Improving Decoy Databases for Protein Folding Algorithms

Predicting protein structures and simulating protein folding motions are two of the most important problems in computational biology today. Modern folding simulation methods rely on a scoring function which attempts to distinguish the native structure (the most energetically stable 3D structure) from one or more non-native structures. Decoy databases are collections of non-native structures that are widely used to test and verify these scoring functions. We present a method to evaluate and improve the quality of decoy databases by adding novel structures and/or removing redundant structures. We test our approach on 13 different decoy databases of varying size and type and show significant improvement across a variety of metrics. The most improvement comes from the addition of novel structures indicating that our improved databases have more informative structures that are more likely to fool scoring functions. We also test our improved databases on a popular modern scoring function. We show that they contain a greater number of native-like structures than the original databases, thereby producing a more rigorous database for testing scoring functions. This work can aid the development and testing of better scoring functions, which in turn, will improve the quality of protein folding simulations

Saving the world’s terrestrial megafauna

From the late Pleistocene to the Holocene, and now the so called Anthropocene, humans have been driving an ongoing series of species declines and extinctions (Dirzo et al. 2014). Large-bodied mammals are typically at a higher risk of extinction than smaller ones (Cardillo et al. 2005). However, in some circumstances terrestrial megafauna populations have been able to recover some of their lost numbers due to strong conservation and political commitment, and human cultural changes (Chapron et al. 2014). Indeed many would be in considerably worse predicaments in the absence of conservation action (Hoffmann et al. 2015). Nevertheless, most mammalian megafauna face dramatic range contractions and population declines. In fact, 59% of the world’s largest carnivores (≥ 15 kg, n = 27) and 60% of the world’s largest herbivores (≥ 100 kg, n = 74) are classified as threatened with extinction on the International Union for the Conservation of Nature (IUCN) Red List (supplemental table S1 and S2). This situation is particularly dire in sub-Saharan Africa and Southeast Asia, home to the greatest diversity of extant megafauna (figure 1). Species at risk of extinction include some of the world’s most iconic animals—such as gorillas, rhinos, and big cats (figure 2 top row)—and, unfortunately, they are vanishing just as science is discovering their essential ecological roles (Estes et al. 2011). Here, our objectives are to raise awareness of how these megafauna are imperiled (species in supplemental table S1 and S2) and to stimulate broad interest in developing specific recommendations and concerted action to conserve them

Transcriptional Landscape of the Prenatal Human Brain

Summary The anatomical and functional architecture of the human brain is largely determined by prenatal transcriptional processes. We describe an anatomically comprehensive atlas of mid-gestational human brain, including de novo reference atlases, in situ hybridization, ultra-high resolution magnetic resonance imaging (MRI) and microarray analysis on highly discrete laser microdissected brain regions. In developing cerebral cortex, transcriptional differences are found between different proliferative and postmitotic layers, wherein laminar signatures reflect cellular composition and developmental processes. Cytoarchitectural differences between human and mouse have molecular correlates, including species differences in gene expression in subplate, although surprisingly we find minimal differences between the inner and human-expanded outer subventricular zones. Both germinal and postmitotic cortical layers exhibit fronto-temporal gradients, with particular enrichment in frontal lobe. Finally, many neurodevelopmental disorder and human evolution-related genes show patterned expression, potentially underlying unique features of human cortical formation. These data provide a rich, freely-accessible resource for understanding human brain development

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis