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Elevated levels of parenting stress have been reported in parents raising an Autistic child. Previous studies have identified a multitude of predictors of parenting stress, including both child-related and parent-related factors, though findings across studies are not always in agreement. In the present study we investigate the determinants of parenting stress using a Network Analysis approach, which is then used to inform a subsequent structural equation model. New Zealand parents ( n =  490) of a child diagnosed with Autism Spectrum Disorder (ASD) provided data on their Autistic child (e.g., ASD core symptoms, problem behaviours) and themselves (i.e., parenting stress). The analysis revealed that both child and parent demographic factors were poor predictors of parenting stress, while the child’s current language and communication ability were correlated with diagnostic age and parenting stress. An earlier diagnostic age, in turn, suggested better behavioural and emotional outcomes for children. Overall, the Network Analysis showed itself to be an informative approach to understanding parenting stress in the ASD context. Findings further advocate for the implementation of ASD-related and language-related interventions as early as possible, and that language delays during early infancy justify prompt clinical assessment.

Mycoplasma bovis (Mbovis) was first detected in cattle in New Zealand (NZ) in July 2017. To prevent further spread, NZ launched a world-first National Eradication Programme in May 2018. Existing diagnostic tests for Mbovis have been applied in countries where Mbovis is endemic, for detecting infection following outbreaks of clinical disease. Diagnostic test evaluation (DTE) under NZ conditions was thus required to inform the Programme. We used Bayesian Latent Class Analysis on paired serum ELISA (ID Screen Mycoplasma bovis Indirect from IDvet) and tonsillar swabs (qPCR) for DTE in the absence of a gold standard. Tested samples were collected at slaughter between June 2018 and November 2019, from infected herds depopulated by the Programme. A first set of models evaluated the detection of active infection, i.e. the presence of Mbovis in the host. At a modified serology positivity threshold of SP %> = 90, estimates of animal-level ELISA sensitivity was 72.8% (95% credible interval 68.5%—77.4%), respectively 97.7% (95% credible interval 97.3%—98.1%) for specificity, while the qPCR sensitivity was 45.2% (95% credible interval 41.0%—49.8%), respectively 99.6% (95% credible interval 99.4%—99.8%) for specificity. In a second set of models, prior information about ELISA specificity was obtained from the National Beef Cattle Surveillance Programme, a population theoretically free—or very low prevalence—of Mbovis. These analyses aimed to evaluate the accuracy of the ELISA test targeting prior exposure to Mbovis, rather than active infection. The specificity of the ELISA for detecting exposure to Mbovis was 99.9% (95% credible interval 99.7%—100.0%), hence near perfect at the threshold SP%=90. This specificity estimate, considerably higher than in the first set of models, was equivalent to the manufacturer’s estimate. The corresponding ELISA sensitivity estimate was 66.0% (95% credible interval 62.7%-70.7%). These results confirm that the IDvet ELISA test is an appropriate tool for determining exposure and infection status of herds, both to delimit and confirm the absence of Mbovis.

Joint matrix factorization is popular for extracting lower dimensional representations of multi-omics data but loses effectiveness with limited samples. Addressing this limitation, we introduce MOTL (Multi-Omics Transfer Learning), a framework that enhances MOFA (Multi-Omics Factor Analysis) by inferring latent factors for small multi-omics target datasets with respect to those inferred from a large heterogeneous learning dataset. We evaluate MOTL by designing simulated and real data protocols and demonstrate that MOTL improves the factorization of limited-sample multi-omics datasets when compared to factorization without transfer learning. When applied to actual glioblastoma samples, MOTL enhances delineation of cancer status and subtype.

The identification of subnetworks of interest—or active modules—by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease. Availability: MOGAMUN is available at https://github.com/elvanov/MOGAMUN and as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/MOGAMUN.html . Contact: anais.baudot@univ-amu.fr

Plant foods generally supply lower quantities of digestible indispensable amino acids (IAAs) relative to the metabolic requirements. Protein quality can therefore be compromised in vegan diets. Targeted complementation of diverse plant foods in optimal proportions can overcome different limiting IAAs in vegan meals. Four-day food diaries from 193 New Zealand vegans were assessed for protein quality. Meals with a Digestible Indispensable Amino Acid Score (DIAAS) < 100% ( n  = 3623) were targeted for improvements in protein quality using a systematic pairwise matching procedure. Meal pairs attaining DIAAS of ≥ 100% were incorporated into the Vegan Protein Quality (VPQ) tool, currently accessible at: vegan-diet-tracking.shinyapps.io/Vegans/. The VPQ tool optimises food quantities to match each user’s energy, total protein and IAA requirements, tailored to user preferences. The pairwise matching approach improved the amino acid profile of > 99% of meals to a DIAAS of at least 75%, with 41% of meals achieving a DIAAS of ≥ 100%. Test scenarios in the VPQ tool demonstrated the ability of the application to optimise meals to fulfil individual requirements, within serving size constraints. The collective findings provide novel empirical evidence that vegan meals can achieve high protein quality when appropriately planned.

