Search Results (3,362)

This study aims to analyse the effect of partially clogged inlets on the behaviour of urban drainage systems at the city scale, particularly regarding intercepted volumes and flood depths. The main challenges were to represent the inlet network in detail at a rather large scale and to avoid the effect of sewer network surcharging on the draining capacity of inlets. This goal has been achieved through a 1D/2D coupled hydraulic model of the whole urban drainage system in La Almunia de Doña Godina (Zaragoza, Spain). The model focuses on the interaction between grated drain inlets and the sewer network under partial clogging conditions. The model is fed with data obtained on field surveys. These surveys identified 948 inlets, classified into 43 types based on geometry and grouped into 7 categories for modelling purposes. Clogging patterns were derived from field observations or estimated using progressive clogging trends. The hydrological model combines a semi-distributed approach for micro-catchments (buildings and courtyards) and a distributed “rain-on-grid” approach for public spaces (streets, squares). The model assesses the impact of inlet clogging on network performance and surface flooding during four rainfall scenarios. Results include inlet interception volumes, flooded surface areas, and flow hydrographs intercepted by single inlets. Specifically, the reduction in intercepted volume ranged from approximately 7% under a mild inlet clogging condition to nearly 50% under severe clogging conditions. Also, the model results show the significant influence of the 2D mesh detail on flood depths. For instance, a mesh with high resolution and break lines representing streets curbs showed a 38% increase in urban areas with flood depths above 1 cm compared to a scenario with a lower-resolution 2D mesh and no curbs. The findings highlight how inlet clogging significantly affects the efficiency of urban drainage systems and increases the surface flood hazard. Further novelties of this work are the extent of the analysis (city scale) and the approach to improve the 2D mesh to assess flood depth. Full article

Frequent building fires seriously threaten the safety of steel structures. According to the data, fire accidents account for about 35% of the total number of production safety accidents. The collapse of steel structures accounted for 42% of the total collapse. The early warning problem of steel structure fire collapse is imminent. This study aims to address this challenge by establishing a novel early warning framework, which is used to quantify the critical early warning threshold of steel frames based on elastic modulus degradation and its correlation with ultrasonic wave velocity under different collapse modes. The sequential thermal–mechanical coupling numerical method is used in the study. Firstly, Pyrosim is used to simulate the high-fidelity fire to obtain the real temperature field distribution, and then it is mapped to the Abaqus finite element model as the temperature load for nonlinear static analysis. The critical point of structural instability is identified by monitoring the mutation characteristics of the displacement and the change rate of the key nodes in real time. The results show that when the steel frame collapses inward as a whole, the three-level early warning elastic modulus thresholds of the beam are 153.6 GPa, 78.6 GPa, and 57.5 GPa, respectively. The column is 168.7 GPa, 122.4 GPa, and 72.6 GPa. Then the three-level warning threshold of transverse and longitudinal wave velocity is obtained. The three-stage shear wave velocity warning thresholds of the fire column are 2828~2843 m/s, 2409~2434 m/s, and 1855~1874 m/s, and the three-stage longitudinal wave velocity warning thresholds are 5742~5799 m/s, 4892~4941 m/s, and 3804~3767 m/s. The core innovation of this study is to quantitatively determine a three-level early warning threshold system, which corresponds to the three stages of significant degradation initiation, local failure, and critical collapse. Based on the theoretical relationship, these elastic modulus thresholds are converted into corresponding ultrasonic wave velocity thresholds. The research results provide a direct and reliable scientific basis for the development of new early warning technology based on acoustic emission real-time monitoring and fill the gap between the mechanism research and engineering application of steel structure fire resistance design. Full article

Alzheimer’s disease is characterized by progressive cognitive decline associated with oxidative stress, neuroinflammation, and impaired neurotrophic signaling. Sulforaphane, a bioactive compound found in broccoli, has demonstrated neuroprotective effects by activating NRF2 and inhibiting NF-κB. However, the efficacy of whole-food-derived sulforaphane remains unclear. This study evaluated the neuroprotective potential of broccoli sprout extract using a scopolamine-induced mouse model of memory impairment. Mice were orally administered broccoli sprout extract once daily at doses of 100 mg/kg or 200 mg/kg for four weeks prior to behavioral and biochemical assessments. Treatment with broccoli sprout extract significantly improved scopolamine-induced deficits in long-term memory, as determined by the passive avoidance test. The spatial working memory remained unaffected. High doses of broccoli sprout extract restored hippocampal brain-derived neurotrophic factor levels and reduced cortical lipid peroxidation, suggesting antioxidant and neurotrophic benefits. Additionally, the low dose preserved striatal choline acetyltransferase expression and reduced systemic tumor necrosis factor-alpha and hippocampal cyclooxygenase-2 levels, indicating its anti-inflammatory and cholinergic protective effects. No significant changes in acetylcholinesterase activity or glutathione levels were observed. Overall, these results imply that broccoli sprout extract has multi-targeted neuroprotective effects, possibly involving redox and inflammatory regulation. Therefore, it may be a safe dietary strategy to support cognition in neurodegenerative conditions. Full article

