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Damaged bridges flex and twist to prevent collapse

Fulminant Viral Myocarditis Associated with Thogotovirus

Replacement of Beta Cells for Type 1 Diabetes

Latent resistance mechanisms of steel truss bridges after critical failures

Abstract Steel truss bridges are constructed by connecting many different types of bars (components) to form a load-bearing structural system. Several disastrous collapses of this type of bridge have occurred as a result of initial component failure(s) propagating to the rest of the structure1–3. Despite the prevalence and importance of these structures, it is still unclear why initial component failures propagate disproportionately in some bridges but barely affect functionality in others4–7. Here we uncover and characterize the fundamental secondary resistance mechanisms that allow steel truss bridges to withstand the initial failure of any main component. These mechanisms differ substantially from the primary resistance mechanisms considered during the design of (undamaged) bridges. After testing a scaled-down specimen of a real bridge and using validated numerical models to simulate the failure of all main bridge components, we show how secondary resistance mechanisms interact to redistribute the loads supported by failed components to other parts of the structure. By studying the evolution of these mechanisms under increasing loads up to global failure, we are able to describe the conditions that enable their effective development. These findings can be used to enhance present bridge design and maintenance strategies, ultimately leading to safer transport networks.

A Fruitful Workup

Interplay between Through-Space and Through-Bond Electronic Coupling in Singlet Fission

Effective SMOTE boost with deep learning for IDC identification in whole-slide images

Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolutional Neural Network (CNN) technology to detect areas impacted by Invasive Ductal Carcinoma (IDC) in whole slide pictures. The trials used a dataset of 162 individuals with IDC, split into training (113 photos) and testing (49 images) groups. Every model was subjected to individual testing. The SMO_CNN model we developed demonstrated exceptional testing and training accuracies of 98.95% and 99.20% respectively, surpassing CNN, VGG19, and ResNet50 models. The results highlight the effectiveness of the created model in properly detecting IDC-affected tissue areas, showing great promise for improving breast cancer diagnosis and treatment planning. We surpassing other models as such, CNN, VGG19, ResNet50.

Examination of a Chimeric Bis-Electrophile for Selective DNA–Protein Cross-Linking and Mechlorethamine Reveals an Unknown Source of Nitrogen Mustard Cytotoxicity

Efficacy of preemptive intravenous ibuprofen and dexketoprofen on postoperative opioid consumption in laparoscopic cholecystectomy: Randomized controlled study

Background To compare the effects of preemptive single-dose intravenous (IV) ibuprofen and dexketoprofen on postoperative pain and opioid consumption in patients undergoing laparoscopic cholecystectomy (LCC). Methods The study included 90 patients aged 18–65 years with an ASA score of I or II who underwent LCC. Patients were equally divided into three groups: Control Group (Group P), 100 cc 0.9% NaCl was infused intravenously over 30 min, Dexketoprofen Group (Group D), 50 mg dexketoprofen in 100 cc 0.9% NaCl was infused intravenously over 30 min, and Ibuprofen Group (Group I), 800 mg ibuprofen in 100 cc 0.9% NaCl was administered intravenously over 30 min. Visual Analog Scale (VAS) scores and opioid requirement were recorded at 1, 2, 4, 6, 12 and 24 hours postoperatively. Results There was no significant difference between the Dexketoprofen and Ibuprofen groups with regard to VAS scores, whereas VAS scores were higher in the control group than other groups in the 1st, 2nd, 4th, 6th,12th, and 24th hours. In addition, fentanyl consumption was higher in the control group at 0–6 hours and at 24 hours compared to the other two groups. Conclusion Preemptive ibuprofen and dexketoprofen administration reduce pain scores and opioid consumption compared with the control group, however, they are non-inferiority to each other.

Cyanobacteria Join the Kahalalide Conversation: Genome and Metabolite Evidence for Structurally Related Peptides

Bundle recommendation methods considering rating data differences for online retailers

Bundling has emerged as a pivotal marketing strategy for online retailers, offering mutual benefits to both merchants and consumers in the rapidly expanding e-commerce landscape. Among various types of user behavior data, user-generated product ratings serve as a critical indicator of individual preferences and satisfaction levels. This research proposes a novel bundle recommendation framework that leverages rating disparities to capture nuanced user preferences and unmet demands. To address the challenges of data sparsity and heterogeneity, we develop a two-stage recommendation method. In the first stage, we enhance the completion of sparse rating matrices by integrating collaborative filtering with deep singular value decomposition. A modified cosine similarity function is introduced, incorporating a rating correction coefficient and an item popularity coefficient to improve similarity estimation. In the second stage, we exploit insights from low-rated items to model user dissatisfaction and latent demands. A dual-layer graph self-attention network is constructed to fuse heterogeneous data, refine inter-item relational representations, and enhance bundle recommendation accuracy. Extensive experiments conducted on benchmark Amazon datasets demonstrate the effectiveness of our approach, achieving 3–6% relative improvements in NDCG and Recall metrics compared to state-of-the-art baselines. Moreover, user satisfaction with the recommended bundles also increased significantly. These results highlight the value of rating differences in understanding user behavior and validate the efficacy of our two-stage model in improving bundle recommendation performance for online retailers.

High-Nuclearity Copper Molecular Catalysts for Electrocatalytic CO-to-Acetate Conversion

ICMC: An Interpretable Cross-domain Multi-modal Classification model for grading teaching plan

Multi-modal classification aims to extract pertinent information from various modalities to assign labels to instances. The advent of deep neural networks has significantly advanced this task. However, the majority of current deep neural networks lack interpretability, leading to skepticism. This issue is particularly pronounced in sensitive domains such as educational assessment. In order to address the trust deficit in deep neural networks for multi-modal classification tasks, we propose an Interpretable Multi-modal Classification framework (ICMC), which enhances confidence in the processes and outcomes of deep neural networks while maintaining interpretability and improving performance. Specifically, our approach incorporates a confidence-driven attention mechanism at the intermediate layer of the deep neural network, assessing attention scores and discerning anomalous information from both local and global perspectives. Furthermore, a confidence probability mechanism is implemented at the output layer, leveraging both local and global perspectives to bolster result confidence. Additionally, we meticulously curate multi-modal datasets for automatic lesson plan scoring research, making them openly available to the community. Quantitative experiments on educational and medical datasets confirm that ICMC outperforms state-of-the-art models (HMCAN, MCAN, HGLNet) by 2.5-6.0% in accuracy and 3.1-7.2% in F1-score, while reducing computational latency by 18%. Cross-domain validation demonstrates 15.7% higher generalizability than transformer-based approaches (CLIP), establishing its interpretability through attention visualization and confidence scoring.

Systematic Bandgap Engineering of a 2D Organic–Inorganic Chalcogenide Semiconductor via Ligand Modification

The effect of leverage manipulation on real estate firms’ financial risk: Based on the interest conflicts perspective

From the perspective of interest conflicts, this study investigates the relationship between corporate leverage manipulation and financial risk using a sample of A-share listed real estate firms in China from 2009 to 2023. Employing a two-way fixed effects model, the main findings are as follows: (1) Leverage manipulation significantly increases the level of financial risk among real estate firms; (2) Mechanism analysis reveals a collusion effect between controlling shareholders and management, as well as between external auditors and management, both of which significantly amplify the impact of leverage manipulation on financial risk. These findings support the collusion effect hypothesis and reject the monitoring effect hypothesis; (3) Heterogeneity tests show that the impact of leverage manipulation on financial risk is more pronounced in non-state-owned enterprises, in firms dominated by transactional institutional investors, and in regions with lower reliance on land finance. This study uncovers the intrinsic link between leverage manipulation and financial risk in the real estate sector and provides important policy implications for regulators aiming to improve and standardize financial risk management in the industry.

Distinct Electric Fields Enable Common Catalytic Function in Structurally Diverse Enzymes

Training and competency frameworks used in the preparation of healthcare professionals for head and neck cancer screening: A scoping review protocol

Objectives This scoping review aims to identify existing training and competency frameworks for healthcare professionals who assess or screen patients on the suspected Head and Neck Cancer (HNC) pathway or in extended practice models, and to explore how skills are developed, and competence assessed, prior to practice. Introduction Up to 97% of people referred with suspected HNC in the UK do not have cancer. This contributes to long wait lists and referral to treatment time in HNC services. A potential solution is for Speech and Language Therapists (SLTs) to assist such services by screening low-risk patients, specifically those presenting with hoarseness or oral-pharyngeal dysphagia. If SLTs are to screen low-risk patients safely and effectively, then specific training and competencies are required. Inclusion criteria International sources written in English between 2014–2025 that involve training and competencies for healthcare professionals who assess or screen patients for HNC. Methods The protocol presents the scoping review process that will be undertaken, which will be conducted as per the Joanna Briggs Institute Manual for Evidence Synthesis and reported in accordance with PRISMA-ScR guidance. Five databases and grey literature will be searched using a peer-reviewed search strategy. Professional organisations will be contacted for unpublished tools from clinical practice. Titles and abstracts will be screened using an a priori protocol. Eligible sources will be charted using an a priori framework and will undergo deductive content analysis. Results will be reported quantitatively and qualitatively using flow charts, tables and visual representations to outline the process and present the results.

