Search Results (16,377)

Background/Objectives: Anthracyclines remain a pivotal element of numerous tumor management regimens; however, their utilization is associated with a range of adverse effects, the most significant of which is cardiotoxicity. Research is constantly being conducted to identify substances that could be incorporated into ongoing cancer chemotherapy to mitigate anthracycline-induced cardiotoxicity. Recently, the apelinergic system has received a lot of attention in this field due to its involvement in cardiovascular regulation. Therefore, the aim of our study was to investigate the ability of the apelinergic system to inhibit the cardiotoxic effects of anthracycline—doxorubicin (DOX). Methods: In this study, 54 Sprague–Dawley rats were divided into seven groups and received intraperitoneal injections with DOX once a week for 4 consecutive weeks. The osmotic pumps provided a continuous release of NaCl (control groups), apelin-13 and elabela at two different doses, and the apelin receptor (APJ) antagonist ML221. Electrocardiography (ECG) and transthoracic echocardiography (TTE) with assessment of left ventricular (LV) systolic parameters were conducted on the first and last days of the experiment. Results: Lower doses of APJ agonists prevented the prolongation of QT and QTc intervals induced by DOX, while higher doses of these drugs exerted no such effect. The TTE examination confirmed DOX-induced LV systolic dysfunction. Moreover, the TTE examination revealed an improvement in the LV systolic parameters in the DOX-treated groups that were simultaneously administered APJ agonists. Conclusions: Our findings support the use of apelin and elabela as potential cardioprotective agents against anthracycline-induced cardiotoxicity. Full article

This paper introduces a comprehensive, data-driven framework for parametrically estimating directional ocean wave spectra from numerically simulated FPSO (Floating Production Storage and Offloading) vessel motions. Leveraging a mid-fidelity digital twin of a spread-moored FPSO vessel in the Guyana Sea, this approach integrates a wide range of statistical values calculated from the time histories of vessel responses—displacements, angular velocities, and translational accelerations. Artificial neural networks (ANNs), trained and optimized through hyperparameter tuning and feature selection, are employed to estimate wave parameters including the significant wave height, peak period, main wave direction, enhancement parameter, and directional-spreading factor. A systematic correlation analysis ensures that informative input features are retained, while extensive sensitivity tests confirm that richer input sets notably improve predictive accuracy. In addition, comparisons against other machine learning (ML) methods—such as Support Vector Machines, Random Forest, Gradient Boosting, and Ridge Regression—demonstrate the present ANN model’s superior ability to capture intricate nonlinear interdependencies between vessel motions and environmental conditions. Full article

Human milk contains a variety of biologically active molecules that are essential for infant growth and development, as well as indicators of maternal health. However, understanding the full potential of these molecules is challenging due to variations in their concentrations among mothers, potential degradation during sample handling and storage, and the limited accessibility of specific human milk analyses. This study aimed to evaluate the effectiveness of a freeze-dried preservative cocktail in maintaining the stability of key milk molecules during collection, transport, and storage. GenV participants (n = 96) were given a sample collection kit and followed the instructions to collect approximately 5 mL of breast milk, which was placed in a collection tube containing the preservative. The samples were mailed at ambient temperature to the GenV laboratory (Murdoch Children’s Research Institute, Melbourne, Victoria, Australia), where they were aliquoted into 1 mL tubes using a liquid handling system (Janus) and stored at −80 °C. These samples were randomly selected and sent to The University of Western Australia (Perth, Western Australia, Australia) on dry ice for biochemical analysis. The average collection day postpartum was 16 ± 14 (range 1–91 days), while the average postal receipt time was 5 ± 3 days (range 1–16 days), and samples were processed within 6 days of receipt (average 3 ± 2 days). The mean concentrations of key molecules—fat (48.6 ± 17.1 g/L), protein (15.5 ± 4.3 g/L), lactose (78.9 ± 13.9 g/L), glucose (0.17 ± 0.17 g/L), lysozyme (0.16 ± 0.16 g/L), and insulin (6.1 ± 4.9 μIU/mL)—were consistent with reported literature values. There were no statistically significant differences in molecular concentrations based on postal transit time, receipt, or processing delays (p > 0.05). These results demonstrate that the preservative cocktail effectively preserved the integrity of key molecules in human milk during handling, postal transport, and storage at ambient temperature. The findings support its use as a valuable tool for human milk research, enabling more flexible sample collection and handling without compromising the quality of the milk or the biochemical analysis. Future research should explore its application in broader contexts to further enhance the accuracy and reliability of milk composition studies across diverse research settings. Full article

