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13,531 results for “Down Syndrome”
Data from the behavioural and Magnetic resonance imaging of the Ts66Yah and Ts65Dn male model of Down syndrome
<p>Please find enclosed the behavioural and Magnetic Resonnance Imaging (MRI) variables used for comparing the Ts66Yah DS models with the parental line Ts65Dn. The raw data are found as two CVS files</p> <p>- Behavioural phenoParameters_Ts65Dn_Ts66Yah.csv</p> <p>- MRI phenoParameters_Ts65Dn_Ts66Yah.csv</p> <p>while the processed data used for the GDAPHEN analysis (https://github.com/YaH44/GDAPHEN/releases/tag/Public) are available as Excel docs.</p> <p> </p> <p>The processing has been done with a low level of imputation for missing data detailed in the Formating_decision_phenoParameters_Ts65Dn_Ts66Yah. ...</p>
DATA SET: Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units
<p>This repository contains the data sets of the article:</p> <p>Mesquida, J., Caballer, A., Cortese, L. <em>et al.</em> Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units. <em>Crit Care</em> <strong>25, </strong>381 (2021). https://doi.org/10.1186/s13054-021-03803-2</p>
Data associated with "A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations"
<p><strong>Datasets used in <em>A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations.</em></strong></p> <p><strong>Datatypes:</strong></p> <ol> <li>Multi-index pandas dataframe (.pkl)</li> <li>Numpy array (.npy)</li> <li>Collection of numpy arrays (.npz)</li> <li>Python dictionary objects (.pkl)</li> </ol> <p><strong>Datasets:</strong></p> <p><strong>alignments.pkl: A dataframe containing numpy arrays of image displacements for each mouse in each memory context.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id. The columns are ['T', 'F1', 'N1', 'F2', 'N2'] for the training, recall 1-hour, neutral, recall 1-day, neutral day 2 memory contexts respectively. Each element of this dataframe is a numpy array of shape images x 2 that hold x and y image displacements respectively. These alignments are computed after the inscopix software motion correction and are used in Supplemental Figure 2 of the paper.</p> <p><strong>behavior_df.pkl: A dataframe of behavior readouts recorded by a camera positioned above the mice in each context chamber.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id.The columns are; sample times (*_time), freezing boolean arrays (*_freeze), x-positions in context chamber (*_x) and y-positions in the context chamber (*_y) for each context (*) in ('Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2').</p> <p><strong>correlated_pairs_df.pkl: A dataframe containing arrays of neuron indices that have a correlation in activity pattern > 0.3.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment ('NA'). The columns contain ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing each memory context. Each element of the dataframe is a numpy array with three columns. The first two columns are the neuron indices that are correlated and the last column is the strength of the correlation.</p> <p><strong>dredd_freezes_df.pkl: A dataframe containing freezing percentages for SOM-Cre and RTT-SOM-Cre mice treated with DREADDS.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment (mcherry, hm3d, hm4d). The columns contain one of ['Neutral', 'Fear', 'Fear_2']. Each element of the dataframe is a freezing percentage for a single mouse. This dataframe is built from reading the dredd_behavior.xlsx excel file. This is used to generate figure 5E of the paper.</p> <p><strong>high_degree_df.pkl: A dataframe containing list of high degree neuron indices.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment ('NA'=not applicable since no DREADD used). The columns contain ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing each memory context. Each element of the dataframe is a list of neuron indices that are high-degree cells.</p> <p><strong>N006_wt_basis.npz: a dict containing three numpy arrays representing the basis images for mouse N006 of genotype wild-type.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 220 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Supplemental Figure 2 of the paper.</p> <p><strong>N006_wt_cxtbasis.pkl: A dictionary containing arrays for basis images and singular values for each context.