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Foods (Basel, Switzerland) May 2024Amplicon-targeted metagenomics is now the standard approach for the study of the composition and dynamics of food microbial communities. Hundreds of papers on this...
Amplicon-targeted metagenomics is now the standard approach for the study of the composition and dynamics of food microbial communities. Hundreds of papers on this subject have been published in scientific journals and the information is dispersed in a variety of sources, while raw sequences and their metadata are available in public repositories for some, but not all, of the published studies. A limited number of web resources and databases allow scientists to access this wealth of information but their level of annotation on studies and samples varies. Here, we report on the release of FoodMicrobionet v5, a comprehensive database of metataxonomic studies on bacterial and fungal communities of foods. The current version of the database includes 251 published studies (11 focusing on fungal microbiota, 230 on bacterial microbiota, and 10 providing data for both bacterial and fungal microbiota) and 14,035 samples with data on bacteria and 1114 samples with data on fungi. The new structure of the database is compatible with interactive apps and scripts developed for previous versions and allows scientists, R&D personnel in industries and regulators to access a wealth of information on food microbial communities.
PubMed: 38890917
DOI: 10.3390/foods13111689 -
Scientific Data Jun 2024Air temperature (Ta), snow depth (Sd), and soil temperature (Tg) are crucial variables for studying the above- and below-ground thermal conditions, especially in high...
Air temperature (Ta), snow depth (Sd), and soil temperature (Tg) are crucial variables for studying the above- and below-ground thermal conditions, especially in high latitudes. However, in-situ observations are frequently sparse and inconsistent across various datasets, with a significant amount of missing data. This study has assembled a comprehensive dataset of in-situ observations of Ta, Sd, and Tg for the Northern Hemisphere (higher than 30°N latitude), spanning 1960-2021. This dataset encompasses metadata and daily data time series for 27,768, 32,417, and 659 gages for Ta, Sd, and Tg, respectively. Using the ERA5-Land reanalysis data product, we applied deep learning methodology to reconstruct the missing data that account for 54.5%, 59.3%, and 74.3% of Ta, Sd, and Tg daily time series, respectively. The obtained high temporal resolution dataset can be used to better understand physical phenomena and relevant mechanisms, such as the dynamics of land-surface-atmosphere energy exchange, snowpack, and permafrost.
PubMed: 38890309
DOI: 10.1038/s41597-024-03483-x -
The Journal of Antimicrobial... Jun 2024MDR and XDR Neisseria gonorrhoeae strains remain major public health concerns internationally, and quality-assured global gonococcal antimicrobial resistance (AMR)...
The novel 2024 WHO Neisseria gonorrhoeae reference strains for global quality assurance of laboratory investigations and superseded WHO N. gonorrhoeae reference strains-phenotypic, genetic and reference genome characterization.
OBJECTIVES
MDR and XDR Neisseria gonorrhoeae strains remain major public health concerns internationally, and quality-assured global gonococcal antimicrobial resistance (AMR) surveillance is imperative. The WHO global Gonococcal Antimicrobial Surveillance Programme (GASP) and WHO Enhanced GASP (EGASP), including metadata and WGS, are expanding internationally. We present the phenotypic, genetic and reference genome characteristics of the 2024 WHO gonococcal reference strains (n = 15) for quality assurance worldwide. All superseded WHO gonococcal reference strains (n = 14) were identically characterized.
MATERIAL AND METHODS
The 2024 WHO reference strains include 11 of the 2016 WHO reference strains, which were further characterized, and four novel strains. The superseded WHO reference strains include 11 WHO reference strains previously unpublished. All strains were characterized phenotypically and genomically (single-molecule PacBio or Oxford Nanopore and Illumina sequencing).
RESULTS
The 2024 WHO reference strains represent all available susceptible and resistant phenotypes and genotypes for antimicrobials currently and previously used (n = 22), or considered for future use (n = 3) in gonorrhoea treatment. The novel WHO strains include internationally spreading ceftriaxone resistance, ceftriaxone resistance due to new penA mutations, ceftriaxone plus high-level azithromycin resistance and azithromycin resistance due to mosaic MtrRCDE efflux pump. AMR, serogroup, prolyliminopeptidase, genetic AMR determinants, plasmid types, molecular epidemiological types and reference genome characteristics are presented for all strains.
CONCLUSIONS
The 2024 WHO gonococcal reference strains are recommended for internal and external quality assurance in laboratory examinations, especially in the WHO GASP, EGASP and other GASPs, but also in phenotypic and molecular diagnostics, AMR prediction, pharmacodynamics, epidemiology, research and as complete reference genomes in WGS analysis.
PubMed: 38889110
DOI: 10.1093/jac/dkae176 -
Journal of Microbiology & Biology... Jun 2024The current and ongoing challenges brought on by climate change will require future scientists who have hands-on experience using advanced molecular techniques, can work...
