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22,597 results for “Regulation”
Fungal community composition and genetic potential regulate fine root decay in northern temperate forests
<p>Understanding how genetic differences among soil microorganisms regulate spatial patterns in litter decay remains a persistent challenge in ecology. Despite fine root litter accounting for ~50% of total litter production in forest ecosystems, far less is known about the microbial decay of fine roots relative to aboveground litter. Here, we evaluated whether fine root decay occurred more rapidly where fungal communities have a greater genetic potential for litter decay. Additionally, we tested if linkages between decay and fungal genes can be adequately captured by delineating saprotrophic and ectomycorrhizal fungal functional groups based on whether they have genes encoding certain ligninolytic class II peroxidase enzymes, which oxidize lignin and polyphenolic compounds. To address these ideas, we used a litterbag study paired with fungal DNA barcoding to characterize fine root decay rates and fungal community composition at the landscape scale in northern temperate forests, and we estimated the genetic potential of fungal communities for litter decay using publicly available genomes. Fine root decay occurred more rapidly where fungal communities had a greater genetic potential for decay, especially of cellulose and hemicellulose. Fine root decay was positively correlated with ligninolytic saprotrophic fungi and negatively correlated with ECM fungi with ligninolytic peroxidases, likely because these saprotrophic and ectomycorrhizal functional groups had the highest and lowest genetic potentials for plant cell wall degradation, respectively. These fungal variables overwhelmed direct environmental controls, suggesting fungal community composition and genetic variation are primary controls over fine root decay in temperate forests at regional scales.</p>
Data for: The cerebellum regulates fear extinction through thalamo-prefrontal cortex interactions in male mice
<p>Fear extinction is a form of inhibitory learning that suppresses the expression of aversive memories and plays a key role in the recovery of anxiety and trauma-related disorders. Here, using male mice, we identify a cerebello-thalamo-cortical pathway regulating fear extinction. The cerebellar fastigial nucleus (FN) projects to the lateral subregion of the mediodorsal thalamic nucleus (MD), which is reciprocally connected with the dorsomedial prefrontal cortex (dmPFC). The inhibition of FN inputs to MD in male mice impairs fear extinction in animals with high fear responses and increases the bursting of MD neurons, a firing pattern known to prevent extinction learning. Indeed, this MD bursting is followed by high levels of the dmPFC 4 Hz oscillations causally associated with fear responses during fear extinction, and the inhibition of FN-MD neurons increases the coherence of MD bursts and oscillations with dmPFC 4 Hz oscillations. Overall, these findings reveal a regulation of fear-related thalamo-cortical dynamics by the cerebellum and its contribution to fear extinction.</p>
Spamming the regulator: Exploring a new lobbying strategy in EU competition procedures
<p>Original dataset associated with the article "Spamming the regulator: Exploring a new lobbying strategy in EU competition procedures". Covers all 108 final decisions in EU merger procedures with a Phase II investigation (Council Regulation 139/2004) of which a public version was available in the electronic registry of DG COMP in September 2020.</p>
Genetic variation in mouse islet Ca2+ oscillations reveals novel regulators of islet function
