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Background Recent advances in Vision Transformer (ViT)-based deep learning have significantly improved the accuracy of lung disease prediction from chest X-ray images. However, limited research exists on comparing the effectiveness of different optimizers for lung disease prediction within ViT models. This study aims to systematically evaluate and compare the performance of various optimization methods for ViT-based models in predicting lung diseases from chest X-ray images. Methods This study utilized a chest X-ray image dataset comprising 19,003 images containing both normal cases and six lung diseases: COVID-19, Viral Pneumonia, Bacterial Pneumonia, Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS), and Tuberculosis. Each ViT model (ViT, FastViT, and CrossViT) was individually trained with each optimization method (Adam, AdamW, NAdam, RAdam, SGDW, and Momentum) to assess their performance in lung disease prediction. Results When tested with ViT on the dataset with balanced-sample sized classes, RAdam demonstrated superior accuracy compared to other optimizers, achieving 95.87%. In the dataset with imbalanced sample size, FastViT with NAdam achieved the best performance with an accuracy of 97.63%. Conclusions We provide comprehensive optimization strategies for developing ViT-based model architectures, which can enhance the performance of these models for lung disease prediction from chest X-ray images.

A high‐performance energy storage device plays an important role in controlling carbon emissions. The emerging additive manufacturing techniques bring a great revolution of electrode fabrication process and promote the performance of energy storage devices through the advanced electrode architecture design. In this paper, recent studies on the three‐dimensional (3D)‐printed electrode with advanced architecture have been mainly reviewed, including interdigitated structure, through‐thickness aligned structure, hierarchical porous structure and fiber and fibric structure of electrodes, and expectations for the development of novel advanced electrode architecture generated and optimized by computational simulation and machine learning. The strategy of advanced electrode architecture design and fabrication enabled by the 3D printing technique represents a promising direction toward future energy storage devices with high electrochemical and mechanical performance. Energy storage devices have been developed for high energy density and power density with a long lifetime. Three‐dimensional (3D) printing is a prominent technology for manipulating the device's electrode structure design to provide better performance. This review summarizes the critical role of 3D printing techniques in advanced electrode architecture design and fabrication and shares our thoughts on future 3D printing electrode development.

Organic-inorganic hybrid perovskites exhibiting exceptional photovoltaic and optoelectronic properties are of fundamental and practical interest, owing to their tunability and low manufacturing cost. For practical applications, however, challenges such as material instability and the photocurrent hysteresis occurring in perovskite solar cells under light exposure need to be understood and addressed. While extensive investigations have suggested that ion migration is a plausible origin of these detrimental effects, detailed understanding of the ion migration pathways remains elusive. Here, we report the characterization of photo-induced ion migration in perovskites using in situ laser illumination inside a scanning electron microscope, coupled with secondary electron imaging, energy-dispersive X-ray spectroscopy and cathodoluminescence with varying primary electron energies. Using methylammonium lead iodide and formamidinium lead iodide as model systems, we observed photo-induced long-range migration of halide ions over hundreds of micrometers and elucidated the transport pathways of various ions both near the surface and inside the bulk of the samples, including a surprising finding of the vertical migration of lead ions. Our study provides insights into ion migration processes in perovskites that can aid perovskite material design and processing in future applications. Ion migration is a plausible origin of material instability and photocurrent hysteresis in perovskite solar cells. Here, authors characterize photo-induced ion migration in perovskites by in situ laser illumination inside scanning electron microscope and observe long-range migration of halide ions.

Trogocytosis is a dynamic cellular process characterized by the exchange of the plasma membrane and associated cytosol during cell-to-cell interactions. Unlike phagocytosis, this transfer maintains the surface localization of transferred membrane molecules. For example, CD4 T cells engaging with antigen-presenting cells undergo trogocytosis, which facilitates the transfer of antigen-loaded major histocompatibility complex (MHC) class II molecules from antigen-presenting cells to CD4 T cells. This transfer results in the formation of antigen-loaded MHC class II molecule-dressed CD4 T cells. These “dressed” CD4 T cells subsequently participate in antigen presentation to other CD4 T cells. Additionally, trogocytosis enables the acquisition of immune-regulatory molecules, such as CTLA-4 and Tim3, in recipient cells, thereby modulating their anti-tumor immunity. Concurrently, donor cells undergo plasma membrane loss, and substantial loss can trigger trogocytosis-mediated cell death, termed trogoptosis. This review aims to explore the trogocytosis-mediated transfer of immune regulatory molecules and their implications within the tumor microenvironment to elucidate the underlying mechanisms of immune evasion in cancers. Trogocytosis Enhances Immune Regulation in the Tumor Microenvironment Trogocytosis is a process where cells exchange membrane proteins, first noted in the 1970s, and it affects immune responses. Membrane proteins are molecules that help cells communicate and interact. Researchers emphasized its importance in how immune cells interact. This review discusses trogocytosis in the tumor microenvironment, influencing both immune and cancer cells. The study explains that trogocytosis allows cells to “nibble” on others, transferring membrane properties, which can change the function of immune cells like T cells and NK cells. Various methods were used to study these interactions, focusing on immune and cancer cells. Findings indicate that trogocytosis can either suppress or enhance immune responses, affecting cancer therapies such as CAR-T cells. Understanding trogocytosis could improve cancer treatments by adjusting immune responses, and future research might explore its mechanisms to boost immunotherapy. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

Human–animal interaction (HAI) has been observed to effectively reduce stress and induce positive emotions owing to the process of directly petting and interacting with animals. Interaction with virtual animals has recently emerged as an alternative due to the limitations in general physical interactions, both due to the COVID-19 pandemic and, more generally, due to the difficulties involved in providing adequate care for animals. This study proposes mixed reality (MR)-based human–animal interaction content along with presenting the experimental verification of its effect on the reduction of mental stress. A mental arithmetic task was employed to induce acute mental stress, which was followed by either MR content, in which a participant interacted with virtual animals via gestures and voice commands, or a slide show of animal images. During the experiment, an electrocardiogram (ECG) was continuously recorded with a patch-type, wireless ECG sensor on the chest of the subject, and their psychological state was evaluated with the help of questionnaires after each task. The findings of the study demonstrate that the MR-based interaction with virtual animals significantly reduces mental stress and induces positive emotions. We expect that this study could provide a basis for the widespread use of MR-based content in the field of mental health.

Background Construction workers working in physically and mentally challenging environments experience high levels of occupational fatigue, which is the primary cause of industrial accidents and illnesses. Therefore, it is very important to measure fatigue in real time to manage the safety and health of construction workers. This study presents a novel approach for simultaneously measuring the subjective and objective fatigue of construction workers using ecological momentary assessment (EMA) and smartwatches. Due to the complexity and diversity of construction site environments, it is necessary to examine whether data collection using smartwatches is suitable in actual construction sites. This study aims to examine the feasibility of the integrated fatigue measurement method. Methods This study comprised two phases: (1) development of an integrated fatigue measurement system for construction workers, and (2) a validation study to evaluate the method’s feasibility based on sensor data acquisition, EMA compliance, and feedback from construction workers in the field ( N  = 80). Three days of biometric data were collected through sensors embedded in the smartwatches for objective fatigue measurement, including heart rate, accelerometer, and gyroscope data. Two types of self-reported data regarding each worker’s fatigue were collected through a researcher-developed EMA application. The acceptability and usability of this system were examined based on the researchers’ observations and unstructured interviews. Results Based on the standardized self-report questionnaire scores, participants were classified into high ( n  = 35, 43.75%) and low ( n  = 45, 56.25%) fatigue groups for comparison. The quantitative outcomes did not show a statistically significant difference between the two fatigue groups. Both groups experienced positive emotions and were able to recognize their health condition at the time of self-reporting, but stated that responding to this measurement system could be burdensome. Conclusions This feasibility study provides a unique understanding of the applications of EMA and smartwatches for safety management in the construction workforce. The developed measurement system shows potential for monitoring fatigue based on the real-time collection of relevant data. It is expected that by expanding this integrated system through further research and onsite application, the health and safety of construction workers can be improved.

