bluuebunny/crossref_metadata_2025 huggingface dataset:
Methods and Means of Eddy Current Testing of Soldered Lap Joints of Electrical Machines
Authors: Gorbunov Anton , Syasko Vladimir , Solomenchuk Pavel , Umanskii Alexander
Published: February 2025

This article is devoted to the non-destructive testing of the soldering integrity of soldered lap joints of the current-carrying busbars in electrical machines’ stator windings. The design of a two-element eddy current probe with tangentially positioned coils and active shielding for measuring the soldering integrity of soldered lap joints is developed. This paper considers the method of suppressing the influence of stray parameters on the testing results and the method of correcting the measurements in the case of a deviation in the electrical conductivity of the conductive busbar material. The test results demonstrate the performance of the developed eddy current probe for determining the actual value of the soldering integrity of soldered lap joints in the range of 0–100% with a permissible relative error of no more than 5%.
Vulnerability Analysis of Column-Supported Reinforced Concrete Silo Structures
Authors: Wang Guiling , Xu Qikeng , Ding Yonggang , Li Jianye , Liu Qiang
Published: February 2025

Under earthquake action, concrete silos can undergo damage over a vast area or may even collapse. To aid seismic design, a numerical simulation of the seismic performance of column-supported reinforced concrete silos was performed, and the performance was quantitatively described. The focus of the research was on determining the damage levels of these silos by adopting an incremental dynamic analysis. The focus of the research was on determining the damage levels of these silos by adopting an incremental dynamic analysis. Four limit states were defined for the first time so as to better determine the damage states of column-supported reinforced concrete silos in the event of earthquakes and the vulnerability analysis of the silo structures was carried out. The analysis results show that volume of the stored grain directly determined its damage behavior. The silo with a greater amount of stored grain entered the plastic state earlier, and the damage effect was more evident. Under the most dangerous working conditions, i.e., the full state of the silo, the 50-year collapse exceedance probability of the silo reaching collapse (LS4) was less than 1% of the 50-year failure risk limit defined in the US seismic design code FEMA P750. This demonstrated that a column-supported reinforced concrete silo can maintain its high anti-collapse reserve capacity under the effect of rare earthquakes.
Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning Classifiers
Authors: Ashraf Jawad , Raza Ghulam Musa , Kim Byung-Seo , Wahid Abdul , Kim Hye-Young
Published: February 2025

Cyber-attacks have become a significant concern today, particularly in IoT environments where security poses a substantial challenge due to the distributed nature and heterogeneity of protocols. To efficiently detect threats in IoT networks, it is crucial to develop a robust intrusion-detection system (IDS) capable of identifying various modern and traditional attacks with high accuracy. Most existing machine-learning-based intrusion-detection systems for IoT have been trained using outdated datasets that do not accurately reflect IoT scenarios. Additionally, current research does not adequately address which machine-learning classifiers are most suitable for developing an efficient IDS in IoT environments. In our research, we have developed and trained a real-time intrusion-detection system for IoT networks that can detect multiple modern and traditional threats with high accuracy. We created seven instances of real-time IDS using state-of-the-art machine-learning algorithms, including Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Naïve Bayes, and Artificial Neural Networks. Using the Pearson Correlation Coefficient, we extracted the most relevant features from the BoT–IoT dataset. After rigorous preprocessing, we used these data to train our algorithms. Our trained model, INIDS, is not only up to date and real-time but also capable of accurately identifying multiple categories of attacks specifically related to IoT networks. To achieve maximum accuracy, instead of selecting only one classifier, we evaluated seven advanced machine-learning algorithms and provided a comprehensive comparison of their performance and efficiency in the context of IoT networks. This analysis can guide future researchers in choosing the right machine-learning algorithms for developing IDS. We found that Random Forest is the most robust classifier for IoT-based network intrusion-detection systems, achieving an accuracy of 99.2%. The second-best performer is Naïve Bayes, with an accuracy of 98.8%.
A Method for the Calibration of a LiDAR and Fisheye Camera System
Authors: Martínez Álvaro , Santo Antonio , Ballesta Monica , Gil Arturo , Payá Luis
Published: February 2025

LiDAR and camera systems are frequently used together to gain a more complete understanding of the environment in different fields, such as mobile robotics, autonomous driving, or intelligent surveillance. Accurately calibrating the extrinsic parameters is crucial for the accurate fusion of the data captured by both systems, which is equivalent to finding the transformation between the reference systems of both sensors. Traditional calibration methods for LiDAR and camera systems are developed for pinhole cameras and are not directly applicable to fisheye cameras. This work proposes a target-based calibration method for LiDAR and fisheye camera systems that avoids the need to transform images to a pinhole camera model, reducing the computation time. Instead, the method uses the spherical projection of the image, obtained with the intrinsic calibration parameters and the corresponding point cloud for LiDAR–fisheye calibration. Thus, unlike a pinhole-camera-based system, a wider field of view is provided, adding more information, which will lead to a better understanding of the environment itself, as well as enabling using fewer image sensors to cover a wider area.
AERQ—A Web-Based Decision Support Tool for Air Quality Assessment
Authors: Cau Pierluigi , Muroni Davide , Satta Guido , Milesi Carlo , Casari Carlino
Published: February 2025

