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Enteroendocrine cells (EECs) are specialized sensory cells in the intestinal epithelium that sense and transduce nutrient information. Consumption of dietary fat contributes to metabolic disorders, but EEC adaptations to high fat feeding were unknown. Here, we established a new experimental system to directly investigate EEC activity in vivo using a zebrafish reporter of EEC calcium signaling. Our results reveal that high fat feeding alters EEC morphology and converts them into a nutrient insensitive state that is coupled to endoplasmic reticulum (ER) stress. We called this novel adaptation 'EEC silencing'. Gnotobiotic studies revealed that germ-free zebrafish are resistant to high fat diet induced EEC silencing. High fat feeding altered gut microbiota composition including enrichment of Acinetobacter bacteria, and we identified an Acinetobacter strain sufficient to induce EEC silencing. These results establish a new mechanism by which dietary fat and gut microbiota modulate EEC nutrient sensing and signaling.

Accurate and reliable detection of landslides plays a crucial role in disaster prevention and mitigation efforts. However, due to unfavorable environmental conditions, uneven surface structures, and other disturbances similar to those of landslides, traditional methods often fail to achieve the desired results. To address these challenges, this study introduces a novel multi-scale cross-layer feature interaction network, specifically designed for landslide segmentation in remote sensing images. In the MCFI-Net framework, we adopt the encoder–decoder as the foundational architecture, and integrate cross-layer feature information to capture fine-grained local textures and broader contextual patterns. Then, we introduce the receptive field block (RFB) into the skip connections to effectively aggregate multi-scale contextual information. Additionally, we design the multi-branch dynamic convolution block (MDCB), which possesses both dynamic perception ability and multi-scale feature representation capability. The comprehensive evaluation conducted on both the Landslide4Sense and Bijie datasets demonstrates the superior performance of MCFI-Net in landslide segmentation tasks. Specifically, on the Landslide4Sense dataset, MCFI-Net achieved a Dice score of 0.7254, a Matthews correlation coefficient (Mcc) of 0.7138, and a Jaccard score of 0.5699. Similarly, on the Bijie dataset, MCFI-Net maintained high accuracy with a Dice score of 0.8201, an Mcc of 0.8004, and a Jaccard score of 0.6951. Furthermore, when evaluated on the optical remote sensing dataset EORSSD, MCFI-Net obtained a Dice score of 0.7770, an Mcc of 0.7732, and a Jaccard score of 0.6571. Finally, ablation experiments carried out on the Landslide4Sense dataset further validated the effectiveness of each proposed module. These results affirm MCFI-Net’s capability in accurately identifying landslide regions from complex remote sensing imagery, and it provides great potential for the analysis of geological disasters in the real world.

Building extraction aims to extract building pixels from remote sensing imagery, which plays a significant role in urban planning, dynamic urban monitoring, and many other applications. UNet3+ is widely applied in building extraction from remote sensing images. However, it still faces issues such as low segmentation accuracy, imprecise boundary delineation, and the complexity of network models. Therefore, based on the UNet3+ model, this paper proposes a 3D Joint Attention (3DJA) module that effectively enhances the correlation between local and global features, obtaining more accurate object semantic information and enhancing feature representation. The 3DJA module models semantic interdependence in the vertical and horizontal dimensions to obtain feature map spatial encoding information, as well as in the channel dimensions to increase the correlation between dependent channel graphs. In addition, a bottleneck module is constructed to reduce the number of network parameters and improve model training efficiency. Many experiments are conducted on publicly accessible WHU,INRIA and Massachusetts building dataset, and the benchmarks, BOMSC-Net, CVNet, SCA-Net, SPCL-Net, ACMFNet, MFCF-Net models are selected for comparison with the 3DJA-UNet3+ model proposed in this paper. The experimental results show that 3DJA-UNet3+ achieves competitive results in three evaluation indicators: overall accuracy, mean intersection over union, and F1-score. The code will be available at https://github.com/EnjiLi/3DJA-UNet3Plus .

