bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization of Black Garlic Protein Extraction Process and Exploration of Its Properties and Functions with Enzymatic Hydrolysis Products
Authors: Liu Jian , Wang Yuanyuan , Wang Bo , Zhang Wei , Ren Xiaoyu , Zhang Youchuang , Jiang Lijun , Dong Chunming , Zhao Guihong
Published: December 2024

This study optimized the process of extracting protein from black garlic using an alkaline dissolution and acid precipitation method through response surface methodology. The optimal extraction conditions were determined as a solid-to-liquid ratio of 1:50, an extraction time of 100 min, an extraction temperature of 30 °C, and an alkaline extraction pH of 9.0. Under these optimized conditions, the actual black garlic protein (BGP) extraction yield was 12.10% ± 0.21%, and the isoelectric point of the obtained BGP was 3.1. Subsequently, this study extracted black garlic protein under optimal conditions and subjected it to enzymatic hydrolysis using different enzymes (trypsin, pepsin, and their mixed enzymes). The functional characteristics, antioxidant activity, and hypoglycemic activity of black garlic protein before and after enzymatic hydrolysis were compared. Among the hydrolysates, the pepsin hydrolysate (BGPH-P) had the smallest particle size (188.57 ± 1.93 nm) and the highest Zeta potential (−29.93 ± 0.42 mV). Scanning electron microscopy showed that BGPH-P had the smallest and most dispersed particles. Fourier-transform infrared (FTIR) spectroscopy revealed that the dual enzymatic hydrolysis hydrolysate (BGPH-PT) exhibited the most stable structure. Compared to BGP, the hydrolysates demonstrated significantly improved solubility, water-holding capacity, and foaming ability (p < 0.05), while their emulsifying activity, emulsion stability, DPPH radical scavenging capacity, and hypoglycemic activity decreased. In summary, the BGP extracted using the optimized process demonstrated good antioxidant and hypoglycemic activities, while its enzymatic hydrolysate BGPH-P exhibited excellent solubility, water-holding capacity, and emulsifying properties, providing valuable insights for the further development of black garlic protein and its hydrolysates.
The Diagnostic Accuracy of the Hyperextension–Internal Rotation (HERI) Test in Assessing Anterior Glenohumeral Instability
Authors: Alahaidib Adel , Almashouq Mohammad , Alsabbagh Laila , Alahaideb Abdulaziz
Published: December 2024

Background: To assess the diagnostic accuracy of the hyperextension–internal rotation (HERI) test to assess anterior glenohumeral instability. Methods: This study evaluated a total of 100 patients. The HERI test was performed on both the stable and unstable shoulder of each patient. The range of extension (°) was measured by a goniometer on both shoulders. Statistical analysis was performed to assess the diagnostic accuracy of the HERI test. Results: A total of 89% of patients showed evidence of an increased extension angle on the abnormal side by a mean of 84.19° compared with the normal contralateral side with a mean of 71.23°. The mean difference in extension angles between the normal and abnormal sides was 12.96°. In diagnosing anterior glenohumeral instability, the HERI test had a sensitivity of 88.8% (95% CI, 79.6–98%), a specificity of 87.3% (95% CI, 80.7–93.9%), and an accuracy of 88%. The positive predictive value was 87% (95% CI, 83.4–92.5%); the negative predictive value was 89% (95% CI, 84.6–93.3%); the positive likelihood ratio was 6.97 (95% CI, 3.4–10.4); the negative likelihood ratio was 0.13 (95% CI, 0.08–0.17); and the diagnostic odds ratio was 54.15 (95% CI, 47.2–61.0). Conclusions: The results of this diagnostic study indicate that the HERI test is a highly sensitive and specific test in assessing anterior glenohumeral instability without causing pain or producing a sense of instability.
Leveraging Next-Generation Sequencing Application from Identity to Purity Profiling of Nucleic Acid-Based Products
Authors: Wadapurkar Rucha , Deo Swarda , Khanzode Renuka , Singh Ajay
Published: December 2024

Background/Objectives: The nucleic acid-based product (NAP) portfolio is expanding continuously and provides safer curative options for many disease indications. Nucleic acid-based products offer several advantages compared to proteins and virus-based products. They represent an emerging field; thus, their quality control and regulatory landscape is evolving to ensure adequate quality and safety. Next-Generation Sequencing (NGS) is mostly recommended for NAP identity testing, and we are leveraging its application for impurity profiling. Methods: We proposed a workflow for the purity assessment of NAPs through short-read Illumina NGS followed by data analysis of mRNA vaccine and pDNA samples. We determined the sequence identity, DNA and RNA contamination, off-target RNA contamination, and poly-A count with the proposed workflow. Results: Our workflow predicted most of the critical quality controls of mRNA vaccine and plasmid DNA samples, especially focusing on the identity and the nucleotide-based impurities. Additionally, NGS data interpretation also assisted in strategic decisions for NAP manufacturing process optimizations. Conclusions: We recommend the adaptation of incremental NGS data by regulatory agencies to identify nucleotide-based impurities in NAPs. Perhaps NGS adaptation under cGMP compliance needs to be deliberated with the regulatory bodies, especially focusing on the methods qualification and validation part, starting from the sample collection, NGS library preparation, NGS run, and its data analysis pipeline.
Climate Change and Vegetation Evolution During the Holocene: Editorial
Authors: Innes James B.
Published: December 2024

