Content Swapping: A New Image Synthesis for Construction Sign Detection in Autonomous Vehicles.

Construction signs alert drivers to the dangers of abnormally blocked roads. In the case of autonomous vehicles, construction signs should be detected automatically to prevent accidents. One might think that we can accomplish the goal easily using the popular deep-learning-based detectors, but it is not the case.

Semiparametric estimation for nonparametric frailty models using nonparametric maximum likelihood approach.

A consequence of using a parametric frailty model with nonparametric baseline hazard for analyzing clustered time-to-event data is that its regression coefficient estimates could be sensitive to the underlying frailty distribution. Recently, there has been a proposal for specifying both the baseline hazard and the frailty distribution nonparametrically, and estimating the unknown parameters by the maximum penalized likelihood method. Instead, in this paper, we propose the nonparametric maximum likelihood method for a general class of nonparametric frailty models, i.

Penalized variable selection for cause-specific hazard frailty models with clustered competing-risks data.

Competing risks data usually arise when an occurrence of an event precludes other types of events from being observed. Such data are often encountered in a clustered clinical study such as a multi-center clinical trial. For the clustered competing-risks data which are correlated within a cluster, competing-risks models allowing for frailty terms have been recently studied.

A Model for Determining Predictors of the MUAC in Acute Malnutrition in Ghana.

The issue of malnutrition is perhaps the most important public health determinant of global wellbeing. It is one of the main causes of improper mental and physical development as well as death of many children. The Mid Upper Arm Circumference (MUAC) rapid text setup is able to diagnose malnutrition due to the fact that the human arm contains subcutaneous fat and muscle mass.

Investigation of Correlated Internet and Smartphone Addiction in Adolescents: Copula Regression Analysis.

Internet and smartphone addiction have become important social issues. Various studies have demonstrated their association with clinical and psychological factors, including depression, anxiety, aggression, anger expression, and behavioral inhibition, and behavioral activation systems. However, these two addictions are also highly correlated with each other, so the consideration of the relationship between internet and smartphone addiction can enhance the analysis.

Associations of personality and clinical characteristics with excessive Internet and smartphone use in adolescents: A structural equation modeling approach.

Adolescent Internet addiction is an important social issue entailing extensive use of Internet and smartphones and its side effects. This study identified relevant psychological factors that affect excessive Internet use (EIU) and excessive smartphone use (ESU) in adolescents using structural equation modeling (SEM). A sample of 714 individuals drawn from lists of middle school students in South Korea completed self-administered questionnaires, including Young's Internet Addiction Test (Y-IAT), the Smartphone Addiction Scale (SAS), and various clinical and psychological scales measuring depression, anxiety, attention deficit/hyperactivity disorder (ADHD), aggression, expression of anger, and the behavioral inhibition system (BIS)/activation system (BAS).

Clustering with varying risks of false assignments in discrete latent variable model.

In clustering problems, to model the intrinsic structure of unlabeled data, the latent variable models are frequently used. These model-based clustering methods often provide a clustering rule minimizing the total false assignment error. However, in many clustering applications, it is desirable to treat false assignment errors for a certain cluster differently.

Cluster-specific nonignorably missing, endogenous, and continuous regressors in multilevel model for binary outcome.

In multilevel regression models for observational clustered data, regressors can be correlated with cluster-level error components, namely endogenous, due to omitted cluster-level covariates, measurement error, and simultaneity. When endogeneity is ignored, regression coefficient estimators can be severely biased. To deal with endogeneity, instrument variable methods have been widely used.

The Impact of Internet Gaming on Alcohol Consumption.

Background: Despite growing evidence of the adverse effects of internet gaming, it has emerged as a popular leisure activity in South Korea and Asia. This is the first study that examines the causal effect of internet gaming on alcohol consumption.

Aims Of The Study: The primary goal of this study is to evaluate the effect of internet gaming on alcohol consumption while controlling for unobserved individual attributes that are omitted in the alcohol consumption regression but are correlated with internet game usage.

CREDO: Highly confident disease-relevant A-to-I RNA-editing discovery in breast cancer.

Adenosine-to-Inosine (A-to-I) RNA editing is the most prevalent post-transcriptional modification of RNA molecules. Researchers have attempted to find reliable RNA editing using next generation sequencing (NGS) data. However, most of these attempts suffered from a high rate of false positives, and they did not consider the clinical relevance of the identified RNA editing, for example, in disease progression.

Frailty modelling approaches for semi-competing risks data.

In the semi-competing risks situation where only a terminal event censors a non-terminal event, observed event times can be correlated. Recently, frailty models with an arbitrary baseline hazard have been studied for the analysis of such semi-competing risks data. However, their maximum likelihood estimator can be substantially biased in the finite samples.

Volume entropy for modeling information flow in a brain graph.

Brain regions send and receive information through neuronal connections in an efficient way. In this paper, we modelled the information propagation in brain networks by a generalized Markov system associated with a new edge-transition matrix, based on the assumption that information flows through brain networks forever. From this model, we derived new global and local network measures, called a volume entropy and the capacity of nodes and edges on FDG PET and resting-state functional MRI.

Association between fever pattern and clinical manifestations of adult-onset Still's disease: unbiased analysis using hierarchical clustering.

Objectives: To perform unbiased analysis of fever patterns and to investigate their association with clinical manifestations and outcome of patients with adult-onset Still's disease (AOSD).

Methods: AOSD patients who were treated as in-patients from 2004 through 2015 were grouped according to 24-hour body temperature (BT) by hierarchical clustering using a Euclidean distance metric with complete linkage. The clinical and laboratory characteristics of the groups were then examined.

