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. To train the deep learning detectors to detect construction signs, we need a large amount of training images which contain construction signs. However, collecting training images including construction signs is very difficult in the real world because construction events do not occur frequently. To make matters worse, the construction signs might have dozens of different construction signs (i.e., contents). To address this problem, we propose a new method named content swapping. Our content swapping divides a construction sign into two parts: the board and the frame. Content swapping generates numerous synthetic construction signs by combining the board images (i.e., contents) taken from the in-domain images and the frames (i.e., geometric shapes) taken from the out-domain images. The generated synthetic construction signs are then added to the background road images via the cut-and-paste mechanism, increasing the number of training images. Furthermore, three fine-tuning methods regarding the region, size, and color of the construction signs are developed to make the generated training images look more realistic. To validate our approach, we applied our method to real-world images captured in South Korea. Finally, we achieve an average precision (AP50) score of 84.98%, which surpasses that of the off-the-shelf method by 9.15%. Full experimental results are available online as a supplemental video. The images used in the experiments are also released as a new dataset CSS138 for the benefit of the autonomous driving community.

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. To the best of our knowledge, however, there is no literature on variable selection methods for cause-specific hazard frailty models. In this article, we propose a variable selection procedure for fixed effects in cause-specific competing risks frailty models using a penalized h-likelihood (HL). Here, we study three penalty functions, LASSO, SCAD, and HL. Simulation studies demonstrate that the proposed procedure using the HL penalty works well, providing a higher probability of choosing the true model than LASSO and SCAD methods without losing prediction accuracy. The proposed method is illustrated by using two kinds of clustered competing-risks cancer data sets.

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.e. models where the frailty distribution is completely unspecified but the baseline hazard can be either parametric or nonparametric. The implementation of the estimation procedure can be based on a combination of either the Broyden-Fletcher-Goldfarb-Shanno or expectation-maximization algorithm and the constrained Newton algorithm with multiple support point inclusion. Simulation studies to investigate the performance of estimation of a regression coefficient by several different model-fitting methods were conducted. The simulation results show that our proposed regression coefficient estimator generally gives a reasonable bias reduction when the number of clusters is increased under various frailty distributions. Our proposed method is also illustrated with two data examples.

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. When proportional food intake increases or reduces, the corresponding increase or reduction in the subcutaneous fat and muscle mass leads to an increase or decrease in the MUAC. In this study, the researchers attempt to develop a model for determining the performance of MUAC in predicting Child malnutrition in Ghana. It focuses on the Joint Generalized Linear Model (Joint-GLM) instead of the traditional Generalized Linear Model (GLM). The analysis is based on primary data measured on children under six years, who were undergoing nutritional treatment at the Princess Marie Louise (PML) Children's Hospital in the Ashiedu Keteke sub-metro area of Accra Metropolis. The study found that a precisely measured weight of a child, height, and albumen levels were positive determinants of the predicted MUAC value. The study also reveals that, of all the variables used in determining the MUAC outcome, the hemoglobin and total protein levels of a child would be the main causes of any variation between the exact nutritional status of a child and that suggested by the MUAC value. The final Joint-GLM suggests that, if there are occasions where the MUAC gave false results, it could be a result of an imbalance in the child's hemoglobin and protein levels. If these two are within acceptable levels in a child, the MUAC is most likely to be consistent in predicting the child's nutritional status accurately. This study therefore recommends the continued use of MUAC in diagnosis of child malnutrition but urges Ghana and countries in Sub-Saharan Africa to roll out an effective nutrition intervention plan targeting the poor and vulnerable suburbs so that the nutritional status of children under five years of age, who were the focus of the current study, may be improved.

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. In this study, we considered the copula regression model to regress the bivariate addictions on clinical and psychological factors. Real data analysis with 555 students (age range: 14-15 years; males, N = 295; females, N = 265) from South Korean public middle schools is illustrated. By fitting the copula regression model, we investigated the dependency between internet and smartphone addiction and determined the risk factors associated with the two addictions. Furthermore, by comparing the model fits of the copula model with linear regression and generalized linear models, the best copula model was proposed in terms of goodness of fit. Our findings revealed that internet and smartphone addiction are not separate problems, and that associations between them should be considered. Psychological factors, such as anxiety, the behavioral inhibition system, and aggression were also significantly associated with both addictions, while ADHD symptoms were related to internet addiction only. We emphasize the need to establish policies on the prevention, management, and education of addiction.

