bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating the Influence of a Gamified Resource on Healthcare Students’ Attitudes, Confidence, and Knowledge Regarding Research Concepts: A Mixed-Method Study
Authors: Johns Lise , Bisset Leanne , Weightman Stacey , Grant Gary , Shuker Mary-Ann , van de Mortel Thea
Published: January 2025

Many university students report anxiety and low confidence when undertaking research courses. In the field, healthcare professionals have further identified that new graduates lack basic research skills. The research aims were to (1) ascertain what research concepts were deemed challenging to inform the development of a gamified resource (named OGRE) and (2) to determine if the resource influenced students’ research attitudes, confidence, and knowledge. Adopting a mixed-method approach, four student group interviews were conducted and pre- and post-course surveys were embedded in two large research courses as measures. Additionally, the qualitative component of student evaluations from six courses served as secondary data. Descriptive statistical analysis and thematic analysis techniques were applied. Whilst students understood the relevance of research to practice, only a minority expressed enthusiasm for the topic. The game did not significantly impact on confidence to undertake research, nor could the increase in student knowledge be attributed to the game alone. Gamified resources can be helpful for visual learners and can assist students with ‘real-life’ applications to practice problems, but are not essential to achieve a course’s learning outcomes. Further multidisciplinary team scenarios could be incorporated in OGRE to enhance practice preparedness.
Dynamic Adaptive Artificial Hummingbird Algorithm-Enhanced Deep Learning Framework for Accurate Transmission Line Temperature Prediction
Authors: Ji Xiu , Lu Chengxiang , Xie Beimin , Han Huanhuan , Li Mingge
Published: January 2025

As power demand increases and the scale of power grids expands, accurately predicting transmission line temperatures is becoming essential for ensuring the stability and security of power systems. Traditional physical and statistical models struggle with complex multivariate time series, often failing to balance short-term fluctuations with long-term dependencies, and their prediction accuracy and adaptability remain limited. To address these challenges, this paper proposes a deep learning model architecture based on the Dynamic Adaptive Artificial Hummingbird Algorithm (DA-AHA), named the DA-AHA-CNN-LSTM-TPA (DA-AHA-CLT). The model integrates convolutional neural networks (CNNs) for local feature extraction, long short-term memory (LSTM) networks for temporal modeling, and temporal pattern attention mechanisms (TPA) for dynamic feature weighting, while the DA-AHA optimizes hyperparameters to enhance prediction accuracy and stability. The traditional artificial hummingbird algorithm (AHA) is further improved by introducing dynamic step-size adjustment, greedy local search, and grouped parallel search mechanisms to balance global exploration and local exploitation. Our experimental results demonstrate that the DA-AHA-CLT model achieves a coefficient of determination (R2) of 0.987, a root-mean-square error (RMSE) of 0.023, a mean absolute error (MAE) of 0.018, and a median absolute error (MedAE) of 0.011, outperforming traditional models such as CNN-LSTM and LSTM-TPA. These findings confirm that the DA-AHA-CLT model effectively captures the complex dynamic characteristics of transmission line temperatures, offering superior performance and robustness in full-time-step prediction tasks, and highlight its potential for solving challenging multivariate time-series forecasting problems in power systems.
Efficient Embedded System for Drowsiness Detection Based on EEG Signals: Features Extraction and Hardware Acceleration
Authors: Zayed Aymen , Trabes Emanuel , Tarrillo Jimmy , Ben Khalifa Khaled , Valderrama Carlos
Published: January 2025

Drowsiness detection is crucial for ensuring the safety of individuals engaged in high-risk activities. Numerous studies have explored drowsiness detection techniques based on EEG signals, but these have typically been validated on computers, which limits their portability. In this paper, we introduce the design and implementation of a drowsiness detection technique utilizing EEG signals, executed on a Zynq7020 System on Chip (SoC) as part of a Pynq-Z2 module. This approach is more suitable for portable applications. We have implemented the Discrete Wavelet Transform (DWT) and feature extraction functions as intellectual property (IP) cores, while other functions run on the ARM processor of the Zynq7020.
Temporal Adaptive Attention Map Guidance for Text-to-Image Diffusion Models
Authors: Jung Sunghoon , Heo Yong Seok
Published: January 2025

Text-to-image generation aims to create visually compelling images aligned with input prompts, but challenges such as subject mixing and subject neglect, often caused by semantic leakage during the generation process, remain, particularly in multi-subject scenarios. To mitigate this, existing methods optimize attention maps in diffusion models, using static loss functions at each time step, often leading to suboptimal results due to insufficient consideration of varying characteristics across diffusion stages. To address this problem, we propose a novel framework that adaptively guides the attention maps by dividing the diffusion process into four intervals: initial, layout, shape, and refinement. We adaptively optimize attention maps using interval-specific strategies and a dynamic loss function. Additionally, we introduce a seed filtering method based on the self-attention map analysis to detect and address the semantic leakage by restarting the generation process with new noise seeds when necessary. Extensive experiments on various datasets demonstrate that our method achieves significant improvements in generating images aligned with input prompts, outperforming previous approaches both quantitatively and qualitatively.
Easy and Straightforward FPGA Implementation of Model Predictive Control Using HDL Coder
Authors: Purraji Marziye , Zamiri Elyas , de Castro Angel
Published: January 2025