</strong></p> <p>This dictionary has keys, ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing the memory contexts. Each value is a 2 element list containing the U-basis images as column vectors and singular values, one per basis image in U. The shape of the basis images is the same shape stored in N006_wt_basis.pkl. This dataset is used in Supplementary Figure 2 to track cells across contexts of the CFC task (see also N006_wt_cxtsources.pkl)</p> <p><strong>N006_wt_cxtsources.pkl: A dictionary containing the independent component source images computed from the basis images for automatically identifying regions of interest (ROIs). </strong></p> <p>The dictionary is keyed on ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] contexts. Each value in the dictionary at a given key is a 3-D numpy array of shape sources x height x width. These data were used to construct the source images and max intensity projection image of the sources in Supplemental Figure 2F-J of the paper.</p> <p><strong>N006_wt_rois.pkl: A dictionary containing the boundaries and annuli coordinates of all rois for mouse N006 of genotype wild-type.</strong></p> <p>This dictionary is keyed on ['boundaries', 'annuli'] contexts and each value is a 179 element list of arrays of boundary line coordinates or annulus point coordinates one per ROI detected for this mouse.</p> <p><strong>N006_wt_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N006 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=205 source images, height=517 pixels and width=704 pixels. This data was used to construct Supplemental Figure 3F.</p> <p><strong>N019_wt_basis.npz: a dict containing three numpy arrays representing the basis images for mouse N019 of genotype wild-type.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 220 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Figure 1C of the paper.</p> <p><strong>N019_wt_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N019 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=204 source images, height=516 pixels and width=698 pixels. This data was used to construct Figure 1C of the paper.</p> <p><strong>P80_animals.pkl: A pandas multi-index object containing the genotype, mouse_id and treatment of the top 80% behavioral performance animals.</strong></p> <p>In this study, we drop the lowest 20% performing WT and RTT animals based on freezing percentage during the recall contexts. This multi-index is used to filter the data before each computation or plot in this study. So for example Figure 1B contains only the top 80% performing WT and RTT mice.</p> <p><strong>pc_sipscs_amps.pkl: A dictionary containing the amplitudes of spontaneous IPSCs recorded in pyramidal cells of WT and RTT mice.</strong></p> <p>This dictionary is keyed on ['wt', 'mecp2_pos', 'mecp2_neg'] representing whether the pyramidal cell was recorded from a wild-type mouse ('wt') or is an MeCP2 negative or MeCP2 positive RTT cell. This value under each key is an array of IPSC amplitudes, one per recorded cell. This data was used to construct Figure 4C in the paper.</p> <p><strong>pc_sipscs_freqs.pkl: A dictionary containing the frequencies of spontaneous IPSCs recorded in pyramidal cells of WT and RTT mice.</strong></p> <p>This dictionary is keyed on ['wt', 'mecp2_pos', 'mecp2_neg'] representing whether the pyramidal cell was recorded from a wild-type mouse ('wt') or is an MeCP2 negative or MeCP2 positive RTT cell. This value under each key is an array of IPSC frequencies, one per recorded cell. This data was used to construct Figure 4C in the paper.</p> <p><strong>rois_df.pkl: A multi-index dataframe containing all ROI information for each non-DREADD treated cell in this study (Figures 1-3).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0. The columns are ['centroid', 'cell_boundary', 'annulus_boundary']. The centroid for each cell is a 2-tuple of row, column pixel centroid coordinates. The cell_boundary is a two-column array of row, col boundary points for each ROI. The annulus_boundary is a two-column array of row, column interior points in the annulus. The annulus region excludes points of overlap with nearby cell bodies (See STAR methods of the paper).</p> <p><strong>signals_df.pkl: A multi-index dataframe containing calcium signals, inferred spikes and metadata for all Non-DREADD experiments used in this study (Figs 1-3).