In order to minimize the carbon emission and energy consumption of fuel cell hybrid vehicles and, at the same time, solve the problem of low accuracy of working condition identification in the working condition identification strategy, this paper proposes an energy management strategy for SUVs on the basis of the working condition identification energy management strategy by using the time classification method. First, the mathematical model of the whole vehicle power system is established, and the driving conditions are constructed using actual collected vehicle driving data. On this basis, the working condition identification model was established, and then the energy management strategy of time working condition classification was established on the basis of the working condition identification model, and the equivalent hydrogen consumption of the two strategies was calculated by the Pontryagin minimization strategy. The results show that the strategy proposed in this paper reduces the equivalent hydrogen consumption by 2.707% compared with the condition identification strategy. This improvement not only greatly improves the energy efficiency of the fuel cell hybrid vehicle but also provides new ideas for the optimization of future energy management strategies.

Detecting irregular or arbitrary shape text in natural scene images is a challenging task that has recently attracted considerable attention from research communities. However, limited by the CNN receptive field, these methods cannot directly capture relations between distant component regions by local convolutional operators. In this paper, we propose a novel method that can effectively and robustly detect irregular text in natural scene images. First, we employ a fully convolutional network architecture based on VGG16_BN to generate text components via the estimated character center points, which can ensure a high text component detection recall rate and fewer noncharacter text components. Second, text line grouping is treated as a problem of inferring the adjacency relations of text components with a graph convolution network (GCN). Finally, to evaluate our algorithm, we compare it with other existing algorithms by performing experiments on three public datasets: ICDAR2013, CTW-1500 and MSRA-TD500. The results show that the proposed method handles irregular scene text well and that it achieves promising results on these three public datasets.

This study proposes a hybrid PSO-EDO algorithm, integrating Particle Swarm Optimization (PSO) and the Exponential Distribution Optimizer (EDO) for efficient and accurate estimation of soil property parameters. The proposed algorithm combines the strengths of Standard PSO (SPSO) and the Exponential Distribution Optimizer (EDO). Three key innovations are introduced: (1) SPM chaotic mapping enhances initial population diversity; (2) dynamic inertia weight balances global exploration and local exploitation; (3) the memoryless property of EDO improves escape capability from local optima. Benchmark tests demonstrate that PSO-EDO achieves near-theoretical optimal convergence errors (mean error ≤ 10−16 for unimodal functions such as F1 and F2) and reduces the computation time by 14.5% compared to EDO. For multimodal functions (e.g., F3), PSO-EDO significantly outperforms PSO-WOA (Particle Swarm Optimization-Whale Optimization Algorithm) with a 22.3% reduction in error. Simulation experiments further validate its engineering practicality: in soil parameter estimation, PSO-EDO completes 1000 iterations in just 1.95 s, with key parameters (e.g., sinkage coefficient n) controlled within a 7.32% error margin. This provides an efficient solution for real-time traversability assessment of autonomous vehicles on soft terrains.

Ensuring the security and reliability of lithium-ion batteries necessitates the development of a robust methodology for detecting defects in battery separators during production. This study initially uses data augmentation techniques in the data processing phase, followed by the utilization of the weighted random sampler method for sampling. Additionally, the dataset is partitioned using the Stratified K-Fold cross-validation method to tackle imbalanced sample data. Subsequently, an ensemble of object detection algorithms involving Faster Region Convolutional Neural Network and RetinaNet is developed. The ensemble method employs a voting mechanism to ascertain the most accurate predictions and utilizes the Adaptive Delta optimization algorithm with adaptive learning rates. This algorithm adjusts the learning rate based on parameter change rates, eliminating the requirement for setting an initial learning rate to ensure result convergence. Finally, a model fine-tuning technique using pre-training transfer learning is applied to improve the detection performance of the ensemble model. Experimental results show that the improved methodology demonstrates a 16.26% increase in recall, a 7.05% improvement in precision, an 11.83% rise in balanced F Score, and a 0.23 increase in the area under the Receiver Operating Characteristic curve. The study results indicate that the proposed method is an effective and accurate approach to detecting defects in lithium-ion battery separators.

