Travel routes to remote ocean targets reveal the map sense resolution for a marine migrant

How animals navigate across the ocean to isolated targets remains perplexing greater than 150 years since this question was considered by Charles Darwin. To help solve this long-standing enigma, we considered the likely resolution of any map sense used in migration, based on the navigational performance across different scales (tens to thousands of kilometres). We assessed navigational performance using a unique high-resolution Fastloc-GPS tracking dataset for post-breeding hawksbill turtles (Eretmochelys imbricata) migrating relatively short distances to remote, isolated targets on submerged banks in the Indian Ocean. Individuals often followed circuitous paths (mean straightness index = 0.54, range 0.14-0.93, s.d. = 0.23, n = 22), when migrating short distances (mean beeline distance to target = 106 km, range 68.7-178.2 km). For example, one turtle travelled 1306.2 km when the beeline distance to the target was only 176.4 km. When off the beeline to their target, turtles sometimes corrected their course both in the open ocean and when encountering shallow water. Our results provide compelling evidence that hawksbill turtles only have a relatively crude map sense in the open ocean. The existence of widespread foraging and breeding areas on isolated oceanic sites points to target searching in the final stages of migration being common in sea turtles

Genetic algorithm dynamics on a rugged landscape

The genetic algorithm is an optimization procedure motivated by biological evolution and is successfully applied to optimization problems in different areas. A statistical mechanics model for its dynamics is proposed based on the parent-child fitness correlation of the genetic operators, making it applicable to general fitness landscapes. It is compared to a recent model based on a maximum entropy ansatz. Finally it is applied to modeling the dynamics of a genetic algorithm on the rugged fitness landscape of the NK model.Comment: 10 pages RevTeX, 4 figures PostScrip

The statistical mechanics of a polygenic characterunder stabilizing selection, mutation and drift

By exploiting an analogy between population genetics and statistical mechanics, we study the evolution of a polygenic trait under stabilizing selection, mutation, and genetic drift. This requires us to track only four macroscopic variables, instead of the distribution of all the allele frequencies that influence the trait. These macroscopic variables are the expectations of: the trait mean and its square, the genetic variance, and of a measure of heterozygosity, and are derived from a generating function that is in turn derived by maximizing an entropy measure. These four macroscopics are enough to accurately describe the dynamics of the trait mean and of its genetic variance (and in principle of any other quantity). Unlike previous approaches that were based on an infinite series of moments or cumulants, which had to be truncated arbitrarily, our calculations provide a well-defined approximation procedure. We apply the framework to abrupt and gradual changes in the optimum, as well as to changes in the strength of stabilizing selection. Our approximations are surprisingly accurate, even for systems with as few as 5 loci. We find that when the effects of drift are included, the expected genetic variance is hardly altered by directional selection, even though it fluctuates in any particular instance. We also find hysteresis, showing that even after averaging over the microscopic variables, the macroscopic trajectories retain a memory of the underlying genetic states.Comment: 35 pages, 8 figure

Stochasticity in the miR-9/Hes1 oscillatory network can account for clonal heterogeneity in the timing of differentiation.

Recent studies suggest that cells make stochastic choices with respect to differentiation or division. However, the molecular mechanism underlying such stochasticity is unknown. We previously proposed that the timing of vertebrate neuronal differentiation is regulated by molecular oscillations of a transcriptional repressor, HES1, tuned by a post-transcriptional repressor, miR-9. Here, we computationally model the effects of intrinsic noise on the Hes1/miR-9 oscillator as a consequence of low molecular numbers of interacting species, determined experimentally. We report that increased stochasticity spreads the timing of differentiation in a population, such that initially equivalent cells differentiate over a period of time. Surprisingly, inherent stochasticity also increases the robustness of the progenitor state and lessens the impact of unequal, random distribution of molecules at cell division on the temporal spread of differentiation at the population level. This advantageous use of biological noise contrasts with the view that noise needs to be counteracted

The Herschel Planetary Nebula Survey (HerPlaNS) I. Data Overview and Analysis Demonstration with NGC 6781