Technological advancements in low-cost devices, the Internet of Things (IoT), numerical models, big data infrastructures, and high-performance computing are revolutionizing urban management, particularly air quality governance. This study examines the application of smart technologies to address urban air quality challenges using integrated sensor networks and predictive models. The decision support system (DSS), AERQ, incorporates the AERMOD modeling tool, achieving a 10 m spatial and 1 h temporal resolution for air quality predictions. It processes hourly climate and traffic data via a high-performance computing (HPC) platform, significantly enhancing prediction accuracy and decision-making efficiency. AERMOD has been calibrated and validated for NO2, showing a good performance against observations. Tested in Cagliari, Sardinia, Italy, AERQ demonstrated a 99% reduction in computation time compared to modern desktop systems, delivering detailed 5-year scenarios in under 15 h. AERQ equips stakeholders with air quality indices, scenario analyses, and mitigation strategies, combining advanced visualization tools with actionable insights. By enabling data-driven decisions, the system empowers policymakers, urban planners, and citizens to improve air quality and public health. This study underscores the transformative potential of integrating advanced technologies into urban management, providing a scalable model for efficient, informed, and responsive air quality governance.
Bioelectrical Impedance Versus Air-Displacement Plethysmography for Body Fat Measurements in Subjects with Abdominal Obesity: A Comparative Study
Authors: Cretescu Iuliana , Horhat Raluca , Mocanu Valeria , Munteanu Oana
Published: February 2025

Obesity is a disorder with an increasing prevalence, associated with cardiovascular and metabolic pathologies. The amount and the distribution pattern of adipose tissue must be considered when managing this disease. Abdominal obesity carries a higher risk of developing obesity-related comorbidities. Body composition methods allow an objective diagnosis and follow-up in obese patients. Although bioelectrical impedance (BIA) is a practical, affordable, and widespread technique to assess body fat percentage (%BF), its accuracy has often been questioned. Therefore, the present study aimed to determine BIA’s accuracy in subjects with abdominal obesity compared to air-displacement plethysmography (AP) as the reference method. Measurements with both body composition techniques were performed on 80 subjects (41 men and 39 women) with a large range of body mass indexes (BMIs). Abdominal obesity, diagnosed as a waist-to-height ratio (WHtR) above 0.5, was present in 28 of them. Agreement between methods was analyzed using t-tests, correlation, and Bland–Altman plots. Compared to AP, BIA underestimated %BF by 3.07 ± 5.81% (CCC = 0.82) in the entire study group. The agreement was comparable in subjects with and without central obesity (2.97 ± 6.21% and 3.26 ± 5.1%, respectively). The changes in body shape geometry due to different adipose tissue distribution patterns only marginally affected BIA’s determinations.
Developing an Urban Landscape Fumigation Service Robot: A Machine-Learned, Gen-AI-Based Design Trade Study
Authors: Chittoor Prithvi Krishna , Dandumahanti Bhanu Priya , Veerajagadheswar Prabakaran , Samarakoon S. M. Bhagya P. , Muthugala M. A. Viraj J. , Elara Mohan Rajesh
Published: February 2025

Generative AI (Gen-AI) revolutionizes design by leveraging machine learning to generate innovative solutions. It analyzes data to identify patterns, creates tailored designs, enhances creativity, and allows designers to explore complex possibilities for diverse industries. This study uses a Gen-AI design generation process to develop an urban landscape fumigation service robot. This study proposes a machine-learned multimodal and feedback-based variational autoencoder (MMF-VAE) model that incorporates a readily available spraying robot dataset and includes design considerations from various research efforts to ensure real-time deployability. The objective is to demonstrate the effectiveness of data-driven and feedback-based approaches in generating design specifications for a fumigation robot with the targeted requirements of autonomous navigation, precision spraying, and an extended runtime. The design generation process comprises three stages: (1) parameter fixation, emphasizing functionality-based and aesthetic-based specifications; (2) design specification generation using the proposed MMF-VAE model with and without a spraying robot dataset; and (3) robot development based on the generated specifications. A comparative analysis evaluated the impact of the dataset-driven design generation. The design generated with the dataset proved more feasible and optimized for real-world deployment with the integration of multimodal inputs and iterative feedback refinement. A real-time prototype was then constructed using the model’s parametric constraints and tested in actual fumigation scenarios to validate operational viability. This study highlights the transformative potential of Gen-AI in robotic design workflows.
Disease Detection Algorithm for Tea Health Protection Based on Improved Real-Time Detection Transformer
Authors: Lin Zhijie , Zhu Zilong , Guo Lingling , Chen Jingjing , Wu Jiyi
Published: February 2025

Traditional disease detection methods typically depend on visual assessments conducted by human experts, which are time-consuming and subjective. Thus, there is an urgent demand for automated and efficient approaches to accurately detect and classify tea diseases. This study presents an enhanced Real-Time Detection Transformer (RT-DETR), tailored for the accurate and efficient identification of tea diseases in natural environments. The proposed method integrates three novel components: Faster-LTNet, CG Attention Module, and RMT Spatial Prior Block, to significantly improve computational efficiency, feature representation, and detection capabilities. Faster-LTNet employs partial convolution and hierarchical design to optimize computational resources, while the CG Attention Module enhances multi-head self-attention by introducing grouped feature inputs and cascading operations to reduce redundancy and increase attention diversity. The RMT Spatial Prior Block integrates a Manhattan distance-based spatial decay matrix and linear decomposition strategy to improve global and local context modeling, reducing attention complexity. The enhanced RT-DETR model achieves a detection precision of 89.20% and a processing speed of 346.40 FPS. While the precision improves, the FPS value also increases by 109, which is superior to the traditional model in terms of precision and real-time processing. Additionally, compared to the baseline model, the FLOPs are reduced by 50%, and the overall model size and parameter size are decreased by approximately 50%. These findings indicate that the proposed algorithm is well-suited for efficient, real-time, and lightweight agricultural disease detection.
Software Fault Localization Based on SALSA Algorithm
Authors: Fan Xin , Shen Zuxiong , Fu Zhenlei , Ge Yun
Published: February 2025

