Constructing a Consciousness Meter Based on the Combination of Non-Linear Measurements and Genetic Algorithm-Based Support Vector Machine.

OBJECTIVE: Constructing a framework to evaluate consciousness is an important issue in neuroscience research and clinical practice. However, there is still no systematic framework for quantifying altered consciousness along the dimensions of both level and content. This study builds a framework to differentiate the following states: coma, general anesthesia, minimally conscious state (MCS), and normal wakefulness. METHODS: This study analyzed electroencephalography (EEG) recorded from frontal channels in patients with disorders of consciousness (either coma or MCS), patients under general anesthesia, and healthy participants in normal waking consciousness (NWC). Four non-linear methods-permutation entropy (PE), sample entropy (SampEn), permutation Lempel-Ziv complexity (PLZC), and detrended fluctuation analysis (DFA)-as well as relative power (RP), extracted features from the EEG recordings. A genetic algorithm-based support vector machine (GA-SVM) classified the states of consciousness based on the extracted features. A multivariable linear regression model then built EEG indices for level and content of consciousness. RESULTS: The PE differentiated all four states of consciousness (p<0.001). Altered contents of consciousness for NWC, MCS, coma, and general anesthesia were best differentiated by the SampEn, and PLZC. In contrast, the levels of consciousness for these four states were best differentiated by RP of Gamma and PE. A multi-dimensional index, combined with the GA-SVM, showed that the integration of PE, PLZC, SampEn, and DFA had the highest classification accuracy (92.3%). The GA-SVM was better than random forest and neural networks at differentiating these four states. The 'coordinate value' in the dimensions of level and content were constructed by the multivariable linear regression model and the non-linear measures PE, PLZC, SampEn, and DFA. CONCLUSIONS: Multi-dimensional measurements, especially the PE, SampEn, PLZC, and DFA, when combined with GA-SVM, are promising methods for constructing a framework to quantify consciousness.

The Statistical Scale Effect as a Source of Positive Genetic Correlation Between Mean and Variability: A Simulation Study.

The selection objective for animal production is the highest income with the lowest production cost, while ensuring the highest animal welfare. A selection experiment for environmental variability of birth weight in mice showed a correlated response in the mean after 20 generations starting from a crossed panmictic population. The relationship between the birth weight and its environmental variability explained the correlated response. The scale effect represents a potential cause of this correlation. The relationship between the mean and the variability implies: the higher the mean, the higher the variability. The study was to quantify by simulation the genetic correlation between a trait and its environmental variability. This can be attributable to the scale effect in a range of coefficients of variation and heritabilities between 0.05 and 0.50. The resulting genetic correlation ranged from 0.1335 to 0.7021 being the highest for the highest heritability and the lowest CV. The scale effect for a trait with heritability between 0.25 and 0.35 and CV between 0.15 and 0.25 generated a genetic correlation between 0.43 and 0.57. The genetic coefficient of variation (GCV) affecting residual variability was modulated by the strength reducing the impact of the scale effect. GCV ranged from 0.0050 to 1.4984. The strength of the scale effect might be in the range between 0 and 1. The scale effect would explain many reported genetic correlation and the additive genetic variance for the variability. This is relevant when increasing the mean of a trait jointly with the reduction of its variability.

Footprints of adaptive evolution revealed by whole Z chromosomes haplotypes in flycatchers.

Detecting positive selection using genomic data is critical to understanding the role of adaptive evolution. Of particular interest in this context is sex chromosomes since they are thought to play a special role in local adaptation and speciation. We sought to circumvent the challenges associated with statistical phasing when using haplotype-based statistics in sweep scans by benefitting from that whole chromosome haplotypes of the sex chromosomes can be obtained by resequencing of individuals of the hemizygous sex. We analyzed whole Z chromosome haplotypes from 100 females from several populations of four black and white flycatcher species (in birds, females are ZW and males ZZ). Based on integrated haplotype score (iHS) and number of segregating sites by length (nSL) statistics, we found strong and frequent haplotype structure in several regions of the Z chromosome in each species. Most of these sweep signals were population-specific, with essentially no evidence for regions under selection shared among species. Some completed sweeps were revealed by the cross-population extended haplotype homozygosity (XP-EHH) statistic. Importantly, by using statistically phased Z chromosome data from resequencing of males, we failed to recover the signals of selection detected in analyses based on whole chromosome haplotypes from females; instead, what likely represent false signals of selection were frequently seen. This highlights the power issues in statistical phasing and cautions against conclusions from selection scans using such data. The detection of frequent selective sweeps on the avian Z chromosome supports a large role of sex chromosomes in adaptive evolution.

