Evolutionary forces shaping genomic islands of population differentiation in humans

Background Levels of differentiation among populations depend both on demographic and selective factors: genetic drift and local adaptation increase population differentiation, which is eroded by gene flow and balancing selection. We describe here the genomic distribution and the properties of genomic regions with unusually high and low levels of population differentiation in humans to assess the influence of selective and neutral processes on human genetic structure. Methods Individual SNPs of the Human Genome Diversity Panel (HGDP) showing significantly high or low levels of population differentiation were detected under a hierarchical-island model (HIM). A Hidden Markov Model allowed us to detect genomic regions or islands of high or low population differentiation. Results Under the HIM, only 1.5% of all SNPs are significant at the 1% level, but their genomic spatial distribution is significantly non-random. We find evidence that local adaptation shaped high-differentiation islands, as they are enriched for non-synonymous SNPs and overlap with previously identified candidate regions for positive selection. Moreover there is a negative relationship between the size of islands and recombination rate, which is stronger for islands overlapping with genes. Gene ontology analysis supports the role of diet as a major selective pressure in those highly differentiated islands. Low-differentiation islands are also enriched for non-synonymous SNPs, and contain an overly high proportion of genes belonging to the 'Oncogenesis' biological process. Conclusions Even though selection seems to be acting in shaping islands of high population differentiation, neutral demographic processes might have promoted the appearance of some genomic islands since i) as much as 20% of islands are in non-genic regions ii) these non-genic islands are on average two times shorter than genic islands, suggesting a more rapid erosion by recombination, and iii) most loci are strongly differentiated between Africans and non-Africans, a result consistent with known human demographic history.

Offline grammar-based recognition of handwritten sentences

This paper proposes a sequential coupling of a Hidden Markov Model (HMM) recognizer for offline handwritten English sentences with a probabilistic bottom-up chart parser using Stochastic Context-Free Grammars (SCFG) extracted from a text corpus. Based on extensive experiments, we conclude that syntax analysis helps to improve recognition rates significantly.

NodePM: A Remote Monitoring Alert System for Energy Consumption Using Probabilistic Techniques

In this paper, we propose an intelligent method, named the Novelty Detection Power Meter (NodePM), to detect novelties in electronic equipment monitored by a smart grid. Considering the entropy of each device monitored, which is calculated based on a Markov chain model, the proposed method identifies novelties through a machine learning algorithm. To this end, the NodePM is integrated into a platform for the remote monitoring of energy consumption, which consists of a wireless sensors network (WSN). It thus should be stressed that the experiments were conducted in real environments different from many related works, which are evaluated in simulated environments. In this sense, the results show that the NodePM reduces by 13.7% the power consumption of the equipment we monitored. In addition, the NodePM provides better efficiency to detect novelties when compared to an approach from the literature, surpassing it in different scenarios in all evaluations that were carried out.

An efficient simulation algorithm for the generalized von Mises distribution of order two

In this article we propose an exact efficient simulation algorithm for the generalized von Mises circular distribution of order two. It is an acceptance-rejection algorithm with a piecewise linear envelope based on the local extrema and the inflexion points of the generalized von Mises density of order two. We show that these points can be obtained from the roots of polynomials and degrees four and eight, which can be easily obtained by the methods of Ferrari and Weierstrass. A comparative study with the von Neumann acceptance-rejection, with the ratio-of-uniforms and with a Markov chain Monte Carlo algorithms shows that this new method is generally the most efficient.

A longitudinal study on delirium in nursing homes

OBJECTIVE To investigate the evolution of delirium of nursing home (NH) residents and their possible predictors. DESIGN Post-hoc analysis of a prospective cohort assessment. SETTING Ninety NHs in Switzerland. PARTICIPANTS Included 14,771 NH residents. MEASUREMENTS The Resident Assessment Instrument Minimum Data Set and the Nursing Home Confusion Assessment Method were used to determine follow-up of subsyndromal or full delirium in NH residents using discrete Markov chain modeling to describe long-term trajectories and multiple logistic regression analyses to determine predictors of the trajectories. RESULTS We identified four major types of delirium time courses in NH. Increasing severity of cognitive impairment and of depressive symptoms at the initial assessment predicted the different delirium time courses. CONCLUSION More pronounced cognitive impairment and depressive symptoms at the initial assessment are associated with different subsequent evolutions of delirium. The presence and evolution of delirium in the first year after NH admission predicted the subsequent course of delirium until death.

