Search Results (2,622)

This study focuses on the impact of wastewater discharges from the Besòs treatment plant on the coastal water quality of Barcelona, particularly under adverse weather conditions. A simplified mathematical model was developed to predict, in real time, the concentration of bacterial indicators (Enterococci and E. coli) along nearby beaches. This model aims to quickly detect contamination events and trigger alerts to evacuate swimming areas before water quality tests are completed. The simulator uses meteorological data—such as wind direction and speed, rainfall intensity, and solar irradiance, among others—to anticipate pollution levels without requiring immediate water sampling. The model was tested against real-world scenarios and validated with historical meteorological and bacteriological data collected over six years. The results show that bacterial pollution occurs mainly during intense rainfall events combined with specific wind conditions, particularly when winds blow from the southeast (SE) or east–southeast (ESE) at moderate to high speeds. These wind patterns carry under-treated wastewater toward the coast. Conversely, winds from the north or northwest tend to disperse the contaminants offshore, posing little to no risk to swimmers. This study confirms that pollution events are relatively rare—about two per year—but pose significant health risks when they do occur. The simulator proved reliable, accurately predicting contamination episodes without producing false alarms. Minor variables such as water temperature or suspended solids showed limited influence, with wind and sunlight being the most critical factors. The model’s rapid response capability allows public authorities to take swift action, significantly reducing the risk to beachgoers. This system enhances current water quality monitoring by offering a predictive, cost-effective, and preventive tool for beach management in urban coastal environments. Full article

Cloudbursts and other extreme rainfall events are becoming more frequent and intense, making precise forecasts and disaster preparedness more challenging. Despite advances in meteorological monitoring, current models often lack the precision needed for hyperlocal extreme rainfall forecasts. This study addresses the research gap in spatial configuration-aware modeling by proposing a novel framework that combines geometry-based weather station selection with advanced deep learning architectures. The primary goal is to utilize real-time data from well-placed Automatic Weather Stations to enhance the precision and reliability of extreme rainfall predictions. Twelve unique datasets were generated using four different geometric topologies—linear, triangular, quadrilateral, and circular—centered around the target station Chinchwad in Pune, India, a site that has recorded diverse rainfall intensities, including a cloudburst event. Using common performance criteria, six deep learning models were trained and assessed across these topologies. The proposed Bi-GRU model under linear topology achieved the highest predictive accuracy (R² = 0.9548, RMSE = 2.2120), outperforming other configurations. These findings underscore the significance of geometric topology in rainfall prediction and provide practical guidance for refining AWS network design in data-sparse regions. In contrast, the Transformer model showed poor generalization with high MAPE values. These results highlight the critical role of spatial station configuration and model architecture in improving prediction accuracy. The proposed framework enables real-time, location-specific early warning systems capable of issuing alerts 2 h before extreme rainfall events. Timely and reliable predictions support disaster risk reduction, infrastructure resilience, and community preparedness, which are essential for safeguarding lives and property in vulnerable regions. Full article

Background: Trastuzumab deruxtecan (T-DXd), a HER2-directed antibody-drug conjugate, has significantly advanced the management of HER2-expressing malignancies. However, interstitial lung disease (ILD) remains a clinically significant adverse effect. Despite increasing clinical use of T-DXd, real-world data on ILD incidence, characteristics, and outcomes—particularly in Middle Eastern populations remain limited. Methods: This retrospective study analyzed medical records of patients who received trastuzumab deruxtecan (T-DXd) at a tertiary care hospital. Data collected included demographics, tumor characteristics, prior treatments, and interstitial lung disease (ILD)-related outcomes. ILD events were identified and graded according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. Descriptive statistics were used to summarize baseline characteristics and ILD features. Univariate logistic regression was performed to assess potential risk factors associated with ILD development. Kaplan–Meier survival analysis was used to evaluate time-to-event outcomes, including time to ILD onset and resolution. Results: Among 65 patients with advanced stage IV cancer (90.8% with breast cancer), 16 (24.6%) developed ILD following T-DXd therapy. The median time to ILD onset was 125.5 days. The most common presenting symptoms were dyspnea and cough (50%). A history of ground-glass opacities was associated with increased odds of ILD (OR 2.7; p = 0.236), though not statistically significant. Patients with Grade ≥ 3 ILD had significantly lower oxygen saturation levels compared to those with milder grades (88.3% vs. 97.7%, p = 0.049). Median time to clinical resolution was 297 days (95% CI: 77.5–516). No significant associations were observed with smoking history, pulmonary metastases, or prior thoracic radiation. Conclusions: In this real-world cohort, ILD occurred in nearly one-quarter of patients receiving T-DXd, predominantly within the first six months of treatment. The findings highlight the importance of early respiratory symptom monitoring and pulse oximetry—particularly in patients with pre-existing pulmonary abnormalities. These results underscore the need for vigilant ILD surveillance strategies and further prospective studies to validate predictive risk factors and optimize management protocols. Full article

