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Li RB, Zhang JD, Cui XR, Cui W. Insomnia is related to long-term atrial fibrillation recurrence following radiofrequency ablation. Ann Med 2024; 56:2323089. [PMID: 38423515 PMCID: PMC10906119 DOI: 10.1080/07853890.2024.2323089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Abstract
OBJECTIVE Atrial fibrillation (AF), the most common cardiac arrhythmia, presents significant health challenges, and the intricate connection between insomnia and AF has garnered substantial attention. This cohort study aims to investigate the relationship between insomnia and AF recurrences following radiofrequency ablation. MATERIALS AND METHODS Data were retrieved from an electronic database of patients who underwent radiofrequency ablation for AF. The primary endpoint was AF recurrence. We utilized a multivariable Cox model, coupled with three propensity score methods, for analysis. RESULTS Between January 1, 2017, and June 1, 2022, 541 patients who underwent radiofrequency ablation for AF were recorded in the database. After excluding 185 patients, the final cohort comprised 356 patients. Among them, 68 were afflicted by insomnia, while 288 were not. Over a median follow-up of 755 days, one patient died, and 130 (36.5%) experienced AF recurrence. Multivariate Cox regression analysis revealed that the insomnia group had a higher risk of AF recurrence compared to the non-insomnia group (HR: 1.83, 95% CI: 1.16-2.89). Further landmark analysis showed no significant difference in AF recurrence rates during the initial 1-year follow-up. However, beyond 1 year, the insomnia group demonstrated a significantly higher AF recurrence rate. As the number of insomnia symptoms increased, the risk of AF recurrence also rose significantly, indicating a dose-response relationship. CONCLUSION This study establishes a significant link between insomnia and long-term AF recurrence following radiofrequency ablation. It underscores the importance of identifying and addressing insomnia in patients with AF undergoing radiofrequency ablation.
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Affiliation(s)
- Rui-bin Li
- Department of Cardiology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Ji-dong Zhang
- Department of Cardiology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xiao-ran Cui
- Department of Cardiology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Wei Cui
- Department of Cardiology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
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2
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Hildingsson I, Johansson M. A cluster analysis of reasons behind fear of birth among women in Sweden. J Psychosom Obstet Gynaecol 2024; 45:2319291. [PMID: 38376114 DOI: 10.1080/0167482x.2024.2319291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 02/11/2024] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Fear of birth is common and complex, caused by a variety of reasons. The aim was to investigate the prevalence of pre-established reasons in relation to fear, and to identify profiles of women based on their reported reasons behind fear of birth. METHODS A cross-sectional Swedish study of women with self-reported fear of birth who completed an online survey. Descriptive statistics, chi-square test, crude and adjusted odds ratios with 95% confidence intervals were used in the analysis of pre-established reasons in relation to self-reported severe fear. A Kappa-means cluster analysis was performed in order to group reasons, that were further investigated in relation to women's background variables. RESULTS A total of 1419 women completed the survey. The strongest reason behind fear of birth was to be forced to give birth vaginally. Four clusters were identified and labeled: minor complexity (reference group), relative minor complexity, relative major complexity, and major complexity. Cesarean section preference, previous mental health problems, being younger, primiparity, and exposure to domestic violence were factors related to cluster grouping. CONCLUSIONS Women with fear of birth have various reasons and diverse complexities behind their fear. Health care providers need to investigate these reasons and support pregnant women with childbirth fear, based on their needs.
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Affiliation(s)
- Ingegerd Hildingsson
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
- Department of Nursing, Mid Sweden University, Sundsvall, Sweden
| | - Margareta Johansson
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
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3
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Saifullah M, Nisar A, Akhtar R, M Husnain S, Imtiaz S, Ahmad B, Ahmed Shafique M, Butt S, Arif M, Majeed Satti A, Shahzad Ahmed M, Kelly SD, Siddique N. Identification of provenance of Basmati rice grown in different regions of Punjab through multivariate analysis. Food Chem 2024; 444:138549. [PMID: 38335678 DOI: 10.1016/j.foodchem.2024.138549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 01/18/2024] [Accepted: 01/21/2024] [Indexed: 02/12/2024]
Abstract
High-priced Basmati rice is vulnerable to deliberate mislabeling to increase profits. This type of fraud may lower consumers' confidence as inferior products can affect brand reputation. To address this problem, there is a need to devise a method that can efficiently distinguish Basmati rice grown in regions that are famous versus the regions that are not suitable for their production. Therefore, in this investigation, thirty-six samples of Basmati rice were collected from two zones of Punjab province (one known for Basmati rice) of Pakistan which is the major producer of Basmati rice. The elemental composition of rice samples was assessed using inductively coupled plasma-optical emission spectrometry and an organic elemental analyzer, whereas data on δ13C was acquired using isotopic ratio-mass spectrometry. Regional clustering of samples based on their respective cultivation zones was observed using multivariate data analysis techniques. Partial least squares-discriminant analysis was found to be effective in grouping rice samples from the different locations and identifying unknown samples belonging to these two regions. Further recommendations are presented to develop a better model for tracing the origin of unidentified rice samples.
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Affiliation(s)
- Muhammad Saifullah
- Chemistry Division, Pakistan Institute of Nuclear Science and Technology (PINSTECH), Islamabad 45650, Pakistan.
| | - Awais Nisar
- Chemistry Division, Pakistan Institute of Nuclear Science and Technology (PINSTECH), Islamabad 45650, Pakistan
| | - Ramzan Akhtar
- Chemistry Division, Pakistan Institute of Nuclear Science and Technology (PINSTECH), Islamabad 45650, Pakistan
| | - Syed M Husnain
- Chemistry Division, Pakistan Institute of Nuclear Science and Technology (PINSTECH), Islamabad 45650, Pakistan.
| | - Shamila Imtiaz
- Chemistry Division, Pakistan Institute of Nuclear Science and Technology (PINSTECH), Islamabad 45650, Pakistan
| | - Bashir Ahmad
- Chemistry Division, Pakistan Institute of Nuclear Science and Technology (PINSTECH), Islamabad 45650, Pakistan
| | - Munib Ahmed Shafique
- Central Analytical Facility Division, Pakistan Institute of Nuclear Science and Technology (PINSTECH), Islamabad 45650, Pakistan
| | - Saira Butt
- Isotope Application Division, Pakistan Institute of Nuclear Science and Technology (PINSTECH), Islamabad 45650, Pakistan
| | - Muhammad Arif
- National Institute of Biotechnology and Genetic Engineering (NIBGE), Faisalabad, Pakistan
| | - Abid Majeed Satti
- Crop Sciences Institute (Rice Program), PARC-National Agriculture Research Center, 44000, Park Road, Islamabad, Pakistan
| | - Muhammad Shahzad Ahmed
- Crop Sciences Institute (Rice Program), PARC-National Agriculture Research Center, 44000, Park Road, Islamabad, Pakistan
| | - Simon D Kelly
- International Atomic Energy Agency, Vienna International Center, PO Box 100, Wagramer Strasse 5, 1400, Vienna, Austria
| | - Naila Siddique
- Chemistry Division, Pakistan Institute of Nuclear Science and Technology (PINSTECH), Islamabad 45650, Pakistan
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4
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Park M, Yu JY, Ko JA, Park HJ. Application of UV-Vis-NIR and FTIR spectroscopy coupled with chemometrics for quality prediction of katsuobushi based on the number of smoking treatments. Food Chem 2024; 442:138604. [PMID: 38306767 DOI: 10.1016/j.foodchem.2024.138604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/16/2024] [Accepted: 01/26/2024] [Indexed: 02/04/2024]
Abstract
Katsuobushi, a smoked, dried skipjack tuna, is a traditional Japanese food additive with a unique flavor and taste. Gas chromatography mass spectrometry (GC-MS), fourier transform infrared (FTIR), and ultraviolet-visible-near infrared spectroscopy (UV-Vis-NIR) combined with chemometric methods were evaluated the quality of katsuobushi according to the number of smoking treatments. Using GC-MS, 46 metabolites were identified and five metabolites were selected as key compounds. All samples were classified according to their smoking number via principal component analysis (PCA), partial least squares-discriminate analysis (PLS-DA) and hierarchical cluster analysis (HCA) of the FTIR and NIR spectra. Partial least squares regression (PLSR) analysis revealed that the FTIR and NIR spectra were highly correlated with the metabolites by GC-MS. These results demonstrated the potential of using the FTIR and NIR spectroscopy combined with chemometrics to assess the quality of katsuobushi based on the smoking treatments, with NIR spectroscopy showed particularly promising.
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Affiliation(s)
- Minjung Park
- Department of Biotechnology, College of Life Science and Biotechnology, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Republic of Korea; Daewang Co. Ltd, 132, Beompyeong-ro, Chodong-myeon, Miryang-si, Gyeongsangnam-do, Republic of Korea
| | - Ji Young Yu
- Department of Food, Nutrition and Packaging Sciences, Clemson University, Clemson, USA
| | - Jung A Ko
- Department of Biotechnology, College of Life Science and Biotechnology, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Republic of Korea.
| | - Hyun Jin Park
- Department of Biotechnology, College of Life Science and Biotechnology, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Republic of Korea.
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Lopez-Rodulfo IM, Tsochatzis ED, Stentoft EW, Martinez-Carrasco P, Bechtner JD, Martinez MM. Partitioning and in vitro bioaccessibility of apple polyphenols during mechanical and physiological extraction: A hierarchical clustering analysis with LC-ESI-QTOF-MS/MS. Food Chem 2024; 441:138320. [PMID: 38199101 DOI: 10.1016/j.foodchem.2023.138320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/09/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024]
Abstract
Polyphenol partitioning during mechanical (cold-pressing) and physiological (digestion) extraction at the individual polyphenol and subclass level was investigated. UHPLC-ESI-QTOF-MS/MS analysis yielded a comprehensive identification of 45 polyphenols whose semi-quantification revealed a hierarchical clustering strongly determined by polyphenol structure and their location within the apple tissue. For instance, pomace retained most flavonols and flavanols (degree of polymerization DP 5-7), which were highly hydrophobic, hydroxylated, or large (>434 Da), and more abundant in peel. In vitro digestion UHPLC-ESI-QTOF-MS/MS analysis of whole apple (and its corresponding matrix-free extract) clustered polyphenols into five main groups according to their interaction with plant cell walls (PCWs) during each digestion phase. This grouping was not reproduced in pomace, which exhibited a greater matrix effect than whole apple during oral and gastric digestion. Nevertheless, the interaction between most polyphenol groups, including dihydrochalcones, flavanols (DP 1-4) and hydroxycinnamic acid derivatives, and pomace PCWs was lost during intestinal digestion.
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Affiliation(s)
- Ivan M Lopez-Rodulfo
- Centre for Innovative Food (CiFOOD), Department of Food Science, Aarhus University, Agro Food Park 48, Aarhus N 8200, Denmark
| | - Emmanouil D Tsochatzis
- Centre for Innovative Food (CiFOOD), Department of Food Science, Aarhus University, Agro Food Park 48, Aarhus N 8200, Denmark
| | - Emil W Stentoft
- Centre for Innovative Food (CiFOOD), Department of Food Science, Aarhus University, Agro Food Park 48, Aarhus N 8200, Denmark
| | - Pamela Martinez-Carrasco
- Centre for Innovative Food (CiFOOD), Department of Food Science, Aarhus University, Agro Food Park 48, Aarhus N 8200, Denmark
| | - Julia D Bechtner
- Centre for Innovative Food (CiFOOD), Department of Food Science, Aarhus University, Agro Food Park 48, Aarhus N 8200, Denmark
| | - Mario M Martinez
- Centre for Innovative Food (CiFOOD), Department of Food Science, Aarhus University, Agro Food Park 48, Aarhus N 8200, Denmark.
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6
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Alasmawi H, Bricker L, Yaqub M. FUSC: Fetal Ultrasound Semantic Clustering of Second-Trimester Scans Using Deep Self-Supervised Learning. Ultrasound Med Biol 2024; 50:703-711. [PMID: 38350787 DOI: 10.1016/j.ultrasmedbio.2024.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/31/2023] [Accepted: 01/14/2024] [Indexed: 02/15/2024]
Abstract
OBJECTIVE The aim of this study was address the challenges posed by the manual labeling of fetal ultrasound images by introducing an unsupervised approach, the fetal ultrasound semantic clustering (FUSC) method. The primary objective was to automatically cluster a large volume of ultrasound images into various fetal views, reducing or eliminating the need for labor-intensive manual labeling. METHODS The FUSC method was developed by using a substantial data set comprising 88,063 images. The methodology involves an unsupervised clustering approach to categorize ultrasound images into diverse fetal views. The method's effectiveness was further evaluated on an additional, unseen data set consisting of 8187 images. The evaluation included assessment of the clustering purity, and the entire process is detailed to provide insights into the method's performance. RESULTS The FUSC method exhibited notable success, achieving >92% clustering purity on the evaluation data set of 8187 images. The results signify the feasibility of automatically clustering fetal ultrasound images without relying on manual labeling. The study showcases the potential of this approach in handling a large volume of ultrasound scans encountered in clinical practice, with implications for improving efficiency and accuracy in fetal ultrasound imaging. CONCLUSION The findings of this investigation suggest that the FUSC method holds significant promise for the field of fetal ultrasound imaging. By automating the clustering of ultrasound images, this approach has the potential to reduce the manual labeling burden, making the process more efficient. The results pave the way for advanced automated labeling solutions, contributing to the enhancement of clinical practices in fetal ultrasound imaging. Our code is available at https://github.com/BioMedIA-MBZUAI/FUSC.
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Affiliation(s)
- Hussain Alasmawi
- Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates.
| | - Leanne Bricker
- Abu Dhabi Health Services Company (SEHA), Abu Dhabi, United Arab Emirates
| | - Mohammad Yaqub
- Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
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7
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Tian J, Han D, Karimi HR, Zhang Y, Shi P. A universal multi-source domain adaptation method with unsupervised clustering for mechanical fault diagnosis under incomplete data. Neural Netw 2024; 173:106167. [PMID: 38359643 DOI: 10.1016/j.neunet.2024.106167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 11/17/2023] [Accepted: 02/06/2024] [Indexed: 02/17/2024]
Abstract
Recently, due to the difficulty of collecting condition data covering all mechanical fault types in industrial scenarios, the fault diagnosis problem under incomplete data is receiving increasing attention where no target prior information can be available. The existing open-set or universal domain adaptation (DA) diagnosis methods typically treat private fault samples in the target as a generalized "unknown" fault class, neglecting their inherent structure. This oversight can lead to confusion in latent feature space representations and difficulties in separating unknown samples. Therefore, a universal DA method with unsupervised clustering is developed to explore the intrinsic structure of the target samples for mechanical fault diagnosis, where multi-source information on different working conditions is considered to transfer complementary knowledge. First, a composite clustering metric combining single-domain and cross-domain evaluation is constructed to recognize shared and unknown health classes on source-target domains. Second, to alleviate the intra-class shift while enlarging the inter-class gap, a class-wise DA algorithm is suggested which operates on the basis of maximum mean discrepancy. Finally, an entropy regularization criterion is utilized to facilitate clustering of different health classes. The efficacy of the presented approach in the fault diagnosis issues when monitoring data is inadequate has been verified through extensive experiments on three rotating machinery datasets.
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Affiliation(s)
- Jinghui Tian
- School of Vehicles and Energy, Yanshan University, Qinhuangdao, Hebei 066004, PR China
| | - Dongying Han
- School of Vehicles and Energy, Yanshan University, Qinhuangdao, Hebei 066004, PR China.
| | - Hamid Reza Karimi
- Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy
| | - Yu Zhang
- School of Vehicles and Energy, Yanshan University, Qinhuangdao, Hebei 066004, PR China
| | - Peiming Shi
- School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, PR China
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8
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Franzo G, de Villiers L, Coetzee LM, Villiers MD, Molini U. Molecular survey of feline immunodeficiency virus (FIV) infection in Namibian cats. Acta Trop 2024; 253:107184. [PMID: 38479467 DOI: 10.1016/j.actatropica.2024.107184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/27/2024] [Accepted: 03/10/2024] [Indexed: 03/26/2024]
Abstract
Feline Immunodeficiency Virus (FIV) is one of the most important infectious diseases of cats, with potential implications in wildlife conservation. Unfortunately, FIV screening and surveillance in domestic cats remains limited in several African countries, including Namibia. In this study, 279 blood samples from domestic cats in Namibia were analyzed for FIV diagnosis by PCR. The cats represented various regions and were cared for by people largely from rural areas with limited financial means. Only 1.43 % of the samples tested positive, unexpectedly low given their outdoor lifestyles. The infected cats, primarily adult and unsterilized, showed no typical FIV symptoms, suggesting subclinical infections. Genetic analysis of the detected strains indicated a unique FIV strain cluster in Namibia, although with a certain within-country variability, in the absence of consistent geographical clustering. The present study represents the first detection and genetic characterization of FIV in the Namibian domestic cat population. Although the infection frequency was low, also in the rural free-roaming population, the features of the enrolled population could have biased the estimation, suggesting the need for more extensive surveys involving diseased and older cats as well. Additionally, because of the long-lasting subclinical nature of the infection, frequent monitoring activities should be performed that allow prompt isolation of infected animals and the implementation of appropriate control measures if necessary.
