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In a stratified analysis, the results from different strata if homogeneity assumption is met are pooled together to obtain a single summary estimate for the common effect parameter. However, the effect can appear homogeneous across strata using one measure but heterogeneous using another. Consequently, two researchers analyzing the same data can arrive at conflicting conclusions if they use different effect measures. In this paper, the author draws on the sufficient component cause model to develop a stratified-analysis method regarding a particular effect measure, the ‘peril ratio’. When there is no mechanistic interaction between the exposure under study and the stratifying variable (i.e., when they do not work together to complete any sufficient cause), the peril ratio is constant across strata. The author presents formulas for the estimation of such a common peril ratio. Three real data are re-analyzed for illustration. When the data is consistent with peril-ratio homogeneity in a stratified analysis, researchers can use the formulas in this paper to pool the strata. [ABSTRACT FROM AUTHOR]

In genetic association studies, tests for Hardy-Weinberg proportions are often employed as a quality control checking procedure. Missing genotypes are typically discarded prior to testing. In this paper we show that inference for Hardy-Weinberg proportions can be biased when missing values are discarded. We propose to use multiple imputation of missing values in order to improve inference for Hardy-Weinberg proportions. For imputation we employ a multinomial logit model that uses information from allele intensities and/or neighbouring markers. Analysis of an empirical data set of single nucleotide polymorphisms possibly related to colon cancer reveals that missing genotypes are not missing completely at random. Deviation from Hardy-Weinberg proportions is mostly due to a lack of heterozygotes. Inbreeding coefficients estimated by multiple imputation of the missings are typically lowered with respect to inbreeding coefficients estimated by discarding the missings. Accounting for missings by multiple imputation qualitatively changed the results of 10 to 17% of the statistical tests performed. Estimates of inbreeding coefficients obtained by multiple imputation showed high correlation with estimates obtained by single imputation using an external reference panel. Our conclusion is that imputation of missing data leads to improved statistical inference for Hardy-Weinberg proportions. [ABSTRACT FROM AUTHOR]

In microarray data analysis, we are often required to combine several dependent partial test results. To overcome this, many suggestions have been made in previous literature; Tippett’s test and Fisher’s omnibus test are most popular. Both tests have known null distributions when the partial tests are independent. However, for dependent tests, their (even, asymptotic) null distributions are unknown and additional numerical procedures are required. In this paper, we revisited Stouffer’s test based on z-scores and showed its advantage over the two aforementioned methods in the analysis of large-scale microarray data. The combined statistic in Stouffer’s test has a normal distribution with mean 0 from the normality of the z-scores. Its variance can be estimated from the scores of genes in the experiment without an additional numerical procedure. We numerically compared the errors of Stouffer’s test and the two p-value based methods, Tippett’s test and Fisher’s omnibus test. We also analyzed our microarray data to find differentially expressed genes by non-genotoxic and genotoxic carcinogen compounds. Both numerical study and the real application showed that Stouffer’s test performed better than Tippett’s method and Fisher’s omnibus method with additional permutation steps. [ABSTRACT FROM AUTHOR]

Statistical Parametric Mapping (SPM) is the dominant paradigm for mass-univariate analysis of neuroimaging data. More recently, a Bayesian approach termed Posterior Probability Mapping (PPM) has been proposed as an alternative. PPM offers two advantages: (i) inferences can be made about effect size thus lending a precise physiological meaning to activated regions, (ii) regions can be declared inactive. This latter facility is most parsimoniously provided by PPMs based on Bayesian model comparisons. To date these comparisons have been implemented by an Independent Model Optimization (IMO) procedure which separately fits null and alternative models. This paper proposes a more computationally efficient procedure based on Savage-Dickey approximations to the Bayes factor, and Taylor-series approximations to the voxel-wise posterior covariance matrices. Simulations show the accuracy of this Savage-Dickey-Taylor (SDT) method to be comparable to that of IMO. Results on fMRI data show excellent agreement between SDT and IMO for second-level models, and reasonable agreement for first-level models. This Savage-Dickey test is a Bayesian analogue of the classical SPM-F and allows users to implement model comparison in a truly interactive manner. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging (fMRI) is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project, along with the relevant R code. The goal is to give statisticians who would like to pursue research in this area a quick tutorial for programming with fMRI data. References of relevant packages and papers are provided for those interested in more advanced analysis. [ABSTRACT FROM AUTHOR]

Public health surveillance systems provide valuable data for reliable predication of future epidemic events. This paper describes a study that used nine types of infectious disease data collected through a national public health surveillance system in mainland China to evaluate and compare the performances of four time series methods, namely, two decomposition methods (regression and exponential smoothing), autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The data obtained from 2005 to 2011 and in 2012 were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The accuracy of the statistical models in forecasting future epidemic disease proved their effectiveness in epidemiological surveillance. Although the comparisons found that no single method is completely superior to the others, the present study indeed highlighted that the SVMs outperforms the ARIMA model and decomposition methods in most cases. [ABSTRACT FROM AUTHOR]

Obesity is an important and intractable public health problem. In addition to the well-known risk factors of behavior, diet, and genetics, gut microbial communities were recently identified as another possible source of risk and a potential therapeutic target. However, human and animal-model studies have yielded conflicting results about the precise nature of associations between microbiome composition and obesity. In this paper, we use publicly available data from the Human Microbiome Project (HMP) and MetaHIT, both surveys of healthy adults that include obese individuals, plus two smaller studies that specifically examined lean versus obese adults. We find that inter-study variability in the taxonomic composition of stool microbiomes far exceeds differences between lean and obese individuals within studies. Our analyses further reveal a high degree of variability in stool microbiome composition and diversity across individuals. While we confirm the previously published small, but statistically significant, differences in phylum-level taxonomic composition between lean and obese individuals in several cohorts, we find no association between BMI and taxonomic composition of stool microbiomes in the larger HMP and MetaHIT datasets. We explore a range of different statistical techniques and show that this result is robust to the choice of methodology. Differences between studies are likely due to a combination of technical and clinical factors. We conclude that there is no simple taxonomic signature of obesity in the microbiota of the human gut. [ABSTRACT FROM AUTHOR]