Artesunate (ART) has anti-cancer activities for a variety of solid tumors. The aim of this study was to investigate the anti-metastatic effect of ART on cervical cancer cells. In vivo anti-metastatic effect of ART was investigated in mice with the lung metastasis model by the subcutaneous injection of ART. The interaction of HOTAIR and COX-2 was measured by RNA immunoprecipitation and RNA pull-down assay. The effect of ART on metastasis of CaSki and Hela cells was evaluated by invasion and migration assay. We found that ART inhibited cervical cancer metastasis and HOTAIR expression. HOTAIR overexpression partially abolished the anti-metastatic effect of ART on cervical cancer cells. In addition, HOTAIR can interact with COX-2 to positively regulate COX-2 expression and catalytic activity. Finally, overexpression of COX-2 reversed the effect of HOTAIR knockdown on Hela cell migration and invasion. Taken together, our data revealed that ART may elicit anti-metastatic effect against cervical cancer by inhibition of HOTAIR expression, which resulted in the decrease of COX-2 expression.

Background Sarcopenia is a potential risk factor for adverse outcomes in haematopoietic cell transplantation (HSCT) recipients. We aimed to explore longitudinal body changes in muscle and adipose mass and their prognostic value in allogeneic HSCT‐treated severe aplastic anaemia (SAA) patients. Methods We retrospectively analysed consecutive SAA patients who underwent allogeneic HSCT between January 2017 and March 2022. Measurements of pectoral muscle and corresponding subcutaneous fat mass were obtained via chest computed tomography at baseline and at 1 month, 3 months, 6 months, and 12 months following HSCT. Sarcopenia was defined as pectoral muscle index (PMI) lower than the sex‐specific median at baseline. Changes in body composition over time were evaluated by generalized estimating equations. Cox regression models were used to investigate prognostic factors affecting overall survival (OS) and failure‐free survival (FFS). A nomogram was constructed from the Cox regression model for OS. Results We included 298 adult SAA patients (including 129 females and 169 males) with a median age of 31 years [interquartile range (IQR), 24–39 years] at baseline. Sarcopenia was present in 148 (148/298, 50%) patients at baseline, 218 (218/285, 76%) patients post‐1 month, 209 (209/262, 80%) patients post‐3 month, 169 (169/218, 78%) patients post‐6 month, and 129 (129/181, 71%) patients post‐12 month. A significant decrease in pectoral muscle mass was observed in SAA patients from the time of transplant to 1 year after HSCT, and the greatest reduction occurred in post 1–3 months (P < 0.001). The sarcopenia group exhibited significantly lower 5‐year OS (90.6% vs. 100%, log‐rank P = 0.039) and 5‐year FFS (89.2% vs. 100%, log‐rank P = 0.021) than the nonsarcopenia group at baseline. Sarcopenia at baseline (hazard ratio, HR, 6.344; 95% confidence interval, CI: 1.570–25.538; P = 0.01; and HR, 3.275; 95% CI: 1.159–9.252; P = 0.025, respectively) and the delta value of the PMI at 6 months post‐transplantation (ΔPMI6) (HR, 0.531; 95% CI: 0.374–0.756; P < 0.001; and HR, 0.666; 95% CI: 0.505–0.879; P = 0.004, respectively) were demonstrated to be independent prognostic factors for OS and FFS in SAA patients undergoing HSCT, and were used to construct the nomogram. The C‐index of the nomogram was 0.75, and the calibration plot showed good agreement between the predictions made by the nomogram and actual observations. Conclusions Sarcopenia persists in SAA patients from the time of transplant to the 1‐year follow‐up after HSCT. Both sarcopenia at baseline and at 6 months following HSCT are associated with poor clinical outcomes, especially in patients with persistent muscle mass loss up to 6 months after transplantation.