Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review.

BACKGROUND: Monitoring free-living physical activity (PA) through wearable devices enables the real-time assessment of activity features associated with health outcomes and provision of treatment recommendations and adjustments. The conclusions of studies on PA and health depend crucially on reliable statistical analyses of digital data. Data analytics, however, are challenging due to the various metrics adopted for measuring PA, different aims of studies, and complex temporal variations within variables. The application, interpretation, and appropriateness of these analytical tools have yet to be summarized. OBJECTIVE: This research aimed to review studies that used analytical methods for analyzing PA monitored by accelerometers. Specifically, this review addressed three questions: (1) What metrics are used to describe an individual's free-living daily PA? (2) What are the current analytical tools for analyzing PA data, particularly under the aims of classification, association with health outcomes, and prediction of health events? and (3) What challenges exist in the analyses, and what recommendations for future research are suggested regarding the use of statistical methods in various research tasks? METHODS: This scoping review was conducted following an existing framework to map research studies by exploring the information about PA. Three databases, PubMed, IEEE Xplore, and the ACM Digital Library, were searched in February 2024 to identify related publications. Eligible articles were classification, association, or prediction studies involving human PA monitored through wearable accelerometers. RESULTS: After screening 1312 articles, 428 (32.62%) eligible studies were identified and categorized into at least 1 of the following 3 thematic categories: classification (75/428, 17.5%), association (342/428, 79.9%), and prediction (32/428, 7.5%). Most articles (414/428, 96.7%) derived PA variables from 3D acceleration, rather than 1D acceleration. All eligible articles (428/428, 100%) considered PA metrics represented in the time domain, while a small fraction (16/428, 3.7%) also considered PA metrics in the frequency domain. The number of studies evaluating the influence of PA on health conditions has increased greatly. Among the studies in our review, regression-type models were the most prevalent (373/428, 87.1%). The machine learning approach for classification research is also gaining popularity (32/75, 43%). In addition to summary statistics of PA, several recent studies used tools to incorporate PA trajectories and account for temporal patterns, including longitudinal data analysis with repeated PA measurements and functional data analysis with PA as a continuum for time-varying association (68/428, 15.9%). CONCLUSIONS: Summary metrics can quickly provide descriptions of the strength, frequency, and duration of individuals' overall PA. When the distribution and profile of PA need to be evaluated or detected, considering PA metrics as longitudinal or functional data can provide detailed information and improve the understanding of the role PA plays in health. Depending on the research goal, appropriate analytical tools can ensure the reliability of the scientific findings.

HYPERREFLECTIVE FOCI AS IMPORTANT PROGNOSTIC INDICATORS OF PROGRESSION OF RETINITIS PIGMENTOSA.

PURPOSE: To investigate the presence and clinical relevance of hyperreflective foci (HRFs) in retinitis pigmentosa. METHODS: Seventy seven retinitis pigmentosa cases were retrospectively reviewed. The 10-mm wide cross-line macular scans in optical coherence tomography were acquired. Hyperreflective foci were classified according to the location in optical coherence tomography: outer layers within the macula (HRF-outer-central), macular border beyond the central 3 mm (HRF-outer-perifoveal), and choroid (HRF-choroidal). The visual acuity at baseline, at 12 months, and other fundus characteristics were collected. RESULTS: The mean logMAR best-corrected visual acuity decreased from 0.59 ± 0.66 (20/78 in Snellen) to 0.74 ± 0.81 (20/106 in Snellen) in 1 year. Sixty-six (42.9%), 105 (68.2%), and 98 (63.6%) eyes were classified to HRF-outer-central, HRF-outer-perifoveal, and HRF-choroidal group, respectively. Hyperreflective foci were positively correlated with poorer vision, central macular thinning, and ellipsoid zone disruption (all P < 0.001). Worse vision was associated with older age, macular involvement, and the coexistence of two or three HRF groups (P = 0.014, 0.047, 0.019, <0.001, respectively). Hyperreflective foci developed more frequently in patients with thick choroid than in those with thin choroid. The coexistence of three HRF groups was correlated with quicker visual deterioration (P = 0.034). CONCLUSION: Hyperreflective foci are common in retinitis pigmentosa and can be a negative prognostic indicator of macular thickness and visual preservation. Thick choroid was associated with all groups of HRFs, especially HRF-choroidal.

Influence of parental behavior on myopigenic behaviors and risk of myopia: analysis of nationwide survey data in children aged 3 to 18 years.

