Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum.

The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4-52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits

Mortality among over 6 million internal and international migrants in Brazil: a study using the 100 Million Brazilian Cohort

Background: To understand if migrants living in poverty in low and middle-income countries (LMICs) have mortality advantages over the non-migrant population, we investigated mortality risk patterns among internal and international migrants in Brazil over their life course. / Methods: We linked socio-economic and mortality data from 1st January 2011 to 31st December 2018 in the 100 Million Brazilian Cohort and calculated all-cause and cause-specific age-standardised mortality rates according to individuals' migration status for men and women. Using Cox regression models, we estimated the age- and sex-adjusted mortality hazard ratios (HR) for internal migrants (i.e., Brazilian-born individuals living in a different Brazilian state than their birth) compared to Brazilian-born non-migrants; and for international migrants (i.e., people born in another country) compared to Brazilian-born individuals. / Findings: The study followed up 45,051,476 individuals, of whom 6,057,814 were internal migrants, and 277,230 were international migrants. Internal migrants had similar all-cause mortality compared to Brazilian non-migrants (aHR = 0.99, 95% CI = 0.98–0.99), marginally higher mortality for ischaemic heart diseases (aHR = 1.04, 95% CI = 1.03–1.05) and higher for stroke (aHR = 1.11, 95% CI = 1.09–1.13). Compared to Brazilian-born individuals, international migrants had 18% lower all-cause mortality (aHR = 0.82, 95% CI = 0.80–0.84), with up to 50% lower mortality from interpersonal violence among men (aHR = 0.50, 95% CI = 0.40–0.64), but higher mortality from avoidable causes related to maternal health (aHR = 2.17, 95% CI = 1.17–4.05). / Interpretation: Although internal migrants had similar all-cause mortality, international migrants had lower all-cause mortality compared to non-migrants. Further investigations using intersectional approaches are warranted to understand the marked variations by migration status, age, and sex for specific causes of death, such as elevated maternal mortality and male lower interpersonal violence-related mortality among international migrants

Genetic diversity of carotenoid-rich bananas evaluated by Diversity Arrays Technology (DArT)

The aim of this work was to evaluate the carotenoid content and genetic variability of banana accessions from the Musa germplasm collection held at Embrapa Cassava and Tropical Fruits, Brazil. Forty-two samples were analyzed, including 21 diploids, 19 triploids and two tetraploids. The carotenoid content was analyzed spectrophotometrically and genetic variability was estimated using 653 DArT markers. The average carotenoid content was 4.73 μg.g -1 , and ranged from 1.06 μg.g -1 for the triploid Nanica (Cavendish group) to 19.24 μg.g -1 for the triploid Saney. The diploids Modok Gier and NBA-14 and the triploid Saney had a carotenoid content that was, respectively, 7-fold, 6-fold and 9-fold greater than that of cultivars from the Cavendish group (2.19 μg.g -1). The mean similarity among the 42 accessions was 0.63 (range: 0.24 to 1.00). DArT analysis revealed extensive genetic variability in accessions from the Embrapa Musa germplasm bank

Influence of ischemic core muscle fibers on surface depolarization potentials in superfused cardiac tissue preparations: a simulation study

Thin-walled cardiac tissue samples superfused with oxygenated solutions are widely used in experimental studies. However, due to decreased oxygen supply and insufficient wash out of waste products in the inner layers of such preparations, electrophysiological functions could be compromised. Although the cascade of events triggered by cutting off perfusion is well known, it remains unclear as to which degree electrophysiological function in viable surface layers is affected by pathological processes occurring in adjacent tissue. Using a 3D numerical bidomain model, we aim to quantify the impact of superfusion-induced heterogeneities occurring in the depth of the tissue on impulse propagation in superficial layers. Simulations demonstrated that both the pattern of activation as well as the distribution of extracellular potentials close to the surface remain essentially unchanged. This was true also for the electrophysiological properties of cells in the surface layer, where most relevant depolarization parameters varied by less than 5.5 %. The main observed effect on the surface was related to action potential duration that shortened noticeably by 53 % as hypoxia deteriorated. Despite the known limitations of such experimental methods, we conclude that superfusion is adequate for studying impulse propagation and depolarization whereas repolarization studies should consider the influence of pathological processes taking place at the core of tissue sample

The spatial distribution of radiodense breast tissue: a longitudinal study

Introduction Mammographic breast density is one of the strongest known markers of susceptibility to breast cancer. To date research into density has relied on a single measure ( for example, percent density (PD)) summarising the average level of density for the whole breast, with no consideration of how the radiodense tissue may be distributed. This study aims to investigate the spatial distribution of density within the breast using 493 mammographic images from a sample of 165 premenopausal women (similar to 3 medio-lateral oblique views per woman).Methods Each breast image was divided into 48 regions and the PD for the whole breast ( overall PD) and for each one of its regions ( regional PD) was estimated. The spatial autocorrelation ( Moran's I value) of regional PD for each image was calculated to investigate spatial clustering of density, whether the degree of clustering varied between a woman's two breasts and whether it was affected by age and other known density correlates.Results The median Moran's / value for 165 women was 0.31 (interquartile range: 0.26, 0.37), indicating a clustered pattern. High-density areas tended to cluster in the central regions of the breast, regardless of the level of overall PD, but with considerable between-woman variability in regional PD. The degree of clustering was similar between a woman's two breasts (mean within-woman difference in Moran's / values between left and right breasts = 0.00 (95% confidence interval (CI) = -0.01, 0.01); P = 0.76) and did not change with aging (mean within-woman difference in I values between screens taken on average 8 years apart = 0.01 (95% CI = -0.01, 0.02); P = 0.30). Neither parity nor age at first birth affected the level of spatial autocorrelation of density, but increasing body mass index (BMI) was associated with a decrease in the degree of spatial clustering.Conclusions This study is the first to demonstrate that the distribution of radiodense tissue within the breast is spatially autocorrelated, generally with the high-density areas clustering in the central regions of the breast. The degree of clustering was similar within a woman's two breasts and between women, and was little affected by age or reproductive factors although it declined with increasing BMI