Background: The aim of this study was to compare microwave ablation (MWA) with and without prior placement of an intra-arterial catheter for the purpose of application of contrast medium (CM). Methods: 148 patients (45 female, 65.1 ± 14.9 years) with liver tumors who underwent CT-guided MWA were included. Of these, 25 patients had an IA catheter placed in the hepatic artery. Results: 37 patients underwent planning imaging for MWA without CM. A total of 86 patients received a standard dose of 80 mL intravenous (IV) CM for the planning scans. The patients with an IA catheter (n = 25) received an IA application of 10 mL CM. A total of 29 patients received contrast-enhanced scans in the PV phase for control of needle positioning after IV application of a standard dose of 80 mL CM. In patients with an IA catheter, control of the needle position was performed by single-slice scans. IA CM application during the ablation enabled monitoring of the ablation zone. Over the entire intervention, patients with IA catheters received less CM as compared to patients without an IA catheter (39.1 ± 10.4 mL vs. 141 ± 39.69 mL; p < 0.001). Conclusions: IA catheter placement was associated with a significant decrease of the amount of CM during MWA and enabled monitoring of the ablation zone. Full article

Owing to the intensive development of electrical and electronic equipment, there is an increasing demand for precious metals, which are often used for its production. Due to their scarce supply, it is important to recover them from secondary sources. A promising way to recover precious metals are impregnated resins. In this research, Amberlite XAD-16 was impregnated with TBP at the weight ratios of 1:2 and 1:3 using the ‘warm impregnation’ method. Studies were carried out on the sorption of Au(III), Pd(II), Pt(IV), and Rh(III) ions from the model chloride solutions as well as the real solution formed after leaching the smart card chips. Only Au(III) ions were efficiently sorbed on the prepared impregnated sorbents. The best results were obtained at 6 M HCl and the sorbent mass: 0.1 g/25 mL. The maximum sorption capacity for the impregnated sorbents was: 147.91 mg/g (ratio 1:2) and 149.66 mg/g (ratio 1:3). Recovery of Au(III) ions from the real leaching solution was: 97.36% and 97.77%, respectively. The Langmuir isotherm was the best-fit model for the experimental results. Thermodynamic studies proved that the investigated sorption process is spontaneous and exothermic. The desorption process can be easily carried out with 1 M HCl/1 M TU. Full article

Patient-level grouped data are prevalent in public health and medical fields, and multiple instance learning (MIL) offers a framework to address the challenges associated with this type of data structure. This study compares four data aggregation methods designed to tackle the grouped structure in classification tasks: post-mean, post-max, post-min, and pre-mean aggregation. We developed a customized AI pipeline that incorporates twelve machine learning algorithms along with the four aggregation methods to detect Parkinson’s disease (PD) using multiple voice recordings from individuals available in the UCI Machine Learning Repository, which includes 756 voice recordings from 188 PD patients and 64 healthy individuals. Seven performance metrics—accuracy, precision, sensitivity, specificity, F1 score, AUC, and MCC—were utilized for model evaluation. Various techniques, such as Bag Over-Sampling (BOS), cross-validation, and grid search, were implemented to enhance classification performance. Among the four aggregation methods, post-mean aggregation combined with XGBoost achieved the highest accuracy (0.880), F1 score (0.922), and MCC (0.672). Furthermore, we identified potential trends in selecting aggregation methods that are suitable for imbalanced data, particularly based on their differences in sensitivity and specificity. These findings provide meaningful implications for the further exploration of grouped imbalanced data. Full article