This is the first of a series of investigations into far-IR characteristics of 11 planetary nebulae (PNs) under the Herschel Space Observatory Open Time 1 program, Herschel Planetary Nebula Survey (HerPlaNS). Using the HerPlaNS data set, we look into the PN energetics and variations of the physical conditions within the target nebulae. In the present work, we provide an overview of the survey, data acquisition and processing, and resulting data products. We perform (1) PACS/SPIRE broadband imaging to determine the spatial distribution of the cold dust component in the target PNs and (2) PACS/SPIRE spectral-energy-distribution (SED) and line spectroscopy to determine the spatial distribution of the gas component in the target PNs. For the case of NGC 6781, the broadband maps confirm the nearly pole-on barrel structure of the amorphous carbon-richdust shell and the surrounding halo having temperatures of 26-40 K. The PACS/SPIRE multi-position spectra show spatial variations of far-IR lines that reflect the physical stratification of the nebula. We demonstrate that spatially-resolved far-IR line diagnostics yield the (T_e, n_e) profiles, from which distributions of ionized, atomic, and molecular gases can be determined. Direct comparison of the dust and gas column mass maps constrained by the HerPlaNS data allows to construct an empirical gas-to-dust mass ratio map, which shows a range of ratios with the median of 195+-110. The present analysis yields estimates of the total mass of the shell to be 0.86 M_sun, consisting of 0.54 M_sun of ionized gas, 0.12 M_sun of atomic gas, 0.2 M_sun of molecular gas, and 4 x 10^-3 M_sun of dust grains. These estimates also suggest that the central star of about 1.5 M_sun initial mass is terminating its PN evolution onto the white dwarf cooling track.Comment: 27 pages, 16 figures, accepted for publication in A&

Discrimination between oral corticosteroid-treated and oral corticosteroid-non-treated severe asthma patients by an electronic nose platform

Rationale: Some severe asthma patients require oral corticosteroids (OCS) likely due to greater disease severity. Exhaled molecular markers can provide phenotypic information in asthma. Objectives: Determine whether patients on OCS (OCS+) have a different breathprint compared with those who were not on OCS (OCS-); determine the classification accuracy of eNose as compared to FEV1 % pred, % sputum eosinophils, and exhaled nitric oxide (FENO). Methods: This was a cross-sectional analysis of the U-BIOPRED cohort. Severe asthma was defined by IMI-criteria [Bel Thorax 2011]. OCS+ patients had daily OCS. OCS- patients had never had OCS and were on maintenance inhaled fluticasone equivalent >1000 μg/day. Exhaled volatile organic compounds trapped on adsorption tubes were analysed by centralized eNose platform (Owlstone Lonestar, Cyranose 320, Comon Invent, Tor Vergata TEN) including a total of 190 sensors. t test was used for comparing groups and support vector machine with leave-one-out cross-validation as a classifier. Results: 33 OCS+ (age 55±11yr, mean±SD, 52% female, 27% smokers, pre-bronchodilator FEV1 64.1±24% pred) and 40 OCS- severe asthma patients (age 54±15yr, mean±SD, 55% female, 35% smokers, pre-bronchodilator FEV1 61.8±24% pred) were studied. Sensor by sensor analysis showed that 56 sensors provided different mean values (change in sensor resistance or frequency) between groups (P<0.05). Accuracy of classification was as follows: eNose 71% (n=73), FENO 71% (n=70), FEV1 62% (n=73) and sputum eosinophils 59% (n=37). Conclusions: Preliminary results suggest OCS+ and OCS- severe asthma patients can be distinguished by an eNose platform

Transient Phenomena in Gene Expression after Induction of Transcription

When transcription of a gene is induced by a stimulus, the number of its mRNA molecules changes with time. Here we discuss how this time evolution depends on the shape of the mRNA lifetime distribution. Analysis of the statistical properties of this change reveals transient effects on polysomes, ribosomal profiles, and rate of protein synthesis. Our studies reveal that transient phenomena in gene expression strongly depend on the specific form of the mRNA lifetime distribution

Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as asthma endotypes. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies