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Zhang J, Shang S, Huo Z, Chen J, Wang Y. Elemental Design of Alkali-Activated Materials with Solid Wastes Using Machine Learning. MATERIALS (BASEL, SWITZERLAND) 2024; 17:4573. [PMID: 39336315 PMCID: PMC11433302 DOI: 10.3390/ma17184573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2024] [Revised: 09/05/2024] [Accepted: 09/12/2024] [Indexed: 09/30/2024]
Abstract
Understanding the strength development of alkali-activated materials (AAMs) with fly ash (FA) and granulated blast furnace slag (GBFS) is crucial for designing high-performance AAMs. This study investigates the strength development mechanism of AAMs using machine learning. A total of 616 uniaxial compressive strength (UCS) data points from FA-GBFS-based AAM mixtures were collected from published literature to train four tree-based machine learning models. Among these models, Gradient Boosting Regression (GBR) demonstrated the highest prediction accuracy, with a correlation coefficient (R-value) of 0.970 and a root mean square error (RMSE) of 4.110 MPa on the test dataset. The SHapley Additive exPlanations (SHAP) analysis revealed that water content is the most influential variable in strength development, followed by curing periods. The study recommends a calcium-to-silicon ratio of around 1.3, a sodium-to-aluminum ratio slightly below 1, and a silicon-to-aluminum ratio slightly above 3 for optimal AAM performance. The proposed design model was validated through laboratory experiments with FA-GBFS-based AAM mixtures, confirming the model's reliability. This research provides novel insights into the strength development mechanism of AAMs and offers a practical guide for elemental design, potentially leading to more sustainable construction materials.
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Affiliation(s)
- Junfei Zhang
- School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China
| | - Shenyan Shang
- School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China
| | - Zehui Huo
- School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China
| | - Junlin Chen
- Arizona College of Technology, Hebei University of Technology, Tianjin 300401, China;
| | - Yuhang Wang
- Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100085, China
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152
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Meda N, Zammarrelli J, Sambataro F, De Leo D. Late-life suicide: machine learning predictors from a large European longitudinal cohort. Front Psychiatry 2024; 15:1455247. [PMID: 39355379 PMCID: PMC11442232 DOI: 10.3389/fpsyt.2024.1455247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Accepted: 08/23/2024] [Indexed: 10/03/2024] Open
Abstract
Background People in late adulthood die by suicide at the highest rate worldwide. However, there are still no tools to help predict the risk of death from suicide in old age. Here, we leveraged the Survey of Health, Ageing, and Retirement in Europe (SHARE) prospective dataset to train and test a machine learning model to identify predictors for suicide in late life. Methods Of more than 16,000 deaths recorded, 74 were suicides. We matched 73 individuals who died by suicide with people who died by accident, according to sex (28.8% female in the total sample), age at death (67 ± 16.4 years), suicidal ideation (measured with the EURO-D scale), and the number of chronic illnesses. A random forest algorithm was trained on demographic data, physical health, depression, and cognitive functioning to extract essential variables for predicting death from suicide and then tested on the test set. Results The random forest algorithm had an accuracy of 79% (95% CI 0.60-0.92, p = 0.002), a sensitivity of.80, and a specificity of.78. Among the variables contributing to the model performance, the three most important factors were how long the participant was ill before death, the frequency of contact with the next of kin and the number of offspring still alive. Conclusions Prospective clinical and social information can predict death from suicide with good accuracy in late adulthood. Most of the variables that surfaced as risk factors can be attributed to the construct of social connectedness, which has been shown to play a decisive role in suicide in late life.
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Affiliation(s)
- Nicola Meda
- Department of Neuroscience, University of Padova, Padova, Italy
| | | | - Fabio Sambataro
- Department of Neuroscience, University of Padova, Padova, Italy
- Padova University Hospital, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Diego De Leo
- De Leo Fund, Research Division, Padova, Italy
- Italian Psychogeriatric Association, Padova, Italy
- Australian Institute for Suicide Research and Prevention, Griffith University, Mt Gravatt Campus, Brisbane, QLD, Australia
- Slovene Centre for Suicide Research, Primorska University, Koper, Slovenia
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153
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Yan H, Xu Z. A novel deep ensemble-based model with outlier removal and order-invariant ranking for carbon dioxide emission prediction. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024:10.1007/s11356-024-34817-2. [PMID: 39287736 DOI: 10.1007/s11356-024-34817-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 08/23/2024] [Indexed: 09/19/2024]
Abstract
Excessive carbon dioxide ( CO 2 ) emissions pose a formidable challenge, driving global climate change and necessitating urgent attention. Striking a balance between curbing CO 2 emissions and fostering economic growth hinges upon the ability to reliably forecast CO 2 emissions. Such forecasts are indispensable for policymakers as they endeavor to make informed decisions and proactively implement mitigation measures. In this research, we introduce an innovative deep ensemble prediction model for CO 2 emissions. This model is constructed around four parallel Long Short-Term Memory (LSTM) neural networks, complemented by a novel Multi-Layer Perception (MLP)-based ensemble framework, equipped with an outlier detection mechanism and an order-invariant ranking module. To enhance prediction accuracy and stability, a k-nearest neighbor (KNN)-based outlier detection module is employed to identify non-outliers and reasonable predictions for the ensemble models. Additionally, a novel feature ranking module is proposed to mitigate prediction fluctuations. The performance evaluation of our model is conducted using historical CO 2 emission data spanning from 1971 to 2021, encompassing six representative countries. Our findings demonstrate that the proposed methodology outperforms existing approaches across various evaluation metrics, offering considerably reduced prediction variances and greater stability. Moreover, long-term CO 2 emission predictions for the corresponding six countries have been provided, which might offer policymakers some basis for making decisions.
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Affiliation(s)
- Huan Yan
- Institute of Industrial Economics, Chinese Academy of Social Science, Beijing, 100006, PR China.
| | - Zhaoyang Xu
- Department of Paediatrics, Cambridge University, Cambridge, UK
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154
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Pan B, Madani MS, Forsberg AP, Brutchey RL, Malmstadt N. Solvent Dependence of Ionic Liquid-Based Pt Nanoparticle Synthesis: Machine Learning-Aided In-Line Monitoring in a Flow Reactor. ACS NANO 2024; 18:25542-25551. [PMID: 39235302 PMCID: PMC11411720 DOI: 10.1021/acsnano.4c05807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/06/2024]
Abstract
Colloidal platinum nanoparticles (Pt NPs) possess a myriad of technologically relevant applications. A potentially sustainable route to synthesize Pt NPs is via polyol reduction in ionic liquid (IL) solvents; however, the development of this synthetic method is limited by the fact that reaction kinetics have not been investigated. In-line analysis in a flow reactor is an appealing approach to obtain such kinetic data; unfortunately, the optical featurelessness of Pt NPs in the visible spectrum complicates the direct analysis of flow chemistry products via ultraviolet-visible (UV-vis) spectrophotometry. Here, we report a machine learning (ML)-based approach to analyze in-line UV-vis spectrophotometric data to determine Pt NP product concentrations. Using a benchtop flow reactor with ML-interpreted in-line analysis, we were able to investigate NP yield as a function of residence time for two IL solvents: 1-butyl-1-methylpyrrolidinium triflate (BMPYRR-OTf) and 1-butyl-2-methylpyridinium triflate (BMPY-OTf). While these solvents are structurally similar, the polyol reduction shows radically different yields of Pt NPs depending on which solvent is used. The approach presented here will help develop an understanding of how the subtle differences in the molecular structures of these solvents lead to distinct reaction behavior. The accuracy of the ML prediction was validated by particle size analysis and the error was found to be as low as 4%. This approach is generalizable and has the potential to provide information on various reaction outcomes stemming from solvent effects, for example, differential yields, orders of reaction, rate coefficients, NP sizes, etc.
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Affiliation(s)
- Bin Pan
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 925 Bloom Walk, Los Angeles, California 90089-1211, United States
| | - Majed S Madani
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 925 Bloom Walk, Los Angeles, California 90089-1211, United States
- Department of Chemical and Materials Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Allison P Forsberg
- Department of Chemistry, University of Southern California, 840 Downey Way, Los Angeles, California 90089-0744, United States
| | - Richard L Brutchey
- Department of Chemistry, University of Southern California, 840 Downey Way, Los Angeles, California 90089-0744, United States
| | - Noah Malmstadt
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 925 Bloom Walk, Los Angeles, California 90089-1211, United States
- Department of Chemistry, University of Southern California, 840 Downey Way, Los Angeles, California 90089-0744, United States
- Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, California 90089-0260, United States
- USC Norris Comprehensive Cancer Center, University of Southern California, 1441 Eastlake Ave, Los Angeles, California 90033, United States
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155
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Shabangu FW, Munoz T, Van Uffelen L, Estabrook BJ, Yemane D, Stafford KM, Branch TA, Vermeulen E, van den Berg MA, Lamont T. Diverse baleen whale acoustic occurrence around two sub-Antarctic islands: A tale of residents and visitors. Sci Rep 2024; 14:21663. [PMID: 39289429 PMCID: PMC11408682 DOI: 10.1038/s41598-024-72696-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 09/10/2024] [Indexed: 09/19/2024] Open
Abstract
Knowledge on the occurrence and behaviour of baleen whales around sub-Antarctic regions is limited, and usually based on short, seasonal sighting research from shore or research vessels and whaling records, neither of which provide accurate and comprehensive year-round perspectives of these animals' ecology. We investigated the seasonal acoustic occurrence and diel vocalizing pattern of baleen whales around the sub-Antarctic Prince Edward Islands (PEIs) using passive acoustic monitoring data from mid-2021 to mid-2023, detecting six distinct baleen whale songs from Antarctic blue whales, Madagascan pygmy blue whales, fin whales, Antarctic minke whales, humpback whales, and sei whales. Antarctic blue and fin whales were detected year-round whereas the other species' songs were detected seasonally, including a new Antarctic minke whale bio-duck song sub-type described here for the first time. Antarctic minke and sei whales were more vocally active at night-time whereas the other species had no clear diel vocalizing patterns. Random forest models identified month and/or sea surface temperature as the most important predictors of all baleen whale acoustic occurrence. These novel results highlight the PEIs as a useful habitat for baleen whales given the number of species that inhabit or transit through this region.
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Affiliation(s)
- Fannie W Shabangu
- Fisheries Management Branch, Department of Forestry, Fisheries and the Environment, Foreshore, Cape Town, South Africa.
- Mammal Research Institute Whale Unit, University of Pretoria, Private Bag X20, Hatfield, 0028, Pretoria, South Africa.
| | - Tessa Munoz
- Department of Ocean Engineering, University of Rhode Island, Narragansett, RI, 02882, USA
- Applied Ocean Sciences, 5242 Port Royal Road #1032, Springfield, VA, 22151, USA
| | - Lora Van Uffelen
- Department of Ocean Engineering, University of Rhode Island, Narragansett, RI, 02882, USA
| | - Bobbi J Estabrook
- K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY, 14850, USA
| | - Dawit Yemane
- Fisheries Management Branch, Department of Forestry, Fisheries and the Environment, Foreshore, Cape Town, South Africa
| | | | - Trevor A Branch
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, 98195, USA
| | - Els Vermeulen
- Mammal Research Institute Whale Unit, University of Pretoria, Private Bag X20, Hatfield, 0028, Pretoria, South Africa
| | - Marcel A van den Berg
- Oceans and Coasts Research Branch, Department of Forestry, Fisheries and the Environment, Foreshore, Cape Town, South Africa
| | - Tarron Lamont
- Oceans and Coasts Research Branch, Department of Forestry, Fisheries and the Environment, Foreshore, Cape Town, South Africa
- Department of Oceanography, University of Cape Town, Cape Town, South Africa
- Nansen-Tutu Centre for Marine Environmental Research, University of Cape Town, Cape Town, South Africa
- Bayworld Centre for Research and Education, Cape Town, South Africa
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156
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Wahid KA, Kaffey ZY, Farris DP, Humbert-Vidan L, Moreno AC, Rasmussen M, Ren J, Naser MA, Netherton TJ, Korreman S, Balakrishnan G, Fuller CD, Fuentes D, Dohopolski MJ. Artificial intelligence uncertainty quantification in radiotherapy applications - A scoping review. Radiother Oncol 2024; 201:110542. [PMID: 39299574 DOI: 10.1016/j.radonc.2024.110542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 08/18/2024] [Accepted: 09/09/2024] [Indexed: 09/22/2024]
Abstract
BACKGROUND/PURPOSE The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ) methods. The purpose of this study was to scope existing literature related to UQ in RT, identify areas of improvement, and determine future directions. METHODS We followed the PRISMA-ScR scoping review reporting guidelines. We utilized the population (human cancer patients), concept (utilization of AI UQ), context (radiotherapy applications) framework to structure our search and screening process. We conducted a systematic search spanning seven databases, supplemented by manual curation, up to January 2024. Our search yielded a total of 8980 articles for initial review. Manuscript screening and data extraction was performed in Covidence. Data extraction categories included general study characteristics, RT characteristics, AI characteristics, and UQ characteristics. RESULTS We identified 56 articles published from 2015 to 2024. 10 domains of RT applications were represented; most studies evaluated auto-contouring (50 %), followed by image-synthesis (13 %), and multiple applications simultaneously (11 %). 12 disease sites were represented, with head and neck cancer being the most common disease site independent of application space (32 %). Imaging data was used in 91 % of studies, while only 13 % incorporated RT dose information. Most studies focused on failure detection as the main application of UQ (60 %), with Monte Carlo dropout being the most commonly implemented UQ method (32 %) followed by ensembling (16 %). 55 % of studies did not share code or datasets. CONCLUSION Our review revealed a lack of diversity in UQ for RT applications beyond auto-contouring. Moreover, we identified a clear need to study additional UQ methods, such as conformal prediction. Our results may incentivize the development of guidelines for reporting and implementation of UQ in RT.
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Affiliation(s)
- Kareem A Wahid
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Zaphanlene Y Kaffey
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David P Farris
- Research Medical Library, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Laia Humbert-Vidan
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Amy C Moreno
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Jintao Ren
- Department of Oncology, Aarhus University Hospital, Denmark
| | - Mohamed A Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tucker J Netherton
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Stine Korreman
- Department of Oncology, Aarhus University Hospital, Denmark
| | | | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David Fuentes
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Michael J Dohopolski
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, USA.
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157
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Majeno A, Granger DA, Bryce CI, Riis JL. Salivary and Serum Analytes and Their Associations with Self-rated Health Among Healthy Young Adults. Int J Behav Med 2024:10.1007/s12529-024-10322-1. [PMID: 39289251 DOI: 10.1007/s12529-024-10322-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/03/2024] [Indexed: 09/19/2024]
Abstract
BACKGROUND Understanding the biological processes underlying poor self-rated health (SRH) can inform prevention efforts. The COVID-19 pandemic highlighted the importance of using self-reported measures and self-collected biospecimens, such as saliva, to understand physiological functioning and assist with health surveillance and promotion. However, the associations between salivary analytes and SRH remain understudied. The current study addresses this gap. METHODS In a laboratory-based study, 99 healthy adults (Mage = 23.8 years, SD = 4.5, 55% men, 43% non-Hispanic White) reported their SRH and provided saliva and blood samples that were assayed for adiponectin, C-reactive protein (CRP), uric acid (UA), and cytokines (IL-1β, IL-6, IL-8, TNF-α). Principal component analyses assessed the component loadings and generated factor scores for saliva and serum analytes. Binary logistic regressions examined the associations between these components and poor SRH. RESULTS Salivary analytes loaded onto two components (component 1: adiponectin and cytokines; component 2: CRP and UA) explaining 58% of the variance. Serum analytes grouped onto three components (component 1: IL-8 and TNF-α; component 2: CRP, IL-1β, and IL-6; component 3: adiponectin and UA) explaining 76% of the variance. Higher salivary component 1 scores predicted higher odds of reporting poor SRH (OR 1.53, 95%CI [1.10, 2.11]). Higher serum component 2 scores predicted higher odds of reporting poor SRH (OR 2.37, 95%CI [1.20, 4.67]). When examined in the same model, salivary component 1 (OR 1.79, 95%CI [1.17, 2.75]) and serum component 2 were associated with poorer SRH (OR 7.74, 95%CI [2.18, 27.40]). CONCLUSIONS In our sample, whether measured in saliva or serum, indices of inflammatory processes were associated with SRH.
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Affiliation(s)
- Angelina Majeno
- Department of Psychological Science, 4201 Social and Behavioral Sciences Gateway, University of California Irvine, Irvine, CA, 92697-7085, USA.
| | - Douglas A Granger
- Institute for Interdisciplinary Salivary Bioscience Research, University of California Irvine, Irvine, CA, USA
- Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Crystal I Bryce
- School of Medicine, The University of Texas at Tyler, Tyler, TX, USA
| | - Jenna L Riis
- Institute for Interdisciplinary Salivary Bioscience Research, University of California Irvine, Irvine, CA, USA
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois Urbana-Champaign, Champaign, IL, USA
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158
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Yin X, Wu Z, Wang H. A novel DRL-guided sparse voxel decoding model for reconstructing perceived images from brain activity. J Neurosci Methods 2024; 412:110292. [PMID: 39299579 DOI: 10.1016/j.jneumeth.2024.110292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 08/31/2024] [Accepted: 09/15/2024] [Indexed: 09/22/2024]
Abstract
BACKGROUND Due to the sparse encoding character of the human visual cortex and the scarcity of paired training samples for {images, fMRIs}, voxel selection is an effective means of reconstructing perceived images from fMRI. However, the existing data-driven voxel selection methods have not achieved satisfactory results. NEW METHOD Here, a novel deep reinforcement learning-guided sparse voxel (DRL-SV) decoding model is proposed to reconstruct perceived images from fMRI. We innovatively describe voxel selection as a Markov decision process (MDP), training agents to select voxels that are highly involved in specific visual encoding. RESULTS Experimental results on two public datasets verify the effectiveness of the proposed DRL-SV, which can accurately select voxels highly involved in neural encoding, thereby improving the quality of visual image reconstruction. COMPARISON WITH EXISTING METHODS We qualitatively and quantitatively compared our results with the state-of-the-art (SOTA) methods, getting better reconstruction results. We compared the proposed DRL-SV with traditional data-driven baseline methods, obtaining sparser voxel selection results, but better reconstruction performance. CONCLUSIONS DRL-SV can accurately select voxels involved in visual encoding on few-shot, compared to data-driven voxel selection methods. The proposed decoding model provides a new avenue to improving the image reconstruction quality of the primary visual cortex.
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Affiliation(s)
- Xu Yin
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Zhengping Wu
- School of Innovations, Sanjiang University, China; School of Electronic Science and Engineering, Nanjing University, China
| | - Haixian Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China.
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159
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Sah S, Haldar D, Singh RN, Das B, Nain AS. Rice yield prediction through integration of biophysical parameters with SAR and optical remote sensing data using machine learning models. Sci Rep 2024; 14:21674. [PMID: 39289440 PMCID: PMC11408675 DOI: 10.1038/s41598-024-72624-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 09/09/2024] [Indexed: 09/19/2024] Open
Abstract
In an era marked by growing global population and climate variability, ensuring food security has become a paramount concern. Rice, being a staple crop for billions of people, requires accurate and timely yield prediction to ensure global food security. This study was undertaken across two rice crop seasons in the Udham Singh Nagar district of Uttarakhand state to predict rice yield at 45, 60 and 90 days after transplanting (DAT) through machine learning (ML) models, utilizing a combination of optical and Synthetic Aperture Radar (SAR) data in conjunction with crop biophysical parameters. Results revealed that the ML models were able to provide relatively accurate early yield estimates. For summer rice, eXtreme gradient boosting (XGB) was the best-performing model at all three stages (45, 60, and 90 DAT), while for kharif rice, the best-performing models at 45, 60, and 90 DAT were XGB, Neural network (NNET), and Cubist, respectively. The combined ranking of ML models showed that prediction accuracy improved as the prediction date approaches harvest, and the best prediction of yield was observed at 90 DAT for both summer and kharif rice. Overall rankings indicate that for summer rice, the top three models were XGB, NNET, and Support vector regression, while for kharif rice, these were Cubist, NNET, and Random Forest, respectively. The findings of this study offer valuable insights into the potential of the combined use of remote sensing and biophysical parameters using ML models, which enhances food security planning and resource management by enabling more informed decision-making by stakeholders such as farmers, policy planners as well as researchers.
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Affiliation(s)
- Sonam Sah
- G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India
- ICAR-National Institute of Abiotic Stress Management, Pune, Maharashtra, India
| | - Dipanwita Haldar
- Indian Institute of Remote Sensing, Dehradun, Uttarakhand, India
| | - R N Singh
- ICAR-National Institute of Abiotic Stress Management, Pune, Maharashtra, India
| | - B Das
- ICAR-Central Coastal Agricultural Research Institute, Goa, Old Goa, India
| | - Ajeet Singh Nain
- G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India.
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160
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Wei ZY, Zhang Z, Zhao DL, Zhao WM, Meng YG. Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer. World J Clin Cases 2024; 12:5908-5921. [PMID: 39286374 PMCID: PMC11287501 DOI: 10.12998/wjcc.v12.i26.5908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 06/19/2024] [Accepted: 07/03/2024] [Indexed: 07/19/2024] Open
Abstract
BACKGROUND Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC. AIM To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI. METHODS The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover independent clinical predictors. These predictors were then used to create a clinical nomogram. Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images, the Mann-Whitney U test, Pearson test, and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features, which were subsequently utilized to generate a radiomic signature. Seven machine learning strategies were used to construct radiomic models that relied on the screening features. The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators. RESULTS Having an accuracy of 0.82 along with an area under the curve (AUC) of 0.915 [95% confidence interval (CI): 0.806-0.986], the random forest method trained on radiomics characteristics performed better than expected. The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram (AUC: 0.75, 95%CI: 0.611-0.899) and the combined nomogram (AUC: 0.869, 95%CI: 0.702-0.986) that integrated clinical parameters and radiomic signature. CONCLUSION The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.
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Affiliation(s)
- Zhi-Yao Wei
- Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
| | - Zhe Zhang
- Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
| | - Dong-Li Zhao
- Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
| | - Wen-Ming Zhao
- National Genomics Data Center and Chinese Academy of Sciences Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100700, China
| | - Yuan-Guang Meng
- Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese People’s Liberation Army General Hospital, Beijing 100700, China
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161
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Mehrbakhsh Z, Hassanzadeh R, Behnampour N, Tapak L, Zarrin Z, Khazaei S, Dinu I. Machine learning-based evaluation of prognostic factors for mortality and relapse in patients with acute lymphoblastic leukemia: a comparative simulation study. BMC Med Inform Decis Mak 2024; 24:261. [PMID: 39285373 PMCID: PMC11404043 DOI: 10.1186/s12911-024-02645-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 08/21/2024] [Indexed: 09/22/2024] Open
Abstract
BACKGROUND Predicting mortality and relapse in children with acute lymphoblastic leukemia (ALL) is crucial for effective treatment and follow-up management. ALL is a common and deadly childhood cancer that often relapses after remission. In this study, we aimed to apply and evaluate machine learning-based models for predicting mortality and relapse in pediatric ALL patients. METHODS This retrospective cohort study was conducted on 161 children aged less than 16 years with ALL. Survival status (dead/alive) and patient experience of relapse (yes/no) were considered as the outcome variables. Ten machine learning (ML) algorithms were used to predict mortality and relapse. The performance of the algorithms was evaluated by cross-validation and reported as mean sensitivity, specificity, accuracy and area under the curve (AUC). Finally, prognostic factors were identified based on the best algorithms. RESULTS The mean accuracy of the ML algorithms for prediction of patient mortality ranged from 64 to 74% and for prediction of relapse, it varied from 64 to 84% on test data sets. The mean AUC of the ML algorithms for mortality and relapse was above 64%. The most important prognostic factors for predicting both mortality and relapse were identified as age at diagnosis, hemoglobin and platelets. In addition, significant prognostic factors for predicting mortality included clinical side effects such as splenomegaly, hepatomegaly and lymphadenopathy. CONCLUSIONS Our results showed that artificial neural networks and bagging algorithms outperformed other algorithms in predicting mortality, while boosting and random forest algorithms excelled in predicting relapse in ALL patients across all criteria. These results offer significant clinical insights into the prognostic factors for children with ALL, which can inform treatment decisions and improve patient outcomes.
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Affiliation(s)
- Zahra Mehrbakhsh
- Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
- Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Roghayyeh Hassanzadeh
- Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
- Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Nasser Behnampour
- Department of Biostatistics and Epidemiology, School of Health, Golestan University of Medical Sciences, Gorgan, Iran
| | - Leili Tapak
- Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
- Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
| | - Ziba Zarrin
- Department of Photogrammetry and Remote Sensing, K.N. Toosi University of Technology, Tehran, Iran
| | - Salman Khazaei
- Health Sciences Research Center, Health Sciences & Technology Research Institute, Hamadan University of Medical Science, Hamadan, Iran
| | - Irina Dinu
- School of Public Health, University of Alberta, Edmonton, Canada
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Amanollahi M, Jameie M, Looha MA, A Basti F, Cattarinussi G, Moghaddam HS, Di Camillo F, Akhondzadeh S, Pigoni A, Sambataro F, Brambilla P, Delvecchio G. Machine learning applied to the prediction of relapse, hospitalization, and suicide in bipolar disorder using neuroimaging and clinical data: A systematic review. J Affect Disord 2024; 361:778-797. [PMID: 38908556 DOI: 10.1016/j.jad.2024.06.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 05/22/2024] [Accepted: 06/17/2024] [Indexed: 06/24/2024]
Abstract
BACKGROUND Bipolar disorder (BD) is associated with increased morbidity/mortality. Adverse outcome prediction might help with the management of patients with BD. METHODS We systematically reviewed the performance of machine learning (ML) studies in predicting adverse outcomes (relapse or recurrence, hospital admission, and suicide-related events) in patients with BD. Demographic, clinical, and neuroimaging-related poor outcome predictors were also reviewed. Three databases (PubMed, Scopus, and Web of Science) were explored from inception to July 2023. RESULTS Eighteen studies, accounting for >30,000 patients, were included. Support vector machine, decision trees, random forest, and logistic regression were the most frequently used ML algorithms. ML models' area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity ranged from 0.71 to 0.98, 72.7-92.8 %, and 59.0-95.2 % for relapse/recurrence prediction (4 studies (3 on relapses and 1 on recurrences). The corresponding values were 0.78-0.88, 21.4-100 %, and 77.0-99.7 % for hospital admissions (3 studies, 21,266 patients), and 0.71-0.99, 44.4-97.9 %, and 38.9-95.0 % for suicide-related events (10 studies, 5558 patients). Also, one study addressed a combination of the interest outcomes. Adverse outcome predictors included early onset BD, BD type I, comorbid psychiatric or substance use disorder, circadian rhythm disruption, hospitalization characteristics, and neuroimaging parameters, including increased dynamic amplitude of low-frequency fluctuation, decreased frontolimbic functional connectivity and aberrant dynamic functional connectivity in corticostriatal circuitry. CONCLUSIONS ML models can predict adverse outcomes of BD with relatively acceptable performance measures. Future studies with larger samples and nested cross-validation validation should be conducted to reach more reliable results.
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Affiliation(s)
- Mobina Amanollahi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Melika Jameie
- Neuroscience Research Center, Iran University of Medical Sciences, Tehran, Iran; Iranian Center of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Azizmohammad Looha
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fatemeh A Basti
- Islamic Azad University, Tehran Medical Branch, Tehran, Iran
| | - Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padova Neuroscience Center, University of Padova, Italy
| | - Hossein Sanjari Moghaddam
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Fabio Di Camillo
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
| | - Shahin Akhondzadeh
- Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Alessandro Pigoni
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padova Neuroscience Center, University of Padova, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
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163
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Yu Q, Zhang L, Ma Q, Da L, Li J, Li W. Predicting all-cause mortality and premature death using interpretable machine learning among a middle-aged and elderly Chinese population. Heliyon 2024; 10:e36878. [PMID: 39281518 PMCID: PMC11399635 DOI: 10.1016/j.heliyon.2024.e36878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 08/15/2024] [Accepted: 08/23/2024] [Indexed: 09/18/2024] Open
Abstract
Objective To develop machine learning-based prediction models for all-cause and premature mortality among the middle-aged and elderly population in China. Method Adults aged 45 years or older at baseline of 2011 from the China Health and Retirement Longitudinal Study (CHARLS) were included. The stacked ensemble model was built utilizing five selected machine learning algorithms. These models underwent training and testing using the CHARLS 2011-2015 cohort (derivation cohort) and subsequently underwent external validation using the CHARLS 2015-2018 cohort (validation cohort). SHapley Additive exPlanations (SHAP) was introduced to quantify the importance of risk factors and explain machine learning algorithms. Result In derivation cohort, a total of 10,677 subjects were included, 478 died during the follow-up. The stacked ensemble model demonstrated the highest efficacy in terms of its discrimination capability for predicting all-cause mortality and premature death, with an AUC[95 % CI] of 0.826[0.792-0.859] and 0.773[0.725-0.821], respectively. In validation cohort, the corresponding AUC[95 % CI] were 0.803[0.743-0.864] and 0.791[0.719-0.863], respectively. Risk factors including age, sex, self-reported health, activities of daily living, cognitive function, ever smoker, levels of systolic blood pressure, Cystatin C and low density lipoprotein were strong predictors for both all-cause mortality and premature death. Conclusion Stacked ensemble models performed well in predicting all-cause and premature death in this Chinese cohort. Interpretable techniques can aid in identifying significant risk factors and non-linear relationships between predictors and mortality.
