cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14594073.v1
title: Additional file 6 of De novo transcriptome analysis and comparative expression profiling of genes associated with the taste-modifying protein neoculin in Curculigo latifolia and Curculigo capitulata fruits

description: Additional file 6: Supplemental Figure 4. Amino acid sequence alignment of 10 C. latifolia transcripts, 15 C. capitulata transcripts, and 17 well-known GNA family members used in the phylogenetic analysis shown in Fig. 5
DOI: 10.6084/m9.figshare.14594079.v1
title: Additional file 8 of De novo transcriptome analysis and comparative expression profiling of genes associated with the taste-modifying protein neoculin in Curculigo latifolia and Curculigo capitulata fruits

description: Additional file 8: Supplemental Table 2. Primer information used for qRT-PCR. qRT-PCR analyses were performed on the neoculin related genes from C. latifolia and C. capitulata fruits
DOI: 10.6084/m9.figshare.14594076.v1
title: Additional file 7 of De novo transcriptome analysis and comparative expression profiling of genes associated with the taste-modifying protein neoculin in Curculigo latifolia and Curculigo capitulata fruits

description: Additional file 7: Supplemental Figure 5. Comparison of the protein sequences of neoculin (NBS and NAS) from public databases and the Curculigo latifolia proteins identified in the present study. Amino acid residues that differ between NBS and NAS are shown in blue for NBS residues and red for NAS residues. Asn-2 of L_16562_c0_g1_i2 was the only residue that differed with NAS, which has Ser at this position (GenBank: BAD29946.1). These de novo assemblies were good matches with the known sequences (NBS and NAS). This level of matching supports the accuracy of the assembly
DOI: 10.6084/m9.figshare.14594070
title: Additional file 5 of De novo transcriptome analysis and comparative expression profiling of genes associated with the taste-modifying protein neoculin in Curculigo latifolia and Curculigo capitulata fruits

description: Additional file 5: Supplemental Table 1. Selection of sequences for phylogenetic analysis using BLAST search of transcripts from C. latifolia and C. capitulata fruits. The query sequences were the amino acid sequences (AA) of GNA (UniProtKB/Swiss-Prot: P30617.1) and the nucleotide sequences (nucl) and AA number of NBS (GenBank: X64110.1, GenBank: CAA45476.1) and NAS (GenBank: AB167079.1, GenBank: BAD29946.1). Each top hit was selected. The contigs that were selected for each query and used as neoculin-related sequences in phylogenetic analysis (Fig. 5) are indicated by checkmarks (✓)
DOI: 10.6084/m9.figshare.14594082
title: Additional file 9 of De novo transcriptome analysis and comparative expression profiling of genes associated with the taste-modifying protein neoculin in Curculigo latifolia and Curculigo capitulata fruits

description: Additional file 9: Supplemental Table 3. Relative quantification of the neoculin related genes from C. latifolia and C. capitulata fruits by qRT-PCR. The mean values of qRT-PCR from three independent biological replicates were normalized to ubiquitin gene of each species
DOI: 10.6084/m9.figshare.14594082.v1
title: Additional file 9 of De novo transcriptome analysis and comparative expression profiling of genes associated with the taste-modifying protein neoculin in Curculigo latifolia and Curculigo capitulata fruits

description: Additional file 9: Supplemental Table 3. Relative quantification of the neoculin related genes from C. latifolia and C. capitulata fruits by qRT-PCR. The mean values of qRT-PCR from three independent biological replicates were normalized to ubiquitin gene of each species
DOI: 10.6084/m9.figshare.14594076
title: Additional file 7 of De novo transcriptome analysis and comparative expression profiling of genes associated with the taste-modifying protein neoculin in Curculigo latifolia and Curculigo capitulata fruits

description: Additional file 7: Supplemental Figure 5. Comparison of the protein sequences of neoculin (NBS and NAS) from public databases and the Curculigo latifolia proteins identified in the present study. Amino acid residues that differ between NBS and NAS are shown in blue for NBS residues and red for NAS residues. Asn-2 of L_16562_c0_g1_i2 was the only residue that differed with NAS, which has Ser at this position (GenBank: BAD29946.1). These de novo assemblies were good matches with the known sequences (NBS and NAS). This level of matching supports the accuracy of the assembly
DOI: 10.6084/m9.figshare.14594079
title: Additional file 8 of De novo transcriptome analysis and comparative expression profiling of genes associated with the taste-modifying protein neoculin in Curculigo latifolia and Curculigo capitulata fruits