Raynaud's phenomenon and anti-nuclear antibody are associated with pulmonary function decline in patients with dermatomyositis and polymyositis.

Objectives: To identify factors associated with deterioration of pulmonary function with disproportional decline in diffusing capacity for carbon monoxide (DLCO) relative to forced vital capacity (FVC) in patients with dermatomyositis (DM) and polymyositis (PM).

Methods: This retrospective cohort study included patients with DM and PM, in whom serial pulmonary function tests were available. Changes in FVC and DLCO over time were estimated using a linear mixed-effects model.

Comparison of the modified unbounded penalty and the LASSO to select predictive genes of response to chemotherapy in breast cancer.

Covariate selection is a fundamental step when building sparse prediction models in order to avoid overfitting and to gain a better interpretation of the classifier without losing its predictive accuracy. In practice the LASSO regression of Tibshirani, which penalizes the likelihood of the model by the L1 norm of the regression coefficients, has become the gold-standard to reach these objectives. Recently Lee and Oh developed a novel random-effect covariate selection method called the modified unbounded penalty (MUB) regression, whose penalization function can equal minus infinity at 0 in order to produce very sparse models.

Automatic detection of significant areas for functional data with directional error control.

In this paper, we propose a large-scale multiple testing procedure to find the significant sub-areas between two samples of curves automatically. The procedure is optimal in that it controls the directional false discovery rate at any specified level on a continuum asymptotically. By introducing a nonparametric Gaussian process regression model for the two-sided multiple test, the procedure is computationally inexpensive.

Hydroxycoumarin OT-55 kills CML cells alone or in synergy with imatinib or Synribo: Involvement of ER stress and DAMP release.

We synthetized and investigated the anti-leukemic potential of the novel cytostatic bis(4-hydroxycoumarin) derivative OT-55 which complied with the Lipinski's rule of 5 and induced differential toxicity in various chronic myeloid leukemia (CML) cell models. OT-55 triggered ER stress leading to canonical, caspase-dependent apoptosis and release of danger associated molecular patterns. Consequently, OT-55 promoted phagocytosis of OT-55-treated CML cells by both murine and human monocyte-derived macrophages.

Distinct patterns of Internet and smartphone-related problems among adolescents by gender: Latent class analysis.

Background and objectives The ubiquitous Internet connections by smartphones weakened the traditional boundaries between computers and mobile phones. We sought to explore whether smartphone-related problems differ from those of computer use according to gender using latent class analysis (LCA). Methods After informed consents, 555 Korean middle-school students completed surveys on gaming, Internet use, and smartphone usage patterns.

Effects of Internet and Smartphone Addictions on Depression and Anxiety Based on Propensity Score Matching Analysis.

The associations of Internet addiction (IA) and smartphone addiction (SA) with mental health problems have been widely studied. We investigated the effects of IA and SA on depression and anxiety while adjusting for sociodemographic variables. In this study, 4854 participants completed a cross-sectional web-based survey including socio-demographic items, the Korean Scale for Internet Addiction, the Smartphone Addiction Proneness Scale, and the subscales of the Symptom Checklist 90 Items-Revised.

Extended negative binomial hurdle models.

Poisson models are widely used for statistical inference on count data. However, zero-inflation or zero-deflation with either overdispersion or underdispersion could occur. Currently, there is no available model for count data, that allows excessive occurrence of zeros along with underdispersion in non-zero counts, even though there have been reported necessity of such models.

Retraction and Republication: A post hoc analysis of intra-subject coefficients of variation in pharmacokinetic measures to calculate optimal sample sizes for bioequivalence studies.

[This retracts the article on p. 179 in vol. 25.

A post hoc analysis of intra-subject coefficients of variation in pharmacokinetic measures to calculate optimal sample sizes for bioequivalence studies.

Because bioequivalence studies are performed using a crossover design, information on the intra-subject coefficient of variation (intra-CV) for pharmacokinetic measures is needed when determining the sample size. However, calculated intra-CVs based on bioequivalence results of identical generic drugs produce different estimates. In this study, we collected bioequivalence results using public resources from the Ministry of Food and Drug Safety (MFDS) and calculated the intra-CVs of various generics.

A Methodological Perspective on the Longitudinal Cognitive Change after Stroke.

Background/aims: Most studies of poststroke cognitive impairment (PSCI) have analyzed cognitive levels at specific time points rather than their changes over time. Furthermore, they seldom consider correlations between cognitive domains. We aimed to investigate the effects of these methodological considerations on determining significant PSCI predictors in a longitudinal stroke cohort.

Robust functional regression model for marginal mean and subject-specific inferences.

We introduce flexible robust functional regression models, using various heavy-tailed processes, including a Student t-process. We propose efficient algorithms in estimating parameters for the marginal mean inferences and in predicting conditional means as well as interpolation and extrapolation for the subject-specific inferences. We develop bootstrap prediction intervals (PIs) for conditional mean curves.

The dialkyl resorcinol stemphol disrupts calcium homeostasis to trigger programmed immunogenic necrosis in cancer.

Stemphol (STP) is a novel druggable phytotoxin triggering mixed apoptotic and non-apoptotic necrotic-like cell death in human acute myeloid leukemia (AML). Use of several chemical inhibitors highlighted that STP-induced non-canonical programmed cell death was Ca-dependent but independent of caspases, poly (ADP-ribose) polymerase-1, cathepsin, or calpains. Similar to thapsigargin, STP led to increased cytosolic Ca levels and computational docking confirmed binding of STP within the thapsigargin binding pocket of the sarco/endoplasmic reticulum (ER) Ca-ATPase (SERCA).