BACKGROUND: Preventive parental behavior may play an important role in the outcomes of children's myopia. We investigated associations between parental behavior and children's myopia status and daily activities using data from the most recent myopia survey in Taiwan. METHODS: In total, 3845 children aged 3 to 18 years who completely responded to the questionnaire were included (total score ranging from 0 to 75). A score of ≥ 50 was considered to indicate beneficial parental behavior. Time allocation data for near-work activities, using electronic devices, and outdoor activities were collected using a separate self-reported questionnaire. Associations between beneficial parental behavior and children's myopia status and activity patterns were analyzed and stratified by school level. RESULTS: Beneficial parental behavior was positively associated with children's myopia in the overall samples [adj. odds ratio (OR): 1.31, 95% confidence interval (CI): 1.08-1.59, p = 0.006)] and at the elementary school level (adj. OR: 1.43, 95% CI: 1.11-1.83, p = 0.005). However, a negative association with high myopia was observed in the overall samples (adj. OR: 0.71, 95% CI: 0.50-0.99, p = 0.049) and high school level (adj. OR: 0.62, 95% CI: 0.41-0.92, p = 0.02). Beneficial parental behavior was associated with less time spent on near work (≥ 180 min/day) and electronic device use (≥ 60 min/day), but not with outdoor activities. CONCLUSION: In Taiwan, children's myopia is associated with higher rate of parents' beneficial behaviors, which suggests that regular vision surveillance is necessary to promote better parental behavior toward children's eye care. Certain parental practices may influence children's behavior pattern and reduce the risk of children's high myopia development in the long run.

Evolution of the Prevalence of Myopia among Taiwanese Schoolchildren: A Review of Survey Data from 1983 through 2017.

PURPOSE: The aim of this study was to evaluate the changes in the prevalence of myopia in Taiwanese schoolchildren over the past few decades and to analyze the risk factors for myopia. DESIGN: Analysis of 8 consecutive population-based myopia surveys conducted from 1983 through 2017. PARTICIPANTS: An average of 8917 (5019-11 656) schoolchildren 3 to 18 years of age were selected using stratified systematic cluster sampling or by probability proportional to size sampling. METHODS: All participants underwent complete ophthalmic evaluations. Three drops of 0.5% tropicamide were used to obtain the cycloplegic refractive status of each participant. Questionnaires were used to acquire participant data from the 1995, 2005, 2010, and 2016 surveys. MAIN OUTCOME MEASURES: Prevalence of myopia (spherical equivalence of ≤-0.25 diopter [D]) and high myopia (≤-6.0 D) was assessed. Multivariate analyses of risk factors were conducted. RESULTS: The prevalence of myopia among all age groups increased steadily. From 1983 through 2017, the weighted prevalence increased from 5.37% (95% confidence interval [CI], 3.50%-7.23%) to 25.41% (95% CI, 21.27%-29.55%) for 7-year-olds (P = 0.001 for trend) and from 30.66% (95% CI, 26.89%-34.43%) to 76.67% (95% CI, 72.94%-80.40%) for 12-year-olds (P = 0.001 for trend). The prevalence of high myopia also increased from 1.39% (95% CI, 0.43%-2.35%) to 4.26% (95% CI, 3.35%-5.17%) for 12-year-olds (P = 0.008 for trend) and from 4.37% (95% CI, 2.91%-5.82%) to 15.36% (95% CI, 13.78%-16.94%) for 15-year-olds (P = 0.039 for trend). In both the 2005 and 2016 survey samples, children who spent less than 180 minutes daily on near-work activities showed significantly lower risks for myopia developing (<60 minutes: odds ratio [OR], 0.48 and 0.56; 60-180 minutes: OR, 0.69 and 0.67). In the 2016 survey, spending more than 60 minutes daily on electronic devices was associated significantly with both myopia and high myopia (OR, 2.43 and 2.31). CONCLUSIONS: The prevalence of myopia among schoolchildren increased rapidly from 1983 through 2017 in Taiwan. The major risk factors are older age and time spent on near-work activities. Use of electronic devices increased the amount of time spent on near-work and may increase the risk of developing myopia.

Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis.