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Affiliation(s)
- Giovanni Franzo
- Dept. of Animal Medicine, Production and Health, University of Padova, viale dell'Università 16, Legnaro 35020, Italy.
| | - Lourens de Villiers
- School of Veterinary Medicine, Faculty of Health Sciences and Veterinary Medicine, University of Namibia, Neudamm Campus, Private Bag, Windhoek 13301, Namibia
| | - Lauren M Coetzee
- Faculty of Veterinary Medicine, University of Teramo, Teramo, Italy
| | - Mari de Villiers
- Rhino Park Veterinary Clinic, 54 Rhino Street, Windhoek North, Windhoek, Namibia
| | - Umberto Molini
- School of Veterinary Medicine, Faculty of Health Sciences and Veterinary Medicine, University of Namibia, Neudamm Campus, Private Bag, Windhoek 13301, Namibia
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9
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Tamakloe R, Zhang K, Hossain A, Kim I, Park SH. Critical risk factors associated with fatal/severe crash outcomes in personal mobility device rider at-fault crashes: A two-step inter-cluster rule mining technique. Accid Anal Prev 2024; 199:107527. [PMID: 38428242 DOI: 10.1016/j.aap.2024.107527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/28/2024] [Accepted: 02/25/2024] [Indexed: 03/03/2024]
Abstract
Personal Mobility Devices (PMDs) have witnessed an extraordinary surge in popularity, emerging as a favored mode of urban transportation. This has sparked significant safety concerns, paralleled by a stark increase in PMD-involved crashes. Research indicates that PMD user behavior, especially in urban areas, is crucial in these crashes, underscoring the need for an extensive investigation into key factors, particularly those causing fatal/severe outcomes. Remarkably, there exists a noticeable gap in the research concerning the analysis of determinants behind fatal/severe PMD crashes, specifically in PMD rider-at-fault collisions. This study addresses this gap by identifying uniform groups of PMD rider-at-fault crashes and investigating cluster-specific key factor associations and determinants of fatal/severe crash outcomes using Seoul's PMD rider-at-fault crash data from 2017 to 2021. A comprehensive two-step framework, integrating Cluster Correspondence Analysis (CCA) and Association Rules Mining (ARM) techniques is employed to segment PMD rider-at-fault crash data into homogeneous groups, revealing unique risk factor patterns within each cluster and further exploring the combination of factors associated with fatal/severe PMD rider-at-fault crash outcomes. CCA revealed three distinct groups: PMD-vehicle, PMD-pedestrian, and single-PMD crashes. From the ARM, it was found that fatal/severe crashes were linked to dry road conditions, male PMD users, and weekdays, irrespective of the cluster. Whereas speeding violations and side collisions were associated with fatal/severe PMD-vehicle rider-at-fault crashes, traffic control violations were related to fatal/severe PMD-pedestrian rider-at-fault crashes at pedestrian crossings. Unsafe riding practices predominantly caused single-PMD crashes during daytime hours. From the findings, engineering improvements, awareness campaigns, education, and law enforcement actions are recommended. The new insights gleaned from this research provide a foundation for informed decision-making and the implementation of policies designed to enhance PMD safety.
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Affiliation(s)
- Reuben Tamakloe
- The Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology, 193 Munji-ro, Yuseong-gu, Daejeon, 34051, South Korea.
| | - Kaihan Zhang
- The Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology, 193 Munji-ro, Yuseong-gu, Daejeon, 34051, South Korea.
| | - Ahmed Hossain
- Department of Civil Engineering, University of Louisiana at Lafayette, Lafayette, LA, 70503, Unites States.
| | - Inhi Kim
- The Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology, 193 Munji-ro, Yuseong-gu, Daejeon, 34051, South Korea.
| | - Shin Hyoung Park
- Department of Transportation Engineering, University of Seoul, 163 Seoulsiripdae-ro Dongdaemun-gu, Seoul 02504, South Korea.
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Sharma A, Wibawa BSS, Andhikaputra G, Solanki B, Sapkota A, Chiang Hsieh LH, Iyer V, Wang YC. Spatial analysis of food and water-borne diseases in Ahmedabad, India: Implications for urban public health planning. Acta Trop 2024; 253:107170. [PMID: 38467234 DOI: 10.1016/j.actatropica.2024.107170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/05/2024] [Accepted: 03/03/2024] [Indexed: 03/13/2024]
Abstract
Spatial analysis of infectious diseases can play an important role in mapping the spread of diseases and can support policy making at local level. Moreover, identification of disease clusters based on local geography and landscape forms the basis for disease control and prevention. Therefore, this study aimed to examine the spatial-temporal variations, hotspot areas, and potential risk factors of infectious diseases (including Viral Hepatitis, Typhoid and Diarrhea) in Ahmedabad city of India. We used Moran's I and Local Indicators of Spatial Association (LISA) mapping to detect spatial clustering of diseases. Spatial and temporal regression analysis was used to identify the association between disease incidence and spatial risk factors. The Moran's I statistics identified presence of positive spatial autocorrelation within the considered diseases, with Moran's I from 0.09 for typhoid to 0.21 for diarrhea (p < 0.001). This indicates a clustering of affected wards for each disease, suggesting that cases were not randomly distributed across the city. LISA mapping demonstrated the clustering of hotspots in central regions of the city, especially towards the east of the river Sabarmati, highlighting key geographical areas with elevated disease risk. The spatial clusters of infectious diseases were consistently associated with slum population density and illiteracy. Furthermore, temporal analysis suggested illiteracy rates could increase risk of viral hepatitis by 13 % (95 % Confidence Interval (CI): 1.01-1.26) and of diarrhea by 18 % (95 % CI: 1.07-1.31). Significant inverse association was also seen between viral hepatitis incidence and the distance of wards from rivers. Conclusively, the study highlight the impact of socio-economic gradients, such as slum population density (indicative of poverty) and illiteracy, on the localized transmission of water and foodborne infections. The evident social stratification between impoverished and affluent households emerges as a notable contributing factor and a potential source of differences in the dynamics of infectious diseases in Ahmedabad.
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Affiliation(s)
- Ayushi Sharma
- Department of Environmental Engineering, College of Engineering, Chung Yuan Christian University, 200 Chung-Pei Road, Zhongli 320, Taiwan; Department of Civil Engineering, College of Engineering, Chung Yuan Christian University, 200 Chung-Pei Road, Zhongli 320, Taiwan
| | - Bima Sakti Satria Wibawa
- Department of Environmental Engineering, College of Engineering, Chung Yuan Christian University, 200 Chung-Pei Road, Zhongli 320, Taiwan
| | - Gerry Andhikaputra
- Department of Environmental Engineering, College of Engineering, Chung Yuan Christian University, 200 Chung-Pei Road, Zhongli 320, Taiwan
| | - Bhavin Solanki
- Medical Officer of Health, Ahmedabad Municipal Corporation, Ahmedabad, Gujarat, India
| | - Amir Sapkota
- Department of Epidemiology and Biostatistics, University of Maryland, School of Public Health, College Park, MD 20742, United States
| | - Lin-Han Chiang Hsieh
- Institute of Environmental Engineering and Management, National Taipei University of Technology, Taiwan.
| | - Veena Iyer
- Indian Institute of Public Health Gandhinagar (IIPHG), Public Health Foundation of India (PHFI), Near Lekwada Bus Stop, Near Lekwada Bus Stop, Opp. New Air Force Station HQ, Palaj. Gandhinagar, 382042, Gujarat, India.
| | - Yu-Chun Wang
- Department of Environmental Engineering, College of Engineering, Chung Yuan Christian University, 200 Chung-Pei Road, Zhongli 320, Taiwan; Research Center for Environmental Changes, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei 11529, Taiwan.
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11
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Dornaika F, El Hajjar S. Towards a unified framework for graph-based multi-view clustering. Neural Netw 2024; 173:106197. [PMID: 38422834 DOI: 10.1016/j.neunet.2024.106197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 11/12/2023] [Accepted: 02/18/2024] [Indexed: 03/02/2024]
Abstract
Recently, clustering data collected from various sources has become a hot topic in real-world applications. The most common methods for multi-view clustering can be divided into several categories: Spectral clustering algorithms, subspace multi-view clustering algorithms, matrix factorization approaches, and kernel methods. Despite the high performance of these methods, they directly fuse all similarity matrices of all views and separate the affinity learning process from the multiview clustering process. The performance of these algorithms can be affected by noisy affinity matrices. To overcome this drawback, this paper presents a novel method called One Step Multi-view Clustering via Consensus Graph Learning and Nonnegative Embedding (OSMGNE). Instead of directly merging the similarity matrices of different views, which may contain noise, a step of learning a consensus similarity matrix is performed. This step forces the similarity matrices of different views to be too similar, which eliminates the problem of noisy data. Moreover, the use of the nonnegative embedding matrix (soft cluster assignment matrix makes it possible to directly obtain the final clustering result without any extra step. The proposed method can solve five subtasks simultaneously. It jointly estimates the similarity matrix of all views, the similarity matrix of each view, the corresponding spectral projection matrix, the unified clustering indicator matrix, and automatically gives the weight of each view without the use of hyper-parameters. In addition, another version of our method is also studied in this paper. This method differs from the first one by using a consensus spectral projection matrix and a consensus Laplacian matrix over all views. An iterative algorithm is proposed to solve the optimization problem of these two methods. The two proposed methods are tested on several real datasets, which prove their superiority.
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Affiliation(s)
- F Dornaika
- University of the Basque Country UPV/EHU, San Sebastian, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao, Spain; Ho Chi Minh City Open University, Ho Chi Minh City, Viet Nam.
| | - S El Hajjar
- University of the Basque Country UPV/EHU, San Sebastian, Spain
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Wu J, Yang B, Xue Z, Zhang X, Lin Z, Chen B. Fast multi-view clustering via correntropy-based orthogonal concept factorization. Neural Netw 2024; 173:106170. [PMID: 38387199 DOI: 10.1016/j.neunet.2024.106170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 01/15/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024]
Abstract
Owing to its ability to handle negative data and promising clustering performance, concept factorization (CF), an improved version of non-negative matrix factorization, has been incorporated into multi-view clustering recently. Nevertheless, existing CF-based multi-view clustering methods still have the following issues: (1) they directly conduct factorization in the original data space, which means its efficiency is sensitive to the feature dimension; (2) they ignore the high degree of factorization freedom of standard CF, which may lead to non-uniqueness factorization thereby causing reduced effectiveness; (3) traditional robust norms they used are unable to handle complex noises, significantly challenging their robustness. To address these issues, we establish a fast multi-view clustering via correntropy-based orthogonal concept factorization (FMVCCF). Specifically, FMVCCF executes factorization on a learned consensus anchor graph rather than directly decomposing the original data, lessening the dimensionality sensitivity. Then, a lightweight graph regularization term is incorporated to refine the factorization process with a low computational burden. Moreover, an improved multi-view correntropy-based orthogonal CF model is developed, which can enhance the effectiveness and robustness under the orthogonal constraint and correntropy criterion, respectively. Extensive experiments demonstrate that FMVCCF can achieve promising effectiveness and robustness on various real-world datasets with high efficiency.
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Affiliation(s)
- Jinghan Wu
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi'an 710049, China; National Engineering Research Center for Visual Information and Applications, Xi'an 710049, China; Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China
| | - Ben Yang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi'an 710049, China; National Engineering Research Center for Visual Information and Applications, Xi'an 710049, China; Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China
| | - Zhiyuan Xue
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi'an 710049, China; National Engineering Research Center for Visual Information and Applications, Xi'an 710049, China; Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China
| | - Xuetao Zhang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi'an 710049, China; National Engineering Research Center for Visual Information and Applications, Xi'an 710049, China; Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China.
| | - Zhiping Lin
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Badong Chen
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi'an 710049, China; National Engineering Research Center for Visual Information and Applications, Xi'an 710049, China; Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China
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Xu G, Gan S, Guo B, Yang L. Application of clustering strategy for automatic segmentation of tissue regions in mass spectrometry imaging. Rapid Commun Mass Spectrom 2024; 38:e9717. [PMID: 38389435 DOI: 10.1002/rcm.9717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/19/2024] [Accepted: 01/21/2024] [Indexed: 02/24/2024]
Abstract
RATIONALE Mass spectrometry imaging (MSI) has been widely used in biomedical research fields. Each pixel in MSI consists of a mass spectrum that reflects the molecule feature of the tissue spot. Because MSI contains high-dimensional datasets, it is highly desired to develop computational methods for data mining and constructing tissue segmentation maps. METHODS To visualize different tissue regions based on mass spectrum features and improve the efficiency in processing enormous data, we proposed a computational strategy that consists of four procedures including preprocessing, data reduction, clustering, and quantitative validation. RESULTS In this study, we examined the combination of t-distributed stochastic neighbor embedding (t-SNE) and hierarchical clustering (HC) for MSI data analysis. Using publicly available MSI datasets, one dataset of mouse urinary bladder, and one dataset of human colorectal cancer, we demonstrated that the generated tissue segmentation maps from this combination were superior to other data reduction and clustering algorithms. Using the staining image as a reference, we assessed the performance of clustering algorithms with external and internal clustering validation measures, including purity, adjusted Rand index (ARI), Davies-Bouldin index (DBI), and spatial aggregation index (SAI). The result indicated that SAI delivered excellent performance for automatic segmentation of tissue regions in MSI. CONCLUSIONS We used a clustering algorithm to construct tissue automatic segmentation in MSI datasets. The performance was evaluated by comparing it with the stained image and calculating clustering validation indexes. The results indicated that SAI is important for automatic tissue segmentation in MSI, different from traditional clustering validation measures. Compared to the reports that used internal clustering validation measures such as DBI, our method offers more effective evaluation of clustering results for MSI segmentation. We envision that the proposed automatic image segmentation strategy can facilitate deep learning in molecular feature extraction and biomarker discovery for the biomedical applications of MSI.