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0 and going up to 5771 cells. The columns are ['channels', 'channel', 'num_pages', 'width', 'height', 'bits', 'Train_signals', 'Fear_signals', 'Neutral_signals', 'Cue_signals', 'Fear_2_signals', 'Neutral_2_signals', 'Cue_2_signals', 'Train_spikes', 'Fear_spikes', 'Neutral_spikes', 'Cue_spikes', 'Fear_2_spikes', 'Neutral_2_spikes', 'Cue_2_spikes', 'sample_rate']. The channels are all the recorded channels, the channels is the channel on which ROIs were detected, the width and height are the image dimensions, the bits is the image bit depth of the calcium movie. The *_signals' are the df/f signals for each cell in each context. Each signal is a numpy array with the first 800 samples have been set to NAN due to settling time of the miniscope. The '*_spikes' are the inferred spikes for each cell stored as an image index. This signal and spike indices can be converted to time using the sample column. This dataframe is used in the construction of Figures 1-3 in the paper.</p> <p><strong>som_behavior_df.pkl: A dataframe of behavior readouts recorded by a camera positioned above the mice in each context chamber.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id. The columns are; sample times (*_time), freezing boolean arrays (*_freeze), x-positions in context chamber (*_x) and y-positions in the context chamber (*_y) for each context in *=('Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'). This dataframe was not used in the paper but may still be useful for further analysis.</p> <p><strong>som_sepsc_amplitudes:</strong> <strong>A dictionary containing the amplitudes of spontaneous EPSCs recorded in SOM cells of WT and RTT mice with and without MeCP2.</strong></p> <p>A dictionary with keys ['som', 'som_rett_pos', 'som_rett_neg'] for WT SOM and RTT-SOM cells with and without MeCP2 respectively. Each value is a list of sEPSC amplitudes. This data was used in Figure 4E-G.</p> <p><strong>som_sepsc_freqs:</strong> <strong>A dictionary containing the amplitudes of spontaneous EPSCs recorded in SOM cells of WT and RTT mice with and without MeCP2.</strong></p> <p>A dictionary with keys ['som', 'som_rett_pos', 'som_rett_neg'] for WT SOM and RTT-SOM cells with and without MeCP2 respectively. Each value is a list of sEPSC frequencies. This data was used in Figure 4E-G.</p> <p><strong>som_signals_df.pkl: A multi-index dataframe containing calcium signals, inferred spikes and metadata for all Non-DREADD SOM cell recordings used in this study (Figs 5).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0 and going up to 710 cells. The columns are ['channels', 'channel', 'num_pages', 'width', 'height', 'bits', 'Train_signals', 'Fear_signals', 'Neutral_signals', 'Cue_signals', 'Fear_2_signals', 'Neutral_2_signals', 'Cue_2_signals', 'Train_spikes', 'Fear_spikes', 'Neutral_spikes', 'Cue_spikes', 'Fear_2_spikes', 'Neutral_2_spikes', 'Cue_2_spikes', 'sample_rate']. The channels are all the recorded channels, the channels is the channel on which ROIs were detected, the width and height are the image dimensions, the bits is the image bit depth of the calcium movie. The *_signals' are the df/f signals for each cell in each context. Each signal is a numpy array with the first 800 samples have been set to NAN due to settling time of the miniscope. The '*_spikes' are the inferred spikes for each cell stored as an image index. This signal and spike indices can be converted to time using the sample column. This data was used to construct Figure 5B-C.</p> <p><strong>ssn33_sstcre_basis.npz: a dict containing three numpy arrays representing the basis images for mouse ssn33 of genotype sst-cre.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 100 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Figure 5A of the paper.</p> <p><strong>ssn33_sstcre_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N019 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=86 source images, height=516 pixels and width=654 pixels. This data was used to construct Figure 5A of the paper.</p>
Raw data of individuals with Down syndromre, individuals with Williams syndrome, healthy children and adults in a visual learning task, a conditional learning task and a transitive inference task.
