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Fig. 2 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 2. Research areas identified in the studies. Research areas subcategories are represented in the left axis, and general research area groups in the right axis. Orange circles represent the number of positive studies, the blue circles the negatives, and the black lines between them represent the differences in the number of studies, in which larger lines represent larger differences between positive and negatives. Orange icons correspond to research areas with larger number of positive studies, i.e., positive studies were more related to Biology (Bio), Ecology (Ecol) and Other (Hum Soc: Human society; Phy Env Geo: Physical environmental geology; Earth Atm: Earth and atmospheric sciences). Negative studies were more frequent in research areas with blue icons, including Medical (Med) and Veterinary sciences (Vet: Veterinarian and agriculture). In the green icon (Eng Inf Mat: Engineering, informatics, and mathematics) there was no major differences between groups. Subcategories: Bio Zoo: Biology and zoology; Bio Evo Gen: Biology, evolution, and genetics; Bio Bioch: Biology and biochemistry; Env Mang: Environmental management and sciences; Eco App: Ecological applications; Math Stat: Mathematical statistics; Inf Comp: informatics and computing; Eng Geom: Engineer and geometrics; Microb: Microbiology; Pub Heal: Public health; Med Micro: Medical microbiology; Med Clin Heal: Medical clinical and health. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 1 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 1. General framework of analyses at study- and variable-levels. In the upper section (study-level, in grey), studies are divided in those that used GBIF-mediated data (positives, in orange) and those that did not (negatives, in blue). Positive studies were group according if GBIF was used as the only data source for all variables (2 studies), or if the variables were based on GBIF together with other data sources (105 studies). In the variable-level section (bottom, white background) the total 358 variables extracted from the positive and negative studies were categorized according to the specific use of GBIF, resulting in five types of variables, four of them extracted from the positive studies. Note that in those studies based on GBIF, the different variables could be based on GBIF alone (33 variables), GBIF together with other sources (85), or specific variables may not be based on GBIF-mediated data at all (81 variables). Finally, each variable was related to different epidemiological roles, resulting in a larger number hosts/reservoirs variables, mostly based on GBIF-mediated data, and a higher presence of pathogen species-variables not using GBIF. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 4 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 4. Variables according to taxon class (Y- axis) epidemiological level (bar colour) and the use of GBIF- mediated data. Bars represent the number of variables by each taxon class (Y-axis), separated in two panels according to the use of GBIF-mediated data. In the right panel, variables in which GBIF-mediated data was used (Used_GBIF), in the left panel variables in which data was not obtained from GBIF (NonGBIF). Next to the bars, the number of variables by each epidemi- ological level, and percentage in rela- tion to the total number of variables of each group (Used_GBIF: 120 and Non- GBIF: 238). Taxon classes are grouped by taxonomic associations (e.g., birds, primates, ticks, mosquitoes); however, some were merged to simplify the figure. For example, mamm/oth/var includes multiple mammal species which were sparsely mentioned; simi- larly, hosts/res var, vector other and path other grouped several species participating as hosts/reservoirs, vec- tors, and pathogens, respectively. Bar colors represent epidemiological levels (pathogens, vectors, hosts/reservoirs), and Other (in sienna) includes species participating as hosts' regulator, predators, among others. GBIF-mediated data was only used in three pathogen variables (purple), representing only 2.5% of the variables in which GBIF-mediated data was used, resulting in a remarkable difference with other sources (NonGBIF), in which pathogens represented a 59.7%. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Extraction of clinical phenotypes for Alzheimer disease dementia from clinical notes using natural language processing
<p><strong>Objectives</strong></p> <p>There is much interest in utilizing clinical data for developing prediction models for Alzheimer disease (AD) risk, progression, and outcomes. Existing studies have mostly utilized curated research registries, image analysis, and structured Electronic Health Record (EHR) data. However, much critical information resides in relatively inaccessible unstructured clinical notes within the EHR.</p> <p><strong>Materials and Methods</strong></p> <p>We developed a natural language processing (NLP)-based pipeline to extract AD-related clinical phenotypes, documenting strategies for success and assessing the utility of mining unstructured clinical notes. We evaluated the pipeline against gold-standard manual annotations performed by two clinical dementia experts for AD-related clinical phenotypes including medical comorbidities, biomarkers, neurobehavioral test scores, behavioral indicators of cognitive decline, family history, and neuroimaging findings.