The study of plant micro- and macrofossil evidence as proxy indicators of past environmental conditions and the influence of factors such as climate change has a long history, originally in Europe but subsequently much more widely [...]
Toward Inculturated Preaching
Authors: Connors Michael E.
Published: December 2024

How do we understand the relationship between preaching and culture? This question is as old as Christianity, even though “culture” in its anthropological sense is a relatively recent development. As every preacher knows, both preacher and listener are shaped by certain pre-understandings and values as they approach the interactive moment of proclamation. Culture shapes the imagination and thought patterns of the preacher, no less than the listener. If preaching is to be considered dialogically—a bidirectional conversation between the preacher and the listeners, in service of a conversation between God and God’s people—then it behooves both human conversation partners to become ever more aware of the cultural milieu(x) in which they are immersed. Every preaching event is the work of constructing an authentic local theology, a theological understanding suited to the particular people, historical moment, and cultural context in which it takes place. This essay seeks to shed light on that question through an engagement with a contemporary approach to the theology of inculturation (or contextualization). The starting point is a theology of preaching and its purpose as a Christian practice. We then turn to a theology of inculturation as it has been developing in recent decades, a theology that frames the interaction of the Christian message with culture in terms of both adaptation and liberation. Drawing upon the work of Robert Schreiter and others in understanding the formation of local theologies, the essay advances some methodological considerations in order for the church to move toward the possibility of authentically inculturated preaching. It concludes with some concrete suggestions for preachers, and an examination of one attempt to think through what it means to preach in a postmodern cultural context.
Atmospheric Turbulence Intensity Image Acquisition Method Based on Convolutional Neural Network
Authors: Mu Yuan , Zhou Liangping , Shao Shiyong , Wang Zhiqiang , Tang Pei , Hu Zhiyuan , Ye Liwen
Published: December 2024

An algorithmic model of a neural network with channel attention and spatial attention (CASANet) is proposed to estimate the value of atmospheric coherence length, which in turn provides a quantitative description of atmospheric turbulence intensity. By processing the acquired spot image data, the channel attention and spatial attention mechanisms are utilized, and the convolutional neural network learns the interdependence between the channel and space of the feature image and adaptively recalibrates the feature response in terms of the channel to increase the contribution of the foreground spot and suppress the background features. Based on the experimental data, an analysis of the CASANet model subject to turbulence intensity perturbations, fluctuations in outgoing power, and fluctuations in beam quality at the outlet is carried out. Comparison of the results of the convolutional neural network with those of the inverse method and the differential image motion method shows that the convolutional neural network is optimal in three evaluation indexes, namely, the mean deviation, the root-mean-square error, and the correlation coefficient, which are 2.74, 3.35, and 0.94, respectively. The convolutional neural network exhibits high accuracy under moderate and weak turbulence, and the estimation values under strong turbulence conditions are still mostly within the 95% confidence interval. The above results fully demonstrate that the proposed convolutional neural network method can effectively estimate the atmospheric coherence length, which provides technical support for the inversion of atmospheric turbulence intensity based on images.
Comparative Analysis of Tillage Indices and Machine Learning Algorithms for Maize Residue Cover Prediction
Authors: Li Jian , Shao Kewen , Du Jia , Song Kaishan , Yu Weilin , Liang Zhengwei , Zhang Weijian , Qin Jie , Zhuo Kaizeng , Zhang Cangming , Han Yu , Zhang Yiwei , Sui Bingrun
Published: December 2024

Remote sensing estimation of maize residue cover (MRC) can rapidly acquire large-scale data on MRC, crucial for monitoring and promoting conservation tillage. Herein, seven tillage indices derived from Sentinel-2 satellite imagery were analyzed alongside measured MRC data to assess their correlation with MRC. MRC estimation models were built using six machine learning algorithms, including back propagation neural network (BPNN), random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), Stacking1, and Stacking2. Model performance was compared using the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The potential for conservation tillage was explored. The results showed that the R2 values of the seven tillage indices in the study area exceeded 0.5, with particularly high correlations for NDTI and STI, with R2 values of 0.755 and 0.751, respectively. When using machine learning algorithms to construct models, the Stacking2 model exhibited the highest estimation accuracy, with an R2 of 0.923, RMSE of 3.32%, and MAE of 0.025, while Stacking1 also demonstrated robust performance, with an R2 of 0.910, RMSE of 3.45%, and MAE of 0.029. Among the base models, XGBoost achieved the highest estimation performance and the lowest error, with R2, RMSE, and MAE values of 0.884, 4.77%, and 0.031, respectively. The R2 values of RF, SVR, and BPNN were 0.865, 0.859, and 0.842, respectively, with RMSE values of 4.06%, 4.76%, and 5.91%, and MAE values of 0.039, 0.047, and 0.059, respectively. These results indicate that the Stacking2 model demonstrates a significant advantage in prediction accuracy. Geostatistical analysis of the inversion results of the Stacking2 model revealed that the proportions of farmland with MRC values exceeding 30% in Changchun, Songyuan, and Siping were 81.90%, 77.96%, and 83.58%, respectively. This indicates that Changchun and Siping have greater potential for implementing conservation tillage. This study demonstrates that the stacking ensemble learning model significantly improves the predictive performance by leveraging the strengths of multiple base models and accurately monitoring the spatial distribution of MRC.
Risk Assessment of Ground Subsidence in Foshan (China) Based on the Integration of SBAS-InSAR Observations and Inducing Factors
Authors: Yi Shouyong , Lai Guilin , Wang Min , Zhang Zongsheng , Chen Yuming , Wen Ningling , Shi Xianlin
Published: December 2024

