ActiveSpaces: Exploring dynamic code deployment for extreme scale data processing

Managing the large volumes of data produced by emerging scientific and engineering simulations running on leadership‐class resources has become a critical challenge. The data have to be extracted off the computing nodes and transported to consumer nodes so that it can be processed, analyzed, visualized, archived, and so on. Several recent research efforts have addressed data‐related challenges at different levels. One attractive approach is to offload expensive input/output operations to a smaller set of dedicated computing nodes known as a staging area. However, even using this approach, the data still have to be moved from the staging area to consumer nodes for processing, which continues to be a bottleneck. In this paper, we investigate an alternate approach, namely moving the data‐processing code to the staging area instead of moving the data to the data‐processing code. Specifically, we describe the ActiveSpaces framework, which provides (1) programming support for defining the data‐processing routines to be downloaded to the staging area and (2) runtime mechanisms for transporting codes associated with these routines to the staging area, executing the routines on the nodes that are part of the staging area, and returning the results. We also present an experimental performance evaluation of ActiveSpaces using applications running on the Cray XT5 at Oak Ridge National Laboratory. Finally, we use a coupled fusion application workflow to explore the trade‐offs between transporting data and transporting the code required for data processing during coupling, and we characterize sweet spots for each option. Copyright © 2014 John Wiley & Sons, Ltd.     

6,5-Fused Ring,C2-Salvinorin Ester,Dual Kappa and Mu Opioid Receptor Agonists as Analgesics Devoid of Anxiogenic Effects**

Current common analgesics are mediated through the mu or kappa opioid receptor agonism. Unfortunately, selective mu or kappa receptor agonists often cause harmful side effects. However, ligands exhibiting dual agonism to the opioid receptors, such as to mu and kappa, or to mu and delta, have been suggested to temper undesirable adverse effects while retaining analgesic activity. Herein we report an introduction of various 6,5-fused rings to C2 of the salvinorin scaffold via an ester linker. In vitro studies showed that many of these compounds have dual agonism on kappa and mu opioid receptors. In vivo studies on the lead dual kappa and mu opioid receptor agonist demonstrated supraspinal thermal analgesic activity while avoiding anxiogenic effects in male mice, thus providing further strong evidence in support of the therapeutic advantages of dual opioid receptor agonists over selective opioid receptor agonists.     

BAPRP: a machine learning approach to blackhole attacks prevention routing protocol in vehicular Ad Hoc networks

International Journal of Information Security - One of the network performance challenges of Vehicular Ad Hoc Networks (VANET) is the Black Hole attack. This destructive attack severely damages the...     

Expanding the toolbox for development of metal-based dual-functional catalytic membranes

Catalytic membranes offer opportunities to develop modular, process-intensified units. Dual-functional materials, which integrate reactive and separation components in a single material, could play an important role in enabling them. Adapting the various characterization tools that are used to analyze the structures of metal-based catalysts to these integrated structures could provide vital information for their design and implementation. In this perspective, we highlight the ways in which these tools can be used to analyze nonreactive membranes and non-integrated systems where the catalyst and the membrane operate as two separate units. A methodology developed to analyze these comparatively simpler systems could be subsequently extended to integrated dual-functional catalytic membranes. Thus, researchers from the catalysis and membranes communities can work together in a way that will not only lead to fundamental advancements in our understanding of catalytic membranes but also enable their transformation into real, scalable process-intensified units.     

Morphology-controlled therapeutic activity of cerium oxide nanoparticles for mild traumatic brain injury

The design and optimization of nanostructures with unique morphologies and properties are at the forefront of biomedical nanotechnology. Cerium oxides are widely used to investigate the effect of morphology on performance. However, elucidating the morphology–activity relationship of cerium oxide nanocrystals in biomedical applications remains challenging. Herein, the therapeutic effects of cerium oxide nanoparticles with different morphologies: cerium oxide nanorods with two different aspect ratios (CeO_x NRs_A and CeO_x NRs_B), cerium oxide nanopolyhedra (CeO_x NPs), and cerium oxide nanocubes (CeO_x NCs) are investigated in in vivo and in vitro mild traumatic brain injury (TBI) models. Cerium oxide nanoparticles inhibit oxidative stress and inflammation after mild TBI, alleviating cognitive impairment; furthermore, the therapeutic effect is significantly affected by their morphology. Owing to the higher Ce³⁺/Ce⁴⁺ ratio, exposure of more active crystal surfaces, and greater number of exposed oxygen vacancies, CeO_x NRs show better activity than CeO_x NPs and CeO_x NCs for mild TBI. Among the two investigated types of cerium oxide nanorods, CeO_x NRs_A, with a higher Ce³⁺/Ce⁴⁺ ratio on the surface, appear to spread better than CeO_x NRs_B in the injured lesions. The factors causing morphology-controlled biomedical performance, such as Ce³⁺/Ce⁴⁺ molar ratio, surface area, and aspect ratio, are discussed.     