Model Predictive Control (MPC) is widely adopted for power electronics converters due to its ability to optimize system performance under dynamic constraints. However, its FPGA implementation remains challenging due to the complexity of Hardware Description Language (HDL) programming. This paper addresses this challenge by introducing a straightforward methodology that simplifies FPGA implementation using MATLAB R2022b Simulink HDL Coder. It is shown that HDL Coder yields favorable synthesis outcomes, both in terms of area and time, compared to hand-coded HDL. Notably, the proposed method achieves a significantly reduced sampling step for the MPC algorithm—down to 32 ns—marking a substantial improvement over state-of-the-art implementations. The Integrated Logic Analyzer (ILA) IP available in the Vivado tool is used during the HIL testing phase to facilitate the real-time observation and analysis required for debugging and confirming the FPGA-implemented controller performance. Additionally, this paper discusses the advantages of utilizing HDL Coder for simplifying the FPGA programming process in power electronics applications and addresses the design challenges encountered using this methodology.
Extended Study of a Multi-Modal Loop Closure Detection Framework for SLAM Applications
Authors: Chghaf Mohammed , Rodríguez Flórez Sergio , El Ouardi Abdelhafid
Published: January 2025

Loop Closure (LC) is a crucial task in Simultaneous Localization and Mapping (SLAM) for Autonomous Ground Vehicles (AGV). It is an active research area because it improves global localization efficiency. The consistency of the global map and the accuracy of the AGV’s location in an unknown environment are highly correlated with the efficiency and robustness of Loop Closure Detection (LCD), especially when facing environmental changes or data unavailability. We propose to introduce multimodal complementary data to increase the algorithms’ resilience. Various methods using different data sources have been proposed to achieve precise place recognition. However, integrating a multimodal loop-closure fusion process that combines multiple information sources within a SLAM system has been explored less. Additionally, existing multimodal place recognition techniques are often difficult to integrate into existing frameworks. In this paper, we propose a fusion scheme of multiple place recognition methods based on camera and LiDAR data for a robust multimodal LCD. The presented approach uses Similarity-Guided Particle Filtering (SGPF) to identify and verify candidates for loop closure. Based on the ORB-SLAM2 framework, the proposed method uses two perception sensors (camera and LiDAR) under two data representation models for each. Our experiments on both KITTI and a self-collected dataset show that our approach outperforms the state-of-the-art methods in terms of place recognition metrics or localization accuracy metrics. The proposed Multi-Modal Loop Closure (MMLC) framework enhances the robustness and accuracy of AGV’s localization by fusing multiple sensor modalities, ensuring consistent performance across diverse environments. Its real-time operation and early loop closure detection enable timely trajectory corrections, reducing navigation errors and supporting cost-effective deployment with adaptable sensor configurations.
Energy Loss of Magnetic Coupling for Pump
Authors: Wang Jiaqiong , Duan Jundong , Zhang Ruijie , Sun Jingru
Published: January 2025

To investigate the energy loss characteristics of magnetic pumps, an experimental setup for magnetic transmission in pumps was constructed. Through experiments, the impact of various factors on the eddy current loss of the magnetic coupling was examined, including rotational speed, electrical conductivity of the isolation sleeve material, axial coupling length of the magnetic coupling, and the physical properties of the fluid medium. Additionally, the temperature variation at different axial positions along the isolation sleeve was measured. The results indicated that eddy current loss increases with higher rotational speeds and shows a nearly linear relationship with the axial coupling length of the magnetic coupling. Furthermore, as the conductivity of the isolation sleeve material rises, the eddy current loss also increases linearly. The conductivity and viscosity of the fluid medium were found to have negligible effects on eddy current loss. Friction losses due to the liquid medium inside the metal isolation sleeve were much smaller than the eddy current losses generated by the sleeve, and thus can be ignored. For non-metallic isolation sleeves, the energy loss is primarily attributed to fluid friction. Finally, the temperature along the axial direction of the isolation sleeve increases progressively from the flange to the bottom.
Experimental Evaluation of a Mobile Charging Station Prototype for Energy Supply Applied to Rural and Isolated Areas in Emergency Situations
Authors: Milón Guzmán Juan José , Braga Sergio Leal , Pradelle Florian Alain Yannick , Díaz Coa Mario Enrique , Infa Mamani Cinthia Katherin
Published: January 2025

A prototype of a mobile electric charging station was developed to simulate the energy supply to a rural medical post. A 20 m2 medical post module was built, divided into two rooms (medical staff room and patient room) and a heater, a freezer, a refrigerator, lights and a personal computer were added inside. The mobile electric charging station was made up of an array of 2.88 kW flexible photovoltaic panels, a 48 V and 19.2 kW·h LiFePO4 battery bank, a charger inverter with a total capacity of 5 kW and a 4 kW electric generator. All of this equipment was placed in an all-terrain pickup truck. Temperature sensors and electrical sensors were installed to evaluate the performance of the prototype in charging and discharging scenarios. Results were obtained according to the operation over 10 months in the city of Arequipa, Peru. The results indicate an indefinite autonomy on clear days, the autonomy varying between 7 and 10 days for a climate with medium cloudiness, and with very cloudy conditions (i.e., with rain), the autonomy is 2 to 3 days. In circumstances of low solar irradiance, the generator had to supply the energy, thereby improving energy autonomy.
Optimizing IoT Video Data: Dimensionality Reduction for Efficient Deep Learning on Edge Computing
Authors: Ortiz-Perez David , Ruiz-Ponce Pablo , Mulero-Pérez David , Benavent-Lledo Manuel , Rodriguez-Juan Javier , Hernandez-Lopez Hugo , Iarovikov Anatoli , Krco Srdjan , Nedic Daliborka , Vukobratovic Dejan , Garcia-Rodriguez Jose
Published: January 2025

