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Casanueva-Morato D, Ayuso-Martinez A, Dominguez-Morales JP, Jimenez-Fernandez A, Jimenez-Moreno G. Bio-inspired computational memory model of the Hippocampus: An approach to a neuromorphic spike-based Content-Addressable Memory. Neural Netw 2024; 178:106474. [PMID: 38941736 DOI: 10.1016/j.neunet.2024.106474] [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: 09/29/2023] [Revised: 04/12/2024] [Accepted: 06/17/2024] [Indexed: 06/30/2024]
Abstract
The brain has computational capabilities that surpass those of modern systems, being able to solve complex problems efficiently in a simple way. Neuromorphic engineering aims to mimic biology in order to develop new systems capable of incorporating such capabilities. Bio-inspired learning systems continue to be a challenge that must be solved, and much work needs to be done in this regard. Among all brain regions, the hippocampus stands out as an autoassociative short-term memory with the capacity to learn and recall memories from any fragment of them. These characteristics make the hippocampus an ideal candidate for developing bio-inspired learning systems that, in addition, resemble content-addressable memories. Therefore, in this work we propose a bio-inspired spiking content-addressable memory model based on the CA3 region of the hippocampus with the ability to learn, forget and recall memories, both orthogonal and non-orthogonal, from any fragment of them. The model was implemented on the SpiNNaker hardware platform using Spiking Neural Networks. A set of experiments based on functional, stress and applicability tests were performed to demonstrate its correct functioning. This work presents the first hardware implementation of a fully-functional bio-inspired spiking hippocampal content-addressable memory model, paving the way for the development of future more complex neuromorphic systems.
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Affiliation(s)
- Daniel Casanueva-Morato
- Escuela Técnica Superior de Ingeniería Informática (ETSII), Universidad de Sevilla, Seville, Avenida de Reina Mercedes s/n, 41012, Spain; Robotics and Tech. of Computers Lab., Universidad de Sevilla, Seville, 41012, Spain; Escuela Politécnica Superior (EPS), Universidad de Sevilla, Sevilla, 41011, Spain.
| | - Alvaro Ayuso-Martinez
- Escuela Técnica Superior de Ingeniería Informática (ETSII), Universidad de Sevilla, Seville, Avenida de Reina Mercedes s/n, 41012, Spain; Robotics and Tech. of Computers Lab., Universidad de Sevilla, Seville, 41012, Spain; Escuela Politécnica Superior (EPS), Universidad de Sevilla, Sevilla, 41011, Spain.
| | - Juan P Dominguez-Morales
- Escuela Técnica Superior de Ingeniería Informática (ETSII), Universidad de Sevilla, Seville, Avenida de Reina Mercedes s/n, 41012, Spain; Robotics and Tech. of Computers Lab., Universidad de Sevilla, Seville, 41012, Spain; Escuela Politécnica Superior (EPS), Universidad de Sevilla, Sevilla, 41011, Spain; Smart Computer Systems Research and Engineering Lab (SCORE), Research Institute of Computer Engineering (I3US), Universidad de Sevilla, Seville, 41012, Spain.
| | - Angel Jimenez-Fernandez
- Escuela Técnica Superior de Ingeniería Informática (ETSII), Universidad de Sevilla, Seville, Avenida de Reina Mercedes s/n, 41012, Spain; Robotics and Tech. of Computers Lab., Universidad de Sevilla, Seville, 41012, Spain; Escuela Politécnica Superior (EPS), Universidad de Sevilla, Sevilla, 41011, Spain; Smart Computer Systems Research and Engineering Lab (SCORE), Research Institute of Computer Engineering (I3US), Universidad de Sevilla, Seville, 41012, Spain.
| | - Gabriel Jimenez-Moreno
- Escuela Técnica Superior de Ingeniería Informática (ETSII), Universidad de Sevilla, Seville, Avenida de Reina Mercedes s/n, 41012, Spain; Robotics and Tech. of Computers Lab., Universidad de Sevilla, Seville, 41012, Spain; Escuela Politécnica Superior (EPS), Universidad de Sevilla, Sevilla, 41011, Spain; Smart Computer Systems Research and Engineering Lab (SCORE), Research Institute of Computer Engineering (I3US), Universidad de Sevilla, Seville, 41012, Spain.
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2
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Madeira D, Madeira C, Calosi P, Vermandele F, Carrier-Belleau C, Barria-Araya A, Daigle R, Findlay HS, Poisot T. Multilayer biological networks to upscale marine research to global change-smart management and sustainable resource use. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 944:173837. [PMID: 38866145 DOI: 10.1016/j.scitotenv.2024.173837] [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: 11/03/2023] [Revised: 05/30/2024] [Accepted: 06/05/2024] [Indexed: 06/14/2024]
Abstract
Human activities are having a massive negative impact on biodiversity and ecological processes worldwide. The rate and magnitude of ecological transformations induced by climate change, habitat destruction, overexploitation and pollution are now so substantial that a sixth mass extinction event is currently underway. The biodiversity crisis of the Anthropocene urges scientists to put forward a transformative vision to promote the conservation of biodiversity, and thus indirectly the preservation of ecosystem functions. Here, we identify pressing issues in global change biology research and propose an integrative framework based on multilayer biological networks as a tool to support conservation actions and marine risk assessments in multi-stressor scenarios. Multilayer networks can integrate different levels of environmental and biotic complexity, enabling us to combine information on molecular, physiological and behaviour responses, species interactions and biotic communities. The ultimate aim of this framework is to link human-induced environmental changes to species physiology, fitness, biogeography and ecosystem impacts across vast seascapes and time frames, to help guide solutions to address biodiversity loss and ecological tipping points. Further, we also define our current ability to adopt a widespread use of multilayer networks within ecology, evolution and conservation by providing examples of case-studies. We also assess which approaches are ready to be transferred and which ones require further development before use. We conclude that multilayer biological networks will be crucial to inform (using reliable multi-levels integrative indicators) stakeholders and support their decision-making concerning the sustainable use of resources and marine conservation.
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Affiliation(s)
- Diana Madeira
- Laboratory for Innovation and Sustainability of Marine Biological Resources (ECOMARE), Centre for Environmental and Marine Studies (CESAM), Department of Biology, University of Aveiro, Aveiro, Portugal.
| | - Carolina Madeira
- Applied Molecular Biosciences Unit, Department of Life Sciences, School of Science and Technology, NOVA University of Lisbon, Caparica, Portugal; i4HB - Institute for Health and Bioeconomy, School of Science and Technology, NOVA University of Lisbon, Caparica, Portugal
| | - Piero Calosi
- Laboratory of Marine Ecological and Evolutionary Physiology, Department of Biology, Chemistry and Geography, University of Quebec in Rimouski, 300 Allée des Ursulines, Rimouski, G5L 3A1, Québec, Canada
| | - Fanny Vermandele
- Laboratory of Marine Ecological and Evolutionary Physiology, Department of Biology, Chemistry and Geography, University of Quebec in Rimouski, 300 Allée des Ursulines, Rimouski, G5L 3A1, Québec, Canada
| | | | - Aura Barria-Araya
- Laboratory of Marine Ecological and Evolutionary Physiology, Department of Biology, Chemistry and Geography, University of Quebec in Rimouski, 300 Allée des Ursulines, Rimouski, G5L 3A1, Québec, Canada
| | - Remi Daigle
- Bedford Institute of Oceanography, Fisheries and Oceans Canada, Dartmouth, Nova Scotia, Canada; Marine Affairs Program, Dalhousie University, Halifax, Nova Scotia, Canada
| | | | - Timothée Poisot
- Department of Biological Sciences, University of Montreal, Montreal, Canada
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Beust C, Valdeolivas A, Baptista A, Brière G, Lévy N, Ozisik O, Baudot A. The Molecular Landscape of Premature Aging Diseases Defined by Multilayer Network Exploration. Adv Biol (Weinh) 2024:e2400134. [PMID: 39123285 DOI: 10.1002/adbi.202400134] [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: 03/10/2024] [Revised: 06/26/2024] [Indexed: 08/12/2024]
Abstract
Premature Aging (PA) diseases are rare genetic disorders that mimic some aspects of physiological aging at an early age. Various causative genes of PA diseases have been identified in recent years, providing insights into some dysfunctional cellular processes. However, the identification of PA genes also revealed significant genetic heterogeneity and highlighted the gaps in this understanding of PA-associated molecular mechanisms. Furthermore, many patients remain undiagnosed. Overall, the current lack of knowledge about PA diseases hinders the development of effective diagnosis and therapies and poses significant challenges to improving patient care. Here, a network-based approach to systematically unravel the cellular functions disrupted in PA diseases is presented. Leveraging a network community identification algorithm, it is delved into a vast multilayer network of biological interactions to extract the communities of 67 PA diseases from their 132 associated genes. It is found that these communities can be grouped into six distinct clusters, each reflecting specific cellular functions: DNA repair, cell cycle, transcription regulation, inflammation, cell communication, and vesicle-mediated transport. That these clusters collectively represent the landscape of the molecular mechanisms that are perturbed in PA diseases, providing a framework for better understanding their pathogenesis is proposed. Intriguingly, most clusters also exhibited a significant enrichment in genes associated with physiological aging, suggesting a potential overlap between the molecular underpinnings of PA diseases and natural aging.
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Affiliation(s)
- Cécile Beust
- Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France
| | - Alberto Valdeolivas
- Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France
| | - Anthony Baptista
- Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France
| | - Galadriel Brière
- Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France
- Aix Marseille Univ, CNRS, I2M, Marseille, France
| | - Nicolas Lévy
- Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France
| | - Ozan Ozisik
- Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France
| | - Anaïs Baudot
- Aix Marseille Univ, INSERM, Marseille Medical Genetics (MMG), Marseille, France
- CNRS, Marseille, France
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
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Kurbucz MT, Keresztély T, Szikszai S, Sugár A, Banász Z. Dataset on global trade networks of COVID-19 medical products. Data Brief 2024; 55:110606. [PMID: 38988730 PMCID: PMC11233917 DOI: 10.1016/j.dib.2024.110606] [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: 02/07/2024] [Revised: 05/20/2024] [Accepted: 06/04/2024] [Indexed: 07/12/2024] Open
Abstract
This paper presents a comprehensive dataset on the global trade dynamics of COVID-19-related medical products for the years 2019 and 2020. The dataset, derived from the BACI database, focuses on eight distinct product categories identified by six-digit codes. The trade flow data for 224 countries is structured as a multilevel network, with countries as nodes and product categories as layers. Directed edges represent trading activities, and edge weights are determined by the difference in exported values between 2019 and 2020. The dataset is provided in an edges-and-nodes format. Additionally, the associated R script transforms the data into the MuxViz R package format, facilitating further analysis and visualization of the dataset. The dataset is valuable for researchers in the field of foreign trade or medical products, and for decision-makers in these fields, whether at company or national level.
