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Abstract Background Pediatric palliative care supports children and young adults with life-limiting conditions and their families, seeking to minimize suffering and enhance quality of life. This study evaluates the impact of specialized palliative care (SPC) on advance care planning (ACP) and patterns of end-of-life care for patients who died in the hospital. Methods This is a retrospective cohort study of medical records extracted from a clinical data warehouse, covering patients who died aged 0–24 in an academic tertiary children’s hospital in South Korea. Participants were categorized into before (2011–2013; pre-period) and after (2017–2019; post-period) the introduction of an SPC service. Within the post-period, patients were further categorized into SPC recipients and non-recipients. Results We identified 274 and 205 patients in the pre-period and post-period, respectively. ACP was conducted more and earlier in the post-period than in the pre-period, and in patients who received palliative care than in those who did not. Patients who received SPC were likely to receive less mechanical ventilation or cardiopulmonary resuscitation and more opioids. A multivariable regression model showed that earlier ACP was associated with not being an infant, receiving SPC, and having a neurological or neuromuscular disease. Conclusions SPC involvement was associated with more and earlier ACP and less intense end-of-life care for children and young adults who died in the hospital. Integrating palliative care into routine care can improve the quality of end-of-life care by reflecting patients’ and their families’ values and preferences.

In this study, we introduce a novel framework for practicing and analyzing arm muscles in motions, such as juggling actions, by estimating the hand motion of the user using a Leap Motion device. The proposed method can map the movement of a ball in a virtual world to the hand motion of the user in real time and visualize the relaxation and contraction of muscles to determine the amount of exercise performed. Our procedure has five sections: 1) The Leap Motion device tracks the hand position of the user. 2) A behavioral pattern in which a user throws a ball is defined as an event. 3) The hand motion is mapped to the ball based on the hand position of the user using the proposed parabola-based particle approach. 4) The quantity of muscle activity is visualized and analyzed in relation to the degree of arm bending. 5) Finally, we propose a method that utilizes the symmetry data-driven approach to extend solvers, enabling the efficient handling of avatar juggling motions in a virtual environment, based on user actions. Moreover, this method enhances the results by allowing diverse control over the virtual ball’s trajectory to match the user’s pose. Consequently, the proposed system enables real-time juggling in a virtual environment, as well as practice and analysis of the arm muscle activity of the user. The outcomes of the analyses are expected to be applied in various industries, including healthcare. In the solver extensions, we do not utilize all the hand position information of the user. Instead, we base the trajectory of one hand on the data of the other hand, synthesizing it through a data-driven approach. This results in a relatively lightweight algorithm that generates the avatar’s juggling motion. Additionally, by leveraging the user’s pose and parabolic motion, we can arbitrarily synthesize the trajectory of the virtual ball, facilitating the easy creation of a variety of scenes.

Children have been reported to be less affected and to have milder severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection than adults during the coronavirus disease 2019 (COVID-19) pandemic. However, children, and particularly those with underlying disorders, are still likely to develop critical illnesses. In the case of SARS‐CoV‐2 infection, most previous studies have focused on adult patients. To aid in the knowledge of in-hospital care of children with COVID‐19, this study presents an expert review of the literature, including the management of respiratory distress or failure, extracorporeal membrane oxygenation (ECMO), multisystem inflammatory syndrome in children (MIS-C), hemodynamic and other organ support, pharmaceutical therapies (anti-viral drugs, anti-inflammatory or antithrombotic therapies) and management of cardiopulmonary arrest.

Background: To investigate Pfizer-BioNTech 162b2 mRNA COVID-19 vaccine (BNT162b2) immunization-related myocarditis and describe the risk factors for consequent hospitalization in the pediatric intensive care unit (PICU) in children between 12 and 18 years. Methods: Children and adolescents 12 years of age and older who presented with discomfort after BNT162b2 immunization (BNTI) and visited pediatric emergency room (PER) at Chang Gung Memorial Hospital from September 22, 2021 to March 21, 2022, were included for analysis. Results: 681 children presented with discomfort after BNTI and visited our PER. The mean age was 15.1 ± 1.7 years. Three hundred and ninety-four (57.9%) and 287 (42.1%) events were after 1st and 2nd dose, respectively. 58.4% (n = 398) were male. The most common complaints were chest pain (46.7%) and chest tightness (27.0%). The median (interquartile range [IQR]) interval of discomfort after BNTI was 3.0 (1.0−12.0) days. BNTI-related pericarditis, myocarditis and myopericarditis were diagnosed in 15 (2.2%), 12 (1.8%) and 2 (0.3%) patients, respectively. Eleven (1.6%) needed hospitalization in PICU. The median (IQR) hospital stay was 4.0 (3.0−6.0) days. There was no mortality. More patients were diagnosed myocarditis (p = 0.004) after 2nd dose BNTI. PICU admission occurred more commonly after 2nd dose BNTI (p = 0.007). Risk factors associated with hospitalization in PICU were abnormal EKG findings (p = 0.047) and abnormal serum troponin levels (p = 0.003) at PER. Conclusion: Myocarditis in children aged 12−18 years occurred more commonly following 2nd dose BNTI. Most cases were of mild or intermediate severity without death. Factors predicting BNTI-related myocarditis and consequent hospitalization in PICU were abnormal EKG findings and abnormal serum troponin levels at PER in this study.

