Multi-GPU Acceleration of the iPIC3D Implicit Particle-in-Cell Code

iPIC3D is a widely used massively parallel Particle-in-Cell code for the simulation of space plasmas. However, its current implementation does not support execution on multiple GPUs. In this paper, we describe the porting of iPIC3D particle mover to GPUs and the optimization steps to increase the performance and parallel scaling on multiple GPUs. We analyze the strong scaling of the mover on two GPU clusters and evaluate its performance and acceleration. The optimized GPU version which uses pinned memory and asynchronous data prefetching outperform their corresponding CPU versions by 5-10x on two different systems equipped with NVIDIA K80 and V100 GPUs.Comment: Accepted for publication in ICCS 201

Recent and future trends in synthetic greenhouse gas radiative forcing

Atmospheric measurements show that emissions of hydrofluorocarbons (HFCs) and hydrochlorofluorocarbons are now the primary drivers of the positive growth in synthetic greenhouse gas (SGHG) radiative forcing. We infer recent SGHG emissions and examine the impact of future emissions scenarios, with a particular focus on proposals to reduce HFC use under the Montreal Protocol. If these proposals are implemented, overall SGHG radiative forcing could peak at around 355 mW m[superscript −2] in 2020, before declining by approximately 26% by 2050, despite continued growth of fully fluorinated greenhouse gas emissions. Compared to “no HFC policy” projections, this amounts to a reduction in radiative forcing of between 50 and 240 mW m[superscript −2] by 2050 or a cumulative emissions saving equivalent to 0.5 to 2.8 years of CO2 emissions at current levels. However, more complete reporting of global HFC emissions is required, as less than half of global emissions are currently accounted for.Natural Environment Research Council (Great Britain) (Advanced Research Fellowship NE/I021365/1)United States. National Aeronautics and Space Administration (Upper Atmospheric Research Program Grant NNX11AF17G)United States. National Oceanic and Atmospheric Administratio

Transcriptomic-metabolomic reprogramming in EGFR-mutant NSCLC early adaptive drug escape linking TGFβ2-bioenergetics-mitochondrial priming.

The impact of EGFR-mutant NSCLC precision therapy is limited by acquired resistance despite initial excellent response. Classic studies of EGFR-mutant clinical resistance to precision therapy were based on tumor rebiopsies late during clinical tumor progression on therapy. Here, we characterized a novel non-mutational early adaptive drug-escape in EGFR-mutant lung tumor cells only days after therapy initiation, that is MET-independent. The drug-escape cell states were analyzed by integrated transcriptomic and metabolomics profiling uncovering a central role for autocrine TGFβ2 in mediating cellular plasticity through profound cellular adaptive Omics reprogramming, with common mechanistic link to prosurvival mitochondrial priming. Cells undergoing early adaptive drug escape are in proliferative-metabolic quiescent, with enhanced EMT-ness and stem cell signaling, exhibiting global bioenergetics suppression including reverse Warburg, and are susceptible to glutamine deprivation and TGFβ2 inhibition. Our study further supports a preemptive therapeutic targeting of bioenergetics and mitochondrial priming to impact early drug-escape emergence using EGFR precision inhibitor combined with broad BH3-mimetic to interrupt BCL-2/BCL-xL together, but not BCL-2 alone

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data

Misalignment between cold gas and stellar components in early-type galaxies

Recent work suggests blue ellipticals form in mergers and migrate quickly from the blue cloud of star-forming galaxies to the red sequence of passively evolving galaxies, perhaps as a result of black hole feedback. Such rapid reddening of stellar populations implies that large gas reservoirs in the pre-merger star-forming pair must be depleted on short time-scales. Here we present pilot observations of atomic hydrogen gas in four blue early-type galaxies that reveal increasing spatial offsets between the gas reservoirs and the stellar components of the galaxies, with advancing post-starburst age. Emission line spectra show associated nuclear activity in two of the merged galaxies, and in one case radio lobes aligned with the displaced gas reservoir. These early results suggest that a kinetic process (possibly feedback from black hole activity) is driving the quick truncation of star formation in these systems, rather than a simple exhaustion of gas suppl

Mathematical Modeling of Risk-Taking in Bipolar Disorder: Evidence of Reduced Behavioral Consistency, With Altered Loss Aversion Specific to Those With History of Substance Use Disorder

