Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns

Identification and Filtering of Uncharacteristic Noise in the CMS Hadron Calorimeter

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Performance of CMS hadron calorimeter timing and synchronization using test beam, cosmic ray, and LHC beam data

This paper discusses the design and performance of the time measurement technique and of the synchronization systems of the CMS hadron calorimeter. Time measurement performance results are presented from test beam data taken in the years 2004 and 2006. For hadronic showers of energy greater than 100 GeV, the timing resolution is measured to be about 1.2 ns. Time synchronization and out-of-time background rejection results are presented from the Cosmic Run At Four Tesla and LHC beam runs taken in the Autumn of 2008. The inter-channel synchronization is measured to be within ±2 ns

Estrogen receptor transcription and transactivation: Structure-function relationship in DNA- and ligand-binding domains of estrogen receptors

Estrogen receptors are members of the nuclear receptor steroid family that exhibit specific structural features, ligand-binding domain sequence identity and dimeric interactions, that single them out. The crystal structures of their DNA-binding domains give some insight into how nuclear receptors discriminate between DNA response elements. The various ligand-binding domain crystal structures of the two known estrogen receptor isotypes (α and β) allow one to interpret ligand specificity and reveal the interactions responsible for stabilizing the activation helix H12 in the agonist and antagonist positions

Health and life insurance as an alternative to malpractice tort law

<p>Abstract</p> <p>Background</p> <p>Tort law has legitimate social purposes of deterrence, punishment and compensation, but medical tort law does none of these well. Tort law could be counterproductive in medicine, encouraging costly defensive practices that harm some patients, restricting access to care in some settings and discouraging innovation.</p> <p>Discussion</p> <p>Patients might be better served by purchasing combined health and life insurance policies and waiving their right to pursue malpractice claims. The combined policy should encourage the insurer to profit by inexpensively delaying policyholders' deaths. A health and life insurer would attempt to minimize mortal risks to policyholders from any cause, including medical mistakes and could therefore pursue systematic quality improvement efforts. If policyholders trust the insurer to seek, develop and reward genuinely effective care; identify, deter and remediate poor care; and compensate survivors through the no-fault process of paying life insurance benefits, then tort law is largely redundant and the right to sue may be waived. If expensive defensive medicine can be avoided, that savings alone could pay for fairly large life insurance policies.</p> <p>Summary</p> <p>Insurers are maligned largely because of their logical response to incentives that are misaligned with the interests of patients and physicians in the United States. Patient, provider and insurer incentives could be realigned by combining health and life insurance, allowing the insurer to use its considerable information access and analytic power to improve patient care. This arrangement would address the social goals of malpractice torts, so that policyholders could rationally waive their right to sue.</p

Outcomes research in the development and evaluation of practice guidelines

BACKGROUND: Practice guidelines have been developed in response to the observation that variations exist in clinical medicine that are not related to variations in the clinical presentation and severity of the disease. Despite their widespread use, however, practice guideline evaluation lacks a rigorous scientific methodology to support its development and application. DISCUSSION: Firstly, we review the major epidemiological foundations of practice guideline development. Secondly, we propose a chronic disease epidemiological model in which practice patterns are viewed as the exposure and outcomes of interest such as quality or cost are viewed as the disease. Sources of selection, information, confounding and temporal trend bias are identified and discussed. SUMMARY: The proposed methodological framework for outcomes research to evaluate practice guidelines reflects the selection, information and confounding biases inherent in its observational nature which must be accounted for in both the design and the analysis phases of any outcomes research study

A Case Study for Large-Scale Human Microbiome Analysis Using JCVI’s Metagenomics Reports (METAREP)