The current and ongoing challenges brought on by climate change will require future scientists who have hands-on experience using advanced molecular techniques, can work with large data sets, and can make correlations between metadata and microbial diversity. A course-embedded research project can prepare students to answer complex research questions that might help plants adapt to climate change. The project described herein uses plants as a host to study the impact of climate change-induced drought on host-microbe interactions through next-generation DNA sequencing and analysis using a command-line program. Specifically, the project studies the impact of simulated drought on the rhizosphere microbiome of Fast Plants rapid cycling using inexpensive greenhouse supplies and 16S rRNA V3/V4 Illumina sequencing. Data analysis is performed with the freely accessible Python-based microbiome bioinformatics platform QIIME 2.
PubMed: 38888313
DOI: 10.1128/jmbe.00046-24 -
Medical Science Educator Jun 2024Problem-based learning (PBL) constructs a curriculum that merges theory and practice by employing clinical scenarios or real-world problems. Originally designed for the...
BACKGROUND
Problem-based learning (PBL) constructs a curriculum that merges theory and practice by employing clinical scenarios or real-world problems. Originally designed for the pre-clinical phase of undergraduate medicine, PBL has since been integrated into diverse aspects of medical education. Therefore, this study aims to map the global scientific landscape related to PBL in medical education in the last ten years.
METHODS
We combined bibliometrics and network analysis to analyze the metadata of related research articles published between 2013 and 2022 and indexed in the Web of Science Core Collection.
RESULTS
Our results show an annual publication rate of 9.42%. The two main journals disseminating research on this subject are and . Education & Educational Research and Health Care Sciences & Services are the two most frequent research areas, and also the two most central nodes of the related network. The USA and China are the most publishing countries, while the Netherlands and Canada are the most collaborative. The Maastricht University holds the position of most publishing and collaborative research organization. The University of California ranks second in publications, while the University of Toronto is the second most central research organization.
CONCLUSIONS
Our study provides an overview of the last ten years of publications related to PBL and medical education, and we hope it can be of interest to educators, researchers, and students involved with this subject.
SUPPLEMENTARY INFORMATION
The online version contains supplementary material available at 10.1007/s40670-024-02003-1.
PubMed: 38887406
DOI: 10.1007/s40670-024-02003-1 -
Microscopy and Microanalysis : the... Jun 2024Atom probe tomography (APT) data analytics have traditionally been based on manual analytics by researchers. As newer atom probes together with focused ion beam-based...
Atom probe tomography (APT) data analytics have traditionally been based on manual analytics by researchers. As newer atom probes together with focused ion beam-based specimen preparation have opened APT to many more materials, yielding much more complex mass spectra, building up a systematic understanding of the pathway from raw data to final interpretation has increasingly become important. This demands a system in which the data and treatment can be traced, ideally by any interested party. Such an approach of findable, accessible, interoperable, and reusable (FAIR) data and analysis policies is becoming increasingly important, not just in APT. In this paper, we present a toolbox, written in MATLAB, which allows the user to store the raw and processed data in a standardized FAIR format (hierarchical data format 5) and process the data in a largely scriptable environment to minimize manual user input. This allows for the experiment data to be interchanged without owner explanations and the analysis to be reproduced. We have devised a metadata scheme that is extensible to other experiments in the materials science domain. With this toolbox, collective knowledge can be built up, and a large number of data sets can be analyzed in a fully automated fashion.
PubMed: 38885135
DOI: 10.1093/mam/ozae031 -
ArXiv Jun 2024Limited universally adopted data standards in veterinary science hinders data interoperability and therefore integration and comparison; this ultimately impedes...
BACKGROUND –
Limited universally adopted data standards in veterinary science hinders data interoperability and therefore integration and comparison; this ultimately impedes application of existing information-based tools to support advancement in veterinary diagnostics, treatments, and precision medicine.
HYPOTHESIS/OBJECTIVES –
Creation of a Vertebrate Breed Ontology (VBO) as a single, coherent logic-based standard for documenting breed names in animal health, production and research-related records will improve data use capabilities in veterinary and comparative medicine.
ANIMALS –
No live animals were used in this study.
METHODS –
A list of breed names and related information was compiled from relevant sources, organizations, communities, and experts using manual and computational approaches to create VBO. Each breed is represented by a VBO term that includes all provenance and the breed's related information as metadata. VBO terms are classified using description logic to allow computational applications and Artificial Intelligence-readiness.
RESULTS –
VBO is an open, community-driven ontology representing over 19,000 livestock and companion animal breeds covering 41 species. Breeds are classified based on community and expert conventions (e.g., horse breed, cattle breed). This classification is supported by relations to the breeds' genus and species indicated by NCBI Taxonomy terms. Relationships between VBO terms, e.g. relating breeds to their foundation stock, provide additional context to support advanced data analytics. VBO term metadata includes common names and synonyms, breed identifiers/codes, and attributed cross-references to other databases.
CONCLUSION AND CLINICAL IMPORTANCE –
Veterinary data interoperability and computability can be enhanced by the adoption of VBO as a source of standard breed names in databases and veterinary electronic health records.
