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13,531 results for “Down Syndrome”
Evolution of invasion syndrome in invasive goldenrod is not constrained by genetic trade-offs
<p>A suite of plant traits is thought to make weed populations highly invasive, including vigorous growth and reproduction, superior competitive ability, and high dispersal ability. Using a breeding design and a common garden experiment, we tested whether such an "invasion syndrome" has evolved in an invasive range of Solidago altissima, and whether the evolution is likely to be genetically constrained. We found an overall shift in invasive phenotypes between native North American and invasive Japanese populations. The invasive populations were taller and produced more leaves, suggesting a superior ability to exploit limited resources. The populations also produced more allelopathic compounds that can suppress competitor growth. Finally, invasive populations produced more seeds, which are smaller and are released from a greater height, indicating a potential for superior dispersal ability than the native populations. Quantitative genetics analyses found a large amount of additive genetic variation in most focal traits across native and invasive populations, with no systematic differences in its magnitude between the ranges. Genetic covariances among three traits representing invasion strategies (leaf mass, polyacetylene concentration and seed size) were small. The R metric, which measures the effect of genetic covariances on the rate of adaptation, indicated that the covariance neither constrains nor accelerates concerted evolution of these traits. The results suggest that the invasion syndrome in S. altissima has evolved in the novel range due to ample additive genetic variation, and relatively free from genetic trade-offs.</p>
Data for Cell-type-specific alternative splicing in the cerebral cortex of a Schinzel-Giedion Syndrome patient variant mouse model
<p><span><strong>data.tar.gz </strong>contains all files from the data directory (except for sam outputs from STAR) associated with the 230926_EJ_Setbp1_AlternativeSplicing GitHub project and includes the following files:</span></p> <p> </p> <p><span><strong>./marvel: </strong>- </span><span>This directory contains rds and Rdata objects that were created using the MARVEL R package</span></p> <p><span>cell_type_goresults.rds - This is the go results split by cell type</span></p> <p><span>marvel_04_split_counts.Rdata - This R data includes all environment objects from MARVEL script 04, and is used for downstream plotting</span></p> <p><span>normalized_sj_expression.Rds - This object is the normalized splice junction expression</span></p> <p><span>Setbp1_marvel_aligned.rds - Final prepared MARVEL object before any SJU analyses have been run</span></p> <p><span>significant_tables.RData - For those who do not want to load multiple massive files, this includes all significant SJU results for each cell type</span></p> <p><span>sj_usage_cell_type.rds - This data object has splice junction usage calculated for each cell type</span></p> <p><span>sj_usage_condition.rds - This data object has splice junction usage calculated for each cell type and also split by condition</span></p> <p> </p> <p><strong><span>./seurat: </span></strong><span>- This directory contains all intermediate and final Seurat single-cell gene expression objects</span></p> <p><span>annotated_brain_samples.rds - This is the final iteration of the processing in Seurat for a final annotated object. Please use this object for any Seurat or single-cell gene expression analyses.</span></p> <p><span>clustered_brain_samples.rds - This is the clustered Seurat object, before cell type annotation based on canonical markers.</span></p> <p><span>filtered_brain_samples_pca.rds - This is the filtered Seurat object, before clustering but after PCA.</span></p> <p><span>filtered_brain_samples.rds - This is the filtered Seurat object, before PCA.</span></p> <p><span>integrated_brain_samples.rds - This the integrated Seurat object, before other steps.</span></p> <p> </p> <p><span><strong>./star: </strong>- </span><span>All files in the STAR directory are outputs from STARsolo, as described in our methods. Each output directory contains the same files, so only one example is included here for brevity. Intermediate SAM files were removed to optimize space.