cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.gdj3m5
title: Occurrence Download

description: A dataset containing 17861 records in GBIF matching the query:
SELECT
 '16' AS fish_cell,
 GBIF_MGRSCode(1000, decimalLatitude, decimalLongitude, 0) AS cell, -- NO randomization
 COUNT(DISTINCT speciesKey) AS species_richness
 FROM occurrence
 WHERE hasCoordinate = TRUE
 AND speciesKey IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND decimalLatitude &gt;= -33.06103686093294
 AND decimalLatitude &lt;= -28.773895969281842
 AND decimalLongitude &gt;= 16.23900266428802
 AND decimalLongitude &lt;= 22.26446617346981
 AND "year" &gt;= 2015
 AND "year" &lt;= 2025
 AND (coordinateUncertaintyInMeters &lt;= 100 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY fish_cell, cell

 The dataset's 17861 records were derived from 94 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040997-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29588713
title: Identification of mitophagy-related biomarkers in human rheumatoid arthritis using machine learning models

description: Rheumatoid arthritis (RA) is a systemic immune-mediated disease characterized by synovitis and joint cartilage destruction. Although many studies have shown that mitophagy is crucial in the development of bone metabolism disorders, its exact function in rheumatoid arthritis (RA) is still not well understood. This study analysed the GSE77298 dataset from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) between rheumatoid arthritis (RA) patients and healthy controls. Mitophagy-related genes (MRGs) were extracted from the literature and screened using bioinformatics techniques, resulting in differentially expressed MRGs (DE-MRGs). The diagnostic value of these genes was assessed using receiver operating characteristic (ROC) curves, and an ANN model was constructed. In the GSE77298 dataset, 267 differentially expressed genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) identified 2191 key module genes, leading to 63 DE-MRGs. Two MRGs, TMEM45A and ZBTB25, were identified as hub genes with areas under the curve (AUC) of 0.991 and 0.911, respectively. The nomogram model demonstrated high diagnostic value. Mitophagy plays a critical role in the progression of rheumatoid arthritis (RA). Identifying two genes associated with mitophagy may aid in the early diagnosis, mechanistic understanding, and treatment of RA.
DOI: 10.6084/m9.figshare.29588713.v1
title: Identification of mitophagy-related biomarkers in human rheumatoid arthritis using machine learning models

description: Rheumatoid arthritis (RA) is a systemic immune-mediated disease characterized by synovitis and joint cartilage destruction. Although many studies have shown that mitophagy is crucial in the development of bone metabolism disorders, its exact function in rheumatoid arthritis (RA) is still not well understood. This study analysed the GSE77298 dataset from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) between rheumatoid arthritis (RA) patients and healthy controls. Mitophagy-related genes (MRGs) were extracted from the literature and screened using bioinformatics techniques, resulting in differentially expressed MRGs (DE-MRGs). The diagnostic value of these genes was assessed using receiver operating characteristic (ROC) curves, and an ANN model was constructed. In the GSE77298 dataset, 267 differentially expressed genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) identified 2191 key module genes, leading to 63 DE-MRGs. Two MRGs, TMEM45A and ZBTB25, were identified as hub genes with areas under the curve (AUC) of 0.991 and 0.911, respectively. The nomogram model demonstrated high diagnostic value. Mitophagy plays a critical role in the progression of rheumatoid arthritis (RA). Identifying two genes associated with mitophagy may aid in the early diagnosis, mechanistic understanding, and treatment of RA.
DOI: 10.5281/zenodo.17775004
title: Supplemental data for 'Vector leaf trees and satellite vision transformer embeddings decode macroalgal genome-environment couplings'

