Inevitable Evolutionary Temporal Elements in Neural Processing: A Study Based on Evolutionary Simulations

Recent studies have suggested that some neural computational mechanisms are based on the fine temporal structure of spiking activity. However, less effort has been devoted to investigating the evolutionary aspects of such mechanisms. In this paper we explore the issue of temporal neural computation from an evolutionary point of view, using a genetic simulation of the evolutionary development of neural systems. We evolve neural systems in an environment with selective pressure based on mate finding, and examine the temporal aspects of the evolved systems. In repeating evolutionary sessions, there was a significant increase during evolution in the mutual information between the evolved agent's temporal neural representation and the external environment. In ten different simulated evolutionary sessions, there was an increased effect of time -related neural ablations on the agents' fitness. These results suggest that in some fitness landscapes the emergence of temporal elements in neural computation is almost inevitable. Future research using similar evolutionary simulations may shed new light on various biological mechanisms

Balanced Input Allows Optimal Encoding in a Stochastic Binary Neural Network Model: An Analytical Study

Recent neurophysiological experiments have demonstrated a remarkable effect of attention on the underlying neural activity that suggests for the first time that information encoding is indeed actively influenced by attention. Single cell recordings show that attention reduces both the neural variability and correlations in the attended condition with respect to the non-attended one. This reduction of variability and redundancy enhances the information associated with the detection and further processing of the attended stimulus. Beyond the attentional paradigm, the local activity in a neural circuit can be modulated in a number of ways, leading to the general question of understanding how the activity of such circuits is sensitive to these relatively small modulations. Here, using an analytically tractable neural network model, we demonstrate how this enhancement of information emerges when excitatory and inhibitory synaptic currents are balanced. In particular, we show that the network encoding sensitivity -as measured by the Fisher information- is maximized at the exact balance. Furthermore, we find a similar result for a more realistic spiking neural network model. As the regime of balanced inputs has been experimentally observed, these results suggest that this regime is functionally important from an information encoding standpoint

Association of Blood Lead (Pb) and Plasma Homocysteine: A Cross Sectional Survey in Karachi, Pakistan

Background: High blood lead (Pb) and hyperhomocysteinemia have been found to be associated with cardiovascular disease (CVD). Mean blood Pb and mean plasma homocysteine levels have been reported to be high in Pakistani population. The objective of the present study was to assess the relationship of blood Pb to the risk of hyperhomocysteinemia in a low income urban population of Karachi, Pakistan. Methodology/Principal Findings: In a cross sectional survey, 872 healthy adults (355 males, 517 females, age 18-60 years) were recruited from a low income urban population of Karachi. Fasting venous blood was obtained and assessed for blood Pb and plasma/serum homocysteine, folate, pyridoxal phosphate (PLP, a coenzymic form of vitamin B6) and vitamin B12. The study population had median (IQR) blood Pb of 10.82 microg/dL (8.29-13.60). Prevalence of high blood Pb (levels\u3e10 microg/dL) was higher in males compared to females (62.5% males vs 56% females, p value=0.05). Mean+/-SD/median (IQR) value of plasma homocysteine was significantly higher in the highest quartile of blood Pb compared to the lowest quartile 16.13+/-11.2 micromol/L vs 13.28+/-9.7micromol/L/13.15 (10.33-17.81) micromol/L vs 11.09 (8.65 14.31) micromol/L (p valu

Phosphorylation of GFAP is associated with injury in the neonatal pig hypoxic-ischemic brain

Glial fibrillary acidic protein (GFAP) is an intermediate filament protein expressed in the astrocyte cytoskeleton that plays an important role in the structure and function of the cell. GFAP can be phosphorylated at six serine (Ser) or threonine (Thr) residues but little is known about the role of GFAP phosphorylation in physiological and pathophysiological states. We have generated antibodies against two phosphorylated GFAP (pGFAP) proteins: p8GFAP, where GFAP is phosphorylated at Ser-8 and p13GFAP, where GFAP is phosphorylated at Ser-13. We examined p8GFAP and p13GFAP expression in the control neonatal pig brain and at 24 and 72 h after an hypoxic-ischemic (HI) insult. Immunohistochemistry demonstrated pGFAP expression in astrocytes with an atypical cytoskeletal morphology, even in control brains. Semi-quantitative western blotting revealed that p8GFAP expression was significantly increased at 24 h post-insult in HI animals with seizures in frontal, parietal, temporal and occipital cortices. At 72 h post-insult, p8GFAP and p13GFAP expression were significantly increased in HI animals with seizures in brain regions that are vulnerable to cellular damage (cortex and basal ganglia), but no changes were observed in brain regions that are relatively spared following an HI insult (brain stem and cerebellum). Increased pGFAP expression was associated with poor neurological outcomes such as abnormal encephalography and neurobehaviour, and increased histological brain damage. Phosphorylation of GFAP may play an important role in astrocyte remodelling during development and disease and could potentially contribute to the plasticity of the central nervous system