In this paper, we propose a learning model for tracking the isolines of fluid based on the physical properties of particles in particle-based fluid simulations. Our method involves analyzing which weights, closely related to surface tracking among the various physical properties of fluid particles, are significant. These weights are used as input values for the learning algorithm, enabling relatively accurate isoline tracking. In addition, compared to existing learning models such as linear regression, LSTM (long short-term memory), and learning representation (1-layer) models, our method obtained superior surface tracking results without accumulating errors. By using our proposed network structure to track the fluid surface, it learns and predicts values derived from existing fluid simulation algorithms, eliminating the need for computational processes for level-set values and enabling real-time surface tracking. As the scale of the simulation increases, our method significantly reduces the time and resources consumed compared to traditional methods and can track the fluid surface without additional resource consumption. Furthermore, due to our method’s simple network structure, the time consumed in the initial process of loading the model into memory is faster than models such as CNN and LSTM. Our proposed model occupies less than 30 kb of storage space, making it suitable for use in middleware. Lastly, to verify the generality of our method, we conducted tests in a total of five scenes, and in all test scenes, visually natural fluid isolines were represented.

In this paper, we propose a data augmentation technique based on Convolutional Neural Networks (CNN or ConvNet) training to efficiently obtain a dataset of images containing concrete cracks. Concrete cracks usually do not have a standardized shape and have complex patterns, making it difficult to obtain images of them, and there is a risk of exposure to dangerous situations when securing data. Therefore, in this paper, we efficiently address the difficulty of dataset collection by using a data augmentation technique based on learning the direction and thickness of cracks, which is cost-effective and time-efficient. Moreover, to improve efficiency, we introduce a method of adaptively handling crack data by constructing a quadtree based on the presence of cracks. To confirm the extent of the improvement in accuracy, we conducted experiments applying the crack detection algorithm to various scenes, and the accuracy was improved in all scenes when measured by IoU (Intersection over union) accuracy. When the algorithm was performed without augmenting the crack data, the false detection rate was about 25%. However, when we augmented the data using our method, the false detection rate significantly decreased to 3%.

In this paper, we propose an efficient sound propagation and remapping technique based on geometry to enhance immersive sound effects in virtual environments, and suggest a method to interactively represent sound by considering the altitude and slope of the terrain. The proposed technique can represent real-time patterns of sound such as waves and flows of sound, as well as diffraction that occur in a physical environment. Our approach involves identifying the position of obstacles from the sound source and calculating the new position of sound that has been refracted or diffracted due to the obstacles. Using a ray-tracing technique, we determine whether there is a collision between obstacles and sound, and calculate the reflected and refracted vectors due to the collision to predict the magnitude of sound that agents beyond the obstacles would hear. In this process, the number of reflected and refracted rays affects the magnitude of sound, allowing us to model sound attenuation based on the distance to the obstacle and the material of the obstacle. In this process, we control the magnitude of sound based on the altitude difference from the sound source and the gradient difference depending on the shape of the surrounding terrain. As a result, we efficiently and quickly represented diffraction patterns expressed in a physically-based approach, and demonstrated that the magnitude of sound diffuses naturally according to the deformed diffraction pattern as the obstacles move and rotate. Furthermore, we realistically represented the sound heard by the audience in terrains with altitude differences, and demonstrated that the magnitude of sound varies depending on the terrain.

This paper proposes a technique for transforming low-resolution (LR) simulations of hair and fur into high-resolution (HR) representations without noise, using strand geometry images in the form of lines and Convolutional Neural Network (CNN or ConvNet). LR and HR data for training are obtained through physics-based simulations, and LR-HR data pairs are set up. The training data involves converting the positions of hair strands into geometry images for use in the learning process. The proposed strand network in this paper is used as an image synthesizer to upscale LR images to HR images. When the obtained HR geometry image is transformed back into HR hair through the nonlinear transform proposed in this paper, it effectively captures the elastic movement of hair that is challenging to represent with a single mapping function alone. Furthermore, we extended the solver to accommodate hair structured in a triangular mesh form. We also propose a method for adaptively refining the hair particles of strands using the Bezier curves algorithm. Additionally, we improve the algorithm to ensure that the proposed strand geometry image can work effectively even in adaptively sampled hair particle structures. As a result, the neural network enables the stable generation of strands not only for straight and curly hair but also in various environments where different external forces are applied. Furthermore, the proposed research generates results faster than previous physics-based simulations and can be easily executed without requiring a deep understanding of complex numerical analysis processes.

In this paper, we propose a path control framework for guiding and simulating the patient’s path of travel to speed up virus testing in pandemic situations, such as COVID-19. We use geographic information and hospital state information to construct graphs to yield optimal travel paths. Pathfinding algorithms A* and Navigation mesh, which have been widely used, are efficient when applied to control agents in a virtual environment. However, they are not suitable for real-time changing cases such as the COVID-19 environment because they guide only predetermined static routes. In order to receive a virus infection test quickly, there are many factors to consider, such as road traffic conditions, hospital size, number of patient movements, and patient processing time, in addition to guiding the shortest distance. In this paper, we propose a framework for digitally twinning various situations by modeling optimization functions considering various environmental factors in real-world urban maps to handle viral infection tests quickly and efficiently.

Background: This study evaluated the prevalence and frequency of serum electrolyte abnormalities (SEAs) in children presenting to a pediatric emergency department (PED) with various diseases. Methods: Pediatric patients (≤18 years) with blood electrolyte panels obtained in the PED of Lin-Kou Chang Gung Memorial Hospital, Taiwan, in the 5 years from January 1, 2016, to August 31, 2021, were enrolled in this retrospective observational study. Patients were divided into three age groups: Group A, < 4 years; Group B, 4–11 years; and Group C, 12–18 years. The associations between SEAs and clinical diseases in children and age-related differences were assessed. Results: This study included 182,058 pediatric patients visiting our PED over a 5-year period. A total of 250 (0.14%) patients with SEAs were included in the analysis. The study population consisted of 127 boys and 123 girls with a median (IQR) age of 9.0 (3.2–14.1) years. Hospital admission was required in 86.4% (n = 216) of the patients, and 32.4% (n = 81) of them were admitted to the pediatric intensive care unit (PICU). The median (IQR) hospital stay and PICU stay was 6.5 (4.0–11.0) and 4.0 (3.0–8.0) days, respectively. The PICU stay was longer in Group A (p 4 years old was hypokalemia. In infants and young children, SEAs were associated with a longer PICU stay.

BackgroundLack of quantifiable biomarkers is a major obstacle in diagnosing and treating depression. In adolescents, increasing suicidality during antidepressant treatment further complicates the problem. ObjectiveWe sought to evaluate digital biomarkers for the diagnosis and treatment response of depression in adolescents through a newly developed smartphone app. MethodsWe developed the Smart Healthcare System for Teens At Risk for Depression and Suicide app for Android-based smartphones. This app passively collected data reflecting the social and behavioral activities of adolescents, such as their smartphone usage time, physical movement distance, and the number of phone calls and text messages during the study period. Our study consisted of 24 adolescents (mean age 15.4 [SD 1.4] years, 17 girls) with major depressive disorder (MDD) diagnosed with Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version and 10 healthy controls (mean age 13.8 [SD 0.6] years, 5 girls). After 1 week’s baseline data collection, adolescents with MDD were treated with escitalopram in an 8-week, open-label trial. Participants were monitored for 5 weeks, including the baseline data collection period. Their psychiatric status was measured every week. Depression severity was measured using the Children’s Depression Rating Scale-Revised and Clinical Global Impressions-Severity. The Columbia Suicide Severity Rating Scale was administered in order to assess suicide severity. We applied the deep learning approach for the analysis of the data. Deep neural network was employed for diagnosis classification, and neural network with weighted fuzzy membership functions was used for feature selection. ResultsWe could predict the diagnosis of depression with training accuracy of 96.3% and 3-fold validation accuracy of 77%. Of the 24 adolescents with MDD, 10 responded to antidepressant treatments. We predicted the treatment response of adolescents with MDD with training accuracy of 94.2% and 3-fold validation accuracy of 76%. Adolescents with MDD tended to move longer distances and use smartphones for longer periods of time compared to controls. The deep learning analysis showed that smartphone usage time was the most important feature in distinguishing adolescents with MDD from controls. Prominent differences were not observed in the pattern of each feature between the treatment responders and nonresponders. The deep learning analysis revealed that the total length of calls received as the most important feature predicting antidepressant response in adolescents with MDD. ConclusionsOur smartphone app demonstrated preliminary evidence of predicting diagnosis and treatment response in depressed adolescents. This is the first study to predict the treatment response of adolescents with MDD by examining smartphone-based objective data with deep learning approaches.

The swarm behavior in nature is a fascinating and complex phenomenon that has been studied extensively for decades. Visually natural swarm animation can be produced by the state-of-the-art rule-based method; however, it still suffers from the drawbacks of low control accuracy and instability in swarm behavior quality when controlled by the user. This study proposes a deep reinforcement learning (DRL) based approach to generate swarm animation that reacts to real-time user control with high quality. A rule-based action generator (RAG) adapted to the actor-critic DRL method is presented to enhance DRL’s action exploration strategy. Various practical dynamic reward functions are also designed for DRL to train agents by rewarding swarm behaviors and penalizing misbehavior. The user controls the swarm by interacting with the swarm’s leader agent, for example by directly changing its speed or orientation, or by specifying a path consisting of waypoints. The second aim of this study is to improve the scalability of the trained policy. This study introduces a new state observation quantity of DRL called the embedded features of swarm (EFS) for allowing the trained policy scaling to a more extensive system than it has been trained on. In the experiments, four different scenarios have been designed to evaluate the control accuracy and quality of the generated swarm behavior by metrics and visualization. Additionally, the experiment has compared the performance of the proposed dynamic reward functions with fixed reward functions. Experimental results show that the proposed approach outperforms state-of-the-art methods in terms of swarm behavior quality and control accuracy. Moreover, the proposed dynamic reward functions are more effective than the existing reward functions.

