bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of Antibiotic Therapy with Ceftazidime Avibactam vs. Best Available Therapy in Adult Patients with Bacteremia Caused by Carbapenem-Resistant Enterobacterales
Authors: Arboleda Daniel , Buitrago Camilo , Vergara Erika Paola , Nocua-Báez Laura Cristina , Saavedra Carlos Humberto , Cortés Jorge Alberto
Published: February 2025

Background/Objectives: Carbapenem-resistant Enterobacterales (CRE) infection is associated with a higher mortality rate. The purpose of this study was to evaluate the effect of ceftazidime avibactam (CZA) for treating bacteremia caused by CRE compared to the best available therapy in an area where these microorganisms are endemic. Methods: A retrospective cohort study of patients with CRE bacteremia was conducted. We included adults with CRE bacteremia who were treated with CZA or the best available therapy (BAT) for more than 48 h, and the hospitalization time was recorded. The outcomes included death during hospitalization, relapse, and microbiological cure. Confounders were adjusted using propensity score-derived stabilized inverse probability of treatment weighting (IPTW). Results: A total of 169 patients with CRE bacteremia were included. About 72.6% of isolates had a class A serin carbapenamase, and 20.4% had metallo-β-lactamase co-production. A total of 107 patients were treated with CZA, 63% in monotherapy and 32% with aztreonam (ATM). Crude mortality during hospitalization was 36 (34.5%) in patients treated with CZA and 21 (33.2%) with BAT. No difference was observed between death rates (HR 0.86: IC 95% 0.40–1.83), microbiological cure (OR 1.31 IC 95% 0.46–3.67), clinical response (OR 1.39 IC 95% 0.35–5.43), acute kidney injury (OR 0.56 IC 95% 0.11–2.80) or relapse (OR 0.99 IC 95% 0.17–5.51) during the hospitalization after the adjustment. Conclusions: Among adult patients with CRE, no differences were observed between treatments with CZA and BAT after adjustment with IPTW.
Trp31 Residue of Trx-1 Is Essential for Maintaining Antioxidant Activity and Cellular Redox Defense Against Oxidative Stress
Authors: He Zongmao , Yan Yi , Guo Xijun , Wang Tong , Liu Xinqiao , Ding Ren-Bo , Fu Yuanfeng , Bao Jiaolin , Qi Xingzhu
Published: February 2025

Thioredoxin-1 (Trx-1) is an important redox protein found in almost all prokaryotic and eukaryotic cells, which has a highly conserved active site sequence: Trp-Cys-Gly-Pro-Cys. To investigate whether the Trp31 residue is essential for the antioxidant activity of human Trx-1 (hTrx-1), we mutated Trx-1 by replacing Trp31 with Ala31 (31Ala) or deleting Trp31 residue (31Del). We introduced 31Ala and 31Del mutations into prokaryotic cells for hTrx-1 protein expression, protein purification and evaluation of antioxidant activity. The results showed that neither the replacing mutation to Ala31 nor the deletion of Trp31 residue affected the efficient expression of hTrx-1 protein in prokaryotic cells, indicating that neither form of Trp31 mutation would disrupt the folded structure of the Trx-1 protein. Comparison of the antioxidant activity of purified hTrx-1 proteins of wild-type, 31Ala and 31Del forms revealed that both mutant forms significantly decreased the antioxidant capacity of hTrx-1. Further investigations on eukaryotic cells showed that H2O2 treatment caused massive cell death in EA.Hy926 human endothelial cells with 31Ala and 31Del mutations compared to wild-type cells, which was associated with increased ROS production and downregulation of antioxidant Nrf2 and HO-1 expression in the mutant cells. These results suggested that mutations in the Trp31 residue of hTrx-1 remarkably disrupted cellular redox defense against oxidative stress. The antioxidant activity of hTrx-1 relies on the thiol–disulfide exchange reaction, in which the content of thiol groups forming disulfide bonds in hTrx-1 is critical. We found that the content of free thiol groups specifically participating in disulfide bond formation was significantly lower in Trp31 mutant hTrx-1 than in wild-type hTrx-1; that was speculated to affect the formation of disulfide bonds between Cys32 and Cys35 by virtual analysis, thus abolishing the antioxidant activity of hTrx-1 in cleaving oxidized groups and defending against oxidative stress. The present study provided valuable insights towards understanding the importance of Trp31 residue of hTrx-1 in maintaining the correct conformation of the Trx fold structure, the antioxidant functionality of hTrx-1 and the cellular redox defense capability against oxidative stress.
Computational Fluid Dynamics of Compartment Fires: A Review of Methods and Applications
Authors: Parsa Vahid , Santiago Aldina , Laím Luís
Published: February 2025

Compartment fires, such as those occurring in buildings and confined spaces, impose modeling challenges due to the complexity of turbulent flows, combustion, and radiative heat transfer. Computational Fluid Dynamics (CFD) has become a vital tool for understanding and predicting fire dynamics in such situations. This review provides an analysis of different available methods and sub-models on the CFD tools which have been applied to compartment fires in the literature, examining current turbulence, combustion, and radiation approaches. Additionally, it identifies challenges and deficiencies in modeling such as combustion, radiation modeling, flame extinction, and ventilation impacts, discussing the balance between accuracy and computational cost. The review also highlights aspects of different sub-models and provides the reader with informative instruction in making the decisions for a more reliable CFD simulation of the compartment fire.
Inception–Attention–BiLSTM Hybrid Network: A Novel Approach for Shear Wave Velocity Prediction Utilizing Well Logging
Authors: Li Jiayi , Lin Yaoting , Gui Zhixian , Wang Peng
Published: February 2025