Copy number variants (CNVs) are the gain or loss of DNA segments in the genome that can vary in dosage and length. CNVs comprise a large proportion of variation in human genomes and impact health conditions. To detect rare CNV associations, kernel-based methods have been shown to be a powerful tool due to their flexibility in modeling the aggregate CNV effects, their ability to capture effects from different CNV features, and their accommodation of effect heterogeneity. To perform a kernel association test, a CNV locus needs to be defined so that locus-specific effects can be retained during aggregation. However, CNV loci are arbitrarily defined and different locus definitions can lead to different performance depending on the underlying effect patterns. In this work, we develop a new kernel-based test called CONCUR (i.e., copy number profile curve-based association test) that is free from a definition of locus and evaluates CNV-phenotype associations by comparing individuals' copy number profiles across the genomic regions. CONCUR is built on the proposed concepts of "copy number profile curves" to describe the CNV profile of an individual, and the "common area under the curve (cAUC) kernel" to model the multi-feature CNV effects. The proposed method captures the effects of CNV dosage and length, accounts for the numerical nature of copy numbers, and accommodates between- and within-locus etiological heterogeneity without the need to define artificial CNV loci as required in current kernel methods. In a variety of simulation settings, CONCUR shows comparable or improved power over existing approaches. Real data analyses suggest that CONCUR is well powered to detect CNV effects in the Swedish Schizophrenia Study and the Taiwan Biobank.

Network hub-node prioritization of gene regulation with intra-network association.

BACKGROUND: To identify and prioritize the influential hub genes in a gene-set or biological pathway, most analyses rely on calculation of marginal effects or tests of statistical significance. These procedures may be inappropriate since hub nodes are common connection points and therefore may interact with other nodes more often than non-hub nodes do. Such dependence among gene nodes can be conjectured based on the topology of the pathway network or the correlation between them. RESULTS: Here we develop a pathway activity score incorporating the marginal (local) effects of gene nodes as well as intra-network affinity measures. This score summarizes the expression levels in a gene-set/pathway for each sample, with weights on local and network information, respectively. The score is next used to examine the impact of each node through a leave-one-out evaluation. To illustrate the procedure, two cancer studies, one involving RNA-Seq from breast cancer patients with high-grade ductal carcinoma in situ and one microarray expression data from ovarian cancer patients, are used to assess the performance of the procedure, and to compare with existing methods, both ones that do and do not take into consideration correlation and network information. The hub nodes identified by the proposed procedure in the two cancer studies are known influential genes; some have been included in standard treatments and some are currently considered in clinical trials for target therapy. The results from simulation studies show that when marginal effects are mild or weak, the proposed procedure can still identify causal nodes, whereas methods relying only on marginal effect size cannot. CONCLUSIONS: The NetworkHub procedure proposed in this research can effectively utilize the network information in combination with local effects derived from marker values, and provide a useful and complementary list of recommendations for prioritizing causal hubs.

Probabilistic prioritization of candidate pathway association with pathway score.

BACKGROUND: Current methods for gene-set or pathway analysis are usually designed to test the enrichment of a single gene-set. Once the analysis is carried out for each of the sets under study, a list of significant sets can be obtained. However, if one wishes to further prioritize the importance or strength of association of these sets, no such quantitative measure is available. Using the magnitude of p-value to rank the pathways may not be appropriate because p-value is not a measure for strength of significance. In addition, when testing each pathway, these analyses are often implicitly affected by the number of differentially expressed genes included in the set and/or affected by the dependence among genes. RESULTS: Here we propose a two-stage procedure to prioritize the pathways/gene-sets. In the first stage we develop a pathway-level measure with three properties. First, it contains all genes (differentially expressed or not) in the same set, and summarizes the collective effect of all genes per sample. Second, this pathway score accounts for the correlation between genes by synchronizing their correlation directions. Third, the score includes a rank transformation to enhance the variation among samples as well as to avoid the influence of extreme heterogeneity among genes. In the second stage, all scores are included simultaneously in a Bayesian logistic regression model which can evaluate the strength of association for each set and rank the sets based on posterior probabilities. Simulations from Gaussian distributions and human microarray data, and a breast cancer study with RNA-Seq are considered for demonstration and comparison with other existing methods. CONCLUSIONS: The proposed summary pathway score provides for each sample an overall evaluation of gene expression in a gene-set. It demonstrates the advantages of including all genes in the set and the synchronization of correlation direction. The simultaneous utilization of all pathway-level scores in a Bayesian model not only offers a probabilistic evaluation and ranking of the pathway association but also presents good accuracy in identifying the top-ranking pathways. The resulting recommendation list of ranked pathways can be a reference for potential target therapy or for future allocation of research resources.

Pre-diagnostic blood immune markers, incidence and progression of B-cell lymphoma and multiple myeloma: Univariate and functionally informed multivariate analyses.

Recent prospective studies have shown that dysregulation of the immune system may precede the development of B-cell lymphomas (BCL) in immunocompetent individuals. However, to date, the studies were restricted to a few immune markers, which were considered separately. Using a nested case-control study within two European prospective cohorts, we measured plasma levels of 28 immune markers in samples collected a median of 6 years before diagnosis (range 2.01-15.97) in 268 incident cases of BCL (including multiple myeloma [MM]) and matched controls. Linear mixed models and partial least square analyses were used to analyze the association between levels of immune marker and the incidence of BCL and its main histological subtypes and to investigate potential biomarkers predictive of the time to diagnosis. Linear mixed model analyses identified associations linking lower levels of fibroblast growth factor-2 (FGF-2 p = 7.2 × 10-4 ) and transforming growth factor alpha (TGF-α, p = 6.5 × 10-5 ) and BCL incidence. Analyses stratified by histological subtypes identified inverse associations for MM subtype including FGF-2 (p = 7.8 × 10-7 ), TGF-α (p = 4.08 × 10-5 ), fractalkine (p = 1.12 × 10-3 ), monocyte chemotactic protein-3 (p = 1.36 × 10-4 ), macrophage inflammatory protein 1-alpha (p = 4.6 × 10-4 ) and vascular endothelial growth factor (p = 4.23 × 10-5 ). Our results also provided marginal support for already reported associations between chemokines and diffuse large BCL (DLBCL) and cytokines and chronic lymphocytic leukemia (CLL). Case-only analyses showed that Granulocyte-macrophage colony stimulating factor levels were consistently higher closer to diagnosis, which provides further evidence of its role in tumor progression. In conclusion, our study suggests a role of growth-factors in the incidence of MM and of chemokine and cytokine regulation in DLBCL and CLL.

Co-modulation analysis of gene regulation in breast cancer reveals complex interplay between ESR1 and ERBB2 genes.

BACKGROUND: Gene regulation is dynamic across cellular conditions and disease subtypes. From the aspect of regulation under modulation, regulation strength between a pair of genes can be modulated by (dependent on) expression abundance of another gene (modulator gene). Previous studies have demonstrated the involvement of genes modulated by single modulator genes in cancers, including breast cancer. However, analysis of multi-modulator co-modulation that can further delineate the landscape of complex gene regulation is, to our knowledge, unexplored previously. In the present study we aim to explore the joint effects of multiple modulator genes in modulating global gene regulation and dissect the biological functions in breast cancer. RESULTS: To carry out the analysis, we proposed the Covariability-based Multiple Regression (CoMRe) method. The method is mainly built on a multiple regression model that takes expression levels of multiple modulators as inputs and regulation strength between genes as output. Pairs of genes were divided into groups based on their co-modulation patterns. Analyzing gene expression profiles from 286 breast cancer patients, CoMRe investigated ten candidate modulator genes that interacted and jointly determined global gene regulation. Among the candidate modulators, ESR1, ERBB2, and ADAM12 were found modulating the most numbers of gene pairs. The largest group of gene pairs was composed of ones that were modulated by merely ESR1. Functional annotation revealed that the group was significantly related to tumorigenesis and estrogen signaling in breast cancer. ESR1-ERBB2 co-modulation was the largest group modulated by more than one modulators. Similarly, the group was functionally associated with hormone stimulus, suggesting that functions of the two modulators are performed, at least partially, through modulation. The findings were validated in majorities of patients (> 99%) of two independent breast cancer datasets. CONCLUSIONS: We have showed CoMRe is a robust method to discover critical modulators in gene regulatory networks, and it is capable of achieving reproducible and biologically meaningful results. Our data reveal that gene regulatory networks modulated by single modulator or co-modulated by multiple modulators play important roles in breast cancer. Findings of this report illuminate complex and dynamic gene regulation under modulation and its involvement in breast cancer.

Distinct seromarkers predict different milestones of chronic hepatitis B progression.

UNLABELLED: Spontaneous seroclearance of hepatitis B e antigen (HBeAg) and hepatitis B virus (HBV) DNA undetectability are important milestones of chronic hepatitis B and major treatment endpoints of antiviral therapy. This study investigated the role of serum hepatitis B surface antigen (HBsAg) levels and established models for predicting HBeAg seroclearance and HBV DNA undetectability. A total of 2,139 HBsAg-seropositive, anti-HCV-seronegative, and treatment-naïve participants without liver cirrhosis at study entry were included. Spontaneous HBeAg seroclearance and HBV DNA undetectability were analyzed in 431 HBeAg-seropositive participants and 1,708 HBeAg-seronegative participants, respectively. Regression coefficients of predictors in Cox proportional hazard models were converted into integer scores for predicting seroclearance and predictive accuracy was assessed with time-dependent receiver operating characteristic (ROC) curves. The HBV DNA level was the most important predictor of HBeAg seroclearance but serum HBsAg level was the most significant predictor of HBV DNA undetectability. Compared to individuals with HBsAg levels ≥ 10,000 IU/mL, the multivariate-adjusted rate ratio (95% confidence interval) of HBV DNA undetectability was 1.20 (0.62-2.30), 2.49 (1.31-4.75), and 6.08 (3.19-11.61) for those with serum HBsAg levels of 1,000-9,999, 100-999, and <100 IU/mL, respectively. The area under the ROC curve (AUROC) of the prediction models for predicting the 5- and 10-year probabilities of HBeAg seroclearance and HBV DNA undetectability were 0.85 (0.80-0.90) and 0.78 (0.73-0.83) for HBeAg seroclearance, and 0.77 (0.72-0.82) and 0.73 (0.70-0.76) for HBV DNA undetectability. CONCLUSION: Prediction models incorporating important host and virus factors can predict HBeAg seroclearance and HBV DNA undetectability. Serum HBsAg levels rather than HBV DNA is the most important predictor of spontaneous HBV DNA undetectability. Serum HBsAg levels should be monitored in the management of patients with HBeAg-seronegative chronic hepatitis B.

Spontaneous seroclearance of hepatitis B seromarkers and subsequent risk of hepatocellular carcinoma.

BACKGROUND AND AIMS: The associations between long-term risk of hepatocellular carcinoma (HCC) and spontaneous seroclearance of HBV e antigen (HBeAg), HBV DNA and HBV surface antigen (HBsAg) have never been examined by a prospective study using serially measured seromarkers. This study aimed to assess the importance of spontaneous HBeAg, HBV DNA and HBsAg seroclearance in the prediction of HCC risk. METHODS: This study included 2946 HBsAg seropositive individuals who were seronegative for antibodies against HCV and free of liver cirrhosis. Serial serum samples collected at study entry and follow-up health examinations were tested for HBeAg, HBV DNA and HBsAg. Cox proportional hazards models were used to calculate the HRs of developing HCC after seroclearance of HBV markers. RESULTS: The HR (95% CI) of developing HCC after seroclearance of HBeAg, HBV DNA and HBsAg during follow-up was 0.63 (0.38 to 1.05), 0.24 (0.11 to 0.57) and 0.18 (0.09 to 0.38), respectively, after adjustment for age, gender and serum level of alanine aminotransferase at study entry. High HBV DNA levels at the seroclearance of HBeAg (mean±SD, 4.35±1.64 log10 IU/mL) may explain the non-significant association between HBeAg seroclearance and HCC risk. Among HBeAg seronegative participants with detectable serum HBV DNA at study entry, the lifetime (30-75-years-old) cumulative incidence of HCC was 4.0%, 6.6% and 14.2%, respectively, for those with seroclearance of both HBV DNA and HBsAg, seroclearance of HBV DNA only, and seroclearance of neither. CONCLUSIONS: Spontaneous seroclearance of HBV DNA and HBsAg are important predictors of reduced HCC risk.

SNP rs10248565 in HDAC9 as a novel genomic aberration biomarker of lung adenocarcinoma in non-smoking women.

BACKGROUND: Numerous efforts have been made to elucidate the etiology and improve the treatment of lung cancer, but the overall five-year survival rate is still only 15%. Although cigarette smoking is the primary risk factor for lung cancer, only 7% of female lung cancer patients in Taiwan have a history of smoking. Since cancer results from progressive accumulation of genetic aberrations, genomic rearrangements may be early events in carcinogenesis. RESULTS: In order to identify biomarkers of early-stage adenocarcinoma, the genome-wide DNA aberrations of 60 pairs of lung adenocarcinoma and adjacent normal lung tissue in non-smoking women were examined using Affymetrix Genome-Wide Human SNP 6.0 arrays. Common copy number variation (CNV) regions were identified by ≥30% of patients with copy number beyond 2 ± 0.5 of copy numbers for each single nucleotide polymorphism (SNP) and at least 100 continuous SNP variant loci. SNPs associated with lung adenocarcinoma were identified by McNemar's test. Loss of heterozygosity (LOH) SNPs were identified in ≥18% of patients with LOH in the locus. Aberration of SNP rs10248565 at HDAC9 in chromosome 7p21.1 was identified from concurrent analyses of CNVs, SNPs, and LOH. CONCLUSION: The results elucidate the genetic etiology of lung adenocarcinoma by demonstrating that SNP rs10248565 may be a potential biomarker of cancer susceptibility.

A predictive scoring system for the seroclearance of HBsAg in HBeAg-seronegative chronic hepatitis B patients with genotype B or C infection.

BACKGROUND & AIMS: Seroclearance of hepatitis B surface antigen (HBsAg) is the most ideal end point in the treatment of chronic hepatitis B. This study develops a predictive scoring system to assess whether the addition of serum levels HBsAg may improve the predictability of HBsAg loss. METHODS: This study included 2491 untreated participants with genotype B or C HBV infection, who were HBsAg-seropositive, HBeAg-seronegative, anti-HCV-seronegative, and cirrhosis free at study entry. Regression coefficients of predictors in Cox Regression models were converted into integer scores for predicting HBsAg seroclearance. Predictive accuracy was assessed with area under the receiver operating characteristic curves (AUROC), and predictive accuracies of models with and without serum HBsAg levels were compared. RESULTS: Low serum levels of both HBsAg and HBV DNA were the strongest predictors of spontaneous HBsAg seroclearance. Compared to baseline serum HBsAg levels ≥1000 IU/ml, the multivariate adjusted rate ratio of spontaneous HBsAg seroclearance was 10.96 (7.92-15.16) for those with baseline serum HBsAg levels <100 IU/ml. The predictive ability of HBsAg levels was modified by HBV viral load, showing a weaker effect in those with higher viral loads, and the strongest effect among those with undetectable viral loads. The inclusion of serum HBsAg levels greatly improved the AUROC for predicting HBsAg seroclearance at the fifth (from 0.79 [0.787-0.792] to 0.89 [0.889-0.891]) and tenth year (from 0.73 [0.728-0.732] to 0.84 [0.839-0.841]) after study entry. CONCLUSIONS: Incorporated into an easy-to-use scoring system, HBV viral load and quantitative serum HBsAg levels can accurately predict HBsAg seroclearance.

A Bayesian approach to differential edges with probabilistic interactions: applications in association and classification.

Motivation: Differential network (D-Net) analysis has attracted great attention in systems biology for its ability to identify genetic variations in response to different conditions. Current approaches either estimate the condition-specific networks separately followed by post-procedures to determine the differential edges or estimate the D-Net directly. Both types of analysis overlook the probabilistic inference and can only provide deterministic inference of the edges. Results: Here, we propose a Bayesian solution and translate the probabilistic estimation in the regression model to an inferential D-Net analysis for genetic association and classification studies. The proposed PRobabilistic Interaction for Differential Edges (PRIDE) focuses on inferring the D-Net with uncertainty so that the existence of the differential edges can be evaluated with probability and even prioritized if comparison among these edges is of interest. The performance of the proposed model is compared with state-of-the-art methods in simulations and is demonstrated in glioblastoma and breast cancer studies. The proposed PRIDE performs comparably to or outperforms most existing tools under deterministic evaluation criteria. Additionally, it offers the unique advantages, including prioritizing the differential edges with probabilities, highlighting the relative importance of hub nodes, and identifying potential sub-networks in a D-Net. Availability and implementation: All the data analyzed in this research can be downloaded at https://xenabrowser.net/datapages/. The R code for implementing PRIDE is available at https://github.com/YJGene0806/PRIDE_Code.

Bayesian Approach to Disease Risk Evaluation Based on Air Pollution and Weather Conditions.

BACKGROUND: Environmental factors such as meteorological conditions and air pollutants are recognized as important for human health, where mortality and morbidity of certain diseases may be related to abrupt climate change or air pollutant concentration. In the literature, environmental factors have been identified as risk factors for chronic diseases such as ischemic heart disease. However, the likelihood evaluation of the disease occurrence probability due to environmental factors is missing. METHOD: We defined people aged 51-90 years who were free from ischemic heart disease (ICD9: 410-414) in 1996-2002 as the susceptible group. A Bayesian conditional logistic regression model based on a case-crossover design was utilized to construct a risk information system and applied to data from three databases in Taiwan: air quality variables from the Environmental Protection Administration (EPA), meteorological parameters from the Central Weather Bureau (CWB), and subject information from the National Health Insurance Research Database (NHIRD). RESULTS: People living in different geographic regions in Taiwan were found to have different risk factors; thus, disease risk alert intervals varied in the three regions. CONCLUSIONS: Disease risk alert intervals can be a reference for weather bureaus to issue health warnings. With early warnings, susceptible groups can take measures to avoid exacerbation of disease when meteorological conditions and air pollution become hazardous to their health.