<p class="MsoNormal">Insufficient insulin secretion to meet metabolic demand results in diabetes. The intracellular flux of Ca<sup>2+</sup> into β-cells triggers insulin release. Since genetics strongly influences variation in islet secretory responses, we surveyed islet Ca<sup>2+</sup> dynamics in eight genetically diverse mouse strains. We found high strain variation in response to four conditions: 1) 8 mM glucose; 2) 8 mM glucose plus amino acids; 3) 8 mM glucose, amino acids, plus 10nM GIP; and 4) 2 mM glucose. These stimuli interrogate β-cell function, α-cell to β-cell signaling, and incretin responses. We then correlated components of the Ca<sup>2+</sup> waveforms to islet protein abundances in the same strains used for the Ca<sup>2+</sup> measurements. To focus on proteins relevant to human islet function, we identified human orthologues of correlated mouse proteins that are proximal to glycemic-associated SNPs in human GWAS. Several orthologues have previously been shown to regulate insulin secretion (e.g. ABCC8, PCSK1, and GCK), supporting our mouse-to-human integration as a discovery platform. By integrating these data, we nominated novel regulators of islet Ca<sup>2+</sup> oscillations and insulin secretion with potential relevance for human islet function. We also provide a resource for identifying appropriate mouse strains in which to study these regulators.</p>
Data from: Extensive, transient, and long-lasting gene regulation in a song-controlling brain area during testosterone-induced song development in adult female canaries
<p>Like other canary reproductive behaviors, song production occurs seasonally and can be triggered by gonadal hormones. Adult female canaries treated with testosterone sing first songs after four days and progressively develop towards typical canary song structure over several weeks, a behavior that females otherwise rarely or never show. We compared gene regulatory networks in the song-controlling brain area HVC after 1 hour (h), 3 h, 8 h, 3 days (d), 7d, and 14d testosterone treatment with placebo-treated control females, paralleling HVC and song development. Rapid onset (1 h or less) of extensive transcriptional changes (2,700 genes) preceded the onset of song production by four days. The highest level of differential gene expression occurred at 14 days when song structure was most elaborate, and song activity was highest. The transcriptomes changed massively several times during the two-week of song production. A total of 9,710 genes were differentially expressed, corresponding to about 60% of the known protein-coding genes of the canary genome. Most (99%) of the differentially expressed genes were regulated only at specific stages. The differentially expressed genes were associated with diverse biological functions, of which cellular level occurring early and nervous system level occurring primarily after prolonged testosterone treatment. Thus, the development of adult songs requires restructuring the entire HVC, including most HVC cell types, rather than altering only neuronal subpopulations or cellular components. Parallel regulation directly by androgen and estrogen receptors and by other hub genes such as the transcription factor SP8, which are under steroidogenic control, lead to massive transcriptomic and neural changes in the specific behavior-controlling brain areas and gradual seasonal occurrence of singing behavior.</p>
Insider trading regulation and trader migration
<p>The following is the supplementary material related to the article 'insider trading regulation and trader migration' published in the Journal of Financial Markets.</p>
N6-Methyladenosine Directly Regulates CD40L Expression In CD4+ T Lymphocytes
<p><strong>Abstract</strong></p> <p>T cell activation is a highly regulated process, modulated via the expression of various immune regulatory proteins including cytokines, surface receptors and co-stimulatory proteins. N<sup>6</sup>-methyladenosine (m<sup>6</sup>A) is an RNA modification that can directly regulate RNA expression levels and it is associated with various biological processes. However, the function of m<sup>6</sup>A in T cell activation remains incompletely understood. We identify m<sup>6</sup>A as a novel regulator of the expression of CD40 ligand (CD40L) in human CD4<sup>+</sup> lymphocytes. Manipulation of the m<sup>6</sup>A ‘eraser’ fat mass and obesity-associated protein (FTO) and m<sup>6</sup>A ‘writer’ protein methyltransferase-like 3 (METTL3) directly affects the expression of CD40L. The m<sup>6</sup>A ‘reader’ protein YT521-B homology domain family-2 (YTHDF2) is able to recognize and bind m<sup>6</sup>A specific sequences on the <em>CD40L</em> mRNA and promotes its degradation. This study demonstrates that CD40L expression in human primary CD4<sup>+</sup> T lymphocytes is regulated via m<sup>6</sup>A modifications, elucidating a new regulatory mechanism in CD4<sup>+</sup> T cell activation that could possibly be leveraged in the future to modulate T cell responses in patients with immune-related diseases.</p> <p> </p> <p><strong>Data deposited</strong></p> <ul> <li>meRIP sequencing: read counts of healthy control peripheral blood-derived CD4+ T lymphocytes (figure 1 in article).</li> <li>meRIP sequencing: per gene TMM normalized log2(CPM + 0.168) expression values of healthy control peripheral blood-derived CD4+ T lymphocytes (figure 1 in article). Also includes Log2FoldChange values or each gene.</li> </ul>
