Novel Connections of Common Childhood Illnesses Based on More Than 5 Million Diary Registrations From Birth Until Age 3 Years

BACKGROUND

All children experience numerous episodes of illness during the first 3 years of life. Most episodes are mild and handled without medical attention but nevertheless burden the families and society. There is a large, and still unexplained, variation in the burden of illness between children.

OBJECTIVE

To describe and provide a better understanding of the disease burden of common childhood diseases through a data-driven approach investigating the communalities between symptom patterns and predefined variables on predispositions, pregnancy, birth, environment, and child development.

METHODS

The study is based on the prospectively followed clinical mother-child cohort COpenhagen Prospective Studies on Asthma in Childhood, which includes 700 children with daily symptom registration in the first 3 years of life, including symptoms of cough, breathlessness, wheeze, cold, pneumonia, sore throat, ear infections, gastrointestinal infections, fever, and eczema. First, we described the number of episodes of symptoms. Next, factor analysis models were used to describe the variation in symptom load in the second year of life (both based on n = 556, with >90% complete diary). Then we characterized patterns of similarity between symptoms using a graphical network model (based on n = 403, with a 3-year monthly compliance of >50%). Finally, predispositions and pregnancy, birth, environmental, and developmental factors were added to the network model.

RESULTS

The children experienced a median of 17 (interquartile range, 12-23) episodes of symptoms during the first 3 years of life, of which most were respiratory tract infections (median, 13; interquartile range, 9-18). The frequency of symptoms was the highest during the second year of life. Eczema symptoms were unrelated to the other symptoms. The strongest association to respiratory symptoms was found for maternal asthma, maternal smoking during the third trimester, prematurity, and CDHR3 genotype. This was in contrast to the lack of associations for the well-established asthma locus at 17q21.

CONCLUSIONS

Healthy young children are burdened by multiple episodes of symptoms during the first 3 years of life. Prematurity, maternal asthma, and CDHR3 genotype were among the strongest drivers of symptom burden.

Dataset on causality analysis of chilling process in beef and pork carcasses using graphical modeling

Appropriate control of carcass temperatures in slaughterhouses requires an accurate understanding of extrinsic and intrinsic factors present after slaughter and dressing. Therefore, we use large amounts of data required under the hazard analysis and critical control point system that are accumulated in daily business reports compiled by food business operators. This data aims to clarify the influencing factors or affectors of the chilling processes for beef and pork carcasses in a slaughterhouse using graphical modeling (GM), which is an explorative method in multivariate data analysis. GM has been widely used for statistical causality analysis in visual and flexible modeling. GM is carried out using the following parameters: outside temperature and humidity, number of carcasses in a chilling room on each operating day and during every afternoon of operation, time of sealing a chilling room, pre-set temperature in a chilling room, chilling room temperature at 16:30 on the day of slaughter and dressing and at 8:00 on the next day, and surface and core temperatures of carcasses. These parameters are set in a three-layered structure comprising (1) cause, (2) intermediate effect, and (3) effect. Covariance selection is performed to statistically eliminate spurious correlation. Path diagrams are drawn for beef and pork in GM for visualization. The data herein has contributed to the first attempt at the use of GM to statistically verify causality in the food manufacturing process. These data can be used to determine causality between carcass temperature and affectors in the chilling process via GM and thus minimize bias. Analyses of the present data are reported in the article "Chilling control of beef and pork carcasses in a slaughterhouse based on causality analysis by graphical modeling" [1].

Correlation Imputation in Single cell RNA-seq using Auxiliary Information and Ensemble Learning

Single cell RNA sequencing is a powerful technique that measures the gene expression of individual cells in a high throughput fashion. However, due to sequencing inefficiency, the data is unreliable due to dropout events, or technical artifacts where genes erroneously appear to have zero expression. Many data imputation methods have been proposed to alleviate this issue. Yet, effective imputation can be difficult and biased because the data is sparse and high-dimensional, resulting in major distortions in downstream analyses. In this paper, we propose a completely novel approach that imputes the gene-by-gene correlations rather than the data itself. We call this method SCENA: Single cell RNA-seq Correlation completion by ENsemble learning and Auxiliary information. The SCENA gene-by-gene correlation matrix estimate is obtained by model stacking of multiple imputed correlation matrices based on known auxiliary information about gene connections. In an extensive simulation study based on real scRNA-seq data, we demonstrate that SCENA not only accurately imputes gene correlations but also outperforms existing imputation approaches in downstream analyses such as dimension reduction, cell clustering, graphical model estimation.

Blood-based metabolic signatures in Alzheimer's disease

Introduction

Identification of blood-based metabolic changes might provide early and easy-to-obtain biomarkers.

Methods

We included 127 Alzheimer's disease (AD) patients and 121 control subjects with cerebrospinal fluid biomarker-confirmed diagnosis (cutoff tau/amyloid β peptide 42: 0.52). Mass spectrometry platforms determined the concentrations of 53 amine compounds, 22 organic acid compounds, 120 lipid compounds, and 40 oxidative stress compounds. Multiple signatures were assessed: differential expression (nested linear models), classification (logistic regression), and regulatory (network extraction).

Results

Twenty-six metabolites were differentially expressed. Metabolites improved the classification performance of clinical variables from 74% to 79%. Network models identified five hubs of metabolic dysregulation: tyrosine, glycylglycine, glutamine, lysophosphatic acid C18:2, and platelet-activating factor C16:0. The metabolite network for apolipoprotein E (APOE) ε4 negative AD patients was less cohesive compared with the network for APOE ε4 positive AD patients.

Discussion

Multiple signatures point to various promising peripheral markers for further validation. The network differences in AD patients according to APOE genotype may reflect different pathways to AD.

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Multiple metabolic signatures point to peripheral AD markers for future validation.

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AD may be described by changes in the metabolism of amines and oxidative stressors.

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APOE ε4-driven AD and non- APOE ε4-driven AD represent different biochemical pathways.

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Network analyses of metabolomics data enable the study of metabolic changes in AD.

Graphical Modeling Meets Systems Pharmacology

A main source of failures in systems projects (including systems pharmacology) is poor communication level and different expectations among the stakeholders. A common and not ambiguous language that is naturally comprehensible by all the involved players is a boost to success. We present bStyle, a modeling tool that adopts a graphical language close enough to cartoons to be a common media to exchange ideas and data and that it is at the same time formal enough to enable modeling, analysis, and dynamic simulations of a system. Data analysis and simulation integrated in the same application are fundamental to understand the mechanisms of actions of drugs: a core aspect of systems pharmacology.

Graphical modeling for gene set analysis: A critical appraisal

Current demand for understanding the behavior of groups of related genes, combined with the greater availability of data, has led to an increased focus on statistical methods in gene set analysis. In this paper, we aim to perform a critical appraisal of the methodology based on graphical models developed in Massa et al. (2010) that uses pathway signaling networks as a starting point to develop statistically sound procedures for gene set analysis. We pay attention to the potential of the methodology with respect to the organizational aspects of dealing with such complex but highly informative starting structures, that is pathways. We focus on three themes: the translation of a biological pathway into a graph suitable for modeling, the role of shrinkage when more genes than samples are obtained, the evaluation of respondence of the statistical models to the biological expectations. To study the impact of shrinkage, two simulation studies will be run. To evaluate the biological expectation we will use data from a network with known behavior that offer the possibility of carrying out a realistic check of respondence of the model to changes in the experimental conditions.