Factors associated with childhood anaemia in Afro-descendant communities in Alagoas, Brazil

Objective: To investigate factors associated with anaemia in preschool children. Design: A home survey was conducted in 2018. Anaemia in children (capillary blood Hb level < 110 g/l) was the outcome, and socio-economic, demographic and health factors of the mother and child were the independent variables. The measure of association was the prevalence ratio, and its 95 % CI was calculated using Poisson’s regression with robust variance and hierarchical selection of independent variables. Setting: Afro-descendants communities living in the state of Alagoas, northeast Brazil. Participants: Children aged 6–59 months and their mothers (n 428 pairs). Results: The prevalence of child anaemia was 38·1 % (95 % CI 33·5, 42·7). The associated factors with child anaemia were male sex, age < 24 months, larger number of residents at home (> 4), relatively taller mothers (highest tertile) and higher z-score of BMI for age, after further adjustment for wealth index, vitamin A supplementation in the past 6 months and clinical visit in the last 30 d. Conclusions: The high prevalence of anaemia observed reveals a relevant public health problem amongst children under five from the quilombola communities of Alagoas. Considering the damage caused to health and multiplicity of risk factors associated with anaemia, the adoption of intersectoral strategies that act on modifiable risk factors and increase vigilance concerning those that are not modifiable becomes urgent.

Bayesian sparse heritability analysis with high-dimensional neuroimaging phenotypes

Summary Heritability analysis plays a central role in quantitative genetics to describe genetic contribution to human complex traits and prioritize downstream analyses under large-scale phenotypes. Existing works largely focus on modeling single phenotype and currently available multivariate phenotypic methods often suffer from scaling and interpretation. In this article, motivated by understanding how genetic underpinning impacts human brain variation, we develop an integrative Bayesian heritability analysis to jointly estimate heritabilities for high-dimensional neuroimaging traits. To induce sparsity and incorporate brain anatomical configuration, we impose hierarchical selection among both regional and local measurements based on brain structural network and voxel dependence. We also use a nonparametric Dirichlet process mixture model to realize grouping among single nucleotide polymorphism-associated phenotypic variations, providing biological plausibility. Through extensive simulations, we show the proposed method outperforms existing ones in heritability estimation and heritable traits selection under various scenarios. We finally apply the method to two large-scale imaging genetics datasets: the Alzheimer’s Disease Neuroimaging Initiative and United Kingdom Biobank and show biologically meaningful results.

Verticillium Wilt in Oilseed Rape—the Microbiome is Crucial for Disease Outbreaks as Well as for Efficient Suppression

Microbiome management is a promising way to suppress verticillium wilt, a severe disease in Brassica caused by Verticillium longisporum. In order to improve current biocontrol strategies, we compared bacterial Verticillium antagonists in different assays using a hierarchical selection and evaluation scheme, and we integrated outcomes of our previous studies. The result was strongly dependent on the assessment method chosen (in vitro, in vivo, in situ), on the growth conditions of the plants and their genotype. The most promising biocontrol candidate identified was a Brassica endophyte Serratia plymuthica F20. Positive results were confirmed in field trials and by microscopically visualizing the three-way interaction. Applying antagonists in seed treatment contributes to an exceptionally low ecological footprint, supporting efficient economic and ecological solutions to controlling verticillium wilt. Indigenous microbiome, especially soil and seed microbiome, has been identified as key to understanding disease outbreaks and suppression. We suggest that verticillium wilt is a microbiome-driven disease caused by a reduction in microbial diversity within seeds and in the soil surrounding them. We strongly recommend integrating microbiome data in the development of new biocontrol and breeding strategies and combining both strategies with the aim of designing healthy microbiomes, thus making plants more resilient toward soil-borne pathogens.

Effect of antibiotics on bacterial populations: a multi-hierarchical selection process

Antibiotics have been widely used for a number of decades for human therapy and farming production. Since a high percentage of antibiotics are discharged from the human or animal body without degradation, this means that different habitats, from the human body to river water or soils, are polluted with antibiotics. In this situation, it is expected that the variable concentration of this type of microbial inhibitor present in different ecosystems may affect the structure and the productivity of the microbiota colonizing such habitats. This effect can occur at different levels, including changes in the overall structure of the population, selection of resistant organisms, or alterations in bacterial physiology. In this review, I discuss the available information on how the presence of antibiotics may alter the microbiota and the consequences of such alterations for human health and for the activity of microbiota from different habitats.