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Nested case control (NCC) is a sampling method widely used for developing and evaluating risk models with expensive biomarkers on large prospective cohort studies. In a typical NCC design, biomarker values are obtained on a subcohort, where cases consist of all the events (subjects who experience the event during the follow-up). However, when the number of events is not small, due to the cost and limited availability of biospecimen, one may select only a subset of events as cases. We refer to such a variation as the untypical NCC. Unfortunately, existing inverse probability weighted (IPW) estimators for the untypical NCC are biased, and they only focus on relative risk parameters under the proportional hazards (PH) model. In this manuscript, we propose new weighting methods that produce consistent IPW estimators for not only relative risk parameters but also several metrics that evaluate a risk model's predictive performance. We also provide the inference procedure via perturbation resampling, which captures all the variance and between-subject covariance induced by the sampling processes for both case and control selections. In addition, our methods are not limited to the PH model, and they can be applied to the time-specific generalized linear model. Under the typical NCC design, our new weights are equivalent to the weight proposed by Samuelsen; under the untypical NCC, the IPW estimators using our weights have smaller bias and variance than the existing methods. We will demonstrate this improved performance via both analytical and numerical investigations.

The official release of Bluetooth Core Specification, version 5.1 in 2019, provides a new feature for direction determination. It introduces a fine-grained angle measurement capability including Angle of Arriva (AoA) and Angle of Departure, which is deemed essential to the development of Internet of Things applications for localization or relative positioning within the area where Global Navigation Satellite System signals cannot be received. In this paper, we set up a series of experiments to empirically evaluate the direction of the Bluetooth Low Energy transmitter based on the AoA mechanism using the commercialised equipment. The experimental evaluation is performed, for the first time to date, under four different testing environments and other various conditions to inspect the fidelity of angle measurement against the high accuracy given in the specifications. In details, an ideal environment (i.e., anechoic chamber with no multi-path effect), an underground mine, an open area and a typical office area are all tested comprehensively. The experimental results reveal that the ideal environment has the best performance with the minimum error and conforms with the provided datasheet. The worst case occurs on the data collected from the office area. We also find a regular pattern always showing up repeatedly on the error plots based on the measurement results and scrutinize the truthfulness of this rule by adding more innumerable tests conducted in the office. Our results show that the performance of accuracy does depend on the data channel selection due to the multi-path effect.

The Soybean Uptake and Network Survey was administered to a random sample of 832 smallholder male and female farmers in northern Ghana to explore gender and other factors related to soybean production. We investigated the effect of receiving a Soybean Success Kit (i.e., certified seed, fertilizer, inoculum) on soybean yield and income from soybean, controlling for factors such as gender. This analysis includes farmers who 1) resided in districts where Kits were distributed, 2) planted soybean in the past 12 months, and 3) for whom we had complete information for district and gender (n = 371). When results were disaggregated by gender among Kit recipients, average soybean yield (ASY) for males was 108% and average soybean income (ASI) was 97% of that for females. When results were disaggregated by gender among Kit non-recipients, ASY for males was 142% and ASI was 147% of that for females. When results for males were disaggregated by whether the respondent received a Kit, ASY for male Kit recipients was 113% and ASI was 112% of that for male non-recipients. When results for females were disaggregated by whether the respondent received a Kit, ASY for female Kit recipients was 148% and ASI was 170% of that for female non-recipients. These results suggest that providing smallholder female farmers with access to low-cost (˂USD6) input bundles to which they customarily have little or no access can help eliminate the gender gap in agricultural productivity. These results may be applicable to other sub-Saharan Africa countries, where targeting smallholder female farmers as input bundle beneficiaries may positively impact agricultural productivity.

Backoff algorithms are used in many distributed systems where multiple devices contend for a shared resource. For the classic balls-into-bins problem, the number of singletons - those bins with a single ball - is important to the analysis of several backoff algorithms; however, existing analyses employ advanced probabilistic tools to obtain concentration bounds. Here, we show that standard Chernoff bounds can be used instead, and the simplicity of this approach is illustrated by re-analyzing some well-known backoff algorithms.

This paper describes ÉIST, automatic speech recogniser for Irish, developed as part of the ongoing ABAIR initiative, combining (1) acoustic models, (2) pronunciation lexicons and (3) language models into a hybrid system. A priority for now is a system that can deal with the multiple diverse native-speaker dialects. Consequently, (1) was built using predominately native-speaker speech, which included earlier recordings used for synthesis development as well as more diverse recordings obtained using the MíleGlór platform. The pronunciation variation across the dialects is a particular challenge in the development of (2) and is explored by testing both Trans-dialect and Multi-dialect letter-to-sound rules. Two approaches to language modelling (3) are used in the hybrid system, a simple n-gram model and recurrent neural network lattice rescoring, the latter garnering impressive performance improvements. The system is evaluated using a test set that is comprised of both native and non-native speakers, which allows for some inferences to be made on the performance of the system on both cohorts.

