Effects of robot rehabilitation on the motor function and gait in children with cerebral palsy: a systematic review and meta-analysis.

This study was to determine the effects of robot rehabilitation on motor function and gait in children with cerebral palsy (CP) and the effect of robot type. Inclusion criteria were children with any type of CP, robot rehabilitation studies, non-robot rehabilitation comparison groups, outcomes related to motor function and gait, and randomized controlled trials. PubMed, Embase, Cochrane Library, CINAHL, and Web of Science databases were searched. Risk of bias was assessed using physiotherapy evidence database. Seven studies with a total of 228 participants were selected. Motor function was significantly improved in three studies comparing robot rehabilitation and control groups (standard mean difference [SMD], 0.79; 95% confidence intervals [CIs], 0.34-1.24; I 2=73%). Gait was not significantly improved in five studies comparing robot rehabilitation and control groups (SMD, 0.27; 95% CI, -0.09 to 0.63; I 2=45%). When comparing effects by robot type, robotic-assisted gate training (RAGT) showed significant improvements in both motor function (SMD, 0.89; 95% CI, 0.36-1.43; I 2=77%) and gait (SMD, 0.62; 95% CI, 0.12-1.11; I 2=44%). Robot rehabilitation effectively improved motor function, and among the robot types, RAGT was found to be effective in improving motor function and gait.

Immediate effects of a vibrating foam roller on dorsiflexion rom, balance, and gait in stroke patients: a randomized controlled trial.

The purpose of this study was to investigate the immediate effects of vibration foam rolling on dorsiflexion range of motion (ROM), balance, and gait in stroke patients. Thirty stroke patients volunteered to participate and were randomly assigned to the vibrating foam roller group (n=15) and the foam roller group (n=15). The vibrating foam roller group performed a 30-min foam roller exercise program, with participants subjected to vibration at 28 Hz. The foam roller group performed the same exercise program as the vibrating foam roller group, but without vibration. Dorsiflexion lunge test, limits of stability, and Timed Up and Go were used to evaluate dorsiflexion ROM, balance, and gait before and after each intervention. The results revealed that the vibration foam roller group showed significant differences in dorsiflexion ROM and gait after the intervention, while the foam roller group exhibited a significant difference only in dorsiflexion ROM (P<0.05). In comparisons between the vibration foam roller group and the foam roller group, significant differences were observed in dorsiflexion ROM and gait (P<0.05). However, there were no significant differences in balance, both before and after the intervention, as well as in the comparisons between the two groups (P>0.05). This study confirmed that a single-session vibrating foam roller exercise program improves dorsiflexion ROM and gait in stroke patients. Further studies with extended exercise program durations are needed to address limitations and explore long-term effects.

Continuous tracking using deep learning-based decoding for noninvasive brain-computer interface.

Brain-computer interfaces (BCI) using electroencephalography provide a noninvasive method for users to interact with external devices without the need for muscle activation. While noninvasive BCIs have the potential to improve the quality of lives of healthy and motor-impaired individuals, they currently have limited applications due to inconsistent performance and low degrees of freedom. In this study, we use deep learning (DL)-based decoders for online continuous pursuit (CP), a complex BCI task requiring the user to track an object in 2D space. We developed a labeling system to use CP data for supervised learning, trained DL-based decoders based on two architectures, including a newly proposed adaptation of the PointNet architecture, and evaluated the performance over several online sessions. We rigorously evaluated the DL-based decoders in a total of 28 human participants, and found that the DL-based models improved throughout the sessions as more training data became available and significantly outperformed a traditional BCI decoder by the last session. We also performed additional experiments to test an implementation of transfer learning by pretraining models on data from other subjects, and midsession training to reduce intersession variability. The results from these experiments showed that pretraining did not significantly improve performance, but updating the models' midsession may have some benefit. Overall, these findings support the use of DL-based decoders for improving BCI performance in complex tasks like CP, which can expand the potential applications of BCI devices and help to improve the quality of lives of healthy and motor-impaired individuals.

Cooling Performance Prediction of Particle-Based Radiative Cooling Film Considering Particle Size Distribution.