Early warning of structural damage from induced seismic events requires rapid and reliable ground motion forecasting. This study presents a novel real-time framework that couples a deep learning approach with structural fragility assessment to generate immediate damage alerts following the onset of seismic shaking. Long Short-Term Memory (LSTM) neural networks are employed to predict full S-wave accelerograms from initial P-wave inputs, trained and tested on accelerometric records from induced seismicity scenarios. The predicted S-wave motion is then used as input for a suite of fragility curves in real time to estimate the probability of structural damage for masonry buildings typical in rural areas of geothermal platforms. The proposed method captures both the temporal evolution of shaking and the structural response potential, offering critical seconds of lead time for automated decision-making systems. Results demonstrate high predictive accuracy of the LSTM model and effective early classification of structural risk. This integrated system provides a practical tool for early warning or rapid response in regions experiencing anthropogenic seismicity, such as those affected by geothermal operations. Full article

Background/Objectives: Cancer patients using implanted venous access devices (ICVADs) for chemotherapy are at increased risk of venous thromboembolism (VTE), but the performance of risk assessment models (RAMs) in this setting is understudied. This study evaluated VTE incidence, risk factors, and the predictive performance of the Khorana, COMPASS-CAT, and ONKOTEV models. Methods: We retrospectively reviewed records of adult cancer patients treated with chemotherapy via ICVADs. The cumulative incidence (CI) of VTEs was estimated using the Fine–Gray method, and RAM performance was assessed by sensitivity, specificity, predictive values, accuracy, and AUC. Overall survival (OS) was analyzed using Kaplan–Meier and log-rank tests. Results: A total of 446 patients were included. The most common cancers were colorectal (29.6%), gastric (26%), pancreatic (18.4%), and breast (13.9%). During a median follow-up of 16.5 months, VTEs occurred in 82 patients (18.4%), including 43 (9.6%) that were ICVAD-related. Median time to VTE was 117 days and 68 days for ICVAD-related events. The CI of VTEs was 9% at 1 year and 18.4% at 2 years. ONKOTEV showed the best performance (accuracy of 74.4%, specificity of 85.7%, and AUC of 0.607), with 1-year incidence higher in the high-risk group (28.5% vs. 12.4%, p < 0.001). In contrast, all RAMs showed limited ability for ICVAD-related VTEs. VTE was independently associated with inferior OS (HR 1.39, p = 0.037). Conclusions: Cancer patients with ICVADs face a substantial risk of early VTEs. Among evaluated RAMs, ONKOTEV performed best for overall but not ICVAD-related events. Prospective studies are needed to guide prophylaxis strategies using validated RAMs. Full article

The exponential growth of space objects in near-Earth and geostationary orbits has posed severe threats to space environment safety, with debris clouds from spacecraft breakup events being a critical concern. Debris cloud tracing, as a key technology for locating breakup points, faces dual challenges of insufficient precision in analytical methods and excessive computational load in numerical methods. To balance traceability accuracy with computational efficiency, this paper proposes a breakup time determination method integrating a clustering algorithm and the minimization of average relative distance. The method first calculates the average relative distance between fragment pairs and preliminarily estimates the breakup epoch using a golden section step-size optimization strategy. Subsequently, the density peak clustering (DPC) algorithm is introduced to eliminate abnormal fragments. The breakup epoch is then refined based on the cleansed fragment dataset, achieving high-precision localization. Validation through simulations of real breakup events demonstrates that this method significantly improves localization accuracy. It establishes a highly reliable temporal benchmark for space collision tracing, debris diffusion prediction, and orbital safety management. Full article

