bluuebunny/crossref_metadata_2025 huggingface dataset:
Plucking Point and Posture Determination of Tea Buds Based on Deep Learning
Authors: Dong Chengju , Wu Weibin , Han Chongyang , Zeng Zhiheng , Tang Ting , Liu Wenwei
Published: January 2025

Tea is a significant cash crop grown widely around the world. Currently, tea plucking predominantly relies on manual work. However, due to the aging population and increasing labor costs, machine plucking has become an important trend in the tea industry. The determination of the plucking position and plucking posture is a critical prerequisite for machine plucking tea leaves. In order to improve the accuracy and efficiency of machine plucking tea leaves, a method is presented in this paper to determine the plucking point and plucking posture based on the instance segmentation deep learning network. In this study, tea images in the dataset were first labeled using the Labelme software (version 4.5.13), and then the LDS-YOLOv8-seg model was proposed to identify the tea bud region and plucking area. The plucking points and the central points of the tea bud’s bounding box were calculated and matched as pairs using the nearest point method (NPM) and the point in range method (PIRM) proposed in this study. Finally, the plucking posture was obtained according to the results of the feature points matching. The matching results on the test dataset show that the PIRM has superior performance, with a matching accuracy of 99.229% and an average matching time of 2.363 milliseconds. In addition, failure cases of feature points matching in the plucking posture determination process were also analyzed in this study. The test results show that the plucking position and posture determination method proposed in this paper is feasible for machine plucking tea.
Salicylic Acid Seed Priming: A Key Frontier in Conferring Salt Stress Tolerance in Barley Seed Germination and Seedling Growth
Authors: Ben Youssef Rim , Jelali Nahida , Acosta Motos Jose Ramón , Abdelly Chedly , Albacete Alfonso
Published: January 2025

The goal of the current study was to investigate the effects of seed priming with salicylic acid (SA) on seed germination parameters, seedling growth traits, nutritional element mobilization, and oxidative stress status in two barley species that were subjected to various salt treatments. The findings demonstrated that salinity reduced a number of germination parameters in unprimed seeds and impacted seedling growth by impeding both species’ necessary nutrient mobilization. Under this abiotic stress, a noticeable rise in malondialdehyde and electrolyte leakage was also noted. Interestingly, pretreating seeds with SA improved seed germination and seedling growth performance under either 100 mM or 200 mM NaCl treatments. In fact, SA improved the length and dry weight of stressed seedlings of both barley species in addition to increasing the germination rate and mean daily germination. Additionally, SA increased the content of calcium, iron, magnesium, and potassium while lowering the concentrations of sodium and malondialdehyde and electrolyte leakage. It is significant to note that, in comparison to Hordeum maritimum, the positive effects of this hormone were more noticeable in stressed Hordeum vulgare species.
Effects of Climatic, Chemical, and Cultural Control Strategies on Community Composition of Auchenorrhyncha and Population Dynamics of Two Major Green Leafhopper Pests in Peach Orchards
Authors: Nascimento Patrícia Monteiro , Neto Ana Carina , Guerreiro Vera , Barateiro Anabela , Anjos Hugo , Coutinho José Pereira , Antunes Marília , Rebelo Maria Teresa
Published: January 2025

A three-year study in two Mediterranean peach orchards monitored Auchenorrhyncha species, including the main vectors of plant pathogens causing significant economic losses. The research focused on the management of two polyphagous leafhoppers, Asymmetrasca decedens and Hebata (Signatasca) solani. Yellow sticky traps were used to track species dynamics and assess population trends in relation to meteorological factors such as temperature, rainfall, and humidity. Chemical treatments and early green pruning were evaluated for pest control effectiveness. Fifty-five Auchenorrhyncha species were identified, including vectors of Xylella fastidiosa. H. solani appeared from spring to early summer, and was gradually replaced by A. decedens, which peaked in late summer. Higher temperatures favoured A. decedens, while rainy days significantly reduced its presence. H. solani responded less consistently to meteorological factors, but increased with higher minimum relative humidity, and decreased with higher total humidity. These results highlight the need for targeted chemical control strategies to manage A. decedens during its peak season and the benefits of early green pruning to protect susceptible trees in spring. The results emphasise the role of climate in pest dynamics and provide important insights for IPM programmes to reduce economic losses in Mediterranean peach orchards worldwide.
Linking Animal Feed Formulation to Milk Quantity, Quality, and Animal Health Through Data-Driven Decision-Making
Authors: Akintan Oreofeoluwa A. , Gebremedhin Kifle G. , Uyeh Daniel Dooyum
Published: January 2025

