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Objective: Sport-related concussion (SRC) affects cognitive and oculomotor function. We evaluated recovery from SRC in athletes with cognitive symptoms and/or oculomotor impairments who were prescribed early aerobic exercise treatment. Design: Secondary exploratory analysis of a randomized controlled trial. Setting: Outpatient sports medicine clinics. Participants: Male and female adolescents (13–18 years) within 10 days of SRC who were prescribed aerobic exercise and who reported either a low (<12 points) or high (≥12 points) burden of cognitive symptom scores on the Post-Concussion Symptom Inventory at initial assessment. Interventions: Early aerobic exercise treatment. Main Outcome Measures: Recovery time and incidence of persisting postconcussive symptoms beyond 1 month. Results: Adolescents with a high burden of cognitive symptoms (n = 24, 16.0 ± 1.3 years old, 50% male, 5.3 ± 2.4 days since injury) took longer to recover (34.4 ± 37.1 vs 15.3 ± 7.6 days) than adolescents with a low burden (n = 32, 14.9 ± 1.4 years old, 69% male, 6.1 ± 2.3 days since injury) and had 6-fold higher odds of developing persisting postconcussive symptoms (odds ratio = 6.17 [1.15–33.15]). Abnormal repetitive saccades and vestibular ocular reflex were independently associated with longer recovery after controlling for multiple comparisons (P < 0.0125), but only abnormal smooth pursuits were a significant effect modifier for the association between cognitive symptoms and recovery (interaction term hazard ratio = 0.133 [0.035–0.504], P = 0.003). Conclusions: Individuals with impaired ability to smoothly track a moving target and whose cognitive symptoms are a significant burden are at substantial risk for delayed recovery from SRC. Early initiation of oculomotor rehabilitation may facilitate recovery in these patients. Trials Registration: Clinicaltrials.gov ID NCT02959216. [ABSTRACT FROM AUTHOR]

Background/Objective: Assessing Orthostatic Intolerance (OI, symptoms upon standing from supine) is recommended in athletes with sport-related concussions (SRCs), as this is caused by impairments in the cardiovascular autonomic nervous system (cANS). Early Exercise Intolerance (Early EI, symptoms on light physical exertion) is also due to impairments in the cANS but is difficult to incorporate into outpatient clinical practice (cost of personnel, time, equipment). The purpose of this study was to determine if we could use OI to screen for Early EI, as well as understand differences between adolescents who do and do not report OI. Methods: Retrospective chart review. Adolescents with physician-diagnosed SRC performed the 2 min supine to 1 min standing OI test and the Buffalo Concussion Treadmill Test (BCTT) during their first post-SRC visit. Early EI was defined as more-than-mild symptom exacerbation at a heart rate (HR) below 135 bpm on the BCTT; OI was defined as new or increased symptoms of dizziness or lightheadedness on postural change. The sensitivity, specificity and diagnostic accuracy were calculated. Participants with and without OI were compared. Results: In total, 166 adolescents (mean 15.4 years, 58.8% male) were seen a mean of 5.5 days after injury; 48.2% had OI and 52.4% had Early EI, but there was no association between the two measures (Phi = 0.122, p = 0.115). The sensitivity and specificity (with 95% confidence intervals) for OI to screen for Early EI were 54.0% (43.5, 64.3) and 58.2% (47.2, 68.7), respectively. Adolescents with OI had a higher incidence of delayed recovery (24% vs. 9%, p = 0.012). Conclusions: Although both measures seem to be related to impaired autonomic function after SRC, OI has limited accuracy in screening for Early EI, which suggests that their etiologies may be different. Nevertheless, the assessment of OI has clinical utility in the management of SRC. [ABSTRACT FROM AUTHOR]

Primary Sjögren's disease (pSD) is a systemic autoimmune disease that has the strongest female predilection of all autoimmune diseases. The underlying mechanisms that govern this sexual dimorphism, however, remain poorly understood. We hypothesized that pSD females would exhibit more robust disease as compared to males, and that Tlr7 controls distinct disease manifestations in males and females. Using a well-established pSD mouse model, we harvested exocrine glands, and pulmonary and renal tissue from males and females and quantified the inflammation present. We then collected salivary glands, spleens, and cervical lymph nodes and performed flow cytometry to assess immune populations implicated in disease. We also harvested sera to examine total and autoreactive antibodies. Our data revealed that pSD mice displayed sex-biased disease, as pSD females showed decreased dacryoadenitis, but increased nephritis as compared to males. Moreover, females exhibited increased proportions of germinal center B cells and CD4+ activated/memory T cells in the periphery. Additionally, salivary gland immune populations were altered in a sex-dependent manner in pSD. Females with pSD also displayed elevated total and autoreactive IgG as compared to males. Additionally, splenic B cell Tlr7 expression was increased in females. We next generated pSD mice that lacked Tlr7 systemically and found that ablation of Tlr7 was primarily protective in pSD females, while Tlr7-deficient pSD males showed heightened disease. Thus, pSD mice display sex-biased disease and these dichotomous manifestations are governed by Tlr7 activation. This study identifies Tlr7 as a druggable target for pSD, and highlights the importance of studying pSD disease mechanisms in both sexes. [ABSTRACT FROM AUTHOR]

