CMS physics technical design report : Addendum on high density QCD with heavy ions

Peer reviewe

Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

Genome-wide association studies (GWAS) have identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs, indicating their potential involvement in regulation of gene expression. Here, we propose a transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) that uses multiple SNPs as instruments and multiple gene expression traits as exposures, simultaneously. Applied to 43 human phenotypes, it uncovers 3,913 putatively causal gene-trait associations, 36% of which have no genome-wide significant SNP nearby in previous GWAS. Using independent association summary statistics, we find that the majority of these loci were missed by GWAS due to power issues. Noteworthy among these links is educational attainment-associated BSCL2, known to carry mutations leading to a Mendelian form of encephalopathy. We also find pleiotropic causal effects suggestive of mechanistic connections. TWMR better accounts for pleiotropy and has the potential to identify biological mechanisms underlying complex traits

Refining Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Genetic Loci by Integrating Summary Data From Genome-wide Association, Gene Expression, and DNA Methylation Studies

Background: Recent genome-wide association studies (GWASs) identified the first genetic loci associated with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The next step is to use these results to increase our understanding of the biological mechanisms involved. Most of the identified variants likely influence gene regulation. The aim of the current study is to shed light on the mechanisms underlying the genetic signals and prioritize genes by integrating GWAS results with gene expression and DNA methylation (DNAm) levels. Methods: We applied summary-data–based Mendelian randomization to integrate ADHD and ASD GWAS data with fetal brain expression and methylation quantitative trait loci, given the early onset of these disorders. We also analyzed expression and methylation quantitative trait loci datasets of adult brain and blood, as these provide increased statistical power. We subsequently used summary-data–based Mendelian randomization to investigate if the same variant influences both DNAm and gene expression levels. Results: We identified multiple gene expression and DNAm levels in fetal brain at chromosomes 1 and 17 that were associated with ADHD and ASD, respectively, through pleiotropy at shared genetic variants. The analyses in brain and blood showed additional associated gene expression and DNAm levels at the same and additional loci, likely because of increased statistical power. Several of the associated genes have not been identified in ADHD and ASD GWASs before. Conclusions: Our findings identified the genetic variants associated with ADHD and ASD that likely act through gene regulation. This facilitates prioritization of candidate genes for functional follow-up studies

Listeria pathogenesis and molecular virulence determinants

The gram-positive bacterium Listeria monocytogenes is the causative agent of listeriosis, a highly fatal opportunistic foodborne infection. Pregnant women, neonates, the elderly, and debilitated or immunocompromised patients in general are predominantly affected, although the disease can also develop in normal individuals. Clinical manifestations of invasive listeriosis are usually severe and include abortion, sepsis, and meningoencephalitis. Listeriosis can also manifest as a febrile gastroenteritis syndrome. In addition to humans, L. monocytogenes affects many vertebrate species, including birds. Listeria ivanovii, a second pathogenic species of the genus, is specific for ruminants. Our current view of the pathophysiology of listeriosis derives largely from studies with the mouse infection model. Pathogenic listeriae enter the host primarily through the intestine. The liver is thought to be their first target organ after intestinal translocation. In the liver, listeriae actively multiply until the infection is controlled by a cell-mediated immune response. This initial, subclinical step of listeriosis is thought to be common due to the frequent presence of pathogenic L. monocytogenes in food. In normal indivuals, the continual exposure to listerial antigens probably contributes to the maintenance of anti-Listeria memory T cells. However, in debilitated and immunocompromised patients, the unrestricted proliferation of listeriae in the liver may result in prolonged low-level bacteremia, leading to invasion of the preferred secondary target organs (the brain and the gravid uterus) and to overt clinical disease. L. monocytogenes and L. ivanovii are facultative intracellular parasites able to survive in macrophages and to invade a variety of normally nonphagocytic cells, such as epithelial cells, hepatocytes, and endothelial cells. In all these cell types, pathogenic listeriae go through an intracellular life cycle involving early escape from the phagocytic vacuole, rapid intracytoplasmic multiplication, bacterially induced actin-based motility, and direct spread to neighboring cells, in which they reinitiate the cycle. In this way, listeriae disseminate in host tissues sheltered from the humoral arm of the immune system. Over the last 15 years, a number of virulence factors involved in key steps of this intracellular life cycle have been identified. This review describes in detail the molecular determinants of Listeria virulence and their mechanism of action and summarizes the current knowledge on the pathophysiology of listeriosis and the cell biology and host cell responses to Listeria infection. This article provides an updated perspective of the development of our understanding of Listeria pathogenesis from the first molecular genetic analyses of virulence mechanisms reported in 1985 until the start of the genomic era of Listeria research