In recent years, the rapid development of the construction economy in Foshan City has led to increasingly apparent geological phenomena of ground subsidence, causing damage to buildings and infrastructures, lowering groundwater level, soil degradation, and environmental pollution. These issues have severely constrained the economic development of Foshan City. This paper utilized SBAS-InSAR technology to monitor ground deformation in Foshan City over the 2017–2022 period and identified 214 potential subsidence hazards. Additionally, integrating extensive surface deformation data from a long time series establishes a risk assessment index system for ground subsidence, primarily driven by construction activities. Further, the mechanism of the ground subsidence was analyzed systematically. Meanwhile, introducing the analytic hierarchy process method (AHP) to assess the risk of Foshan City, the results showed that the proportion of area from high-risk to low-risk zones is 8.55%, 8.64%, 11.79%, 22.38% and 48.64%, respectively. Moreover, this paper proposed corresponding measures to prevent and control ground subsidence. The evaluation results can provide a scientific reference for future geological disaster prevention and management in Foshan City.
Deep Learning Unravels Differences Between Kinematic and Kinetic Gait Cycle Time Series from Two Control Samples of Healthy Children Assessed in Two Different Gait Laboratories
Authors: de Gorostegui Alfonso , Kiernan Damien , Martín-Gonzalo Juan-Andrés , López-López Javier , Pulido-Valdeolivas Irene , Rausell Estrella , Zanin Massimiliano , Gómez-Andrés David
Published: December 2024

We investigate the application of deep learning in comparing gait cycle time series from two groups of healthy children, each assessed in different gait laboratories. Both laboratories used similar gait analysis protocols with minimal differences in data collection. Utilizing a ResNet-based deep learning model, we successfully identified the source laboratory of each dataset, achieving a high classification accuracy across multiple gait parameters. To address the inter-laboratory differences, we explored various pre-processing methods and time series properties that may have been detected by the algorithm. We found that the standardization of the time series values was a successful approach to decrease the ability of the model to distinguish between the two centers. Our findings also reveal that differences in the power spectra and autocorrelation structures of the datasets play a significant role in the model performance. Our study emphasizes the importance of standardized protocols and robust data pre-processing to enhance the transferability of machine learning models across clinical settings, particularly for deep learning approaches.
Highly Responsive Robotic Prosthetic Hand Control Considering Electrodynamic Delay
Authors: Won Jiwoong , Iwase Masami
Published: December 2024

As robots become increasingly integrated into human society, the importance of human–machine interfaces continues to grow. This study proposes a faster and more accurate control system for myoelectric prostheses by considering the Electromechanical Delay (EMD), a key characteristic of Electromyography (EMG) signals. Previous studies have focused on systems designed for wrist movements without attempting implementation. To overcome this, we expanded the system’s capability to handle more complex movements, such as those of fingers, by replacing the existing four-channel wired EMG sensor with an eight-channel wireless EMG sensor. This replacement improved the number of channels and user convenience. Additionally, we analyzed the communication delay introduced by this change and validated the feasibility of utilizing EMD. Furthermore, to address the limitations of the SISO-NARX model, we proposed a MISO-NARX model. To resolve issues related to model complexity and reduced accuracy due to the increased number of EMG channels, we introduced ridge regression, improving the system identification accuracy. Finally, we applied the ZPETC+PID controller to an actual servo motor and verified its performance. The results showed that the system reached the target value approximately 0.240 s faster than the response time of 0.428 s without the controller. This study significantly enhances the responsiveness and accuracy of myoelectric prostheses and is expected to contribute to the development of practical devices in the future.
Assessing Changes in Reaction Time Following RAMP Warm-Up and Short-Term Repeated Volleyball Specific Exercise in Young Players
Authors: Cieśluk Kacper , Sadowska Dorota , Krzepota Justyna
Published: December 2024

The assessment of the various skills of athletes is carried out in terms of their ability to perform sport-specific tasks. The cognitive abilities of the players have significance for their effectiveness. In volleyball, a player’s ability to react quickly appears to be crucial in responding to an opponent’s dynamic play. The aim of the study was to evaluate the changes in reaction time to light signals following warm-up and physical exercise. Eighteen volleyball players (15.58 ± 2.01 years) participated in the study. Four FITLIGHT TrainerTM discs were placed on the wall facing the participant to conduct the reaction time test. The participant’s task was to react as quickly as possible to the illuminated disc by touching it with the palm of their hand. The procedure was repeated five times: before the warm-up, after the warm-up, and after each of the three agility tests. Friedman’s ANOVA showed statistically significant differences in reaction time between the tests (Chi2 ANOVA = 61.23; p < 0.001). All tests performed after the warm-up according to the RAMP protocol showed statistically significantly better results than those before the warm-up (p ≤ 0.05). At the same time, no differences were observed between the tests performed after successive agility tests. The results indicated that a well-chosen warm-up plays an important role in shortening the time of visual-motor reaction to a light stimulus (RT). Subsequent studies should be expanded to include other research groups and assess other parameters.
Human Occupancy Monitoring and Positioning with Speed-Responsive Adaptive Sliding Window Using an Infrared Thermal Array Sensor
Authors: Lin Yukai , Zhao Qiangfu
Published: December 2024