<p>Raw data of 17 individuals with Down syndrome (8 girls/women; average age: 17.8 years; range: 7.2-30.8 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p> <p>Raw data of 27 individuals with Williams syndrome (16 girls/women; average age: 23.7; range: 9.4-43.8 at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p> <p>Raw data of 71<strong> </strong>healthy children (31 girls; average age: 6.42 years; range: 2.95-11.64 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p> <p>Raw data of 22 healthy adults (11 femaleswomen; average age: 26.05 years; range: 20.32-29.76 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p>
DATA to support Dyrk1a function in glutamatergic neurons in mouse models of Mental Retardation Disease 7 (MRD7) and Down syndrome (or trisomy 21)
<p>Four datasets are provided here to support the function of Dyrk1a in glutamatergic neurons in mouse models of Mental Retardation Disease 7 (MRD7) and Down syndrome (or trisomy 21):</p> <p>- RNAseq data to compare hippocampal expressed genes at postnatal day 30, in the complete inactivation of Dyrk1a in glutamatergic neurons using a Dyrk1a floxed-allele and the Camk2:Cre transgene</p> <p>- data from all the figures</p> <p>-data from all the supplementary figures </p> <p>-data from the quantitative proteomic analysis made from hippocampal extract of wt, Dyrk1a heterozygote, Dp(16)1Yey and Dp(16)1Yey with only two functional copies of Dyrk1a</p> <p>Detailed information are available in the article by Brault et al 2021, deposited in Biorachiv https://doi.org/10.1101/2021.05.01.442242 </p>
Acute Respiratory Distress Syndrome-Database of Genes (ARDS-DB)
<p>To better understand the gene level associations that are most relevant to Acute Respiratory Distress Syndrome (ARDS), a comprehensive resource is needed. There’s currently no freely available database dedicated to ARDS that provides comprehensive gene lists from experimentally verifiable studies, gene function, gene location, and additional metadata for tracking related link out resources. The need for such a database is only accentuated by the steep rise in ARDS cases due to the 2020 Coronavirus pandemic, in which infected patients admitted to the ICU develop ARDS at a rate of 67% to 85%, calling for an increase into ARDS research. </p> <p>Our goal was to develop such a resource for use by the scientific community to enhance our studies of ARDS and associated genes. Our first step was to perform data mining and curation of scientific literature through a robust review process. Subsequent steps enabled us to refine our data by capturing specific metadata and incorporating these into our database. The version 1 of the database will provide users with access to the database flat file with current genes, gene location, chromosomal information, and more in a freely accessible and downloadable format. Future project goals are to develop a standalone web portal that will integrate the gene level information with network analysis, and other visualizations for users. </p> <p> </p>
Dataset for the paper "Prolonged prothrombin time as an early prognostic indicator of severe acute respiratory distress syndrome in patients with COVID-19 related pneumonia"
<p>The dataset contains the data on ICU-transferred (N=100) and Stable (N=131) patients with COVID-19 (N=156) and Non-COVID-19 viral pneumonia (N=75). Among COVID-19 patients of this study, 82 patients developed Refractory Respiratory Failure (RRF) or Severe Acute Respiratory Distress Syndrome (SARDS) and were transferred to Intensive Care Unit (ICU), 74 patients had a Stable course of disease and were not transferred to ICU. Collected data are presented as a table with columns:<br> - Gender;<br> - Age (years);<br> - SARS-CoV-2 RT-PCR testing results;<br> - Time between the disease onset and admission to the hospital (days);<br> - Time between admission to the hospital and transfer to ICU (days);<br> - Artificial lung ventilation in ICU needed;<br> - C-reactive protein (CRP) upon admission (mg/L);<br> - International Normalized Ratio (INR) upon admission;<br> - Prothrombin Time (PT) upon admission (sec.);<br> - Fibrinogen upon admission (mg/L);<br> - Chest Computed Tomography (CT) upon admission: lung tissue affected (%);<br> - Platelet count upon admission (10^9/L);<br> - Chest CT, 1 week after admission: lung tissue affected (%);<br> - CRP, 1 week after admission (mg/L);<br> - Platelet count, 1 week after admission (10^9/L).</p>
