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Scientific Data Dec 2023Metadata from epidemiological studies, including chronic disease outcome metadata (CDOM), are important to be findable to allow interpretability and reusability. We...
Metadata from epidemiological studies, including chronic disease outcome metadata (CDOM), are important to be findable to allow interpretability and reusability. We propose a comprehensive metadata schema and used it to assess public availability and findability of CDOM from German population-based observational studies participating in the consortium National Research Data Infrastructure for Personal Health Data (NFDI4Health). Additionally, principal investigators from the included studies completed a checklist evaluating consistency with FAIR principles (Findability, Accessibility, Interoperability, Reusability) within their studies. Overall, six of sixteen studies had complete publicly available CDOM. The most frequent CDOM source was scientific publications and the most frequently missing metadata were availability of codes of the International Classification of Diseases, Tenth Revision (ICD-10). Principal investigators' main perceived barriers for consistency with FAIR principles were limited human and financial resources. Our results reveal that CDOM from German population-based studies have incomplete availability and limited findability. There is a need to make CDOM publicly available in searchable platforms or metadata catalogues to improve their FAIRness, which requires human and financial resources.
Topics: Humans; Metadata; Publications; Chronic Disease
PubMed: 38052810
DOI: 10.1038/s41597-023-02726-7 -
Frontiers in Big Data 2023Text Reuse reveals meaningful reiterations of text in large corpora. Humanities researchers use text reuse to study, e.g., the posterior reception of influential texts...
Text Reuse reveals meaningful reiterations of text in large corpora. Humanities researchers use text reuse to study, e.g., the posterior reception of influential texts or to reveal evolving publication practices of historical media. This research is often supported by interactive visualizations which highlight relations and differences between text segments. In this paper, we build on earlier work in this domain. We present Text Reuse at Scale, the to our knowledge first interface which integrates text reuse data with other forms of semantic enrichment to enable a versatile and scalable exploration of intertextual relations in historical newspaper corpora. The Text Reuse at Scale interface was developed as part of the project and combines powerful search and filter operations with close and distant reading perspectives. We integrate text reuse data with enrichments derived from topic modeling, named entity recognition and classification, language and document type detection as well as a rich set of newspaper metadata. We report on historical research objectives and common user tasks for the analysis of historical text reuse data and present the prototype interface together with the results of a user evaluation.
PubMed: 38025945
DOI: 10.3389/fdata.2023.1249469 -
Microbial Genomics Nov 2023In the province of Alberta, Canada, invasive disease caused by serogroup 20 (serotypes 20A/20B) has been increasing in incidence. Here, we characterize provincial...
In the province of Alberta, Canada, invasive disease caused by serogroup 20 (serotypes 20A/20B) has been increasing in incidence. Here, we characterize provincial invasive serogroup 20 isolates collected from 1993 to 2019 alongside invasive and non-invasive serogroup 20 isolates from the Global Pneumococcal Sequencing (GPS) Project collected from 1998 to 2015. Trends in clinical metadata and geographic location were evaluated, and serogroup 20 isolate genomes were subjected to molecular sequence typing, virulence and antimicrobial resistance factor mining, phylogenetic analysis and pangenome calculation. Two hundred and seventy-four serogroup 20 isolates from Alberta were sequenced, and analysed along with 95 GPS Project genomes. The majority of invasive Alberta serogroup 20 isolates were identified after 2007 in primarily middle-aged adults and typed predominantly as ST235, a sequence type that was rare among GPS Project isolates. Most Alberta isolates carried a full-length capsular gene, suggestive of serotype 20B. All Alberta and GPS Project genomes carried molecular resistance determinants implicated in fluoroquinolone and macrolide resistance, with a few Alberta isolates exhibiting phenotypic resistance to azithromycin, clindamycin, erythromycin, tetracycline and trimethoprim-sulfamethoxazole, as well as non-susceptibility to tigecycline. All isolates carried multiple virulence factors including those involved in adherence, immune modulation and nutrient uptake, as well as exotoxins and exoenzymes. Phylogenetically, Alberta serogroup 20 isolates clustered with predominantly invasive GPS Project isolates from the USA, Israel, Brazil and Nepal. Overall, this study highlights the increasing incidence of invasive serogroup 20 disease in Alberta, Canada, and provides insights into the genetic and clinical characteristics of these isolates within a global context.
