cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17889933
title: Dataset for Single-molecule Detection of Amino Acid Phosphorylation using electron tunnelling currents: Toward Neurodegenerative Disease Diagnosis

description: Dataset and codes for Single-molecule Detection of Amino Acid Phosphorylation using electron tunnelling currents: Toward Neurodegenerative Disease Diagnosis
DOI: 10.15468/dl.z3b889
title: Occurrence Download

description: A dataset containing 392 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dremomys lokriah"
 ]
}

 The dataset includes 392 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021903-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n464p8
title: Occurrence Download

description: A dataset containing 635567 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Egypt",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 635567 records from 737 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021862-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17911214
title: Data Analysis for "Single-Cell Transcriptomic Signatures Enable Stratified Combination Therapy for Platinum-Resistant Ovarian Cancer"

description: This repository contains the data processing and analysis scripts used in the manuscript “Single-Cell Transcriptomic Signatures Enable Stratified Combination Therapy for Platinum-Resistant Ovarian Cancer”.

 

Abstract

In high-grade serous carcinoma (HGSC), extensive intra-tumoral heterogeneity hinders complete cancer eradication and remains a major obstacle to developing combination therapies capable of eliminating subpopulations resistant to standard-of-care treatment. Using single-cell RNA sequencing of 72 samples from 54 patients with HGSC, spanning treatment-naïve, post-neoadjuvant chemotherapy and relapse stages, we established a carboplatin-anchored discovery framework that identifies transcriptional signatures of both intrinsic (pre-existing) and adaptive (therapy-induced) resistance in individual tumors, and prioritizes drugs that mechanistically target these programs to potentiate carboplatin efficacy. Candidate drugs were prioritized through integration of orthogonal resources - viability (GDSC, PRISM) and perturbational transcriptomics (L1000, Perturb-seq) - to reduce context bias and identify mechanism-matched agents. Of 64 drug candidates, three carboplatin adjuvants enhanced long-term efficacy in patient-derived organoids (PDOs), with pevonedistat further significantly reducing tumor burden in orthotopic xenografts. This tiered validation pipeline, spanning short-term and long-term PDOs and in vivo orthoptic xenografts, establishes a translational framework linking single cell resistance programs to actionable, tumor-specific, carboplatin-anchored combinations for HGSC.
DOI: 10.5281/zenodo.17911213
title: Data Analysis for "Single-Cell Transcriptomic Signatures Enable Stratified Combination Therapy for Platinum-Resistant Ovarian Cancer"

description: This repository contains the data processing and analysis scripts used in the manuscript “Single-Cell Transcriptomic Signatures Enable Stratified Combination Therapy for Platinum-Resistant Ovarian Cancer”.

 

Abstract

In high-grade serous carcinoma (HGSC), extensive intra-tumoral heterogeneity hinders complete cancer eradication and remains a major obstacle to developing combination therapies capable of eliminating subpopulations resistant to standard-of-care treatment. Using single-cell RNA sequencing of 72 samples from 54 patients with HGSC, spanning treatment-naïve, post-neoadjuvant chemotherapy and relapse stages, we established a carboplatin-anchored discovery framework that identifies transcriptional signatures of both intrinsic (pre-existing) and adaptive (therapy-induced) resistance in individual tumors, and prioritizes drugs that mechanistically target these programs to potentiate carboplatin efficacy. Candidate drugs were prioritized through integration of orthogonal resources - viability (GDSC, PRISM) and perturbational transcriptomics (L1000, Perturb-seq) - to reduce context bias and identify mechanism-matched agents. Of 64 drug candidates, three carboplatin adjuvants enhanced long-term efficacy in patient-derived organoids (PDOs), with pevonedistat further significantly reducing tumor burden in orthotopic xenografts. This tiered validation pipeline, spanning short-term and long-term PDOs and in vivo orthoptic xenografts, establishes a translational framework linking single cell resistance programs to actionable, tumor-specific, carboplatin-anchored combinations for HGSC.
DOI: 10.48804/slppdc
title: Analyzing Colorimetric Characterization Models for Virtual Reality Headsets: A Comparative Study

description: This dataset contains the data used to evaluate color characterization models for two virtual reality head-mounted displays (Meta Quest 2 and Meta Quest Pro). The dataset includes training data, test data, and stability measurements collected across multiple devices. It also contains the Python code used for data processing, model training, and evaluation, along with figures and tables from the associated publication.