description: Marine macroalgae span extreme environments, but the genomic basis of their salinity and temperature tolerance is unclear. We sequenced nine Arabian Gulf macroalgae and combined them with 117 published genomes (126 total; 69 Rhodophyta, 43 Ochrophyta, 14 Chlorophyta) to test genome–environment links. Protein domain (Pfam) profiles were paired with two environmental frameworks: satellite-derived variables (sea surface temperature, salinity, chlorophyll-a, bathymetry via Google Earth Engine) and learned vision-transformer embeddings from petabyte-scale imagery (AlphaEarth Foundations). Gradient-boosted trees with phylum-aware cross-validation classified salinity at 87% ± 4.8% accuracy, indicating conserved osmotic solutions, whereas temperature prediction failed across phyla (R² ≈ 0) but improved within Ochrophyta (R² ≈ 0.28), pointing to lineage-restricted thermal architectures dominated by uncharacterized domains. Correlation screens (6,533 Pfams × 20 variables) yielded 76 FDR-significant and four Bonferroni-significant associations, led by DUF3570 (PF12094; r = -0.54 with temperature), and adhesion-related vWF-A domains were enriched in Arabian Gulf brown algae across both environmental frameworks. These results suggest salinity tolerance arises from conserved, multi-pathway mechanisms, while thermal tolerance mechanisms in macroalgae are lineage-specific and driven largely by unannotated protein families.
DOI: 10.6084/m9.figshare.28517870
title: Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning

description: Ageing significantly contributes to osteoarthritis (OA) and metabolic syndrome (MetS) pathogenesis, yet the underlying mechanisms remain unknown. This study aimed to identify ageing-related biomarkers in OA patients with MetS. OA and MetS datasets and ageing-related genes (ARGs) were retrieved from public databases. The limma package was used to identify differentially expressed genes (DEGs), and weighted gene coexpression network analysis (WGCNA) screened gene modules, and machine learning algorithms, such as random forest (RF), support vector machine (SVM), generalised linear model (GLM), and extreme gradient boosting (XGB), were employed. The nomogram and receiver operating characteristic (ROC) curve assess the diagnostic value, and CIBERSORT analysed immune cell infiltration. We identified 20 intersecting genes among DEGs of OA, key module genes of MetS, and ARGs. By comparing the accuracy of the four machine learning models for disease prediction, the SVM model, which includes CEBPB, PTEN, ARPC1B, PIK3R1, and CDC42, was selected. These hub ARGs not only demonstrated strong diagnostic values based on nomogram data but also exhibited a significant correlation with immune cell infiltration. Building on these findings, we have identified five hub ARGs that are associated with immune cell infiltration and have constructed a nomogram aimed at early diagnosing OA patients with MetS.
DOI: 10.6084/m9.figshare.28517870.v1
title: Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning

description: Ageing significantly contributes to osteoarthritis (OA) and metabolic syndrome (MetS) pathogenesis, yet the underlying mechanisms remain unknown. This study aimed to identify ageing-related biomarkers in OA patients with MetS. OA and MetS datasets and ageing-related genes (ARGs) were retrieved from public databases. The limma package was used to identify differentially expressed genes (DEGs), and weighted gene coexpression network analysis (WGCNA) screened gene modules, and machine learning algorithms, such as random forest (RF), support vector machine (SVM), generalised linear model (GLM), and extreme gradient boosting (XGB), were employed. The nomogram and receiver operating characteristic (ROC) curve assess the diagnostic value, and CIBERSORT analysed immune cell infiltration. We identified 20 intersecting genes among DEGs of OA, key module genes of MetS, and ARGs. By comparing the accuracy of the four machine learning models for disease prediction, the SVM model, which includes CEBPB, PTEN, ARPC1B, PIK3R1, and CDC42, was selected. These hub ARGs not only demonstrated strong diagnostic values based on nomogram data but also exhibited a significant correlation with immune cell infiltration. Building on these findings, we have identified five hub ARGs that are associated with immune cell infiltration and have constructed a nomogram aimed at early diagnosing OA patients with MetS.
DOI: 10.6084/m9.figshare.29643457.v1
title: Revealing the impact of gut microbiota-derived metabolites on depression through network pharmacology