Topics: Adult; Middle Aged; Humans; Alberta; Serogroup; Streptococcus pneumoniae; Anti-Bacterial Agents; Phylogeny; Drug Resistance, Bacterial; Macrolides; Genomics
PubMed: 38015202
DOI: 10.1099/mgen.0.001141 -
MBio Oct 2023The salivary microbiome has been proven to play a crucial role in local and systemic diseases. Moreover, the effects of biological and lifestyle factors such as oral...
The salivary microbiome has been proven to play a crucial role in local and systemic diseases. Moreover, the effects of biological and lifestyle factors such as oral hygiene and smoking on this microbial community have already been explored. However, what was not yet well understood was the natural variation of the saliva microbiome in healthy women and how this is associated with specific use of hormonal contraception and with the number of different sexual partners with whom microbiome exchange is expected regularly. In this paper, we characterized the salivary microbiome of 255 healthy women of reproductive age using an in-depth questionnaire and self-sampling kits. Using the large metadata set, we were able to investigate the associations of several host-related and lifestyle variables with the salivary microbiome profiles. Our study shows a high preservation between individuals.
Topics: Humans; Female; Reproduction; Saliva; Sexual Partners; Microbiota; Health Status; RNA, Ribosomal, 16S
PubMed: 37655878
DOI: 10.1128/mbio.00300-23 -
Journal of Biomedical Informatics Jan 2024The development and deployment of machine learning (ML) models for biomedical research and healthcare currently lacks standard methodologies. Although tools for model...
The development and deployment of machine learning (ML) models for biomedical research and healthcare currently lacks standard methodologies. Although tools for model replication are numerous, without a unifying blueprint it remains difficult to scientifically reproduce predictive ML models for any number of reasons (e.g., assumptions regarding data distributions and preprocessing, unclear test metrics, etc.) and ultimately, questions around generalizability and transportability are not readily answered. To facilitate scientific reproducibility, we built upon the Predictive Model Markup Language (PMML) to capture essential information. As a key component of the PREdictive Model Index and Exchange REpository (PREMIERE) platform, we present the Automated Metadata Pipeline (AMP) for conversion of a given predictive ML model into an extended PMML file that autocompletes an ML-based checklist, assessing model elements for interoperability and reproducibility. We demonstrate this pipeline on multiple test cases with three different ML algorithms and health-related datasets, providing a foundation for future predictive model reproducibility, sharing, and comparison.
Topics: Reproducibility of Results; Biomedical Research; Algorithms; Records; Metadata
PubMed: 38000765
DOI: 10.1016/j.jbi.2023.104551 -
Journal of Global Antimicrobial... Mar 2024Mycoplasma and Ureaplasma spp. especially M. hominis, U. parvum, and U. urealyticum recognized as an important cause of urogenital infections. Sake of the presence of... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Mycoplasma and Ureaplasma spp. especially M. hominis, U. parvum, and U. urealyticum recognized as an important cause of urogenital infections. Sake of the presence of antibiotic resistance and a continuous rise in resistance, the treatment options are limited, and treatment has become more challenging and costlier.
OBJECTIVES
Therefore, this meta-analysis aimed to estimate worldwide resistance rates of genital Mycoplasmas and Ureaplasma to fluoroquinolones (ciprofloxacin, ofloxacin, moxifloxacin, and levofloxacin) agents.
METHODS
We searched the relevant published studies in PubMed, Scopus, and Embase from until 3, March 2022. All statistical analyses were carried out using the statistical package R.
RESULTS
The 30 studies included in the analysis were performed in 16 countries. In the metadata, the proportions of ciprofloxacin, ofloxacin, moxifloxacin, and levofloxacin resistance in Mycoplasma and Ureaplasma urogenital isolates were reported 59.8% (95% CI 49.6, 69.1), 31.2% (95% CI 23, 40), 7.3% (95% CI 1, 31), and 5.3% (95% CI 1, 2), respectively. According to the meta-regression, the ciprofloxacin, ofloxacin, moxifloxacin, and levofloxacin rate increased over time. There was a statistically significant difference in the fluoroquinolones resistance rates between different continents/countries (P < 0.05).
CONCLUSIONS
Based on the results obtained in this systematic review and meta-analysis we recommend the use of the newer group of fluoroquinolones especially levofloxacin as the first choice for the treatment of genital mycoplasmosis, as well as ofloxacin for the treatment of genital infections caused by U. parvum.
Topics: Humans; Ureaplasma; Mycoplasma; Fluoroquinolones; Levofloxacin; Ureaplasma urealyticum; Moxifloxacin; Mycoplasma hominis; Microbial Sensitivity Tests; Ureaplasma Infections; Urinary Tract Infections; Ciprofloxacin
PubMed: 38016593
DOI: 10.1016/j.jgar.2023.11.007 -
Philosophy, Ethics, and Humanities in... Nov 2023Neuroscientific approaches have historically triggered changes in the conception of creativity and artistic experience, which can be revealed by noting the intersection... (Review)
Review
BACKGROUND
Neuroscientific approaches have historically triggered changes in the conception of creativity and artistic experience, which can be revealed by noting the intersection of these fields of study in terms of variables such as global trends, methodologies, objects of study, or application of new technologies; however, these neuroscientific approaches are still often considered as disciplines detached from the arts and humanities. In this light, the question arises as to what evidence the history of neurotechnologies provides at the intersection of creativity and aesthetic experience.
METHODS
We conducted a century-long bibliometric analysis of key parameters in multidisciplinary studies published in the Scopus database. Screening techniques based on the PRISMA method and advanced data analysis techniques were applied to 3612 documents metadata from the years 1922 to 2022. We made graphical representations of the results applying algorithmic and clusterization processes to keywords and authors relationships.
RESULTS
From the analyses, we found a) a shift from a personality-focus quantitative analysis to a field-focus qualitative approach, considering topics such as art, perception, aesthetics and beauty; b) The locus of interest in fMRI-supported neuroanatomy has been shifting toward EEG technologies and models based on machine learning and deep learning in recent years; c) four main clusters were identified in the study approaches: humanistic, creative, neuroaesthetic and medical; d) the neuroaesthetics cluster is the most central and relevant, mediating between creativity and neuroscience; e) neuroaesthetics and neuroethics are two of the neologism that better characterizes the challenges that this convergence of studies will have in the next years.
CONCLUSIONS
Through a longitudinal analysis, we evidenced the great influence that neuroscience is having on the thematic direction of the arts and humanities. The perspective presented shows how this field is being consolidated and helps to define it as a new opportunity of great potential for future researchers.
Topics: Humanities; Cognition; Art; Neurosciences; Creativity
PubMed: 37946225
DOI: 10.1186/s13010-023-00147-3 -
Nucleic Acids Research Jan 2024The molecular causes and mechanisms of neurodegenerative diseases remain poorly understood. A growing number of single-cell studies have implicated various neural,...
The molecular causes and mechanisms of neurodegenerative diseases remain poorly understood. A growing number of single-cell studies have implicated various neural, glial, and immune cell subtypes to affect the mammalian central nervous system in many age-related disorders. Integrating this body of transcriptomic evidence into a comprehensive and reproducible framework poses several computational challenges. Here, we introduce ZEBRA, a large single-cell and single-nucleus RNA-seq database. ZEBRA integrates and normalizes gene expression and metadata from 33 studies, encompassing 4.2 million human and mouse brain cells sampled from 39 brain regions. It incorporates samples from patients with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease, and Multiple sclerosis, as well as samples from relevant mouse models. We employed scVI, a deep probabilistic auto-encoder model, to integrate the samples and curated both cell and sample metadata for downstream analysis. ZEBRA allows for cell-type and disease-specific markers to be explored and compared between sample conditions and brain regions, a cell composition analysis, and gene-wise feature mappings. Our comprehensive molecular database facilitates the generation of data-driven hypotheses, enhancing our understanding of mammalian brain function during aging and disease. The data sets, along with an interactive database are freely available at https://www.ccb.uni-saarland.de/zebra.