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Affiliation(s)
- Marcell T Kurbucz
- Department of Computational Sciences, Institute for Particle and Nuclear Physics, HUN-REN Wigner Research Centre for Physics, 29-33 Konkoly-Thege Miklós Street, H-1121 Budapest, Hungary
- Department of Statistics, Institute of Data Analytics and Information Systems, Corvinus University of Budapest, 8 Fővám Square, H-1093 Budapest, Hungary
| | - Tibor Keresztély
- Department of Statistics, Institute of Data Analytics and Information Systems, Corvinus University of Budapest, 8 Fővám Square, H-1093 Budapest, Hungary
| | - Szabolcs Szikszai
- Department of Economics, University of Pannonia, 10 Egyetem Street, H-8200 Veszprém, Hungary
| | - András Sugár
- Department of Statistics, Institute of Data Analytics and Information Systems, Corvinus University of Budapest, 8 Fővám Square, H-1093 Budapest, Hungary
| | - Zsuzsanna Banász
- Department of Economics, University of Pannonia, 10 Egyetem Street, H-8200 Veszprém, Hungary
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Sehgal NKR, Rader B, Brownstein JS. Examining the Role of Physician Characteristics in Web-Based Verified Primary Care Physician Reviews: Observational Study. J Med Internet Res 2024; 26:e51672. [PMID: 39074363 PMCID: PMC11319894 DOI: 10.2196/51672] [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: 08/07/2023] [Revised: 11/17/2023] [Accepted: 06/12/2024] [Indexed: 07/31/2024] Open
Abstract
BACKGROUND Doctor review websites have become increasingly popular as a source of information for patients looking to select a primary care provider. Zocdoc is one such platform that allows patients to not only rate and review their experiences with doctors but also directly schedule appointments. This study examines how several physician characteristics including gender, age, race, languages spoken in a physician's office, education, and facial attractiveness impact the average numerical rating of primary care doctors on Zocdoc. OBJECTIVE The aim of this study was to investigate the association between physician characteristics and patient satisfaction ratings on Zocdoc. METHODS A data set of 1455 primary care doctor profiles across 30 cities was scraped from Zocdoc. The profiles contained information on the physician's gender, education, and languages spoken in their office. Age, facial attractiveness, and race were imputed from profile pictures using commercial facial analysis software. Each doctor profile listed an average overall satisfaction rating, bedside manner rating, and wait time rating from verified patients. Descriptive statistics, the Wilcoxon rank sum test, and multivariate logistic regression were used to analyze the data. RESULTS The average overall rating on Zocdoc was highly positive, with older age, lower facial attractiveness, foreign degrees, allopathic degrees, and speaking more languages negatively associated with the average rating. However, the effect sizes of these factors were relatively small. For example, graduates of Latin American medical schools had a mean overall rating of 4.63 compared to a 4.77 rating for US graduates (P<.001), a difference roughly equivalent to a 2.8% decrease in appointments. On multivariate analysis, being Asian and having a doctor of osteopathic medicine degree were positively associated with higher overall ratings, while attending a South Asian medical school and speaking more European and Middle Eastern languages in the office were negatively associated with higher overall ratings. CONCLUSIONS Overall, the findings suggest that age, facial attractiveness, education, and multilingualism do have some impact on web-based doctor reviews, but the numerical effect is small. Notably, bias may play out in many forms. For example, a physician's appearance or accent may impact a patient's trust, confidence, or satisfaction with their physician, which could in turn influence their take-up of preventative services and lead to either better or worse health outcomes. The study highlights the need for further research in how physician characteristics influence patient ratings of care.
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Affiliation(s)
- Neil K R Sehgal
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States
- Computational Epidemiology Group, Boston Children's Hospital, Boston, MA, United States
| | - Benjamin Rader
- Computational Epidemiology Group, Boston Children's Hospital, Boston, MA, United States
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
| | - John S Brownstein
- Computational Epidemiology Group, Boston Children's Hospital, Boston, MA, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
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Ourang SA, Sohrabniya F, Mohammad-Rahimi H, Dianat O, Aminoshariae A, Nagendrababu V, Dummer PMH, Duncan HF, Nosrat A. Artificial intelligence in endodontics: Fundamental principles, workflow, and tasks. Int Endod J 2024. [PMID: 39056554 DOI: 10.1111/iej.14127] [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: 05/21/2024] [Revised: 06/25/2024] [Accepted: 07/13/2024] [Indexed: 07/28/2024]
Abstract
The integration of artificial intelligence (AI) in healthcare has seen significant advancements, particularly in areas requiring image interpretation. Endodontics, a specialty within dentistry, stands to benefit immensely from AI applications, especially in interpreting radiographic images. However, there is a knowledge gap among endodontists regarding the fundamentals of machine learning and deep learning, hindering the full utilization of AI in this field. This narrative review aims to: (A) elaborate on the basic principles of machine learning and deep learning and present the basics of neural network architectures; (B) explain the workflow for developing AI solutions, from data collection through clinical integration; (C) discuss specific AI tasks and applications relevant to endodontic diagnosis and treatment. The article shows that AI offers diverse practical applications in endodontics. Computer vision methods help analyse images while natural language processing extracts insights from text. With robust validation, these techniques can enhance diagnosis, treatment planning, education, and patient care. In conclusion, AI holds significant potential to benefit endodontic research, practice, and education. Successful integration requires an evolving partnership between clinicians, computer scientists, and industry.
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Affiliation(s)
- Seyed AmirHossein Ourang
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fatemeh Sohrabniya
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Omid Dianat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, University of Maryland School of Dentistry, Baltimore, Maryland, USA
- Private Practice, Irvine Endodontics, Irvine, California, USA
| | - Anita Aminoshariae
- Department of Endodontics, School of Dental Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | | | | | - Henry F Duncan
- Division of Restorative Dentistry, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland
| | - Ali Nosrat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, University of Maryland School of Dentistry, Baltimore, Maryland, USA
- Private Practice, Centreville Endodontics, Centreville, Virginia, USA
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Sadurski J, Polak-Berecka M, Staniszewski A, Waśko A. Step-by-Step Metagenomics for Food Microbiome Analysis: A Detailed Review. Foods 2024; 13:2216. [PMID: 39063300 PMCID: PMC11276190 DOI: 10.3390/foods13142216] [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: 06/11/2024] [Revised: 07/11/2024] [Accepted: 07/12/2024] [Indexed: 07/28/2024] Open
Abstract
This review article offers a comprehensive overview of the current understanding of using metagenomic tools in food microbiome research. It covers the scientific foundation and practical application of genetic analysis techniques for microbial material from food, including bioinformatic analysis and data interpretation. The method discussed in the article for analyzing microorganisms in food without traditional culture methods is known as food metagenomics. This approach, along with other omics technologies such as nutrigenomics, proteomics, metabolomics, and transcriptomics, collectively forms the field of foodomics. Food metagenomics allows swift and thorough examination of bacteria and potential metabolic pathways by utilizing foodomic databases. Despite its established scientific basis and available bioinformatics resources, the research approach of food metagenomics outlined in the article is not yet widely implemented in industry. The authors believe that the integration of next-generation sequencing (NGS) with rapidly advancing digital technologies such as artificial intelligence (AI), the Internet of Things (IoT), and big data will facilitate the widespread adoption of this research strategy in microbial analysis for the food industry. This adoption is expected to enhance food safety and product quality in the near future.
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Affiliation(s)
- Jan Sadurski
- Department of Biotechnology, Microbiology and Human Nutrition, Faculty of Food Science and Biotechnology, University of Life Sciences in Lublin, 20-704 Lublin, Poland; (M.P.-B.); (A.S.); (A.W.)
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Banerjee S, Dunn P, Conard S, Ali A. Mental Health Applications of Generative AI and Large Language Modeling in the United States. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:910. [PMID: 39063487 PMCID: PMC11276907 DOI: 10.3390/ijerph21070910] [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: 05/26/2024] [Revised: 07/09/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024]
Abstract
(1) Background: Artificial intelligence (AI) has flourished in recent years. More specifically, generative AI has had broad applications in many disciplines. While mental illness is on the rise, AI has proven valuable in aiding the diagnosis and treatment of mental disorders. However, there is little to no research about precisely how much interest there is in AI technology. (2) Methods: We performed a Google Trends search for "AI and mental health" and compared relative search volume (RSV) indices of "AI", "AI and Depression", and "AI and anxiety". This time series study employed Box-Jenkins time series modeling to forecast long-term interest through the end of 2024. (3) Results: Within the United States, AI interest steadily increased throughout 2023, with some anomalies due to media reporting. Through predictive models, we found that this trend is predicted to increase 114% through the end of the year 2024, with public interest in AI applications being on the rise. (4) Conclusions: According to our study, we found that the awareness of AI has drastically increased throughout 2023, especially in mental health. This demonstrates increasing public awareness of mental health and AI, making advocacy and education about AI technology of paramount importance.
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Affiliation(s)
- Sri Banerjee
- School of Health Sciences and Public Policy, Walden University, Minneapolis, MN 55401, USA
| | - Pat Dunn
- Center for Health Technology & Innovation American Heart Association, Dallas, TX 75231, USA;
| | | | - Asif Ali
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA;
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9
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Calcote MJ, Mann JR, Adcock KG, Duckworth S, Donald MC. Big Data in Health Care: An Interprofessional Course. Nurse Educ 2024; 49:E187-E191. [PMID: 37994454 DOI: 10.1097/nne.0000000000001571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
BACKGROUND The widespread adoption of the electronic health record (EHR) has resulted in vast repositories of EHR big data that are being used to identify patterns and correlations that translate into data-informed health care decision making. PROBLEM Health care professionals need the skills necessary to navigate a digitized, data-rich health care environment as big data plays an increasingly integral role in health care. APPROACH Faculty incorporated the concept of big data in an asynchronous online course allowing an interprofessional mix of students to analyze EHR big data on over a million patients. OUTCOMES Students conducted a descriptive analysis of cohorts of patients with selected diagnoses and presented their findings. CONCLUSIONS Students collaborated with an interprofessional team to analyze EHR big data on selected variables. The teams used data visualization tools to describe an assigned diagnosis patient population.
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Affiliation(s)
- Margaret J Calcote
- Author Affiliations: Assistant Professor (Dr Calcote), The University of Mississippi Medical Center School of Nursing, Jackson; Professor and Chair (Dr Mann), Department of Preventive Medicine, The University of Mississippi Medical Center School of Medicine, Jackson; Professor (Dr Adcock), Pharmacy Division, The University of Mississippi Medical Center School of Pharmacy, Jackson; Professor (Dr Duckworth), The University of Mississippi Medical Center Division of Internal Medicine, Jackson; and Medical Student M3 (Mr Donald), The University of Mississippi Medical Center School of Medicine, Jackson
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Berriche L, Larabi-Marie-Sainte S. Unveiling ChatGPT text using writing style. Heliyon 2024; 10:e32976. [PMID: 38984302 PMCID: PMC11231544 DOI: 10.1016/j.heliyon.2024.e32976] [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: 03/06/2024] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 07/11/2024] Open
Abstract
Extensive use of AI-generated texts culminated recently after the advent of large language models. Although the use of AI text generators, such as ChatGPT, is beneficial, it also threatens the academic level as students may resort to it. In this work, we propose a technique leveraging the intrinsic stylometric features of documents to detect ChatGPT-based plagiarism. The stylometric features were normalized and fed to classical classifiers, such as k-Nearest Neighbors, Decision Tree, and Naïve Bayes, as well as ensemble classifiers, such as XGBoost and Stacking. A thorough examination of the classifier was conducted by using Cross-Fold validation, hyperparameters tuning, and multiple training iterations. The results show the efficacy of both classical and ensemble learning classifiers in distinguishing between human and ChatGPT writing styles with a noteworthy performance of XGBoost where 100 % was achieved for accuracy, recall, and precision metrics. Moreover, the proposed XGBoost classifier outperformed the state-of-the-art result on the same dataset and same classifier highlighting the superiority of the proposed feature style extraction method over TF-IDF techniques. The ensemble learning classifiers were also applied to the generated dataset with mixed texts, where paragraphs are written by ChatGPT and humans. The results show that 98 % of the documents were classified correctly as either mixed or human. The last contribution consists in the authorship attribution of the paragraphs of a single document where the accuracy reached 92.3 %.
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Affiliation(s)
- Lamia Berriche
- College of Computer & Information Sciences, Prince Sultan University, Saudi Arabia
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11
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Bassetti D, Pospíšil L, Horenko I. On Entropic Learning from Noisy Time Series in the Small Data Regime. ENTROPY (BASEL, SWITZERLAND) 2024; 26:553. [PMID: 39056915 PMCID: PMC11276242 DOI: 10.3390/e26070553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024]
Abstract
In this work, we present a novel methodology for performing the supervised classification of time-ordered noisy data; we call this methodology Entropic Sparse Probabilistic Approximation with Markov regularization (eSPA-Markov). It is an extension of entropic learning methodologies, allowing the simultaneous learning of segmentation patterns, entropy-optimal feature space discretizations, and Bayesian classification rules. We prove the conditions for the existence and uniqueness of the learning problem solution and propose a one-shot numerical learning algorithm that-in the leading order-scales linearly in dimension. We show how this technique can be used for the computationally scalable identification of persistent (metastable) regime affiliations and regime switches from high-dimensional non-stationary and noisy time series, i.e., when the size of the data statistics is small compared to their dimensionality and when the noise variance is larger than the variance in the signal. We demonstrate its performance on a set of toy learning problems, comparing eSPA-Markov to state-of-the-art techniques, including deep learning and random forests. We show how this technique can be used for the analysis of noisy time series from DNA and RNA Nanopore sequencing.
