cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7177790.v2
title: RBM-5 modulates U2AF large subunit-dependent alternative splicing in <i>C. elegans</i>

description: A key step in pre-mRNA splicing is the recognition of 3ʹ splicing sites by the U2AF large and small subunits, a process regulated by numerous <i>trans</i>-acting splicing factors. How these <i>trans</i>-acting factors interact with U2AF <i>in vivo</i> is unclear. From a screen for suppressors of the temperature-sensitive (ts) lethality of the <i>C. elegans</i> U2AF large subunit gene <i>uaf-1(n4588)</i> mutants, we identified mutations in the RNA binding motif gene <i>rbm-5</i>, a homolog of the tumor suppressor gene <i>RBM5. rbm-5</i> mutations can suppress <i>uaf-1(n4588)</i> ts-lethality by loss of function and neuronal expression of <i>rbm-5</i> was sufficient to rescue the suppression. Transcriptome analyses indicate that <i>uaf-1(n4588)</i> affected the expression of numerous genes and <i>rbm-5</i> mutations can partially reverse the abnormal gene expression to levels similar to that of wild type. Though <i>rbm-5</i> mutations did not obviously affect alternative splicing per se, they can suppress or enhance, in a gene-specific manner, the altered splicing of genes in <i>uaf-1(n4588)</i> mutants. Specifically, the recognition of a weak 3ʹ splice site was more susceptible to the effect of <i>rbm-5</i>. Our findings provide novel <i>in vivo</i> evidence that RBM-5 can modulate UAF-1-dependent RNA splicing and suggest that RBM5 might interact with U2AF large subunit to affect tumor formation.
DOI: 10.6084/m9.figshare.7177790.v1
title: RBM-5 modulates U2AF large subunit-dependent alternative splicing in <i>C. elegans</i>

description: A key step in pre-mRNA splicing is the recognition of 3ʹ splicing sites by the U2AF large and small subunits, a process regulated by numerous <i>trans</i>-acting splicing factors. How these <i>trans</i>-acting factors interact with U2AF <i>in vivo</i> is unclear. From a screen for suppressors of the temperature-sensitive (ts) lethality of the <i>C. elegans</i> U2AF large subunit gene <i>uaf-1(n4588)</i> mutants, we identified mutations in the RNA binding motif gene <i>rbm-5</i>, a homolog of the tumor suppressor gene <i>RBM5. rbm-5</i> mutations can suppress <i>uaf-1(n4588)</i> ts-lethality by loss of function and neuronal expression of <i>rbm-5</i> was sufficient to rescue the suppression. Transcriptome analyses indicate that <i>uaf-1(n4588)</i> affected the expression of numerous genes and <i>rbm-5</i> mutations can partially reverse the abnormal gene expression to levels similar to that of wild type. Though <i>rbm-5</i> mutations did not obviously affect alternative splicing per se, they can suppress or enhance, in a gene-specific manner, the altered splicing of genes in <i>uaf-1(n4588)</i> mutants. Specifically, the recognition of a weak 3ʹ splice site was more susceptible to the effect of <i>rbm-5</i>. Our findings provide novel <i>in vivo</i> evidence that RBM-5 can modulate UAF-1-dependent RNA splicing and suggest that RBM5 might interact with U2AF large subunit to affect tumor formation.
DOI: 10.6084/m9.figshare.11370720
title: Comparison from Compressor performance modelling method based on support vector machine nonlinear regression algorithm

description: To overcome the difficulty of having only part of compressor characteristic maps including on-design operating point, and accurately calculate compressor thermodynamic performance under variable working conditions, this paper proposes a novel compressor performance modelling method based on support vector machine nonlinear regression algorithm. It is compared with the other three neural network algorithms (i.e. BP, RBF and Elman neural networks) from the perspective of interpolation and extrapolation accuracy as well as calculation time, to prove the validity of the proposed method. Application analyses indicate that the proposed method has better interpolation and extrapolation performance than the other three neural networks. In terms of flow characteristic map representation, the root mean square error (RMSE) of the extrapolation performance at higher and lower speed operating area by the proposed method is 0.89% and 2.57%, respectively. And the total RMSE by the proposed method is 2.72%, which is more accurate by 47% than the Elman algorithm. For efficiency characteristic map representation, the RMSE of the extrapolation performance at higher and lower speed operating area by the proposed method is 2.85% and 1.22%, respectively. And the total RMSE by the proposed method is 1.81%, which is more accurate by 35% than the BP algorithm. Moreover, the proposed method has better real-time performance compared with the other three neural network algorithms.
DOI: 10.6084/m9.figshare.13467935
title: Additional file 1 of Deep neural networks for inferring binding sites of RNA-binding proteins by using distributed representations of RNA primary sequence and secondary structure