</p> <p><strong>Results</strong></p> <p>Documentation rates for each phenotype varied in the structured versus unstructured EHR. Inter-annotator agreement was high (Cohen's kappa = 0.72–1) and positively correlated with the NLP-based phenotype extraction pipeline's performance (average F1-score = 0.65-0.99) for each phenotype.</p> <p><strong>Discussion</strong></p> <p>We developed an automated NLP-based pipeline to extract informative phenotypes that may improve the performance of eventual machine-learning predictive models for AD. In the process, we examined documentation practices for each phenotype relevant to the care of AD patients and identified factors for success.</p> <p><strong>Conclusion</strong></p> <p>Success of our NLP-based phenotype extraction pipeline depended on domain-specific knowledge and focus on a specific clinical domain instead of maximizing generalizability. </p>
Smoking-induced subgingival dysbiosis precedes clinical signs of periodontal disease
<p>Using 16 rRNA sequencing, a total of 233 subgingival sites from 8 smokers and 9 non-smokers over 6-12 months(804 subgingival samples total) were analyzed to study subgingival microbiome dysbiosis in smokers over time.</p>
Elevated urine BMP phospholipids in LRRK2 and VPS35 mutation carriers with and without Parkinson's disease
<p><strong>Participant demographic and clinical characteristics, and urine BMP phospholipid levels. </strong></p> <p>For each participant, sample collection site is provided as: BCN (Barcelona), VIE (Vienna), DND (Dundee), or SSB (San Sebastian). Also provided are age at study participation, age at PD diagnosis (where applicable), sex (M, for male, and F, for female), experimental group (control, iPD –idiopathic PD –, LRRK2 G2019S, LRRK2 R1441G/C, VPS35 D620N, GBA, or other), and PD status (NMC for non-manifesting mutation carriers, or PD). Values for all measured BMP species presented as ng of BMP per mg of creatinine are provided. Additionally, urine creatinine (mg/ml) and non-normalized BMP levels are provided. BQL designates BMP levels that were below quantification level and NM designates values that were not measured for a particular individual.</p>
Spatial transcriptomics stratifies health and psoriatic disease severity by emergent cellular ecosystems
<p>While human inflammatory skin diseases' cellular and molecular features are well-characterized, their tissue context and systemic impact remain poorly understood. We thus profiled human psoriasis (PsO) as a prototypic immune-mediated condition with a high preference for extra-cutaneous involvement. Spatial transcriptomics (ST) analyses of 25 healthy, active, and clinically uninvolved skin biopsies, and integration with public single-cell transcriptomics data revealed striking differences in immune microniches between healthy and inflamed skin. Tissue scale-cartography further identified core disease features across all active lesions, including the emergence of an inflamed suprabasal epidermal state and the presence of B lymphocytes in lesional skin. Notably, both lesional and distal non-lesional samples were stratified by skin disease severity, and not by the presence of systemic disease. This segregation was driven by macrophage-, fibroblast- and lymphatic-enriched spatial regions with gene signatures associated with metabolic dysfunction. Taken together, these findings suggest that mild and severe forms of PsO have distinct molecular features and that severe PsO may profoundly alter the cellular and metabolic make up of distal unaffected skin sites. Additionally, our study provides an unprecedented resource for the research community to study spatial gene organization of healthy and inflamed human skin. </p>
Datasets for article Effect of disease prevalence and growth stage on symptoms severity in the Turnip mosaic virus - Arabidopsis thaliana pathosystem
<p>Plants generate volatile organic compounds (VOCs) in response to biotic and abiotic stimuli that provide information about the physiological status of emitters to other individuals in the community. Nearby receivers adjust their own defenses in response to these chemical cues. The majority of studies to date has concentrated on the communication of abiotic stressors (<em>e.g</em>. salinity or drought) or herbivory. Less attention had received the role of VOCs during microbial infections and almost nothing has been done for viruses. Here we investigated the function of VOCs during turnip mosaic virus infection of <em>Arabidopsis thaliana</em>. First, we looked at the influence of two factors on the kinetics of symptoms progression in receivers, namely the prevalence of infection in the population and the growth stage of the receiver plants at inoculation. We found that young plants were more sensitive to the protective effect of VOCs than older ones, and that high infection prevalence results in a slower disease progression in receivers. Second, we looked into the possibility that jasmonates could be VOC candidates. To do this, we examined the kinetics of symptoms progression in jasmonate-insensitive and wild-type plants, and the results showed that the protective effect vanished in the mutant plants. Third, we investigated the possibility that root communication would be also relevant. Our findings showed that the kinetics of symptom progression across receivers was further slowed down when plants were housed in the same pot. Together, our preliminary findings point to a potential function for disease prevalence in plant communities in regulating the severity of symptoms, this effect being mediated by VOCs.</p>