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Affiliation(s)
- Davide Bassetti
- Faculty of Mathematics, RPTU Kaiserslautern-Landau, Gottlieb-Daimler-Str. 48, 67663 Kaiserslautern, Germany
| | - Lukáš Pospíšil
- Department of Mathematics, Faculty of Civil Engineering, VŠB-TUO, Ludvika Podeste 1875/17, 708 33 Ostrava, Czech Republic;
| | - Illia Horenko
- Faculty of Mathematics, RPTU Kaiserslautern-Landau, Gottlieb-Daimler-Str. 48, 67663 Kaiserslautern, Germany
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12
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Guo H, Liang D. Big Earth Data and its role in sustainability. Sci Bull (Beijing) 2024; 69:1623-1627. [PMID: 38553343 DOI: 10.1016/j.scib.2024.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2024]
Affiliation(s)
- Huadong Guo
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Dong Liang
- International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
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Li S, Tang Z, Kim KS, Smith JS. On the Use and Construction of Wi-Fi Fingerprint Databases for Large-Scale Multi-Building and Multi-Floor Indoor Localization: A Case Study of the UJIIndoorLoc Database. SENSORS (BASEL, SWITZERLAND) 2024; 24:3827. [PMID: 38931610 PMCID: PMC11207647 DOI: 10.3390/s24123827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 06/10/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024]
Abstract
Large-scale multi-building and multi-floor indoor localization has recently been the focus of intense research in indoor localization based on Wi-Fi fingerprinting. Although significant progress has been made in developing indoor localization algorithms, few studies are dedicated to the critical issues of using existing and constructing new Wi-Fi fingerprint databases, especially for large-scale multi-building and multi-floor indoor localization. In this paper, we first identify the challenges in using and constructing Wi-Fi fingerprint databases for large-scale multi-building and multi-floor indoor localization and then provide our recommendations for those challenges based on a case study of the UJIIndoorLoc database, which is the most popular publicly available Wi-Fi fingerprint multi-building and multi-floor database. Through the case study, we investigate its statistical characteristics with a focus on the three aspects of (1) the properties of detected wireless access points, (2) the number, distribution and quality of labels, and (3) the composition of the database records. We then identify potential issues and ways to address them using the UJIIndoorLoc database. Based on the results from the case study, we not only provide valuable insights on the use of existing databases but also give important directions for the design and construction of new databases for large-scale multi-building and multi-floor indoor localization in the future.
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Affiliation(s)
- Sihao Li
- School of Advanced Technology, Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou 215123, China; (S.L.); (Z.T.)
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK;
| | - Zhe Tang
- School of Advanced Technology, Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou 215123, China; (S.L.); (Z.T.)
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK;
| | - Kyeong Soo Kim
- School of Advanced Technology, Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou 215123, China; (S.L.); (Z.T.)
| | - Jeremy S. Smith
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK;
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14
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de Kok JWTM, van Bussel BCT, Schnabel R, van Herpt TTW, Driessen RGH, Meijs DAM, Goossens JA, Mertens HJMM, van Kuijk SMJ, Wynants L, van der Horst ICC, van Rosmalen F. Table 0; documenting the steps to go from clinical database to research dataset. J Clin Epidemiol 2024; 170:111342. [PMID: 38574979 DOI: 10.1016/j.jclinepi.2024.111342] [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: 12/07/2023] [Revised: 02/01/2024] [Accepted: 03/26/2024] [Indexed: 04/06/2024]
Abstract
OBJECTIVES Data-driven decision support tools have been increasingly recognized to transform health care. However, such tools are often developed on predefined research datasets without adequate knowledge of the origin of this data and how it was selected. How a dataset is extracted from a clinical database can profoundly impact the validity, interpretability and interoperability of the dataset, and downstream analyses, yet is rarely reported. Therefore, we present a case study illustrating how a definitive patient list was extracted from a clinical source database and how this can be reported. STUDY DESIGN AND SETTING A single-center observational study was performed at an academic hospital in the Netherlands to illustrate the impact of selecting a definitive patient list for research from a clinical source database, and the importance of documenting this process. All admissions from the critical care database admitted between January 1, 2013, and January 1, 2023, were used. RESULTS An interdisciplinary team collaborated to identify and address potential sources of data insufficiency and uncertainty. We demonstrate a stepwise data preparation process, reducing the clinical source database of 54,218 admissions to a definitive patient list of 21,553 admissions. Transparent documentation of the data preparation process improves the quality of the definitive patient list before analysis of the corresponding patient data. This study generated seven important recommendations for preparing observational health-care data for research purposes. CONCLUSION Documenting data preparation is essential for understanding a research dataset originating from a clinical source database before analyzing health-care data. The findings contribute to establishing data standards and offer insights into the complexities of preparing health-care data for scientific investigation. Meticulous data preparation and documentation thereof will improve research validity and advance critical care.
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Affiliation(s)
- Jip W T M de Kok
- Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Bas C T van Bussel
- Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands; Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - Ronny Schnabel
- Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Thijs T W van Herpt
- Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Rob G H Driessen
- Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands; Department of Cardiology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Daniek A M Meijs
- Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Joep A Goossens
- Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands; Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Iwan C C van der Horst
- Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Frank van Rosmalen
- Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands.
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15
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Liu H, Jing N, Li F, Wang K, Tang J, Zhao Q, Zhang Y, Noushahi HA, Xu R, Wang X, Zhu W, Feng S, Shu S, Mei Z. An omics-based characterization of Wolfiporia cocos reveals three CYP450 members involved in the biosynthetic pathway of pachymic acid. Commun Biol 2024; 7:666. [PMID: 38816492 PMCID: PMC11139888 DOI: 10.1038/s42003-024-06323-1] [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: 11/21/2022] [Accepted: 05/13/2024] [Indexed: 06/01/2024] Open
Abstract
Wolfiporia cocos is a medicinal mushroom used in China. It biosynthesizes pachymic acid (PA), a main therapeutic triterpene associated with therapies. Nowadays, the unknown PA biosynthesis leads to difficulties in increasing its content in W. cocos. Herein, we report sequencing, assembling, and characterization of the genome and several transcriptomes of W. cocos. Sequence mining determined candidate genes that encode lanosterol synthase, sterol O-acyltransferase, and sterol C-24 methyltransferase likely involved in the steps from lanosterol to PA. Gene cluster analysis identified four CYP450 cDNAs likely involved in the biosynthesis of PA, namely WcCYP64-1, WcCYP64-2, WcCYP52, and WcCYP_FUM15, which were subjected to both overexpression and silencing in mycelia. The overexpression of each of WcCYP64-1, WcCYP52 and WcCYP_FUM15 increased the content of PA, 16α-hydroxytrametenolic acid, eburicoic acid, and tumulosic acid, while the silencing of each gene either significantly or slightly decreased the contents of these four compounds, indicating their involvement in the PA biosynthesis. In addition, different temperatures affected the expression of these genes and the formation of PA. By contrast, the overexpression and silencing of WcCYP64-2 did not alter the formation of these compounds. Taken together, these findings determine more potential steps in the biosynthetic pathway of PA for metabolic engineering.
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Affiliation(s)
- Heping Liu
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei Province, China
| | - Naliang Jing
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei Province, China
| | - Fengfeng Li
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei Province, China
| | - Keyue Wang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei Province, China
| | - Jing Tang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei Province, China
| | - Qin Zhao
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei Province, China
| | - Yipeng Zhang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei Province, China
| | - Hamza Armghan Noushahi
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei Province, China
| | - Ran Xu
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, Hubei Province, China
| | | | - Wenjun Zhu
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, Hubei Province, China.
| | - Shengqiu Feng
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei Province, China.
| | - Shaohua Shu
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei Province, China.
| | - Zhinan Mei
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei Province, China
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16
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Mhlanga D. The role of big data in financial technology toward financial inclusion. Front Big Data 2024; 7:1184444. [PMID: 38774056 PMCID: PMC11106479 DOI: 10.3389/fdata.2024.1184444] [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: 03/11/2023] [Accepted: 04/22/2024] [Indexed: 05/24/2024] Open
Abstract
In the rapidly evolving landscape of financial technology (FinTech), big data stands as a cornerstone, driving significant transformations. This study delves into the pivotal role of big data in FinTech and its implications for financial inclusion. Employing a comprehensive literature review methodology, we analyze diverse sources including academic journals, industry reports, and online articles. Our findings illuminate how big data catalyzes the development of novel financial products and services, enhances risk management, and boosts operational efficiency, thereby fostering financial inclusion. Particularly, big data's capability to offer insightful customer behavior analytics is highlighted as a key driver for creating inclusive financial services. However, challenges such as data privacy and security, and the need for ethical algorithmic practices are also identified. This research contributes valuable insights for policymakers, regulators, and industry practitioners, suggesting a need for balanced regulatory frameworks to harness big data's potential ethically and responsibly. The outcomes of this study underscore the transformative power of big data in FinTech, indicating a pathway toward a more inclusive financial ecosystem.
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Affiliation(s)
- David Mhlanga
- College of Business and Economics, University of Johannesburg, Johannesburg, South Africa
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17
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Robinson B, Bhamidi S, Dayan E. The spatial distribution of coupling between tau and neurodegeneration in amyloid-β positive mild cognitive impairment. Neurobiol Aging 2024; 136:70-77. [PMID: 38330641 PMCID: PMC10940182 DOI: 10.1016/j.neurobiolaging.2024.01.014] [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: 07/20/2023] [Revised: 01/24/2024] [Accepted: 01/26/2024] [Indexed: 02/10/2024]
Abstract
Synergies between amyloid-β (Aβ), tau, and neurodegeneration persist along the Alzheimer's disease (AD) continuum. This study aimed to evaluate the extent of spatial coupling between tau and neurodegeneration (atrophy) and its relation to Aβ positivity in mild cognitive impairment (MCI). Data from 409 participants were included (95 cognitively normal controls, 158 Aβ positive (Aβ+) MCI, and 156 Aβ negative (Aβ-) MCI). Florbetapir PET, Flortaucipir PET, and structural MRI were used as biomarkers for Aβ, tau and atrophy, respectively. Individual correlation matrices for tau load and atrophy were used to layer a multilayer network, with separate layers for tau and atrophy. A measure of coupling between corresponding regions of interest (ROIs) in the tau and atrophy layers was computed, as a function of Aβ positivity. Fewer than 25% of the ROIs across the brain showed heightened coupling between tau and atrophy in Aβ+ , relative to Aβ- MCI. Coupling strengths in the right rostral middle frontal and right paracentral gyri, in particular, mediated the association between Aβ burden and cognition in this sample.
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Affiliation(s)
- Belfin Robinson
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Shankar Bhamidi
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Eran Dayan
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA; Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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18
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Sundaram A, Subramaniam H, Ab Hamid SH, Mohamad Nor A. An adaptive data-driven architecture for mental health care applications. PeerJ 2024; 12:e17133. [PMID: 38563009 PMCID: PMC10984189 DOI: 10.7717/peerj.17133] [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: 08/16/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024] Open
Abstract
Background In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven architectures are pivotal for organizing, manipulating, and presenting data to facilitate positive computing through ensemble machine learning models. Moreover, the COVID-19 pandemic underscored a substantial need for a flexible mental health care architecture. This architecture, inclusive of machine learning predictive models, has the potential to benefit a larger population by identifying individuals at a heightened risk of developing various mental disorders. Objective Therefore, this research aims to create a flexible mental health care architecture that leverages data-driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data-driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated. Method Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale. Results Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data-driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture's effectiveness. To further assess the architecture's practical application, a prototype architecture for predicting pandemic anxiety was developed.
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Affiliation(s)
- Aishwarya Sundaram
- Institute for Advanced Studies, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Hema Subramaniam
- Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Siti Hafizah Ab Hamid
- Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Azmawaty Mohamad Nor
- Department of Educational Psychology and Counselling, Faculty of Education, Universiti Malaya, Kuala Lumpur, Malaysia
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19
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Madden K, Mayes S, Cook J, Ferrar K. The Effects of Pointe Shoes on Ballet Dancers' Biomechanics, Muscle Activity, Movement and Symptoms: A Scoping Review. J Dance Med Sci 2024; 28:57-71. [PMID: 38174848 DOI: 10.1177/1089313x231218305] [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] [Indexed: 01/05/2024]
Abstract
Introduction: Dancing en pointe requires ballet dancers to stand on the tips of their toes while wearing a structured pointe shoe. Understanding the effect of pointe shoes on ballet dancers' biomechanics, function, symptoms and skin health is essential to guide shoe development and ultimately improve dancer performance. Therefore, the purpose of this scoping review was to map the evidence and identify knowledge gaps related to the effect of wearing pointe shoes on professional and recreational ballet dancers. Method: A scoping review was conducted by searching 6 electronic databases and the International Association for Dance Medicine and Science Bibliography. Results: Thirty-five studies were grouped into 5 categories: pointe shoe factors (eg, toe box and shank, pointe shoe status; 9 studies), shoe types (eg, flat shoes vs demi pointe vs pointe; 10 studies), ballet movements (11 studies), symptoms (5 studies), and intrinsic dancer factors (eg, foot type/toe length; 7 studies). Studies were published between 1979 and 2023, with 72% (n = 23) published between 2006 and 2020. Most (86%) of the studies were cross-sectional. Most studies explored biomechanical outcomes and the most common data collection device was force plates (19 studies). Overall, there was an insufficient volume of evidence for specific research aims. Significant gaps in knowledge exist regarding functional and performance-based outcomes, injury outcomes including exploration of factors such as shoe age/usage, and pointe shoe treatment factors. Conclusions: Currently the field of research suffers from threats to ecological validity, with many study methods not reflecting ballet-specific demands or environments. The impact of pointe shoes on ballet dancers is a developing research area, and this scoping review can help guide future research decisions. Studies need to target the knowledge gaps and employ rigorous ecologically valid study designs and ensure that findings inform shoe design and dancer education to minimize injury and maximize comfort and performance.