Cell-free DNA Fragmentomics and Second Malignant Neoplasm Risk in Patients with PTEN Hamartoma Tumor Syndrome
<p><strong>Supplementary dataset for manuscript:</strong></p> <p><strong>Cell-free DNA Fragmentomics and Second Malignant Neoplasm Risk in Patients with PTEN Hamartoma Tumor Syndrome</strong></p> <p>Affiliations:</p> <p><sup>1</sup>Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA</p> <p><sup>2</sup>Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH 44195, USA</p> <p><sup>3</sup>Rose Ella Burkhardt Brain Tumor and Neuro-Oncology Center, Cleveland Clinic, Cleveland, OH 44195, USA</p> <p><sup>4</sup>Center for Personalized Genetic Healthcare, Medical Specialties Institute, Cleveland Clinic, Cleveland, OH 44195, USA</p> <p><sup>5</sup>Center for Immunotherapy and Precision Immuno-oncology, Cleveland Clinic, Cleveland, OH 44195, USA</p> <p><sup>6</sup>Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH 44195, USA</p> <p><sup>7</sup>Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA</p> <p><sup>8</sup>Germline High Risk Cancer Focus Group, Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH 44106, USA</p> <p><strong><em>Correspondence</em></strong>:</p> <p>Darren Liu, MD, MS (<a href="https://orcid.org/0000-0002-6703-1843">https://orcid.org/0000-0002-3693-5145)</a></p> <p>Cleveland Clinic Genomic Medicine Institute</p> <p>9500 Euclid Avenue, Cleveland, OH 44195, USA</p> <p>E-mail: liud5@ccf.org </p>
Genome-wide association analyses identify novel Brugada syndrome risk loci and highlight a new mechanism of sodium channel regulation in disease susceptibility
<p>The Brugada syndrome GWAS summary statistics</p> <p>Brugada syndrome is a cardiac arrhythmia disorder associated with sudden death in young adults. With the exception of <em>SCN5A</em>, encoding the cardiac sodium channel Na<sub>V</sub>1.5, susceptibility genes remain largely unknown. We performed a genome-wide association meta-analysis comprising 2,820 unrelated cases with Brugada syndrome and 10,001 controls.</p> <p> </p>
Dysglycemias in patients admitted to ICUs with severe acute respiratory syndrome due to COVID-19 versus other causes – A cohort study - Dataset
<p>Dataset of a cohort whose summary is described below.</p> <p>Abstract</p> <p>Importance: Dysglycemias have been associated with worse prognosis in critically ill patients with or without diabetes, but data on their association with severe COVID-19 and outcomes are lacking. Objectives: To analyze the relationship of dysglycemias with COVID-19 in hospitalized patients with severe acute respiratory syndrome (SARS) and assess the influence of dysglycemias on mortality. Design, Setting and Participants: Cohort of consecutive patients with SARS and suspected COVID-19 hospitalized in intensive care units (ICUs) across eight hospitals in Curitiba-Brazil. Main Outcomes and Measures: The primary outcome was the influence of COVID-19 on the variation of the following parameters of dysglycemia: highest glucose level at admission, mean and highest glucose levels during ICU stay, mean glucose variation, and percentage of days with hyperglycemia. The secondary outcome was the influence of COVID-19 and each of the five parameters of dysglycemia on hospital mortality within 30 days from ICU admission. Results: We compared 703 patients with COVID-19 and 138 without COVID-19 admitted to the ICUs due to SARS. Compared with patients without COVID-19, those with COVID-19 had significantly higher glucose peaks at admission (198.1mg/dL vs. 167.8mg/dL, respectively) and during ICU stay (285.9mg/Dl vs. 230.9md/dL), higher mean daily glucose values (167.9mg/dL vs. 149.8mg/dL), higher percentage of days with hyperglycemia during ICU stay (vs. 45.0 vs. 31.5), and greater mean daily glucose variations (85.3mg/dL vs. 63.5mg/dL). However, these associations were lost after adjustment for APACHE II scores, SOFA scores, CRP level, corticosteroid use and nosocomial infection. Dysglycemia and COVID-19 were each independent risk factors for mortality. Conclusions and Relevance: Patients with SARS due to COVID-19 had higher mortality and more frequent dysglycemia than patients with SARS due to other causes. This association seemed to be related to disease severity and inflammation and was independent of corticosteroid use, suggesting no specific relationship with the SARS-CoV-2 infection.</p>