Here, we present a novel protocol concept for quantifying the cooling performance of particle-based radiative cooling (PBRC). PBRC, known for its high flexibility and scalability, emerges as a promising method for practical applications. The cooling power, one of the cooling performance indexes, is the typical quantitative performance index, representing its cooling capability at the surface. One of the primary obstacles to predicting cooling power is the difficulty of simulating the non-uniform size and shape of micro-nanoparticles in the PBRC film. The present work aims to develop an accurate protocol for predicting the cooling power of PBRC film using image processing and regression analysis techniques. Specifically, the protocol considers the particle size distribution through circle object detection on SEM images and determines the probability density function based on a chi-square test. To validate the proposed protocol, a PBRC structure with PDMS/Al2O3 micro-nanoparticles is fabricated, and the proposed protocol precisely predicts the measured cooling power with a 7.8% error. Through this validation, the proposed protocol proves its potential and reliability for the design of PBRC.

Differing Efficacy of Dapagliflozin Versus Empagliflozin on the Risk of Incident Atrial Fibrillation in Patients With Type 2 Diabetes: A Real-World Observation Using a Nationwide, Population-Based Cohort.

BACKGROUND: Meta-analyses of large clinical trials investigating SGLT2 (sodium-glucose cotransporter-2) inhibitors have suggested their protective effects against atrial fibrillation in patients with type 2 diabetes. However, the results were predominantly driven from trials involving dapagliflozin. METHODS AND RESULTS: We used a nationwide, population-based cohort of patients with type 2 diabetes who initiated either dapagliflozin or empagliflozin between May 2016 and December 2018. An active-comparator, new-user design was used, and the 2 groups of patients were matched using propensity scores. The primary outcome was incident nonvalvular atrial fibrillation, which was analyzed using both the main intention-to-treat and sensitivity analysis that censored patients who skipped their medications for ≥30 days. Men ≥55 years of age and women ≥60 years of age with ≥1 traditional risk factor or those with established cardiovascular disease were categorized as high cardiovascular risk group. Patients not included in the high-risk group were categorized as low risk. After 1:1 propensity-score matching, a total of 137 928 patients (mean age, 55 years; 58% men) were included and followed up for 2.2±0.6 years. The risk of incident atrial fibrillation was significantly lower in the dapagliflozin group in both the main (hazard ratio [HR], 0.885 [95% CI, 0.789-0.992]) and sensitivity analyses (HR, 0.835 [95% CI, 0.719-0.970]). Notably, this was consistent in both the low and high cardiovascular risk groups. There was no effect modification by age, sex, body mass index, duration of diabetes, or renal function. CONCLUSIONS: This real-world, population-based study demonstrates that patients with type 2 diabetes using dapagliflozin may have a lower risk of developing nonvalvular atrial fibrillation than those using empagliflozin.

A Survey on the Use of Outcome Measures during Physical Therapy Interventions by Physical Therapists in Korea.

The purpose of this study was to determine the current status of patient care provided by Korean physical therapists (KPTs) in clinical practice by studying the outcome measures (OMs) used in physical therapy interventions among KPTs with experience in treating patients. A total of 225 KPTs with experience in treating patients in clinical settings participated in the study and completed the online questionnaire. The questionnaire included questions about the use of OMs and the reasons for using them, as well as the types, benefits, and barriers of OMs. The participants' responses were analyzed and reported in terms of frequencies and percentages. A total of 220 questionnaires were analyzed. The results show that the majority of KPTs in clinical practice used OMs during interventions. The main reasons for using OMs were to check the patient's condition and to determine the direction and effectiveness of treatment. In terms of the types of OMs used, the highest percentage of subjects used both patient-reported OMs (PROMs) and performance-based OMs (PBOMs). They chose OMs that were quick and easy to use and used them voluntarily. Barriers to and reasons for not using OMs were similar, including lack of benefits, lack of time, and problems with patient performance and uncooperative behavior. When analyzing the effect of demographic characteristics on the use of OMs, we found that physical therapists specializing in musculoskeletal and neurological systems, physical therapists with longer treatment times, and physical therapists who valued OMs were more likely to use them. Based on the results of this study, it is recommended that improvements in the work environment and healthcare system are needed to enhance the professionalism of KPTs working in the field of physical agent therapy by improving their awareness of Oms and improving the quality of physical therapy interventions.

