cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11474253.v2
title: miR-29c-3p inhibits autophagy and cisplatin resistance in ovarian cancer by regulating FOXP1/ATG14 pathway

description: Autophagy, characterized by the elevator of autophagy-related gene 14 (ATG14) and the dysregulation of autophagy-related proteins, contributes to the cisplatin (DDP) resistance in ovarian cancer. Forkhead box protein P1 (FOXP1), which is a well-defined transcription factor, is reported to have the oncogenic effect on ovarian cancer. This study aims to identify the effect of miR-29c-3p/FOXP1/ATG14 pathway in regulating autophagy and DDP resistance in ovarian cancer. The expressions of miR-29c-3p, FOXP1, ATG14 and autophagy-related proteins were detected in DDP-sensitive ovarian cancer cell lines (SKOV3 and A2780) and DDP-resistant cell lines (SKOV3/DDP and A2780/DDP). Cell viability was detected using the MTT assay. The therapeutic effect of miR-29c-3p overexpression was observed in the xenograft model of nude mice.Compared with DDP-sensitive cells, miR-29c-3p was decreased in DDP-resistant cells, and an enhancement of FOXP1, ATG14, autophagy, and drug resistance was shown in DDP-resistant cells. The anti-resistant effect of miR-29c-3p was observed as overexpressing miR-29c-3p inhibited cell viability of DDP-resistant cells. Moreover, FOXP1 was a target of miR-29c-3p, which was confirmed by the luciferase reporter assay, and ATG14 was transactivated by FOXP1, which was confirmed by the ChIP assay. Overexpression of miR-29c-3p increased DDP sensitivity by downregulating FOXP1/ATG14 in vitro. The tumor volume was reduced after the injection of miR-29c-3p-overexpressing SKOV3/DDP cells in vivo. Overexpression of miR-29c-3p inhibited autophagy and DDP resistance partly via downregulating FOXP1/ATG14 pathway, suggesting miR-29c-3p as a novel target in overcoming DDP resistance in ovarian cancer.
DOI: 10.6084/m9.figshare.13046996
title: Additional file 1 of Overexpression of microRNA-367 inhibits angiogenesis in ovarian cancer by downregulating the expression of LPA1

description: Additional file 1 The original immunoblotting bands.
DOI: 10.6084/m9.figshare.13046996.v1
title: Additional file 1 of Overexpression of microRNA-367 inhibits angiogenesis in ovarian cancer by downregulating the expression of LPA1

description: Additional file 1 The original immunoblotting bands.
DOI: 10.6084/m9.figshare.11998422.v1
title: Additional file 1 of Transcriptome analysis of metabolic pathways associated with oil accumulation in developing seed kernels of Styrax tonkinensis, a woody biodiesel species

description: Additional file 1. Predicted biodiesel fuel properties of developing S. tonkinensis kernels.
DOI: 10.6084/m9.figshare.11998425.v1
title: Additional file 2 of Transcriptome analysis of metabolic pathways associated with oil accumulation in developing seed kernels of Styrax tonkinensis, a woody biodiesel species

description: Additional file 2. List of differentially expressed unigenes (DEGs) between different time points.
DOI: 10.6084/m9.figshare.11998434.v1
title: Additional file 5 of Transcriptome analysis of metabolic pathways associated with oil accumulation in developing seed kernels of Styrax tonkinensis, a woody biodiesel species

description: Additional file 5. Annotation and expression of key unigenes related to oil accumulation.
DOI: 10.6084/m9.figshare.13047210.v1
title: Additional file 1 of Highly multiplexed quantifications of 299 somatic mutations in colorectal cancer patients by automated MALDI-TOF mass spectrometry

description: Additional file 1 Table S1. Mutation list of CRC panel. Table S2. Primer sequences for mutation detection by CRC panel. Table S3. Primer sequences for mutation validation by Sanger sequencing. (XLS 145 kb)
DOI: 10.6084/m9.figshare.19862837
title: Additional file 1 of MiR-135a-5p suppresses trophoblast proliferative, migratory, invasive, and angiogenic activity in the context of unexplained spontaneous abortion

description: Additional file 1.
DOI: 10.6084/m9.figshare.13047210
title: Additional file 1 of Highly multiplexed quantifications of 299 somatic mutations in colorectal cancer patients by automated MALDI-TOF mass spectrometry