No evidence for association with APOL1 kidney disease risk alleles and Human African Trypanosomiasis in two Ugandan populations:

Human African trypanosomiasis (HAT) manifests as an acute form caused by Trypanosoma brucei rhodesiense (Tbr) and a chronic form caused by Trypanosoma brucei gambiense (Tbg). Previous studies have suggested a host genetic role in infection outcomes, particularly for APOL1. We have undertaken a candidate gene association studies (CGAS) in a Ugandan Tbr and a Tbg HAT endemic area, to determine whether polymorphisms in IL10, IL8, IL4, HLAG, TNFA, TNX4LB, IL6, IFNG, MIF, APOL1, HLAA, IL1B, IL4R, IL12B, IL12R, HP, HPR, and CFH have a role in HAT

Inferring structural connectivity using Ising couplings in models of neuronal networks

Functional connectivity metrics have been widely used to infer the underlying structural connectivity in neuronal networks. Maximum entropy based Ising models have been suggested to discount the effect of indirect interactions and give good results in inferring the true anatomical connections. However, no benchmarking is currently available to assess the performance of Ising couplings against other functional connectivity metrics in the microscopic scale of neuronal networks through a wide set of network conditions and network structures. In this paper, we study the performance of the Ising model couplings to infer the synaptic connectivity in in silico networks of neurons and compare its performance against partial and cross-correlations for different correlation levels, firing rates, network sizes, network densities, and topologies. Our results show that the relative performance amongst the three functional connectivity metrics depends primarily on the network correlation levels. Ising couplings detected the most structural links at very weak network correlation levels, and partial correlations outperformed Ising couplings and cross-correlations at strong correlation levels. The result was consistent across varying firing rates, network sizes, and topologies. The findings of this paper serve as a guide in choosing the right functional connectivity tool to reconstruct the structural connectivity

Identification of Direct Target Genes Using Joint Sequence and Expression Likelihood with Application to DAF-16

A major challenge in the post-genome era is to reconstruct regulatory networks from the biological knowledge accumulated up to date. The development of tools for identifying direct target genes of transcription factors (TFs) is critical to this endeavor. Given a set of microarray experiments, a probabilistic model called TRANSMODIS has been developed which can infer the direct targets of a TF by integrating sequence motif, gene expression and ChIP-chip data. The performance of TRANSMODIS was first validated on a set of transcription factor perturbation experiments (TFPEs) involving Pho4p, a well studied TF in Saccharomyces cerevisiae. TRANSMODIS removed elements of arbitrariness in manual target gene selection process and produced results that concur with one's intuition. TRANSMODIS was further validated on a genome-wide scale by comparing it with two other methods in Saccharomyces cerevisiae. The usefulness of TRANSMODIS was then demonstrated by applying it to the identification of direct targets of DAF-16, a critical TF regulating ageing in Caenorhabditis elegans. We found that 189 genes were tightly regulated by DAF-16. In addition, DAF-16 has differential preference for motifs when acting as an activator or repressor, which awaits experimental verification. TRANSMODIS is computationally efficient and robust, making it a useful probabilistic framework for finding immediate targets