In the current era of advanced IoT technology, human occupancy monitoring and positioning technology is widely used in various scenarios. For example, it can optimize passenger flow in public transportation systems, enhance safety in large shopping malls, and adjust smart home devices based on the location and number of occupants for energy savings. Additionally, in homes requiring special care, it can provide timely assistance. However, this technology faces limitations such as privacy concerns, environmental factors, and costs. Traditional cameras may not effectively address these issues, but infrared thermal sensors can offer similar applications while overcoming these challenges. Infrared thermal sensors detect the infrared heat emitted by the human body, protecting privacy and functioning effectively day and night with low power consumption, making them ideal for continuous monitoring scenarios like security systems or elderly care. In this study, we propose a system using the AMG8833, an 8 × 8 Infrared Thermal Array Sensor. The sensor data are processed through interpolation, adaptive thresholding, and blob detection, and the merged human heat signatures are separated. To enhance stability in human position estimation, a dynamic sliding window adjusts its size based on movement speed, effectively handling environmental changes and uncertainties.
Photovoltaic Array Fault Diagnosis and Localization Method Based on Modulated Photocurrent and Machine Learning
Authors: Tao Yebo , Yu Tingting , Yang Jiayi
Published: December 2024

Photovoltaic arrays are exposed to outdoor conditions year-round, leading to degradation, cracks, open circuits, and other faults. Hence, the establishment of an effective fault diagnosis system for photovoltaic arrays is of paramount importance. However, existing fault diagnosis methods often trade off between high accuracy and localization. To address this concern, this paper proposes a fault identification and localization approach for photovoltaic arrays based on modulated photocurrent and machine learning. By irradiating different frequency-modulated light, this method separates photocurrent and directly measures the photoelectric conversion efficiency of each panel, achieving both high accuracy and localization. Through machine learning classification algorithms, the current amplitude and frequency of each photovoltaic panel are identified to achieve fault identification and localization. Compared to other methods, the strengths of this method lie in its ability to achieve high-speed and high-accuracy fault identification and localization by measuring only the short-circuit current. Additionally, the equipment cost is low. The feasibility of the proposed method is demonstrated through practical experimentation. It is determined that when utilizing a neural network algorithm, the fault identification speed meets measurement requirements (5800 obs/s), and the fault diagnosis accuracy is optimal (97.8%).
Focusing on Cracks with Instance Normalization Wavelet Layer
Authors: Guo Lei , Xiong Fengguang , Cao Yaming , Xue Hongxin , Cui Lei , Han Xie
Published: December 2024

Automatic crack detection is challenging, owing to the complex and thin topologies, diversity, and background noises of cracks. Inspired by the wavelet theory, we present an instance normalization wavelet (INW) layer and embed the layer into the deep model for segmentation. The proposed layer employs prior knowledge in the wavelets to capture the crack features and filter the high-frequency noises simultaneously, accelerating the convergence of model training. Furthermore, instance normalization in our layer is utilized to mitigate the feature differences, boosting the generalization capability. In addition, a fusion layer is added to merge the information across the different layers. The comparison experiments and ablation studies demonstrate that the INW layer steadily enhances recognition and convergence performance on the DeepCrack dataset and CRACK500 dataset.
Magnetic Induction Phase Difference for Cerebral Hemorrhage Detection
Authors: Liu Jie , Yan Lian , Deng Huangsen , Qin Mingxin , Chen Mingsheng
Published: December 2024

Magnetic induction phase shift is a promising technology for the detection of cerebral hemorrhage, owing to its nonradioactive, noninvasive, and real-time detection properties. To enhance the detection sensitivity and linearity, a zero-flow sensor was proposed. The uniform primary magnetic field and its counteraction were achieved. Phase-change responses to solutions of varying conductivities and rabbits with cerebral hemorrhage were investigated and compared with traditional sensors. The sensitivities in detecting solutions with different conductivities were 1.84, 1.39, and 1.22 times higher than those for a low-pass birdcage coil, planar gradiometer, and Bx-sensor, respectively. The results for rabbits with cerebral hemorrhage showed that the sensitivities increased by 1.17, 1.67, and 6.3 times compared with a low-pass birdcage coil, symmetric cancelation-type sensor, and single co-axial coil, respectively. This sensor could accurately detect three stages in the pathological process. Blood loss of 1 mL meant that the compensatory mechanism of cerebrospinal fluid began to fail, and 1.4 mL of blood loss meant that the compensatory mechanism failed completely. The adjusted R-squared value of the first-order linear fit was above 0.98 in both physical and animal experiments, indicating that high detection linearity was achieved. The proposed sensor provides a more accurate method for cerebral hemorrhage detection and facilitates the practical application of magnetic induction phase shift in pre-hospital and bedside real-time detection.
WGAN-GP for Synthetic Retinal Image Generation: Enhancing Sensor-Based Medical Imaging for Classification Models
Authors: Anaya-Sánchez Héctor , Altamirano-Robles Leopoldo , Díaz-Hernández Raquel , Zapotecas-Martínez Saúl
Published: December 2024