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Affiliation(s)
- Guang Xu
- College of Computer, Hubei University of Education, Wuhan, China
| | - Shengfeng Gan
- College of Computer, Hubei University of Education, Wuhan, China
| | - Bo Guo
- College of Computer, Hubei University of Education, Wuhan, China
| | - Li Yang
- College of Computer, Hubei University of Education, Wuhan, China
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Zhao X, Wang S, Du T, Jiang Y, Zhao Y, Ma Y, Shen D, Shen Y, Ma J. Demystifying the landscape of endometrial immune microenvironment in luteal-phase from cuprotosis: Implications for the mechanism and treatment of RPL. Gene 2024; 903:148191. [PMID: 38253297 DOI: 10.1016/j.gene.2024.148191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/22/2023] [Accepted: 01/18/2024] [Indexed: 01/24/2024]
Abstract
BACKGROUND Adaptive changes in the endometrial immune microenvironment during the luteal phase are essential for pregnancy, and their abnormalities are associated with recurrent pregnancy loss (RPL). Nevertheless, the specific mechanism is still unknown. Cuprotosis, an innovatively discovered type of programmed cell death, provides us with a pioneering perspective to decipher the landscape of luteal-phase endometrial immune microenvironment in RPL. This study aimed to analyze the immune landscape of luteal-phase endometrial microenvironment in RPL and explore the association of cuprotosis with it through integrative bioinformatics analysis. METHODS The microarrays involving the luteal phase endometrial tissue of RPL were obtained from the GEO database. Differentially expressed genes (DEGs) of RPL were screened and key modules were detected by WGCNA. GO, KEGG, and GSEA immune enrichment analyses were performed on the DEGs in the most relevant modules to RPL. Then, the endometrial immune microenvironment landscape of RPL was analyzed, including immune infiltration analysis and correlation analysis between immune cells or immune functions. The interaction of cuprotosis-related genes (CRGs), the expression level between groups, the immune localization and their correlation with immune cells and immune function were analyzed. LASSO regression and Nomogram evaluated the diagnostic value of immune-related CRGS in RPL. Functional enrichment analysis was performed on the RPL signature CRGs. And RPL samples were grouped according to the expression of 7 RPL signature CRGs through unsupervised clustering analysis. After that, we analyzed the expression level of CRGs and immune infiltration, as well as performed immune function enrichment analysis in subtypes. In addition, we also screened potential drugs that might act on CRGs to improve the pathological mechanism of RPL. RESULTS In this study, we uncovered that DEGs and genes in key modules derived from weighted gene co-expression network analysis (WGCNA) were involved in immune regulation. And the immune infiltration landscape of RPL was significantly different from healthy controls. Furthermore, six hub genes were screened from CRGs based on Cytohubba, and their expression profilings were verified in RPL and normal mouse samples. Besides, seven CRGs closely associated with the immune regulation of RPL were identified by Spearman correlation analysis, including SLC31A1, LIAS, DLD, DLAT, DBT, ATP7B, and ATP7A, named as immune-related CRGs. Furthermore, three subgroups clustered according to these seven genes showed significant differences in immune landscape, suggesting a remarkable effect of CRGs on immune regulation. Last but not least, we analyzed the regulation network of transcription factors, miRNAs, and CRGs, and screened potential compounds for the treatment of RPL by targeting CRGs. CONCLUSIONS The abnormal endometrial immune microenvironment in the luteal phase was associated with the pathomechanism of RPL, and cuprotosis was closely involved in the immune microenvironment in the luteal phase endometrium of RPL. Collectively, this study revealed the potential contribution of CRGs to the pathogenesis of RPL, providing a novel breakthroughs in insights into the pathogenesis, diagnosis, and treatment of RPL.
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Affiliation(s)
- Xiaoxuan Zhao
- Department of Traditional Chinese Medicine (TCM) Gynecology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Sihui Wang
- Department of Traditional Chinese Medicine (TCM) Gynecology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Tingting Du
- Zhejiang Chinese Medical University, Hangzhou, China
| | - Yuepeng Jiang
- Zhejiang Chinese Medical University, Hangzhou, China
| | - Yang Zhao
- The First Clinical Medical College of Nanjing University of Chinese Medicine, Nanjing, China
| | - Yiming Ma
- Zhejiang Chinese Medical University, Hangzhou, China
| | - Dan Shen
- Department of Traditional Chinese Medicine (TCM) Gynecology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Yi Shen
- Department of Traditional Chinese Medicine (TCM) Gynecology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Jing Ma
- Department of Traditional Chinese Medicine (TCM) Gynecology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.
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Jian J, Wu D, Cao J, Dong F, Liu J, Wang D, Zhang S. Grouped Multivariate Variational Mode Decomposition With Application to EEG Analysis. IEEE Trans Biomed Eng 2024; 71:1332-1344. [PMID: 37983148 DOI: 10.1109/tbme.2023.3334379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
OBJECTIVE In this paper, a novel extended form of multivariate variational mode decomposition (MVMD) method to multigroup data named as grouped MVMD (GMVMD) is proposed. GMVMD is distinct from MVMD as it extracts common frequencies with strong correlations among regional channels. METHODS Firstly, GMVMD utilizes a new clustering algorithm named as frequencies grouping algorithm to classify the nearest common frequencies among all channels to specified groups. Secondly, a generic variational optimization model which is extended from MVMD is formulated. Thirdly, alternating direction method of multipliers (ADMM) is utilized to obtain optimal solution of GMVMD model. RESULTS The proposed method introduces an extra parameter to decide the number of clusterings which need to be specified by the user. The effectiveness and superiority of the algorithm are demonstrated on a series of experiments. The utility of GMVMD is verified by grouping real-world electroencephalogram (EEG) data having similar center frequencies successfully. CONCLUSION GMVMD outperforms MVMD in mode-alignment, signal reduction error and et al. Significance: GMVMD can obtain more accurate center frequencies and less signal reduction error than MVMD.
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Golozar M, Motlagh AV, Mahdevar M, Peymani M, InanlooRahatloo K, Ghaedi K. TBX15 and SDHB expression changes in colorectal cancer serve as potential prognostic biomarkers. Exp Mol Pathol 2024; 136:104890. [PMID: 38378070 DOI: 10.1016/j.yexmp.2024.104890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 01/07/2024] [Accepted: 02/16/2024] [Indexed: 02/22/2024]
Abstract
Alterations in the expression of certain genes could be associated with both patient mortality rates and drug resistance. This study aimed to identify genes in colorectal cancer (CRC) that potentially serve as hub genes influencing patient survival rates. RNA-Seq data were downloaded from the cancer genome atlas database, and differential expression analysis was performed between tumors and healthy controls. Through the utilization of univariate and multivariate Cox regression analyses, in combination with the MCODE clustering module, the genes whose expression changes were related to survival rate and the hub genes related to them were identified. The mortality risk model was computed using the hub genes. CRC samples and the RT-qPCR method were utilized to confirm the outcomes. PharmacoGx data were employed to link the expression of potential genes to medication resistance and sensitivity. The results revealed the discovery of seven hub genes, which emerged as independent prognostic markers. These included HOXC6, HOXC13, HOXC8, and TBX15, which were associated with poor prognosis and overexpression, as well as SDHB, COX5A, and UQCRC1, linked to favorable prognosis and downregulation. Applying the risk model developed with the mentioned genes revealed a markedly higher incidence of deceased patients in the high-risk group compared to the low-risk group. RT-qPCR results indicated a decrease in SDHB expression and an elevation in TBX15 levels in cancer samples relative to adjacent healthy tissue. Also, PharmacoGx data indicated that the expression level of SDHB was correlated with drug sensitivity to Crizotinib and Dovitinib. Our findings highlight the potential association between alterations in the expression of genes such as HOXC6, HOXC13, HOXC8, TBX15, SDHB, COX5A, and UQCRC1 and increased mortality rates in CRC patients. As revealed by the PPI network, these genes exhibited the most connections with other genes linked to survival.
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Affiliation(s)
- Melika Golozar
- Kish International Campus, School of Biology, College of Science, University of Tehran, Tehran, Iran
| | - Ali Valipour Motlagh
- Department of Animal Biotechnology, Cell Science Research Center, Royan Institute for Biotechnology, ACECR, Isfahan 8165131378, Iran
| | - Mohammad Mahdevar
- Genius Gene, Genetics and Biotechnology Company, Tehran, Iran; Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Maryam Peymani
- Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
| | - Kolsoum InanlooRahatloo
- Kish International Campus, School of Biology, College of Science, University of Tehran, Tehran, Iran
| | - Kamran Ghaedi
- Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran.
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Cheng P, Liu Z, Sun M, Zhang W, Guo R, Hu A, Long Y. The relations of psychotic-like experiences (PLEs) and depressive symptoms and the bias of depressive symptoms during the clustering among Chinese adolescents: Findings from the network perspective. J Affect Disord 2024; 350:867-876. [PMID: 38272370 DOI: 10.1016/j.jad.2024.01.180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 01/16/2024] [Accepted: 01/18/2024] [Indexed: 01/27/2024]
Abstract
BACKGROUND There are rare studies about the network structure of psychotic-like experiences (PLEs) and depressive symptoms among adolescents. Studies have widely acknowledged that PLEs in adolescents confer a higher risk of depressive symptoms, but the complex interactions remain inadequately understood. Our study aimed to examine the hierarchy and inter-associations of PLEs and depressive symptoms in a large adolescent sample from the network analysis perspective. METHODS A total of 5008 Chinese adolescents were enrolled in our sample. Community Assessment of Psychic Experiences-42 (CAPE-42) was applied to build the network. Centrality indexes were calculated to represent the significance of nodes in the network. Community detection was conducted to figure out the specific clustering of nodes. Demographic information was collected for the sub-network comparisons. Accuracy and stability of the network were also tested. RESULTS "Failure", "External control", and "Lack of activity" were the most central nodes. The main bridge nodes linking PLEs and depressive symptoms were "Failure", "Guilty", and "No future". Positive PLE "Odd looks" and negative PLE "Unable to terminate" are the two PLEs that were most relevant to depressive nodes. Community detection further demonstrated the bias of depressive nodes in the data-driven clustering. Comparative sub-network analysis suggested that age was the only demographic factor related to the current network. CONCLUSION In this study of a large adolescent sample, we first demonstrated the network structure and specific clustering preference of PLEs and depressive symptoms. Our findings may enhance the understanding of the relationship between PLE and depressive symptoms.
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Affiliation(s)
- Peng Cheng
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Zhening Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Meng Sun
- Department of Social Psychiatry, the Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Wen Zhang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Rui Guo
- Hunan Xinyang Culture Communication Co., LTD, China
| | - Aimin Hu
- College of Medicine, Jishou University, Jishou, Hunan, China
| | - Yicheng Long
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China.
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France AM, Panneer N, Farnham PG, Oster AM, Viguerie A, Gopalappa C. Simulation of Full HIV Cluster Networks in a Nationally Representative Model Indicates Intervention Opportunities. J Acquir Immune Defic Syndr 2024; 95:355-361. [PMID: 38412046 PMCID: PMC10901443 DOI: 10.1097/qai.0000000000003367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 12/07/2023] [Indexed: 02/29/2024]
Abstract
BACKGROUND Clusters of rapid HIV transmission in the United States are increasingly recognized through analysis of HIV molecular sequence data reported to the National HIV Surveillance System. Understanding the full extent of cluster networks is important to assess intervention opportunities. However, full cluster networks include undiagnosed and other infections that cannot be systematically observed in real life. METHODS We replicated HIV molecular cluster networks during 2015-2017 in the United States using a stochastic dynamic network simulation model of sexual transmission of HIV. Clusters were defined at the 0.5% genetic distance threshold. Ongoing priority clusters had growth of ≥3 diagnoses/year in multiple years; new priority clusters first had ≥3 diagnoses/year in 2017. We assessed the full extent, composition, and transmission rates of new and ongoing priority clusters. RESULTS Full clusters were 3-9 times larger than detected clusters, with median detected cluster sizes in new and ongoing priority clusters of 4 (range 3-9) and 11 (range 3-33), respectively, corresponding to full cluster sizes with a median of 14 (3-74) and 94 (7-318), respectively. A median of 36.3% (range 11.1%-72.6%) of infections in the full new priority clusters were undiagnosed. HIV transmission rates in these clusters were >4 times the overall rate observed in the entire simulation. CONCLUSIONS Priority clusters reflect networks with rapid HIV transmission. The substantially larger full extent of these clusters, high proportion of undiagnosed infections, and high transmission rates indicate opportunities for public health intervention and impact.
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Affiliation(s)
- Anne Marie France
- Division of HIV Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention
| | - Nivedha Panneer
- Division of HIV Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention
| | - Paul G. Farnham
- Division of HIV Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention
| | - Alexandra M. Oster
- Division of HIV Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention
| | - Alex Viguerie
- Division of HIV Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention
| | - Chaitra Gopalappa
- Division of HIV Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention
- University of Massachusetts Amherst, Amherst, MA, United States
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Ming Z, Pogosyan A, Gao C, Colbert CM, Wu HH, Finn JP, Ruan D, Hu P, Christodoulou AG, Nguyen KL. ECG-free cine MRI with data-driven clustering of cardiac motion for quantification of ventricular function. NMR Biomed 2024; 37:e5091. [PMID: 38196195 PMCID: PMC10947936 DOI: 10.1002/nbm.5091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 11/14/2023] [Accepted: 11/22/2023] [Indexed: 01/11/2024]
Abstract
BACKGROUND Despite the widespread use of cine MRI for evaluation of cardiac function, existing real-time methods do not easily enable quantification of ventricular function. Moreover, segmented cine MRI assumes periodicity of cardiac motion. We aim to develop a self-gated, cine MRI acquisition scheme with data-driven cluster-based binning of cardiac motion. METHODS A Cartesian golden-step balanced steady-state free precession sequence with sorted k-space ordering was designed. Image data were acquired with breath-holding. Principal component analysis and k-means clustering were used for binning of cardiac phases. Cluster compactness in the time dimension was assessed using temporal variability, and dispersion in the spatial dimension was assessed using the Caliński-Harabasz index. The proposed and the reference electrocardiogram (ECG)-gated cine methods were compared using a four-point image quality score, SNR and CNR values, and Bland-Altman analyses of ventricular function. RESULTS A total of 10 subjects with sinus rhythm and 8 subjects with arrhythmias underwent cardiac MRI at 3.0 T. The temporal variability was 45.6 ms (cluster) versus 24.6 ms (ECG-based) (p < 0.001), and the Caliński-Harabasz index was 59.1 ± 9.1 (cluster) versus 22.0 ± 7.1 (ECG based) (p < 0.001). In subjects with sinus rhythm, 100% of the end-systolic and end-diastolic images from both the cluster and reference approach received the highest image quality score of 4. Relative to the reference cine images, the cluster-based multiphase (cine) image quality consistently received a one-point lower score (p < 0.05), whereas the SNR and CNR values were not significantly different (p = 0.20). In cases with arrhythmias, 97.9% of the end-systolic and end-diastolic images from the cluster approach received an image quality score of 3 or more. The mean bias values for biventricular ejection fraction and volumes derived from the cluster approach versus reference cine were negligible. CONCLUSION ECG-free cine cardiac MRI with data-driven clustering for binning of cardiac motion is feasible and enables quantification of cardiac function.
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Affiliation(s)
- Zhengyang Ming
- Physics and Biology in Medicine Graduate Program, University of California, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Arutyun Pogosyan
- Division of Cardiology, David Geffen School of Medicine at UCLA and VA Greater Los Angeles Healthcare System, Los Angeles, California, USA
| | - Chang Gao
- Physics and Biology in Medicine Graduate Program, University of California, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Caroline M Colbert
- Physics and Biology in Medicine Graduate Program, University of California, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
- Division of Cardiology, David Geffen School of Medicine at UCLA and VA Greater Los Angeles Healthcare System, Los Angeles, California, USA
| | - Holden H Wu
- Physics and Biology in Medicine Graduate Program, University of California, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, California, USA
| | - J Paul Finn
- Physics and Biology in Medicine Graduate Program, University of California, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Dan Ruan
- Physics and Biology in Medicine Graduate Program, University of California, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, California, USA
- Department of Radiation Oncology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Peng Hu
- Physics and Biology in Medicine Graduate Program, University of California, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Anthony G Christodoulou
- Department of Bioengineering, University of California, Los Angeles, California, USA
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Kim-Lien Nguyen
- Physics and Biology in Medicine Graduate Program, University of California, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
- Division of Cardiology, David Geffen School of Medicine at UCLA and VA Greater Los Angeles Healthcare System, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, California, USA
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Wang M, Wang J, Rong Z, Wang L, Xu Z, Zhang L, He J, Li S, Cao L, Hou Y, Li K. A bidirectional interpretable compound-protein interaction prediction framework based on cross attention. Comput Biol Med 2024; 172:108239. [PMID: 38460309 DOI: 10.1016/j.compbiomed.2024.108239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 02/25/2024] [Accepted: 02/26/2024] [Indexed: 03/11/2024]
Abstract
The identification of compound-protein interactions (CPIs) plays a vital role in drug discovery. However, the huge cost and labor-intensive nature in vitro and vivo experiments make it urgent for researchers to develop novel CPI prediction methods. Despite emerging deep learning methods have achieved promising performance in CPI prediction, they also face ongoing challenges: (i) providing bidirectional interpretability from both the chemical and biological perspective for the prediction results; (ii) comprehensively evaluating model generalization performance; (iii) demonstrating the practical applicability of these models. To overcome the challenges posed by current deep learning methods, we propose a cross multi-head attention oriented bidirectional interpretable CPI prediction model (CmhAttCPI). First, CmhAttCPI takes molecular graphs and protein sequences as inputs, utilizing the GCW module to learn atom features and the CNN module to learn residue features, respectively. Second, the model applies cross multi-head attention module to compute attention weights for atoms and residues. Finally, CmhAttCPI employs a fully connected neural network to predict scores for CPIs. We evaluated the performance of CmhAttCPI on balanced datasets and imbalanced datasets. The results consistently show that CmhAttCPI outperforms multiple state-of-the-art methods. We constructed three scenarios based on compound and protein clustering and comprehensively evaluated the model generalization ability within these scenarios. The results demonstrate that the generalization ability of CmhAttCPI surpasses that of other models. Besides, the visualizations of attention weights reveal that CmhAttCPI provides chemical and biological interpretation for CPI prediction. Moreover, case studies confirm the practical applicability of CmhAttCPI in discovering anticancer candidates.
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Affiliation(s)
- Meng Wang
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Jianmin Wang
- School of Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon, 21983, Republic of Korea
| | - Zhiwei Rong
- School of Public Health, Peking University, Beijing, 100871, China
| | - Liuying Wang
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Zhenyi Xu
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Liuchao Zhang
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Jia He
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Shuang Li
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Lei Cao
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Yan Hou
- School of Public Health, Peking University, Beijing, 100871, China
| | - Kang Li
- School of Public Health, Harbin Medical University, Harbin, 150081, China.
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Liu M, Palade V, Zheng Z. Learning the consensus and complementary information for large-scale multi-view clustering. Neural Netw 2024; 172:106103. [PMID: 38219678 DOI: 10.1016/j.neunet.2024.106103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/25/2023] [Accepted: 01/04/2024] [Indexed: 01/16/2024]
Abstract
The multi-view data clustering has attracted much interest from researchers, and the large-scale multi-view clustering has many important applications and significant research value. In this article, we fully make use of the consensus and complementary information, and exploit a bipartite graph to depict the duality relationship between original points and anchor points. To be specific, representative anchor points are selected for each view to construct corresponding anchor representation matrices, and all views' anchor points are utilized to construct a common representation matrix. Using anchor points also reduces the computation complexity. Next, the bipartite graph is built by fusing these representation matrices, and a Laplacian rank constraint is enforced on the bipartite graph. This will make the bipartite graph have k connected components to obtain accurate clustering labels, where the bipartite graph is specifically designed for a large-scale dataset problem. In addition, the anchor points are also updated by dictionary learning. The experimental results on the four benchmark image processing datasets have demonstrated superior performance of the proposed large-scale multi-view clustering algorithm over other state-of-the-art multi-view clustering algorithms.
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Affiliation(s)
- Maoshan Liu
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China.
| | - Vasile Palade
- Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry CV1 2TL, UK.
| | - Zhonglong Zheng
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China.
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22
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Nguyen Y, Nocturne G, Henry J, Ng WF, Belkhir R, Desmoulins F, Bergé E, Morel J, Perdriger A, Dernis E, Devauchelle-Pensec V, Sène D, Dieudé P, Couderc M, Fauchais AL, Larroche C, Vittecoq O, Salliot C, Hachulla E, Le Guern V, Gottenberg JE, Mariette X, Seror R. Identification of distinct subgroups of Sjögren's disease by cluster analysis based on clinical and biological manifestations: data from the cross-sectional Paris-Saclay and the prospective ASSESS cohorts. Lancet Rheumatol 2024; 6:e216-e225. [PMID: 38437852 PMCID: PMC10949202 DOI: 10.1016/s2665-9913(23)00340-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 03/06/2024]
Abstract
BACKGROUND Sjögren's disease is a heterogenous autoimmune disease with a wide range of symptoms-including dryness, fatigue, and pain-in addition to systemic manifestations and an increased risk of lymphoma. We aimed to identify distinct subgroups of the disease, using cluster analysis based on subjective symptoms and clinical and biological manifestations, and to compare the prognoses of patients in these subgroups. METHODS This study included patients with Sjögren's disease from two independent cohorts in France: the cross-sectional Paris-Saclay cohort and the prospective Assessment of Systemic Signs and Evolution of Sjögren's Syndrome (ASSESS) cohort. We first used an unsupervised multiple correspondence analysis to identify clusters within the Paris-Saclay cohort using 26 variables comprising patient-reported symptoms and clinical and biological manifestations. Next, we validated these clusters using patients from the ASSESS cohort. Changes in disease activity (measured by the European Alliance of Associations for Rheumatology [EULAR] Sjögren's Syndrome Disease Activity Index [ESSDAI]), patient-acceptable symptom state (measured by the EULAR Sjögren's Syndrome Patient Reported Index [ESSPRI]), and lymphoma incidence during follow-up were compared between clusters. Finally, we compared our clusters with the symptom-based subgroups previously described by Tarn and colleagues. FINDINGS 534 patients from the Paris-Saclay cohort (502 [94%] women, 32 [6%] men, median age 54 years [IQR 43-64]), recruited between 1999 and 2022, and 395 patients from the ASSESS cohort (370 [94%] women, 25 [6%] men, median age 53 years [43-63]), recruited between 2006 and 2009, were included in this study. In both cohorts, hierarchical cluster analysis revealed three distinct subgroups of patients: those with B-cell active disease and low symptom burden (BALS), those with high systemic disease activity (HSA), and those with low systemic disease activity and high symptom burden (LSAHS). During follow-up in the ASSESS cohort, disease activity and symptom states worsened for patients in the BALS cluster (67 [36%] of 186 patients with ESSPRI score <5 at month 60 vs 92 [49%] of 186 at inclusion; p<0·0001). Lymphomas occurred in patients in the BALS cluster (five [3%] of 186 patients; diagnosed a median of 70 months [IQR 42-104] after inclusion) and the HSA cluster (six [4%] of 158 patients; diagnosed 23 months [13-83] after inclusion). All patients from the Paris-Saclay cohort with a history of lymphoma were in the BALS and HSA clusters. This unsupervised clustering classification based on symptoms and clinical and biological manifestations did not correlate with a previous classification based on symptoms only. INTERPRETATION On the basis of symptoms and clinical and biological manifestations, we identified three distinct subgroups of patients with Sjögren's disease with different prognoses. Our results suggest that these subgroups represent different heterogeneous pathophysiological disease mechanisms, stages of disease, or both. These findings could be of interest when stratifying patients in future therapeutic trials. FUNDING Fondation pour la Recherche Médicale, French Ministry of Health, French Society of Rheumatology, Innovative Medicines Initiative 2 Joint Undertaking, Medical Research Council UK, and Foundation for Research in Rheumatology.
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Affiliation(s)
- Yann Nguyen
- Department of Rheumatology, Hôpital Bicêtre, Assistance Publique - Hôpitaux de Paris, Université Paris-Saclay, Paris, France; Center for Immunology of Viral Infections and Auto-immune Diseases (IMVA), Institut pour la Santé et la Recherche Médicale (INSERM) UMR 1184, Université Paris-Saclay, Paris, France
| | - Gaëtane Nocturne
- Department of Rheumatology, Hôpital Bicêtre, Assistance Publique - Hôpitaux de Paris, Université Paris-Saclay, Paris, France; Center for Immunology of Viral Infections and Auto-immune Diseases (IMVA), Institut pour la Santé et la Recherche Médicale (INSERM) UMR 1184, Université Paris-Saclay, Paris, France
| | - Julien Henry
- Department of Rheumatology, Hôpital Bicêtre, Assistance Publique - Hôpitaux de Paris, Université Paris-Saclay, Paris, France
| | - Wan-Fai Ng
- Faculty of Medical Sciences, Clinical and Translational Research Institute, Newcastle University, NIHR Newcastle Biomedical Research Centre and NIHR Newcastle Clinical Research Facility, Newcastle upon Tyne NHS Hospitals Foundation Trust, Newcastle upon Tyne, UK
| | - Rakiba Belkhir
- Department of Rheumatology, Hôpital Bicêtre, Assistance Publique - Hôpitaux de Paris, Université Paris-Saclay, Paris, France
| | - Frédéric Desmoulins
- Department of Rheumatology, Hôpital Bicêtre, Assistance Publique - Hôpitaux de Paris, Université Paris-Saclay, Paris, France
| | - Elisabeth Bergé
- Department of Rheumatology, Hôpital Bicêtre, Assistance Publique - Hôpitaux de Paris, Université Paris-Saclay, Paris, France
| | - Jacques Morel
- Rheumatology Department, CHU de Montpellier, PhyMedExp, Université de Montpellier, INSERM, CNRS, Montpellier, France
| | - Aleth Perdriger
- Rheumatology Department, CHU Rennes, Université Rennes, Rennes, France
| | - Emmanuelle Dernis
- Department of Rheumatology and Clinical Immunology, General Hospital, Le Mans, France
| | - Valérie Devauchelle-Pensec
- Department of Rheumatology, CHU de Brest, INSERM 1227, LBAI, Université de Bretagne Occidentale, Centre de Référence des Maladies Auto-Immunes Rares de l'Adulte, Brest, France
| | - Damien Sène
- Department of Internal Medicine, Hôpital Lariboisière, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Philippe Dieudé
- Department of Rheumatology, Hôpital Bichat-Claude Bernard, Assistance Publique - Hôpitaux de Paris, INSERM UMR1152, Paris-Cité University, Paris, France
| | - Marion Couderc
- Department of Rheumatology, CHU de Clermont-Ferrand, INSERM UMR 1240, Clermont Auvergne University, Clermont-Ferrand, France
| | - Anne-Laure Fauchais
- Department of Internal Medicine, University Hospital of Limoges, Limoges, France
| | - Claire Larroche
- Department of Internal Medicine, Assistance Publique - Hôpitaux de Paris, Hôpital Avicenne, Bobigny, France
| | - Olivier Vittecoq
- Department of Rheumatology, Rouen University Hospital, Rouen, France
| | - Carine Salliot
- Department of Rheumatology, Centre Hospitalier Universitaire d'Orléans, Orléans, France
| | - Eric Hachulla
- Department of Internal Medicine and Clinical Immunology, Hôpital Claude Huriez, University of Lille, Lille, France
| | - Véronique Le Guern
- National Referral Centre for Rare Autoimmune and Systemic Diseases, Department of Internal Medicine, Hôpital Cochin, Assistance Publique - Hôpitaux de Paris Centre, Université Paris Cité, Paris, France
| | - Jacques-Eric Gottenberg
- Rheumatology Department, EA 3432, Hôpitaux Universitaires de Strasbourg, Université de Strasbourg, France
| | - Xavier Mariette
- Department of Rheumatology, Hôpital Bicêtre, Assistance Publique - Hôpitaux de Paris, Université Paris-Saclay, Paris, France; Center for Immunology of Viral Infections and Auto-immune Diseases (IMVA), Institut pour la Santé et la Recherche Médicale (INSERM) UMR 1184, Université Paris-Saclay, Paris, France
| | - Raphaèle Seror
- Department of Rheumatology, Hôpital Bicêtre, Assistance Publique - Hôpitaux de Paris, Université Paris-Saclay, Paris, France; Center for Immunology of Viral Infections and Auto-immune Diseases (IMVA), Institut pour la Santé et la Recherche Médicale (INSERM) UMR 1184, Université Paris-Saclay, Paris, France.
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Erdin M, Polat C, Smura T, Irmak S, Cetintas O, Cogal M, Colak F, Karatas A, Sozen M, Matur F, Vapalahti O, Sironen T, Oktem IMA. Phylogenetic Characterization of Orthohantavirus dobravaense (Dobrava Virus). Emerg Infect Dis 2024; 30:779-782. [PMID: 38526228 DOI: 10.3201/eid3004.230912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024] Open
Abstract
We report complete coding sequences of Orthohantavirus dobravaense (Dobrava virus) Igneada strains and phylogenetic characterization of all available complete coding sequences. Our analyses suggested separation of host-dependent lineages, followed by geographic clustering. Surveillance of orthohantaviruses using complete genomes would be useful for assessing public health threats from Dobrava virus.
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24
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Chen J, Wen B. Bi-level gene selection of cancer by combining clustering and sparse learning. Comput Biol Med 2024; 172:108236. [PMID: 38471351 DOI: 10.1016/j.compbiomed.2024.108236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 02/07/2024] [Accepted: 02/25/2024] [Indexed: 03/14/2024]
Abstract
The diagnosis of cancer based on gene expression profile data has attracted extensive attention in the field of biomedical science. This type of data usually has the characteristics of high dimensionality and noise. In this paper, a hybrid gene selection method based on clustering and sparse learning is proposed to choose the key genes with high precision. We first propose a filter method, which combines the k-means clustering algorithm and signal-to-noise ratio ranking method, and then, a weighted gene co-expression network has been applied to the reduced data set to identify modules corresponding to biological pathways. Moreover, we choose the key genes by using group bridge and sparse group lasso as wrapper methods. Finally, we conduct some numerical experiments on six cancer datasets. The numerical results show that our proposed method has achieved good performance in gene selection and cancer classification.
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Affiliation(s)
- Junnan Chen
- School of Science, Hebei University of Technology, Tianjin, PR China.
| | - Bo Wen
- Institute of Mathematics, Hebei University of Technology, Tianjin, PR China.
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25
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Bittante G, Amalfitano N, Ferragina A, Lombardi A, Tagliapietra F. Interrelationships among physical and chemical traits of cheese: Explanatory latent factors and clustering of 37 categories of cheeses. J Dairy Sci 2024; 107:1980-1992. [PMID: 37949396 DOI: 10.3168/jds.2023-23538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023]
Abstract
Cheese presents extensive variability in physical, chemical, and sensory characteristics according to the variety of processing methods and conditions used to create it. Relationships between the many characteristics of cheeses are known for single cheese types or by comparing a few of them, but not for a large number of cheese types. This case study used the properties recorded on 1,050 different cheeses from 107 producers grouped into 37 categories to analyze and quantify the interrelationships among the chemical and physical properties of many cheese types. The 15 cheese traits considered were ripening length, weight, firmness, adhesiveness, 6 different chemical characteristics, and 5 different color traits. As the 105 correlations between the 15 cheese traits were highly variable, a multivariate analysis was carried out. Four latent explanatory factors were extracted, representing 86% of the covariance matrix: the first factor (38% of covariance) was named Solids because it is mainly linked positively to fat, protein, water-soluble nitrogen, ash, firmness, adhesiveness, and ripening length, and negatively to moisture and lightness; the second factor (24%) was named Hue because it is linked positively to redness/blueness, yellowness/greenness, and chroma, and negatively to hue; the third factor (17%) was named Size because it is linked positively to weight, ripening length, firmness, and protein; and the fourth factor (7%) was named Basicity because it is linked positively to pH. The 37 cheese categories were grouped into 8 clusters and described using the latent factors: the Grana Padano cluster (characterized mainly by high Size scores); hard mountain cheeses (mainly high Solids scores); very soft cheeses (low Solids scores); blue cheeses (high Basicity scores), yellowish cheeses (high Hue scores), and 3 other clusters (soft cheeses, pasta filata and treated rind, and firm mountain cheeses) according to specific combinations of intermediate latent factors and cheese traits. In this case study, the high variability and interdependence of 15 major cheese traits can be substantially explained by only 4 latent factors, allowing us to identify and characterize 8 cheese type clusters.
