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tRNA modifications are crucial for efficient protein synthesis, impacting codon recognition, tRNA stability, and translation rates. RNA viruses hijack the host’s translational machinery, including the pool of modified tRNA, to translate their own genomes. However, the mismatch between viral and host codon usage can lead to a limited availability of specific tRNA leading to ribosome stalling, posing a significant challenge for efficient protein translation. While some viruses address this challenge through codon optimization, we show here that SARS-CoV-2 (Coronavirus) and the Zika virus (ZIKV; Flavivirus) adopt a different approach, manipulating the host tRNA epitranscriptome. Analysis of codon bias indices confirmed a substantial divergence between viral and host codon usage, revealing a strong preference in viral genes for codons decoded by tRNAs requiring U34 wobble modification. Monitoring tRNA modification dynamics in infected cells showed that both SARS-CoV2 and ZIKV enhance U34 tRNA modifications during infection. Strikingly, impairing U34 tRNAs profoundly impacted viral replication, underscoring the strict reliance of SARS-CoV-2 and ZIKV on manipulating the host tRNA epitranscriptome to support the efficient translation of their genome. Full article

Timely and accurate identification of surface damage in hydraulic structures is essential for maintaining structural integrity and ensuring operational safety. Traditional manual inspections are time-consuming, labor-intensive, and prone to subjectivity, especially for large-scale or inaccessible infrastructure. Leveraging advancements in aerial imaging, unmanned aerial vehicles (UAVs) enable efficient acquisition of high-resolution visual data across expansive hydraulic environments. However, existing deep learning (DL) models often lack architectural adaptations for the visual complexities of UAV imagery, including low-texture contrast, noise interference, and irregular crack patterns. To address these challenges, this study proposes a lightweight, robust, and high-precision segmentation framework, called LFPA-EAM-Fast-SCNN, specifically designed for pixel-level damage detection in UAV-captured images of hydraulic concrete surfaces. The developed DL-based model integrates an enhanced Fast-SCNN backbone for efficient feature extraction, a Lightweight Feature Pyramid Attention (LFPA) module for multi-scale context enhancement, and an Edge Attention Module (EAM) for refined boundary localization. The experimental results on a custom UAV-based dataset show that the proposed damage detection method achieves superior performance, with a precision of 0.949, a recall of 0.892, an F1 score of 0.906, and an IoU of 87.92%, outperforming U-Net, Attention U-Net, SegNet, DeepLab v3+, I-ST-UNet, and SegFormer. Additionally, it reaches a real-time inference speed of 56.31 FPS, significantly surpassing other models. The experimental results demonstrate the proposed framework’s strong generalization capability and robustness under varying noise levels and damage scenarios, underscoring its suitability for scalable, automated surface damage assessment in UAV-based remote sensing of civil infrastructure. Full article

Artificial intelligence (AI) has become the mainstream of analysis tools in remote sensing. Various semantic segmentation models have been introduced to segment land cover from aerial or satellite images, and remarkable results have been achieved. However, they often lack universal performance on unseen images, making them challenging to provide as a service. One of the primary reasons for the lack of robustness is overfitting, resulting from errors and inconsistencies in the ground truth (GT). In this study, we propose a method to mitigate these inconsistencies by utilizing redundant models and verify the improvement using a public dataset based on Google Earth images. Redundant models share the same network architecture and hyperparameters but are trained with different combinations of training and validation data on the same dataset. Because of the variations in sample exposure during training, these models yield slightly different inference results. This variability allows for the estimation of pixel-level confidence levels for the GT. The confidence level is incorporated into the GT to influence the loss calculation during the training of the enhanced model. Furthermore, we implemented a consensus model that employs modified masks, where classes with low confidence are substituted by the dominant classes identified through a majority vote from the redundant models. To further improve robustness, we extended the same approach to fuse the dataset with different class compositions based on imagery from the Korea Multipurpose Satellite 3A (KOMPSAT-3A). Performance evaluations were conducted on three network architectures: a simple network, U-Net, and DeepLabV3. In the single-dataset case, the performance of the enhanced and consensus models improved by an average of 2.49% and 2.59% across the network architectures. In the multi-dataset scenario, the enhanced models and consensus models showed an average performance improvement of 3.37% and 3.02% across the network architectures, respectively, compared to an average increase of 1.55% without the proposed method. Full article