Shear wave velocity prediction is critical for applications in petrophysics, reservoir characterization, and unconventional energy resource development. While empirical formulas and theoretical rock physics models offer solutions, they are often limited by geological complexity, high cost, and computational inefficiency. After the emergence of deep learning methods, a series of new approaches have been provided to tackle these problems. In this study, a novel Inception–attention–BiLSTM hybrid network is proposed to enhance shear wave prediction accuracy and stability by integrating the strengths of three components: Inception for multi-scale feature extraction, attention mechanisms for dynamically highlighting key temporal features, and BiLSTM for capturing long-term dependencies in logging data. The test dataset of this network comes from the Jurassic Badaowan Formation in the Junggar Basin, achieving superior performance compared to standalone Inception and BiLSTM networks. The proposed hybrid network demonstrated MAE and R2 values of 0.211 and 0.994, respectively, outperforming Inception (MAE 0.671, R2 0.981) and BiLSTM (MAE 0.215, R2 0.991). These results underscore its robustness in handling complex logging data, providing a more accurate and generalizable framework for Vs prediction while addressing limitations of traditional methods. This work highlights the potential of hybrid deep learning architectures in advancing logging data analysis and reservoir characterization.
Risk-Based Optimization of Renewable Energy Investment Portfolios: A Multi-Stage Stochastic Approach to Address Uncertainty
Authors: Ogunniran Olufemi , Babatunde Olubayo , Akintayo Busola , Adisa Kolawole , Ighravwe Desmond , Ogbemhe John , Olanrewaju Oludolapo Akanni
Published: February 2025

This work presents a multi-stage stochastic optimization model intended to improve investment decision-making for energy projects by incorporating uncertainty in contexts and changes in market pricing. In contrast to conventional deterministic models, which generally concentrate on a singular stage while neglecting the intricacies associated with policy and market uncertainties, our methodology incorporates Conditional Value at Risk as a pivotal risk metric. Across a span of five years, the model predicts how investments will be distributed among three types of electricity projects: Solar Farm, Wind Farm, and Hydro Plant. The stochastic model strategically allocates an investment of USD 16.5 million to achieve an expansion in the capacity of 925 megawatts and an expected portfolio return of USD 1,822,500. Notably, the model maintains a Conditional Value at Risk of USD 100,000 and an impressive Sharpe Ratio of 18.2250, demonstrating its ability to offer improved risk-adjusted returns. This study illustrates the effectiveness of stage stochastic optimization in enhancing diverse and robust renewable energy portfolios.
Profit-Efficient Elastic Allocation of Cloud Resources Using Two-Stage Adaptive Workload Prediction
Authors: Li Lei , Gao Xue
Published: February 2025

Internet services are increasingly being deployed using cloud computing. However, the workload of an Internet service is not constant; therefore, the required cloud computing resources need to be allocated elastically to minimize the associated costs. Thus, this study proposes a proactive cloud resource scheduling framework. First, we propose a new workload prediction method—named the adaptive two-stage multi-neural network based on long short-term memory (LSTM)—which can adaptively route prediction tasks to the corresponding LSTM sub-model according to the workload change trend (i.e., uphill and downhill categories), in order to improve the predictive accuracy. To avoid the cost associated with manual labeling of the training data, the first-order gradient feature is used with the k-means algorithm to cluster and label the original training data set automatically into uphill and downhill training data sets. Then, based on stochastic queueing theory and the proposed prediction method, a maximum cloud service profit resource search algorithm based on the network workload prediction algorithm is proposed to identify a suitable number of virtual machines (VMs) in order to avoid delays in resource adjustment and increase the service profit. The experimental results demonstrate that the proposed proactive adaptive elastic resource scheduling framework can improve the workload prediction accuracy (MAPE: 0.0276, RMSE: 3.7085, R2: 0.9522) and effectively allocate cloud resources.
Self-Heating Effect Coupled Compact Model to Predict Hot Carrier Injection Degradation in Nanoscale Bulk FinFETs Under Different Conditions
Authors: Liu Bingrui , Chen Lan
Published: February 2025

The HCI effect has been the focus of research as a common reliability consideration under advanced nodes in semiconductors. In this paper, a new compact model that takes into account the self-heating effect, width dependence, and substrate voltage dependence is proposed in the framework of a self-saturated power–law model containing oxide defects. The compact model employs different parameters in different carrier energy regions to improve the accuracy of the model. The predictions of the model fit well with experimental data extracted from the literature and the TCAD data, proving the validity of the model. Meanwhile, the model is used in this paper to predict and analyze the HCI’s degradation as well as lifetime under different conditions.
Optimum Multilevel Thresholding for Medical Brain Images Based on Tsallis Entropy, Incorporating Bayesian Estimation and the Cauchy Distribution
Authors: Wang Xianwen , Yang Yingyuan , Nan Minhang , Bao Guanjun , Liang Guoyuan
Published: February 2025