Cerebral 3-[18F]fluoro-5-(2-pyridinylethynyl)benzonitrile ([18F]FPEB) uptake and fragile X messenger ribonucleoprotein in men with fragile X syndrome
<p>Legends. Cerebral3-[<sup>18</sup>F]fluoro-5-(2-pyridinylethynyl)benzonitrile([<sup>18</sup>F]FPEB)uptake and fragileX messenger ribonucleoprotein in men with fragile X syndrome</p> <p>Table S1. Concomitant medications of participants from the Institute for Neurodegenerative Disorders (IND)</p> <p>Table S2. Demographic and clinical characteristics of participants from the Institute for Neurodegenerative Disorders (IND)</p> <p>Table S3. Demographic and clinical characteristics of participants from the Johns Hopkins University (JHU)</p> <p>Table S4. Genetic and psychological assessments of participants from the Institute for Neurodegenerative Disorders (IND)</p> <p>Table S5. Genetic and psychological assessments of participants from the Johns Hopkins University (JHU)</p> <p>Table S6. Fragile X Mental Retardation Protein (FMRP) in nanogram per microgram total protein for ten healthy people with typical development (TD) with normal CGG repeat sizes [range (20, 37)] utilizing the same analyses as the other participants</p> <p>Table S7. Positron emission tomography (PET) data and analyses for participants from the Institute for Neurodegenerative Disorders (IND)</p> <p>Table S8. Positron emission tomography (PET) data and analysis of participants from the Johns Hopkins University (JHU)</p> <p>Table S9. Correlation coefficients between Fragile X Mental Retardation Protein (FMRP) (Table S4) and [18F]FPEB uptake (Wong DF et al. 2013) for participants with fragile X syndrome (Table S7)</p> <p>Table S10. Correlation coefficients between Fragile X Mental Retardation Protein (FMRP) (Table S4) and [18F]FPEB uptake (Wong DF et al. 2013) for participants with fragile X syndrome and fragile X syndrome-mosaicism (Table S7)</p> <p>Poster. Cerebral3-[<sup>18</sup>F]fluoro-5-(2-pyridinylethynyl)benzonitrile([<sup>18</sup>F]FPEB)uptake and fragileX messenger ribonucleoprotein in men with fragile X syndrome</p> <p>Video. Cerebral3-[<sup>18</sup>F]fluoro-5-(2-pyridinylethynyl)benzonitrile([<sup>18</sup>F]FPEB)uptake and fragileX messenger ribonucleoprotein in men with fragile X syndrome</p> <p>The authors thank Flora Tassone, Ph.D., Department of Biochemistry and Molecular Medicine, School of Medicine, UC Davis Health, Sacramento, California, for providing genetic and protein data about participants.</p>
A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Syndromes Dataset
<p>Simulated sydromes measurement of quantum surface code error correction.<br>Used for the paper: "<a href="https://doi.org/10.48550/arXiv.2307.09463">A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction</a>".</p> <p>File names: <code>d-<surface_code_distance>_pfr-<physical_fault_rate>_nb-<number_of_samples></code></p> <p>Each file is formatted as csv with the following columns:</p> <ul> <li>label: binary label (0: no error, 1: error)</li> <li>syndromes: syndrome measurement sequence (tuples of the form (round, syndromes))</li> <li>quantity: number of samples for this label + syndrome sequence</li> </ul> <p>Only distance 3 is currently available with 10M samples for each physical fault rate.</p> <p>The data generation relies on <a href="https://github.com/quantumlib/Stim" target="_blank" rel="noopener">Stim</a>.</p>
Dataset related to article "Molecular Studies and ex vivo Complement assay on Endothelium Highlight the Genetic Complexity of Atypical Hemolytic Uremic Syndrome: The Case of a Pedigree With a Null CD46 Variant".
