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The genomes of thousands of individuals are profiled within Dutch healthcare and research each year. However, this valuable genomic data, associated clinical data and consent are captured in different ways and stored across many systems and organizations. This makes it difficult to discover rare disease patients, reuse data for personalized medicine and establish research cohorts based on specific parameters. FAIR Genomes aims to enable NGS data reuse by developing metadata standards for the data descriptions needed to FAIRify genomic data while also addressing ELSI issues. We developed a semantic schema of essential data elements harmonized with international FAIR initiatives. The FAIR Genomes schema v1.1 contains 110 elements in 9 modules. It reuses common ontologies such as NCIT, DUO and EDAM, only introducing new terms when necessary. The schema is represented by a YAML file that can be transformed into templates for data entry software (EDC) and programmatic interfaces (JSON, RDF) to ease genomic data sharing in research and healthcare. The schema, documentation and MOLGENIS reference implementation are available at https://fairgenomes.org.

Background Scientific literature carries a wealth of information crucial for research, but only a fraction of it is present as structured information in databases and therefore can be analyzed using traditional data analysis tools. Natural language processing (NLP) is often and successfully employed to support humans by distilling relevant information from large corpora of free text and structuring it in a way that lends itself to further computational analyses. For this pilot, we developed a pipeline that uses NLP on biological literature to produce knowledge networks. We focused on the flesh color of potato, a well-studied trait with known associations, and we investigated whether these knowledge networks can assist us in formulating new hypotheses on the underlying biological processes. Results We trained an NLP model based on a manually annotated corpus of 34 full-text potato articles, to recognize relevant biological entities and relationships between them in text (genes, proteins, metabolites and traits). This model detected the number of biological entities with a precision of 97.65% and a recall of 88.91% on the training set. We conducted a time series analysis on 4023 PubMed abstract of plant genetics-based articles which focus on 4 major Solanaceous crops (tomato, potato, eggplant and capsicum), to determine that the networks contained both previously known and contemporaneously unknown leads to subsequently discovered biological phenomena relating to flesh color. A novel time-based analysis of these networks indicates a connection between our trait and a candidate gene (zeaxanthin epoxidase) already two years prior to explicit statements of that connection in the literature. Conclusions Our time-based analysis indicates that network-assisted hypothesis generation shows promise for knowledge discovery, data integration and hypothesis generation in scientific research.

Background A quantitative trait locus (QTL) is a genomic region that correlates with a phenotype. Most of the experimental information about QTL mapping studies is described in tables of scientific publications. Traditional text mining techniques aim to extract information from unstructured text rather than from tables. We present QTLTableMiner ++ (QTM), a table mining tool that extracts and semantically annotates QTL information buried in (heterogeneous) tables of plant science literature. QTM is a command line tool written in the Java programming language. This tool takes scientific articles from the Europe PMC repository as input, extracts QTL tables using keyword matching and ontology-based concept identification. The tables are further normalized using rules derived from table properties such as captions, column headers and table footers. Furthermore, table columns are classified into three categories namely column descriptors, properties and values based on column headers and data types of cell entries. Abbreviations found in the tables are expanded using the Schwartz and Hearst algorithm. Finally, the content of QTL tables is semantically enriched with domain-specific ontologies (e.g. Crop Ontology, Plant Ontology and Trait Ontology) using the Apache Solr search platform and the results are stored in a relational database and a text file. Results The performance of the QTM tool was assessed by precision and recall based on the information retrieved from two manually annotated corpora of open access articles, i.e. QTL mapping studies in tomato ( Solanum lycopersicum ) and in potato ( S. tuberosum ). In summary, QTM detected QTL statements in tomato with 74.53% precision and 92.56% recall and in potato with 82.82% precision and 98.94% recall. Conclusion QTM is a unique tool that aids in providing QTL information in machine-readable and semantically interoperable formats.