Nitrate leaching from soil presents a significant threat to soil health, as it can result in nutrient loss, soil acidification, and structural damage. It is crucial to quantify the spatial heterogeneity of nitrate leaching and its drivers. A total of 509 observational data points regarding nitrate leaching in northern China were collected, capturing the spatial and temporal variations across crops such as winter wheat, maize, and greenhouse vegetables. A machine learning (ML) model for predicting nitrate leaching was then developed, with the random forest (RF) model outperforming the support vector machine (SVM), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) models, achieving an R² of 0.75. However, the performance improved significantly after integrating the four models with Bayesian optimization (all models had R² > 0.56), which realized quantitative prediction capabilities for nitrate leaching loss concentrations. Moreover, the XGBoost model exhibited the highest fitting accuracy and the smallest error in estimating nitrate leaching losses, with an R² value of 0.79 and an average absolute error (MAE) of 3.87 kg/ha. Analyses of the feature importance and SHAP values in the optimal XGBoost model identified soil organic matter, chemical nitrogen fertilizer input, and water input (including rainfall and irrigation) as the main indicators of nitrate leaching loss. The ML-based modeling method developed overcomes the difficulty of the determination of the functional relationship between nitrate loss intensity and its influencing factors, providing a data-driven solution for estimating nitrate–nitrogen loss in farmlands in North China and strengthening sustainable agricultural practices. Full article

Object detection is a pivotal research domain within computer vision, with applications spanning from autonomous vehicles to medical diagnostics. This comprehensive survey presents an in-depth analysis of the evolution and significant advancements in object detection, emphasizing the critical role of machine learning (ML) and deep learning (DL) techniques. We explore a wide spectrum of methodologies, ranging from traditional approaches to the latest DL models, thoroughly evaluating their performance, strengths, and limitations. Additionally, the survey delves into various metrics for assessing model effectiveness, including precision, recall, and intersection over union (IoU), while addressing ongoing challenges in the field, such as managing occlusions, varying object scales, and improving real-time processing capabilities. Furthermore, we critically examine recent breakthroughs, including advanced architectures like Transformers, and discuss challenges and future research directions aimed at overcoming existing barriers. By synthesizing current advancements, this survey provides valuable insights for enhancing the robustness, accuracy, and efficiency of object detection systems across diverse and challenging applications. Full article

This paper provides the complete details of current challenges and solutions in the cybersecurity of cyber-physical systems (CPS) within the context of the IIoT and its integration with edge computing (IIoT–edge computing). We systematically collected and analyzed the relevant literature from the past five years, applying a rigorous methodology to identify key sources. Our study highlights the prevalent IIoT layer attacks, common intrusion methods, and critical threats facing IIoT–edge computing environments. Additionally, we examine various types of cyberattacks targeting CPS, outlining their significant impact on industrial operations. A detailed taxonomy of primary security mechanisms for CPS within IIoT–edge computing is developed, followed by a comparative analysis of our approach against existing research. The findings underscore the widespread vulnerabilities across the IIoT architecture, particularly in relation to DoS, ransomware, malware, and MITM attacks. The review emphasizes the integration of advanced security technologies, including machine learning (ML), federated learning (FL), blockchain, blockchain–ML, deep learning (DL), encryption, cryptography, IT/OT convergence, and digital twins, as essential for enhancing the security and real-time data protection of CPS in IIoT–edge computing. Finally, the paper outlines potential future research directions aimed at advancing cybersecurity in this rapidly evolving domain. Full article

Conducting penetration testing (pentesting) in cybersecurity is a crucial turning point for identifying vulnerabilities within the framework of Information Technology (IT), where real malicious offensive behavior is simulated to identify potential weaknesses and strengthen preventive controls. Given the complexity of the tests, time constraints, and the specialized level of expertise required for pentesting, analysis and exploitation tools are commonly used. Although useful, these tools often introduce uncertainty in findings, resulting in high rates of false positives. To enhance the effectiveness of these tests, Machine Learning (ML) has been integrated, showing significant potential for identifying anomalies across various security areas through detailed detection of underlying malicious patterns. However, pentesting environments are unpredictable and intricate, requiring analysts to make extensive efforts to understand, explore, and exploit them. This study considers these challenges, proposing a recommendation system based on a context-rich, vocabulary-aware transformer capable of processing questions related to the target environment and offering responses based on necessary pentest batteries evaluated by a Reinforcement Learning (RL) estimator. This RL component assesses optimal attack strategies based on previously learned data and dynamically explores additional attack vectors. The system achieved an F1 score and an Exact Match rate over $97.0$%, demonstrating its accuracy and effectiveness in selecting relevant pentesting strategies. Full article