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Affiliation(s)
- Kate Madden
- La Trobe University, Bundoora, VIC, Australia
| | - Sue Mayes
- The Australian Ballet, Southbank, VIC, Australia
| | - Jill Cook
- La Trobe Sport and Exercise Medicine Research Centre, La Trobe University, Bundoora, VIC, Australia
| | - Katia Ferrar
- The Australian Ballet, Southbank, VIC, Australia
- La Trobe Sport and Exercise Medicine Research Centre, La Trobe University, Bundoora, VIC, Australia
- Aboriginal Health Equity, SAHMRI, Adelaide, SA, Australia
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20
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Neely L, Oyama S, Chen Q, Qutub A, Chen C. Tutorial: Lessons Learned for Behavior Analysts from Data Scientists. Perspect Behav Sci 2024; 47:203-223. [PMID: 38660507 PMCID: PMC11035514 DOI: 10.1007/s40614-023-00376-z] [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: 04/30/2023] [Indexed: 04/26/2024] Open
Abstract
Big data is a computing term used to refer to large and complex data sets, typically consisting of terabytes or more of diverse data that is produced rapidly. The analysis of such complex data sets requires advanced analysis techniques with the capacity to identify patterns and abstract meanings from the vast data. The field of data science combines computer science with mathematics/statistics and leverages artificial intelligence, in particular machine learning, to analyze big data. This field holds great promise for behavior analysis, where both clinical and research studies produce large volumes of diverse data at a rapid pace (i.e., big data). This article presents basic lessons for the behavior analytic researchers and clinicians regarding integration of data science into the field of behavior analysis. We provide guidance on how to collect, protect, and process the data, while highlighting the importance of collaborating with data scientists to select a proper machine learning model that aligns with the project goals and develop models with input from human experts. We hope this serves as a guide to support the behavior analysts interested in the field of data science to advance their practice or research, and helps them avoid some common pitfalls.
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Affiliation(s)
- Leslie Neely
- Department of Educational Psychology, University of Texas at San Antonio, 501 West Cesar Chavez, San Antonio, TX 78207 USA
| | - Sakiko Oyama
- Department of Educational Psychology, University of Texas at San Antonio, 501 West Cesar Chavez, San Antonio, TX 78207 USA
| | - Qian Chen
- Department of Educational Psychology, University of Texas at San Antonio, 501 West Cesar Chavez, San Antonio, TX 78207 USA
| | - Amina Qutub
- Department of Educational Psychology, University of Texas at San Antonio, 501 West Cesar Chavez, San Antonio, TX 78207 USA
| | - Chen Chen
- University of Central Florida, Orlando, FL USA
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21
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Tang Z, Li S, Kim KS, Smith JS. Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization. SENSORS (BASEL, SWITZERLAND) 2024; 24:1026. [PMID: 38339745 PMCID: PMC10857661 DOI: 10.3390/s24031026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 02/12/2024]
Abstract
Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)-i.e., one of the state-of-the-art multi-building and multi-floor localization models-and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of "by a single building", where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of 8.42 m.
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Affiliation(s)
- Zhe Tang
- School of Advanced Technology, Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou 215123, China; (Z.T.); (S.L.)
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK;
| | - Sihao Li
- School of Advanced Technology, Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou 215123, China; (Z.T.); (S.L.)
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK;
| | - Kyeong Soo Kim
- School of Advanced Technology, Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou 215123, China; (Z.T.); (S.L.)
| | - Jeremy S. Smith
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK;
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22
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Koide-Majima N, Nishimoto S, Majima K. Mental image reconstruction from human brain activity: Neural decoding of mental imagery via deep neural network-based Bayesian estimation. Neural Netw 2024; 170:349-363. [PMID: 38016230 DOI: 10.1016/j.neunet.2023.11.024] [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: 06/10/2023] [Revised: 09/22/2023] [Accepted: 11/08/2023] [Indexed: 11/30/2023]
Abstract
Visual images observed by humans can be reconstructed from their brain activity. However, the visualization (externalization) of mental imagery is challenging. Only a few studies have reported successful visualization of mental imagery, and their visualizable images have been limited to specific domains such as human faces or alphabetical letters. Therefore, visualizing mental imagery for arbitrary natural images stands as a significant milestone. In this study, we achieved this by enhancing a previous method. Specifically, we demonstrated that the visual image reconstruction method proposed in the seminal study by Shen et al. (2019) heavily relied on low-level visual information decoded from the brain and could not efficiently utilize the semantic information that would be recruited during mental imagery. To address this limitation, we extended the previous method to a Bayesian estimation framework and introduced the assistance of semantic information into it. Our proposed framework successfully reconstructed both seen images (i.e., those observed by the human eye) and imagined images from brain activity. Quantitative evaluation showed that our framework could identify seen and imagined images highly accurately compared to the chance accuracy (seen: 90.7%, imagery: 75.6%, chance accuracy: 50.0%). In contrast, the previous method could only identify seen images (seen: 64.3%, imagery: 50.4%). These results suggest that our framework would provide a unique tool for directly investigating the subjective contents of the brain such as illusions, hallucinations, and dreams.
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Affiliation(s)
- Naoko Koide-Majima
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, Osaka 565-0871, Japan; Graduate School of Frontier Biosciences, Osaka University, Osaka 565-0871, Japan
| | - Shinji Nishimoto
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, Osaka 565-0871, Japan; Graduate School of Frontier Biosciences, Osaka University, Osaka 565-0871, Japan; Graduate School of Medicine, Osaka University, Osaka 565-0871, Japan
| | - Kei Majima
- Institute for Quantum Life Science, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan; JST PRESTO, Saitama 332-0012, Japan.
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23
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Singh S, Kumar M, Kumar A, Verma BK, Abhishek K, Selvarajan S. Efficient pneumonia detection using Vision Transformers on chest X-rays. Sci Rep 2024; 14:2487. [PMID: 38291130 PMCID: PMC10827725 DOI: 10.1038/s41598-024-52703-2] [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: 06/20/2023] [Accepted: 01/22/2024] [Indexed: 02/01/2024] Open
Abstract
Pneumonia is a widespread and acute respiratory infection that impacts people of all ages. Early detection and treatment of pneumonia are essential for avoiding complications and enhancing clinical results. We can reduce mortality, improve healthcare efficiency, and contribute to the global battle against a disease that has plagued humanity for centuries by devising and deploying effective detection methods. Detecting pneumonia is not only a medical necessity but also a humanitarian imperative and a technological frontier. Chest X-rays are a frequently used imaging modality for diagnosing pneumonia. This paper examines in detail a cutting-edge method for detecting pneumonia implemented on the Vision Transformer (ViT) architecture on a public dataset of chest X-rays available on Kaggle. To acquire global context and spatial relationships from chest X-ray images, the proposed framework deploys the ViT model, which integrates self-attention mechanisms and transformer architecture. According to our experimentation with the proposed Vision Transformer-based framework, it achieves a higher accuracy of 97.61%, sensitivity of 95%, and specificity of 98% in detecting pneumonia from chest X-rays. The ViT model is preferable for capturing global context, comprehending spatial relationships, and processing images that have different resolutions. The framework establishes its efficacy as a robust pneumonia detection solution by surpassing convolutional neural network (CNN) based architectures.
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Affiliation(s)
| | - Manoj Kumar
- JSS Academy of Technical Education, Noida, India
| | - Abhay Kumar
- National Institute of Technology Patna, Patna, India
| | | | | | - Shitharth Selvarajan
- School of Built Environment, Engineering and Computing, Leeds Beckett University, LS1 3HE, Leeds, UK.
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Xue F, Knight S, Connolly E, O’Halloran A, Shirsath MA, Newman L, Duggan E, Kenny RA, Romero-Ortuno R. Were Frailty Identification Criteria Created Equal? A Comparative Case Study on Continuous Non-Invasively Collected Neurocardiovascular Signals during an Active Standing Test in the Irish Longitudinal Study on Ageing (TILDA). SENSORS (BASEL, SWITZERLAND) 2024; 24:442. [PMID: 38257535 PMCID: PMC10818961 DOI: 10.3390/s24020442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/22/2023] [Accepted: 12/27/2023] [Indexed: 01/24/2024]
Abstract
BACKGROUND In this observational study, we compared continuous physiological signals during an active standing test in adults aged 50 years and over, characterised as frail by three different criteria, using data from The Irish Longitudinal Study on Ageing (TILDA). METHODS This study utilised data from TILDA, an ongoing landmark prospective cohort study of community-dwelling adults aged 50 years or older in Ireland. The initial sampling strategy in TILDA was based on random geodirectory sampling. Four independent groups were identified: those characterised as frail only by one of the frailty tools used (the physical Frailty Phenotype (FP), the 32-item Frailty Index (FI), or the Clinical Frailty Scale (CFS) classification tree), and a fourth group where participants were not characterised as frail by any of these tools. Continuous non-invasive physiological signals were collected during an active standing test, including systolic (sBP) and diastolic (dBP) blood pressure, as well as heart rate (HR), using digital artery photoplethysmography. Additionally, the frontal lobe cerebral oxygenation (Oxy), deoxygenation (Deoxy), and tissue saturation index (TSI) were also non-invasively measured using near-infrared spectroscopy (NIRS). The signals were visualised across frailty groups and statistically compared using one-dimensional statistical parametric mapping (SPM). RESULTS A total of 1124 participants (mean age of 63.5 years; 50.2% women) were included: 23 were characterised as frail only by the FP, 97 by the FI, 38 by the CFS, and 966 by none of these criteria. The SPM analyses revealed that only the group characterised as frail by the FI had significantly different signals (p < 0.001) compared to the non-frail group. Specifically, they exhibited an attenuated gain in HR between 10 and 15 s post-stand and larger deficits in sBP and dBP between 15 and 20 s post-stand. CONCLUSIONS The FI proved to be more adept at capturing distinct physiological responses to standing, likely due to its direct inclusion of cardiovascular morbidities in its definition. Significant differences were observed in the dynamics of cardiovascular signals among the frail populations identified by different frailty criteria, suggesting that caution should be taken when employing frailty identification tools on physiological signals, particularly the neurocardiovascular signals in an active standing test.