Data for: Multilayered regulation of developmentally programmed pre-anthesis tip degeneration of the barley inflorescence
<p><span>In cereal crops such as barley (<em>Hordeum vulgare</em> L.), pre-anthesis tip degeneration (PTD) starts with growth arrest of the inflorescence meristem dome, followed basipetally by the degeneration of floral primordia and the central axis. Due to its quantitative nature and environmental sensitivity, inflorescence PTD constitutes a complex, multilayered trait affecting final grain number. This trait was studied by microscopic dissection of immature inflorescence meristems under standardized growth conditions. We combined spatiotemporal metabolomic, transcriptomic, and genetic approaches to elucidate the mechanism of barley inflorescence PTD in two- and six-rowed barley cultivars 'Bowman' and 'Morex,' respectively. Metabolome profiling includes hormones and primary metabolites such as sugars, TCA intermediates, and amino acids by dividing spike meristems into dying apical and viable central and basal parts at four developmental stages during the spike growth phase. </span>Similarly, RNA sequencing was performed for three developmental stages in both genotypes. RNA sequencing data analyses were performed to identify differentially expressed and tissue-specific transcripts. Further, PTD-associated hub genes were identified by weighted gene coexpression network analysis. Based on transcriptome analyses, we identified an important modulator of inflorescence PTD and functionally validated it using Cas9-mediated mutagenesis and gene-based associated study using a diverse panel of barley accessions. </p>
Source data for "Regulation of replication origin licensing by ORC phosphorylation reveals a two-step mechanism for Mcm2-7 ring closing"
<p>Source data for "Regulation of replication origin licensing by ORC phosphorylation reveals a two-step mechanism for Mcm2-7 ring closing" </p> <p>The data is organized by Figure and associated Supp Figure(s). A README file is included in each figure folder to explain the files.</p> <p>(note: Data is included for Figs2-7 and SuppFigs 2-8. Fig.1 and SuppFig.1 did not have any associated data matrices, so there is no upload for them here). </p> <p>Briefly, the single molecule data is included in several different formats, all generated from single-molecule TIRF-microscopy video files using Matlab:</p> <p>Integrated trace files "traces" include integrated fluorescence intensity at individual DNA molecules over a 20 minute reaction.</p> <p>Background-corrected trace files normalize the integrated fluorescence intensity to a local-average background, as described in the Methods section of the paper-- these are used for EFRET calculations.</p> <p>"Intervals" files include the start, end time, and duration of protein-DNA interactions and were generated from the trace files and the raw videos using the Imscroll program (available at: https://github.com/gelles-brandeis/CoSMoS_Analysis).</p> <p>EFRET vectors are concatenated vectors of the EFRET vs time values of all protein-DNA interactions in the experiment-- these are used to generate EFRET heat maps. </p> <p>Matlab analysis scripts used in the paper are uploaded in a separate folder "ALA_scripts_used_final". Several of these scripts reference custom Matlab functions from the Gelles lab which are available here: https://github.com/gelles-brandeis/jganalyze and should be downloaded along with my attached scripts file. </p> <p>note: Raw single-molecule video files have not been uploaded here due to large sizes, but can be provided upon request. </p>
Data from: Biochemical, structural and dynamical characterizations of the lactate dehydrogenase from Selenomonas ruminantium provide information about an intermediate evolutionary step prior to complete allosteric regulation acquisition in the super family of lactate and malate dehydrogenases.