The rapid loss of biodiversity significantly impacts birds’ environments and behaviors, highlighting the importance of analyzing bird behavior for ecological insights. With the growing adoption of Machine Learning (ML) algorithms in the Internet of Things (IoT) domain, edge computing has become essential to ensure data privacy and enable real-time predictions by processing high-dimensional data, such as video streams, efficiently. This paper introduces a set of dimensionality reduction techniques tailored for video sequences based on cutting-edge methods for this data representation. These methods drastically compress video data, reducing bandwidth and storage requirements while enabling the creation of compact ML models with faster inference speeds. Comprehensive experiments on bird behavior classification in rural environments demonstrate the effectiveness of the proposed techniques. The experiments incorporate state-of-the-art deep learning techniques, including pre-trained video vision models, Autoencoders, and single-frame feature extraction. These methods demonstrated superior performance to the baseline, achieving up to a 6000-fold reduction in data size while reaching a classification accuracy of 60.7% on the Visual WetlandBirds Dataset and obtaining state-of-the-art performance on this dataset. These findings underline the potential of using dimensionality reduction to enhance the scalability and efficiency of bird behavior analysis.
Extraction of Biomolecules from Coffee and Cocoa Agroindustry Byproducts Using Alternative Solvents
Authors: Prado José Pedro Zanetti , Basso Rodrigo Corrêa , Rodrigues Christianne Elisabete da Costa
Published: January 2025

Coffee and cocoa agribusinesses generate large volumes of byproducts, including coffee husk, coffee pulp, parchment skin, silver skin, and cocoa bean shell. Despite the rich composition of these materials, studies on biomolecule extraction with green solvents are still scarce, and further research is needed. Extraction methods using alternative solvents to obtain biomolecules must be developed to enhance the byproducts’ value and align with biorefinery concepts. This article reviews the compositions of coffee and cocoa byproducts, their potential applications, and biomolecule extraction methods, focusing on alternative solvents. The extraction methods currently studied include microwave-assisted, ultrasound-assisted, pulsed electric field-assisted, supercritical fluid, and pressurized liquid extraction. At the same time, the alternative solvents encompass the biobased ones, supercritical fluids, supramolecular, ionic liquids, and eutectic solvents. Considering the biomolecule caffeine, using alternative solvents such as pressurized ethanol, supercritical carbon dioxide, ionic liquids, and supramolecular solvents resulted in extraction yields of 2.5 to 3.3, 4.7, 5.1, and 1.1 times higher than conventional solvents. Similarly, natural deep eutectic solvents led to a chlorogenic acid extraction yield 84 times higher than water. The results of this research provide a basis for the development of environmentally friendly and efficient biomolecule extraction methods, improving the utilization of agricultural waste.
The Use of High-Protein Preparations in Ice Cream Production
Authors: Kiełczewska Katarzyna , Smoczyński Michał , Gutkowska Marta
Published: January 2025

The aim of this study was to evaluate the applicability of high-protein preparations in the production of ice cream. Ice cream for the experiment was produced with the addition of the following high-protein preparations: micellar casein concentrate (CN) obtained from skimmed milk, buttermilk protein concentrate (BMP), whey protein concentrate (WPC) with 80% protein content, and skimmed milk powder (SMP) as the control sample. The ice cream mix (composition, colour, and consistency index) and the ice cream (overrun, melting rate, hardness, and sensory attributes) were analysed in this study. The addition of high-protein preparations increased the protein content of the ice cream mix, thus modifying selected properties of the mix and the produced ice cream. Mixes fortified with high-protein preparations were characterised by a higher consistency index (maximum values for WPC) and larger particle size (maximum values for CN) than those of the control sample. The whiteness index was lower in high-protein ice cream mixes than in the control sample. Depending on the type of preparation added to the ice cream mix, the resulting ice cream differed in hardness (hardness was highest in samples containing WPC, 276.54 N), overrun (lowest in samples containing WPC, 52.40%), and melting rate (lowest in samples containing BMP and highest in samples containing WPC, 0.24 g/min). High-protein preparations did not induce significant changes in ice cream palatability, except for ice cream fortified with WPC, which scored lower in the sensory analysis due to lower fluffiness, higher brittleness, and sour aroma and taste.
Data Augmentation for Deep Learning-Based Speech Reconstruction Using FOC-Based Methods
Authors: Yazgaç Bilgi Görkem , Kırcı Mürvet
Published: January 2025

