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Aichi virus (AiV), a small non-enveloped RNA virus, hijacks the endoplasmic reticulum (ER)–Golgi cholesterol transport machinery to form cholesterol-rich replication sites originating from Golgi membranes. Interferon-induced transmembrane proteins (IFITMs) are antiviral restriction factors, whose involvement in intracellular cholesterol transport is suggested. Here, we describe the roles of IFITM1 in cholesterol transport that affect AiV RNA replication. IFITM1 stimulated AiV RNA replication and its knockdown significantly reduced the replication. In replicon RNA-transfected or infected cells, endogenous IFITM1 localized to the viral RNA replication sites. Further, IFITM1 interacted with viral proteins and host Golgi proteins, ACBD3, PI4KB, OSBP, which constitute the replication sites. When overexpressed, IFITM1 localized to the Golgi as well as endosomes, and this phenotype was also observed for endogenous IFITM1 early in AiV RNA replication, leading to the distribution of cholesterol at the Golgi-derived replication sites. The pharmacological inhibition of ER–Golgi cholesterol transport or endosomal cholesterol export impaired AiV RNA replication and cholesterol accumulation at the replication sites. Such defects were corrected by expression of IFITM1. Overexpressed IFITM1 facilitated late endosome–Golgi cholesterol transport without any viral proteins. In summary, we propose a model in which IFITM1 enhances cholesterol transport to the Golgi to accumulate cholesterol at Golgi-derived replication sites, providing a novel mechanism by which IFITM1 enables efficient genome replication of non-enveloped RNA virus.

Dental pulp stem cells (DPSC) are capable of differentiating into vascular endothelial cells. Although the capacity of vascular endothelial growth factor (VEGF) to induce endothelial differentiation of stem cells is well established, mechanisms that maintain stemness and prevent vasculogenic differentiation remain unclear. Here, we tested the hypothesis that p53 signaling through p21 and Bmi-1 maintains stemness and inhibits vasculogenic differentiation. To address this hypothesis, we used primary human DPSC from permanent teeth and Stem cells from Human Exfoliated Deciduous (SHED) teeth as models of postnatal mesenchymal stem cells. DPSC seeded in biodegradable scaffolds and transplanted into immunodeficient mice generated mature human blood vessels invested with smooth muscle actin-positive mural cells. Knockdown of p53 was sufficient to induce vasculogenic differentiation of DPSC (without vasculogenic differentiation medium containing VEGF), as shown by increased expression of endothelial markers (VEGFR2, Tie-2, CD31, VE-cadherin), increased capillary sprouting in vitro; and increased DPSC-derived blood vessel density in vivo. Conversely, induction of p53 expression with small molecule inhibitors of the p53-MDM2 binding (MI-773, APG-115) was sufficient to inhibit VEGF-induced vasculogenic differentiation. Considering that p21 is a major downstream effector of p53, we knocked down p21 in DPSC and observed an increase in capillary sprouting that mimicked results observed when p53 was knocked down. Stabilization of ubiquitin activity was sufficient to induce p53 and p21 expression and reduce capillary sprouting. Interestingly, we observed an inverse and reciprocal correlation between p53/p21 and the expression of Bmi-1, a major regulator of stem cell self-renewal. Further, direct inhibition of Bmi-1 with PTC-209 resulted in blockade of capillary-like sprout formation. Collectively, these data demonstrate that p53/p21 functions through Bmi-1 to prevent the vasculogenic differentiation of DPSC.