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Affiliation(s)
- Qi Yu
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Lingzhi Zhang
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Qian Ma
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Lijuan Da
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Jiahui Li
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Wenyuan Li
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
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164
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Saad Alotaibi B, Ibrahim Shema A, Umar Ibrahim A, Awad Abuhussain M, Abdulmalik H, Aminu Dodo Y, Atakara C. Assimilation of 3D printing, Artificial Intelligence (AI) and Internet of Things (IoT) for the construction of eco-friendly intelligent homes: An explorative review. Heliyon 2024; 10:e36846. [PMID: 39286162 PMCID: PMC11403525 DOI: 10.1016/j.heliyon.2024.e36846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 08/02/2024] [Accepted: 08/22/2024] [Indexed: 09/19/2024] Open
Abstract
The construction industry is witnessing a transformative shift towards sustainable and intelligent housing solutions driven by advancements in 3D printing, Artificial Intelligence (AI), and the Internet of Things (IoT). Several architectural and construction firms have adopted innovative technologies to make construction easier, sustainable, efficient, cheap, fast, low generation of waste etc. This explorative review critically examines the integration of these technologies in the construction of eco-friendly intelligent homes. Drawing on a comprehensive analysis of literature spanning from 2010 to 2024, the review explores the synergistic potential and challenges associated with amalgamating 3D printing, AI, and IoT in construction processes. The increase need of smart homes equipped with sensors that can sense and regulate temperature, prevent or control fire, sense gas leakage, motion detectors and alarms for security and other application is in high demand. These types of smart homes can only be achieved by integrating different technologies together which include 3D printing (3DP), AI and Internet of Things (IoT). Despite the growing research in the field of automated construction, there are few articles that attempt to integrate these technologies together for futuristic smart homes and potential of smart cities. This study is aim at providing up-to-date advancement in technological innovation within the construction sector with regards to applications of 3DP, IoT, and AI. Key findings highlight how 3D printing enables rapid prototyping and customization of building components, AI enhances energy efficiency and occupant comfort through predictive analytics and automation, while IoT facilitates real-time monitoring and control of building systems. Furthermore, the review discusses the environmental benefits, cost-effectiveness, and societal implications of adopting such integrated approaches. However, challenges such as regulatory barriers, technological limitations, and the need for skilled labor are identified as critical barriers to widespread implementation. Future research directions are proposed to address these challenges and further optimize the integration of 3D printing, AI, and IoT for the construction of sustainable intelligent homes. In this review article, the need for 3DP in construction, advantage and disadvantage of 3DP, (AI) and IoT and the application of these technologies in addressing challenges regarding 3DP and promoting sustainability in the construction industries were comprehensively explored.
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Affiliation(s)
- Badr Saad Alotaibi
- Architectural Engineering Department, College of Engineering, Najran University, Najran, 66426, Kingdom of Saudi Arabia
| | | | | | - Mohammed Awad Abuhussain
- Architectural Engineering Department, College of Engineering, Najran University, Najran, 66426, Kingdom of Saudi Arabia
| | - Halima Abdulmalik
- Department of Architecture, International University of East Africa, Kampala, 759125, Uganda
| | - Yakubu Aminu Dodo
- Architectural Engineering Department, College of Engineering, Najran University, Najran, 66426, Kingdom of Saudi Arabia
| | - Cemil Atakara
- Department of Architecture, Cyprus International University, Nicosia, 99010, Cyprus
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165
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Khan AR, Bhatti SA, Tawfiq F, Siddiqui MK, Hussain S, Ali MA. On degree-based operators and topological descriptors of molecular graphs and their applications to QSPR analysis of carbon derivatives. Sci Rep 2024; 14:21543. [PMID: 39278960 PMCID: PMC11403011 DOI: 10.1038/s41598-024-72621-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2024] [Accepted: 09/09/2024] [Indexed: 09/18/2024] Open
Abstract
This work initiates a concept of reduced reverse degree basedRR D M -Polynomial for a graph, and differential and integral operators by using thisRR D M -Polynomial. In this study twelve reduced reverse degree-based topological descriptors are formulated using theRR D M -Polynomial. The topological descriptors, denoted as T D 's, are numerical invariants that offer significant insights into the molecular topology of a molecular graph. These descriptors are essential for conducting QSPR investigations and accurately estimating physicochemical attributes. The structural and algebraic characteristics of the graphene and graphdiyne are studied to apply this methodology. The study involves the analysis and estimation of Reduced reverse degree-based topological descriptors and physicochemical features of graphene derivatives using best-fit quadratic regression models. This work opens up new directions for scientists and researchers to pursue, taking them into new fields of study.
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Affiliation(s)
- Abdul Rauf Khan
- Department of Mathematics, Faculty of Sciences, Ghazi University, Dera Ghazi Khan, 32200, Pakistan
| | - Saad Amin Bhatti
- Department of Mathematics, Faculty of Sciences, Ghazi University, Dera Ghazi Khan, 32200, Pakistan
| | - Ferdous Tawfiq
- Mathematics Department, College of Science, King Saud University, P.O. Box 22452, Riyadh, 11495, Saudi Arabia
| | | | - Shahid Hussain
- Energy Engineering Division, Department of Engineering Science and Mathematics, Lulea University of Technology, Lulea, Sweden
| | - Mustafa Ahmed Ali
- Department of Mathematics, Faculty of Science, Somali National University, Mogadishu, Somalia.
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166
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Lai CHL, Kwok APK, Wong KC. Cheminformatic Identification of Tyrosyl-DNA Phosphodiesterase 1 (Tdp1) Inhibitors: A Comparative Study of SMILES-Based Supervised Machine Learning Models. J Pers Med 2024; 14:981. [PMID: 39338235 PMCID: PMC11433629 DOI: 10.3390/jpm14090981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2024] [Revised: 09/13/2024] [Accepted: 09/14/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND Tyrosyl-DNA phosphodiesterase 1 (Tdp1) repairs damages in DNA induced by abortive topoisomerase 1 activity; however, maintenance of genetic integrity may sustain cellular division of neoplastic cells. It follows that Tdp1-targeting chemical inhibitors could synergize well with existing chemotherapy drugs to deny cancer growth; therefore, identification of Tdp1 inhibitors may advance precision medicine in oncology. OBJECTIVE Current computational research efforts focus primarily on molecular docking simulations, though datasets involving three-dimensional molecular structures are often hard to curate and computationally expensive to store and process. We propose the use of simplified molecular input line entry system (SMILES) chemical representations to train supervised machine learning (ML) models, aiming to predict potential Tdp1 inhibitors. METHODS An open-sourced consensus dataset containing the inhibitory activity of numerous chemicals against Tdp1 was obtained from Kaggle. Various ML algorithms were trained, ranging from simple algorithms to ensemble methods and deep neural networks. For algorithms requiring numerical data, SMILES were converted to chemical descriptors using RDKit, an open-sourced Python cheminformatics library. RESULTS Out of 13 optimized ML models with rigorously tuned hyperparameters, the random forest model gave the best results, yielding a receiver operating characteristics-area under curve of 0.7421, testing accuracy of 0.6815, sensitivity of 0.6444, specificity of 0.7156, precision of 0.6753, and F1 score of 0.6595. CONCLUSIONS Ensemble methods, especially the bootstrap aggregation mechanism adopted by random forest, outperformed other ML algorithms in classifying Tdp1 inhibitors from non-inhibitors using SMILES. The discovery of Tdp1 inhibitors could unlock more treatment regimens for cancer patients, allowing for therapies tailored to the patient's condition.
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Affiliation(s)
- Conan Hong-Lun Lai
- Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong 999077, China
- Data Science and Policy Studies Programme, School of Governance and Policy Science, Faculty of Social Science, The Chinese University of Hong Kong, Hong Kong 999077, China
| | - Alex Pak Ki Kwok
- Data Science and Policy Studies Programme, School of Governance and Policy Science, Faculty of Social Science, The Chinese University of Hong Kong, Hong Kong 999077, China
| | - Kwong-Cheong Wong
- Data Science and Policy Studies Programme, School of Governance and Policy Science, Faculty of Social Science, The Chinese University of Hong Kong, Hong Kong 999077, China
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167
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Rakhimzhanova T, Kuzdeuov A, Varol HA. AnyFace++: Deep Multi-Task, Multi-Domain Learning for Efficient Face AI. SENSORS (BASEL, SWITZERLAND) 2024; 24:5993. [PMID: 39338738 PMCID: PMC11436022 DOI: 10.3390/s24185993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2024] [Revised: 09/11/2024] [Accepted: 09/13/2024] [Indexed: 09/30/2024]
Abstract
Accurate face detection and subsequent localization of facial landmarks are mandatory steps in many computer vision applications, such as emotion recognition, age estimation, and gender identification. Thanks to advancements in deep learning, numerous facial applications have been developed for human faces. However, most have to employ multiple models to accomplish several tasks simultaneously. As a result, they require more memory usage and increased inference time. Also, less attention is paid to other domains, such as animals and cartoon characters. To address these challenges, we propose an input-agnostic face model, AnyFace++, to perform multiple face-related tasks concurrently. The tasks are face detection and prediction of facial landmarks for human, animal, and cartoon faces, including age estimation, gender classification, and emotion recognition for human faces. We trained the model using deep multi-task, multi-domain learning with a heterogeneous cost function. The experimental results demonstrate that AnyFace++ generates outcomes comparable to cutting-edge models designed for specific domains.
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Affiliation(s)
| | | | - Huseyin Atakan Varol
- Institute of Smart Systems and Artificial Intelligence, Nazarbayev University, Astana 010000, Kazakhstan; (T.R.); (A.K.)
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168
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Koh HJW, Gašević D, Rankin D, Heritier S, Frydenberg M, Talic S. Variational Bayes machine learning for risk adjustment of general outcome indicators with examples in urology. NPJ Digit Med 2024; 7:249. [PMID: 39277683 PMCID: PMC11401950 DOI: 10.1038/s41746-024-01244-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 09/01/2024] [Indexed: 09/17/2024] Open
Abstract
Risk adjustment is often necessary for outcome quality indicators (QIs) to provide fair and accurate feedback to healthcare professionals. However, traditional risk adjustment models are generally oversimplified and not equipped to disentangle complex factors influencing outcomes that are out of a healthcare professional's control. We present VIRGO, a novel variational Bayes model trained on routinely collected, large administrative datasets to risk-adjust outcome QIs. VIRGO uses detailed demographics, diagnosis, and procedure codes to provide individualized risk adjustment and explanations on patient factors affecting outcomes. VIRGO achieves state-of-the-art on external datasets and features capabilities of uncertainty expression, explainable features, and counterfactual analysis capabilities. VIRGO facilitates risk adjustment by explaining how patient factors led to adverse outcomes and expresses the uncertainty of each prediction, allowing healthcare professionals to not only explore patient factors with unexplained variance that are associated with worse outcomes but also reflect on the quality of their clinical practice.
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Affiliation(s)
- Harvey Jia Wei Koh
- Centre for Learning Analytics, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
- Digital Health Cooperative Research Centre, Sydney, NSW, Australia
- School of Public Health and Preventative Medicine, Monash University, Melbourne, VIC, Australia
| | - Dragan Gašević
- Centre for Learning Analytics, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
- Digital Health Cooperative Research Centre, Sydney, NSW, Australia
| | - David Rankin
- Digital Health Cooperative Research Centre, Sydney, NSW, Australia
- School of Public Health and Preventative Medicine, Monash University, Melbourne, VIC, Australia
| | - Stephane Heritier
- School of Public Health and Preventative Medicine, Monash University, Melbourne, VIC, Australia
| | - Mark Frydenberg
- Cabrini Healthcare, Malvern, VIC, Australia
- Department of Surgery, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Stella Talic
- Centre for Learning Analytics, Faculty of Information Technology, Monash University, Clayton, VIC, Australia.
- Digital Health Cooperative Research Centre, Sydney, NSW, Australia.
- School of Public Health and Preventative Medicine, Monash University, Melbourne, VIC, Australia.
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169
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Habib F, Ali Z, Azam A, Kamran K, Pasha FM. Navigating pathways to automated personality prediction: a comparative study of small and medium language models. Front Big Data 2024; 7:1387325. [PMID: 39345825 PMCID: PMC11427259 DOI: 10.3389/fdata.2024.1387325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 08/28/2024] [Indexed: 10/01/2024] Open
Abstract
Introduction Recent advancements in Natural Language Processing (NLP) and widely available social media data have made it possible to predict human personalities in various computational applications. In this context, pre-trained Large Language Models (LLMs) have gained recognition for their exceptional performance in NLP benchmarks. However, these models require substantial computational resources, escalating their carbon and water footprint. Consequently, a shift toward more computationally efficient smaller models is observed. Methods This study compares a small model ALBERT (11.8M parameters) with a larger model, RoBERTa (125M parameters) in predicting big five personality traits. It utilizes the PANDORA dataset comprising Reddit comments, processing them on a Tesla P100-PCIE-16GB GPU. The study customized both models to support multi-output regression and added two linear layers for fine-grained regression analysis. Results Results are evaluated on Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), considering the computational resources consumed during training. While ALBERT consumed lower levels of system memory with lower heat emission, it took higher computation time compared to RoBERTa. The study produced comparable levels of MSE, RMSE, and training loss reduction. Discussion This highlights the influence of training data quality on the model's performance, outweighing the significance of model size. Theoretical and practical implications are also discussed.
