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427 results for “Modularity”
Characterisation of modular response of E. coli Keio collection mutant library to acid adaptation
GEO Series GSE13361. Escherichia coli; Escherichia coli K-12; Escherichia coli str. K-12 substr. MG1655. 62 samples. Type: Expression profiling by array.
A modular differentiation system maps multiple human kidney lineages from pluripotent stem cells [RNA-Seq]
GEO Series GSE146117. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Gene Regulatory Network inference in long-lived C. elegans reveals modular properties that are predictive of novel ageing genes
GEO Series GSE166512. Caenorhabditis elegans. 115 samples. Type: Expression profiling by high throughput sequencing.
Modular repertoire analyses identify dynamic type I and type II interferon transcriptional signatures in adult SLE patients
GEO Series GSE49454. Homo sapiens. 177 samples. Type: Expression profiling by array.
Modular architecture of the STING C-terminal tail allows interferon and NF-κB signaling adaptation
GEO Series GSE128363. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Modular expression analysis reveals functional conservation between human Langerhans Cells and mouse cross-priming dendritic cells
GEO Series GSE66355. Homo sapiens. 17 samples. Type: Expression profiling by array.
A modular dCas9-based recruitment platform for combinatorial epigenome editing
GEO Series GSE241460. Homo sapiens. 23 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.
Stellate cell expression of SPARC-related modular calcium-binding protein 2 is associated with human non-alcoholic fatty liver disease severity
GEO Series GSE207310. Homo sapiens. 30 samples. Type: Expression profiling by high throughput sequencing.
Using a modular massively parallel reporter assay to discover context-specific regulatory grammars in type 2 diabetes - Kyono library
GEO Series GSE279057. Rattus norvegicus; synthetic construct. 4 samples. Type: Other.
CRISPR Display: A modular method for locus-specific targeting of long noncoding RNAs and synthetic RNA devices in vivo [RNA-Seq]
GEO Series GSE66755. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
Using a modular massively parallel reporter assay to discover context-specific regulatory grammars in type 2 diabetes - Tovar library
GEO Series GSE279071. synthetic construct; Rattus norvegicus. 6 samples. Type: Other.
A modular dCas9-SunTag DNMT3A epigenome editing system overcomes pervasive off-target activity of direct fusion dCas9-DNMT3A constructs
GEO Series GSE107607. Homo sapiens. 166 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.
Single-cell multi-scale footprinting reveals the modular organization of DNA regulatory elements
GEO Series GSE216464. Mus musculus; Homo sapiens. 41 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other.
Long noncoding RNA as modular scaffold of histone modification complexes
GEO Series GSE22345. Homo sapiens. 14 samples. Type: Genome binding/occupancy profiling by genome tiling array.
Evaluation Results for Modular Collaborative Program Analysis in OPAL
<p>This are the full evaluation results for "Modular Collaborative Program Analysis in OPAL", published at ESEC/FSE 2020.</p> <p>The zip file contains csv files with the data of individual analysis executions (raw-results.csv) and the respective medians (aggregated-results.csv).</p> <p>Please note that the dataset refers to OPAL as 'BlaSt' as this name was used during double-blind review.</p>
Data from: Modular tagging of amplicons using a single PCR for high-throughput sequencing
High-throughput sequencing (HTS) of PCR amplicons is becoming the method of choice to sequence one or several targeted loci for phylogenetic and DNA barcoding studies. Although the development of HTS has allowed rapid generation of massive amounts of DNA sequence data, preparing amplicons for HTS remains a rate-limiting step. For example, HTS platforms require platform-specific adapter sequences to be present at the 5′ and 3′ end of the DNA fragment to be sequenced. In addition, short multiplex identifier (MID) tags are typically added to allow multiple samples to be pooled in a single HTS run. Existing methods to incorporate HTS adapters and MID tags into PCR amplicons are either inefficient, requiring multiple enzymatic reactions and clean-up steps, or costly when applied to multiple samples or loci (fusion primers). We describe a method to amplify a target locus and add HTS adapters and MID tags via a linker sequence using a single PCR. We demonstrate our approach by generating reference sequence data for two mitochondrial loci (COI and 16S) for a diverse suite of insect taxa. Our approach provides a flexible, cost-effective and efficient method to prepare amplicons for HTS.