Hydrologists need to predict extreme hydrological and meteorological events for design purposes, whose magnitude and probability are estimated using a probability distribution function (PDF). The choice of an appropriate PDF is crucial in describing the behavior of the phenomenon and the predictions can differ significantly depending on the PDF. So, the success of the probability distribution function in representing the data of extreme value series of natural events such as hydrology and climatology is of great importance. Depending on whether the series consists of maximum or minimum values, the theoretical probability density function must be appropriately fit to the right or left tail of the extreme data, which contains the most critical information. This study includes a combined evaluation of the performance of four different tests for selecting the appropriate probability distribution of maximum rainfall in Türkiye: Kolmogorov–Smirnov (KS) test, Anderson–Darling (AD) test, Probability Plot Correlation Coefficient (PPCC) test, and L-Moments Z^DIST test. Within the scope of the study, maximum rainfall series of seven rainfall durations from 15 to 1440 min, at rain gauge stations in 81 provinces of Türkiye, were examined. Goodness of fit was performed based on ranking using a combination of four different numerical tests (KS, AD, PPCC, Z^DIST). The probabilistic character of maximum rainfall was evaluated using a large dataset consisting of 567 time series with record lengths ranging from 45 to 80 years. The goodness of fit of distributions was examined from three different perspectives. The first is an examination considering rainfall durations, the second is a province-based examination, and the third is a general country-based assessment. In all three different perspectives, the Wakeby distribution was determined as the best fit candidate to represent the maximum rainfall in Türkiye. Full article

To achieve accurate monitoring of dust intensity, this study developed a coupled model based on a convolutional neural network (CNN) and a bidirectional long short-term memory network (Bi-LSTM) to monitor dust intensity in a 24 h dynamic pattern. During this process, progressive dust temporal (PDT) features reflecting the temporal dynamics of dust events, including clear-sky state values, adjacent observation state values, and current observation state values for spectral indices and brightness temperatures, were first designed. Then, a PCBNet model combining CNN and Bi-LSTM was established and compared with PCLNet (CNN and LSTM), random forest (RF), and support vector machine (SVM) using only single-time observations, as well as PDT-RF and PDT-SVM, which used PDT features as inputs. Finally, a dust intensity product was generated by the optimal model, and its relationship with PM10 concentrations at air quality stations was examined. Furthermore, a dust storm event in April 2021 was analyzed to evaluate the ability of the products to capture event dynamics. The results indicate that PCBNet achieved the highest accuracy among all models on the validation dataset. Predicted dust intensity levels were well correlated with PM10 concentrations, and the monitoring product effectively tracked the spatiotemporal evolution of dust event. Full article

Background/Objectives: Acute renal injury (AKI) after surgical aortic valve replacement (SAVR) is a serious postoperative complication, associated with an increased need for resources and an increase in early mortality. Methods: In 2006–2017, 1548 patients underwent SAVR with or without an associated procedure. Preoperative and operative factors, as well as adverse postoperative events, were registered. The outcome was AKI defined by a decrease in the estimated glomerular filtration rate (eGFR) of at least 25%. Statistical analysis was performed with chi-square test and Student’s t-test. Significant factors were entered into a logistic regression analysis. AKI’s effect on long-term survival was determined via Kaplan–Meier analysis and Cox’s proportional hazard analysis. Results: AKI occurred in 447/1548 or 30.7% of the patients. Most preoperative cardiac and non-cardiac factors were associated with AKI. Prior endocarditis and a decreased eGFR were the dominant preoperative factors for early mortality, while a need for reintervention was the dominant postoperative event. AKI was also associated with prolonged surgical time and an increased need for resources. In patients who died within 30 days, AKI was not the sole complication. AKI also significantly reduced survival in the univariate analysis, revealing that AKI was a significant, independent predictor of survival, albeit the least strong. Conclusions: AKI is a serious postoperative complication associated with mostly non-modifiable factors. Postoperative AKI predicts reduced long-term survival. Full article