Continuous Tracking using Deep Learning-based Decoding for Non-invasive Brain-Computer Interface.

Brain-computer interfaces (BCI) using electroencephalography (EEG) provide a non-invasive method for users to interact with external devices without the need for muscle activation. While noninvasive BCIs have the potential to improve the lives of both healthy and motor impaired individuals, they currently have limited applications due to inconsistent performance and low degrees of freedom. In this study, we use deep-learning (DL)-based decoders for online Continuous Pursuit (CP), a complex BCI task requiring the user to track an object in 2D space. We developed a new labelling system to use CP data for supervised learning, trained DL-based decoders based on two architectures, including a newly proposed adaptation of the PointNet architecture, and evaluated the performance over several online sessions. We rigorously evaluated the DL-based decoders in a total of 28 human subjects, and found that the DL-based models improved throughout the sessions as more training data became available and significantly outperformed a traditional BCI decoder by the last session. We also performed additional experiments to test an implementation of transfer learning by pre-training models on data from other subjects, and mid-session training to reduce inter-session variability. The results from these experiments show that pre-training did not significantly improve performance, but updating the models mid-session may have some benefit. Overall, these findings support the use of DL-based decoders for improving BCI performance in complex tasks like CP, which can expand the potential applications of BCI devices and help improve the lives of both healthy individuals and motor-impaired patients.

Comparative cardiovascular outcomes in type 2 diabetes patients taking dapagliflozin versus empagliflozin: a nationwide population-based cohort study.

BACKGROUND: Sodium-glucose co-transporter-2 inhibitors displayed cardiovascular benefits in type 2 diabetes mellitus in previous studies; however, there were some heterogeneities regarding respective cardiovascular outcomes within the class. Furthermore, their efficacies in Asians, females, and those with low cardiovascular risks were under-represented. Thus, we compared the cardiovascular outcomes between new users of dapagliflozin and empagliflozin in a broad range of patients with type 2 diabetes mellitus using a nationwide population-based real-world cohort from Korea. METHODS: Korean National Health Insurance registry data between May 2016 and December 2018 were extracted, and an active-comparator new-user design was applied. The primary outcome was a composite of heart failure (HF)-related events (i.e., hospitalization for HF and HF-related death), myocardial infarction, ischemic stroke, and cardiovascular death. The secondary outcomes were individual components of the primary outcome. RESULTS: A total of 366,031 new users of dapagliflozin or empagliflozin were identified. After 1:1 nearest-neighbor propensity score matching, 72,752 individuals (mean age approximately 56 years, 42% women) from each group were included in the final analysis, with a follow-up of 150,000 ~ person-years. Approximately 40% of the patients included in the study had type 2 diabetes mellitus as their sole cardiovascular risk factor, with no other risk factors. The risk of the primary outcome was not different significantly between dapagliflozin and empagliflozin users (hazard ratio [HR] 0.93, 95% confidence interval [CI] 0.855-1.006). The risks of secondary outcomes were also similar, with the exception of the risks of HF-related events (HR 0.84, 95% CI 0.714-0.989) and cardiovascular death (HR 0.76, 95% CI 0.618-0.921), which were significantly lower in the dapagliflozin users. CONCLUSIONS: This large-scale nationwide population-based real-world cohort study revealed no significant difference in composite cardiovascular outcomes between new users of dapagliflozin and empagliflozin. However, dapagliflozin might be associated with lower risks of hospitalization or death due to HF and cardiovascular death than empagliflozin in Asian patients with type 2 diabetes mellitus.

Development and Validation of a Joint Attention-Based Deep Learning System for Detection and Symptom Severity Assessment of Autism Spectrum Disorder.

Importance: Joint attention, composed of complex behaviors, is an early-emerging social function that is deficient in children with autism spectrum disorder (ASD). Currently, no methods are available for objectively quantifying joint attention. Objective: To train deep learning (DL) models to distinguish ASD from typical development (TD) and to differentiate ASD symptom severities using video data of joint attention behaviors. Design, Setting, and Participants: In this diagnostic study, joint attention tasks were administered to children with and without ASD, and video data were collected from multiple institutions from August 5, 2021, to July 18, 2022. Of 110 children, 95 (86.4%) completed study measures. Enrollment criteria were 24 to 72 months of age and ability to sit with no history of visual or auditory deficits. Exposures: Children were screened using the Childhood Autism Rating Scale. Forty-five children were diagnosed with ASD. Three types of joint attention were assessed using a specific protocol. Main Outcomes and Measures: Correctly distinguishing ASD from TD and different levels of ASD symptom severity using the DL model area under the receiver operating characteristic curve (AUROC), accuracy, precision, and recall. Results: The analytical population consisted of 45 children with ASD (mean [SD] age, 48.0 [13.4] months; 24 [53.3%] boys) vs 50 with TD (mean [SD] age, 47.9 [12.5] months; 27 [54.0%] boys). The DL ASD vs TD models showed good predictive performance for initiation of joint attention (IJA) (AUROC, 99.6% [95% CI, 99.4%-99.7%]; accuracy, 97.6% [95% CI, 97.1%-98.1%]; precision, 95.5% [95% CI, 94.4%-96.5%]; and recall, 99.2% [95% CI, 98.7%-99.6%]), low-level response to joint attention (RJA) (AUROC, 99.8% [95% CI, 99.6%-99.9%]; accuracy, 98.8% [95% CI, 98.4%-99.2%]; precision, 98.9% [95% CI, 98.3%-99.4%]; and recall, 99.1% [95% CI, 98.6%-99.5%]), and high-level RJA (AUROC, 99.5% [95% CI, 99.2%-99.8%]; accuracy, 98.4% [95% CI, 97.9%-98.9%]; precision, 98.8% [95% CI, 98.2%-99.4%]; and recall, 98.6% [95% CI, 97.9%-99.2%]). The DL-based ASD symptom severity models showed reasonable predictive performance for IJA (AUROC, 90.3% [95% CI, 88.8%-91.8%]; accuracy, 84.8% [95% CI, 82.3%-87.2%]; precision, 76.2% [95% CI, 72.9%-79.6%]; and recall, 84.8% [95% CI, 82.3%-87.2%]), low-level RJA (AUROC, 84.4% [95% CI, 82.0%-86.7%]; accuracy, 78.4% [95% CI, 75.0%-81.7%]; precision, 74.7% [95% CI, 70.4%-78.8%]; and recall, 78.4% [95% CI, 75.0%-81.7%]), and high-level RJA (AUROC, 84.2% [95% CI, 81.8%-86.6%]; accuracy, 81.0% [95% CI, 77.3%-84.4%]; precision, 68.6% [95% CI, 63.8%-73.6%]; and recall, 81.0% [95% CI, 77.3%-84.4%]). Conclusions and Relevance: In this diagnostic study, DL models for identifying ASD and differentiating levels of ASD symptom severity were developed and the premises for DL-based predictions were visualized. The findings suggest that this method may allow digital measurement of joint attention; however, follow-up studies are necessary for further validation.

Data-driven mortality risk prediction of severe degenerative mitral regurgitation patients undergoing mitral valve surgery.

AIMS: The outcomes of mitral valve replacement/repair (MVR) in severe degenerative mitral regurgitation (MR) patients depend on various risk factors. We aimed to develop a risk prediction model for post-MVR mortality in severe degenerative MR patients using machine learning. METHODS AND RESULTS: Consecutive severe degenerative MR patients undergoing MVR were analysed (n = 1521; 70% training/30% test sets). A random survival forest (RSF) model was constructed, with 3-year post-MVR all-cause mortality as the outcome. Partial dependency plots were used to define the thresholds of each risk factor. A simple scoring system (MVR-score) was developed to stratify post-MVR mortality risk. At 3 years following MVR, 90 patients (5.9%) died in the entire cohort (59 and 31 deaths in the training and test sets). The most important predictors of mortality in order of importance were age, haemoglobin, valve replacement, glomerular filtration rate, left atrial dimension, and left ventricular (LV) end-systolic diameter. The final RSF model with these six variables demonstrated high predictive performance in the test set (3-year C-index 0.880, 95% confidence interval 0.834-0.925), with mortality risk increased strongly with left atrial dimension >55 mm, and LV end-systolic diameter >45 mm. MVR-score demonstrated effective risk stratification and had significantly higher predictability compared to the modified Mitral Regurgitation International Database score (3-year C-index 0.803 vs. 0.750, P = 0.034). CONCLUSION: A data-driven machine learning model provided accurate post-MVR mortality prediction in severe degenerative MR patients. The outcome following MVR in severe degenerative MR patients is governed by both clinical and echocardiographic factors.