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). The final model, fitted using SEM, showed that both clinical characteristics, including ADHD symptoms, aggression, expression of anger, depression, and anxiety, and personality characteristics, represented by BIS/BAS, played important roles in the severity of EIU or ESU. In particular, affective components such as depression and anxiety were significantly associated with both EIU and ESU, whereas aggression, the expression of anger, and ADHD symptoms affected only EIU. Furthermore, the association between ESU and EIU was significant. Although personality characteristics measured by the BIS and BAS scores did not have direct effects on addiction, they were associated with clinical features and might be risk factors for addiction. The model revealed significant pathways from personality and clinical features to EIU and ESU in adolescents and informed our basic understanding of the meaningful predictors of these addictions and their direct and indirect influences.

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. In this paper, we introduce the false assignment rate for clustering and estimate it by using the extended likelihood approach. We propose VRclust, a novel clustering rule that controls various errors differently across clusters. Real data examples illustrate the usage of estimation of false assignment rate and a simulation study shows that error controls are consistent as the sample size increases.

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. In this paper, we propose effective modifications to reduce such bias using the hierarchical likelihood. We also investigate the relationship between marginal and hierarchical likelihood approaches. Simulation results are provided to validate performance of the proposed method. The proposed method is illustrated through analysis of semi-competing risks data from a breast cancer study.

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. However, the instrument variable method often requires external instrument variables with certain conditions that cannot be verified empirically. Methods that use the within-cluster variations of the endogenous variable work under the restriction that either the outcome or the endogenous variable has a linear relationship with the cluster-level random effect. We propose a new method for binary outcome when it follows a logistic mixed-effects model and the endogenous variable is normally distributed but not linear in the random effect. The proposed estimator capitalizes on the nested data structure without requiring external instrument variables. We show that the proposed estimator is consistent and asymptotically normal. Furthermore, our method can be applied when the endogenous variable is missing in a cluster-specific nonignorable mechanism, without requiring that the missing mechanism be correctly specified. We evaluate the finite sample performance of the proposed approach via simulation and apply the method to a health care study using a San Diego inpatient dataset. Our study demonstrates that the clustered structure can be exploited to draw valid analysis of multilevel data with correlated effects.

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. METHODS: We use data from a survey of 5,003 men and women who lived in Seoul and the surrounding metropolitan area of South Korea during the year 2014. We use the instrumental variable regressions and partially linear regressions. RESULTS: We first find that the age at which an individual starts internet gaming and being a member of an internet gaming club are significantly associated with the average hours spent internet gaming in adulthood. Using these two instrumental variables, we show that longer hours of internet gaming is associated with less consumption of alcohol among men, but more consumption of alcohol among women. The opposite effects of internet gaming on alcohol consumption for male and female users are robust to alternative specifications and estimation methods. DISCUSSION: We investigate potential channels through which men and women are differently affected by internet gaming on alcohol consumption. We find large disparities in types of gaming devices and playing partners between men and women and that these factors account for part of the different gaming effects by gender. Other gaming preferences contributing to the heterogeneous game effects are not examined due to lack of data, which is the limitation of this study. IMPLICATIONS FOR HEALTH POLICIES: The empirical findings suggest that female users of internet games, in particular those who are vulnerable to social isolation, can reap the most benefit toward reducing the risk of developing Alcohol Use Disorder (AUD) from health interventions that aim to monitor unhealthy use of internet games. IMPLICATIONS FOR HEALTH CARE PROVISION AND USE: Understanding the impact of internet gaming on other substance use such as alcohol will be useful for the design of effective clinical treatments and preventative health care provision. IMPLICATIONS FOR FURTHER RESEARCH: Based on the finding that men are likely to sit for longer periods of time indulging in games, further research may examine how the prolonged sedentary leisure activity of internet gaming affect obesity and other physical health problems.

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. We devised an effective RNA-editing discovery pipeline called CREDO, which includes novel statistical filtering modules based on integration of DNA- and RNA-seq data from matched tumor-normal tissues. CREDO was compared with three other RNA-editing discovery pipelines and found to give significantly fewer false positives. Application of CREDO to breast cancer data from the Cancer Genome Atlas (TCGA) project discovered highly confident RNA editing with clinical relevance to cancer progression in terms of patient survival. RNA-editing detection using DNA- and RNA-seq data from matched tumor-normal tissues should be more routinely performed as multiple omics data are becoming commonly available from each patient sample. We believe CREDO is an effective and reliable tool for this problem.

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. Volume entropy of a metric graph, a global measure of information, measures the exponential growth rate of the number of network paths. Capacity of nodes and edges, a local measure of information, represents the stationary distribution of information propagation in brain networks. On the resting-state functional MRI of healthy normal subjects, these measures revealed that volume entropy was significantly negatively correlated to the aging and capacities of specific brain nodes and edges underpinned which brain nodes or edges contributed these aging-related changes.

