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Background/Objectives: Squamous cell carcinoma (SCC), a prevalent form of skin cancer, presents diagnostic challenges, particularly in resource-limited settings with a low-quality imaging infrastructure. The accurate classification of SCC margins is essential to guide effective surgical interventions and reduce recurrence rates. This study proposes a vision transformer (ViT)-based model to improve SCC margin classification by addressing the limitations of convolutional neural networks (CNNs) in analyzing low-quality histopathological images. Methods: This study introduced a transfer learning approach using a ViT architecture customized with additional flattening, batch normalization, and dense layers to enhance its capability for SCC margin classification. A performance evaluation was conducted using machine learning metrics averaged over five-fold cross-validation and comparisons were made with the leading CNN models. Ablation studies have explored the effects of architectural configuration on model performance. Results: The ViT-based model achieved superior SCC margin classification with 0.928 ± 0.027 accuracy and 0.927 ± 0.028 AUC, surpassing the highest performing CNN model, InceptionV3 (accuracy: 0.86 ± 0.049; AUC: 0.837 ± 0.029), demonstrating robustness of ViT over CNN for low-quality histopathological images. Ablation studies have reinforced the importance of tailored architectural configurations for enhancing diagnostic performance. Conclusions: This study underscores the transformative potential of ViTs in histopathological analysis, especially in resource-limited settings. By enhancing diagnostic accuracy and reducing dependence on high-quality imaging and specialized expertise, it presents a scalable solution for global cancer diagnostics. Future research should prioritize optimizing ViTs for such environments and broadening their clinical applications.

Background/Objective: The global spread of Monkeypox (Mpox) has highlighted the urgent need for rapid, accurate diagnostic tools. Traditional methods like polymerase chain reaction (PCR) are resource-intensive, while skin image-based detection offers a promising alternative. This study evaluates the effectiveness of vision transformers (ViTs) for automated Mpox detection. Methods: By fine-tuning a pre-trained ViT model on an Mpox lesion image dataset, a robust ViT-based transfer learning (TL) model was created. Performance was assessed relative to convolutional neural network (CNN)-based TL models and ViT models trained from scratch across key metrics: accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Furthermore, a transferability measure was utilized to assess the effectiveness of feature transfer to Mpox images. Results: The results show that the ViT model outperformed a CNN, achieving an AUC of 0.948 and an accuracy of 0.942 with a p-value of less than 0.05 across all metrics, highlighting its potential for accurate and scalable Mpox detection. Moreover, the ViT models yielded a better hypothesis margin-based transferability measure, highlighting its effectiveness in transferring useful learning weights to Mpox images. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations also confirmed that the ViT model attends to clinically relevant features, supporting its interpretability and reliability for diagnostic use. Conclusions: The results from this study suggest that ViT offers superior accuracy, making it a valuable tool for Mpox early detection in field settings, especially where conventional diagnostics are limited. This approach could support faster outbreak response and improved resource allocation in public health systems.

Medical image analysis is essential for accurate disease diagnosis, yet progress in developing high-performing deep learning models for medical image analysis remains limited by the scarcity of large, high-quality annotated datasets. Conventional transfer learning (CTL) from natural image pretrained models offers partial benefits but frequently encounters domain mismatch, resulting in limited generalizability to medical imaging tasks. This study reports the development and evaluation of a multistage transfer learning (MSTL) framework designed to improve domain adaptation and enhance diagnosis performance. The MSTL framework introduces an intermediate pretraining stage using cell line microscopic images to provide a more relevant source domain between ImageNet pretraining and downstream medical imaging tasks. The workflow consists of sequential pretraining on ImageNet, fine-tuning on cell line images, and final adaptation to medical datasets, including mammograms, ultrasounds, and X-rays. The study assessed MSTL performance using convolutional neural networks (CNNs) and vision transformers (ViTs) and compared results against CTL and training from scratch. The findings show that ViTs consistently outperform CNNs, with ViTB-16 achieving the highest accuracy across all datasets. Additionally, transferability metrics, Log Expected Empirical Prediction, Negative Conditional Entropy, and H-Score, exhibited strong positive correlations with model accuracy, particularly for mammography and X-ray tasks with ViTB-16 exceeding Pearson correlation coefficients 0.95. Overall, the MSTL framework substantially narrowed the gap between general image pretraining and specialized medical imaging tasks. By improving domain adaptation and generalization, it offers a robust and scalable pathway for advancing diagnostic performance in medical image analysis.

