Exact quantum quasiclassical, and semiclassical reaction probabilities for the collinear F+D_2 → FD+D reaction

Exact quantum, quasiclassical, and semiclassical reaction probabilities and rate constants for the collinear reaction F+D_2 → FD+D are presented. In all calculations, a high degree of population inversion is predicted with P^R_(03) and P^R(04) being the dominant reaction probabilities. In analogy with the F+H_2 reaction (preceding paper), the exact quantum 0→3 and 0→4 probabilities show markedly different energy dependence with PR03 having a much smaller effective threshold energy (E_T=0.014 eV) than P^R_(04) (0.055 eV). The corresponding quasiclassical forward probabilities P^R_(03) and P^R_(04) are in poor agreement with the exact quantum ones, while their quasiclassical reverse and semiclassical counterparts provide much better approximations to the exact results. Similar comparisons are also made in the analysis of the corresponding EQ, QCF, QCR, and USC rate constants. An information theoretic analysis of the EQ and QCF reaction probabilities indicates nonlinear surprisal behavior as well as a significant isotope dependence. Additional quantum results at higher energies are presented and discussed in terms of threshold behavior and resonances. Exact quantum reaction probabilities for the related F+HD → FH+D and F+DH → FD+H reactions are given and an attempt to explain the observed isotope effects is made

Large quantum effects in the collinear F+H2-->FH+H reaction

We have performed accurate quantum mechanical calculations of reaction probabilities for the collinear F+H2-->FH+H reaction as well as corresponding quasiclassical trajectory calculations. A comparison of these results shows that very significant quantum mechanical effects are present in this reaction

Computational thinking in the era of big data biology

It is fall again, and another class of students has arrived in the Watson School of Biological Sciences at Cold Spring Harbor Laboratory (CSHL). Building on the lab's 100-year history as a leading center for research and education, the Watson School was established in 1998 as a graduate program in biology with a focus on molecular, cellular and structural biology, and neuroscience, cancer, plant biology and genetics. All students in the program complete the same courses, centered around these research topics, with an emphasis on the principles of scientific reasoning and logic, as well as the importance of ethics and effective communication. Three years ago the curriculum was expanded to include a new course on quantitative biology (QB) and I, along with my co-instructor Mickey Atwal and other members of the QB program, have been teaching it ever since

Biological data sciences in genome research

The last 20 years have been a remarkable era for biology and medicine. One of the most significant achievements has been the sequencing of the first human genomes, which has laid the foundation for profound insights into human genetics, the intricacies of regulation and development, and the forces of evolution. Incredibly, as we look into the future over the next 20 years, we see the very real potential for sequencing more than 1 billion genomes, bringing even deeper insight into human genetics as well as the genetics of millions of other species on the planet. Realizing this great potential for medicine and biology, though, will only be achieved through the integration and development of highly scalable computational and quantitative approaches that can keep pace with the rapid improvements to biotechnology. In this perspective, I aim to chart out these future technologies, anticipate the major themes of research, and call out the challenges ahead. One of the largest shifts will be in the training used to prepare the class of 2035 for their highly interdisciplinary world

Social Capital and Adolescent Student At-Risk Behaviors

The purpose of this study was to investigate the differences between six at-risk behaviors of adolescent students in relation to levels of social capital of 9–12 grade students. Data were gathered from the Youth Risk and Protective Factors Survey (YRPFS), administered to ninth through twelfth grade students in a school district located in a medium size midwest city. Factor analysis reduced the 114 questions on Survey A and B to two independent factors. Independent Factor I, Family Social Capital, was the sum of issues pertaining to the parents\u27 educational background, rules at home, and educational expectations for their children. Independent Factor II, School and Community Social Capital, was the sum of issues pertaining to school involvement by parents, the discussions parents had about school with their adolescent, and involvement in community activities. Summated ratings generated six dependent variables of alcohol usage, drug usage, tobacco usage, sexual behaviors, trouble at school, and violence. Multivariate analysis of variance was used to determine the level of difference within the three levels of Family Social Capital and School and Community Social Capital and the dependent variables. A further analysis using a univariate analysis of variance test was conducted to determine if there were significant differences between the dependent at-risk variables and three levels of independent social capital variables. And finally, Bonferroni\u27s post hoc comparisons were conducted for each dependent at-risk variable to determine the level of significant difference between the levels of each independent social capital variable. The attainment of social capital was determined through the relationships students developed with their family, school, and community. At-risk factors to include alcohol use, drug use, tobacco use, sexual behaviors, trouble at school, and violence were analyzed to determine their relationship to the different levels of social capital. The results suggest that family, school, and community social capital have a significant influence on the social development of adolescents. It was determined that when levels of social capital were high, participation in at-risk behaviors decreased. The results of this study indicate that, overall, Family Social Capital is somewhat more important than School and Community Social Capital when considering the level of at-risk behavior engaged in by adolescents

The Influence of Reaction Rates on the Final p-Abundances

The astrophysical p-process is responsible for the origin of the proton rich nuclei,which are heavier than iron. A huge network involving thousands of reaction rates is necessary to calculate the final p-abundances. But not all rates included in the network have a strong influence on the p-nuclei abundances. The p-process was investigated using a full nuclear reaction network for a type II supernovae explosion when the shock front passes through the O/Ne layer. Calculations were done with a multi-layer model adopting the seed of a pre-explosion evolution of a 25 mass star. In extensive simulations we investigated the impact of single reaction rates on the final p-abundances. The results are important for the strategy of future experiments in this field.Comment: 4 page

Genomic dark matter: the reliability of short read mapping illustrated by the genome mappability score

Motivation: Genome resequencing and short read mapping are two of the primary tools of genomics and are used for many important applications. The current state-of-the-art in mapping uses the quality values and mapping quality scores to evaluate the reliability of the mapping. These attributes, however, are assigned to individual reads and do not directly measure the problematic repeats across the genome. Here, we present the Genome Mappability Score (GMS) as a novel measure of the complexity of resequencing a genome. The GMS is a weighted probability that any read could be unambiguously mapped to a given position and thus measures the overall composition of the genome itself. Results: We have developed the Genome Mappability Analyzer to compute the GMS of every position in a genome. It leverages the parallelism of cloud computing to analyze large genomes, and enabled us to identify the 5-14% of the human, mouse, fly and yeast genomes that are difficult to analyze with short reads. We examined the accuracy of the widely used BWA/SAMtools polymorphism discovery pipeline in the context of the GMS, and found discovery errors are dominated by false negatives, especially in regions with poor GMS. These errors are fundamental to the mapping process and cannot be overcome by increasing coverage. As such, the GMS should be considered in every resequencing project to pinpoint the 'dark matter' of the genome, including of known clinically relevant variations in these regions