cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35092/yhjc.12424685.v1
title: MRI dataset supporting "Layer-Specific Contributions to Imagined and Executed Hand Movements in Human Primary Motor Cortex"

description: The data file contains nifti files for each run of each participant in our study. The zipped file also contains a 'readMe' file with more information.<br><br>Dataset supports publication: Persichetti, A. S., Avery, J. A., Huber, L., Merriam, E. P., & Martin, A. (2020). Layer-Specific Contributions to Imagined and Executed Hand Movements in Human Primary Motor Cortex. <i>Current Biology</i>. https://doi.org/10.1016/j.cub.2020.02.046<br>Abstract: The human ability to imagine motor actions without executing them (i.e., motor imagery) is crucial to a number of cognitive functions, including motor planning and learning, and has been shown to improve response times and accuracy of subsequent motor actions [ 1 , 2 ]. Although these behavioral findings suggest the possibility that imagined movements directly influence primary motor cortex (M1), how this might occur remains unknown [ 3 ]. Here, we use a non-blood-oxygen-level-dependent (BOLD) method for collecting fMRI data, called vascular space occupancy (VASO) [ 4 , 5 ], to measure neural activations across cortical laminae in M1 while participants either tapped their thumb and forefinger together or simply imagined doing so. We report that, whereas executed movements (i.e., finger tapping) evoked neural responses in both the superficial layers of M1 that receive cortical input and the deep layers of M1 that send output to the spinal cord to support movement, imagined movements evoked responses in superficial cortical layers only. Furthermore, we found that finger tapping preceded by both imagined and executed movements showed a reduced response in the superficial layers (repetition suppression) coupled with a heightened response in the deep layers (repetition enhancement). Taken together, our results provide evidence for a mechanism whereby imagined movements can directly affect motor performance and might explain how neural repetition effects lead to improvements in behavior (e.g., repetition priming).
DOI: 10.35092/yhjc.12441953
title: Diffusion MRI data supporting "Tractography Processing with the Sparse Closest Point Transform"

description: Diffusion MRI data supporting the manuscript entitled "Tractography Processing with the Sparse Closest Point Transform". This includes a population-averaged diffusion tensor imaging dataset, tractography reconstructions of fiber bundles, and classification model parameters for selecting bundles in subject data.<br>Abstract:<br>"We propose a novel approach for processing diffusion MRI tractography datasets using the sparse closest point transform (SCPT). Tractography enables the 3D geometry of white matter pathways to be reconstructed; however, algorithms for processing them are often highly customized, and thus, do not leverage the existing wealth of machine learning (ML) algorithms. We investigated a vector-space tractography representation that aims to bridge this gap by using the SCPT, which consists of two steps: first, extracting sparse and representative landmarks from a tractography dataset, and second transforming curves relative to these landmarks with a closest point transform. We explore its use in three typical tasks: fiber bundle clustering, simplification, and selection across a population. The clustering algorithm groups fibers from single whole-brain datasets using a non-parametric k-means clustering algorithm, with performance compared with three alternative methods and across four datasets. The simplification algorithm removes redundant curves to improve interactive visualization, with performance gauged relative to random subsampling. The selection algorithm extracts bundles across a population using a one-class Gaussian classifier derived from an atlas prototype, with performance gauged by scan-rescan reliability and sensitivity to normal aging, as compared to manual mask-based selection. Our results demonstrate how the SCPT enables the novel application of existing vector-space ML algorithms to create effective and efficient tools for tractography processing. Our experimental data is available online, and our software implementation is available in the Quantitative Imaging Toolkit."
DOI: 10.35092/yhjc.12441953.v1
title: Diffusion MRI data supporting "Tractography Processing with the Sparse Closest Point Transform"

description: Diffusion MRI data supporting the manuscript entitled "Tractography Processing with the Sparse Closest Point Transform". This includes a population-averaged diffusion tensor imaging dataset, tractography reconstructions of fiber bundles, and classification model parameters for selecting bundles in subject data.<br>Abstract:<br>"We propose a novel approach for processing diffusion MRI tractography datasets using the sparse closest point transform (SCPT). Tractography enables the 3D geometry of white matter pathways to be reconstructed; however, algorithms for processing them are often highly customized, and thus, do not leverage the existing wealth of machine learning (ML) algorithms. We investigated a vector-space tractography representation that aims to bridge this gap by using the SCPT, which consists of two steps: first, extracting sparse and representative landmarks from a tractography dataset, and second transforming curves relative to these landmarks with a closest point transform. We explore its use in three typical tasks: fiber bundle clustering, simplification, and selection across a population. The clustering algorithm groups fibers from single whole-brain datasets using a non-parametric k-means clustering algorithm, with performance compared with three alternative methods and across four datasets. The simplification algorithm removes redundant curves to improve interactive visualization, with performance gauged relative to random subsampling. The selection algorithm extracts bundles across a population using a one-class Gaussian classifier derived from an atlas prototype, with performance gauged by scan-rescan reliability and sensitivity to normal aging, as compared to manual mask-based selection. Our results demonstrate how the SCPT enables the novel application of existing vector-space ML algorithms to create effective and efficient tools for tractography processing. Our experimental data is available online, and our software implementation is available in the Quantitative Imaging Toolkit."
DOI: 10.35092/yhjc.12459554
title: Dataset from Mouse 2 for Sit & Goard: Distributed and Retinotopically Asymmetric Processing of Coherent Motion in Mouse Visual Cortex

description: Accompanying dataset for Sit KK, Goard MJ. Distributed and Retinotopically Asymmetric Processing of Coherent Motion in Mouse Visual Cortex.<br><br>This dataset contains the data for mouse 2 / 10<br>Mouse SKKS058<br>See all data in collection here: https://doi.org/10.35092/yhjc.c.5018363<br>These datasets contain calcium imaging data taken from L2/3 of the primary visual cortex in mice. Mice were head fixed and passively watching a random dot kinematogram stimulus. The stimulus data vector for processing these data are also included. The accompanying code to analyze these data can be found at: https://github.com/kevinksit/CoherentMotionProject<br>Data are either in preprocessed DFF matrices, which represent the change in neural activity compared to a baseline or raw images from the microscope. All data are processed with MATLAB 2018b.<br>The purpose of these data is to understand the organization of neural activity in response to coherent motion in the murine visual cortex.<br>The manuscript associated with these data is Sit KK, Goard MJ. Distributed and Retinotopically Asymmetric Processing of Coherent Motion in Mouse Visual Cortex.<br>ABSTRACTPerception of visual motion is important for a range of ethological behaviors in mammals. In primates, specific visual cortical regions are specialized for processing of coherent visual motion. However, whether mouse visual cortex has a similar organization remains unclear, despite powerful genetic tools available for measuring population neural activity. Here, we use widefield and 2-photon calcium imaging of transgenic mice to measure mesoscale and cellular responses to coherent motion. Imaging of primary visual cortex (V1) and higher visual areas (HVAs) during presentation of natural movies and random dot kinematograms (RDKs) reveals varied responsiveness to coherent motion, with stronger responses in dorsal stream areas compared to ventral stream areas. Moreover, there is considerable anisotropy within visual areas, such that neurons representing the lower visual field are more responsive to coherent motion. These results indicate that processing of visual motion in mouse cortex is distributed heterogeneously both across and within visual areas.
DOI: 10.15156/bio/sh1229249.08fu
title: SH1229249.08FU

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.
DOI: 10.15156/bio/sh1229250.08fu
title: SH1229250.08FU

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.
DOI: 10.15156/bio/th028945
title: TH028945
title: Heligmonellidae

description: Here we present the novel 'taxon hypothesis' (TH) concept to accommodate fungal 'species hypothesis' (SH) into existing classification frameworks for taxonomic communication. As supplied with Digital Object Identifiers (DOIs), THs are flexible in time and enable direct cross-communication of higher level taxa along with evolving classification schemes. Every TH includes given number of SHs and consequently fixed number of fungal rDNA ITS sequences. DNA sequences generated by High-throughput or Sanger sequencing studies and identified against UNITE SHs will be automatically assigned to THs as well.
DOI: 10.15156/bio/th028946
title: TH028946
title: Chabertiidae

description: Here we present the novel 'taxon hypothesis' (TH) concept to accommodate fungal 'species hypothesis' (SH) into existing classification frameworks for taxonomic communication. As supplied with Digital Object Identifiers (DOIs), THs are flexible in time and enable direct cross-communication of higher level taxa along with evolving classification schemes. Every TH includes given number of SHs and consequently fixed number of fungal rDNA ITS sequences. DNA sequences generated by High-throughput or Sanger sequencing studies and identified against UNITE SHs will be automatically assigned to THs as well.
DOI: 10.25549/one-c4-49453
title: Ed. F., letters (1966-1969)

description: Ed. F., letter and response from Lewis Bonham (1966-1969)
DOI: 10.35092/yhjc.12344351.v1
title: The fine-mapped associations of 35 lab biomarkers described in 'Genetics of 35 blood and urine biomarkers in the UK Biobank'

description: The dataset contains the output from FINEMAP, a software to identify causal variants from genome-wide association summary statistics, for 35 biomarker traits described in the following preprint:<br>N. Sinnott-Armstrong*, Y. Tanigawa*, et al, Genetics of 38 blood and urine biomarkers in the UK Biobank. bioRxiv, 660506 (2019). doi:10.1101/660506<br><br>Note that we are preparing a revised version of the manuscript and this dataset contains 35 (instead of 38) biomarker phenotypes.<br><br>For each trait, we provide a tar archive file which contains the full output from FINEMAP for the regions with at least one genome-wide significant associations (p &lt; 5e-9) from the multi-ethnic GWAS meta-analysis within UK Biobank. The content of the tar archive is organized by directories, named as <code>chr[CHROM]/[TRAIT]/range[RANGE]</code>, and contains the following files: <code>GLOBAL_[TRAIT]_chr[CHROM]_range[RANGE].bdose.zst</code><code>GLOBAL_[TRAIT]_chr[CHROM]_range[RANGE].config.zst</code><code>GLOBAL_[TRAIT]_chr[CHROM]_range[RANGE].cred.zst</code><code>GLOBAL_[TRAIT]_chr[CHROM]_range[RANGE].ld.zst</code><code>GLOBAL_[TRAIT]_chr[CHROM]_range[RANGE].master.zst</code><code>GLOBAL_[TRAIT]_chr[CHROM]_range[RANGE].snp.zst</code><code>GLOBAL_[TRAIT]_chr[CHROM]_range[RANGE].z.zst</code> where, [TRAIT]<code></code><code></code>: trait name[CHROM]<code></code><code></code>: chromosome[RANGE]<code></code><code></code>: the range of the region in the format of <code>[start position]-[end position]</code>. We provide the list of traits and regions included in this data release in <code>FINEMAP.index.tsv</code>. This is a flat table with 3 columns: trait: the biomarker traitchr: chromosomerange: the range Note that we used GRCh37/hg19 genome reference in the analysis and the BETA is always reported for the alternate allele. Please check the FINEMAP paper and software documentation for the detailed explanation of the file formats. Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).Christian Benner. FINEMAP. http://christianbenner.com/. Also, all the files in the tar archive is compressed with Zstandard (as indicated by the <code>.zst</code> extension). You can check the contents with <code>zstdcat</code> command and uncompress the files with <code>zstd -d [file.zst]</code>. The Zstandard software can be built from source, or simply available from conda (https://anaconda.org/conda-forge/zstd), pip (https://pypi.org/project/zstd/) or brew (https://formulae.brew.sh/formula/zstd). Please check Zstandard website (http://facebook.github.io/zstd/) for more information.
DOI: 10.35092/yhjc.12355424.v1
title: The multi-PRS weights computed with the 35 lab biomarkers described in 'Genetics of 35 blood and urine biomarkers in the UK Biobank'

description: The dataset contains the multi-PRS weights computed with the 35 biomarker traits described in the following preprint:<br>N. Sinnott-Armstrong*, Y. Tanigawa*, et al, Genetics of 38 blood and urine biomarkers in the UK Biobank. bioRxiv, 660506 (2019). doi:10.1101/660506<br><br>Note that we are preparing a revised version of the manuscript and this dataset contains 35 (instead of 38) biomarker phenotypes.<br><br>The list of disease endpoints included in this dataset is: angina, alcoholic cirrhosis, gallstones, hypertension, cholecystitis, kidney failure, heart failure, myocardial infarction, gout, and type 2 diabetes (T2D).<br><br>We provide weights of the 23 polygenic risk scores characterized by multi-PRS. The list of models are summarized in list_of_multi-PRS-models.tsv. This index file the following columns: Filename: the filename of polygenic risk score weights in this dataset.Trait: the disease outcome.Covariate_adjustment: a binary variable indicating whether the multi-PRS model is trained with covariate (age, sex, and PC1-10) adjustment.Family_history_adjustment: a binary variable indicating whether the multi-PRS model is trained with family history.Note: Additional information when relevant. For the PRS models listed with "Covariate_adjustment == TRUE", we fit multi-PRS regression model adjusted by age, sex, and 10 principal components where as the ones with "Covariate_adjustment == FALSE" we did not use those covariates.<br><br>For T2D, we have two sets of models: (1) models trained for Eastwood et al. T2D cases in UK Biobank and (2) models trained for Eastwood et al. T2D cases in UK Biobank vs. filtered controls with HbA1c &lt; 39.<br><br>For myocardial infarction, we provide a model with family history adjustment, <code>weights_familyhistory.HC326.tsv.gz</code>. This model is trained with covariates (age, sex, and 10 principal components) and family history of heart disease as covariates.<br><br>Please read our manuscript for more details. For each PRS model listed in <code>list_of_multi-PRS-models.tsv</code>, we provide a compressed tab-delimited files, which contain the multi-PRS weights. The files have the following columns: CHROM: the chromosomePOS: the positionID: the variant identifierREF: the reference alleleALT: the alternate alleleA1: the risk alleleweights.: the coefficients (weights) of the PRS Note that we used GRCh37/hg19 genome reference in the analysis and the BETA is always reported for the alternate allele.<br><br>The multi-PRS weights files are compressed with <code>gzip</code>. One should be able to read those files with the standard <code>gzip</code>/<code>zcat</code>.
DOI: 10.35092/yhjc.12401162.v1
title: Supplemental Table 6: Gene ontology analysis of the CTNNB1 profile feminization-suppressible genes in chicken embryonic gonads after in ovo exposure to ethynylestradiol

description: Supplemental Table 6 supporting: Keiko Shioda et al. “Transcriptomic and epigenetic preservation of genetic sex identity in estrogen-feminized male chicken embryonic gonads” <br>Supplemental Table 6: Gene ontology analysis of CTNNB1 group differentially expressed genes.<br><br>Chicken embryos (29 total) were exposed to 20 microgram/egg ethynylestradiol or vehicle oil emulsion from incubation day 6, and their gonads were collected on day 19. Expression of several genes, represented by CTNNB1, in chicken embryonic gonads were strongly suppressed upon feminization after in ovo exposure to ethynylestradiol. This table shows outcome of a gene ontology analysis of the CTNNB1 group genes using the NIH LHRI DAVID service.
DOI: 10.35092/yhjc.12457805
title: Dataset from Mouse 10 for Sit & Goard: Distributed and Retinotopically Asymmetric Processing of Coherent Motion in Mouse Visual Cortex