Background: Poisoning by household cleaning products(HCPs) is common in children. Some HCPs are toxic and may cause severe complications. We assessed HCP poisonings treated in a pediatric emergency department(PED). Methods: This was a retrospective study of patients aged under 18 years with HCP poisoning admitted to the largest PED in Taiwan from 2011 to 2020 were recruited. Results: The records over a 10-year period from 103 children admitted to the PED because of HCP poisoning(70 boys [68%] and 33 girls [32%]), mean age 3.54 years(standard deviation [SD] = 3.15 years) were evaluated. Most poisonings were unintentional(99%, n = 102) and occurred at home(96%, n = 99). The HCPs included alkaline(74%, n = 76), acidic(25%, n = 26), and neutral(1%, n = 1) agents. Most were orally ingested(86%, n = 89). Panendoscopy was performed in 25 patients(24%), and the endoscopic(Zargar) grade was used to determine the severity of injury. Medications(steroids [9%, n = 9], antibiotics [10%, n = 10], or antacids [30%, n = 23]) were prescribed. Alkaline HCP ingestion induced severe esophageal injury(p = 0.04) and esophageal stricture(p = 0.04). Five patients(5%) exhibited esophageal strictures and required balloon dilation. On multivariate analysis, alkaline HCP ingestion(p = 0.04), severe esophageal caustic injury(Zargar grade ≥ 3) (p

In this paper, we propose a highly efficient method for synthesizing high-resolution(HR) smoke simulations based on deep learning. A major issue for physics-based HR fluid simulations is that they require large amounts of physical memory and long execution times. In recent years, this issue has been addressed by developing deep-learning-based super-resolution(SR) methods that convert low-resolution(LR) fluid simulation results to HR(High-resolution) versions. However, these methods were not very efficient because they performed operations even in areas with low density or no density. In this paper, we propose a method that can maximize its efficiency by introducing a downscaled and binarized adaptive octree. However, even if it is divided by octree, because the number of nodes increases when the resolution of the simulation space is large, we reduce the size of the space by multiscaling and at the same time perform binarization to preserve the density that may be lost in this process. The octree calculated in this process has a structure similar to that of a multigrid solver, and the octree calculated at coarse resolution is restored to its original size and used for HR expression. Finally, we apply the SR process only to those areas having significant density values. Using the proposed method, the SR process is significantly faster and the memory efficiency is improved. The performance of our method is compared with that of an existing SR method to demonstrate its efficiency.

In this paper, we present a novel method that can stably express the directional ice form caused by freezing of flowing water. The key to the proposed framework is to reflect the flow of fluids with viscosity in the direction of ice growth. Water is simulated by applying a new viscous technique to the implicit incompressible fluid simulation, and the proposed anisotropic freezing solution is used to express directional ice and glaze effects. The conditions under which water particles turn into ice particles are calculated according to a new energy function based on humidity and water flow. The humidity is approximated based on the virtual water film on the surface of the object, and the flow of fluid is incorporated into our anisotropic freezing solution to guide the growth direction of the ice. As a result, the proposed technique reliably produces glaze and directional freezing effects according to the flow direction of viscous water.

Physically-based simulation is being expanded to simulate various materials such as deformable bodies, sand, and lava as well as liquid, and has been widely used as plug-in and software in actual related industries. Contrary to this trend, however, most of the simulation software products are built as an in-house solution, making it difficult for users to add new features not provided by the software. In this paper, we propose a post-processing framework that can improve the visual quality of liquid surfaces by receiving only the positions of liquid particles created in the existing simulation tools. Our framework includes the following methods: 1) analyzing liquid flow from particle position, 2) sampling technique for creating liquid sheets. Our framework improves the detail of liquid surfaces by temporarily adding new liquid particles through sampling of particles from existing simulation techniques. Our technique does not depend on any particular simulation tool. Experiments with various scenarios have also shown that liquid sheet detail, which was difficult to express in traditional software, has been improved, and problems such as break-up fluid surfaces and noisy surfaces have been mitigated. In addition, our research is a post-processing framework that uses only particle position as input data, making it easy to integrate with existing simulation tools that provide particle position.

Abstract Background The increase in the number of pediatric patients with complex health conditions necessitates the application of advance care planning for children. Earlier, withdrawal of life-sustaining treatment was taboo in the medical society in South Korea due to the history of such practice being punishable by law, and physicians tended to pursue aggressive treatment. With changes in public opinion on end-of-life care, the Korean government enacted a new law that protect human dignity by respecting patients’ self-determination and facilitating advance care planning. However, little is known about current state of advance care planning for pediatric patients. The study aimed to assess perceptions regarding advance care planning among South Korean pediatricians and clarify any differences in perception among pediatric subspecialties. Methods This study was an observational cross-sectional survey that used a web-based self-report questionnaire. Participants comprised of pediatricians currently caring for children with life-limiting conditions in 2018. Results Of the 96 respondents, 89 were included in the analysis. In a hypothetical patient scenario, more hemato-oncologists and intensivists than neonatologists and neurologists preferred to provide comfort care than aggressive treatment. While 72.2% of hemato-oncologists reported that they usually or always discuss advance care plans with parents during treatment, more than half of other pediatricians reported that they seldom do so. Furthermore, 65% of respondents said that they never discuss advance care planning with adolescent patients. Moreover, there were no notable differences among subspecialties. The most prevalent answers to factors impeding advance care planning were lack of systemic support after performing advance care planning (82.0%) and uncertain legal responsibilities (70.8%). Conclusions The pediatricians differed in their experiences and attitudes toward advance care planning based on their subspecialty. Consequently, institutional support and education should be provided to physicians so that they can include children and families in discussions on prognosis.