Vibrio parahaemolyticus is a major foodborne pathogen worldwide, responsible for seafood-associated poisoning. Among its toxin genes, tdh2 is the most critical. To investigate the role of tdh2 in V. parahaemolyticus under gastrointestinal conditions, we constructed tdh2 deletion and complementation strains and compared their survival under acid (pH 3 and 4) and bile stress (2%). The results showed that tdh2 expression was significantly upregulated under cold (4 °C) and bile stress (0.9%). Survival assays and PI staining revealed that the tdh2 mutant strain (VP: △tdh2) was more sensitive to acid and bile stress than the wild-type (WT), and this sensitivity was rescued by tdh2 complementation. These findings suggest that tdh2 plays a protective role in enhancing V. parahaemolyticus tolerance to acid and bile stress. In the VP: △tdh2 strain, seven genes were significantly upregulated and six were downregulated as a result of tdh2 deletion. These genes included VPA1332 (vtrA), VPA1348 (vtrB), VP2467 (ompU), VP0301 and VP1995 (ABC transporters), VP0527 (nhaR), and VP2553 (rpoS), among others. Additionally, LC-MS/MS analysis identified 12 differential metabolites between the WT and VP: △tdh2 strains, including phosphatidylserine (PS) (17:2 (9Z,12Z) /0:0 and 20:1 (11Z) /0:0), phosphatidylglycerol (PG) (17:0/0:0), flavin mononucleotide (FMN), and various nucleotides. The protective mechanism of tdh2 may involve preserving cell membrane permeability through regulation of ompU and ABC transporters and enhancing electron transfer efficiency via regulation of nhaR. The resulting reduction in ATP, DNA, and RNA synthesis—along with changes in membrane permeability and electron transfer due to decreased FMN—likely contributed to the reduced survival of the VP: △tdh2 strain. Meanwhile, the cells actively synthesized phospholipids to repair membrane damage, leading to increased levels of PS and PG. This study provides important insights into strategies for preventing and controlling food poisoning caused by tdh⁺ V. parahaemolyticus. Full article

Sensing transparent objects has many applications in human daily life, including robot navigation and grasping. However, this task presents significant challenges due to the unpredictable nature of scenes that extend beyond/behind transparent objects, particularly the lack of fixed visual patterns and strong background interference. This paper aims to solve the transparent object segmentation problem by leveraging the intrinsic global modeling capabilities of transformer architectures. We design a Query Parsing Module (QPM) that innovatively formulates segmentation as a dictionary lookup problem, differing fundamentally from conventional pixel-wise mechanisms, e.g., via attention-based prototype matching, and a set of learnable class prototypes as query inputs. Based on QPM, we propose a high-performance transformer-based end-to-end segmentation model, Transparent Object Segmentation through Query (TOSQ). TOSQ’s encoder is based on the Segformer’s backbone, and its decoder consists of a series of QPM modules, which progressively refine segmentation masks by the proposed QPMs. TOSQ achieves state-of-the-art performance on the Trans10K-V2 dataset (76.63% mIoU, 95.34% Acc), with particularly significant gains in challenging categories like windows (+23.59%) and glass doors (+11.22%), demonstrating its superior capability in transparent object segmentation. Full article

Visual inspection of civil infrastructure for structural health assessment, as performed by structural engineers, is expensive and time-consuming. Therefore, automating this process is highly attractive, which has received significant attention in recent years. With the increasing capabilities of computers, deep neural networks have become a standard tool and can be used for structural health inspections. A key challenge, however, is the availability of reliable datasets. In this work, the U-net and DeepLab v3+ convolutional neural networks are trained on a synthetic Tokaido dataset. This dataset comprises images representative of data acquired by unmanned aerial vehicle (UAV) imagery and corresponding ground truth data. The data includes semantic segmentation masks for both categorizing structural elements (slabs, beams, and columns) and assessing structural damage (concrete spalling or exposed rebars). Data augmentation, including both image quality degradation (e.g., brightness modification, added noise) and image transformations (e.g., image flipping), is applied to the synthetic dataset. The selected neural network architectures achieve excellent performance, reaching values of 97% for accuracy and 87% for Mean Intersection over Union (mIoU) on the validation data. It also demonstrates promising results in the semantic segmentation of real-world structures captured in photographs, despite being trained solely on synthetic data. Additionally, based on the obtained results of semantic segmentation, it can be concluded that DeepLabV3+ outperforms U-net in structural component identification. However, this is not the case in the damage identification task. Full article