Bipolar disorder (BD) is associated with excessive pleasure-seeking risk-taking behaviors that often characterize its clinical presentation. However, the mechanisms of risk-taking behavior are not well-understood in BD. Recent data suggest prior substance use disorder (SUD) in BD may represent certain trait-level vulnerabilities for risky behavior. This study examined the mechanisms of risk-taking and the role of SUD in BD via mathematical modeling of behavior on the Balloon Analogue Risk Task (BART). Three groups—18 euthymic BD with prior SUD (BD+), 15 euthymic BD without prior SUD (BD–), and 33 healthy comparisons (HC)—completed the BART. We modeled behavior using four competing hierarchical Bayesian models, and model comparison results favored the Exponential-Weight Mean-Variance (EWMV) model, which encompasses and delineates five cognitive components of risk-taking: prior belief, learning rate, risk preference, loss aversion, and behavioral consistency. Both BD groups, regardless of SUD history, showed lower behavioral consistency than HC. BD+ exhibited more pessimistic prior beliefs (relative to BD– and HC) and reduced loss aversion (relative to HC) during risk-taking on the BART. Traditional measures of risk-taking on the BART (adjusted pumps, total points, total pops) detected no group differences. These findings suggest that reduced behavioral consistency is a crucial feature of risky decision-making in BD and that SUD history in BD may signal additional trait vulnerabilities for risky behavior even when mood symptoms and substance use are in remission. This study also underscores the value of using mathematical modeling to understand behavior in research on complex disorders like BD

Barriers and facilitators to change in the organisation and delivery of endoscopy services in England and Wales: a focus group study

Objective: Explore professional views of changes to gastroenterology service organisation and delivery and barriers and facilitators impacting on change. The work was undertaken as part of an evaluation in endoscopy service provision catalysed by the Modernising Endoscopy Services Programme of the Modernisation Agency. Design: Focus groups followed by analysis and group-working activities identifying key themes. Setting: English and Welsh secondary care gastroenterology units. Participants: 20 professionals working in gastroenterology in England and Wales. Medical, surgical and nursing specialists including endoscopy nurses. Opportunistic sampling to include senior people in leadership and management roles who were directly involved in service modernisation, excluding those involved in the Modernisation Endoscopy Services Programme. Results: Four 1.5 h focus groups took place in 2007. Summative and thematic analyses captured essential aspects of text and achieved consensus on key themes. 4 themes were revealed: 'loss of personal autonomy and erosion of professionalism', 'lack of senior management understanding', 'barriers and facilitators to change' and 'differences between English and Welsh units'. Themes indicated that low staff morale, lack of funding and senior management support were barriers to effective change. Limitations to the study include the disproportionately low number of focus group attendees from English units and the time delay in reporting these findings. Conclusions: Despite ambitions to implement change, ineffective management support continued to hamper modernisation of service organisation and delivery. While the National Health Service Modernisation Agency Modernising Endoscopy Services Programme acted as a catalyst for change, affecting the way staff work, communicate and think, it was not effective in heralding change itself. However, gastroenterologists were keen to consider the potential for change and future service modernisation. The methodological framework of innovative qualitative enquiry offers comprehensive and rigorous enhancement of quantitative studies, including randomised trials, when a mixed methods approach is needed.7 page(s

Effects Of Heat On Seven Endodontic Sealers

Purpose: To examine the microscopic surface features, chemical composition, and thermodynamic profile of seven endodontic sealers (AH Plus, Adseal, MTA-Fillapex, RoekoSeal, GuttaFlow 2, GuttaFlow BioSeal, and EndoRez) exposed to high-temperature changes using an endodontic obturation device. Methods: The thermal properties were examined using scanning calorimetry (DSC) and thermogravimetric analysis (TGA). Then, six disc-shaped specimens of each sealer were prepared and divided into two groups – a room temperature group and a heat exposure group – for analysis of surface and chemical changes using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). Results: DSC analysis showed that AH Plus had the highest exothermal signal (122.9°C), while TGA analysis showed that MTA-Fillapex was most affected by increased temperature (32.4% mass loss at 230ºC). SEM analysis showed that while AH Plus and GuttaFlow BioSeal maintained their surface integrity after heat exposure, the EDS profiles demonstrated changes in the chemical composition of the sealers after heat exposure for 5 s. High-temperature exposure had a negative impact on the properties of five of the sealers (Adseal, MTA-Fillapex, RoekoSeal, GuttaFlow 2, and EndoRez). Conclusion: AH Plus and GuttaFlow BioSeal showed minimal changes upon high-temperature exposure, suggesting their suitability for thermal endodontic obturation techniques