Constraints from Solar and Reactor Neutrinos on Unparticle Long-Range Forces

We have investigated the impact of long-range forces induced by unparticle operators of scalar, vector and tensor nature coupled to fermions in the interpretation of solar neutrinos and KamLAND data. If the unparticle couplings to the neutrinos are mildly non-universal, such long-range forces will not factorize out in the neutrino flavour evolution. As a consequence large deviations from the observed standard matter-induced oscillation pattern for solar neutrinos would be generated. In this case, severe limits can be set on the infrared fix point scale, Lambda_u, and the new physics scale, M, as a function of the ultraviolet (d_UV) and anomalous (d) dimension of the unparticle operator. For a scalar unparticle, for instance, assuming the non-universality of the lepton couplings to unparticles to be of the order of a few per mil we find that, for d_UV=3 and d=1.1, M is constrained to be M > O(10^9) TeV (M > O(10^10) TeV) if Lambda_u= 1 TeV (10 TeV). For given values of Lambda_u and d, the corresponding bounds on M for vector [tensor] unparticles are approximately 100 [3/Sqrt(Lambda_u/TeV)] times those for the scalar case. Conversely, these results can be translated into severe constraints on universality violation of the fermion couplings to unparticle operators with scales which can be accessible at future colliders.Comment: 13 pages, 3 figures. Minor changes due to precision in numerical factors and correction in figure labels. References added. Conclusions remain unchange

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Evolutionary Computation Group

Abstract In this paper, we present a genetic algorithm with a very small population and a reinitialization process (a mi-cro genetic algorithm) for solving multiobjective optimization problems. Our approach uses three forms of elitism, includ-ing an external memory (or secondary population) to keep the nondominated solutions found along the evolutionary pro-cess. We validate our proposal using several engineering op-timization problems taken from the specialized literature, and we compare our results with respect to two other algorithms (the NSGA-II and PAES) using three different metrics. Our results indicate that our approach is very efficient (computa

Uncertainty of protein-ligand binding constants: asymmetric confidence intervals versus standard errors

Equilibrium binding constants (Kb) between chemical compounds and target proteins or between interacting proteins provide a quantitative understanding of biological interaction mechanisms. Reporting uncertainties of measured experimental parameters are critical for decision making in many scientific areas, e.g., in lead compound discovery processes and in comparing computational predictions with experimental results. Uncertainties in measured Kb values are commonly represented by a symmetric normal distribution, often quoted in terms of the experimental value plus-minus the standard deviation. However, in general the distributions of measured Kb (and equivalent Kd) values and the corresponding free energy change DeltaGb are all asymmetric to varying degree. Here, using a simulation approach, we illustrate the effect of asymmetric Kb distributions within the realm of isothermal titration calorimetry (ITC) experiments. Further we illustrate the known, but perhaps not widely appreciated, fact that when distributions of any of Kb, Kd and DeltaGb are transformed into each other their degree of asymmetry is changed. Consequently, we recommend that a more accurate way of expressing the uncertainties of Kb, Kd, and DeltaGb values is to consistently report 95% confidence intervals, in line with other author’s suggestions. The ways to obtain such error ranges are discussed in detail and exemplified for a binding reaction obtained by ITC