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Affiliation(s)
- Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals, and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Nicolò Amalfitano
- Department of Agronomy, Food, Natural Resources, Animals, and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy.
| | - Alessandro Ferragina
- Food Quality and Sensory Science Department, Teagasc Food Research Centre, Ashtown D15 KN3K, Dublin, Ireland
| | - Angiolella Lombardi
- Department of Agronomy, Food, Natural Resources, Animals, and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
| | - Franco Tagliapietra
- Department of Agronomy, Food, Natural Resources, Animals, and Environment (DAFNAE), University of Padova (Padua), 35020 Legnaro (PD), Italy
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26
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Le Y, Zhu H, Ye C, Lin J, Wang N, Yang T. CT radiomics analysis discriminates pulmonary lesions in patients with pulmonary MALT lymphoma and non-pulmonary MALT lymphoma. Methods 2024; 224:54-62. [PMID: 38369073 DOI: 10.1016/j.ymeth.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 02/10/2024] [Accepted: 02/13/2024] [Indexed: 02/20/2024] Open
Abstract
PURPOSE The aim of this study is to create and validate a radiomics model based on CT scans, enabling the distinction between pulmonary mucosa-associated lymphoid tissue (MALT) lymphoma and other pulmonary lesion causes. METHODS Patients diagnosed with primary pulmonary MALT lymphoma and lung infections at Fuzhou Pulmonary Hospital were randomly assigned to either a training group or a validation group. Meanwhile, individuals diagnosed with primary pulmonary MALT lymphoma and lung infections at Fujian Provincial Cancer Hospital were chosen as the external test group. We employed ITK-SNAP software for delineating the Region of Interest (ROI) within the images. Subsequently, we extracted radiomics features and convolutional neural networks using PyRadiomics, a component of the Onekey AI software suite. Relevant radiomic features were selected to build an intelligent diagnostic prediction model utilizing CT images, and the model's efficacy was assessed in both the validation group and the external test group. RESULTS Leveraging radiomics, ten distinct features were carefully chosen for analysis. Subsequently, this study employed the machine learning techniques of Logistic Regression (LR), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) to construct models using these ten selected radiomics features within the training groups. Among these, SVM exhibited the highest performance, achieving an accuracy of 0.868, 0.870, and 0.90 on the training, validation, and external testing groups, respectively. For LR, the accuracy was 0.837, 0.863, and 0.90 on the training, validation, and external testing groups, respectively. For KNN, the accuracy was 0.884, 0.859, and 0.790 on the training, validation, and external testing groups, respectively. CONCLUSION We established a noninvasive radiomics model utilizing CT imaging to diagnose pulmonary MALT lymphoma associated with pulmonary lesions. This model presents a promising adjunct tool to enhance diagnostic specificity for pulmonary MALT lymphoma, particularly in populations where pulmonary lesion changes may be attributed to other causes.
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Affiliation(s)
- Yuyin Le
- Department of Hematology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China; Department of Oncology Medicine, Fuzhou Pulmonary Hospital of Fujian Province, The Teaching Hospital of Fujian Medical University, 2 Hubian Rd, 350001 Fuzhou, Fujian, China
| | - Haojie Zhu
- Department of Hematology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
| | - Chenjing Ye
- Fujian Medical University, Fuzhou, Fujian, China
| | - Jiexiang Lin
- The Shengli Clinical Medical College, Fujian Medical University, Fuzhou, Fujian 350001, China
| | - Nila Wang
- Department of Oncology Medicine, Fuzhou Pulmonary Hospital of Fujian Province, The Teaching Hospital of Fujian Medical University, 2 Hubian Rd, 350001 Fuzhou, Fujian, China
| | - Ting Yang
- Department of Hematology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China; Institute of Precision Medicine, Fujian Medical University, Fuzhou 350005, China.
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27
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Guo Z, Liang R, Tao J, Li N, Cheng Z, Yan B, Chen G. Pyrolytic homogeneity enhancement of municipal solid waste using a clustering-based sorting strategy. Waste Manag 2024; 177:232-242. [PMID: 38342060 DOI: 10.1016/j.wasman.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 01/16/2024] [Accepted: 02/01/2024] [Indexed: 02/13/2024]
Abstract
Heterogeneity of pyrolytic parameters in municipal solid waste (MSW) significantly hinders its waste-to-energy efficiency. So far, hardly any light has been shed on current pyrolytic heterogeneity conditions or feasible pyrolytic homogeneity enhancement approaches of MSW. Accordingly, pyrolytic properties (Ea and logA) of 130 MSW samples in 6 categories were collected from literature. A kinetic parameters clustering-based sorting strategy for MSW was proposed. A so-called C index was established to compare their sorting performance for Ea and logA against two traditional sorting strategies (substance categorization and density clustering). Results showed that the proposed sorting strategies outperformed the traditional ones in pyrolytic homogeneity enhancement, where the optimal C_Ea and C_logA reached 1578.30 kJ/mol and 93.11 -log min. Among investigated clustering methods, k-means clustering outperformed hierarchical clustering, which could be attributed to its adaptability to the sample structure. Future perspectives involving data set expansion, model framework development, and downstream technologies matching were also discussed. The index C established in this study can be used to evaluate other clustering models.
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Affiliation(s)
- Zilin Guo
- Tianjin University, Tianjin 300350, China
| | - Rui Liang
- Tianjin University, Tianjin 300350, China
| | - Junyu Tao
- School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China.
| | - Ning Li
- Tianjin University, Tianjin 300350, China
| | | | - Beibei Yan
- Tianjin University, Tianjin 300350, China
| | - Guanyi Chen
- School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China; School of Ecology and Environment, Tibet University, Lhasa 850012, China
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28
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Gu C, Jewett PI, Yabroff KR, Vogel RI, Parsons HM, Gangnon RE, Purani H, Blaes AH. Forgoing physician visits due to cost: regional clustering among cancer survivors by age, sex, and race/ethnicity. J Cancer Surviv 2024; 18:385-397. [PMID: 35316473 PMCID: PMC9492897 DOI: 10.1007/s11764-022-01201-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 03/08/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND Innovative treatments have improved cancer survival but also increased financial hardship for patients. While demographic factors associated with financial hardship among cancer survivors are known in the USA, the role of geography is less clear. METHODS We evaluated prevalence of forgoing care due to cost within 12 months by US Census region (Northeast, North Central/Midwest [NCMW], South, West) by demographic factors (age, sex, race/ethnicity) among 217,981 cancer survivors aged 18 to 82 years from the 2015-2019 Behavioral Risk Factor Surveillance System survey. We summarized region- and group-specific prevalence of forgoing physician visits due to cost and used multilevel logistic regression models to compare regions. RESULTS The prevalence of forgoing physician visits due to cost was highest in the South (aged < 65 years: 19-38%; aged ≥ 65: 4-21%; adjusted odds ratios [OR], NCMW versus South, OR: 0.63 [0.56-0.71]; Northeast versus South, OR: 0.63 [0.55-0.73]; West versus South, OR: 0.73 [0.64-0.84]). Across the USA, including regions with broad Medicaid expansion, younger, female, and persons of color most often reported cost-related forgoing physician visits. CONCLUSION Forgoing physician visits due to cost among cancer survivors is regionally clustered, raising concerns for concentrated poor long-term cancer outcomes. Underlying factors likely include variation in regional population compositions and contextual factors, such as Medicaid expansion and social policies. Disproportionate cost burden among survivors of color in all regions highlight systemic barriers, underscoring the need to improve access to the entire spectrum of care for cancer survivors, and especially for those most vulnerable.
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Affiliation(s)
- Christina Gu
- Division of Hematology and Oncology, Department of Medicine, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN, 55455, USA.
| | - Patricia I Jewett
- Division of Hematology and Oncology, Department of Medicine, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN, 55455, USA
- Division of Gynecologic Oncology, Department of Obstetrics, Gynecology and Women's Health, University of Minnesota, Minneapolis, MN, USA
| | - K Robin Yabroff
- Surveillance & Health Equity Science Department, American Cancer Society, Atlanta, GA, USA
| | - Rachel I Vogel
- Division of Gynecologic Oncology, Department of Obstetrics, Gynecology and Women's Health, University of Minnesota, Minneapolis, MN, USA
| | - Helen M Parsons
- Division of Health Policy and Management, University of Minnesota, Minneapolis, MN, USA
| | - Ronald E Gangnon
- Department of Population Health Sciences, University of Wisconsin, Madison, WI, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA
| | - Himal Purani
- Division of Hematology and Oncology, Department of Medicine, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN, 55455, USA
| | - Anne H Blaes
- Division of Hematology and Oncology, Department of Medicine, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN, 55455, USA
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29
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Wang H, Zhang W, Ma X. Contrastive and adversarial regularized multi-level representation learning for incomplete multi-view clustering. Neural Netw 2024; 172:106102. [PMID: 38219677 DOI: 10.1016/j.neunet.2024.106102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/20/2023] [Accepted: 01/04/2024] [Indexed: 01/16/2024]
Abstract
Incomplete multi-view clustering is a significant task in machine learning, given that complex systems in nature and society cannot be fully observed; it provides an opportunity to exploit the structure and functions of underlying systems. Current algorithms are criticized for failing either to balance data restoration and clustering or to capture the consistency of the representation of various views. To address these problems, a novel Multi-level Representation Learning Contrastive and Adversarial Learning (aka MRL_CAL) for incomplete multi-view clustering is proposed, in which data restoration, consistent representation, and clustering are jointly learned by exploiting features in various subspaces. Specifically, MRL_CAL employs v auto-encoder to obtain a low-level specific-view representation of instances, which restores data by estimating the distribution of the original incomplete data with adversarial learning. Then, MRL_CAL extracts a high-level representation of instances, in which the consistency of various views and labels of clusters is incorporated with contrastive learning. In this case, MRL_CAL simultaneously learns multi-level features of instances in various subspaces, which not only overcomes the confliction of representations but also improves the quality of features. Finally, MRL_CAL transforms incomplete multi-view clustering into an overall objective, where features are learned under the guidance of clustering. Extensive experimental results indicate that MRL_CAL outperforms state-of-the-art algorithms in terms of various measurements, implying that the proposed method is promising for incomplete multi-view clustering.
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Affiliation(s)
- Haiyue Wang
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China
| | - Wensheng Zhang
- School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, 510006, China
| | - Xiaoke Ma
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China.
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Locatelli G, Iovino P, Pasta A, Jurgens CY, Vellone E, Riegel B. Cluster analysis of heart failure patients based on their psychological and physical symptoms and predictive analysis of cluster membership. J Adv Nurs 2024; 80:1380-1392. [PMID: 37788062 DOI: 10.1111/jan.15890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 08/16/2023] [Accepted: 09/18/2023] [Indexed: 10/04/2023]
Abstract
AIM Patients with heart failure experience multiple co-occurring symptoms that lower their quality of life and increase hospitalization and mortality rates. So far, no heart failure symptom cluster study recruited patients from community settings or focused on symptoms predicting most clinical outcomes. Considering physical and psychological symptoms together allows understanding how they burden patients in different combinations. Moreover, studies predicting symptom cluster membership using variables other than symptoms are lacking. We aimed to (a) cluster heart failure patients based on physical and psychological symptoms and (b) predict symptom cluster membership using sociodemographic/clinical variables. DESIGN Secondary analysis of MOTIVATE-HF trial, which recruited 510 heart failure patients from a hospital, an outpatient and a community setting in Italy. METHODS Cluster analysis was performed based on the two scores of the Hospital Anxiety-Depression scale and two scores of the Heart-Failure Somatic Perception Scale predicting most clinical outcomes. ANOVA and chi-square test were used to compare patients' characteristics among clusters. For the predictive analysis, we split the data into a training set and a test set and trained three classification models on the former to predict patients' symptom cluster membership based on 11 clinical/sociodemographic variables. Permutation analysis investigated which variables best predicted cluster membership. RESULTS Four clusters were identified based on the intensity and combination of psychological and physical symptoms: mixed distress (high psychological, low physical symptoms), high distress, low distress and moderate distress. Clinical and sociodemographic differences were found among clusters. NYHA-class (New York Heart Association) and sleep quality were the most important variables in predicting symptom cluster membership. CONCLUSIONS These results can support the development of tailored symptom management intervention and the investigation of symptom clusters' effect on patient outcomes. The promising results of the predictive analysis suggest that such benefits may be obtained even when direct access to symptoms-related data is absent. IMPLICATIONS These results may be particularly useful to clinicians, patients and researchers because they highlight the importance of addressing clusters of symptoms, instead of individual symptoms, to facilitate symptom detection and management. Knowing which variables best predict symptom cluster membership can allow to obtain such benefits even when direct access to symptoms-data is absent. IMPACT Four clusters of heart failure patients characterized by different intensity and combination of psychological and physical symptoms were identified. NYHA class and sleep quality appeared important variables in predicting symptom cluster membership. REPORTING METHOD The authors have adhered to the EQUATOR guidelines STROBE to report observational cross-sectional studies. PATIENT OR PUBLIC CONTRIBUTION Patients were included only for collecting their data.
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Affiliation(s)
- Giulia Locatelli
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
- School of Nursing, Midwifery and Paramedicine, Faculty of Health Sciences, Australian Catholic University, New South Wales, Sydney, Australia
| | - Paolo Iovino
- Health Sciences Department, University of Florence, Florence, Italy
| | - Alessandro Pasta
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Corrine Y Jurgens
- Connell School of Nursing, Boston College, Massachusetts, Boston, USA
| | - Ercole Vellone
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
- Department of Nursing and Obstetrics, Wroclaw Medical University, Wroclaw, Poland
| | - Barbara Riegel
- School of Nursing, Midwifery and Paramedicine, Faculty of Health Sciences, Australian Catholic University, New South Wales, Sydney, Australia
- School of Nursing, University of Pennsylvania, Pennsylvania, Philadelphia, USA
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Tian Q, Zhao M. Generation, division and training: A promising method for source-free unsupervised domain adaptation. Neural Netw 2024; 172:106142. [PMID: 38281364 DOI: 10.1016/j.neunet.2024.106142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 12/20/2023] [Accepted: 01/21/2024] [Indexed: 01/30/2024]
Abstract
Conventional unsupervised domain adaptation (UDA) methods often presuppose the existence of labeled source domain samples while adapting the source model to the target domain. Nevertheless, this premise is not always tenable in the context of source-free UDA (SFUDA) attributed to data privacy considerations. Some existing methods address this challenging SFUDA problem by self-supervised learning. But inaccurate pseudo-labels are always unavoidable to degrade the performance of the target model among these methods. Therefore, we propose a promising SFUDA method, namely Generation, Division and Training (GDT) which aims to promote the reliability of pseudo-labels for self-supervised learning and encourage similar features to have closer predictions than dissimilar ones by contrastive learning. Specifically in our GDT method, we first refine pseudo-labels with deep clustering for target samples and then split them into reliable samples and unreliable samples. After that, we adopt self-supervised learning and information maximization for reliable samples training. And for unreliable samples, we conduct contrastive learning via the perspective of similarity and disparity to attract similar samples and repulse dissimilar samples, which helps pull the similar features closed and push the dissimilar features away, leading to efficient feature clustering. Thorough experimentation on three benchmark datasets substantiates the excellence of our proposed approach.
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Affiliation(s)
- Qing Tian
- School of Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China; Wuxi Institute of Technology, Nanjing University of Information Science and Technology, Wuxi, 214000, China; State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China.
| | - Mengna Zhao
- School of Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
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32
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Rabideau DJ, Li F, Wang R. Multiply robust generalized estimating equations for cluster randomized trials with missing outcomes. Stat Med 2024; 43:1458-1474. [PMID: 38488532 DOI: 10.1002/sim.10027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/04/2024] [Accepted: 01/18/2024] [Indexed: 03/19/2024]
Abstract
Generalized estimating equations (GEEs) provide a useful framework for estimating marginal regression parameters based on data from cluster randomized trials (CRTs), but they can result in inaccurate parameter estimates when some outcomes are informatively missing. Existing techniques to handle missing outcomes in CRTs rely on correct specification of a propensity score model, a covariate-conditional mean outcome model, or require at least one of these two models to be correct, which can be challenging in practice. In this article, we develop new weighted GEEs to simultaneously estimate the marginal mean, scale, and correlation parameters in CRTs with missing outcomes, allowing for multiple propensity score models and multiple covariate-conditional mean models to be specified. The resulting estimators are consistent provided that any one of these models is correct. An iterative algorithm is provided for implementing this more robust estimator and practical considerations for specifying multiple models are discussed. We evaluate the performance of the proposed method through Monte Carlo simulations and apply the proposed multiply robust estimator to analyze the Botswana Combination Prevention Project, a large HIV prevention CRT designed to evaluate whether a combination of HIV-prevention measures can reduce HIV incidence.