</span></p> <p><span>J1/ - This directory contains outputs for brain sample J1</span></p> <p><span>J13/ - This directory contains outputs for brain sample J13</span></p> <p><span>J15/ - This directory contains outputs for brain sample J15</span></p> <p><span>J2/ - This directory contains outputs for brain sample J2</span></p> <p><span>J3/ - This directory contains outputs for brain sample J3</span></p> <p><span>J4/ - This directory contains outputs for brain sample J4</span></p> <p><span>K1/ - This directory contains outputs for kidney sample K1</span></p> <p><span>K2/ - This directory contains outputs for kidney sample K2</span></p> <p><span>K3/ - This directory contains outputs for kidney sample K3</span></p> <p><span>K4/ - This directory contains outputs for kidney sample K4</span></p> <p><span>K5/ - This directory contains outputs for kidney sample K5</span></p> <p><span>K6/ - This directory contains outputs for kidney sample K6</span></p> <p> </p> <p><span><strong>./star/genome:</strong> - This directory contains outputs from running STAR genomeGenerate. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>chrLength.txt</span></p> <p><span>chrNameLength.txt</span></p> <p><span>chrName.txt</span></p> <p><span>chrStart.txt</span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>Genome</span></p> <p><span>genomeParameters.txt</span></p> <p><span>Log.out</span></p> <p><span>SA</span></p> <p><span>SAindex</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p> <p> </p> <p><span><strong>./star/J1:</strong> - This is the head STAR directory for sample J1. It contains logs, basic QC, and gene and splice junction counts. For more information about the STAR pipeline and its outputs, please refer to the STAR documentation</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>Log.final.out</span></p> <p><span>Log.out</span></p> <p><span>Log.progress.out</span></p> <p><span>SJ.out.tab</span></p> <p><span>Solo.out/</span></p> <p><span>STARgenome/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out:</strong>- This directory contains the outputs used for downstream analysis</span></p> <p><span>Barcodes.stats</span></p> <p><span>GeneFull_Ex50pAS/</span></p> <p><span>SJ/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS: </strong>- This directory contains the filtered and raw barcodes, features, and matrix files for gene expression (including introns)</span></p> <p><span>Features.stats</span></p> <p><span>filtered/</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p><span>UMIperCellSorted.txt</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/filtered: </strong>- This directory contains the filtered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages)</span></p> <p><span>barcodes.tsv.gz - This file contains filtered cell barcodes</span></p> <p><span>features.tsv.gz - This file contains filtered features (genes)</span></p> <p><span>matrix.mtx.gz - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/raw: </strong>- This directory contains the unfiltered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages). Files are the same as previously described for filtered.</span></p> <p><span>barcodes.tsv</span></p> <p><span>features.tsv</span></p> <p><span>matrix.mtx</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ: </strong>- This directory contains the QC and raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>Features.stats</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ/raw:</strong> - This directory contains the raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>barcodes.tsv - This file contains filtered cell barcodes</span></p> <p><span>features.tsv - This file contains filtered features (splice junctions)</span></p> <p><span>matrix.mtx - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/_STARgenome:</strong> - This directory contains the STARgenome created and used by STAR for this sample. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p>
Emergence of inflammatory fibroblasts with aging in Hermansky-Pudlak syndrome associated pulmonary fibrosis
<p>The longitudinal cellular interactions that drive pulmonary fibrosis are not well understood. To investigate the disease underpinnings associated with fibrosis onset and progression, we generated a scRNAseq atlas of lungs from young and aged mouse models of multiple subtypes of Hermansky-Pudlak syndrome (HPS), a collection of rare autosomal recessive diseases associated with albinism, platelet dysfunction, and pulmonary fibrosis. We identified an age-dependent increase in SAA3<sup>+</sup> inflammatory lung fibroblasts in HPS mice, including in double-mutant HPS1-2 mice which develop spontaneous fibrosis. HPS1 fibroblasts showed increased expression of IL-1R1, whereas alveolar type II epithelial cells from HPS2 mice induced the inflammatory gene signature in co-cultured fibroblasts. scRNAseq of lung tissue from three HPS1 patients similarly showed the presence of inflammatory fibroblasts and increased <em>IL1R1</em> expression on fibroblasts. These data