description: A total of 208 metabolites and 223 targets were initially extracted from the gutMGene v1.0 database. In addition, 1,630 and 1,321 targets were identified using the Similarity Ensemble Approach and Swiss Target Prediction databases, respectively, resulting in 921 overlapping targets. By integrating data from gutMGenev1.0, 13 core targets were finally identified. A microbiota–metabolite–target–signal pathway–disease network was constructed using Cytoscape 3.9.1, revealing 15 metabolites associated with the IL-17, TLR, and PI3K-Akt signalling pathways. Among these, five metabolites exhibited favourable drug similarity and acceptable toxicological profiles. Molecular docking confirmed the stable binding of two key metabolites—succinate and phenylacetylglutamine—to their respective targets. The results showed that succinate and phenylacetylglutamine demonstrated strong binding affinities to IL-1β and GSK3B, both involved in the IL-17, TLR, and PI3K-Akt signalling pathways. IL-17 and TLR4, as important pro-inflammatory cytokines, are closely associated with the development of depression, while the PI3K/AKT signalling pathway plays a key role in its pathogenesis. The present study reveals the potential mechanisms by which gut microbiota influence MDD and provides a scientific basis and a comprehensive research framework for future investigations. The research methodology involves utilising databases to identify gut microbiota metabolites and targets associated with major depressive disorder (MDD), constructing interaction networks, analysing gene ontology and pathways, evaluating drug-likeness and toxicity, and conducting molecular docking tests to understand the influence of gut microbiota metabolites on MDD.
DOI: 10.6084/m9.figshare.29949859
title: Forecast Selection in Unstable Environments

description: This article leverages the time-series properties of forecast loss differences for out-of-sample forecast selection. Our framework predicts the conditional distribution of future loss differences while accommodating for time-contingent unstable forecasting environments. We establish distributional theory to quantify the sampling uncertainty of our predictions, enabling the development of advanced selection rules. Through simulations and an empirical application to inflation forecasting, we demonstrate the efficacy of our selection methodology and the potential for our advanced selection rules to achieve second-order forecasting objectives.
DOI: 10.6084/m9.figshare.29949859.v1
title: Forecast Selection in Unstable Environments

description: This article leverages the time-series properties of forecast loss differences for out-of-sample forecast selection. Our framework predicts the conditional distribution of future loss differences while accommodating for time-contingent unstable forecasting environments. We establish distributional theory to quantify the sampling uncertainty of our predictions, enabling the development of advanced selection rules. Through simulations and an empirical application to inflation forecasting, we demonstrate the efficacy of our selection methodology and the potential for our advanced selection rules to achieve second-order forecasting objectives.
DOI: 10.6084/m9.figshare.29643457
title: Revealing the impact of gut microbiota-derived metabolites on depression through network pharmacology

description: A total of 208 metabolites and 223 targets were initially extracted from the gutMGene v1.0 database. In addition, 1,630 and 1,321 targets were identified using the Similarity Ensemble Approach and Swiss Target Prediction databases, respectively, resulting in 921 overlapping targets. By integrating data from gutMGenev1.0, 13 core targets were finally identified. A microbiota–metabolite–target–signal pathway–disease network was constructed using Cytoscape 3.9.1, revealing 15 metabolites associated with the IL-17, TLR, and PI3K-Akt signalling pathways. Among these, five metabolites exhibited favourable drug similarity and acceptable toxicological profiles. Molecular docking confirmed the stable binding of two key metabolites—succinate and phenylacetylglutamine—to their respective targets. The results showed that succinate and phenylacetylglutamine demonstrated strong binding affinities to IL-1β and GSK3B, both involved in the IL-17, TLR, and PI3K-Akt signalling pathways. IL-17 and TLR4, as important pro-inflammatory cytokines, are closely associated with the development of depression, while the PI3K/AKT signalling pathway plays a key role in its pathogenesis. The present study reveals the potential mechanisms by which gut microbiota influence MDD and provides a scientific basis and a comprehensive research framework for future investigations. The research methodology involves utilising databases to identify gut microbiota metabolites and targets associated with major depressive disorder (MDD), constructing interaction networks, analysing gene ontology and pathways, evaluating drug-likeness and toxicity, and conducting molecular docking tests to understand the influence of gut microbiota metabolites on MDD.
DOI: 10.6084/m9.figshare.30460445
title: Optimized PEGylated paclitaxel and 6-gingerol co-loaded liposomes induce G2/M phase arrest and apoptosis in MDA-MB-231 breast and A549 lung cancer cells