Accurate synthetic image generation is crucial for addressing data scarcity challenges in medical image classification tasks, particularly in sensor-derived medical imaging. In this work, we propose a novel method using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and nearest-neighbor interpolation to generate high-quality synthetic images for diabetic retinopathy classification. Our approach enhances training datasets by generating realistic retinal images that retain critical pathological features. We evaluated the method across multiple retinal image datasets, including Retinal-Lesions, Fine-Grained Annotated Diabetic Retinopathy (FGADR), Indian Diabetic Retinopathy Image Dataset (IDRiD), and the Kaggle Diabetic Retinopathy dataset. The proposed method outperformed traditional generative models, such as conditional GANs and PathoGAN, achieving the best performance on key metrics: a Fréchet Inception Distance (FID) of 15.21, a Mean Squared Error (MSE) of 0.002025, and a Structural Similarity Index (SSIM) of 0.89 in the Kaggle dataset. Additionally, expert evaluations revealed that only 56.66% of synthetic images could be distinguished from real ones, demonstrating the high fidelity and clinical relevance of the generated data. These results highlight the effectiveness of our approach in improving medical image classification by generating realistic and diverse synthetic datasets.
GFA-Net: Geometry-Focused Attention Network for Six Degrees of Freedom Object Pose Estimation
Authors: Lin Shuai , Yu Junhui , Su Peng , Xue Weitao , Qin Yang , Fu Lina , Wen Jing , Huang Hong
Published: December 2024

Six degrees of freedom (6-DoF) object pose estimation is essential for robotic grasping and autonomous driving. While estimating pose from a single RGB image is highly desirable for real-world applications, it presents significant challenges. Many approaches incorporate supplementary information, such as depth data, to derive valuable geometric characteristics. However, the challenge of deep neural networks inadequately extracting features from object regions in RGB images remains. To overcome these limitations, we introduce the Geometry-Focused Attention Network (GFA-Net), a novel framework designed for more comprehensive feature extraction by analyzing critical geometric and textural object characteristics. GFA-Net leverages Point-wise Feature Attention (PFA) to capture subtle pose differences, guiding the network to localize object regions and identify point-wise discrepancies as pose shifts. In addition, a Geometry Feature Aggregation Module (GFAM) integrates multi-scale geometric feature maps to distill crucial geometric features. Then, the resulting dense 2D–3D correspondences are passed to a Perspective-n-Point (PnP) module for 6-DoF pose computation. Experimental results on the LINEMOD and Occlusion LINEMOD datasets indicate that our proposed method is highly competitive with state-of-the-art approaches, achieving 96.54% and 49.35% accuracy, respectively, utilizing the ADD-S metric with a 0.10d threshold.
Characterizing Subsurface Environments Using Borehole Magnetic Gradiometry
Authors: Asgharzadeh Mohammad Forman , Ghasemzadeh Hasan , von Frese Ralph , Ighani Kamran
Published: December 2024

Forward modeling the magnetic effects of an inferred source is the basis of magnetic anomaly inversion for estimating subsurface magnetization parameters. This study uses numerical least-squares Gauss–Legendre quadrature (GLQ) integration to evaluate the magnetic potential, anomaly, and gradient components of a cylindrical prism element. Relative to previous studies, it quantifies for the first time the magnetic gradient components, enabling their applications in the interpretation of cylindrical bodies. A comparison of this method to other methods of evaluating the vertical component of the magnetic field associated with a full cylinder shows that it has comparable to improved performance in computational accuracy and speed. Based on the developed theory, a conceptual design is presented for an instrument to measure the magnetic gradient effects of subsurface material in the vicinity of a borehole. The significance of this instrument relative to conventional borehole magnetometers is in its ability to determine the azimuthal directions of magnetic sources within the borehole environment.
Driving-Related Cognitive Abilities Prediction Based on Transformer’s Multimodal Fusion Framework
Authors: Li Yifan , Liu Bo , Zhang Wenli
Published: December 2024

With the increasing complexity of urban roads and rising traffic flow, traffic safety has become a critical societal concern. Current research primarily addresses drivers’ attention, reaction speed, and perceptual abilities, but comprehensive assessments of cognitive abilities in complex traffic environments are lacking. This study, grounded in cognitive science and neuropsychology, identifies and quantitatively evaluates ten cognitive components related to driving decision-making, execution, and psychological states by analyzing video footage of drivers’ actions. Physiological data (e.g., Electrocardiogram (ECG), Electrodermal Activity (EDA)) and non-physiological data (e.g., Eye Tracking (ET)) are collected from simulated driving scenarios. A dual-branch Transformer network model is developed to extract temporal features from multimodal data, integrating these features through a weight adjustment strategy to predict driving-related cognitive abilities. Experiments on a multimodal driving dataset from the Computational Physiology Laboratory at the University of Houston, USA, yield an Accuracy (ACC) of 0.9908 and an F1-score of 0.9832, confirming the model’s effectiveness. This method effectively combines scale measurements and driving behavior under secondary tasks to assess cognitive abilities, providing a novel approach for driving risk assessment and traffic safety strategy development.
Task Offloading with LLM-Enhanced Multi-Agent Reinforcement Learning in UAV-Assisted Edge Computing
Authors: Zhu Feifan , Huang Fei , Yu Yantao , Liu Guojin , Huang Tiancong
Published: December 2024