In software development, debugging is the most tedious and time-consuming phase. Therefore, various automated fault localization techniques have been proposed to assist debugging. Among existing fault localization techniques, Spectrum-Based Fault Localization (SBFL) is one of the most extensively researched methods. Traditional SBFL techniques rely solely on the coverage of program execution for fault localization, which means they neglect the interactions between program entities and fault propagation paths during the execution of the program, resulting in a tie problem that reduces the accuracy of fault localization. To solve the above problem, this paper proposes SA-SBFL, a fault localization method based on the SALSA (Random Method for Link Structure Analysis) algorithm. First, a link graph of program entities is constructed, which includes interactions between program entities and fault propagation paths. Then, the suspicion values obtained from traditional SBFL methods are used as the initial weights of the link graph. Finally, the random walk model is employed to simulate the propagation of faults among program entities, analyze the importance of program entities in the fault propagation process, and obtain a ranking list of suspicious program entities. The experiments in this paper demonstrate that the SA-SBFL method significantly outperforms general SBFL methods. For instance, in the Defects4J dataset, the SA-SBFL technique outperforms traditional SBFL in terms of fault localization accuracy, with a 47% improvement in the Top-1 metric and a 10% increase in the Top-5 metric, and it also showed an average improvement of 19% in the EXAM metric.
Preparation and Characterization of Poly(butylene succinate) Films Modified with Sea Buckthorn (Hippophae rhamnoides L.) Extract for Packaging Applications
Authors: Macieja Szymon , Bartkowiak Artur , Mizielińska Małgorzata
Published: February 2025

Food packaging has to preserve food products, not only providing protection against mechanical factors, but also providing antioxidant and antimicrobial protection. This article describes the effects of PBS film modification with two sea buckthorn extracts (chloroform extract and supercritical CO2 extract) at two different concentrations (1 or 5 g per 100 g of polymer). The films were tested to determine the effect of the active additive on optical properties, mechanical properties, moisture sorption, antioxidant and antimicrobial properties (against Escherichia coli, Staphylococcus aureus, and Candida albicans). The extracts improved free radical scavenging significantly (up to 41.13 ± 1.31% for PBS-CO2 0.05) and reduced the number of microorganisms studied (more than a 6000-fold reduction for E. coli, a 1400-fold reduction in S. aureus and a 1200-fold reduction in C. albicans). The ability to block UV radiation was dependent on the concentration of extracts in the polymer matrix. No significant changes were noticed for mechanical properties or FTIR spectra. The films obtained appear to be potential packaging materials for food products with special protective properties.
The Lightweight Deep Learning Model in Sunflower Disease Identification: A Comparative Study
Authors: Zhang Liqian , Wu Xiao
Published: February 2025

(1) With the development of artificial intelligence, people expect to use modern information technology to solve the critical problems encountered in agriculture. How to identify sunflower diseases as early and quickly as possible and take corresponding measures has become a key issue for increasing crop production and farmers’ income. Sunflowers, as an important oil crop, are vulnerable to infections by various diseases, such as downy mildew, leaf scar, gray mold, etc. (2) In order to select a better lightweight model that can be embedded into mobile devices or embedded devices for sunflower disease detection, we compared five lightweight deep learning models in this study, including SqueezeNet, ShuffleNetV2, MnasNet-A1, MobileNetV3-Small, and EfficientNetV2-Small. The dataset used to train and test the models included 1892 images. These images were divided into four categories, namely, downy mildew, gray mold, leaf scar, and fresh leaves. (3) By evaluating the accuracy, precision, recall, and F1 score of each model, we found that EfficeintNetV2-Small exhibited the highest performance with an accuracy of 90.19%. Whereas the other models, SqueezeNet, ShuffleNetV2, MnasNet-A1, and MobileNetV3-Small, achieved accuracies of 84.08%, 79.31%, 88.59%, and 84.08%, respectively. To address the problem of poor generalization ability of models caused by small datasets, we adopted the transfer learning technique. After doing that, the recognition accuracies of the five models, SqueezeNet, ShuffleNetV2, MnasNet-A1, MobileNetV3-Small, and EfficeintNetV2-Small, reached 96.02%, 95.23%, 94.96%, 96.92%, and 99.20%, respectively. The accuracies of these five models were improved by 14.2%, 20%, 7.2%, 15.2%, and 10%. Based on the comparative results, we found EfficeintNetV2-Small was an optimal choice for sunflower disease identification due to its high detection accuracy.
Robust and Unbiased Estimation of Robot Pose and Pipe Diameter for Natural Gas Pipeline Inspection Using 3D Time-of-Flight (ToF) Sensors
Authors: Nguyen Hoa-Hung , Park Jae-Hyun , Kim Jae-Jun , Yoo Kwanghyun , Kim Dong-Kyu , Jeong Han-You
Published: February 2025