Performance and Operational Feasibility of Epstein-Barr Virus-Based Screening for Detection of Nasopharyngeal Carcinoma: Direct Comparison of Two Alternative Approaches.

PURPOSE: Two Epstein-Barr virus (EBV)-based testing approaches have shown promise for early detection of nasopharyngeal carcinoma (NPC). Neither has been independently validated nor their performance compared. We compared their diagnostic performance in an independent population. METHODS: We tested blood samples from 819 incident Taiwanese NPC cases (213 early-stage, American Joint Committee on Cancer version 7 stages I and II) diagnosed from 2010 to 2014 and from 1,768 controls from the same region, frequency matched to cases on age and sex. We compared an EBV antibody score using immunoglobulin A antibodies measured by enzyme-linked immunosorbent assay (EBV antibody score) and plasma EBV DNA load measured by real-time PCR followed by next-generation sequencing (NGS) among EBV DNA-positive individuals (EBV DNA algorithm). RESULTS: EBV antibodies and DNA load were measured for 2,522 (802 cases; 1,720 controls) and 2,542 (797 cases; 1,745 controls) individuals, respectively. Of the 898 individuals positive for plasma EBV DNA and therefore eligible for NGS, we selected 442 (49%) for NGS testing. The EBV antibody score had a sensitivity of 88.4% (95% CI, 86.1 to 90.6) and a specificity of 94.9% (95% CI, 93.8 to 96.0) for NPC. The EBV DNA algorithm yielded significantly higher sensitivity (93.2%; 95% CI, 91.3 to 94.9; P = 1.33 × 10-4) and specificity (98.1%; 95% CI, 97.3 to 98.8; P = 3.53 × 10-7). For early-stage NPC, the sensitivities were 87.1% (95% CI, 82.7 to 92.4) for the EBV antibody score and 87.0% (95% CI, 81.9 to 91.5) for the EBV DNA algorithm (P = .514). For regions with a NPC incidence of 20-100/100,000 person-years (eg, residents in southern China and Hong Kong), these two approaches yielded similar numbers needed to screen (EBV antibody score: 5,656-1,131; EBV DNA algorithm: 5,365-1,073); positive predictive values ranged from 0.4% to 1.7% and 1.0% to 4.7%, respectively. CONCLUSION: We demonstrated high sensitivity and specificity of EBV antibody and plasma EBV DNA for NPC detection, with slightly inferior performance of the EBV antibody score. Cost-effectiveness studies are needed to guide screening implementation.

Changes in serum levels of HBV DNA and alanine aminotransferase determine risk for hepatocellular carcinoma.

BACKGROUND & AIMS: It is not clear whether risk for hepatocellular carcinoma can be accurately determined from long-term changes in serum levels of hepatitis B virus (HBV) DNA or alanine aminotransferase (ALT). METHODS: We measured serum levels of HBV DNA and ALT at enrollment and during follow-up analysis of 3160 participants in the REVEAL-HBV study. Development of hepatocellular carcinoma was determined from follow-up examinations and computerized linkage with National Cancer Registry and National Death Certification profiles. Multivariate-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox regression models. RESULTS: During 38,330 person-years of follow-up, 81 participants developed hepatocellular carcinoma (incidence rate, 211.3/100,000 person-years). The risk for hepatocellular carcinoma was only slightly higher for participants whose follow-up levels of HBV DNA spontaneously decreased to <10,000 copies/mL compared with those with baseline levels of HBV DNA<10,000 copies/mL (control group; HR, 2.25; 95% CI, 0.68-7.37). Compared with the control group, the HRs (95% CI) for long-term levels of HBV DNA that persisted at 10,000 to 100,000 copies/mL, decreased to/persisted at 100,000 to 1,000,000 copies/mL, or decreased to/persisted at 1,000,000 to 10,000,000 copies/mL were 3.12 (1.09-8.89), 8.85 (3.85-20.35), and 16.78 (7.33-38.39), respectively. A gradient in ALT level was significantly associated with hepatocellular carcinoma risk: from all low-normal, to ever high-normal, to transient abnormal, to persistent abnormal (Ptrend<.001). CONCLUSIONS: Long-term changes in serum levels of HBV DNA and ALT are independent predictors of risk for hepatocellular carcinoma. Regular monitoring of levels of HBV DNA and ALT is important in clinical management of chronic carriers of HBV.