The clinical serial interval of an infectious disease is the time between date of symptom onset in an index case and the date of symptom onset in one of its secondary cases. It is a quantity which is commonly collected during a pandemic and is of fundamental importance to public health policy and mathematical modelling. In this paper we present a novel method for calculating the serial interval distribution for a Markovian model of household transmission dynamics. This allows the use of Bayesian MCMC methods, with explicit evaluation of the likelihood, to fit to serial interval data and infer parameters of the underlying model. We use simulated and real data to verify the accuracy of our methodology and illustrate the importance of accounting for household size. The output of our approach can be used to produce posterior distributions of population level epidemic characteristics. [ABSTRACT FROM AUTHOR]

Graph theoretical approaches have successfully revealed abnormality in brain connectivity, in particular, for contrasting patients from healthy controls. Besides the group comparison analysis, a correlational study is also challenging. In studies with patients, for example, finding brain connections that indeed deepen specific symptoms is interesting. The correlational study is also beneficial since it does not require controls, which are often difficult to find, especially for old-age patients with cognitive impairment where controls could also have cognitive deficits due to normal ageing. However, one of the major difficulties in such correlational studies is too conservative multiple comparison correction. In this paper, we propose a novel method for identifying brain connections that are correlated with a specific cognitive behavior by employing cluster-based statistics, which is less conservative than other methods, such as Bonferroni correction, false discovery rate procedure, and extreme statistics. Our method is based on the insight that multiple brain connections, rather than a single connection, are responsible for abnormal behaviors. Given brain connectivity data, we first compute a partial correlation coefficient between every edge and the behavioral measure. Then we group together neighboring connections with strong correlation into clusters and calculate their maximum sizes. This procedure is repeated for randomly permuted assignments of behavioral measures. Significance levels of the identified sub-networks are estimated from the null distribution of the cluster sizes. This method is independent of network construction methods: either structural or functional network can be used in association with any behavioral measures. We further demonstrated the efficacy of our method using patients with subcortical vascular cognitive impairment. We identified sub-networks that are correlated with the disease severity by exploiting diffusion tensor imaging techniques. The identified sub-networks were consistent with the previous clinical findings having valid significance level, while other methods did not assert any significant findings. [ABSTRACT FROM AUTHOR]

In this paper we introduce a new method to expressly use live/corporeal data in quantifying differences of time series data with an underlying limit cycle attractor; and apply it using an example of gait data. Our intention is to identify gait pattern differences between diverse situations and classify them on group and individual subject levels. First we approximated the limit cycle attractors, from which three measures were calculated: δM amounts to the difference between two attractors (a measure for the differences of two movements), δD computes the difference between the two associated deviations of the state vector away from the attractor (a measure for the change in movement variation), and δF, a combination of the previous two, is an index of the change. As an application we quantified these measures for walking on a treadmill under three different conditions: normal walking, dual task walking, and walking with additional weights at the ankle. The new method was able to successfully differentiate between the three walking conditions. Day to day repeatability, studied with repeated trials approximately one week apart, indicated excellent reliability for δM (ICCave > 0.73 with no differences across days; p > 0.05) and good reliability for δD (ICCave  =  0.414 to 0.610 with no differences across days; p > 0.05). Based on the ability to detect differences in varying gait conditions and the good repeatability of the measures across days, the new method is recommended as an alternative to expensive and time consuming techniques of gait classification assessment. In particular, the new method is an easy to use diagnostic tool to quantify clinical changes in neurological patients. [ABSTRACT FROM AUTHOR]

Advances in next-generation sequencing technologies have enabled the identification of multiple rare single nucleotide polymorphisms involved in diseases or traits. Several strategies for identifying rare variants that contribute to disease susceptibility have recently been proposed. An important feature of many of these statistical methods is the pooling or collapsing of multiple rare single nucleotide variants to achieve a reasonably high frequency and effect. However, if the pooled rare variants are associated with the trait in different directions, then the pooling may weaken the signal, thereby reducing its statistical power. In the present paper, we propose a backward support vector machine (BSVM)-based variant selection procedure to identify informative disease-associated rare variants. In the selection procedure, the rare variants are weighted and collapsed according to their positive or negative associations with the disease, which may be associated with common variants and rare variants with protective, deleterious, or neutral effects. This nonparametric variant selection procedure is able to account for confounding factors and can also be adopted in other regression frameworks. The results of a simulation study and a data example show that the proposed BSVM approach is more powerful than four other approaches under the considered scenarios, while maintaining valid type I errors. [ABSTRACT FROM AUTHOR]

Background: The confirmatory diagnosis of Osteogenesis Imperfecta (OI) requires invasive, commonly bone biopsy, time consuming and destructive methods. This paper proposes an alternative method using a combination of two-photon excitation fluorescence (TPEF) and second-harmonic generation (SHG) microscopies from easily obtained human skin biopsies. We show that this method can distinguish subtypes of human OI. Methodology/Principal Findings: Different aspects of collagen microstructure of skin fresh biopsies and standard H&E-stained sections of normal and OI patients (mild and severe forms) were distinguished by TPEF and SHG images. Moreover, important differences between subtypes of OI were identified using different methods of quantification such as collagen density, ratio between collagen and elastic tissue, and gray-level co-occurrence matrix (GLCM) image-pattern analysis. Collagen density was lower in OI dermis, while the SHG/autofluorescence index of the dermis was significantly higher in OI as compared to that of the normal skin. We also showed that the energy value of GLCM texture analysis is useful to discriminate mild from severe OI and from normal skin. Conclusions/Significance: This work demonstrated that nonlinear microscopy techniques in combination with image-analysis approaches represent a powerful tool to investigate the collagen organization in skin dermis in patients with OI and has the potential to distinguish the different types of OI. The procedure outlined in this paper requires a skin biopsy, which is almost painless as compared to the bone biopsy commonly used in conventional methods. The data presented here complement existing clinical diagnostic techniques and can be used as a diagnostic procedure to confirm the disease, evaluate its severity and treatment efficacy. [ABSTRACT FROM AUTHOR]