Corresponding paper: Bhaumik, U., Van de Perre, L. and Leloup, F.B., 2024. Analyzing Colorimetric Characterization Models for Virtual Reality Headsets: A Comparative Study. Journal of Imaging Science & Technology.
DOI: 10.6084/m9.figshare.31240998
title: Age-associated differences in XBB.1.5 trivalent booster vaccine-induced adaptive responses revealed by single-cell RNA sequencing

description: Older adults remain highly vulnerable to severe SARS-CoV-2 outcomes despite multiple vaccinations, yet age-associated differences in immune responses to updated COVID-19 booster vaccines remain incompletely characterized. Here, we administered an XBB.1.5 trivalent recombinant protein booster (WSK-V102C) to 22 individuals (&lt;38 years) and 20 individuals (≥73 years), all of whom had previously received 2–3 doses of inactivated COVID-19 vaccines. Neutralizing antibody responses against multiple SARS-CoV-2 variants were quantified and compared between age groups. Meanwhile, single-cell RNA sequencing was also performed on peripheral blood mononuclear cells (PBMCs) collected at baseline and 28 days post-vaccination to profile age-associated immune features following boosting. Following booster immunization, both age groups achieved significantly elevated antibody titres against all tested strains. Nevertheless, the magnitude of antibody fold increase was consistently lower in elderly individuals than in younger adults. Single-cell analyses revealed age-associated differences in post-vaccination immune organization. In elderly individuals, B-cell state transitions were characterized by transcriptional signatures consistent with memory B cell-to-plasmablast differentiation, whereas younger individuals predominantly exhibited transitions from naïve B cells. CD4+ T cells from elderly individuals displayed altered transcriptional trajectories and reduced T-cell receptor diversity relative to younger adults. In contrast, younger individuals showed coordinated B- and T-cell-associated transcriptional programmes, including enrichment of transcription factors such as KLF7, CEBPB, CEBPD, and MAFB. Collectively, our study describes age-associated differences in immune coordination and cellular response patterns following XBB.1.5 booster vaccination. Further longitudinal and functional studies will be required to clarify the mechanistic basis and clinical implications of these observations.
DOI: 10.6084/m9.figshare.31240998.v1
title: Age-associated differences in XBB.1.5 trivalent booster vaccine-induced adaptive responses revealed by single-cell RNA sequencing

description: Older adults remain highly vulnerable to severe SARS-CoV-2 outcomes despite multiple vaccinations, yet age-associated differences in immune responses to updated COVID-19 booster vaccines remain incompletely characterized. Here, we administered an XBB.1.5 trivalent recombinant protein booster (WSK-V102C) to 22 individuals (&lt;38 years) and 20 individuals (≥73 years), all of whom had previously received 2–3 doses of inactivated COVID-19 vaccines. Neutralizing antibody responses against multiple SARS-CoV-2 variants were quantified and compared between age groups. Meanwhile, single-cell RNA sequencing was also performed on peripheral blood mononuclear cells (PBMCs) collected at baseline and 28 days post-vaccination to profile age-associated immune features following boosting. Following booster immunization, both age groups achieved significantly elevated antibody titres against all tested strains. Nevertheless, the magnitude of antibody fold increase was consistently lower in elderly individuals than in younger adults. Single-cell analyses revealed age-associated differences in post-vaccination immune organization. In elderly individuals, B-cell state transitions were characterized by transcriptional signatures consistent with memory B cell-to-plasmablast differentiation, whereas younger individuals predominantly exhibited transitions from naïve B cells. CD4+ T cells from elderly individuals displayed altered transcriptional trajectories and reduced T-cell receptor diversity relative to younger adults. In contrast, younger individuals showed coordinated B- and T-cell-associated transcriptional programmes, including enrichment of transcription factors such as KLF7, CEBPB, CEBPD, and MAFB. Collectively, our study describes age-associated differences in immune coordination and cellular response patterns following XBB.1.5 booster vaccination. Further longitudinal and functional studies will be required to clarify the mechanistic basis and clinical implications of these observations.
DOI: 10.5281/zenodo.18659564
title: Mitigating Residual FAI for Air-Stable FAPbI3 LEDs
DOI: 10.5281/zenodo.18659563
title: Mitigating Residual FAI for Air-Stable FAPbI3 LEDs
DOI: 10.15159/phytop.205
title: Metstoa Kaasikjärv

