Relevance of forage fish in the diet of Magellanic penguins breeding in northern Patagonia,Argentina

We quantified the trophic niche of Magellanic penguins (Spheniscus magellanicus) breeding and moulting in Golfo San Jorge, Argentina, through conventional stomach content and stable isotope analysis. A total of 112 adults were flushed during the early and late chick stages of 2011 and 2012 at Isla Vernacci Norte, and at least 15 prey taxa were found, including fishes, cephalopods, crustaceans and polychaetes. Overall, Argentine anchovy (Engraulis anchoita) showed the highest contribution in terms of importance by mass (68.1–85.3%, depending on chick stage and year), except for the old chick stage in 2011 when the shortfin squid (Illex argentinus) was the main prey consumed (56.0%). Based on carbon and nitrogen isotopic values from a total of 256 blood samples, corresponding to young and old chicks and to adults of both sexes sampled throughout the incubation, chick and moult stages at the above mentioned colony and years, Bayesian mixing model outputs showed that Argentine anchovy was always the main prey (48–86%). Bayesian mixing model outputs obtained from adults of both sexes and their chicks during the late chick stage of 2013 at Isla Vernacci Norte, Isla Tova and Isla Leones also showed that Argentine anchovy was the main prey consumed. This is the first comprehensive assessment of Magellanic penguin diet composition in northern Patagonia, quantifying the relative contribution of prey in the diet of adults and chicks at different stages of the annual cycle and years, and confirms the relevance of a forage fish such as the Argentine anchovy in its trophic ecology.     

Genomic inference accurately predicts the timing and severity of a recent bottleneck in a nonmodel insect population

The analysis of molecular data from natural populations has allowed researchers to answer diverse ecological questions that were previously intractable. In particular, ecologists are often interested in the demographic history of populations, information that is rarely available from historical records. Methods have been developed to infer demographic parameters from genomic data, but it is not well understood how inferred parameters compare to true population history or depend on aspects of experimental design. Here, we present and evaluate a method of SNP discovery using RNA sequencing and demographic inference using the program δaδi, which uses a diffusion approximation to the allele frequency spectrum to fit demographic models. We test these methods in a population of the checkerspot butterfly Euphydryas gillettii. This population was intentionally introduced to Gothic, Colorado in 1977 and has as experienced extreme fluctuations including bottlenecks of fewer than 25 adults, as documented by nearly annual field surveys. Using RNA sequencing of eight individuals from Colorado and eight individuals from a native population in Wyoming, we generate the first genomic resources for this system. While demographic inference is commonly used to examine ancient demography, our study demonstrates that our inexpensive, all‐in‐one approach to marker discovery and genotyping provides sufficient data to accurately infer the timing of a recent bottleneck. This demographic scenario is relevant for many species of conservation concern, few of which have sequenced genomes. Our results are remarkably insensitive to sample size or number of genomic markers, which has important implications for applying this method to other nonmodel systems.     

Negative effects of elevated testosterone on female fecundity in zebra finches

Although factors influencing androgen deposition in the avian egg and its effects on nestling fitness are recently receiving considerable attention, little is known about the potential costs of high testosterone levels in the females. Our study aimed at determining the effect of injections of testosterone (T) in female zebra finches (Taeniopygia guttata), on clutch size, egg mass, yolk mass, and yolk androgen content. Females were given a single bolus injection of T in a range of doses after laying the first egg. Results show that administration of T negatively affected clutch size; the strength of this effect increased with increasing doses of T. Females injected with the highest testosterone dose showed suppressed oviposition of the third and the fourth eggs. Interestingly, testosterone administration made females produce eggs with relatively large yolks, suggesting that T may mediate the trade-off between number and size of eggs. Testosterone injection resulted in elevated levels of androgen in the eggs, in contrast to control clutches, which showed a decreasing pattern of androgen concentration along the laying sequence. We conclude that high androgen investment in eggs may be limited by physiological requirements of the ovulatory process.     

Case-control and case-only designs with genotype and family history data: estimating relative risk, residual familial aggregation, and cumulative risk

In case-control studies of inherited diseases, participating subjects (probands) are often interviewed to collect detailed data about disease history and age-at-onset information in their family members. Genotype data are typically collected from the probands, but not from their relatives. In this article, we introduce an approach that combines case-control analysis of data on the probands with kin-cohort analysis of disease history data on relatives. Assuming a marginally specified multivariate survival model for joint risk of disease among family members, we describe methods for estimating relative risk, cumulative risk, and residual familial aggregation. We also describe a variation of the methodology that can be used for kin-cohort analysis of the family history data from a sample of genotyped cases only. We perform simulation studies to assess performance of the proposed methodologies with correct and mis-specified models for familial aggregation. We illustrate the proposed methodologies by estimating the risk of breast cancer from BRCA1/2 mutations using data from the Washington Ashkenazi Study.     

Design and validation of a novel method to measure cross-sectional area of neck muscles included during routine MR brain volume imaging

Introduction

Low muscle mass secondary to disease and ageing is an important cause of excess mortality and morbidity. Many studies include a MR brain scan but no peripheral measure of muscle mass. We developed a technique to measure posterior neck muscle cross-sectional area (CSA) on volumetric MR brain scans enabling brain and muscle size to be measured simultaneously.

Methods

We performed four studies to develop and test: feasibility, inter-rater reliability, repeatability and external validity. We used T1-weighted MR brain imaging from young and older subjects, obtained on different scanners, and collected mid-thigh MR data.