PubMed: 38883236
DOI: No ID Found -
Digital Health 2024This study aimed to determine the status of scientific production on biosensor usage for human health monitoring.
OBJECTIVE
This study aimed to determine the status of scientific production on biosensor usage for human health monitoring.
METHODS
We used bibliometrics based on the data and metadata retrieved from the Web of Science between 2007 and 2022. Articles unrelated to health and medicine were excluded. The databases were processed using the VOSviewer software and auxiliary spreadsheets. Data extraction yielded 275 articles published in 161 journals, mainly concentrated on 13 journals and 881 keywords plus.
RESULTS
The keywords plus of high occurrences were estimated at 27, with seven to 30 occurrences. From the 1595 identified authors, 125 were consistently connected in the coauthorship network in the total set and were grouped into nine clusters. Using Lotka's law, we identified 24 prolific authors, and Hirsch index analysis revealed that 45 articles were cited more than 45 times. Crosses were identified between 17 articles in the Hirsch index and 17 prolific authors, highlighting the presence of a large set of prolific authors from various interconnected clusters, a triad, and a solitary prolific author.
CONCLUSION
An exponential trend was observed in biosensor research for health monitoring, identifying areas of innovation, collaboration, and technological challenges that can guide future research on this topic.
PubMed: 38882252
DOI: 10.1177/20552076241256876 -
Health Informatics Journal 2024This study aims to address the critical challenges of data integrity, accuracy, consistency, and precision in the application of electronic medical record (EMR) data...
This study aims to address the critical challenges of data integrity, accuracy, consistency, and precision in the application of electronic medical record (EMR) data within the healthcare sector, particularly within the context of Chinese medical information data management. The research seeks to propose a solution in the form of a medical metadata governance framework that is efficient and suitable for clinical research and transformation. The article begins by outlining the background of medical information data management and reviews the advancements in artificial intelligence (AI) technology relevant to the field. It then introduces the "Service, Patient, Regression, base/Away, Yeast" (SPRAY)-type AI application as a case study to illustrate the potential of AI in EMR data management. The research identifies the scarcity of scientific research on the transformation of EMR data in Chinese hospitals and proposes a medical metadata governance framework as a solution. This framework is designed to achieve scientific governance of clinical data by integrating metadata management and master data management, grounded in clinical practices, medical disciplines, and scientific exploration. Furthermore, it incorporates an information privacy security architecture to ensure data protection. The proposed medical metadata governance framework, supported by AI technology, offers a structured approach to managing and transforming EMR data into valuable scientific research outcomes. This framework provides guidance for the identification, cleaning, mining, and deep application of EMR data, thereby addressing the bottlenecks currently faced in the healthcare scenario and paving the way for more effective clinical research and data-driven decision-making.
Topics: Artificial Intelligence; China; Humans; Electronic Health Records; Data Management; Metadata
PubMed: 38881290
DOI: 10.1177/14604582241262961 -
Journal of Biomedical Informatics Jun 2024Studies confirm that significant biases exist in online recommendation platforms, exacerbating pre-existing disparities and leading to less-than-optimal outcomes for...
BACKGROUND
Studies confirm that significant biases exist in online recommendation platforms, exacerbating pre-existing disparities and leading to less-than-optimal outcomes for underrepresented demographics. We study issues of bias in inclusion and representativeness in the context of healthcare information disseminated via videos on the YouTube social media platform, a widely used online channel for multi-media rich information. With one in three US adults using the Internet to learn about a health concern, it is critical to assess inclusivity and representativeness regarding how health information is disseminated by digital platforms such as YouTube.
METHODS
Leveraging methods from fair machine learning (ML), natural language processing and voice and facial recognition methods, we examine inclusivity and representativeness of video content presenters using a large corpus of videos and their metadata on a chronic condition (diabetes) extracted from the YouTube platform. Regression models are used to determine whether presenter demographics impact video popularity, measured by the video's average daily view count. A video that generates a higher view count is considered to be more popular.
RESULTS
The voice and facial recognition methods predicted the gender and race of the presenter with reasonable success. Gender is predicted through voice recognition (accuracy = 78 %, AUC = 76 %), while the gender and race predictions use facial recognition (accuracy = 93 %, AUC = 92 % and accuracy = 82 %, AUC = 80 %, respectively). The gender of the presenter is more significant for video views only when the face of the presenter is not visible while videos with male presenters with no face visibility have a positive relationship with view counts. Furthermore, videos with white and male presenters have a positive influence on view counts while videos with female and non - white group have high view counts.
CONCLUSION
Presenters' demographics do have an influence on average daily view count of videos viewed on social media platforms as shown by advanced voice and facial recognition algorithms used for assessing inclusion and representativeness of the video content. Future research can explore short videos and those at the channel level because popularity of the channel name and the number of videos associated with that channel do have an influence on view counts.
PubMed: 38880237
DOI: 10.1016/j.jbi.2024.104669