A bijective product k-cordial labeling f of a graph G with vertex set V and edge set E is a bijection from V to $\{1,2,\dots ,|V|\}$ such that the induced edge labeling $f^{\times }:E\left(G\right)\to {\mathbb{Z}}_k=\{i|0\le i\le k-1\}$ defined as $f^{\times }\left(uv\right)\equiv f\left(u\right)f\left(v\right)(\mathrm{mod}k)$ for every edge $uv\in E$ satisfies the condition $|e_f^{\times }\left(i\right)-e_f^{\times }\left(j\right)|\le 1$, where $i,j\in {\mathbb{Z}}_k$ and $e_f^{\times }\left(i\right)$ is the number of edges labeled with i under $f^{\times }$. A graph which admits a bijective product k-cordial labeling is called a bijective product k-cordial graph. In this paper, we study bijective product $\pi$-cordiality for paths and cycles, where $\pi$ is an odd prime. We determine bijective product $\pi$-cordiality for paths and cycles for $3\le \pi \le 13$. Also, we establish the bijective product k-cordial labeling of stars. Further, we find the bijective product 4-cordial labeling of bistars and the splitting graphs of stars and bistars. Full article

For a vertex subset S of a graph $G,$ if each vertex of G is either in S or adjacent to some vertex in $S,$ then S is a dominating set of $G.$ Let S be a dominating set of a graph $G.$ If each vertex v not in S has a neighbor u in S such that $(S\backslash \{u\left\}\right)\cup \left\{v\right\}$ is also a dominating set of $G,$ then S is a secure dominating set of $G.$ If each vertex u in S has a neighbor v not in S such that $(S\backslash \{u\left\}\right)\cup \left\{v\right\}$ is also a dominating set of G, then S is a co-secure dominating set of G. The minimum cardinality of a secure (resp. co-secure) dominating set of G is the secure (resp. co-secure) domination number of $G.$ Arumugam et al. proposed the questions to characterize a graph G such that the co-secure domination number of G equals the independence number and the secure domination number of G, respectively. Inspired by those questions, in this paper, we obtain two classes of claw-free graphs such that the co-secure domination number equal the independence number and the secure domination number. Our results provide some theoretical basis of claw-free graphs for networks. Full article

Over efficient photocatalysts, CO₂ photoreduction typically converts CO₂ into low-carbon chemicals, which serve as raw materials for downstream synthesis processes. Here, an efficient composite photocatalyst heterojunction (Cu₂O-u/g-C₃N₄) has been fabricated to reduce CO₂. Graphitic carbon nitride (g-C₃N₄) was synthesized via thermal polymerization of urea at 550 °C, while pre-dispersed Cu₂O derived from urea pyrolysis (Cu₂O-u) was prepared by thermal reduction of urea and CuCl₂·2H₂O at 180 °C. The heterojunction Cu₂O-u/g-C₃N₄ was subsequently constructed through hydrothermal treatment at 180 °C. This heterojunction exhibited a bandgap of 2.10 eV, with dual optical absorption edges at 485 nm and above 800 nm, enabling efficient harvesting of solar light. Under 175 W mercury lamp irradiation, the heterojunction catalyzed liquid-phase CO₂ photoreduction to formic acid, acetic acid, and methanol. Its formic acid production activity surpassed that of pristine g-C₃N₄ by 3.14-fold and TiO₂ by 8.72-fold. Reaction media, hole scavengers, and reaction duration modulated product selectivity. In acetonitrile/isopropanol systems, formic acid and acetic acid production reached 579.4 and 582.8 μmol·h⁻¹·g_cat⁻¹. Conversely, in water/triethanolamine systems, methanol production reached 3061.6 μmol·h⁻¹·g_cat⁻¹, with 94.79% of the initial conversion retained after three cycles. Finally, this work ends with the conclusions of the CO₂ photocatalytic reduction to formic acid, acetic acid, and methanol, and recommends prospects for future research. Full article

Achieving state-of-the-art performance (82.5% IoU, 85.6% F1), this paper proposes an enhanced DeepLabV3+ model for robust underwater crack detection through three integrated innovations: a physics-based light propagation correction model for illumination distortion, multi-scale feature extraction for variable crack dimensions, and curvature flow-guided loss for boundary precision. Our approach significantly outperforms DeepLabV3+, SCTNet, and LarvSeg by 10.6–13.4% IoU, demonstrating particular strength in detecting small cracks (78.1% IoU) under challenging low-light/high-turbidity conditions. The solution provides a practical framework for automated underwater infrastructure inspection. Full article

The introduction of the ”dual carbon” target has increased the need for products that can accurately measure carbon dioxide levels, reflecting the rising demand. Due to challenges in achieving the required spatiotemporal resolution, accuracy, and spatial continuity with current carbon dioxide concentration products, it is essential to explore methods for obtaining carbon dioxide concentration products with completeness in space and time. Based on the 2018 OCO-2 carbon dioxide products and environmental variables such as vegetation coverage (FVC, LAI), net primary productivity (NPP), relative humidity (RH), evapotranspiration (ET), temperature (T) and wind (U, V), this study constructed a multiple regression model to obtain the spatial continuous carbon dioxide concentration products in the provinces of Huai River Basin. Using indicators such as correlation coefficient, root mean square error (RMSE), local variance, and percentage of valid pixels, the performance of model was validated. The validation results are shown as follows: (1) Among the selected environmental variables, the primary factors affecting the spatiotemporal distribution of carbon dioxide concentration are ET, LAI, FVC, NPP, T, U, and RH. (2) Compared with the OCO-2 carbon dioxide products, the percentage of valid pixels of the reconstructed carbon dioxide concentration data increased from less than 1% to over 90%. (3) The local variance in reconstructed data was significantly larger than that of original OCO-2 ${\mathrm{C}\mathrm{O}}_2$ products. (4) The average monthly RMSE is 2.69. Therefore, according to the model developed in this study, we can obtain a carbon dioxide concentration dataset that is spatially complete, meets precision requirements, and is rich in local detail information, which can better reflect the spatial pattern of carbon dioxide concentration and can be used to examine the carbon cycle between the terrestrial environment, biosphere, and atmosphere. Full article

Accurate classification of chest X-ray (CXR) images is crucial for diagnosing lung diseases in medical imaging. Existing deep learning models for CXR image classification face challenges in distinguishing non-lung features. In this work, we propose a new segmentation-assisted fusion-based classification method. The method involves two stages: first, we use a lightweight segmentation model, Partial Convolutional Segmentation Network (PCSNet) designed based on an encoder–decoder architecture, to accurately obtain lung masks from CXR images. Then, a fusion of the masked CXR image with the original image enables classification using the improved lightweight ShuffleNetV2 model. The proposed method is trained and evaluated on segmentation datasets including the Montgomery County Dataset (MC) and Shenzhen Hospital Dataset (SH), and classification datasets such as Chest X-Ray Images for Pneumonia (CXIP) and COVIDx. Compared with seven segmentation models (U-Net, Attention-Net, SegNet, FPNNet, DANet, DMNet, and SETR), five classification models (ResNet34, ResNet50, DenseNet121, Swin-Transforms, and ShuffleNetV2), and state-of-the-art methods, our PCSNet model achieved high segmentation performance on CXR images. Compared to the state-of-the-art Attention-Net model, the accuracy of PCSNet increased by 0.19% (98.94% vs. 98.75%), and the boundary accuracy improved by 0.3% (97.86% vs. 97.56%), while requiring 62% fewer parameters. For pneumonia classification using the CXIP dataset, the proposed strategy outperforms the current best model by 0.14% in accuracy (98.55% vs. 98.41%). For COVID-19 classification with the COVIDx dataset, the model reached an accuracy of 97.50%, the absolute improvement in accuracy compared to CovXNet was 0.1%, and clinical metrics demonstrate more significant gains: specificity increased from 94.7% to 99.5%. These results highlight the model’s effectiveness in medical image analysis, demonstrating clinically meaningful improvements over state-of-the-art approaches. Full article