The global demand for high-quality animal products, particularly dairy, has intensified the need for more precise and efficient livestock feed formulation. This review connects data-driven decision-making in optimizing feed formulation to enhance milk quantity and quality while addressing animal health implications. Modern feed formulation has evolved into a sophisticated, data-centric process by integrating diverse data sources such as nutritional databases, environmental data, and animal performance metrics. Leveraging advanced analytical techniques, such as machine learning and optimization algorithms, have created highly accurate feed formulations tailored to specific livestock needs. These innovations increase milk yield and contribute to developing dairy products with higher nutritional value. Decision Support Systems play a complementary role by offering real-time decision-making capabilities, enabling farmers to make data-informed adjustments composition based on changing conditions. However, despite its potential, the widespread adoption of data-driven feed formulation faces challenges such as data quality, technological limitations, and industry resistance, mostly disjointed processes. The objectives of this review are: (i) to explore the current advancements and challenges of data-driven decision-making in feed formulation, focusing on its connection to milk quantity and quality, and (ii) to highlight how this optimized feed formulation strategy improves sustainable dairy production.
Emodin Improves Juvenile Largemouth Bass (Micropterus salmoides) Liver Health Through Nrf2/NF-κB Pathway and Fat Metabolism: Growth Performance, Immune Response and Resistance Against Aeromonas veronii Infection
Authors: Zhao Zhenxin , Zhao Fei , Luo Tianxun , Zhou Zhou , Zhang Xianbo
Published: January 2025

The experiment was aimed at examining the influence of adding emodin to feeds on the growth performance, liver immunity, and resistance against Aeromonas veronii infection among juvenile largemouth basses and other potential mechanisms. A total of 540 fish (45 ± 0.3 g) were randomly divided into 6 diets, including EM-0, EM-250, EM-500, EM-1000, EM-2000, and EM-4000 diets, in which 0, 250, 500, 1000, 2000, and 4000 mg kg−1 emodin was added. Following a 60-day feeding test, it demonstrated that the feed conversion ratio (FCR) of juveniles within the EM-500 and EM-1000 groups remarkably exceeded that of the EM-0 group. Subsequently, unlike those in EM-0 group, the fish in the EM-1000 group showed heightened hepatocyte count, induced hepatic lipolysis-associated expression of peroxisome proliferators-activated receptor α (PPARα) and acyl-coenzyme an oxidase (ACO), and reduced the hepatic triglyceride (TG) levels. Additionally, EM-1000 could up-regulate the expressions of nuclear factor erythroid 2-associated factor 2 (Nrf2) and heme oxygenase-1 (HO-1) in livers compared with controls and boost antioxidant enzymes activities of superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and catalase (CAT), along with a lower content of reactive oxygen species (ROS) and malondialdehyde (MDA). Meanwhile, the EM-1000 group increased anti-inflammatory cytokines of interleukin-10 (IL-10) while suppressing the interleukin-8 (IL-8) expression of pro-inflammatory cytokines in livers by contrast to controls. In the end, juvenile largemouth bass in the EM-1000 group showed a comparatively highest survival rate, whereas fish in the EM-2000 and EM-4000 groups exhibited a little higher mortality than that of the EM-0 group. To sum up, our study exposed that supplementing emodin with 1000 mg kg−1 in diet could enhance the hepatic antioxidant status and unspecific immunity to reinforce the protective effect on disease resistance, resulting in improving the growth performance in juvenile largemouth bass.
Advances in Fluorescence Techniques for the Detection of Hydroxyl Radicals near DNA and Within Organelles and Membranes
Authors: Ransdell-Green Eleanor C. , Baranowska-Kortylewicz Janina , Wang Dong
Published: January 2025

Hydroxyl radicals (•OH), the most potent oxidants among reactive oxygen species (ROS), are a major contributor to oxidative damage of biomacromolecules, including DNA, lipids, and proteins. The overproduction of •OH is implicated in the pathogenesis of numerous diseases such as cancer, neurodegenerative disorders, and some cardiovascular pathologies. Given the localized nature of •OH-induced damage, detecting •OH, specifically near DNA and within organelles, is crucial for understanding their pathological roles. The major challenge of •OH detection results from their short half-life, high reactivity, and low concentrations within biological systems. As a result, there is a growing need for the development of highly sensitive and selective probes that can detect •OH in specific cellular regions. This review focuses on the advances in fluorescence probes designed to detect •OH near DNA and within cellular organelles and membranes. The key designs of the probes are highlighted, with emphasis on their strengths, applications, and limitations. Recommendations for future research directions are given to further enhance probe development and characterization.
An Improved Bio-Inspired Material Generation Algorithm for Engineering Optimization Problems Including PV Source Penetration in Distribution Systems
Authors: Gafar Mona , Sarhan Shahenda , Ginidi Ahmed R. , Shaheen Abdullah M.
Published: January 2025