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Affiliation(s)
- Fatima Habib
- FAST School of Management, National University of Computer and Emerging Sciences, Lahore, Pakistan
| | - Zeeshan Ali
- Oxford Brookes Business School, Oxford Brookes University, Oxford, United Kingdom
| | - Akbar Azam
- FAST School of Management, National University of Computer and Emerging Sciences, Lahore, Pakistan
| | - Komal Kamran
- FAST School of Management, National University of Computer and Emerging Sciences, Lahore, Pakistan
| | - Fahad Mansoor Pasha
- Faculty of Business Administration, Lahore School of Economics, Lahore, Pakistan
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170
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Covelli V, Buonocore M, Grimaldi M, Scrima M, Santoro A, Marino C, De Simone V, van Baarle L, Biscu F, Scala MC, Sala M, Matteoli G, D'Ursi AM, Rodriquez M. Peptides as modulators of FPPS enzyme: A multifaceted evaluation from the design to the mechanism of action. Eur J Med Chem 2024; 279:116871. [PMID: 39303514 DOI: 10.1016/j.ejmech.2024.116871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2024] [Revised: 09/02/2024] [Accepted: 09/10/2024] [Indexed: 09/22/2024]
Abstract
Bone diseases are medical conditions caused by the loss of bone homeostasis consecutive to increased osteoclast activity and diminished osteoblast activity. The mevalonate pathway (MVA) is crucial for maintaining this balance since it drives the post-translational prenylation of small guanosine triphosphatases (GTPases) proteins. Farnesyl pyrophosphate synthase (FPPS) plays a crucial role in the MVA pathway. Consequently, in the treatment of bone-related diseases, FPPS is the target of FDA-approved nitrogen-containing bisphosphonates (N-BPs), which have tropism mainly for bone tissue due to their poor penetration in soft tissues. The development of inhibitors targeting the FPPS enzyme has garnered significant interest in recent decades due to FPPS's role in the biosynthesis of cholesterol and other isoprenoids, which are implicated in cancer, bone diseases, and other conditions. In this study, we describe a multidisciplinary approach to designing novel FPPS inhibitors, combining computational modeling, biochemical assays, and biophysical techniques. A series of peptides and phosphopeptides were designed, synthesized, and evaluated for their ability to inhibit FPPS activity. Molecular docking was employed to predict the binding modes of these compounds to FPPS, while Surface Plasmon Resonance (SPR) and Nuclear Magnetic Resonance (NMR) spectroscopy experiments - based on Saturation Transfer Difference (STD) and an enzymatic NMR assay - were used to measure their binding affinities and kinetics. The biological activity of the most promising compounds was further assessed in cellular assays using murine colorectal cancer (CRC) cells. Additionally, genomics and metabolomics profiling allowed to unravel the possible mechanisms underlying the activity of the peptides, confirming their involvement in the modulation of the MVA pathway. Our findings demonstrate that the designed peptides and phosphopeptides exhibit significant inhibitory activity against FPPS and possess antiproliferative effects on CRC cells, suggesting their potential as therapeutic agents for cancer.
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Affiliation(s)
- Verdiana Covelli
- Department of Pharmacy, University of Naples Federico II, Via Domenico Montesano, 49, 80131, Naples, Italy.
| | - Michela Buonocore
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy; Department of Chemical Sciences and Research Centre on Bioactive Peptides (CIRPEB), University of Naples Federico II, Strada Comunale Cintia, 80126, Naples, Italy.
| | - Manuela Grimaldi
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy.
| | - Mario Scrima
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy.
| | - Angelo Santoro
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy; Department of Pharmacy, Scuola di Specializzazione in Farmacia Ospedaliera, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy.
| | - Carmen Marino
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy.
| | - Veronica De Simone
- Department of Chronic Diseases, Metabolism and Ageing (CHROMETA)-Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Herestraat, 49, 3000, Leuven, Belgium.
| | - Lies van Baarle
- Department of Chronic Diseases, Metabolism and Ageing (CHROMETA)-Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Herestraat, 49, 3000, Leuven, Belgium.
| | - Francesca Biscu
- Department of Chronic Diseases, Metabolism and Ageing (CHROMETA)-Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Herestraat, 49, 3000, Leuven, Belgium.
| | - Maria Carmina Scala
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy.
| | - Marina Sala
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy.
| | - Gianluca Matteoli
- Department of Chronic Diseases, Metabolism and Ageing (CHROMETA)-Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Herestraat, 49, 3000, Leuven, Belgium.
| | - Anna Maria D'Ursi
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy.
| | - Manuela Rodriquez
- Department of Pharmacy, University of Naples Federico II, Via Domenico Montesano, 49, 80131, Naples, Italy.
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171
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Tsushima Y, Nakayama K, Okuya T, Koiwa H, Ando H, Watanabe Y. Brain activities in the auditory area and insula represent stimuli evoking emotional response. Sci Rep 2024; 14:21335. [PMID: 39266687 PMCID: PMC11393461 DOI: 10.1038/s41598-024-72112-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 09/03/2024] [Indexed: 09/14/2024] Open
Abstract
Cinema, a modern titan of entertainment, holds power to move people with the artful manipulation of auditory and visual stimuli. Despite this, the mechanisms behind how sensory stimuli elicit emotional responses are unknown. Thus, this study evaluated which brain regions were involved when sensory stimuli evoke auditory- or visual-driven emotions during film viewing. Using functional magnetic resonance imaging (fMRI) decoding techniques, we found that brain activities in the auditory area and insula represent the stimuli that evoke emotional response. The observation of brain activities in these regions could provide further insights to these mechanisms for the improvement of film-making, as well as the development of novel neural techniques in neuroscience. In near feature, such a "neuro-designed" products/ applications might gain in popularity.
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Affiliation(s)
- Yoshiaki Tsushima
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), 1-4 Yamadaoka, Suita, Osaka, 565-0871, Japan.
| | - Koharu Nakayama
- Faculty of Life and Medical Sciences, Doshisha University, 1-3 Tataramiyakodani, Kyotanabe, Kyoto, 610-0321, Japan
| | - Teruhisa Okuya
- Panasonic Holdings Corporation, 3-1-1 Yagumo-Naka-Machi, Moriguchi City, Osaka, 570-8501, Japan
| | - Hiroko Koiwa
- Electric Works Company, Panasonic Corporation, Kadoma, Osaka, 571-8686, Japan
| | - Hiroshi Ando
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), 1-4 Yamadaoka, Suita, Osaka, 565-0871, Japan
- Universal Communication Research Institue, National Institute of Information and Communications Technology (NICT), 3-5 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0289, Japan
| | - Yoshiaki Watanabe
- Faculty of Life and Medical Sciences, Doshisha University, 1-3 Tataramiyakodani, Kyotanabe, Kyoto, 610-0321, Japan
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172
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Arnold M, Liou L, Boland MR. Development, evaluation and comparison of machine learning algorithms for predicting in-hospital patient charges for congestive heart failure exacerbations, chronic obstructive pulmonary disease exacerbations and diabetic ketoacidosis. BioData Min 2024; 17:35. [PMID: 39267093 PMCID: PMC11395859 DOI: 10.1186/s13040-024-00387-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Accepted: 08/30/2024] [Indexed: 09/14/2024] Open
Abstract
BACKGROUND Hospitalizations for exacerbations of congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD) and diabetic ketoacidosis (DKA) are costly in the United States. The purpose of this study was to predict in-hospital charges for each condition using machine learning (ML) models. RESULTS We conducted a retrospective cohort study on national discharge records of hospitalized adult patients from January 1st, 2016, to December 31st, 2019. We constructed six ML models (linear regression, ridge regression, support vector machine, random forest, gradient boosting and extreme gradient boosting) to predict total in-hospital cost for admission for each condition. Our models had good predictive performance, with testing R-squared values of 0.701-0.750 (mean of 0.713) for CHF; 0.694-0.724 (mean 0.709) for COPD; and 0.615-0.729 (mean 0.694) for DKA. We identified important key features driving costs, including patient age, length of stay, number of procedures, and elective/nonelective admission. CONCLUSIONS ML methods may be used to accurately predict costs and identify drivers of high cost for COPD exacerbations, CHF exacerbations and DKA. Overall, our findings may inform future studies that seek to decrease the underlying high patient costs for these conditions.
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Affiliation(s)
- Monique Arnold
- Department of Emergency Medicine, The Mount Sinai Hospital at the Icahn School of Medicine, 306 E 96th Street, #4A, New York, NY, 10128, USA.
| | - Lathan Liou
- Icahn School of Medicine at Mount Sinai Hospital, New York City, NY, USA
| | - Mary Regina Boland
- Data Science, Department of Mathematics, Herbert W. Boyer School of Natural Sciences, Mathematics, and Computing, Saint Vincent College, Latrobe, PA, USA
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173
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Strobl EV, Gamazon ER. Transcriptome-Wide Root Causal Inference. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.22.24310837. [PMID: 39108507 PMCID: PMC11302617 DOI: 10.1101/2024.07.22.24310837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Root causal genes correspond to the first gene expression levels perturbed during pathogenesis by genetic or non-genetic factors. Targeting root causal genes has the potential to alleviate disease entirely by eliminating pathology near its onset. No existing algorithm discovers root causal genes from observational data alone. We therefore propose the Transcriptome-Wide Root Causal Inference (TWRCI) algorithm that identifies root causal genes and their causal graph using a combination of genetic variant and unperturbed bulk RNA sequencing data. TWRCI uses a novel competitive regression procedure to annotate cis and trans-genetic variants to the gene expression levels they directly cause. The algorithm simultaneously recovers a causal ordering of the expression levels to pinpoint the underlying causal graph and estimate root causal effects. TWRCI outperforms alternative approaches across a diverse group of metrics by directly targeting root causal genes while accounting for distal relations, linkage disequilibrium, patient heterogeneity and widespread pleiotropy. We demonstrate the algorithm by uncovering the root causal mechanisms of two complex diseases, which we confirm by replication using independent genome-wide summary statistics.
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174
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Düsing C, Cimiano P, Rehberg S, Scherer C, Kaup O, Köster C, Hellmich S, Herrmann D, Meier KL, Claßen S, Borgstedt R. Integrating federated learning for improved counterfactual explanations in clinical decision support systems for sepsis therapy. Artif Intell Med 2024; 157:102982. [PMID: 39277983 DOI: 10.1016/j.artmed.2024.102982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 08/29/2024] [Accepted: 09/01/2024] [Indexed: 09/17/2024]
Abstract
In recent years, we have witnessed both artificial intelligence obtaining remarkable results in clinical decision support systems (CDSSs) and explainable artificial intelligence (XAI) improving the interpretability of these models. In turn, this fosters the adoption by medical personnel and improves trustworthiness of CDSSs. Among others, counterfactual explanations prove to be one such XAI technique particularly suitable for the healthcare domain due to its ease of interpretation, even for less technically proficient staff. However, the generation of high-quality counterfactuals relies on generative models for guidance. Unfortunately, training such models requires a huge amount of data that is beyond the means of ordinary hospitals. In this paper, we therefore propose to use federated learning to allow multiple hospitals to jointly train such generative models while maintaining full data privacy. We demonstrate the superiority of our approach compared to locally generated counterfactuals. Moreover, we prove that generative models for counterfactual generation that are trained using federated learning in a suitable environment perform only marginally worse compared to centrally trained ones while offering the benefit of data privacy preservation. Finally, we integrate our method into a prototypical CDSS for treatment recommendation for sepsis patients, thus providing a proof of concept for real-world application as well as insights and sanity checks from clinical application.
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Affiliation(s)
- Christoph Düsing
- Center for Cognitive Interaction Technology, Bielefeld University, Inspiration 1, Bielefeld, 33619, Germany.
| | - Philipp Cimiano
- Center for Cognitive Interaction Technology, Bielefeld University, Inspiration 1, Bielefeld, 33619, Germany.
| | - Sebastian Rehberg
- Department of Anaesthesiology, Intensive Care, Emergency Medicine, Transfusion Medicine and Pain Therapy, University Hospital OWL, Campus Bielefeld-Bethel, Protestant Hospital of the Bethel Foundation, Burgsteig 13, Bielefeld, 33617, Germany.
| | - Christiane Scherer
- Institute of Laboratory Medicine, Microbiology and Hygiene, University Hospital OWL, Campus Bielefeld-Bethel, Protestant Hospital of the Bethel Foundation, Burgsteig 13, Bielefeld, 33617, Germany.
| | - Olaf Kaup
- Institute of Laboratory Medicine, Microbiology and Transfusion Medicine, University Hospital OWL, Campus Bielefeld Hospital, Teutoburger Straße 50, Bielefeld, 33604, Germany.
| | - Christiane Köster
- University Clinic for Cardiology and Internal Intensive Care Medicine, University Hospital OWL, Campus Bielefeld Hospital, Teutoburger Straße 50, Bielefeld, 33604, Germany.
| | - Stefan Hellmich
- Department of Anesthesiology, Surgical Intensive Care Medicine, Emergency Medicine and Pain Therapy, University Hospital OWL, Campus Bielefeld Hospital, Teutoburger Straße 50, Bielefeld, 33604, Germany.
| | - Daniel Herrmann
- Department of Anesthesiology, Surgical Intensive Care Medicine, Emergency Medicine and Pain Therapy, University Hospital OWL, Campus Bielefeld Hospital, Teutoburger Straße 50, Bielefeld, 33604, Germany.
| | - Kirsten Laura Meier
- Department of Anesthesiology, Surgical Intensive Care Medicine, Emergency Medicine and Pain Therapy, University Hospital OWL, Campus Bielefeld Hospital, Teutoburger Straße 50, Bielefeld, 33604, Germany.
| | - Simon Claßen
- Department of Anesthesiology, Surgical Intensive Care Medicine, Emergency Medicine and Pain Therapy, University Hospital OWL, Campus Bielefeld Hospital, Teutoburger Straße 50, Bielefeld, 33604, Germany.
| | - Rainer Borgstedt
- Department of Anaesthesiology, Intensive Care, Emergency Medicine, Transfusion Medicine and Pain Therapy, University Hospital OWL, Campus Bielefeld-Bethel, Protestant Hospital of the Bethel Foundation, Burgsteig 13, Bielefeld, 33617, Germany.
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175
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Zhang T, Wang S, Chai Y, Yu J, Zhu W, Li L, Li B. Prediction and Interpretability Study of the Glass Transition Temperature of Polyimide Based on Machine Learning with Quantitative Structure-Property Relationship ( Tg-QSPR). J Phys Chem B 2024; 128:8807-8817. [PMID: 38979707 DOI: 10.1021/acs.jpcb.4c00756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
The glass transition temperature (Tg) is a crucial characteristic of polyimides (PIs). Developing a Tg predictive model using machine learning methodologies can facilitate the design of PI structures and expedite the development process. In this investigation, a data set comprising 1257 PIs was assembled, with Tg values determined using differential scanning calorimetry. 210 molecular descriptors were computed using RDKit, and subsequently, six distinct feature selection methodologies were employed to refine the descriptor set. Quantitative structure-property relationship models targeting Tg (Tg-QSPR) were then constructed using five ensemble learning algorithms and one deep learning algorithm. These models exhibited high predictive accuracy and robustness, with the CATBoost model demonstrating the highest accuracy, achieving a coefficient of determination of 0.823 for the test set, a mean absolute error of 20.1 °C, and a root-mean-square error of 29.0 °C. The study identified the NumRotatableBonds descriptor as particularly influential on Tg, showing a negative correlation with the property. Additionally, the model's accuracy was validated using ten new PI films not included in the original data set, resulting in absolute errors ranging from 2.5 to 26.9 °C and absolute percentage error rates of 1.0-12.8%. These findings underscore the importance of utilizing extensive and diverse data sets for predictive modeling to enhance accuracy and stability. Furthermore, exploring the interpretability of the model and experimentally validating newly synthesized PIs have augmented the practical utility of the model. Under the guidance of the Tg-QSPR models, it will be possible to accelerate the performance prediction and structural design of PIs in the future.
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Affiliation(s)
- Tianyong Zhang
- Tianjin Key Laboratory of Applied Catalysis Science and Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300354, China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300072, China
- Tianjin Engineering Research Center of Functional Fine Chemicals, Tianjin 300354, China
| | - Suisui Wang
- Tianjin Key Laboratory of Applied Catalysis Science and Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300354, China
| | - Yamei Chai
- Tianjin Key Laboratory of Applied Catalysis Science and Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300354, China
| | - Jianing Yu
- Tianjin Key Laboratory of Applied Catalysis Science and Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300354, China
| | - Wenxuan Zhu
- Tianjin Key Laboratory of Applied Catalysis Science and Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300354, China
| | - Liang Li
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
| | - Bin Li
- Tianjin Key Laboratory of Applied Catalysis Science and Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300354, China
- Tianjin Engineering Research Center of Functional Fine Chemicals, Tianjin 300354, China
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176
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Gregory S, Robertson S, Aughey R, Spencer B, Alexander J. Assigning goal-probability value to high intensity runs in football. PLoS One 2024; 19:e0308749. [PMID: 39264891 PMCID: PMC11392333 DOI: 10.1371/journal.pone.0308749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 07/11/2024] [Indexed: 09/14/2024] Open
Abstract
High intensity run counts-defined as the number of runs where a player reaches and maintains a speed above a certain threshold-are a popular football running statistic in sport science research. While the high intensity run number gives an insight into the volume or intensity of a player's work rate it does not give any indication about the effectiveness of their runs or whether or not they provided value to the team. To provide the missing context of value this research borrows the concept of value models from sports analytics which assign continuous values to each frame of optical tracking data. In this research the value model takes the form of goal-probability for the in-possession team. By aligning the value model with high intensity runs this research identifies positive correlations between speed and acceleration with high value runs, as well as a negative correlation between tortuosity (a measure of path curvature) and high value runs. There is also a correlation between the number of players making high intensity runs concurrently and the value generated by the team, suggesting a form of movement coordination. Finally positional differences are explored demonstrating that attacking players make more in-possession high intensity runs when goal probability is high, whereas defensive players make more out-of-possession high intensity runs while goal probability is high. By assigning value to high-intensity runs practitioners are able to add new layers of context to traditional sport science metrics and answer more nuanced questions.
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Affiliation(s)
- Sam Gregory
- Institute for Health & Sport, Victoria University, Melbourne, Australia
- Inter Miami CF, Miami, Florida, United States of America
| | - Sam Robertson
- Institute for Health & Sport, Victoria University, Melbourne, Australia
| | - Robert Aughey
- Institute for Health & Sport, Victoria University, Melbourne, Australia
| | | | - Jeremy Alexander
- Institute for Health & Sport, Victoria University, Melbourne, Australia
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177
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Yuan W, Li Y, Han Z, Chen Y, Xie J, Chen J, Bi Z, Xi J. Evolutionary Mechanism Based Conserved Gene Expression Biclustering Module Analysis for Breast Cancer Genomics. Biomedicines 2024; 12:2086. [PMID: 39335599 PMCID: PMC11428256 DOI: 10.3390/biomedicines12092086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Revised: 08/23/2024] [Accepted: 09/02/2024] [Indexed: 09/30/2024] Open
Abstract
The identification of significant gene biclusters with particular expression patterns and the elucidation of functionally related genes within gene expression data has become a critical concern due to the vast amount of gene expression data generated by RNA sequencing technology. In this paper, a Conserved Gene Expression Module based on Genetic Algorithm (CGEMGA) is proposed. Breast cancer data from the TCGA database is used as the subject of this study. The p-values from Fisher's exact test are used as evaluation metrics to demonstrate the significance of different algorithms, including the Cheng and Church algorithm, CGEM algorithm, etc. In addition, the F-test is used to investigate the difference between our method and the CGEM algorithm. The computational cost of the different algorithms is further investigated by calculating the running time of each algorithm. Finally, the established driver genes and cancer-related pathways are used to validate the process. The results of 10 independent runs demonstrate that CGEMGA has a superior average p-value of 1.54 × 10-4 ± 3.06 × 10-5 compared to all other algorithms. Furthermore, our approach exhibits consistent performance across all methods. The F-test yields a p-value of 0.039, indicating a significant difference between our approach and the CGEM. Computational cost statistics also demonstrate that our approach has a significantly shorter average runtime of 5.22 × 100 ± 1.65 × 10-1 s compared to the other algorithms. Enrichment analysis indicates that the genes in our approach are significantly enriched for driver genes. Our algorithm is fast and robust, efficiently extracting co-expressed genes and associated co-expression condition biclusters from RNA-seq data.
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Affiliation(s)
- Wei Yuan
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511436, China
| | - Yaming Li
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511436, China
| | - Zhengpan Han
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511436, China
| | - Yu Chen
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511436, China
| | - Jinnan Xie
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511436, China
| | - Jianguo Chen
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511436, China
| | - Zhisheng Bi
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511436, China
| | - Jianing Xi
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511436, China
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178
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Jeong SM, Song YD, Seok CL, Lee JY, Lee EC, Kim HJ. Machine learning-based classification of Parkinson's disease using acoustic features: Insights from multilingual speech tasks. Comput Biol Med 2024; 182:109078. [PMID: 39265476 DOI: 10.1016/j.compbiomed.2024.109078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 04/26/2024] [Accepted: 08/09/2024] [Indexed: 09/14/2024]
Abstract
This study advances the automation of Parkinson's disease (PD) diagnosis by analyzing speech characteristics, leveraging a comprehensive approach that integrates a voting-based machine learning model. Given the growing prevalence of PD, especially among the elderly population, continuous and efficient diagnosis is of paramount importance. Conventional monitoring methods suffer from limitations related to time, cost, and accessibility, underscoring the need for the development of automated diagnostic tools. In this paper, we present a robust model for classifying speech patterns in Korean PD patients, addressing a significant research gap. Our model employs straightforward preprocessing techniques and a voting-based machine learning approach, demonstrating superior performance, particularly when training data is limited. Furthermore, we emphasize the effectiveness of the eGeMAPSv2 feature set in PD analysis and introduce new features that substantially enhance classification accuracy. The proposed model, achieving an accuracy of 84.73 % and an area under the ROC (AUC) score of 92.18 % on a dataset comprising 100 Korean PD patients and 100 healthy controls, offers a practical solution for automated diagnosis applications, such as smartphone apps. Future research endeavors will concentrate on enhancing the model's performance and delving deeper into the relationship between high-importance features and PD.
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Affiliation(s)
- Seung-Min Jeong
- Department of AI & Informatics, Graduate School, Sangmyung University, Hongjimun 2-Gil 20, Jongno-gu, Seoul, 03016, Republic of Korea
| | - Young-Do Song
- Department of AI & Informatics, Graduate School, Sangmyung University, Hongjimun 2-Gil 20, Jongno-gu, Seoul, 03016, Republic of Korea
| | - Chae-Lin Seok
- Department of AI & Informatics, Graduate School, Sangmyung University, Hongjimun 2-Gil 20, Jongno-gu, Seoul, 03016, Republic of Korea
| | - Jun-Young Lee
- Department of Psychiatry, Seoul National University College of Medicine & SMG-SNU Boramae Medical Center, 20, Boramae-Ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea
| | - Eui Chul Lee
- Department of Human-Centered Artificial Intelligence, Sangmyung University, Hongjimun 2-Gil 20, Jongno-gu, Seoul, 03016, Republic of Korea.
| | - Han-Joon Kim
- Department of Neurology, Seoul National University College of Medicine, Seoul National University Hospital, Daehak-ro 101, Jongno-gu, Seoul, 03080, Republic of Korea
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179
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Zhou Y, Yao J, Hong F, Zhang Y, Wang Y. Balanced Destruction-Reconstruction Dynamics for Memory-Replay Class Incremental Learning. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2024; 33:4966-4981. [PMID: 39236120 DOI: 10.1109/tip.2024.3451932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/07/2024]
Abstract
Class incremental learning (CIL) aims to incrementally update a trained model with the new classes of samples (plasticity) while retaining previously learned ability (stability). To address the most challenging issue in this goal, i.e., catastrophic forgetting, the mainstream paradigm is memory-replay CIL, which consolidates old knowledge by replaying a small number of old classes of samples saved in the memory. Despite effectiveness, the inherent destruction-reconstruction dynamics in memory-replay CIL are an intrinsic limitation: if the old knowledge is severely destructed, it will be quite hard to reconstruct the lossless counterpart. Our theoretical analysis shows that the destruction of old knowledge can be effectively alleviated by balancing the contribution of samples from the current phase and those saved in the memory. Motivated by this theoretical finding, we propose a novel Balanced Destruction-Reconstruction module (BDR) for memory-replay CIL, which can achieve better knowledge reconstruction by reducing the degree of maximal destruction of old knowledge. Specifically, to achieve a better balance between old knowledge and new classes, the proposed BDR module takes into account two factors: the variance in training status across different classes and the quantity imbalance of samples from the current phase and memory. By dynamically manipulating the gradient during training based on these factors, BDR can effectively alleviate knowledge destruction and improve knowledge reconstruction. Extensive experiments on a range of CIL benchmarks have shown that as a lightweight plug-and-play module, BDR can significantly improve the performance of existing state-of-the-art methods with good generalization. Our code is publicly available here.
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180
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Quan Y, Zhao Y, Musa RM, Morgans R, Silva RM, Hung CH, Chen YS. Assessing physical fitness adaptations in collegiate male soccer players through training load parameters: a two-arm randomized study on combined small-sided games and running-based high-intensity interval training. Front Physiol 2024; 15:1466386. [PMID: 39351281 PMCID: PMC11440478 DOI: 10.3389/fphys.2024.1466386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Accepted: 08/30/2024] [Indexed: 10/04/2024] Open
Abstract
Objective To evaluate the effects of a 4-week intervention combining small-sided games (SSGs) and high-intensity interval training (HIIT) on physical fitness in collegiate male soccer players. Methods Twenty-one soccer players were randomly assigned to either the HIIT + SSGs group (n = 11) or a control group (n = 10). Physical fitness was assessed at baseline and 1-week post-intervention, including countermovement jump (CMJ), change of direction (COD) test, sprint test, repeated sprint ability (RSA) test, and 30-15 Intermittent Fitness Test (30-15IFT). The intervention comprised eight sessions over 4 weeks: four SSGs and four HIIT. Results The intervention group showed small to moderate improvements: mean RSA improved by 4.5% (p = 0.07), CMJ increased by 3.2% (p = 0.12), and 30-15IFT scores enhanced by 6.8% (p = 0.09). Key predictors of group membership included heart rate load per minute (OR 1.602) and various GPS variables. Conclusion The 4-week intervention combining SSGs with HIIT did not produce statistically significant improvements in most physical fitness variables compared to the control group. Although there were positive trends in variables such as RSA and 30-15IFT, these changes were modest and not statistically significant. The results suggest that while the combined SSGs and HIIT approach shows potential, its impact on physical fitness over a 4-week period is limited, with some variables, like CMJ, even showing decreases.
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Affiliation(s)
- YanXiu Quan
- College of Physical Education, China West Normal University, Nanchong, Sichuan, China
| | - YongXing Zhao
- College of Physical Education, Chizhou University, Chizhou, Anhui, China
| | - Rabiu Muazu Musa
- Centre for Fundamental and Continuing Education, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
- Faculty of Applied Social Sciences, Universiti Sultan Zainal Abidin, Kuala Terengganu, Malaysia
| | - Ryland Morgans
- Football Performance Hub, University of Central Lancashire, Preston, United Kingdom
| | - Rui Miguel Silva
- Escola Superior de Desporto e Lazer, Instituto Politécnico de Viana do Castelo, Rua Escola Industrial e Comercial de Nun'Álvares, Viana doCastelo, Portugal
- Research Center in Sports Performance, Recreation, Innovation and Technology (SPRINT), Melgaço, Portugal
| | - Chin-Hwai Hung
- Department of Physical Education, Fu Jen Catholic University, New Taipei, Taiwan
| | - Yung-Sheng Chen
- Department of Exercise and Health Sciences, University of Taipei, Taipei, Taiwan
- Exercise and Health Promotion Association, New Taipei, Taiwan
- Tanyu Research Laboratory, Taipei, Taiwan
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181
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Borra D, Paissan F, Ravanelli M. SpeechBrain-MOABB: An open-source Python library for benchmarking deep neural networks applied to EEG signals. Comput Biol Med 2024; 182:109097. [PMID: 39265481 DOI: 10.1016/j.compbiomed.2024.109097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 08/20/2024] [Accepted: 08/30/2024] [Indexed: 09/14/2024]
Abstract
Deep learning has revolutionized EEG decoding, showcasing its ability to outperform traditional machine learning models. However, unlike other fields, EEG decoding lacks comprehensive open-source libraries dedicated to neural networks. Existing tools (MOABB and braindecode) prevent the creation of robust and complete decoding pipelines, as they lack support for hyperparameter search across the entire pipeline, and are sensitive to fluctuations in results due to network random initialization. Furthermore, the absence of a standardized experimental protocol exacerbates the reproducibility crisis in the field. To address these limitations, we introduce SpeechBrain-MOABB, a novel open-source toolkit carefully designed to facilitate the development of a comprehensive EEG decoding pipeline based on deep learning. SpeechBrain-MOABB incorporates a complete experimental protocol that standardizes critical phases, such as hyperparameter search and model evaluation. It natively supports multi-step hyperparameter search for finding the optimal hyperparameters in a high-dimensional space defined by the entire pipeline, and multi-seed training and evaluation for obtaining performance estimates robust to the variability caused by random initialization. SpeechBrain-MOABB outperforms other libraries, including MOABB and braindecode, with accuracy improvements of 14.9% and 25.2% (on average), respectively. By enabling easy-to-use and easy-to-share decoding pipelines, our toolkit can be exploited by neuroscientists for decoding EEG with neural networks in a replicable and trustworthy way.