Entropy-based thresholding is a widely used technique for medical image segmentation. Its principle is to determine the optimal threshold by maximizing or minimizing the image’s entropy, dividing the image into different regions or categories. The intensity distributions of objects and backgrounds often overlap and contain many outliers, making segmentation extremely difficult. In this paper, we introduce a novel thresholding method that incorporates the Cauchy distribution into the Tsallis entropy framework based on Bayesian estimation. By introducing Bayesian prior probability estimation to address the overlap in intensity distributions between the two classes, we enhance the estimation of the probability that a pixel belongs to either class. Additionally, we utilize the Cauchy distribution, known for its heavy-tailed characteristics, to fit grayscale pixel distributions with outliers, enhancing tolerance to extreme values. The optimal threshold is derived through the optimization of an information measure formulated using updated Tsallis entropy. Experimental results demonstrate that the proposed method, called Cauchy-TB, achieves significant superiority to existing approaches on two public medical brain image datasets.
A Local Discrete Feature Histogram for Point Cloud Feature Representation
Authors: Jia Linjing , Li Cong , Xi Guan , Liu Xuelian , Xie Da , Wang Chunyang
Published: February 2025

Local feature descriptors are a critical problem in computer vision; the majority of current approaches find it difficult to achieve a balance between descriptiveness, robustness, compactness, and efficiency. This paper proposes the local discrete feature histogram (LDFH), a novel local feature descriptor, as a solution to this problem. The LDFH descriptor is constructed based on a robust local reference frame (LRF). It partitions the local space based on radial distance and calculates three geometric features, including the normal deviation angle, polar angle, and normal lateral angle, in each subspace. These features are then discretized to generate three feature statistical histograms, which are combined using a weighted fusion strategy to generate the final LDFH descriptor. Experiments on public datasets demonstrate that, compared with the existing methods, LDFH strikes an excellent balance between descriptiveness, robustness, compactness, and efficiency, making it suitable for various scenes and sensor datasets.
Optimizing Energy Efficiency and Sustainability in Winter Climate Control: Innovative Use of Variable Refrigerant Flow (VRF) Systems in University Buildings
Authors: Arroyo Gómez Yolanda , San José-Alonso Julio F. , San José-Gallego Luis J. , Rey-Hernández Javier M. , Sanz-Tejedor Ascensión , Rey-Martínez Francisco J.
Published: February 2025

This study presents a comprehensive analysis of the energy efficiency and sustainability of Variable Refrigerant Flow (VRF) systems in university buildings during the winter season, offering significant contributions to the field. A novel methodology is introduced to accurately assess the real Seasonal Coefficient of Performance (SCOP) of VRF systems, benchmarked against conventional Heating, Ventilation, and Air Conditioning (HVAC) technologies, such as natural gas-fueled boiler systems. The findings demonstrate outstanding seasonal energy performance, with the VRF system achieving a SCOP of 5.349, resulting in substantial energy savings and enhanced sustainability. Key outcomes include a 67% reduction in primary energy consumption and a 79% decrease in greenhouse gas emissions per square meter when compared to traditional boiler systems. Furthermore, VRF systems meet 83% of the building’s energy demand through renewable energy sources, exceeding the regulatory SCOP threshold of 2.5. These results underscore the transformative potential of VRF systems in achieving nearly Zero-Energy Building (nZEB) objectives, illustrating their ability to exceed stringent sustainability standards. The research emphasizes the strategic importance of adopting advanced HVAC solutions, particularly in regions with high heating demands, such as those characterized by continental climates. VRF systems emerge as a superior alternative, optimizing energy consumption while significantly reducing the environmental footprint of buildings. By contributing to global sustainable development and climate change mitigation efforts, this study advocates for the widespread adoption of VRF systems, positioning them as a critical component in the transition toward a sustainable, zero-energy building future.
Experimental Study on the Influence of Permeation Deformation of Limestone with Different Moisture Contents in Deep-Water Environments
Authors: Hou Chunyao , Cheng Heng , Tan Dawen , Lei Yanan , Jiang Chenfang , Zhao Yuntian , Tian Jingjie
Published: February 2025

This study deeply explores the permeation deformation mechanism of limestone cores in deep-water environments. Through a customized test device, the mechanical responses of deep-water rocks under different moisture content conditions are simulated. The device is composed of a pressurization system, a pressure vessel, and a data acquisition system, which can accurately apply high water pressure and monitor the stress and strain changes in the core in real time. The cores used in the experiment were cut to standardized sizes and subjected to strict saturation and drying treatments to ensure the consistency of test conditions and the accuracy of data. The mechanical behaviors of cores under four working conditions of dryness, 50% moisture content, and 100% moisture content are analyzed. Through the cyclic process of pressurization and depressurization, the changes in the strain characteristics of the cores are observed. The research results show that when pressure is applied for the first time, low moisture content cores will absorb water and expand, and the strain will increase and then tend to be stable as the pressure increases. There is no such process for cores with 100% moisture content. Water pressure is positively correlated with the elastic modulus of rocks, negatively correlated with the strain rate during pressurization, and positively correlated during depressurization. Moreover, an increase in moisture content reduces the average strain mutation, reduces the average strain rate amplitude, and increases the elastic modulus. This study provides important theoretical support for the design and construction of deep-water rock engineering and provides a reference for further research in related fields.
Characterization and Identification of Temperature and Humidity Properties of Varied Winter Covering Techniques for Wine Grapes in the North Foothills of the Tianshan Region
Authors: Ma Yunlong , Yang Jinyue , He Jiaxin , Wang Ping , Sun Qinming
Published: February 2025

Overwintering frost damage is a major challenge for the wine grape industry in northern China. This study investigates overwintering treatments to improve survival rates and mitigate frost damage in the wine grape production area of the northern foothills of the Tianshan Mountains. Seven overwintering treatments were tested: soil-covered striped cloth, striped cloth, sandwiched striped cloth, thickened striped cloth, double-layered striped cloth, heat-insulating striped cloth, and heat-insulating sandwich striped cloth. Temperature and humidity were continuously monitored during the overwintering period, both aboveground and at depths of 20 and 40 cm underground. By analyzing temperature trends, the duration of low temperatures, and temperature fluctuations, comprehensive overwintering indices were derived through principal component analysis to assess heat retention, moisture preservation, and the impact on grapevine survival. The results showed that the sandwiched striped cloth treatment provided the best insulation, with a 4.4 °C higher minimum daily temperature and a 356% increase in overwintering indices compared to striped cloth alone. The double-layer striped cloth treatment also improved safety, with a 130% increase in overwintering indices. Other treatments, including the soil-covered and the heat-insulating striped cloth, showed reduced performance. The sandwiched striped cloth and double-layer striped cloth treatments are recommended for northern China’s wine grape regions, with further research needed to evaluate their economic viability.
The Use of Mixed Reality in Training Trainers—A Single-Centre Study
Authors: Malhotra Prabhjot Singh , Jain Swati , Karcheva Silvia Stefanova , Helmy Adel
Published: February 2025

There has been an exponential increase in the utility of mixed-reality (MR) software as a tool for medical education and training due to its immersive and interactive capabilities. Whilst it has been progressively used in surgical training or in simulation training, there is a significant lack of using it to train the “trainers”. In this single-centre prospective study, MR technology was used to deliver a dedicated 2-h tutorial in surgical training to two cohorts of postgraduate students attending a course on clinical research and education. The Microsoft HoloLens 2 was used to run mixed-reality software capable of rendering CT scan images of a normal brain, an MRI of a large meningioma, an abdominal–pelvic CT scan, and a 3D-printed cranioplasty scan. The participants were then asked to complete a post-usage questionnaire in an anonymous manner. Fourteen participants attended the teaching session and completed the post-usage questionnaire. Scores obtained on the User Experience Questionnaire (UEQ) revealed that MR technology is rated “Excellent” on quality aspects for Attractiveness, Stimulation and Novelty. This prospective study provides insight into incorporating MR in training the trainers, allowing them to be equipped with the technology to imparting education to the next generation across various disciplines.
Multiphysics Analysis Process of Front-End Process for Induction Hardening of 3D Structures to Predict Structural Deformation
Authors: Hwang Soonjae , Yi Sarang , Park Jongkyu , Hong Seokmoo
Published: February 2025

The torsion beam, integral to rear suspension systems in vehicles, is a critical component where strength and durability must be prioritized in specific regions. To enhance the strength of these regions, induction hardening, a localized heat treatment method, is employed. However, the application of this treatment introduces distortion, which compromises the precision of welding between components and raises significant concerns about product quality and safety. To address these challenges, the present study introduces a front-end analysis process for induction hardening aimed at predicting distortion following heat treatment. A three-dimensional model of the product was utilized to simulate the front-end process of induction hardening. A coupled analysis of electrical and thermal fields was conducted to replicate the heating effect induced by coils during the heat treatment process. Additionally, a thermo-structural coupled analysis was performed to predict the distortion occurring during the cooling phase. A comparative analysis with actual product measurements demonstrated that the proposed method achieved a distortion displacement prediction accuracy of 98%, thereby validating the efficacy of the proposed analysis process.
Fast Path Generation Algorithm for Mobile Robot Navigation Using Hybrid Map
Authors: Baek Youngmi , Park Jung Kyu
Published: February 2025

In this paper, we address the challenge of memory consumption in mobile robot navigation, particularly when using grid-based maps for accurate environment representation. While grid-based maps are widely used, their large memory requirements make them unsuitable for embedded systems. To overcome this limitation, we propose a novel path planning algorithm that rapidly generates secure paths using a hybrid map. This hybrid map significantly reduces memory usage compared to grid-based maps while maintaining the efficiency of a topological map. Experimental results demonstrate that the proposed method requires only 1.5% of the computation time needed for grid-based path planning, ensuring faster and more efficient navigation. Furthermore, the approach provides robust and secure paths, making it ideal for mobile robot applications. This method holds promise for advancing autonomous navigation in resource-constrained environments.
CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
Authors: Feng Shu , Gao Luhan , Shi Leyi
Published: February 2025