Unmanned aerial vehicles (UAVs) furnished with computational servers enable user equipment (UE) to offload complex computational tasks, thereby addressing the limitations of edge computing in remote or resource-constrained environments. The application of value decomposition algorithms for UAV trajectory planning has drawn considerable research attention. However, existing value decomposition algorithms commonly encounter obstacles in effectively associating local observations with the global state of UAV clusters, which hinders their task-solving capabilities and gives rise to reduced task completion rates and prolonged convergence times. To address these challenges, this paper introduces an innovative multi-agent deep learning framework that conceptualizes multi-UAV trajectory optimization as a decentralized partially observable Markov decision process (Dec-POMDP). This framework integrates the QTRAN algorithm with a large language model (LLM) for efficient region decomposition and employs graph convolutional networks (GCNs) combined with self-attention mechanisms to adeptly manage inter-subregion relationships. The simulation results demonstrate that the proposed method significantly outperforms existing deep reinforcement learning methods, with improvements in convergence speed and task completion rate exceeding 10%. Overall, this framework significantly advances UAV trajectory optimization and enhances the performance of multi-agent systems within UAV-assisted edge computing environments.
Research Progress in Fiber Bragg Grating-Based Ocean Temperature and Depth Sensors
Authors: Zhao Xinyu , Wei Chenxi , Zeng Lina , Sun Li , Li Zaijin , Chen Hao , Liu Guojun , Qiao Zhongliang , Qu Yi , Xu Dongxin , Li Lianhe , Li Lin
Published: December 2024

Fiber Bragg gratings (FBGs) are widely used in stress and temperature sensing due to their small size, light weight, high resistance to high temperatures, corrosion, electromagnetic interference, and low cost. In recent years, various structural enhancements and sensitization to FBGs have been explored to improve the performance of ocean temperature and depth sensors, thereby enhancing the accuracy and detection range of ocean temperature and depth data. This paper reviews advancements in temperature, pressure, and dual-parameter enhancement techniques for FBG-based sensors. Additionally, the advantages and disadvantages of each method are compared and analyzed, providing new directions for the application of FBG sensors in marine exploration.
Causal Effects of Air Pollution, Noise, and Shift Work on Unstable Angina and Myocardial Infarction: A Mendelian Randomization Study
Authors: Ma Qiye , Chen Lin , Xu Hao , Weng Yiru
Published: December 2024

Cardiovascular disease continues to be a major contributor to global morbidity and mortality, with environmental and occupational factors such as air pollution, noise, and shift work increasingly recognized as potential contributors. Using a two-sample Mendelian randomization (MR) approach, this study investigates the causal relationships of these risk factors with the risks of unstable angina (UA) and myocardial infarction (MI). Leveraging single nucleotide polymorphisms (SNPs) as genetic instruments, a comprehensive MR study was used to assess the causal influence of four major air pollutants (PM2.5, PM10, NO2, and NOx), noise, and shift work on unstable angina and myocardial infarction. Summary statistics were derived from large genome-wide association studies (GWASs) from the UK Biobank and the FinnGen consortium (Helsinki, Finland), with replication using an independent GWAS data source for myocardial infarction. The inverse-variance weighted (IVW) approach demonstrated a significant positive correlation between shift work and the increased risk of both unstable angina (OR with 95% CI: 1.62 [1.12–2.33], p = 0.010) and myocardial infarction (OR with 95% CI: 1.46 [1.00–2.14], p = 0.052). MR-PRESSO analysis identified outliers, and after correction, the association between shift work and myocardial infarction strengthened (OR with 95% CI: 1.58 [1.11–2.27], p = 0.017). No notable causal associations were identified for air pollution or noise with either outcome. The replication of myocardial infarction findings using independent data supported a possible causal link between shift work and myocardial infarction (OR with 95% CI: 1.41 [1.08–1.84], p = 0.012). These results provide novel evidence supporting shift work as a likely causal risk factor for unstable angina and myocardial infarction, underscoring the need for targeted public health strategies to mitigate its cardiovascular impact. However, further investigation is necessary to elucidate the role of air pollution and noise in cardiovascular outcomes.
Highly Pathogenic Avian Influenza Contributes to the Population Decline of the Peregrine Falcon (Falco peregrinus) in The Netherlands
Authors: Caliendo Valentina , Bellido Martin Beatriz , Fouchier Ron A. M. , Verdaat Hans , Engelsma Marc , Beerens Nancy , Slaterus Roy
Published: December 2024