description: The study aimed to optimize and develop novel PEGylated co-loaded nanoliposome entrapped with paclitaxel (PTX) and 6-gingerol (Gn) (PEG-Lipo-PTX-Gn) by response surface methodology (RSM) approach to prevent the diffusion and resistant-related issues of PTX in oncotherapy. Physiochemical characterization studies results revealed that the prepared PEG-Lipo-PTX-Gn attained optimum particle size, shape, charge, polydispersity index (PDI) as well as showed synergistic entrapment of PTX and Gn, sustained drug release, better colloidal stability and structural integrity. PEG-Lipo-PTX-Gn exhibited a noteworthy antiproliferative effect, apoptotic percentage and a higher proportion of G2/M cell cycle arrest in MDA-MB-231 and A549 cell lines. Meanwhile, no significant toxicity was observed in normal cell lines (HEK 293 kidney embryonic cells and L929 fibroblast cells). Another testament to its efficacy is the dramatic decline in Bcl-2 levels in the PEG-Lipo-PTX-Gn-treated group. Hence, optimized PEG-Lipo-PTX-Gn could be a promising novel approach in cancer treatment regimens.
DOI: 10.6084/m9.figshare.30596253
title: Green synthesis gold nanoparticle from <i>Rosa damascena</i>: antioxidant, antimicrobial, cytotoxic activities on nerve cells and inhibitory effects on Parkinson’s disease

description: This study investigates the green synthesis of gold nanoparticles (AuNPs) using <i>Rosa damascena</i> extract. . The objective is to assess the antioxidant, antimicrobial and cytotoxic activities of the biosynthesized AuNPs and to evaluate their inhibitory effects on proteins associated with Parkinson’s disease and experimental study incorporating <i>in vitro</i> biological assays, physicochemical characterization techniques and molecular docking analysis was also performed. Characterization tests were performed <i>via</i> UV–Vis spectroscopy, Fourier Transform Infra-red Spectroscopy (FTIR), dynamic light scattering (DLS), Zeta Potential, X-ray Diffraction (XRD) and transmission electron microscopy (TEM). Antioxidant activity was assessed using the DPPH assay, while antimicrobial efficacy was tested against both Gram-positive and Gram-negative bacterial strains. Cytotoxicity was evaluated the MTT assay. The biosynthesized AuNPs exhibited strong antioxidant and antimicrobial activities, along with reduced cytotoxicity in neuronal models. Molecular docking revealed favourable binding affinities of key phytoconstituents (such as quercetin, kaempferol and geranyl acetate) with neurodegenerative disease-associated proteins, supporting their potential therapeutic relevance. Rosa damascena-mediated green synthesis of AuNPs yields nanoparticles with promising antioxidant, antimicrobial, neuroprotective properties and the bioactive compounds of this plant demonstrate a highly significant impact on the inhibition of deleterious proteins and the preservation of neuronal integrity. These findings suggest the potential utility in treating neurodegenerative disorders, including Parkinson’s disease. .
DOI: 10.6084/m9.figshare.30596253.v1
title: Green synthesis gold nanoparticle from <i>Rosa damascena</i>: antioxidant, antimicrobial, cytotoxic activities on nerve cells and inhibitory effects on Parkinson’s disease