Screens for specific phenotypes have long been a cornerstone of biology. Here, we present an updated synthesis of our large-scale visual screens for Arabidopsis (Arabidopsis thaliana) mutants that exhibit leaf morphology defects. In our 2009 review, we used phenotypes to group the leaf mutants that we had isolated and characterized since 1992; here, by contrast, we functionally classified the mutations that we studied over the last 16 years based on the biological programs they disrupt. Since 2009, we have identified and analyzed 38 genes required for proper leaf development; these genes are involved in translation, chloroplast function, cell wall construction, auxin homeostasis, microRNA biogenesis, and epigenetic regulation. Many of the identified mutants have pleiotropic phenotypes, consistent with the central roles of the affected pathways in development. In this review, we systematically link morphological traits to specific molecular dysfunctions, highlighting the enduring utility of forward genetic approaches. We found that the Arabidopsis leaf is a model organ of a model organism, and we have used this model-in-a-model system to dissect whole-plant traits such as cell proliferation and expansion, and to improve our understanding of the genetic control of plant form and size. Full article

Computer-aided systems can assist doctors in detecting cancer at an early stage using medical image analysis. In estrogen receptor immunohistochemistry (ER-IHC)-stained whole-slide images, automated cell identification and segmentation are helpful in the prediction scoring of hormone receptor status, which aids pathologists in determining whether to recommend hormonal therapy or other therapies for a patient. Accurate scoring can be achieved with accurate segmentation and classification of the nuclei. This paper presents two main objectives: first is to identify the top three models for this classification task and establish an ensemble model, all using 10-fold cross-validation strategy; second is to detect recurring misclassifications within the dataset to identify “misclassified nuclei” or “incorrectly labeled nuclei” for the nuclei class ground truth. The classification task is carried out using 32 pre-trained deep learning models from Keras Applications, focusing on their effectiveness in classifying negative, weak, moderate, and strong nuclei in the ER-IHC histopathology images. An ensemble learning with logistic regression approach is employed for the three best models. The analysis reveals that the top three performing models are EfficientNetB0, EfficientNetV2B2, and EfficientNetB4 with an accuracy of 94.37%, 94.36%, and 94.29%, respectively, and the ensemble model’s accuracy is 95%. We also developed a web-based platform for the pathologists to rectify the “faulty-class” nuclei in the dataset. The complete flow of this work can benefit the field of medical image analysis especially when dealing with intra-observer variability with a large number of images for ground truth validation. Full article

As an important traffic and transportation roadway, tunnel engineering is widely used in important fields such as highways, railways, water conservancy, subways and mining. It is limited by complex geological conditions, harsh construction environments and poor robustness of the monitoring system. If the construction process and monitoring method are not properly designed, it will often directly induce disasters such as tunnel deformation, collapse, leakage and rockburst. This seriously threatens the safety of tunnel construction and operation and the protection of the regional ecological environment. Therefore, based on distributed fiber optic sensing technology, the full–cycle spatiotemporally continuous sensing information of the tunnel structure is obtained in real time. Accordingly, the health status of the tunnel is dynamically grasped, which is of great significance to ensure the intrinsic safety of the whole life cycle for the tunnel project. Firstly, this manuscript systematically sorts out the development and evolution process of the theory and technology of structural health monitoring in tunnel engineering. The scope of application, advantages and disadvantages of mainstream tunnel engineering monitoring equipment and main optical fiber technology are compared and analyzed from the two dimensions of equipment and technology. This provides a new path for clarifying the key points and difficulties of tunnel engineering monitoring. Secondly, the mechanism of action of four typical optical fiber sensing technologies and their application in tunnel engineering are introduced in detail. On this basis, a spatiotemporal continuous perception method for tunnel engineering based on DFOS is proposed. It provides new ideas for safety monitoring and early warning of tunnel engineering structures throughout the life cycle. Finally, a high–speed rail tunnel in northern China is used as the research object to carry out tunnel structure health monitoring. The dynamic changes in the average strain of the tunnel section measurement points during the pouring and curing period and the backfilling period are compared. The force deformation characteristics of different positions of tunnels in different periods have been mastered. Accordingly, scientific guidance is provided for the dynamic adjustment of tunnel engineering construction plans and disaster emergency prevention and control. At the same time, in view of the development and upgrading of new sensors, large models and support processes, an innovative tunnel engineering monitoring method integrating “acoustic, optical and electromagnetic” model is proposed, combining with various machine learning algorithms to train the long–term monitoring data of tunnel engineering. Based on this, a risk assessment model for potential hazards in tunnel engineering is developed. Thus, the potential and disaster effects of future disasters in tunnel engineering are predicted, and the level of disaster prevention, mitigation and relief of tunnel engineering is continuously improved. Full article

Background/Objectives: Breast cancer (BC) is the most prevalent cancer worldwide, with approximately 40% of early-stage BC patients developing recurrence despite initial treatments. Current diagnostic methods, such as mammography and solid tissue biopsies, face limitations in sensitivity, accessibility, and the ability to characterize tumor heterogeneity or monitor systemic disease progression. Methods: To address these gaps, this study investigates a fully automated analysis workflow using data derived from fluorescent Whole-Slide Imaging (fWSI) for detecting and classifying rare cells (circulating tumor and tumor microenvironment cells) in peripheral blood samples. Our methodology integrates supervised machine learning algorithms for rare event detection, immunofluorescence-based classification, and statistical quantification of cellular features. Results: Using a fWSI dataset of 534 cancer and non-cancer peripheral blood samples, the automated model demonstrated high concordance with manual annotation, achieving up to 98.9% accuracy and a precision-sensitivity AUC of 83.2%. Morphometric analysis of rare cells identified significant differences between normal donors, early-stage BC, and late-stage BC cohorts, with distinct clusters emerging in late-stage BC. Conclusions: These findings highlight the potential of liquid biopsy and algorithmic approaches for improving BC diagnostics and staging, offering a scalable, minimally invasive solution to enhance clinical decision-making. Future work aims to refine the automated framework to minimize errors and improve the robustness across diverse cohorts. Full article

A homogenisation procedure for energy-buffering structural layers with integrated electrical energy storage systems (capacitors) is described with the aim of calculating their shielding effectiveness to the electromagnetic waves when they are installed inside building walls. In fact, these storage systems may attenuate electromagnetic fields in the frequency ranges employed by mobile telephony, radio broadcasting, and wireless data transmission, thus impairing the operation of Internet of Things infrastructures. The capacitors inside the individual energy-buffering modules have a multilayered structure, in which the layers have very small thicknesses, making an analytical solution of the electromagnetic field for this kind of object practically impossible. Similarly, numerical solutions may not be practical due to the very small thickness of the layers compared to the overall object size. Therefore, this paper presents a simple and effective analytical method to model multilayered structures consisting of homogenising the whole capacitor, which can then be treated as a unique block of material with fictitious (but effective) electric and magnetic parameters. The method is based on multi-section transmission lines, and a quick and reliable analytical methodology is proposed to evaluate the shielding capabilities using the homogenised capacitor’s effective parameters. Moreover, experimental measurements on a real prototype have also been carried out to validate the methodology. Results show that the trend of the simulated and measured SE is the same, proving that the method can be employed to obtain a conservative estimation of the SE from numerical simulations. Full article

With the ongoing transformation of the energy structure and the advancement of smart grid development, green and sustainable development of substations has become an inevitable trend. As the core driving force of substation transformation, novel technologies remain at the pilot application stage, and their performance evaluations are yet to be clarified. In view of this, this paper proposes a comprehensive evaluation framework for the application effectiveness of novel technologies in green construction for substations. Firstly, based on the feature for the whole life cycle of the technologies, an evaluation index system is established covering multiple dimensions and stages, including resource conservation, technical performance enhancement, and economic benefits. Secondly, on the basis of AHP group decision and EWM combination weights, a variable-weight model is constructed by combining projection gray target evaluation to enable significant differentiation in cross-technology comparative analysis. Finally, a case study is conducted on pilot applications of multiple novel technologies in substations within a specific region, and the results indicate that novel technologies which demonstrate better sustainable development effects throughout the entire life cycle have a broader prospect for promotion. Full article

Introduction/Background: Patients with locally advanced rectal cancer (LARC) with pathological complete response (pCR) after neoadjuvant chemo-radiotherapy (NCRT) are a privileged group because of the favorable progression of their disease. However, their follow-up patterns after surgery are similar to those of other groups with worse prognosis, with the consequent psychological and economic impact. Methods: This is a retrospective observational multicenter study with data obtained from the Spanish Rectal Cancer Project. Patients with LARC who underwent surgery with curative intent after NCRT and achieved pCR were selected. The last follow-up update was conducted in December 2021. A conditional survival model was used to analyze oncological outcomes during follow-up. Recurrence-free survival (RFS) was analyzed for the entire cohort of patients and for those who survived at one, two, and three years. Results: A total of 815 patients from 32 hospitals were included. Their mean age was 65.1 years, and 36.1% of them were women. Of the 815 patients, 35 died or experienced recurrence (local or systemic) in the first postoperative year, and 780 were included in the conditional survival analysis one year after surgery. The probability of RFS at 5 years was 86.5% in the whole cohort and 89.4%, 92.9%, and 95.2% for survivors at one, two, and three years, respectively. The probability of recurrence in these same groups was 6.5%, 4.3%, 1.8%, and 0.6%. Conclusions: Follow-up of patients with LARC and pCR after NCRT followed by surgery could be adapted based on conditional survival data showing that the probability of RFS increases as patients remain recurrence-free, and recurrences more than 3 years after treatment are exceptional. Full article

Modeling drug-induced transcriptional responses at the single-cell level is essential for advancing human healthcare, particularly in understanding disease mechanisms, assessing therapeutic efficacy, and anticipating adverse effects. However, existing approaches often impose a rigid constraint by enforcing pointwise alignment of latent representations to a standard normal prior, which limits expressiveness and results in biologically uninformative embeddings, especially in complex biological systems. Additionally, many methods inadequately address the challenges of unpaired data, typically relying on naive averaging strategies that ignore cell-type specificity and intercellular heterogeneity. To overcome these limitations, we propose scOTM, a deep learning framework designed to predict single-cell perturbation responses from unpaired data, focusing on generalization to unseen cell types. scOTM integrates prior biological knowledge of perturbations and cellular states, derived from large language models specialized for molecular and single-cell corpora. These informative representations are incorporated into a variational autoencoder with maximum mean discrepancy regularization, allowing flexible modeling of transcriptional shifts without imposing a strict constraint of alignment to a standard normal prior. scOTM further employs optimal transport to establish an efficient and interpretable mapping between control and perturbed distributions, effectively capturing the transcriptional shifts underlying response variation. Extensive experiments demonstrate that scOTM outperforms existing methods in predicting whole-transcriptome responses and identifying top differentially expressed genes. Furthermore, scOTM exhibits superior robustness in data-limited settings and strong generalization capabilities across cell types. Full article

Background: Corynebacterium ulcerans is an emerging zoonotic pathogen capable of cau-sing diphtheria-like infections in humans. Objectives: we report, for the first time in Brazil, the detection and phenotypic/genomic characterization of three atoxigenic ST-339 strains isolated from domestic animals, including one with a ciprofloxacin resistance profile linked to double GyrA mutations (S89L, D93G). Methods: species identification was performed by MALDI-TOF MS, followed by in vitro antimicrobial susceptibility testing, whole-genome sequencing, and bioinformatic analyses to predict virulence determinants, antimicrobial resistance genes, CRISPR–Cas systems, mobile genetic elements, and in silico structural analysis as well as phylogenetic reconstruction. Results: whole-genome sequencing confirmed species identity, revealed high genetic similarity, and identified distinct phylogenetic subclades, suggesting potential international dissemination. Genomic analyses showed conserved virulence determinants, such as incomplete pilus clusters, iron acquisition systems, and the pld gene, with the absence of the tox gene. Molecular modeling and dynamics simulations indicated that GyrA mutations disrupt critical ciprofloxacin–magnesium–water interactions, reducing binding stability. Mobile genetic elements, prophages, and CRISPR–Cas systems underscored the genomic plasticity of these isolates. Conclusions: these findings document a little-studied antimicrobial resistance mechanism in zoonotic C. ulcerans, highlighting the need for strengthened surveillance and further research on virulence and resistance, even in ato-xigenic strains. Full article

