Smart Grid U.S. Transmission Grid: Issues And Opportunities

No one can tell you today exactly what technologies will make up the smart grid of the future, but smart grid is not just about the technology. It will involve designing an architecture that will utilize the data that is generated by the technology to automate the grid. It will also involve a paradigm shift in the utility industry with the active participation of customers in the energy delivery process. Deploying smart grid technologies will not be measured in months, but in years and decades. Public policy stands to have a huge impact on this time frame

Machine Learning Analysis of τRAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times

Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, τ, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the τRAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce τ. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating τ and to decipher the molecular features leading to longer τ values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of τ for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of τ. Thus, we find that machine learning helps to overcome inaccuracies in the modeling of protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the τRAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization

Mind Mapping as a Pragmatic Solution for Evaluation: A Critical Reflection through Two Case Studies

Funders of social interventions that address complex child and family welfare concerns for highly vulnerable populations are increasingly seeking cost-effective and rapid mixed method evaluations of their services. This paper describes a mind mapping approach that was used to collect valid and reliable qualitative data from large numbers of informants across two separate evaluation projects. The mind mapping approach provided a rapid, credible solution to the need to extract and summarize views from a diverse range of informants, and to gain consensus agreement on themes arising from the data. Through the use of two case studies to illustrate the application of the technique, we explore the advantages and disadvantages of the method and reflect upon the utility of mind mapping for quality improvement evaluation within the human services

Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data.

Electroconvulsive therapy (ECT) works rapidly and has been widely used to treat depressive disorders (DEP). However, identifying biomarkers predictive of response to ECT remains a priority to individually tailor treatment and understand treatment mechanisms. This study used a connectome-based predictive modeling (CPM) approach in 122 patients with DEP to determine if pre-ECT whole-brain functional connectivity (FC) predicts depressive rating changes and remission status after ECT (47 of 122 total subjects or 38.5% of sample), and whether pre-ECT and longitudinal changes (pre/post-ECT) in regional brain network biomarkers are associated with treatment-related changes in depression ratings. Results show the networks with the best predictive performance of ECT response were negative (anti-correlated) FC networks, which predict the post-ECT depression severity (continuous measure) with a 76.23% accuracy for remission prediction. FC networks with the greatest predictive power were concentrated in the prefrontal and temporal cortices and subcortical nuclei, and include the inferior frontal (IFG), superior frontal (SFG), superior temporal (STG), inferior temporal gyri (ITG), basal ganglia (BG), and thalamus (Tha). Several of these brain regions were also identified as nodes in the FC networks that show significant change pre-/post-ECT, but these networks were not related to treatment response. This study design has limitations regarding the longitudinal design and the absence of a control group that limit the causal inference regarding mechanism of post-treatment status. Though predictive biomarkers remained below the threshold of those recommended for potential translation, the analysis methods and results demonstrate the promise and generalizability of biomarkers for advancing personalized treatment strategies

Survey of sediment quality in Sabine Lake, Texas and vicinity

The toxicity of sediments in Sabine Lake, Texas, and adjoining Intracoastal Waterway canals was determined as part of bioeffects assessment studies managed by NOAA’s National Status and Trends Program. The objectives of the survey were to determine: (1) the incidence and degree of toxicity of sediments throughout the study area; (2) the spatial patterns (or gradients) in chemical contamination and toxicity, if any, throughout the study area; (3) the spatial extent of chemical contamination and toxicity; and (4) the statistical relationships between measures of toxicity and concentrations of chemicals in the sediments. Surficial sediment samples were collected during August, 1995 from 66 randomly-chosen locations. Laboratory toxicity tests were performed as indicators of potential ecotoxicological effects in sediments. A battery of tests was performed to generate information from different phases (components) of the sediments. Tests were selected to represent a range in toxicological endpoints from acute to chronic sublethal responses. Toxicological tests were conducted to measure: reduced survival of adult amphipods exposed to solid-phase sediments; impaired fertilization success and abnormal morphological development in gametes and embryos, respectively, of sea urchins exposed to pore waters; reduced metabolic activity of a marine bioluminescent bacteria exposed to organic solvent extracts; and induction of a cytochrome P-450 reporter gene system in exposures to solvent extracts of the sediments. Chemical analyses were performed on portions of each sample to quantify the concentrations of trace metals, polynuclear aromatic hydrocarbons, and chlorinated organic compounds. Correlation analyses were conducted to determine the relationships between measures of toxicity and concentrations of potentially toxic substances in the samples. Based upon the compilation of results from chemical analyses and toxicity tests, the quality of sediments in Sabine Lake and vicinity did not appear to be severely degraded. Chemical concentrations rarely exceeded effects-based numerical guidelines, suggesting that toxicant-induced effects would not be expected in most areas. None of the samples was highly toxic in acute amphipod survival tests and a minority (23%) of samples were highly toxic in sublethal urchin fertilization tests. Although toxic responses occurred frequently (94% of samples) in urchin embryo development tests performed with 100% pore waters, toxicity diminished markedly in tests done with diluted pore waters. Microbial bioluminescent activity was not reduced to a great degree (no EC50 <0.06 mg/ml) and cytochrome P-450 activity was not highly induced (6 samples exceeded 37.1 ug/g benzo[a]pyrene equivalents) in tests done with organic solvent extracts. Urchin embryological development was highly correlated with concentrations of ammonia and many trace metals. Cytochrome P450 induction was highly correlated with concentrations of a number of classes of organic compounds (including the polynuclear aromatic hydrocarbons and chlorinated compounds). (PDF contains 51 pages