Lexicon optimisation strategies, addressing the problem of dialect divergence, are tested in an ASR system for Irish. As in many endangered languages, Irish has no spoken standard, but rather, three very different dialects of Ulster (Ul), Connaught (Co) and Munster (Mu). Furthermore, the complex sound system and ancient, opaque writing system result in sound-to-grapheme mappings that differ considerably across dialects. A hybrid ASR system was trained on (predominantly) native speaker speech data, balanced across the dialects. Experiment 1 tested whether a Global lexicon, which captures dialect variant forms with relatively abstract representations, can perform as well as a Multi-dialect lexicon containing all dialect variants. Three dialect-specific lexicons were also included in the tests. The Global lexicon did yield the best performance and experiment 2 tested whether further reductions to its phoneset might further enhance its performance. These included (i) merging a Tense-Lax contrast among coronal sonorants, not common to all dialects, and (ii) merging the contrast of voiceless-voiced sonorants, as the voiceless member is relatively infrequent. Results showed but a slight enhancement and only for Mu dialect, which is the one most aligned to the phoneset reduction.

A range of lexicons and language models were tested in the development of ASR for Irish. One problem, common among minority languages, is the multiplicity of dialects, with no one spoken standard. To address this challenge, in a hybrid ASR system two alternative cross-dialect lexicons are tested, which draw on research in dialect phonology. First, individual lexicons were built for the three main dialects of Ulster (Ul), Connaught (Co) and Munster (Mu). With these, a Multi-dialect lexicon incorporated all dialect-varying word forms. An alternative Global lexicon, essentially a trans-dialect lexicon, used abstract representations of dialect-varying forms (phoneme or morpheme sized units). These two cross-dialect lexicons were tested along with the three dialect-specific lexicons. Several different language models were also tested. Results for the Global and Multi-dialect lexicons were found to yield the highest performance, with the lowest overall WER for the latter. There were considerable differences in results for the individual dialect lexicons: this may reflect a bias in the datasets used or could be indicators of the linguistic distance between the dialects - competing hypotheses that will need more rigorous testing. Results showed a strong effect of the language model used. Error patterns show frequent substitutions involving inflected forms.

Porcine reproductive and respiratory syndrome virus (PRRSV) causes an economically important global swine disease. Here we report the development of subunit PRRSV-2 vaccines by expressing swine leucocyte antigen (SLA) class I and class II allele-specific epitope antigens in a robust adenovirus vector. SLA I-specific CD8 and SLA II-specific CD4 T cell epitopes of PRRSV-2 NADC20 were predicted in silico. Stable murine leukaemia cell lines (RMA-S), which are TAP-deficient and lacking endogenous class I epitope loading, were established to express different SLA I alleles. The binding stability of PRRSV T cell epitope peptides with SLA I alleles expressed on RMA-S cells was characterized. Two PRRSV poly-T cell epitope peptides were designed. NADC20-PP1 included 39 class I epitopes, consisting of 8 top-ranked epitopes specific to each of 5 SLA I alleles, and fused to 5 class II epitopes specific to SLA II alleles. NADC20-PP2, a subset of PP1, included two top-ranked class I epitopes specific to each of the five SLA I alleles. Two vaccine candidates, Ad-NADC20-PP1 and Ad-NADC20-PP2, were constructed by expressing the polytope peptides in a replication-incompetent human adenovirus 5 vector. A vaccination and challenge study in 30 piglets showed that animals vaccinated with the vaccines had numerically lower gross and histopathology lung lesions, and numerically lower PRRSV RNA loads in lung and serum after challenge compared to the controls, although there was no statistical significance. The results suggested that the Ad-NADC20-PP1 and Ad-NADC20-PP2 vaccines provided little or no protection, further highlighting the tremendous challenges faced in developing an effective subunit PRRSV-2 vaccine.

In this paper, we propose a goodness-of-fit test, named pseudo “in-and-out-of-likelihood” (PIOL) ratio test, to check for misspecification in semi-parametric copula models for univariate time series. The proposed test extends the idea of the IOS test by Presnell and Boos (J Am Stat Assoc 99:216–227, 2004) and PIOS test by Zhang et al. (J Econom, 193:215–233, 2016), which are problematic for direct application to univariate time series. The PIOL test provides an integrated framework for both independent data and time series data. In addition, an approximation method is implemented to alleviate the computational burden of calculating the test statistics. Asymptotic properties of the proposed test statistics are discussed. The finite-sample performance is examined through simulation studies. We also demonstrate the proposed method through the analysis of a time series of daily transactions of Apple trade.

Objective:Women are less likely than men to be arrested for driving under the influence (DUI) of alcohol or another drug, yet their proportion of DUI offenders is growing. Understanding how DUI rec...

Additional file 1 Supplementary material. The supplementary material includes (i) the histograms of the predicted AOF risk obtained from the prescribed-dose model and ovarian-dose model for individuals with and without AOF, respectively, (ii) the procedure of obtaining the true values of the IncV metrics under the distributional assumptions of the numerical study, (iii) the results of the numerical study scenarios in which neither of the working risk models is the true model, including plots of the values of each IncV metric for all the scenarios under different event rates, and the scatter plots of each pair of the IncV metrics, and (iv) the results for the scenarios where the two-marker model is the true model, including plots of the values of each IncV metric for all the scenarios under different event rates, plots of their summary statistics, and a table listing the Pearson correlation of each pair of the IncV metrics. (PDF file)