The term heart disease refers to a wide range of conditions that impact the heart and blood vessels. It continues to be a major global cause of morbidity and mortality. The narrowing or blockage of blood vessels, which can result in major medical events like heart attacks, angina (chest pain) or strokes, is a common issue linked to heart disease. In order to lower the risk of serious complications and facilitate prompt medical intervention, early diagnosis and prediction are essential. This study developed predictive models that can precisely identify people at risk by applying a variety of machine learning algorithms to a structured dataset on heart disease. Blood pressure, cholesterol, age, gender, and other health-related indicators are among the 13 essential characteristics that make up the dataset. Numerous machine learning models such as Naïve Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, and others were trained using these features. Using the RapidMiner platform, which offered a visual environment for data preprocessing, model training, and performance analysis, all models were created and assessed. The best-performing model was the Naïve Bayes classifier which achieved an impressive accuracy rate of 90% after extensive testing and comparison of performance metrics like accuracy precision and recall. This outcome shows how well the model can predict heart disease in actual clinical settings. By supporting individualized health recommendations, enabling early diagnosis, and facilitating timely treatment, the effective application of such models can significantly benefit patients and healthcare professionals. Furthermore, heart disease incidence can be considerably decreased by identifying and addressing modifiable risk factors such as high blood pressure, elevated cholesterol, smoking, diabetes, and physical inactivity. In summary, machine learning has the potential to improve the identification and treatment of heart-related disorders. This study highlights the value of data-driven methods in healthcare and indicates that incorporating predictive models into standard medical procedures may enhance patient outcomes, lower healthcare expenses, and improve public health administration. Full article

Background and Objectives: To evaluate the prognostic value of the Clinical–Pathologic Stage–Estrogen receptor status and Grade (CPS+EG) staging system, which combines clinical staging, pathological staging, oestrogen receptor (ER) status, and tumour grade in predicting survival outcomes in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer receiving neoadjuvant therapy (NACT). Materials and Methods: A retrospective review was performed on 245 female breast cancer patients who received anti-HER2 therapy alongside NACT at the Medical Oncology Department of Kartal Dr Lütfi Kırdar City Hospital, University of Health Sciences, from April 2012 to June 2024. The CPS+EG score was calculated using the MD Anderson Cancer Centre neoadjuvant treatment response calculator. Patients were categorised into two groups based on their CPS+EG score < 3 and ≥3. The primary outcomes assessed were disease-free survival (DFS) and overall survival (OS). Kaplan–Meier and log-rank tests were utilised for time-to-event analysis; Cox regression was used for multivariate analysis. A significance level of ≤0.05 was considered. Results: The median age of the patient cohort was 51 years (range: 27–82 years). Among these patients, 183 (74.6%) had a CPS+EG score less than 3, while 62 (25.3%) exhibited a score of 3 or higher. The median follow-up duration was 37.6 months. The pathological complete response (pCR) rate across the entire cohort was 51.8%. Specifically, the pCR rate was 56.3% in the group with CPS+EG scores below 3, and 38.7% in those with scores of 3 or higher (p = 0.017). Patients with CPS+EG scores less than 3 demonstrated superior overall survival (OS), which reached statistical significance in univariate analysis. Multivariate analysis identified the CPS+EG score as an independent prognostic factor for both overall survival and disease-free survival (DFS), with hazard ratios of 0.048 (95% CI: 0.004–0.577, p = 0.017) and 0.35 (95% CI: 0.14–0.86, p = 0.023), respectively. Conclusions: The CPS+EG score is an independent and practical prognostic marker, particularly for overall survival, in patients with HER2-positive breast cancer who have received neoadjuvant therapy. Patients with a CPS+EG score < 3 have higher pCR rates and survival rates. When used in conjunction with pCR, it can improve risk categorisation and contribute to the individualisation of adjuvant strategies in the post-neoadjuvant period. Due to its ease of calculation and lack of additional costs, this score can be instrumental in clinical practice for identifying high-risk patients. Our findings support the integration of the CPS+EG score into routine clinical decision-making processes, although prospective validation studies are necessary. Full article

This study explores the impact of assimilating Doppler radar data on short-term numerical weather forecasting for a heavy rainfall event in Southern China, focusing on different spatial scales. Results show that radar data assimilation significantly improves the initial analysis and enhances the accuracy of hourly precipitation forecasts by providing more detailed mesoscale system information, compared to assimilating only wind profiler radar data. The Barnes filter analysis reveals that radar data assimilation has a more pronounced effect on mesoscale systems, with improvements primarily concentrated in the first 2 h of the forecast. However, this improvement diminishes rapidly beyond the 2 h lead time, indicating the inherent predictability limits of mesoscale systems. In contrast, large-scale systems exhibit a greater stability and predictability, with radar data assimilation having a relatively smaller but still positive impact. The study emphasizes the importance of radar data assimilation for short-term forecasts at different spatial scales and suggests that future work prioritize extending mesoscale predictability. Full article

