Gain control network conditions in early sensory coding

Gain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network of excitatory and inhibitory neurons. We consider two scenarios for the variation of input into the system. In the first case, the intensity of the sensory input controls the input currents to a fixed proportion of neurons of the excitatory and inhibitory populations. In the second case, increasing intensity of the sensory stimulus will both, recruit an increasing number of neurons that receive input and change the input current that they receive. Using a mean field approximation for the network activity we derive relationships between the parameters of the network that ensure that the overall level of activity of the excitatory population remains unchanged for increasing intensity of the external stimulation. We find that, first, the main parameters that regulate network gain are the probabilities of connections from the inhibitory population to the excitatory population and of the connections within the inhibitory population. Second, we show that strict gain control is not achievable in a random network in the second case, when the input recruits an increasing number of neurons. Finally, we confirm that the gain control conditions derived from the mean field approximation are valid in simulations of firing rate models and Hodgkin-Huxley conductance based models

Fluctuating selection models and Mcdonald-Kreitman type analyses

It is likely that the strength of selection acting upon a mutation varies through time due to changes in the environment. However, most population genetic theory assumes that the strength of selection remains constant. Here we investigate the consequences of fluctuating selection pressures on the quantification of adaptive evolution using McDonald-Kreitman (MK) style approaches. In agreement with previous work, we show that fluctuating selection can generate evidence of adaptive evolution even when the expected strength of selection on a mutation is zero. However, we also find that the mutations, which contribute to both polymorphism and divergence tend, on average, to be positively selected during their lifetime, under fluctuating selection models. This is because mutations that fluctuate, by chance, to positive selected values, tend to reach higher frequencies in the population than those that fluctuate towards negative values. Hence the evidence of positive adaptive evolution detected under a fluctuating selection model by MK type approaches is genuine since fixed mutations tend to be advantageous on average during their lifetime. Never-the-less we show that methods tend to underestimate the rate of adaptive evolution when selection fluctuates

Acrylamide and Glycidamide Hemoglobin Adducts and Epithelial Ovarian Cancer: A Nested Case-Control Study in Nonsmoking Postmenopausal Women from the EPIC Cohort

Background: Acrylamide was classified as “probably carcinogenic to humans (group 2A)” by the International Agency for Research on Cancer. Epithelial ovarian cancer (EOC) is the fourth cause of cancer mortality in women. Five epidemiological studies have evaluated the association between EOC risk and dietary acrylamide intake assessed using food frequency questionnaires, and one nested case–control study evaluated hemoglobin adducts of acrylamide (HbAA) and its metabolite glycidamide (HbGA) and EOC risk; the results of these studies were inconsistent. Methods: A nested case–control study in nonsmoking postmenopausal women (334 cases, 417 controls) was conducted within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Unconditional logistic regression models were used to estimate ORs and 95% confidence intervals (CI) for the association between HbAA, HbGA, HbAA+HbGA, and HbGA/HbAA and EOC and invasive serous EOC risk. Results: No overall associations were observed between biomarkers of acrylamide exposure analyzed in quintiles and EOC risk; however, positive associations were observed between some middle quintiles of HbGA and HbAA+HbGA. Elevated but nonstatistically significant ORs for serous EOC were observed for HbGA and HbAA+HbGA (ORQ5vsQ1, 1.91; 95% CI, 0.96–3.81 and ORQ5vsQ1, 1.90; 95% CI, 0.94–3.83, respectively); however, no linear dose–response trends were observed. Conclusion: This EPIC nested case–control study failed to observe a clear association between biomarkers of acrylamide exposure and the risk of EOC or invasive serous EOC. Impact: It is unlikely that dietary acrylamide exposure increases ovarian cancer risk; however, additional studies with larger sample size should be performed to exclude any possible association with EOC risk