As metagenomic studies continue to increase in their number, sequence volume and complexity, the scalability of biological analysis frameworks has become a rate-limiting factor to meaningful data interpretation. To address this issue, we have developed JCVI Metagenomics Reports (METAREP) as an open source tool to query, browse, and compare extremely large volumes of metagenomic annotations. Here we present improvements to this software including the implementation of a dynamic weighting of taxonomic and functional annotation, support for distributed searches, advanced clustering routines, and integration of additional annotation input formats. The utility of these improvements to data interpretation are demonstrated through the application of multiple comparative analysis strategies to shotgun metagenomic data produced by the National Institutes of Health Roadmap for Biomedical Research Human Microbiome Project (HMP) (http://nihroadmap.nih.gov). Specifically, the scalability of the dynamic weighting feature is evaluated and established by its application to the analysis of over 400 million weighted gene annotations derived from 14 billion short reads as predicted by the HMP Unified Metabolic Analysis Network (HUMAnN) pipeline. Further, the capacity of METAREP to facilitate the identification and simultaneous comparison of taxonomic and functional annotations including biological pathway and individual enzyme abundances from hundreds of community samples is demonstrated by providing scenarios that describe how these data can be mined to answer biological questions related to the human microbiome. These strategies provide users with a reference of how to conduct similar large-scale metagenomic analyses using METAREP with their own sequence data, while in this study they reveal insights into the nature and extent of variation in taxonomic and functional profiles across body habitats and individuals. Over one thousand HMP WGS datasets and the latest open source code are available at http://www.jcvi.org/hmp-metarep

Pooled extracellular receptor-ligand interaction screening using CRISPR activation.

Extracellular interactions between cell surface receptors are necessary for signaling and adhesion but identifying them remains technically challenging. We describe a cell-based genome-wide approach employing CRISPR activation to identify receptors for a defined ligand. We show receptors for high-affinity antibodies and low-affinity ligands can be unambiguously identified when used in pools or as individual binding probes. We apply this technique to identify ligands for the adhesion G-protein-coupled receptors and show that the Nogo myelin-associated inhibitory proteins are ligands for ADGRB1. This method will enable extracellular receptor-ligand identification on a genome-wide scale

Influence of nutrients and currents on the genomic composition of microbes across an upwelling mosaic

Metagenomic data sets were generated from samples collected along a coastal to open ocean transect between Southern California Bight and California Current waters during a seasonal upwelling event, providing an opportunity to examine the impact of episodic pulses of cold nutrient-rich water into surface ocean microbial communities. The data set consists of ∼5.8 million predicted proteins across seven sites, from three different size classes: 0.1–0.8, 0.8–3.0 and 3.0–200.0 μm. Taxonomic and metabolic analyses suggest that sequences from the 0.1–0.8 μm size class correlated with their position along the upwelling mosaic. However, taxonomic profiles of bacteria from the larger size classes (0.8–200 μm) were less constrained by habitat and characterized by an increase in Cyanobacteria, Bacteroidetes, Flavobacteria and double-stranded DNA viral sequences. Functional annotation of transmembrane proteins indicate that sites comprised of organisms with small genomes have an enrichment of transporters with substrate specificities for amino acids, iron and cadmium, whereas organisms with larger genomes have a higher percentage of transporters for ammonium and potassium. Eukaryotic-type glutamine synthetase (GS) II proteins were identified and taxonomically classified as viral, most closely related to the GSII in Mimivirus, suggesting that marine Mimivirus-like particles may have played a role in the transfer of GSII gene functions. Additionally, a Planctomycete bloom was sampled from one upwelling site providing a rare opportunity to assess the genomic composition of a marine Planctomycete population. The significant correlations observed between genomic properties, community structure and nutrient availability provide insights into habitat-driven dynamics among oligotrophic versus upwelled marine waters adjoining each other spatially

Socially and biologically inspired computing for self-organizing communications networks

The design and development of future communications networks call for a careful examination of biological and social systems. New technological developments like self-driving cars, wireless sensor networks, drones swarm, Internet of Things, Big Data, and Blockchain are promoting an integration process that will bring together all those technologies in a large-scale heterogeneous network. Most of the challenges related to these new developments cannot be faced using traditional approaches, and require to explore novel paradigms for building computational mechanisms that allow us to deal with the emergent complexity of these new applications. In this article, we show that it is possible to use biologically and socially inspired computing for designing and implementing self-organizing communication systems. We argue that an abstract analysis of biological and social phenomena can be made to develop computational models that provide a suitable conceptual framework for building new networking technologies: biologically inspired computing for achieving efficient and scalable networking under uncertain environments; socially inspired computing for increasing the capacity of a system for solving problems through collective actions. We aim to enhance the state-of-the-art of these approaches and encourage other researchers to use these models in their future work