The estimation of robot pose and pipe diameter is an essential task for reliable in-line inspection (ILI) operations and the accurate assessment of pipeline attributes. This paper addresses the problem of robot pose and pipe diameter estimation for natural gas pipelines based on 3D time-of-flight (ToF) sensors. To tackle this challenge, we model the problem as a non-linear least-squares optimization that fits 3D ToF sensor measurements in its local coordinates to an elliptic cylindrical model of the pipe inner surface. We identify and prove that the canonical ellipse-based estimation method (C-EPD), which uses a canonical residual function, suffers from bias in diameter estimation due to its asymmetry to depth errors. To overcome this limitation, we propose the robust and unbiased estimation of pose and diameter (RU-EPD) approach, which employs a novel error-based residual function. The proposed function is symmetric to depth errors, effectively reducing estimation bias. Extensive numerical simulations and prototype pipeline experiments demonstrate that RU-EPD outperforms C-EPD, achieving an at least six times lower estimation bias and a 2.5 times smaller estimation error range in pipe diameter and about a 2 times smaller estimation error range in pose estimation.
MCP: A Named Entity Recognition Method for Shearer Maintenance Based on Multi-Level Clue-Guided Prompt Learning
Authors: Cao Xiangang , Shi Luyang , Wang Xulong , Duan Yong , Yang Xin , Zhang Xinyuan
Published: February 2025

The coal mining industry has accumulated a vast amount of knowledge on shearer accident analysis and handling during its development. Accurately identifying and extracting entity information related to shearer maintenance is crucial for advancing downstream tasks in intelligent shearer operations and maintenance. Currently, named entity recognition in the field of shearer maintenance primarily relies on fine-tuning-based methods; however, a gap exists between pretraining and downstream tasks. In this paper, we introduce prompt learning and large language models (LLMs), proposing a named entity recognition method for shearer maintenance based on multi-level clue-guided prompt learning (MCP). This method consists of three key components: (1) the prompt learning layer, which encapsulates the information to be identified and forms multi-level sub-clues into structured prompts based on a predefined format; (2) the LLM layer, which employs a decoder-only architecture-based large language model to deeply process the connection between the structured prompts and the information to be identified through multiple stacked decoder layers; and (3) the answer layer, which maps the output of the LLM layer to a structured label space via a parser to obtain the recognition results of structured named entities in the shearer maintenance domain. By designing multi-level sub-clues, MCP enables the model to extract and learn trigger words related to entity recognition from the prompts, acquiring context-aware prompt tokens. This allows the model to make accurate predictions, bridging the gap between fine-tuning and pretraining while eliminating the reliance on labeled data for fine-tuning. Validation was conducted on a self-constructed knowledge corpus in the shearer maintenance domain. Experimental results demonstrate that the proposed method outperforms mainstream baseline models in the field of shearer maintenance.
Design Study of Hole Types for Improved Cooling of Experimental Heatsinks Manufactured by SLM Technology Using an AlSi10Mg Alloy
Authors: Madaj Rudolf , Kohar Robert , Brumercik Frantisek , Veres Matus
Published: February 2025

The purpose of this article is to inform the reader about the results of an experimental investigation into the appropriate manifold geometry for an air-cooled inverter, which is manufactured from an AlSi10Mg powder material using SLM technology. The best approach is to optimize the part geometry for SLM technology so that the placement of support structures required for model fabrication is eliminated as much as possible. A suitable solution was selected based on the design of the most appropriate cross-sectional shape of the openings with the smallest dimensional accuracy deviation and shape deformation. In the experiment, three test specimens were designed; each of them contained eight holes of different shapes, particularly square, rhombic, and circular, with a given range of sizes. The results of the experimental study can help designers select the optimal design of vents and cavities for the chosen AM technology, e.g., for conformal cooling systems.
SALM: A Unified Model for 2D and 3D Region of Interest Segmentation in Lung CT Scans Using Vision Transformers
Authors: Gayap Hadrien T. , Akhloufi Moulay A.
Published: February 2025

Accurate segmentation of Regions of Interest (ROI) in lung Computed Tomography (CT) is crucial for early lung cancer diagnosis and treatment planning. However, the variability in size, shape, and location of lung lesions, along with the complexity of 3D spatial relationships, poses significant challenges. In this work, we propose SALM (Segment Anything in Lung Model), a deep learning model for 2D and 3D ROI segmentation. SALM leverages Vision Transformers, proposing an adaptation of positional encoding functions to effectively capture spatial relationships in both 2D slices and 3D volumes using a single, unified model. Evaluation on the LUNA16 dataset demonstrated strong performance in both modalities. In 2D segmentation, SALM achieved a Dice score of 93% on 124,662 slices. For 3D segmentation using 174 3D images from the same dataset, SALM attained a Dice score of 81.88%. We also tested SALM on an external database (PleThora) on a subset of 255 pulmonary CT from diseased patients, where it achieved a Dice score of 78.82%. These results highlight SALM’s ability to accurately segment lung ROI in both 2D and 3D, demonstrating its potential to improve the accuracy and efficiency of computer-aided diagnosis for lung cancer.
Numerical Simulation of Perkins Instability in the Midlatitude F-Region Ionosphere: The Influence of Background Ionospheric Multi-Factors
Authors: Liu Yi , Lan Ting , Zhou Yufeng , Zhu Yunzhou , Fan Zhiqiang , Wu Yewen , Zhang Yuqiang , Wang Xiang
Published: February 2025

A numerical simulation of Perkins instability in the midlatitude F-region ionosphere is developed in this study. The growth of nighttime plasma density perturbation excited by Perkins instability was successfully reproduced. The simulated results show that the ionospheric perturbation structure elongated from northwest (NW) to southeast (SE) was generated from initial random seeding by applying a very large southeastward neutral wind (200 m/s). The domain wave vector direction agreed with the linear Perkins theory. Our simulated results were consistent with the previous observations and simulations. To investigate the influence of background ionospheric multi-factors on the generation of nighttime medium-scale traveling ionospheric disturbance (MSTID), we simulated the evolution process of ionospheric perturbations under initial background ionospheric conditions. The simulated results indicate the importance of neutral scale height on the development of nighttime MSTID and suggest that a smaller neutral scale height would amplify the amplitude of ionospheric perturbations. The influences of gravity wave (GW) activity and polarized electric field seeding from plasma instability in the E region are also discussed in this study. We conclude that the additional seeding processes play a major role in the accelerated Perkins instability and amplify ionospheric perturbations. The electrodynamic coupling process has a greatly significant effect on the growth rate of Perkins instability compared to GW activity.
Special Issue Editorial: Urban and Regional Nitrogen Cycle and Risk Management
Authors: Xian Chaofan , Shen Yu-Sheng , Gong Cheng
Published: February 2025

Disturbance of urban and regional nitrogen cycles due to urbanization have resulted in the greenhouse effect, acid rain, eutrophication, and reductions in biodiversity [...]
Effects of Cadmium Pollution on Human Health: A Narrative Review
Authors: Yang Yunxi , Hassan Mohammad Farooque , Ali Waseem , Zou Hui , Liu Zongping , Ma Yonggang
Published: February 2025

Cadmium (Cd) is a pervasive environmental and industrial toxin that poses significant health risks. It readily moves through soil–plant systems, leading to global contamination and human exposure through diet, smoking, and pollution. The main purpose of this review is to explore the effect of Cd on physiological processes of different bodies’ organs, including the bones, kidneys, and liver, as well as the immune, cerebrovascular, cardiovascular, and reproductive systems. Accumulation of Cd in the body can result in poisoning with severe impacts on bone and kidney health, as well as reduced bone mineral density due to renal damage. Research has linked Cd to lung cancer and pulmonary toxicity, and elevated urinary biomarkers suggest compromised renal function. Cd also affects the cardiovascular, cerebrovascular, and immune systems; the liver; and reproductive systems, contributing to various diseases by disrupting blood pressure and calcium regulation, causing oxidative stress and DNA damage, and impairing cell functions. Ongoing research is essential to fully understand Cd-induced toxicological effects and to develop effective interventions to prevent exposure and mitigate health risks.
Application of Fuzzy Subordinations and Superordinations for an Analytic Function Connected with q-Difference Operator
Authors: Ali Ekram E. , El-Ashwah Rabha M. , Albalahi Abeer M.
Published: February 2025

This paper extends the idea of subordination from the theory of fuzzy sets to the geometry theory of analytic functions with a single complex variable. The purpose of this work is to define fuzzy subordination and illustrate its main characteristics. New fuzzy differential subordinations will be introduced with the help of this effort. We define a linear operator Iq,ρs(ν,ς) using the concept of the q-calculus operators. New fuzzy differential subordinations are created by employing the previously described operator, functions from the new class, and well-known lemmas. Specific corollaries derived from the operator proved the many examples created for the fuzzy differential subordinations, as well as the theorems, and demonstrate how the new theoretical conclusions apply to the fuzzy differential superordinations provided in this research.
Formulation Design and Functional Characterization of a Novel Fermented Beverage with Antioxidant, Anti-Inflammatory and Antibacterial Properties
Authors: Abdi Ameni , Gatri Emna , Filannino Pasquale , M’Hir Sana , Ayed Lamia
Published: February 2025

The aim of this study was to use different concentrations of lemon juice and honey to improve the formulation of a green tea water kefir (GTWK) beverage by applying a central composite design (CCD). Honey’s concentration was 10–50% and lemon juice concentration was 1–5%, these were used as the independent factors, whereas pH, bacteria and yeasts’ count, total phenolic content, % DPPH. scavenging activity, and overall acceptability were used as the dependent factors. The optimal concentration of honey and lemon juice for highest microbial count, antioxidant activities and overall acceptability was 42.85% and 1.771%, respectively. The analysis of variance revealed that the model was well-fitting, with R2 ranging from 87.27% to 96.95%, adj-R2 ranging from 78.17% to 94.26% and a non-significant lack of fits. The optimized fermented beverage showed antibacterial potential against Echerichia coli ATCC11229, Staphylococcus aureus ATCC6538 and Salmonella typhimirium ATCC14028 strains. The anti-inflammatory activity was evaluated on CaCo-2 and RAW 264.7 cells. According to ELISA assay, a significant decrease (p < 0.05) in TNF-α concentration was found after inflammatory stimulation, from 1205.41 ± 55.87 pg/mL to 478.17 ± 69.12 pg/mL.
Increased Interleukin-6 Levels in Responders with Treatment-Resistant Depression After Bright Light Therapy
Authors: Kosanovic Rajacic Biljana , Sagud Marina , Begic Drazen , Nikolac Perkovic Matea , Kozmar Ana , Rogic Dunja , Mihaljevic Peles Alma , Bozicevic Marija , Pivac Nela
Published: February 2025