description: Additional file 8: Supplemental Table 2. Primer information used for qRT-PCR. qRT-PCR analyses were performed on the neoculin related genes from C. latifolia and C. capitulata fruits
DOI: 10.6084/m9.figshare.14594088
title: Additional file 1 of IPD 2.0: To derive insights from an evolving SARS-CoV-2 genome

description: Additional file 1. Supplementary Tables (S1–S6).
DOI: 10.6084/m9.figshare.14594088.v1
title: Additional file 1 of IPD 2.0: To derive insights from an evolving SARS-CoV-2 genome

description: Additional file 1. Supplementary Tables (S1–S6).
DOI: 10.6084/m9.figshare.14594165
title: Additional file 2 of Effects of transition on HIV and non-HIV services and health systems in Kenya: a mixed methods evaluation of donor transition

description: Additional file 2.
DOI: 10.6084/m9.figshare.14594165.v1
title: Additional file 2 of Effects of transition on HIV and non-HIV services and health systems in Kenya: a mixed methods evaluation of donor transition

description: Additional file 2.
DOI: 10.6084/m9.figshare.14594171
title: Additional file 4 of Effects of transition on HIV and non-HIV services and health systems in Kenya: a mixed methods evaluation of donor transition

description: Additional file 4.
DOI: 10.6084/m9.figshare.14594171.v1
title: Additional file 4 of Effects of transition on HIV and non-HIV services and health systems in Kenya: a mixed methods evaluation of donor transition

description: Additional file 4.
DOI: 10.6084/m9.figshare.14594184
title: Additional file 2 of Multi-omic profiling of lung and liver tumor microenvironments of metastatic pancreatic cancer reveals site-specific immune regulatory pathways

description: Additional file 2: Table S1. 1st Mass Cytometry Antibody Panel.
DOI: 10.6084/m9.figshare.14594187
title: Additional file 3 of Multi-omic profiling of lung and liver tumor microenvironments of metastatic pancreatic cancer reveals site-specific immune regulatory pathways

description: Additional file 3: Table S2. Mass Cytometry Major Immune Cell Annotation from 1st Panel Data.
DOI: 10.6084/m9.figshare.14594190
title: Additional file 4 of Multi-omic profiling of lung and liver tumor microenvironments of metastatic pancreatic cancer reveals site-specific immune regulatory pathways

description: Additional file 4: Table S3. Mass Cytometry T Cell Subset Annotation from 1st Panel Data.
DOI: 10.6084/m9.figshare.14594190.v1
title: Additional file 4 of Multi-omic profiling of lung and liver tumor microenvironments of metastatic pancreatic cancer reveals site-specific immune regulatory pathways

description: Additional file 4: Table S3. Mass Cytometry T Cell Subset Annotation from 1st Panel Data.
DOI: 10.6084/m9.figshare.14594193
title: Additional file 5 of Multi-omic profiling of lung and liver tumor microenvironments of metastatic pancreatic cancer reveals site-specific immune regulatory pathways

description: Additional file 5: Table S4. 2nd Mass Cytometry Antibody Panel.
DOI: 10.6084/m9.figshare.14594184.v1
title: Additional file 2 of Multi-omic profiling of lung and liver tumor microenvironments of metastatic pancreatic cancer reveals site-specific immune regulatory pathways

description: Additional file 2: Table S1. 1st Mass Cytometry Antibody Panel.
DOI: 10.6084/m9.figshare.14594193.v1
title: Additional file 5 of Multi-omic profiling of lung and liver tumor microenvironments of metastatic pancreatic cancer reveals site-specific immune regulatory pathways

description: Additional file 5: Table S4. 2nd Mass Cytometry Antibody Panel.
DOI: 10.6084/m9.figshare.14594199
title: Additional file 7 of Multi-omic profiling of lung and liver tumor microenvironments of metastatic pancreatic cancer reveals site-specific immune regulatory pathways

description: Additional file 7: Table S6. Rapid Autopsy Samples Additional Detail.
DOI: 10.6084/m9.figshare.14594199.v1
title: Additional file 7 of Multi-omic profiling of lung and liver tumor microenvironments of metastatic pancreatic cancer reveals site-specific immune regulatory pathways

description: Additional file 7: Table S6. Rapid Autopsy Samples Additional Detail.
DOI: 10.6084/m9.figshare.14594187.v1
title: Additional file 3 of Multi-omic profiling of lung and liver tumor microenvironments of metastatic pancreatic cancer reveals site-specific immune regulatory pathways