<p><em>The files contain raw data related to the article "Molecular Studies and ex vivo Complement assay on Endothelium Highlight the Genetic Complexity of Atypical Hemolytic Uremic Syndrome: The Case of a Pedigree With a Null CD46 Variant", available from <a href="https://www.frontiersin.org/articles/10.3389/fmed.2020.579418/full">https://www.frontiersin.org/articles/10.3389/fmed.2020.579418/ful</a>l.</em></p> <p>File <strong>"Genetic and clinical data"</strong>:</p> <ul> <li>In the sheet "485 aHUS patients" are reported data obtained from the screening of 485 unrelated patients with aHUS including rare variants (RVs) in complement disease-associated genes (<em>CFH, CD46, CFI, C3, CFB </em>and <em>THBD</em>), the presence of <em>CFH-CFHR</em> genomic rearrangements and/or anti-FH antibodies.</li> <li>In the sheet "Pedigrees with c.286+2T>G" are listed all pedigrees carrying the c.286+2T>G variant, the diseases status of all subjects and the age of disease onset of patients. In bold are indicated pedigrees (n=7) used to study the penetrance of aHUS in c.286+2T>G carriers.</li> <li>In the sheet "Haplotypes" are reported genotypes used to evaluate the association between the presence of <em>CFH-H3</em> and <em>CD46<sub>GGAAC</sub></em> risk haplotypes and aHUS. Results of this analysis are reported in Table 3 of the published paper.</li> <li>In the sheet "Raw data Fig.2" are reported data of "platelet count" and "serum creatinine" of the proband used to elaborate Figure 2.</li> </ul> <p>In the file <strong>"C3 and C5b-9 deposition"</strong> is reported the quantification of serum-induced C3 and C5b-9 deposition on human microvascular endothelial cell line (HMEC-1). The fluorescent staining was evaluated with Image J and expressed as pixel<sup>2 </sup>per field analyzed. The fields with the lowest and highest values were excluded from calculation. These values were used to elaborate data included in Table 2 and in Figure 5.</p> <p>In the file <strong>"CD46 protein expression"</strong> are reported data of CD46 expression on peripheral blood mononuclear cells (PBMCs) isolated from the proband, his relatives and healthy volunteers. Data of specific expression of CD46 (evaluated for SCR1 or for SCR4 as reported in the materials and methods section) are indicated as median fluorescence intensity (MFI) percentage compared with the control.</p> <p>In the ppt file <strong>"cDNA amplification and sequencing results"</strong> is reported:</p> <ul> <li>the agarose gel image of the amplified cDNA from the control (ctr), the proband (IV-8) and his healthy father (III-7).</li> <li>Electropherograms obtained from the cDNA sequencing of the control (ctr), the proband (IV-8) and his healthy father (III-7).</li> </ul> <p>Additional data will be made available by the authors, without undue reservation, to any qualified researcher. </p>
Risk factor prediction for Secondary Glaucoma amongst patients presenting with Pseudo exfoliation Syndrome (PEX) at Ophthalmology OPD in a Tertiary Care Centre in Ahmedabad
<p>Here we are uploading a data sheet of the<strong> "Risk factor prediction for Secondary Glaucoma amongst patients presenting with Pseudo exfoliation Syndrome (PEX) at Ophthalmology OPD in a Tertiary Care Centre in Ahmedabad." </strong></p>
Data, scripts, and figures of the article: The effect of oregano essential oils on Feed Passage Syndrome in broilers: 2. Assessment under a challenge model
<p>This data set contains the data, JMP scripts, and figures of the article titled "The effect of oregano essential oil on Feed Passage Syndrome in broilers: 2. Assessment under a challenge model" to be published in the journal Animal - Open Space.</p>
Data, scripts, and figures of the article: The effect of oregano essential oils on Feed Passage Syndrome in broilers: 1. Assessment under field conditions
<p>This data set contains the data, JMP scripts, and figures of the article titled "The effect of oregano essential oils on Feed Passage Syndrome in broilers: 1. Assessment under field conditions" to be published in the journal Animal - Open Space.</p>
Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome, caregives and researchers
<p>Nine animation videos used in the questionnaire described in the articles "<strong>Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome</strong>" (<a href="https://doi.org/10.1177%2F1545968321989331">https://doi.org/10.1177/1545968321989331</a>) and "<strong>Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome, caregivers and researchers</strong>" (<a href="https://doi.org/10.1080/17483107.2021.1958932">https://doi.org/10.1080/17483107.2021.1958932</a>). <em>Video animations were designed and produced by Merel Horsmeier.</em></p>
Dataset related to the article "Clinical and Molecular Data Define a Diagnosis of Arrhythmogenic Cardiomyopathy in a Carrier of a Brugada-Syndrome-Associated PKP2 Mutation"