The implementation of comprehensive security measures is a critical factor in the rapid growth of industrial control networks. Federated Learning has emerged as a viable solution for safeguarding privacy in machine learning. The effectiveness of pattern detection in models is diminished as a result of the difficulty in extracting attack information from extremely large datasets and obtaining an adequate number of examples for specific types of attacks. A robust Federated Learning method, CGFL, is introduced in this study to resolve the challenges presented by data distribution discrepancies and client class imbalance. By employing a data generation strategy to generate balanced datasets for each client, CGFL enhances the global model. It employs a data generator that integrates artificially generated data with the existing data from local clients by employing label correction and data generation techniques. The geometric median aggregation technique was implemented to enhance the security of the aggregation process. The model was simulated and evaluated using the CIC-IDS2017 dataset, NSL-KDD dataset, and CSE-CIC-IDS2018 dataset. The experimental results indicate that CGFL does an effective job of enhancing the accuracy of ICS attack detection in Federated Learning under imbalanced sample conditions.
A Comprehensive Review of the Influence of Sensitizers on the Detonation Properties of Emulsion Explosives
Authors: Maranda Andrzej , Markowska Dorota , Kukfisz Bożena , Jakubczak Weronika
Published: February 2025

Emulsion explosives are extensively utilized in the global mining industry due to their superior water resistance, high safety standards, cost-efficiency, and robust performance. The basic component of these explosives is a water-in-oil emulsion matrix, which, in its initial state, lacks the capacity for detonation. The sensitization process, achieved through either physical or chemical means, is a critical step that enhances the emulsion’s sensitivity to detonation, thereby improving its operational efficiency in blasting applications. This review presents a comprehensive and systematic analysis of the current scientific literature and experimental investigations concerning the impact of key sensitizing methods and agents on the detonation characteristics of emulsion explosives. Particular emphasis is placed on the classification of sensitizers, their physicochemical properties, and their interactions with the emulsion matrix. By examining various sensitization mechanisms, this study provides insights into the role and efficacy of both established and emerging sensitizing agents. The findings of this review highlight the pivotal role of sensitizer selection in defining the detonation performance of emulsion explosives, with implications for enhancing safety standards and ensuring the protection of both industrial operations and public safety. The most optimal sensitization method is chemical, utilizing cost-effective components that generate gas bubbles within the matrix. A key advantage is the in situ production of emulsion explosives, which eliminates the need for their transport on public roads, thereby enhancing safety and reducing the risk of terrorist threats.
Flow-Induced Strength Analysis of Large Francis Turbine Under Extended Load Range
Authors: Liu Xingping , Huang Xingxing , Chen Weijiang , Wang Zhengwei
Published: February 2025

To meet the load requirements of the power grid, the hydroelectric power plants need to extend the operational load range of the turbine units, which are often operated under off-design operating conditions. This new challenge significantly changes the flow characteristics of the hydro turbine units. Strong vibrations and high stresses caused by pressure pulsations at various loads directly lead to severe damage to the runner blades, threatening the safe operation of the hydropower unit. In this study, the detailed flow dynamics analysis under three loading conditions of a large-scale Francis turbine, i.e., 33.3%, 66.6%, and 100% of the Francis turbine’s rated power, is investigated with computational fluid dynamics (CFD) calculations. The pressure files at different operating conditions are adopted to carry out the corresponding flow-induced strength analysis of the Francis runner prototype. The pressure distributions and flow velocity distributions at these three typical operating conditions are studied, and the maximum stress of the runner gradually increases with the power output of the turbine, but it is only around one-third of the yield stress of the runner material. It reveals that the runner is safe to operate in the extended operation range from a 33.3% to 100% of the rated power load. The analysis approach in this work can be applied to other hydraulic machinery including Francis turbines, pumps and pump–turbines.
De Novo Leaf Transcriptome Assembly and Metagenomic Studies of Coast Live Oak (Quercus agrifolia)
Authors: Senn Savanah , Enke Ray A. , Carrell Steven J. , Nations Bradley , Best Meika , Kostoglou Mathew , Smith Karu , Yan Jieyao , Ford Jillian M. , Vion Les , Presley Gerald
Published: February 2025

Coast Live Oak (Quercus agrifolia) is a native keystone hardwood species of the California coastal and semi-arid forest environment. Q. agrifolia is threatened by pathogens such as the oomycete Phytophthora ramorum, which is known to cause Sudden Oak Death in environments from Southern California to Oregon. This study considers oaks and their rootzone microbes recovering from moderate and low-intensity fires in rapid succession, compared to high- and low-intensity fires with a large time gap between them. cDNA libraries from nine oak leaf tissue samples were sequenced on DNBseq. Soil samples were sent out for shotgun metagenomics and for 16S community profiling. The de novo Q. agrifolia assembly yielded 521,817 transcripts with an average length of 805.2 bp. Among identified DEGs (differentially expressed genes) between the trail areas, several candidate genes were identified including shikimate dehydrogenase and myrcene synthase. The MegaBLAST results showed a high degree of similarity to WGS sequences from Q. agrifolia that had been previously annotated in other closely related Quercus species. There was a differential abundance of microbial genera associated with the different burn areas, including Pedobacter, Filimonas, Cohnella, and Sorangium. The data embody the first Q. agrifolia transcriptome that with further development could be used to screen oak seedlings for resistance; beneficial microbial populations have been identified that are associated with fire recovery under varied conditions.
Investigation of Carbon Monoxide, Carbon Dioxide, and Methane Source Variability at the WMO/GAW Station of Lamezia Terme (Calabria, Southern Italy) Using the Ratio of Ozone to Nitrogen Oxides as a Proximity Indicator
Authors: D’Amico Francesco , Lo Feudo Teresa , Gullì Daniel , Ammoscato Ivano , De Pino Mariafrancesca , Malacaria Luana , Sinopoli Salvatore , De Benedetto Giorgia , Calidonna Claudia Roberta
Published: February 2025

