Photophobia Associated with Traumatic Brain Injury: A Systematic Review and Meta-analysis.

SIGNIFICANCE: This study reports the prevalence and relative risk of photophobia in patients with traumatic brain injury (TBI). OBJECTIVES: This study aimed to conduct a systematic review and meta-analysis to determine the prevalence and relative risk of photophobia in patients with TBI. DATA SOURCES: Three databases were used for literature search: PubMed, EMBASE, and Cochrane Library. STUDY APPRAISAL AND SYNTHESIS METHODS: Publications reporting the prevalence of photophobia after TBI in patients of any age were included. A series of meta-regression analyses based on a generalized linear mixed model was performed to identify potential sources of heterogeneity in the prevalence estimates. RESULTS: Seventy-five eligible publications were identified. The prevalence of photophobia was 30.46% (95% confidence interval [CI], 20.05 to 40.88%) at 1 week after the injury. Prevalence decreased to 19.34% (95% CI, 10.40 to 28.27%) between 1 week and 1 month after TBI and to 13.51% (95% CI, 5.77 to 21.24%) between 1 and 3 months after the injury. The rapid decrease in the prevalence of photophobia in the first 3 months after a TBI injury was significant (P < .001). Three months post-TBI, the prevalence of photophobia leveled off to a near plateau with nonsignificant variability, increasing between 3 and 6 months (17.68%; 95% CI, 9.05 to 26.32%) and decreasing between 6 and 12 months since TBI (14.85%; 95% CI, 6.80 to 22.90%). Subgroup analysis of 14 publications that contained control data showed that the estimated risk ratio for photophobia was significantly higher in the TBI than in the control group during the entire 12 months after TBI. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: This study demonstrates that photophobia is a frequent complaint after TBI, which largely resolves for many individuals within 3 months after the injury. For some patients, however, photophobia can last up to 12 months and possibly longer. Developing an objective quantitative methodology for measuring photophobia, validating a dedicated photophobia questionnaire, and having a specific photophobia International Classification of Diseases, Tenth Revision code would greatly improve data gathering and analysis.

Adaptive local false discovery rate procedures for highly spiky data and their application RNA sequencing data of yeast SET4 deletion mutants.

Chromatin dynamics are central to the regulation of gene expression and genome stability. In order to improve understanding of the factors regulating chromatin dynamics, the genes encoding these factors are deleted and the differential gene expression profiles are determined using approaches such as RNA sequencing. Here, we analyzed a gene expression dataset aimed at uncovering the function of the relatively uncharacterized chromatin regulator, Set4, in the model system Saccharomyces cerevisiae (budding yeast). The main theme of this paper focuses on identifying the highly differentially expressed genes in cells deleted for Set4 (referred to as Set4 Δ mutant dataset) compared to the wild-type yeast cells. The Set4 Δ mutant data produce a spiky distribution on the log-fold changes of their expressions, and it is reasonably assumed that genes which are not highly differentially expressed come from a mixture of two normal distributions. We propose an adaptive local false discovery rate (FDR) procedure, which estimates the null distribution of the log-fold changes empirically. We numerically show that, unlike existing approaches, our proposed method controls FDR at the aimed level (0.05) and also has competitive power in finding differentially expressed genes. Finally, we apply our procedure to analyzing the Set4 Δ mutant dataset.

Visual Deficits and Dysfunctions Associated with Traumatic Brain Injury: A Systematic Review and Meta-analysis.

SIGNIFICANCE: This study reports prevalence data combined independently for accommodative dysfunction, convergence insufficiency, visual field loss, and visual acuity loss in patients with traumatic brain injury in the absence of eye injury. OBJECTIVE: The objective of this study was to conduct a systematic review and meta-analysis to determine the prevalence rates of accommodative dysfunction, convergence insufficiency, visual field loss, and visual acuity loss in TBI patients without concomitant eye injury. DATA SOURCES: The data sources used in this study were PubMed, EMBASE, EBSCO, and Cochrane Library. STUDY APPRAISAL AND SYNTHESIS METHODS: Publications reporting the prevalence of diagnosed accommodative dysfunction, convergence insufficiency, visual field loss, or visual acuity loss to the level of legal blindness in TBI patients of any age were included. Univariate metaregression analyses and subgroup analyses were performed to account for statistical heterogeneity. RESULTS: Twenty-two eligible publications were identified across the four visual conditions. Random-effects models yielded combined prevalence estimates: accommodative dysfunction (42.8; 95% confidence interval [CI], 31.3 to 54.7), convergence insufficiency (36.3%; 95% CI, 28.2 to 44.9%), visual field loss (18.2%; 95% CI, 10.6 to 27.1%), and visual acuity loss (0.0%; 95% CI, 0.0 to 1.1%). Metaregression and subgroup analyses revealed that visual field loss was significantly more prevalent in moderate to severe (39.8%; 95% CI, 29.8 to 50.3%) compared with mild TBI (6.6%; 95% CI, 0 to 19.5%). CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: This study demonstrates that accommodative dysfunction, convergence insufficiency, and visual field loss are common sequelae of TBI. Prospective longitudinal research with rigorous and uniform methodology is needed to better understand short- and long-term effects of TBI on the vision system.

Oncodomains: A protein domain-centric framework for analyzing rare variants in tumor samples.

The fight against cancer is hindered by its highly heterogeneous nature. Genome-wide sequencing studies have shown that individual malignancies contain many mutations that range from those commonly found in tumor genomes to rare somatic variants present only in a small fraction of lesions. Such rare somatic variants dominate the landscape of genomic mutations in cancer, yet efforts to correlate somatic mutations found in one or few individuals with functional roles have been largely unsuccessful. Traditional methods for identifying somatic variants that drive cancer are 'gene-centric' in that they consider only somatic variants within a particular gene and make no comparison to other similar genes in the same family that may play a similar role in cancer. In this work, we present oncodomain hotspots, a new 'domain-centric' method for identifying clusters of somatic mutations across entire gene families using protein domain models. Our analysis confirms that our approach creates a framework for leveraging structural and functional information encapsulated by protein domains into the analysis of somatic variants in cancer, enabling the assessment of even rare somatic variants by comparison to similar genes. Our results reveal a vast landscape of somatic variants that act at the level of domain families altering pathways known to be involved with cancer such as protein phosphorylation, signaling, gene regulation, and cell metabolism. Due to oncodomain hotspots' unique ability to assess rare variants, we expect our method to become an important tool for the analysis of sequenced tumor genomes, complementing existing methods.

Empirical null estimation using zero-inflated discrete mixture distributions and its application to protein domain data.

In recent mutation studies, analyses based on protein domain positions are gaining popularity over gene-centric approaches since the latter have limitations in considering the functional context that the position of the mutation provides. This presents a large-scale simultaneous inference problem, with hundreds of hypothesis tests to consider at the same time. This article aims to select significant mutation counts while controlling a given level of Type I error via False Discovery Rate (FDR) procedures. One main assumption is that the mutation counts follow a zero-inflated model in order to account for the true zeros in the count model and the excess zeros. The class of models considered is the Zero-inflated Generalized Poisson (ZIGP) distribution. Furthermore, we assumed that there exists a cut-off value such that smaller counts than this value are generated from the null distribution. We present several data-dependent methods to determine the cut-off value. We also consider a two-stage procedure based on screening process so that the number of mutations exceeding a certain value should be considered as significant mutations. Simulated and protein domain data sets are used to illustrate this procedure in estimation of the empirical null using a mixture of discrete distributions. Overall, while maintaining control of the FDR, the proposed two-stage testing procedure has superior empirical power.

The Moderated Mediating Effect of Self-Efficacy on Exercise Among Older Adults in an Online Bone Health Intervention Study: A Parallel Process Latent Growth Curve Model.

This secondary data analyses of a longitudinal study assessed whether self-efficacy for exercise (SEE) mediated online intervention effects on exercise among older adults and whether age (50-64 vs. ≥65 years) moderated the mediation. Data were from an online bone health intervention study. Eight hundred sixty-six older adults (≥50 years) were randomized to three arms: Bone Power (n = 301), Bone Power Plus (n = 302), or Control (n = 263). Parallel process latent growth curve modeling (LGCM) was used to jointly model growths in SEE and in exercise and to assess the mediating effect of SEE on the effect of intervention on exercise. SEE was a significant mediator in 50- to 64-year-old adults (0.061, 95 BCI: 0.011, 0.163) but not in the ≥65 age group (-0.004, 95% BCI: -0.047, 0.025). Promotion of SEE is critical to improve exercise among 50- to 64-year-olds.

Testing independence of bivariate interval-censored data using modified Kendall's tau statistic.

In this paper, we study a nonparametric procedure to test independence of bivariate interval censored data; for both current status data (case 1 interval-censored data) and case 2 interval-censored data. To do it, we propose a score-based modification of the Kendall's tau statistic for bivariate interval-censored data. Our modification defines the Kendall's tau statistic with expected numbers of concordant and disconcordant pairs of data. The performance of the modified approach is illustrated by simulation studies and application to the AIDS study. We compare our method to alternative approaches such as the two-stage estimation method by Sun et al. (Scandinavian Journal of Statistics, 2006) and the multiple imputation method by Betensky and Finkelstein (Statistics in Medicine, 1999b).

A protein domain-centric approach for the comparative analysis of human and yeast phenotypically relevant mutations.

BACKGROUND: The body of disease mutations with known phenotypic relevance continues to increase and is expected to do so even faster with the advent of new experimental techniques such as whole-genome sequencing coupled with disease association studies. However, genomic association studies are limited by the molecular complexity of the phenotype being studied and the population size needed to have adequate statistical power. One way to circumvent this problem, which is critical for the study of rare diseases, is to study the molecular patterns emerging from functional studies of existing disease mutations. Current gene-centric analyses to study mutations in coding regions are limited by their inability to account for the functional modularity of the protein. Previous studies of the functional patterns of known human disease mutations have shown a significant tendency to cluster at protein domain positions, namely position-based domain hotspots of disease mutations. However, the limited number of known disease mutations remains the main factor hindering the advancement of mutation studies at a functional level. In this paper, we address this problem by incorporating mutations known to be disruptive of phenotypes in other species. Focusing on two evolutionarily distant organisms, human and yeast, we describe the first inter-species analysis of mutations of phenotypic relevance at the protein domain level. RESULTS: The results of this analysis reveal that phenotypic mutations from yeast cluster at specific positions on protein domains, a characteristic previously revealed to be displayed by human disease mutations. We found over one hundred domain hotspots in yeast with approximately 50% in the exact same domain position as known human disease mutations. CONCLUSIONS: We describe an analysis using protein domains as a framework for transferring functional information by studying domain hotspots in human and yeast and relating phenotypic changes in yeast to diseases in human. This first-of-a-kind study of phenotypically relevant yeast mutations in relation to human disease mutations demonstrates the utility of a multi-species analysis for advancing the understanding of the relationship between genetic mutations and phenotypic changes at the organismal level.

Domain landscapes of somatic mutations in cancer.

BACKGROUND: Large-scale tumor sequencing projects are now underway to identify genetic mutations that drive tumor initiation and development. Most studies take a gene-based approach to identifying driver mutations, highlighting genes mutated in a large percentage of tumor samples as those likely to contain driver mutations. However, this gene-based approach usually does not consider the position of the mutation within the gene or the functional context the position of the mutation provides. Here we introduce a novel method for mapping mutations to distinct protein domains, not just individual genes, in which they occur, thus providing the functional context for how the mutation contributes to disease. Furthermore, aggregating mutations from all genes containing a specific protein domain enables the identification of mutations that are rare at the gene level, but that occur frequently within the specified domain. These highly mutated domains potentially reveal disruptions of protein function necessary for cancer development. RESULTS: We mapped somatic mutations from the protein coding regions of 100 colon adenocarcinoma tumor samples to the genes and protein domains in which they occurred, and constructed topographical maps to depict the "mutational landscapes" of gene and domain mutation frequencies. We found significant mutation frequency in a number of genes previously known to be somatically mutated in colon cancer patients including APC, TP53 and KRAS. In addition, we found significant mutation frequency within specific domains located in these genes, as well as within other domains contained in genes having low mutation frequencies. These domain "peaks" were enriched with functions important to cancer development including kinase activity, DNA binding and repair, and signal transduction. CONCLUSIONS: Using our method to create the domain landscapes of mutations in colon cancer, we were able to identify somatic mutations with high potential to drive cancer development. Interestingly, the majority of the genes involved have a low mutation frequency. Therefore, the method shows good potential for identifying rare driver mutations in current, large-scale tumor sequencing projects. In addition, mapping mutations to specific domains provides the necessary functional context for understanding how the mutations contribute to the disease, and may reveal novel or more refined gene and domain target regions for drug development.

Semiparametric transformation models for multivariate panel count data with dependent observation process.

This article discusses regression analysis of multivariate panel count data in which the observation process may contain relevant information about or be related to the underlying recurrent event processes of interest. Such data occur if a recurrent event study involves several related types of recurrent events and the observation scheme or process may be subject-specific. For the problem, a class of semiparametric transformation models is presented, which provides a great flexibility for modelling the effects of covariates on the recurrent event processes. For estimation of regression parameters, an estimating equation-based inference procedure is developed and the asymptotic properties of the resulting estimates are established. Also the proposed approach is evaluated by simulation studies and applied to the data arising from a skin cancer chemoprevention trial.

Set4 regulates stress response genes and coordinates histone deacetylases within yeast subtelomeres.

The yeast chromatin protein Set4 is a member of the Set3-subfamily of SET domain proteins which play critical roles in the regulation of gene expression in diverse developmental and environmental contexts. We previously reported that Set4 promotes survival during oxidative stress and regulates expression of stress response genes via stress-dependent chromatin localization. In this study, global gene expression analysis and investigation of histone modification status identified a role for Set4 in maintaining gene repressive mechanisms within yeast subtelomeres under both normal and stress conditions. We show that Set4 works in a partially overlapping pathway to the SIR complex and the histone deacetylase Rpd3 to maintain proper levels of histone acetylation and expression of stress response genes encoded in subtelomeres. This role for Set4 is particularly critical for cells under hypoxic conditions, where the loss of Set4 decreases cell fitness and cell wall integrity. These findings uncover a new regulator of subtelomeric chromatin that is key to stress defense pathways and demonstrate a function for Set4 in regulating repressive, heterochromatin-like environments.

Repression of Middle Sporulation Genes in Saccharomyces cerevisiae by the Sum1-Rfm1-Hst1 Complex Is Maintained by Set1 and H3K4 Methylation.

The conserved yeast histone methyltransferase Set1 targets H3 lysine 4 (H3K4) for mono, di, and trimethylation and is linked to active transcription due to the euchromatic distribution of these methyl marks and the recruitment of Set1 during transcription. However, loss of Set1 results in increased expression of multiple classes of genes, including genes adjacent to telomeres and middle sporulation genes, which are repressed under normal growth conditions because they function in meiotic progression and spore formation. The mechanisms underlying Set1-mediated gene repression are varied, and still unclear in some cases, although repression has been linked to both direct and indirect action of Set1, associated with noncoding transcription, and is often dependent on the H3K4me2 mark. We show that Set1, and particularly the H3K4me2 mark, are implicated in repression of a subset of middle sporulation genes during vegetative growth. In the absence of Set1, there is loss of the DNA-binding transcriptional regulator Sum1 and the associated histone deacetylase Hst1 from chromatin in a locus-specific manner. This is linked to increased H4K5ac at these loci and aberrant middle gene expression. These data indicate that, in addition to DNA sequence, histone modification status also contributes to proper localization of Sum1 Our results also show that the role for Set1 in middle gene expression control diverges as cells receive signals to undergo meiosis. Overall, this work dissects an unexplored role for Set1 in gene-specific repression, and provides important insights into a new mechanism associated with the control of gene expression linked to meiotic differentiation.

Frontline Science: Myeloid-derived suppressor cells (MDSCs) facilitate maternal-fetal tolerance in mice.