Primary Sjögren's disease (pSD) (also referred to as Sjögren's syndrome) is an autoimmune disease that primarily occurs in women. In addition to exocrine gland dysfunction, pSD patients exhibit B cell hyperactivity. B cell–intrinsic TLR7 activation is integral to the pathogenesis of systemic lupus erythematosus, a disease that shares similarities with pSD. The role of TLR7-mediated B cell activation in pSD, however, remains poorly understood. We hypothesized that age-associated B cells (ABCs) were expanded in pSD and that TLR7-stimulated ABCs exhibited pathogenic features characteristic of disease. Our data revealed that ABC expansion and TLR7 expression were enhanced in a pSD mouse model in a Myd88-dependent manner. Splenocytes from pSD mice showed enhanced sensitivity to TLR7 agonism as compared with those derived from control animals. Sort-purified marginal zone B cells and ABCs from pSD mice showed enhanced inflammatory cytokine secretion and were enriched for antinuclear autoantibodies following TLR7 agonism. Finally, IgG from pSD patient sera showed elevated antinuclear autoantibodies, many of which were secreted preferentially by TLR7-stimulated murine marginal zone B cells and ABCs. These data indicate that pSD B cells are hyperresponsive to TLR7 agonism and that TLR7-activated B cells contribute to pSD through cytokine and autoantibody production. Thus, therapeutics that target TLR7 signaling cascades in B cells may have utility in pSD patients. [ABSTRACT FROM AUTHOR]

This manuscript reviews some recent and popular error control methods for tree structured hypothesis testing. We review a common setting/definition for hypotheses arranged in a tree structure and we discuss two common Type I errors present in multiple testing: family wise error rates (FWERs) and false discovery rate (FDR). We also contrast these methods with a recent development designed to control the false selection rate (FSR). We discuss the algorithms used to implement these error controls and the strategies used to navigate tree structures in light of these errors. We highlight the assumptions necessary in these strategies, summarize the available R software packages to implement these approaches, and show them at work on an example. This article is categorized under:Data: Types and Structure > Image and Spatial DataApplications of Computational Statistics > Genomics/Proteomics/GeneticsData: Types and Structure > Microarrays [ABSTRACT FROM AUTHOR]

Classical continuous goodness-of-fit (GOF) testing is employed for examining whether the data come from an assumed parametric model. In many cases, GOF tests assume a uniform null distribution and examine extreme values of the order statistics of the samples. Many of these statistics can be expressed by a function of the order statistics and the p-values amount to a joint probability statement based on the uniform order statistics. In this paper, we utilize Steck's recursion method and propose two high precision computing algorithms to compute the p-values for these GOF statistics. The numerical difficulties in implementing Steck's method are discussed and compared with solutions provided in high precision libraries. [ABSTRACT FROM AUTHOR]

Abstract Background Numerous gel-based softwares exist to detect protein changes potentially associated with disease. The data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. A particularly important topic is how the various softwares handle missing data. To date, no one has extensively studied the impact that interpolating missing data has on subsequent analysis of protein spots. Results This work highlights the existing algorithms for handling missing data in two-dimensional gel analysis and performs a thorough comparison of the various algorithms and statistical tests on simulated and real datasets. For imputation methods, the best results in terms of root mean squared error are obtained using the least squares method of imputation along with the expectation maximization (EM) algorithm approach to estimate missing values with an array covariance structure. The bootstrapped versions of the statistical tests offer the most liberal option for determining protein spot significance while the generalized family wise error rate (gFWER) should be considered for controlling the multiple testing error. Conclusions In summary, we advocate for a three-step statistical analysis of two-dimensional gel electrophoresis (2-DE) data with a data imputation step, choice of statistical test, and lastly an error control method in light of multiple testing. When determining the choice of statistical test, it is worth considering whether the protein spots will be subjected to mass spectrometry. If this is the case a more liberal test such as the percentile-based bootstrap t can be employed. For error control in electrophoresis experiments, we advocate that gFWER be controlled for multiple testing rather than the false discovery rate.

Abstract Background An estimated 12% of females in the United States will develop breast cancer in their lifetime. Although, there are advances in treatment options including surgery and chemotherapy, breast cancer is still the second most lethal cancer in women. Thus, there is a clear need for better methods to predict prognosis for each breast cancer patient. With the advent of large genetic databases and the reduction in cost for the experiments, researchers are faced with choosing from a large pool of potential prognostic markers from numerous breast cancer gene expression profile studies. Methods Five microarray datasets related to breast cancer were examined using gene set analysis and the cancers were categorized into different subtypes using a scoring system based on genetic pathway activity. Results We have observed that significant genes in the individual studies show little reproducibility across the datasets. From our comparative analysis, using gene pathways with clinical variables is more reliable across studies and shows promise in assessing a patient's prognosis. Conclusions This study concludes that, in light of clinical variables, there are significant gene pathways in common across the datasets. Specifically, several pathways can further significantly stratify patients for survival. These candidate pathways should help to develop a panel of significant biomarkers for the prognosis of breast cancer patients in a clinical setting.