Characterisation of flash floods in small ungauged mountain basins of Central Spain using an integrated approach

One of the main problems of flood hazard assessment in ungauged or poorly gauged basins is the lack of runoff data. In an attempt to overcome this problem we have combined archival records, dendrogeomorphic time series and instrumental data (daily rainfall and discharge) from four ungauged and poorly gauged mountain basins in Central Spain with the aim of reconstructing and compiling information on 41 flash flood events since the end of the 19th century. Estimation of historical discharge and the incorporation of uncertainty for the at-site and regional flood frequency analysis were performed with an empirical rainfall–runoff assessment as well as stochastic and Bayesian Markov Chain Monte Carlo (MCMC) approaches. Results for each of the ungauged basins include flood frequency, severity, seasonality and triggers (synoptic meteorological situations). The reconstructed data series clearly demonstrates how uncertainty can be reduced by including historical information, but also points to the considerable influence of different approaches on quantile estimation. This uncertainty should be taken into account when these data are used for flood risk management.

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon’s implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike’s preceding ISI. As we show, the EIF’s exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron’s ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing computational theories about UP states during slow wave sleep and present possible extensions of the model in the context of spike-frequency adaptation.

Genome profiling of sterol synthesis shows convergent evolution in parasites and guides chemotherapeutic attack

Sterols are an essential class of lipids in eukaryotes, where they serve as structural components of membranes and play important roles as signaling molecules. Sterols are also of high pharmacological significance: cholesterol-lowering drugs are blockbusters in human health, and inhibitors of ergosterol biosynthesis are widely used as antifungals. Inhibitors of ergosterol synthesis are also being developed for Chagas's disease, caused by Trypanosoma cruzi. Here we develop an in silico pipeline to globally evaluate sterol metabolism and perform comparative genomics. We generate a library of hidden Markov model-based profiles for 42 sterol biosynthetic enzymes, which allows expressing the genomic makeup of a given species as a numerical vector. Hierarchical clustering of these vectors functionally groups eukaryote proteomes and reveals convergent evolution, in particular metabolic reduction in obligate endoparasites. We experimentally explore sterol metabolism by testing a set of sterol biosynthesis inhibitors against trypanosomatids, Plasmodium falciparum, Giardia, and mammalian cells, and by quantifying the expression levels of sterol biosynthetic genes during the different life stages of T. cruzi and Trypanosoma brucei. The phenotypic data correlate with genomic makeup for simvastatin, which showed activity against trypanosomatids. Other findings, such as the activity of terbinafine against Giardia, are not in agreement with the genotypic profile.

Absolute polarity determination of teeth cementum by phase sensitive second harmonic generation microscopy

The absolute sign of local polarity in relation to the biological growth direction has been investigated for teeth cementum using phase sensitive second harmonic generation microscopy (PS-SHGM) and a crystal of 2-cyclooctylamino-5-nitropyridine (COANP) as a nonlinear optic (NLO) reference material. A second harmonic generation (SHG) response was found in two directions of cementum: radial (acellular extrinsic fibers that are oriented more or less perpendicular to the root surface) and circumferential (cellular intrinsic fibers that are oriented more or less parallel to the surface). A mono-polar state was demonstrated for acellular extrinsic cementum. However, along the different parts of cementum in circumferential direction, two corresponding domains were observed featuring an opposite sign of polarity indicative for a bi-polar microscopic state of cellular intrinsic cementum. The phase information showed that the orientation of radial collagen fibrils of cementum is regularly organized with the donor (D) groups pointing to the surface. Circumferential collagen molecules feature orientational disorder and are oriented up and down in random manner showing acceptor or donor groups at the surface of cementum. Considering that the cementum continues to grow in thickness throughout life, we can conclude that the cementum is growing circumferentially in two opposite directions and radially in one direction. A Markov chain type model for polarity formation in the direction of growth predicts D-groups preferably appearing at the fiber front.