Genetic-based feature selection for efficient motion imaging of a brain-computer interface framework.

OBJECTIVE: A brain-computer interface (BCI) equips humans with the ability to control computers and technical devices mentally. However, the enormous data and the existing irrelevant features of the electrocorticogram signal limit the performance of the classifier. To address these problems, a novel signal processing framework for a binary motor imagery-based BCI system (MI-BCI) is proposed in this paper. APPROACH: Stockwell transform and Bayesian linear discriminant analysis were applied to feature extraction and classification, respectively, and a genetic algorithm (GA) was used in the process of feature selection to extract the most relevant features for classification. The superiority of the algorithm is demonstrated through test results based on the BCI Competition III dataset I. MAIN RESULTS: By comparing the processes with or without feature selection, the performance of the classification was proven to improve using the GA. By adjusting the parameters of the GA, the best feature set (selected 48.6% features) was selected to achieve classification sensitivity, specificity, precision, and accuracy of 94%, 98%, 97.9%, and 96%, respectively, exceeding the results of the existing state-of-the art algorithms. SIGNIFICANCE: As the proposed method can reduce the number of features and select the best feature set, its classification performance was improved and the classification time was shortened; thus, it can be applied to various BCI systems.

Taking Stock of the Drosophila Research Ecosystem.

With a century-old history of fundamental discoveries, the fruit fly has long been a favored experimental organism for a wide range of scientific inquiries. But Drosophila is not a "legacy" model organism; technical and intellectual innovations continue to revitalize fly research and drive advances in our understanding of conserved mechanisms of animal biology. Here, we provide an overview of this "ecosystem" and discuss how to address emerging challenges to ensure its continued productivity. Drosophila researchers are fortunate to have a sophisticated and ever-growing toolkit for the analysis of gene function. Access to these tools depends upon continued support for both physical and informational resources. Uncertainty regarding stable support for bioinformatic databases is a particular concern, at a time when there is the need to make the vast knowledge of functional biology provided by this model animal accessible to scientists studying other organisms. Communication and advocacy efforts will promote appreciation of the value of the fly in delivering biomedically important insights. Well-tended traditions of large-scale tool development, open sharing of reagents, and community engagement provide a strong basis for coordinated and proactive initiatives to improve the fly research ecosystem. Overall, there has never been a better time to be a fly pusher.

Genetic algorithm optimized triply compensated pulses in NMR spectroscopy.

Sensitivity and resolution in NMR experiments are affected by magnetic field inhomogeneities (of both external and RF), errors in pulse calibration, and offset effects due to finite length of RF pulses. To remedy these problems, built-in compensation mechanisms for these experimental imperfections are often necessary. Here, we propose a new family of phase-modulated constant-amplitude broadband pulses with high compensation for RF inhomogeneity and heteronuclear coupling evolution. These pulses were optimized using a genetic algorithm (GA), which consists in a global optimization method inspired by Nature's evolutionary processes. The newly designed π and π/2 pulses belong to the 'type A' (or general rotors) symmetric composite pulses. These GA-optimized pulses are relatively short compared to other general rotors and can be used for excitation and inversion, as well as refocusing pulses in spin-echo experiments. The performance of the GA-optimized pulses was assessed in Magic Angle Spinning (MAS) solid-state NMR experiments using a crystalline U-(13)C, (15)N NAVL peptide as well as U-(13)C, (15)N microcrystalline ubiquitin. GA optimization of NMR pulse sequences opens a window for improving current experiments and designing new robust pulse sequences.