Treatment-resistant depression (TRD) remains a challenge despite the growing number of interventions. Peripheral interleukin-6 (IL-6) levels have repeatedly been associated with both the presence and response to different treatments in TRD. There is currently no information available on the effects of bright light therapy (BLT) on serum IL-6 levels. This study assessed the effects of BLT on serum IL-6 levels in TRD patients. Serum IL-6 was determined at two points in TRD patients—at baseline and after 4 weeks of BLT—and at a single point in the healthy controls. Depression severity was measured by the Hamilton Rating Scale for Depression (HAMD)-17 and the Montgomery–Åsberg Depression Rating Scale (MADRS). The study included 104 females, 54 diagnosed with TRD (median age 52.5) and 50 healthy controls (median age 44.5). At baseline, patients had higher IL-6 levels than the controls. BLT treatment reduced HAMD-17 and MADRS scores. Serum IL-6 levels were not significantly affected by the 4 weeks of BLT. However, when patients were divided according to treatment response, IL-6 levels were increased in responders to BLT. The neuroinflammatory mechanism may be involved in the etiopathogenesis and the treatment of TRD, while changes in serum IL-6 levels may be potential indicators of response to treatment.
Pulmozyme Ameliorates LPS-Induced Lung Fibrosis but Provokes Residual Inflammation by Modulating Cell-Free DNA Composition and Controlling Neutrophil Phenotype
Authors: Alekseeva Ludmila A. , Sen’kova Aleksandra V. , Sounbuli Khetam , Savin Innokenty A. , Zenkova Marina A. , Mironova Nadezhda L.
Published: February 2025

Pulmonary fibrosis, a chronic progressive lung disorder, can be the result of previous acute inflammation-associated lung injury and involves a wide variety of inflammatory cells, causing the deposition of extracellular matrix (ECM) components in the lungs. Such lung injury is often associated with excessive neutrophil function and the formation of DNA networks in the lungs, which are also some of the most important factors for fibrosis development. Acute lung injury with subsequent fibrosis was initiated in C57Bl/6 mice by a single intranasal (i.n.) administration of LPS. Starting from day 14, human recombinant DNase I in the form of Pulmozyme for topical administration was instilled i.n. twice a week at a dose of 50 U/mouse. Cell-free DNA (cfDNA), DNase activity, and cell content were analyzed in blood serum and bronchoalveolar lavage fluid (BALF). Inflammatory and fibrotic changes in lung tissue were evaluated by histological analysis. The gene expression profile in spleen-derived neutrophils was analyzed by RT-qPCR. We demonstrated that Pulmozyme significantly reduced connective tissue expansion in the lungs. However, despite the reliable antifibrotic effect, complete resolution of inflammation in the respiratory system of mice treated with Pulmozyme was not achieved, possibly due to enhanced granulocyte recruitment and changes in the nuclear/mitochondrial cfDNA balance in the BALF. Moreover, Pulmozyme introduction caused the enrichment of the spleen-derived neutrophil population by those with an unusual phenotype, combining pro-inflammatory and anti-inflammatory features, which can also maintain lung inflammation. Pulmozyme can be considered a promising drug for lung fibrosis management; however, the therapy may be accompanied by residual inflammation.
SNX19 Interacts with Caveolin-1 and Flotillin-1 to Regulate D1R Endocytosis and Signaling
Authors: Amatya Bibhas , Polzin Jacob Q. M. , Villar Van A. M. , Yang Jiang , Konkalmatt Prasad , Wang Xiaoyan , Cadme Raisha C. , Xu Peng , Gildea John J. , Cuevas Santiago , Armando Ines , Felder Robin A. , Jose Pedro A. , Lee Hewang
Published: February 2025

Background: Sorting nexin 19 (SNX19) is important in the localization and trafficking of the dopamine D1 receptor (D1R) to lipid raft microdomains. However, the interaction between SNX19 and the lipid raft components caveolin-1 or flotillin-1 and, in particular, their roles in the cellular endocytosis and cell membrane trafficking of the D1R have not been determined. Methods: Caveolin-1 and flotillin-1 motifs were analyzed by in silico analysis; colocalization was observed by confocal immunofluorescence microscopy; protein-protein interaction was determined by co-immunoprecipitation. Results: In silico analysis revealed the presence of putative caveolin-1 and flotillin-1 binding motifs within SNX19. In mouse and human renal proximal tubule cells (RPTCs), SNX19 was localized mainly in lipid rafts. In mouse RPTCs transfected with wild-type (WT) Snx19, fenoldopam (FEN), a D1-like receptor agonist, increased the colocalization of SNX19 with caveolin-1 and flotillin-1. FEN also increased the co-immunoprecipitation of SNX19 with caveolin-1 and flotillin-1, effects that were prevented by SCH39166, a D1-like receptor antagonist. The FEN-mediated increase in the residence of SNX19 in lipid rafts and the colocalization of the D1R with caveolin-1 and flotilin-1 were attenuated by the deletion of a caveolin-1 (YHTVNRRYREF) (ΔCav1) or a flotillin-1 (EEGPGTETETGLPVS) (ΔFlot1) binding motif. The FEN-mediated increase in intracellular cAMP production was also impaired by the deletion of either the flotillin-1 or caveolin-1 binding motif. Nocodazole, a microtubule depolymerization inhibitor, interfered with the FEN-mediated increase in the colocalization between SNX19 and D1R. Conclusion: SNX19 contains caveolin-1 and flotillin-1 binding motifs, which play an important role in D1R endocytosis and signaling.
EGFR-Mutant Lung Adenocarcinoma Cell-Derived Exosomal miR-651-5p Induces CD8+ T Cell Apoptosis via Downregulating BCL2 Expression
Authors: Zhao Chao , Cheng Lei , Li Aiwu , Wang Haowei , Li Xuefei , Xu Jun
Published: February 2025