Chronic hepatitis E virus (HEV) infection is a significant clinical problem in immunocompromised individuals such as organ transplant recipients, although the mechanism remains unknown because of the lack of an animal model. We successfully developed a pig model of chronic HEV infection and examined immune correlates leading to chronicity. The conditions of immunocompromised patients were mimicked by treating pigs with an immunosuppressive regimen including cyclosporine, azathioprine, and prednisolone. Immunocompromised pigs infected with HEV progressed to chronicity, because 8/10 drug-treated HEV-infected pigs continued fecal virus shedding beyond the acute phase of infection, whereas the majority (7/10) of mock-treated HEV-infected pigs cleared fecal viral shedding at 8 wk postinfection. During chronic infection, serum levels of the liver enzyme γ-glutamyl transferase and fecal virus shedding were significantly higher in immunocompromised HEV-infected pigs. To identify potential immune correlates of chronic infection, we determined serum levels of cytokines and cell-mediated immune responses in pigs. Results showed that HEV infection of immunocompromised pigs reduced the serum levels of Th1 cytokines IL-2 and IL-12, and Th2 cytokines IL-4 and IL-10, particularly during the acute phase of infection. Furthermore IFN-γ–specific CD4+ T-cell responses were reduced in immunocompromised pigs during the acute phase of infection, but TNF-α–specific CD8+ T-cell responses increased during the chronic phase of infection. Thus, active suppression of cell-mediated immune responses under immunocompromised conditions may facilitate the establishment of chronic HEV infection. This pig model will aid in delineating the mechanisms of chronic HEV infection and in developing effective therapeutics against chronic hepatitis E.

Women are less likely than men to be arrested for driving under the influence (DUI) of alcohol or another drug, yet their proportion of DUI offenders is growing. Understanding how DUI recidivism risk varies for men and women is of practical utility for DUI assessment and intervention programs. The goals of the current study are to determine if there are different sets of predictors for men and women and whether gender-specific DUI recidivism risk scales perform better than a single recidivism scale for both men and women.We rely on statistically driven techniques to develop gender-specific and total sample recidivism risk scales. We then test the ability of the scales to predict recidivism within 12 months among a large sample (N = 10,827, 22.3% female) of DUI offenders court mandated to a DUI intervention in Mississippi.Predictors of recidivism were drawn from measures of criminal history, substance use disorders, driving behaviors, and accidents. Gender-specific models yielded different sets of recidivism risk factors for men and women, with minimal overlap between the two. Male risk factors were criminal history and heavy alcohol consumption. For women, evidence of a substance use disorder was a unique predictor. Having a prior DUI arrest, driving behaviors, and a physical health consequence of alcohol or drug use (i.e., weight loss) were shared predictors for both sexes.Findings suggest that within broad categories of risk factors, the predictive validity of specific assessment items may vary by sex. Our methods represent progression toward more efficient prediction of DUI recidivists.

Agingis characterized by a progressive loss of physiological integrity, while cancer represents one of the primarypathologicalfactorsthat severely threatenhuman lifespan and healthspan.In clinical oncology, drug resistance limits the efficacy of most anticancer treatments, andidentification of major mechanisms remains a key to solve thischallenging issue. Here we highlight the multifaceted senescence-associated secretory phenotype (SASP), which comprises numerous soluble factors including amphiregulin (AREG). Production of AREG is triggered by DNA damage to stromal cells which passively enter senescence in the tumor microenvironment (TME), aprocess that remarkably enhances cancer malignancy including acquired resistance mediated by EGFR. Furthermore, Paracrine AREG induces PD-L1 expression in recipient cancer cells and createsan immunosuppressive TME via immune checkpoint activation against cytotoxic lymphocytes. Targeting AREG not only minimizedchemoresistanceof cancer cells, but also restored immunocompetency when combined with classical chemotherapy in humanized animals. Our study underscores the potential of in vivoSASP in driving the TME-mediated drug resistance and shaping an immunosuppressive niche, and provides the proof-of-principle of targeting major SASP factors to improve therapeutic outcome in cancer medicine, the success of which can substantially reduce aging-related morbidity and mortality.

Immuno-stimulatory class I-restricted cytotoxic T lymphocytes (CTL) epitopes of porcine reproductive and respiratory syndrome virus (PRRSV) are important for vaccine development. In this study we first determined the expression frequency of swine leukocyte antigen (SLA) class I alleles in commercial pigs in the United States. The SLA genotyping result allowed us to predict potential CTL epitopes from a contemporary strain of PRRSV (RFLP 1-7-4) by using bioinformatic tools. The predicted epitopes were then evaluated in an ex vivo stimulation assay with peripheral blood mononuclear cells isolated from pigs experimentally-infected with PRRSV. Using flow-cytometry analysis, we identified a number of immuno-stimulatory CTL epitopes, including two peptides from GP3 and two from Nsp9 that significantly improved both degranulation marker CD107a and IFN-γ production in cytotoxic CD4+CD8+ T cells, CD8+ T cells, and γδ T cells, and two peptides that inhibited IFN-γ production. These CTL epitopes will aid future vaccine development against PRRSV.

Porcine reproductive and respiratory syndrome virus (PRRSV) is arguably the most economically-important global swine pathogen. Here we demonstrated that PRRSV down-regulates Swine Leukocyte Antigen class I (SLA-I) expression in porcine alveolar macrophages, PK15-CD163 cells and monocyte-derived dendritic cells. To identify the viral protein(s) involved in SLA-I down-regulation, we tested all 22 PRRSV structural and non-structural proteins and identified that Nsp1α and Nsp2TF, and GP3 significantly down-regulated SLA-I expression with Nsp2TF showing the greatest effect. We further generated a panel of mutant viruses in which the Nsp2TF protein synthesis was abolished, and found that the two mutants with disrupted -2 ribosomal frameshifting elements and additional stop codons in the TF domain were unable to down-regulate SLA-I expression. Additionally we demonstrated that the last 68 amino acids of TF domain in Nsp2TF are critical for this function. Collectively, the results indicate a novel function of Nsp2TF in negative modulation of SLA-I expression.

The U.S. swine industry have been bearing the financial impact of Porcine Reproductive and Respiratory Syndrome (PRRS) for decades. Absent of a safe and efficacious vaccine to combat PRRS virus's genetic heterogeneity, it remains a costly disease on pig farms across the country. We have developed virus-like-particle (VLP) based vaccines that incorporate 4 PRRSV epitopes in the hepatitis B core antigen (HBcAg) backbone. Administration of the vaccines in female BALB/C mice resulted in extremely significant PRRSV epitope specific antibody response. One vaccine candidate GP3-4 was able to mount a significant viral neutralizing response against both parental PRRSV strain VR2385 and heterologous PRRSV strain NADC20, showing a promising potential for cross-protection against PRRSV.

Viruses subvert macromolecular pathways in infected host cells to aid in viral gene amplification or to counteract innate immune responses. Roles for host-encoded, noncoding RNAs, including microRNAs, have been found to provide pro- and anti-viral functions. Recently, circular RNAs (circRNAs), that are generated by a nuclear back-splicing mechanism of pre-mRNAs, have been implicated to have roles in DNA virus-infected cells. This study examines the circular RNA landscape in uninfected and hepatitis C virus (HCV)-infected liver cells. Results showed that the abundances of distinct classes of circRNAs were up-regulated or down-regulated in infected cells. Identified circRNAs displayed pro-viral effects. One particular up-regulated circRNA, circPSD3, displayed a very pronounced effect on viral RNA abundances in both hepatitis C virus- and Dengue virus-infected cells. Though circPSD3 has been shown to bind factor eIF4A3 that modulates the cellular nonsense-mediated decay (NMD) pathway, circPSD3 regulates RNA amplification in a pro-viral manner at a post-translational step, while eIF4A3 exhibits the anti-viral property of the NMD pathway. Findings from the global analyses of the circular RNA landscape argue that pro-, and likely, anti-viral functions are executed by circRNAs that modulate viral gene expression as well as host pathways. Because of their long half-lives, circRNAs likely play hitherto unknown, important roles in viral pathogenesis., Author summary Usually, cells are infected by one or a few virus particles that carry genomes with limited expression capacity. Thus, the expression of viral genomes has to compete with a sea of cellular components that aid in viral translation, replication and virion production. Depending on their lifestyle, viruses have evolved to avoid or to subvert cellular pathways, especially those that display anti-viral functions. Host-derived circular RNA molecules have recently been discovered in the cytoplasm of cells, although, as-of yet, few functions have been assigned to them. Here, we describe alterations in the circular RNA landscape in hepatitis C virus-infected liver cells. Up-regulated and down-regulated circular RNAs were identified, and three of the upregulated RNAs were shown to promote HCV infection. One of them, circPSD3, inhibited viral RNA abundance at a post-translational step. Because circular RNAs are more stable than linear RNAs, they may have important functions during viral infection, dictating the outcomes of innate immune responses and viral pathogenesis.

Backoff algorithms are used in many distributed systems where multiple devices contend for a shared resource. For the classic balls-into-bins problem, the number of singletons -- those bins with a single ball -- is important to the analysis of several backoff algorithms; however, existing analyses employ advanced probabilistic tools to obtain concentration bounds. Here, we show that standard Chernoff bounds can be used instead, and the simplicity of this approach is illustrated by re-analyzing some well-known backoff algorithms., Comment: Corrections to first version

Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of ~30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC = 0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD.

Prediction performance of a risk scoring system needs to be carefully assessed before its adoption in clinical practice. Clinical preventive care often uses risk scores to screen asymptomatic population. The primary clinical interest is to predict the risk of having an event by a pre-specified future time t(0). Accuracy measures such as positive predictive values have been recommended for evaluating the predictive performance. However, for commonly used continuous or ordinal risk score systems, these measures require a subjective cut-off threshold value that dichotomizes the risk scores. The need for a cut-off value created barriers for practitioners and researchers. In this paper, we propose a threshold-free summary index of positive predictive values that accommodates time-dependent event status and competing risks. We develop a nonparametric estimator and provide an inference procedure for comparing this summary measure between two risk scores for censored time to event data. We conduct a simulation study to examine the finite-sample performance of the proposed estimation and inference procedures. Lastly, we illustrate the use of this measure on a real data example, comparing two risk score systems for predicting heart failure in childhood cancer survivors.