Not the time or the place: the missing spatio-temporal link in publicly available genetic data.

Genetic data are being generated at unprecedented rates. Policies of many journals, institutions and funding bodies aim to ensure that these data are publicly archived so that published results are reproducible. Additionally, publicly archived data can be 'repurposed' to address new questions in the future. In 2011, along with other leading journals in ecology and evolution, Molecular Ecology implemented mandatory public data archiving (the Joint Data Archiving Policy). To evaluate the effect of this policy, we assessed the genetic, spatial and temporal data archived for 419 data sets from 289 articles in Molecular Ecology from 2009 to 2013. We then determined whether archived data could be used to reproduce analyses as presented in the manuscript. We found that the journal's mandatory archiving policy has had a substantial positive impact, increasing genetic data archiving from 49 (pre-2011) to 98% (2011-present). However, 31% of publicly archived genetic data sets could not be recreated based on information supplied in either the manuscript or public archives, with incomplete data or inconsistent codes linking genetic data and metadata as the primary reasons. While the majority of articles did provide some geographic information, 40% did not provide this information as geographic coordinates. Furthermore, a large proportion of articles did not contain any information regarding date of sampling (40%). Although the inclusion of spatio-temporal data does require an increase in effort, we argue that the enduring value of publicly accessible genetic data to the molecular ecology field is greatly compromised when such metadata are not archived alongside genetic data.

Comparison of variants of canonical correlation analysis and partial least squares for combined analysis of MRI and genetic data.

The standard analysis approach in neuroimaging genetics studies is the mass-univariate linear modeling (MULM) approach. From a statistical view, however, this approach is disadvantageous, as it is computationally intensive, cannot account for complex multivariate relationships, and has to be corrected for multiple testing. In contrast, multivariate methods offer the opportunity to include combined information from multiple variants to discover meaningful associations between genetic and brain imaging data. We assessed three multivariate techniques, partial least squares correlation (PLSC), sparse canonical correlation analysis (sparse CCA) and Bayesian inter-battery factor analysis (Bayesian IBFA), with respect to their ability to detect multivariate genotype-phenotype associations. Our goal was to systematically compare these three approaches with respect to their performance and to assess their suitability for high-dimensional and multi-collinearly dependent data as is the case in neuroimaging genetics studies. In a series of simulations using both linearly independent and multi-collinear data, we show that sparse CCA and PLSC are suitable even for very high-dimensional collinear imaging data sets. Among those two, the predictive power was higher for sparse CCA when voxel numbers were below 400 times sample size and candidate SNPs were considered. Accordingly, we recommend Sparse CCA for candidate phenotype, candidate SNP studies. When voxel numbers exceeded 500 times sample size, the predictive power was the highest for PLSC. Therefore, PLSC can be considered a promising technique for multivariate modeling of high-dimensional brain-SNP-associations. In contrast, Bayesian IBFA cannot be recommended, since additional post-processing steps were necessary to detect causal relations. To verify the applicability of sparse CCA and PLSC, we applied them to an experimental imaging genetics data set provided for us. Most importantly, application of both methods replicated the findings of this data set.

Genética da leishmaniose cutânea em humanos: identificação de vias imunológicas que contribuem para a suscetibilidade à doença / Genetics of cutaneous leishmaniasis in humans: identification of immunological pathways that contribute to disease susceptibility

A leishmaniose cutânea (LC) é a forma clínica mais comum do complexo de doenças causadas por protozoários do gênero Leishmania. Interessantemente, alguns indivíduos infectados com espécies dermotrópicas do parasito não desenvolvem a LC, enquanto outros desenvolvem lesões crônicas. Os mecanismos envolvidos nesta variação permanecem amplamente desconhecidos, embora fatores genéticos do hospedeiro podem influenciar o risco de desenvolver a doença. No primeiro estudo apresentado nesta tese, foi mostrado que a sinalização IL-2/IL-2R desempenha um papel crucial na resposta imune contra espécies dermotrópicas de Leishmania. Os transcritos de vários genes da via de sinalização IL-2 são mais abundantes em úlceras cutâneas causadas por Leishmania braziliensis do que em amostras de pele normal de dadores não infectados. Um estudo de associação em famílias brasileiras (209 famílias nucleares) identificou dois polimorfismos no gene IL2RA associados à LC causada por L. braziliensis [rs10905669 (p = 3x10-4) e rs706778 (p = 3x10-4)]...