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Affiliation(s)
- Davide Borra
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Cesena, Forlì-Cesena, Italy.
| | | | - Mirco Ravanelli
- Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada; Mila - Quebec AI Institute, Montreal, Quebec, Canada
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Pham NP, Gingras H, Godin C, Feng J, Groppi A, Nikolski M, Leprohon P, Ouellette M. Holistic understanding of trimethoprim resistance in Streptococcus pneumoniae using an integrative approach of genome-wide association study, resistance reconstruction, and machine learning. mBio 2024; 15:e0136024. [PMID: 39120145 PMCID: PMC11389379 DOI: 10.1128/mbio.01360-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 07/08/2024] [Indexed: 08/10/2024] Open
Abstract
Antimicrobial resistance (AMR) is a public health threat worldwide. Next-generation sequencing (NGS) has opened unprecedented opportunities to accelerate AMR mechanism discovery and diagnostics. Here, we present an integrative approach to investigate trimethoprim (TMP) resistance in the key pathogen Streptococcus pneumoniae. We explored a collection of 662 S. pneumoniae genomes by conducting a genome-wide association study (GWAS), followed by functional validation using resistance reconstruction experiments, combined with machine learning (ML) approaches to predict TMP minimum inhibitory concentration (MIC). Our study showed that multiple additive mutations in the folA and sulA loci are responsible for TMP non-susceptibility in S. pneumoniae and can be used as key features to build ML models for digital MIC prediction, reaching an average accuracy within ±1 twofold dilution factor of 86.3%. Our roadmap of in silico analysis-wet-lab validation-diagnostic tool building could be adapted to explore AMR in other combinations of bacteria-antibiotic. IMPORTANCE In the age of next-generation sequencing (NGS), while data-driven methods such as genome-wide association study (GWAS) and machine learning (ML) excel at finding patterns, functional validation can be challenging due to the high numbers of candidate variants. We designed an integrative approach combining a GWAS on S. pneumoniae clinical isolates, followed by whole-genome transformation coupled with NGS to functionally characterize a large set of GWAS candidates. Our study validated several phenotypic folA mutations beyond the standard Ile100Leu mutation, and showed that the overexpression of the sulA locus produces trimethoprim (TMP) resistance in Streptococcus pneumoniae. These validated loci, when used to build ML models, were found to be the best inputs for predicting TMP minimal inhibitory concentrations. Integrative approaches can bridge the genotype-phenotype gap by biological insights that can be incorporated in ML models for accurate prediction of drug susceptibility.
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Affiliation(s)
- Nguyen-Phuong Pham
- Centre de Recherche en Infectiologie du Centre de Recherche du CHU de Québec and Département de Microbiologie, Infectiologie et Immunologie, Faculté de Médecine, Université Laval, Québec City, Québec, Canada
| | - Hélène Gingras
- Centre de Recherche en Infectiologie du Centre de Recherche du CHU de Québec and Département de Microbiologie, Infectiologie et Immunologie, Faculté de Médecine, Université Laval, Québec City, Québec, Canada
| | - Chantal Godin
- Centre de Recherche en Infectiologie du Centre de Recherche du CHU de Québec and Département de Microbiologie, Infectiologie et Immunologie, Faculté de Médecine, Université Laval, Québec City, Québec, Canada
| | - Jie Feng
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Alexis Groppi
- Bordeaux Bioinformatics Center and CNRS, Institut de Biochimie et Génétique Cellulaires (IBGC) UMR 5095, Université de Bordeaux, Bordeaux, France
| | - Macha Nikolski
- Bordeaux Bioinformatics Center and CNRS, Institut de Biochimie et Génétique Cellulaires (IBGC) UMR 5095, Université de Bordeaux, Bordeaux, France
| | - Philippe Leprohon
- Centre de Recherche en Infectiologie du Centre de Recherche du CHU de Québec and Département de Microbiologie, Infectiologie et Immunologie, Faculté de Médecine, Université Laval, Québec City, Québec, Canada
| | - Marc Ouellette
- Centre de Recherche en Infectiologie du Centre de Recherche du CHU de Québec and Département de Microbiologie, Infectiologie et Immunologie, Faculté de Médecine, Université Laval, Québec City, Québec, Canada
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183
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Demir B, Ayna Altuntaş S, Kurt İ, Ulukaya S, Erdem O, Güler S, Uzun C. Cognitive activity analysis of Parkinson's patients using artificial intelligence techniques. Neurol Sci 2024:10.1007/s10072-024-07734-y. [PMID: 39256279 DOI: 10.1007/s10072-024-07734-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 08/19/2024] [Indexed: 09/12/2024]
Abstract
PURPOSE The development of modern Artificial Intelligence (AI) based models for the early diagnosis of Parkinson's disease (PD) has been gaining deep attention by researchers recently. In particular, the use of different types of datasets (voice, hand movements, gait, etc.) increases the variety of up-to-date models. Movement disorders and tremors are also among the most prominent symptoms of PD. The usage of drawings in the detection of PD can be a crucial decision-support approach that doctors can benefit from. METHODS A dataset was created by asking 40 PD and 40 Healthy Controls (HC) to draw spirals with and without templates using a special tablet. The patient-healthy distinction was achieved by classifying drawings of individuals using Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) algorithms. Prior to classification, the data were normalized by applying the min-max normalization method. Moreover, Leave-One-Subject-Out (LOSO) Cross-Validation (CV) approach was utilized to eliminate possible overfitting scenarios. To further improve the performances of classifiers, Principal Component Analysis (PCA) dimension reduction technique were also applied to the raw data and the results were compared accordingly. RESULTS The highest accuracy among machine learning based classifiers was obtained as 90% with SVM classifier using non-template drawings with PCA application. CONCLUSION The model can be used as a pre-evaluation system in the clinic as a non-invasive method that also minimizes environmental and educational level differences by using simple hand gestures such as hand drawing, writing numbers, words, and syllables. As a result of our study, preliminary preparation has been made so that hand drawing analysis can be used as an auxiliary system that can save time for health professionals. We plan to work on more comprehensive data in the future.
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Affiliation(s)
- Bahar Demir
- Department of Computational Sciences, Trakya University, Edirne, 22030, Turkey.
| | - Sinem Ayna Altuntaş
- Department of Computational Sciences, Trakya University, Edirne, 22030, Turkey
- Department of Biomedical Device Technology, Trakya University, Edirne, 22030, Turkey
| | - İlke Kurt
- Department of Computational Sciences, Trakya University, Edirne, 22030, Turkey
- Department of Biomedical Device Technology, Trakya University, Edirne, 22030, Turkey
| | - Sezer Ulukaya
- Department of Electrical and Electronics Engineering, Trakya University, Edirne, 22030, Turkey
| | - Oğuzhan Erdem
- Department of Electrical and Electronics Engineering, Trakya University, Edirne, 22030, Turkey
| | - Sibel Güler
- Department of Neurology, Yalova University Faculty of Medicine, Yalova, 77200, Turkey.
| | - Cem Uzun
- Department of Otorhinolaryngology, Head and Neck Surgery, Koç University School of Medicine, İstanbul, 34010, Turkey
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184
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Chakraborty C, Bhattacharya M, Lee SS, Wen ZH, Lo YH. The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges. MOLECULAR THERAPY. NUCLEIC ACIDS 2024; 35:102295. [PMID: 39257717 PMCID: PMC11386122 DOI: 10.1016/j.omtn.2024.102295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug discovery has come to the forefront. It reduces the time and expenditure. Due to these advantages, pharmaceutical industries are concentrating on AI-driven drug discovery. Several drug molecules have been discovered using AI-based techniques and tools, and several newly AI-discovered drug molecules have already entered clinical trials. In this review, we first present the data and their resources in the pharmaceutical sector for AI-driven drug discovery and illustrated some significant algorithms or techniques used for AI and ML which are used in this field. We gave an overview of the deep neural network (NN) models and compared them with artificial NNs. Then, we illustrate the recent advancement of the landscape of drug discovery using AI to deep learning, such as the identification of drug targets, prediction of their structure, estimation of drug-target interaction, estimation of drug-target binding affinity, design of de novo drug, prediction of drug toxicity, estimation of absorption, distribution, metabolism, excretion, toxicity; and estimation of drug-drug interaction. Moreover, we highlighted the success stories of AI-driven drug discovery and discussed several collaboration and the challenges in this area. The discussions in the article will enrich the pharmaceutical industry.
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Affiliation(s)
- Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha 756020, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-Do 24252, Republic of Korea
| | - Zhi-Hong Wen
- Department of Marine Biotechnology and Resources, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
| | - Yi-Hao Lo
- Department of Family Medicine, Zuoying Armed Forces General Hospital, Kaohsiung 813204, Taiwan
- Shu-Zen Junior College of Medicine and Management, Kaohsiung 821004, Taiwan
- Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 804201, Taiwan
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185
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Behera SK, Karthika S, Mahanty B, Meher SK, Zafar M, Baskaran D, Rajamanickam R, Das R, Pakshirajan K, Bilyaminu AM, Rene ER. Application of artificial intelligence tools in wastewater and waste gas treatment systems: Recent advances and prospects. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 370:122386. [PMID: 39260284 DOI: 10.1016/j.jenvman.2024.122386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 08/17/2024] [Accepted: 08/31/2024] [Indexed: 09/13/2024]
Abstract
The non-linear complex relationships among the process variables in wastewater and waste gas treatment systems possess a significant challenge for real-time systems modelling. Data driven artificial intelligence (AI) tools are increasingly being adopted to predict the process performance, cost-effective process monitoring, and the control of different waste treatment systems, including those involving resource recovery. This review presents an in-depth analysis of the applications of emerging AI tools in physico-chemical and biological processes for the treatment of air pollutants, water and wastewater, and resource recovery processes. Additionally, the successful implementation of AI-controlled wastewater and waste gas treatment systems, along with real-time monitoring at the industrial scale are discussed.
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Affiliation(s)
- Shishir Kumar Behera
- Process Simulation Research Group, School of Chemical Engineering, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India.
| | - S Karthika
- Department of Chemical Engineering, Alagappa College of Technology, Anna University, Chennai, 600 025, Tamil Nadu, India
| | - Biswanath Mahanty
- Division of Biotechnology, Karunya Institute of Technology & Sciences, Coimbatore, 641 114, Tamil Nadu, India
| | - Saroj K Meher
- Systems Science and Informatics Unit, Indian Statistical Institute, Bangalore, 560059, India
| | - Mohd Zafar
- Department of Applied Biotechnology, College of Applied Sciences & Pharmacy, University of Technology and Applied Sciences - Sur, P.O. Box: 484, Zip Code: 411, Sur, Oman
| | - Divya Baskaran
- Department of Chemical and Biomolecular Engineering, Chonnam National University, Yeosu, Jeonnam, 59626, South Korea; Department of Biomaterials, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, 600 077, Tamil Nadu, India
| | - Ravi Rajamanickam
- Department of Chemical Engineering, Annamalai University, Chidambaram, 608002, Tamil Nadu, India
| | - Raja Das
- Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India
| | - Kannan Pakshirajan
- Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, 781 039, Assam, India
| | - Abubakar M Bilyaminu
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, P. O. Box 3015, 2601, DA Delft, the Netherlands
| | - Eldon R Rene
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, P. O. Box 3015, 2601, DA Delft, the Netherlands
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186
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Agarwal P, Mathur V, Kasturi M, Srinivasan V, Seetharam RN, S Vasanthan K. A Futuristic Development in 3D Printing Technique Using Nanomaterials with a Step Toward 4D Printing. ACS OMEGA 2024; 9:37445-37458. [PMID: 39281933 PMCID: PMC11391532 DOI: 10.1021/acsomega.4c04123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 07/27/2024] [Accepted: 08/06/2024] [Indexed: 09/18/2024]
Abstract
3D bioprinting has shown great promise in tissue engineering and regenerative medicine for creating patient-specific tissue scaffolds and medicinal devices. The quickness, accurate imaging, and design targeting of this emerging technology have excited biomedical engineers and translational medicine researchers. Recently, scaffolds made from 3D bioprinted tissue have become more clinically effective due to nanomaterials and nanotechnology. Because of quantum confinement effects and high surface area/volume ratios, nanomaterials and nanotechnological techniques have unique physical, chemical, and biological features. The use of nanomaterials and 3D bioprinting has led to scaffolds with improved physicochemical and biological properties. Nanotechnology and nanomaterials affect 3D bioprinted tissue engineered scaffolds for regenerative medicine and tissue engineering. Biomaterials and cells that respond to stimuli change the structural shape in 4D bioprinting. With such dynamic designs, tissue architecture can change morphologically. New 4D bioprinting techniques will aid in bioactuation, biorobotics, and biosensing. The potential of 4D bioprinting in biomedical technologies is also discussed in this article.