Machine Learning-Based Prediction of Attention-Deficit/Hyperactivity Disorder and Sleep Problems With Wearable Data in Children.

Importance: Early detection of attention-deficit/hyperactivity disorder (ADHD) and sleep problems is paramount for children's mental health. Interview-based diagnostic approaches have drawbacks, necessitating the development of an evaluation method that uses digital phenotypes in daily life. Objective: To evaluate the predictive performance of machine learning (ML) models by setting the data obtained from personal digital devices comprising training features (ie, wearable data) and diagnostic results of ADHD and sleep problems by the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version for Diagnostic and Statistical Manual of Mental Disorders, 5th edition (K-SADS) as a prediction class from the Adolescent Brain Cognitive Development (ABCD) study. Design, Setting, and Participants: In this diagnostic study, wearable data and K-SADS data were collected at 21 sites in the US in the ABCD study (release 3.0, November 2, 2020, analyzed October 11, 2021). Screening data from 6571 patients and 21 days of wearable data from 5725 patients collected at the 2-year follow-up were used, and circadian rhythm-based features were generated for each participant. A total of 12 348 wearable data for ADHD and 39 160 for sleep problems were merged for developing ML models. Main Outcomes and Measures: The average performance of the ML models was measured using an area under the receiver operating characteristics curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, the Shapley Additive Explanations value was used to calculate the importance of features. Results: The final population consisted of 79 children with ADHD problems (mean [SD] age, 144.5 [8.1] months; 55 [69.6%] males) vs 1011 controls and 68 with sleep problems (mean [SD] age, 143.5 [7.5] months; 38 [55.9%] males) vs 3346 controls. The ML models showed reasonable predictive performance for ADHD (AUC, 0.798; sensitivity, 0.756; specificity, 0.716; PPV, 0.159; and NPV, 0.976) and sleep problems (AUC, 0.737; sensitivity, 0.743; specificity, 0.632; PPV, 0.036; and NPV, 0.992). Conclusions and Relevance: In this diagnostic study, an ML method for early detection or screening using digital phenotypes in children's daily lives was developed. The results support facilitating early detection in children; however, additional follow-up studies can improve its performance.

A Method of Generating Real-Time Natural Light Color Temperature Cycle for Circadian Lighting Service.

The light intensity and color temperature of natural light periodically change and promote the circadian entrainment of the human body. In addition, the color temperature cycle of natural light that is unique to each region is formed by its location and geographic and environmental factors, affecting the health of its residents. Research on lighting and construction to provide the color temperature of real-time natural light has continued to provide the beneficial effect of natural indoor lighting. However, lighting technology that provides the real-time color temperature of natural light could not be realized since it is challenging to select a color temperature cycle zone due to abrupt color temperature changes at sunrise and sunset. Such drastic shifts cause an irregular measurement of color temperature over time due to general weather or atmospheric conditions. In a previous study, a method of generating a color temperature cycle using deep learning was introduced, but the performance at the beginning and end of the color temperature cycle was unreliable. Therefore, this study proposes generating a real-time natural light color temperature cycle for the circadian lighting service. The characteristics of the daily color temperature cycle were analyzed based on the measured natural light characteristics database, and a data set for learning was established. To improve the color temperature cycle generation performance, a deep learning (TadGAN) model was implemented by searching for the lowest point of the color temperature at the start and end points of the color temperature cycle and applying the boot and ending datasets to these points. The color temperature cycle zone was accurately detected in real-time in the experiment, and the generation performance of the color temperature cycle was maintained at the beginning and end of the color temperature cycle. The mean absolute error decreased by about 67%, confirming the generation of a more accurate real-time color temperature cycle.