Neural audio reconstruction is an important subtopic of Neural Audio Synthesis (NAS), which is a current emerging topic of modern Artificial Intelligence (AI) applications. The objective of a neural audio reconstruction model is to achieve a viable audio waveform from an audio feature representation that excludes the phase information. Since the data-dependent nature of such systems demands an increased quantity of data, methods of increasing the quantity of data for neural network training arise as a topic of substantial interest. Although the applications of data augmentation methods for classification tasks are well documented, there is still room for development for applications of such methods on signal synthesis tasks. Additionally, the Fractional-Order Calculus (FOC) framework provides possibilities for quality applications for the signal processing domain. Still, it is important to show that the methods based on the FOC framework can be applied to different application domains to show the capabilities of this framework. In this paper, FOC-based methods are applied to a speech dataset for data augmentation purposes to increase the audio reconstruction performance of a neural network, a spectral consistency-based neural audio reconstruction model called Deep Griffin-Lim Iteration (DeGLI), with respect to objective measures PESQ and STOI. An FOC-based method for rescaling linear frequency for augmenting magnitude spectrogram data is proposed. Furthermore, together with an FOC-based phase estimation method, it is shown that an augmentation strategy that has the objective of increased spectral consistency should be considered in data augmentation for audio reconstruction tasks. The test results reveal that this type of strategy increases the performance of a spectral consistency-based neural audio reconstruction model by over 13% for smaller depths.
Study on the Efficiency and Complexity of Chinese Energy Market Based on Multiple Events
Authors: Shen Xiaoyu , Zuo Weizhen , Chang Jiaxin , Zhou Weijie
Published: January 2025

Energy supports national economic development, and its financial attributes are continuously being enhanced. Analyzing the efficiency and risk of the energy market is of significant importance in energy security and sustainable development. This paper employs the multifractal detrended fluctuation analysis (MFDFA) method to study the efficiency, complexity, and risk of the energy exchange-traded fund (ETF) market in China. Additionally, the market characteristics are investigated under different events. The empirical results indicate that the Chinese energy ETF market is anti-persistence and has not yet reached weak efficiency. The market exhibits multifractal characteristics, with variations in multifractal structures across different stages, reflecting varying degrees of complexity. Particularly, the market displays more complex multifractal characteristics when price fluctuations are caused by supply and demand changes. Furthermore, external policies or information and supply–demand relationships have a certain impact on market prices. Based on the current state of China’s energy ETF market, some suggestions are proposed to provide valuable references for the development of the energy market.
APCAD Part 2: A Novel Method for Detection of Meiotic Aneuploidy in Preimplantation Embryos
Authors: Verdyck Pieter , Berckmoes Veerle , Fernandez Gallardo Elia , Keymolen Kathelijn , Olsen Catharina , De Rycke Martine
Published: January 2025

Background/Objectives: Preimplantation genetic testing methods to detect aneuploidy (PGT-A) based on genomewide single nucleotide polymorphism (SNP) data were scarce and did not meet our needs. Methods: Hence, we developed a novel method for this purpose. After the raw B-allele frequency (rBAF) values of Single Nucleotide Polymorfisms (SNPs) are obtained from a sample of interest with SNP array, the BAF values for specific categories of SNPs (cBAF) are visualized separately. Results: The analysis of the cBAF, rBAF and Log2R profiles enables to distinguish all common types of chromosomal abnormalities without haplotyping. This was demonstrated by reanalyzing data from 359 embryos which had previously been analyzed with Karyomapping. We identified additional underrepresented maternal haplotypes in five samples that we could not detect with Karyomapping. In addition, we identified all chromosomes with meiotic-origin copy number gains (both parental homolog (BPH)) (n = 70) and all chromosomes with a non-mosaic copy number loss larger than 5 Mb (n = 93) that had been detected with Karyomapping. Conclusions: We conclude that the proposed method can be used to reliably detect meiotic-origin aneuploidy without haplotyping and, hence without the need for a phasing reference.
Composition and Dynamics of the Sonosphere Along a Soil-Surface Ecotone at an Agricultural Site in Northern Italy: A Preliminary Approach
Authors: Farina Almo , Mullet Timothy C.
Published: January 2025

Investigating the sonosphere can serve as a valuable proxy for understanding various ecosystem processes. Consequently, an ecoacoustic perspective broadens our capacity to understand how airborne sounds interact along an ecotone at the soil surface with the subterranean sounds generated within a pedon. We explored techniques that could detect, quantify, and analyze the sonic dimensions of a sonosphere in the form of sounds within a unit of soil (sonopedon), sounds from a landscape unit (sonotope), and the sonic ecotone (sonotone) where these phenomena converge. We recorded sounds for 24 h over 20 days in September 2024 at 40 sites distributed evenly across a small rural parcel of agricultural land in Northern Italy. We utilized a sound recording device fabricated with a sonic probe that simultaneously operated inside the soil and the grounds’ surface, which successfully captured sounds attributable both to the soilscape and to the landscape. We calculated the Sonic Heterogeneity Indices, SHItf and SHIft, and analyzed the Spectral and Temporal Sonic Signatures along with Spectral Sonic Variability, Effective Number of Frequency Bins, and Sonic Dissimilarity. Each calculation contributed to a detailed description of how the sonosphere is characterized across the frequency spectrum, temporal dynamics, and sound sources. The sonosphere in our study area, primarily characterized by the low-frequency spectra, possessed a mix of biological, geophysical, and anthropogenic sounds displaying distinct temporal patterns (sonophases) that coincided with astronomic divisions of the day (daytime, twilights, and nighttime).
Factors for Predicting Morbidity and Mortality of Gastric Cancer Patients After Laparoscopic Surgery: A Retrospective Study
Authors: Olmo Juan Carlos Martín-del , López-Mestanza Cristina , Trujillo Díaz Jean Carlo , Vaquero-Puerta Carlos , Concejo-Cutoli Pilar , Gómez-López Juan Ramón
Published: January 2025