In second language acquisition research, the critical period hypothesis (cph) holds that the function between learners' age and their susceptibility to second language input is non-linear. This paper revisits the indistinctness found in the literature with regard to this hypothesis's scope and predictions. Even when its scope is clearly delineated and its predictions are spelt out, however, empirical studies–with few exceptions–use analytical (statistical) tools that are irrelevant with respect to the predictions made. This paper discusses statistical fallacies common in cph research and illustrates an alternative analytical method (piecewise regression) by means of a reanalysis of two datasets from a 2010 paper purporting to have found cross-linguistic evidence in favour of the cph. This reanalysis reveals that the specific age patterns predicted by the cph are not cross-linguistically robust. Applying the principle of parsimony, it is concluded that age patterns in second language acquisition are not governed by a critical period. To conclude, this paper highlights the role of confirmation bias in the scientific enterprise and appeals to second language acquisition researchers to reanalyse their old datasets using the methods discussed in this paper. The data and R commands that were used for the reanalysis are provided as supplementary materials. [ABSTRACT FROM AUTHOR]

Methicillin resistant Staphylococcus aureus (MRSA) is currently a major cause of skin and soft tissue infections (SSTI) in the United States. Seasonal variation of MRSA infections in hospital settings has been widely observed. However, systematic time-series analysis of incidence data is desirable to understand the seasonality of community acquired (CA)-MRSA infections at the population level. In this paper, using data on monthly SSTI incidence in children aged 0–19 years and enrolled in Medicaid in Maricopa County, Arizona, from January 2005 to December 2008, we carried out time-series and nonlinear regression analysis to determine the periodicity, trend, and peak timing in SSTI incidence in children at different age: 0–4 years, 5–9 years, 10–14 years, and 15–19 years. We also assessed the temporal correlation between SSTI incidence and meteorological variables including average temperature and humidity. Our analysis revealed a strong annual seasonal pattern of SSTI incidence with peak occurring in early September. This pattern was consistent across age groups. Moreover, SSTIs followed a significantly increasing trend over the 4-year study period with annual incidence increasing from 3.36% to 5.55% in our pediatric population of approximately 290,000. We also found a significant correlation between the temporal variation in SSTI incidence and mean temperature and specific humidity. Our findings could have potential implications on prevention and control efforts against CA-MRSA. [ABSTRACT FROM AUTHOR]

This work aims at identifying a set of humoral immunologic parameters that improve prediction of the activation process in HIV patients. Starting from the well-known impact of humoral immunity in HIV infection, there is still a lack of knowledge in defining the role of the modulation of functional activity and titers of serum antibodies from early stage of infection to the development of AIDS. We propose an integrated approach that combines humoral and clinical parameters in defining the host immunity, implementing algorithms associated with virus control. A number of humoral parameters were simultaneously evaluated in a whole range of serum samples from HIV-positive patients. This issue has been afforded accounting for estimation problems typically related to “feasibility” studies where small sample size in each group and large number of parameters are jointly estimated. We used nonparametric statistical procedures to identify biomarkers in our study which included 42 subjects stratified on five different stages of HIV infection, i.e., Elite Controllers (EC), Long Term Non Progressors (LTNP), HAART, AIDS and Acute Infection (AI). The main goal of the paper is to illustrate a novel profiling method for helping to design a further confirmatory study. A set of seventeen different HIV-specific blood humoral factors were analyzed in all subjects, i.e. IgG and IgA to gp120IIIB, to gp120Bal, to whole gp41, to P1 and T20 gp41 epitopes of the MPER-HR2 region, to QARILAV gp41 epitope of the HR1 region and to CCR5; neutralization activity against five different virus strains and ADCC were also evaluated. Patients were selected on the basis of CD4 cell counts, HIV/RNA and clinical status. The Classification and Regression Trees (CART) approach has been used to uncover specific patterns of humoral parameters in different stages of HIV disease. Virus neutralization of primary virus strains and antibodies to gp41 were required to classify patients, suggesting that clinical profiles strongly rely on functional activity against HIV. [ABSTRACT FROM AUTHOR]

Introduction: Systematic reviewer authors intending to include all randomized participants in their meta-analyses need to make assumptions about the outcomes of participants with missing data. Objective: The objective of this paper is to provide systematic reviewer authors with a relatively simple guidance for addressing dichotomous data for participants excluded from analyses of randomized trials. Methods: This guide is based on a review of the Cochrane handbook and published methodological research. The guide deals with participants excluded from the analysis who were considered ‘non-adherent to the protocol’ but for whom data are available, and participants with missing data. Results: Systematic reviewer authors should include data from ‘non-adherent’ participants excluded from the primary study authors' analysis but for whom data are available. For missing, unavailable participant data, authors may conduct a complete case analysis (excluding those with missing data) as the primary analysis. Alternatively, they may conduct a primary analysis that makes plausible assumptions about the outcomes of participants with missing data. When the primary analysis suggests important benefit, sensitivity meta-analyses using relatively extreme assumptions that may vary in plausibility can inform the extent to which risk of bias impacts the confidence in the results of the primary analysis. The more plausible assumptions draw on the outcome event rates within the trial or in all trials included in the meta-analysis. The proposed guide does not take into account the uncertainty associated with assumed events. Conclusions: This guide proposes methods for handling participants excluded from analyses of randomized trials. These methods can help in establishing the extent to which risk of bias impacts meta-analysis results. [ABSTRACT FROM AUTHOR]