<p>This record contains raw data related to the article “ Molecular Data Define a Diagnosis of Arrhythmogenic Cardiomyopathy in a Carrier of a Brugada-Syndrome-Associated PKP2 Mutation”. </p> <p>Plakophilin-2 (<em>PKP2</em>) is the most frequently mutated desmosomal gene in arrhythmogenic cardiomyopathy (ACM), a disease characterized by structural and electrical alterations predominantly affecting the right ventricular myocardium. Notably, ACM cases without overt structural alterations are frequently reported, mainly in the early phases of the disease. Recently, the <em>PKP2</em> p.S183N mutation was found in a patient affected by Brugada syndrome (BS), an inherited arrhythmic channelopathy most commonly caused by sodium channel gene mutations. We here describe a case of a patient carrier of the same BS-related <em>PKP2</em> p.S183N mutation but with a clear diagnosis of ACM. Specifically, we report how clinical and molecular investigations can be integrated for diagnostic purposes, distinguishing between ACM and BS, which are increasingly recognized as syndromes with clinical and genetic overlaps. This observation is fundamentally relevant in redefining the role of genetics in the approach to the arrhythmic patient, progressing beyond the concept of "one mutation, one disease", and raising concerns about the most appropriate approach to patients affected by structural/electrical cardiomyopathy. The merging of genetics, electroanatomical mapping, and tissue and cell characterization summarized in our patient seems to be the most complete diagnostic algorithm, favoring a reliable diagnosis.</p>
Gestational Cd exposure in the CD-1 mouse induces sex-specific hepatic insulin insensitivity, obesity and metabolic syndrome in adult female offspring
<p>There is compelling evidence that developmental exposure to some toxic metals increases risk for obesity and obesity-related morbidity including cardiovascular disease and type 2 diabetes in adults. To explore the hypothesis that developmental Cd exposure increased risk of obesity later in life, male and female CD-1 mice were maternally exposed to 500 ppb CdCl<sub>2</sub> in drinking water during a human gestational equivalent period (GD0 - PND10). Hallmark indicators of metabolic disruption, hepatic steatosis, and metabolic syndrome were evaluated prior to birth through adulthood. Blood Cd levels in dams were similar to those observed in human pregnancy cohorts. There were no observed impacts of exposure on dams or pregnancy-related outcomes. Results of glucose and insulin tolerance testing revealed that Cd-exposure impaired glucose homeostasis in young adult offspring. Exposure-related increases in circulating triglycerides and hepatic steatosis were apparent only in females. By PND120, Cd-exposed females had become 30% heavier with 700% more perigonadal fat than unexposed control females. There was no evidence of dyslipidemia, steatosis, increased weight gain, nor increased adiposity in Cd-exposed male offspring. Hepatic transcriptome analysis at PND1, PND21, and PND42 revealed evidence for female-specific increases in oxidative stress and mitochondrial dysfunction with significant early disruption of retinoic acid signaling and altered insulin receptor signaling consistent with hepatic insulin sensitivity in adult females. The observed steatosis and metabolic syndrome-like phenotypes resulting from exposure to 500 ppb CdCl<sub>2</sub> during the pre- and perinatal period of development equivalent to human gestation indicate that Cd acts developmentally as a sex-specific delayed obesogen.</p>
Data for: The rediscovery of the putative ant social parasite Manica parasitica syn. nov. (Hymenoptera: Formicidae) reveals an unexpected endoparasite syndrome
<p>Parasitism is ubiquitous across the tree of life, comprising approximately half of all animal species. Social insect colonies attract many pathogens, endo- and ectoparasites, and are exploited by social parasites, which usurp the social environment of their hosts for survival and reproduction. Exploitation by parasites and pathogens versus social parasites may cause similar behavioral and morphological modifications. Ants possess two overlapping syndromes: the social parasite and endoparasite syndromes. Upon rediscovering two populations of the putative social parasite <em>Manica parasitica</em> in the Sierra Nevadas, we test the hypothesis that <em>M. parasitica</em> is an independently evolving social parasite species relative to its host <em>M. bradleyi</em>. We evaluate traits used to discriminate <em>M. parasitica</em> from <em>M. bradleyi</em>, and examine the morphology and behavior of <em>M. parasitica</em> in the context of ant parasitic syndromes. We find that <em>M. parasitica</em> is not a social parasite species. Instead, <em>M. parasitica </em>individuals represents cestode-infected <em>M. bradleyi </em>workers. We propose that <em>Manica parasitica</em> should be regarded as a junior synonym of <em>Manica bradleyi</em>. Our results emphasize that an integrative approach is essential for unraveling the complex life histories of social insects and their symbionts.</p>
ScienceDex guides
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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.