Background/Objectives: Gastric cancer is a major global health concern and a leading cause of cancer-related death. While surgical resection remains the cornerstone of curative treatment, postoperative morbidity and mortality are significant issues. This study aimed to identify factors associated with postoperative morbidity and mortality in patients undergoing laparoscopic gastrectomy for gastric adenocarcinoma. Results: The median age was 73 years. Eighteen total gastrectomies (15%) and eighty-eight subtotal gastrectomies (66.6%) were performed. Major complications (Clavien-Dindo ≥ III) occurred in 22.3% of patients, and the hospital mortality rate was 7.5%. Independent predictors of morbidity included anemia (OR 2.49, p = 0.047), extended lymphadenectomy (OR 5.09, p = 0.002), and conversion to open surgery (OR 9.40, p = 0.031). Coagulopathy was a significant predictor of mortality (OR 5.01, p = 0.049). Methods: A retrospective, single-center observational study was conducted on 120 patients who underwent laparoscopic surgery between January 2004 and December 2022. Preoperative assessments included endoscopy, imaging, and staging according to the TNM classification. Risk factors were analyzed using univariate and multivariate logistic regression. Conclusions: Laparoscopic gastrectomy is a technically challenging procedure with substantial risks of morbidity and mortality. Identifying modifiable risk factors, such as anemia and coagulopathy, provides opportunities for improved outcomes through preoperative prehabilitation and optimized patient selection.
Carbon-Supported Pt-SiO2 Catalysts for Oxygen Reduction Reaction in Low-Temperature Range: Rotating Disk Electrode Study
Authors: Mensharapov Ruslan M. , Spasov Dmitry D. , Sinyakov Matvey V. , Grineva Darya E. , Nagorny Seraphim V. , Chumakov Ratibor G. , Bakirov Artem V. , Ivanova Nataliya A.
Published: January 2025

The activities of Pt electrocatalysts modified with a prepared silica powder (with SiO2 contents of 3 and 7 wt%) in the oxygen reduction reaction in the temperature range from 0 °C to 50 °C were investigated by the rotating disk electrode technique to evaluate their efficiency in the process of the cold start of a proton-exchange membrane fuel cell (PEMFC). An increase in the mass activity of the Pt-SiO2/C electrocatalyst in comparison with Pt/C was observed, which can be attributed to a more dispersed distribution of platinum particles on the support surface and a decrease in their size. The activity values of the silica-modified electrocatalysts in the oxygen reduction reaction were approximately two-fold higher at 1 °C and four-fold higher at elevated temperatures of up to 50 °C in comparison with Pt/C, which makes their application in PEMFCs at low temperatures, including in the process of cold start, a promising avenue for further investigation.
Forecasting Model for Danube River Water Temperature Using Artificial Neural Networks
Authors: Ionescu Cristina-Sorana , Opriș Ioana , Nistoran Daniela-Elena Gogoașe , Baciu Constantin-Alexandru
Published: January 2025

The objective of this paper is to propose an artificial neural network (ANN) model to forecast the Danube River temperature at Chiciu–Călărași, Romania, bordered by Romanian and Bulgarian ecological sites, and situated upstream of the Cernavoda nuclear power plant. Given the temperature increase trend, the potential of thermal pollution is rising, impacting aquatic and terrestrial ecosystems. The available data covered a period of eight years, between 2008 and 2015. Using as input data actual air and water temperatures, and discharge, as well as air temperature data provided by weather forecasts, the ANN model predicts the Danube water temperature one week in advance with a root mean square deviation (RMSE) of 0.954 °C for training and 0.803 °C for testing. The ANN uses the Levenberg–Marquardt feedforward backpropagation algorithm. This feature is useful for the irrigation systems and for the power plants in the area that use river water for different purposes. The results are encouraging for developing similar studies in other locations and extending the ANN model to include more parameters that can have a significant influence on water temperature.
The Potassium Utilization Gene Network in Brassica napus and Functional Validation of BnaZSHAK5.2 Gene in Response to Potassium Deficiency
Authors: Qian Xingzhi , Liu Hanrong , Zhou Jie , Zhu Wenyu , Hu Liping , Yang Xiaoya , Yang Xiwen , Zhao Huiyan , Wan Huafang , Yin Nengwen , Li Jiana , Qu Cunmin , Du Hai
Published: January 2025