CD137 is a costimulatory molecule transiently expressed on activated T cells after mitogen or antigen stimulation that can be exploited for isolating antigen-specific T cells as reported in mouse models. By utilizing an antiporcine CD137 monoclonal antibody (mAb, clone 3B9) developed in our laboratory, we isolated virus-specific CD8β T cells from peripheral blood of pigs experimentally infected with different porcine reproductive and respiratory syndrome virus (PRRSV) strains. Similar to mouse, porcine CD8β T cells also express CD137 transiently upon Concavalin A stimulation while the unstimulated cells did not. Most frequently, virus-specific CD8β T cells were isolated at low levels from peripheral blood of pigs experimentally infected with PRRSV strains VR2385, NADC20, and MN184B at 49 and 63 days postinfection. The results suggest that porcine CD137-specific mAb is a useful tool for isolating virus-specific CD8 T cells from peripheral blood and tissues of pigs after in vitro stimulation with viral antigen.

Accurate risk prediction models are needed to identify different risk groups for individualized prevention and treatment strategies. In the Nurses’ Health Study, to examine the effects of several biomarkers and genetic markers on the risk of rheumatoid arthritis (RA), a three-phase nested case-control (NCC) design was conducted, in which two sequential NCC subcohorts were formed with one nested within the other, and one set of new markers measured on each of the subcohorts. One objective of the study is to evaluate clinical values of novel biomarkers in improving upon existing risk models because of potential cost associated with assaying biomarkers. In this paper, we develop robust statistical procedures for constructing risk prediction models for RA and estimating the incremental value (IncV) of new markers based on three-phase NCC studies. Our method also takes into account possible time-varying effects of biomarkers in risk modeling, which allows us to more robustly assess the biomarker utility and address the question of whether a marker is better suited for short-term or long-term risk prediction. The proposed procedures are shown to perform well in finite samples via simulation studies.

Diagnostics for heteroscedasticity in linear regression models have been intensively investigated in the literature. However, limited attention has been paid on how to identify covariates associated with heteroscedastic error variances. This problem is critical in correctly modelling the variance structure in weighted least squares estimation, which leads to improved estimation efficiency. We propose covariate-specific statistics based on information ratios formed as comparisons between the model-based and sandwich variance estimators. A two-step diagnostic procedure is established, first to detect heteroscedasticity in error variances, and then to identify covariates the error variance structure might depend on. This proposed method is generalized to accommodate practical complications, such as when covariates associated with the heteroscedastic variances might not be associated with the mean structure of the response variable, or when strong correlation is present amongst covariates. The performance of the proposed method is assessed via a simulation study and is illustrated through a data analysis in which we show the importance of correct identification of covariates associated with the variance structure in estimation and inference. The Canadian Journal of Statistics 43: 358–377; 2015 © 2015 Statistical Society of Canada Resume Les outils de diagnostic pour l'heteroscedasticite dans les modeles de regression lineaire sont largement etudies dans la litterature. Toutefois, l'identification des covariables associees aux variances heteroscedastiques n'a suscite que peu d'interet. Ce probleme joue pourtant un role cle pour l'estimation par les moindres carres ponderes, puisque la modelisation correcte de la structure de variance accroi t l'efficacite de l'estimation. Les auteurs proposent des statistiques specifiques aux covariables fondees sur un ratio d'information comparant l'estimateur de la variance base sur le modele a l'estimateur sandwich de la variance. Ils developpent une procedure diagnostique en deux etapes, detectant d'abord l'heteroscedasticite et identifiant ensuite les covariables dont peut dependre la structure de variance. Ils generalisent la methode proposee afin d'accommoder des complications pratiques telles que l'absence de lien entre la structure de la moyenne et une covariable associee avec l'heteroscedasticite, ou la forte correlation des covariables. Les auteurs evaluent la performance de la methode proposee a l'aide d'une etude de simulation et l'illustrent en analysant un jeu de donnees montrant l'importance d'identifier correctement les covariables associees avec la structure de variance pour l'estimation et l'inference. La revue canadienne de statistique xx: 1–20; 2015 © 2015 Societe statistique du Canada

Providing accurate and dynamic age-specific risk prediction is a crucial step in precision medicine. In this manuscript, we introduce an approach for estimating the τ-year age-specific absolute risk directly via a flexible varying coefficient model. The approach facilitates the utilization of predictors varying over an individual's lifetime. By using a nonparametric inverse probability weighted kernel estimating equation, the age-specific effects of risk factors are estimated without requiring the specification of the functional form. The approach allows borrowing information across individuals of similar ages, and therefore provides a practical solution for situations where the longitudinal information is only measured sparsely. We evaluate the performance of the proposed estimation and inference procedures with numerical studies, and make comparisons with existing methods in the literature. We illustrate the performance of our proposed approach by developing a dynamic prediction model using data from the Framingham Study.