posit complex interactions between dysfunctional epithelial cells, inflammatory fibroblasts, and recruited immune cells, suggesting potential opportunities for mitigation of the fibrotic cascade. </p> <p>This record contains gzipped anndata h5ad (hdf5) files corresponding to the human scRNA-seq data analyzed during this study. One file (`adata_processed_data_generated_in_study_zenodo.h5ad`) contains only data derived from new samples processed in this study. Another file (`adata_processed_combined_zenodo.h5ad`) contains the aforementioned data from new samples, as well as data from previously published studies that are publicly available (Guo et. al., 2023, Tsukui et.al., 2022, Habermann et. al., 2020). Raw gene expression counts, as well as normalized counts, are provided as values in the `layers` attribute of the anndata object. Cell annotations and projection coordinates are similarly provided in the `obs` and `obsm` attributes of the object, respectively. The AnnData and Scanpy python libraries are required to load and re-process these data. </p>
Rett Syndrome Omics Datasets integrated into RettDb
<p>This datasets collection is part of the web resource: RettDb, the Rett Syndrome Omics Database.</p> <p>RettDb is fully described in this paper submitted to DATABASE: Cillàri, N., Neri, G., Pisanti, N., Milazzo, P., Borello, U. "RettDb: the Rett Syndrome Omics Database to navigate the Rett Syndrome genomic landscape" </p> <p> </p> <p>The table below contains a brief description of the data</p> <p>For a full description of the data, refer to the web resourse or this publication</p> <p> </p> <table> <tbody> <tr> <td><strong>filenames</strong></td> <td><strong>description</strong></td> <td><strong>file-type</strong></td> </tr> <tr> <td>H3K4me3_wt_Chip_seq.bedgraph.gz</td> <td>H3K4me3 ChipSeq</td> <td>bedgraph </td> </tr> <tr> <td>H3K4me3_wt_Chip_seq.bedgraph.gz.tbi</td> <td>H3K4me3 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>H3K9me3_wt_Chip_seq.bedgraph.gz</td> <td>H3K9me3 ChipSeq</td> <td>bedgraph </td> </tr> <tr> <td>H3K9me3_wt_Chip_seq.bedgraph.gz.tbi</td> <td>H3K9me3 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>H3K27ac_wt_Chip_seq.bedgraph.gz</td> <td>H3K27ac ChipSeq</td> <td>bedgraph </td> </tr> <tr> <td>H3K27ac_wt_Chip_seq.bedgraph.gz.tbi</td> <td>H3K27ac ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>H3K36me3_wt_Chip_seq.bedgraph.gz</td> <td>H3K36me3 ChipSeq</td> <td>bedgraph </td> </tr> <tr> <td>H3K36me3_wt_Chip_seq.bedgraph.gz.tbi</td> <td>H3K36me3 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>H4K20me3_wt_Chip_seq.bedgraph.gz</td> <td>H4K20me3 ChipSeq</td> <td>bedgraph </td> </tr> <tr> <td>H4K20me3_wt_Chip_seq.bedgraph.gz.tbi</td> <td>H4K20me3 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>Mecp2_wt_ChiP_seq_1.bedgraph.gz</td> <td>Mecp2 ChipSeq</td> <td>bedgraph </td> </tr> <tr> <td>Mecp2_wt_ChiP_seq_1.bedgraph.gz.tbi</td> <td>Mecp2 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>Mecp2_wt_ChiP_seq_2.bedgraph.gz</td> <td>Mecp2 ChipSeq</td> <td>bedgraph </td> </tr> <tr> <td>Mecp2_wt_ChiP_seq_2.bedgraph.gz.tbi</td> <td>Mecp2 ChipSeq Index</td> <td>bedgraph index</td> </tr> <tr> <td>Mecp2_wt_ChiP_seq_3.bw.gz.bw</td> <td>Mecp2 ChipSeq</td> <td>bigwig</td> </tr> <tr> <td>Mecp2_RNA_seq_WT_vs_KO.bed.gz</td> <td>Mecp2 RNASeq </td> <td>bed</td> </tr> <tr> <td>Mecp2_RNA_seq_WT_vs_KO.bed.gz.tbi</td> <td>Mecp2 RNASeq Index</td> <td>bed index</td> </tr> <tr> <td>Mecp2_wt.hic</td> <td>Mecp2 WT chromatin conformation data</td> <td>hic</td> </tr> <tr> <td>Mecp2_ko.hic</td> <td>Mecp2 KO chromatin conformation data</td> <td>hic</td> </tr> <tr> <td>CpG_island_mm10.bed.gz</td> <td>CpG island</td> <td>bed </td> </tr> <tr> <td>CpG_island_mm10.bed.gz.tbi</td> <td>CpG island Index</td> <td>bed index</td> </tr> <tr> <td>FIMO_CISBP_TFBS_predictions.bed.gz</td> <td>FIMO CISBP TFBS Predictions</td> <td>bed </td> </tr> <tr> <td>FIMO_CISBP_TFBS_predictions.bed.gz.tbi</td> <td>FIMO CISBP TFBS Predictions Index</td> <td>bed index</td> </tr> <tr> <td>remap2022_CRMs.bb</td> <td>Remap2022 Cis Regulatory Modules</td> <td>bigbed</td> </tr> <tr> <td>remap2022_non_redundant_peak.bb</td> <td>Remap2022 Non Redundant Peaks</td> <td>bigbed</td> </tr> <tr> <td>hub.config.json</td> <td>Tracks configuration file for WashU</td> <td>json</td> </tr> </tbody> </table> <p> </p>
Data from: Repeated evolution of vertebrate pollination syndromes in a recently diverged Andean plant clade