Potassium, an essential inorganic cation, is crucial for the growth of oil crops like Brassica napus L. Given the scarcity of potassium in soil, enhancing rapeseed’s potassium utilization efficiency is of significant importance. This study identified 376 potassium utilization genes in the genome of B. napus ZS11 through homologous retrieval, encompassing 7 functional and 12 regulatory gene families. These genes are unevenly distributed across 19 chromosomes, and the proteins encoded by these genes are mainly localized in the cell membrane, vacuoles, and nucleus. Microsynteny analysis highlighted the role of small-scale replication events and allopolyploidization in the expansion of potassium utilization genes, identifying 77 distinct types of cis-acting elements within their promoter regions. The regulatory mechanisms of potassium utilization genes were provided by analyses of transcription factors, miRNA, and protein interaction networks. Under low potassium stress, the potassium utilization genes, particularly those belonging to the KUP and CBL families, demonstrate pronounced co-expression. RNA-seq and RT-qPCR analysis identified the BnaZSHAK5.2 gene, which is a high-affinity potassium ion transporter, playing a crucial role in the stress response to potassium deficiency in B. napus, as its expression is strongly induced by low potassium stress. A functional complementation study demonstrates that the BnaZSHAK5.2 gene could rescue the primary root growth of the Athak5 mutant under low potassium conditions, confirming its role in response to low potassium stress by sustaining root development.
Genome-Wide Identification and Expression Profile of Farnesyl Pyrophosphate Synthase (FPS) Gene Family in Euphorbia Hirta L.
Authors: Bian Xinyi , Wu Tingkai , Qiang Runrun , Deng Zhi , Rehman Fazal , Han Qiyu , Xu Dong , Yuan Yuan , Wang Xiaobo , An Zewei , Wu Wenguan , Wang Huasen , Cheng Han
Published: January 2025

The biosynthesis of isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), which are essential for sesquiterpenes and triterpenes, respectively, is primarily governed by the mevalonate pathway, wherein farnesyl pyrophosphate synthase (FPS) plays a pivotal role. This study identified eight members of the FPS gene family in Euphorbia hirta, designated EhFPS1–EhFPS8, through bioinformatics analysis, revealing their distribution across several chromosomes and a notable tandem gene cluster. The genes exhibited strong hydrophilic properties and key functional motifs crucial for enzyme activity. An in-depth analysis of the EhFPS genes highlighted their significant involvement in isoprenoid metabolism and lipid biosynthesis, with expression patterns influenced by hormones such as jasmonic acid and salicylic acid. Tissue-specific analysis demonstrated that certain FPS genes, particularly EhFPS1, EhFPS2, and EhFPS7, showed elevated expression levels in latex, suggesting their critical roles in terpenoid biosynthesis. Furthermore, subcellular localization studies have indicated that these proteins are primarily found in the cytoplasm, reinforcing their function in metabolic processes. These findings provide a foundational understanding of the FPS genes in E. hirta, including their gene structures, conserved domains, and evolutionary relationships. This study elucidates the potential roles of these genes in response to environmental factors, hormone signaling, and stress adaptation, thereby paving the way for future functional analyses aimed at exploring the regulation of terpenoid biosynthesis and enhancing stress tolerance in this species.
Comparative Transcriptome Analysis Reveals Potential Molecular Regulation of Organic Acid Metabolism During Fruit Development in Late-Maturing Hybrid Citrus Varieties
Authors: Tang Xiaoyu , Huang Mengqi , Deng Lijun , Li Yixuan , Jin Xiaojun , Xu Jiaqi , Xiong Bo , Liao Ling , Zhang Mingfei , He Jiaxian , Sun Guochao , He Siya , Wang Zhihui
Published: January 2025

Late-maturing hybrid citrus is a significant fruit that combines the best traits of both parents and is highly prized for its unique flavor. Not only can organic acids alter the flavor of citrus pulp, but they are also essential for cellular metabolism, energy conversion, and maintaining the acidbase balance in plant tissues. Although organic acids play a key role in the quality formation of citrus fruits, there is still insufficient research on the metabolic processes of organic acids in late-maturing hybrid citrus varieties. In this study, three late-maturing citrus varieties with different acidity levels, namely ‘Huangjinjia’ (HJ), ‘Kiyomi’ (QJ), and ‘Harumi’ (CJ), were selected to systematically investigate the metabolic regulation mechanism of organic acids in late-maturing citrus through transcriptome sequencing technology, combined with physiological and biochemical analyses. This study revealed gene expression differences related to organic acid synthesis and degradation. Through gene expression profiling, several genes closely associated with organic acid metabolism were identified, and a preliminary gene network related to the regulation of organic acid metabolism was constructed. The results showed that there were significant differences in the organic acid metabolic pathways between different varieties and growth stages of the fruit. Specifically, HJ had a higher TA content than QJ and CJ, primarily due to the significantly higher citric acid and malic acid contents in HJ compared to the other two varieties. Further analysis revealed that four gene modules showed a high correlation with the levels of major organic acids in the fruits. The genes involved in these modules are closely related to organic acid synthesis, degradation, and transport. Additionally, we also identified several key genes (AS1, BZP44, COL4, TCP4, IDD10, YAB2, and GAIPB) that might be involved in the regulation of organic acid metabolism. The functions of these genes could have a significant impact on the expression levels changes of enzymes related to organic acid metabolism. This study provides a foundation for exploring the intrinsic mechanisms regulating the organic acid content in late-maturing hybrid citrus fruits and contributes to the functional research of organic acids in late-maturing hybrid citrus and the molecular design of high-quality varieties.
Autophagy and Mitophagy in Diabetic Kidney Disease—A Literature Review
Authors: Stanigut Alina Mihaela , Tuta Liliana , Pana Camelia , Alexandrescu Luana , Suceveanu Adrian , Blebea Nicoleta-Mirela , Vacaroiu Ileana Adela
Published: January 2025

