1,670 research outputs found
Infrastructure for mobile sensor network in the Singapore coastal zone
URL to conference page. Scroll down to 2010 conference, click on "Paper and session list," and search the PDF for Patrikalakis.Singapore is an island nation located at southern tip of the Malaysian
Peninsula. She is at a strategic location along major shipping routes and
therefore has one of the busiest harbors in the world. Having a safe and
secure harbor environment is vital to maintain trade and growth in the
country and region. To help build and maintain a safe harbor
environment, the Center of Environmental Sensing and Modelling
(CENSAM) under the Singapore-MIT Alliance for Research and
Technology (SMART) is developing a mobile sensor network in the
Singapore coastal zone
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Biological, clinical and population relevance of 95 loci for blood lipids.
Plasma concentrations of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides are among the most important risk factors for coronary artery disease (CAD) and are targets for therapeutic intervention. We screened the genome for common variants associated with plasma lipids in >100,000 individuals of European ancestry. Here we report 95 significantly associated loci (P < 5 x 10(-8)), with 59 showing genome-wide significant association with lipid traits for the first time. The newly reported associations include single nucleotide polymorphisms (SNPs) near known lipid regulators (for example, CYP7A1, NPC1L1 and SCARB1) as well as in scores of loci not previously implicated in lipoprotein metabolism. The 95 loci contribute not only to normal variation in lipid traits but also to extreme lipid phenotypes and have an impact on lipid traits in three non-European populations (East Asians, South Asians and African Americans). Our results identify several novel loci associated with plasma lipids that are also associated with CAD. Finally, we validated three of the novel genes-GALNT2, PPP1R3B and TTC39B-with experiments in mouse models. Taken together, our findings provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD
Modeling precision treatment of breast cancer
Background: First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets. Results: We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples. Conclusions: These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified
Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche.
Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P < 5 × 10(-8)) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signalling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition
A Mouse Model of Post-Arthroplasty Staphylococcus aureus Joint Infection to Evaluate In Vivo the Efficacy of Antimicrobial Implant Coatings
Post-arthroplasty infections represent a devastating complication of total joint replacement surgery, resulting in multiple reoperations, prolonged antibiotic use, extended disability and worse clinical outcomes. As the number of arthroplasties in the U.S. will exceed 3.8 million surgeries per year by 2030, the number of post-arthroplasty infections is projected to increase to over 266,000 infections annually. The treatment of these infections will exhaust healthcare resources and dramatically increase medical costs.To evaluate novel preventative therapeutic strategies against post-arthroplasty infections, a mouse model was developed in which a bioluminescent Staphylococcus aureus strain was inoculated into a knee joint containing an orthopaedic implant and advanced in vivo imaging was used to measure the bacterial burden in real-time. Mice inoculated with 5x10(3) and 5x10(4) CFUs developed increased bacterial counts with marked swelling of the affected leg, consistent with an acute joint infection. In contrast, mice inoculated with 5x10(2) CFUs developed a low-grade infection, resembling a more chronic infection. Ex vivo bacterial counts highly correlated with in vivo bioluminescence signals and EGFP-neutrophil fluorescence of LysEGFP mice was used to measure the infection-induced inflammation. Furthermore, biofilm formation on the implants was visualized at 7 and 14 postoperative days by variable-pressure scanning electron microscopy (VP-SEM). Using this model, a minocycline/rifampin-impregnated bioresorbable polymer implant coating was effective in reducing the infection, decreasing inflammation and preventing biofilm formation.Taken together, this mouse model may represent an alternative pre-clinical screening tool to evaluate novel in vivo therapeutic strategies before studies in larger animals and in human subjects. Furthermore, the antibiotic-polymer implant coating evaluated in this study was clinically effective, suggesting the potential for this strategy as a therapeutic intervention to combat post-arthroplasty infections
Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas
This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing
molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin
Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context
Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and
correlation with overall survival. TIL map structural patterns were grouped using standard
histopathological parameters. These patterns are enriched in particular T cell subpopulations
derived from molecular measures. TIL densities and spatial structure were differentially enriched
among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial
infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic
patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for
the TCGA image archives with insights into the tumor-immune microenvironment
The Critical Role of Notch Ligand Delta-like 1 in the Pathogenesis of Influenza A Virus (H1N1) Infection
Influenza A viral infections have been identified as the etiologic agents for historic pandemics, and contribute to the annual mortality associated with acute viral pneumonia. While both innate and acquired immunity are important in combating influenza virus infection, the mechanism connecting these arms of the immune system remains unknown. Recent data have indicated that the Notch system is an important bridge between antigen-presenting cells (APCs) and T cell communication circuits and plays a central role in driving the immune system to overcome disease. In the present study, we examine the role of Notch signaling during influenza H1N1 virus infection, focusing on APCs. We demonstrate here that macrophages, but not dendritic cells (DCs), increased Notch ligand Delta-like 1 (Dll1) expression following influenza virus challenge. Dll1 expression on macrophages was dependent on retinoic acid-inducible gene-I (RIG-I) induced type-I IFN pathway, and not on the TLR3-TRIF pathway. We also found that IFNα-Receptor knockout mice failed to induce Dll1 expression on lung macrophages and had enhanced mortality during influenza virus infection. Our results further showed that specific neutralization of Dll1 during influenza virus challenge induced higher mortality, impaired viral clearance, and decreased levels of IFN-γ. In addition, we blocked Notch signaling by using γ-secretase inhibitor (GSI), a Notch signaling inhibitor. Intranasal administration of GSI during influenza infection also led to higher mortality, and higher virus load with excessive inflammation and an impaired production of IFN-γ in lungs. Moreover, Dll1 expression on macrophages specifically regulates IFN-γ levels from CD4+and CD8+T cells, which are important for anti-viral immunity. Together, the results of this study show that Dll1 positively influences the development of anti-viral immunity, and may provide mechanistic approaches for modifying and controlling the immune response against influenza H1N1 virus infection
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