While specialized interactions, including those involving plants and their pollinators, are often invoked to explain high species diversity, they are rarely explored at macroevolutionary scales. We investigate the dynamic evolution of hummingbird and bat pollination syndromes in the centropogonid clade (Lobelioideae: Campanulaceae), an Andean-centered group of ∼550 angiosperm species. We demonstrate that flowers hypothesized to be adapted to different pollinators based on flower color fall into distinct regions of morphospace, and this is validated by morphology of species with known pollinators. This supports the existence of pollination syndromes in centropogonids, an idea corroborated by ecological studies. We further demonstrate that hummingbird pollination is ancestral, and that bat pollination has evolved ∼13 times independently, with ∼11 reversals. This convergence is associated with correlated evolution of floral traits within selective regimes corresponding to pollination syndrome. Collectively, our results suggest that floral morphological diversity is extremely labile, likely resulting from selection imposed by pollinators. Finally, even though this clade's rapid diversification is partially attributed to their association with vertebrate pollinators, we detect no difference in diversification rates between hummingbird- and bat-pollinated lineages. Our study demonstrates the utility of pollination syndromes as a proxy for ecological relationships in macroevolutionary studies of certain species-rich clades.
Barth Syndrome Cardiac Tissue Proteomics Dataset
<p>Spectral counts from LC/MS proteomics of left ventricular tissue from deidentified Barth Syndrome patients (BTHS), along with tissue from age-matched non-failing controls (NF) and patients with idiopathic dilated cardiomyopathy. </p>
data set regarding to project- Malnutrition, sarcopenia and malnutrition-sarcopenia syndrome in Older Adults with COPD
<p><strong>data set regarding to project- Malnutrition, sarcopenia and malnutrition-sarcopenia syndrome in Older Adults with COPD</strong></p>
Neurophysiological assessment in a patient affected by Marfan syndrome
<p>Marfan syndrome (MS) is a hereditary connective tissue disorder characterized by different multiorgan patterns. The guidelines for MS diagnosis do not highlight the usefulness—or even the use—of any neurophysiological techniques for diagnosing this disease. Moreover, few neurophysiological studies assessing the central and peripheral nervous systems in MS subjects have been reported to date. Case presentation: We describe a male patient affected by MS. To assess sensory and nociceptive pathways in this patient, a neurophysiological assessment was performed using electroencephalogram, nerve conduction studies, and somatosensory and laser-evoked potentials. To the best of our knowledge, this is the first published case report to evaluate the role of evoked potential assessments for the study of sensory and nociceptive pathways in MS. Future studies should investigate the use of a complete neurophysiological approach for the clinical and therapeutic management of MS patients in a large sample.</p>
Brain functional connectivity in chronic tic disorders and Gilles de la Tourette syndrome
<p>The pathophysiology of chronic tic disorder (cTD) and Gilles de la Tourette syndrome (GTS) is characterized by the dysfunction of both motor and non - motor cortico - striatal - thalamo - cortical (CSTC) circuitries, which leads to tic release and comorbids. A role of fronto - parietal network (FPN) connectivity breakdown has been postulated for tic pathogenesis, given that the FPN entertain connections with limbic, paralimbic, and CSTC networks. Our study was aimed at characterizing the FPN functional connectivity in cTD and GTS in order to assess the role of its deterioration in tic severity and the degree of comorbids. We recorded scalp EEG during resting state in patients with cTD and GTS. The eLORETA current source densities were analyzed, and the lagged phase synchronization (LPS) was calculated to estimate nonlinear functional connectivity between cortical areas. We found that the FPN functional connectivity in delta band was more detrimental in more severe GTS patients. Also, the sensorimotor functional connectivity in beta2 band was stronger in more severe cTD and GTS patients. FPN functional connectivity deterioration correlated with comorbids presence and severity in patients with GTS. Our data suggest that a FPN disconnection may contribute to the motoric symptomatology and comorbid severity in GTS, whereas sensorimotor disconnection may contribute to tic severity in cTD and GTS. Although preliminary, our study points out a differently disturbed brain connectivity between patients with cTD and GTS. This may serve as diagnostic marker and potentially interesting base to develop pharmacological and noninvasive neuromodulation trials aimed at reducing tic symptomatology.</p>