description: This study investigates the green synthesis of gold nanoparticles (AuNPs) using <i>Rosa damascena</i> extract. . The objective is to assess the antioxidant, antimicrobial and cytotoxic activities of the biosynthesized AuNPs and to evaluate their inhibitory effects on proteins associated with Parkinson’s disease and experimental study incorporating <i>in vitro</i> biological assays, physicochemical characterization techniques and molecular docking analysis was also performed. Characterization tests were performed <i>via</i> UV–Vis spectroscopy, Fourier Transform Infra-red Spectroscopy (FTIR), dynamic light scattering (DLS), Zeta Potential, X-ray Diffraction (XRD) and transmission electron microscopy (TEM). Antioxidant activity was assessed using the DPPH assay, while antimicrobial efficacy was tested against both Gram-positive and Gram-negative bacterial strains. Cytotoxicity was evaluated the MTT assay. The biosynthesized AuNPs exhibited strong antioxidant and antimicrobial activities, along with reduced cytotoxicity in neuronal models. Molecular docking revealed favourable binding affinities of key phytoconstituents (such as quercetin, kaempferol and geranyl acetate) with neurodegenerative disease-associated proteins, supporting their potential therapeutic relevance. Rosa damascena-mediated green synthesis of AuNPs yields nanoparticles with promising antioxidant, antimicrobial, neuroprotective properties and the bioactive compounds of this plant demonstrate a highly significant impact on the inhibition of deleterious proteins and the preservation of neuronal integrity. These findings suggest the potential utility in treating neurodegenerative disorders, including Parkinson’s disease. .
DOI: 10.6084/m9.figshare.30460445.v1
title: Optimized PEGylated paclitaxel and 6-gingerol co-loaded liposomes induce G2/M phase arrest and apoptosis in MDA-MB-231 breast and A549 lung cancer cells

description: The study aimed to optimize and develop novel PEGylated co-loaded nanoliposome entrapped with paclitaxel (PTX) and 6-gingerol (Gn) (PEG-Lipo-PTX-Gn) by response surface methodology (RSM) approach to prevent the diffusion and resistant-related issues of PTX in oncotherapy. Physiochemical characterization studies results revealed that the prepared PEG-Lipo-PTX-Gn attained optimum particle size, shape, charge, polydispersity index (PDI) as well as showed synergistic entrapment of PTX and Gn, sustained drug release, better colloidal stability and structural integrity. PEG-Lipo-PTX-Gn exhibited a noteworthy antiproliferative effect, apoptotic percentage and a higher proportion of G2/M cell cycle arrest in MDA-MB-231 and A549 cell lines. Meanwhile, no significant toxicity was observed in normal cell lines (HEK 293 kidney embryonic cells and L929 fibroblast cells). Another testament to its efficacy is the dramatic decline in Bcl-2 levels in the PEG-Lipo-PTX-Gn-treated group. Hence, optimized PEG-Lipo-PTX-Gn could be a promising novel approach in cancer treatment regimens.
DOI: 10.6084/m9.figshare.29958074
title: Elucidating the integrative role and possible molecular mechanisms of <i>Salvia miltiorrhiza</i> ingredients and gut microbiota-derived metabolites in alleviating pyroptosis-mediated hepatic ischemia–reperfusion injury through network pharmacology

description: Currently, therapeutic options for hepatic ischemia–reperfusion injury (HIRI) remain limited and challenging. An emerging alternative involves the combination of ingredients from traditional Chinese medicine (TCM) and beneficial gut microbiota (GM) metabolites. This study integrates ingredients of <i>Salvia miltiorrhiza</i> (SM) and metabolites of GM to assess their combined therapeutic efficacy against HIRI through pyroptosis using network pharmacology. Twenty-nine final targets were recognized as key proteins responsible for the alleviation of HIRI by SM ingredients and GM metabolites through pyroptosis, with GAPDH, AKT1, ILB1 emerging as central targets in the protein–protein interaction (PPI) network. The Toll-like receptor (TLR), NOD-like receptor (NLR), IL-17, TNF and MAPK signalling pathways were identified as key pathways in the therapeutic effects of SM ingredients and GM metabolites. Eight microRNAs (miRNAs) were predicted to be potential miRNAs exerting the most influence. Four SM ingredients and 11 GM metabolites were identified as non-toxic, promising candidates against HIRI. Moreover, the results of molecular docking showed all compounds were well combined with the corresponding proteins. This study highlights the therapeutic potential of TCM and beneficial GM in HIRI treatment and provides a foundational dataset for future research on their combined application. Further <i>in vitro</i> and <i>in vivo</i> studies are needed to validate these findings.