Highly pathogenic avian influenza (HPAI) epizootics have caused repeated mass mortality events among wild birds. The effect of the infection is potentially detrimental for a variety of bird species, including the Peregrine Falcon (Falco peregrinus). The numbers of wintering and breeding Peregrine Falcons in the Netherlands have recently declined. We investigated the changes in population trends in relation to HPAI H5 virus outbreaks. For this purpose, we analyzed variations in annual numbers of wintering and breeding birds, the virology of reported dead birds, and the presence of the HPAI H5 virus in unhatched eggs. We showed that significant mortalities of Peregrine Falcons had occurred in 2016–2017 and 2020–2023, years of major HPAI H5 virus outbreaks. In particular, the highest rates of bird mortality and HPAI virus infection were reported in 2023. In this year, over 80% (28/32) of the tested birds were positive for HPAI H5 virus. No HPAI H5 virus was present in the eggs. Based on these findings, we concluded that HPAI represents a serious threat to the Peregrine Falcon population in the Netherlands, and, in combination with anthropogenic factors, may contribute to the decline of this species. Targeted HPAI surveillance and disease mitigation measures are necessary for the conservation of this species.
eEF-2K Deficiency Boosts the Virus-Specific Effector CD8+ T Cell Responses During Viral Infection
Authors: Wang Liqing , Song Benny Shone , Poojary Rayansh , Xiong Xiaofang , Ren Xingcong , Yang Jin-Ming , Song Jianxun
Published: December 2024

In this study, we revealed a critical role of eukaryotic elongation factor-2 kinase (eEF-2K), a negative regulator of protein synthesis, in regulating T cells during vaccinia virus (VACV) infection. We found that eEF-2K-deficient (eEF-2K⁻/⁻) mice exhibited a significantly higher proportion of VACV-specific effector CD8+ T cells without compromising the development of VACV-specific memory CD8+ T cells. RNA sequencing demonstrated that eEF-2K⁻/⁻ VACV-specific effector CD8+ T cells had enhanced functionality, which improves their capacity to combat viral infection during the effector phase. Moreover, we identified tumor necrosis factor receptor-associated factor 3 (TRAF3) as a critical mediator of the stronger antiviral response observed in eEF-2K⁻/⁻ effector CD8+ T cells. These findings suggest that targeting eEF-2K may provide a novel strategy to augmenting effector CD8+ T cell responses against viral infections.
Assessing the Burden of Neglected Tropical Diseases in Low-Income Communities: Challenges and Solutions
Authors: Branda Francesco , Ali Abdisalam Yusuf , Ceccarelli Giancarlo , Albanese Mattia , Binetti Erica , Giovanetti Marta , Ciccozzi Massimo , Scarpa Fabio
Published: December 2024

Neglected tropical diseases (NTDs) represent a group of chronic and debilitating infections that affect more than one billion people, predominantly in low-income communities with limited health infrastructure. This paper analyzes the factors that perpetuate the burden of NTDs, highlighting how poor health infrastructure, unfavorable socioeconomic conditions and lack of therapeutic resources exacerbate their impact. The effectiveness of current interventions, such as mass drug administration (MDA) programs and improved sanitation, in reducing disease prevalence is examined. In addition, the role of climate change, which alters transmission dynamics and expands affected territories, is discussed as an emerging challenge. The analysis suggests that integrated, multisectoral approaches, including health education and infrastructure interventions, are essential to breaking the cycle of poverty and disease. Although international programs have marked significant progress, achieving elimination targets by 2030 requires sustained commitment, innovation, and increased research capacity in endemic countries.
Decrease in HBsAg After TAF Switching from Entecavir During Long-Term Treatment of Chronic Hepatitis B Virus Infection
Authors: Tajiri Kazuto , Hayashi Yuka , Murayama Aiko , Muraishi Nozomu , Minemura Masami , Yasuda Ichiro
Published: December 2024

Achieving HBsAg seroclearance is a key goal in treating chronic hepatitis B virus (HBV) infection but remains difficult with nucleos(t)ide analogues (NAs). Tenofovir alafenamide fumarate (TAF), a recommended NA for managing chronic HBV infection (CHB), has uncertain effects on HBsAg levels and potential adverse events when used long-term after switching from entecavir (ETV). We retrospectively evaluated 77 CHB patients, including 47 who switched from ETV to TAF with a median follow-up of 40 months post-switch and a median of 60 months of HBsAg monitoring pre-switch. No significant change in HBsAg levels was observed in the overall cohort post-switch, consistent with the ETV continuation group. However, a significant decrease in HBsAg was noted in patients with HBsAg < 100 IU/mL at the time of switching. HBsAg loss occurred in three patients who switched to TAF. No adverse effects were observed, and TAF was well tolerated. The most significant factor associated with achieving HBsAg < 100 IU/mL was the Fib-4 index, a marker of liver fibrosis, at the time of switching. Switching from ETV to TAF is an effective strategy in CHB management, with hepatic inflammation potentially playing an essential role in achieving HBsAg decrease. Patients with increased Fib-4 index were significantly more likely to show decreased HBsAg. This finding suggests patients with mild to moderate fibrosis may respond better to TAF in terms of HBsAg reduction.
Virological Aspects of COVID-19 in Patients with Hematological Malignancies: Duration of Viral Shedding and Genetic Analysis
Authors: Themlaoui Asma , Ancora Massimo , Ghedira Kais , Mhalla Yosra , Hamdoun Manel , Bahri Maroua , Aissaoui Lamia , Ben Lakhal Raihane , Di Pasquale Adriano , Camma Cesare , Bahri Olfa
Published: December 2024