Dispersal syndromes are poorly associated with climatic niche differences in the Azorean seed plants
<p><b>Aim: </b>Environmental niche tracking is linked to the species ability to disperse. While well investigated on large spatial scales, dispersal constraints also influence small-scale processes and may explain the difference between the potential and the realized niche of species at small-scales. Here we test whether niche size and niche fill differ systematically according to dispersal syndrome within isolated oceanic islands. We expect species with higher dispersal abilities (anemochorous or endozoochorous) will have a higher niche fill, despite of their environmental niche size.</p> <p><b>Location:</b> Azores archipelago</p> <p><b>Taxon:</b> Native seed plants</p> <p><b>Methods:</b> We combined a georeferenced database of the species distribution within the archipelago (Azorean Biodiversity Portal/GBIF) with an expert-based dispersal syndrome categorization and a high-resolution climatic grid (CIELO model). Using four climatic variables (Annual Mean Temperature, Mean Diurnal Range, Annual Precipitation, Precipitation Seasonality), we calculated a 4-dimensional hypervolume to estimate the niche size of each species. Niche fill was quantified as the suitable climatic space of the island that was occupied by the focal species.</p> <p><b>Results:</b> Endozoochorous species display higher niche fill compared to epizoochorous and hydrochorous species, and larger niches than anemochorous and epizoochorous. Differences among the remaining groups are not significant neither for niche fill nor for niche size.</p> <p><b>Main Conclusions:</b> Although endozoochorous species track their niche more efficiently at small-scales than other dispersal syndromes, the differences between dispersal syndromes are not consistent. The ability of a species to track its niche at small-scales is not tightly related to its dispersal syndrome. Although intuitively appealing, dispersal syndrome classifications might not be the most appropriate tools for understanding dispersal processes at small-scales.</p>
Big brown bat (Eptesicus fuscus) capture records before and after white-nose syndrome
<p>We collated 30 years of big brown bat capture records collected between 1990 to 2020. We collected data from wildlife agencies and researchers in the eastern US. We kept capture records that fell within the months of March through October, representing spring through fall months when bats are surveyed outside of hibernacula. We then paired this data with spatiotemporal spread of the fungal pathogen <em>Pseudogymnoascus destructans</em> (causal agent of white-nose syndrome that kills North American temperate bats), of which, big brown bats are susceptible to infection. The completed dataset represents 30,497 individual big brown bat captures across 3,797 unique sites.</p>
URInary peptidomic patterns of Long-COVID syndrome "UriCoV"
<p>Post acute sequelae of SARS-CoV-2 infection (PASC), also referred to as long COVID, is the most frequent, yet poorly characterized sequelae of COVID-19. It has tremendous, unpredictable consequences on personal health and socioeconomic status of affected individuals, and on global economic issues. UriCov is a multidisciplinary, comprehensive project with the aim to investigate in depth the molecular phenotype(s) of individual patients previously infected by SARS-CoV-2 and identify patients at risk of PASC. This will be achieved through multi-disciplinary research based on omics and clinical data in a bioinformatics framework, based on the hypothesis that endothelial damage is a key event in PASC. The developed molecular tools may allow patient stratification to initiate personalized treatment for prevention of PASC prior to symptoms and to decrease the PASC incidence. UriCoV will also provide missing fundamental knowledge on the molecular pathophysiology of PASC.</p>
Drugs prescribed in Phelan-McDermid Syndrome differentially impact sensory behaviors in shank3 zebrafish models
