We study the temperature dependence of the lifetime of geometric and geometric/energetic water hydrogen-bonds (H-bonds), down to supercooled water, through molecular dynamics. The probability and lifetime of H-bonds that break either by translational or librational motions and those of energetic broken H-bonds, along with the effects of transient broken H-bonds and transient H-bonds, are considered. We show that the fraction of transiently broken energetic H-bonds increases at low temperatures and that this energetic breakdown is caused by oxygen-oxygen electrostatic repulsions upon too small amplitude librations to disrupt geometric H-bonds. Hence, differences between geometric and energetic continuous H-bond lifetimes are associated with large H-bond energy fluctuations, in opposition to moderate geometric fluctuations, within common energetic and geometric H-bond definition thresholds. Exclusion of transient broken H-bonds and transient H-bonds leads to H-bond definition-independent mean lifetimes and activation energies, ~11 kJ/mol, consistent with the reactive flux method and experimental scattering results. Further, we show that power law decay of specific temporal H-bond lifetime probability distributions is associated with librational and translational motions that occur on the time scale (~0.1 ps) of H-bond breaking /re-forming dynamics. While our analysis is diffusion-free, the effect of diffusion on H-bond probability distributions where H-bonds are allowed to break and re-form, switching acceptors in between, is shown to result in neither exponential nor power law decay, similar to the reactive flux correlation function.

Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases present highly heterogeneous genotype, which difficult biological marker identification. Machine learning methods are widely used to identify these markers, but their performance is highly dependent upon the size and quality of available data. In this study, we demonstrated that machine learning classifiers trained on gene functional similarities, using Gene Ontology (GO), can improve the identification of genes involved in complex diseases. For this purpose, we developed a supervised machine learning methodology to predict complex disease genes. The proposed pipeline was assessed using Autism Spectrum Disorder (ASD) candidate genes. A quantitative measure of gene functional similarities was obtained by employing different semantic similarity measures. To infer the hidden functional similarities between ASD genes, various types of machine learning classifiers were built on quantitative semantic similarity matrices of ASD and non-ASD genes. The classifiers trained and tested on ASD and non-ASD gene functional similarities outperformed previously reported ASD classifiers. For example, a Random Forest (RF) classifier achieved an AUC of 0. 80 for predicting new ASD genes, which was higher than the reported classifier (0.73). Additionally, this classifier was able to predict 73 novel ASD candidate genes that were enriched for core ASD phenotypes, such as autism and obsessive-compulsive behavior. In addition, predicted genes were also enriched for ASD co-occurring conditions, including Attention Deficit Hyperactivity Disorder (ADHD). We also developed a KNIME workflow with the proposed methodology which allows users to configure and execute it without requiring machine learning and programming skills. Machine learning is an effective and reliable technique to decipher ASD mechanism by identifying novel disease genes, but this study further demonstrated that their performance can be improved by incorporating a quantitative measure of gene functional similarities. Source code and the workflow of the proposed methodology are available at https://github.com/Muh-Asif/ASD-genes-prediction.

ABSTRACT:Electronic properties of H 2 O 2 (H 2 O) 1−6 and (H 2 O) 1−7 clusters are reported. Emphasis was placed on the changes induced by the presence of hydrogen peroxide on the electronic properties of water aggregates. Theoretical results for excitation energies as well as vertical and adiabatic ionization energies are reported. Excitation energies were calculated with time-dependent density functional theory and equation-of-motion coupled cluster with single and double excitations (EOM-CCSD). A many-body energy decomposition scheme recently proposed was coupled to the EOM-CCSD method making possible an accurate prediction of the first vertical excitation energy of peroxide-water clusters. In comparison with water clusters, our results show that the presence of hydrogen peroxide in water clusters is characterized by a [1.5-1.8] eV red-shift of the first excitation energy. The first excitation is localized on the HOOH moiety, and no significant dependence of the first excitation energy on the cluster size is observed. The differences between vertical and adiabatic ionization energies for both water and hydrogen peroxide-water clusters reflect the feature that ionization leads to proton transfer and to a significant structural and electronic density reorganization.

We present the results of a systematic literature review that examines the main paradigms and properties of programming languages developed for and used in High Performance Computing for Big Data processing. The systematic literature review is based on a combination of automated keyword-based search in the Elsevier Science Direct database and further digital databases for articles published in international peer-reviewed journals and conferences, leading to an initial sample of 420 articles, which was then narrowed down in a second phase to 152 articles found relevant and published 2006-2018. The manual analysis of these articles allowed us to identify 26 languages used in 33 of these articles for HPC for Big Data processing. We analyzed the languages and their usage in these articles by 22 criteria and summarize the results in this article. We evaluate the outcomes of the literature review by comparing them with opinions of domain experts. Our results indicate that, for instance, the majority of the used HPC languages in the context of Big Data are text-based general-purpose programming languages and target the end-user community.