Development and Validation of a Dynamically Updated Prediction Model for Attrition from Marine Recruit Training

Dijksma, I, Hof, MHP, Lucas, C, and Stuiver, MM. Development and validation of a dynamically updated prediction model for attrition from Marine recruit training. J Strength Cond Res 36(9): 2523-2529, 2022-Whether fresh Marine recruits thrive and complete military training programs, or fail to complete, is dependent on numerous interwoven variables. This study aimed to derive a prediction model for dynamically updated estimation of conditional dropout probabilities for Marine recruit training. We undertook a landmarking analysis in a Cox proportional hazard model using longitudinal data from 744 recruits from existing databases of the Marine Training Center in the Netherlands. The model provides personalized estimates of dropout from Marine recruit training given a recruit's baseline characteristics and time-varying mental and physical health status, using 21 predictors. We defined nonoverlapping landmarks at each week and developed a supermodel by stacking the landmark data sets. The final supermodel contained all but one a priori selected baseline variables and time-varying health status to predict the hazard of attrition from Marine recruit training for each landmark as comprehensive as possible. The discriminative ability (c-index) of the prediction model was 0.78, 0.75, and 0.73 in week one, week 4 and week 12, respectively. We used 10-fold cross-validation to train and evaluate the model. We conclude that this prediction model may help to identify recruits at an increased risk of attrition from training throughout the Marine recruit training and warrants further validation and updates for other military settings

Sparse redundancy analysis of high-dimensional genetic and genomic data

Motivation: Recent technological developments have enabled the possibility of genetic and genomic integrated data analysis approaches, where multiple omics datasets from various biological levels are combined and used to describe (disease) phenotypic variations. The main goal is to explain and ultimately predict phenotypic variations by understanding their genetic basis and the interaction of the associated genetic factors. Therefore, understanding the underlying genetic mechanisms of phenotypic variations is an ever increasing research interest in biomedical sciences. In many situations, we have a set of variables that can be considered to be the outcome variables and a set that can be considered to be explanatory variables. Redundancy analysis (RDA) is an analytic method to deal with this type of directionality. Unfortunately, current implementations of RDA cannot deal optimally with the high dimensionality of omics data (p >> n). The existing theoretical framework, based on Ridge penalization, is suboptimal, since it includes all variables in the analysis. As a solution, we propose to use Elastic Net penalization in an iterative RDA framework to obtain a sparse solution. Results: We proposed sparse redundancy analysis (sRDA) for high dimensional omics data analysis. We conducted simulation studies with our software implementation of sRDA to assess the reliability of sRDA. Both the analysis of simulated data, and the analysis of 485 512 methylation markers and 18,424 gene-expression values measured in a set of 55 patients with Marfan syndrome show that sRDA is able to deal with the usual high dimensionality of omics dat

From population reference to national standard: new and improved birthweight charts