In our research six different mint species (peppermint, spearmint (five different chemotypes), Horse mint, mojito mint, apple mint (two different chemotypes), bergamot mint) have been evaluated by referring to their chemical (essential oil (EO) content and composition) and in vitro biological (antibacterial, antioxidant effect) characteristics. The EO amount of the analyzed mint populations varied between 1.99 and 3.61 mL/100 g d.w. Altogether, 98 volatile compounds have been detected in the oils. Antibacterial effects (inhibition zones, MIC, IC₅₀ and MBC) were evaluated against Escherichia coli, Salmonella enterica, Bacillus cereus and Staphylococcus aureus. The best antibacterial effect was given by a carvacrol–thymol chemotype spearmint population (inhibition zone: 18.00–20.00 mm, MIC: 0.06 v/v%, IC₅₀: 0.01–0.03 v/v%, MBC: 0.06, >2.00 v/v%). The least effective oil in the case of Gram-negative bacteria was bergamot mint (inhibition zone: 7.67–8.67 mm, MIC: 2.00, >2.00 v/v%, IC₅₀: 0.11–0.25 v/v%, MBC: 2.00, >2.00 v/v%), while in the case of Gram-positive bacteria, oils containing dihydrocarvone as the main compound possessed the weakest antibacterial effect (inhibition zone: 9.00–10.00 mm, MIC: 1.00–2.00 v/v%, IC₅₀: 0.22–0.37 v/v%, MBC: >2.00 v/v%). Interestingly, none of the oils could kill B. cereus in the applied concentrations. Full article

Species of the family Echeneidae are renowned for their capacity to adhere to various hosts using a sucking disc. This study aimed to examine the mitochondrial genome characteristics of three fish species (Echeneis naucrates, Remora albescens, and Remora remora) within the family Echeneidae and determine their phylogenetic relationships. The findings revealed that the mitochondrial genome lengths of the three species were 16,611 bp, 16,648 bp, and 16,623 bp, respectively, containing 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), two ribosomal RNA genes (rRNAs), and a D-loop region. Most PCGs utilized ATG as the initiation codon, while only cox I used the GTG as the initiation codon. Additionally, seven genes employed incomplete termination codons (T and TA). The majority of PCGs in the three species displayed negative AT-skew and GC-skew values, with the GC-skew amplitude being greater than the AT-skew. The Ka/Ks ratios of the 13 PCGs did not exceed 1, demonstrating these species had been subjected to purification selection. Furthermore, only tRNA-Ser (GCT) lacked the D arm, while other tRNAs exhibited a typical cloverleaf secondary structure. Bayesian inference (BI) and maximum likelihood (ML) methods were utilized to construct a phylogenetic tree of the three species based on the 13 PCGs. Remora remora was identified as a distinct group, while R. osteochir and R. brachyptera were classified as sister taxa. This study contributes to the mitochondrial genome database of the family Echeneidae and provides a solid foundation for further systematic classification research in this fish group. Full article