Data from: Modularity speeds up motor learning by overcoming mechanical bias in musculoskeletal geometry
We can easily learn and perform a variety of movements that fundamentally require complex neuromuscular control. Many empirical findings have demonstrated that a wide range of complex muscle activation patterns could be well captured by the combination of a few functional modules, the so-called muscle synergies. Modularity represented by muscle synergies would simplify the control of a redundant neuromuscular system. However, how the reduction of neuromuscular redundancy through a modular controller contributes to sensorimotor learning remains unclear. To clarify such roles, we constructed a simple neural network model of the motor control system that included three intermediate layers representing neurons in the primary motor cortex, spinal interneurons organized into modules and motoneurons controlling upper-arm muscles. After a model learning period to generate the desired shoulder and/or elbow joint torques, we compared the adaptation to a novel rotational perturbation between modular and non-modular models. A series of simulations demonstrated that the modules reduced the effect of the bias in the distribution of muscle pulling directions, as well as in the distribution of torques associated with individual cortical neurons, which led to a more rapid adaptation to multi-directional force generation. These results suggest that modularity is crucial not only for reducing musculoskeletal redundancy but also for overcoming mechanical bias due to the musculoskeletal geometry allowing for faster adaptation to certain external environments.
Data from: Modularity induced gating and delays in neuronal networks
Neural networks, despite their highly interconnected nature, exhibit distinctly localized and gated activation. Modularity, a distinctive feature of neural networks, has been recently proposed as an important parameter determining the manner by which networks support activity propagation. Here we use an engineered biological model, consisting of engineered rat cortical neurons, to study the role of modular topology in gating the activity between cell populations. We show that pairs of connected modules support conditional propagation (transmitting stronger bursts with higher probability), long delays and propagation asymmetry. Moreover, large modular networks manifest diverse patterns of both local and global activation. Blocking inhibition decreased activity diversity and replaced it with highly consistent transmission patterns. By independently controlling modularity and disinhibition, experimentally and in a model, we pose that modular topology is an important parameter affecting activation localization and is instrumental for population-level gating by disinhibition.
Projection matrices for numerical examples of "An adaptive model order reduction technique for parameter-dependent modular structures"
<p>This data set contains supplementary data for the paper:<br> 'An adaptive model order reduction technique for parameter-dependent modular structures'</p> <p>The Authors are:<br> Stephan Ritzert (a), Domen Macek (a), Jaan-Willen Simon (b), Stefanie Reese (a)</p> <p>Affiliations:<br> (a) Institute of Applied Mechanics, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, 52074 Aachen, Germany<br> (b) Civil Engineering Mechanics, University of Wuppertal, Pauluskirchstr. 7, 42285 Wuppertal, Germany</p> <p>Contact: stephan.ritzert@ifam.rwth-aachen.de</p> <p>### The Dataset ###</p> <p>The dataset contains all mesh files and all projection matrices used for the numerical examples 2,3, and 4.</p> <p>Example 2: Naming of projections matrices<br> -Square: The projection matrices depend on the parameter alpha: {0,5,10,...,90}<br> The name of the projection matrix is 'PsiTransIso' + alpha + '.txt'</p> <p> -Rectangle: The projection matrices depend on the parameter alpha: {0,5,10,...,180}, and the length lx: {600,700,800,900,1000,1100,1200}<br> The name of the projection matrix is 'PsiTransIso' + alpha + '-' + lx + '.txt'</p> <p>Example 3: Naming of projections matrices<br> -Square: The projection matrices depend on the parameter alpha: {0,5,10,...,90}<br> The name of the projection matrix is 'PsiTransIso' + alpha + '.txt'</p> <p> -Rectangle: The projection matrices depend on the parameter alpha: {0,5,10,...,180}, and the length lx: {600,700,800,900,1000,1100,1200}<br> The name of the projection matrix is 'PsiTransIso' + alpha + '-' + lx + '.txt'</p> <p><br> Example 4: Naming of projections matrices<br> -curved: The projection matrices depend on the parameter phi: {20,30,...,90}<br> The name of the projection matrix is 'PsiIso' + phi + '.txt'</p> <p> -rectangle: The projection matrices depend on the parameter lx: {600,800,1000,1200}<br> The name of the projection matrix is 'PsiIso' + lx + '.txt'</p> <p> </p> <p> </p>
Replication Package for the Paper "A Lazy and Modular Approach to Int-Blasting"
<p>Replication package for the paper:<br>"A Lazy and Modular Approach to Int-Blasting" by Max Barth, Matthias Heizmann and Jochen Hoenicke.</p> <p>It contains the results presented in the evaluation of our paper. Addtionally, it contains the necessary tools and benchmarks to reproduce our results.<br>The included README contains detailed instructions.</p> <p> </p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.