How Emergent Social Patterns in Allogrooming Combat Parasitic Infections

Members of social groups risk infection through contact with those in their social network. Evidence that social organization may protect populations from pathogens in certain circumstances prompts the question as to how social organization affects the spread of ectoparasites. The same grooming behaviors that establish social bonds also play a role in the progression of ectoparasitic outbreaks. In this paper, we model the interactions between social organization and allogrooming efficiency to consider how ectoparasitic threats may have shaped the evolution of social behaviors. To better understand the impacts of social grooming on organizational structure, we consider several dynamic models of social organization using network centrality measures as the basis of neighbor selection. Within this framework, we consider the impact of varying levels of social grooming on both the group structure and the overall ectoparasitic disease burden. Our results demonstrate that allogrooming, along with ongoing dynamic social organization, may be protective with respect to both the timing and the magnitude of ectoparasitic epidemics. These results support the idea that ectoparasitic threat should not be considered a single evolutionary factor in the evolution of host social systems, and may have operated in different ways depending on the broader ecology of the host-ectoparasite interaction

A detailed hydrothermal investigation of a helical micro double-tube heat exchanger for a wide range of helix pitch length

The present study was numerically inquired the heat transfer performance and fluid flow characteristic of a helical micro double-tube heat exchanger (HMDTHX) using the finite volume method. The tube length was considered to be constantly equal to 30 mm, and 12 different configurations were modeled by changing in turn number and pitch length (P) for Reynolds numbers of 50, 100, 150, and 200. The findings indicated that the heat transfer would enhance by applying any helix angle in the straight tube. However, it had an optimum point which varied by Reynolds number (Re). Rising Re caused overall heat transfer coefficient (OHTC), pressure drop, and pumping power augment for all cases. Increasing P in overall reduced OHTC, pressure drop, and pumping power which had different maximum points between P = 0.5 to 3. Maximum overall heat transfer coefficient (OHTC) enhancement was equal to 45% for Re = 200 and P = 2. Also, maximum effectiveness was 11.5% for P = 2 and Re = 200. Moreover, a 42% maximum increment was achieved for pressure drop, pumping power, and friction factor at Re = 200 and P = 2. Shear stress for Re = 100 to 200 showed that the values are almost the same for P = 0.5 and 1. Then by increasing P, the shear stress decreases. While, for Re = 50, a maximum is seen at P = 2. The temperature distribution was indicated that the maximum temperature of the straight tube and helical tube are the same, but the difference is in the average temperature, which was 3.2 K between straight and helical tubes. Finally, by investigating the velocity contour, it was determined that a secondary flow through the HMDTHX, affected by centrifugal force, was existed, enhancing the fluid flow turbulency and heat transfer rate

Identification of polymorphic inversions from genotypes

Background: Polymorphic inversions are a source of genetic variability with a direct impact on recombination frequencies. Given the difficulty of their experimental study, computational methods have been developed to infer their existence in a large number of individuals using genome-wide data of nucleotide variation. Methods based on haplotype tagging of known inversions attempt to classify individuals as having a normal or inverted allele. Other methods that measure differences between linkage disequilibrium attempt to identify regions with inversions but unable to classify subjects accurately, an essential requirement for association studies. Results: We present a novel method to both identify polymorphic inversions from genome-wide genotype data and classify individuals as containing a normal or inverted allele. Our method, a generalization of a published method for haplotype data [1], utilizes linkage between groups of SNPs to partition a set of individuals into normal and inverted subpopulations. We employ a sliding window scan to identify regions likely to have an inversion, and accumulation of evidence from neighboring SNPs is used to accurately determine the inversion status of each subject. Further, our approach detects inversions directly from genotype data, thus increasing its usability to current genome-wide association studies (GWAS). Conclusions: We demonstrate the accuracy of our method to detect inversions and classify individuals on principled-simulated genotypes, produced by the evolution of an inversion event within a coalescent model [2]. We applied our method to real genotype data from HapMap Phase III to characterize the inversion status of two known inversions within the regions 17q21 and 8p23 across 1184 individuals. Finally, we scan the full genomes of the European Origin (CEU) and Yoruba (YRI) HapMap samples. We find population-based evidence for 9 out of 15 well-established autosomic inversions, and for 52 regions previously predicted by independent experimental methods in ten (9+1) individuals [3,4]. We provide efficient implementations of both genotype and haplotype methods as a unified R package inveRsion

Health, Lifestyle, and Psycho-Social Determinants of Poor Sleep Quality During the Early Phase of the COVID-19 Pandemic: A Focus on UK Older Adults Deemed Clinically Extremely Vulnerable