Abstract Background The aim of this study was to examine the psychometric properties of the Korean version of Screen for Child Anxiety Related Emotional Disorders (SCARED) on a sample of Korean youths and to examine the cross-cultural differences in adolescents’ anxiety. Methods Our study included 147 adolescents (ages 12–17, 92 girls), 93 with major depressive disorder and 54 as controls. Participants were evaluated using the Kiddie-Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL), SCARED, Child Behavior Checklist (CBCL), Disruptive Behavioral Disorder Scale (DBD) and Attention Deficit Hyperactivity Disorder Rating Scale (ADHD-RS). Pearson’s r and Cronbach’s α values of the SCARED were calculated, and exploratory factor analysis was conducted. Results The Korean SCARED scores were correlated with the total anxiety scores of K-SADS-PL (r = 0.74) and the CBCL anxious/depressed subscale scores (r = 0.35). Results showed a five-factor structure with good internal consistency, in which some items were loaded on different factors compared to previous studies. Conclusions The Korean SCARED demonstrated promising psychometric properties, and could be a valid scale for screening anxiety symptoms in primary care. The fact that different items comprised the factors may reflect the cultural difference between United States and Korea in experiencing anxiety.

We propose an anisotropic constrained-boundary convolutional neural networks (hereafter, AnisoCBConvNet) that can stably express high-quality meshes without oscillation by applying super-resolution operations to low-resolution cloth meshes. As a training set for the neural network, we use a pair between simulation data of low resolution (LR) cloth and data obtained by applying the same simulation to high resolution (HR) cloth with increased quad mesh resolution of LR cloth. The actual data used for training are 2D geometry images converted from 3D meshes. The proposed AnisoCBConvNet is used to train an image synthesizer that converts LR geometry images to HR geometry images. In particular, by controlling the weights anisotropically near the boundary, the problem of surface wrinkling caused by oscillation is alleviated. When the HR geometry image obtained through AnisoCBConvNet is converted back to the HR cloth mesh, details including wrinkles are expressed better than the input cloth mesh. In addition, our results improved the noise problem in the existing geometry image approach. We tested AnisoCBConvNet-based super-resolution in various simulation scenarios, and confirmed stable and efficient performance in most of the results. By using our method, it will be possible to effectively produce CG VFX created using high-quality cloth simulation in games and movies.

Background: Phthalates are synthetic chemicals with endocrine-disrupting properties. They are reportedly associated with various neurotoxic outcomes. Studies on exposure to phthalates and children’s autistic traits have shown inconsistent results with respect to sex and susceptible time periods. We investigated the association of phthalate exposure during the prenatal period and childhood with autistic traits over time using a birth cohort in South Korea. Methods: Five phthalate metabolites were measured during mid-term pregnancy and children's follow-up at ages of 4, 6, and 8 years among a total of 547 mother–child pairs. The social communication questionnaire (SCQ) was used to assess autistic traits of children at each time point. The relationship between phthalate metabolites and SCQ scores were analyzed by exposure windows and sex. Results: A 2.7 fold increase in di-(2-ethylhexyl) phthalate metabolite levels, mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) and mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP) during pregnancy was associated with increased SCQ scores at 4 years by 8.5% (95% confidence intervals [CI]: 1.9%, 15.5%) and 7.4% (95% CI: 0.3%, 15.0%), respectively, but not at the age of 6 or 8 years. Moreover, MEHHP levels at ages of 4 and 8 years were associated with increased SCQ scores at 8 years by 9.9% (95% CI: 1.8%, 18.6%) and 9.6% (95% CI: 1.3%, 18.6%), respectively. Boys showed stronger associations between phthalate exposure and SCQ scores than girls. Conclusion: The study suggested different susceptible time windows of phthalate exposure: exposure during pregnancy is associated with autistic traits in young children, whereas exposure during early childhood years leads to autistic traits in school-aged children, particularly boys.

Background: Pediatric poisoning is a common emergency worldwide. Routine surveillance is required for public health authorities and physicians to update strategies for prevention and management of pediatric poisoning. This study investigated the epidemiology of poisoning among children admitted to an emergency department (ED). Methods: This was a retrospective descriptive study. Data were collected from patients under 18 years old (y/o) presenting with poisoning at the largest ED in North Taiwan from 2011 to 2015. Results: Five-year records of 590 patients—309 (52.3%) boys and 281 (47.7%) girls—were analyzed. The mean age was 5.07 y/o (Standard Deviation [SD] = 5.02 years), and 94.7% of events occurred at home. Incidence was highest from 6 p.m. to 12 a.m. (42.2%, n = 249). Most patients younger than 11 y/o were male, but this gender distribution was reversed in adolescents (11–17 y/o). Pharmaceutical ingestion (41.4%, n = 244) was the leading cause of poisoning; pesticide was the most common non-pharmaceutical poison ingested (9.5%, n = 55). Carbon monoxide (CO) intoxication (87.6%, n = 99) and snakebite (75%, n = 9) were the common causes of inhalation (n = 113) and venom (n = 12) poisoning, respectively. The mean duration of the ED stay was 5.45 h (SD = 7.39 h), and 101 cases (17.2%), including 21 cases (3.6%) requiring intensive care, were admitted to the hospital. All patients survived. Conclusion: Most poisonings occurred in young children, at home, by unintentional ingestion of a single substance, from 6 p.m. to 12 a.m. Female adolescents were the common intentional poisoning patients and pharmaceutical ingestion was the leading cause of poisoning. This kind of information enables ED physicians to improve preparations for pediatric poisoning cases and allows public health authorities to sharpen the focus of poisoning prevention efforts. Key Words: children, emergency department, intentional, poisoning, toxicology

In this paper, we propose a sprite animation synthesis technique that can efficiently represent the fire-flake effects seen in the natural phenomenon of fire. The proposed method uses the actual fire video or animated fire video as inputs and performs the following steps: 1) Extraction of feature vectors that can predict the direction of flame from image, 2) calculation of artificial buoyancy field, 3) creation and advection of fire-flake texture, 4) calculation of artificial motion blur using buoyancy flow, and 5) high quality composition. First, we detect the edges from the image and calculate the feature vectors needed to calculate the artificial buoyancy field. The computed 2D feature vectors are integrated into the Navier-Stokes equation and used to calculate the buoyancy field, which generates and advects anisotropic fire-flake textures. Finally, we apply artificial motion blur according to buoyancy direction to improve composition result of sprite animation. As a result, this method is based on image synthesis, which is faster than the existing 3D simulation-based approach. Experimental results show that high quality results can be easily and reliably obtained. In addition, since the final result is a sprite animation format, it can be easily used in existing game engines.