Cytokines are often used as adjuvants to improve vaccine immunogenicity, since they are important in initiating and shaping the immune response. The available commercial modified live-attenuated vaccines (MLVs) against porcine reproductive and respiratory syndrome virus (PRRSV) are unable to mount sufficient heterologous protection, as they typically induce weak innate and inadequate T cell responses. In this study, we investigated the immunogenicity and vaccine efficacy of recombinant PRRSV MLVs incorporated with the porcine cytokine interleukin-15 (IL-15) or IL-18 gene fused to a glycosylphosphatidylinositol (GPI) modification signal that can anchor the cytokines to the cell membrane. We demonstrated that both cytokines were successfully expressed on the cell membrane of porcine alveolar macrophages after infection with recombinant MLVs. Pigs vaccinated with recombinant MLVs or the parental Suvaxyn MLV had significantly reduced lung lesions and viral RNA loads in the lungs after heterologous challenge with the PRRSV NADC20 strain. The recombinant MLVs SUV-IL-15 and SUV-IL-18 recovered the inhibition of the NK cell response seen with Suvaxyn MLV. The recombinant MLV SUV-IL-15 significantly increased the numbers of gamma interferon (IFN-γ)-producing cells in circulation at 49 days postvaccination (dpv), especially for IFN-γ-producing CD4 − CD8 + T cells and γδ T cells, compared to the Suvaxyn MLV and SUV-IL-18. Additionally, MLV SUV-IL-15-vaccinated pigs also had elevated levels of γδ T cell responses observed at 7 dpv, 49 dpv, and 7 days postchallenge. These data demonstrate that the recombinant MLV expressing membrane-bound IL-15 enhances NK and T cell immune responses after vaccination and confers improved heterologous protection, although this was not statistically significant compared to the parental MLV. IMPORTANCE Porcine reproductive and respiratory syndrome (PRRS) has arguably been the most economically important global swine disease, causing immense economic losses worldwide. The available commercial modified live-attenuated vaccines (MLVs) against PRRS virus (PRRSV) are generally effective against only homologous or closely related virus strains but are ineffective against heterologous strains, partially due to the insufficient immune response induced by the vaccine virus. To improve the immunogenicity of MLVs, in this study, we present a novel approach of using porcine IL-15 or IL-18 as an adjuvant by directly incorporating its encoding gene into a PRRSV MLV and expressing it as an adjuvant. Importantly, we directed the expression of the incorporated cytokines to the cell membrane surface by fusing the genes with a membrane-targeting signal from CD59. The recombinant MLV virus expressing the membrane-bound IL-15 cytokine greatly enhanced NK cell and γδ T cell responses and also conferred improved protection against heterologous challenge with the PRRSV NADC20 strain.

In this article, we develop a new goodness-of-fit test for multivariate jump diffusion models. The test statistic is constructed by a contrast between an “in-sample” likelihood (or a likelihood of observed data) and an“out-of-sample” likelihood (or a likelihood of predicted data). We show that under the null hypothesis of a jump diffusion process being correctly specified, the proposed test statistic converges in probability to a constant that equals to the number of model parameters in the null model. We also establish the asymptotic normality for the proposed test statistic. To implement this method, we invoke a closed-form approximation to transition density functions, which results in a computationally efficient algorithm to evaluate the test. Using Monte Carlo simulation experiments, we illustrate that both exact and approximate versions of the proposed test perform satisfactorily. In addition, we demonstrate the proposed testing method in several popular stochastic volatility models for time series of weekly S&P 500 index during the period of January 1990 and December 2014, in which we invoke a linear affine relationship between latent stochastic volatility and the implied volatility index.

Immunogenicity of protein subunit vaccines may be dramatically improved by targeting them through antibodies specific to c-type lectin receptors (CLRs) of dendritic cells in mice, cattle, and primates. This novel vaccine development approach has not yet been explored in pigs or other species largely due to the lack of key reagents. In this study, we demonstrate that porcine reproductive and respiratory syndrome virus (PRRSV) antigen was targeted efficiently to dendritic cells through antibodies specific to a porcine CLR molecule DC-SIGN (dendritic cell-specific intercellular adhesion molecule-3-grabbing non-integrin) in pigs. A recombinant PRRSV antigen (shGP45M) was constructed by fusing secretory-competent subunits of GP4, GP5 and M proteins derived from genetically-shuffled strains of PRRSV. In vaccinated pigs, when the PRRSV shGP45M antigen was delivered through a recombinant mouse-porcine chimeric antibody specific to the porcine DC-SIGN (pDC-SIGN) neck domain, porcine dendritic cells rapidly internalized them in vitro and induced higher numbers of antigen-specific interferon-γ producing CD4T cells compared to the pigs receiving non-targeted PRRSV shGP45M antigen. The pDC-SIGN targeting of recombinant antigen subunits may serve as an alternative or complementary strategy to existing vaccines to improve protective immunity against PRRSV by inducing efficient T cell responses.

Synthetic attenuated virus engineering (SAVE) is an emerging technology that enables rapid attenuation of viruses. In this study, by using SAVE we demonstrated rapid attenuation of an arterivirus, porcine reproductive and respiratory syndrome virus (PRRSV). The major envelope GP5 gene of PRRSV was codon-pair deoptimized aided by a computer algorithm. The codon-pair deoptimized virus, designated as SAVE5 with a deoptimized GP5 gene, was successfully rescued in vitro. The SAVE5 virus replicated at a lower level in vitro with a significant decrease of GP5 protein expression compared to the wild-type PRRSV VR2385 virus. Pigs experimentally infected with the SAVE5 virus had significantly lower viremia level up to 14 days post-infection as well as significantly reduced gross and histological lung lesions when compared to wild-type PRRSV VR2385 virus-infected pigs, indicating the attenuation of the SAVE5 virus. This study proved the feasibility of rapidly attenuating PRRSV by SAVE.