Autophagy and mitophagy are critical cellular processes that maintain homeostasis by removing damaged organelles and promoting cellular survival under stress conditions. In the context of diabetic kidney disease, these mechanisms play essential roles in mitigating cellular damage. This review provides an in-depth analysis of the recent literature on the relationship between autophagy, mitophagy, and diabetic kidney disease, highlighting the current state of knowledge, existing research gaps, and potential areas for future investigations. Diabetic nephropathy (DN) is traditionally defined as a specific form of kidney disease caused by long-standing diabetes, characterized by the classic histological lesions in the kidney, including mesangial expansion, glomerular basement membrane thickening, nodular glomerulosclerosis (Kimmelstiel–Wilson nodules), and podocyte injury. Clinical markers for DN are albuminuria and the gradual decline in glomerular filtration rate (GFR). Diabetic kidney disease (DKD) is a broader and more inclusive term, for all forms of chronic kidney disease (CKD) in individuals with diabetes, regardless of the underlying pathology. This includes patients who may have diabetes-associated kidney damage without the typical histological findings of diabetic nephropathy. It also accounts for patients with other coexisting kidney diseases (e.g., hypertensive nephrosclerosis, ischemic nephropathy, tubulointerstitial nephropathies), even in the absence of albuminuria, such as a reduction in GFR.
The Glycosylation of Serum IgG Antibodies in Post-COVID-19 and Post-Vaccination Patients
Authors: Váradi Csaba
Published: January 2025

The signature of human serum IgG glycosylation is critical in the defense against pathogens. Alterations of IgG N-glycome were associated with COVID-19 (Coronavirus disease 2019) severity, although knowledge on the response to vaccination is limited. IgG N-glycome was analyzed in this study in post-COVID-19 and post-vaccination patients to reveal potential glycosylation-based alterations using hydrophilic interaction liquid chromatography (HILIC-UPLC) with fluorescence (FLR) and mass-spectrometric (MS) detection. IgG antibodies were purified from serum samples through protein G affinity chromatography followed by PNGase F digestion-based deglycosylation. The released glycans were fluorescently derivatized by procainamide labeling and purified via solid-phase extraction. Higher levels of sialylation and afucosylation were identified in post-COVID-19 patients, which was further expanded by vaccination, but only in those who were previously SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) infected.
Environmental Exposure to Bisphenol A Enhances Invasiveness in Papillary Thyroid Cancer
Authors: Huang Chien-Yu , Xie Ren-Hao , Li Pin-Hsuan , Chen Chong-You , You Bo-Hong , Sun Yuan-Chin , Chou Chen-Kai , Chang Yen-Hsiang , Lin Wei-Che , Chen Guan-Yu
Published: January 2025

Bisphenol A (BPA) is a prevalent environmental contaminant found in plastics and known for its endocrine-disrupting properties, posing risks to both human health and the environment. Despite its widespread presence, the impact of BPA on papillary thyroid cancer (PTC) progression, especially under realistic environmental conditions, is not well understood. This study examined the effects of BPA on PTC using a 3D thyroid papillary tumor spheroid model, which better mimicked the complex interactions within human tissues compared to traditional 2D models. Our findings demonstrated that BPA, at environmentally relevant concentrations, could induce significant changes in PTC cells, including a decrease in E-cadherin expression, an increase in vimentin expression, and reduced thyroglobulin (TG) secretion. These changes suggest that BPA exposure may promote epithelial–mesenchymal transition (EMT), enhance invasiveness, and reduce cell differentiation, potentially complicating treatment, including by increasing resistance to radioiodine therapy. This research highlights BPA’s hazardous nature as an environmental contaminant and emphasizes the need for advanced in vitro models, like 3D tumor spheroids, to better assess the risks posed by such chemicals. It provides valuable insights into the environmental implications of BPA and its role in thyroid cancer progression, enhancing our understanding of endocrine-disrupting chemicals.
Challenges in Humoral Immune Response to Adeno-Associated Viruses Determination
Authors: Naumova Daria A. , Krokunova Tatyana , Maksimov Denis , Mityaeva Olga N. , Astakhova Ekaterina A. , Volchkov Pavel Yu
Published: January 2025

Adeno-associated viruses (AAVs) are non-pathogenic, replication-deficient viruses that have gained widespread attention for their application as gene therapy vectors. While these vectors offer high transduction efficiency and long-term gene expression, the host immune response poses a significant challenge to their clinical success. This review focuses on the obstacles to evaluating the humoral response to AAVs. We discuss the problems with the validation of in vitro tests and the possible approaches to overcome them. Using published data on neutralizing titers of AAV serotypes, we built the first antigenic maps of AAVs in order to visualize the antigenic relationships between varying serotypes.
A Study on Potential Sources of Perineuronal Net-Associated Sema3A in Cerebellar Nuclei Reveals Toxicity of Non-Invasive AAV-Mediated Cre Expression in the Central Nervous System
Authors: Gimenez Geoffrey-Alexander , Romijn Maurits , van den Herik Joëlle , Meijer Wouter , Eggers Ruben , Hobo Barbara , De Zeeuw Chris I. , Canto Cathrin B. , Verhaagen Joost , Carulli Daniela
Published: January 2025