description: Additional file 1 The file includes the performance measures of competitive methods on the RBP-24 dataset. The AUC values of using DeepRKE- and without RNA secondary structure sequence, as well as the AUC values using DeepRKE- without RNA secondary structure and distributed representation of sequences.
DOI: 10.6084/m9.figshare.13467937.v1
title: Additional file 2 of Deep neural networks for inferring binding sites of RNA-binding proteins by using distributed representations of RNA primary sequence and secondary structure

description: Additional file 2 The file includes the performance measures of competitive methods on the RBP-31 dataset. The AUCs of using DeepRKE- and without RNA secondary structure sequence, as well as the AUC values of using DeepRKE- without RNA secondary structure and distributed representation of sequences.
DOI: 10.6084/m9.figshare.7176500
title: CEJ Toluene Steam Reforming.xlsx

description: Data of OES and tar reforming
DOI: 10.6084/m9.figshare.25249096
title: Metagenome-assembled genomes (MAGs) from biofloc microbiota.

description: We recovered over 500 metagenome-assembled genomes (MAGs) from the floc-associated microbiota of commercial biofloc aquaculture systems. These MAGs were then functionally annotated to understand the metabolic potential.
DOI: 10.6084/m9.figshare.17139446
title: Athlete Sub305

description: EEG and ET mat format data of one Shooting (Pistol) Athlete Sub305 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139446.v1
title: Athlete Sub305

description: EEG and ET mat format data of one Shooting (Pistol) Athlete Sub305 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139455
title: Athlete Sub307

description: EEG and ET mat format data of one Shooting (Pistol) Athlete Sub307 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139455.v1
title: Athlete Sub307

description: EEG and ET mat format data of one Shooting (Pistol) Athlete Sub307 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139464
title: Athlete Sub308

description: EEG and ET mat format data of one Archery Athlete Sub308 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139464.v1
title: Athlete Sub308

description: EEG and ET mat format data of one Archery Athlete Sub308 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139467
title: Athlete Sub309

description: EEG and ET mat format data of one Archery Athlete Sub309 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139467.v1
title: Athlete Sub309

description: EEG and ET mat format data of one Archery Athlete Sub309 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139491
title: Athlete Sub311

description: EEG and ET mat format data of one Shooting (Pistol) Athlete Sub311 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139491.v1
title: Athlete Sub311

description: EEG and ET mat format data of one Shooting (Pistol) Athlete Sub311 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139494
title: Athlete Sub313

description: EEG and ET mat format data of one Shooting (Pistol) Athlete Sub313 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139494.v1
title: Athlete Sub313

description: EEG and ET mat format data of one Shooting (Pistol) Athlete Sub313 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139497
title: Athlete Sub314

description: EEG and ET mat format data of one Shooting (Pistol) Athlete Sub314 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139497.v1
title: Athlete Sub314

description: EEG and ET mat format data of one Shooting (Pistol) Athlete Sub314 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139500
title: Athlete Sub315

description: EEG and ET mat format data of one Shooting (Rifle) Athlete Sub315 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139500.v1
title: Athlete Sub315

description: EEG and ET mat format data of one Shooting (Rifle) Athlete Sub315 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139503
title: Athlete Sub317

description: EEG and ET mat format data of one Shooting (Rifle) Athlete Sub317 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139503.v1
title: Athlete Sub317

description: EEG and ET mat format data of one Shooting (Rifle) Athlete Sub317 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139506
title: Athlete Sub318

description: EEG and ET mat format data of one Shooting (Rifle) Athlete Sub318 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.17139506.v1
title: Athlete Sub318

description: EEG and ET mat format data of one Shooting (Rifle) Athlete Sub318 in ABT and CCT experiments.
DOI: 10.6084/m9.figshare.25249285
title: ChiPSeq and ATAC-Seq Tracks Bulk Multicardiac Cell