The Material Generation Optimization (MGO) algorithm is an innovative approach inspired by material chemistry which emulates the processes of chemical compound formation and stabilization to thoroughly explore and refine the parameter space. By simulating the bonding processes—such as the formation of ionic and covalent bonds—MGO generates new solution candidates and evaluates their stability, guiding the algorithm toward convergence on optimal parameter values. To improve its search efficiency, this paper introduces an Enhanced Material Generation Optimization (IMGO) algorithm, which integrates a Quadratic Interpolated Learner Process (QILP). Unlike conventional random selection, QILP strategically selects three distinct chemical compounds, resulting in increased diversity, a more thorough exploration of the solution space, and improved resistance to local optima. The adaptable and non-linear adjustments of QILP’s quadratic function allow the algorithm to traverse complex landscapes more effectively. This innovative IMGO, along with the original MGO, is developed to support applications across three phases, showcasing its versatility and enhanced optimization capabilities. Initially, both the original and improved MGO algorithms are evaluated using several mathematical benchmarks from the CEC 2017 test suite and benchmarks to measure their optimization capabilities. Following this, both algorithms are applied to the following three well-known engineering optimization problems: the welded beam design, rolling element bearing design, and pressure vessel design. The simulation results are then compared to various established bio-inspired algorithms, including Artificial Ecosystem Optimization (AEO), Fitness–Distance-Balance AEO (FAEO), Chef-Based Optimization Algorithm (CBOA), Beluga Whale Optimization Algorithm (BWOA), Arithmetic-Trigonometric Optimization Algorithm (ATOA), and Atomic Orbital Searching Algorithm (AOSA). Moreover, MGO and IMGO are tested on a real Egyptian power distribution system to optimize the placement of PV and the capacitor units with the aim of minimizing energy losses. Lastly, the PV parameters estimation problem is successfully solved via IMGO, considering the commercial RTC France cell. Comparative studies demonstrate that the IMGO algorithm not only achieves significant energy loss reduction but also contributes to environmental sustainability by reducing emissions, showcasing its overall effectiveness in practical energy optimization applications. The IMGO algorithm improved the optimization outcomes of 23 benchmark models with an average accuracy enhancement of 65.22% and a consistency of 69.57% compared to the MGO method. Also, the application of IMGO in PV parameter estimation achieved a reduction in computational errors of 27.8% while maintaining superior optimization stability compared to alternative methods.
Research on Multi-Strategy Fusion of the Chimpanzee Optimization Algorithm and Its Application in Path Planning
Authors: He Xing , Guo Chenxv
Published: January 2025

In this paper, a multi-strategy enhanced chimpanzee optimization algorithm (MSEChOA) acting on path planning for delivery vehicles is proposed to achieve the goal of shortening global path lengths for the delivery unmanned vehicles and obtaining safer paths. In the initialization phase, the algorithm introduces a hybrid good point set and chaos initialization strategy, combining the advantages of both to enhance the randomness and homogeneity of the initial population. After that, it incorporates a benchmark weight strategy and Gaussian-modulated cosine factor to adaptively adjust algorithm parameters, thus balancing the global and local search capabilities and improving the search efficiency. In the end, the algorithm incorporates a global search enhancer (GEE) to further enhance the global search capability in the later phases, thereby avoiding local optima. Experiments on several benchmark test functions show that MSEChOA outperforms traditional ChOA and other optimization algorithms in optimization accuracy and convergence speed. In simulation experiments, MSEChOA shows stronger path planning ability and good computational efficiency in both simple and complex environments, proving its feasibility and superiority in the field of path planning.
Uranium Mineral Transport in the Peña Blanca Desert: Dissolution or Fragmentation? Simulation in Sediment Column Systems
Authors: Pérez-Reyes Victoria , Cabral-Lares Rocio M. , Canche-Tello Jesús G. , Rentería-Villalobos Marusia , González-Sánchez Guillermo , Carmona-Lara Blanca P. , Hernández-Herrera Cristina , Faudoa-Gómez Fabián , Rodríguez-Guerra Yair , Vázquez-Olvera Gregorio , Carrillo-Flores Jorge , Reyes-Cortés Ignacio A. , Hernández-Cruz Daniel , Loredo-Portales René , Montero-Cabrera María E.
Published: January 2025

The Sierra Peña Blanca (SPB) region in Chihuahua, Mexico contains a significant uranium deposit representing about 40% of the country’s reserves. Common uranium minerals in this area include uranophane, schoepite, and weeksite/boltwoodite, with several superficial occurrences. Mining activities in the 1980s left unprocessed uranium ore exposed to weathering, with potential transport towards Laguna del Cuervo. This study presents an experimental simulation of uranium transport in SPB sediments using three approaches: (i) a batch experiment to evaluate the ideal adsorption of (UO2)2+ by fine sediment; (ii) a column system fed with 569 mgU L−1 UO2(NO3)2 to simulate adsorption by different sediment particle sizes; (iii) a column system with an upper horizon of uranophane from the area, fed with deionized water, to simulate uranium weathering and transport in particulate material, determined by liquid scintillation counting, revealed that the clay fraction had the highest adsorption capacity for U. X-ray Absorption Fine Structure (XAFS) analysis at the U L3 edge confirmed the U(IV) oxidation state and the fittings of the extended XAFS spectra confirmed the presence of the uranophane group of minerals. X-ray tomography further corroborated the distribution of particulate minerals along the column. The results suggest that the primary transport mechanism in SPB involves the fragmentation of uranium minerals, accompanied by eventual dissolution and subsequent adsorption of U onto sediments.
Bulk System Reliability Assessment Incorporating Nodal Correlations in Supply–Demand Variabilities and Uncertainties Created with Net-Zero Emission Targets
Authors: Sharma Deeksha , Karki Rajesh
Published: January 2025