Background: The efficacy of programmed cell death 1 (PD-1) or ligand 1 (PD-L1) inhibitors in epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC) patients is not satisfactory. Studies have indicated that the ratio of CD8+ tumor infiltration lymphocytes (TILs) was associated with immunotherapy efficacy; however, it was significantly lower in EGFR-mutant than wild type patients. The underlying mechanisms need to be studied. Methods: Database analysis, clinical specimens, small RNA sequencing, and single-cell sequencing were used to analyze miRNA expression and immune cell infiltration. Cell co-culture and flow cytometry were conducted to detect immune cell apoptosis. The mouse model was performed to analyze the influence of miR-651-5p antagomirs on the tumor microenvironment. Results: The miR-651-5p was found to be highly expressed in EGFR-mutant lung adenocarcinoma cell-derived exosomes, which could promote CD8+ T cell apoptosis, while the miR-651-5p inhibitor decreased the ratio of PC9-secreted exosomes and induced apoptosis. Mechanistically, the EGFR signaling pathway promoted the expression of miR-651-5p by activating the transcription factor Fos proto-oncogene (FOS) in EGFR-mutant lung adenocarcinoma cell lines. B-cell lymphoma 2 (BCL2) was the target of miR-651-5p, and miR-651-5p could promote T cell apoptosis by inhibiting BCL2 expression. In addition, the miR-651-5p antagomir increased T cell infiltration and enhanced the efficacy of the PD-1 inhibitor treating the EGFR-mutant lung adenocarcinoma humanized mouse model. Conclusions: EGFR-mutant lung adenocarcinoma promotes T cell apoptosis through exosomal miR-651-5p. miR-651-5p antagonists increase immune cell infiltration and enhance the anti-tumor effect of PD-1 inhibitor, suggesting a new combination therapy to improve the efficacy of immunotherapy in EGFR-mutant NSCLC patients.
Characteristics of Patients with Newly Diagnosed High-Grade Advanced Ovarian, Fallopian Tube, and Primary Peritoneal Cancer in 2018–2023 and the Impact of Molecular Diagnostics on Chemotherapy in Clinical Practice
Authors: Millert-Kalińska Sonja , Stawicka-Niełacna Małgorzata , Tomczak Lidia , Pruski Dominik , Przybylski Marcin , Jaszczyńska-Nowinka Karolina , Poprawski Grzegorz , Mądry Radosław
Published: February 2025

Background: High-grade advanced ovarian, fallopian tube, and primary peritoneal (HGAOC) cancers require both surgical and systemic treatment. The introduction of polyADP-ribose polymerase inhibitors (PARPis) has significantly improved outcomes. This study presents an analysis of HGAOC patients treated at a single center, following updated guidelines. Methods: We observed 437 women newly diagnosed with HGAOC at the Department of Gynecological Oncology between January 2018 and December 2023. Results: Since November 2022, first-line treatment has included bevacizumab and PARPi, regardless of residual disease post-cytoreductive surgery. In both BRCA1/2-mutated and non-mutated groups, PARPi-based regimens increased significantly after May 2021 (p < 0.01). Recurrence number emerged as a strong prognostic factor for survival (p < 0.001), with each recurrence raising mortality risk by 80%. Median survival was 21 months for paclitaxel + platinum derivatives (PC), 27 months for PC + bevacizumab (BEV), and 30 months for PC + BEV + PARPi. Conclusions: The rapid adoption of modern therapies at our center has aligned treatment strategies with HRD status and global standards. However, variations in financial regulations and drug accessibility persist across countries. Despite these challenges, physicians should prioritize the most effective therapies available.
Analyzing and Assisting Finger Motions for Spoon Scooping
Authors: Tanizaki Yuto , Tortós-Vinocour Pablo E. , Matsunaga Fuko , Kamijo Naoki , Yoshida Koki , Kokubu Shota , Gomez-Tames Jose , Yu Wenwei
Published: February 2025

Assisting patients with weakened hand and wrist strength during meals is essential. While various feeding devices have been developed, many do not utilize patients’ residual finger functions, leading to an increase in the risk of disuse syndrome and loss of joy in life. Recently, assist-as-needed support for spoon grasping by soft hand rehabilitation devices has been studied. Moreover, in our previous study, we investigated finger motions for the required scooping angle and verified them with a dummy hand driven by soft actuators. However, eating with a spoon requires not only spoon grasping and rotating but also plunging the spoon into food and lifting it afterward. The goal of this study is to achieve self-feeding with spoons using soft actuators for individuals with partial finger disabilities. To address this, we measured scooping movements using inertial measurement units, identified feasible finger motions for spoon plunging and lifting, and verified our findings through experiments with a dummy hand driven by soft actuators. As a result, we found a way to achieve the two motions by regulating the moment applied to the spoon. These results highlight the potential of soft actuators for assisting scooping movements. This study marks an important step toward feeding assistance that leverages patients’ residual finger functions.
Spatio-Temporal Characterization of Nest-Niche in Passerine Birds Co-Occurring in Olive Agro-Ecosystems: Partitioning of Nesting Sites, Breeding Chronology, and Productivity
Authors: Squalli Wafae , Douini Ikram , Mansouri Ismail , Achiban Hamid , Tahiri Hassane , Fadil Fatima , Wink Michael , Dakki Mohamed
Published: February 2025