Atrial Functional Tricuspid Regurgitation: Importance of Atrial Fibrillation and Right Atrial Remodeling and Prognostic Significance.

BACKGROUND: Little is known about the determinants and outcomes of significant atrial functional tricuspid regurgitation (AFTR). OBJECTIVES: The authors aimed to identify risk factors for significant TR in relation to atrial fibrillation-flutter (AF-AFL) and assess its prognostic implications. METHODS: The authors retrospectively studied patients with mild TR with follow-up echocardiography examinations. Significant TR was defined as greater than or equal to moderate TR. AFTR was defined as TR, attributed to right atrial (RA) remodeling or isolated tricuspid annular dilatation, without other primary or secondary etiology, except for AF-AFL. The Mantel-Byar test was used to compare clinical outcomes by progression of AFTR. RESULTS: Of 833 patients with mild TR, 291 (34.9%) had AF-AFL. During the median 4.6 years, significant TR developed in 35 patients, including 33 AFTRs. Significant AFTR occurred in patients with AF-AFL more predominantly than in those patients without AF-AFL (10.3% vs 0.6%; P < 0.001). In Cox analysis, AF-AFL was a strong risk factor for AFTR (adjusted HR: 8.33 [95% CI: 2.34-29.69]; P = 0.001). Among patients with AF-AFL, those who developed significant AFTR had larger baseline RA areas (23.8 vs 19.4 cm2; P < 0.001) and RA area-to-right ventricle end-systolic area ratio (3.0 vs 2.3; P < 0.001) than those who did not. These parameters were independent predictors of AFTR progression. The 10-year major adverse cardiovascular event was significantly higher after progression of AFTR than before or without progression (79.8% vs 8.6%; Mantel-Byar P < 0.001). CONCLUSIONS: In patients with mild TR, significant AFTR developed predominantly in patients with AF-AFL, conferring poor prognosis. RA enlargement, especially with increased RA area-to-right ventricle end-systolic area ratio, was a strong risk factor for progression of AFTR.

Long-term outcomes in distinct phenogroups of patients with primary mitral regurgitation undergoing valve surgery.

OBJECTIVES: Patients with mitral regurgitation (MR) may be heterogeneous with different risk profiles. We aimed to identify distinct phenogroups of patients with severe primary MR and investigate their long-term prognosis after mitral valve (MV) surgery. METHODS: The retrospective cohort of patients with severe primary MR undergoing MV surgery (derivation, n=1629; validation, n=692) was analysed. Latent class analysis was used to classify patients into subgroups using 15 variables. The primary outcome was all-cause mortality after MV surgery. RESULTS: During follow-up (median 6.0 years), 149 patients (9.1%) died in the derivation cohort. In the univariable Cox analysis, age, female, atrial fibrillation, left ventricular (LV) end-systolic dimension/volumes, LV ejection fraction, left atrial dimension and tricuspid regurgitation peak velocity were significant predictors of mortality following MV surgery. Five distinct phenogroups were identified, three younger groups (group 1-3) and two older groups (group 4-5): group 1, least comorbidities; group 2, men with LV enlargement; group 3, predominantly women with rheumatic MR; group 4, low-risk older patients; and group 5, high-risk older patients. Cumulative survival was the lowest in group 5, followed by groups 3 and 4 (5-year survival for groups 1-5: 98.5%, 96.0%, 91.7%, 95.6% and 83.4%; p<0.001). Phenogroups had similar predictive performance compared with the Mitral Regurgitation International Database score in patients with degenerative MR (3-year C-index, 0.763 vs 0.750, p=0.602). These findings were reproduced in the validation cohort. CONCLUSION: Five phenogroups of patients with severe primary MR with different risk profiles and outcomes were identified. This phenogrouping strategy may improve risk stratification when optimising the timing and type of interventions for severe MR.

TadGAN-Based Daily Color Temperature Cycle Generation Corresponding to Irregular Changes of Natural Light.

