Contractile properties of myofibers are dictated by the abundance of myosin heavy chain (MyHC) isoforms. MyHC composition designates muscle function, and its alterations could unravel differential muscle involvement in muscular dystrophies and aging. Current analyses are limited to visual assessments in which myofibers expressing multiple MyHC isoforms are prone to misclassification. As a result, complex patterns and subtle alterations are unidentified. We developed a high-throughput, data-driven myofiber analysis to quantitatively describe the variations in myofibers across the muscle. We investigated alterations in myofiber composition between genotypes, 2 muscles, and 2 age groups. We show that this analysis facilitates the discovery of complex myofiber compositions and its dependency on age, muscle type, and genetic conditions.-Raz, V., Raz, Y., van de Vijver, D., Bindellini, D., van Putten, M., van den Akker, E. B. High-throughput data-driven analysis of myofiber composition reveals muscle-specific disease and age-associated patterns.
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http://dx.doi.org/10.1096/fj.201801714R | DOI Listing |
Angew Chem Int Ed Engl
December 2024
University of York, Department of Chemistry, Heslington, YO10 5DD, York, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
High-throughput combinatorial metal complex synthesis has emerged as a powerful tool for rapidly generating and screening diverse libraries of metal complexes, enabling accelerated discovery in fields such as catalysis, medicinal chemistry, and materials science. By systematically combining building blocks (BBs) under mild and efficient conditions, researchers can explore broad chemical spaces, increasing the likelihood of identifying complexes with desired properties. This method streamlines hit identification and optimisation, especially when integrated with high-throughput screening (HTS) and data-driven approaches like machine learning.
View Article and Find Full Text PDFJ Pharm Sci
December 2024
Research and Development, AbbVie Inc., 1 North Waukegan Road, North Chicago, Illinois, 60064, United States.
Biopharmaceutical companies generate a wealth of data, ranging from in silico physicochemical properties and machine learning models to both low and high-throughput in vitro assays and in vivo studies. To effectively harnesses this extensive data, we introduce a statistical methodology facilitated by Accuracy, Utility, and Rank Order Assessment (AURA), which combines basic statistical analyses with dynamic data visualizations to evaluate endpoint effectiveness in predicting intestinal absorption. We demonstrated that various physicochemical properties uniquely influence intestinal absorption on a project-specific basis, considering factors like intestinal efflux, passive permeability, and clearance.
View Article and Find Full Text PDFBMC Bioinformatics
December 2024
Department of Statistics, University of Florida, Gainesville, FL, 32611, USA.
Background: Metabolomics is a high-throughput technology that measures small molecule metabolites in cells, tissues or biofluids. Analysis of metabolomics data is a multi-step process that involves data processing, quality control and normalization, followed by statistical and bioinformatics analysis. The latter step often involves pathway analysis to aid biological interpretation of the data.
View Article and Find Full Text PDFBrief Bioinform
November 2024
State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan, Nanjing 210023, China.
High-throughput sequencing methods have brought about a huge change in omics-based biomedical study. Integrating various omics data is possibly useful for identifying some correlations across data modalities, thus improving our understanding of the underlying biological mechanisms and complexity. Nevertheless, most existing graph-based feature extraction methods overlook the complementary information and correlations across modalities.
View Article and Find Full Text PDFSci Technol Adv Mater
November 2024
Department of Physics, Nagoya University, Nagoya, Japan.
Computational approaches using theoretical calculations and data scientific methods have become increasingly important in materials science and technology, with the development of relevant methodologies and algorithms, the availability of large materials data, and the enhancement of computer performance. As reviewed herein, we have developed computational methods for the design and prediction of inorganic materials with a particular focus on the exploration of semiconductors and dielectrics. High-throughput first-principles calculations are used to systematically and accurately predict the local atomic and electronic structures of polarons, point defects, surfaces, and interfaces, as well as bulk fundamental properties.
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