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Affiliation(s)
- Feng Xue
- Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, D02 PN40 Dublin, Ireland
| | - Silvin Knight
- Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, D02 PN40 Dublin, Ireland
| | - Emma Connolly
- Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, D02 PN40 Dublin, Ireland
| | - Aisling O’Halloran
- Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, D02 PN40 Dublin, Ireland
| | - Morgana Afonso Shirsath
- Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, D02 PN40 Dublin, Ireland
| | - Louise Newman
- Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, D02 PN40 Dublin, Ireland
| | - Eoin Duggan
- Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, D02 PN40 Dublin, Ireland
| | - Rose Anne Kenny
- Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, D02 PN40 Dublin, Ireland
| | - Roman Romero-Ortuno
- Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, D02 PN40 Dublin, Ireland
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Morita PP, Kaur J, Miranda PADSES. Enhancing public health research: a viewpoint report on the transition to secure, cloud-based systems. Front Public Health 2024; 11:1270450. [PMID: 38259746 PMCID: PMC10800498 DOI: 10.3389/fpubh.2023.1270450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Affiliation(s)
- Plinio Pelegrini Morita
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
- Research Institute for Aging, University of Waterloo, Waterloo, ON, Canada
- Centre for Digital Therapeutics, Techna Institute, University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Jasleen Kaur
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
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Schwartz JL, Lasser EC, Kitchen C, Gwynn KB, Pandya C, Weiner JP, Gudzune KA. Prevalence of lifestyle-related behavioral information in claims data in the U.S. Prev Med 2024; 178:107826. [PMID: 38122938 DOI: 10.1016/j.ypmed.2023.107826] [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: 08/28/2023] [Revised: 12/01/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023]
Abstract
OBJECTIVE Given their association with varying health risks, lifestyle-related behaviors are essential to consider in population-level disease prevention. Health insurance claims are a key source of information for population health analytics, but the availability of lifestyle information within claims data is unknown. Our goal was to assess the availability and prevalence of data items that describe lifestyle behaviors across several domains within a large U.S. claims database. METHODS We conducted a retrospective, descriptive analysis to determine the availability of the following claims-derived lifestyle domains: nutrition, eating habits, physical activity, weight status, emotional wellness, sleep, tobacco use, and substance use. To define these domains, we applied a serial review process with three physicians to identify relevant diagnosis and procedure codes within claims for each domain. We used enrollment files and medical claims from a large national U.S. health plan to identify lifestyle relevant codes filed between 2016 and 2020. We calculated the annual prevalence of each claims-derived lifestyle domain and the proportion of patients by count within each domain. RESULTS Approximately half of all members within the sample had claims information that identified at least one lifestyle domain (2016 = 41.9%; 2017 = 46.1%; 2018 = 49.6%; 2019 = 52.5%; 2020 = 50.6% of patients). Most commonly identified domains were weight status (19.9-30.7% across years), nutrition (13.3-17.8%), and tobacco use (7.9-9.8%). CONCLUSION Our study demonstrates the feasibility of using claims data to identify key lifestyle behaviors. Additional research is needed to confirm the accuracy and validity of our approach and determine its use in population-level disease prevention.
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Affiliation(s)
- Jessica L Schwartz
- Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elyse C Lasser
- Center for Population Health IT, Department of Health Policy & Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Christopher Kitchen
- Center for Population Health IT, Department of Health Policy & Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kendrick B Gwynn
- Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Johns Hopkins Community Physicians, Baltimore, MD, USA
| | - Chintan Pandya
- Center for Population Health IT, Department of Health Policy & Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jonathan P Weiner
- Center for Population Health IT, Department of Health Policy & Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kimberly A Gudzune
- Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Center for Population Health IT, Department of Health Policy & Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Mayer B, Kringel D, Lötsch J. Artificial intelligence and machine learning in clinical pharmacological research. Expert Rev Clin Pharmacol 2024; 17:79-91. [PMID: 38165148 DOI: 10.1080/17512433.2023.2294005] [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: 08/28/2023] [Accepted: 12/08/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Clinical pharmacology research has always involved computational analysis. With the abundance of drug-related data available, the integration of artificial intelligence (AI) and machine learning (ML) methods has emerged as a promising way to enhance clinical pharmacology research. METHODS Based on an accepted definition of clinical pharmacology as a field of research dealing with all aspects of drug-human interactions, the analysis included publications from institutes specializing in clinical pharmacology. Research topics and the most used machine learning methods in clinical pharmacology were retrieved from the PubMed database and summarized. RESULTS ML was identified in 674 publications attributed to clinical pharmacology research, with a significant increase in publication activity over the last decade. Notable research topics addressed by ML/AI included Covid-19-related clinical pharmacology research, clinical neuropharmacology, drug safety and risk assessment, clinical pharmacology related to cancer research, and antimicrobial and antiviral research unrelated to Covid-19. In terms of ML methods, neural networks, random forests, and support vector machines were frequently mentioned in the abstracts of the retrieved papers. CONCLUSIONS ML, and AI in general, is increasingly being used in various research areas within clinical pharmacology. This report presents specific examples of applications and highlights the most used ML methods.
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Affiliation(s)
- Benjamin Mayer
- Medical Faculty, Institute of Clinical Pharmacology, Goethe - University, Frankfurt am Main, Germany
| | - Dario Kringel
- Medical Faculty, Institute of Clinical Pharmacology, Goethe - University, Frankfurt am Main, Germany
| | - Jörn Lötsch
- Medical Faculty, Institute of Clinical Pharmacology, Goethe - University, Frankfurt am Main, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Frankfurt am Main, Germany
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Edwards-Bailey L, Piggott K, Dalton L, Horton J, Parks C, White S, Wright G, Kearney A. The Craniofacial Collaboration UK: Developmental Outcomes in 7- and 10-Year-Old Children With Metopic Synostosis. J Craniofac Surg 2024; 35:96-103. [PMID: 38294298 DOI: 10.1097/scs.0000000000009803] [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: 07/20/2022] [Accepted: 08/26/2023] [Indexed: 02/01/2024] Open
Abstract
The Craniofacial Collaboration (CC-UK) is a shared initiative across the Psychology teams attached to 4 highly specialized craniofacial centers in the United Kingdom. The CC-UK aims to address key limitations in the existing craniofacial literature by analyzing data for homogenous samples of children with craniosynostosis. This article presents the fifth wave of CC-UK data collection, focused on 7- and 10-year olds who have undergone primary corrective surgery for metopic synostosis (MS). Data for children with sagittal synostosis and MS have previously been presented at 3 and 5 years. This paper continues to build on this with consideration to older age groups, presenting the first CC-UK analysis of cognitive assessment data using the Wechsler Abbreviated Scale of Intelligence-Second Edition. Results show that the majority of children with MS fall within the average ranges across behavioral and neurodevelopmental domains. However, several domains indicated a trend of heightened concern when compared with normative data, particularly for parent-reported outcomes, suggesting that there may be some subtle difficulties for children with MS. Consideration of how these findings compare with that of previous CC-UK analyses is explored. Further, implications for clinical practice and future research are considered, with the need for longitudinal analyses, as well as data from multiple perspectives (eg, school, parents, and self) at older age points to establish patterns over time. Through collaboration across the highly specialized craniofacial centers, the CC-UK hopes to work toward this goal moving forward.
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Affiliation(s)
| | - Katie Piggott
- Craniofacial Unit, Alder Hey Children's NHS Foundation Trust, Liverpool
| | - Louise Dalton
- Oxford Craniofacial Unit, Oxford University Hospitals NHS Foundation Trust, Oxford
| | - Jo Horton
- Craniofacial Unit, Birmingham Women's and Children's Hospital, Birmingham
| | - Christopher Parks
- Craniofacial Unit, Alder Hey Children's NHS Foundation Trust, Liverpool
| | - Samuel White
- Oxford Craniofacial Unit, Oxford University Hospitals NHS Foundation Trust, Oxford
| | - Gillian Wright
- Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
| | - Anna Kearney
- Craniofacial Unit, Alder Hey Children's NHS Foundation Trust, Liverpool
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Ko JT, Li YY, Chen PY, Liu PY, Ho MY. Use of 16S rRNA gene sequences to identify cyanobacteria that can grow in far-red light. Mol Ecol Resour 2024; 24:e13871. [PMID: 37772760 DOI: 10.1111/1755-0998.13871] [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: 05/26/2023] [Revised: 09/10/2023] [Accepted: 09/18/2023] [Indexed: 09/30/2023]
Abstract
Although most cyanobacteria use visible light (VL; λ = 400-700 nm) for photosynthesis, some have evolved strategies to use far-red light (FRL; λ = 700-800 nm). These cyanobacteria are defined as far-red light-utilizing cyanobacteria (FRLCyano), including two groups: (1) chlorophyll d-producing Acaryochloris spp. and (2) polyphyletic cyanobacteria that produce chlorophylls d and f in response to FRL. Numerous ecological studies examine pigments, such as chlorophylls d and f, to investigate the presence of FRLCyano in the environment. This method is not ideal because it can only detect FRLCyano that have made chlorophylls d or f. Here we develop a new method, far-red cyanobacteria identification (FRCI), to identify FRLCyano based on 16S rRNA gene sequences. From public databases and published articles, 62 16S rRNA gene sequences of FRLCyano were extracted. Comparing with related lineages, we determined that 97% sequence identity is the optimal cut-off for distinguishing FRLCyano from other cyanobacteria. To test the method experimentally, we collected samples from 17 sites in Taipei, Taiwan, and conducted VL and FRL enrichments. Our results demonstrate that FRCI can detect FRLCyano during FRL enrichments more sensitively than pigment analysis. FRCI can also resolve the composition of FRLCyano at the genus level, which pigment analysis cannot do. In addition, we applied FRCI to published datasets and discovered putative FRLCyano in diverse environments, including soils, hot springs and deserts. Overall, our results indicate that FRCI is a sensitive and high-resolution method using 16S rRNA gene sequences to identify FRLCyano.
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Affiliation(s)
- Jui-Tse Ko
- Department of Life Science, National Taiwan University, Taipei, Taiwan
| | - Ying-Yang Li
- Department of Life Science, National Taiwan University, Taipei, Taiwan
- Institute of Plant Biology, National Taiwan University, Taipei, Taiwan
| | - Pa-Yu Chen
- Department of Life Science, National Taiwan University, Taipei, Taiwan
| | - Po-Yu Liu
- School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Ming-Yang Ho
- Department of Life Science, National Taiwan University, Taipei, Taiwan
- Institute of Plant Biology, National Taiwan University, Taipei, Taiwan
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30
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Kniha E, Dvořák V, Koblmüller S, Prudhomme J, Ivović V, Hoxha I, Oerther S, Heitmann A, Lühken R, Bañuls AL, Sereno D, Michelutti A, Toniolo F, Alarcón-Elbal PM, Bravo-Barriga D, González MA, Lucientes J, Colella V, Otranto D, Bezerra-Santos MA, Kunz G, Obwaller AG, Depaquit J, Alić A, Kasap OE, Alten B, Omeragic J, Volf P, Walochnik J, Sebestyén V, Trájer AJ. Reconstructing the post-glacial spread of the sand fly Phlebotomus mascittii Grassi, 1908 (Diptera: Psychodidae) in Europe. Commun Biol 2023; 6:1244. [PMID: 38066195 PMCID: PMC10709326 DOI: 10.1038/s42003-023-05616-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 11/21/2023] [Indexed: 12/18/2023] Open
Abstract
Phlebotomine sand flies (Diptera: Phlebotominae) are the principal vectors of Leishmania spp. (Kinetoplastida: Trypanosomatidae). In Central Europe, Phlebotomus mascittii is the predominant species, but largely understudied. To better understand factors driving its current distribution, we infer patterns of genetic diversity by testing for signals of population expansion based on two mitochondrial genes and model current and past climate and habitat suitability for seven post-glacial maximum periods, taking 19 climatic variables into account. Consequently, we elucidate their connections by environmental-geographical network analysis. Most analyzed populations share a main haplotype tracing back to a single glacial maximum refuge area on the Mediterranean coasts of South France, which is supported by network analysis. The rapid range expansion of Ph. mascittii likely started in the early mid-Holocene epoch until today and its spread possibly followed two routes. The first one was through northern France to Germany and then Belgium, and the second across the Ligurian coast through present-day Slovenia to Austria, toward the northern Balkans. Here we present a combined approach to reveal glacial refugia and post-glacial spread of Ph. mascittii and observed discrepancies between the modelled and the current known distribution might reveal yet overlooked populations and potential further spread.