<p>Altered sensory processing is a pervasive symptom in individuals with Autism Spectrum Disorders (ASD). Especially in cases of profound autism, individuals are often medicated to manage behaviors like aggression and/or self-harm and/or epilepsy, and it remains unclear how these medications might impact perception/sensory processing. To test this, we screened three medications, risperidone, Lithium Chloride (LiCl), and carbamazepine (CBZ), prescribed to individuals with Phelan-McDermid Syndrome (PMS) and one drug, 2-Methyl-6-(phenylethynyl) pyridine (MPEP) tested in rodent models of PMS, for their effects on a sensory-induced behavior in two zebrafish PMS models with frameshift mutations in the N- or C- termini. PMS is caused by deletions of the terminal end of chromosome 22 or point mutations in <em>Shank3</em>. People with PMS can present with an array of symptoms including ASD, epilepsy, gastrointestinal distress, and reduced responses to sensory stimuli. Our zebrafish mutant shank3ab models likewise show reduced responses in a Visual Motor Response (VMR) assay, in which increased locomotion is triggered by light-to-dark transitions. To test how pharmacological treatments affect the VMR, we exposed larvae to selected drugs for twenty-four hours and then quantified their locomotion during four ten-minute cycles of lights on-to-off stimuli. We found that risperidone normalized the VMR in <em>shank3</em> models, LiCl and CBZ had no effect on the VMR in any of the three genotypes. MPEP reduced the VMR in WT to levels seen in <em>shank3</em> models but caused no changes in either <em>shank3</em> model. Our work shows that the effects of drugs on sensory processing is varied in a way that can be highly genotype- and drug-dependent.</p>
Sources of variation in reciprocal herkogamy in the distyly floral syndrome of Linum tenue (Linaceae)
<p>Distyly is a floral polymorphism involving reciprocal herkogamy shaped by selection for pollen transfer efficiency. The variation of the floral organs involved in pollen transfer can be individually affected by environmental and genetic sources of variance, but the organ development will be canalized to minimize reciprocal inaccuracy between anthers and stigmas, as this is the focus of selection. We measured floral organ and cell length of both morphs of distylous <em>Linum tenue</em> (Linaceae) at different developmental stages of field- and glasshouse-grown plants. We analyzed the results to measure reciprocal inaccuracy and identify sources of variance.</p>
Dataset: A three-dimensional approach to general plant fire syndromes
<p>1. Plant fire syndromes are usually defined as combinations of fire response traits, the most common being resprouting (R) and seeding (S). Plant flammability (F), on the other hand, refers to a plant's effects on communities and ecosystems. Despite its important ecological and evolutionary implications, F has rarely been considered to define plant fire syndromes and, if so, usually separated from response syndromes.</p> <p>2. We propose a three-dimensional model that combines R, S and F, encapsulating both plant response to fire regimes and the capacity to promote them. Each axis is divided into three possible standardized categories, reflecting low, medium and high values of each variable, with a total of 27 possible combinations of R, S and F.</p> <p>3. We hypothesized that different fire histories should be reflected in the position of species within the three-dimensional space and that this should help assess the importance of fire as an evolutionary force in determining R-S-F syndromes.</p> <p>4. To illustrate our approach we compiled information on the fire syndromes of 24 dominant species of different growth forms from the Chaco seasonally-dry forest of central Argentina, and we compared them to 33 species from different Mediterranean-type climate ecosystems (MTCEs) of the world.</p> <p>5. Chaco and MTCEs species differed in the range (seven syndromes vs. thirteen syndromes, respectively) and proportion of extreme syndromes (i.e. species with extreme values of R, S and/or F) representing 29% of species in the Chaco vs. 45% in the MTCEs.</p> <p>6. Additionally, we explored the patterns of R, S and F of 4032 species from seven regions with contrasting fire histories, and found significantly higher frequencies of extreme values (predominantly high) of all three variables in MTCEs compared to the other regions, where intermediate and low values predominated, broadly supporting our general hypothesis.</p> <p>7. The proposed three-dimensional approach should help standardize comparisons of fire syndromes across taxa, growth forms and regions with different fire histories. This will contribute to the understanding of the role of fire in the evolution of plant traits and assist vegetation modelling in the face of changes in fire regimes.</p>
Video supplement to "A randomized, controlled, crossover trial of median nerve stimulation for treatment of Tourette syndrome"
<p>Video from the last 60 s of Block 5 (stimulation off) and the first 60 s of Block 6 (stimulation on) from the rhythmic <span class="math-tex">\(\pm \)</span> arrhythmic visits of three study participants who reported substantial improvement in tic severity on at least one visit.</p>