The phenomenon of colour constancy in human visual perception keeps surface colours constant, despite changes in their reflected light due to changing illumination. Although colour constancy has evolved under a constrained subset of illuminations, it is unknown whether its underlying mechanisms, thought to involve multiple components from retina to cortex, are optimised for particular environmental variations. Here we demonstrate a new method for investigating colour constancy using illumination matching in real scenes which, unlike previous methods using surface matching and simulated scenes, allows testing of multiple, real illuminations. We use real scenes consisting of solid familiar or unfamiliar objects against uniform or variegated backgrounds and compare discrimination performance for typical illuminations from the daylight chromaticity locus (approximately blue-yellow) and atypical spectra from an orthogonal locus (approximately red-green, at correlated colour temperature 6700 K), all produced in real time by a 10-channel LED illuminator. We find that discrimination of illumination changes is poorer along the daylight locus than the atypical locus, and is poorest particularly for bluer illumination changes, demonstrating conversely that surface colour constancy is best for blue daylight illuminations. Illumination discrimination is also enhanced, and therefore colour constancy diminished, for uniform backgrounds, irrespective of the object type. These results are not explained by statistical properties of the scene signal changes at the retinal level. We conclude that high-level mechanisms of colour constancy are biased for the blue daylight illuminations and variegated backgrounds to which the human visual system has typically been exposed. [ABSTRACT FROM AUTHOR]

Background: Graphical representation of data is one of the most easily comprehended forms of explanation. The current study describes a simple visualization tool which may allow greater understanding of medical and epidemiological data. Method: We propose a simple tool for visualization of data, known as a “quilt plot”, that provides an alternative to presenting large volumes of data as frequency tables. Data from the Australian Needle and Syringe Program survey are used to illustrate “quilt plots”. Conclusion: Visualization of large volumes of data using “quilt plots” enhances interpretation of medical and epidemiological data. Such intuitive presentations are particularly useful for the rapid assessment of problems in the data which cannot be readily identified by manual review. We recommend that, where possible, “quilt plots” be used along with traditional quantitative assessments of the data as an explanatory data analysis tool. [ABSTRACT FROM AUTHOR]

When 5-lipoxygenase (5-LO) is inhibited, roughly half of the CNS effect of the prototypic endocannabinoid anandamide (AEA) is lost. Therefore, we decided to investigate whether inhibiting this enzyme would influence physiological functions classically described as being under control of the endocannabinoid system. Although 5-LO inhibition by MK-886 reduced lipoxin A4 levels in the brain, no effect was found in the elevated plus maze (EPM), even at the highest possible doses, via i.p. (10 mg/kg,) or i.c.v. (500 pmol/2 µl) routes. Accordingly, no alterations in anxiety-like behavior in the EPM test were observed in 5-LO KO mice. Interestingly, aged mice, which show reduced circulating lipoxin A4 levels, were sensitive to MK-886, displaying an anxiogenic-like state in response to treatment. Moreover, exogenous lipoxin A4 induced an anxiolytic-like profile in the EPM test. Our findings are in line with other reports showing no difference between FLAP KO or 5-LO KO and their control strains in adult mice, but increased anxiety-like behavior in aged mice. We also show for the first time that lipoxin A4 affects mouse behavior. In conclusion, we propose an age-dependent relevancy of endogenous 5-LO derivatives in the modulation of anxiety-like behavior, in addition to a potential for exogenous lipoxin A4 in producing an anxiolytic-like state. [ABSTRACT FROM AUTHOR]

We describe a supervised prediction method for diagnosis of acute myeloid leukemia (AML) from patient samples based on flow cytometry measurements. We use a data driven approach with machine learning methods to train a computational model that takes in flow cytometry measurements from a single patient and gives a confidence score of the patient being AML-positive. Our solution is based on an regularized logistic regression model that aggregates AML test statistics calculated from individual test tubes with different cell populations and fluorescent markers. The model construction is entirely data driven and no prior biological knowledge is used. The described solution scored a 100% classification accuracy in the DREAM6/FlowCAP2 Molecular Classification of Acute Myeloid Leukaemia Challenge against a golden standard consisting of 20 AML-positive and 160 healthy patients. Here we perform a more extensive validation of the prediction model performance and further improve and simplify our original method showing that statistically equal results can be obtained by using simple average marker intensities as features in the logistic regression model. In addition to the logistic regression based model, we also present other classification models and compare their performance quantitatively. The key benefit in our prediction method compared to other solutions with similar performance is that our model only uses a small fraction of the flow cytometry measurements making our solution highly economical. [ABSTRACT FROM AUTHOR]

Antigenic characterization based on serological data, such as Hemagglutination Inhibition (HI) assay, is one of the routine procedures for influenza vaccine strain selection. In many cases, it would be impossible to measure all pairwise antigenic correlations between testing antigens and reference antisera in each individual experiment. Thus, we have to combine and integrate the HI tables from a number of individual experiments. Measurements from different experiments may be inconsistent due to different experimental conditions. Consequently we will observe a matrix with missing data and possibly inconsistent measurements. In this paper, we develop a new mathematical model, which we refer to as Joint Matrix Completion and Filtering, for HI data integration. In this approach, we simultaneously handle the incompleteness and uncertainty of observations by assuming that the underlying merged HI data matrix has low rank, as well as carefully modeling different levels of noises in each individual table. An efficient blockwise coordinate descent procedure is developed for optimization. The performance of our approach is validated on synthetic and real influenza datasets. The proposed joint matrix completion and filtering model can be adapted as a general model for biological data integration, targeting data noises and missing values within and across experiments. [ABSTRACT FROM AUTHOR]