Many birds regard partitioning feeding and breeding resources as a strategy to escape competition and co-occurring breeding species. During the breeding season, breeding requirements are essential, yet the patterns and drivers of segregation between and within populations are poorly understood. We monitored the habitat use and chronology of the four co-occurring passerines Eurasian Blackbird, European Greenfinch, Common Chaffinch, and European Serin to examine levels of habitat partitioning and success rates during the breeding season. We recorded nesting sites, nest supports, nest placement, breeding chronology, and productivity in four olive orchards in Fez, central Morocco, from March to August 2018–2019. Our results showed substantial spatial segregation in nesting sites, nest supports, and tree vertical placements. Breeding birds showed significant temporal segregation during the nesting, laying, and hatching phases. These findings suggest distinct spatial and temporal differences in habitat selection among co-occurring passerines, which are suggested to increase avoidance of intra and interspecific competition for resources. Breeding success was very high in European Serin and Eurasian Blackbird. Failure factors were recorded: eggs of European Serin were threatened principally by desertion, abortion, and destruction, and eggs of European Greenfinch were impacted by poaching by children. Our findings provide the first data on the spatial and temporal partitioning of breeding resources among passerines species in Northwest African and probably Mediterranean agroecosystems.
The Endothelial Cell-Related Genes EIF1 and HSPA1B Contribute to the Pathogenesis of Alzheimer’s Disease by Modulating Peripheral Immunoinflammatory Responses
Authors: Gu Yucheng , Chen Nihong , Qi Jingwen , Zhu Lin , Chen Xiangliang , Wang Feng , Zhang Yingdong , Jiang Teng
Published: February 2025

Background: Emerging evidence suggests that peripheral immunoinflammatory responses contribute to Alzheimer’s disease (AD) pathogenesis, and endothelial cells (ECs) are involved in these responses. Nevertheless, the potential molecular mechanisms and signaling pathways by which ECs modulate peripheral immunoinflammatory responses and thus contribute to AD pathogenesis are not fully understood. Methods: The single-cell RNA sequencing dataset GSE157827 was analyzed, and AD key genes were screened using LASSO regression and random forest algorithms. Functional enrichment analyses of these AD key genes were conducted using gene set enrichment analysis (GSEA) and gene set variation analysis. Immune cell infiltration analyses for AD key genes were performed using single-sample GSEA, and their correlations with immunoinflammatory factors were assessed using the TISIDB database. Peripheral blood RNA sequencing data from our cohort were utilized to validate the expression patterns of EC-related AD key genes in peripheral blood and to investigate their association with cognition. Results: ECs are the most significant contributors to AD among all brain cell subpopulations. For the first time, the EC-related genes EIF1 and HSPA1B were identified as key genes associated with AD progression. These two EC-related key genes may participate in AD pathogenesis by modulating peripheral immunoinflammatory responses. The levels of EIF1 and HSPA1B were significantly altered in the peripheral blood during AD progression, and EIF1 levels correlated with cognitive functions in AD clinical continuum patients. Conclusions: These findings underscore the critical roles of the EC-related genes EIF1 and HSPA1B in AD pathogenesis and their potential as biomarkers for this disease.
Simultaneous Multi-Treatment Strategy for Brain Tumor Reduction via Nonlinear Control
Authors: Arsalan Muhammad , Yu Xiaojun , Sadiq Muhammad Tariq , Almogren Ahmad
Published: February 2025

Background: Recently proposed brain-tumor treatment strategies prioritize fast reduction of tumor cell population while often neglecting the radiation or chemotherapeutic drug dosage requirements to achieve it. Moreover, these techniques provide chemotherapy based treatment strategies, while ignoring the toxic side effects of the drugs employed by it. Methods: This study updates the recently proposed brain-tumor system dynamics by incorporating radiotherapy along with chemotherapy to simultaneously initiate both therapies for a more comprehensive and effective response against tumor proliferation. Afterwards, based on the upgraded system dynamics, this study proposes a novel multi-input sigmoid-based smooth synergetic nonlinear controller with the aim to reduce the dosage requirements of both therapies while keeping the overall system response robust and efficient. The novelty of this study lies in the combination of radiotherapy and chemotherapy inputs in a way that prioritizes patients health and well-being, while integrating advanced synergetic control technique with a sigmoid function based smoothing agent. Results: The proposed method reduced baseline radiation and chemo drug dosages by 57% and 33% respectively while effectively suppressing tumor growth and proliferation. Similarly, the proposed controller reduced the time required for complete tumor mitigation by 60% while reducing the radiation and chemotherapeutic drug intensity by 93.8% and 21.3% respectively. Conclusions: This study offers significant improvement in tumor treatment methodologies by providing a safer, less riskier brain-tumor treatment strategy that has promising potential to improve survival rates against this menacing health condition so that the affected patients may lead a healthier and better quality of life.