description: Accompanying dataset for Sit KK, Goard MJ. Distributed and Retinotopically Asymmetric Processing of Coherent Motion in Mouse Visual Cortex.<br><br>This dataset contains the data for mouse 10 / 10<br>Mouse SKKS053<br><br>See all data in collection here: https://doi.org/10.35092/yhjc.c.5018363<br>These datasets contain calcium imaging data taken from L2/3 of the primary visual cortex in mice. Mice were head fixed and passively watching a random dot kinematogram stimulus. The stimulus data vector for processing these data are also included. The accompanying code to analyze these data can be found at: https://github.com/kevinksit/CoherentMotionProject<br>Data are either in preprocessed DFF matrices, which represent the change in neural activity compared to a baseline or raw images from the microscope. All data are processed with MATLAB 2018b.<br>The purpose of these data is to understand the organization of neural activity in response to coherent motion in the murine visual cortex.<br>The manuscript associated with these data is Sit KK, Goard MJ. Distributed and Retinotopically Asymmetric Processing of Coherent Motion in Mouse Visual Cortex.<br>ABSTRACTPerception of visual motion is important for a range of ethological behaviors in mammals. In primates, specific visual cortical regions are specialized for processing of coherent visual motion. However, whether mouse visual cortex has a similar organization remains unclear, despite powerful genetic tools available for measuring population neural activity. Here, we use widefield and 2-photon calcium imaging of transgenic mice to measure mesoscale and cellular responses to coherent motion. Imaging of primary visual cortex (V1) and higher visual areas (HVAs) during presentation of natural movies and random dot kinematograms (RDKs) reveals varied responsiveness to coherent motion, with stronger responses in dorsal stream areas compared to ventral stream areas. Moreover, there is considerable anisotropy within visual areas, such that neurons representing the lower visual field are more responsive to coherent motion. These results indicate that processing of visual motion in mouse cortex is distributed heterogeneously both across and within visual areas.
DOI: 10.35092/yhjc.12459299
title: Dataset from Mouse 6 for Sit & Goard: Distributed and Retinotopically Asymmetric Processing of Coherent Motion in Mouse Visual Cortex

description: Accompanying dataset for Sit KK, Goard MJ. Distributed and Retinotopically Asymmetric Processing of Coherent Motion in Mouse Visual Cortex.<br><br>This dataset contains the data for mouse 6 / 10<br>Mouse SKKS047<br><br>See all data in collection here: https://doi.org/10.35092/yhjc.c.5018363<br>These datasets contain calcium imaging data taken from L2/3 of the primary visual cortex in mice. Mice were head fixed and passively watching a random dot kinematogram stimulus. The stimulus data vector for processing these data are also included. The accompanying code to analyze these data can be found at: https://github.com/kevinksit/CoherentMotionProject<br>Data are either in preprocessed DFF matrices, which represent the change in neural activity compared to a baseline or raw images from the microscope. All data are processed with MATLAB 2018b.<br>The purpose of these data is to understand the organization of neural activity in response to coherent motion in the murine visual cortex.<br>The manuscript associated with these data is Sit KK, Goard MJ. Distributed and Retinotopically Asymmetric Processing of Coherent Motion in Mouse Visual Cortex.<br>ABSTRACTPerception of visual motion is important for a range of ethological behaviors in mammals. In primates, specific visual cortical regions are specialized for processing of coherent visual motion. However, whether mouse visual cortex has a similar organization remains unclear, despite powerful genetic tools available for measuring population neural activity. Here, we use widefield and 2-photon calcium imaging of transgenic mice to measure mesoscale and cellular responses to coherent motion. Imaging of primary visual cortex (V1) and higher visual areas (HVAs) during presentation of natural movies and random dot kinematograms (RDKs) reveals varied responsiveness to coherent motion, with stronger responses in dorsal stream areas compared to ventral stream areas. Moreover, there is considerable anisotropy within visual areas, such that neurons representing the lower visual field are more responsive to coherent motion. These results indicate that processing of visual motion in mouse cortex is distributed heterogeneously both across and within visual areas.