This paper focuses on the challenges in food safety governance in megacities, taking Shanghai as the research object. Aiming at the pain points in food sampling inspections, it proposes a risk prediction and regulatory optimization scheme combining text mining and machine learning. First, the paper uses the LDA method to conduct in-depth mining on over 78,000 pieces of food sampling data across 34 categories in Shanghai, so as to identify core risk themes. Second, it applies SMOTE oversampling to the sampling data with an extremely low unqualified rate (0.5%). Finally, a machine learning prediction model for food safety risks is constructed, and predictions are made based on this model. The research findings are as follows: ① Food risks in Shanghai show significant characteristics in terms of time, category, and pollution causes. ② Supply chain links, regulatory intensity, and consumption scenarios are among the core influencing factors. ③ The traditional “full coverage” model is inefficient, and resources need to be tilted toward high-risk categories. ④ Public attention (e.g., the “You Order, We Inspect” initiative) can drive regulatory responses to improve the qualified rate. Based on these findings, this paper suggests that relevant authorities should ① classify three levels of risks for categories, increase inspection frequency for high-risk products in summer, adjust sampling intensity for different business entities, and establish a dynamic hierarchical regulatory mechanism; ② tackle source governance, reduce environmental pollution, upgrade process supervision, and strengthen whole-chain risk prevention and control; and ③ promote public participation, strengthen the enterprise responsibility system, and deepen the social co-governance pattern. This study effectively addresses the risk early warning problems in food safety supervision of megacities, providing a scientific basis and practical path for optimizing the allocation of regulatory resources and improving governance efficiency. Full article

Wheat is a staple crop widely sown in Northwest China, and understanding and modelling evapotranspiration (ET) during the wheat-growing stage is important for irrigation scheduling and the efficient use of agricultural water resources. In this study, a four-year observation was conducted on a spring wheat field with border irrigation (BI) treatment and drip irrigation (DI) treatment, based on two Bowen ratio energy balance (BREB) systems. The results showed that the average ET across the whole growing stage scale was 512.0 mm for the BI treatment and 446.9 mm for the DI treatment, and the DI treatment reduced ET by 65.1 mm across the growing stage scale. The driving factors of the changes in ET in the two treatments were investigated using partial correlation analysis after understanding the changing pattern of ET. Net radiation (Rn), soil water content (SWC), and leaf area index (LAI) were the main meteorological, soil, and crop factors leading to the changes in ET in the two treatments. In terms of ET simulation, the SWAP model and different types of machine learning algorithms were used in this study to numerically simulate ET at a daily scale. The total ET values simulated by the SWAP model at the interannual scale were 11.0–14.2% lower than the observed values of ET, and the simulation accuracy varied at different growing stages. In terms of the machine learning simulation of ET, this study is the first to apply five machine learning algorithms to simulate a typical irrigated wheat field in the arid region of Northwest China. It was found that the Stacking algorithm as well as the SWAP model had the optimal simulation among all machine learning algorithms. These findings can provide a scientific basis for irrigation management and the efficient use of agricultural water resources in spring wheat fields in arid regions. Full article

The growing number of older adults with cancer confront practical and organizational limitations that hinder their ability to obtain care that is adapted to their health status, needs, expectations, and life choices. The integration into practice of evidence-based and institutional recommendations for a geriatric approach and person-centered high-quality care remains incomplete. This study uses an action research design to explore stakeholders’ perspectives of the challenges involved in translating the established care priorities into a compassionate geriatric approach in oncology and identify promising pathways to improvement. Fifty-three stakeholders participated in focus groups to create cognitive maps representing perceived relationships between concepts related to compassionate care of older adults with cancer. Combining maps results in a single model constructed in Mental Modeler software to weigh relationships and calculate concept centrality (importance in the model). The model represents stakeholders’ collective perspective of the determinants of compassionate care that need to be addressed at different decision-making levels. The results reveal pathways to improvement at systemic, organizational, practice, and societal levels. These include connecting policies on ageing and national cancer programs, addressing fragmented care through interdisciplinary teamwork, promoting person-centered care, cultivating relational proximity, and combatting ageism. Translating evidence-based practices and priority orientations into compassionate care rests on collective capacities across multiple providers to address the whole person and their unique trajectory. Full article