description: ChIP-Seq and ATAC-Seq Tracks for Multiple Cardiac Cell Types
DOI: 10.6084/m9.figshare.25249285.v1
title: ChiPSeq and ATAC-Seq Tracks Bulk Multicardiac Cell

description: ChIP-Seq and ATAC-Seq Tracks for Multiple Cardiac Cell Types
DOI: 10.6084/m9.figshare.19929908.v1
title: NMR and GC data

description: NMR and HRMS characterization data on 13C labeled products.
DOI: 10.6084/m9.figshare.11370699
title: Elman from Compressor performance modelling method based on support vector machine nonlinear regression algorithm

description: To overcome the difficulty of having only part of compressor characteristic maps including on-design operating point, and accurately calculate compressor thermodynamic performance under variable working conditions, this paper proposes a novel compressor performance modelling method based on support vector machine nonlinear regression algorithm. It is compared with the other three neural network algorithms (i.e. BP, RBF and Elman neural networks) from the perspective of interpolation and extrapolation accuracy as well as calculation time, to prove the validity of the proposed method. Application analyses indicate that the proposed method has better interpolation and extrapolation performance than the other three neural networks. In terms of flow characteristic map representation, the root mean square error (RMSE) of the extrapolation performance at higher and lower speed operating area by the proposed method is 0.89% and 2.57%, respectively. And the total RMSE by the proposed method is 2.72%, which is more accurate by 47% than the Elman algorithm. For efficiency characteristic map representation, the RMSE of the extrapolation performance at higher and lower speed operating area by the proposed method is 2.85% and 1.22%, respectively. And the total RMSE by the proposed method is 1.81%, which is more accurate by 35% than the BP algorithm. Moreover, the proposed method has better real-time performance compared with the other three neural network algorithms.
DOI: 10.6084/m9.figshare.11370705.v1
title: The compressor characteristics prediction points data from Compressor performance modelling method based on support vector machine nonlinear regression algorithm

description: Different algorithms in this paper are used to predict compressor characteristic curves at different rotational speeds.
DOI: 10.6084/m9.figshare.11370711
title: The compressor characteristics test points data from Compressor performance modelling method based on support vector machine nonlinear regression algorithm

description: Extracting compressor parameter values at different rotating speeds from a specific type of compressor
DOI: 10.6084/m9.figshare.11370714
title: RMSE from Compressor performance modelling method based on support vector machine nonlinear regression algorithm

description: To overcome the difficulty of having only part of compressor characteristic maps including on-design operating point, and accurately calculate compressor thermodynamic performance under variable working conditions, this paper proposes a novel compressor performance modelling method based on support vector machine nonlinear regression algorithm. It is compared with the other three neural network algorithms (i.e. BP, RBF and Elman neural networks) from the perspective of interpolation and extrapolation accuracy as well as calculation time, to prove the validity of the proposed method. Application analyses indicate that the proposed method has better interpolation and extrapolation performance than the other three neural networks. In terms of flow characteristic map representation, the root mean square error (RMSE) of the extrapolation performance at higher and lower speed operating area by the proposed method is 0.89% and 2.57%, respectively. And the total RMSE by the proposed method is 2.72%, which is more accurate by 47% than the Elman algorithm. For efficiency characteristic map representation, the RMSE of the extrapolation performance at higher and lower speed operating area by the proposed method is 2.85% and 1.22%, respectively. And the total RMSE by the proposed method is 1.81%, which is more accurate by 35% than the BP algorithm. Moreover, the proposed method has better real-time performance compared with the other three neural network algorithms.
DOI: 10.6084/m9.figshare.11370714.v1
title: RMSE from Compressor performance modelling method based on support vector machine nonlinear regression algorithm