description: Phytoplankton biomass, diversity and abundance in samples collected from Metstoa Kaasikjärv during the period 1981 and 1993.
DOI: 10.5281/zenodo.18657906
title: Interlayer stacking controls the electronic properties of the van der Waals material 1T-TaS2

description: Dataset and Jupyter notebook (Python) for the figures in publication "Interlayer stacking controls the electronic properties of the van der Waals material 1T-TaS2" (https://doi.org/10.1103/6t8x-mbsd)
DOI: 10.15468/dl.qqdhjx
title: Occurrence Download

description: A dataset containing 11649 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Phengaris teleius"
 ]
}

 The dataset includes 11649 records from 100 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021905-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18657905
title: Interlayer stacking controls the electronic properties of the van der Waals material 1T-TaS2

description: Dataset and Jupyter notebook (Python) for the figures in publication "Interlayer stacking controls the electronic properties of the van der Waals material 1T-TaS2" (https://doi.org/10.1103/6t8x-mbsd)
DOI: 10.6084/m9.figshare.29118028
title: AI-enhanced computational discovery of promising ALK5 inhibitors in a ultra-large chemical space library for cardiovascular Disease therapy

description: Cardiac fibrosis, characterized by excessive extracellular matrix deposition, is a critical contributor to cardiovascular diseases, including heart failure. Transforming growth factor-beta 1 signaling, especially through activin receptor-like kinase 5 (ALK5), plays a key role in cardiac fibroblast activation and fibrosis. Traditional drug discovery approaches face challenges in identifying ALK5 inhibitors. This study leverages computational methods to expedite the discovery of potential ALK5 inhibitors. An active learning model was trained to screen a vast compound library, resulting in the selection of promising candidates. Molecular fingerprint clustering analysis and the absorption, distribution, metabolism, excretion, toxicity evaluation further characterized these compounds. Machine learning-based quantitative structure - activity relationship models predicted their activity. Molecular dynamics simulations assessed binding stability in different environments. DE50349483 and DE21377883 emerged as promising candidates with potential inhibitory effects. This study showcases the power of computational methods in drug discovery, offering hope for innovative therapies in cardiac fibrosis.
DOI: 10.6084/m9.figshare.29118028.v1
title: AI-enhanced computational discovery of promising ALK5 inhibitors in a ultra-large chemical space library for cardiovascular Disease therapy

description: Cardiac fibrosis, characterized by excessive extracellular matrix deposition, is a critical contributor to cardiovascular diseases, including heart failure. Transforming growth factor-beta 1 signaling, especially through activin receptor-like kinase 5 (ALK5), plays a key role in cardiac fibroblast activation and fibrosis. Traditional drug discovery approaches face challenges in identifying ALK5 inhibitors. This study leverages computational methods to expedite the discovery of potential ALK5 inhibitors. An active learning model was trained to screen a vast compound library, resulting in the selection of promising candidates. Molecular fingerprint clustering analysis and the absorption, distribution, metabolism, excretion, toxicity evaluation further characterized these compounds. Machine learning-based quantitative structure - activity relationship models predicted their activity. Molecular dynamics simulations assessed binding stability in different environments. DE50349483 and DE21377883 emerged as promising candidates with potential inhibitory effects. This study showcases the power of computational methods in drug discovery, offering hope for innovative therapies in cardiac fibrosis.
DOI: 10.6084/m9.figshare.28007188
title: Exploring the anti-cancer properties of <i>Carissa carandas</i> as a multi-targeted approach against breast cancer

