Structural modeling of COVID-19 spread in relation to human mobility

Human mobility is considered as one of the prominent non-pharmaceutical interventions to control the spread of the pandemic (positive effect from mobility to infection). Conversely, the spread of the pandemic triggered massive changes to people's daily schedules by limiting their movement (negative effect from infection to mobility). The purpose of this study is to investigate this bi-directional relationship between human mobility and COVID-19 spread across U.S. counties during the early phase of the pandemic when infection rates were stabilizing and activity-travel behavior reflected a fairly steady return to normal following the drastic changes observed during the pandemic's initial shock. In particular, we applied Structural Regression (SR) model to investigate a bi-directional relationship between COVID-19 infection rate and the degree of human mobility in a county in association with socio-demographic and location characteristics of that county, and state-wide COVID-19 policies. Combining U.S. county-level cross-sectional data from multiple sources, our model results suggested that during the study period, human mobility and infection rate in a county both influenced each other, but in an opposite direction. Metropolitan counties experienced higher infection and lower mobility than non-metropolitan counties in the early stage of the pandemic. Counties with highly infected neighboring counties and more external trips had a higher infection rate. During the study period, community mitigation strategies, such as stay at home order, emergency declaration, and non-essential business closure significantly reduced mobility whereas public mask mandate significantly reduced infection rates. The findings of this study will provide important insights to policy makers in understanding the two-way relationship between human mobility and COVID-19 spread and to derive mobility-driven policy actions accordingly.

Copy Number Variation Detection by Single-Cell DNA Sequencing with SCOPE

Whole-genome single-cell DNA sequencing (scDNA-seq) enables the characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we describe SCOPE, a normalization and copy number estimation method for scDNA-seq data. We give an overview of the methodology and illustrate SCOPE with step-by-step demonstrations.

Revealing heavy metal correlations with water quality and tracking its latent factors by canonical correlation analysis and structural equation modeling in Dongjianghu Lake

Decreasing levels of water quality and elevated concentrations of heavy metals in freshwaters can pose global challenges for drinking water sources. Multivariate statistical techniques have been applied on data matrices of water quality and heavy metals for keen characterization of their spatio-temporal variations, exploration of latent factors, and identification of pollution sources. Non-metric multidimensional scaling (nMDS), canonical correlation analysis (CCA), and structural equation modeling (SEM) were employed to process data matrices of the water quality and heavy metals with 14 parameters measured at 13 sampling sites in Dongjianghu Lake in March, June, August, and December 2016. The sampling sites were grouped into three clusters using the nMDS, suggesting that the increasing order of the water quality levels was approximately midstream < downstream < upstream and lake. The CCA of 14 parameters proved that the Escherichia coli, COD_Mn, TP, TN, TEMP, DO, and pH were the latent factors to distinguish the sampling sites, suggesting that the natural disturbances further influenced the lake and upstream, while the anthropogenic activities further influenced the midstream and downstream. The CCA of the heavy metals exhibited that the COD_Mn, F^-, and E. coli were the latent factors of the Cu, Zn, and As, while the DO and TEMP were the latent factors of the Cd. This indicated that the Cu, As, and Zn were mainly associated with the anthropogenic activities, while the Cd was predominantly relative to the natural conditions. The SEM of the water quality and heavy metals showed that the weights of COD_Mn (28.64%), NH₃-N (14.96%), BOD₅ (14.32%), TN (12.88%), and TP (10.18%) were higher than those of the pH (8.37%), DO (7.73%), TEMP (2.58%), and E. coli (0.34%). This indicated that the former exhibited strong influences on the heavy metals than the latter. Moreover, the COD_Mn and BOD₅ were the key factors of the heavy metals, which should be attributed to the no-point sources, especially the exploitation mining and mill tailings. The water quality assessment by the nMDS, CCA, and SEM can determine the status, trend corresponding to its standards, and trace latent factors and identify possible pollution sources. The study could provide a guide for water quality evaluation and pollution control.