dataset related to article "A NOVEL BIO-INSPIRED STRATEGY TO PREVENT AMYLOIDOGENESIS AND SYNAPTIC DAMAGE IN ALZHEIMER'S DISEASE"
<p><strong>Levels of A</strong><strong>beta40, Abeta42 and aggregated Abeta</strong></p> <p><strong>results obtained from plaque count</strong></p> <p><strong>densitometric analysis of ctf and synaptic proteins</strong></p> <p><strong>levels of antibodies against Abeta42 and Abeta1-6</strong></p>
datset related to article "THE NOVEL I213S MUTATION IN PSEN1 GENE IS LOCATED IN A HOTSPOT CODON ASSOCIATED WITH FAMILIAL EARLY-ONSET ALZHEIMER'S DISEASE"
<p><strong>Electropherogram of the proband psen1 exon 7</strong></p> <p><strong>ngs analysis of causal and risk genes associated to dementia</strong></p>
RNA-Seq and proteomics of Crohn's disease: Terminal ileum of inflamed and non inflamed paired tissue biopsy
<p>The cause of chronic inflammation manifestations such as inflammatory bowel disease (IBD) is still not yet fully understood. Evidence however points to the convergence of environmental factors, genetic factors, intestinal microbiota and the immune response at the intestinal epithelial surface that consequently leads to a breakdown of barrier function in IBD. An increasing number of theories implicate a dysfunctional intestinal epithelial surface in the pathogenesis of IBD and progression to complicated disease, which contributes significantly to the IBD burden. As yet, the significance of the intestinal epithelial surface in IBD manifestation and progression has not been explored using a systems biology approach, that compares the mucosa of healthy patients to those with IBD. In the context of dyregulation of the epithelial barrier, there is a scarcity of publications that explore the contribution of proteins- the effectors of the cells and RNA message in the pathways affected by CD and in particular the localization of inflammatory response. We aim to evaluate the functional pathways of chronic inflammation at the intestinal epithelial surface using a combination of transcriptomic and proteomic analyses on intestinal epithelial tissue samples from paired inflamed and non-inflammed Crohn's disease (CD) and healthy control subjects. Concordance of several biological pathways from both data sets was found to be altered in CD patients' epithelia when compared to healthy controls. This information could be helpful in identifying novel therapeutic targets that aim to restore barrier function at the intestinal epithelial surface and to guide therapy.</p>
dataset related to article: "PMCA-Based Detection of Prions in the Olfactory Mucosa of Patients With Sporadic Creutzfeldt-Jakob Disease"
<ol> <li>ESTIMATING PRION CONCENTRATION IN THE OM OF SCJD MM1, MV2 AND VV2 PATIENTS BY MEANS OF QUANTITATIVE PMCA (QPMCA)</li> <li>EVALUATION OF THE BIOCHEMICAL PROPERTIES OF PMCA GENERATED PRODUCTS</li> <li>PMCA ANALYSIS OF SCJD BRAIN HOMOGENATES</li> <li>PMCA ANALYSIS OF SCJD OLFACTORY MUCOSA</li> <li>RT-QuIC ANALYSIS OF OM AND BH OF SCJD PATIENTS AND CONTROLS</li> <li>WESTERN BLOT ANALYSIS OF BRAIN HOMOGENATES</li> <li>STATISTICAL ANALYSIS</li> </ol>
Medication and condition codes used to develop a computable phenotype for Crohn's Disease incident cases
<p>Lists of medication and condition concepts used for Crohn's disease incident case phenotyping as described in my Master's Thesis "Machine Learning Based Prediction of Incident Cases of Crohn’s Disease Using Electronic Health Records From a Large Integrated Health System".</p> <ul> <li>ibd_medication.csv contains medication names, OMOP Concept IDs, RxNorm codes and a flag indicating whether the medication is IBD-specific (i.e., antibiotics and glucocorticoides are marked as unspecific)</li> <li>ibd_conditions.csv contains condition names, OMOP Concept IDs, SNOMED CT codes and a categorical column indicating whether the condition refers to Crohn's Disease (CD), Ulcerative Colitis (UC), or Inflammatory bowel disease unclassified (IBD-U)</li> <li>ibd_symptoms.csv contains symptom names, OMOP Concept IDs, containing symptom group categories, and a flag indicating whether the symptom was added because it is a SNOMED CT descendent code of another code on the list. The list was created based on the IBD symptoms Read Code list provided by Blackwell et. al, 2021, doi:10.1093/ecco-jcc/jjaa146</li> </ul> <p>IBD, Inflammatory Bowel Disease; OMOP, Observational Medical Outcomes Partnership; SNOMED CT, Systematized Nomenclature of Medicine Clinial Terms.</p>
Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease
<p>This dataset repository corresponds to the project Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease.</p>
Supplementary data and code to "Known allosteric proteins have central roles in genetic disease" by G. Abrusan, D. Ascher and M. Inouye, PLOS Computational Biology 18(2):e1009806.