In this study, we examined the natural recovery of mangroves in abandoned shrimp ponds located in the Wunbaik Mangrove Forest (WMF) in Myanmar using artificial neural network (ANN) classification and a change detection approach with Sentinel-2 satellite images. In 2020, we conducted various experiments related to mangrove classification by tuning input features and hyper-parameters. The selected ANN model was used with a transfer learning approach to predict the mangrove distribution in 2015. Changes were detected using classification results from 2015 and 2020. Naturally recovering mangroves were identified by extracting the change detection results of three abandoned shrimp ponds selected during field investigation. The proposed method yielded an overall accuracy of 95.98%, a kappa coefficient of 0.92, mangrove and non-mangrove precisions of 0.95 and 0.98, respectively, recalls of 0.96, and F1 scores of 0.96 for the 2020 classification. For the 2015 prediction, transfer learning improved model performance, resulting in an overall accuracy of 97.20%, a kappa coefficient of 0.94, mangrove and non-mangrove precisions of 0.98 and 0.96, respectively, recalls of 0.98 and 0.97, and F1 scores of 0.96. The change detection results showed that mangrove forests in the WMF slightly decreased between 2015 and 2020. Naturally recovering mangroves were detected at approximately 50% of each abandoned site within a short abandonment period. This study demonstrates that the ANN method using Sentinel-2 imagery and topographic and canopy height data can produce reliable results for mangrove classification. The natural recovery of mangroves presents a valuable opportunity for mangrove rehabilitation at human-disturbed sites in the WMF.

Reanalysis data and a parametric typhoon model formula are often used to prepare wind and pressure fields for storm surge hindcasting. However, their optimum selection and usage have not been well established. To enhance the accuracy of wind and pressure fields, two hybrid methods were proposed by applying a parametric typhoon model of Mitsuta–Fujii’s formula, which is determined from the typhoon center to a certain radius of R b , and then switching to the reanalysis data of ERA-Interim in the outer region through the interpolated transition bandwidth of W b . In hybrid model I, R b and W b were fixed because the time-varying radius of the maximum wind speed was determined by the typhoon formula. In hybrid model II, these parameters were determined to minimize the mean difference between the reanalysis data and the fields obtained by typhoon formula in the transition band at each time step of the typhoon track. The hindcasting of eight significant typhoon events approaching Tokyo Bay was performed. This validated the proposed methods in comparison with the observed storm surge anomalies. Both models performed satisfactorily. Hybrid model II was found to be superior in terms of the balance of accuracy and preparation cost.

Phosphatidylinositol 4‐kinase IIIβ (PI4KB) is a host factor required for genome RNA replication of enteroviruses, small non‐enveloped viruses belonging to the family Picornaviridae . Here, we demonstrated that PI4KB is also essential for genome replication of another picornavirus, Aichi virus (AiV), but is recruited to the genome replication sites by a different strategy from that utilized by enteroviruses. AiV non‐structural proteins, 2B, 2BC, 2C, 3A, and 3AB, interacted with a Golgi protein, acyl‐coenzyme A binding domain containing 3 (ACBD3). Furthermore, we identified previously unknown interaction between ACBD3 and PI4KB, which provides a novel manner of Golgi recruitment of PI4KB. Knockdown of ACBD3 or PI4KB suppressed AiV RNA replication. The viral proteins, ACBD3, PI4KB, and phophatidylinositol‐4‐phosphate (PI4P) localized to the viral RNA replication sites. AiV replication and recruitment of PI4KB to the RNA replication sites were not affected by brefeldin A, in contrast to those in enterovirus infection. These results indicate that a viral protein/ACBD3/PI4KB complex is formed to synthesize PI4P at the AiV RNA replication sites and plays an essential role in viral RNA replication. Picornavirus genome replication occurs on intracellular membranes, to which the polymerase localizes by binding the lipid PI4P. Aichi virus proteins interact with the Golgi protein ACBD3, which in turn recruits PI4‐kinase to stimulate PI4P synthesis, thus promoting replication.

Seagrass meadows are one of the blue carbon ecosystems that continue to decline worldwide. Frequent mapping is essential to monitor seagrass meadows for understanding change processes including seasonal variations and influences of meteorological and oceanic events such as typhoons and cyclones. Such mapping approaches may also enhance seagrass blue carbon strategy and management practices. Although unmanned aerial vehicle (UAV) aerial photography has been widely conducted for this purpose, there have been challenges in mapping accuracy, efficiency, and applicability to subtidal water meadows. In this study, a novel method was developed for mapping subtidal and intertidal seagrass meadows to overcome such challenges. Ground truth seagrass orthophotos in four seasons were created from the Futtsu tidal flat of Tokyo Bay, Japan, using vertical and oblique UAV photography. The feature pyramid network (FPN) was first applied for automated seagrass classification by adjusting the spatial resolution and normalization parameters and by considering the combinations of seasonal input data sets. The FPN classification results ensured high performance with the validation metrics of 0.957 overall accuracy (OA), 0.895 precision, 0.942 recall, 0.918 F1-score, and 0.848 IoU, which outperformed the conventional U-Net results. The FPN classification results highlighted seasonal variations in seagrass meadows, exhibiting an extension from winter to summer and demonstrating a decline from summer to autumn. Recovery of the meadows was also detected after the occurrence of Typhoon No. 19 in October 2019, a phenomenon which mainly happened before summer 2020.