In situ H2O2 Generation by Choline Oxidase and Its Application in Amino Polysaccharide Degradation by Coupling to Lytic Polysaccharide Monooxygenase

Chitin, the most abundant amino polysaccharide in Nature, has many applications in different fields. However, processing of this recalcitrant biopolymer in an environmentally friendly manner remains a major challenge. In this context, lytic polysaccharide monooxygenases (LPMOs) are of interest, as they can act on the most recalcitrant parts of chitin and related insoluble biopolymers such as cellulose. Efficient LPMO catalysis can be achieved by feeding reactions with H₂O₂, but careful control of H₂O₂ is required to avoid autocatalytic enzyme inactivation. Herein, we present a coupled enzyme system in which a choline oxidase from Arthrobacter globiformis is employed for controlled in situ generation of H₂O₂ that fuels LPMO-catalyzed oxidative degradation of chitin. We show that the rate, stability and extent of the LPMO reaction can be manipulated by varying the amount of choline oxidase and/or its substrate, choline chloride, and that efficient peroxygenase reactions may be achieved using sub-μM concentrations of the H₂O₂-generating enzyme. This coupled system requires only sub-stoichiometric amounts of the reductant that is needed to keep the LPMO in its active, reduced state. It is conceivable that this enzyme system may be used for bioprocessing of chitin in choline-based natural deep eutectic solvents.     

1,2,4-Triazole-3-Thione Analogues with a 2-Ethylbenzoic Acid at Position 4 as VIM-type Metallo-β-Lactamase Inhibitors

Metallo-β-lactamases (MBLs) are increasingly involved as a major mechanism of resistance to carbapenems in relevant opportunistic Gram-negative pathogens. Unfortunately, clinically efficient MBL inhibitors still represent an unmet medical need. We previously reported several series of compounds based on the 1,2,4-triazole-3-thione scaffold. In particular, Schiff bases formed between diversely 5-substituted-4-amino compounds and 2-carboxybenzaldehyde were broad-spectrum inhibitors of VIM-type, NDM-1 and IMP-1 MBLs. Unfortunately, these compounds were unable to restore antibiotic susceptibility of MBL-producing bacteria, probably because of poor penetration and/or susceptibility to hydrolysis. To improve their microbiological activity, we synthesized and characterized compounds where the hydrazone-like bond of the Schiff base analogues was replaced by a stable ethyl link. This small change resulted in a narrower inhibition spectrum, as all compounds were poorly or not inhibiting NDM-1 and IMP-1, but showed a significantly better activity on VIM-type enzymes, with K_i values in the μM to sub-μM range. The resolution of the crystallographic structure of VIM-2 in complex with one of the best inhibitors yielded valuable information about their binding mode. Interestingly, several compounds were shown to restore the β-lactam susceptibility of VIM-type-producing E. coli laboratory strains and also of K. pneumoniae clinical isolates. In addition, selected compounds were found to be devoid of toxicity toward human cancer cells at high concentration, thus showing promising safety.     

Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete

Fiber-reinforced self-compacting concrete (FRSCC) is a typical construction material, and its compressive strength (CS) is a critical mechanical property that must be adequately determined. In the machine learning (ML) approach to estimating the CS of FRSCC, the current research gaps include the limitations of samples in databases, the applicability constraints of models owing to limited mixture components, and the possibility of applying recently proposed models. This study developed different ML models for predicting the CS of FRSCC to address these limitations. Artificial neural network, random forest, and categorical gradient boosting (CatBoost) models were optimized to derive the best predictive model with the aid of a 10-fold cross-validation technique. A database of 381 samples was created, representing the most significant FRSCC dataset compared with previous studies, and it was used for model development. The findings indicated that CatBoost outperformed the other two models with excellent predictive abilities (root mean square error of 2.639 MPa, mean absolute error of 1.669 MPa, and coefficient of determination of 0.986 for the test dataset). Finally, a sensitivity analysis using a partial dependence plot was conducted to obtain a thorough understanding of the effect of each input variable on the predicted CS of FRSCC. The results showed that the cement content, testing age, and superplasticizer content are the most critical factors affecting the CS.