During successful pregnancy, a woman is immunologically tolerant of her genetically and antigenically disparate fetus, a state known as maternal-fetal tolerance. How this state is maintained has puzzled investigators for more than half a century. Diverse, immune and nonimmune mechanisms have been proposed; however, these mechanisms appear to be unrelated and to act independently. A population of immune suppressive cells called myeloid-derived suppressor cells (MDSCs) accumulates in pregnant mice and women. Given the profound immune suppressive function of MDSCs, it has been suggested that this cell population may facilitate successful pregnancy by contributing to maternal-fetal tolerance. We now report that myeloid cells with the characteristics of MDSCs not only accumulate in the circulation and uterus of female mice following mating but also suppress T cell activation and function in pregnant mice. Depletion of cells with the phenotype and function of MDSCs from gestation d 0.5 through d 7.5 resulted in implantation failure, increased T cell activation, and increased T cell infiltration into the uterus, whereas induction of MDSCs restored successful pregnancy and reduced T cell activation. MDSC-mediated suppression during pregnancy was accompanied by the down-regulation of L-selectin on naïve T cells and a reduced ability of naïve T cells to enter lymph nodes and become activated. Because MDSCs regulate many of the immune and nonimmune mechanisms previously attributed to maternal-fetal tolerance, MDSCs may be a unifying mechanism promoting maternal-fetal tolerance, and their induction may facilitate successful pregnancy in women who spontaneously abort or miscarry because of dysfunctional maternal-fetal tolerance.

The histone methyltransferases Set5 and Set1 have overlapping functions in gene silencing and telomere maintenance.

Genes adjacent to telomeres are subject to transcriptional repression mediated by an integrated set of chromatin modifying and remodeling factors. The telomeres of Saccharomyces cerevisiae have served as a model for dissecting the function of diverse chromatin proteins in gene silencing, and their study has revealed overlapping roles for many chromatin proteins in either promoting or antagonizing gene repression. The H3K4 methyltransferase Set1, which is commonly linked to transcriptional activation, has been implicated in telomere silencing. Set5 is an H4 K5, K8, and K12 methyltransferase that functions with Set1 to promote repression at telomeres. Here, we analyzed the combined role for Set1 and Set5 in gene expression control at native yeast telomeres. Our data reveal that Set1 and Set5 promote a Sir protein-independent mechanism of repression that may primarily rely on regulation of H4K5ac and H4K8ac at telomeric regions. Furthermore, cells lacking both Set1 and Set5 have highly correlated transcriptomes to mutants in telomere maintenance pathways and display defects in telomere stability, linking their roles in silencing to protection of telomeres. Our data therefore provide insight into and clarify potential mechanisms by which Set1 contributes to telomere silencing and shed light on the function of Set5 at telomeres.

ROC Estimation from Clustered Data with an Application to Liver Cancer Data.

In this article, we propose a regression model to compare the performances of different diagnostic methods having clustered ordinal test outcomes. The proposed model treats ordinal test outcomes (an ordinal categorical variable) as grouped-survival time data and uses random effects to explain correlation among outcomes from the same cluster. To compare different diagnostic methods, we introduce a set of covariates indicating diagnostic methods and compare their coefficients. We find that the proposed model defines a Lehmann family and can also introduce a location-scale family of a receiver operating characteristic (ROC) curve. The proposed model can easily be estimated using standard statistical software such as SAS and SPSS. We illustrate its practical usefulness by applying it to testing different magnetic resonance imaging (MRI) methods to detect abnormal lesions in a liver.

Mobile Diabetes Intervention for Glycemic Control in 45- to 64-Year-Old Persons With Type 2 Diabetes.

The purpose of this study was to assess effects of a mobile coaching system on glycated hemoglobin (HbA1c) levels in younger versus older patients over 1 year. Participants (n = 118) included adult patients with Type 2 diabetes cared for by community physicians. Intervention patients received mobile phone coaching and individualized web portal. Control patients received usual care. Patients were stratified into two age groups: younger (<55 years) and older (≥ 55 years). The intervention resulted in greater 12-month declines in HbA1c, compared with usual care, for patients in both age groups (p < .0001). Among older patients, HbA1c changed by -1.8% (95% confidence interval [CI] = [-2.4, -1.1]) in the intervention group and -0.3% (95% CI = [-0.9, +0.3]) in the control group. Among younger patients, HbA1c changed by -2.0% (95% CI = [-2.5, -1.5]) in the intervention group and -1.0% (95% CI = [-1.6, -0.4]) in the control group. The mobile health intervention was as effective at managing Type 2 diabetes in older adults as younger persons.