Cutaneous leishmaniasis (CL) is the most common clinical form of leishmaniasis and can be caused by several dermotropic Leishmania species. Interestingly, some infected individuals do not develop cutaneous lesions, while others are severely affected. The basis of this variation remains largely unknown, although host genetic factors seem to influence disease risk. In the first study presented in this thesis, it was shown that IL-2 plays a crucial role in human immunity against dermotropic Leishmania species. It was observed that the transcripts of several genes of the IL-2 pathway were more abundant in skin ulcers caused by Leishmania braziliensis than in normal skin samples. A primary association study on Brazilians (754 individuals from 209 families) identified two polymorphisms in the IL2RA gene associated with CL caused by L. braziliensis [rs10905669 (p = 3x10-4) and rs706778 (p = 3x10-4)]...

Statistical genetics with application to population-based study design: a primer for clinicians.

With the completion of the entire human genome sequence and remarkable advances in genotyping technologies, there has been an increased interest in the application of genetics and genomics in biomedical research over the last decade. Large-scale population-based genetic association studies have now become routine and their application to several multifactorial diseases such as cardiovascular disorders has led to the identification of a number of novel susceptibility genes. However, to be able to interpret results from such studies, clinicians need to have a basic understanding of unique concepts and issues related to this fast-moving area of research. In this primer, we provide a broad overview of design, analysis, and methodological issues with a focus on population-based study design.

Estudios de transformación genética en arveja voluble cultivar Santa Isabel / Genetic transformation study in climbing pea Santa Isabel cultivar

Se probaron diferentes alternativas de transformación genética en arveja cultivar "Santa Isabel" con el fin de estudiar los factores que afectan el proceso. Se emplearon los métodos de infiltración mediante vacío, infección directa de explantes, transformación de polen, y microinyección de ovarios. La prueba histoquímica de expresión gus fue escogida como método de análisis en la determinación de transformantes positivos. Con las metodologías empleadas se detectaron puntos azules en el tejido vegetal, lo cual indica la expresión transitoria del transgen en los explantes utilizados. Los resultados obtenidos sugieren que la transformación genética en arveja cultivada en Colombia puede ser utilizada para la introducción de genes de interés como apoyo a los procesos de mejoramiento genético.

Different genetic transformation alternatives were tested in pea, "Santa Isabel" cultivar, with the purpose of studying the factors that affect the process. The methods of infiltration with vacuum, direct infection of the explants, pollen transformation and ovary microinjection were used. The hystochemical test of the gus expression was chosen as analysis method in the determination of positive transformants. With the used methodologies, blue spots in the plant tissue were detected, which indicates transient expression of the transgene in utilized explants. The obtained results suggest that the genetic transformation in pea genotypes planted in Colombia can be utilized for the introduction of genes of interest as support to genetic improvement.

DNA repair: a changing geography? (1964-2008).

This article aims to explain the current state of DNA Repair studies' global geography by focusing on the genesis of the community. Bibliometric data is used to localize scientific activities related to DNA Repair at the city level. The keyword "DNA Repair" was introduced first by American scientists. It started to spread after 1964 that is to say, after P. Howard-Flanders (Yale University), P. Hanawalt (Stanford University) and R. Setlow (Oak Ridge Laboratories) found evidence for Excision Repair mechanisms. It was the first stage in the emergence of an autonomous scientific community. In this article, we will try to assess to what extent the geo-history of this scientific field is determinant in understanding its current geography. In order to do so, we will localize the places where the first "DNA Repair" publications were signed fifty years ago and the following spatial diffusion process, which led to the current geography of the field. Then, we will focus on the evolution of the research activity of "early entrants" in relation to the activity of "latecomers". This article is an opportunity to share with DNA Repair scientists some research results of a dynamic field in Science studies: spatial scientometrics.