<p>This data accompanies the paper entitled <strong><em>Biochemical, structural and dynamical characterizations of the lactate dehydrogenase from Selenomonas ruminantium provide information about an intermediate evolutionary step prior to complete allosteric regulation acquisition in the super family of lactate and malate dehydrogenases.</em></strong></p> <p>The zip archive contains the results of molecular dynamics simulations of the 2 systems investigated in the paper: <em>S. rum</em> and <em>T. mar</em> LDHs. The systems have been simulated at 315 K for <em>S. rum </em>and 340 K for <em>T. mar</em>. Final configurations of the proteins after productions are provided for all the systems in GRO Gromos87 format. Trajectories with the positions of the proteins every 100 ps are provided for all the systems in XTC gromacs format.</p>
Decreased FAM13B expression increases atrial fibrillation susceptibility by regulating sodium current and calcium handling
<p><strong>Objectives</strong>: To determine the causal genetic variant and gene and the mechanism for the atrial fibrillation (AF) genome wide association study (GWAS) locus on chromosome 5q31.</p> <p><strong>Background</strong>: <em>FAM13B</em> expression is strongly associated with the lead AF GWAS variant at 5q31. However, the regulatory variant controlling <em>FAM13B</em> expression and the mechanism by which <em>FAM13B</em> impacts AF susceptibility are not known.</p> <p><strong>Methods</strong>: Bioinformatics, reporter gene transfections, gel shifts, and gene editing were used to identify the variant regulating <em>FAM13B</em> expression. RNAseq after <em>FAM13B</em> knockdown in stem cell-derived cardiomyocytes (iCMs) identified downstream processes. Patch clamp and calcium transient assays were used to assess downstream mechanisms. <em>Fam13b</em> knockout (KO) mice were studied for heart structural and functional changes, and pacing-induced arrhythmia.</p> <p><strong>Results</strong>: rs17171731 was identified as the regulatory variant controlling <em>FAM13B</em> expression, with decreased enhancer activity of the risk allele. Knockdown of <em>FAM13B</em> in iCMs altered expression of >1000 genes including <em>SCN2B</em> and led to pro-arrhythmogenic changes in the late sodium current and Ca<sup>2+</sup> cycling. FAM13B is a member of the Rho GTPase-activating protein (RhoGAP) gene family, but failed to demonstrate RhoGAP activity. GFP-tagged <em>FAM13B</em> expressed in iCMs localized at the Z-disc and plasma membrane. Fam13b knockout mice had increased basal p-wave duration and QT interval, and were more susceptible to pacing-induced arrhythmias vs. controls.</p> <p><strong>Conclusions</strong>: This study went from an AF GWAS locus to identify the causal variant and gene, mechanisms for this association, and confirmed arrhythmia susceptibility in <em>Fam13b</em> KO mice. FAM13B and downstream effectors are potential targets for patient-specific therapeutics.</p>
Figure 2 in Response of Little Fire Ant (Hymenoptera: Formicidae) Colonies to Insect Growth Regulators and Hydramethylnon
Figure 2. Number* of sexual brood and abnormal alates produced within Wasmannia auropunctata colonies after exposure to baits containing IGRs.
Figure 1 in Response of Little Fire Ant (Hymenoptera: Formicidae) Colonies to Insect Growth Regulators and Hydramethylnon
Figure 1. Mean worker mortality* (%) in Wasmannia auropunctata colonies after IGR-bait exposure over time.
Data for investigation of cell-type-specific expression and regulation in Setbp1 (p.S858R) atypical SGS male mice
<p><strong>processed.tar.gz, contains all files from the data directory associated with this projects and includes the following:</strong></p> <ul> <li><strong>setbp1_targets.csv</strong> : list of human SETP1 gene targets from ChIP-seq experiments converted to mouse and enriched for additional interactions from literature references (https://pubmed.ncbi.nlm.nih.gov/35685777/ and https://www.nature.com/articles/s41467-018-04462-8, </li> <li><strong>seurat_objects/</strong> <ul> <li><strong>kidney_integrated_celltypes.Rdata </strong>: filtered and preprocessed seurat object with cell type assignments for input to SoupX processing</li> <li><strong>kidney_integrated_celltypes_postSoup.Rdata</strong> : preprocessed and quality controlled seurat object with final cell type assignments after SoupX processing</li> <li><strong>setbp1_cerebralintcelltypes.Rdata</strong> : preprocessed and quality controlled seurat object with final cell type assignments</li> </ul> </li> <li><strong>decoupleR_CollecTRI/</strong> <ul> <li><strong>mouse_prior_tri.csv</strong> : accessed May 2023, it is unclear how versioning or accessions are used so we provided