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. RESULTS: A total of 103 patients were included. During follow-up, 31 (30.1%) and 37 (35.9%) had a disproportionally better (ΔDLCO/ΔFVC>mean slope + 95% CI) or a disproportionally worse (ΔDLCO/ΔFVC

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. It can cope with problems with multidimensional covariates and accommodate different sampling designs across the samples. We further propose the significant curve/surface, giving an insight on dynamic significant differences between two curves. Simulation studies demonstrate that the proposed procedure enjoys superior performance with strong power and good directional error control. The procedure is also illustrated with the application to two executive function studies in hemiplegia.

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. Furthermore, given an excessive zero rate, we need a model that allows a larger degree of overdispersion than existing models. In this paper, we use a random-effect model to produce a general statistical model for accommodating such phenomenon occurring in real data analyses.

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. RESULTS: Hierarchical clustering partitioned 70 AOSD patients into three distinct groups. Group 1 (n=14) had the highest mean BT (38.1± 0.4°C) and the widest variation in BT (2.7±0.9°C). Group 2 (n=35) had a lower mean BT (37.4±0.3°C) and a smaller variation (2.1±0.7°C). Group 3 (n=21) had the lowest mean BT (36.7±0.3°C) and the smallest variation (1.5±0.6°C). Clinical features and extent of organ involvement did not differ significantly between groups. However, Group 1 had lower platelet counts and higher lactate dehydrogenase, ferritin levels, and prothrombin time than the other groups. In addition, Group 1 exhibited higher risk of having a macrophage activation syndrome (MAS) and tended to require more intense treatment with corticosteroids and immunosuppressant to achieve clinical remission as compared to other groups. CONCLUSIONS: Hierarchical clustering identified three distinct fever patterns in patients with AOSD. Higher BT was associated with wider variations in diurnal temperature, higher risk of developing MAS, more intense treatment, and longer time to clinical remission, suggesting that fever pattern is a prognostic factor for AOSD.

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. We sought to compare the predictive accuracy and the number of covariates selected by these two methods in several high-dimensional datasets, consisting in genes expressions measured to predict response to chemotherapy in breast cancer patients. These comparisons were performed by building the Receiver Operating Characteristics (ROC) curves of the classifiers obtained with the selected genes and by comparing their area under the ROC curve (AUC) corrected for optimism using several variants of bootstrap internal validation and cross-validation. We found consistently in all datasets that the MUB penalization selected a remarkably smaller number of covariates than the LASSO while offering a similar-and encouraging-predictive accuracy. The models selected by the MUB were actually nested in the ones obtained with the LASSO. Similar findings were observed when comparing these results to those obtained in their first publication by other authors or when using the area under the Precision-Recall curve (AUCPR) as another measure of predictive performance. In conclusion, the MUB penalization seems therefore to be one of the best options when sparsity is required in high-dimension. Further investigation in other datasets is however required to validate these findings.

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. Moreover, OT-55 inhibited tumor necrosis factor α-induced activation of nuclear factor-кB and produced synergistic effects when used in combination with imatinib to inhibit colony formation in vitro and Bcr-Abl+ patient blast xenograft growth in zebrafish. Furthermore, OT-55 synergized with omacetaxine in imatinib-resistant KBM-5 R cells to inhibit the expression of Mcl-1, triggering apoptosis. In imatinib-resistant K562 R cells, OT-55 triggered necrosis and blocked tumor formation in zebrafish in combination with omacetaxine.

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. They also completed various psychosocial instruments. LCA was performed for the whole group and by gender. In addition to ANOVA and χ2 tests, post-hoc tests were conducted to examine differences among the LCA subgroups. Results In the whole group (n = 555), four subtypes were identified: dual-problem users (49.5%), problematic Internet users (7.7%), problematic smartphone users (32.1%), and "healthy" users (10.6%). Dual-problem users scored highest for addictive behaviors and other psychopathologies. The gender-stratified LCA revealed three subtypes for each gender. With dual-problem and healthy subgroup as common, problematic Internet subgroup was classified in the males, whereas problematic smartphone subgroup was classified in the females in the gender-stratified LCA. Thus, distinct patterns were observed according to gender with higher proportion of dual-problem present in males. While gaming was associated with problematic Internet use in males, aggression and impulsivity demonstrated associations with problematic smartphone use in females. Conclusions An increase in the number of digital media-related problems was associated with worse outcomes in various psychosocial scales. Gaming may play a crucial role in males solely displaying Internet-related problems. The heightened impulsivity and aggression seen in our female problematic smartphone users requires further research.