Sensitivity to missing not at random dropout in clinical trials:Use and interpretation of the trimmed means estimator

Outcome values in randomized controlled trials (RCTs) may be missing not at random (MNAR), if patients with extreme outcome values are more likely to drop out (eg, due to perceived ineffectiveness of treatment, or adverse effects). In such scenarios, estimates from complete case analysis (CCA) and multiple imputation (MI) will be biased. We investigate the use of the trimmed means (TM) estimator for the case of univariable missingness in one continuous outcome. The TM estimator operates by setting missing values to the most extreme value, and then “trimming” away equal fractions of both groups, estimating the treatment effect using the remaining data. The TM estimator relies on two assumptions, which we term the “strong MNAR” and “location shift” assumptions. We derive formulae for the TM estimator bias resulting from the violation of these assumptions for normally distributed outcomes. We propose an adjusted TM estimator, which relaxes the location shift assumption and detail how our bias formulae can be used to establish the direction of bias of CCA and TM estimates, to inform sensitivity analyses. The TM approach is illustrated in a sensitivity analysis of the CoBalT RCT of cognitive behavioral therapy (CBT) in 469 individuals with 46 months follow‐up. Results were consistent with a beneficial CBT treatment effect, with MI estimates closer to the null and TM estimates further from the null than the CCA estimate. We propose using the TM estimator as a sensitivity analysis for data where extreme outcome value dropout is plausible

Blood pressure and mortality:using offspring blood pressure as an instrument for own blood pressure in the HUNT study

Given that observational associations may be inaccurate, we used offspring blood pressure (BP) to provide alternative estimates of the associations between own BP and mortality. Observational associations between BP and mortality, estimated as hazard ratios (HRs) from Cox regression, were compared to HRs obtained using offspring BP as an instrumental variable (IV) for own BP (N = 32,227 mother-offspring and 27,535 father-offspring pairs). Observationally, there were positive associations between own BP and mortality from all-causes, cardiovascular disease (CVD), coronary heart disease (CHD), stroke and diabetes. Point estimates of the associations between BP and mortality from all-causes, CVD and CHD were amplified in magnitude when using offspring BP as an IV. For example, the HR for all-cause mortality per standard deviation (SD) increase in own systolic BP (SBP) obtained in conventional observational analyses increased from 1.10 (95% CI: 1.09–1.12; P < 0.0001) to 1.31 (95% CI: 1.19–1.43; P < 0.0001). Additionally, SBP was positively associated with diabetes and cancer mortality (HRs: 2.00; 95% CI: 1.12–3.35; P = 0.02 and 1.20; 95% CI: 1.02–1.42; P = 0.03, respectively) and diastolic BP (DBP) with stroke mortality (HR: 1.30; 95% CI: 1.02–1.66; P = 0.03). Results support positive associations between BP and mortality from all-causes, CVD and CHD, SBP on cancer mortality and DBP on stroke mortality

National HPCC Software Exchange

This report describes an effort to construct a National HPCC Software Exchange (NHSE). This system shows how the evolving National Information Infrastructure (NII) can be used to facilitate sharing of software and information among members of the High Performance Computing and Communications (HPCC) community. To access the system use the URL: http://www.netlib.org/nse/