The mechanical and material properties of elderly human articular cartilage subject to impact and slow loading

Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.Peer reviewedPostprin

Identifying and Training Deep Learning Neural Networks on Biomedical-Related Datasets

This manuscript describes the development of a resources module that is part of a learning platform named \u27NIGMS Sandbox for Cloud-based Learning\u27 https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on implementing deep learning algorithms for biomedical image data in an interactive format that uses appropriate cloud resources for data access and analyses. Biomedical-related datasets are widely used in both research and clinical settings, but the ability for professionally trained clinicians and researchers to interpret datasets becomes difficult as the size and breadth of these datasets increases. Artificial intelligence, and specifically deep learning neural networks, have recently become an important tool in novel biomedical research. However, use is limited due to their computational requirements and confusion regarding different neural network architectures. The goal of this learning module is to introduce types of deep learning neural networks and cover practices that are commonly used in biomedical research. This module is subdivided into four submodules that cover classification, augmentation, segmentation and regression. Each complementary submodule was written on the Google Cloud Platform and contains detailed code and explanations, as well as quizzes and challenges to facilitate user training. Overall, the goal of this learning module is to enable users to identify and integrate the correct type of neural network with their data while highlighting the ease-of-use of cloud computing for implementing neural networks. This manuscript describes the development of a resource module that is part of a learning platform named NIGMS Sandbox for Cloud-based Learning https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses

Sequential Use of Transcriptional Profiling, Expression Quantitative Trait Mapping, and Gene Association Implicates MMP20 in Human Kidney Aging

Kidneys age at different rates, such that some people show little or no effects of aging whereas others show rapid functional decline. We sequentially used transcriptional profiling and expression quantitative trait loci (eQTL) mapping to narrow down which genes to test for association with kidney aging. We first performed whole-genome transcriptional profiling to find 630 genes that change expression with age in the kidney. Using two methods to detect eQTLs, we found 101 of these age-regulated genes contain expression-associated SNPs. We tested the eQTLs for association with kidney aging, measured by glomerular filtration rate (GFR) using combined data from the Baltimore Longitudinal Study of Aging (BLSA) and the InCHIANTI study. We found a SNP association (rs1711437 in MMP20) with kidney aging (uncorrected p = 3.6×10−5, empirical p = 0.01) that explains 1%–2% of the variance in GFR among individuals. The results of this sequential analysis may provide the first evidence for a gene association with kidney aging in humans

Transcriptional repression of the human collagenase-1 (MMP-1) gene in MDA231 breast cancer cells by all-trans-retinoic acid requires distal regions of the promoter

In the present study, we investigated the mechanisms controlling constitutive transcription of collagenase-1 and its repression by all-trans-retinoic acid (RA) in the highly invasive metastatic and oestrogen-receptor-negative breast cancer cell line MDA231. A combination of in vivo and in vitro experiments that include DNAase I hypersensitivity assays, transient transfection of collagenase-1 promoter constructs, and electrophoretic mobility shift assays implicate several PEA3 sites, binding sites for Ets-related transcription factors, in the constitutive expression of the human collagenase-1 promoter. Transient transfection of promoter constructs linked to the luciferase reporter, along with gel retardation assays, revealed that repression of collagenase-1 transcription by RA is not dependent on the proximal AP-1 site, but, rather, requires sequences located in distal regions of the promoter. Transcriptional analyses and electrophoretic mobility shift assays suggest that the PEA3 site located at –3108 bp facilitates, at least in part, the transcriptional repression of the human collagenase-1 gene in MDA231 cells. We conclude that collagenase-1 repression in MDA231 cells occurs by a novel regulatory pathway that does not depend on the proximal AP-1 site at –73 bp, but does depend on distal regions in the collagenase-1 promoter. © 1999 Cancer Research Campaig

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A Transglutaminase Immunologically Related to Tissue Transglutaminase Catalyzes Cross-Linking of Cell Wall Proteins in<i>Chlamydomonas reinhardtii</i>

Abstract The addition of primary amines to the growth medium of the unicellular green alga Chlamydomonas reinhardtii disrupts cell wall assembly in both vegetative and zygotic cells. Primary amines are competitive inhibitors of the protein-cross-linking activity of transglutaminases. Two independent assays for transglutaminase confirmed a burst of extracellular activity during the early stages of cell wall formation in both vegetative cells and zygotes. When non-inhibiting levels of a radioactive primary amine (14C-putrescine) were added to the growth medium, both cell types were labeled in a reaction catalyzed by extracellular transglutaminase. The radioactive label was found specifically in the cell wall proteins of both cell types, and acid hydrolysis of the labeled material released unmodified 14C-putrescine. Western blots of the proteins secreted at the times of maximal transglutaminase activity in both cell types revealed a single highly cross-reactive 72-kD band when screened with antibodies to guinea pig tissue transglutaminase. Furthermore, the proteins immunoprecipitated by this antiserum in vivo exhibited transglutaminase activity. We propose that this transglutaminase is responsible for an early cell wall protein cross-linking event that temporally precedes the oxidative cross-linking mediated by extracellular peroxidases.</jats:p