Traumatic brain injury (TBI) is a leading cause of pediatric morbidity and mortality and is categorized as abusive head trauma (AHT) and accidental head injury. A retrospective chart review of 124 children aged

In this paper, we propose a technique that can efficiently express the preservation and breakup of liquid sheets by eliminating over-preserved liquid sheets using the motion of water particles projected onto the screen. First, we project three-dimensional water particles onto a two-dimensional screen. When multiple particles are projected onto the same pixel, we select one of the front most particles as a screened particle by comparing their depth values. Based on the anisotropic kernel and density, the motion of the screened water particles is tracked to determine whether preservation and breakup should be performed. As a result, new water particles are added or existing ones are deleted, which makes it possible to express two characteristics of particle-based fluids: preservation and breakup of liquid sheets. The proposed technique is based on particle-based fluids, which can be used to remove the over-preserved liquid sheets and thus improve the quality of liquid sheets without surface noise.

In this paper, we propose a new interface to control VR(Virtual reality) contents, games, and animations in real-time using the user's breath and the acceleration sensor of a mobile device. Although interaction techniques are very important in VR and physically-based animations, UI(User interface) methods using different types of devices or controllers have not been covered. Most of the proposed interaction techniques have focused on screen touch and motion recognition. The direction of the breath is calculated using the position and angle between the user and the mobile device, and the control position to handle the contents is determined using the acceleration sensor built into the mobile device. Finally, to remove the noise contained in the input breath, the magnitude of the wind is filtered using a kernel modeling a pattern similar to the actual breath. To demonstrate the superiority of this study, we produced real-time interaction results by applying the breath as an external force of VR contents, games, and animations.

In this paper, we propose an angular momentum-based advection technique that can express the turbulent foam effect. The motion of foam particles, which are strongly bound to the motion of the underlying fluid, is viscous, and sometimes clumping problems occur. This problem is a decisive factor that makes it difficult to express realistic foam effects. Since foam particles, which are secondary effects, depend on the motion of the underlying water, in order to exaggerate the foam effects or express more lively foam effects, it is inevitable to tune the motion of the underlying water and then readjust the foam particles. Because of such a cumbersome process, the readjustment of the foam effects requires a change in the motion of the underlying water, and it is not easy to produce such a scene because the water and foam effects must change at the same time. In this paper, we present a method to maintain angular momentum-based force from water particles without tuning the motion of the underlying water. We can restore the lost turbulent flow by additional advection of foam particles based on this force. In addition, our method can be integrated with screen-space projection frameworks, allowing us to fully embrace all the advantages of this approach. In this paper, the turbulence of the foam particles was improved by minimizing the viscous motion of the foam particles without tuning the motion of the underlying water, and as a result, lively foam effects can be expressed.

Surface tension has a great influence on the shape of the fluid interface, and is an important physical characteristic in expressing not only liquids but also liquid metals such as mercury and gallium. In the field of physics-based particle fluid simulations, it is a challenging problem to express the high surface tension generated by fluid-air or fluid-solid interaction in real time. The main reasons for this are (1) The magnitude of the force that can be stably expressed in real-time fluid simulation is limited, so when the magnitude of the surface tension increases at a large time-step, the simulation stability decreases, and (2) If we use a small time-step, a stronger force can be expressed. However, it becomes difficult to operate in real time because the computational cost increases. Techniques were proposed to solve this problem for a few specific scenes, but there has not yet been a general approach that can reliably express high surface tension in various scenarios. In this paper, we propose a real-time particle-based fluid simulation framework that can efficiently and stably express high surface tension. Unlike the previous methods, we newly model the surface tension so that the strong surface tension force generated in the droplet area with a large curvature is applied evenly in the normal and tangent directions regardless of the size of the droplet. We also propose new pressure constraints that converge quickly and accurately using this force. Our method can be effectively used in various physics-based simulation scenarios because it can easily express and control surface tension effects that appear in materials such as liquid metal as well as water.

Abstract Background The effect of diabetes mellitus (DM) and chronic kidney disease (CKD) on long-term outcomes in patients receiving percutaneous coronary intervention (PCI) is unclear. Methods A total of 1394 patients who underwent PCI were prospectively enrolled and divided into 4 groups according to the presence or absence of DM or CKD. Baseline characteristics, risk factors, medications, and angiographic findings were compared. Determinants of long-term outcomes in patients undergoing PCI were analyzed. Results Patients with DM and CKD had the highest all-cause mortality and cardiovascular mortality (both P