Leaf senescence is the final, programmed stage of leaf development, marked by nutrient remobilization and tightly regulated molecular events. Although alternative splicing has emerged as a major regulator of plant development, its role in isoform switching during leaf aging remains poorly understood. To address this, we conducted a genome-wide analysis of isoform switching in Arabidopsis, leveraging publicly available RNA-seq data from mature (16-day-old) and senescent (30-day-old) leaves, analyzed with the IsoformSwitchAnalyzeR package. Between these two developmental stages, we identified 269 genes exhibiting 377 significant isoform switches collectively predicted to alter protein localization, coding potential, and transcript stability. Experimental validation confirmed predicted switching at the PUS3 (Pseudouridine Synthase 3) locus, with sequence analysis revealing an age-dependent shift from mitochondrial-targeted to cytoplasmic isoforms. Gene Ontology enrichment analysis of switching genes revealed 82 significant terms, prominently associated with metabolism, gene expression, developmental regulation, and stress responses. Interestingly, we found nearly one-third of switching genes to overlap with known targets of the splicing factor SR45, with enrichment in pathways related to nucleotide and amino acid metabolism, energy production, and developmental processes. Correspondingly, dark-induced senescence assays revealed accelerated senescence in the sr45 mutant, confirming SR45′s role in regulating leaf aging. Specific complementation of SR45′s two isoforms revealed contrasting functions, with SR45.1 restoring normal senescence timing while SR45.2 failed to complement. Taken together, our findings demonstrate that differential isoform usage, orchestrated by specific splicing regulators, plays a critical role in leaf aging. This insight opens new avenues for manipulating senescence and engineering stay-green traits in crops. Full article

EsBax (bcl-2 Associated X protein), a member of the bcl-2 family involved in the mitochondrial apoptosis pathway, plays a crucial role in immune response and defense in invertebrates. In this study, we successfully cloned the full-length cDNA of EsBax from the Chinese mitten crab (Eriocheir sinensis) and investigated its immune-related functions. The EsBax cDNA is 3374 bp in length, including a 1563 bp open reading frame (ORF) encoding 521 amino acids, a 142 bp 5′ untranslated region (UTR), and a 1699 bp 3′ UTR. The predicted EsBax protein has a molecular weight of 58.0786 kD, a theoretical isoelectric point of 7.28, and contains three conserved BH domains (BH1-BH3), and a transmembrane domain (TM). Amino acid sequence analysis revealed the highest sequence identity (99.42%) with E. sinensis. For the expression analysis, three biological replicates were performed for each tissue and treatment group. Real-time quantitative PCR showed that EsBax mRNA was ubiquitously expressed in all examined tissues, with the highest expression in the hepatopancreas, followed by hemocytes, intestine, gill, and the lowest in muscle. Upon stimulation with lipopolysaccharide (LPS), Aeromonas hydrophila (AH), or cycloheximide (CHX), EsBax expression increased and peaked at 24 h (LPS and CHX) or 48 h (A. hydrophila), then decreased. These results suggest that EsBax expression is dynamically responsive to exogenous stimulants (LPS, A. hydrophila, and CHX) in E. sinensis, implying a potential role of EsBax in the molecular events associated with pathogen-induced apoptosis in this species. Full article

In recent years, frequent wildfires have posed significant threats to both the ecological environment and socioeconomic development. Investigating the mechanisms underlying the influencing factors of forest fires and accurately predicting the likelihood of such events are crucial for effective prevention strategies. However, the field currently faces challenges, including the unclear characterization of influencing factors, limited accuracy in forest fire predictions, and the absence of models for mountain fire scenarios. To address these issues, this study proposes a research framework of “decoupling analysis-model prediction-scenario validation” and employs Principal Component Analysis (PCA) and Shapley Additive Explanations (SHAP) value quantification to elucidate the significant roles of meteorological as well as combustible state indicators through multifactor coupling. Furthermore, the Attention-based Long Short-Term Memory (ALSTM) network trained on PCA-decoupled data achieved mean accuracy, recall, and area under the precision-recall curve (PR-AUC) values of 97.82%, 94.61%, and 99.45%, respectively, through 10-time cross-validation, significantly outperforming traditional LSTM neural networks and logistic regression (LR) methods. Based on digital twin technology, a three-dimensional mountain fire scenario evolution model is constructed to validate the accuracy of the ALSTM network’s predictions and to quantify the impact of key factors on fire evolution. This approach offers an interpretable solution for predicting forest fires in complex environments and provides theoretical and technical support for the digital transformation of forest fire prevention and management. Full article