<p>Scripts and data to reproduce the figures and supplementary figures of "G. Abrusan, D. Ascher and M. Inouye (2022) Known allosteric proteins have central roles in genetic disease." PLOS Computational Biology 18(2):e1009806. https://doi.org/10.1371/journal.pcbi.1009806.</p>
NTDs2RDF: A heterogeneous and integrated knowledge graph for the exploration of neglected tropical diseases
<p>Neglected tropical diseases (NTDs) are a heterogeneous group of 20 bacterial, viral, parasitic, and fungal conditions that generally occur in developing tropical countries in the Americas, Africa, and Asia. NTDs mainly affect poor populations that do not have access to safe water, sanitation, and high-quality healthcare. Because of the severe effects of NTDs (i.e., they can cause long-lasting disabilities), they reinforce the cycle of poverty in vulnerable communities.</p> <p>The <strong>NTDs2RDF</strong> project aimed to create a knowledge graph (KG) of genes, proteins, metabolic pathways, gene ontologies, single nucleotide variants, drugs, and other relevant data for three NTDs (Chagas disease, leishmaniasis, and African trypanosomiasis), integrating all the information in a single data structure that can be explored through a query interface implemented with a Streamlit web application. This software provides a user-friendly platform to extract information from the KG using SPARQL queries.</p> <p>The project represents an initial step towards the creation of a heterogeneous database for different NTDs with several potential applications in advancing the understanding of NTDs biology and providing insights that cannot be obtained through alternative resources.</p>
Dopamine transporter and synaptic vesicle sorting defects underlie auxilin-associated Parkinson's disease
<p>Auxilin participates in clathrin uncoating to facilitate presynaptic endocytosis. Loss-of-function mutations of auxilin (<em>PARK19</em>) cause Parkinson’s disease. Using auxilin KO mice, Vidyadhara et al. (2023) show that synaptic vesicle sorting deficits, cytoplasmic dopamine accumulation, dopamine transporter mistrafficking, and synaptic autophagic overload may lead to pathogenesis of Parkinson’s disease in <em>PARK19</em> patients. This file contains the data set used to generate all the main figures.</p>
Celluloepidemiology: a novel paradigm for quantifying infectious disease dynamics through T-cell modelling on a population level
<p>T-cell receptor sequencing (TCR-seq) was performed on enriched CD8+ T-cells. TCR clonotype annotation was performed using MiXCR v.3.0.13 with the default input parameters.</p> <p>Full origin and method description available in:<br>Celluloepidemiology: a novel paradigm for quantifying infectious disease dynamics through T-cell modelling on a population level</p>
Statistics "Bioderived therapeutics for chronic liver diseases"
<p>Details to the statistics for the chapter 4 in the thesis "Bioderived therapeutics for chronic liver diseases"</p>
Synaptic oligomeric tau in Alzheimer's disease – a potential culprit in the spread of tau pathology through the brain
<p>In Alzheimer’s disease (AD), fibrillar tau pathology accumulates and spreads through the brain and synapses are lost. Evidence from mouse models indicates that tau spreads trans-synaptically from pre- to postsynapses and that oligomeric tau is synaptotoxic, but data on synaptic tau in human brain is scarce. Here we used sub-diffraction-limit microscopy to study synaptic tau accumulation in post-mortem temporal and occipital cortices of human AD and control donors. Oligomeric tau is present in both pre- and postsynaptic terminals even in areas without abundant fibrillar tau deposition. Further, there is a higher proportion of oligomeric tau compared to phosphorylated or misfolded tau found at synaptic terminals. These data suggest that accumulation of oligomeric tau in synapses is an early event in disease pathogenesis, and that tau pathology may progress through the brain via trans-synaptic spread in human disease. Thus, specifically reducing oligomeric tau at synapses may be a promising therapeutic strategy for AD.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.