description: Additional file 3: Table S2. Mass Cytometry Major Immune Cell Annotation from 1st Panel Data.
DOI: 10.6084/m9.figshare.14594196
title: Additional file 6 of Multi-omic profiling of lung and liver tumor microenvironments of metastatic pancreatic cancer reveals site-specific immune regulatory pathways

description: Additional file 6: Table S5. Mass Cytometry Cell Subset Annotation for 2nd Panel Data.
DOI: 10.6084/m9.figshare.14594196.v1
title: Additional file 6 of Multi-omic profiling of lung and liver tumor microenvironments of metastatic pancreatic cancer reveals site-specific immune regulatory pathways

description: Additional file 6: Table S5. Mass Cytometry Cell Subset Annotation for 2nd Panel Data.
DOI: 10.6084/m9.figshare.14593980
title: Datasets for Group Technology

description: Datasets are used for evaluation of method related to cellular manufacturing
DOI: 10.6084/m9.figshare.14593980.v1
title: Datasets for Group Technology

description: Datasets are used for evaluation of method related to cellular manufacturing
DOI: 10.6084/m9.figshare.14594297
title: Additional file 2 of Risk of cardiovascular mortality, stroke and coronary heart mortality associated with aircraft noise around Congonhas airport, São Paulo, Brazil: a small-area study

description: Additional file 2: Table S1. Model selection. Information regarding the forward stepwise regression models fitted including model name, description and Akaike information criterion (AIC) for cardiovascular diseases (CVD), stroke and coronary disease. Shaded those presented in the manuscript. Bolded the model with the best fit; shaded, models fully presented in the full text.
DOI: 10.6084/m9.figshare.14594297.v1
title: Additional file 2 of Risk of cardiovascular mortality, stroke and coronary heart mortality associated with aircraft noise around Congonhas airport, São Paulo, Brazil: a small-area study

description: Additional file 2: Table S1. Model selection. Information regarding the forward stepwise regression models fitted including model name, description and Akaike information criterion (AIC) for cardiovascular diseases (CVD), stroke and coronary disease. Shaded those presented in the manuscript. Bolded the model with the best fit; shaded, models fully presented in the full text.
DOI: 10.6084/m9.figshare.14594300
title: Additional file 3 of Risk of cardiovascular mortality, stroke and coronary heart mortality associated with aircraft noise around Congonhas airport, São Paulo, Brazil: a small-area study

description: Additional file 3: Table S2. Relative risk (RR) and 95% confidence intervals (CI) for the association between CVD, stroke and CHD with 10 dB increase in noise. Adjusted model only (i.e. Adjusted for age and sex (standardization) + smoking proxy + IDHM 2010 + Black and Mixed ethnicity + road traffic density). Trends estimated using WHO standard methods, i.e. generalized least square estimation.
DOI: 10.6084/m9.figshare.14594300.v1
title: Additional file 3 of Risk of cardiovascular mortality, stroke and coronary heart mortality associated with aircraft noise around Congonhas airport, São Paulo, Brazil: a small-area study

description: Additional file 3: Table S2. Relative risk (RR) and 95% confidence intervals (CI) for the association between CVD, stroke and CHD with 10 dB increase in noise. Adjusted model only (i.e. Adjusted for age and sex (standardization) + smoking proxy + IDHM 2010 + Black and Mixed ethnicity + road traffic density). Trends estimated using WHO standard methods, i.e. generalized least square estimation.
DOI: 10.6084/m9.figshare.14594369.v1
title: Additional file 2 of Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study

description: Additional file 2: UMAP representation of the topological space occupied by known active DRD2 molecules and de novo molecules.
DOI: 10.6084/m9.figshare.14594369
title: Additional file 2 of Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study

description: Additional file 2: UMAP representation of the topological space occupied by known active DRD2 molecules and de novo molecules.
DOI: 10.6084/m9.figshare.14594373.v1
title: Additional file 2 of Exploring genetic resistance to infectious salmon anaemia virus in Atlantic salmon by genome-wide association and RNA sequencing

description: Additional file 2.
DOI: 10.6084/m9.figshare.14594373
title: Additional file 2 of Exploring genetic resistance to infectious salmon anaemia virus in Atlantic salmon by genome-wide association and RNA sequencing

description: Additional file 2.
DOI: 10.6084/m9.figshare.14594376
title: Additional file 3 of Exploring genetic resistance to infectious salmon anaemia virus in Atlantic salmon by genome-wide association and RNA sequencing

