Objective To examine the correlation between the Frontal Assessment Battery (FAB) test, which is used to assess the frontal lobe function, and anatomical lesions as well as the ability of the test to detect frontal lobe dysfunction.Methods Records of stroke patients undergoing a FAB test and Mini-Mental State Examination (MMSE) were retrospectively reviewed. The patients were divided into three groups according to the lesions determined by an imaging study: frontal lobe cortex lesions, frontal subcortical circuit lesions, and other lesions. The FAB scores of the three groups were compared using the Kruskal-Wallis test. The validity of the FAB test to detect frontal lobe dysfunction was assessed by a comparison with the Computerized Neuropsychological Function Test (CNT) using the Spearman correlation coefficient. The correlation coefficients between the FAB test and MMSE were analyzed further based on the MMSE cutoff score.Results Patients with frontal cortex lesions had significantly lower total and subtest scores according to the FAB test than the other patients. The FAB test correlated better with the CNT than the MMSE, particularly in the executive function and memory domains. A high MMSE score (r=0.435) indicated a lower correlation with the FAB test score than a low MMSE score (r=0.714).Conclusion The FAB test could differentiate frontal lobe lesions from others in stroke patients and showed a good correlation with the CNT. Moreover, the FAB test can be used in patients with high MMSE scores to detect frontal lobe dysfunction and determine the treatment strategies for stroke patients.

Fatal casualties resulting from explosions of electric vehicles and energy storage systems equipped with lithium-ion batteries have become increasingly common worldwide. As a result, interest in developing safer and more advanced battery systems has grown. Aqueous batteries are emerging as a promising alternative to lithium-ion batteries, which offer advantages such as low cost, safety, high ionic conductivity, and environmental friendliness. In this Review, we discuss the challenges and recent strategies for various aqueous battery systems that use lithium, zinc, sodium, magnesium, and aluminium ions as carrier ions. We also highlight the three key factors that need the most improvement in these aqueous battery systems: higher operating voltage for the cathode, a more stable metal anode interface, and a larger electrochemical stability window of the electrolyte.

This study aimed to estimate the discharge in ungauged watersheds. To this end, we herein deviated from the model development methodology of previous studies and used convolution neural network (CNN), a deep training algorithm, and hydrological images. As the CNN model was developed for solving classification issues in general, it is unsuitable for simulating the discharge, which is a continuous variable. Therefore, the fully connected layer of the CNN model was improved. Moreover, images reflecting the hydrological conditions rather than a general photograph were used as input data for the CNN model. Three study areas that have discharge gauged data were set for the model’s training and testing. The data from two of the three study areas were used for CNN model training, and the data of the other were used to evaluate model prediction performance. The results of this study demonstrate a moderate predictive success of the discharge of an ungauged watershed using the CNN model and hydrological images. Therefore, it can be suitable as a methodology for the discharge estimation of ungauged watersheds. Simultaneously, it is expected that our methodology can be applied to the field of remote sensing or to the field of real-time discharge simulation using satellite imagery on a global scale or across a wide area.

Recently, the concentration of 〖CO〗_2, one of the major air pollutants for greenhouse effect, is increasing due to the massive use of fossil fuels. Thus, research about gas sensors for monitoring 〖CO〗_2 gas have performed, and conventional methods have the challenge of requiring complex structures. Thus, research about gas sensors using nanomaterials has been conducted, and graphene-based gas sensors have been actively researched since its extraordinary conductivity. However, there are challenges that the gas absorption site is limited in chemically unstable site. In this study, ZnO/graphene heterostructure to improve the gas absorption area with high conductivity through ZnO on graphene was presented. Each layer acted as a gas adsorption and a carrier conducting layer respectively, and the sensitivity by the thickness of ZnO and the effect of the annealing temperature were evaluated. This work exhibited a sensitivity of 78% at room temperature, and repeatability and selectivity were also studied.

Preterm birth (PTB), defined as birth at less than 37 weeks of gestation, is a major determinant of neonatal mortality and morbidity. Early diagnosis of PTB risk followed by protective interventions are essential to reduce adverse neonatal outcomes. However, due to the redundant nature of the clinical conditions with other diseases, PTB-associated clinical parameters are poor predictors of PTB. To identify molecular signatures predictive of PTB with high accuracy, we performed mRNA sequencing analysis of PTB patients and full-term birth (FTB) controls in Korean population and identified differentially expressed genes (DEGs) as well as cellular pathways represented by the DEGs between PTB and FTB. By integrating the gene expression profiles of different ethnic groups from previous studies, we identified the core T-cell activation pathway associated with PTB, which was shared among all previous datasets, and selected three representative DEGs (CYLD, TFRC, and RIPK2) from the core pathway as mRNA signatures predictive of PTB. We confirmed the dysregulation of the candidate predictors and the core T-cell activation pathway in an independent cohort. Our results suggest that CYLD, TFRC, and RIPK2 are potentially reliable predictors for PTB.