Alterações genéticas em casais com antecedentes de aborto recorrente no primeiro trimestre da gestação / Genetic alterations in couples with a history of recurrent miscarriage in the first trimester of pregnancy

O abortamento é considerado um problema multifatorial, cujas principais causas envolvidas na sua etiologia são os fatores ambientais (como exposição a substâncias tóxicas), genéticos, anatômicos, endócrinos, imunológicos, trombofílicos e doenças infecciosas (como toxoplasmose, rubéola). No entanto, os fatores genéticos são atribuídos principalmente aos abortamentos de primeiro trimestre da gestação. As alterações cromossômicas, o polimorfismo C677T, no gene da metilenotetrahidrofolato redutase (MTHFR677C>T); o polimorfismo G1691A, no gene do Fator V de Leiden (FVL1691G>A), e o polimorfismo G20210A, no gene da protrombina (PRT20210G>A), têm sido associados a problemas obstétricos, incluindo aborto recorrente. O objetivo deste trabalho foi investigar associação entre as mutações relacionadas à trombofilia, presença de alterações cromossômican e a ocorrência de aborto espontâneo recorrente e avaliar possíveis interações entre as referidas mutações e as alterações cromossômicas. A casuística foi composta por 151 mulheres com história de aborto recorrente, 94 parceiros e 100 controles (mulheres sem histórico de aborto). A investigação das mutações foi realizada pela técnica de Reação em Cadeia da Polimerase- Polimorfismo de Tamanho de Fragmento de Restrição. As alterações cromossômicas foram investigadas pela cariotipagem com banda–G. A frequência das alterações cromossômicas foi de 7,3% nas mulheres com abortamento recorrente e 1% nos controles (p=0,022), e de 2,1% nos parceiros. No entanto, a frequência dos alelos MTHR677C>T (23% versus 22,5%), FVL1691G>A (1,5% versus 1% ) e PRT20210G>A (1,45% versus 0%) foi similar entre casos e controles, respectivamente. No grupo investigado, foi observada associação entre aborto recorrente e alterações cromossômicas, mas não foi encontrada associação com os polimorfismos gênicos investigados.

Abortion is considered a multifactorial problem, the most important causes involved in its etiology are, environmental factors ( as exposure to toxic chemicals), genetic, anatomic, endocrine, immunological, thrombophilic and infectious diseases (such as toxoplasmosis, rubella). However, genetic factors are mainly attributed to abortions of the first trimester of pregnancy. Chromosomal abnormalities, MTHFR 677C>T, factor V Leiden 1691G>A and prothrombin 20210G>A mutations have been associated with obstetric problems, including recurrent miscarriage. The objective of this research was to investigate associations between mutations in three genes commonly associated to thrombophilic events, chromosomal abnormalities and the occurrence of recurrent miscarriage. As well evaluate possible interactions between these mutations and chromosomal abnormalities. The sample was comprised of 151 women with history of recurrent miscarriages, 94 partners and 100 control (women with no history of abortion). The investigation of the mutations was performed by Polymerase Chain Reaction (PCR)/ Restriction Fragment Length Polymorphism (RFLP). Chromosomal aberrations were investigated by karyotyping with G-banda. The frequency of chromosomal abnormalities was 7.3% in women with recurrent miscarriage and 1% in controls (p = 0.022), and 2.1% in the partners. However, the frequency of allele MTHR677C> T (23% versus 22.5%), FVL1691G> A (1.5% vs. 1%) and PRT20210G> A (1.45% vs. 0%) was similar for cases and controls, respectively. In the investigated group was found association between recurrent miscarriage and chromosomal abnormalities, but no association was found with the genetic polymorphisms investigated.