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Affiliation(s)
- Edwin Kniha
- Institute of Specific Prophylaxis and Tropical Medicine, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Vít Dvořák
- Department of Parasitology, Faculty of Science, Charles University, Prague, Czech Republic
| | | | - Jorian Prudhomme
- UMR MIVEGEC (Université de Montpellier-IRD-CNRS), Institute of Research for Development, Montpellier, France
- INTHERES, Université de Toulouse, INRAE, ENVT, Toulouse, France
| | - Vladimir Ivović
- Department of Biodiversity, FAMNIT, University of Primorska, Koper-Capodistria, Slovenia
| | - Ina Hoxha
- Institute of Specific Prophylaxis and Tropical Medicine, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Sandra Oerther
- Institute of Global Health, Heidelberg University, Heidelberg, Germany
- German Mosquito Control Association (KABS), Speyer, Germany
- Institute for Dipterology (IfD), Speyer, Germany
| | - Anna Heitmann
- Department of Arbovirology, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany
| | - Renke Lühken
- Department of Arbovirology, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany
- Research Group Vector Control, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany
| | - Anne-Laure Bañuls
- UMR MIVEGEC (Université de Montpellier-IRD-CNRS), Institute of Research for Development, Montpellier, France
| | - Denis Sereno
- UMR MIVEGEC (Université de Montpellier-IRD-CNRS), Institute of Research for Development, Montpellier, France
- Institut de Recherche pour le Développement, Université de Montpellier, UMR INTERTRYP, Parasite Infectiology and Public Health Research group. IRD, CIRAD, Montpellier, France
| | - Alice Michelutti
- Laboratory of Parasitology, Micology and Medical Entomology, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, Italy
| | - Federica Toniolo
- Laboratory of Parasitology, Micology and Medical Entomology, Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, Italy
| | - Pedro M Alarcón-Elbal
- Departamento de Producción y Sanidad Animal, Salud Pública Veterinaria y Ciencia y Tecnología de los Alimentos (PASAPTA), Facultad de Veterinaria, Universidad CEU Cardenal Herrera, Valencia, Spain
- Laboratorio de investigación de Entomología, Departamento de Zoología, Facultad de Ciencias Biológicas, Bloque B, Universidad de Valencia, Valencia, Spain
| | - Daniel Bravo-Barriga
- Department of Animal Health, Animal Health and Zoonosis Research Group (GISAZ), UIC Zoonosis and Emerging Diseases (ENZOEM), University of Cordoba, Cordoba, Spain
| | - Mikel A González
- Department of Animal Production and Health, Veterinary Public Health and Food Science and Technology (PASAPTA), Facultad de Veterinaria, Universidad Cardenal Herrera-CEU, CEU Universities, Valencia, Spain
- Applied Zoology and Animal Conservation Group, University of the Balearic Islands (UIB), Palma de Mallorca, Spain
| | - Javier Lucientes
- Animal Health Department, The AgriFood Institute of Aragon (IA2), School of Veterinary Medicine, University of Zaragoza, Zaragoza, Spain
| | - Vito Colella
- Faculty of Science, The University of Melbourne, Parkville, Australia
| | - Domenico Otranto
- Department of Veterinary Medicine, University of Bari, Bari, Italy
- Faculty of Veterinary Sciences, Bu-Ali Sina University, Hamedan, Iran
| | | | - Gernot Kunz
- Institute of Biology, University of Graz, Graz, Austria
| | - Adelheid G Obwaller
- Division of Science, Research and Development, Federal Ministry of Defence, Vienna, Austria
| | - Jerome Depaquit
- Université de Reims Champagne Ardenne, ESCAPE EA7510, USC ANSES VECPAR, SFR Cap Santé, UFR de Pharmacie, Reims, France
| | - Amer Alić
- Department of Clinical Sciences of Veterinary Medicine, Faculty of Veterinary Medicine, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Ozge Erisoz Kasap
- Department of Biology, Ecology Section, Faculty of Science, VERG Laboratories, Hacettepe University, Ankara, Turkey
| | - Bulent Alten
- Department of Biology, Ecology Section, Faculty of Science, VERG Laboratories, Hacettepe University, Ankara, Turkey
| | - Jasmin Omeragic
- Department of Pathobiology and Epidemiology, Veterinary Faculty, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Petr Volf
- Department of Parasitology, Faculty of Science, Charles University, Prague, Czech Republic
| | - Julia Walochnik
- Institute of Specific Prophylaxis and Tropical Medicine, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Viktor Sebestyén
- University of Pannonia, Sustainability Solutions Research Lab, Veszprém, Hungary
| | - Attila J Trájer
- University of Pannonia, Sustainability Solutions Research Lab, Veszprém, Hungary.
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Putra RH, Astuti ER, Nurrachman AS, Putri DK, Ghazali AB, Pradini TA, Prabaningtyas DT. Convolutional neural networks for automated tooth numbering on panoramic radiographs: A scoping review. Imaging Sci Dent 2023; 53:271-281. [PMID: 38174035 PMCID: PMC10761295 DOI: 10.5624/isd.20230058] [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: 03/08/2023] [Revised: 06/14/2023] [Accepted: 07/14/2023] [Indexed: 01/05/2024] Open
Abstract
Purpose The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved through classification, detection, and segmentation tasks. Material and Methods An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review. Results Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentation tasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture. Conclusion CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs. Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes.
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Affiliation(s)
- Ramadhan Hardani Putra
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
| | - Eha Renwi Astuti
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
| | - Aga Satria Nurrachman
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
| | - Dina Karimah Putri
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
- Division of Dental Informatics and Radiology, Tohoku University Graduate School of Dentistry, Sendai, Japan
| | - Ahmad Badruddin Ghazali
- Oral Radiology Unit, Department of Oral Maxillofacial Surgery and Oral Diagnosis, Kulliyyah of Dentistry, International Islamic University Malaysia, Malaysia
| | - Tjio Andrinanti Pradini
- Undergraduate Program, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
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Lang L, Tavadze P, Prusinowski M, Andrews Z, Neumann C, Trejos T, Romero AH. Using convolutional neural networks to support examiners in duct tape physical fit comparisons. Forensic Sci Int 2023; 353:111884. [PMID: 37989070 DOI: 10.1016/j.forsciint.2023.111884] [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: 01/19/2023] [Revised: 10/30/2023] [Accepted: 11/07/2023] [Indexed: 11/23/2023]
Abstract
This paper describes the construction and use of a machine-learning model to provide objective support for a physical fit examination of duct tapes. We present the ForensicFit package that can preprocess and database raw tape images. Using the processed tape image, we trained a convolutional neural network to compare tape edges and predict membership scores (i.e., fit or non-fit category). A dataset of nearly 2000 tapes and 4000 images was evaluated, including various quality grades: low, medium, and high, as well as two separation methods, scissor-cut and hand-torn. The model predicts medium-quality and high-quality scissor-cut tape more accurately than hand-torn, whereas for low-quality tape predicts the hand-torn tapes more accurately. These results are consistent with previous studies performed on the same datasets by analyst examinations. A method of pixel importance was also implemented to show which pixels are used to make the decision. This method can confirm some fit features that correspond with analyst-identified features, like edge morphology and backing pattern. This pilot study demonstrates the feasibility of computational algorithms to build physical fit databases and automated comparisons using deep neural networks, which can be used as a model for other materials.
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Affiliation(s)
- Logan Lang
- West Virginia University, Department of Physics and Astronomy, Morgantown, WV 26506, USA
| | - Pedram Tavadze
- West Virginia University, Department of Forensic and Investigative Science, Morgantown, WV 26506, USA
| | - Meghan Prusinowski
- West Virginia University, Department of Forensic and Investigative Science, Morgantown, WV 26506, USA
| | - Zachary Andrews
- West Virginia University, Department of Forensic and Investigative Science, Morgantown, WV 26506, USA
| | | | - Tatiana Trejos
- West Virginia University, Department of Forensic and Investigative Science, Morgantown, WV 26506, USA
| | - Aldo H Romero
- West Virginia University, Department of Physics and Astronomy, Morgantown, WV 26506, USA.
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Grady SK, Dojcsak L, Harville EW, Wallace ME, Vilda D, Donneyong MM, Hood DB, Valdez RB, Ramesh A, Im W, Matthews-Juarez P, Juarez PD, Langston MA. Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:124201. [PMID: 38109119 PMCID: PMC10727037 DOI: 10.1289/ehp12901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 11/22/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND The exposome serves as a popular framework in which to study exposures from chemical and nonchemical stressors across the life course and the differing roles that these exposures can play in human health. As a result, data relevant to the exposome have been used as a resource in the quest to untangle complicated health trajectories and help connect the dots from exposures to adverse outcome pathways. OBJECTIVES The primary aim of this methods seminar is to clarify and review preprocessing techniques critical for accurate and effective external exposomic data analysis. Scalability is emphasized through an application of highly innovative combinatorial techniques coupled with more traditional statistical strategies. The Public Health Exposome is used as an archetypical model. The novelty and innovation of this seminar's focus stem from its methodical, comprehensive treatment of preprocessing and its demonstration of the positive effects preprocessing can have on downstream analytics. DISCUSSION State-of-the-art technologies are described for data harmonization and to mitigate noise, which can stymie downstream interpretation, and to select key exposomic features, without which analytics may lose focus. A main task is the reduction of multicollinearity, a particularly formidable problem that frequently arises from repeated measurements of similar events taken at various times and from multiple sources. Empirical results highlight the effectiveness of a carefully planned preprocessing workflow as demonstrated in the context of more highly concentrated variable lists, improved correlational distributions, and enhanced downstream analytics for latent relationship discovery. The nascent field of exposome science can be characterized by the need to analyze and interpret a complex confluence of highly inhomogeneous spatial and temporal data, which may present formidable challenges to even the most powerful analytical tools. A systematic approach to preprocessing can therefore provide an essential first step in the application of modern computer and data science methods. https://doi.org/10.1289/EHP12901.
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Affiliation(s)
- Stephen K. Grady
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, Tennessee, USA
| | - Levente Dojcsak
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee, USA
| | - Emily W. Harville
- Department Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Maeve E. Wallace
- Department of Social, Behavioral, and Population Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Dovile Vilda
- Department of Social, Behavioral, and Population Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | | | - Darryl B. Hood
- Division of Environmental Health Sciences, College of Public Health, Ohio State University, Columbus, Ohio, USA
| | - R. Burciaga Valdez
- Department of Economics, University of New Mexico, Albuquerque, New Mexico, USA
| | - Aramandla Ramesh
- Department of Biochemistry, Cancer Biology, Neuroscience & Pharmacology, Meharry Medical College, Nashville, Tennessee, USA
| | - Wansoo Im
- Department of Family and Community Medicine, Meharry Medical College, Nashville, Tennessee, USA
| | | | - Paul D. Juarez
- Department of Family and Community Medicine, Meharry Medical College, Nashville, Tennessee, USA
- Institute on Health Disparities, Equity, and the Exposome, Meharry Medical College, Nashville, Tennessee, USA
| | - Michael A. Langston
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee, USA
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Cinaglia P, Milano M, Cannataro M. Multilayer network alignment based on topological assessment via embeddings. BMC Bioinformatics 2023; 24:416. [PMID: 37932663 PMCID: PMC10629033 DOI: 10.1186/s12859-023-05508-5] [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: 08/10/2023] [Accepted: 10/02/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Network graphs allow modelling the real world objects in terms of interactions. In a multilayer network, the interactions are distributed over layers (i.e., intralayer and interlayer edges). Network alignment (NA) is a methodology that allows mapping nodes between two or multiple given networks, by preserving topologically similar regions. For instance, NA can be applied to transfer knowledge from one biological species to another. In this paper, we present DANTEml, a software tool for the Pairwise Global NA (PGNA) of multilayer networks, based on topological assessment. It builds its own similarity matrix by processing the node embeddings computed from two multilayer networks of interest, to evaluate their topological similarities. The proposed solution can be used via a user-friendly command line interface, also having a built-in guided mode (step-by-step) for defining input parameters. RESULTS We investigated the performance of DANTEml based on (i) performance evaluation on synthetic multilayer networks, (ii) statistical assessment of the resulting alignments, and (iii) alignment of real multilayer networks. DANTEml over performed a method that does not consider the distribution of nodes and edges over multiple layers by 1193.62%, and a method for temporal NA by 25.88%; we also performed the statistical assessment, which corroborates the significance of its own node mappings. In addition, we tested the proposed solution by using a real multilayer network in presence of several levels of noise, in accordance with the same outcome pursued for the NA on our dataset of synthetic networks. In this case, the improvement is even more evident: +4008.75% and +111.72%, compared to a method that does not consider the distribution of nodes and edges over multiple layers and a method for temporal NA, respectively. CONCLUSIONS DANTEml is a software tool for the PGNA of multilayer networks based on topological assessment, that is able to provide effective alignments both on synthetic and real multi layer networks, of which node mappings can be validated statistically. Our experimentation reported a high degree of reliability and effectiveness for the proposed solution.
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Affiliation(s)
- Pietro Cinaglia
- Department of Health Sciences, Magna Graecia University, 88100, Catanzaro, Italy.
| | - Marianna Milano
- Department of Experimental and Clinical Medicine, Magna Graecia University, 88100, Catanzaro, Italy
| | - Mario Cannataro
- Data Analytics Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, 88100, Catanzaro, Italy
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Barak H, Fuchs N, Liddor-Naim M, Nir I, Sivan A, Kushmaro A. Microbial dark matter sequences verification in amplicon sequencing and environmental metagenomics data. Front Microbiol 2023; 14:1247119. [PMID: 38029171 PMCID: PMC10656735 DOI: 10.3389/fmicb.2023.1247119] [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: 06/25/2023] [Accepted: 10/04/2023] [Indexed: 12/01/2023] Open
Abstract
Although microorganisms constitute the most diverse and abundant life form on Earth, in many environments, the vast majority of them remain uncultured. As it is based on information gleaned mainly from cultivated microorganisms, our current body of knowledge regarding microbial life is partial and does not reflect actual microbial diversity. That diversity is hidden in the uncultured microbial majority, termed by microbiologists as "microbial dark matter" (MDM), a term borrowed from astrophysics. Metagenomic sequencing analysis techniques (both 16S rRNA gene and shotgun sequencing) compare gene sequences to reference databases, each of which represents only a small fraction of the existing microorganisms. Unaligned sequences lead to groups of "unknown microorganisms" that are usually ignored and rarefied from diversity analysis. To address this knowledge gap, we analyzed the 16S rRNA gene sequences of microbial communities from four different environments-a living organism, a desert environment, a natural aquatic environment, and a membrane bioreactor for wastewater treatment. From those datasets, we chose representative sequences of potentially unknown bacteria for additional examination as "microbial dark matter sequences" (MDMS). Sequence existence was validated by specific amplification and re-sequencing. These sequences were screened against databases and aligned to the Genome Taxonomy Database to build a comprehensive phylogenetic tree for additional sequence classification, revealing potentially new candidate phyla and other lineages. These putative MDMS were also screened against metagenome-assembled genomes from the explored environments for additional validation and for taxonomic and metabolic characterizations. This study shows the immense importance of MDMS in environmental metataxonomic analyses of 16S rRNA gene sequences and provides a simple and readily available methodology for the examination of MDM hidden behind amplicon sequencing results.
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Affiliation(s)
- Hana Barak
- Department of Civil and Environmental Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Naomi Fuchs
- Avram and Stella Goldstein-Goren Department of Biotechnology Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Michal Liddor-Naim
- Avram and Stella Goldstein-Goren Department of Biotechnology Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Irit Nir
- Avram and Stella Goldstein-Goren Department of Biotechnology Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Alex Sivan
- Avram and Stella Goldstein-Goren Department of Biotechnology Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Ariel Kushmaro
- Avram and Stella Goldstein-Goren Department of Biotechnology Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- The Ilse Katz Center for Nanoscale Science and Technology, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- School of Sustainability and Climate Change, Ben-Gurion University of the Negev, Beer-Sheva, Israel
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Russell M, Aqil A, Saitou M, Gokcumen O, Masuda N. Gene communities in co-expression networks across different tissues. PLoS Comput Biol 2023; 19:e1011616. [PMID: 37976327 PMCID: PMC10691702 DOI: 10.1371/journal.pcbi.1011616] [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: 05/19/2023] [Revised: 12/01/2023] [Accepted: 10/19/2023] [Indexed: 11/19/2023] Open
Abstract
With the recent availability of tissue-specific gene expression data, e.g., provided by the GTEx Consortium, there is interest in comparing gene co-expression patterns across tissues. One promising approach to this problem is to use a multilayer network analysis framework and perform multilayer community detection. Communities in gene co-expression networks reveal groups of genes similarly expressed across individuals, potentially involved in related biological processes responding to specific environmental stimuli or sharing common regulatory variations. We construct a multilayer network in which each of the four layers is an exocrine gland tissue-specific gene co-expression network. We develop methods for multilayer community detection with correlation matrix input and an appropriate null model. Our correlation matrix input method identifies five groups of genes that are similarly co-expressed in multiple tissues (a community that spans multiple layers, which we call a generalist community) and two groups of genes that are co-expressed in just one tissue (a community that lies primarily within just one layer, which we call a specialist community). We further found gene co-expression communities where the genes physically cluster across the genome significantly more than expected by chance (on chromosomes 1 and 11). This clustering hints at underlying regulatory elements determining similar expression patterns across individuals and cell types. We suggest that KRTAP3-1, KRTAP3-3, and KRTAP3-5 share regulatory elements in skin and pancreas. Furthermore, we find that CELA3A and CELA3B share associated expression quantitative trait loci in the pancreas. The results indicate that our multilayer community detection method for correlation matrix input extracts biologically interesting communities of genes.
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Affiliation(s)
- Madison Russell
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, United States of America
| | - Alber Aqil
- Department of Biological Sciences, State University of New York at Buffalo, Buffalo, New York, United States of America
| | - Marie Saitou
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
| | - Omer Gokcumen
- Department of Biological Sciences, State University of New York at Buffalo, Buffalo, New York, United States of America
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, United States of America
- Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, New York, United States of America
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Petti M, Farina L. Network medicine for patients' stratification: From single-layer to multi-omics. WIREs Mech Dis 2023; 15:e1623. [PMID: 37323106 DOI: 10.1002/wsbm.1623] [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: 10/14/2022] [Revised: 03/08/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023]
Abstract
Precision medicine research increasingly relies on the integrated analysis of multiple types of omics. In the era of big data, the large availability of different health-related information represents a great, but at the same time untapped, chance with a potentially fundamental role in the prevention, diagnosis and prognosis of diseases. Computational methods are needed to combine this data to create a comprehensive view of a given disease. Network science can model biomedical data in terms of relationships among molecular players of different nature and has been successfully proposed as a new paradigm for studying human diseases. Patient stratification is an open challenge aimed at identifying subtypes with different disease manifestations, severity, and expected survival time. Several stratification approaches based on high-throughput gene expression measurements have been successfully applied. However, few attempts have been proposed to exploit the integration of various genotypic and phenotypic data to discover novel sub-types or improve the detection of known groupings. This article is categorized under: Cancer > Biomedical Engineering Cancer > Computational Models Cancer > Genetics/Genomics/Epigenetics.
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Affiliation(s)
- Manuela Petti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
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Yousefi B, Firoozbakht F, Melograna F, Schwikowski B, Van Steen K. PLEX.I: a tool to discover features in multiplex networks that reflect clinical variation. Front Genet 2023; 14:1274637. [PMID: 37928248 PMCID: PMC10620964 DOI: 10.3389/fgene.2023.1274637] [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: 08/09/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023] Open
Abstract
Molecular profiling technologies, such as RNA sequencing, offer new opportunities to better discover and understand the molecular networks involved in complex biological processes. Clinically important variations of diseases, or responses to treatment, are often reflected, or even caused, by the dysregulation of molecular interaction networks specific to particular network regions. In this work, we propose the R package PLEX.I, that allows quantifying and testing variation in the direct neighborhood of a given node between networks corresponding to different conditions or states. We illustrate PLEX.I in two applications in which we discover variation that is associated with different responses to tamoxifen treatment and to sex-specific responses to bacterial stimuli. In the first case, PLEX.I analysis identifies two known pathways i) that have already been implicated in the same context as the tamoxifen mechanism of action, and ii) that would have not have been identified using classical differential gene expression analysis.
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Affiliation(s)
- Behnam Yousefi
- Computational Systems Biomedicine Lab, Institut Pasteur, Université Paris Cité, Paris, France
- École Doctorale Complexite du Vivant, Sorbonne Université, Paris, France
- BIO3—Laboratory for Systems Medicine, KU Leuven, Leuven, Belgium
| | - Farzaneh Firoozbakht
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | | | - Benno Schwikowski
- Computational Systems Biomedicine Lab, Institut Pasteur, Université Paris Cité, Paris, France
| | - Kristel Van Steen
- BIO3—Laboratory for Systems Medicine, KU Leuven, Leuven, Belgium
- BIO3—Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium
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Zaki M, Rowles LS, Adjeroh DA, Orner KD. A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste. ACS ES&T ENGINEERING 2023; 3:1424-1467. [PMID: 37854077 PMCID: PMC10580293 DOI: 10.1021/acsestengg.3c00043] [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: 01/31/2023] [Revised: 09/11/2023] [Accepted: 09/11/2023] [Indexed: 10/20/2023]
Abstract
Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002-2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to cost-effectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.
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Affiliation(s)
- Mohammed
T. Zaki
- Wadsworth
Department of Civil and Environmental Engineering, West Virginia University, Morgantown, West Virginia 26505, United States
| | - Lewis S. Rowles
- Department
of Civil Engineering and Construction, Georgia
Southern University, Statesboro, Georgia 30458, United States
| | - Donald A. Adjeroh
- Lane
Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, West Virginia 26505, United States
| | - Kevin D. Orner
- Wadsworth
Department of Civil and Environmental Engineering, West Virginia University, Morgantown, West Virginia 26505, United States
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40
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Milano M, Agapito G, Cannataro M. An Exploratory Application of Multilayer Networks and Pathway Analysis in Pharmacogenomics. Genes (Basel) 2023; 14:1915. [PMID: 37895264 PMCID: PMC10606656 DOI: 10.3390/genes14101915] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 09/26/2023] [Accepted: 10/05/2023] [Indexed: 10/29/2023] Open
Abstract
Over the years, network analysis has become a promising strategy for analysing complex system, i.e., systems composed of a large number of interacting elements. In particular, multilayer networks have emerged as a powerful framework for modelling and analysing complex systems with multiple types of interactions. Network analysis can be applied to pharmacogenomics to gain insights into the interactions between genes, drugs, and diseases. By integrating network analysis techniques with pharmacogenomic data, the goal consists of uncovering complex relationships and identifying key genes to use in pathway enrichment analysis to figure out biological pathways involved in drug response and adverse reactions. In this study, we modelled omics, disease, and drug data together through multilayer network representation. Then, we mined the multilayer network with a community detection algorithm to obtain the top communities. After that, we used the identified list of genes from the communities to perform pathway enrichment analysis (PEA) to figure out the biological function affected by the selected genes. The results show that the genes forming the top community have multiple roles through different pathways.
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Affiliation(s)
- Marianna Milano
- Department of Experimental and Clinical Medicine, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy
- Data Analytics Research Center, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy; (G.A.); (M.C.)
| | - Giuseppe Agapito
- Data Analytics Research Center, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy; (G.A.); (M.C.)
- Department of Law, Economics and Social Sciences, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy
| | - Mario Cannataro
- Data Analytics Research Center, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy; (G.A.); (M.C.)
- Department of Medical and Surgical Sciences, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy
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Khilwani R, Singh S. Systems Biology and Cytokines Potential Role in Lung Cancer Immunotherapy Targeting Autophagic Axis. Biomedicines 2023; 11:2706. [PMID: 37893079 PMCID: PMC10604646 DOI: 10.3390/biomedicines11102706] [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: 08/03/2023] [Revised: 09/11/2023] [Accepted: 09/11/2023] [Indexed: 10/29/2023] Open
Abstract
Lung cancer accounts for the highest number of deaths among men and women worldwide. Although extensive therapies, either alone or in conjunction with some specific drugs, continue to be the principal regimen for evolving lung cancer, significant improvements are still needed to understand the inherent biology behind progressive inflammation and its detection. Unfortunately, despite every advancement in its treatment, lung cancer patients display different growth mechanisms and continue to die at significant rates. Autophagy, which is a physiological defense mechanism, serves to meet the energy demands of nutrient-deprived cancer cells and sustain the tumor cells under stressed conditions. In contrast, autophagy is believed to play a dual role during different stages of tumorigenesis. During early stages, it acts as a tumor suppressor, degrading oncogenic proteins; however, during later stages, autophagy supports tumor cell survival by minimizing stress in the tumor microenvironment. The pivotal role of the IL6-IL17-IL23 signaling axis has been observed to trigger autophagic events in lung cancer patients. Since the obvious roles of autophagy are a result of different immune signaling cascades, systems biology can be an effective tool to understand these interconnections and enhance cancer treatment and immunotherapy. In this review, we focus on how systems biology can be exploited to target autophagic processes that resolve inflammatory responses and contribute to better treatment in carcinogenesis.
