Investigation of factors regarding the effects of COVID-19 pandemic on college students' depression by quantum annealer.

Diverse cases regarding the impact, with its related factors, of the COVID-19 pandemic on mental health have been reported in previous studies. In this study, multivariable datasets were collected from 751 college students who could be easily affected by pandemics based on the complex relationships between various mental health factors. We utilized quantum annealing (QA)-based feature selection algorithms that were executed by commercial D-Wave quantum computers to determine the changes in the relative importance of the associated factors before and after the pandemic. Multivariable linear regression (MLR) and XGBoost models were also applied to validate the QA-based algorithms. Based on the experimental results, we confirm that QA-based algorithms have comparable capabilities in factor analysis research to the MLR models that have been widely used in previous studies. Furthermore, the performance of the QA-based algorithms was validated through the important factor results from the algorithms. Pandemic-related factors (e.g., confidence in the social system) and psychological factors (e.g. decision-making in uncertain situations) were more important in post-pandemic conditions. Although the results should be validated using other mental health variables or national datasets, this study will serve as a reference for researchers regarding the use of the quantum annealing approach in factor analysis with validation through real-world survey dataset analysis.

Identification and comparison of pandemic-to-symptom networks of South Korea and the United States.

Background: The Coronavirus (COVID-19) pandemic resulted in a dramatic increase in the prevalence of anxiety and depression globally. Although the impact on the mental health of young adults was especially strong, its underlying mechanisms remain elusive. Materials and methods: Using a network approach, the present study investigated the putative pathways between pandemic-related factors and anxiety and depressive symptoms among young adults in South Korea and the U.S. Network analyses were conducted on cross-country data collected during the COVID-19 lockdown period (n = 1,036). Our model included depression symptoms (PHQ-9), generalized anxiety symptoms (GAD-7), and COVID-19-related factors (e.g., COVID-19-related traumatic stress, pandemic concerns, access to medical/mental health services). Results: The overall structure of pandemic-to-symptom networks of South Korea and the U.S. were found to be similar. In both countries, COVID-related stress and negative future anticipation (an anxiety symptom) were identified as bridging nodes between pandemic-related factors and psychological distress. In addition, worry-related symptoms (e.g., excessive worry, uncontrollable worry) were identified as key contributors in maintaining the overall pandemic-to-symptom network in both countries. Conclusion: The similar network structures and patterns observed in both countries imply that there may exist a stable relationship between the pandemic and internalizing symptoms above and beyond the sociocultural differences. The current findings provide new insights into the common potential pathway between the pandemic and internalizing symptoms in South Korea and in the U.S. and inform policymakers and mental health professionals of potential intervention targets to alleviate internalizing symptoms.

Fast Detection of Missing Thin Propagating Cracks during Deep-Learning-Based Concrete Crack/Non-Crack Classification.

Existing deep learning (DL) models can detect wider or thicker segments of cracks that occupy multiple pixels in the width direction, but fail to distinguish the thin tail shallow segment or propagating crack occupying fewer pixels. Therefore, in this study, we proposed a scheme for tracking missing thin/propagating crack segments during DL-based crack identification on concrete surfaces in a computationally efficient manner. The proposed scheme employs image processing as a preprocessor and a postprocessor for a 1D DL model. Image-processing-assisted DL as a precursor to DL eliminates labor-intensive labeling and the plane structural background without any distinguishable features during DL training and testing; the model identifies potential crack candidate regions. Iterative differential sliding-window-based local image processing as a postprocessor to DL tracks missing thin cracks on segments classified as cracks. The capability of the proposed method is demonstrated on low-resolution images with cracks of single-pixel width, captured using unmanned aerial vehicles on concrete structures with different surface textures, different scenes with complicated disturbances, and optical variability. Due to the multi-threshold-based image processing, the overall approach is invariant to the choice of initial sensitivity parameters, hyperparameters, and the sequence of neuron arrangement. Further, this technique is a computationally efficient alternative to semantic segmentation that results in pixelated mapping/classification of thin crack regimes, which requires labor-intensive and skilled labeling.

Mutation of Rice Early Flowering3.1 (OsELF3.1) delays leaf senescence in rice.

In Arabidopsis, EARLY FLOWERING3 (ELF3) has pivotal roles in controlling circadian rhythm and photoperiodic flowering. In addition, ELF3 negatively regulates leaf senescence by repressing the transcription of PHYTOCHROME-INTERACTING FACTOR4 (PIF4) and PHYTOCHROME-INTERACTING FACTOR5 (PIF5); elf3 mutants senesce earlier and ELF3-overexpressing (ELF3-OX) plants senesce later than wild type (WT). Here, we show that in contrast to Arabidopsis ELF3, which represses senescence, the rice homolog OsELF3.1 promotes leaf senescence; oself3.1 mutants showed delayed senescence and OsELF3.1-OX plants senesced earlier under both dark-induced and natural senescence conditions. Microarray analysis revealed that in the senescing leaves, a number of senescence-associated genes, phytohormone-related genes, and NAC and WRKY family genes (OsNAP, ONAC106, and OsWRKY42) were differentially expressed in oself3.1 mutants compared with WT. Interestingly, we found that Arabidopsis plants overexpressing OsELF3.1 show delayed leaf senescence, produce short petioles, and flower late in long days, just like Arabidopsis ELF3-OX plants. This demonstrates that the regulatory functions of ELF3 and OsELF3.1 are conserved between Arabidopsis and rice, but the downstream regulatory cascades have opposite effects.