Semaphorin 3A (Sema3A) is an axon guidance molecule, which is also abundant in the adult central nervous system (CNS), particularly in perineuronal nets (PNNs). PNNs are extracellular matrix structures that restrict plasticity. The cellular sources of Sema3A in PNNs are unknown. Most Sema3A-bearing neurons do not express Sema3A mRNA, suggesting that Sema3A may be released from other neurons. Another potential source of Sema3A is the choroid plexus. To identify sources of PNN-associated Sema3A, we focused on the cerebellar nuclei, which contain Sema3A+ PNNs. Cerebellar nuclei neurons receive prominent input from Purkinje cells (PCs), which express high levels of Sema3A mRNA. By using a non-invasive viral vector approach, we overexpressed Cre in PCs, the choroid plexus, or throughout the CNS of Sema3Afl/fl mice. Knocking out Sema3A in PCs or the choroid plexus was not sufficient to decrease the amount of PNN-associated Sema3A. Alternatively, knocking out Sema3A throughout the CNS induced a decrease in PNN-associated Sema3A. However, motor deficits, microgliosis, and neurodegeneration were observed, which were due to Cre toxicity. Our study represents the first attempt to unravel cellular sources of PNN-associated Sema3A and shows that non-invasive viral-mediated Cre expression throughout the CNS could lead to toxicity, complicating the interpretation of Cre-mediated Sema3A knock-out.
Differential Cytotoxicity and Inflammatory Responses to Particulate Matter Components in Airway Structural Cells
Authors: Faruqui Nilofar , Orell Sofie , Dondi Camilla , Leni Zaira , Kalbermatter Daniel M. , Gefors Lina , Rissler Jenny , Vasilatou Konstantina , Mudway Ian S. , Kåredal Monica , Shaw Michael , Larsson-Callerfelt Anna-Karin
Published: January 2025

Particulate matter (PM) is a major component of ambient air pollution. PM exposure is linked to numerous adverse health effects, including chronic lung diseases. Air quality guidelines designed to regulate levels of ambient PM are currently based on the mass concentration of different particle sizes, independent of their origin and chemical composition. The objective of this study was to assess the relative hazardous effects of carbonaceous particles (soot), ammonium nitrate, ammonium sulfate, and copper oxide (CuO), which are standard components of ambient air, reflecting contributions from primary combustion, secondary inorganic constituents, and non-exhaust emissions (NEE) from vehicular traffic. Human epithelial cells representing bronchial (BEAS-2B) and alveolar locations (H441 and A549) in the airways, human lung fibroblasts (HFL-1), and rat precision-cut lung slices (PCLS) were exposed in submerged cultures to different concentrations of particles for 5–72 h. Following exposure, cell viability, metabolic activity, reactive oxygen species (ROS) formation, and inflammatory responses were analyzed. CuO and, to a lesser extent, soot reduced cell viability in a dose-dependent manner, increased ROS formation, and induced inflammatory responses. Ammonium nitrate and ammonium sulfate did not elicit any significant cytotoxic responses but induced immunomodulatory alterations at very high concentrations. Our findings demonstrate that secondary inorganic components of PM have a lower hazard cytotoxicity compared with combustion-derived and indicative NEE components, and alveolar epithelial cells are more sensitive to PM exposure. This information should help to inform which sources of PM to target and feed into improved, targeted air quality guidelines.
Comparison of Methods of Detecting IL-1β in the Blood of Alzheimer’s Disease Subjects
Authors: Remnitz Alexandra D. , Hadad Roey , Keane Robert W. , Dietrich W. Dalton , de Rivero Vaccari Juan Pablo
Published: January 2025

Interleukin (IL)-1β is a pro-inflammatory cytokine whose levels are increased in the brains of Alzheimer’s disease (AD) patients. Despite the role of IL-1β in the pathology of AD, the fact that it is expressed at very low levels makes it a challenging cytokine to measure, hence limiting its potential use as a reliable biomarker. Moreover, being able to accurately and reliably measure the levels of IL-1 β in blood makes it possible to evaluate this cytokine as a potential biomarker of the inflammatory response in AD. In this study, we compared three quantification methodologies, Meso-Scale Discovery (MSD), both V-Plex and S-Plex versions, and Quanterix’s SIMOA (Single-Molecule Array), to measure IL-1β in the serum of AD patients and age-matched controls. These assays are routinely used to measure IL-1β serum levels with high specificity and sensitivity in human AD patients, yet to the best of our knowledge, no study has compared all three techniques for their accuracy to measure IL-1β as biomarkers. Our findings indicate the two MSD assays can be used to measure IL-1β levels in AD and control serum, but the SIMOA assay showed the highest receiver operating characteristics (ROCs), with an area under the curve (AUC) of 0.9532, which can be compared to the AUC values for the V-Plex assay, 0.5660, and the S-Plex assay, 0.6632. Taken together, these data show that although all technologies are useful in the measurement of IL-1β in the blood, the SIMOA IL-1β 3.0 assay is more reliable and sensitive in measuring biomarkers of AD.