description: Additional file 1 Table S1. Mutation list of CRC panel. Table S2. Primer sequences for mutation detection by CRC panel. Table S3. Primer sequences for mutation validation by Sanger sequencing. (XLS 145 kb)
DOI: 10.48518/00014
title: Plankton image dataset from a cabled observatory system (JEDI System/OCEANS) deployed at coastal area of Oshima island, Tokyo, Japan
DOI: 10.15468/d3prvg
title: Zooplankton biomass and species composition and abundance in the southeastern Caribbean Sea (Cariaco Basin, October 2001 – January 2017) collected by the CARIACO Ocean Time-Series Program

description: The CARIACO Ocean Time-Series Program (formerly known as CArbon Retention In A Colored Ocean) started on November 1995 (CAR-001) and ended on January 2017 (CAR-232). Monthly cruises were conducted to the CARIACO station (10.50° N, 64.67° W) onboard the R/V Hermano Ginés of the Fundación La Salle de Ciencias Naturales de Venezuela. The program studied the relationship between surface primary production, physical forcing variables like the wind, and the settling flux of particulate carbon in the Cariaco Basin. This depression, located on the continental shelf of Venezuela, shows marked seasonal and interannual variation in hydrographic properties and primary production (carbon fixation rates by photosynthesis of planktonic algae). Zooplankton sampling was done during each CARIACO time-series cruise from October 2001 to January 2017 (cruises CAR071 - CAR232). Oblique BONGO net tow samples from 200 m to the surface were analyzed to determine biomass (dry weight and ash content) and taxonomic composition.
DOI: 10.15468/35g64d
title: Great Australian Bight Research Program, Australia (2015-2017)

description: This dataset is the collections of benthic biota (and their derived data) from two Marine National Facility charter voyages conducted under the Benthic Characterisation Project (3.1) of the Great Australian Bight Research Program (GABRP): Southern Surveyor voyage SS2013_C02 and RV Investigator voyage IN2015_C02 and Project 4 - Benthic biota of volcanic seamounts, seeps and canyons of the GAB - of the Great Australian Bight Deepwater Marine Program (GABDMP): RV Investigator voyages IN2015_C01 and IN2017_C01. There is also data from REM Etive voyage RE2017_C01. Voyage details (dataset metadata, maps, projects, publications, data reports and data) are at SS2013_C02, IN2015_C01, IN2015_C02, IN2017_C01 and RE2017_C01. This data has been republished (2022-08-29 with additional details of each occurrence where they can be one of three types: catch composition (counts per taxa and optional aggregated weights by trawl event), catch measurements (length and/or weight) of individual taxa and/or catch specimens and their stored location. The occurrence type is recorded in occurrenceremarks.
DOI: 10.6084/m9.figshare.19862837.v1
title: Additional file 1 of MiR-135a-5p suppresses trophoblast proliferative, migratory, invasive, and angiogenic activity in the context of unexplained spontaneous abortion

description: Additional file 1.
DOI: 10.25345/c5z60cc93
title: MassIVE MSV000094197 - Quantitative proteomic analysis reveals unique Hsp90 cycle-dependent client interactions
title: Authors: Erick I. Rios, Davi Goncalves, Kevin A. Morano, and Jill L. Johnson Abstract: Hsp90 is an abundant and essential molecular chaperone that mediates the folding and activation of client proteins in a nucleotide-dependent cycle. Hsp90 inhibition directly or indirectly impacts the function of 10-15% of all proteins due to degradation of client proteins or indirect downstream effects. Due to its role in chaperoning oncogenic proteins, Hsp90 is an important drug target. However, compounds that occupy the ATP-binding pocket and broadly inhibit function have not achieved widespread use due to negative effects. More selective inhibitors are needed; however, it is unclear how to achieve selective inhibition. We conducted a quantitative proteomic analysis of soluble proteins in yeast strains expressing wild-type Hsp90 or mutants that disrupt different steps in the client folding pathway. Out of 2,482 proteins in our sample set (approximately 38% of yeast proteins), we observed statistically significant changes in abundance of 350 (14%) of those proteins (log2 fold change &gt;= 1.5). Of these, 257/350 (~73%) with the strongest differences in abundance were previously connected to Hsp90 function. Principal component analysis of the entire dataset revealed that the effects of the mutants could be separated into three primary clusters. As evidence that Hsp90 mutants affect different pools of clients, simultaneous co-expression of two mutants in different clusters restored wild-type growth. Our data suggests that the ability of Hsp90 to sample a wide range of conformations allows the chaperone to mediate folding of a broad array of clients and that disruption of conformational flexibility results in client defects dependent on those states.
DOI: 10.6073/pasta/d2ba2a7b896a1a3a10725ae81e547f92
title: Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Half-hourly soil moisture and temperature data, 2010-2022