An exploration of the DSM-5 posttraumatic stress disorder symptom latent variable network

BACKGROUND

Both the latent variable model and the network model have been widely used to conceptualize mental disorders. However, it has been pointed out that there is no clear dichotomy between the two models, and a combination of these two model could enable a better understanding of psychopathology. The recently proposed latent network model (LNM) has provided a statistical framework to enable this combination. Evidence has shown that posttraumatic stress disorder (PTSD) could be a suitable candidate disorder to study the combined model. In the current study, we initiated the first investigation of the latent network of PTSD symptoms.

METHODS

The latent network of DSM-5 PTSD symptoms was estimated in 1196 adult survivors of China's 2008 Wenchuan earthquake. Validation testing of the latent network was conducted in a replication sample of children and adolescent who experienced various trauma types. PTSD symptoms were measured by the PTSD Checklist for DSM-5 (PCL-5). The latent network was estimated using the seven-factor hybrid model of DSM-5 PTSD symptoms, analysed using the R package lvnet.

RESULTS

The latent network model demonstrated good fit in both samples. A strong weighted edge between the intrusion and avoidance dimensions was identified (regularized partial correlation = 0.75). The externalizing behaviour dimension demonstrated the highest centrality in the latent network.

CONCLUSIONS

This study is the first to investigate the latent network of DSM-5 PTSD symptoms. Results suggest that both latent symptom dimension and associations between the dimensions should be considered in future PTSD studies and clinical practices.

CODEX2: full-spectrum copy number variation detection by high-throughput DNA sequencing

High-throughput DNA sequencing enables detection of copy number variations (CNVs) on the genome-wide scale with finer resolution compared to array-based methods but suffers from biases and artifacts that lead to false discoveries and low sensitivity. We describe CODEX2, as a statistical framework for full-spectrum CNV profiling that is sensitive for variants with both common and rare population frequencies and that is applicable to study designs with and without negative control samples. We demonstrate and evaluate CODEX2 on whole-exome and targeted sequencing data, where biases are the most prominent. CODEX2 outperforms existing methods and, in particular, significantly improves sensitivity for common CNVs.

Maternal Prenatal Psychological Distress and Preschool Cognitive Functioning: the Protective Role of Positive Parental Engagement

Considerable animal research and available human studies suggest that psychological distress experienced by mothers during gestation is associated with later neurodevelopmental deficits in offspring; however, little research has examined potential protective factors that might mitigate this risk. The current study examined the impact of maternal prenatal psychological distress during pregnancy on cognitive outcomes in preschoolers (ages 2.5-5 years) and positive parenting as a potential protective factor. Mother-child dyads (N = 162, mean child age = 44 months, 49 % female) were recruited from a longitudinal cohort of women who had previously participated in a study of maternal mood disorders during pregnancy. Maternal prenatal distress was assessed with multiple measures collected throughout pregnancy. During a follow-up visit, mothers were interviewed about their psychological symptoms since the birth of the child, parenting behaviors were recorded during a parent-child interaction, and children's cognitive abilities were measured using the Differential Ability Scales, 2nd Edition. Maternal prenatal distress significantly predicted lower general cognitive abilities; however, this relationship was strongest for children whose mothers exhibited low levels of positive engagement and not significant when mothers exhibited high levels of positive engagement. Results suggest that positive parental engagement can protect against the detrimental effects of maternal prenatal distress on preschoolers' cognitive abilities.

Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications

We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging-genetic studies to identify genetic variants that are associated with brain imaging phenotypes, often in the form of high dimensional tensor fields. GRRLF identifies from the structure in the data the effective dimensionality of the data, and then jointly performs dimension reduction of the covariates, dynamic identification of latent factors, and nonparametric estimation of both covariate and latent response fields. After accounting for the latent and covariate effects, GRLLF performs a nonparametric test on the remaining factor of interest. GRRLF provides a better factorization of the signals compared with common solutions, and is less susceptible to overfitting because it exploits the effective dimensionality. The generality and the flexibility of GRRLF also allow various statistical models to be handled in a unified framework and solutions can be efficiently computed. Within the field of neuroimaging, it improves the sensitivity for weak signals and is a promising alternative to existing approaches. The operation of the framework is demonstrated with both synthetic datasets and a real-world neuroimaging example in which the effects of a set of genes on the structure of the brain at the voxel level were measured, and the results compared favorably with those from existing approaches.