the Mouse CollecTRI (https://github.com/saezlab/CollecTRI) used in this study to calculate cell-type-specific TF activity</li> </ul> </li> <li><strong>motif_inputs/</strong> <ul> <li><strong>Mus_motif_all.txt </strong>: TF-motif PANDA input for mm10 reference genome enriched for</li> </ul> </li> <li><strong>expression_inputs/</strong> : expression matrices for all cell types for both tissues and conditions (n = 50) used to construct cell-type-specific PANDA networks</li> </ul> <p><strong>The below files are from the data/results directory of this associated project and include the following:</strong></p> <ul> <li><strong>alpaca.tar.gz</strong> : directory containing differential community analysis outputs from ALPACA</li> <li><strong>seurat.tar.gz </strong>: directory containing SoupX intermediates, as well as the</li> </ul>
Data from: Single-nucleus RNA-seq and ATAC-seq in outbred rats with divergent cocaine addiction behaviors reveal long-term changes in gene regulation and GABAergic inhibition in the amygdala
<p>This dataset accompanies our publication titled: "Single-nucleus RNA-seq and ATAC-seq in outbred rats with divergent cocaine addiction behaviors reveal long-term changes in gene regulation and GABAergic inhibition in the amygdala."</p> <p><strong>Files Included:</strong></p> <p><strong>1. geno.N26.vcf.gz</strong><br> - Description: Contains genotypes for 26 Heterogeneous Stock rats whose gene expression was predicted.</p> <p><strong>2. pred_expr.Brain.N26.tsv</strong><br> - Description: This tab-delimited table contains predicted relative gene expression in the brain for 26 Heterogeneous Stock rats. <br> - Details: Predictions were made for 8,997 genes from linear models based on cis-eQTLs from whole brain hemisphere tissue downloaded from the <a href="https://ratgtex.org/download/">RatGTEx Portal</a>. A gene is included in the table if it had at least one significant cis-eQTL, and if its predicted expression in these 26 animals had nonzero variance. The values in the table give the predicted log2(relative expression), where log2(2) = 1 is the baseline expression from the two haplotypes of the gene if it had only reference alleles at all its regulatory loci.<br> - Additional Info: Predictions were generated using <strong>gene_expr_pred.py</strong> available at https://github.com/PejLab/gene_expr_pred<br> An explanation of the prediction model is given in https://doi.org/10.1101/2022.01.28.478116</p> <p><strong>3. Behavioral data.xlsx</strong><br> - Description: Contains behavioral data for the Heterogeneous Stock (HS) rats.<br> - Organization: Each sheet in the file corresponds to data for a specific figure.</p> <p><strong>Additional Dataset Locations:</strong></p> <p>The primary datasets generated during this study can be found on the Gene Expression Omnibus under accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE212417">GSE212417</a></p> <p><strong>Publicly Available Datasets Utilized:</strong></p> <p>- Rattus norvegicus Ensembl v98 reference genome and genome assembly: <a href="http://useast.ensembl.org/Rattus_norvegicus/Info/Index">Rnor_6.0 </a><br> - JASPAR2022 transcription factor binding profiles for vertebrates: <a href="https://jaspar.genereg.net/">JASPAR</a><br> - ENCODE Honeybadger 2 ChIP-seq: <a href="https://personal.broadinstitute.org/meuleman/reg2map/">Broad Institute</a><br> - Liu et al. 2019106 GWAS for tobacco and nicotine addiction summary statistics: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6358542/">PubMed</a><br> - RatGTEx Portal tissue-specific cis-eQTLs: <a href="https://ratgtex.org/download/">RatGTEx Portal </a><br> - 1000 Genomes European reference panel: <a href="https://alkesgroup.broadinstitute.org/LDSCORE/">Alkes Group</a><br> - KEGG pathways: <a href="https://www.kegg.jp/kegg/rest/keggapi.html">KEGG API</a></p>
Analysis Products: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains analysis products for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen <em>et al</em>. Please refer to the READMEs in the directories, which are summarized below.</p> <p>The record contains the following files:<br> <br> `clusters.tsv`: <strong> </strong>contains the cluster id, name and colour of clusters in the paper</p> <p><strong>scATAC.zip</strong></p> <p>Analysis products for the single-cell ATAC-seq data. Contains:</p> <p>- `cells.tsv`: list of barcodes that pass QC. Columns include:<br> - `barcode`<br> - `sample`: (time point)<br> - `umap1`<br> - `umap2`<br> - `cluster`<br> - `dpt_pseudotime_fibr_root`: pseudotime values treating a fibroblast cell as root<br> - `dpt_pseudotime_xOSK_root`: pseudotime values treating xOSK cell as root<br> - `peaks.bed`: list of peaks of 500bp across all cell states. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.<br> - `features.tsv`: 50 dimensional representation of each cell <br> - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed` </p> <p><strong>scATAC_clusters.zip</strong></p> <p>Analysis products corresponding to cluster pseudo-bulks of the single-cell ATAC-seq data. </p> <p>- `clusters.tsv`: contains the cluster id, name and colour used in the paper<br> - `peaks`: contains `overlap_reproducibilty/overlap.optimal_peak` peaks called using ENCODE bulk ATAC-seq pipeline in the narrowPeak format.<br> - `fragments`: contains per cluster fragment files </p> <p><strong>scATAC_scRNA_integration.zip</strong></p> <p>Analysis products from the integration of scATAC with scRNA. Contains:</p> <p>- `peak_gene_links_fdr1e-4.tsv`: file with peak gene links passing FDR 1e-4. For analyses in the paper, we filter to peaks with absolute correlation >0.45.<br> - `harmony.cca.30.feat.tsv`: 30 dimensional co-embedding for scATAC and scRNA cells obtained by CCA followed by applying Harmony over assay type.<br> - `harmony.cca.metadata.tsv`: UMAP coordinates for scATAC and scRNA cells derived from the Harmony CCA embedding. First column contains barcode.</p> <p><strong>scRNA.zip</strong></p> <p>Analysis products for the single-cell RNA-seq data. Contains:</p> <p>- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca), knn graphs, all associated metadata. Note that barcode suffix (1-9 corresponds to samples D0, D2, ..., D14, iPSC)<br> - `genes.txt`: list of all genes<br> - `cells.tsv`: list of barcodes that pass QC across samples. Contains:<br> - `barcode_sample`: barcode with index of sample (1-9 corresponding to D0, D2, ..., D14, iPSC) <br> - `sample`: sample name (D0, D2, .., D14, iPSC)<br> - `umap1`<br> - `umap2`<br> - `nCount_RNA`<br> - `nFeature_RNA`<br> - `cluster`<br> - `percent.mt`: percent of mitochondrial transcripts in cell<br> - `percent.oskm`: percent of OSKM transcripts in cell<br> - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt` <br> - `pca.tsv`: first 50 PC of each cell<br> - `oskm_endo_sendai.tsv`: estimated raw counts (cts, may not be integers) and log(1+ tp10k) normalized expression (norm) for endogenous and exogenous (Sendai derived) counts of POU5F1 (OCT4), SOX2, KLF4 and MYC genes. Rows are consistent with `seurat.rds` and `cells.tsv`</p> <p><strong>multiome.zip</strong></p> <p><em>multiome/snATAC:</em></p> <p>These files are derived from the integration of nuclei from multiome (D1M and D2M), with cells from day 2 of scATAC-seq (labeled D2). </p> <p>- `cells.tsv`: This is the list of nuclei barcodes that pass QC from multiome AND also cell barcodes from D2 of scATAC-seq. Includes:<br> - `barcode`<br> - `umap1`: These are the coordinates used for the figures involving multiome in the paper.<br> - `umap2`: ^^^ <br> - `sample`: D1M and D2M correspond to multiome, D2 corresponds to day 2 of scATAC-seq<br> - `cluster`: For multiome barcodes, these are labels transfered from scATAC-seq. For D2 scATAC-seq, it is the original cluster labels. <br> - `peaks.bed`: This is the same file as scATAC/peaks.bed. List of peaks of 500bp. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.<br> - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`.<br> - `features.no.harmony.50d.tsv`: 50 dimensional representation of each cell prior to running Harmony (to correct for batch effect between D2 scATAC and D1M,D2M snMultiome). Rows correspond to cells from `cells.tsv`.<br> - `features.harmony.10d.tsv`: 10 dimensional representation of each cell after running Harmony. Rows correspond to cells from `cells.tsv`.</p> <p><em>multiome/snRNA:</em></p> <p>- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca),associated metadata. Note that barcode suffix (1,2 corresponds to samples D1M, D2M). Please use the UMAP/features from snATAC/ for consistency.<br> - `genes.txt`: list of all genes (this is different from the list in scRNA analysis)<br> - `cells.tsv`: list of barcodes that pass QC across samples. Contains:<br> - `barcode_sample`: barcode with index of sample (1,2 corresponding to D1M, D2M respectively) <br> - `sample`: sample name (D1M, D2M)<br> - `nCount_RNA`<br> - `nFeature_RNA`<br> - `percent.oskm`: percent of OSKM genes in cell<br> - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt` </p>
Reinforcement-based processes actively regulate motor exploration along redundant solution manifolds