Residual antibiotics in agricultural soils can be of concern due to the development of antibiotic resistant microorganisms. Among various antibiotics, oxytetracycline (OTC), streptomycin (ST), and validamycin A (VA) have been used for agricultural purposes in South Korea; however, studies on the biodegradation of these antibiotics in soil are limited. Therefore, this study investigated the effects of pH (5.5, 6.8, and 7.4) and temperature (1.8, 23.0, and 31.2 °C) conditions on the biodegradation of these antibiotics in soil. The biodegradation tests were carried out in the field soil (FS) and rice paddy soil (RS) for 30 d with OTC and ST and 10 d with VA, and the residual antibiotics concentrations were monitored over the degradation period. Under various conditions, the degradation rates of ST was lower (11–69%) than that of OTC (60–90%) and VA (15–96%). The degradation half-lives of OTC and VA tend to decrease with increasing pH value, while the degradation half-life of ST tend to increase with increasing pH value. But, the effect of soil pH on the antibiotics degradation was not statistically significant, except for ST in the FS and RS and VA in the FS. The degradation of three antibiotics was greater at higher temperatures (23.0 °C and 31.2 °C) than at lower temperature (1.8 °C), and the degradation half-lives decreased with increasing temperature. The different degradation characteristics of different antibiotics in soil can be explained by the different characteristics of the antibiotics (e.g., sorption affinity, chemical forms) and soil (e.g., organic matter content). The results suggest that the degradation characteristics of antibiotics need to be considered in order to properly manage the residual antibiotics in soil.

The total dinoflagellate cyst community and the cysts of Cochlodinium polykrikoides in the surface sediments of South Sea (Tongyeong coast), South Korea, were analysed using next-generation sequencing (NGS) and morphological approaches. Dinoflagellate cysts can be highly abundant (111–4,087 cysts g −1 dry weight) and have diverse species composition. A total of 35 taxa of dinoflagellate cysts representing 16 genera, 21 species (including four unconfirmed species), and 14 complex species were identified by NGS analysis. Cysts of Scrippsiella spp (mostly Scrippsiella trochoidea ) were the most dominant and Polykrikos schwartzii , Pentapharsodinium dalei , Ensiculifera carinata , and Alexandrium catenella/tamarense were common. Thus, a combination of NGS and morphological analysis is effective for studying the cyst communities present in a given environment. Although C. polykrikoides developed massive blooms during 2013–2014, microscopy revealed low density of their cysts, whereas no cysts were detected by NGS. However, the vegetative C. polykrikoides not appeared during 2015–2017 in spite of the observation of C. polykrikoides cysts. This suggests that the C. polykrikoides blooms were not due to development of their cysts but to other factors such as currents transporting them to a marine environment suitable for their growth.

The hippocampus is a brain region responsible for learning and memory functions. The purpose of this study was to investigate the effects of low-intensity exercise and bright light exposure on neurogenesis and brain-derived neurotrophic factor expression in adult rat hippocampus. Male Sprague-Dawley rats were randomly assigned to control, exercise, light, or exercise ＋ light groups （n = 9 per group）. The rats in the exercise group were subjected to treadmill exercise （5 days per week, 30 minutes per day, over a 4-week period）, the light group rats were irradiated （5 days per week, 30 minutes per day, 10 000 Ix, over a 4-week period）, the exercise ＋ light group rats were subjected to treadmill exercise in combination with bright light exposure, and the control group rats remained sedentary over a 4-week period. Compared with the control group, there was a significant increase in neurogenesis in the hippocampal dentate gyrus of rats in the exercise, light, and exercise ＋ light groups. Moreover, the expression level of brain-derived neurotrophic factor in the rat hippocampal dentate gyrus was significantly higher in the exercise group and light group than that in the control group. Interestingly, there was no significant difference in brain-derived neurotrophic factor expression between the control group and exercise ＋ light group. These results indicate that low-intensity treadmill exercise （first 5 minutes at a speed of 2 m/min, second 5 minutes at a speed of 5 m/min, and the last 20 minutes at a speed of 8 m/min） or bright-light exposure therapy induces positive biochemical changes in the brain. In view of these findings, we propose that moderate exercise or exposure to sunlight during childhood can be beneficial for neural development.

Microalgae are photosynthetic microorganisms that function as primary producers in aquatic ecosystems. Some species of microalgae undergo rapid growth and cause harmful blooms in marine ecosystems. Heterocapsa triquetra is one of the most common bloom-forming species in estuarine and coastal waters worldwide. Although this species does not produce toxins, unlike some other Heterocapsa species, the high density of its blooms can cause significant ecological damage. We developed a H. triquetra species-specific nuclease protection assay sandwich hybridization (NPA-SH) probe that targets the large subunit of ribosomal RNA (LSU rRNA). We tested probe specificity and sensitivity with five other dinoflagellates that also cause red tides. Our assay detected H. triquetra at a concentration of 1.5×104 cells/mL, more sensitive than required for a red-tide guidance warning by the Korea Ministry of Oceans and Fisheries in 2015 (3.0×10 4 cells/mL). We also used the NPA-SH assay to monitor H. triquetra in the Tongyeong region of the southern sea area of Korea during 2014. This method could detect H. triquetra cells within 3 h. Our assay is useful for monitoring H. triquetra under field conditions.

For the past three decades, scholars have conducted field experiments to examine gender-based hiring discrimination in the United States. However, these studies have produced mixed results. To further interpret these findings, we performed a meta-analysis of 37 audit studies conducted between 1990 and 2022. Using an aggregated sample of 243,202 fictitious job applications, the study finds no evidence of statistically significant gender discrimination at the study level. However, a series of more focused meta-analyses reveal important variations in the extent of discrimination by occupation type and applicant race. First, the gender composition of an occupation predicts gender bias in hiring. Second, the intersection of gender and race is critical—in female-dominated jobs, White female applicants receive more callbacks than their male counterparts, but Black female applicants experience no such benefit. The study contributes to the literature on labor market and gender (in)equality by synthesizing the findings of field experiments.

Viruses are the most abundant biological entities in the oceans, and account for a significant amount of the genetic diversity of marine ecosystems. However, there is little detailed information about the biodiversity of viruses in marine environments. Rapid advances in metagenomics have enabled the identification of previously unknown marine viruses. We performed metagenomic profiling of seawater samples collected at 6 sites in Goseong Bay (South Sea, Korea) during the spring, summer, autumn, and winter of 2014. The results indicated the presence of highly diverse virus communities. The DNA libraries from samples collected during four seasons were sequenced using Illumina HiSeq 2000. The number of viral reads was 136,850 during March, 70,651 during June, 66,165 during September, and 111,778 during December. Species identification indicated that Pelagibacter phage HTVC010P, Ostreococcus lucimarinus OIV5 and OIV1, and Roseobacter phage SIO1 were the most common species in all samples. For viruses with at least 10 reads, there were 204 species during March, 189 during June, 170 during September, and 173 during December. Analysis of virus families indicated that the Myoviridae was the most common during all four seasons, and viruses in the Polyomaviridae were only present during March. Viruses in the Iridoviridae were only present during three seasons. Additionally, viruses in the Iridoviridae, Herpesviridae, and Poxviridae, which may affect fish and marine animals, appeared during different seasons. These results suggest that seasonal changes in temperature contribute to the dynamic structure of the viral community in the study area. The information presented here will be useful for comparative analyses with other marine viral communities.

We conducted a seroprevalence study of a large ongoing outbreak of human adenovirus type 55 (HAdV-55) among the military in South Korea. Serum samples were collected between 2018 and 2019 from military-exposed (military group) and non-exposed (non-military group) populations. The plaque reduction neutralization test (PRNT) was used to assess neutralization activity against HAdV-55. A total of 100 sera was collected from the non-military group, of which 18.8% showed HAdV-55 neutralizing antibody activity. Ninety-six sera were tested from the military group, which had significantly higher prevalence of neutralizing antibodies (56.0%, P <0.001). A significantly higher proportion of the military group had PRNT titers [greater than or equal to]1:1,000 than the non-military group (85.7% vs. 50.0%, P = 0.004). Among the military group, 48.9% of active-duty soldiers had PRNT titers [greater than or equal to]1:5,000, while none of the discharged civilians did (P = 0.007). In conclusion, Koreans were exposed to HAdV-55 in their communities, but the exposure risk was higher among people in military service.