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Affiliation(s)
- Prachi Agarwal
- Manipal Centre for Biotherapeutics Research, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
| | - Vidhi Mathur
- Manipal Centre for Biotherapeutics Research, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
| | - Meghana Kasturi
- Department of Mechanical Engineering, University of Michigan, Dearborn, Michigan 48128, United States
| | - Varadharajan Srinivasan
- Manipal Institute of Technology, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
| | - Raviraja N Seetharam
- Manipal Centre for Biotherapeutics Research, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
| | - Kirthanashri S Vasanthan
- Manipal Centre for Biotherapeutics Research, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
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187
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Ebtekar A, Hutter M. Modeling the Arrows of Time with Causal Multibaker Maps. ENTROPY (BASEL, SWITZERLAND) 2024; 26:776. [PMID: 39330109 PMCID: PMC11431034 DOI: 10.3390/e26090776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 07/22/2024] [Accepted: 09/07/2024] [Indexed: 09/28/2024]
Abstract
Why do we remember the past, and plan the future? We introduce a toy model in which to investigate emergent time asymmetries: the causal multibaker maps. These are reversible discrete-time dynamical systems with configurable causal interactions. Imposing a suitable initial condition or "Past Hypothesis", and then coarse-graining, yields a Pearlean locally causal structure. While it is more common to speculate that the other arrows of time arise from the thermodynamic arrow, our model instead takes the causal arrow as fundamental. From it, we obtain the thermodynamic and epistemic arrows of time. The epistemic arrow concerns records, which we define to be systems that encode the state of another system at another time, regardless of the latter system's dynamics. Such records exist of the past, but not of the future. We close with informal discussions of the evolutionary and agential arrows of time, and their relevance to decision theory.
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Affiliation(s)
- Aram Ebtekar
- Independent Researcher, Vancouver, BC V5Y 3J6, Canada
| | - Marcus Hutter
- Google DeepMind, London N1C 4AG, UK
- School of Computing, Australian National University, Canberra, ACT 2601, Australia
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188
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Banks R, Greene BR, Morrow I, Ciesla M, Woolever D, Tobyne S, Gomes-Osman J, Jannati A, Showalter J, Bates D, Pascual-Leone A. Digital speech hearing screening using a quick novel mobile hearing impairment assessment: an observational correlation study. Sci Rep 2024; 14:21157. [PMID: 39256446 PMCID: PMC11387469 DOI: 10.1038/s41598-024-67539-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 07/12/2024] [Indexed: 09/12/2024] Open
Abstract
By 2050, 1 in 4 people worldwide will be living with hearing impairment. We propose a digital Speech Hearing Screener (dSHS) using short nonsense word recognition to measure speech-hearing ability. The importance of hearing screening is increasing due to the anticipated increase in individuals with hearing impairment globally. We compare dSHS outcomes with standardized pure-tone averages (PTA) and speech-recognition thresholds (SRT). Fifty participants (aged 55 or older underwent pure-tone and speech-recognition thresholding. One-way ANOVA was used to compare differences between hearing impaired and hearing not-impaired groups, by the dSHS, with a clinical threshold of moderately impaired hearing at 35 dB and severe hearing impairment at 50 dB. dSHS results significantly correlated with PTAs/SRTs. ANOVA results revealed the dSHS was significantly different (F(1,47) = 38.1, p < 0.001) between hearing impaired and unimpaired groups. Classification analysis using a 35 dB threshold, yielded accuracy of 85.7% for PTA-based impairment and 81.6% for SRT-based impairment. At a 50 dB threshold, dSHS classification accuracy was 79.6% for PTA-based impairment (Negative Predictive Value (NPV)-93%) and 83.7% (NPV-100%) for SRT-based impairment. The dSHS successfully differentiates between hearing-impaired and unimpaired individuals in under 3 min. This hearing screener offers a time-saving, in-clinic hearing screening to streamline the triage of those with likely hearing impairment to the appropriate follow-up assessment, thereby improving the quality of services. Future work will investigate the ability of the dSHS to help rule out hearing impairment as a cause or confounder in clinical and research applications.
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Affiliation(s)
- Russell Banks
- Linus Health, Inc., Boston, MA, USA.
- Department of Communicative Sciences & Disorders, Michigan State University, East Lansing, MI, USA.
| | - Barry R Greene
- Linus Health, Inc., Boston, MA, USA
- Linus Health Europe, Dublin, Ireland
| | | | | | | | | | - Joyce Gomes-Osman
- Linus Health, Inc., Boston, MA, USA
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Ali Jannati
- Linus Health, Inc., Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
| | | | | | - Alvaro Pascual-Leone
- Linus Health, Inc., Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Hinda and Arthur Marcus Institute for Aging Research and Deanna and Sidney Wolk Center for Memory Health, Hebrew Senior Life, Boston, MA, USA
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189
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Powers A, Angelos PA, Bond A, Farina E, Fredericks C, Gandhi J, Greenwald M, Hernandez-Busot G, Hosein G, Kelley M, Mourgues C, Palmer W, Rodriguez-Sanchez J, Seabury R, Toribio S, Vin R, Weleff J, Woods S, Benrimoh D. A computational account of the development and evolution of psychotic symptoms. Biol Psychiatry 2024:S0006-3223(24)01584-1. [PMID: 39260466 DOI: 10.1016/j.biopsych.2024.08.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 08/08/2024] [Accepted: 08/13/2024] [Indexed: 09/13/2024]
Abstract
The mechanisms of psychotic symptoms like hallucinations and delusions are often investigated in fully-formed illness, well after symptoms emerge. These investigations have yielded key insights, but are not well-positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We will make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We will argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing a compensatory relative over-reliance on prior beliefs. This over-reliance on priors predisposes to hallucinations and covaries with hallucination severity. An over-reliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We will identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptomatology as a point of equilibrium among competing biological forces.
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Affiliation(s)
- Albert Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA.
| | - P A Angelos
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Alexandria Bond
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Emily Farina
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Carolyn Fredericks
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Jay Gandhi
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Maximillian Greenwald
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | | | - Gabriel Hosein
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Megan Kelley
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Catalina Mourgues
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - William Palmer
- Yale University Department of Psychology, New Haven, CT, USA
| | | | - Rashina Seabury
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Silmilly Toribio
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Raina Vin
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Jeremy Weleff
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Scott Woods
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - David Benrimoh
- Department of Psychiatry, McGill University, Montreal, Canada
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190
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Hayes T, Baraldi AN, Coxe S. Random forest analysis and lasso regression outperform traditional methods in identifying missing data auxiliary variables when the MAR mechanism is nonlinear (p.s. Stop using Little's MCAR test). Behav Res Methods 2024:10.3758/s13428-024-02494-1. [PMID: 39251529 DOI: 10.3758/s13428-024-02494-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/19/2024] [Indexed: 09/11/2024]
Abstract
The selection of auxiliary variables is an important first step in appropriately implementing missing data methods such as full information maximum likelihood (FIML) estimation or multiple imputation. However, practical guidelines and statistical tests for selecting useful auxiliary variables are somewhat lacking, leading to potentially biased estimates. We propose the use of random forest analysis and lasso regression as alternative methods to select auxiliary variables, particularly in situations in which the missing data pattern is nonlinear or otherwise complex (i.e., interactive relationships between variables and missingness). Monte Carlo simulations demonstrate the effectiveness of random forest analysis and lasso regression compared to traditional methods (t-tests, Little's MCAR test, logistic regressions), in terms of both selecting auxiliary variables and the performance of said auxiliary variables when incorporated in an analysis with missing data. Both techniques outperformed traditional methods, providing a promising direction for improvement of practical methods for handling missing data in statistical analyses.
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Affiliation(s)
- Timothy Hayes
- Department of Psychology, Florida International University, 11200 SW 8 Street, Miami, FL, DM 381B, USA.
| | - Amanda N Baraldi
- Department of Psychology, Oklahoma State University, Stillwater, OK, USA
| | - Stefany Coxe
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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191
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Damos PT. On formal limitations of causal ecological networks. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230170. [PMID: 39034692 PMCID: PMC11293863 DOI: 10.1098/rstb.2023.0170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 12/02/2023] [Accepted: 02/22/2024] [Indexed: 07/23/2024] Open
Abstract
Causal multivariate time-series analysis, combined with network theory, provide a powerful tool for studying complex ecological interactions. However, these methods have limitations often underestimated when used in graphical modelling of ecological systems. In this opinion article, I examine the relationship between formal logic methods used to describe causal networks and their inherent statistical and epistemological limitations. I argue that while these methods offer valuable insights, they are restricted by axiomatic assumptions, statistical constraints and the incompleteness of our knowledge. To prove that, I first consider causal networks as formal systems, define causality and formalize their axioms in terms of modal logic and use ecological counterexamples to question the axioms. I also highlight the statistical limitations when using multivariate time-series analysis and Granger causality to develop ecological networks, including the potential for spurious correlations among other data characteristics. Finally, I draw upon Gödel's incompleteness theorems to highlight the inherent limits of fully understanding complex networks as formal systems and conclude that causal ecological networks are subject to initial rules and data characteristics and, as any formal system, will never fully capture the intricate complexities of the systems they represent. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.
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Affiliation(s)
- Petros T. Damos
- Minstry of Education, Religious and Sports, Directorate of Secondary Education Veroia, Ergohori59132, Greece
- Department of Agriculture, School of Agricultural Studies, University of Western Macedonia, Florina, 53100, Greece
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Western Macedonia, Kozani50100, Greece
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192
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Rahman MS, Islam KR, Prithula J, Kumar J, Mahmud M, Alam MF, Reaz MBI, Alqahtani A, Chowdhury MEH. Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3. BMC Med Inform Decis Mak 2024; 24:249. [PMID: 39251962 PMCID: PMC11382400 DOI: 10.1186/s12911-024-02655-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Accepted: 08/27/2024] [Indexed: 09/11/2024] Open
Abstract
BACKGROUND Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques offer advantages over traditional methods for predicting outcomes. This study aimed to develop a prognostic model using a Stacking-based Meta-Classifier to predict 30-day mortality risks in Sepsis-3 patients from the MIMIC-III database. METHODS A cohort of 4,240 Sepsis-3 patients was analyzed, with 783 experiencing 30-day mortality and 3,457 surviving. Fifteen biomarkers were selected using feature ranking methods, including Extreme Gradient Boosting (XGBoost), Random Forest, and Extra Tree, and the Logistic Regression (LR) model was used to assess their individual predictability with a fivefold cross-validation approach for the validation of the prediction. The dataset was balanced using the SMOTE-TOMEK LINK technique, and a stacking-based meta-classifier was used for 30-day mortality prediction. The SHapley Additive explanations analysis was performed to explain the model's prediction. RESULTS Using the LR classifier, the model achieved an area under the curve or AUC score of 0.99. A nomogram provided clinical insights into the biomarkers' significance. The stacked meta-learner, LR classifier exhibited the best performance with 95.52% accuracy, 95.79% precision, 95.52% recall, 93.65% specificity, and a 95.60% F1-score. CONCLUSIONS In conjunction with the nomogram, the proposed stacking classifier model effectively predicted 30-day mortality in Sepsis patients. This approach holds promise for early intervention and improved outcomes in treating Sepsis cases.
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Affiliation(s)
- Md Sohanur Rahman
- Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Khandaker Reajul Islam
- Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, 56000, Kuala Lumpur, Malaysia
| | - Johayra Prithula
- Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Jaya Kumar
- Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, 56000, Kuala Lumpur, Malaysia.
| | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - Mohammed Fasihul Alam
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, 2713, Qatar
| | - Mamun Bin Ibne Reaz
- Department of Electrical Engineering, Independent University, Bangladesh, Dhaka, Bangladesh
| | - Abdulrahman Alqahtani
- Department of Biomedical Technology, College of Applied Medical Sciences in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
- Department of Medical Equipment Technology, College of Applied, Medical Science, Majmaah University, Majmaah City, 11952, Saudi Arabia
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193
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Lochner S, Honerkamp D, Valada A, Straw AD. Reinforcement learning as a robotics-inspired framework for insect navigation: from spatial representations to neural implementation. Front Comput Neurosci 2024; 18:1460006. [PMID: 39314666 PMCID: PMC11416953 DOI: 10.3389/fncom.2024.1460006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Accepted: 08/20/2024] [Indexed: 09/25/2024] Open
Abstract
Bees are among the master navigators of the insect world. Despite impressive advances in robot navigation research, the performance of these insects is still unrivaled by any artificial system in terms of training efficiency and generalization capabilities, particularly considering the limited computational capacity. On the other hand, computational principles underlying these extraordinary feats are still only partially understood. The theoretical framework of reinforcement learning (RL) provides an ideal focal point to bring the two fields together for mutual benefit. In particular, we analyze and compare representations of space in robot and insect navigation models through the lens of RL, as the efficiency of insect navigation is likely rooted in an efficient and robust internal representation, linking retinotopic (egocentric) visual input with the geometry of the environment. While RL has long been at the core of robot navigation research, current computational theories of insect navigation are not commonly formulated within this framework, but largely as an associative learning process implemented in the insect brain, especially in the mushroom body (MB). Here we propose specific hypothetical components of the MB circuit that would enable the implementation of a certain class of relatively simple RL algorithms, capable of integrating distinct components of a navigation task, reminiscent of hierarchical RL models used in robot navigation. We discuss how current models of insect and robot navigation are exploring representations beyond classical, complete map-like representations, with spatial information being embedded in the respective latent representations to varying degrees.
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Affiliation(s)
- Stephan Lochner
- Institute of Biology I, University of Freiburg, Freiburg, Germany
| | - Daniel Honerkamp
- Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Abhinav Valada
- Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Andrew D. Straw
- Institute of Biology I, University of Freiburg, Freiburg, Germany
- Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany
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194
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Medina CA, Heuschele DJ, Zhao D, Lin M, Beil CT, Sheehan MJ, Xu Z. Multi-trait modeling and machine learning discover new markers associated with stem traits in alfalfa. FRONTIERS IN PLANT SCIENCE 2024; 15:1429976. [PMID: 39315379 PMCID: PMC11418689 DOI: 10.3389/fpls.2024.1429976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 07/30/2024] [Indexed: 09/25/2024]
Abstract
Alfalfa biomass can be fractionated into leaf and stem components. Leaves comprise a protein-rich and highly digestible portion of biomass for ruminant animals, while stems constitute a high fiber and less digestible fraction, representing 50 to 70% of the biomass. However, little attention has focused on stem-related traits, which are a key aspect in improving the nutritional value and intake potential of alfalfa. This study aimed to identify molecular markers associated with four morphological traits in a panel of five populations of alfalfa generated over two cycles of divergent selection based on 16-h and 96-h in vitro neutral detergent fiber digestibility in stems. Phenotypic traits of stem color, presence of stem pith cells, winter standability, and winter injury were modeled using univariate and multivariate spatial mixed linear models (MLM), and the predicted values were used as response variables in genome-wide association studies (GWAS). The alfalfa panel was genotyped using a 3K DArTag SNP markers for the evaluation of the genetic structure and GWAS. Principal component and population structure analyses revealed differentiations between populations selected for high- and low-digestibility. Thirteen molecular markers were significantly associated with stem traits using either univariate or multivariate MLM. Additionally, support vector machine (SVM) and random forest (RF) algorithms were implemented to determine marker importance scores for stem traits and validate the GWAS results. The top-ranked markers from SVM and RF aligned with GWAS findings for solid stem pith, winter standability, and winter injury. Additionally, SVM identified additional markers with high variable importance for solid stem pith and winter injury. Most molecular markers were located in coding regions. These markers can facilitate marker-assisted selection to expedite breeding programs to increase winter hardiness or stem palatability.