Abstract Drought stress significantly retards the plant production. Melatonin is a vital hormone, signaling molecule, and bio-regulator of diverse physiological growth and development processes. Its role in boosting agronomic traits under diverse stress conditions has received considerable attention. However, the underlying molecular mechanism of action and how they increase drought stress tolerance has not been fully interpreted. The current study aimed to ascertain the protective role of melatonin in fortifying the antioxidant defense system, modulating the phytohormone profile, and improving agronomic traits of tomato seedlings under drought stress. After the V1 stage (1st leaf fully emerged), tomato seedlings were exposed to PEG-6000 to mimic drought-induced stress (DR 10% and DR 20%), followed by exogenous application of 100 µM soil drench. Drought-induced stress negatively impacted tomato seedlings by reducing growth and development and biomass accumulation, diminishing salicylic acid (SA) and chlorophyll levels, and dramatically lowering the antioxidant defense ability. However, melatonin protected them by activating the defense system, which decreased the oxidative burst and increased the activities of SOD, CAT, and APX. Administration of 100 µM melatonin by soil drench most remarkably downregulated the transcription factors of SlDREB3 and SlNCED3. This study has validated the moderating potential of melatonin against drought-induced stress by maintaining plant growth and development, enhancing hormone levels, elevating antioxidant enzyme activities, and suppressing the relative expression of drought-responsive genes. These findings also provide a basis for the potential use of MT in agricultural research and other relevant fields of study.

Abstract Bacillus pumilus plays an essential role in agricultural applications as a beneficial microbe and for sustainable agriculture production. However, the underlying mechanisms of B. pumilus strains remain unclear as to how they are beneficial for plants as stress tolerant and growth promoters. Bacillus pumilus was isolated from the rhizosphere soil of Artemisia vulgaris. NGS (next-generation sequencing) was performed for the strain to gain new insights into the molecular mechanisms underlying plant-microbial interactions. NGS revealed 3,910 genes, 3294 genes with protein-coding, and 11 functional genomic regions related to diverse agronomic traits including stress tolerance. We identified the two possible phytohormone biosynthesis approaches from metabolic regions1(terpense→diterpense→betacarotene→xanthoxin→ABA)2(terpense→diterpense→geranyl diphosphate →C20 →GA). Several gene clusters related to the biosynthesis of phytohormones, stress tolerance, and agricultural diversification were predicted. The genome provides insights into the possible mechanisms of this bacterium for stress tolerance and its future applications. The genomic organization of B. pumilus revealed several hallmarks of its plant growth promotion and pathogen suppression activities. Our results provide detailed genomic information for the strain and reveal its potential stress tolerance mechanisms, laying the foundation for developing effective stress tolerance strategies against abiotic stress.

The present study examined patterns of number-related utterances and actions directed to 9-month-old infants by their parents. An ethnically and economically diverse sample of 86 families participated in structured play sessions conducted separately with the mothers and fathers of each infant. Data were coded for eight math talk categories, and these codes were analyzed for frequency, as well as complexity (number of categories mentioned within each 30-sec time interval) and diversity (number of categories mentioned across the entire play session). Number-related utterances and actions were frequent, with cardinality, one-to-one correspondence, and non-numerical quantitative terms (e.g., "some, less") being most frequent. Mothers and fathers both provided frequent and diverse input, though mothers provided significantly more math talk than fathers for abstract counting and equivalence/non-equivalence, and mothers mentioned significantly more categories overall than fathers. Both non-numerical quantitative relations and cardinality were significantly associated with children's preschool numeracy outcomes, though the association was negative for cardinality. Furthermore, fathers' numeracy input and mothers' total talk were also significantly and positively associated with children's numeracy outcomes.

In this paper, we propose a method to efficiently control the path of non-playable characters (NPC) in an interactive virtual environment such as a game or virtual reality (VR) by calculating a weight map and path similarity based on the user’s path. Our method automatically constructs a navigation mesh that provides a new route to the NPC by referring to the user’s trajectory. Our method finds more paths that users usually go through as time passes, and the number of users increases. Accordingly, the paths that NPCs can traverse automatically are updated adaptively to the virtual environment. In addition, NPC agents can move smartly by assigning high weights to the user’s preferred paths. We tested the usefulness of the proposed method through several example scenarios in an interactive environment such as a video game or VR, and this method can be easily applied to various types of navigation based on the interactive environment.

The imperialist competitive algorithm combined with chaos theory (CICA) demonstrates excellent performance in global optimization problems. However, its computational complexity increases with the introduction of chaotic maps. To address this, we integrate CICA with a dropout strategy that randomly samples the dimensions of each solution at each iteration of the computation. We investigate the potential of the proposed algorithm with different chaotic maps through six symmetric and six asymmetric benchmark functions. We also apply the proposed algorithm to AUVs’ path planning application showing its performance and effectiveness in solving real problems. The simulation results show that the proposed algorithm not only has low computational complexity but also enhances local search capability near the globally optimal solution with an insignificant loss in the success rate.