Abstract Background Concomitant with the rise in the popularity of DNA microarrays has been a surge of proposed methods for the analysis of microarray data. Fully controlled "spike-in" datasets are an invaluable but rare tool for assessing the performance of various methods. Results We generated a new wholly defined Affymetrix spike-in dataset consisting of 18 microarrays. Over 5700 RNAs are spiked in at relative concentrations ranging from 1- to 4-fold, and the arrays from each condition are balanced with respect to both total RNA amount and degree of positive versus negative fold change. We use this new "Platinum Spike" dataset to evaluate microarray analysis routes and contrast the results to those achieved using our earlier Golden Spike dataset. Conclusions We present updated best-route methods for Affymetrix GeneChip analysis and demonstrate that the degree of "imbalance" in gene expression has a significant effect on the performance of these methods.

Abstract Background We provide a re-analysis of the Golden Spike dataset, a first generation "spike-in" control microarray dataset. The original analysis of the Golden Spike dataset was presented in a manuscript by Choe et al. and raised questions concerning the performance of several statistical methods for the control of the false discovery rate (across a set of tests for differential expression). These original findings are now in question as it has been reported that the p-values associated with the tests of differential expression for null probesets (i.e., probesets designed to be fold change 1 across the two arms of the experiment) are not uniformly distributed. Two recent publications have speculated as to the reasons the null distributions are non-uniform. A publication by Dabney and Storey concludes that the non-uniform distributions of null p-values are the direct consequence of an experimental design which requires technical replicates to approximate biological replicates. Irizarry et al. identify four characteristics of the feature level data (three related to experimental design and one artifact). Irizarry et al. argue that the four observed characteristics imply that the assumptions common to most pre-processing algorithms are not satisfied and hence the expression measure methodologies considered by Choe et al. are likely to be flawed. Results We replicate and extend the analyses of Dabney and Storey and present our results in the context of a two stage analysis. We provide evidence that the Stage I pre-processing algorithms considered in Dabney and Storey fail to provide expression values that are adequately centered or scaled. Furthermore, we demonstrate that the distributions of the p-values, test statistics, and probabilities associated with the relative locations and variabilities of the Stage II expression values vary with signal intensity. We provide diagnostic plots and a simple logistic regression based test statistic to detect these intensity related defects in the processed data. Conclusion We agree with Dabney and Storey that the null p-values considered in Choe et al. are indeed non-uniform. We also agree with the conclusion that, given current pre-processing technologies, the Golden Spike dataset should not serve as a reference dataset to evaluate false discovery rate controlling methodologies. However, we disagree with the assessment that the non-uniform p-values are merely the byproduct of testing for differential expression under the incorrect assumption that chip data are approximate to biological replicates. Whereas Dabney and Storey attribute the non-uniform p-values to violations of the Stage II model assumptions, we provide evidence that the non-uniformity can be attributed to the failure of the Stage I analyses to correct for systematic biases in the raw data matrix. Although we do not speculate as to the root cause of these systematic biases, the observations made in Irizarry et al. appear to be consistent with our findings. Whereas Irizarry et al. describe the effect of the experimental design on the feature level data, we consider the effect on the underlying multivariate distribution of putative null p-values. We demonstrate that the putative null distributions corresponding to the pre-processing algorithms considered in Choe et al. are all intensity dependent. This dependence serves to invalidate statistical inference based upon standard two sample test statistics. We identify a flaw in the characterization of the appropriate "null" probesets described in Choe et al. and we provide a corrected analysis which reduces (but does not eliminate) the intensity dependent effects.

1. Introduction Pin-tip microarray technology was invented in the early 1990s [1]. The technology has grown tremendously, and now there are numerous types of probes and target elements. Target elements [...], The main focus in pin-tip (or print-tip) microarray analysis is determining which probes, genes, or oligonucleotides are differentially expressed. Specifically in array comparative genomic hybridization (aCGH) experiments, researchers search for chromosomal imbalances in the genome. To model this data, scientists apply statistical methods to the structure of the experiment and assume that the data consist of the signal plus random noise. In this paper we propose 'SmoothArray', a new method to preprocess comparative genomic hybridization (CGH) bacterial artificial chromosome (BAC) arrays and we show the effects on a cancer dataset. As part of our R software package 'aCGHplus,' this freely available algorithm removes the variation due to the intensity effects, pin/print-tip, the spatial location on the microarray chip, and the relative location from the well plate. removal of this variation improves the downstream analysis and subsequent inferences made on the data. Further, we present measures to evaluate the quality of the dataset according to the arrayer pins, 384-well plates, plate rows, and plate columns. We compare our method against competing methods using several metrics to measure the biological signal. With this novel normalization algorithm and quality control measures, the user can improve their inferences on datasets and pinpoint problems that may arise in their BAC aCGH technology.