This study to develop lighting is advanced for reproducing natural light color temperature beneficial to humans. Methods were introduced to provide daily color temperature cycles through formulas based on the measured natural light characteristics or real-time reproduction of natural light color temperature linking sensors. Analysis results for the measured natural light showed that irregular color temperature cycles were observed for more than 90% of the year due to the influence of regional weather and atmospheric conditions. Regular color temperature cycles were observed only on some clear days. The color temperature cycle dramatically affects the health of the occupants. However, since irregular color temperatures are difficult to predict and cannot easily generate cycles, only the color temperatures of some clear days are currently used, and the actual color temperature of natural light cannot be reproduced. There is little research on deriving real-time periodic characteristics and lighting services targeting irregular color temperatures of natural light. Therefore, this paper proposes a TadGAN (Time Series Anomaly Detection Using Generative Adversarial Networks)-based daily color temperature cycle generation method that responds to irregular changes in the natural light color temperature. A TadGAN model for generating the natural light color temperature cycle was built, and learning was performed based on the dataset extracted through the measured natural light characteristic Database. After that, the generator of TadGAN was repeatedly applied to generate a color temperature cycle close to the change of natural light. In the performance test of the proposed method, it was possible to generate periodic characteristics of the irregular natural light color temperature distribution.

α-Fe2O3 nanoparticles and hazardous air pollutants release during cooking using cast iron wok in a commercial Chinese restaurant.

Long-term exposure to fine particles (PM2.5), ultrafine particles (UFPs), and volatile organic compounds (VOCs) emissions from cooking has been linked to adverse human health effects. Here, we measured the real-time number size distribution of particles emitted when cooking two served food in Chinese restaurants and estimated the emission rate of UFPs and PM2.5. Experiments were conducted under a control hood, and both online measurement and offline analysis of PM2.5 were carried out. The measured emission rates of PM2.5 generated from deep-frying and grilling were 0.68 ± 0.11 mg/min and 1.58 ± 0.25 mg/min, respectively. Moreover, the UFPs emission rate of deep-frying (4.3 × 109 #/min) is three times higher than that of grilling (1.4 × 109 #/min). Additionally, the PM2.5 emission of deep-frying was comprised of a considerable amount of α-Fe2O3 (5.7% of PM2.5 total mass), which is more toxic than other iron oxide species. A total of six carcinogenic HAPs were detected, among which formaldehyde, acrolein, and acetaldehyde were found to exceed the inhalation reference concentration (RfC) for both cooking methods. These findings can contribute to future evaluation of single particle and HAPs emission from cooking to better support toxicity assessment.

Multiview child motor development dataset for AI-driven assessment of child development.

BACKGROUND: Children's motor development is a crucial tool for assessing developmental levels, identifying developmental disorders early, and taking appropriate action. Although the Korean Developmental Screening Test for Infants and Children (K-DST) can accurately assess childhood development, its dependence on parental surveys rather than reliable, professional observation limits it. This study constructed a dataset based on a skeleton of recordings of K-DST behaviors in children aged between 20 and 71 months, with and without developmental disorders. The dataset was validated using a child behavior artificial intelligence (AI) learning model to highlight its possibilities. RESULTS: The 339 participating children were divided into 3 groups by age. We collected videos of 4 behaviors by age group from 3 different angles and extracted skeletons from them. The raw data were used to annotate labels for each image, denoting whether each child performed the behavior properly. Behaviors were selected from the K-DST's gross motor section. The number of images collected differed by age group. The original dataset underwent additional processing to improve its quality. Finally, we confirmed that our dataset can be used in the AI model with 93.94%, 87.50%, and 96.31% test accuracy for the 3 age groups in an action recognition model. Additionally, the models trained with data including multiple views showed the best performance. CONCLUSION: Ours is the first publicly available dataset that constitutes skeleton-based action recognition in young children according to the standardized criteria (K-DST). This dataset will enable the development of various models for developmental tests and screenings.

A DNN-Based UVI Calculation Method Using Representative Color Information of Sun Object Images.