With the rapid advancement of UAV-based remote sensing and image recognition techniques, identifying environmental risk factors from aerial imagery has emerged as a focal point in intelligent inspection during the power transmission and distribution projects construction phase. The uneven spatial distribution of risk factors on construction sites, their weak texture signatures, and the inherently multi-scale nature of UAV imagery pose significant detection challenges. To address these issues, we propose a one-stage SRW-YOLO algorithm built upon the YOLOv11 framework. First, a P2-scale shallow feature detection layer is added to capture high-resolution fine details of small targets. Second, we integrate a reparameterized convolution based on channel shuffle (RCS) of a one-shot aggregation (RCS-OSA) module into the backbone and neck’s shallow layers, enhancing feature extraction while significantly reducing inference latency. Finally, a dynamic non-monotonic focusing mechanism WIoU v3 loss function is employed to reweigh low-quality annotations, thereby improving small-object localization accuracy. Experimental results demonstrate that SRW-YOLO achieves an overall precision of 80.6% and mAP of 79.1% on the State Grid dataset, and exhibits similarly superior performance on the VisDrone2019 dataset. Compared with other one-stage detectors, SRW-YOLO delivers markedly higher detection accuracy, offering critical technical support for multi-scale, heterogeneous environmental risk monitoring during the power transmission and distribution projects construction phase, and establishes the theoretical foundation for rapid and accurate inspection using UAV-based intelligent imaging. Full article

Background: Limited data are available regarding the mechanical performance of 3D-printed fixed partial dentures (FPDs) fabricated from different materials and connector geometries. The purpose of this in vitro study was to evaluate the influence of connector size and material type on the fracture resistance of three-unit posterior FPDs fabricated with two commercially available 3D-printable dental resins. Methods: A standardized metal model with two cylindrical abutments was used to design three-unit FPDs. A total of sixty samples were produced, considering three connector sizes (3 × 3 mm, 4 × 4 mm, and 5 × 5 mm) and two different resins: Temp Print (GC Corp., Tokyo, Japan) and V-Print c&b temp (Voco GmbH, Cuxhaven, Germany) (n = 10). Specimens were fabricated with a DLP printer (Asiga MAX UV), post-processed per manufacturer recommendations, and tested for fracture resistance under occlusal loading using a universal testing machine. Data were analyzed using nonparametric tests (Mann–Whitney U and Kruskal–Wallis; α = 0.05). Results: Significant differences were found between material and connector size groups (p < 0.001). Temp Print (GC Corp., Tokyo, Japan) demonstrated higher mean fracture loads (792.34 ± 578.36 N) compared to V-Print c&b temp (Voco GmbH, Cuxhaven, Germany) (359.74 ± 131.64 N), with statistically significant differences at 4 × 4 and 5 × 5 mm connectors. Fracture strength proportionally increased with connector size. FPDs with 5 × 5 mm connectors showed the highest resistance, reaching values above 1500 N. Conclusions: Both connector geometry and material composition significantly affected the fracture resistance of 3D-printed FPDs. Larger connector dimensions and the use of Temp Print (GC Corp., Tokyo, Japan) resin enhanced mechanical performance. Full article

Background/Objectives: Hispanic adolescents experience elevated rates of disordered eating behaviors and body dissatisfaction, yet limited research has examined how gender and weight status interact to shape these risks within this population. Methods: A cross-sectional survey was conducted among 680 Hispanic adolescents (ages 9–15) from a predominantly Mexican-American middle school. Participants completed the Modified Kids Eating Disorder Survey (M-KEDS), and height and weight were objectively measured to determine BMI-for-age percentile. Chi-square tests, Mann–Whitney U tests, and logistic regression were used to assess differences by gender and weight status, including interaction effects. Bonferroni correction was applied for multiple comparisons. Effect sizes (Cramér’s V, odds ratios with 95% CI) were reported. Results: Approximately 73% of participants reported body dissatisfaction, with significant differences observed by gender and weight status. Adolescents with overweight/obesity reported significantly higher negative weight attitudes and extreme weight control behaviors than healthy-weight peers (p < 0.001), with large effect sizes. Females endorsed more disordered attitudes and behaviors, except for exercise to lose weight, which was more common among overweight/obese males. Conclusions: These findings underscore the high prevalence and significance of disordered eating behaviors in Hispanic adolescents, including those at a healthy weight. Results highlight the importance of culturally tailored, gender-sensitive screening and prevention strategies. Schools serve as critical settings for early identification, and tools like the M-KEDS can help address disparities in care access and improve outcomes among Hispanic youth. Full article