It has been shown that ideas of reference in the context of paranoia (IoR-P) and schizophrenia spectrum disorders (IoR-S) are caused by different psychological constructs. Although it is well known that both IoR-P and IoR-S are frequently evoked during the same period of life, how they interact with each other is unknown. The purpose of the present study was to develop the Japanese version of the Referential Thinking Scale (J-REF) to assess IoR-S, examine its validity and reliability, and explore the predictors of IoR-P and IoR-S. In this study, several subgroups of Japanese individuals in their 20s were included in the analysis. The J-REF had high internal consistency, high test-retest reliability, good convergent, and discriminant validity. Two hierarchical regression analyses showed that public self-consciousness predicted the manifestation of IoR-P, while the dimensions of schizotypy predicted that of IoR-S. Moreover, social anxiety and negative moods could cause IoR-P and IoR-S. This study directly showed the existence of two different types of ideas of reference in terms of their predictors. It is also significant in that it first examined referential thinking using the REF scale in the context of Asia and showed that there may not be much difference in the frequency of ideas of reference from other cultures. Future research directions are also discussed.

This paper presents the development of a U-Net model using four basic optical bands and SRTM data to analyze changes in mangrove forests from 1990 to 2024, with an emphasis on the impact of restoration programs. The model, which employed supervised learning for binary classification by fusing multi-temporal Landsat 8 and Sentinel-2 imagery, achieved a superior accuracy of 99.73% for the 2020 image classification. It was applied to predict the long-term mangrove maps in Wunbaik Mangrove Forest (WMF) and to detect the changes at five-year intervals. The change detection results revealed significant changes in the mangrove forests, with 29.3% deforestation, 5.75% reforestation, and −224.52 ha/yr of annual rate of changes over 34 years. The large areas of mangrove forests have increased since 2010, primarily due to naturally recovered and artificially planted mangroves. Approximately 30% of the increased mangroves from 2015 to 2024 were attributed to mangrove plantations implemented by the government. This study contributes to developing a deep learning model with multi-temporal and multi-source imagery for long-term mangrove monitoring by providing accurate performance and valuable information for effective conservation strategies and restoration programs.

Surface Pre-Reacted Glass-ionomer (S-PRG) filler, which releases strontium (Sr2+), borate (BO33−), fluoride (F−), sodium (Na+), silicate (SiO32−), and aluminum (Al3+) ions at high concentrations, is a unique glass filler that are utilized in dentistry. Because of its multiple-ion releasing characteristics, S-PRG filler exhibits several bioactivities such as tooth strengthening, acid neutralization, promotion of mineralization, inhibition of bacteria and fungi, inhibition of matrix metalloproteinases, and enhancement of cell activity. Therefore, S-PRG filler per se and S-PRG filler-containing materials have the potential to be beneficial for various dental treatments and care. Those include restorative treatment, caries prevention/management, vital pulp therapy, endodontic treatment, prevention/treatment of periodontal disease, prevention of denture stomatitis, and perforation repair/root end filling. This review summarizes bioactive functions exhibited by S-PRG filler and its possible contribution to oral health.

In Japan, there are many flood and tsunami disasters caused by typhoons, heavy rains and earthquakes. In this case, the residents have an evacuation time to evacuate to shelters after evacuation alerts from a governmental office. However, some people don’t try to evacuate soon. As a result, it is problem that some victims of disasters are caused because without or late evacuation and supporting. In order to prevent such a damage, we developed Regional Information Sharing System (RISS) for disaster risk reduction. The RISS includes daily required life information from regional government and regional shops, as well as disaster information, shelters’ condition and the information of support required people and support teams. In this paper, we introduce the concept of RISS and its evaluation results on a field test and the effects in a regional disaster case by multi-agent simulation.