Growing global concerns over reducing carbon emissions in the electricity market have accelerated the integration of renewable energy sources and electric vehicles, increasing variabilities and uncertainties across various nodes of power networks. System planners and operators recognize the importance of probabilistic bulk system reliability assessment methods capable of capturing the real-time behavior of components in the emerging systems. In this regard, the paper proposes a methodology for conducting bulk system reliability assessments of power system networks characterized by variable supply and demand profiles at different bulk power points. This paper implements a nodal negative load modeling method to integrate wind power generation in reliability assessment, capturing the cross-correlation between demand–supply variabilities at any node of the network. The multi-state load model employs the load cut-off strategy to reduce the number of demand scenarios, enhancing the computational efficiency. Moreover, the multi-state wind modeling approach considers the penetration levels, ensuring the impact of increasing penetration is appropriately captured. The methodology determines a list of a reduced set of scenarios for which consequence assessment needs to be conducted. The proposed framework and methods can readily be applied by power utilities, as these methods can be incorporated into most commercial software that uses an analytical approach for CSR assessment. The methodology is illustrated using the Roy Billinton Test System (RBTS) and can be effectively applied to other networks.
Seismic Prediction of Porosity in the Norne Field: Utilizing Support Vector Regression and Empirical Models Driven by Bayesian Linearized Inversion
Authors: Teruya Monroe Jorge A. , de Figueiredo Jose J. S. , Amanajas Carlos E. S.
Published: January 2025

This work aims to improve the characterization of petrophysical properties by accurately estimating subsurface porosity using seismic and well data. The study includes Bayesian Linearized Inversion to obtain elastic parameters (e.g., compressional e shear wave velocities and densities). This reduces processing uncertainty and provides a reliable substitute for the standard Amplitude versus Offset inversion method. Furthermore, incorporating sparse spike wavelets with Bayesian Linearized Inversion refines the inversion output, facilitating the extraction of petrophysical properties. Combined with log data from seventeen wells, these inverted parameters serve as inputs for two porosity prediction models: the empirical Han’s equation and a more adaptable Support Vector Regression model, the latter demonstrating superior precision in most cases due to its flexible fitting and calibration capabilities. Results from the Norne field in the North Sea confirm the approach’s viability, with the Support Vector Regression model achieving a significant Pearson correlation coefficient of 90% in porosity prediction, underscoring the potential of machine learning techniques in improving subsurface exploration results.
Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory–Attention Mechanism
Authors: Yang Geunseok , Ji Jinfeng , Kim Jaehee
Published: January 2025

The rapid expansion of software applications has led to an increase in the frequency of bugs, which are typically reported through user-submitted bug reports. Developers prioritize these reports based on severity and project schedules. However, the manual process of assigning bug priorities is time-consuming and prone to inconsistencies. To address these limitations, this study presents a Priority-Sensitive LSTM–Attention mechanism for automating bug priority prediction. The proposed approach extracts features such as product and component details from bug repositories and preprocesses the data to ensure consistency. Priority-based feature selection is applied to align the input data with the task of bug prioritization. These features are processed through a Long Short-Term Memory (LSTM) network to capture sequential dependencies, and the outputs are further refined using an Attention mechanism to focus on the most relevant information for prediction. The effectiveness of the proposed model was evaluated using datasets from the Eclipse and Mozilla open-source projects. Compared to baseline models such as Naïve Bayes, Random Forest, Decision Tree, SVM, CNN, LSTM, and CNN-LSTM, the proposed model achieved a superior performance. It recorded an accuracy of 93.00% for Eclipse and 84.11% for Mozilla, representing improvements of 31.11% and 40.39%, respectively, over the baseline models. Statistical verification confirmed that these performance gains were significant. This study distinguishes itself by integrating priority-based feature selection with a hybrid LSTM–Attention architecture, which enhances prediction accuracy and robustness compared to existing methods. The results demonstrate the potential of this approach to streamline bug prioritization, improve project management efficiency, and assist developers in resolving high-priority issues.
PROMISE: Prognostic Radiomic Outcome Measurement in Acute Subdural Hematoma Evacuation Post-Craniotomy
Authors: Guranda Alexandru , Richter Antonia , Wach Johannes , Güresir Erdem , Vychopen Martin
Published: January 2025

Background/Objectives: Traumatic acute subdural hematoma (aSDH) often requires surgical intervention, such as craniotomy, to relieve mass lesions and pressure. The extent of hematoma evacuation significantly impacts patient outcomes. This study utilizes 3D Slicer software to analyse post-craniotomy hematoma volume changes and evaluate their prognostic significance in aSDH patients. Methods: Among 178 adult patients diagnosed with aSDH from January 2015 to December 2022, 64 underwent hematoma evacuation via craniotomy. Initial scans were performed within 24 h of trauma, followed by routine postoperative scans to assess residual hematoma. We conducted radiomic analysis of preoperative and postoperative volumes, surface area, Feret diameter, sphericity, flatness, and elongation. Clinical parameters, including SOFA score, APACHE score, pupillary response, comorbidities, age, anticoagulation status, and preoperative haematocrit and haemoglobin levels, were also evaluated. Results: Changes in Δ surface area significantly correlated with 30-day outcomes (p = 0.03) and showed moderate predictive accuracy (AUC = 0.65). Patients with a Δ surface area > 30,090 mm2 experienced poorer outcomes (OR = 6.66, p = 0.02). Significant features included preoperative surface area (p = 0.009), Feret diameter (p = 0.0012). In multivariate analysis, only the Feret diameter remained significant (p = 0.01). Conclusions: Postoperative Δ surface area is, among other variables, a strong predictor of 30-day outcomes, while in multivariate analysis, preoperative Feret diameter remains the only independent predictor. Radiomic analysis with 3D Slicer may enhance prognostic accuracy and inform tailored therapeutic strategies.
Shared Paths to Well-Being: The Impact of Group Therapy
Authors: Sequeira-Nazaré Elena Renée , Schmitz Bernhard
Published: January 2025