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Affiliation(s)
| | - Shailza Singh
- Systems Medicine Laboratory, National Centre for Cell Science, SPPU Campus, Ganeshkhind Road, Pune 411007, India;
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Chunduri RK, Perera DG. Neuromorphic Sentiment Analysis Using Spiking Neural Networks. SENSORS (BASEL, SWITZERLAND) 2023; 23:7701. [PMID: 37765758 PMCID: PMC10536645 DOI: 10.3390/s23187701] [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: 06/27/2023] [Revised: 08/25/2023] [Accepted: 09/02/2023] [Indexed: 09/29/2023]
Abstract
Over the past decade, the artificial neural networks domain has seen a considerable embracement of deep neural networks among many applications. However, deep neural networks are typically computationally complex and consume high power, hindering their applicability for resource-constrained applications, such as self-driving vehicles, drones, and robotics. Spiking neural networks, often employed to bridge the gap between machine learning and neuroscience fields, are considered a promising solution for resource-constrained applications. Since deploying spiking neural networks on traditional von-Newman architectures requires significant processing time and high power, typically, neuromorphic hardware is created to execute spiking neural networks. The objective of neuromorphic devices is to mimic the distinctive functionalities of the human brain in terms of energy efficiency, computational power, and robust learning. Furthermore, natural language processing, a machine learning technique, has been widely utilized to aid machines in comprehending human language. However, natural language processing techniques cannot also be deployed efficiently on traditional computing platforms. In this research work, we strive to enhance the natural language processing traits/abilities by harnessing and integrating the SNNs traits, as well as deploying the integrated solution on neuromorphic hardware, efficiently and effectively. To facilitate this endeavor, we propose a novel, unique, and efficient sentiment analysis model created using a large-scale SNN model on SpiNNaker neuromorphic hardware that responds to user inputs. SpiNNaker neuromorphic hardware typically can simulate large spiking neural networks in real time and consumes low power. We initially create an artificial neural networks model, and then train the model using an Internet Movie Database (IMDB) dataset. Next, the pre-trained artificial neural networks model is converted into our proposed spiking neural networks model, called a spiking sentiment analysis (SSA) model. Our SSA model using SpiNNaker, called SSA-SpiNNaker, is created in such a way to respond to user inputs with a positive or negative response. Our proposed SSA-SpiNNaker model achieves 100% accuracy and only consumes 3970 Joules of energy, while processing around 10,000 words and predicting a positive/negative review. Our experimental results and analysis demonstrate that by leveraging the parallel and distributed capabilities of SpiNNaker, our proposed SSA-SpiNNaker model achieves better performance compared to artificial neural networks models. Our investigation into existing works revealed that no similar models exist in the published literature, demonstrating the uniqueness of our proposed model. Our proposed work would offer a synergy between SNNs and NLP within the neuromorphic computing domain, in order to address many challenges in this domain, including computational complexity and power consumption. Our proposed model would not only enhance the capabilities of sentiment analysis but also contribute to the advancement of brain-inspired computing. Our proposed model could be utilized in other resource-constrained and low-power applications, such as robotics, autonomous, and smart systems.
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Affiliation(s)
| | - Darshika G. Perera
- Department of Electrical and Computer Engineering, University of Colorado Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, CO 80918, USA;
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Miller JD, Fry AC, Ciccone AB, Poggio J. Analysis of Rate of Force Development as a Vertical Jump Height Predictor. RESEARCH QUARTERLY FOR EXERCISE AND SPORT 2023; 94:638-645. [PMID: 35442167 DOI: 10.1080/02701367.2022.2036315] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 01/25/2022] [Indexed: 06/14/2023]
Abstract
Purpose: Many researchers and coaches hold that the ability to generate force rapidly is an important factor in athletic performance. This concept is often studied by analyzing the rate of ground reaction force development (RFD) during vertical jumps; however, many such studies disagree on whether estimates of RFD are true predictors of vertical jump height, have limited sample sizes, and have not employed multiple regression analysis. Therefore, the purpose of the study was to assess the utility of RFD as a predictor of vertical jump height. Methods: Forward sequential multiple regression models were performed using kinematic, kinetic, and demographic variables from a database of maximal countermovement vertical jumps collected via motion capture system from 2,258 NCAA Division I athletes. Results: Peak RFD was a significant bivariate predictor of vertical jump height (r = 0.408, p < .001). However, when other variables were included in the prediction model the partial variance in vertical jump height accounted for by peak RFD was nearly eliminated (r = -0.051, β = -0.051), but sex (r = 0.246, β = 0.94) and peak ground reaction force (r = 0.503, β = 1.109) emerged as predictors of partial variance in jump height. Furthermore, mediation analysis revealed the direct effect of peak RFD on vertical jump height was only 0.004. Conclusions: Multiple regression analysis enabled by a large sample size suggests Peak RFD may not be uniquely useful as a predictor of vertical jump height during maximal countermovement jumps.
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Gupta R, Kumari S, Senapati A, Ambasta RK, Kumar P. New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease. Ageing Res Rev 2023; 90:102013. [PMID: 37429545 DOI: 10.1016/j.arr.2023.102013] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/26/2023] [Accepted: 07/06/2023] [Indexed: 07/12/2023]
Abstract
Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA.
| | - Smita Kumari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA
| | | | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA.
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Machado TM, Berssaneti FT. Literature review of digital twin in healthcare. Heliyon 2023; 9:e19390. [PMID: 37809792 PMCID: PMC10558347 DOI: 10.1016/j.heliyon.2023.e19390] [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: 09/29/2022] [Revised: 05/26/2023] [Accepted: 08/21/2023] [Indexed: 10/10/2023] Open
Abstract
This article aims to make a bibliometric literature review using systematic scientific mapping and content analysis of digital twins in healthcare to know the evolution, domain, keywords, content type, and kind and purpose of digital twin's implementation in healthcare, so a consolidation and future improvement of existing knowledge can be made and gaps for new studies can be identified. The increase in publications of digital twins in healthcare is quite recent and it is still concentrated in the domain of technology sources. The subject is majorly concentrated in patient's digital twin group and in precision medicine and aspects, issues and/or policies subgroups, although the publications keywords mirror it only at the group side. Digital twins in healthcare are probably stepping out of the infancy phase. On the other hand, digital twins in hospital group and the device and facilities management subgroups are more mature with all knowledge gathered from the manufacturing sector. There is an absence of some publication's types in general, device and care subgroup and no whole body or hospital digital twin was reported. Based on the presented arguments, guidelines for future research were presented: advance in the creation of general frameworks, in subgroups not as much explored, and in groups and subgroups already explored, but that need more advancement to achieve the main goals of a whole human or hospital digital twin with the main issues resolved.
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Affiliation(s)
- Tatiana Mallet Machado
- Production Engineering Department, Polytechnic School University of São Paulo, Av. Prof. Almeida Prado, Brazil
| | - Fernando Tobal Berssaneti
- Production Engineering Department, Polytechnic School University of São Paulo, Av. Prof. Almeida Prado, Brazil
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46
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Soy S, Lakra U, Prakash P, Suravajhala P, Nigam VK, Sharma SR, Bayal N. Exploring microbial diversity in hot springs of Surajkund, India through 16S rRNA analysis and thermozyme characterization from endogenous isolates. Sci Rep 2023; 13:14221. [PMID: 37648773 PMCID: PMC10469164 DOI: 10.1038/s41598-023-41515-5] [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: 02/15/2023] [Accepted: 08/28/2023] [Indexed: 09/01/2023] Open
Abstract
Hot springs are a valuable source of biologically significant chemicals due to their high microbial diversity. To investigate the possibilities for industrial uses of these bacteria, researchers collected water and sediment samples from variety of hot springs. Our investigation employed both culture-dependent and culture-independent techniques, including 16S-based marker gene analysis of the microbiota from the hot springs of Surajkund, Jharkhand. In addition, we cultivated thermophilic isolates and screened for their ability to produce amylase, xylanase, and cellulase. After the optimized production of amylase the enzyme was partially purified and characterized using UPLC, DLS-ZP, and TGA. The retention time for the amylase was observed to be around 0.5 min. We confirmed the stability of the amylase at higher temperatures through observation of a steady thermo gravimetric profile at 400 °C. One of the thermophilic isolates obtained from the kund, demonstrated the potential to degrade lignocellulosic agricultural waste.
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Affiliation(s)
- S Soy
- Department of Bioengineering and Biotechnology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India
| | - U Lakra
- Department of Bioengineering and Biotechnology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India
| | - P Prakash
- Department of Bioengineering and Biotechnology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India
| | - P Suravajhala
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Clappana, Kerala, India
- Systems Genomics Lab, Bioclues.org, Hyderabad, India
| | - V K Nigam
- Department of Bioengineering and Biotechnology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India
| | - S R Sharma
- Department of Bioengineering and Biotechnology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India.
| | - N Bayal
- National Centre for Cell Science, Ganeshkhind, Pune, India
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Menegatti D, Giuseppi A, Delli Priscoli F, Pietrabissa A, Di Giorgio A, Baldisseri F, Mattioni M, Monaco S, Lanari L, Panfili M, Suraci V. CADUCEO: A Platform to Support Federated Healthcare Facilities through Artificial Intelligence. Healthcare (Basel) 2023; 11:2199. [PMID: 37570439 PMCID: PMC10418332 DOI: 10.3390/healthcare11152199] [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: 07/13/2023] [Revised: 07/26/2023] [Accepted: 07/31/2023] [Indexed: 08/13/2023] Open
Abstract
Data-driven algorithms have proven to be effective for a variety of medical tasks, including disease categorization and prediction, personalized medicine design, and imaging diagnostics. Although their performance is frequently on par with that of clinicians, their widespread use is constrained by a number of obstacles, including the requirement for high-quality data that are typical of the population, the difficulty of explaining how they operate, and ethical and regulatory concerns. The use of data augmentation and synthetic data generation methodologies, such as federated learning and explainable artificial intelligence ones, could provide a viable solution to the current issues, facilitating the widespread application of artificial intelligence algorithms in the clinical application domain and reducing the time needed for prevention, diagnosis, and prognosis by up to 70%. To this end, a novel AI-based functional framework is conceived and presented in this paper.
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Affiliation(s)
| | | | | | | | | | - Federico Baldisseri
- Department of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy; (D.M.); (A.G.); (F.D.P.); (A.P.); (A.D.G.); (M.M.); (S.M.); (L.L.); (M.P.); (V.S.)
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Hasman M, Mayr M, Theofilatos K. Uncovering Protein Networks in Cardiovascular Proteomics. Mol Cell Proteomics 2023; 22:100607. [PMID: 37356494 PMCID: PMC10460687 DOI: 10.1016/j.mcpro.2023.100607] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 05/01/2023] [Accepted: 06/20/2023] [Indexed: 06/27/2023] Open
Abstract
Biological networks have been widely used in many different diseases to identify potential biomarkers and design drug targets. In the present review, we describe the main computational techniques for reconstructing and analyzing different types of protein networks and summarize the previous applications of such techniques in cardiovascular diseases. Existing tools are critically compared, discussing when each method is preferred such as the use of co-expression networks for functional annotation of protein clusters and the use of directed networks for inferring regulatory associations. Finally, we are presenting examples of reconstructing protein networks of different types (regulatory, co-expression, and protein-protein interaction networks). We demonstrate the necessity to reconstruct networks separately for each cardiovascular tissue type and disease entity and provide illustrative examples of the importance of taking into consideration relevant post-translational modifications. Finally, we demonstrate and discuss how the findings of protein networks could be interpreted using single-cell RNA-sequencing data.
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Affiliation(s)
- Maria Hasman
- King's British Heart Foundation Centre, Kings College London, London, United Kingdom
| | - Manuel Mayr
- King's British Heart Foundation Centre, Kings College London, London, United Kingdom
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Sobhaninia Z, Karimi N, Khadivi P, Samavi S. Brain tumor segmentation by cascaded multiscale multitask learning framework based on feature aggregation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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50
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Toșa C, Tarigan AK. Comparing sustainable product hashtags: Insights from a historical twitter dataset. Data Brief 2023; 49:109427. [PMID: 37538954 PMCID: PMC10393594 DOI: 10.1016/j.dib.2023.109427] [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: 05/16/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 08/05/2023] Open
Abstract
This data article describes the process of data collection and analysis of Twitter conversations about sustainable products. The dataset contains the IDs of tweets tagged with the hashtags #sustainableproducts, #ecoproducts, #ecofriendlyproducts, and #greenproducts. The time period spans 10 years and includes a total of over 140 thousand tweets from around the world. The article describes the process of obtaining the data using Twarc and the Twitter developer's academic researcher API and describes the preprocessing techniques used to identify keywords, hashtags, topics, and sentiments expressed in the conversations. The analysis identifies key attributes of each sustainable product category as well as commonalities and differences within and across categories. The data have the potential to be reused in future research related to sustainable consumption and production, including further analysis of the sentiments and attitudes expressed in the Twitter conversations and comparison with other social media platforms or survey data. In addition, the data can serve as a basis for marketing strategies and product design by enterprises or organizations seeking to promote sustainable products.
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