Background: Antenatal detection of intrauterine growth restriction remains a major obstetrical challenge, with the majority of cases not detected before birth. In these infants with undetected intrauterine growth restriction, the diagnosis must be made after birth. Clinicians use birthweight charts to identify infants as small-for-gestational-age if their birthweights are below a predefined threshold for gestational age. The choice of birthweight chart strongly affects the classification of small-for-gestational-age infants and has an impact on both research findings and clinical practice. Despite extensive literature on pathological risk factors associated with small-for-gestational-age, controversy exists regarding the exclusion of affected infants from a reference population. Objective: This study aims to identify pathological risk factors for abnormal fetal growth, to quantify their effects, and to use these findings to calculate prescriptive birthweight charts for the Dutch population. Materials and Methods: We performed a retrospective cross-sectional study, using routinely collected data of 2,712,301 infants born in The Netherlands between 2000 and 2014. Risk factors for abnormal fetal growth were identified and categorized in 7 groups: multiple gestation, hypertensive disorders, diabetes, other pre-existing maternal medical conditions, maternal substance (ab)use, medical conditions related to the pregnancy, and congenital malformations. The effects of these risk factors on mean birthweight were assessed using linear regression. Prescriptive birthweight charts were derived from live-born singleton infants, born to ostensibly healthy mothers after uncomplicated pregnancies and spontaneous onset of labor. The Box-Cox-t distribution was used to model birthweight and to calculate sex-specific percentiles. The new charts were compared to various existing birthweight and fetal-weight charts. Results: We excluded 111,621 infants because of missing data on birthweight, gestational age or sex, stillbirth, or a gestational age not between 23 and 42 weeks. Of the 2,599,640 potentially eligible infants, 969,552 (37.3%) had 1 or more risk factors for abnormal fetal growth and were subsequently excluded. Large absolute differences were observed between the mean birthweights of infants with and without these risk factors, with different patterns for term and preterm infants. The final low-risk population consisted of 1,629,776 live-born singleton infants (50.9% male), from which sex-specific percentiles were calculated. Median and 10th percentiles closely approximated fetal-weight charts but consistently exceeded existing birthweight charts. Conclusion: Excluding risk factors that cause lower birthweights results in prescriptive birthweight charts that are more akin to fetal-weight charts, enabling proper discrimination between normal and abnormal birthweight. This proof of concept can be applied to other populations

Growth patterns from birth to overweight at age 5-6 years of children with various backgrounds in socioeconomic status and country of origin: the ABCD study

Introduction: Children from minority groups are at increased risk of overweight. This study compared BMI growth patterns from birth onwards of boys and girls with overweight at 5-6 years, according to socioeconomic status (SES) and country of origin, in order to gain more insight into the critical periods of growth to overweight. Methods: A total of 3714 singletons of the multi-ethnic ABCD study were included. Within children with overweight at age 5-6 years (N = 487, prevalence boys: 11.6%, girls: 14.6%), BMI growth patterns from birth onwards (12.8 serial measurements; SD = 3.1) were compared between children from European (69.4%) and non-European mothers (30.6%), and between children from low (20.8%), mid (37.0%) or high SES (42.2%), based on maternal educational level. Results: BMI growth to overweight did not differ between children of European or non-European mothers, but it did differ according to maternal SES. Children with overweight in the low and mid SES group had a lower BMI in the first 2 years of life, an earlier adiposity rebound and increased in BMI more rapidly after age 2, resulting in a higher BMI at age 7 years compared to children with overweight in the high SES group [∆BMI (kg/m2) between high and low SES: boys 1.43(95%CI:0.16;3.01) and girls 1.91(0.55;3.27)]. Conclusion: Children with overweight from low SES have an early adiposity rebound and accelerated growth to a higher BMI at age 5-6 years compared to children with overweight from the high SES group. These results imply that timing of critical periods for overweight development is earlier in children with a low socioeconomic background as compared to other children

Recurrence risk of preterm birth in subsequent singleton pregnancy after preterm twin delivery

OBJECTIVE: The purpose of this study was to investigate the recurrence risk of preterm birth ( <37 weeks' gestation) in a subsequent singleton pregnancy after a previous nulliparous preterm twin delivery. STUDY DESIGN: We included 1957 women who delivered a twin gestation and a subsequent singleton pregnancy from the Netherlands Perinatal Registry. We compared the outcome of subsequent singleton pregnancy of women with a history of preterm delivery to the pregnancy outcome of women with a history of term twin delivery. RESULTS: Preterm birth in the twin pregnancy occurred in 1075 women (55%) vs 882 women (45%) who delivered at term. The risk of subsequent spontaneous singleton preterm birth was significantly higher after preterm twin delivery (5.2% vs 0.8%; odds ratio, 6.9; 95% confidence interval, 3.1-15.2). CONCLUSION: Women who deliver a twin pregnancy are at greater risk for delivering prematurely in a subsequent singleton pregnanc