Saxitoxin in Alaskan commercial crab species

Paralytic shellfish poisoning (PSP) is a pervasive human health concern associated with subsistence, recreationally and commercially harvested Alaskan shellfish. PSP is caused by saxitoxins (STX), a family of structurally similar neurotoxins produced by the marine microalgae Alexandrium catenella (formerly A. fundyense). These toxins accumulate in filter-feeding shellfish such as clams, mussels and oysters. While PSP is commonly associated with consuming bivalves, toxic STX levels can also be found in crab viscera (crab butter). The first cases of PSP from consuming Dungeness crab viscera (Metacarcinus magister) were reported in 1992. Although this incident and others did not involve commercially harvested crab, they did impact management of the Dungeness crab fishery in Alaska. Current regulations in southeast Alaska permit the sale of whole Dungeness crab, whereas those in the Kodiak Archipelago must have their viscera removed post-harvest to prevent PSP. This study examines the impacts of STXs and current regulations on the Alaskan crab fishery, with a focus on Dungeness crab. Data on commercial landings and the value of harvested Dungeness crab and processed products showed that regulations to protect human health, combined with market forces over the past 30 years, have shifted the fishery’s focus toward Dungeness crab products without viscera. The study also presents time series data on STX concentrations in Dungeness crab from 1992 to 2023, along with maps indicating collection locations and their associated toxicity levels. The same data for King crab (Paralithodes or Lithodes spp.) and Tanner (Snow) crab (Chionoecetes spp.) are included to assess the prevalence of STX in these commercially harvested species. Further, a preliminary analysis suggests regional variations in the toxicity of A. catenella strains could affect regional shellfish toxicity.

Improving the accuracy of cybersecurity spam email detection using ensemble techniques: A stacking approach Machine learning for spam email detection

With the widespread adoption of internet technologies and email communication systems, the exponential growth in email usage has precipitated a corresponding surge in spam proliferation. These unsolicited messages not only consume users’ valuable time through information overload but also pose significant cybersecurity threats through malware distribution and phishing schemes, thereby jeopardizing both digital security and user experience. This emerging challenge underscores the critical importance of developing effective spam detection mechanisms as a cornerstone of modern cybersecurity infrastructure. Through empirical analysis of machine learning (ML) performance on publicly available spam datasets, we established that algorithmic ensemble methods consistently outperform individual models in detection accuracy. We propose an optimized stacking ensemble framework that strategically combines predictions from four heterogeneous base models (NBC, k-NN, LR, XGBoost) through meta-learner integration. Our methodology incorporates grid search cross-validation with hyperparameter space optimization, enabling systematic identification of parameter configurations that maximize detection performance. The enhanced model was rigorously evaluated using comprehensive metrics including accuracy (99.79%), precision, recall, and F1-score, demonstrating statistically significant improvements over both baseline models and existing solutions documented in the literature.

The spatiotemporal differentiation and influencing factors of urban vitality in the border areas of China

Based on the concept of urban vitality and the unique geographical location of border areas, an evaluation framework for urban vitality in border areas was constructed from four dimensions: economy, society, openness, and culture. The entropy weight method was used to calculate the urban vitality levels of 45 border cities in 2000, 2010, 2019, and 2022, and the influencing factors were quantitatively analyzed. The results indicated that from 2000–2022, the vitality level of China’s border cities continued to rise, from 1.38 to 6.57 with an average annual growth rate of 7.35%. The southwestern border region exhibited the highest growth rate, which was 5.3 times greater than that of the western border region with the slowest growth. Spatially, the areas with high urban vitality shifted from the northeast area to the southwest area, with key vitality centers emerging in Mudanjiang (northeast), Baotou (north), Bortala Mongolian Autonomous Prefecture (northwest), and Chongzuo (southwest). Among the influencing factors, openness vitality was found as the primary internal constraint on urban vitality, while border policies and innovation capabilities were identified as the most significant external drivers. This research aims to provide both theoretical insights and empirical evidence to guide the enhancement of border city vitality, contributing to the broader global discourse on border region development.