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Affiliation(s)
- Cesar A Medina
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, United States
| | - Deborah J Heuschele
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, United States
- Plant Science Research Unit, USDA-ARS, Saint Paul, MN, United States
| | - Dongyan Zhao
- Breeding Insight, Cornell University, Ithaca, NY, United States
| | - Meng Lin
- Breeding Insight, Cornell University, Ithaca, NY, United States
| | - Craig T Beil
- Breeding Insight, Cornell University, Ithaca, NY, United States
| | - Moira J Sheehan
- Breeding Insight, Cornell University, Ithaca, NY, United States
| | - Zhanyou Xu
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, United States
- Plant Science Research Unit, USDA-ARS, Saint Paul, MN, United States
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195
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Mehdi S, Tiwary P. Thermodynamics-inspired explanations of artificial intelligence. Nat Commun 2024; 15:7859. [PMID: 39251574 PMCID: PMC11385982 DOI: 10.1038/s41467-024-51970-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 08/20/2024] [Indexed: 09/11/2024] Open
Abstract
In recent years, predictive machine learning models have gained prominence across various scientific domains. However, their black-box nature necessitates establishing trust in them before accepting their predictions as accurate. One promising strategy involves employing explanation techniques that elucidate the rationale behind a model's predictions in a way that humans can understand. However, assessing the degree of human interpretability of these explanations is a nontrivial challenge. In this work, we introduce interpretation entropy as a universal solution for evaluating the human interpretability of any linear model. Using this concept and drawing inspiration from classical thermodynamics, we present Thermodynamics-inspired Explainable Representations of AI and other black-box Paradigms, a method for generating optimally human-interpretable explanations in a model-agnostic manner. We demonstrate the wide-ranging applicability of this method by explaining predictions from various black-box model architectures across diverse domains, including molecular simulations, text, and image classification.
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Affiliation(s)
- Shams Mehdi
- Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park, 20742, USA
| | - Pratyush Tiwary
- Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park, 20742, USA.
- University of Maryland Institute for Health Computing, Bethesda, Maryland, 20852, USA.
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196
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Reddy RS, Alahmari KA, Alshahrani MS, Alkhamis BA, Tedla JS, ALMohiza MA, Elrefaey BH, Koura GM, Gular K, Alnakhli HH, Mukherjee D, Rao VS, Al-Qahtani KA. Exploring the impact of physiotherapy on health outcomes in older adults with chronic diseases: a cross-sectional analysis. Front Public Health 2024; 12:1415882. [PMID: 39314794 PMCID: PMC11416960 DOI: 10.3389/fpubh.2024.1415882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 08/05/2024] [Indexed: 09/25/2024] Open
Abstract
Objective This study evaluates the impact of physiotherapy interventions on health outcomes and explores the correlation between physiotherapy session characteristics and improvements in health among older individuals. Methods In a cross-sectional design, 384 older adults with chronic conditions such as arthritis, osteoporosis, Chronic Obstructive Pulmonary Disease (COPD), diabetes, and hypertension were recruited. Results The proportion of arthritis (39.1%) and hypertension (45.8%) was notably high. Participants receiving physiotherapy showed significant improvements in pain levels (mean reduction from 5.09 to 2.95), mobility scores (improvement from 3.0 to 3.96), and functional independence. A positive correlation was identified between the frequency of physiotherapy sessions and pain reduction (r = 0.26, p = 0.035), and a stronger correlation between session duration and both pain reduction (r = 0.38, p = 0.002) and mobility improvement (r = 0.43, p = 0.001). High satisfaction rates with physiotherapy were reported, and age was found to be a significant negative predictor of health outcomes (Coef. = -0.3402, p = 0.0009). Conclusion Physiotherapy interventions significantly improve health outcomes in older adults with chronic diseases.
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Affiliation(s)
- Ravi Shankar Reddy
- Program of Physical Therapy, Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Khalid A. Alahmari
- Program of Physical Therapy, Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Mastour Saeed Alshahrani
- Program of Physical Therapy, Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Batool Abdulelah Alkhamis
- Program of Physical Therapy, Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Jaya Shanker Tedla
- Program of Physical Therapy, Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Mohammad A. ALMohiza
- Department of Health Rehabilitation Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Basant Hamdy Elrefaey
- Program of Physical Therapy, Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Ghada M. Koura
- Program of Physical Therapy, Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Kumar Gular
- Program of Physical Therapy, Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Hani Hassan Alnakhli
- Program of Physical Therapy, Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Debjani Mukherjee
- Program of Physical Therapy, Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Vikram Sreenivasa Rao
- Department of Anatomy, College of Medicine, King Khalid University, Abha, Saudi Arabia
| | - Khalid Awad Al-Qahtani
- Program of Physical Therapy, Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
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197
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Duan G, Zheng C, Jiang Y, Leng C, Liu Y, Wang B, He D, Wen Z. Effects of different soil and water conservation measures on plant functional traits in the Loess Plateau. FRONTIERS IN PLANT SCIENCE 2024; 15:1381807. [PMID: 39315374 PMCID: PMC11418278 DOI: 10.3389/fpls.2024.1381807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 08/12/2024] [Indexed: 09/25/2024]
Abstract
Soil and water conservation measures (SWCM) have wide-ranging effects on vegetation and soil, and their effects on the ecosystem are multifaceted, with complex mechanisms. While numerous studies have focused on the impact of such measures on soil, the improvement of plant functional traits is a major factor in the ecological recovery of the Loess Plateau. This survey extensively investigated no measure plots, vegetation measure plots, and engineering measure plots in the Loess Plateau. The impact of SWCM on plant functional traits was investigated using structural equation modeling. We examined six plant functional traits-leaf dry weight (LD), specific leaf area (SLA), leaf tissue density (LTD), leaf total phosphorus (LTP), leaf total nitrogen (LTN), and leaf volume (LV)-correlated with resource acquisition and allocation. In 122 plots, we explored the effects of measures, soil, diversity, and community structure on the weighted average of plant functional traits. The findings showed substantial positive correlations between LD and SLA, LD and LV, SLA and LV, SLA and LTP, and LTP and LTN. LTD has a substantial negative correlation with LD, LTD with SLA, and LTD with LV. SWCM limits diversity, and the mechanisms by which it affects plant functional traits vary. In the structural equation model (SEM) of vegetation measures, improving community structure enhances plant functional traits, but soil factors have the greatest influence on plant functional traits in SEM engineering measures. Plant functional trait differences on the Loess Plateau result are due to differential plant responses to diverse soil properties and community structure. Vegetation measures enhance the chemical properties of plant functional traits, while engineering measures improve physical properties. The study provides a theoretical foundation for vegetation restoration and management following the implementation of diverse SWCM.
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Affiliation(s)
- Gaohui Duan
- College of Grassland Agriculture, Northwest A&F University, Xianyang, China
| | - Cheng Zheng
- College of Grassland Agriculture, Northwest A&F University, Xianyang, China
| | - Yanmin Jiang
- Institute of Soil and Water Conservation, Chinese Academy of Sciences & Ministry of Water Resources, Yangling, China
| | - Chunqian Leng
- School of Chemical Engineering, Shandong Institute of Petroleum and Chemical Technology, Dongying, China
| | - Yangyang Liu
- College of Grassland Agriculture, Northwest A&F University, Xianyang, China
| | - Boheng Wang
- First Department of Forest and Grass Comprehensive Monitoring, East China Survey and Planning Institute of National Forest and Grassland Administration, Hangzhou, China
| | - Dianjing He
- Ecological Engineering Department, Northwest China Survey and Planning Institute of National Forest and Grassland Administration, Xian, China
| | - Zhongming Wen
- College of Grassland Agriculture, Northwest A&F University, Xianyang, China
- Institute of Soil and Water Conservation, Chinese Academy of Sciences & Ministry of Water Resources, Yangling, China
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198
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Wang Y, Liu G. Self-Supervised Dam Deformation Anomaly Detection Based on Temporal-Spatial Contrast Learning. SENSORS (BASEL, SWITZERLAND) 2024; 24:5858. [PMID: 39275768 PMCID: PMC11397878 DOI: 10.3390/s24175858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2024] [Revised: 09/06/2024] [Accepted: 09/07/2024] [Indexed: 09/16/2024]
Abstract
The detection of anomalies in dam deformation is paramount for evaluating structural integrity and facilitating early warnings, representing a critical aspect of dam health monitoring (DHM). Conventional data-driven methods for dam anomaly detection depend extensively on historical data; however, obtaining annotated data is both expensive and labor-intensive. Consequently, methodologies that leverage unlabeled or semi-labeled data are increasingly gaining popularity. This paper introduces a spatiotemporal contrastive learning pretraining (STCLP) strategy designed to extract discriminative features from unlabeled datasets of dam deformation. STCLP innovatively combines spatial contrastive learning based on temporal contrastive learning to capture representations embodying both spatial and temporal characteristics. Building upon this, a novel anomaly detection method for dam deformation utilizing STCLP is proposed. This method transfers pretrained parameters to targeted downstream classification tasks and leverages prior knowledge for enhanced fine-tuning. For validation, an arch dam serves as the case study. The results reveal that the proposed method demonstrates excellent performance, surpassing other benchmark models.
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Affiliation(s)
- Yu Wang
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
| | - Guohua Liu
- College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
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199
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Aydın Y, Cakiroglu C, Bekdaş G, Geem ZW. Explainable Ensemble Learning and Multilayer Perceptron Modeling for Compressive Strength Prediction of Ultra-High-Performance Concrete. Biomimetics (Basel) 2024; 9:544. [PMID: 39329567 PMCID: PMC11430366 DOI: 10.3390/biomimetics9090544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Revised: 08/23/2024] [Accepted: 09/05/2024] [Indexed: 09/28/2024] Open
Abstract
The performance of ultra-high-performance concrete (UHPC) allows for the design and creation of thinner elements with superior overall durability. The compressive strength of UHPC is a value that can be reached after a certain period of time through a series of tests and cures. However, this value can be estimated by machine-learning methods. In this study, multilayer perceptron (MLP) and Stacking Regressor, an ensemble machine-learning models, is used to predict the compressive strength of high-performance concrete. Then, the ML model's performance is explained with a feature importance analysis and Shapley additive explanations (SHAPs), and the developed models are interpreted. The effect of using different random splits for the training and test sets has been investigated. It was observed that the stacking regressor, which combined the outputs of Extreme Gradient Boosting (XGBoost), Category Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Extra Trees regressors using random forest as the final estimator, performed significantly better than the MLP regressor. It was shown that the compressive strength was predicted by the stacking regressor with an average R2 score of 0.971 on the test set. On the other hand, the average R2 score of the MLP model was 0.909. The results of the SHAP analysis showed that the age of concrete and the amounts of silica fume, fiber, superplasticizer, cement, aggregate, and water have the greatest impact on the model predictions.
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Affiliation(s)
- Yaren Aydın
- Department of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, Turkey
| | - Celal Cakiroglu
- Department of Civil Engineering, Turkish-German University, 34820 Istanbul, Turkey
| | - Gebrail Bekdaş
- Department of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, Turkey
| | - Zong Woo Geem
- Department of Smart City, Gachon University, Seongnam 13120, Republic of Korea
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200
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Harshe K, Conner BC, Lerner ZF. Predicting Steady-State Metabolic Power in Cerebral Palsy, Stroke, and the Elderly During Walking With and Without Assistive Devices. Ann Biomed Eng 2024:10.1007/s10439-024-03614-w. [PMID: 39245696 DOI: 10.1007/s10439-024-03614-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Accepted: 09/01/2024] [Indexed: 09/10/2024]
Abstract
PURPOSE Individuals with walking impairment, such as those with cerebral palsy, often face challenges in leading physically active lives due to the high energy cost of movement. Assistive devices like powered exoskeletons aim to alleviate this burden and improve mobility. Traditionally, optimizing the effectiveness of such devices has relied on time-consuming laboratory-based measurements of energy expenditure, which may not be feasible for some patient populations. To address this, our study aimed to enhance the state-of-the-art predictive model for estimating steady-state metabolic rate from 2-min walking trials to include individuals with and without walking disabilities and for a variety of terrains and wearable device conditions. METHODS Using over 200 walking trials collected from eight prior exoskeleton-related studies, we trained a simple linear machine learning model to predict metabolic power at steady state based on condition-specific factors, such as whether the trial was conducted on a treadmill (level or incline) or outdoors, as well as demographic information, such as the participant's weight or presence of walking impairment, and 2 minutes of metabolic data. RESULTS We demonstrated the ability to predict steady-state metabolic rate to within an accuracy of 4.71 ± 2.7% on average across all walking conditions and patient populations, including with assistive devices and on different terrains. CONCLUSION This work seeks to unlock the use of in-the-loop optimization of wearable assistive devices in individuals with limited walking capacity. A freely available MATLAB application allows other researchers to easily apply our model.
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Affiliation(s)
- Karl Harshe
- Department of Mechanical Engineering, Northern Arizona University, Flagstaff, AZ, 86001, USA
| | - Benjamin C Conner
- College of Medicine - Phoenix, University of Arizona, Phoenix, AZ, USA
| | - Zachary F Lerner
- Department of Mechanical Engineering, Northern Arizona University, Flagstaff, AZ, 86001, USA.
- College of Medicine - Phoenix, University of Arizona, Phoenix, AZ, USA.
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