<p>From a baby's babbling to a songbird practicing a new tune, exploration is critical to motor learning. A hallmark of exploration is the emergence of random walk behaviour along solution manifolds, where successive motor actions are not independent but rather become serially dependent. Such exploratory random walk behaviour is ubiquitous across species, neural firing, gait patterns, and reaching behaviour. Past work has suggested that exploratory random walk behaviour arises from an accumulation of movement variability and a lack of error-based corrections. Here we test a fundamentally different idea—that reinforcement-based processes regulate random walk behaviour to promote continual motor exploration to maximize success. Across three human-reaching experiments, we manipulated the size of both the visually displayed target and an unseen reward zone, as well as the probability of reinforcement feedback. Our empirical and modelling results parsimoniously support the notion that exploratory random walk behaviour emerges by utilizing knowledge of movement variability to update intended reach aim towards recently reinforced motor actions. This mechanism leads to active and continuous exploration of the solution manifold, currently thought by prominent theories to arise passively. The ability to continually explore muscle, joint, and task redundant solution manifolds is beneficial while acting in uncertain environments, during motor development, or when recovering from a neurological disorder to discover and learn new motor actions.</p>
Global flows of insect transport and establishment: the role of biogeography, trade, and regulations
<p><strong><span>Aim</span></strong><span>:</span><span> Non-native species are part of almost every biological community worldwide, yet numbers of species establishments have an uneven global distribution. Asymmetrical exchanges of species between regions are likely influenced by a range of mechanisms, including propagule pressure, native species pools, environmental conditions, and biosecurity. While the importance of different mechanisms is likely to vary among invasion stages, </span><span>those occurring prior to establishment are difficult to account for. We used records of unintentional insect introductions </span><span>to test 1) whether insects from some biogeographic regions are more likely to be successful invaders, 2) whether the intensity of trade flows between regions determines how many species are intercepted and how many successfully establish, and 3) whether the variables driving invasion success differ pre- and post-introduction.</span></p> <p><strong><span>Location</span></strong><span>: </span><span>Canada, mainland USA, Hawaii, Japan, Australia, New Zealand, Great Britain, South Korea, South Africa.</span></p> <p><strong><span>Methods</span></strong><span>:</span> <span>To </span><span>disentangle processes occurring during the transport and establishment stages</span><span>, we analysed border interceptions of 8,199 insect species as a proxy for transported species flows, and lists of 2,076 established non-native insect species in eight areas.</span></p> <p><strong><span>Results</span></strong><span>: </span><span>During transport, the largest species flows generally originated from the Nearctic, Panamanian and Neotropical regions. Insects native to eight of twelve biogeographic regions were able to establish, with the largest flows of established species on average coming from the Western Palearctic, Neotropical, and Australasian/Oceanian regions.</span><span> Both the </span><span>biogeographic region of origin and trade intensity significantly influenced the size of species flows between regions during transport and establishment. The transported species richness increased with Gross National Income in the source country and decreased with geographic distance. More species were able to establish when introduced within their native biogeographic region.</span></p> <p><strong><span>Main conclusions</span></strong><span>: </span><span>Our results suggest that accounting for processes occurring prior to establishment is crucial for understanding invasion asymmetry in insects, and quantifying regional biosecurity risks.</span></p>
Data for "Coupled carbon and nitrogen cycling regulates the cnidarian-algal symbiosis"
<p>Raw data associated with the publication "Coupled carbon and nitrogen cycling regulates the cnidarian-algal symbiosis". Data associated with individual figures and corresponding analyses are uploaded as separate tabs in the Excel file. Radecker_etal_NanoSIMS.zip contains the individual NanoSIMS images (names according to treatment). Radecker_etal_Chlorophyll_Fluorescence_Images.zip contains exemplary photographs of chlorophyll fluorescence of Aiptasia (names according to treatment).</p> <p> </p> <p> </p>
Using Mobile Technology to Improve Self-Regulation
ClinicalTrials.gov study NCT03774433. IPD Sharing: YES. Countries: 1. Publications: 1.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.