Quantitative trait locus mapping and candidate gene analysis for plant architecture traits using whole genome re-sequencing in rice.

Plant breeders have focused on improving plant architecture as an effective means to increase crop yield. Here, we identify the main-effect quantitative trait loci (QTLs) for plant shape-related traits in rice (Oryza sativa) and find candidate genes by applying whole genome re-sequencing of two parental cultivars using next-generation sequencing. To identify QTLs influencing plant shape, we analyzed six traits: plant height, tiller number, panicle diameter, panicle length, flag leaf length, and flag leaf width. We performed QTL analysis with 178 F7 recombinant in-bred lines (RILs) from a cross of japonica rice line 'SNUSG1' and indica rice line 'Milyang23'. Using 131 molecular markers, including 28 insertion/deletion markers, we identified 11 main- and 16 minor-effect QTLs for the six traits with a threshold LOD value > 2.8. Our sequence analysis identified fifty-four candidate genes for the main-effect QTLs. By further comparison of coding sequences and meta-expression profiles between japonica and indica rice varieties, we finally chose 15 strong candidate genes for the 11 main-effect QTLs. Our study shows that the whole-genome sequence data substantially enhanced the efficiency of polymorphic marker development for QTL fine-mapping and the identification of possible candidate genes. This yields useful genetic resources for breeding high-yielding rice cultivars with improved plant architecture.

Infrared thermal imaging in patients with medial collateral ligament injury of the knee - a retrospective study.

OBJECTIVES: Digital infrared thermographic imaging (DITI) has been used widely for various inflammatory diseases, circulatory diseases, skin diseases, musculoskeletal diseases and cancers. In cases of ligament injury, obviously the temperature of the damaged area increases due to local inflammation; however, whether the temperature also increases due to DITI has not been determined. The purpose of the present study was to identify whether or not the changes of temperature in patient's with medial collateral ligament injury were really due to infrared thermography and to determine the applicability of DITI for assessing ligament injuries. METHODS: Twenty patient's who underwent DITI for a medial collateral ligament injury from September 2012 to June 2014 were included in the current study. The thermographic images from the patient's knees were divided to cover seven sub-areas: the middle of the patella, and the inferomedial, the inferolateral, the superomedial, the superolateral, the medial, and the lateral regions of patella. The temperatures of the seven regions were measured, and the temperature differences between affected and unaffected regions were analyzed by using the Wilcoxon signed rank test. RESULTS: The 20 patient's were composed of 14 women (70%) and 6 men (30%), with a mean age of 62.15 ± 15.71 (mean ± standard deviation (SD)) years. The temperature of the affected side, which included the middle of the patella, and the inferomedial, the superomedial, the superolateral, and the medial regions, showed a significant increase compared to that of the unaffected side (P < 0.05). The inferolateral and the lateral regions showed no significant changes. CONCLUSION: Our study results suggest that DITI can show temperature changes if a patient has a ligament injury and that it can be applied in the evaluation of a medial collateral ligament injury.

Effects of Takju intake and moderate exercise training on brain acetylcholinesterase activity and learning ability in rats.

Takju is a Korean alcoholic beverage made from rice, and is brewed with the yeast Saccharomyces cerevisiae. This study was conducted to evaluate the effects of exercise training and moderate Takju consumption on learning ability in 6-week old Sprague-Dawley male rats. The rats were treated with exercise and alcohol for 4 weeks in six separate groups as follows: non-exercised control (CC), exercised control (EC), non-exercised consuming ethanol (CA), exercised consuming ethanol (EA), non-exercised consuming Takju (CT), and exercised consuming Takju (ET). An AIN-93M diet was provided ad libitum. Exercise training was performed at a speed of 10 m/min for 15 minutes per day. Ethanol and Takju were administered daily for 6-7 hours to achieve an intake of about 10 ml after 12 hours of deprivation, and, thereafter, the animals were allowed free access to deionized water. A Y-shaped water maze was used from the third week to understand the effects of exercise and alcohol consumption on learning and memory. After sacrifice, brain acetylcholinesterase (AChE) activity was analyzed. Total caloric intake and body weight changes during the experiment were not significantly different among the groups. AChE activity was not significantly different among the groups. The number of errors for position reversal training in the maze was significantly smaller in the EA group than that in the CA and ET groups, and latency times were shorter in the EA group than those in the CC, EC, CT, and ET groups. The latency difference from the first to the fifth day was shortest in the ET group. The exercised groups showed more errors and latency than those of the non-exercised groups on the first day, but the data became equivalent from the second day. The results indicate that moderate exercise can increase memory and learning and that the combination of exercise and Takju ingestion may enhance learning ability.