The present study was undertaken to study the clinical and hemato-biochemical alterations, ultrasonography, and surgical treatment of bovine suffering from cecal dilatation and cecal impaction. The present study was conducted on 11 bovines (9 buffaloes and 2 cattle) suffering from cecal dilatation (n=6) and cecal impaction (n=5). The diagnosis of surgical affections of cecum was made on the basis of clinical examination, hematobiochemistry, ultrasonography, and exploratory laparotomy. A marked decrease in serum total protein, albumin, chloride, potassium, and calcium levels while an increase in lactate concentrations was recorded. Peritoneal fluid examination revealed an increase in total protein concentration. Per rectal examination along with ultrasonography was used as a confirmatory diagnostic tool for cecal dilatation and cecal impaction. Ultrasonographic features of cecal dilatation and cecal impaction were recorded. Left flank laparorumenotomy was performed in six animals with dilated cecum along with colonic fecalith. Post-rumenotomy, these animals were treated with massage of cecum along with kneading of colonic fecalith. Right flank typhlotomy was done in the remaining five animals having impacted cecum for decompression of the dilated cecum. 9 of 11 animals survived which underwent surgery and remained healthy up to 3-month follow-up. Ultrasonography was reliable in the diagnosis of cecal dilatation and cecal impaction in bovine. Left flank exploration after laparorumenotomy is an ideal surgical technique for the management of cecal dilatation, while right flank typhlotomy is ideal for the management of cecal impaction in bovine.

Genetics research is increasingly focusing on mining fully sequenced genomes and their annotations to identify the causal genes associated with traits (phenotypes) of interest. However, a complex trait is typically associated with multiple quantitative trait loci (QTLs), each comprising many genes, that can positively or negatively affect the trait of interest. To help breeders in ranking candidate genes, we developed an analytical platform called pbg-ld that provides semantically integrated geno- and phenotypic data on Solanaceae species. This platform combines both unstructured data from scientific literature and structured data from publicly available biological databases using the Linked Data approach. In particular, QTLs were extracted from tables of full-text articles from the Europe PubMed Central (PMC) repository using QTLTableMiner++ (QTM), while the genomic annotations were obtained from the Sol Genomics Network (SGN), UniProt and Ensembl Plants databases. These datasets were transformed into Linked Data graphs, which include cross-references to many other relevant databases such as Gramene, Plant Reactome, InterPro and KEGG Orthology (KO). Users can query and analyze the integrated data through a web interface or programmatically via the SPARQL and RESTful services (APIs). We illustrate the usability of pbg-ld by querying genome annotations, by comparing genome graphs, and by two biological use cases in Jupyter Notebooks. In the first use case, we performed a comparative genomics study using pbg-ld to compare the difference in the genetic mechanism underlying tomato fruit shape and potato tuber shape. In the second use case, we developed a seamlessly integrated workflow that uses genomic data from pbg-ld knowledge graphs and prioritization pipelines to predict candidate genes within QTL regions for metabolic traits of tomato.

Hypertension (HTN), often dubbed the “silent killer,” poses a significant global health challenge, affecting over 1.3 billion individuals. Despite advances in treatment, effective long-term blood pressure (BP) control remains elusive, necessitating novel therapeutic approaches. Poor control of BP remains a leading cause of cardiovascular morbidity and mortality worldwide and is becoming an even larger global health problem due to the aging population, rising rates of obesity, poorer dietary patterns and overall cardiometabolic health, and suboptimal rates of patient adherence and optimal BP control. Ribonucleic acid interference (RNAi) technology, which leverages the body’s natural gene-silencing mechanism, has emerged as a promising strategy for several diseases and has recently been tested for its antihypertensive effects. We systematically reviewed peer-reviewed articles from databases including PubMed, EMBASE, and Scopus for studies examining RNAi’s role in managing HTN, focusing on mechanisms, clinical utility, and safety profile. Key early-phase trials of some RNAi-leading candidate drugs are detailed. Also highlighted are challenges such as target specificity, delivery mechanisms, durability of effect, and immunogenicity. We conclude by summarizing how RNAi has a significant potential role in HTN therapy due to their unique benefits, such as long-term duration of action, infrequent dosing, and lack of major side effects.