description: This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warmign affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieve using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011.This data set includes half-hourly values of surface moisture content (gravimetric, 0-5cm), depth-integrated soil moisture (volumetric, 0-20 cm), and soil temperature in winter warming and summer warming, drying, and control treatment plots at DryPEHR.
DOI: 10.17632/98sf9jfjxy.1
title: Lazrak-dataset

description: Electrophysiology data of ENaC activity in human and mouse alveolar epithelial cells in-situ, in freshly cut lung slices, and in culture using patch clamp techniques and Ussing chamber system for measuring short circuit currents across alveolar cells confluents monolayers. In addition, the project will generate lung tissue images for the distribution and the expression of the calcium sensing receptor in the alveolar epithelium. The expression of ENaC and CaSR proteins will be measured by western blot producing more data to help clarify the mechanism by which hyaluronan fragments activate calcium-sensing receptor and inhibits ions and water transport across the alveolar epithelium causing lung edema. The collected data from the experiments mentioned above will approximatively total 20 to 30 Gigabytes. The following data files will be produced in the course of the project: Axon binary files (ABF files) when measuring ENaC activity in alveolar cells in-situ, in lung slices. These files are usually larges, tens of megabytes each, depending on the sampling rate and the time necessary to collect enough data and test compounds of interest. In addition, the data analysis will produce new files, axon text files (ATF files), and finally the processed data will be transferred to a third-party analysis and presentation programs ( IGOR from Wavemetrics and Origin from Origin labs) that generate the final files suitable for presentation and publication. The imaging files are very large and remain at the same size after analysis. Western blot files and in-vivo data will be presented as an aggregate and do not generate large files excel and PowerPoint will be used to pool the numbers together and put in a presentable form, respectively.
DOI: 10.6084/m9.figshare.19863008
title: Additional file 2 of Serum proteomics unveil characteristic protein diagnostic biomarkers and signaling pathways in patients with esophageal squamous cell carcinoma

description: Additional file 2: Quantitative proteins in ESCC and Control groups.
DOI: 10.6084/m9.figshare.19863008.v1
title: Additional file 2 of Serum proteomics unveil characteristic protein diagnostic biomarkers and signaling pathways in patients with esophageal squamous cell carcinoma

description: Additional file 2: Quantitative proteins in ESCC and Control groups.
DOI: 10.6084/m9.figshare.11998647
title: Additional file 5 of The different dietary sugars modulate the composition of the gut microbiota in honeybee during overwintering

description: Additional file 5: Table S3. OTU abundances and taxonomic classifications within each sample at different levels.
DOI: 10.6084/m9.figshare.13047278.v1
title: Additional file 2 of Development of a relevant strategy using de novo transcriptome assembly method for transcriptome comparisons between Muscovy and common duck species and their reciprocal inter-specific mule and hinny hybrids fed ad libitum and overfed

description: Additional file 2. GO Biological process enrichments in DEG data.
DOI: 10.6084/m9.figshare.13047278
title: Additional file 2 of Development of a relevant strategy using de novo transcriptome assembly method for transcriptome comparisons between Muscovy and common duck species and their reciprocal inter-specific mule and hinny hybrids fed ad libitum and overfed

description: Additional file 2. GO Biological process enrichments in DEG data.
DOI: 10.6084/m9.figshare.11998647.v1
title: Additional file 5 of The different dietary sugars modulate the composition of the gut microbiota in honeybee during overwintering

