The Birth-Death-Mutation process: a new paradigm for fat tailed distributions

Fat tailed statistics and power-laws are ubiquitous in many complex systems. Usually the appearance of of a few anomalously successful individuals (bio-species, investors, websites) is interpreted as reflecting some inherent "quality" (fitness, talent, giftedness) as in Darwin's theory of natural selection. Here we adopt the opposite, "neutral", outlook, suggesting that the main factor explaining success is merely luck. The statistics emerging from the neutral birth-death-mutation (BDM) process is shown to fit marvelously many empirical distributions. While previous neutral theories have focused on the power-law tail, our theory economically and accurately explains the entire distribution. We thus suggest the BDM distribution as a standard neutral model: effects of fitness and selection are to be identified by substantial deviations from it

Metastable Ar(1s\u3csub\u3e5\u3c/sub\u3e) Density Dependence on Pressure and Argon-helium Mixture in a High Pressure Radio Frequency Dielectric Barrier Discharge

Simulations of an α-mode radio frequency dielectric barrier discharge are performed for varying mixtures of argon and helium at pressures ranging from 200 to 500 Torr using both zero and one-dimensional models. Metastable densities are analyzed as a function of argon-helium mixture and pressure to determine the optimal conditions, maximizing metastable density for use in an optically pumped rare gas laser. Argon fractions corresponding to the peak metastable densities are found to be pressure dependent, shifting from approximately 15% Ar in He at 200 Torr to 10% at 500 Torr. A decrease in metastable density is observed as pressure is increased due to a diminution in the reduced electric field and a quadratic increase in metastable loss rates through Ar*2 formation. A zero-dimensional effective direct current model of the dielectric barrier discharge is implemented, showing agreement with the trends predicted by the one-dimensional fluid model in the bulk plasma

Adaptations to Climate in Candidate Genes for Common Metabolic Disorders

Evolutionary pressures due to variation in climate play an important role in shaping phenotypic variation among and within species and have been shown to influence variation in phenotypes such as body shape and size among humans. Genes involved in energy metabolism are likely to be central to heat and cold tolerance. To test the hypothesis that climate shaped variation in metabolism genes in humans, we used a bioinformatics approach based on network theory to select 82 candidate genes for common metabolic disorders. We genotyped 873 tag SNPs in these genes in 54 worldwide populations (including the 52 in the Human Genome Diversity Project panel) and found correlations with climate variables using rank correlation analysis and a newly developed method termed Bayesian geographic analysis. In addition, we genotyped 210 carefully matched control SNPs to provide an empirical null distribution for spatial patterns of allele frequency due to population history alone. For nearly all climate variables, we found an excess of genic SNPs in the tail of the distributions of the test statistics compared to the control SNPs, implying that metabolic genes as a group show signals of spatially varying selection. Among our strongest signals were several SNPs (e.g., LEPR R109K, FABP2 A54T) that had previously been associated with phenotypes directly related to cold tolerance. Since variation in climate may be correlated with other aspects of environmental variation, it is possible that some of the signals that we detected reflect selective pressures other than climate. Nevertheless, our results are consistent with the idea that climate has been an important selective pressure acting on candidate genes for common metabolic disorders.</p

Sex differences in mathematics and reading achievement are inversely related: within- and across-nation assessment of 10 years of PISA data

We analyzed one decade of data collected by the Programme for International Student Assessment (PISA), including the mathematics and reading performance of nearly 1.5 million 15 year olds in 75 countries. Across nations, boys scored higher than girls in mathematics, but lower than girls in reading. The sex difference in reading was three times as large as in mathematics. There was considerable variation in the extent of the sex differences between nations. There are countries without a sex difference in mathematics performance, and in some countries girls scored higher than boys. Boys scored lower in reading in all nations in all four PISA assessments (2000, 2003, 2006, 2009). Contrary to several previous studies, we found no evidence that the sex differences were related to nations’ gender equality indicators. Further, paradoxically, sex differences in mathematics were consistently and strongly inversely correlated with sex differences in reading: Countries with a smaller sex difference in mathematics had a larger sex difference in reading and vice versa. We demonstrate that this was not merely a between-nation, but also a within-nation effect. This effect is related to relative changes in these sex differences across the performance continuum: We did not find a sex difference in mathematics among the lowest performing students, but this is where the sex difference in reading was largest. In contrast, the sex difference in mathematics was largest among the higher performing students, and this is where the sex difference in reading was smallest. The implication is that if policy makers decide that changes in these sex differences are desired, different approaches will be needed to achieve this for reading and mathematics. Interventions that focus on high-achieving girls in mathematics and on low achieving boys in reading are likely to yield the strongest educational benefits

Genome sequence of the pattern forming Paenibacillus vortex bacterium reveals potential for thriving in complex environments

<p>Abstract</p> <p>Background</p> <p>The pattern-forming bacterium <it>Paenibacillus vortex </it>is notable for its advanced social behavior, which is reflected in development of colonies with highly intricate architectures. Prior to this study, only two other <it>Paenibacillus </it>species (<it>Paenibacillus </it>sp. JDR-2 and <it>Paenibacillus larvae</it>) have been sequenced. However, no genomic data is available on the <it>Paenibacillus </it>species with pattern-forming and complex social motility. Here we report the <it>de novo </it>genome sequence of this Gram-positive, soil-dwelling, sporulating bacterium.</p> <p>Results</p> <p>The complete <it>P. vortex </it>genome was sequenced by a hybrid approach using 454 Life Sciences and Illumina, achieving a total of 289× coverage, with 99.8% sequence identity between the two methods. The sequencing results were validated using a custom designed Agilent microarray expression chip which represented the coding and the non-coding regions. Analysis of the <it>P. vortex </it>genome revealed 6,437 open reading frames (ORFs) and 73 non-coding RNA genes. Comparative genomic analysis with 500 complete bacterial genomes revealed exceptionally high number of two-component system (TCS) genes, transcription factors (TFs), transport and defense related genes. Additionally, we have identified genes involved in the production of antimicrobial compounds and extracellular degrading enzymes.</p> <p>Conclusions</p> <p>These findings suggest that <it>P. vortex </it>has advanced faculties to perceive and react to a wide range of signaling molecules and environmental conditions, which could be associated with its ability to reconfigure and replicate complex colony architectures. Additionally, <it>P. vortex </it>is likely to serve as a rich source of genes important for agricultural, medical and industrial applications and it has the potential to advance the study of social microbiology within Gram-positive bacteria.</p

You Name It – How Memory and Delay Govern First Name Dynamics

The adoption and abandonment of first names through time is a fascinating phenomenon that may shed light on social dynamics and the forces that determine cultural taste in general. Here we show that baby name dynamics is governed almost solely by deterministic forces, even though the emerging abundance statistics resembles the one obtained from a pure drift model. Exogenous events are shown to affect the name dynamics very rarely, and most of the year-to-year fluctuations around the deterministic trend may be attributed solely to demographic noise. We suggest that the rise and fall of a name reflect an “infection” process with delay and memory. The symmetry between adoption and abandonment speed emerges from our model without further assumptions

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research

Financial time series prediction using spiking neural networks

In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments. © 2014 Reid et al