One of the major global challenges of today is to meet the food demands of an everincreasing population (food demand will increase by 50% in 2030). One approach to address this challenge is to breed new crop varieties that yield more even under unfavorable conditions e.g. have improved tolerance to drought and/or resistance to pathogens. However, designing a breeding program is a laborious and time consuming effort that often lacks the capacity to generate new cultivars quickly in response to the required traits. Recent advances in biotechnology and genomics data science have the potential to accelerate and precise breeding programs greatly. As large-scale genomic data sets for crop species are available in multiple independent data sources and scientific literature, this thesis provides innovative technologies that use natural language processing (NLP) and semantic web technologies to address challenges of integrating genomic data for improving plant breeding.Firstly, in this research study, we developed a supervised Natural language processing (NLP) model with the help of IBM Watson, to extract knowledge networks containing genotypic-phenotypic associations of potato tuber flesh color from the scientific literature. Secondly, a table mining tool called QTLTableMiner++ (QTM) was developed which enables knowledge discovery of novel genomic regions (such as QTL regions), which positively or negatively affect the traits of interest. The objective of both above mentioned, NLP techniques was to extract information which is implicitly described in the literature and is not available in structured resources, like databases. Thirdly, with the help of semantic web technology, a linked-data platform called Solanaceae linked data platform(pbg-ld) was developed, to semantically integrates geno- and pheno-typic data of Solanaceae species. This platform combines both unstructured data from scientific literature and structured data from publicly available biological databases using the Linked Data approach. Lastly, analysis workflows for prioritizing candidate genes with QTL regions were tested using pbg-ld. Hence, this research provides in-silico knowledge discovery tools and genomic data infrastructure, which aids researchers and breeders in the design of a precise and improved breeding program.

Background: An important public health issue is tobacco use. Smoking has a direct impact on the oral cavity in addition to its well-known negative consequences on the human body. The present study was conducted to assess periodontal status in smokers. Materials & Methods: 50 smokers were put in group I and 50 non- smokers were put in group Il(control). Parameters such as probing depth (PD) and clinical attachment loss (CAL) was recorded in all subjects. Results: The group I had 30 males and 20 females and group II had 25 males and 25 females. The mean probing depth (PD) in group I was 2.8 and in group II was 1.3. The difference was significant (P< 0.05). CAL in group I was 4.0 mm and in group II was 2.8 mm. The difference was significant (P< 0.05). Conclusion: Smoking has a negative impact on periodontal health. In comparison to non-smokers, smokers had worse periodontal health.

The vast majority of time-series forecasting approaches require a substantial training dataset. However, many real-life forecasting applications have very little initial observations, sometimes just 40 or fewer. Thus, the applicability of most forecasting methods is restricted in data-sparse commercial applications. While there is recent work in the setting of very limited initial data (so-called `zero-shot' forecasting), its performance is inconsistent depending on the data used for pretraining. In this work, we take a different approach and devise ForecastPFN, the first zero-shot forecasting model trained purely on a novel synthetic data distribution. ForecastPFN is a prior-data fitted network, trained to approximate Bayesian inference, which can make predictions on a new time series dataset in a single forward pass. Through extensive experiments, we show that zero-shot predictions made by ForecastPFN are more accurate and faster compared to state-of-the-art forecasting methods, even when the other methods are allowed to train on hundreds of additional in-distribution data points.

Applying good data management and FAIR data principles (Findable, Accessible, Interoperable, and Reusable) in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object. We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub. Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice. The FAIR4RS principles provide guidelines to enhance the discovery and reuse of research software. FAIR Digital Objects support Findability, Accessibility, Interoperability, and Reusability by both humans and machines. We here demonstrate the implementation multi-omics data analysis workflow and share it as a FAIR Digital Object.