Coronavirus disease 2019 (COVID-19) has been associated with a significant fatality rate and persistent evolution in immunocompromised patients. In this prospective study, we aimed to determine the duration of excretion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 37 Tunisian patients with hematological malignancies (40.5% with lymphoma and 37.8% with leukemia). In order to investigate the accumulation of viral mutations, we carried out genetic investigation on longitudinal nasopharyngeal samples using RT-PCR and whole-genome sequencing. Patients’ samples were collected until the RT-PCR results became negative. SARS-CoV-2 infection was symptomatic in 48.6% of cases with fever, and cough was symptomatic in 61% of cases; the mortality rate was estimated to be 13.5%. The duration of viral RNA shedding ranged from 7 to 92 days after onset; it exceeded 18 days in 79.4% of cases. An intermittent PCR positivity was observed in two symptomatic patients. Persistent PCR positivity, defined as the presence of viral RNA for more than 30 days, was found in 51.4% of cases. No significant differences were observed for age, sex, type of hematological malignancy, or COVID-19 evolution between this group and a second one characterized by non-persistent PCR positivity. Lymphopenia was an independent predictor of prolonged SARS-CoV-2 RNA detection (p = 0.04). Three types of variants were detected; the most frequent was the Omicron. Globally, the mean intra-host variability in the SARS-CoV-2 genome was 1.31 × 10−3 mutations per site per year; it was 1.44 × 10−3 in the persistent group and 1.3 × 10−3 in the non-persistent group. Three types of mutations were detected; the most frequent were nucleotide substitutions in the spike (S) gene. No statistically significant difference was observed between the two groups as to the type and mean number of observed mutations in the whole genome and the S region (p = 0.650). Sequence analysis revealed the inclusion of one to eight amino acid-changing events in seventeen cases; it was characterized by genetic stability from the third to the twentieth day of evolution in six cases. For the two patients with intermittent PCR positivity, sequences obtained from samples before and after negative PCR were identical in the whole genome, confirming an intra-host evolution of the same viral strain. This study confirms the risk of persistent viral shedding in patients with hematological malignancies. However, persistence of PCR positivity seems to be correlated only with a continuous elimination of viral RNA debris. Additional studies based on cell culture analysis are needed to confirm these findings.
Difference in Optical Coherence Tomography Angiography Parameters After SARS-CoV-2 Infection During the Alpha and Delta Variant Dominance Periods
Authors: Kal Magdalena , Brzdęk Michał , Karska-Basta Izabella , Rzymski Piotr , Pinna Antonio , Mackiewicz Jerzy , Odrobina Dominik , Zarębska-Michaluk Dorota , Flisiak Robert
Published: December 2024

The SARS-CoV-2 infection manifests with diverse clinical manifestations, with severity potentially influenced by the viral variant. COVID-19 has also been shown to impact ocular microcirculation in some patients, but whether this effect varies by viral lineage remains unclear. This prospective study compared clinical features and ocular parameters assessed via optical coherence tomography angiography (OCTA) in patients recovering from SARS-CoV-2 infections during the dominance of two distinctive viral lineages, Alpha (B.1.1.7) and Delta (B.1.617.2), and compared them to a control group. The following parameters were measured: vessel density (VD) in the superficial capillary plexus (SCP), deep capillary plexus (DCP), and choriocapillaris (CCP) using OCTA, with a manual assessment of the foveal avascular zones in the SCP (FAZs) and DCP (FAZd). A control group was also included. Among 63 patients in the Alpha group and 41 in the Delta group, no eye-related symptoms were reported during the examination. However, the Delta group showed significantly lower VD in the SCP and DCP across all quadrants (p < 0.001–0.039), while the Alpha group showed reduced VD in the foveal CCP (p = 0.005) and significantly wider FAZs and FAZd (p = 0.002 for both). In conclusion, ocular microcirculatory changes differed between the two variants, with Alpha associated with foveal choroidal VD reduction and larger FAZs and Delta linked to lower SCP and DCP VD across multiple regions. These findings highlight the potential for SARS-CoV-2 variants to differentially impact ocular vasculature, underscoring the need for variant-specific follow-up in COVID-19 patients.
Modeling BK Virus Infection in Renal Transplant Recipients
Authors: Myers Nicholas , Droz Dana , Rogers Bruce W. , Tran Hien , Flores Kevin B. , Chan Cliburn , Knechtle Stuart J. , Jackson Annette M. , Luo Xunrong , Chambers Eileen T. , McCarthy Janice M.
Published: December 2024

Kidney transplant recipients require a lifelong protocol of immunosuppressive therapy to prevent graft rejection. However, these same medications leave them susceptible to opportunistic infections. One pathogen of particular concern is human polyomavirus 1, also known as BK virus (BKPyV). This virus attacks kidney tubule epithelial cells and is a direct threat to the health of the graft. Current standard of care in BK virus-infected transplant recipients is reduction in immunosuppressant therapy, to allow the patient’s immune system to control the virus. This requires a delicate balance; immune suppression must be strong enough to prevent rejection, yet weak enough to allow viral clearance. We seek to model viral and immune dynamics with the ultimate goal of applying optimal control methods to this problem. In this paper, we begin with a previously published model and make simplifying assumptions that reduce the number of parameters from 20 to 14. We calibrate our model using newly available patient data and a detailed sensitivity analysis. Numerical results for multiple patients are given to show that the newer model reflects observed dynamics well.