The objectives of this paper are to predict life years lost associated with obesity-related diseases (ORDs) for U.S. non-smoking adults, and to examine the relationship between those ORDs and mortality. Data from the National Health Interview Survey, 1997–2000, were used. We employed mixed proportional hazard models to estimate the association between those ORDs and mortality and used simulations to project life years lost associated with the ORDs. We found that obesity-attributable comorbidities are associated with large decreases in life years and increases in mortality rates. The life years lost associated with ORDs is more marked for younger adults than older adults, for blacks than whites, for males than females, and for the more obese than the less obese. Using U.S. non-smoking adults aged 40 to 49 years as an example to illustrate percentage of the life years lost associated with ORDs, we found that the mean life years lost associated with ORDs for U.S. non-smoking black males aged 40 to 49 years with a body mass index above 40 kg/m2 was 5.43 years, which translates to a 7.5% reduction in total life years. White males of the same age range and same degree of obesity lost 5.23 life years on average – a 6.8% reduction in total life years, followed by black females (5.04 years, a 6.5% reduction in life years), and white females (4.7 years, a 5.8% reduction in life years). Overall, ORDs increased chances of dying and lessened life years by 0.2 to 11.7 years depending on gender, race, BMI classification, and age. [ABSTRACT FROM AUTHOR]

Several approaches have been proposed for near real-time detection and prediction of the spread of influenza. These include search query data for influenza-related terms, which has been explored as a tool for augmenting traditional surveillance methods. In this paper, we present a method that uses Internet search query data from Baidu to model and monitor influenza activity in China. The objectives of the study are to present a comprehensive technique for: (i) keyword selection, (ii) keyword filtering, (iii) index composition and (iv) modeling and detection of influenza activity in China. Sequential time-series for the selected composite keyword index is significantly correlated with Chinese influenza case data. In addition, one-month ahead prediction of influenza cases for the first eight months of 2012 has a mean absolute percent error less than 11%. To our knowledge, this is the first study on the use of search query data from Baidu in conjunction with this approach for estimation of influenza activity in China. [ABSTRACT FROM AUTHOR]

Background: An opioid peptide neuron/humoral feedback regulation might be involved in changes of intraocular pressure (IOP). The aims of this study are to investigate the effects of arcuate nucleus (ARC) and opioid peptides on intraocular pressure (IOP). Methods: Fifty-four healthy purebred New Zealand white rabbits (108eyes) were randomly divided into 4 groups, including control group, electrical stimulation group, [D-Ala2, N-Me-Phe4, Gly5-ol]-enkephalin (DAMGO) group, and [D-Pen 2, D-Pen5]- enkephalin (DPDPE) group. Bilateral IOP was measured after unilateral electrical stimulation of the ARC or unilateral microinjection into the ARC of the selective μ-opioid receptor agonist DAMGO or the selective δ opioid receptor agonist DPDPE, both alone and after pre-administration of either the non-selective opioid receptor antagonist naloxone or saline. Results: Both electrical stimulation in ARC and micro-injection either or opioid receptor agonists, DAMGO or DPDPE, respectively, caused a significant bilateral reduction in IOP (P<0.05) which was more pronounced in the ipsilateral than in the contralateral eye. Pretreatment with naloxone prevented some, but not all IOP reductions. Conclusion: The ARC takes part in the negative regulation of IOP, an action that may involve opioid neurons. [ABSTRACT FROM AUTHOR]

Evidence suggests that individual variability in lifetime exposures influences how cognitive performance changes with advancing age. Brain maintenance and cognitive reserve are theories meant to account for preserved performance despite advancing age. These theories differ in their causal mechanisms. Brain maintenance predicts more advantageous lifetime exposures will reduce age-related neural differences. Cognitive reserve predicts that lifetime exposures will not directly reduce these differences but minimize their impact on cognitive performance. The present work used moderated-mediation modeling to investigate the contributions of these mechanisms at explaining variability in cognitive performance among a group of 39 healthy younger (mean age (standard deviation) 25.9 (2.92) and 45 healthy older adults (65.2 (2.79)). Cognitive scores were computed using composite measures from three separate domains (speed of processing, fluid reasoning, and memory), while their lifetime exposures were estimated using education and verbal IQ measures. T1-weighted MR images were used to measure cortical thickness and subcortical volumes. Results suggest a stronger role for cognitive reserve mechanisms in explaining age-related cognitive variability: even with age-related reduced gray matter, individuals with greater lifetime exposures could perform better given their quantity of brain measures. [ABSTRACT FROM AUTHOR]

Tuberculosis still remains a major public health problem even though it is treatable and curable. Weight gain measurement during anti tuberculosis (TB) treatment period is an important component to assess the progress of TB patients. In this study, Latent Growth Models (LGMs) were implemented in a longitudinal design to predict the change in weight of TB patients who were given three different regimens under randomized controlled clinical trial for anti-TB treatment. Linear and Quadratic LGMs were fitted using Mplus software. The age, sex and treatment response of the TB patients were used as time invariant independent variables of the growth trajectories. The quadratic trend was found to be better in explaining the changes in weight without grouping than the quadratic model for three group comparisons. A significant increase in the change of weight over time was identified while a significant quadratic effect indicated that weights were sustained over time. The growth rate was similar in both the groups. The treament response had significant association with the growth rate of weight scores of the patients. [ABSTRACT FROM AUTHOR]