description: The escalating incidence of breast cancer globally presents a formidable challenge within oncology. Our research pursued an examination of the anti-cancer potential of <i>Carissa carandas</i>, a shrub traditionally used for medicinal purposes and known for its composition of vital nutrients and phytochemicals. We employed a network pharmacology strategy combined with molecular docking and molecular dynamics simulations to elucidate the intricate relationships between the phytochemical constituents of <i>C. carandas</i>, critical breast cancer proteins, and associated signaling pathways. The study highlighted a complex network of protein interactions, identifying AKT1, HIF1A, PTGS2, and GSK3B as key nodes within this network. These proteins are engaged by numerous investigated compounds from <i>C. carandas</i> and are fundamental in modulating crucial signaling pathways such as those involving Estrogen, HIF-1, Prolactin, VEGF, and Th17 cell differentiation—each of which plays a recognized role in breast cancer progression, affecting tumor growth, proliferation, and metastatic potential. Our analysis suggests that the phytochemicals in <i>C. carandas</i> may exert anti-cancer activity by synergistically modulating these pathways, highlighting the benefit of multi-targeted therapeutic approaches over single-targeted ones. In summary, through the application of advanced network pharmacology, molecular docking, MD simulations, and MM/PBSA analysis, our study offers a detailed exploration of the potential mechanisms by which <i>C. carandas</i> may exert anti-cancer effects. This sets a foundation for further in-depth experimental and clinical trials to validate these mechanisms and support the advancement of novel plant-derived therapeutic options towards breast cancer, with the possibility of significantly advancing the therapeutic options for this prevalent disease.
DOI: 10.6084/m9.figshare.28007188.v1
title: Exploring the anti-cancer properties of <i>Carissa carandas</i> as a multi-targeted approach against breast cancer

description: The escalating incidence of breast cancer globally presents a formidable challenge within oncology. Our research pursued an examination of the anti-cancer potential of <i>Carissa carandas</i>, a shrub traditionally used for medicinal purposes and known for its composition of vital nutrients and phytochemicals. We employed a network pharmacology strategy combined with molecular docking and molecular dynamics simulations to elucidate the intricate relationships between the phytochemical constituents of <i>C. carandas</i>, critical breast cancer proteins, and associated signaling pathways. The study highlighted a complex network of protein interactions, identifying AKT1, HIF1A, PTGS2, and GSK3B as key nodes within this network. These proteins are engaged by numerous investigated compounds from <i>C. carandas</i> and are fundamental in modulating crucial signaling pathways such as those involving Estrogen, HIF-1, Prolactin, VEGF, and Th17 cell differentiation—each of which plays a recognized role in breast cancer progression, affecting tumor growth, proliferation, and metastatic potential. Our analysis suggests that the phytochemicals in <i>C. carandas</i> may exert anti-cancer activity by synergistically modulating these pathways, highlighting the benefit of multi-targeted therapeutic approaches over single-targeted ones. In summary, through the application of advanced network pharmacology, molecular docking, MD simulations, and MM/PBSA analysis, our study offers a detailed exploration of the potential mechanisms by which <i>C. carandas</i> may exert anti-cancer effects. This sets a foundation for further in-depth experimental and clinical trials to validate these mechanisms and support the advancement of novel plant-derived therapeutic options towards breast cancer, with the possibility of significantly advancing the therapeutic options for this prevalent disease.
DOI: 10.6084/m9.figshare.28776751
title: Deciphering multifaceted molecular mechanisms of matairesinol in inhibiting triple-negative breast cancer through comprehensive systems biology investigations