In the last decade, the production of guinea pig meat in Andean countries has increased due to the growing number of consumers of this meat. Objective: To evaluate the effect of including different doses (0.50, 1.00, and 1.50 mL) of agro-industrial substrates (molasses distillery waste) fermented with lactic acid bacteria and yeasts on productive performance, hematological profile, relative weight changes in digestive tract organs, and changes in the intestinal microbiota in guinea pigs (Cavia porcellus). Materials: A total of 300 guinea pigs, Kuri breed, aged 20 days and weighing 330 g, were distributed into 10 groups of 30 animals each. Ctrl, Control. La, substrate fermented with Lactobacillus acidophilus (8.1 × 10⁷ CFU/mL). Kf, substrate fermented with Kluyveromyces fragilis (7.4 × 10⁶ CFU/mL). La + Kf, substrate fermented with bacteria and yeasts; the evaluated doses were 0.50, 1.00, and 1.50 mL/animal. The indicators evaluated in the study included weight gain, health, hematological profile, relative weight of digestive tract organs, and changes in the intestinal microbiota. Results: The parameters evaluated were toxicity, productive parameters, occurrence of diarrhea and mortality, and blood profile. The results showed a significant increase in the weight of the animals consuming probiotics, especially at higher doses. Additionally, an improvement in the intestinal microbiota was observed, with an increase in beneficial bacteria such as Lactobacillus and a decrease in pathogenic bacteria. Probiotics also influenced the hematological parameters and the weight of digestive tract organs, suggesting a positive effect on the overall health of the animals. Conclusions: Supplementation with probiotics proved to be a promising strategy for improving productive performance and intestinal health in guinea pigs. Supplementation with L. acidophilus and K. fragilis significantly enhances guinea pig growth and modulates the intestinal microbiota. The combination of strains and appropriate doses maximizes benefits. These results promise applications in animal production, requiring further studies to confirm their efficacy in other species and developmental stages. Full article

Food sustainability has become a major global concern in recent years. Multiple complimentary strategies to deal with this issue have been developed; one of these approaches is regenerative farming. The identification and analysis of crop type phenology are required to achieve sustainable regenerative faming. Earth Observation (EO) data have been widely applied to crop type identification using supervised Machine Learning (ML) and Deep Learning (DL) classifications, but these methods commonly rely on large amounts of ground truth data, which usually prevent historical analysis and may be impractical in very remote, very extensive or politically unstable regions. Thus, the development of a robust but intelligent unsupervised classification model is attractive for the long-term and sustainable prediction of agricultural yields. Here, we propose FastDTW-HC, a combination of Fast Dynamic Time Warping (DTW) and Hierarchical Clustering (HC), as a significantly improved method that requires no ground truth input for the classification of winter food crop varieties of barley, wheat and rapeseed, in Norfolk, UK. A series of variables is first derived from the EO products, and these include spectral indices from Sentinel-2 multispectral data and backscattered amplitude values at dual polarisations from Sentinel-1 Synthetic Aperture Radar (SAR) data. Then, the phenological patterns of winter barley, winter wheat and winter rapeseed are analysed using the FastDTW-HC applied to the time-series created for each variable, between Nov 2019 and June 2020. Future research will extend this winter food crop mapping analysis using FastDTW-HC modelling to a regional scale. Full article

(1) Background: Carbohydrate mouth rinsing (CMR) stimulates the central nervous system and improves motor control. However, no studies have examined the effects of CMR on softball batting performance. The purpose of this study was to investigate the effect of CMR on softball batting performance. (2) Methods: Fifteen trained female collegiate softball players (age: 20.6 ± 0.9 years; height: 159.5 ± 5.2 cm; body weight: 58.1 ± 6.9 kg) completed two trials in a randomization crossover trail, in which they rinsed their mouths for 20 s with 25 mL of either 6.4% maltodextrin (CMR) or a placebo (PLA). After rinsing, the Posner cueing task and grip force, counter-movement jump (CMJ) and batting tests were performed in sequence. A tanner tee was utilized to hit five sets of five balls at a time, with a minimum 3 min rest between sets. The batting test recorded the average exit velocity, maximum exit velocity and launch angle consistency. The standardized standard deviation (SD) for launch angle represents the standardized variability. (3) Results: The consistency of the launch angle of the CMR trial was significantly greater (p = 0.025; Cohen’s d = 0.69) than that of the PLA trial. There were no significant differences in the Posner cueing task, grip strength, vertical jump, or exit velocity. (4) Conclusions: The findings of this study indicate that CMR enhances the launch angle consistency of all-out-effort batting, but does not influence the exit velocity of softball hitting. Full article