Background Tuber bruising in tetraploid potatoes ( Solanum tuberosum ) is a trait of economic importance, as it affects tubers’ fitness for sale. Understanding the genetic components affecting tuber bruising is a key step in developing potato lines with increased resistance to bruising. As the tetraploid setting renders genetic analyses more complex, there is still much to learn about this complex phenotype. Here, we used capture sequencing data on a panel of half-sibling populations from a breeding programme to perform a genome-wide association analysis (GWAS) for tuber bruising. In addition, we collected transcriptomic data to enrich the GWAS results. However, there is currently no satisfactory method to represent both GWAS and transcriptomics analysis results in a single visualisation and to compare them with existing knowledge about the biological system under study. Results When investigating population structure, we found that the STRUCTURE algorithm yielded greater insights than discriminant analysis of principal components (DAPC). Importantly, we found that markers with the highest (though non-significant) association scores were consistent with previous findings on tuber bruising. In addition, new genomic regions were found to be associated with tuber bruising. The GWAS results were backed by the transcriptomics differential expression analysis. The differential expression notably highlighted for the first time the role of two genes involved in cellular strength and mechanical force sensing in tuber resistance to bruising. We proposed a new visualisation, the HIDECAN plot, to integrate the results from the genomics and transcriptomics analyses, along with previous knowledge about genomic regions and candidate genes associated with the trait. Conclusion This study offers a unique genome-wide exploration of the genetic components of tuber bruising. The role of genetic components affecting cellular strength and resistance to physical force, as well as mechanosensing mechanisms, was highlighted for the first time in the context of tuber bruising. We showcase the usefulness of genomic data from breeding programmes in identifying genomic regions whose association with the trait of interest merit further investigation. We demonstrate how confidence in these discoveries and their biological relevance can be increased by integrating results from transcriptomics analyses. The newly proposed visualisation provides a clear framework to summarise of both genomics and transcriptomics analyses, and places them in the context of previous knowledge on the trait of interest.

The early detection of health problems in dairy cattle is crucial to reduce economic losses. Mid-infrared (MIR) spectrometry has been used for identifying the composition of cow milk in routine tests. As such, it is a potential tool to detect diseases at an early stage. Partial least squares discriminant analysis (PLS-DA) has been widely applied to identify illness such as lameness by using MIR spectrometry data. However, this method suffers some limitations. In this study, a series of machine learning techniques—random forest, support vector machine, neural network (NN), convolutional neural network and ensemble models—were used to test the feasibility of identifying cow sickness from 1909 milk sample MIR spectra from Holstein-Friesian, Jersey and crossbreed cows under grazing conditions. PLS-DA was also performed to compare the results. The sick cow records had a time window of 21 days before and 7 days after the milk sample was analysed. NN showed a sensitivity of 61.74%, specificity of 97% and positive predicted value (PPV) of nearly 60%. Although the sensitivity of the PLS-DA was slightly higher than NN (65.6%), the specificity and PPV were lower (79.59% and 15.25%, respectively). This indicates that by using NN, it is possible to identify a health problem with a reasonable level of accuracy.

Background/Objectives: Inadequate intake of indispensable amino acids (IAAs) is a significant challenge in vegan diets. Since IAAs are not produced or stored over long durations in the human body, regular and balanced dietary protein consumption throughout the day is essential for metabolic function. The objective of this study is to investigate the variation in protein and IAA intake across 24 h among New Zealand vegans with time-series clustering, using Dynamic Time Warping (DTW). Methods: This data-driven approach objectively categorised vegan dietary data into distinct clusters for protein intake and protein quality analysis. Results: Total protein consumed per eating occasion (EO) was 11.1 g, with 93.5% of the cohort falling below the minimal threshold of 20 g of protein per EO. The mean protein intake for each EO in cluster 1 was 6.5 g, cluster 2 was 11.4 g and only cluster 3 was near the threshold at 19.0 g. IAA intake was highest in cluster 3, with lysine and leucine being 3× higher in cluster 3 than cluster 1. All EOs in cluster 1 were below the reference protein intake relative to body weight, closely followed by cluster 2 (91.5%), while cluster 3 comparatively had the lowest EOs under this reference (31.9%). Conclusions: DTW produced three distinct dietary patterns in the vegan cohort. Further exploration of plant protein combinations could inform recommendations to optimise protein quality in vegan diets.

Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designed to infer gene regulatory networks. The fifth \"Dialogue for Reverse Engineering Assessments and Methods\" (DREAM5) challenges are aimed at assessing methods and associated algorithms devoted to the inference of biological networks. Challenge 3 on \"Systems Genetics\" proposed to infer causal gene regulatory networks from different genetical genomics data sets. We investigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the 16 teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics.

People diagnosed with schizophrenia can have functional impairments in multiple domains. Cognitive impairment is central to schizophrenia and has substantial prognostic value compared with other symptoms of schizophrenia. However, no study has previously investigated directed relationships in a complex system of cognitive, sociodemographic, clinical and quality of life (QOL) variables in people diagnosed with schizophrenia. To identify the complex relationships of components of cognition with other cognitive components, as well as with clinical and QOL variables. This study included data from 1450 participants in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. The present study reconstructed a Bayesian network from this data using cognition, clinical, sociodemographic and QOL variables. Processing speed was centrally associated with all other cognitive domains. Cognitive domains were conditionally independent of positive symptoms but moderately associated with negative symptoms ( = -0.25; < 0.001). The positive symptoms subscale was independent of QOL, conditioning on third variables. Negative symptoms were moderately associated with QOL ( = -0.33; < 0.001), and processing speed had a weak association with QOL ( = -0.12; < 0.001). Processing speed was a central variable in the network. Intervening with respect to processing speed may be the most beneficial way of improving other cognitive functions. More research is needed on directed networks that include social cognition and global levels of functioning.