description: To overcome the difficulty of having only part of compressor characteristic maps including on-design operating point, and accurately calculate compressor thermodynamic performance under variable working conditions, this paper proposes a novel compressor performance modelling method based on support vector machine nonlinear regression algorithm. It is compared with the other three neural network algorithms (i.e. BP, RBF and Elman neural networks) from the perspective of interpolation and extrapolation accuracy as well as calculation time, to prove the validity of the proposed method. Application analyses indicate that the proposed method has better interpolation and extrapolation performance than the other three neural networks. In terms of flow characteristic map representation, the root mean square error (RMSE) of the extrapolation performance at higher and lower speed operating area by the proposed method is 0.89% and 2.57%, respectively. And the total RMSE by the proposed method is 2.72%, which is more accurate by 47% than the Elman algorithm. For efficiency characteristic map representation, the RMSE of the extrapolation performance at higher and lower speed operating area by the proposed method is 2.85% and 1.22%, respectively. And the total RMSE by the proposed method is 1.81%, which is more accurate by 35% than the BP algorithm. Moreover, the proposed method has better real-time performance compared with the other three neural network algorithms.
DOI: 10.6084/m9.figshare.13467915
title: Additional file 23 of A Cas9-mediated adenosine transient reporter enables enrichment of ABE-targeted cells

description: Additional file 23.
DOI: 10.6084/m9.figshare.13467937
title: Additional file 2 of Deep neural networks for inferring binding sites of RNA-binding proteins by using distributed representations of RNA primary sequence and secondary structure

description: Additional file 2 The file includes the performance measures of competitive methods on the RBP-31 dataset. The AUCs of using DeepRKE- and without RNA secondary structure sequence, as well as the AUC values of using DeepRKE- without RNA secondary structure and distributed representation of sequences.
DOI: 10.6084/m9.figshare.13467915.v1
title: Additional file 23 of A Cas9-mediated adenosine transient reporter enables enrichment of ABE-targeted cells

description: Additional file 23.
DOI: 10.6084/m9.figshare.16613765.v2
title: Identification of hsa_circ_0002024 as a prognostic competing endogenous RNA (ceRNA) through the hsa_miR_129-5p/Anti-Silencing Function 1B Histone Chaperone (ASF1B) axis in renal cell carcinoma

description: We aimed to identify novel circular RNAs (circRNAs) as prognostic competing endogenous RNAs (ceRNAs) to serve as genetic biomarkers and therapeutic targets for renal cell carcinoma (RCC). High-throughput sequencing data of circRNAs from Gene Expression Omnibus (GEO) and of microRNAs (miRNAs) and messenger RNAs (mRNAs) from The Cancer Genome Atlas (TCGA) were retrieved to identify differentially expressed RNAs (DERNAs). DEmRNAs were subjected to weighted gene coexpression network analysis (WGCNA) to identify prognostic DEmRNA (proDEmRNA) modules. Overlapping DEcircRNA-DEmiRNA and DEmiRNA-proDEmRNA interactions among the TargetScan, miRanda and RNAhybrid databases were constructed and identified. The circRNA-miRNA-mRNA ceRNA network was constructed using mutual DEmiRNAs in two interaction networks as nodes. mRNAs validated as significantly overexpressed in RCC by Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA) and quantitative polymerase chain reaction (q-PCR), along with the correlative miRNAs, were used for survival analysis. Finally, a ceRNA network with 13 upregulated circRNAs, 8 downregulated miRNAs and 21 upregulated mRNAs was constructed, in which Anti-Silencing Function 1B Histone Chaperone (ASF1B) and Forkhead Box M1 (FOXM1) were considered significant by Oncomine, GEPIA and q-PCR. Survival analysis showed that ASF1B, FOXM1 and hsa_miR_1254 were significantly negatively correlated but hsa_miR_129-5p was positively correlated with overall survival time. Exploration of the ceRNA network revealed the prognostic hsa_circ_0002024/hsa_miR_129-5p/ASF1B axis. Therefore, hsa_circ_0002024 was identified as a prognostic ceRNA that might sponge hsa_miR_129-5p to regulate ASF1B and affect RCC prognosis. However, further validation is needed.
DOI: 10.6084/m9.figshare.23816796.v1
title: Mild pH-decoupling Aqueous Flow Battery with Practical pH Recovery (data)

description: Data for Mild pH-decoupling Aqueous Flow Battery with Practical pH Recovery
DOI: 10.6084/m9.figshare.22689790.v1
title: RNA-seq of WT and ypg

description: RNA sequence results of wild type vs ygp mutant
DOI: 10.6084/m9.figshare.14516579.v1
title: Additional file 3 of Ten-eleven translocation protein 1 modulates medulloblastoma progression

