Maternal asthma symptoms during pregnancy on child behaviour and executive function: A Bayesian phenomics approach.

OBJECTIVE: Maternal history of inflammatory conditions has been linked to offspring developmental and behavioural outcomes. This phenomenon may be explained by the maternal immune activation (MIA) hypothesis, which posits that dysregulation of the gestational immune environment affects foetal neurodevelopment. The timing of inflammation is critical. We aimed to understand maternal asthma symptoms during pregnancy, in contrast with paternal asthma symptoms during the same period, on child behaviour problems and executive function in a population-based cohort. METHODS: Data were obtained from 844 families from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) birth cohort. Parent asthma symptoms during the prenatal period were reported. Asthma symptoms in children were reported longitudinally from two to five years old, while behavioural problems and executive functioning were obtained at seven years old. Parent and child measures were compared between mothers with and without prenatal asthma symptoms. Generalized linear and Bayesian phenomics models were used to determine the relation between parent or child asthma symptoms and child outcomes. RESULTS: Children of mothers with prenatal asthma symptoms had greater behavioural and executive problems than controls (Cohen's d: 0.43-0.75; all p < 0.05). This association remained after adjustments for emerging asthma symptoms during the preschool years and fathers' asthma symptoms during the prenatal period. After adjusting for dependence between child outcomes, the Bayesian phenomics model showed that maternal prenatal asthma symptoms were associated with child internalising symptoms and higher-order executive function, while child asthma symptoms were associated with executive function skills. Paternal asthma symptoms during the prenatal period were not associated with child outcomes. CONCLUSIONS: Associations between child outcomes and maternal but not paternal asthma symptoms during the prenatal period suggests a role for MIA. These findings need to be validated in larger samples, and further research may identify behavioural and cognitive profiles of children with exposure to MIA.

Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases.

We propose a novel approach to the estimation of multiple Graphical Models to analyse temporal patterns of association among a set of metabolites over different groups of patients. Our motivating application is the Southall And Brent REvisited (SABRE) study, a tri-ethnic cohort study conducted in the UK. We are interested in identifying potential ethnic differences in metabolite levels and associations as well as their evolution over time, with the aim of gaining a better understanding of different risk of cardio-metabolic disorders across ethnicities. Within a Bayesian framework, we employ a nodewise regression approach to infer the structure of the graphs, borrowing information across time as well as across ethnicities. The response variables of interest are metabolite levels measured at two time points and for two ethnic groups, Europeans and South-Asians. We use nodewise regression to estimate the high-dimensional precision matrices of the metabolites, imposing sparsity on the regression coefficients through the dynamic horseshoe prior, thus favouring sparser graphs. We provide the code to fit the proposed model using the software Stan, which performs posterior inference using Hamiltonian Monte Carlo sampling, as well as a detailed description of a block Gibbs sampling scheme.

Joint modelling of mental health markers through pregnancy: a Bayesian semi-parametric approach.

Maternal depression and anxiety through pregnancy have lasting societal impacts. It is thus crucial to understand the trajectories of its progression from preconception to postnatal period, and the risk factors associated with it. Within the Bayesian framework, we propose to jointly model seven outcomes, of which two are physiological and five non-physiological indicators of maternal depression and anxiety over time. We model the former two by a Gaussian process and the latter by an autoregressive model, while imposing a multidimensional Dirichlet process prior on the subject-specific random effects to account for subject heterogeneity and induce clustering. The model allows for the inclusion of covariates through a regression term. Our findings reveal four distinct clusters of trajectories of the seven health outcomes, characterising women's mental health progression from before to after pregnancy. Importantly, our results caution against the loose use of hair corticosteroids as a biomarker, or even a causal factor, for pregnancy mental health progression. Additionally, the regression analysis reveals a range of preconception determinants and risk factors for depressive and anxiety symptoms during pregnancy.

Joint modeling of association networks and longitudinal biomarkers: An application to childhood obesity.

