Explore the vast range of titles available.

The 2024 Noto Peninsula earthquake (M JMA 7.6) occurred, followed by a tsunami on January 1, 2024. The earthquake caused considerable damage by strong ground motion over a wide area centered on the Noto Peninsula. The tsunami also damaged coastal areas. Japan Agency for Marine–Earth Science and Technology (JAMSTEC), Earthquake Research Institute (ERI) and Atmosphere and Ocean Research Institute (AORI) of the University of Tokyo conducted emergency survey cruises with various observations such as ocean bottom seismometers (OBSs) deployment and recovery operations to identify aftershock activity promptly and acquisition of multibeam bathymetry data from the northeast area of the Noto Peninsula and Toyama Bay. Our survey mainly covered the northeast part of the aftershock area and the downstream part of the Toyama Deep–Sea Channel (TDSC) in the Toyama Bay and the southern Toyama Trough. Survey lines in the northern survey area generally trend NNE–SSW, roughly parallel to the channel; some lines in the NNW–SSE direction cross the active faults. We compiled a bathymetric map (with 25 m grid spacing), using all available multibeam echo sounder (MBES) data. We identified some important bathymetric features, such as gentle wavy topography developed over the levee and horseshoe-shaped landforms. In addition, we conducted a dense survey to detect depth differences before and after the earthquake in the area along TDSC in the middle of our survey area. Results revealed four small-scale landslide areas in the dense survey area. Obtaining detailed topographic data using modern multibeam sonar is extremely important to assess the risk of tsunami damage for various marine infrastructures as well as the potential occurrence of submarine landslides and slope failures. Graphical Abstract

In recent years, significant progress has been made in image recognition technology based on deep neural networks. However, improving recognition performance under low-light conditions remains a significant challenge. This study addresses the enhancement of recognition model performance in low-light conditions. We propose an image-adaptive learnable module which apply appropriate image processing on input images and a hyperparameter predictor to forecast optimal parameters used in the module. Our proposed approach allows for the enhancement of recognition performance under low-light conditions by easily integrating as a front-end filter without the need to retrain existing recognition models designed for low-light conditions. Through experiments, our proposed method demonstrates its contribution to enhancing image recognition performance under low-light conditions.

In this paper, we propose a novel low-light image enhancement method aimed at improving the performance of recognition models. Despite recent advances in deep learning, the recognition of images under low-light conditions remains a challenge. Although existing low-light image enhancement methods have been developed to improve image visibility for human vision, they do not specifically focus on enhancing recognition model performance. Our proposed low-light image enhancement method consists of two key modules: the Global Enhance Module, which adjusts the overall brightness and color balance of the input image, and the Pixelwise Adjustment Module, which refines image features at the pixel level. These modules are trained to enhance input images to improve downstream recognition model performance effectively. Notably, the proposed method can be applied as a frontend filter to improve low-light recognition performance without requiring retraining of downstream recognition models. Experimental results demonstrate that our method improves the performance of pretrained recognition models under low-light conditions and its effectiveness.

In recent years, significant progress has been made in image recognition technology based on deep neural networks. However, improving recognition performance under low-light conditions remains a significant challenge. This study addresses the enhancement of recognition model performance in low-light conditions. We propose an image-adaptive learnable module which apply appropriate image processing on input images and a hyperparameter predictor to forecast optimal parameters used in the module. Our proposed approach allows for the enhancement of recognition performance under low-light conditions by easily integrating as a front-end filter without the need to retrain existing recognition models designed for low-light conditions. Through experiments, our proposed method demonstrates its contribution to enhancing image recognition performance under low-light conditions.

Tissue fibrosis and organ dysfunction are hallmarks of age-related diseases including heart failure, but it remains elusive whether there is a common pathway to induce both events. Through single-cell RNA-seq, spatial transcriptomics, and genetic perturbation, we elucidate that high-temperature requirement A serine peptidase 3 (Htra3) is a critical regulator of cardiac fibrosis and heart failure by maintaining the identity of quiescent cardiac fibroblasts through degrading transforming growth factor-β (TGF-β). Pressure overload downregulates expression of Htra3 in cardiac fibroblasts and activated TGF-β signaling, which induces not only cardiac fibrosis but also heart failure through DNA damage accumulation and secretory phenotype induction in failing cardiomyocytes. Overexpression of Htra3 in the heart inhibits TGF-β signaling and ameliorates cardiac dysfunction after pressure overload. Htra3-regulated induction of spatio-temporal cardiac fibrosis and cardiomyocyte secretory phenotype are observed specifically in infarct regions after myocardial infarction. Integrative analyses of single-cardiomyocyte transcriptome and plasma proteome in human reveal that IGFBP7, which is a cytokine downstream of TGF-β and secreted from failing cardiomyocytes, is the most predictable marker of advanced heart failure. These findings highlight the roles of cardiac fibroblasts in regulating cardiomyocyte homeostasis and cardiac fibrosis through the Htra3-TGF-β-IGFBP7 pathway, which would be a therapeutic target for heart failure. Cardiac fibrosis is a hallmark of heart failure. Here the authors use single-cell RNA-sequencing, spatial transcriptomics, and genetic manipulations, to show that Htra3 regulates cardiac fibrosis by keeping fibroblasts quiescent and by degrading TGF-beta.