Star Cluster Classification using Deep Transfer Learning with PHANGS-HST

Currently available star cluster catalogues from HST imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming nature of this process has limited the production of reliable catalogues and thus also post-observation analysis. To address this problem, deep transfer learning has recently been used to create neural network models which accurately classify star cluster morphologies at production scale for nearby spiral galaxies (D < 20 Mpc). Here, we use HST UV-optical imaging of over 20,000 sources in 23 galaxies from the Physics at High Angular Resolution in Nearby GalaxieS (PHANGS) survey to train and evaluate two new sets of models: i) distance-dependent models, based on cluster candidates binned by galaxy distance (9-12 Mpc, 14-18 Mpc, 18-24 Mpc), and ii) distance-independent models, based on the combined sample of candidates from all galaxies. We find that the overall accuracy of both sets of models is comparable to previous automated star cluster classification studies (~60-80 per cent) and show improvement by a factor of two in classifying asymmetric and multi-peaked clusters from PHANGS-HST. Somewhat surprisingly, while we observe a weak negative correlation between model accuracy and galactic distance, we find that training separate models for the three distance bins does not significantly improve classification accuracy. We also evaluate model accuracy as a function of cluster properties such as brightness, colour, and SED-fit age. Based on the success of these experiments, our models will provide classifications for the full set of PHANGS-HST candidate clusters (N ~ 200,000) for public release.Comment: 16 pages, 10 figure

Resource assessment of the marine current developed in the Cozumel Channel

Renewable energy based systems are expected to contribute on the reduction of greenhouse gases and carbon emission, while satisfying global energy demands. In Mexico, the Cozumel Channel located in the Caribbean Sea has been identified as a potential energy source in the region. Preliminary studies have shown that the ocean current is characterized by almost uniform and unidirectional flow velocities of up to 2.0 m/s within its mid-section with water depths > 500 m. Nevertheless, a detailed resource assessment in shallow waters of the Cozumel Channel is required to address sites potentially suitable for the installation of marine energy converters. Field measurements were taken during September 23rd-29th, 2018 to describe the spatial variation of the marine current velocities at various points along the east-side of the Cozumel Channel, at water depths less than 50 m. Flow velocities higher than 1.0 m/s were identified on the northern east of the Cozumel Channel, at a distance >600 m from the shoreline and over the continental shelf with water depths <50 m. Both energy and power intensity exceedance curves were developed from depth averaged velocities from ADCP measurements. Potential sites were identified where an array of marine energy converters could be installed preventing the devastation of the rich ecosphere renown in the region

Inflammatory potential of the diet and risk of colorectal cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC) study

Pro-inflammatory diets are associated with risk of developing colorectal cancer (CRC), however inconsistencies exist in subsite- and sex-specific associations. The relationship between CRC and combined lifestyle-related factors that contribute towards a low-grade inflammatory profile has not yet been explored. We examined the association between the dietary inflammatory potential and an inflammatory profile and CRC risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. This cohort included 476,160 participants followed-up of 14 years and 5,991 incident CRC cases (3,897 colon and 2,094 rectal tumours). Dietary inflammatory potential was estimated using an Inflammatory Score of the Diet (ISD). An Inflammatory Profile Score (IPS) was constructed, incorporating the ISD, physical activity level and abdominal obesity. The associations between the ISD and CRC and IPS and CRC were assessed using multivariable regression models. More pro- inflammatory diets were related to a higher CRC risk, particularly for colon cancer; Hazar Ratio (HR) for highest versus lowest ISD quartile was 1.15 (95% confidence interval (CI) 1.04-1.27) for CRC, 1.24 (95% CI 1.09-1.41) for colon cancer and 0.99 (95% CI 0.83-1.17) for rectal cancer. Associations were more pronounced in men and not significant in women. The IPS was associated with CRC risk, particularly colon cancer among men; HRs for the highest versus lowest IPS were 1.62 (95% CI 1.31- 2.01) for colon cancer overall and 2.11 (95% CI 1.50-2.97) for colon cancer in men. This study shows that more pro-inflammatory diets and a more inflammatory profile are associated with higher risk of CRC, principally colon cancer and in men. This article is protected by copyright. All rights reserved

In vivo biocompatibility testing of nanoparticle-functionalized alginate–chitosan scaffolds for tissue engineering applications