Body Mass Index and Employment-Based Health Insurance

<p>Abstract</p> <p>Background</p> <p>Obese workers incur greater health care costs than normal weight workers. Possibly viewed by employers as an increased financial risk, they may be at a disadvantage in procuring employment that provides health insurance. This study aims to evaluate the association between body mass index [BMI, weight in kilograms divided by the square of height in meters] of employees and their likelihood of holding jobs that include employment-based health insurance [EBHI].</p> <p>Methods</p> <p>We used the 2004 Household Components of the nationally representative Medical Expenditure Panel Survey. We utilized logistic regression models with provision of EBHI as the dependent variable in this descriptive analysis. The key independent variable was BMI, with adjustments for the domains of demographics, social-economic status, workplace/job characteristics, and health behavior/status. BMI was classified as normal weight (18.5–24.9), overweight (25.0–29.9), or obese (≥ 30.0). There were 11,833 eligible respondents in the analysis.</p> <p>Results</p> <p>Among employed adults, obese workers [adjusted probability (AP) = 0.62, (0.60, 0.65)] (<it>P </it>= 0.005) were more likely to be employed in jobs with EBHI than their normal weight counterparts [AP = 0.57, (0.55, 0.60)]. Overweight workers were also more likely to hold jobs with EBHI than normal weight workers, but the difference did not reach statistical significance [AP = 0.61 (0.58, 0.63)] (<it>P </it>= 0.052). There were no interaction effects between BMI and gender or age.</p> <p>Conclusion</p> <p>In this nationally representative sample, we detected an association between workers' increasing BMI and their likelihood of being employed in positions that include EBHI. These findings suggest that obese workers are more likely to have EBHI than other workers.</p

Maternal exposure to air pollution before and during pregnancy related to changes in newborn's cord blood lymphocyte subpopulations. The EDEN study cohort

<p>Abstract</p> <p>Background</p> <p>Toxicants can cross the placenta and expose the developing fetus to chemical contamination leading to possible adverse health effects, by potentially inducing alterations in immune competence. Our aim was to investigate the impacts of maternal exposure to air pollution before and during pregnancy on newborn's immune system.</p> <p>Methods</p> <p>Exposure to background particulate matter less than 10 μm in diameter (PM₁₀) and nitrogen dioxide (NO₂) was assessed in 370 women three months before and during pregnancy using monitoring stations. Personal exposure to four volatile organic compounds (VOCs) was measured in a subsample of 56 non-smoking women with a diffusive air sampler during the second trimester of pregnancy. Cord blood was analyzed at birth by multi-parameter flow cytometry to determine lymphocyte subsets.</p> <p>Results</p> <p>Among other immunophenotypic changes in cord blood, decreases in the CD4+CD25+ T-cell percentage of 0.82% (p = 0.01), 0.71% (p = 0.04), 0.88% (p = 0.02), and 0.59% (p = 0.04) for a 10 μg/m³increase in PM₁₀levels three months before and during the first, second and third trimester of pregnancy, respectively, were observed after adjusting for confounders. A similar decrease in CD4+CD25+ T-cell percentage was observed in association with personal exposure to benzene. A similar trend was observed between NO₂exposure and CD4+CD25+ T-cell percentage; however the association was stronger between NO₂exposure and an increased percentage of CD8+ T-cells.</p> <p>Conclusions</p> <p>These data suggest that maternal exposure to air pollution before and during pregnancy may alter the immune competence in offspring thus increasing the child's risk of developing health conditions later in life, including asthma and allergies.</p

Evaluating deterministic motif significance measures in protein databases

<p>Abstract</p> <p>Background</p> <p>Assessing the outcome of motif mining algorithms is an essential task, as the number of reported motifs can be very large. Significance measures play a central role in automatically ranking those motifs, and therefore alleviating the analysis work. Spotting the most interesting and relevant motifs is then dependent on the choice of the right measures. The combined use of several measures may provide more robust results. However caution has to be taken in order to avoid spurious evaluations.</p> <p>Results</p> <p>From the set of conducted experiments, it was verified that several of the selected significance measures show a very similar behavior in a wide range of situations therefore providing redundant information. Some measures have proved to be more appropriate to rank highly conserved motifs, while others are more appropriate for weakly conserved ones. Support appears as a very important feature to be considered for correct motif ranking. We observed that not all the measures are suitable for situations with poorly balanced class information, like for instance, when positive data is significantly less than negative data. Finally, a visualization scheme was proposed that, when several measures are applied, enables an easy identification of high scoring motifs.</p> <p>Conclusion</p> <p>In this work we have surveyed and categorized 14 significance measures for pattern evaluation. Their ability to rank three types of deterministic motifs was evaluated. Measures were applied in different testing conditions, where relations were identified. This study provides some pertinent insights on the choice of the right set of significance measures for the evaluation of deterministic motifs extracted from protein databases.</p