Modelling Energy Consumption based on Resource Utilization

Power management is an expensive and important issue for large computational infrastructures such as datacenters, large clusters, and computational grids. However, measuring energy consumption of scalable systems may be impractical due to both cost and complexity for deploying power metering devices on a large number of machines. In this paper, we propose the use of information about resource utilization (e.g. processor, memory, disk operations, and network traffic) as proxies for estimating power consumption. We employ machine learning techniques to estimate power consumption using such information which are provided by common operating systems. Experiments with linear regression, regression tree, and multilayer perceptron on data from different hardware resulted into a model with 99.94\% of accuracy and 6.32 watts of error in the best case.Comment: Submitted to Journal of Supercomputing on 14th June, 201

A90V TDP-43 variant results in the aberrant localization of TDP-43 in vitro

AbstractTAR DNA-binding protein-43 (TDP-43) is a highly conserved, ubiquitously expressed nuclear protein that was recently identified as the disease protein in frontotemporal lobar degeneration with ubiquitin-positive inclusions (FTLD-U) and amyotrophic lateral sclerosis (ALS). Pathogenic TDP-43 gene (TARDBP) mutations have been identified in familial ALS kindreds, and here we report a TARDBP variant (A90V) in a FTLD/ALS patient with a family history of dementia. Significantly, A90V is located between the bipartite nuclear localization signal sequence of TDP-43 and the in vitro expression of TDP-43-A90V led to its sequestration with endogenous TDP-43 as insoluble cytoplasmic aggregates. Thus, A90V may be a genetic risk factor for FTLD/ALS because it predisposes nuclear TDP-43 to redistribute to the cytoplasm and form pathological aggregates

Cuckoo Algorithm Based on Global Feedback

This article proposes a cuckoo algorithm (GFCS) based on the global feedback strategy and innovatively introduces a “re-fly” mechanism. In GFCS, the process of the algorithm is adjusted and controlled by a dynamic global variable, and the dynamic global parameter also serves as an indicator of whether the algorithm has fallen into a local optimum. According to the change of the global optimum value of the algorithm in each round, the dynamic global variable value is adjusted to optimize the algorithm. In addition, we set new formulas for the other main parameters, which are also adjusted by the dynamic global variable as the algorithm progresses. When the algorithm converges prematurely and falls into a local optimum, the current optimum is retained, and the algorithm is initialized and re-executed to find a better value. We define the previous process as “re-fly.” To verify the effectiveness of GFCS, we conducted extensive experiments on the CEC2013 test suite. The experimental results show that the GFCS algorithm has better performance compared to other algorithms when considering the quality of the obtained solution

Glutathione transferase Ti and M1 genotype polymorphism in the normal population of Shanghai

Glutathione transferases are known to be important enzymes in the metabolism of xenobiotics. In humans genetic polymorphisms have been reported for the hGSTM1 and hGSTT1 genes leading to individual differences in susceptibility towards toxic effects, such as cancer. This study describes the distribution of the two polymorphisms of hGSTT1 and hGSTM1 in the normal Chinese population of Shanghai. Out of 219 healthy individuals having been genotyped for GSTTI and GSTMI, 108 (49%) were identified to be homozygously deficient for the GSTT1 gene and 107 (49%) for the GSTM1 gene

A eutectic high-entropy alloy coating of novel hard-surface and soft-core structure with improved tribological properties

Structures with a hard surface and soft core have always been the goal of surface-coating technology. In this study, this goal was achieved in a eutectic high-entropy alloy (EHEA) surface by using aging treatment at elevated temperature. The EHEA surface structure comprised L21 and B2 phases, and a substantial amount of nanoprecipitates were observed inside the B2 and L21 phases. Interestingly, the structure near the interface resembled a “flowerpot seed”, whereas the structure away from the interface resembled a “flower-like” one. After aging treatment, Al diffused to the area away from the interface, resulting in increased volume fraction of L21. Cr and Fe diffused toward the interface, resulting in increased B2 phase. The hardness gradually decreased from the surface to the interior, creating a structure with a hard surface and soft core. The tribological properties of the surface also significantly improved, with decreased coefficient of friction from 0.514 to 0.471. Therefore, this EHEA is a promising candidate as a coating material for long-term applications at elevated temperatures

Correlation between Disease Severity and the Intestinal Microbiome in Mycobacterium tuberculosis-Infected Rhesus Macaques

Why some but not all individuals infected with Mycobacterium tuberculosis develop disease is poorly understood. Previous studies have revealed an important influence of the microbiota on host resistance to infection with a number of different disease agents. Here, we investigated the possible role of the individual’s microbiome in impacting the outcome of M. tuberculosis infection in rhesus monkeys experimentally exposed to this important human pathogen. Although M. tuberculosis infection itself caused only minor alterations in the composition of the gut microbiota in these animals, we observed a significant correlation between an individual monkey’s microbiome and the severity of pulmonary disease. More importantly, this correlation between microbiota structure and disease outcome was evident even prior to infection. Taken together, our findings suggest that the composition of the microbiome may be a useful predictor of tuberculosis progression in infected individuals either directly because of the microbiome’s direct influence on host resistance or indirectly because of its association with other host factors that have this influence. This calls for exploration of the potential of the microbiota composition as a predictive biomarker through carefully designed prospective studies.The factors that determine host susceptibility to tuberculosis (TB) are poorly defined. The microbiota has been identified as a key influence on the nutritional, metabolic, and immunological status of the host, although its role in the pathogenesis of TB is currently unclear. Here, we investigated the influence of Mycobacterium tuberculosis exposure on the microbiome and conversely the impact of the intestinal microbiome on the outcome of M. tuberculosis exposure in a rhesus macaque model of tuberculosis. Animals were infected with different strains and doses of M. tuberculosis in three independent experiments, resulting in a range of disease severities. The compositions of the microbiotas were then assessed using a combination of 16S rRNA and metagenomic sequencing in fecal samples collected pre- and postinfection. Clustering analyses of the microbiota compositions revealed that alterations in the microbiome after M. tuberculosis infection were of much lower magnitude than the variability seen between individual monkeys. However, the microbiomes of macaques that developed severe disease were noticeably distinct from those of the animals with less severe disease as well as from each other. In particular, the bacterial families Lachnospiraceae and Clostridiaceae were enriched in monkeys that were more susceptible to infection, while numbers of Streptococcaceae were decreased. These findings in infected nonhuman primates reveal that certain baseline microbiome communities may strongly associate with the development of severe tuberculosis following infection and can be more important disease correlates than alterations to the microbiota following M. tuberculosis infection itself