This study explored the impact of an art of living intervention within group psychotherapy for depression, focusing on constructs like life satisfaction, self-efficacy, and depression. Mental illness prevalence often exceeds available treatment options, particularly in Germany, where group psychotherapy is a viable alternative. While less researched, group therapy effectively improves well-being, especially through interpersonal exchange. Meta-analyses confirm cognitive behavioral group therapy’s effectiveness against depression, encouraging further investigation. This study employed a two-factor experimental design with randomized group allocation. The control group (CG) participated in weekly 50 min sessions for four weeks, while the experimental group (EG) received identical therapy plus reflective life-stimulating questions. Measures of depression, art of living, life satisfaction, and self-efficacy were taken before, after, and three months post-intervention. Among 107 participants, 52 were in the EG and 55 were in the CG. The results showed a significant 24% reduction in depression scores in the experimental group, a significant 16% increase in the art of living and a significant 19% increase in life satisfaction, while the CG showed no significant changes. Self-efficacy did not significantly improve in the EG. Follow-up data indicated sustained improvements in depression and art of living for the EG. The limitations of this study include a limited scope, practical constraints, randomization challenges and confounding variables, which are typical for experimental studies. These findings highlight the intervention’s potential, suggesting future research focusing on long-term effects, personality factors and disorder-specific applications.
Integrated Scheduling Optimization for Multi-Line Production and Transportation of Prefabricated Components Considering Shared Resources
Authors: Dan Yiran , Sun Chengshuang , Luo Qianmai
Published: January 2025

With the increasing popularity of prefabricated buildings, more and more precast plants have been equipped with multiple production lines to increase productivity and meet the growing market demand. Sharing equipment, human, and transportation resources is a typical feature of integrated scheduling management for precast production and transportation on multiple production lines. In response to these characteristics, this article studies the integrated scheduling optimization of multi-line production and transportation for prefabricated components. With on-time delivery and lower costs as the goals, a scheduling optimization mathematical model is established for this scenario. This article adopts the genetic algorithm to design the solution algorithm for this model, and the effectiveness of the model and algorithm is verified through an example. The results show that compared with the traditional scheduling scheme, this method can prominently reduce costs while promoting on-time delivery. The model and method can help the precast plant with multiple production lines improve efficiency and reduce costs, as well as enhancing the practicability of the precast production and transportation scheduling scheme.
Evaluating the Performance of Fixed 3D-Printed and Dynamic Fabric Modules in a Second-Skin Façade System: A Residential Case Study in Southern Italy at Building and District Scales
Authors: Spanodimitriou Yorgos , Ciampi Giovanni , Tufano Luigi , Scorpio Michelangelo , Sibilio Sergio
Published: January 2025

The building sector accounts for 30% of worldwide final energy usage and 26% of global energy-linked emissions. In construction, innovative materials and systems can offer flexible, lightweight, energy-efficient solutions to achieve more efficient buildings. This study addresses the energy analysis and environmental impacts of retrofitting residential buildings in Monterusciello, Italy, using an innovative second-skin façade system design that incorporates 3D-printed and fabric modules. The purpose is to enhance energy efficiency and reduce the environmental impact of residential buildings originally constructed with prefabricated elements that have degraded over time. This research employed TRNSYS modelling to simulate energy consumption and environmental impacts at the single-building and whole-district levels, analysing the system’s effectiveness in reducing cooling and heating demands and using different materials for optimal performance. The results show that retrofitting with the second-skin façade system significantly reduces cooling energy demand by 30.2% and thermal energy demand by 3.84%, reaching a primary energy saving of 16.4% and 285 tons of CO2 emissions reduction for the whole district. The results highlight the potential of second-skin façade systems in improving energy efficiency and environmental sustainability, suggesting future research directions in material innovation and adaptive system development for district-wide applications.
Assessment of Masonry-Infilled Steel Frames Using Existing Experimental Tests Database in Comparison to Standards
Authors: Bratu Ciprian , Šokić Dario , Ademović Naida , Isik Ercan , Bulajić Borko , Radu Dorin , Hadzima-Nyarko Marijana
Published: January 2025

Although the influence of infill masonry on horizontal load structure behavior is well-documented, current standards and regulations have yet to fully incorporate or explicitly define the load-bearing capacity of this complex system. Canadian and American standards present more comprehensive and specific methodologies for calculating the load-bearing capacity of infill masonry and frame systems. In contrast, European standards tend to focus on offering general guidelines for the design of these systems without delving into the detailed calculation procedures. However, extensive data and experimental studies on this topic are available in the literature. The primary aim of this paper was to compile a database of experiments involving frames with different types of infill masonry. Subsequently, the empirical results obtained through the application of analytical expressions from various standards are compared to the experimental data included in the compiled database. The obtained load-bearing values were compared to different standards and work conducted by various researchers found in the literature in order to assess their reliability. Based on the obtained results, important conclusions were drawn, specifically to the most accurate equivalent diagonal model used and the analytical expressions to be used in the assessment of the masonry-infilled steel frame behavior. The equivalent diagonal model, utilized in all analytical expressions, can provide highly accurate estimations of load-bearing capacities that closely align with the experimental results. Regardless of the type of infill element, the analytical expressions consistently overestimated the load-bearing capacity. In the presence of longitudinal force, analytical expressions tend to be conservative, providing significantly lower load-bearing values compared with experimental results, which ensures a safety margin. The database can be utilized to develop numerical models, which can subsequently serve as the foundation for probabilistic methods used in conducting reliability assessments.
Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm Optimization
Authors: Abood Emad A. , Al-Kamal Mustafa Kamal , Muhodir Sabih Hashim , Al-Abdaly Nadia Moneem , Bernardo Luís Filipe Almeida , De Domenico Dario , Imran Hamza
Published: January 2025

In structures with reinforced concrete walls, coupling beams join individual walls to produce a rigid assembly that withstands sideways forces. A precise forecasting of the critical shear capacity is essential to avoid early shear failure and attain the desired ductility performance of coupled shear wall systems in earthquake design. This paper examines the ability of Support Vector Regression (SVR) in predicting the shear performance of coupling beams. SVR is a distinguished machine learning regression method that has been positively utilized in former works to forecast the performance of several structural members. Nevertheless, the capability of this regression method deeply relies on picking its best hyperparameters. To handle this, a heuristic optimization procedure named Particle Swarm Optimization (PSO) was merged with SVR to select the optimal hyperparameters. The data of RC coupling beams collected from the previous works were utilized to build the proposed model. Several performance metrics, including RMSE, R2, and MAE, were employed to compare the performance of the optimized model against a baseline SVR model and previous approaches. Analytical results indicate that the new optimized prediction model can assist civil engineers in designing RC coupling beam structures more effectively and outperforms existing models in predicting the shear strength of such beams.
The Role of Metastasectomies and Immunotherapy in the Management of Melanoma Lung Metastases: An Analysis of the National Cancer Database
Authors: Tasoudis Panagiotis , Manaki Vasiliki , Parness Shannon , Khoury Audrey L. , Agala Chris B. , Haithcock Benjamin E. , Mody Gita N. , Long Jason M.
Published: January 2025

Introduction: Patients with metastatic melanoma to the lung typically have poor outcomes. Although a pulmonary metastasectomy for selected patients has been shown to improve survival, the role of surgical resection following the introduction of immunotherapy for metastatic melanoma is unknown. The objective of this study was to determine predictors of survival for patients with melanoma metastatic to the lung in the era of immunotherapy. Methods: In this retrospective study, data from the National Cancer Database were abstracted for patients with melanoma lung metastases. The overall survival was evaluated using Kaplan–Meier and Cox proportional hazard analysis, adjusting for previously described risk factors for mortality. Patients with concomitant metastases to organs other than the lung were excluded from the study. Results: A total of 625 patients with lung metastases at the time of a skin melanoma diagnosis were identified. A total of 280 patients underwent a pulmonary metastasectomy, 267 received immunotherapy, and 78 were treated with both a metastasectomy and immunotherapy. During a median follow-up time of 34.6 months [IQR: 14.2, 75.9], a metastasectomy was found to offer significantly improved survival compared to immunotherapy alone. No difference was noted between a metastasectomy and a combination of a metastasectomy and immunotherapy in the adjusted Cox proportional hazard model. Conclusions: When statistical models were adjusted for risk factors, a metastasectomy maintained a significant survival advantage compared to immunotherapy. The addition of immunotherapy to the treatment of patients treated with a pulmonary metastasectomy did not improve survival. Our findings support the role of surgery for patients with pulmonary metastatic melanoma.
Medicaid Expansion Increases Treatment for Patients with Colon Cancer
Authors: Lyons John Morgan , Danos Denise M. , Maniscalco Lauren S. , Yi Yong , Moaven Omeed , Wu Xiaocheng , Chu Quyen
Published: January 2025

Introduction: Medicaid expansion (ME) has positively impacted colon cancer screening. ME’s effect on colon cancer treatment is less clear. This study analyses the effect of ME on patterns of colon cancer treatment. Methods: Patients with primary invasive colon cancer were identified using the Louisiana Tumor Registry. Patients diagnosed with colon cancer prior to ME (2014–2015) were compared to those diagnosed after (2017–2018). Coordinate variables were analyzed using Fisher’s exact test. Treatment status was modeled with multivariable logistic regression and the results are reported as adjusted odds ratios. Results: The proportion of uninsured patients decreased following ME (5.5 versus 1.9, p < 0.001), with the greatest reductions among patients between 45 and 54 years old (13.5% to 3.5%, p < 0.0001), African Americans (8.9 to 2.1%, p < 0.0001), and those in high-poverty neighborhoods (7.1 to 2.1%, p < 0.0001). Following ME, all patients with Stage I-III disease were more likely to receive surgery (OR = 1.95; 95%: CI 1.21–3.14)—especially the extremely impoverished (OR = 2.39; 95% CI 1.41–4.02). Young patients with Stage IV colon cancer were more likely to receive chemotherapy (OR-1.6; 95% CI 1.03–2.4). Patients with Stage IV colon cancer were less likely to receive treatment within 30 days of diagnosis (OR = 0.7; 95% CI 0.5–0.9), but, on subset analysis, this was only observed in non-Medicaid patients. Conclusion: ME is associated with increased treatment for patients with colon cancer, and it did not appear to affect time to treatment. However, it seems to affect different subsets of the population differently.
PROTAC-Mediated GSPT1 Degradation Impairs the Expression of Fusion Genes in Acute Myeloid Leukemia
Authors: Perzolli Alicia , Steinebach Christian , Krönke Jan , Gütschow Michael , Zwaan C. Michel , Barneh Farnaz , Heidenreich Olaf
Published: January 2025