Domain landscapes of somatic mutations in cancer.

The fight against cancer has been hindered by its highly heterogeneous nature. Recent genome-wide sequencing studies have shown that individual malignancies contain many mutations that range from those commonly found in tumor genomes to rare cancer somatic mutations present only in a small fraction of lesions. For instance, the genome of a colorectal cancer in one patient can have somewhere between 50 to 100 somatic mutations, but might share only 2 or 3 mutated genes with colorectal tumor genomes from other patients. Somatic mutations that are frequently found in tumor genomes often play a significant role in tumor development and are thus classified as cancer driver mutations. However, efforts to correlate somatic mutations found in one or few individual tumor genomes with critical functional roles in tumor development have so far been unsuccessful. In this paper, we analyze cancer somatic mutations from lung and other types of cancer patients using a new approach based on aggregation of mutational data at the protein domain level. Our preliminary analysis confirms that our approach creates a framework for leveraging structural genomics and evolution into the analysis of somatic cancer mutations. We found that by incorporating information about classification of proteins and protein sites we are able to detect novel clusters of cancer somatic mutations.

Incorporating molecular and functional context into the analysis and prioritization of human variants associated with cancer.

BACKGROUND AND OBJECTIVE: With recent breakthroughs in high-throughput sequencing, identifying deleterious mutations is one of the key challenges for personalized medicine. At the gene and protein level, it has proven difficult to determine the impact of previously unknown variants. A statistical method has been developed to assess the significance of disease mutation clusters on protein domains by incorporating domain functional annotations to assist in the functional characterization of novel variants. METHODS: Disease mutations aggregated from multiple databases were mapped to domains, and were classified as either cancer- or non-cancer-related. The statistical method for identifying significantly disease-associated domain positions was applied to both sets of mutations and to randomly generated mutation sets for comparison. To leverage the known function of protein domain regions, the method optionally distributes significant scores to associated functional feature positions. RESULTS: Most disease mutations are localized within protein domains and display a tendency to cluster at individual domain positions. The method identified significant disease mutation hotspots in both the cancer and non-cancer datasets. The domain significance scores (DS-scores) for cancer form a bimodal distribution with hotspots in oncogenes forming a second peak at higher DS-scores than non-cancer, and hotspots in tumor suppressors have scores more similar to non-cancers. In addition, on an independent mutation benchmarking set, the DS-score method identified mutations known to alter protein function with very high precision. CONCLUSION: By aggregating mutations with known disease association at the domain level, the method was able to discover domain positions enriched with multiple occurrences of deleterious mutations while incorporating relevant functional annotations. The method can be incorporated into translational bioinformatics tools to characterize rare and novel variants within large-scale sequencing studies.

Input permutation method to detect active voxels in fMRI study.

Correctly identifying voxels or regions of interest (ROI) that actively respond to a given stimulus is often an important objective/step in many functional magnetic resonance imaging (fMRI) studies. In this article, we study a nonparametric method to detect active voxels, which makes minimal assumption about the distribution of blood oxygen level-dependent (BOLD) signals. Our proposal has several interesting features. It uses time lagged correlation to take into account the delay in response to the stimulus, due to hemodynamic variations. We introduce an input permutation method (IPM), a type of block permutation method, to approximate the null distribution of the test statistic. Also, we propose to pool the permutation-derived statistics of preselected voxels for a better approximation to the null distribution. Finally, we control multiple testing error rate using the local false discovery rate (FDR) by Efron [Correlation and large-scale simultaneous hypothesis testing. J Am Stat Assoc 102 (2007) 93-103] and Park et al. [Estimation of empirical null using a mixture of normals and its use in local false discovery rate. Comput Stat Data Anal 55 (2011) 2421-2432] to select the active voxels.