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Affiliation(s)
- Dustin J Rabideau
- Biostatistics, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, Connecticut, USA
| | - Rui Wang
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
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Li J, Wei Y, Liu J, Cheng S, Zhang X, Qiu H, Li J, He C. Integrative analysis of metabolism subtypes and identification of prognostic metabolism-related genes for glioblastoma. Biosci Rep 2024; 44:BSR20231400. [PMID: 38419527 DOI: 10.1042/bsr20231400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 02/01/2024] [Accepted: 02/28/2024] [Indexed: 03/02/2024] Open
Abstract
Increasing evidence has demonstrated that cancer cell metabolism is a critical factor in tumor development and progression; however, its role in glioblastoma (GBM) remains limited. In the present study, we classified GBM into three metabolism subtypes (MC1, MC2, and MC3) through cluster analysis of 153 GBM samples from the RNA-sequencing data of The Cancer Genome Atlas (TCGA) based on 2752 metabolism-related genes (MRGs). We further explored the prognostic value, metabolic signatures, immune infiltration, and immunotherapy sensitivity of the three metabolism subtypes. Moreover, the metabolism scoring model was established to quantify the different metabolic characteristics of the patients. Results showed that MC3, which is associated with a favorable survival outcome, had higher proportions of isocitrate dehydrogenase (IDH) mutations and lower tumor purity and proliferation. The MC1 subtype, which is associated with the worst prognosis, shows a higher number of segments and homologous recombination defects and significantly lower mRNA expression-based stemness index (mRNAsi) and epigenetic-regulation-based mRNAsi. The MC2 subtype has the highest T-cell exclusion score, indicating a high likelihood of immune escape. The results were validated using an independent dataset. Five MRGs (ACSL1, NDUFA2, CYP1B1, SLC11A1, and COX6B1) correlated with survival outcomes were identified based on metabolism-related co-expression module analysis. Laboratory-based validation tests further showed the expression of these MRGs in GBM tissues and how their expression influences cell function. The results provide a reference for developing clinical management approaches and treatments for GBM.
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Affiliation(s)
- Jiahui Li
- Department of Rehabilitation Medicine, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, Jiangsu Province 215228, China
- Center of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - Yutian Wei
- Neurovascular Center, Changhai Hospital, Naval Medical University, Shanghai, 200433, China
| | - Jiali Liu
- Center of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - Shupeng Cheng
- Center of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - Xia Zhang
- Center of Rehabilitation Medicine, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shanxi Province 710054, China
| | - Huaide Qiu
- Faculty of Rehabilitation Science, Nanjing Normal University of Special Education, Nanjing, Jiangsu Province 210038, China
| | - Jianan Li
- Center of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210029, China
| | - Chuan He
- Department of Rehabilitation Medicine, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, Jiangsu Province 215228, China
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Wu AL, Chow JC. Developing a novel algorithm for comparing cluster patterns in networks on journal articles during and after COVID-19: Bibliometric analysis. Medicine (Baltimore) 2024; 103:e37530. [PMID: 38518002 PMCID: PMC10956958 DOI: 10.1097/md.0000000000037530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 02/16/2024] [Indexed: 03/24/2024] Open
Abstract
BACKGROUND Cluster analysis is vital in bibliometrics for deciphering large sets of academic data. However, no prior research has employed a cluster-pattern algorithm to assess the similarities and differences between 2 clusters in networks. The study goals are 2-fold: to create a cluster-pattern comparison algorithm tailored for bibliometric analysis and to apply this algorithm in presenting clusters of countries, institutes, departments, authors (CIDA), and keywords on journal articles during and after COVID-19. METHODS We analyzed 9499 and 5943 articles from the Journal of Medicine (Baltimore) during and after COVID-19 in 2020 to 2021 and 2022 to 2023, sourced from the Web of Science (WoS) Core Collection. Follower-leading clustering algorithm (FLCA) was compared to other 8 counterparts in cluster validation and effectiveness and a cluster-pattern-comparison algorithm (CPCA) was developed using the similarity coefficient, collaborative maps, and thematic maps to evaluate CIDA cluster patterns. The similarity coefficients were categorized as identical, similar, dissimilar, or different for values above 0.7, between 0.5 and 0.7, between 0.3 and 0.5, and below 0.3, respectively. RESULTS Both stages displayed similar trends in annual publications and average citations, although these trends are decreasing. The peak publication year was 2020. Similarity coefficients of cluster patterns in these 2 stages for CIDA entities and keywords were 0.73, 0.35, 0.80, 0.02, and 0.83, respectively, suggesting the existence of identical patterns (>0.70) in countries, departments, and keywords plus, but dissimilar (<0.5) and different patterns (<0.3) found in institutes and 1st and corresponding authors, during and after COVID-19. CONCLUSIONS This research effectively created and utilized CPCA to analyze cluster patterns in bibliometrics. It underscores notable identical patterns in country-/department-/keyword based clusters, but dissimilar and different in institute-/author- based clusters, between these 2 stages during and after COVID-19, offering a framework for future bibliographic studies to compare cluster patterns beyond just the CIDA entities, as demonstrated in this study.
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Affiliation(s)
- Alice-Like Wu
- Department of Medical Statistics and Analytics, Coding Research Center, Toronto, Canada
| | - Julie Chi Chow
- Department of Pediatrics, Chi Mei Medical Center, Tainan, Taiwan
- Department of Pediatrics, School of Medicine, College of Medicine, Chung Shan Medical University, Taichung, Taiwan
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35
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Ceccato A, Forne C, Bos LD, Camprubí-Rimblas M, Areny-Balagueró A, Campaña-Duel E, Quero S, Diaz E, Roca O, De Gonzalo-Calvo D, Fernández-Barat L, Motos A, Ferrer R, Riera J, Lorente JA, Peñuelas O, Menendez R, Amaya-Villar R, Añón JM, Balan-Mariño A, Barberà C, Barberán J, Blandino-Ortiz A, Boado MV, Bustamante-Munguira E, Caballero J, Carbajales C, Carbonell N, Catalán-González M, Franco N, Galbán C, Gumucio-Sanguino VD, de la Torre MDC, Estella Á, Gallego E, García-Garmendia JL, Garnacho-Montero J, Gómez JM, Huerta A, Jorge-García RN, Loza-Vázquez A, Marin-Corral J, Martínez de la Gándara A, Martin-Delgado MC, Martínez-Varela I, Messa JL, Muñiz-Albaiceta G, Nieto MT, Novo MA, Peñasco Y, Pozo-Laderas JC, Pérez-García F, Ricart P, Roche-Campo F, Rodríguez A, Sagredo V, Sánchez-Miralles A, Sancho-Chinesta S, Socias L, Solé-Violan J, Suarez-Sipmann F, Tamayo-Lomas L, Trenado J, Úbeda A, Valdivia LJ, Vidal P, Bermejo J, Gonzalez J, Barbe F, Calfee CS, Artigas A, Torres A. Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort. Crit Care 2024; 28:91. [PMID: 38515193 PMCID: PMC10958830 DOI: 10.1186/s13054-024-04876-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 03/14/2024] [Indexed: 03/23/2024] Open
Abstract
BACKGROUND Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster. METHODS Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3. RESULTS Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3. CONCLUSIONS During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis.
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Affiliation(s)
- Adrian Ceccato
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Department of Medicine, Universitat Autonoma de Barcelona, Plaça Torre de L'Aigua, S/N, 08208, Sabadell, Spain.
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain.
- Intensive Care Unit, Hospital Universitari Sagrat Cor, Grupo Quironsalud, Barcelona, Spain.
| | - Carles Forne
- Heorfy Consulting, Lleida, Spain
- Department of Basic Medical Sciences, University of Lleida, Lleida, Spain
| | - Lieuwe D Bos
- Intensive Care and Laboratory of Experimental Intensive Care and Anesthesiology (LEICA), Amsterdam UMC Location AMC, University of Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
| | - Marta Camprubí-Rimblas
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Department of Medicine, Universitat Autonoma de Barcelona, Plaça Torre de L'Aigua, S/N, 08208, Sabadell, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Aina Areny-Balagueró
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Department of Medicine, Universitat Autonoma de Barcelona, Plaça Torre de L'Aigua, S/N, 08208, Sabadell, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Elena Campaña-Duel
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Department of Medicine, Universitat Autonoma de Barcelona, Plaça Torre de L'Aigua, S/N, 08208, Sabadell, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Sara Quero
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Department of Medicine, Universitat Autonoma de Barcelona, Plaça Torre de L'Aigua, S/N, 08208, Sabadell, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Emili Diaz
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Department of Medicine, Universitat Autonoma de Barcelona, Plaça Torre de L'Aigua, S/N, 08208, Sabadell, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Oriol Roca
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Department of Medicine, Universitat Autonoma de Barcelona, Plaça Torre de L'Aigua, S/N, 08208, Sabadell, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - David De Gonzalo-Calvo
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Translational Research in Respiratory Medicine, Respiratory Department, Hospital Universitari Aranu de Vilanova and Santa Maria, IRBLleida, Lleida, Spain
| | - Laia Fernández-Barat
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Department of Pneumology, Hospital Clinic of Barcelona, August Pi i Sunyer Biomedical Research Institute-IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Anna Motos
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Department of Pneumology, Hospital Clinic of Barcelona, August Pi i Sunyer Biomedical Research Institute-IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Ricard Ferrer
- Intensive Care Department, Hospital Universitari Vall d'Hebron, Vall d'Hebron Institut de Recerca, Barcelona, Spain
| | - Jordi Riera
- Intensive Care Department, Hospital Universitari Vall d'Hebron, Vall d'Hebron Institut de Recerca, Barcelona, Spain
| | - Jose A Lorente
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Hospital Universitario de Getafe, Universidad Europea, Madrid, Spain
- Department of Bioengineering, Universidad Carlos III, Madrid, Spain
| | - Oscar Peñuelas
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Hospital Universitario de Getafe, Universidad Europea, Madrid, Spain
| | - Rosario Menendez
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Pulmonary Department, University and Polytechnic Hospital La Fe, Valencia, Spain
| | - Rosario Amaya-Villar
- Intensive Care Clinical Unit, Hospital Universitario Virgen de Rocío, Seville, Spain
| | - José M Añón
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Servicio de Medicina Intensiva, Hospital Universitario La Paz, IdiPAZ, Madrid, Spain
| | | | | | - José Barberán
- Hospital Universitario HM Montepríncipe, Facultad HM Hospitales de Ciencias de La Salud, Universidad Camilo Jose Cela, Madrid, Spain
| | - Aaron Blandino-Ortiz
- Servicio de Medicina Intensiva, Hospital Universitario Ramón y Cajal, Madrid, Spain
- Intensive Care Unit, and Emergency Medicine, Universidad de Alcalá, Madrid, Spain
| | | | - Elena Bustamante-Munguira
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Department of Intensive Care Medicine, Hospital Clínico Universitario Valladolid, Valladolid, Spain
| | - Jesús Caballero
- Critical Intensive Medicine Department, Hospital Universitari Arnau de Vilanova de Lleida, IRBLleida, Lleida, Spain
| | | | - Nieves Carbonell
- Intensive Care Unit, Hospital Clínico Universitario, Valencia, Spain
| | | | | | - Cristóbal Galbán
- Department of Critical Care Medicine, CHUS, Complejo Hospitalario Universitario de Santiago, Santiago, Spain
| | - Víctor D Gumucio-Sanguino
- Department of Intensive Care, Hospital Universitari de Bellvitge, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Maria Del Carmen de la Torre
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Hospital de Mataró de Barcelona, Barcelona, Spain
| | - Ángel Estella
- Department of Medicine, Intensive Care Unit University Hospital of Jerez, University of Cádiz, INIBiCA, Cádiz, Spain
| | - Elena Gallego
- Unidad de Cuidados Intensivos, Hospital Universitario San Pedro de Alcántara, Cáceres, Spain
| | | | - José Garnacho-Montero
- Intensive Care Clinical Unit, Hospital Universitario Virgen Macarena, Seville, Spain
| | - José M Gómez
- Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Arturo Huerta
- Pulmonary and Critical Care Division, Emergency Department, Clínica Sagrada Família, Barcelona, Spain
| | | | - Ana Loza-Vázquez
- Unidad de Medicina Intensiva, Hospital Universitario Virgen de Valme, Seville, Spain
| | | | | | | | | | | | - Guillermo Muñiz-Albaiceta
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Departamento de Biología Funcional, Instituto Universitario de Oncología del Principado de Asturias, Instituto de Investigación Sanitaria del Principado de Asturias, Hospital Central de Asturias, Universidad de Oviedo, Oviedo, Spain
| | | | - Mariana Andrea Novo
- Servei de Medicina Intensiva, Hospital Universitari Son Espases, Palma, Illes Balears, Spain
| | - Yhivian Peñasco
- Servicio de Medicina Intensiva, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - Juan Carlos Pozo-Laderas
- UGC-Medicina Intensiva, Hospital Universitario Reina Sofia, Instituto Maimonides IMIBIC, Córdoba, Spain
| | - Felipe Pérez-García
- Servicio de Microbiología Clínica, Facultad de Medicina, Departamento de Biomedicina y Biotecnología, Hospital Universitario Príncipe de Asturias - Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain
| | - Pilar Ricart
- Servei de Medicina Intensiva, Hospital Universitari Germans Trias, Badalona, Spain
| | - Ferran Roche-Campo
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Hospital Verge de la Cinta, Tortosa, Tarragona, Spain
| | - Alejandro Rodríguez
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Critical Care Department, Hospital Universitario Joan XXIII, CIBERES, Rovira and Virgili University, IISPV, Tarragona, Spain
| | | | - Angel Sánchez-Miralles
- Intensive Care Unit, Hospital Universitario Sant Joan d'Alacant, Sant Joan d'Alacant, Alicante, Spain
| | - Susana Sancho-Chinesta
- Servicio de Medicina Intensiva, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Lorenzo Socias
- Intensive Care Unit, Hospital Son Llàtzer, Illes Balears, Palma, Spain
| | - Jordi Solé-Violan
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Critical Care Department, Hospital Universitario de GC Dr. Negrín, Universidad Fernando Pessoa Canarias, Las Palmas, Gran Canaria, Spain
| | - Fernando Suarez-Sipmann
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Intensive Care Unit, Hospital Universitario La Princesa, Madrid, Spain
| | - Luis Tamayo-Lomas
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Critical Care Department, Hospital Universitario Río Hortega de Valladolid, Valladolid, Spain
| | - José Trenado
- Servicio de Medicina Intensiva, Hospital Universitario Mútua de Terrassa, Terrassa, Barcelona, Spain
| | - Alejandro Úbeda
- Servicio de Medicina Intensiva, Hospital Punta de Europa, Algeciras, Spain
| | | | - Pablo Vidal
- Complexo Hospitalario Universitario de Ourense, Orense, Spain
| | - Jesus Bermejo
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Hospital Universitario Río Hortega de Valladolid, Valladolid, Spain
- Instituto de Investigación Biomédica de Salamanca (IBSAL), Gerencia Regional de Salud de Castilla y León, Salamanca, Spain
| | - Jesica Gonzalez
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Translational Research in Respiratory Medicine, Respiratory Department, Hospital Universitari Aranu de Vilanova and Santa Maria, IRBLleida, Lleida, Spain
| | - Ferran Barbe
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Translational Research in Respiratory Medicine, Respiratory Department, Hospital Universitari Aranu de Vilanova and Santa Maria, IRBLleida, Lleida, Spain
| | - Carolyn S Calfee
- Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Antonio Artigas
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Department of Medicine, Universitat Autonoma de Barcelona, Plaça Torre de L'Aigua, S/N, 08208, Sabadell, Spain.
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain.
| | - Antoni Torres
- Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Department of Pneumology, Hospital Clinic of Barcelona, August Pi i Sunyer Biomedical Research Institute-IDIBAPS, University of Barcelona, Barcelona, Spain
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Ren L, Wang J, Li W, Guo M, Yu G. Single-cell RNA-seq data clustering by deep information fusion. Brief Funct Genomics 2024; 23:128-137. [PMID: 37208992 DOI: 10.1093/bfgp/elad017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 02/13/2023] [Indexed: 05/21/2023] Open
Abstract
Determining cell types by single-cell transcriptomics data is fundamental for downstream analysis. However, cell clustering and data imputation still face the computation challenges, due to the high dropout rate, sparsity and dimensionality of single-cell data. Although some deep learning based solutions have been proposed to handle these challenges, they still can not leverage gene attribute information and cell topology in a sensible way to explore the consistent clustering. In this paper, we present scDeepFC, a deep information fusion-based single-cell data clustering method for cell clustering and data imputation. Specifically, scDeepFC uses a deep auto-encoder (DAE) network and a deep graph convolution network to embed high-dimensional gene attribute information and high-order cell-cell topological information into different low-dimensional representations, and then fuses them to generate a more comprehensive and accurate consensus representation via a deep information fusion network. In addition, scDeepFC integrates the zero-inflated negative binomial (ZINB) into DAE to model the dropout events. By jointly optimizing the ZINB loss and cell graph reconstruction loss, scDeepFC generates a salient embedding representation for clustering cells and imputing missing data. Extensive experiments on real single-cell datasets prove that scDeepFC outperforms other popular single-cell analysis methods. Both the gene attribute and cell topology information can improve the cell clustering.