Background: Several studies have assessed the impact of COVID-19-relatedlockdownson sleep quality across global populations. However, no study to date has specifically assessed at-riskpopulations, particularly those at highest risk of complications from coronavirus infection deemed “clinically-extremely-vulnerable-(COVID-19CEV)” [as defined by Public Health England, 2020].Methods: In this cross-sectional study, we surveyed 5,558 adults aged ≥50 years (of whom 523 met criteria for COVID-19CEV) during the first pandemic wave that resulted in a nationwide-lockdown (April-June 2020) with assessments of sleep quality (an adapted sleep scale that captured multiple sleep indices before and during the lockdown), health/medical, lifestyle, psychosocial and socio demographic factors. We examined associations between these variablesand sleep quality;and explored interactions of COVID-19CEV status with significant predictors of poor sleep,to identify potential moderating factors. Results: 37% of participants reported poor sleep quality which was associated with younger age, female sex and multimorbidity. Significant associations with poor sleep included health/medical factors: COVID-19 CEV status, higher BMI, arthritis, pulmonary disease, and mental health disorders; and the following lifestyle and psychosocial factors: living alone, higher alcohol consumption, an unhealthy diet and higher depressive and anxiety symptoms. Moderators of the negative relationship between COVID-19 CEV status and good sleep quality were marital status, loneliness, anxiety and diet. Within this subgroup, less anxious and less lonely males, as well as females with healthier diets, reported better sleep. Conclusions: Sleep quality in older adults was compromised during the sudden unprecedented nation-wide lockdown due to distinct modifiable factors. An important contribution of our study is the assessment of a “clinically-extremely-vulnerable” population and the sex differences identified within this group. Male and female older adults deemed COVID-19 CEV may benefit from targeted mental health and dietary interventions, respectively. This work extends the available evidence on the notable impact of lack of social interactions during the COVID-19 pandemic on sleep, and provides recommendations towards areas for future work, including research into vulnerability factors impacting sleep disruption and COVID-19-related complications. Study results may inform tailored interventions targeted at modifiable risk factors to promote optimal sleep; additionally, providing empirical data to support health policy development in this area

Effect of a reduced fat and sugar maternal dietary intervention during lactation on the infant gut microbiome

PUBLISHED 17 August 2022Objective: A growing body of literature has shown that maternal diet during pregnancy is associated with infant gut bacterial composition. However, whether maternal diet during lactation affects the exclusively breastfed infant gut microbiome remains understudied. This study sets out to determine whether a two-week of a reduced fat and sugar maternal dietary intervention during lactation is associated with changes in the infant gut microbiome composition and function. Design: Stool samples were collected from four female and six male (n = 10) infants immediately before and after the intervention. Maternal baseline diet from healthy mothers aged 22–37 was assessed using 24-h dietary recall. During the 2-week dietary intervention, mothers were provided with meals and their dietary intake was calculated using FoodWorks 10 Software. Shotgun metagenomic sequencing was used to characterize the infant gut microbiome composition and function. Results: In all but one participant, maternal fat and sugar intake during the intervention were significantly lower than at baseline. The functional capacity of the infant gut microbiome was significantly altered by the intervention, with increased levels of genes associated with 28 bacterial metabolic pathways involved in biosynthesis of vitamins (p = 0.003), amino acids (p = 0.005), carbohydrates (p = 0.01), and fatty acids and lipids (p = 0.01). Although the dietary intervention did not affect the bacterial composition of the infant gut microbiome, relative difference in maternal fiber intake was positively associated with increased abundance of genes involved in biosynthesis of storage compounds (p = 0.016), such as cyanophycin. Relative difference in maternal protein intake was negatively associated with Veillonella parvula (p = 0.006), while positively associated with Klebsiella michiganensis (p = 0.047). Relative difference in maternal sugar intake was positively associated with Lactobacillus paracasei (p = 0.022). Relative difference in maternal fat intake was positively associated with genes involved in the biosynthesis of storage compounds (p = 0.015), fatty acid and lipid (p = 0.039), and metabolic regulator (p = 0.038) metabolic pathways. Conclusion: This pilot study demonstrates that a short-term maternal dietary intervention during lactation can significantly alter the functional potential, but not bacterial taxonomy, of the breastfed infant gut microbiome. While the overall diet itself was not able to change the composition of the infant gut microbiome, changes in intakes of maternal protein and sugar during lactation were correlated with changes in the relative abundances of certain bacterial species. Clinical trial registration: Australian New Zealand Clinical Trials Registry (ACTRN12619000606189).Azhar S. Sindi, Lisa F. Stinson, Soo Sum Lean, Yit-Heng Chooi, Gabriela E. Leghi, Merryn J. Netting, Mary E. Wlodek, Beverly S. Muhlhausler, Donna T. Geddes and Matthew S. Payn

On the power and the systematic biases of the detection of chromosomal inversions by paired-end genome sequencing

One of the most used techniques to study structural variation at a genome level is paired-end mapping (PEM). PEM has the advantage of being able to detect balanced events, such as inversions and translocations. However, inversions are still quite difficult to predict reliably, especially from high-throughput sequencing data. We simulated realistic PEM experiments with different combinations of read and library fragment lengths, including sequencing errors and meaningful base-qualities, to quantify and track down the origin of false positives and negatives along sequencing, mapping, and downstream analysis. We show that PEM is very appropriate to detect a wide range of inversions, even with low coverage data. However, % of inversions located between segmental duplications are expected to go undetected by the most common sequencing strategies. In general, longer DNA libraries improve the detectability of inversions far better than increments of the coverage depth or the read length. Finally, we review the performance of three algorithms to detect inversions -SVDetect, GRIAL, and VariationHunter-, identify common pitfalls, and reveal important differences in their breakpoint precisions. These results stress the importance of the sequencing strategy for the detection of structural variants, especially inversions, and offer guidelines for the design of future genome sequencing projects

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015

SummaryBackground The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding Bill & Melinda Gates Foundation

Consistency-based detection of potential tumor-specific deletions in matched normal/tumor genomes

Wittler R, Chauve C. Consistency-based detection of potential tumor-specific deletions in matched normal/tumor genomes. BMC Bioinformatics. 2011;12(Suppl. 9):S21