The prevalence of chronic non-communicable diseases such as obesity has noticeably increased in the last decade. The study of these diseases in early life is of paramount importance in determining their course in adult life and in supporting clinical interventions. Recently, attention has been drawn to approaches that study the alteration of metabolic pathways in obese children. In this work, we propose a novel joint modeling approach for the analysis of growth biomarkers and metabolite associations, to unveil metabolic pathways related to childhood obesity. Within a Bayesian framework, we flexibly model the temporal evolution of growth trajectories and metabolic associations through the specification of a joint nonparametric random effect distribution, with the main goal of clustering subjects, thus identifying risk sub-groups. Growth profiles as well as patterns of metabolic associations determine the clustering structure. Inclusion of risk factors is straightforward through the specification of a regression term. We demonstrate the proposed approach on data from the Growing Up in Singapore Towards healthy Outcomes cohort study, based in Singapore. Posterior inference is obtained via a tailored MCMC algorithm, involving a nonparametric prior with mixed support. Our analysis has identified potential key pathways in obese children that allow for the exploration of possible molecular mechanisms associated with childhood obesity.

Perinatal Trajectories of Maternal Depressive Symptoms in Prospective, Community-Based Cohorts Across 3 Continents.

Importance: Depressive symptoms during pregnancy influence the development and health of the offspring, underscoring the need for timely intervention. However, the course of depressive symptoms across the perinatal period remains unclear, thus complicating screening and referral guidelines. Objective: To examine the course and stability of depressive symptoms across the perinatal period in multiple, ethnically diverse independent observational cohorts. Design, Setting, and Participants: This cohort study included self-reported depressive symptoms at multiple time points from 7 prospective cohorts spanning 3 continents (United Kingdom: Avon Longitudinal Study of Parents and Children from 1991 to 1995; Canada: Maternal Adversity, Vulnerability and Neurodevelopment from 2003 to 2007; Montreal Antenatal Well-being Study from 2019 to 2022; Alberta Pregnancy Outcomes and Nutrition from 2009 to 2014; and Singapore: Growing Up in Singapore Toward Healthy Outcomes from 2009 to 2013; Singapore Preconception Study of Long-Term Maternal and Child Outcomes from 2015 to 2019; and Mapping Antenatal Maternal Stress from 2019 to 2022). Participants were recruited either during preconception or pregnancy and observed into the postnatal period. All data from each cohort were analyzed from July 2022 to April 2023. Main Outcomes and Measures: Self-reported depressive symptoms from pregnancy to 2 years following childbirth using either the Edinburgh Postnatal Depression Scale or the Center for Epidemiological Studies Depression were analyzed independently within each cohort using item response theory (IRT) techniques. K-means clustering was used to identify groups of participants with similar trajectories. Results: A total of 11 563 pregnant women (mean [SD] age, 29 [5] years; 569 [4.9%] East Asian women; 304 [2.6%] Southeast Asian women; 10 133 [87.6%] White women) self-reported depressive symptoms from pregnancy to 2 years following childbirth. Analytic methods from Item Response Theory identified 3 groups of mothers based on depressive symptoms: low, mild, and high levels in each of the 7 cohorts. Mothers within and across all cohorts had stable trajectories of maternal depressive symptoms from pregnancy onwards. Mothers with clinical levels of depressive symptoms likewise showed stable trajectories from pregnancy into the postnatal period. Conclusions and Relevance: In this study, trajectories of depressive symptoms remained stable from pregnancy across the perinatal period, a finding that conflicts with a continuing emphasis on postpartum or postnatal onset of depression that persists in some health policy guidelines. Interventions and public health initiatives should focus on reducing depressive symptoms during pregnancy in addition to following birth.

Bayesian modelling of response to therapy and drug-sensitivity in acute lymphoblastic leukemia.