In the field of Atmospheric Sciences, source apportionment and a more detailed understanding of local and remote contributions to observed concentrations of greenhouse gases (GHGs) across international networks, such as the World Meteorological Organization—Global Atmosphere Watch (WMO/GAW), can be achieved via the implementation of new atmospheric tracers. One tool for achieving a more precise understanding of GHG emissions is the evaluation of air mass aging indicators, which can serve as proximity indicators. In this study, the ratio between ozone (O3) and nitrogen oxides (NOx) is applied to nine continuous years (2015–2023) of measurements at the Lamezia Terme (LMT) observation site in Calabria, Southern Italy, to differentiate the aging of air masses and identify four distinct categories: LOC (local), N–SRC (near source), R–SRC (remote source), and BKG (atmospheric background). Due to possible overestimation of nitrogen dioxide (NO2) caused by heated (~300–400 °C) molybdenum converters used in the employed instruments, a correction factor based on a previous study has been integrated to further analyze the results. Additionally, this work introduces a second correction factor based on the local behavior of surface ozone and the diurnal peaks observed during boreal warm seasons in an area characterized by a Mediterranean climate. The results of this study confirm the hypotheses of previous works on local sources of pollution: the LOC category yields the highest concentrations observed at the site, which are in accordance with the northeastern wind sector and anthropogenic sources. R–SRC and BKG are more representative of atmospheric background levels and characterize westerly winds from the Tyrrhenian Sea. N–SRC, as expected, shows an intermediate behavior between local and remote/background levels. Differences in results between standard O3/NOx categories and corrected measurements will need to be investigated in future studies.
Reliability and Performance Optimization of Multi-Subsystem Systems Using Copula-Based Repair
Authors: Elshoubary Elsayed E. , Radwan Taha , Attwa Rasha Abd El-Wahab
Published: February 2025

This paper proposes a system made up of four subsystems connected in sequence. The first and third subsystems each have one unit, the second has two, and the fourth has three. Every subsystem operates in parallel and is governed by the K-Out-of-n:G rule. Nonetheless, each subsystem needs at least one operational unit in order for the system to work. While a unit’s failure has an exponential distribution, repair is simulated using a general distribution and a distribution from the Gumbel–Hougaard family of copula. This study’s primary objective is to assess and contrast the system performance while our system is running under these two different repair policies. The problem is solved by combining the supplementary variable technique with Laplace transforms. We use reliability metrics to assess system performance. The second objective of this study is to present a reduction approach plan aimed at improving the overall reliability metrics of our system.
Effects of Experimentally Induced Lower Limb Muscle Fatigue on Healthy Adults’ Gait: A Systematic Review
Authors: Wang Liangsen , Ma Wenyue , Zhu Wenfei , Zhai Lin , Sun Yuliang
Published: February 2025

Lower limb fatigue reduces muscle strength, alters joint biomechanics, affects gait, and increases injury risk. In addition, it is of great clinical significance to explore local muscle fatigue or weakness caused by fatigue to understand its compensatory effect on the ipsilateral or contralateral joints. We systematically searched multiple databases, including five databases, using key terms such as “Muscle Fatigue” and “Gait”. Only studies that experimentally induced fatigue through sustained muscle activities in healthy adults were included. This review examined 11 studies exploring the effects of lower limb muscle fatigue on gait and lower limb biomechanics. The findings indicated that muscle fatigue significantly influenced spatiotemporal parameters, joint angles, and moments. Most studies that were reviewed reported an increase in step width and a decrease in knee joint moments following fatigue. Additionally, muscle activation levels tended to decline. In summary, compensatory mechanisms can lead to new walking strategies, such as increasing step width or enhancing the strength of muscles in adjacent joints. These adjustments impact dynamic balance differently: wider steps may enhance medial–lateral stability, while reduced muscle strength could lead to higher heel contact velocity and longer slip distances. Although these changes might influence dynamic balance, compensatory strategies may help mitigate the overall effect of fall risk. Future studies should use appropriate protocols, such as moderate or severe fatigue interventions with isokinetic dynamometry.
Qualitative and Quantitative Evaluation of a Deep Learning-Based Reconstruction for Accelerated Cardiac Cine Imaging
Authors: Ma Junjie , Zhu Xucheng , Kaushik Suryanarayanan , Ali Eman , Li Liangliang , Manickam Kavitha , Li Ke , Janich Martin A.
Published: February 2025