description: Additional file 3.
DOI: 10.6084/m9.figshare.14594376.v1
title: Additional file 3 of Exploring genetic resistance to infectious salmon anaemia virus in Atlantic salmon by genome-wide association and RNA sequencing

description: Additional file 3.
DOI: 10.6084/m9.figshare.14594379
title: Additional file 4 of Exploring genetic resistance to infectious salmon anaemia virus in Atlantic salmon by genome-wide association and RNA sequencing

description: Additional file 4.
DOI: 10.6084/m9.figshare.14594379.v1
title: Additional file 4 of Exploring genetic resistance to infectious salmon anaemia virus in Atlantic salmon by genome-wide association and RNA sequencing

description: Additional file 4.
DOI: 10.6084/m9.figshare.14594382
title: Additional file 5 of Exploring genetic resistance to infectious salmon anaemia virus in Atlantic salmon by genome-wide association and RNA sequencing

description: Additional file 5.
DOI: 10.6084/m9.figshare.14594382.v1
title: Additional file 5 of Exploring genetic resistance to infectious salmon anaemia virus in Atlantic salmon by genome-wide association and RNA sequencing

description: Additional file 5.
DOI: 10.6084/m9.figshare.14594385
title: Additional file 6 of Exploring genetic resistance to infectious salmon anaemia virus in Atlantic salmon by genome-wide association and RNA sequencing

description: Additional file 6.
DOI: 10.6084/m9.figshare.14594385.v1
title: Additional file 6 of Exploring genetic resistance to infectious salmon anaemia virus in Atlantic salmon by genome-wide association and RNA sequencing

description: Additional file 6.
DOI: 10.6084/m9.figshare.14594388
title: Additional file 7 of Exploring genetic resistance to infectious salmon anaemia virus in Atlantic salmon by genome-wide association and RNA sequencing

description: Additional file 7.
DOI: 10.6084/m9.figshare.14594388.v1
title: Additional file 7 of Exploring genetic resistance to infectious salmon anaemia virus in Atlantic salmon by genome-wide association and RNA sequencing

description: Additional file 7.
DOI: 10.6084/m9.figshare.14594391.v1
title: Additional file 1 of OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality

description: Additional file 1. Supplementary Tables S1–S4.
DOI: 10.6084/m9.figshare.14594394
title: Additional file 2 of OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality

description: Additional file 2. PostGreSQL script for retrieving data from the MIMIC-III database
DOI: 10.6084/m9.figshare.14594391
title: Additional file 1 of OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality

description: Additional file 1. Supplementary Tables S1–S4.
DOI: 10.6084/m9.figshare.14594394.v1
title: Additional file 2 of OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality

description: Additional file 2. PostGreSQL script for retrieving data from the MIMIC-III database
DOI: 10.6084/m9.figshare.14594459
title: Additional file 1 of Compound heterozygous missense mutations in a Chinese mucopolysaccharidosis type VI patient: a case report

description: Additional file 1.
DOI: 10.6084/m9.figshare.14594459.v1
title: Additional file 1 of Compound heterozygous missense mutations in a Chinese mucopolysaccharidosis type VI patient: a case report

description: Additional file 1.
DOI: 10.6084/m9.figshare.14594495
title: Additional file 11 of High copy number variations, particular transcription factors, and low immunity contribute to the stemness of prostate cancer cells

description: Additional file 11. Table S5. Results of correlation between different immune cells.
DOI: 10.6084/m9.figshare.14594495.v1
title: Additional file 11 of High copy number variations, particular transcription factors, and low immunity contribute to the stemness of prostate cancer cells

description: Additional file 11. Table S5. Results of correlation between different immune cells.
DOI: 10.6084/m9.figshare.14594501
title: Additional file 13 of High copy number variations, particular transcription factors, and low immunity contribute to the stemness of prostate cancer cells

description: Additional file 13. Table S6. Correlation results between stemness genes and different immune cells in the MEmagenta module.
DOI: 10.6084/m9.figshare.14594510
title: Additional file 3 of High copy number variations, particular transcription factors, and low immunity contribute to the stemness of prostate cancer cells

description: Additional file 3. Table S2. Results of differential expression of stemness genes in MEmagenta module.
DOI: 10.6084/m9.figshare.14594507
title: Additional file 2 of High copy number variations, particular transcription factors, and low immunity contribute to the stemness of prostate cancer cells