Acute lymphoblastic leukemia (ALL) is a heterogeneous haematologic malignancy involving the abnormal proliferation of immature lymphocytes and accounts for most paediatric cancer cases. The management of ALL in children has seen great improvement in the last decades thanks to greater understanding of the disease leading to improved treatment strategies evidenced through clinical trials. Common therapy regimens involve a first course of chemotherapy (induction phase), followed by treatment with a combination of anti-leukemia drugs. A measure of the efficacy early in the course of therapy is the presence of minimal residual disease (MRD). MRD quantifies residual tumor cells and indicates the effiectiveness of the treatment over the course of therapy. MRD positivity is defined for values of MRD greater than 0.01%, yielding left-censored MRD observations. We propose a Bayesian model to study the relationship between patient features (leukemia subtype, baseline characteristics, and drug sensitivity profile) and MRD observed at two time points during the induction phase. Specifically, we model the observed MRD values via an auto-regressive model, accounting for left-censoring of the data and for the fact that some patients are already in remission after the first stage of induction therapy. Patient characteristics are included in the model via linear regression terms. In particular, patient-specific drug sensitivity based on ex vivo assays of patient samples is exploited to identify groups of subjects with similar profiles. We include this information as a covariate in the model for MRD. We adopt horseshoe priors for the regression coefficients to perform variable selection to identify important covariates. We fit the proposed approach to data from three prospective paediatric ALL clinical trials carried out at the St. Jude Children's Research Hospital. Our results highlight that drug sensitivity profiles and leukemic subtypes play an important role in the response to induction therapy as measured by serial MRD measures.

Bayesian clustering of multiple zero-inflated outcomes.

Several applications involving counts present a large proportion of zeros (excess-of-zeros data). A popular model for such data is the hurdle model, which explicitly models the probability of a zero count, while assuming a sampling distribution on the positive integers. We consider data from multiple count processes. In this context, it is of interest to study the patterns of counts and cluster the subjects accordingly. We introduce a novel Bayesian approach to cluster multiple, possibly related, zero-inflated processes. We propose a joint model for zero-inflated counts, specifying a hurdle model for each process with a shifted Negative Binomial sampling distribution. Conditionally on the model parameters, the different processes are assumed independent, leading to a substantial reduction in the number of parameters as compared with traditional multivariate approaches. The subject-specific probabilities of zero-inflation and the parameters of the sampling distribution are flexibly modelled via an enriched finite mixture with random number of components. This induces a two-level clustering of the subjects based on the zero/non-zero patterns (outer clustering) and on the sampling distribution (inner clustering). Posterior inference is performed through tailored Markov chain Monte Carlo schemes. We demonstrate the proposed approach on an application involving the use of the messaging service WhatsApp. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Genome-wide association study for antibody response to Mycobacterium avium ssp. paratubeculosis in goats.

Mycobacterium avium ssp. paratuberculosis (MAP), causes Johne's disease (JD), or paratuberculosis, a chronic enteritis of ruminants, which in goats is characterized by ileal lesions. The work described here is a case-control association study using the Illumina Caprine SNP50 BeadChip to unravel the genes involved in susceptibility of goats to JD. Goats in herds with a high occurrence of Johne's disease were classified as healthy or infected based on the level of serum antibodies against MAP, and 331 animals were selected for the association study. Goats belonged to the Jonica (157) and Siriana breeds (174). Whole-genome association analysis identified one region suggestive of significance associated with an antibody response to MAP on chromosome 7 (p-value = 1.23 × 10-5 ). These results provide evidence for genetic loci involved in the antibody response to MAP in goats.

Immunohistochemical Distribution of Serotonin Transporter (SERT) in the Optic Lobe of the Honeybee, Apis mellifera.

Visual information is processed in the optic lobes, which consist of three retinotopic neuropils. These are the lamina, the medulla and the lobula. Biogenic amines play a crucial role in the control of insect responsiveness, and serotonin is clearly related to aggressiveness in invertebrates. Previous studies suggest that serotonin modulates aggression-related behaviours, possibly via alterations in optic lobe activity. The aim of this investigation was to immunohistochemically localize the distribution of serotonin transporter (SERT) in the optic lobe of moderate, docile and aggressive worker honeybees. SERT-immunoreactive fibres showed a wide distribution in the lamina, medulla and lobula; interestingly, the highest percentage of SERT immunoreactivity was observed across all the visual neuropils of the docile group. Although future research is needed to determine the relationship between the distribution of serotonin fibres in the honeybee brain and aggressive behaviours, our immunohistochemical study provides an anatomical basis supporting the role of serotonin in aggressive behaviour in the honeybee.

Analysis of Complementary Sex-Determiner (csd) Allele Diversity in Different Honeybee Subspecies from Italy Based on NGS Data.