BACKGROUND Lower deep venous thrombosis (DVT) is one of the major complications of patients with tumors or patients undergoing major surgery. Electrical acupoint stimulation, an established technique of traditional Chinese medicine (TCM), can be well combined with Western medicine to reduce the incidence of postoperative DVT, especially in elderly patients. OBJECTIVE The objectives of this study were to assess the efficiency of electrical acupoint stimulation in the prevention of postsurgery DVT in elderly patients with gastrointestinal malignant tumors and to validate an effective and safe nursing approach that integrates TCM and Western medicine. METHODS A total of 120 patients (none aged

A popular technique for tolerating malicious faults in open distributed systems is to establish small groups of participants, each of which has a non-faulty majority. These groups are used as building blocks to design attack-resistant algorithms. Despite over a decade of active research, current constructions require group sizes of $O(\log n)$, where $n$ is the number of participants in the system. This group size is important since communication and state costs scale polynomially with this parameter. Given the stubbornness of this logarithmic barrier, a natural question is whether better bounds are possible. Here, we consider an attacker that controls a constant fraction of the total computational resources in the system. By leveraging proof-of-work (PoW), we demonstrate how to reduce the group size exponentially to $O(\log\log n)$ while maintaining strong security guarantees. This reduction in group size yields a significant improvement in communication and state costs., Comment: This work is supported by the National Science Foundation grant CCF 1613772 and a C Spire Research Gift

Highlights • Targeting PEDV spike protein domains to Langerin-expressing dendritic cells through a single chain antibody. • Transdermal Langerin-targeting of PEDV S antigen significantly enhanced CD4 T cell immunity. • Systemic Langerin-targeting of PEDV S antigen significantly augmented serum IgG and IgA responses., Porcine epidemic diarrhea virus (PEDV) first emerged in the United States in 2013 causing high mortality and morbidity in neonatal piglets with immense economic losses to the swine industry. PEDV is an alpha-coronavirus replicating primarily in porcine intestinal cells. PEDV vaccines are available in Asia and Europe, and conditionally-licensed vaccines recently became available in the United States but the efficacies of these vaccines in eliminating PEDV from swine populations are questionable. In this study, the immunogenicity of a subunit vaccine based on the spike protein of PEDV, which was directly targeted to porcine dendritic cells (DCs) expressing Langerin, was assessed. The PEDV S antigen was delivered to the dendritic cells through a single-chain antibody specific to Langerin and the targeted cells were stimulated with cholera toxin adjuvant. This approach, known as “dendritic cell targeting,” greatly improved PEDV S antigen-specific T cell interferon-γ responses in the CD4posCD8pos T cell compartment in pigs as early as 7 days upon transdermal administration. When the vaccine protein was targeted to Langerinpos DCs systemically through intramuscular vaccination, it induced higher serum IgG and IgA responses in pigs, though these responses require a booster dose, and the magnitude of T cell responses were lower as compared to transdermal vaccination. We conclude that PEDV spike protein domains targeting Langerin-expressing dendritic cells significantly increased CD4 T cell immune responses in pigs. The results indicate that the immunogenicity of protein subunit vaccines can be greatly enhanced by direct targeting of the vaccine antigens to desirable dendritic cell subsets in pigs.

We develop a hypothesis testing approach to checking model misspecification on parametric structures in continuous-time stochastic diffusion models. The key idea behind the development of our test statistic is rooted in a ratio of two types of information matrices, the negative sensitivity matrix and the variability matrix, in the context of martingale estimating equations. We propose a bootstrap resampling method to implement numerically the proposed diagnostic procedure. Through intensive simulation studies, we compare the proposed approach with several currently popular methods and show that our approach is advantageous in the aspects of type I error control, power improvement as well as computational efficiency. Two real-world data examples are included to illustrate the practical use of our proposed testing procedure.

In this article, we focus on the circumstances in quasi-likelihood inference that the estimation accuracy of mean structure parameters is guaranteed by correct specification of the first moment, but the estimation efficiency could be diminished due to misspecification of the second moment. We propose an information ratio (IR) statistic to test for model misspecification of the variance/covariance structure through a comparison between two forms of information matrix: the negative sensitivity matrix and the variability matrix. We establish asymptotic distributions of the proposed IR test statistics. We also suggest an approximation to the asymptotic distribution of the IR statistic via a perturbation resampling method. Moreover, we propose a selection criterion based on the IR test to select the best fitting variance/covariance structure from a class of candidates. Through simulation studies, it is shown that the IR statistic provides a powerful statistical tool to detect different scenarios of misspecific...

The purpose of this study is to primarily evaluate the lumen area and secondarily evaluate wall area measurements of in vivo lower extremity peripheral vein bypass grafts patients using high spatial resolution, limited field of view, cardiac gated, black blood inner volume three-dimensional fast spin echo MRI. Fifteen LE-PVBG patients prospectively underwent ultrasound followed by T1-weighted and T2-weighted magnetic resonance (MR) imaging. Lumen and vessel wall areas were measured by direct planimetry. For graft lumen areas, T1- and T2-weighted measurements were compared with ultrasound. For vessel wall areas, differences between T1- and T2-weighted measurements were evaluated. There was no significant difference between ultrasound and MR lumen measurements, reflecting minimal MR blood suppression artifact. Graft wall area measured from T1-weighted images was significantly larger than that measured from T2-weighted images (P < 0.001). The mean of the ratio of T1- versus T2-weighted vessel wall areas was 1.59 (95% CI: 1.48-1.69). The larger wall area measured on T1-weighted images was due to a significantly larger outer vessel wall boundary. Very high spatial resolution LE-PVBG vessel wall MR imaging can be performed in vivo, enabling accurate measurements of lumen and vessel wall areas and discerning differences in those measures between different tissue contrast weightings. Vessel wall area differences suggest that LE-PVBG vessel wall tissues produce distinct signal characteristics under T1 and T2 MR contrast weightings.