Two-dimensional (2D) cine imaging is essential in routine clinical cardiac MR (CMR) exams for assessing cardiac structure and function. Traditional cine imaging requires patients to hold their breath for extended periods and maintain consistent heartbeats for optimal image quality, which can be challenging for those with impaired breath-holding capacity or irregular heart rhythms. This study aims to systematically assess the performance of a deep learning-based reconstruction (Sonic DL Cine, GE HealthCare, Waukesha, WI, USA) for accelerated cardiac cine acquisition. Multiple retrospective experiments were designed and conducted to comprehensively evaluate the technique using data from an MR-dedicated extended cardiac torso anatomical phantom (digital phantom) and healthy volunteers on different cardiac planes. Image quality, spatiotemporal sharpness, and biventricular cardiac function were qualitatively and quantitatively compared between Sonic DL Cine-reconstructed images with various accelerations (4-fold to 12-fold) and fully sampled reference images. Both digital phantom and in vivo experiments demonstrate that Sonic DL Cine can accelerate cine acquisitions by up to 12-fold while preserving comparable SNR, contrast, and spatiotemporal sharpness to fully sampled reference images. Measurements of cardiac function metrics indicate that function measurements from Sonic DL Cine-reconstructed images align well with those from fully sampled reference images. In conclusion, this study demonstrates that Sonic DL Cine is able to reconstruct highly under-sampled (up to 12-fold acceleration) cine datasets while preserving SNR, contrast, spatiotemporal sharpness, and quantification accuracy for cardiac function measurements. It also provides a feasible approach for thoroughly evaluating the deep learning-based method.
Petroselinic Acid from Apiaceae Family Plants Ameliorates Autoimmune Disorders Through Suppressing Cytosolic-Nucleic-Acid-Mediated Type I Interferon Signaling
Authors: Guo Yue , Wang Yun-Ying , Wang Yao , Liu Yan-Hong , Liu Jia-Yu , Shen Yan-Yan , Cao Ai-Ping , Wang Rui-Bo , Xie Bo-Yang , Pan Xin , Li Ai-Ling , Zhou Tao , Wang Na , Xia Qing , Zhang Wei-Na
Published: February 2025

The recognition of cytosolic nucleic acids is a critical step in the host immune response against danger signals, such as molecular patterns from pathogens or tissue damage. Nonetheless, over-reactivity to self-nucleic acids leads to the sustained production of type I interferon (IFN), mediated either by cGAS or RLR, contributing to the pathogenesis of certain autoimmune diseases, such as Aicardi–Goutières syndrome (AGS). Therefore, inhibiting excessive IFN production represents a potential therapeutic strategy for such autoimmune conditions. In this study, we discovered that petroselinic acid (PA), a natural compound isolated from Apiaceae family plants, effectively suppresses type I IFN production induced by cytosolic nucleic acids. Mechanistic investigations revealed that PA inhibits the phosphorylation of TBK1 and IRF3, which are key nodal proteins within the type I interferon pathway. Notably, molecular docking suggests potential binding between PA and cytosolic nucleic acid sensors, such as cGAS and RIG-I. Moreover, we found that PA effectively attenuates the expression of type I IFN and their downstream interferon-stimulated genes (ISGs) in models of AGS autoimmune disease characterized by excessive nucleic acid accumulation. Thus, our research identifies a natural compound that offers a promising strategy for treating autoimmune diseases resulting from aberrant self-nucleic acid recognition and the hyperactivation of type I interferon.
A Review of the Sustainability, Chemical Composition, Bioactive Compounds, Antioxidant and Antidiabetic Activity, Neuroprotective Properties, and Health Benefits of Microalgae
Authors: Dimopoulou Maria , Kolonas Alexandros , Stagos Dimitris , Gortzi Olga
Published: February 2025

Microalgae have emerged as a valuable source of essential nutrients and bioactive compounds, such as proteins, polyphenols, and polysaccharides, which are critical for overall health. Recent research has demonstrated the therapeutic potential of microalgae in addressing a variety of health conditions, including inflammation, oxidative stress, Type 2 diabetes mellitus (T2DM), and neurological disorders. The aim of this paper is to investigate the chemical composition, nutritional value, and biological properties of microalgae. Relevant information was gathered through a comprehensive search of scientific databases, including PubMed, Science Direct, Google Scholar, and the Cochrane Library. Key microalgal strains such as Spirulina platensis, Chlorella vulgaris, Haematococcus pluvialis, and Dunaliella salina have shown notable health-promoting properties. For instance, Spirulina platensis is rich in proteins, vitamins, and polyunsaturated fatty acids, while Chlorella vulgaris offers significant levels of chlorophyll and carotenoids. Haematococcus pluvialis is recognized for its high astaxanthin content and Dunaliella salina for its beta-carotene content. These microalgae strains have demonstrated beneficial effects in managing type 2 diabetes mellitus, alleviating oxidative stress, and offering neuroprotective potential. This paper provides an overview of microalgae’s nutritional composition, their medicinal properties, and their promising role in treating chronic diseases, with a particular focus on their applications in antidiabetic and neuroprotective therapies.
Angiotensin 1–7 and the Non-Peptide MAS-R Agonist AVE0991 Inhibit Breast Cancer Cell Migration and Invasion
Authors: Alfoudiry Mariam M. , Khajah Maitham A.
Published: February 2025

Background: Endocrine resistance in breast cancer is associated with the epithelial-to-mesenchymal transition (EMT), resulting in enhanced cell proliferation, motility, and invasion and leading to a poor prognosis. There are few studies regarding the role of Angiotensin II (Ang II) and Angiotensin 1–7 (Ang 1–7) in relation to breast cancer, with contradictory outcomes. This study aims to investigate the expression of Ang 1–7 and MAS-R and evaluate the effects of Ang II, Ang 1–7, and the MAS-R agonist AVE0991 on EMT induction and reversal. Methods: The effects of Ang II and Ang 1–7 on normal and breast cancer cell lines were determined using various techniques for cell proliferation (MTT), motility (scratch assay), and invasion (Cultrex assay). Also, the expression/localization profiles of Ang 1–7 and its receptor (MAS-R), as well as various EMT markers, were determined using immunofluorescence, western blot, and ELISA. Results: Ang II significantly decreased the motility of the tested cell lines; however, it did not have a significant effect on their proliferation or invasion. The expression profiles of the tested EMT markers were not affected by Ang II treatment. The expression levels of Ang 1–7 and MAS-R were significantly higher in the normal breast epithelial cells and estrogen receptor ER compared to the ER+ breast cancer cells. Treatment with Ang 1–7 or the non-peptide MAS-R agonist AVE0991 significantly reduced the migration and invasion of the tested cell lines without modulating the tested EMT markers. Compared to Ang 1–7, AVE0991 exhibited a more prominent dose-dependent inhibitory effect on the proliferation, motility, and invasion of the ER− breast cancer cells. Conclusions: Ang 1–7 and AVE0991 play a promising therapeutic role in breast cancer, in part by reducing cell motility and invasion.
Severe Intra- and Post-Operative Lactic Acidosis in a Patient Who Underwent Robotic Thoracoscopic Surgery
Authors: Smirnov Alexander , Semionov Michael , Ishay Shlomo Yaron , Zlotnik Alexander , Fraifeld Vadim E. , Frank Dmitry
Published: February 2025

Background/Objectives: Lactic acidosis is one of the most common causes of metabolic acidosis in hospitalized patients. It happens when lactic acid production exceeds lactic acid clearance. The elevation of lactate was commonly improved after the restoration of tissue perfusion. However, there are rare cases of severe lactate elevation (greater than 8 mmol/L) in the intraoperative period of thoracoscopic surgery. A poor prognosis with high morbidity and mortality characterizes these cases. Case Description: A 72-year-old man was admitted to the Soroka University Medical Center for thoracoscopic robotic left upper lobe lobectomy due to squamous cell carcinoma. At the end of surgery (overall, 8.5 h), the lactate level reached 10.2 mmol/L with the development of severe lactic metabolic acidosis. Thiamine was successfully given to patients to stimulate lactate clearance towards the cycle of tricarboxylic acids via pyruvate. Conclusions: Though the pathogenesis of this state in our case is not fully clear, it may have been induced by chemotherapy and during tumor manipulation by a surgeon. The successful recovery of blood lactic levels after thiamine treatment is suggestive of thiamine deficiency as a possible cause of lactic acidosis in our patient. Although we do not have data on the plasma thiamine level, we suggest that its determination in the perioperative period would be beneficial for excluding a probable thiamine deficiency in the case of severe lactic acidosis.
Beyond the Surface: Nutritional Interventions Integrated with Diagnostic Imaging Tools to Target and Preserve Cartilage Integrity: A Narrative Review
Authors: Lavalle Salvatore , Scapaticci Rosa , Masiello Edoardo , Salerno Valerio Mario , Cuocolo Renato , Cannella Roberto , Botteghi Matteo , Orro Alessandro , Saggini Raoul , Donati Zeppa Sabrina , Bartolacci Alessia , Stocchi Vilberto , Piccoli Giovanni , Pegreffi Francesco
Published: February 2025

This narrative review provides an overview of the various diagnostic tools used to assess cartilage health, with a focus on early detection, nutrition intervention, and management of osteoarthritis. Early detection of cartilage damage is crucial for effective patient management. Traditional diagnostic tools like radiography and conventional magnetic resonance imaging (MRI) sequences are more suited to detecting late-stage structural changes. This paper highlights advanced imaging techniques, including sodium MRI, T2 mapping, T1ρ imaging, and delayed gadolinium-enhanced MRI of cartilage, which provide valuable biochemical information about cartilage composition, particularly the glycosaminoglycan content and its potential links to nutrition-related factors influencing cartilage health. Cartilage degradation is often linked with inflammation and measurable via markers like CRP and IL-6 which, although not specific to cartilage breakdown, offer insights into the inflammation affecting cartilage. In addition to imaging techniques, biochemical markers, such as collagen breakdown products and aggrecan fragments, which reflect metabolic changes in cartilage, are discussed. Emerging tools like optical coherence tomography and hybrid positron emission tomography–magnetic resonance imaging (PET-MRI) are also explored, offering high-resolution imaging and combined metabolic and structural insights, respectively. Finally, wearable technology and biosensors for real-time monitoring of osteoarthritis progression, as well as the role of artificial intelligence in enhancing diagnostic accuracy through pattern recognition in imaging data are addressed. While these advanced diagnostic tools hold great potential for early detection and monitoring of osteoarthritis, challenges remain in clinical translation, including validation in larger populations and integration into existing clinical workflows and personalized treatment strategies for cartilage-related diseases.