Objective: To project the HIV/AIDS epidemics among men who have sex with men (MSM) under different combinations of HIV testing and linkage to care (TLC) interventions including antiretroviral therapy (ART) in Beijing, China. Design: Mathematical modeling. Methods: Using a mathematical model to fit prevalence estimates from 2000–2010, we projected trends in HIV prevalence and incidence during 2011–2020 under five scenarios: (S1) current intervention levels by averaging 2000–2010 coverage; (S2) increased ART coverage with current TLC; (S3) increased TLC/ART coverage; (S4) increased condom use; and (S5) increased TLC/ART plus increased condom use. Results: The basic reproduction number based upon the current level of interventions is significantly higher than 1 ( confidence interval (CI), 1.83–2.35), suggesting that the HIV epidemic will continue to increase to 2020. Compared to the 2010 prevalence of 7.8%, the projected HIV prevalence in 2020 for the five prevention scenarios will be: (S1) Current coverage: 21.4% (95% CI, 9.9–31.7%); (S2) Increased ART: 19.9% (95% CI, 9.9–28.4%); (S3) Increased TLC/ART: 14.5% (95% CI, 7.0–23.8%); (S4) Increased condom use: 13.0% (95% CI, 9.8–28.4%); and (S5) Increased TLC/ART and condom use: 8.7% (95% CI, 5.4–11.5%). HIV epidemic will continue to rise () for S1–S4 even with hyperbolic coverage in the sensitivity analysis, and is expected to decline () for S5. Conclusion: Our transmission model suggests that Beijing MSM will have a rapidly rising HIV epidemic. Even enhanced levels of TLC/ART will not interrupt epidemic expansion, despite optimistic assumptions for coverage. Promoting condom use is a crucial component of combination interventions. [ABSTRACT FROM AUTHOR]

Background: End-Stage Renal Disease (ESRD) is a worldwide public health problem. Currently, many genome-wide association studies have suggested a potential association between human leukocyte antigen (HLA) and ESRD by uncovering a causal relationship between HLA and glomerulonephritis. However, previous studies, which investigated the HLA polymorphism and its association with ESRD, were performed with the modest data sets and thus might be limited. On the other hand, few researches were conducted to tackle the Chinese population with ESRD. Therefore, this study aims to detect the susceptibilities of HLA polymorphism to ESRD within the Cantonese community, a representative southern population of China. Methods: From the same region, 4541 ESRD patients who were waiting for kidney transplantation and 3744 healthy volunteer bone marrow donors (controls) were randomly chosen for this study. Polymerase chain reaction-sequence specific primer method was used to analyze the HLA polymorphisms (including HLA-A, HLA-B and HLA-DRB1 loci) in both ESRD patients and controls. The frequencies of alleles at these loci and haplotypes were compared between ESRD patients and controls. Results: A total of 88 distinct HLA alleles and 1361 HLA A-B-DRB1 haplotypes were detected. The frequencies of five alleles, HLA-A*24, HLA-B*55, HLA-B*54, HLA-B*40(60), HLA-DRB1*04, and one haplotype (HLA-A*11-B*27-DRB1*04) in ESRD patients are significantly higher than those in the controls, respectively. Conclusions: Five HLA alleles and one haplotype at the HLA-A, HLA-B and HLA-DRB1 loci appear to be associated with ESRD within the Cantonese population. [ABSTRACT FROM AUTHOR]

Objective: To use multilevel functional principal component analysis to exploit the information inherent in the shape of longitudinally sampled glucose curves during pregnancy, and to analyse the impact of glucose curve characteristics on neonatal birth weight, percentage fat and cord blood C-peptide. Study Design and Setting: A cohort study of healthy, pregnant women (n = 884). They underwent two oral glucose tolerance tests (gestational weeks 14–16 and 30–32), which gave two glucose curves per woman. Results: Glucose values were higher, and peaked later in third trimester than in early pregnancy. The curve characteristic “general glucose level” accounted for 91% of the variation across visits, and 72% within visits. The curve characteristics “timing of postprandial peak”, and “oscillating glucose levels” accounted for a larger part of the variation within visits (15% and 8%), than across visits (7% and <2%). A late postprandial peak during pregnancy, and high general glucose levels in third trimester had significant, positive effects on birth weight (p<0.05). Generally high glucose levels during pregnancy had a significant, positive impact on neonatal percentage fat (p = 0.04). High general glucose level in third trimester had a significant, positive impact on cord blood C-peptide (p = 0.004). Conclusion: Shape information in entire OGTT curves provides significant physiological information of importance for several outcomes, and may contribute to the understanding of the metabolic changes during pregnancy. [ABSTRACT FROM AUTHOR]

Objectives: This study was conducted to generate knowledge useful for developing public health interventions for more effective tuberculosis control in Arkansas. Methods: The study population included 429 culture-confirmed reported cases (January 1, 2004–December 31, 2010). Mycobacterium tuberculosis genotyping data were used to identify cases likely due to recent transmission (clustered) versus reactivation (non-clustered). Poisson regression models estimated average decline rate in incidence over time and assessed the significance of differences between subpopulations. A multinomial logistic model examined differences between clustered and non-clustered incidence. Results: A significant average annual percent decline was found for the overall incidence of culture-confirmed (9%; 95% CI: 5.5%, 16.9%), clustered (6%; 95% CI: 0.5%, 11.6%), and non-clustered tuberculosis cases (12%; 95% CI: 7.6%, 15.9%). However, declines varied among demographic groups. Significant declines in clustered incidence were only observed in males, non-Hispanic blacks, 65 years and older, and the rural population. Conclusions: These findings suggest that the Arkansas tuberculosis control program must target both traditional and non-traditional risk groups for successful tuberculosis elimination. The present study also demonstrates that a thorough analysis of TB trends in different population subgroups of a given geographic region or state can lead to the identification of non-traditional risk factors for TB transmission. Similar studies in other low incidence populations would provide beneficial data for how to control and eventually eliminate TB in the U.S. [ABSTRACT FROM AUTHOR]