Background: Proteolysis targeting chimeras (PROTACs) are heterobifunctional small molecules that utilize the ubiquitin–proteasome system to selectively degrade target proteins. This innovative technology has shown remarkable efficacy and specificity in degrading oncogenic proteins and has progressed through various stages of preclinical and clinical development for hematologic malignancies, including adult acute myeloid leukemia (AML). However, the application of PROTACs in pediatric AML remains largely unexplored. Methods: In this study, we show the potent effect of GSPT1 degradation against AML cells induced by either a GSPT1-selective cereblon modulator CC-90009 or by an off-target effect caused by a CDK6-PROTAC named GU3341. Results: Both in vitro and ex vivo experiments revealed that GSPT1 degradation significantly inhibited tumor growth, induced cell cycle arrest, and triggered apoptosis in two pediatric AML subtypes characterized by RUNX1::RUNX1T1 and FUS::ERG fusion genes. Furthermore, the degradation of GSPT1 impaired the expression of RUNX1::RUNX1T1 and its cooperating transcription factors RUNX1 and ERG. Similarly, GSPT1 degradation also reduced FUS::ERG fusion transcript levels in AML cells harboring the translocation t(16;24)(p11:q22). Conclusions: These findings suggest a new role of GSPT1 in regulating leukemic transcriptional networks and open a new therapeutic strategy to target leukemic fusion genes in pediatric AML patients.
Analysis of Differences in the Classification of Endometrial Cancer Patients in Poland
Authors: Szatkowski Wiktor , Kluz Tomasz , Cieślak-Steć Małgorzata , Śliwińska Magdalena , Nowak-Jastrząb Małgorzata , Winkler Izabela , Tomaszewski Jacek , Blecharz Paweł
Published: January 2025

Background: Endometrial cancer (EC) incidence and mortality have been steadily rising globally over recent decades. The introduction of advanced molecular technologies, such as next-generation sequencing (NGS) alongside the FIGO 2023 classification, presents opportunities for refined diagnostics and risk stratification. This study aimed to analyze differences in EC classification among oncology centers in southeastern Poland. Methods: Data were collected from 461 consecutive patients newly diagnosed with EC between 2022 and 2024 at four major oncology centers in southeastern Poland. Molecular and immunohistochemical (IHC) analyses were conducted on formalin-fixed paraffin-embedded (FFPE) tissues to identify key markers, including POLE mutations, MSI-H, and p53 status. Results: The application of the FIGO 2023 staging system revealed statistically significant inter-center differences, with Centers 1 and 4 diagnosing a higher proportion of early-stage cases. The most prevalent subtype was NSMP, observed in 51% of cases. MSI-H occurred in 13–36% of patients, depending on the center. p53 mutations ranged from 9% to 26%. POLE mutations were identified in 4% of patients overall. Significant variations in the molecular subtype distribution across centers highlight potential differences in diagnostic access or tumor biology. Conclusions: The findings demonstrate regional differences in EC staging and molecular profiles in Poland, potentially reflecting disparities in diagnostic resources, methodologies, or tumor characteristics. Addressing these variations through standardized diagnostic protocols and equitable access to molecular tools is critical for optimizing patient outcomes. Future research should focus on evaluating the impact of molecular markers on therapy response and prognosis to guide personalized treatment strategies.
MicroRNA-21 as a Regulator of Cancer Stem Cell Properties in Oral Cancer
Authors: Jaksic Karisik Milica , Lazarevic Milos , Mitic Dijana , Milosevic Markovic Maja , Riberti Nicole , Jelovac Drago , Milasin Jelena
Published: January 2025

Oral squamous cell carcinoma (OSCC) is a highly aggressive malignancy with poor prognosis, mainly due to the presence of cancer stem cells (CSCs), a small subpopulation of cells that contribute to therapy resistance and tumor progression. The principal objective of this study was to investigate the role of miRNA-21 in the maintenance of cancer cell stemness and the possibility of altering it. The CD44 antigen was used as a marker for CSC isolation from oral cancer cell cultures. CD44+ and CD44− populations were sorted via magnetic separation. miRNA-21 inhibition was performed in CD44+ cells via transfection. CD44+ cells possessed a significantly higher migration and invasion potential compared to CD44− cells, higher levels of miRNA-21 (p = 0.004) and β-catenin (p = 0.005), and lower levels of BAX (p = 0.015). miRNA-21 inhibition in CD44+ cells reduced migration, invasion, and colony formation while increasing apoptosis. Stemness markers were significantly downregulated following miRNA-21 inhibition: OCT4 (p = 0.013), SOX2 (p = 0.008), and NANOG (p = 0.0001), as well as β-catenin gene (CTNNB1) (p < 0.05), an important member of WNT signaling pathway. Apoptotic activity was enhanced, with a significant downregulation of the antiapoptotic Bcl-2 (p = 0.008) gene. In conclusion, miRNA-21 plays a critical role in the regulation of oral cancer CD44+ cells properties. Targeting and inhibiting miRNA-21 in CD44+ cells could represent a promising novel strategy in OSCC treatment.
Fluorescence Lifetime Imaging of NAD(P)H in Patients’ Lymphocytes: Evaluation of Efficacy of Immunotherapy
Authors: Yuzhakova Diana V. , Sachkova Daria A. , Izosimova Anna V. , Yashin Konstantin S. , Yusubalieva Gaukhar M. , Baklaushev Vladimir P. , Mozherov Artem M. , Shcheslavskiy Vladislav I. , Shirmanova Marina V.
Published: January 2025