description: Additional file 5: Table S3. OTU abundances and taxonomic classifications within each sample at different levels.
DOI: 10.6084/m9.figshare.19863107.v1
title: Additional file 22 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 22 Supplementary Table S21. Comparison of survival classification using single gene CNV and two gene CoCNV.
DOI: 10.6084/m9.figshare.19863110
title: Additional file 2 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 2. Supplementary Table S1. PMI/PCC networks recall TR relationships.
DOI: 10.6084/m9.figshare.19863116.v1
title: Additional file 4 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 4. Supplementary Table S3. PMI/PCC networks recall miRCoTR relationships.
DOI: 10.6084/m9.figshare.19863119
title: Additional file 5 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 5. Supplementary Table S4. PMI/PCC networks recall SCGD relationships.
DOI: 10.6084/m9.figshare.19863071
title: Additional file 11 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 11. Supplementary Table S10. Zscores of PMI/PCC networks recall CoDM relationships.
DOI: 10.6084/m9.figshare.19863119.v1
title: Additional file 5 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 5. Supplementary Table S4. PMI/PCC networks recall SCGD relationships.
DOI: 10.6084/m9.figshare.19863125.v1
title: Additional file 7 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 7. Supplementary Table S6. Zscores of PMI networks recall CoCNV relationship.
DOI: 10.6084/m9.figshare.19863068
title: Additional file 10 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 10. Supplementary Table S9. PMI/PCC networks recall CoCNV LL relationships.
DOI: 10.6084/m9.figshare.19863077.v1
title: Additional file 13 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 13. Supplementary Table S12. PMI/PCC networks recall CoDM HL relationships.
DOI: 10.6084/m9.figshare.19863080
title: Additional file 14 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 14. Supplementary Table S13. PMI/PCC networks recall CoDM LH relationships.
DOI: 10.6084/m9.figshare.19863083
title: Additional file 15 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 15. Supplementary Table S14.PMI/PCC networks recall CoDM LL relationships.
DOI: 10.6084/m9.figshare.19863104
title: Additional file 21 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 21 Supplementary Table S20. The p-value distribution of randomly subsampled LUSC00 Cox multivariate modules using clinical covariates and both CoCNV and CoDM scores.
DOI: 10.6084/m9.figshare.19863131.v1
title: Additional file 9 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 9. Supplementary Table S8. PMI/PCC networks recall CoCNV GG relationships.
DOI: 10.6084/m9.figshare.19863071.v1
title: Additional file 11 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 11. Supplementary Table S10. Zscores of PMI/PCC networks recall CoDM relationships.
DOI: 10.6084/m9.figshare.19863089
title: Additional file 17 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 17. Supplementary Table S16. PMI recalled CoDM survival analysis.
DOI: 10.6084/m9.figshare.19863095
title: Additional file 19 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 19. Supplementary Table S18. Cox multivariate analysis for LUSC00.
DOI: 10.6084/m9.figshare.19863083.v1
title: Additional file 15 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 15. Supplementary Table S14.PMI/PCC networks recall CoDM LL relationships.
DOI: 10.6084/m9.figshare.19863086
title: Additional file 16 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 16. Supplementary Table S15. PMI recalled CoCNV survival analysis.
DOI: 10.6084/m9.figshare.19863089.v1
title: Additional file 17 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 17. Supplementary Table S16. PMI recalled CoDM survival analysis.
DOI: 10.6084/m9.figshare.19863074.v1
title: Additional file 12 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 12. Supplementary Table S11. PMI/PCC networks recall CoDM HH relationships.
DOI: 10.6084/m9.figshare.19863095.v1
title: Additional file 19 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 19. Supplementary Table S18. Cox multivariate analysis for LUSC00.
DOI: 10.6084/m9.figshare.19863110.v1
title: Additional file 2 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 2. Supplementary Table S1. PMI/PCC networks recall TR relationships.
DOI: 10.6084/m9.figshare.19863074
title: Additional file 12 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 12. Supplementary Table S11. PMI/PCC networks recall CoDM HH relationships.
DOI: 10.6084/m9.figshare.19863077
title: Additional file 13 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 13. Supplementary Table S12. PMI/PCC networks recall CoDM HL relationships.
DOI: 10.6084/m9.figshare.19863086.v1
title: Additional file 16 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 16. Supplementary Table S15. PMI recalled CoCNV survival analysis.
DOI: 10.6084/m9.figshare.19863092
title: Additional file 18 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 18. Supplementary Table S17. Cox multivariate analysis for BRCA0000 and BRCA0112.
DOI: 10.6084/m9.figshare.19863116
title: Additional file 4 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 4. Supplementary Table S3. PMI/PCC networks recall miRCoTR relationships.
DOI: 10.6084/m9.figshare.19863122.v1
title: Additional file 6 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 6. Supplementary Table S5. PMI/PCC networks recall PPI relationships.
DOI: 10.6084/m9.figshare.19863068.v1
title: Additional file 10 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 10. Supplementary Table S9. PMI/PCC networks recall CoCNV LL relationships.
DOI: 10.6084/m9.figshare.19863080.v1
title: Additional file 14 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 14. Supplementary Table S13. PMI/PCC networks recall CoDM LH relationships.
DOI: 10.6084/m9.figshare.19863101
title: Additional file 20 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 20 Supplementary Table S19. The p-value distribution of randomly subsampled LUSC00 Cox multivariate models using only clinical covariates.
DOI: 10.6084/m9.figshare.19863092.v1
title: Additional file 18 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 18. Supplementary Table S17. Cox multivariate analysis for BRCA0000 and BRCA0112.
DOI: 10.6084/m9.figshare.19863101.v1
title: Additional file 20 of Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

description: Additional file 20 Supplementary Table S19. The p-value distribution of randomly subsampled LUSC00 Cox multivariate models using only clinical covariates.