Probabilistic edge inference of gene networks with markov random field-based bayesian learning.

Current algorithms for gene regulatory network construction based on Gaussian graphical models focuses on the deterministic decision of whether an edge exists. Both the probabilistic inference of edge existence and the relative strength of edges are often overlooked, either because the computational algorithms cannot account for this uncertainty or because it is not straightforward in implementation. In this study, we combine the Bayesian Markov random field and the conditional autoregressive (CAR) model to tackle simultaneously these two tasks. The uncertainty of edge existence and the relative strength of edges can be measured and quantified based on a Bayesian model such as the CAR model and the spike-and-slab lasso prior. In addition, the strength of the edges can be utilized to prioritize the importance of the edges in a network graph. Simulations and a glioblastoma cancer study were carried out to assess the proposed model's performance and to compare it with existing methods when a binary decision is of interest. The proposed approach shows stable performance and may provide novel structures with biological insights.

The misuse of distributional assumptions in functional class scoring gene-set and pathway analysis.

Gene-set analysis (GSA) is a standard procedure for exploring potential biological functions of a group of genes. The development of its methodology has been an active research topic in recent decades. Many GSA methods, when newly proposed, rely on simulation studies to evaluate their performance with an implicit assumption that the multivariate expression values are normally distributed. This assumption is commonly adopted in GSAs, particularly those in the group of functional class scoring (FCS) methods. The validity of the normality assumption, however, has been disputed in several studies, yet no systematic analysis has been carried out to assess the effect of this distributional assumption. Our goal in this study is not to propose a new GSA method but to first examine if the multi-dimensional gene expression data in gene sets follow a multivariate normal (MVN) distribution. Six statistical methods in three categories of MVN tests were considered and applied to a total of 24 RNA data sets. These RNA values were collected from cancer patients as well as normal subjects, and the values were derived from microarray experiments, RNA sequencing, and single-cell RNA sequencing. Our first finding suggests that the MVN assumption is not always satisfied. This assumption does not hold true in many applications tested here. In the second part of this research, we evaluated the influence of non-normality on the statistical power of current FCS methods, both parametric and nonparametric ones. Specifically, the scenario of mixture distributions representing more than one population for the RNA values was considered. This second investigation demonstrates that the non-normality distribution of the RNA values causes a loss in the statistical power of these GSA tests, especially when subtypes exist. Among the FCS GSA tools examined here and among the scenarios studied in this research, the N-statistics outperform the others. Based on the results from these two investigations, we conclude that the assumption of MVN should be used with caution when evaluating new GSA tools, since this assumption cannot be guaranteed and violation may lead to spurious results, loss of power, and incorrect comparison between methods. If a newly proposed GSA tool is to be evaluated, we recommend the incorporation of a wide range of multivariate non-normal distributions or sampling from large databases if available.

Mitochondrial DNA methylation profiling of the human prefrontal cortex and nucleus accumbens: correlations with aging and drug use.

BACKGROUND: Despite the brain's high demand for energy, research on its epigenetics focuses on nuclear methylation, and much of the mitochondrial DNA methylation remains seldom investigated. With a focus on the nucleus accumbens (NAcc) and the prefrontal cortex (PFC), we aimed to identify the mitochondrial methylation signatures for (1) distinguishing the two brain areas, (2) correlating with aging, and (3) reflecting the influence of illicit drugs on the brain. RESULT: We collected the brain tissue in the NAcc and the PFC from the deceased individuals without (n = 39) and with (n = 14) drug use and used whole-genome bisulfite sequencing to cover cytosine sites in the mitochondrial genome. We first detected differential methylations between the NAcc and the PFC in the nonusers group (P = 3.89 × 10-9). These function-related methylation differences diminished in the drug use group due to the selective alteration in the NAcc. Then, we found the correlation between the methylation levels and the chronological ages in the nonusers group (R2 = 0.34 in the NAcc and 0.37 in the PFC). The epigenetic clocks in illicit drug users, especially in the ketamine users, were accelerated in both brain regions by comparison with the nonusers. Finally, we summarized the effect of the illicit drugs on the methylation, which could significantly differentiate the drug users from the nonusers (AUC = 0.88 in the NAcc, AUC = 0.94 in the PFC). CONCLUSION: The mitochondrial methylations were different between different brain areas, generally accumulated with aging, and sensitive to the effects of illicit drugs. We believed this is the first report to elucidate comprehensively the importance of mitochondrial DNA methylation in human brain.