Background: There is a strong interest in designing new scaffolds for their potential application in tissue engineering and regenerative medicine. The incorporation of functionalization molecules can lead to the enhancement of scaffold properties, resulting in variations in scaffold compatibility. Therefore, the efficacy of the therapy could be compromised by the foreign body reaction triggered after implantation.Methods: In this study, the biocompatibilities of three scaffolds made from an alginate–chitosan combination and functionalized with gold nanoparticles (AuNp) and alginate-coated gold nanoparticles (AuNp + Alg) were evaluated in a subcutaneous implantation model in Wistar rats. Scaffolds and surrounding tissue were collected at 4-, 7- and 25-day postimplantation and processed for histological analysis and quantification of the expression of genes involved in angiogenesis, macrophage profile, and proinflammatory (IL-1β and TNFα) and anti-inflammatory (IL-4 and IL-10) cytokines.Results: Histological analysis showed a characteristic foreign body response that resolved 25 days postimplantation. The intensity of the reaction assessed through capsule thickness was similar among groups. Functionalizing the device with AuNp and AuNp + Alg decreased the expression of markers associated with cell death by apoptosis and polymorphonuclear leukocyte recruitment, suggesting increased compatibility with the host tissue. Similarly, the formation of many foreign body giant cells was prevented. Finally, an increased detection of alpha smooth muscle actin was observed, showing the angiogenic properties of the elaborated scaffolds.Conclusion: Our results show that the proposed scaffolds have improved biocompatibility and exhibit promising potential as biomaterials for elaborating tissue engineering constructs

Plasma Elaidic Acid Level as Biomarker of Industrial Trans Fatty Acids and Risk of Weight Change: Report from the EPIC Study

Background Few epidemiological studies have examined the association between dietary trans fatty acids and weight gain, and the evidence remains inconsistent. The main objective of the study was to investigate the prospective association between biomarker of industrial trans fatty acids and change in weight within the large study European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Methods Baseline plasma fatty acid concentrations were determined in a representative EPIC sample from the 23 participating EPIC centers. A total of 1,945 individuals were followed for a median of 4.9 years to monitor weight change. The association between elaidic acid level and percent change of weight was investigated using a multinomial logistic regression model, adjusted by length of follow-up, age, energy, alcohol, smoking status, physical activity, and region. Results In women, doubling elaidic acid was associated with a decreased risk of weight loss (odds ratio (OR) = 0.69, 95% confidence interval (CI) = 0.55-0.88, p = 0.002) and a trend was observed with an increased risk of weight gain during the 5-year follow-up (OR = 1.23, 95% CI = 0.97-1.56, p = 0.082) (p-trend<.0001). In men, a trend was observed for doubling elaidic acid level and risk of weight loss (OR = 0.82, 95% CI = 0.66-1.01, p = 0.062) while no significant association was found with risk of weight gain during the 5-year follow-up (OR = 1.08, 95% CI = 0.88-1.33, p = 0.454). No association was found for saturated and cis-monounsaturated fatty acids. Conclusions These data suggest that a high intake of industrial trans fatty acids may decrease the risk of weight loss, particularly in women. Prevention of obesity should consider limiting the consumption of highly processed foods, the main source of industrially-produced trans fatty acids

Statistical Inference of Selection and Divergence from a Time-Dependent Poisson Random Field Model

We apply a recently developed time-dependent Poisson random field model to aligned DNA sequences from two related biological species to estimate selection coefficients and divergence time. We use Markov chain Monte Carlo methods to estimate species divergence time and selection coefficients for each locus. The model assumes that the selective effects of non-synonymous mutations are normally distributed across genetic loci but constant within loci, and synonymous mutations are selectively neutral. In contrast with previous models, we do not assume that the individual species are at population equilibrium after divergence. Using a data set of 91 genes in two Drosophila species, D. melanogaster and D. simulans, we estimate the species divergence time (or 1.68 million years, assuming the haploid effective population size years) and a mean selection coefficient per generation . Although the average selection coefficient is positive, the magnitude of the selection is quite small. Results from numerical simulations are also presented as an accuracy check for the time-dependent model