Background: The wide variability in clinical responses to anti-tumor immunotherapy drives the search for personalized strategies. One of the promising approaches is drug screening using patient-derived models composed of tumor and immune cells. In this regard, the selection of an appropriate in vitro model and the choice of cellular response assay are critical for reliable predictions. Fluorescence lifetime imaging microscopy (FLIM) is a powerful, non-destructive tool that enables direct monitoring of cellular metabolism on a label-free basis with a potential to resolve metabolic rearrangements in immune cells associated with their reactivity. Objective: The aim of the study was to develop a patient-derived glioma explant model enriched by autologous peripheral lymphocytes and explore FLIM of the redox-cofactor NAD(P)H in living lymphocytes to measure the responses of the model to immune checkpoint inhibitors. Methods: The light microscopy, FLIM of NAD(P)H and flow cytometry were used. Results: The results demonstrate that the responsive models displayed a significant increase in the free NAD(P)H fraction α1 after treatment, associated with a shift towards glycolysis due to lymphocyte activation. The non-responsive models exhibited no alterations or a decrease in the NAD(P)H α1 after treatment. The FLIM data correlated well with the standard assays of immunotherapy drug response in vitro, including morphological changes, the T-cells activation marker CD69, and the tumor cell proliferation index Ki67. Conclusions: The proposed platform that includes tumor explants co-cultured with lymphocytes and the NAD(P)H FLIM assay represents a promising solution for the patient-specific immunotherapeutic drug screening.
GV1001, hTERT Peptide Fragment, Prevents Doxorubicin-Induced Endothelial-to-Mesenchymal Transition in Human Endothelial Cells and Atherosclerosis in Mice
Authors: Chen Wei , Kim Seojin , Kim Sharon Y. , Beheshtian Cheyenne , Kim Naryung , Shin Ki-Hyuk , Kim Reuben H. , Kim Sangjae , Park No-Hee
Published: January 2025

Doxorubicin is a highly effective anticancer agent, but its clinical use is restricted by severe side effects, including atherosclerosis and cardiomyopathy. These complications are partly attributed to doxorubicin’s ability to induce endothelial-to-mesenchymal transition (EndMT) in vascular endothelial cells, a critical process in the initiation and progression of atherosclerosis and cardiomyopathy. GV1001, a multifunctional peptide with anti-inflammatory, anti-cancer, antioxidant, and anti-Alzheimer’s properties, has demonstrated inhibition of EndMT. We investigated whether GV1001 could counteract doxorubicin-induced EndMT in endothelial cells and prevent atherosclerosis in a mouse model. The results revealed that GV1001 significantly suppressed EndMT induced by doxorubicin, likely through its protective effects on mitochondria. By mitigating mitochondrial damage, GV1001 reduced the accumulation of mitochondrial and cellular reactive oxygen species (ROS), repressed the activation of nuclear factor kappa B (NF-κB), and reduced the production of proinflammatory cytokines in endothelial cells. Additionally, GV1001 reduced systemic and vascular inflammation, lipid accumulation, and monocyte/macrophage infiltration within arterial walls in mice. In conclusion, GV1001 appears to prevent doxorubicin-induced atherosclerosis by safeguarding vascular endothelial cells from mitochondrial dysfunction, inflammation, and phenotypic changes. These findings suggest the potential of GV1001 as a therapeutic agent to mitigate the long-term cardiovascular side effects associated with doxorubicin treatment in humans.
Acidic Microenvironment Enhances Cisplatin Resistance in Bladder Cancer via Bcl-2 and XIAP
Authors: Hiruma Kaede , Bilim Vladimir , Kazama Akira , Shirono Yuko , Murata Masaki , Tomita Yoshihiko
Published: January 2025

Cisplatin (CDDP) remains a key drug for patients with advanced bladder cancer (BC), despite the emergence of new therapeutic agents; thus, the identification of factors contributing to CDDP treatment resistance is crucial. As acidity of the tumor microenvironment has been reported to be associated with treatment resistance and poor prognosis across various cancer types, our objectives in this study were to investigate the effects of an acidic environment on BC cells and elucidate the mechanisms behind CDDP resistance. Our findings show that BC cells cultured under acidic conditions developed cisplatin resistance as acidity increased. Notably, CDDP administered to BC cells in a pH 6.0 environment required double the concentration, compared to those in a pH 7.5 environment, to achieve equivalent toxicity. Using chloroquine and navitoclax, we identified the involvement of the Bcl-2 and LC3B pathways in the acquisition of CDDP resistance under acidic conditions. A Western blot analysis revealed that the activations of Bcl-2 and XIAP expression appear to inhibit both apoptotic and autophagic cell death. Taken together, these results suggest that alleviating the acidity of the tumor microenvironment in clinical settings might enhance BC sensitivity to CDDP.