Topics: Animals; Humans; Mice; Alzheimer Disease; Brain; Neurodegenerative Diseases; Parkinson Disease; Transcriptome; Gene Expression; Single-Cell Analysis
PubMed: 37941147
DOI: 10.1093/nar/gkad990 -
Database : the Journal of Biological... Oct 2023With the rapidly growing amount of biological data, powerful but also flexible data management and visualization systems are of increasingly crucial importance. The...
With the rapidly growing amount of biological data, powerful but also flexible data management and visualization systems are of increasingly crucial importance. The COVID-19 pandemic has more than highlighted this need and the challenges scientists are facing. Here, we provide an example and a step-by-step template for non-IT personnel to easily implement an intuitive, interactive data management solution to manage and visualize the high influx of biological samples and associated metadata in a laboratory setting. Our approach is illustrated with the genomic surveillance for SARS-CoV-2 in Germany, covering over 11 600 internal and 130 000 external samples from multiple datasets. We compare three data management options used in laboratories: (i) simple, yet error-prone and inefficient spreadsheets, (ii) complex and long-to-implement laboratory information management systems and (iii) high-performance database management systems. We highlight the advantages and pitfalls of each option and outline why a document-oriented NoSQL option via MongoDB Atlas can be a suitable solution for many labs. Our example can be treated as a template and easily adapted to allow scientists to focus on their core work and not on complex data administration.
Topics: Humans; SARS-CoV-2; COVID-19; Pandemics; Genomics; Database Management Systems
PubMed: 37847816
DOI: 10.1093/database/baad071 -
PeerJ. Computer Science 2023The fourth industrial revolution, often referred to as Industry 4.0, has revolutionized the manufacturing sector by integrating emerging technologies such as artificial...
The fourth industrial revolution, often referred to as Industry 4.0, has revolutionized the manufacturing sector by integrating emerging technologies such as artificial intelligence (AI), machine and deep learning, Industrial Internet of Things (IIoT), cloud computing, cyber physical systems (CPSs) and cognitive computing, throughout the production life cycle. Predictive maintenance (PdM) emerges as a critical component, utilizing data analytic to track machine health and proactively detect machinery failures. Deep learning (DL), is pivotal in this context, offering superior accuracy in prediction through neural networks' data processing capabilities. However, DL adoption in PdM faces challenges, including continuous model updates and domain dependence. Meanwhile, centralized DL models, prevalent in PdM, pose security risks such as central points of failure and unauthorized access. To address these issues, this study presents an innovative decentralized PdM system integrating DL, blockchain, and decentralized storage based on the InterPlanetary File System (IPFS) for accurately predicting Remaining Useful Lifetime (RUL). DL handles predictive tasks, while blockchain secures data orchestration. Decentralized storage safeguards model metadata and training data for dynamic models. The system features synchronized two DL pipelines for time series data, encompassing prediction and training mechanisms. The detailed material and methods of this research shed light on the system's development and validation processes. Rigorous validation confirms the system's accuracy, performance, and security through an experimental testbed. The results demonstrate the system's dynamic updating and domain independence. Prediction model surpass state-of-the-art models in terms of the root mean squared error (RMSE) score. Blockchain-based scalability performance was tested based on smart contract gas usage, and the analysis shows efficient performance across varying input and output data scales. A comprehensive CIA analysis highlights the system's robust security features, addressing confidentiality, integrity, and availability aspects. The proposed decentralized predictive maintenance (PdM) system, which incorporates deep learning (DL), blockchain technology, and decentralized storage, has the potential to improve predictive accuracy and overcome significant security and scalability obstacles. Consequently, this system holds promising implications for the advancement of predictive maintenance in the context of Industry 4.0.
PubMed: 38192482
DOI: 10.7717/peerj-cs.1712