Source Data for EMBOR-2023-58131V2
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus (mouse)
SUBMITTER:
PROVIDER: S-BSST1371 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description: Not available
| S-BSST1400 | biostudies-other
Project description: Not available
| S-BSST1139 | biostudies-other
Project description:Understanding of TMZ therapy during and after treatment in a mouse model
2023-08-10 | GSE227348 | GEO
Project description:BackgroundAccording to the WHO/UNICEF Joint Monitoring Programme, unimproved drinking water sources include unprotected wells and springs, surface water (e.g., rivers, lakes), vendor-provided water, bottled water (without improved alternatives), and tanker truck-provided water. In East Africa, 68.7% of water at very high risk comes from such sources. Using unimproved drinking water sources can lead to serious health risks, including waterborne diseases such as cholera, dysentery, typhoid fever, and hepatitis. Therefore, this study aimed to map the spatial distribution of unimproved drinking water sources in the region.MethodsWe analyzed recent Demographic and Health Survey (DHS) data from 12 East African countries (Burundi, Comoros, Ethiopia, Kenya, Madagascar, Mozambique, Malawi, Rwanda, Tanzania, Uganda, Zambia, and Zimbabwe). A total of 206,748 households were sampled in 12 East African countries. Data management and analysis were carried out in several stages, beginning with data cleaning, followed by statistical weighting and data merging. This was then followed by geospatial analysis and mapping, and finally, spatial cluster detection.ResultsSpatial clusters of unimproved drinking water sources were identified within the study area (Global Moran's I: 0.018, z-score: 87.10, p < 0.05). A total of 167 significant spatial windows containing primary and secondary clusters were identified. The first spatial window contained the primary clusters, while the remaining 166 spatial windows contained secondary clusters. Primary clusters were found in Madagascar and coastal Mozambique, with secondary clusters distributed across all 12 countries analyzed.ConclusionsThis study identified significant clusters, hotspots, and outliers (high-high clusters) of unimproved drinking water sources across various East African countries. To address these issues effectively, priority should be given to the identified clusters, hotspots, and high-high clusters. Primary recommendations include expanding water treatment facilities, improving water distribution systems, and protecting drinking water sources. Secondary recommendations emphasize strengthening regulations, conducting research, and fostering public-private partnerships to ensure sustainable access to clean water. Finally, we urge collaboration among governments, international organizations, and NGOs to enhance water infrastructure. Their efforts should focus on providing technical assistance, financial support, capacity building, project implementation, advocacy, and financing for drinking water infrastructure in the region. Further research integrating health outcome data with spatial analysis could help identify high-risk regions where the impacts of unimproved water sources are most pronounced.
| S-EPMC11925304 | biostudies-literature
Project description:Quantitative activity and species source data of natural products (NPs) are important for drug discovery, medicinal plant research, and microbial investigations. Activity values of NPs against specific targets are useful for discovering targeted therapeutic agents and investigating the mechanism of medicinal plants. Composition/concentration values of NPs in individual species facilitate the assessments and investigations of the therapeutic quality of herbs and phenotypes of microbes. Here, we describe an update of the NPASS natural product activity and species source database previously featured in NAR. This update includes: (i) new data of ∼95 000 records of the composition/concentration values of ∼1 490 NPs/NP clusters in ∼390 species, (ii) extended data of activity values of ∼43 200 NPs against ∼7 700 targets (∼40% and ∼32% increase, respectively), (iii) extended data of ∼31 600 species sources of ∼94 400 NPs (∼26% and ∼32% increase, respectively), (iv) new species types of ∼440 co-cultured microbes and ∼420 engineered microbes, (v) new data of ∼66 600 NPs without experimental activity values but with estimated activity profiles from the established chemical similarity tool Chemical Checker, (vi) new data of the computed drug-likeness properties and the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties for all NPs. NPASS update version is freely accessible at http://bidd.group/NPASS.
| S-EPMC9825494 | biostudies-literature
Project description:Given trained models from multiple source domains, how can we predict the labels of unlabeled data in a target domain? Unsupervised multi-source domain adaptation (UMDA) aims for predicting the labels of unlabeled target data by transferring the knowledge of multiple source domains. UMDA is a crucial problem in many real-world scenarios where no labeled target data are available. Previous approaches in UMDA assume that data are observable over all domains. However, source data are not easily accessible due to privacy or confidentiality issues in a lot of practical scenarios, although classifiers learned in source domains are readily available. In this work, we target data-free UMDA where source data are not observable at all, a novel problem that has not been studied before despite being very realistic and crucial. To solve data-free UMDA, we propose DEMS (Data-free Exploitation of Multiple Sources), a novel architecture that adapts target data to source domains without exploiting any source data, and estimates the target labels by exploiting pre-trained source classifiers. Extensive experiments for data-free UMDA on real-world datasets show that DEMS provides the state-of-the-art accuracy which is up to 27.5% point higher than that of the best baseline.
| S-EPMC8270218 | biostudies-literature
Project description:DrugCentral monitors new drug approvals and standardizes drug information. The current update contains 285 drugs (131 for human use). New additions include: (i) the integration of veterinary drugs (154 for animal use only), (ii) the addition of 66 documented off-label uses and iii) the identification of adverse drug events from pharmacovigilance data for pediatric and geriatric patients. Additional enhancements include chemical substructure searching using SMILES and 'Target Cards' based on UniProt accession codes. Statistics of interests include the following: (i) 60% of the covered drugs are on-market drugs with expired patent and exclusivity coverage, 17% are off-market, and 23% are on-market drugs with active patents and exclusivity coverage; (ii) 59% of the drugs are oral, 33% are parenteral and 18% topical, at the level of the active ingredients; (iii) only 3% of all drugs are for animal use only; however, 61% of the veterinary drugs are also approved for human use; (iv) dogs, cats and horses are by far the most represented target species for veterinary drugs; (v) the physicochemical property profile of animal drugs is very similar to that of human drugs. Use cases include azaperone, the only sedative approved for swine, and ruxolitinib, a Janus kinase inhibitor.
| S-EPMC9825566 | biostudies-literature