Sexual regulation in Apis mellifera is controlled by the complementary sex-determiner (csd) gene: females (queens and workers) are heterozygous at this locus and males (drones) are hemizygous. When homozygous diploid drones develop, they are eaten by worker bees. High csd allelic diversity in honeybee populations is a priority for colony survival. The focus of this study is to investigate csd variability in the genomic sequence of the hypervariable region (HVR) of the csd gene in honeybee subspecies sampled in Italy. During the summer of 2017 and 2018, worker bees belonging to 125 colonies were sampled. The honeybees belonged to seven different A. mellifera subspecies: A. m. ligustica, A. m. sicula, A. m cecropia, A. m. carnica, A. m. mellifera, Buckfast and hybrid Carnica. Illumina genomic resequencing of all samples was performed and used for the characterization of global variability among colonies. In this work, a pipeline using existing resequencing data to explore the csd gene allelic variants present in the subspecies collection, based on de novo assembly of sequences falling within the HVR region, is described. On the whole, 138 allelic sequences were successfully reconstructed. Among these, 88 different alleles were identified, 68 of which match with csd alleles present in the NCBI GenBank database.

Automated image analysis to assess hygienic behaviour of honeybees.

Focus of this study is to design an automated image processing pipeline for handling uncontrolled acquisition conditions of images acquired in the field. The pipeline has been tested on the automated identification and count of uncapped brood cells in honeybee (Apis Mellifera) comb images to reduce the workload of beekeepers during the study of the hygienic behavior of honeybee colonies. The images used to develop and test the model were acquired by beekeepers on different days and hours in summer 2020 and under uncontrolled conditions. This resulted in images differing for background noise, illumination, color, comb tilts, scaling, and comb sizes. All the available 127 images were manually cropped to approximately include the comb area. To obtain an unbiased evaluation, the cropped images were randomly split into a training image set (50 images), which was used to develop and tune the proposed model, and a test image set (77 images), which was solely used to test the model. To reduce the effects of varied illuminations or exposures, three image enhancement algorithms were tested and compared followed by the Hough Transform, which allowed identifying individual cells to be automatically counted. All the algorithm parameters were automatically chosen on the training set by grid search. When applied to the 77 test images the model obtained a correlation of 0.819 between the automated counts and the experts' counts. To provide an assessment of our model with publicly available images acquired by a different equipment and under different acquisition conditions, we randomly extracted 100 images from a comb image dataset made available by a recent literature work. Though it has been acquired under controlled exposure, the images in this new set have varied illuminations; anyhow, our pipeline obtains a correlation between automatic and manual counts equal to 0.997. In conclusion, our tests on the automatic count of uncapped honey bee comb cells acquired in the field and on images extracted from a publicly available dataset suggest that the hereby generated pipeline successfully handles varied noise artifacts, illumination, and exposure conditions, therefore allowing to generalize our method to different acquisition settings. Results further improve when the acquisition conditions are controlled.

Bayesian inference on the number of recurrent events: A joint model of recurrence and survival.

The number of recurrent events before a terminating event is often of interest. For instance, death terminates an individual's process of rehospitalizations and the number of rehospitalizations is an important indicator of economic cost. We propose a model in which the number of recurrences before termination is a random variable of interest, enabling inference and prediction on it. Then, conditionally on this number, we specify a joint distribution for recurrence and survival. This novel conditional approach induces dependence between recurrence and survival, which is often present, for instance, due to frailty that affects both. Additional dependence between recurrence and survival is introduced by the specification of a joint distribution on their respective frailty terms. Moreover, through the introduction of an autoregressive model, our approach is able to capture the temporal dependence in the recurrent events trajectory. A non-parametric random effects distribution for the frailty terms accommodates population heterogeneity and allows for data-driven clustering of the subjects. A tailored Gibbs sampler involving reversible jump and slice sampling steps implements posterior inference. We illustrate our model on colorectal cancer data, compare its performance with existing approaches and provide appropriate inference on the number of recurrent events.

Investigating Genetic and Phenotypic Variability of Queen Bees: Morphological and Reproductive Traits.