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Affiliation(s)
- Liangrui Ren
- School of Software, Shandong University, 250101 Ji'nan, China
| | - Jun Wang
- Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, 250101 Ji'nan, China
| | - Wei Li
- School of Control Science and Engineering, Shandong University, 250061 Ji'nan, China
| | - Maozu Guo
- College of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, 100044,Bei'jing, China
| | - Guoxian Yu
- School of Software, Shandong University, 250101 Ji'nan, China
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37
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Elsamani Y, Kajikawa Y. How teleworking adoption is changing the labor market and workforce dynamics? PLoS One 2024; 19:e0299051. [PMID: 38502670 PMCID: PMC10950259 DOI: 10.1371/journal.pone.0299051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 02/04/2024] [Indexed: 03/21/2024] Open
Abstract
This article investigates how teleworking adoption influenced the labor market and workforce dynamic using bibliometric methods to overview 86 years of teleworking research [1936-2022]. By grouping the retrieved articles available on the Web of Science (WOS) core collection database, we revealed a holistic and topical view of teleworking literature using clustering and visualization techniques. Our results reflect the situation where the adoption of teleworking in the last three years was accelerated by the pandemic and facilitated by innovation in remote work technologies. We discussed the factors influencing one's decision to join the workforce or a specific company, besides the unintended consequences of the rapid adoption of teleworking. The study can aid organizations in developing adequate teleworking arrangements, enhancing employee outcomes, and improving retention rates. Furthermore, it can help policymakers design more effective policies to support employees, improve labor force participation rates, and improve societal well-being.
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Affiliation(s)
- Yousif Elsamani
- Department of Innovation Science, School of Environment & Society, Tokyo Institute of Technology, Tokyo, Japan
| | - Yuya Kajikawa
- Department of Innovation Science, School of Environment & Society, Tokyo Institute of Technology, Tokyo, Japan
- Institute for Future Initiatives, The University of Tokyo, Tokyo, Japan
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38
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Duan B, Chen S, Cheng X, Liu Q. Multi-slice spatial transcriptome domain analysis with SpaDo. Genome Biol 2024; 25:73. [PMID: 38504325 PMCID: PMC10949687 DOI: 10.1186/s13059-024-03213-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 03/08/2024] [Indexed: 03/21/2024] Open
Abstract
With the rapid advancements in spatial transcriptome sequencing, multiple tissue slices are now available, enabling the integration and interpretation of spatial cellular landscapes. Herein, we introduce SpaDo, a tool for multi-slice spatial domain analysis, including modules for multi-slice spatial domain detection, reference-based annotation, and multiple slice clustering at both single-cell and spot resolutions. We demonstrate SpaDo's effectiveness with over 40 multi-slice spatial transcriptome datasets from 7 sequencing platforms. Our findings highlight SpaDo's potential to reveal novel biological insights in multi-slice spatial transcriptomes.
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Affiliation(s)
- Bin Duan
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
| | - Shaoqi Chen
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
| | - Xiaojie Cheng
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
| | - Qi Liu
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
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Zhao J, Bai Y, Yang Y, Li X. The impact of aerobics on mental health and stress levels: A visualization analysis of the CiteSpace map. PLoS One 2024; 19:e0300677. [PMID: 38502660 PMCID: PMC10950220 DOI: 10.1371/journal.pone.0300677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 03/01/2024] [Indexed: 03/21/2024] Open
Abstract
This study aims to integrate research in the field of aerobics and mental health through the visualization analysis method of the CiteSpace map, to clarify the impact of aerobics on mental health and stress levels. Firstly, based on the literature method, pieces of literature related to aerobics and mental health are searched and collected. Secondly, the visualization analysis method of the CiteSpace map is employed to summarize and analyze the contents of the literature, involving statistical analysis of the annual number of publications, analysis of author characteristics, and analysis of publishing institution characteristics. In addition, keyword co-occurrence analysis and keyword cluster analysis are also conducted in related research fields. Among them, the Log-Likelihood Ratio is used in keyword cluster analysis. Finally, the results are analyzed using the visualization analysis method of the CiteSpace map and the statistics-based comprehensive results. The results demonstrate that in the recent 20 years, the average annual number of articles in related fields exceeds 190. The high-yield authors are distributed in economically developed areas, and the cooperation among authors is scattered. In the keyword clustering results, a total of 77 cluster labels are obtained. The Q value of the clustering module is 0.89, and the average clustering profile silhouette (S) value is 0.92, indicating that the clustering structure is significant and the results are reasonable. The aerobics cluster contains the most closely related keywords, covering mental health and stress levels. Data analysis based on existing studies reveals that aerobics has a significant impact on mental health and stress levels. Individuals participating in aerobics show obvious improvement in mental health inventory (MHI) scores (t(100) = 4.32, p<0.05). Individuals participating in aerobics present a remarkable reduction in the questionnaire evaluation of stress levels (t(100) = -3.91, p<0.05). This study's results support aerobics' positive effects on mental health and stress levels.
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Affiliation(s)
- Jianxin Zhao
- Department of Physical Education and Teaching, Hebei Finance University, Baoding, Hebei, China
| | - Yabing Bai
- Department of Physical Education and Teaching, Hebei Finance University, Baoding, Hebei, China
| | - Yongjing Yang
- School of Accounting and Finance, Changsha Commerce & Tourism College, Changsha, Hunan, China
| | - Xiaolei Li
- Department of Physical Education and Teaching, Hebei Finance University, Baoding, Hebei, China
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Marziali RA, Franceschetti C, Dinculescu A, Nistorescu A, Kristály DM, Moșoi AA, Broekx R, Marin M, Vizitiu C, Moraru SA, Rossi L, Di Rosa M. Reducing Loneliness and Social Isolation of Older Adults Through Voice Assistants: Literature Review and Bibliometric Analysis. J Med Internet Res 2024; 26:e50534. [PMID: 38498039 DOI: 10.2196/50534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 10/13/2023] [Accepted: 11/24/2023] [Indexed: 03/19/2024] Open
Abstract
BACKGROUND Loneliness and social isolation are major public health concerns for older adults, with severe mental and physical health consequences. New technologies may have a great impact in providing support to the daily lives of older adults and addressing the many challenges they face. In this scenario, technologies based on voice assistants (VAs) are of great interest and potential benefit in reducing loneliness and social isolation in this population, because they could overcome existing barriers with other digital technologies through easier and more natural human-computer interaction. OBJECTIVE This study aims to investigate the use of VAs to reduce loneliness and social isolation of older adults by performing a systematic literature review and a bibliometric cluster mapping analysis. METHODS We searched PubMed, Embase, and Scopus databases for articles that were published in the last 6 years, related to the following main topics: voice interface, VA, older adults, isolation, and loneliness. A total of 40 articles were found, of which 16 (40%) were included in this review. The included articles were then assessed through a qualitative scoring method and summarized. Finally, a bibliometric analysis was conducted using VOSviewer software (Leiden University's Centre for Science and Technology Studies). RESULTS Of the 16 articles included in the review, only 2 (13%) were considered of poor methodological quality, whereas 9 (56%) were of medium quality and 5 (31%) were of high quality. Finally, through bibliometric analysis, 221 keywords were extracted, of which 36 (16%) were selected. The most important keywords, by number of occurrences and by total link strength; results of the analysis with the Association Strength normalization method; and default values were then presented. The final bibliometric network consisted of 36 selected keywords, which were grouped into 3 clusters related to 3 main topics (ie, VA use for social isolation among older adults, the significance of age in the context of loneliness, and the impact of sex factors on well-being). For most of the selected articles, the effect of VA on social isolation and loneliness of older adults was a minor theme. However, more investigations were done on user experience, obtaining preliminary positive results. CONCLUSIONS Most articles on the use of VAs by older adults to reduce social isolation and loneliness focus on usability, acceptability, or user experience. Nevertheless, studies directly addressing the impact that using a VA has on the social isolation and loneliness of older adults find positive and promising results and provide important information for future research, interventions, and policy development in the field of geriatric care and technology.
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Affiliation(s)
- Rachele Alessandra Marziali
- Centre for Innovative Models for Aging Care and Technology, IRCCS INRCA-National Institute of Health and Science on Aging, Ancona, Italy
| | - Claudia Franceschetti
- Centre for Innovative Models for Aging Care and Technology, IRCCS INRCA-National Institute of Health and Science on Aging, Ancona, Italy
| | - Adrian Dinculescu
- The Space Applications and Technologies Laboratory, Institute of Space Science - Subsidiary of INFLPR (National Institute for Laser, Plasma and Radiation Physics), Magurele, Romania
| | - Alexandru Nistorescu
- The Space Applications and Technologies Laboratory, Institute of Space Science - Subsidiary of INFLPR (National Institute for Laser, Plasma and Radiation Physics), Magurele, Romania
| | - Dominic Mircea Kristály
- Department of Automatics and Information Technology, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, Brasov, Romania
| | - Adrian Alexandru Moșoi
- Department of Psychology and Education Sciences, Faculty of Psychology and Education Sciences, Transilvania University of Brasov, Brasov, Romania
| | | | - Mihaela Marin
- The Space Applications and Technologies Laboratory, Institute of Space Science - Subsidiary of INFLPR (National Institute for Laser, Plasma and Radiation Physics), Magurele, Romania
| | - Cristian Vizitiu
- The Space Applications and Technologies Laboratory, Institute of Space Science - Subsidiary of INFLPR (National Institute for Laser, Plasma and Radiation Physics), Magurele, Romania
- Department of Automatics and Information Technology, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, Brasov, Romania
| | - Sorin-Aurel Moraru
- Department of Automatics and Information Technology, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, Brasov, Romania
| | - Lorena Rossi
- Centre for Innovative Models for Aging Care and Technology, IRCCS INRCA-National Institute of Health and Science on Aging, Ancona, Italy
| | - Mirko Di Rosa
- Centre for Biostatistics and Applied Geriatric Clinical Epidemiology, IRCCS INRCA-National Institute of Health and Science on Aging, Ancona, Italy
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41
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Lu Q, Zou J, Ye Y, Wang Z. Design and implementation of a Li River water quality monitoring and analysis system based on outlier data analysis. PLoS One 2024; 19:e0299435. [PMID: 38498583 PMCID: PMC10947683 DOI: 10.1371/journal.pone.0299435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 02/11/2024] [Indexed: 03/20/2024] Open
Abstract
The detection of water quality indicators such as Temperature, pH, Turbidity, Conductivity, and TDS involves five national standard methods. Chemically based measurement techniques may generate liquid residue, causing secondary pollution. The water quality monitoring and data analysis system can effectively address the issues that conventional methods require multiple pieces of equipment and repeated measurements. This paper analyzes the distribution characteristics of the historical data from five sensors at a specific time, displays them graphically in real time, and provides an early warning of exceeding the standard; It selects four water samples from different sections of the Li River, based on the national standard method, the average measurement errors of Temperature, PH, TDS, Conductivity and Turbidity are 0.98%, 2.23%, 2.92%, 3.05% and 3.98%.;It further uses the quartile method to analyze the outlier data over 100,000 records and five historical periods are selected. Experiment results show the system is relatively stable in measuring Temperature, PH and TDS, and the proportion of outlier is 0.42%, 0.84% and 1.24%. When Turbidity and Conductivity are measured, the proportion is 3.11% and 2.92%. In the experiment of using 7 methods to fill outlier, K nearest neighbor algorithm is better than others. The analysis of data trends, outliers, means, and extreme values assists in making decisions, such as updating and maintaining equipment, addressing extreme water quality situations, and enhancing regional water quality oversight.
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Affiliation(s)
- Qirong Lu
- College of Information Science and Engineering, Guilin University of Technology, Guilin, China
- Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, China
| | - Jian Zou
- College of Information Science and Engineering, Guilin University of Technology, Guilin, China
- Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, China
| | - Yingya Ye
- College of Information Science and Engineering, Guilin University of Technology, Guilin, China
- Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, China
| | - Zexin Wang
- College of Information Science and Engineering, Guilin University of Technology, Guilin, China
- Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, China
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42
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Chen X, Wang M, Liu X, Zhang W, Yan H, Lan X, Xu Y, Tang S, Xie J. Clustering analysis for the evolutionary relationships of SARS-CoV-2 strains. Sci Rep 2024; 14:6428. [PMID: 38499639 PMCID: PMC10948388 DOI: 10.1038/s41598-024-57001-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 03/13/2024] [Indexed: 03/20/2024] Open
Abstract
To explore the differences and relationships between the available SARS-CoV-2 strains and predict the potential evolutionary direction of these strains, we employ the hierarchical clustering analysis to investigate the evolutionary relationships between the SARS-CoV-2 strains utilizing the genomic sequences collected in China till January 7, 2023. We encode the sequences of the existing SARS-CoV-2 strains into numerical data through k-mer algorithm, then propose four methods to select the representative sample from each type of strains to comprise the dataset for clustering analysis. Three hierarchical clustering algorithms named Ward-Euclidean, Ward-Jaccard, and Average-Euclidean are introduced through combing the Euclidean and Jaccard distance with the Ward and Average linkage clustering algorithms embedded in the OriginPro software. Experimental results reveal that BF.28, BE.1.1.1, BA.5.3, and BA.5.6.4 strains exhibit distinct characteristics which are not observed in other types of SARS-CoV-2 strains, suggesting their being the majority potential sources which the future SARS-CoV-2 strains' evolution from. Moreover, BA.2.75, CH.1.1, BA.2, BA.5.1.3, BF.7, and B.1.1.214 strains demonstrate enhanced abilities in terms of immune evasion, transmissibility, and pathogenicity. Hence, closely monitoring the evolutionary trends of these strains is crucial to mitigate their impact on public health and society as far as possible.
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Affiliation(s)
- Xiangzhong Chen
- School of Computer Science, Shaanxi Normal University, Xian, 710119, China
| | - Mingzhao Wang
- School of Computer Science, Shaanxi Normal University, Xian, 710119, China
| | - Xinglin Liu
- School of Computer Science, Shaanxi Normal University, Xian, 710119, China
| | - Wenjie Zhang
- School of Computer Science, Shaanxi Normal University, Xian, 710119, China
| | - Huan Yan
- School of Computer Science, Shaanxi Normal University, Xian, 710119, China
| | - Xiang Lan
- School of Computer Science, Shaanxi Normal University, Xian, 710119, China
| | - Yandi Xu
- School of Computer Science, Shaanxi Normal University, Xian, 710119, China
- College of Life Sciences, Shaanxi Normal University, Xian, 710119, China
| | - Sanyi Tang
- School of Mathematics and Statistics, Shaanxi Normal University, Xian, 710119, China.
| | - Juanying Xie
- School of Computer Science, Shaanxi Normal University, Xian, 710119, China.
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Kalkan BM, Ozcan SC, Cicek E, Gonen M, Acilan C. Nek2A prevents centrosome clustering and induces cell death in cancer cells via KIF2C interaction. Cell Death Dis 2024; 15:222. [PMID: 38493150 PMCID: PMC10944510 DOI: 10.1038/s41419-024-06601-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 03/05/2024] [Accepted: 03/07/2024] [Indexed: 03/18/2024]
Abstract
Unlike normal cells, cancer cells frequently exhibit supernumerary centrosomes, leading to formation of multipolar spindles that can trigger cell death. Nevertheless, cancer cells with supernumerary centrosomes escape the deadly consequences of unequal segregation of genomic material by coalescing their centrosomes into two poles. This unique trait of cancer cells presents a promising target for cancer therapy, focusing on selectively attacking cells with supernumerary centrosomes. Nek2A is a kinase involved in mitotic regulation, including the centrosome cycle, where it phosphorylates linker proteins to separate centrosomes. In this study, we investigated if Nek2A also prevents clustering of supernumerary centrosomes, akin to its separation function. Reduction of Nek2A activity, achieved through knockout, silencing, or inhibition, promotes centrosome clustering, whereas its overexpression results in inhibition of clustering. Significantly, prevention of centrosome clustering induces cell death, but only in cancer cells with supernumerary centrosomes, both in vitro and in vivo. Notably, none of the known centrosomal (e.g., CNAP1, Rootletin, Gas2L1) or non-centrosomal (e.g., TRF1, HEC1) Nek2A targets were implicated in this machinery. Additionally, Nek2A operated via a pathway distinct from other proteins involved in centrosome clustering mechanisms, like HSET and NuMA. Through TurboID proximity labeling analysis, we identified novel proteins associated with the centrosome or microtubules, expanding the known interaction partners of Nek2A. KIF2C, in particular, emerged as a novel interactor, confirmed through coimmunoprecipitation and localization analysis. The silencing of KIF2C diminished the impact of Nek2A on centrosome clustering and rescued cell viability. Additionally, elevated Nek2A levels were indicative of better patient outcomes, specifically in those predicted to have excess centrosomes. Therefore, while Nek2A is a proposed target, its use must be specifically adapted to the broader cellular context, especially considering centrosome amplification. Discovering partners such as KIF2C offers fresh insights into cancer biology and new possibilities for targeted treatment.
