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Raw data of the published paper - DOI: 10.1021/acsomega.5c09558

Using a multi-polygenic score approach, we characterized the relationship between genome-wide information and the history of PPD in patients with mood disorders, with the hypothesis that multiple polygenic risk scores (PRSs) could potentially influence the development of PPD. The PLS linear regression in the whole sample defined a model explaining 27.12% of the variance in the presence of PPD history, 56.73% of variance among MDD, and 42.96% of variance in BD. Our findings highlight that multiple genetic factors related to circadian rhythms, inflammation, and psychiatric diagnoses are top contributors to the prediction of PPD. Specifically, in MDD, the top contributing PRS was monocyte count, while in BD, it was chronotype, with PRSs for inflammation and psychiatric diagnoses significantly contributing to both groups.

The neurobiological differences between women who have experienced a peripartum episode and those who have only had episodes outside of this period are not well understood. Our findings demonstrate that women who have experienced a peripartum episode are neurobiologically distinct from women who have no history of PPD in a cluster within the basal ganglia, an area important for motivation, decision making, and emotional processing. Furthermore, we show that the genetic load for estradiol has a differing effect in this area based on PPD status, which supports the claim that PPD is associated with sensitivity to sex steroid hormones.

The introduction of letermovir (LTV) in prophylaxis after allogeneic-hematopoietic stem cell transplantation (allo-HSCT) has reduced the incidence of Cytomegalovirus (CMV) clinically relevant reactivations (CRE), but these events increase after LTV cessation. CMV-specific T cells protect the patients against CRE, but the mechanisms stimulating their emergence during or immediately after LTV treatment need to be further explored, especially in the setting of post-transplant cyclophosphamide (PT-Cy). In this study, we analyzed 42 CMV-seropositive adult patients with hematological malignancies undergoing allo-HSCT with PT-Cy and calcineurin inhibitor (CNI)-free GvHD prophylaxis in a single-center observational study. Fifteen patients received LTV as prophylaxis in the first 100 days after transplant. CMV-specific CD8+ T cells were quantified by flow cytometry using Dextramer® CMV-Kit (IVD, Immudex) in PBMC frozen 90 (D90) and 180 (D180) days after allo-HSCT, and protective anti-viral immunity was defined based on the threshold that we had previously identified of 0.5 CMV-specific cells/ul. Our findings reinforce the protective role of LTV against clinically relevant CMV-CRE and confirm that LTV prophylaxis is associated with a delayed reconstitution of CMV-specific CD8⁺ T cells compared to no-LTV patients. Importantly, our data underscore the pivotal role of antigen exposure—even transient and at low levels, such as during CMV blips—in promoting the expansion of protective levels of CMV-specific T lymphocytes. This suggests that minimal antigenic stimulation is sufficient to boost protective CMV-specific immune responses in the context of ongoing reconstitution.

This database originates from an educational research study exploring how the “dual-agent-assisted writing” model influences students with varying levels of critical thinking skills.

The replication dataset (data.dta) contains an unbalanced panel of Chinese listed firms from 2009 to 2021 and includes all variables required to construct the climate risk perception index, the three perception-bias measures, firm characteristics, carbon-risk indicators, and climate-sensitivity classifications. The dataset allows full replication of all tables and figures in “Risk-Shaped Cognition: How Climate-Risk Bias Influences Corporate M&A Decisions”, including baseline results, robustness checks, and heterogeneity analyses. All variables are derived from publicly available sources or standard databases, and detailed construction steps are provided in the Supplementary Material.

The data presented here support the research paper “Provenance Modelling of Fossil Dinosaur Bones Using Geochemistry and Machine Learning”, intended for a submission to "Paleoworld". The dataset contains trace elements concentrations from fossil dinosaur bones from the Upper Cretaceous Nemegt and Djadokhta formations. For the analytical purposes, the dataset was divided into two subsets: the first one consisting of long bones (tibiae, femora, radii and humeri) and the other including trabecular bones (ribs and vertebrae) and metatarsals. Locality labels were used to train and evaluate several machine learning classifiers (logistic regression, random forest, AdaBoost, XGBoost) to assess the potential of bone geochemistry for provenance prediction. Feature selection was conducted on the best-performing models to identify the elements contributing the most to the model performance. These results were compared with those obtained using Linear Discriminant Analysis. The data are provided in CSV format in the “Data” folder. The folder “Plots and figures” contains the figures used in the manuscript, including the plots. The folder “Supplementary files” contains additional files. These files are: - interactive HTML plot ("Element profiles.html") showing the all the concentration profiles across each analysed sample, including the ones measured along several profiles - concentration profiles presented in a PDF file ("All profiles.pdf") - XLSX file with the statistical summary of the data ("Data description WK.xlsx") - LDA scalings in CSV file ("LDA scalings.csv") - The tables comparing predictions and performance of the algorithms using test part of each subset ("Predictions and accuracy.xlsx") Besides that, the Jupyter Notebook with data analysis is also provided ("Bones from Gobi - loc prediction.ipynb").

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Emerging evidence suggests that amino acid homorepeats (HRs) in proteins (HRPs) contribute to protein interactability. What is the role of HRs in human-pathogen protein interactions? We find that pathogens engage physiologically important human HRPs, thereby affecting diverse host physiological processes. From the pathogen standpoint, (i) eukaryotic pathogens engage more HRPs but with host-sparse HRs, leading to disparate and discriminate interactions, (ii) prokaryotic pathogens engage less HRPs but with host-abundant non-polar HRs via host protein proxies bringing about discriminate or promiscuous interactions and (iii) viral pathogens engage more HRPs with host-abundant polar uncharged HRs affecting promiscuous interactions using host-partner HR tract mimicry. To propel further research, we introduce a resource Hi-PHI (http://hiphi.iisertirupati.ac.in/) cataloging critical information about human and pathogen HRPs and HRs. We propose mechanisms to (i) repurpose drugs targeting human HRPs engaged by pathogens for treating different infections and (ii) exploit HRs and their flanks as targets for pathogen-targeted anti-infectives. Here, we have uploaded the assembled and curated human-pathogen protein interactome (HPI), which has 19,535 interactions between human and pathogen proteins. We have also provided the source code to facilitate repetition of this work and address other fundamental systems- and molecular-level questions. The instructions regarding usage of the codes are provided in individual scripts. All the datasets assembled, curated, generated and used in this study is available as a resource, Hi-PHI database (http://hiphi.iisertirupati.ac.in/).

The data of ferromanganese nodules.