Objective: Patients treated for nonfunctioning pituitary macroadenoma (NFMA) with suprasellar extension show disturbed sleep characteristics, possibly related to hypothalamic dysfunction. In addition to hypopituitarism, both structural hypothalamic damage and sleep restriction per se are associated with the metabolic syndrome. However, the prevalence of the metabolic syndrome in patients with NFMA is not well established. Our objective was to study the prevalence and risk factors for (components of) the metabolic syndrome in patients treated for NFMA. Design: The metabolic syndrome (NCEP-ATP III criteria) was studied in an unselected cohort of 145 NFMA patients (aged 26–88yr, 44% female) in long-term remission after treatment, receiving adequate stable hormone replacement for any pituitary deficiencies. The results were compared to population data of 63,995 Dutch inhabitants by standardization (LifeLines cohort study). Results: NFMA patients showed increased risk for reduced HDL-cholesterol (SMR 1.59, 95% CI 1.13–2.11), increased triglyceride levels (SMR 2.31, 95% CI 1.78–2.90) and the metabolic syndrome (SMR 1.60, 95% CI 1.22–2.02), but not for increased blood pressure, waist circumference or hyperglycemia. Preoperative visual field defects independently affected the risk for increased blood pressure (OR 6.5, 95% CI 1.9–22.2), and hypopituitarism was associated with a body mass index - dependent risk for increased waist circumference (OR 1.6, 95% CI 1.2–2.2) and the metabolic syndrome (OR 1.4, 95% CI 1.0–1.9). Conclusions: Patients treated for NFMA are increased at risk for developing the metabolic syndrome, mainly due to decreased HDL-cholesterol and increased triglycerides. Risk factors included hypopituitarism and preoperative visual field defects. Hypothalamic dysfunction may explain the metabolic abnormalities, in addition to intrinsic imperfections of hormone replacement therapy. Additional research is required to explore the relation between derangements in circadian rhythmicity and metabolic syndrome in these patients. [ABSTRACT FROM AUTHOR]

One major drawback of deception detection is its vulnerability to countermeasures, whereby participants wilfully modulate their physiological or neurophysiological response to critical guilt-determining stimuli. One reason for this vulnerability is that stimuli are usually presented slowly. This allows enough time to consciously apply countermeasures, once the role of stimuli is determined. However, by increasing presentation speed, stimuli can be placed on the fringe of awareness, rendering it hard to perceive those that have not been previously identified, hindering the possibility to employ countermeasures. We tested an identity deception detector by presenting first names in Rapid Serial Visual Presentation and instructing participants to lie about their own identity. We also instructed participants to apply a series of countermeasures. The method proved resilient, remaining effective at detecting deception under all countermeasures. [ABSTRACT FROM AUTHOR]

Background: Human endogenous retroviruses (HERVs) are repetitive sequences derived from ancestral germ-line infections by exogenous retroviruses and different HERV families have been integrated in the genome. HERV-Fc1 in chromosome X has been previously associated with multiple sclerosis (MS) in Northern European populations. Additionally, HERV-Fc1 RNA levels of expression have been found increased in plasma of MS patients with active disease. Considering the North-South latitude gradient in MS prevalence, we aimed to evaluate the role of HERV-Fc1on MS risk in three independent Spanish cohorts. Methods: A single nucleotide polymorphism near HERV-Fc1, rs391745, was genotyped by Taqman chemistry in a total of 2473 MS patients and 3031 ethnically matched controls, consecutively recruited from: Northern (569 patients and 980 controls), Central (883 patients and 692 controls) and Southern (1021 patients and 1359 controls) Spain. Our results were pooled in a meta-analysis with previously published data. Results: Significant associations of the HERV-Fc1 polymorphism with MS were observed in two Spanish cohorts and the combined meta-analysis with previous data yielded a significant association [rs391745 C-allele carriers: pM-H = 0.0005; ORM-H (95% CI) = 1.27 (1.11–1.45)]. Concordantly to previous findings, when the analysis was restricted to relapsing remitting and secondary progressive MS samples, a slight enhancement in the strength of the association was observed [pM-H = 0.0003, ORM-H (95% CI) = 1.32 (1.14–1.53)]. Conclusion: Association of the HERV-Fc1 polymorphism rs391745 with bout-onset MS susceptibility was confirmed in Southern European cohorts. [ABSTRACT FROM AUTHOR]

Background: The measurement of activity energy expenditure (AEE) via accelerometry is the most commonly used objective method for assessing human daily physical activity and has gained increasing importance in the medical, sports and psychological science research in recent years. Objective: The purpose of this study was to determine which of the following procedures is more accurate to determine the energy cost during the most common everyday life activities; a single regression or an activity based approach. For this we used a device that utilizes single regression models (GT3X, ActiGraph Manufacturing Technology Inc., FL., USA) and a device using activity-dependent calculation models (move II, movisens GmbH, Karlsruhe, Germany). Material and Methods: Nineteen adults (11 male, 8 female; 30.4±9.0 years) wore the activity monitors attached to the waist and a portable indirect calorimeter (IC) as reference measure for AEE while performing several typical daily activities. The accuracy of the two devices for estimating AEE was assessed as the mean differences between their output and the reference and evaluated using Bland-Altman analysis. Results: The GT3X overestimated the AEE of walking (GT3X minus reference, 1.26 kcal/min), walking fast (1.72 kcal/min), walking up−/downhill (1.45 kcal/min) and walking upstairs (1.92 kcal/min) and underestimated the AEE of jogging (−1.30 kcal/min) and walking upstairs (−2.46 kcal/min). The errors for move II were smaller than those for GT3X for all activities. The move II overestimated AEE of walking (move II minus reference, 0.21 kcal/min), walking up−/downhill (0.06 kcal/min) and stair walking (upstairs: 0.13 kcal/min; downstairs: 0.29 kcal/min) and underestimated AEE of walking fast (−0.11 kcal/min) and jogging (−0.93 kcal/min). Conclusions: Our data suggest that the activity monitor using activity-dependent calculation models is more appropriate for predicting AEE in daily life than the activity monitor using a single regression model. [ABSTRACT FROM AUTHOR]