Cardiomyopathy develops through an interaction of genetic and environmental factors. The clinical manifestations of both dilated cardiomyopathy and hypertrophic cardiomyopathy are diverse, but genetic testing defines the causative genes in about half of cases and can predict clinical prognosis. It has become clear that cardiomyopathy is caused not only by single rare variants but also by combinations of multiple common variants, and genome-wide genetic research is important for accurate disease risk assessment. Single-cell analysis research aimed at understanding the pathophysiology of cardiomyopathy is progressing rapidly, and it is expected that genomic analysis and single-cell molecular profiling will be combined to contribute to more detailed stratification of cardiomyopathy.

Dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) are genetically and phenotypically heterogeneous. Cardiac function is improved after treatment in some cardiomyopathy patients, but little is known about genetic predictors of long-term outcomes and myocardial recovery following medical treatment. To elucidate the genetic basis of cardiomyopathy in Japan and the genotypes involved in prognosis and left ventricular reverse remodeling (LVRR), we performed targeted sequencing on 120 DCM (70 sporadic and 50 familial) and 52 HCM (15 sporadic and 37 familial) patients and integrated their genotypes with clinical phenotypes. Among the 120 DCM patients, 20 (16.7%) had TTN truncating variants and 13 (10.8%) had LMNA variants. TTN truncating variants were the major cause of sporadic DCM (21.4% of sporadic cases) as with Caucasians, whereas LMNA variants, which include a novel recurrent LMNA E115M variant, were the most frequent in familial DCM (24.0% of familial cases) unlike Caucasians. Of the 52 HCM patients, MYH7 and MYBPC3 variants were the most common (12 (23.1%) had MYH7 variants and 11 (21.2%) had MYBPC3 variants) as with Caucasians. DCM patients harboring TTN truncating variants had better prognosis than those with LMNA variants. Most patients with TTN truncating variants achieved LVRR, unlike most patients with LMNA variants.

There are a few studies about the clinical impacts of plasma B-type natriuretic peptide (BNP) at discharge with the occurrence of worsening renal function (WRF) on mortality in patients with heart failure (HF). We divided total 301 patients with acute decompensated HF into four groups by the median value (278.7 pg/mL) of BNP level at discharge and by the occurrence of WRF. WRF developed in 100 patients (33.2%). Cardiovascular mortality was significantly different between the four groups (P = 0.0002). Patients with WRF and elevated BNP had a higher cardiovascular mortality than patients without WRF and elevated BNP in Cox proportional hazard models (hazard ratio [HR], 10.48; 95% confident interval [95% CI], 1.27–225.53; P = 0.03). Patients with either WRF or elevated BNP did not have an increased risk of cardiovascular mortality compared to patients without WRF and elevated BNP. Regarding HF readmission and cardiovascular mortality, patients with WRF and elevated BNP had the highest risk (HR, 5.17; 95% CI, 2.07–14.30, P = 0.0003) and patients with either WRF or elevated BNP had a higher risk than patients without WRF and elevated BNP. The occurrence of WRF combined with elevated BNP at discharge was associated with increased 1-year cardiovascular mortality and HF readmission.

This single-centre prospective feasibility study (UMIN000030232) evaluated whether zinc supplementation was safe and effective for improving outcomes among patients with acute myocardial infarction (AMI). Within 24 h after successful primary percutaneous coronary intervention, consenting patients with AMI were randomly assigned 1:1 to receive conventional treatment (conventional treatment group) or conventional treatment plus zinc acetate supplementation (zinc supplementation group). The two groups were compared in terms of major adverse cardiovascular events (MACE), and scar size, which was evaluated using cardiac magnetic resonance imaging (CMR) at 4 weeks after discharge. A total of 56 patients underwent randomization (with 26 assigned to the zinc supplementation group and 27 to the conventional treatment group). The two groups had generally similar laboratory findings and clinical characteristics. The two groups also had similar lengths of hospital stay and rates of MACE. Forty of the 53 patients underwent CMR and it revealed that % core zone was numerically lower in the zinc supplementation group than in the conventional treatment group (9.3 ± 6.9% vs. 14.2 ± 9.1%, P = 0.07). This small single-centre study failed to detect a significant reduction in mid-term MACE after AMI among patients who received zinc supplementation.