Individual differences in learners learning styles can have a significant effect on their acceptance of collaboration technologies to facilitate the sharing of learning information in technology-based collaborative learning. There is, however, a lack of research in exploring the impact of learning styles on the acceptance of open learner models for information sharing in this area. This study investigates the impact of learners learning styles on their acceptance of open learner models for information sharing. A total of 240 undergraduate students in a university in Malaysia have participated in the online survey. A chi-square test is performed to explore the relationship between learning styles and the acceptance of open learner models for information sharing in a higher education setting. The result reveals that learning styles have no significant impact on learners acceptance of open learner models for information sharing. The implications of this study can assist education instructors to apply appropriate instructional strategies in adopting open learner models for improving the performance of technology-mediated learning., Comment: ISBN# 978-0-646-95337-3 Presented at the Australasian Conference on Information Systems 2015 (arXiv:1605.01032)

This paper concerns goodness-of-fit test for semiparametric copula models. Our contribution is two-fold: we first propose a new test constructed via the comparison between "in-sample" and "out-of-sample" pseudolikelihoods, which avoids the use of any probability integral transformations. Under the null hypothesis that the copula model is correctly specified, we show that the proposed test statistic converges in probability to a constant equal to the dimension of the parameter space and establish the asymptotic normality for the test. Second, we introduce a hybrid mechanism to combine several test statistics, so that the resulting test will make a desirable test power among the involved tests. This hybrid method is particularly appealing when there exists no single dominant optimal test. We conduct comprehensive simulation experiments to compare the proposed new test and hybrid approach with the best "blank test" shown in Genest et al. (2009). For illustration, we apply the proposed tests to analyze three real datasets.

In many clinical applications, understanding when measurement of new markers is necessary to provide added accuracy to existing prediction tools could lead to more cost effective disease management. Many statistical tools for evaluating the incremental value (IncV) of the novel markers over the routine clinical risk factors have been developed in recent years. However, most existing literature focuses primarily on global assessment. Since the IncVs of new markers often vary across subgroups, it would be of great interest to identify subgroups for which the new markers are most/least useful in improving risk prediction. In this paper we provide novel statistical procedures for systematically identifying potential traditional-marker based subgroups in whom it might be beneficial to apply a new model with measurements of both the novel and traditional markers. We consider various conditional time-dependent accuracy parameters for censored failure time outcome to assess the subgroup-specific IncVs. We provide non-parametric kernel-based estimation procedures to calculate the proposed parameters. Simultaneous interval estimation procedures are provided to account for sampling variation and adjust for multiple testing. Simulation studies suggest that our proposed procedures work well in finite samples. The proposed procedures are applied to the Framingham Offspring Study to examine the added value of an inflammation marker, C-reactive protein, on top of the traditional Framingham risk score for predicting 10-year risk of cardiovascular disease.

We propose and investigate the properties of a digital ferromagnetic heterostructure (DFH) consisting of a $��$-doped layer of Mn in Si, using \textit{ab initio} electronic-structure methods. We find that (i) ferromagnetic order of the Mn layer is energetically favorable relative to antiferromagnetic, and (ii) the heterostructure is a two-dimensional half metallic system. The metallic behavior is contributed by three majority-spin bands originating from hybridized Mn-$d$ and nearest-neighbor Si-$p$ states, and the corresponding carriers are responsible for the ferromagnetic order in the Mn layer. The minority-spin channel has a calculated semiconducting gap of 0.25 eV. Analysis of the total and partial densities of states, band structure, Fermi surfaces and associated charge density reveals the marked two-dimensional nature of the half metallicity. The band lineup is found to be favorable for retaining the half metal character to near the Curie temperature ($T_{C}$). Being Si based and possibly having a high $T_{C}$ as suggested by an experiment on dilutely doped Mn in Si, the heterostructure may be of special interest for integration into mature Si technologies for spintronic applications., 4 pages, 4 figures, Revised version, to appear in Phys. Rev. Lett

3594 Background: Functional SNPs in VEGF-A are prognostic for OS in several malignancies, but not described definitively in mCRC. In bevacizumab-treated patients with advanced breast cancer, VEGF -2578AA and -1154A were predictive of OS—the latter SNP reported in a recent mCRC series as well. We examined the association between five functional VEGF-A SNPs and OS in a large cohort of bevacizumab-treated patients with mCRC. Methods: 403 patients with mCRC treated from 2004-2010 were included in a retrospective analysis. DNA extraction, genotyping, and SNP evaluation were performed according to standard protocols. Survival was calculated from the time of Stage IV diagnosis until death. Data were censored as of 12/31/2010. Results: There were 279 deaths in this group of 403 patients (69%). Median age was 55.7 (24-86 y), and 54% of patients were male. Age, sex, race, tumor grade, chemotherapies, and curative surgery (metastatectomy to negative margins) were considered. Significant clinical predictors of OS in univariate Cox modeling were cetuximab treatment [HR=1.46; 95% CI 1.13-1.88; p=0.0014], irinotecan treatment [HR=2.10; 1.32-3.35; p