description: Additional file 3: Table S2. MB sample summary.
DOI: 10.6084/m9.figshare.16613765.v1
title: Identification of hsa_circ_0002024 as a prognostic competing endogenous RNA (ceRNA) through the hsa_miR_129-5p/Anti-Silencing Function 1B Histone Chaperone (ASF1B) axis in renal cell carcinoma

description: We aimed to identify novel circular RNAs (circRNAs) as prognostic competing endogenous RNAs (ceRNAs) to serve as genetic biomarkers and therapeutic targets for renal cell carcinoma (RCC). High-throughput sequencing data of circRNAs from Gene Expression Omnibus (GEO) and of microRNAs (miRNAs) and messenger RNAs (mRNAs) from The Cancer Genome Atlas (TCGA) were retrieved to identify differentially expressed RNAs (DERNAs). DEmRNAs were subjected to weighted gene coexpression network analysis (WGCNA) to identify prognostic DEmRNA (proDEmRNA) modules. Overlapping DEcircRNA-DEmiRNA and DEmiRNA-proDEmRNA interactions among the TargetScan, miRanda and RNAhybrid databases were constructed and identified. The circRNA-miRNA-mRNA ceRNA network was constructed using mutual DEmiRNAs in two interaction networks as nodes. mRNAs validated as significantly overexpressed in RCC by Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA) and quantitative polymerase chain reaction (q-PCR), along with the correlative miRNAs, were used for survival analysis. Finally, a ceRNA network with 13 upregulated circRNAs, 8 downregulated miRNAs and 21 upregulated mRNAs was constructed, in which Anti-Silencing Function 1B Histone Chaperone (ASF1B) and Forkhead Box M1 (FOXM1) were considered significant by Oncomine, GEPIA and q-PCR. Survival analysis showed that ASF1B, FOXM1 and hsa_miR_1254 were significantly negatively correlated but hsa_miR_129-5p was positively correlated with overall survival time. Exploration of the ceRNA network revealed the prognostic hsa_circ_0002024/hsa_miR_129-5p/ASF1B axis. Therefore, hsa_circ_0002024 was identified as a prognostic ceRNA that might sponge hsa_miR_129-5p to regulate ASF1B and affect RCC prognosis. However, further validation is needed.
DOI: 10.6084/m9.figshare.14516555.v1
title: Additional file 13 of Ten-eleven translocation protein 1 modulates medulloblastoma progression

description: Additional file 13: Table S12. Summary of MSigPathway results (GREAT analysis).
DOI: 10.6084/m9.figshare.14516588
title: Additional file 6 of Ten-eleven translocation protein 1 modulates medulloblastoma progression

description: Additional file 6: Table S5. Motif analysis results of human 5hmC gain.
DOI: 10.6084/m9.figshare.14516558
title: Additional file 14 of Ten-eleven translocation protein 1 modulates medulloblastoma progression

description: Additional file 14: Table S13. Summary of transcript ID and gene names identified by RNA-seq.
DOI: 10.6084/m9.figshare.14516552.v1
title: Additional file 12 of Ten-eleven translocation protein 1 modulates medulloblastoma progression

description: Additional file 12: Table S11. Motif analysis results of mouse 5hmC gain.
DOI: 10.6084/m9.figshare.14516546.v1
title: Additional file 10 of Ten-eleven translocation protein 1 modulates medulloblastoma progression

description: Additional file 10: Table S9. Summary of similarity between human MBs and SmoA1 MBs (Genes).
DOI: 10.6084/m9.figshare.14516555
title: Additional file 13 of Ten-eleven translocation protein 1 modulates medulloblastoma progression

description: Additional file 13: Table S12. Summary of MSigPathway results (GREAT analysis).
DOI: 10.6084/m9.figshare.14516564
title: Additional file 16 of Ten-eleven translocation protein 1 modulates medulloblastoma progression

description: Additional file 16: Table S15. Summary of 5hmdC and dC in human normal cerebella (n = 5) and human MBs (n = 24).
DOI: 10.6084/m9.figshare.14516594.v1
title: Additional file 8 of Ten-eleven translocation protein 1 modulates medulloblastoma progression

description: Additional file 8: Table S7. Summary of 5hmC gain and loss regions in SmoA1-MBs.
DOI: 10.15156/bio/sh2763026.08fu
title: SH2763026.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.