Datset related to article: "ACTB gene mutation in combined Dystonia-Deafness syndrome with parkinsonism: Expanding the phenotype and highlighting the long-term GPi DBS outcome"
<p>Sanger sequences (.abi files) validating variants found with whole exome sequencing</p>
Metabolomics data: glutaminase inhibition in combination with azacytidine in myelodysplastic syndromes
<p>Malignancies can become reliant on glutamine as an alternative energy source and as a facilitator of aberrant DNA methylation, thus implicating glutaminase (GLS) as a potential therapeutic target. We demonstrate preclinical synergy of Telaglenastat (CB-839), a selective GLS inhibitor, when combined with AZA, <em>in vitro</em> and <em>in vivo</em>, followed by a phase Ib/II study of the combination in patients with advanced MDS. Treatment with Telaglenastat/AZA led to an ORR of 70% with CRs in 66% patients and a median overall survival of 11.6 months. scRNAseq and flow cytometry demonstrated a myeloid differentiation program at the stem cell level in clinical responders. Expression of non-canonical glutamine transporter, SLC38A1, was found to be overexpressed in MDS stem cells and was associated with clinical responses to Telaglenastat/AZA; was predictive of worse prognosis in a large MDS cohort. These data demonstrate the safety and efficacy of a combined metabolic and epigenetic approach in MDS.</p>
Recurrent, founder and hypomorphic variants contribute to shaping the genetic landscape of Joubert syndrome
<p><strong>Introduction: </strong>This database includes the raw data linked with the paper “Recurrent, founder and hypomorphic variants contribute to the genetic landscape of Joubert syndrome”. In this paper we reported eleven recurrent variants in seven distinct JS genes occurring in our large European JS cohort, including a previously unreported variant in KIAA0586 (c.1006C>T).</p> <p><strong>Methods</strong>: We evaluated the frequencies of these variants in our cohort of >500 European JS patients and compared them with controls (from three large Italian non-JS cohorts and from the gnomAD database), and with an independent cohort of about 600 JS probands from the United States.</p> <p><strong>Results: </strong>All variants were markedly enriched in the European JS cohort compared to all controls. When comparing allele frequencies in the two JS cohorts, the Ashkenazi Jewish founder variant (TMEM216 c.218G>T) was significantly enriched in American JS compared to European JS patients, while the MKS1 c.1476T>G variant was about ten times more frequent among European JS. Frequencies of all remaining variants were comparable in the two cohorts. Genotyping of several microsatellite markers across the gene loci in carriers of seven variants identified four novel founder haplotypes. Of note, MKS1 c.1476T>G was consistently detected in compound heterozygosity with deleterious variants in JS patients, while it was found in homozygosity in an unaffected parent. Functional studies on fibroblasts from this healthy carrier and her affected son showed a similarly reduced percentage of ciliated cells compared to unaffected controls, but much shorter cilia in the patient than in the unaffected homozygous parent, consistent with a hypomorphic effect.</p>
Genetic dissection of triplicated chromosome 21 orthologs yields varying skeletal traits in Down syndrome model mice
<p><span class="normaltextrun">Down syndrome (DS) phenotypes result from triplicated genes, but effects of three copy genes are not well known. A mouse mapping panel genetically dissecting human chromosome 21 (Hsa21) syntenic regions was used to investigate the contributions and interactions of triplicated Hsa21 orthologous genes on mouse chromosome 16 (Mmu16) on skeletal phenotypes. Skeletal structure and mechanical properties were assessed in femurs of male and female Dp9Tyb, Dp2Tyb, Dp3Tyb, Dp4Tyb, Dp5Tyb, Dp6Tyb, Ts1Rhr, and Dp1Tyb;<em>Dyrk1a</em><sup>+/+/-</sup> mice. Dp1Tyb mice, with the entire Hsa21 homologous region of Mmu16 triplicated, display bone deficits similar to those of humans with DS and served as a baseline for other strains in the panel. Bone phenotypes varied based on triplicated gene content, sex, and bone compartment. Three copies of <em>Dyrk1a</em> played a sex-specific, essential role in trabecular deficits and may interact with other genes to influence cortical deficits related to DS. Triplicated genes in Dp9Tyb and Dp2Tyb mice improved some skeletal parameters. As triplicated genes can both improve and worsen bone deficits, it is important to understand the interaction between and molecular mechanisms of skeletal alterations affected by these genes.</span></p>
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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.