As outdoor activities are necessary for maintaining our health, research interest in environmental conditions such as the weather, atmosphere, and ultraviolet (UV) radiation is increasing. In particular, UV radiation, which can benefit or harm the human body depending on the degree of exposure, is recognized as an essential environmental factor that needs to be identified. However, unlike the weather and atmospheric conditions, which can be identified to some extent by the naked eye, UV radiation corresponds to wavelength bands that humans cannot recognize; hence, the intensity of UV radiation cannot be measured. Recently, although devices and sensors that can measure UV radiation have been launched, it is very difficult for ordinary users to acquire ambient UV radiation information directly because of the cost and inconvenience caused by operating separate devices. Herein, a deep neural network (DNN)-based ultraviolet index (UVI) calculation method is proposed using representative color information of sun object images. First, Mask-region-based convolutional neural networks (R-CNN) are applied to sky images to extract sun object regions and then detect the representative color of the sun object regions. Then, a deep learning model is constructed to calculate the UVI by inputting RGB color values, which are representative colors detected later along with the altitude angle and azimuth of the sun at that time. After selecting each day of spring and autumn, the performance of the proposed method was tested, and it was confirmed that accurate UVI could be calculated within a range of mean absolute error of 0.3.

Risk of Atrial Fibrillation According to Cancer Type: A Nationwide Population-Based Study.

BACKGROUND: Patients with cancer have an increased risk of atrial fibrillation (AF). However, there is a paucity of information regarding the association between cancer type and risk of AF. OBJECTIVES: This study sought to evaluate the risk of AF according to the type of cancer. METHODS: We enrolled 816,811 patients who were diagnosed with cancer from the Korean National Health Insurance Service database between 2009 and 2016. Age- and sex-matched noncancer control subjects (1:2; n = 1,633,663) were also selected. Newly diagnosed AF was identified based on the type of cancer. RESULTS: During a median follow-up of 4.5 years, AF was newly diagnosed in 25,356 patients with cancer (6.6 per 1,000 person-years). In multivariable Fine and Gray's regression analysis, cancer was an independent risk factor for incident AF (adjusted subdistribution hazard ratio [aHR]: 1.63; 95% confidence interval [CI]: 1.61 to 1.66). Multiple myeloma showed a higher association with incident AF (aHR: 3.34; 95% CI: 2.98 to 3.75). Esophageal cancer showed the highest risk among solid cancers (aHR: 2.69; 95% CI: 2.45 to 2.95), and stomach cancer showed the lowest association with AF risk (aHR: 1.27; 95% CI 1.23 to 1.32). CONCLUSIONS: Although patients with cancer were found to have a higher risk of AF, the impact on AF development varied by cancer type.

Exercise and the Risk of Dementia in Patients with Newly Diagnosed Atrial Fibrillation: A Nationwide Population-Based Study.

BACKGROUND: It is unclear whether exercise would reduce dementia in patients with a new diagnosis of atrial fibrillation (AF). Therefore, we aimed to evaluate the association between the change in physical activity (PA) before and after new-onset AF and the risk of incident dementia. METHODS: Using the Korean National Health Insurance Service database, we enrolled a total of 126,555 patients with newly diagnosed AF between 2010 and 2016, who underwent health examinations within two years before and after their diagnosis of AF. The patients were divided into four groups: persistent non-exercisers, exercise starters, exercise quitters, and exercise maintainers. RESULTS: Based on a total of 396,503 person-years of follow-up, 5943 patients were diagnosed with dementia. Compared to persistent non-exercisers, exercise starters (adjusted hazard ratio (aHR) 0.87; 95% confidence interval (CI) 0.81-0.94), and exercise maintainers (aHR 0.66; 95% CI 0.61-0.72) showed a lower risk of incident dementia; however, the risk was similar in exercise quitters (aHR 0.98; 95% CI 0.92-1.05) (p-trend < 0.001). There was a J-shaped relationship between the dose of exercise and the risk of dementia, with the risk reduction maximized at 5-6 times per week of moderate-to-vigorous PA among exercise starters. CONCLUSION: Patients who initiated or continued regular exercise after diagnosis of AF were associated with a lower risk of dementia than persistent non-exercisers, with no risk reduction associated with exercise cessation. Our findings may provide evidence for the benefit of exercise prescription to patients with new-onset AF to prevent incident dementia regardless of their current exercise status.