description: Additional file 2. Table S1. The relationship between genes and color modules in WGCNA analysis results.
DOI: 10.6084/m9.figshare.14594510.v1
title: Additional file 3 of High copy number variations, particular transcription factors, and low immunity contribute to the stemness of prostate cancer cells

description: Additional file 3. Table S2. Results of differential expression of stemness genes in MEmagenta module.
DOI: 10.6084/m9.figshare.14594516
title: Additional file 5 of High copy number variations, particular transcription factors, and low immunity contribute to the stemness of prostate cancer cells

description: Additional file 5. Table S3. The result of copy number amplification in the genome location of the stemness genes in the MEmagenta module.
DOI: 10.6084/m9.figshare.14594501.v1
title: Additional file 13 of High copy number variations, particular transcription factors, and low immunity contribute to the stemness of prostate cancer cells

description: Additional file 13. Table S6. Correlation results between stemness genes and different immune cells in the MEmagenta module.
DOI: 10.6084/m9.figshare.14594519
title: Additional file 6 of High copy number variations, particular transcription factors, and low immunity contribute to the stemness of prostate cancer cells

description: Additional file 6. Table S4. The result of copy number deletion in the genome location of the stemness genes in the MEmagenta module.
DOI: 10.6084/m9.figshare.14594519.v1
title: Additional file 6 of High copy number variations, particular transcription factors, and low immunity contribute to the stemness of prostate cancer cells

description: Additional file 6. Table S4. The result of copy number deletion in the genome location of the stemness genes in the MEmagenta module.
DOI: 10.6084/m9.figshare.14594507.v1
title: Additional file 2 of High copy number variations, particular transcription factors, and low immunity contribute to the stemness of prostate cancer cells

description: Additional file 2. Table S1. The relationship between genes and color modules in WGCNA analysis results.
DOI: 10.6084/m9.figshare.14594516.v1
title: Additional file 5 of High copy number variations, particular transcription factors, and low immunity contribute to the stemness of prostate cancer cells

description: Additional file 5. Table S3. The result of copy number amplification in the genome location of the stemness genes in the MEmagenta module.
DOI: 10.6084/m9.figshare.14594547
title: Additional file 1 of Association of TNF-α -308G &gt; A polymorphism with susceptibility to tendinopathy in athletes: a case–control study

description: Additional file 1.
DOI: 10.6084/m9.figshare.14594547.v1
title: Additional file 1 of Association of TNF-α -308G &gt; A polymorphism with susceptibility to tendinopathy in athletes: a case–control study

description: Additional file 1.
DOI: 10.5281/zenodo.4759854
title: Test private dataset

description: This is a test private dataset for crawling purposes
DOI: 10.5281/zenodo.4759853
title: Test private dataset

description: This is a test private dataset for crawling purposes
DOI: 10.5281/zenodo.4759426
title: Earthquake Data around the Palu-Koro fault and Minahassa thrust

description: This earthquake data is used in my submitted paper.
DOI: 10.5281/zenodo.4759425
title: Earthquake Data around the Palu-Koro fault and Minahassa thrust

description: This earthquake data is used in my submitted paper.
DOI: 10.6084/m9.figshare.14169623
title: Readily available biomarkers predict poor survival in metastatic pancreatic cancer

description: Identification of metastatic pancreatic cancer (mPC) patients with the worst prognosis could help to tailor therapy. We evaluated readily available biomarkers for the prediction of 90-day mortality in a nationwide cohort of mPC patients. Patients with synchronous mPC were included from the Netherlands Cancer Registry (2015–2017). Baseline CA19-9, albumin, CRP, LDH, CRP/albumin ratio, and (modified) Glasgow Prognostic Score ((m)GPS composed of albumin and CRP) were evaluated. Multivariable logistic regression analyses were performed to identify predictors of 90-day mortality. Prognostic value per predictor was quantified by Nagelkerke’s partial R<sup>2</sup>. Overall, 4248 patients were included. Median overall survival was 2.2 months and 90-day mortality was 59.4% (n = 1629). All biomarkers predicted 90-day mortality in univariable analysis, and remained statistically significant after adjustment for clinically relevant factors and all other biomarkers (all <i>p</i> In mPC patients, albumin, CA19-9, CRP, LDH, CRP/albumin ratio, and (m)GPS are prognostic for poor survival. Biomarkers did not predict response to chemotherapy. These readily available biomarkers can be used to better inform patients and to stratify in clinical trials.