Results

After developing the technique and demonstrating feasibility, we tested it for inter-rater reliability in 40 subjects. Intraclass correlation coefficients (ICC) between raters were 0.99 (95% confidence intervals (CI) 0.98–1.00) for the combined group (trapezius, splenius and semispinalis), 0.92 (CI 0.85–0.96) for obliquus and 0.92 (CI 0.85–0.96) for sternocleidomastoid. The first unrotated principal component explained 72.2% of total neck muscle CSA variance and correlated positively with both right (r = 0.52, p = .001) and left (r = 0.50, p = .002) grip strength. The 14 subjects in the repeatability study had had two MR brain scans on three different scanners. The ICC for between scanner variation for total neck muscle CSA was high at 0.94 (CI 0.86–0.98). The ICCs for within scanner variations were also high, with values of 0.95 (CI 0.86–0.98), 0.97 (CI 0.92–0.99) and 0.96 (CI 0.86–0.99) for the three scanners. The external validity study found a correlation coefficient for total thigh CSA and total neck CSA of 0.88.

Discussion

We present a feasible, valid and reliable method for measuring neck muscle CSA on T1-weighted MR brain scans. Larger studies are needed to validate and apply our technique with subjects differing in age, ethnicity and geographical location.     

Littoral benthic macroinvertebrates of mountain lakes in the Tatra Mountains (Slovakia,Poland)

Littoral benthic macroinvertebrates of 45 mountain lakes in the Tatra Mountains were sampled using a semi-quantitative method in September 2000. A total of 32,852 specimens were identified to 93 taxa belonging to 14 higher taxonomic groups. Multivariate statistics (CCA, RDA) and nine biotic metrics (AQEM/STAR) were used to explain relationships between macroinvertebrate assemblages and environmental variables. Up to 57% of the ecological position of littoral macroinvertebrate assemblages were explained by variance of environmental variables divided into chemical, trophic, physical, catchment and location. Five types of Tatra lakes were recognized using CCA: A — strongly acidified lakes (small catchment, low pH, high concentration of TP, DOC, highest amount of POM in littoral); B — alpine acidified lakes (low amount of POM, low values of biotic metrics); C — alpine non-acidified lakes (high value of diversity index, predominance of Diptera); D — subalpine acidified lakes (high values of biotic metrics: number of families, proportion of crenal and rhithral taxa/total taxa); E — subalpine non-acidified lakes (high values of biotic metrics: number of families, number of genera, BMWP score, number of taxa and abundance of EPT taxa). RDA was used to design five levels of macroinvertebrate taxa acidification tolerance. The Tatra Acidification Index (TAI) was established to assess the acidification status of the lakes in the Tatra Mts.     

Use of 16S rRNA Gene for Identification of a Broad Range of Clinically Relevant Bacterial Pathogens

According to World Health Organization statistics of 2011, infectious diseases remain in the top five causes of mortality worldwide. However, despite sophisticated research tools for microbial detection, rapid and accurate molecular diagnostics for identification of infection in humans have not been extensively adopted. Time-consuming culture-based methods remain to the forefront of clinical microbial detection. The 16S rRNA gene, a molecular marker for identification of bacterial species, is ubiquitous to members of this domain and, thanks to ever-expanding databases of sequence information, a useful tool for bacterial identification. In this study, we assembled an extensive repository of clinical isolates (n = 617), representing 30 medically important pathogenic species and originally identified using traditional culture-based or non-16S molecular methods. This strain repository was used to systematically evaluate the ability of 16S rRNA for species level identification. To enable the most accurate species level classification based on the paucity of sequence data accumulated in public databases, we built a Naïve Bayes classifier representing a diverse set of high-quality sequences from medically important bacterial organisms. We show that for species identification, a model-based approach is superior to an alignment based method. Overall, between 16S gene based and clinical identities, our study shows a genus-level concordance rate of 96% and a species-level concordance rate of 87.5%. We point to multiple cases of probable clinical misidentification with traditional culture based identification across a wide range of gram-negative rods and gram-positive cocci as well as common gram-negative cocci.     

The effect of polyphenols and vitamin E on the antioxidant status and meat quality of broiler chickens exposed to high temperature

The aim of this study was to determine the effect of a polyphenol product (PP) (Proviox) and vitamin E on the antioxidant status and meat quality of broiler chickens exposed to high temperature. The experimental materials comprised 120 ROSS 308 broilers (6 treatments, 10 replications, 2 birds per replication). Dietary supplementation with vitamin E and PP was applied in the following experimental design: group I (negative control) – without supplementation; group II (positive control) – without supplementation; group III – supplementation with 100 mg vitamin E/kg; group IV – 200 mg vitamin E/kg; group V – 100 mg vitamin E/kg and 100 mg PP/kg; group VI – 200 mg PP/kg. In groups II–VI, broiler chickens aged 21–35 d were exposed to increased temperature (34°C for 10 h daily). In chickens exposed to high temperature, dietary supplementation with antioxidants, mostly PP, improved growth performance parameters, including body weight, body weight gain and feed intake until 28 d of age. Vitamin E added to broiler chicken diets at 200 mg/kg and vitamin E combined with PP was most effective in improving the total antioxidant status of birds, enhancing blood antioxidant enzyme activities and increasing vitamin E concentrations in the liver and breast muscles. Broilers fed diets supplemented with 200 mg/kg of vitamin E alone and vitamin E in combination with PP were characterised by a higher percentage content of breast muscles in the carcass. Dietary supplementation with antioxidants improved the water-holding capacity of meat, reduced natural drip loss and increased the crude ash content of meat. The breast muscles of chickens fed diets supplemented with PP had a lower contribution of yellowness. The breast muscles of chickens receiving diets with 100 mg vitamin E/kg(group III) and diets supplemented with PP (groups V and VI) were characterised by the highest concentrations of polyunsaturated fatty acids. The PP can be a valuable component of diets for broiler chickens exposed to high temperature.     

Fusing Data Mining,Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

Background

Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study.

Methods

The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009–2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators.

Results

After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001).

Conclusion

The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.