The white color in the larval integument of the silkworm Bombyx mori is considered the result of uric acid accumulation in its epidermal cells. Larvae of the eri silkworm Samia ricini (Lepidoptera; Saturniidae) also have a white and opaque integument, but little is known about its coloration mechanism. In this study, we first performed a feeding assay of S. ricini larvae using allopurinol, an inhibitor of xanthine oxidase, which catalyzes the degradation of xanthine to uric acid. This treatment induced a clear translucent integument phenotype, indicating that the larval color of S. ricini is also determined by uric acid accumulation. Next, to investigate the genetic basis that controls uric acid accumulation in S. ricini larvae, we isolated and characterized the S. ricini homolog of mammalian biogenesis of lysosome-related organelles complex 1, subunit 2 (BLOS2), which is known to play a crucial role in urate granule biosynthesis. We created a transcription activator-like effector nuclease (TALEN)-mediated gene knockout of S. ricini BLOS2 (SrBLOS2) and succeeded in establishing SrBLOS2 knockout strains (SrBLOS2KO). SrBLOS2KO mutants exhibited a translucent larval integument phenotype and lacked uric acid in the epidermis, as also observed in allopurinol-fed larvae. In addition, electron microscopy revealed that urate granules were rarely observed in the epidermis of SrBLOS2KO larvae, whereas abundant granules were found in the epidermis of wild-type larvae. These results clearly demonstrated that larval S. ricini accumulates uric acid as urate granules in the epidermis and that the genetic basis that controls uric acid accumulation is evolutionarily conserved in S. ricini and B. mori.

When simulating fluids, such as water or fire, interacting with solids, it is a challenging problem to represent details of diffusion front in physical reaction. Previous approaches commonly use isotropic or anisotropic diffusion to model the transport of a quantity through a medium or long interface. We have identified unrealistic monotonous patterns with previous approaches and therefore, propose to extend these approaches by integrating the deformation of the material with the diffusion process. Specifically, stretching deformation represented by strain is incorporated in a divergence-constrained diffusion model. A novel diffusion model is introduced to increase the global rate at which the solid acquires relevant quantities, such as heat or saturation. This ensures that the equations describing fluid flow are linked to the change of solid geometry, and also satisfy the divergence-free condition. Experiments show that our method produces convincing results.

In this paper, we propose a method for automatically detecting the times during which game players exhibit specific behavior, such as when players commonly show excitement, concentration, immersion, and surprise. The proposed method detects such outlying behavior based on the game players’ characteristics. These characteristics are captured non-invasively in a general game environment. In this paper, cameras were used to analyze observed data such as facial expressions and player movements. Moreover, multimodal data from the game players (i.e., data regarding adjustments to the volume and the use of the keyboard and mouse) was used to analyze high-dimensional game-player data. A support vector machine was used to efficiently detect outlying behaviors. We verified the effectiveness of the proposed method using games from several genres. The recall rate of the outlying behavior pre-identified by industry experts was approximately 70%. The proposed method can also be used for feedback analysis of various interactive content provided in PC environments.

When we perform particle-based water simulation, water particles are often increased dramatically because of particle splitting around breaking holes to maintain the thin fluid sheets. Because most of the existing approaches do not consider the volume of the water particles, the water particles must have a very low mass to satisfy the law of the conservation of mass. This phenomenon smears the motion of the water, which would otherwise result in splashing, thereby resulting in artifacts such as numerical dissipation. Thus, we propose a new fluid-implicit, particle-based framework for maintaining and representing the thin sheets and turbulent flows of water. After splitting the water particles, the proposed method uses the ghost density and ghost mass to redistribute the difference in mass based on the volume of the water particles. Next, small-scale turbulent flows are formed in local regions and transferred in a smooth manner to the global flow field. Our results show us the turbulence details as well as the thin sheets of water, thereby obtaining an aesthetically pleasing improvement compared with existing methods.

In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it.

Objective Next generation sequencing is commonly used to characterize the microbiome structure. MiSeq is most commonly used to analyze the microbiome due to its relatively long read length. Illumina also introduced the 250 × 2 chip for NovaSeq. The purpose of this study was to compare the performance of MiSeq and NovaSeq in the context of oral microbiome study.Methods Total read count, read quality score, relative bacterial abundance, community diversity, and correlation between two platforms were analyzed. Phylogenetic trees were analyzed for Streptococcus and periodontopathogens.Results NovaSeq produced significantly more read counts and assigned more operational taxonomic units (OTUs) compared to MiSeq. Community diversity was similar between MiSeq and NovaSeq. NovaSeq were able to detect more unique OTUs compared to MiSeq. When phylogenetic trees were constructed for Streptococcus and periodontopathogens, both platforms detected OTUs for most of the clades.Conclusion Taken together, while both MiSeq and NovaSeq platforms effectively characterize the oral microbiome, NovaSeq outperformed MiSeq in terms of read counts and detection of unique OTUs, highlighting its potential as a valuable tool for large scale oral microbiome studies.

Xerostomia, commonly known as dry mouth, is characterized by reduced salivary secretion, which can lead to various oral health issues and discomfort. In this paper, we propose a novel, non-invasive method for predicting xerostomia through the analysis of tongue images. To predict salivary gland secretion from tongue images, we collected images from patients who visited the hospital with complaints of dry mouth and measured their saliva secretion. Features were extracted from these tongue images, and correlation analysis was performed using machine learning techniques to assess the relationship between the extracted features and measured saliva secretion. We obtained tongue images and saliva secretion measurements from 176 patients. Images were cropped to 100 × 100 pixels, resulting in 462 features. The dataset was divided into training and test sets, consisting of 160 and 16 samples, respectively. The correlation coefficients for the training and test datasets were 0.9496 and 0.9415, respectively, while the correlation coefficient for the entire dataset was 0.9482. The estimated linear equation was y = 0.9244x + 2.1664. This study aimed to predict salivary gland secretion based on tongue images. By extracting features from color images and employing a neural network machine learning model, we estimated salivary gland secretion. With a sufficiently large dataset of tongue images, further advancements in regression analysis using deep learning techniques could enhance the accuracy of these predictions.