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Affiliation(s)
- Batuhan Mert Kalkan
- Koç University, Graduate School of Health Sciences, Istanbul, Turkey
- Koç University, Research Center for Translational Medicine, Istanbul, Turkey
| | | | - Enes Cicek
- Koç University, Graduate School of Health Sciences, Istanbul, Turkey
- Koç University, Research Center for Translational Medicine, Istanbul, Turkey
| | - Mehmet Gonen
- Koç University, School of Medicine, Istanbul, Turkey
- Koç University, College of Engineering, Department of Industrial Engineering, Istanbul, Turkey
| | - Ceyda Acilan
- Koç University, Research Center for Translational Medicine, Istanbul, Turkey.
- Koç University, School of Medicine, Istanbul, Turkey.
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44
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Liu R, Qian K, He X, Li H. Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation. BMC Bioinformatics 2024; 25:116. [PMID: 38493095 PMCID: PMC10944609 DOI: 10.1186/s12859-024-05706-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 02/15/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND The integration of single-cell RNA sequencing data from multiple experimental batches and diverse biological conditions holds significant importance in the study of cellular heterogeneity. RESULTS To expedite the exploration of systematic disparities under various biological contexts, we propose a scRNA-seq integration method called scDisco, which involves a domain-adaptive decoupling representation learning strategy for the integration of dissimilar single-cell RNA data. It constructs a condition-specific domain-adaptive network founded on variational autoencoders. scDisco not only effectively reduces batch effects but also successfully disentangles biological effects and condition-specific effects, and further augmenting condition-specific representations through the utilization of condition-specific Domain-Specific Batch Normalization layers. This enhancement enables the identification of genes specific to particular conditions. The effectiveness and robustness of scDisco as an integration method were analyzed using both simulated and real datasets, and the results demonstrate that scDisco can yield high-quality visualizations and quantitative outcomes. Furthermore, scDisco has been validated using real datasets, affirming its proficiency in cell clustering quality, retaining batch-specific cell types and identifying condition-specific genes. CONCLUSION scDisco is an effective integration method based on variational autoencoders, which improves analytical tasks of reducing batch effects, cell clustering, retaining batch-specific cell types and identifying condition-specific genes.
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Affiliation(s)
- Renjing Liu
- School of Mathematics and Physics, China University of Geosciences (Wuhan), Wuhan, 430074, China
| | - Kun Qian
- School of Mathematics and Physics, China University of Geosciences (Wuhan), Wuhan, 430074, China
| | - Xinwei He
- School of Mathematics and Physics, China University of Geosciences (Wuhan), Wuhan, 430074, China
| | - Hongwei Li
- School of Mathematics and Physics, China University of Geosciences (Wuhan), Wuhan, 430074, China.
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Holsten L, Dahm K, Oestreich M, Becker M, Ulas T. hCoCena: A toolbox for network-based co-expression analysis and horizontal integration of transcriptomic datasets. STAR Protoc 2024; 5:102922. [PMID: 38427570 PMCID: PMC10918327 DOI: 10.1016/j.xpro.2024.102922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/09/2024] [Accepted: 02/13/2024] [Indexed: 03/03/2024] Open
Abstract
As the number and complexity of transcriptomic datasets increase, there is a rising demand for accessible and user-friendly analysis tools. Here, we present hCoCena (horizontal construction of co-expression networks and analysis), a toolbox facilitating the analysis of a single dataset, as well as the joint analysis of multiple datasets. We describe steps for workspace setup, formatting tables, data processing, and network integration. We then detail procedures for gene clustering, gene set enrichment analysis, and transcription factor enrichment analysis. For complete details on the use and execution of this protocol, please refer to Oestreich et al.1.
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Affiliation(s)
- Lisa Holsten
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany; PRECISE Platform for Single Cell Genomics and Epigenomics, DZNE, and University of Bonn, 53127 Bonn, Germany; Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, 53115 Bonn, Germany; Department of Pediatrics, University Hospital Würzburg, 97080 Würzburg, Germany.
| | - Kilian Dahm
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany; PRECISE Platform for Single Cell Genomics and Epigenomics, DZNE, and University of Bonn, 53127 Bonn, Germany; Department of Pediatrics, University Hospital Würzburg, 97080 Würzburg, Germany
| | - Marie Oestreich
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany; Modular High-Performance Computing and Artificial Intelligence, German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Matthias Becker
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany; Modular High-Performance Computing and Artificial Intelligence, German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Thomas Ulas
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany; PRECISE Platform for Single Cell Genomics and Epigenomics, DZNE, and University of Bonn, 53127 Bonn, Germany; Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, 53115 Bonn, Germany.
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Shen Y, Yang X, Liu H, Li Z. Advancing mortality rate prediction in European population clusters: integrating deep learning and multiscale analysis. Sci Rep 2024; 14:6255. [PMID: 38491097 PMCID: PMC10942990 DOI: 10.1038/s41598-024-56390-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
Accurately predicting population mortality rates is crucial for effective retirement insurance and economic policy formulation. Recent advancements in deep learning time series forecasting (DLTSF) have led to improved mortality rate predictions compared to traditional models like Lee-Carter (LC). This study focuses on mortality rate prediction in large clusters across Europe. By utilizing PCA dimensionality reduction and statistical clustering techniques, we integrate age features from high-dimensional mortality data of multiple countries, analyzing their similarities and differences. To capture the heterogeneous characteristics, an adaptive adjustment matrix is generated, incorporating sequential variation and spatial geographical information. Additionally, a combination of graph neural networks and a transformer network with an adaptive adjustment matrix is employed to capture the spatiotemporal features between different clusters. Extensive numerical experiments using data from the Human Mortality Database validate the superiority of the proposed GT-A model over traditional LC models and other classic neural networks in terms of prediction accuracy. Consequently, the GT-A model serves as a powerful forecasting tool for global population studies and the international life insurance field.
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Affiliation(s)
- Yuewen Shen
- School of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215000, China
| | - Xinhao Yang
- School of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215000, China.
| | - Hao Liu
- School of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215000, China
| | - Ze Li
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215000, China
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Stamou MI, Smith KT, Kim H, Balasubramanian R, Gray KJ, Udler MS. Polycystic Ovary Syndrome Physiologic Pathways Implicated Through Clustering of Genetic Loci. J Clin Endocrinol Metab 2024; 109:968-977. [PMID: 37967238 PMCID: PMC10940264 DOI: 10.1210/clinem/dgad664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 11/05/2023] [Accepted: 11/09/2023] [Indexed: 11/17/2023]
Abstract
CONTEXT Polycystic ovary syndrome (PCOS) is a heterogeneous disorder, with disease loci identified from genome-wide association studies (GWAS) having largely unknown relationships to disease pathogenesis. OBJECTIVE This work aimed to group PCOS GWAS loci into genetic clusters associated with disease pathophysiology. METHODS Cluster analysis was performed for 60 PCOS-associated genetic variants and 49 traits using GWAS summary statistics. Cluster-specific PCOS partitioned polygenic scores (pPS) were generated and tested for association with clinical phenotypes in the Mass General Brigham Biobank (MGBB, N = 62 252). Associations with clinical outcomes (type 2 diabetes [T2D], coronary artery disease [CAD], and female reproductive traits) were assessed using both GWAS-based pPS (DIAMANTE, N = 898,130, CARDIOGRAM/UKBB, N = 547 261) and individual-level pPS in MGBB. RESULTS Four PCOS genetic clusters were identified with top loci indicated as following: (i) cluster 1/obesity/insulin resistance (FTO); (ii) cluster 2/hormonal/menstrual cycle changes (FSHB); (iii) cluster 3/blood markers/inflammation (ATXN2/SH2B3); (iv) cluster 4/metabolic changes (MAF, SLC38A11). Cluster pPS were associated with distinct clinical traits: Cluster 1 with increased body mass index (P = 6.6 × 10-29); cluster 2 with increased age of menarche (P = 1.5 × 10-4); cluster 3 with multiple decreased blood markers, including mean platelet volume (P = 3.1 ×10-5); and cluster 4 with increased alkaline phosphatase (P = .007). PCOS genetic clusters GWAS-pPSs were also associated with disease outcomes: cluster 1 pPS with increased T2D (odds ratio [OR] 1.07; P = 7.3 × 10-50), with replication in MGBB all participants (OR 1.09, P = 2.7 × 10-7) and females only (OR 1.11, 4.8 × 10-5). CONCLUSION Distinct genetic backgrounds in individuals with PCOS may underlie clinical heterogeneity and disease outcomes.
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Affiliation(s)
- Maria I Stamou
- Reproductive Endocrine Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Kirk T Smith
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA 02114, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Hyunkyung Kim
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA 02114, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Ravikumar Balasubramanian
- Reproductive Endocrine Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Kathryn J Gray
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Miriam S Udler
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA 02114, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
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Liang S, Dou J, Iqbal R, Chen K. Label-aware distance mitigates temporal and spatial variability for clustering and visualization of single-cell gene expression data. Commun Biol 2024; 7:326. [PMID: 38486077 PMCID: PMC10940680 DOI: 10.1038/s42003-024-05988-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 02/28/2024] [Indexed: 03/18/2024] Open
Abstract
Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. The batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Label-Aware Distance (LAD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate LAD on simulated data as well as apply it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). LAD provides better cell embedding than state-of-the-art batch correction methods on longitudinal datasets. It can be used in distance-based clustering and visualization methods to combine the power of multiple samples to help make biological findings.
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Affiliation(s)
- Shaoheng Liang
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA.
- Department of Computer Science, Rice University, Houston, TX, USA.
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Jinzhuang Dou
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Ramiz Iqbal
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA.
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Tokuoka Y, Seo M, Hayakawa H, Yamasaki F, Kimura K, Takashima K, Hashigoe K, Matsui H, Oka M. Different divergence processes of isoglosses of folk nomenclature between wild trees and rice landraces imply the need for different conservation planning based on the type of plant resources. J Ethnobiol Ethnomed 2024; 20:35. [PMID: 38486237 PMCID: PMC10941470 DOI: 10.1186/s13002-024-00675-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/08/2024] [Indexed: 03/17/2024]
Abstract
BACKGROUND The intensification of production and socio-economic changes have accelerated the loss of local traditional knowledge and plant resources. Understanding the distribution and determinants of such biocultural diversity is essential in planning efficient surveys and conservation efforts. Because the concept of biocultural diversity in socio-ecological adaptive systems comprises biological, cultural, and linguistic diversity, linguistic information should serve as a surrogate for the distribution of local biological and cultural diversity. In this study, we spatio-linguistically evaluated the names of local trees and rice landraces recorded in Ehime Prefecture, southwestern Japan. METHODS Hierarchical clustering was performed separately for the names of local trees and rice landraces. By considering innate flora differences and species having multiple local names, a novel distance index was adopted for local tree names. For the names of rice landraces, Jaccard distance was adopted. V-measure and factor detector analysis were used to evaluate the spatial association between the isogloss maps of the folk nomenclature derived from the clustering and multiple thematic maps. RESULTS Local tree names showed stronger spatial association with geographical factors than rice landrace names. One folk nomenclature group of trees overlapped well with the slash-and-burn cultivation area, suggesting a link between the naming of trees and the traditional production system. In contrast, rice landraces exhibited stronger associations with folklore practices. Moreover, influences of road networks and pilgrimages on rice landraces indicated the importance of human mobility and traditional rituals on rice seed transfer. High homogeneity and low completeness in the V-measure analysis indicated that the names of local trees and rice landraces were mostly homogenous within current municipalities and were shared with a couple of adjacent municipalities. The isogloss maps help to illustrate how the biological and cultural diversity of wild trees and rice landraces are distributed. They also help to identify units for inter-municipal collaboration for effective conservation of traditional knowledge related to those plant resources and traditional rice varieties themselves. CONCLUSIONS Our spatio-linguistic evaluation indicated that complex geographical and sociological processes influence the formation of plant folk nomenclature groups and implies a promising approach using quantitative lexico-statistical analysis to help to identify areas for biocultural diversity conservation.
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Affiliation(s)
- Yoshinori Tokuoka
- Faculty for Collaborative Regional Innovation, Ehime University, 3, Bunkyo, Matsuyama, Ehime, 790-8577, Japan.
| | - Mincheol Seo
- Faculty of Law and Letters, Ehime University, 3, Bunkyo, Matsuyama, Ehime, 790-8577, Japan
| | - Hiroshi Hayakawa
- Curatorial Division, Museum of Natural and Environmental History, 5762, Oya, Suruga, Shizuoka, Shizuoka, 422-8017, Japan
| | - Fukuhiro Yamasaki
- Research Center of Genetic Resources, National Agriculture and Food Research Organization, 2-1-2 Kannondai, Tsukuba, Ibaraki, 305-8602, Japan
| | - Kenichiro Kimura
- Institute for Rural Engineering, National Agriculture and Food Research Organization, 2-1-6 Kannondai, Tsukuba, Ibaraki, 305-8609, Japan
| | - Kenji Takashima
- Sadamisaki Hanto Museum, 293 Shionashi Otsu, Ikata, Nishiuwa, Ehime, 796-0506, Japan
| | - Kiyokazu Hashigoe
- Center for Research in Science Education, Ehime University, 3, Bunkyo, Matsuyama, Ehime, 790-8577, Japan
| | | | - Mitsunori Oka
- Tokyo NODAI Research Institute, Tokyo University of Agriculture, 1-1-1, Sakuragaoka, Setagaya, Tokyo, 156-8502, Japan
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Cojic M, Klisic A, Sahmanovic A, Petrovic N, Kocic G. Cluster analysis of patient characteristics, treatment modalities, renal impairments, and inflammatory markers in diabetes mellitus. Sci Rep 2024; 14:5994. [PMID: 38472402 PMCID: PMC10933260 DOI: 10.1038/s41598-024-56451-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 03/06/2024] [Indexed: 03/14/2024] Open
Abstract
Type 2 diabetes mellitus (T2DM) is caused by an interplay of various factors where chronic hyperglycemia and inflammation have central role in its onset and progression. Identifying patient groups with increased inflammation in order to provide more personalized approach has become crucial. We hypothesized that grouping patients into clusters according to their clinical characteristics could identify distinct unique profiles that were previously invisible to the clinical eye. A cross-sectional record-based study was performed at the Primary Health Care Center Podgorica, Montenegro, on 424 T2DM patients aged between 30 and 85. Using hierarchical clustering patients were grouped into four distinct clusters based on 12 clinical variables, including glycemic and other relevant metabolic indicators. Inflammation was assessed through neutrophil-to-lymphocyte (NLR) and platelet to lymphocyte ratio (PLR). Cluster 3 which featured the oldest patients with the longest T2DM duration, highest hypertension rate, poor glycemic control and significant GFR impairment had the highest levels of inflammatory markers. Cluster 4 which featured the youngest patients, with the best glycemic control, the highest GFR had the lowest prevalence of coronary disease, but not the lowest levels of inflammatory markers. Identifying these clusters offers physicians opportunity for more personalized T2DM management, potentially mitigating its associated complications.
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Affiliation(s)
- Milena Cojic
- University of Montenegro-Faculty of Medicine, Podgorica, Montenegro.
- Primary Health Care Center, Podgorica, Montenegro.
| | - Aleksandra Klisic
- University of Montenegro-Faculty of Medicine, Podgorica, Montenegro
- Primary Health Care Center, Podgorica, Montenegro
| | - Amina Sahmanovic
- University of Montenegro-Faculty of Medicine, Podgorica, Montenegro
- Primary Health Care Center, Podgorica, Montenegro
| | | | - Gordana Kocic
- Department of Medical Biochemistry, School of Medicine, University of Nis, Niš, Serbia
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