Cellular protein degradation requires a complex molecular machine, the proteasome. To mitigate the fundamental challenge of assembling the 66-subunit proteasome, cells utilize dedicated chaperones to order subunit addition. However, recent evidence suggests that proteasome assembly is not simply a series of subunit additions, but each step may be scrutinized so that only correct assembly events advance to proteasomes. Here, we find an unexpected mechanism of quality control (QC) during proteasome assembly—via the proteasomal nuclear localization signal (NLS). This mechanism specifically sequesters defective assembly intermediates to the nucleus, away from ongoing assembly in the cytoplasm, thereby antagonizing defective proteasome formation. This NLS, a bona fide proteasomal component, provides continuous surveillance throughout proteasome assembly. Even a single incorrect event activates spatial QC. Our findings illuminate a two-decade-old mystery in proteasome regulation; proteasomal NLSs, dispensable for proteasome localization, instead provide QC by compartmentalizing assembly defects to ensure that only correct proteasomes form. Cellular protein degradation requires a molecular machine, the 66-subunit proteasome. Here, the authors show a quality control mechanism during proteasome assembly via its nuclear localization signal, sequestering incorrect subassemblies into the nucleus to antagonize defective proteasome formation.

Anthropogenic threats from human activity have significantly altered global ecosystems, resulting in habitat destruction and fragmentation, biodiversity loss, and species extinction. The identification and establishment of protected areas for the conservation of threatened species is thus required to mitigate the impacts of these threats. This study identified priority conservation areas (PCAs) for endangered mammals, birds, amphibians, and reptiles in South Korea using ecological modeling. In particular, we used the MaxEnt model to determine the potential distributions of endangered species and Zonation software to prioritize conservation areas. In doing so, we aimed to identify key environmental factors that influence the distributions of the target species and to identify ecologically critical areas using gap analysis based on the first-grade areas on the ecology and nature map (ENM). The results indicated that the highest priority areas for endangered species varied by taxon, with endangered mammals found primarily in forested areas, endangered birds on inland plains, and endangered amphibians and reptiles within marine and stream regions. The gap analysis revealed a very low overlap between ecological areas and high-priority areas for endangered birds and amphibians. This suggests that the first-grade areas on the ENM mainly represent areas with high vegetational coverage. These findings highlight the need to reassess existing protected areas and designate PCAs for endangered species independently from the ENM. This study represents a foundational assessment that can be used to inform conservation planning and prompt continued ecological research in support of biodiversity conservation efforts.

The aim of this study was to evaluate the biocompatibility of calcium silicate-based sealers (CeraSeal and EndoSeal TCS) and epoxy resin-based sealer (AH-Plus) in terms of cell viability, inflammatory response, expression of mesenchymal phenotype, osteogenic potential, cell attachment, and morphology, of human periodontal ligament stem cells (hPDLSCs). hPDLSCs were acquired from the premolars (n = 4) of four subjects, whose ages extended from 16 to 24 years of age. Flow cytometry analysis showed stemness of hPDLSCs was maintained in all materials. In cell viability test, AH-Plus showed the lowest cell viability, and CeraSeal showed significantly higher cell viability than others. In ELISA test, AH-Plus showed higher expression of IL-6 and IL-8 than calcium silicate-based sealers. In an osteogenic potential test, AH-Plus showed a lower expression level than other material; however, EndoSeal TCS showed a better expression level than others. All experiments were repeated at least three times per cell line. Scanning electronic microscopy studies showed low degree of cell proliferation on AH-Plus, and high degree of cell proliferation on calcium silicate-based sealers. In this study, calcium silicate-based sealers appear to be more biocompatible and less cytotoxic than epoxy-resin based sealers.

Container-based deep learning has emerged as a cutting-edge trend in modern AI applications. Containers have several merits compared to traditional virtual machine platforms in terms of resource utilization and mobility. Nevertheless, containers still pose challenges in executing deep learning workloads efficiently with respect to resource usage and performance. In particular, multi-tenant environments are vulnerable to the performance of container-based deep learning due to conflicts of resource usage. To quantify the container effect in deep learning, this article captures various event traces related to deep learning performance using containers and compares them with those captured on a host machine without containers. By analyzing the system calls invoked and various performance metrics, we quantify the effect of containers in terms of resource consumption and interference. We also explore the effects of executing multiple containers to highlight the issues that arise in multi-tenant environments. Our observations show that containerization can be a viable solution for deep learning workloads, but it is important to manage resources carefully to avoid excessive contention and interference, especially for storage write-back operations. We also suggest a preliminary solution to avoid the performance bottlenecks of page-faults and storage write-backs by introducing an intermediate non-volatile flushing layer, which improves I/O latency by 82% on average.