The quality of the honeybee queen has an important effect on a colony's development, productivity, and survival. Queen failure or loss is considered a leading cause for colonies' mortality worldwide. The queen's quality, resulting from her genetic background, developmental conditions, mating success, and environment, can be assessed by some morphological measures. The study aims to investigate variability for traits that could assess the quality of the queen. Related animals were enrolled in this study. Variance components were estimated fitting a mixed animal model to collected data. Heritabilities of body and tagmata weights ranged from 0.46 to 0.54, whereas lower estimates were found for the tagmata width and wing length. Heritabilities estimated for the spermatheca diameter and volume, number of ovarioles, and number of sperms were 0.17, 0.88, 0.70, and 0.57, respectively. Many phenotypic correlations related to size were high and positive, while weak correlations were found between morphology and reproductive traits. Introducing a queen's traits in a selection program could improve colonies' survivability. Further research should focus on better defining the correlations between the individual qualities of a queen and her colony's performance.

A Bayesian nonparametric approach to dynamic item-response modeling: An application to the GUSTO cohort study.

Statistical analysis of questionnaire data is often performed employing techniques from item-response theory. In this framework, it is possible to differentiate respondent profiles and characterize the questions (items) included in the questionnaire via interpretable parameters. These models are often crosssectional and aim at evaluating the performance of the respondents. The motivating application of this work is the analysis of psychometric questionnaires taken by a group of mothers at different time points and by their children at one later time point. The data are available through the GUSTO cohort study. To this end, we propose a Bayesian semiparametric model and extend the current literature by: (i) introducing temporal dependence among questionnaires taken at different time points; (ii) jointly modeling the responses to questionnaires taken from different, but related, groups of subjects (in our case mothers and children), introducing a further dependency structure and therefore sharing of information; (iii) allowing clustering of subjects based on their latent response profile. The proposed model is able to identify three main groups of mother/child pairs characterized by their response profiles. Furthermore, we report an interesting maternal reporting bias effect strongly affecting the clustering structure of the mother/child dyads.

Using Omics Approaches in the Discovery of Biomarkers for Early Diagnosis of Johne's Disease in Sheep and Goats.

Johne's disease (JD) is caused by Mycobacterium avium subsp. paratuberculosis (MAP) and is an important and emerging problem in livestock; therefore, its control and prevention is a priority to reduce economic losses and health risks. Most JD research has been carried out on cattle, but interest in the pathogenesis and diagnosis of this disease in sheep and goats is greatest in developing countries. Sheep and goats are also a relevant part of livestock production in Europe and Australia, and these species provide an excellent resource to study and better understand the mechanism of survival of MAP and gain insights into possible approaches to control this disease. This review gives an overview of the literature on paratuberculosis in sheep and goats, highlighting the immunological aspects and the potential for "omics" approaches to identify effective biomarkers for the early detection of infection. As JD has a long incubation period before the disease becomes evident, early diagnosis is important to control the spread of the disease.

Whole-Genome Sequence Analysis of Italian Honeybees (Apis mellifera).

At the end of the last glaciation, Apis mellifera was established in northern Europe. In Italy, Apis melliferaligustica adapted to the mild climate and to the rich floristic biodiversity. Today, with the spread of Varroa destructor and with the increasing use of pesticides in agriculture, the Ligustica subspecies is increasingly dependent on human action for its survival. In addition, the effects of globalization of bee keeping favored the spread in Italy of other honeybee stocks of A. mellifera, in particular the Buckfast bee. The purpose of this study was to characterize the Italian honeybee's population by sequencing the whole genome of 124 honeybees. Whole genome sequencing was performed by Illumina technology, obtaining a total coverage of 3720.89X, with a mean sample coverage of 29.77X. A total of 4,380,004 SNP variants, mapping on Amel_HAv3.1 chromosomes, were detected. Results of the analysis of the patterns of genetic variation allowed us to identify and subgroup bees according to their type. The investigation revealed the genetic originality of the Sicula, and in A.m. ligustica limited genetic introgression from the other breeds. Morphometric analysis of 5800 worker bees was in agreement with genomic data.

Bayesian splines versus fractional polynomials in network meta-analysis.

BACKGROUND: Network meta-analysis (NMA) provides a powerful tool for the simultaneous evaluation of multiple treatments by combining evidence from different studies, allowing for direct and indirect comparisons between treatments. In recent years, NMA is becoming increasingly popular in the medical literature and underlying statistical methodologies are evolving both in the frequentist and Bayesian framework. Traditional NMA models are often based on the comparison of two treatment arms per study. These individual studies may measure outcomes at multiple time points that are not necessarily homogeneous across studies. METHODS: In this article we present a Bayesian model based on B-splines for the simultaneous analysis of outcomes across time points, that allows for indirect comparison of treatments across different longitudinal studies. RESULTS: We illustrate the proposed approach in simulations as well as on real data examples available in the literature and compare it with a model based on P-splines and one based on fractional polynomials, showing that our approach is flexible and overcomes the limitations of the latter. CONCLUSIONS: The proposed approach is computationally efficient and able to accommodate a large class of temporal treatment effect patterns, allowing for direct and indirect comparisons of widely varying shapes of longitudinal profiles.

Modelling ethnic differences in the distribution of insulin resistance via Bayesian nonparametric processes: an application to the SABRE cohort study.

We analyse data from the Southall And Brent REvisited (SABRE) tri-ethnic study, where measurements of metabolic and anthropometric variables have been recorded. In particular, we focus on modelling the distribution of insulin resistance which is strongly associated with the development of type 2 diabetes. We propose the use of a Bayesian nonparametric prior to model the distribution of Homeostasis Model Assessment insulin resistance, as it allows for data-driven clustering of the observations. Anthropometric variables and metabolites concentrations are included as covariates in a regression framework. This strategy highlights the presence of sub-populations in the data, characterised by different levels of risk of developing type 2 diabetes across ethnicities. Posterior inference is performed through Markov Chains Monte Carlo (MCMC) methods.

Combinatorial Discriminant Analysis Applied to RNAseq Data Reveals a Set of 10 Transcripts as Signatures of Exposure of Cattle to Mycobacterium avium subsp. paratuberculosis.

Paratuberculosis or Johne's disease in cattle is a chronic granulomatous gastroenteritis caused by infection with Mycobacterium avium subspecies paratuberculosis (MAP). Paratuberculosis is not treatable; therefore, the early identification and isolation of infected animals is a key point to reduce its incidence. In this paper, we analyse RNAseq experimental data of 5 ELISA-negative cattle exposed to MAP in a positive herd, compared to 5 negative-unexposed controls. The purpose was to find a small set of differentially expressed genes able to discriminate between exposed animals in a preclinical phase from non-exposed controls. Our results identified 10 transcripts that differentiate between ELISA-negative, clinically healthy, and exposed animals belonging to paratuberculosis-positive herds and negative-unexposed animals. Of the 10 transcripts, five (TRPV4, RIC8B, IL5RA, ERF, CDC40) showed significant differential expression between the three groups while the remaining 5 (RDM1, EPHX1, STAU1, TLE1, ASB8) did not show a significant difference in at least one of the pairwise comparisons. When tested in a larger cohort, these findings may contribute to the development of a new diagnostic test for paratuberculosis based on a gene expression signature. Such a diagnostic tool could allow early interventions to reduce the risk of the infection spreading.

Joint modeling of recurrent events and survival: a Bayesian non-parametric approach.

Heart failure (HF) is one of the main causes of morbidity, hospitalization, and death in the western world, and the economic burden associated with HF management is relevant and expected to increase in the future. We consider hospitalization data for HF in the most populated Italian Region, Lombardia. Data were extracted from the administrative data warehouse of the regional healthcare system. The main clinical outcome of interest is time to death and research focus is on investigating how recurrent hospitalizations affect the time to event. The main contribution of the article is to develop a joint model for gap times between consecutive rehospitalizations and survival time. The probability models for the gap times and for the survival outcome share a common patient specific frailty term. Using a flexible Dirichlet process model for %Bayesian nonparametric prior as the random-effects distribution accounts for patient heterogeneity in recurrent event trajectories. Moreover, the joint model allows for dependent censoring of gap times by death or administrative reasons and for the correlations between different gap times for the same individual. It is straightforward to include covariates in the survival and/or recurrence process through the specification of appropriate regression terms. The main advantages of the proposed methodology are wide applicability, ease of interpretation, and efficient computations. Posterior inference is implemented through Markov chain Monte Carlo methods.