Bayesian network prior: network analysis of biological data using external knowledge

Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological knowledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we call Bayesian Network Prior (BNP). BNP depicts the relation between various evidence types that contribute to the event ‘gene interaction’ and is used to calculate the probability of a candidate graph (G) in the structure learning process. Results: Our simulation results on synthetic, simulated and real biological data show that the proposed approach can identify the underlying interaction network with high accuracy even when the prior information is distorted and outperforms existing methods

Spectroelectrochemistry of potassium ethylxanthate, bis(ethylxanthato)nickel(II) and tetraethylammonium tris(ethylxanthato)-nickelate(II)

Electrochemical and chemical oxidation of S2COEt-, Ni(S2COEt)2, and [Ni(S2COEt)3]- have been studied by CV and in situ UV-VIS spectroscopy in acetonitrile. Cyclic voltammograms of S2COEt- and Ni(S2COEt)2 display one (0.00 V) and two (0.35 and 0.80 V) irreversible oxidation peaks, respectively, referenced to an Ag/Ag+ (0.10 M) electrode. However, the cyclic voltammogram of [Ni(S2COEt)3]- displays one reversible (-0.15 V) and two irreversible (0.35, 0.80 V) oxidation peaks, referenced to an Ag/Ag+ electrode. The low temperature EPR spectrum of the oxidatively electrolyzed solution of (NEt4)[Ni(S2COEt)3] indicates the presence of [NiIII(S2COEt)3], which disproportionates to Ni(S2COEt)2, and the dimer of the oxidized ethylxanthate ligand, (S2COEt)2 ((S2COEt)2=C2H5OC(S)SS(S)COC 2H5), with a second order rate law. The final products of constant potential electrolysis at the first oxidation peak potentials of S2COEt-, Ni(S2COEt)2, and [Ni(S2COEt)3]- are (S2COEt)2; Ni2+ (sol) and (S2COEt)2; and Ni(S2COEt)2 and (S2COEt)2, respectively. The chemical oxidation of S2COEt- to (S2COEt)2, and [Ni(S2COEt)3]- to (S2COEt)2 and Ni(S2COEt)2 were also achieved with iodine. The oxidized ligand in the dimer form can be reduced to S2COEt- with CN- in solution

Energy aware approach for HPC systems

International audienceHigh‐performance computing (HPC) systems require energy during their full life cycle from design and production to transportation to usage and recycling/dismanteling. Because of increase of ecological and cost awareness, energy performance is now a primary focus. This chapter focuses on the usage aspect of HPC and how adapted and optimized software solutions could improve energy efficiency. It provides a detailed explanation of server power consumption, and discusses the application of HPC, phase detection, and phase identification. The chapter also suggests that having the load and memory access profiles is insufficient for an effective evaluation of the power consumed by an application. The available leverages in HPC systems are also shown in detail. The chapter proposes some solutions for modeling the power consumption of servers, which allows designing power prediction models for better decision making.These approaches allow the deployment and usage of a set of available green leverages, permitting energy reduction

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Chromo- and Fluorogenic Organometallic Sensors

Compounds that change their absorption and/or emission properties in the presence of a target ion or molecule have been studied for many years as the basis for optical sensing. Within this group of compounds, a variety of organometallic complexes have been proposed for the detection of a wide range of analytes such as cations (including H+), anions, gases (e.g. O 2, SO2, organic vapours), small organic molecules, and large biomolecules (e.g. proteins, DNA). This chapter focuses on work reported within the last few years in the area of organometallic sensors. Some of the most extensively studied systems incorporate metal moieties with intense long-lived metal-to-ligand charge transfer (MLCT) excited states as the reporter or indicator unit, such as fac-tricarbonyl Re(I) complexes, cyclometallated Ir(III) species, and diimine Ru(II) or Os(II) derivatives. Other commonly used organometallic sensors are based on Pt-alkynyls and ferrocene fragments. To these reporters, an appropriate recognition or analyte-binding unit is usually attached so that a detectable modification on the colour and/or the emission of the complex occurs upon binding of the analyte. Examples of recognition sites include macrocycles for the binding of cations, H-bonding units selective to specific anions, and DNA intercalating fragments. A different approach is used for the detection of some gases or vapours, where the sensor's response is associated with changes in the crystal packing of the complex on absorption of the gas, or to direct coordination of the analyte to the metal centre

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

Bayesian network prior: network analysis of biological data using external knowledge

Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological knowledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we call Bayesian Network Prior (BNP). BNP depicts the relation between various evidence types that contribute to the event ‘gene interaction’ and is used to calculate the probability of a candidate graph (G) in the structure learning process. Results: Our simulation results on synthetic, simulated and real biological data show that the proposed approach can identify the underlying interaction network with high accuracy even when the prior information is distorted and outperforms existing methods

Bayesian Pathway Analysis of Cancer Microarray Data

High Throughput Biological Data (HTBD) requires detailed analysis methods and from a life science perspective, these analysis results make most sense when interpreted within the context of biological pathways. Bayesian Networks (BNs) capture both linear and nonlinear interactions and handle stochastic events in a probabilistic framework accounting for noise making them viable candidates for HTBD analysis. We have recently proposed an approach, called Bayesian Pathway Analysis (BPA), for analyzing HTBD using BNs in which known biological pathways are modeled as BNs and pathways that best explain the given HTBD are found. BPA uses the fold change information to obtain an input matrix to score each pathway modeled as a BN. Scoring is achieved using the Bayesian-Dirichlet Equivalent method and significance is assessed by randomization via bootstrapping of the columns of the input matrix. In this study, we improve on the BPA system by optimizing the steps involved in ‘‘Data Preprocessing and Discretization’’, ‘‘Scoring’’, ‘‘Significance Assessment’’, and ‘‘Software and Web Application’’. We tested the improved system on synthetic data sets and achieved over 98% accuracy in identifying the active pathways. The overall approach was applied on real cancer microarray data sets in order to investigate the pathways that are commonly active in different cancer types. We compared our findings on the real data sets with a relevant approach called the Signaling Pathway Impact Analysis (SPIA)