Chronic subdural hematoma (CSDH) is a relatively common disorder in neurosurgery on elderly patients, though the mechanism that causes the disease remains unclear. Studies have suggested that local anticoagulation and inflammatory changes may be important in its pathogenesis. Most studies have used a basic bivariate statistical analysis to assess complex immunological responses in patients with this disorder, hence a more sophisticated multivariate statistical approach might be warranted. Our objective was to assess the association and correlation between the pro- and anti-inflammatory responses in a cohort of patients with chronic subdural hematoma (n = 57) using an exploratory and confirmatory factor analysis. Thirteen assigned pro-inflammatory (TNF-α, IL-1β, IL-2, IL-2R, IL-6, IL-7, IL-12, IL-15, IL-17, CCL2, CXCL8, CXCL9 and CXCL10) and five assigned anti-inflammatory (IL-1RA, IL-4, IL-5, IL-10 and IL-13) cytokines from blood and hematoma fluid samples were examined. Exploratory factor analysis indicated two major underlying immunological processes expressed by the cytokines in both blood and hematoma fluid, but with a different pattern and particularly regarding the cytokines IL-13, IL-6, IL-4 and TNF-α. Scores from confirmatory factor analysis models exhibited a higher correlation between pro- and anti-inflammatory activities in blood (r  = 0.98) than in hematoma fluid samples (r  = 0.92). However, correlations of inflammatory processes between blood and hematoma fluid samples were lower and non-significant. A structural equation model showed a significant association between increased anti-inflammatory activity in hematoma fluid samples and a lower risk of recurrence, but this relationship was not statistically significant in venous blood samples. Moreover, these findings indicate that anti-inflammatory activities in the hematoma may play a role in the risk of a recurrence of CSDH. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT. Methods and Materials: Quality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3+ xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R2, chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC. Results: Eight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R2 was satisfactory and corresponded well with the expected values. Conclusions: Multivariate NTCP models with LASSO can be used to predict patient-rated xerostomia after IMRT. [ABSTRACT FROM AUTHOR]

Background: Due to the lack of timely access to resources for critical care, strategic use of antiviral drugs is crucial for mitigating the impact of novel influenza viruses with pandemic potential in remote and isolated communities. We sought to evaluate the effect of antiviral treatment and prophylaxis of close contacts in a Canadian remote northern community. Methods: We used an agent-based, discrete-time simulation model for disease spread in a remote community, which was developed as an in-silico population using population census data. Relative and cumulative age-specific attack rates, and the total number of infections in simulated model scenarios were obtained. Results: We found that early initiation of antiviral treatment is more critical for lowering attack rates in a remote setting with a low population-average age compared to an urban population. Our results show that a significant reduction in the relative, age-specific attack rates due to increasing treatment coverage does not necessarily translate to a significant reduction in the overall arrack rate. When treatment coverage varies from low to moderate, targeted prophylaxis has a very limited impact in reducing attack rates and should be offered at a low level (below 10%) to avoid excessive waste of drugs. Conclusions: In contrast to previous work, for conservative treatment coverages, our results do not provide any convincing evidence for the implementation of targeted prophylaxis. The findings suggest that public health strategies in remote communities should focus on the wider availability (higher coverage) and timely distribution of antiviral drugs for treatment of clinically ill individuals. [ABSTRACT FROM AUTHOR]

Antrodia cinnamomea is a traditional healthy food that has been demonstrated to possess anti-inflammatory, antioxidative, and anticacer effects. The purpose of this study was to evaluate whether the ethanolic extract of A. cinnamomea (EEAC) can affect the efflux function of P-glycoprotein (P-gp) and the effect of ABCB1 genetic variants on the interaction between EEAC and P-gp. To investigate the mechanism of this interaction, Flp-In™-293 cells stably transfected with various genotypes of human P-gp were established and the expression of P-gp was confirmed by Western blot. The results of the rhodamine 123 efflux assay demonstrated that EEAC efficiently inhibited wild-type P-gp function at an IC50 concentration of 1.51±0.08 µg/mL through non-competitive inhibition. The IC50 concentrations for variant-type 1236T-2677T-3435T P-gp and variant-type 1236T-2677A-3435T P-gp were 5.56±0.49 µg/mL and 3.33±0.67 µg/mL, respectively. In addition, the inhibition kinetics of EEAC also changed to uncompetitive inhibition in variant-type 1236T-2677A-3435T P-gp. The ATPase assay revealed that EEAC was an ATPase stimulator and was capable of reducing verapamil-induced ATPase levels. These results indicate that EEAC may be a potent P-gp inhibitor and higher dosages may be required in subjects carrying variant-types P-gp. Further studies are required to translate this basic knowledge into clinical applications. [ABSTRACT FROM AUTHOR]

Understanding the impact of multiple genetic variants and their interactions on the disease penetrance of familial multiple prostate cancer is very relevant to the overall understanding of carcinogenesis. We assessed the joint effect of two loci on rs4242382 at 8q24 and rs10486567 at 7p15.2 to this end. We analyzed the data from a Finnish family-based genetic study, which was composed of 947 men including 228 cases in 75 families, to evaluate the respective effects of the two loci on the disease penetrance; in particular, the occurrence and number of prostate cancer cases within a family were utilized to evaluate the interactions between the two loci under the additive and multiplicative Poisson regression models. The risk alleles A at rs4242382 (OR = 1.14, 95% CI 1.08–1.19, P<0.0001) and a risk allele A at rs10486567 (OR = 1.06, 96%CI 1.01–1.11, P = 0.0208) were found to be associated with an increased risk of familial PrCa, especially with four or more cases within a family. A multiplicative model fitted the joint effect better than an additive model (likelihood ratio test X2 = 13.89, P<0.0001). The influence of the risk allele A at rs10486567 was higher in the presence of the risk allele A at rs4242382 (OR = 1.09 (1.01–1.18) vs. 1.01 (0.95–1.07)). Similar findings were observed in non-aggressive PrCa, but not in aggressive PrCa. We demonstrated that two loci (rs4242382 and rs10486567) are highly associated with familial multiple PrCa, and the gene-gene interaction or statistical epistasis was consistent with the Fisher's multiplicative model. These loci's association and epistasis were observed for non-aggressive but not for aggressive tumors. The proposed statistical model can be further developed to accommodate multi-loci interactions to provide further insights into epistasis. [ABSTRACT FROM AUTHOR]