description: Triple-negative breast cancer (TNBC), characterized by the absence of Estrogen Receptor (ER), Progesterone Receptor (PR), and amplified HER2, represents an aggressive subtype devoid of targeted therapies, contributing to heightened mortality rates. Matairesinol (MAT) has demonstrated anti-cancer, anti-inflammatory, immunomodulatory, anti-migratory, and antiangiogenic activities. This study investigates MAT’s therapeutic potential for TNBC, employing network pharmacology, molecular docking, and molecular dynamics simulations. Through the integration of MAT and TNBC targets from public databases, we identified 47 potential therapeutic targets. Top 10 hub targets, including HIF1A, ESR1, AKT1, EGFR, HSP90AA1, Src, ERBB2, IGF1, ANXA5, and MAPK1, were revealed through protein-protein interaction analysis. Biological enrichments, encompassing GO and KEGG pathway analyses, unveiled insights into functional roles and associated pathways. The Compound-Targets-Pathways-Disease (C-T-P-D) network illustrated relationships between MAT, its targets, and pertinent pathways. Exploring protein-protein interactions with STRING, followed by validation and supplementation using the GeneMANIA-based functional association (GMFA) method and David web wizard, emphasized the MAPK signaling pathway as a more potential target of MAT against TNBC. The biological significance of these findings underscores MAT’s potential as a multi-target inhibitor within multiple signaling pathways related to TNBC, showcasing its efficacy against TNBC. Molecular docking and dynamics simulations substantiated the interaction between MAT and the identified hub targets. In conclusion, our in-silico analysis proposes that MAT could mediate a multi-target and multi-pathway anti-TNBC effect with the MAPK pathway as its novel target pathway. These insights into the potential therapeutic mechanisms of MAT offer valuable directions for further research and the development of interventions against TNBC.
DOI: 10.6084/m9.figshare.28776751.v1
title: Deciphering multifaceted molecular mechanisms of matairesinol in inhibiting triple-negative breast cancer through comprehensive systems biology investigations

description: Triple-negative breast cancer (TNBC), characterized by the absence of Estrogen Receptor (ER), Progesterone Receptor (PR), and amplified HER2, represents an aggressive subtype devoid of targeted therapies, contributing to heightened mortality rates. Matairesinol (MAT) has demonstrated anti-cancer, anti-inflammatory, immunomodulatory, anti-migratory, and antiangiogenic activities. This study investigates MAT’s therapeutic potential for TNBC, employing network pharmacology, molecular docking, and molecular dynamics simulations. Through the integration of MAT and TNBC targets from public databases, we identified 47 potential therapeutic targets. Top 10 hub targets, including HIF1A, ESR1, AKT1, EGFR, HSP90AA1, Src, ERBB2, IGF1, ANXA5, and MAPK1, were revealed through protein-protein interaction analysis. Biological enrichments, encompassing GO and KEGG pathway analyses, unveiled insights into functional roles and associated pathways. The Compound-Targets-Pathways-Disease (C-T-P-D) network illustrated relationships between MAT, its targets, and pertinent pathways. Exploring protein-protein interactions with STRING, followed by validation and supplementation using the GeneMANIA-based functional association (GMFA) method and David web wizard, emphasized the MAPK signaling pathway as a more potential target of MAT against TNBC. The biological significance of these findings underscores MAT’s potential as a multi-target inhibitor within multiple signaling pathways related to TNBC, showcasing its efficacy against TNBC. Molecular docking and dynamics simulations substantiated the interaction between MAT and the identified hub targets. In conclusion, our in-silico analysis proposes that MAT could mediate a multi-target and multi-pathway anti-TNBC effect with the MAPK pathway as its novel target pathway. These insights into the potential therapeutic mechanisms of MAT offer valuable directions for further research and the development of interventions against TNBC.