bluuebunny/crossref_metadata_2025 huggingface dataset:
Potential of Calcium-Modified Biochar for Soil Nutrient and Carbon Sequestration in Citrus Orchards
Authors: Bai Yinnan , Huang Rui , Li Shu , Li Xianliang , Fan Qijun , Liu Shengqiu , Hu Lening
Published: December 2024

To examine the mechanisms of organic carbon transformation and sequestration by biochar in citrus orchard soil, a 100-day organic carbon mineralization test was conducted using citrus orchard soil from a 5-year-old forest. Calcium-modified citrus peel biochar (OBC-Ca) was applied at rates of 0%, 1%, 2%, and 4%. The results indicated that different percentages of OBC-Ca significantly influenced the mineralization processes in citrus orchards. Specifically, the cumulative mineralization of soil organic carbon was notably reduced by 8.68% and 17.00% with the application of 2% and 4% OBC-Ca, respectively, compared to the control group. Random forest analysis revealed that microbial biomass carbon (MBC), readily oxidizable carbon (ROC), and dissolved organic carbon (DOC) were critical indicators for predicting the cumulative mineralization of soil organic carbon. MBC and ROC were found to inhibit the cumulative mineralization, while DOC promoted it. As the proportion of OBC-Ca applications increased, MBC rose by 2.63% to 10.46%, ROC increased by 16.41% to 108.59%, and DOC increased by 0.48% to 11.67%. Correlation analysis demonstrated a significant negative correlation between the cumulative mineralization rate of soil organic carbon and soil enzyme activity, with soil sucrase content increasing significantly by 216.42% to 393.44% compared to the control. The application of calcium-modified biochar effectively reduces carbon dioxide emissions from citrus orchard soils, with a 4% application yielding the most favorable outcomes for enhancing soil carbon sinks, thereby positively impacting the carbon sequestration potential of citrus orchard soil.
Antifungal Activity of Bacillus velezensis X3-2 Against Plant Pathogens and Biocontrol Effect on Potato Late Blight
Authors: Wei Peixia , Gao Mengying , Zhou Shuang , Liu Guohui , Wang Pan , Liu Chunguang , Yang Fengshan , Fu Haiyan
Published: December 2024

Late blight of potato is caused by the pathogen Phytophthora infestans, which has been considered to be the most destructive disease affecting potato crops worldwide. In recent years, the use of antagonistic microorganisms to control potato late blight has become a green and environmentally friendly means of disease control, greatly reducing the use of chemical pesticides. To obtain antagonistic bacteria with a high biocontrol effect against potato late blight, a total of 16 antagonistic bacterial strains with an inhibition rate of more than 50% against P. infestans were screened from potato rhizosphere soil by double-culture method, among which the bacterial isolate (X3-2) had the strongest inhibitory activity against P. infestans, with an inhibition rate of 81.97 ± 4.81%, respectively, and a broad-spectrum inhibitory activity. The bacterial isolate (X3-2) was identified as Bacillus velezensis based on its 16S rDNA gene sequence and morphological as well as biochemical properties. The results of our in vitro experiments demonstrated that X3-2 was a potent inducer of resistance in potato tubers and leaflets against late blight. In greenhouse experiments, it was confirmed that the biological preparation X3-2 exhibits an anti-oomycete effect, demonstrating a significant control efficacy on potato late blight. Further analyses showed that the antagonistic substances of X3-2 were distributed both intracellularly and extracellularly. In addition, screening for plant-growth-promoting (PGP) traits showed that X3-2 has the ability to produce siderophores and secrete indole acetic acid (IAA). The findings from this research suggest that B. velezensis X3-2 exhibits promise as a biocontrol agent for managing late blight. In the future, the composition and mechanism of the action of its antimicrobial substances can be studied in depth, and field trials can be carried out to assess its actual prevention and control effects.
Assessment of Fumonisin, Deoxynivalenol, and Zearalenone Levels and the Occurrence of Mycotoxigenic Fusarium Species in Cereal Grains from Muscat, Sultanate of Oman
Authors: Al-Rashdi Fatma Khuseib Hamed , Al-Sadi Abdullah Mohammed , Waly Mostafa Ibrahim , Hussain Shah , Velazhahan Rethinasamy
Published: December 2024

Mycotoxin contamination in agricultural goods is a major global problem due to its negative impact on human and animal health. The principal mycotoxin producers are fungal species from the genera Fusarium, Aspergillus, Alternaria, and Penicillium. The toxigenic fungal species produce the mycotoxins as secondary metabolites when they invade agricultural commodities during crop cultivation in the field (preharvest) or after harvesting or during transport and storage. This study was designed to investigate the levels of Fusarium mycotoxins, viz., fumonisin (FUM), zearalenone (ZEN), and deoxynivalenol (DON) in cereal grain samples collected from Muscat, Sultanate of Oman during 2023-24. A total of 90 cereal grain (wheat, corn, rice, barley) samples from local markets at Muscat, the Plant Quarantine Department, Oman, and Oman Flour Mills Company were analyzed using competitive enzyme immunoassay kits. Furthermore, Fusarium spp. associated with the contaminated grain samples were isolated, and their mycotoxin-producing potential was assessed. The results indicated that FUM, ZEN, and DON levels were below the detection limit (LOD) in 81%, 97%, and 44% of the samples, respectively. Two out of fifteen corn samples and one out of thirty-seven wheat samples tested exceeded the maximum permissible limit for FUM and ZEN, respectively, as set by the European Commission. A total of 19 Fusarium spp. associated with the contaminated grain samples were isolated and identified through molecular techniques. Sixteen isolates of F. verticillioides, one isolate of F. thapsinum, and two new Fusarium species were identified based on nuclear ribosomal DNA internal transcribed spacer and elongation factor 1-alpha sequences. Two isolates of F. verticillioides (FQD-1 and FQD-20) produced FUM levels exceeding 2000 µg kg−1. The maximum ZEN concentration was observed in F. verticillioides FQD-20 (9.2 µg kg−1), followed by F. verticillioides FQD-2 (2.8 µg kg−1) and Fusarium sp. FOFMC-26 (2.5 µg kg−1). All tested Fusarium strains produced DON, with levels ranging from 25.6 to 213 µg kg−1, with F. thapsinum FQD-4 producing the highest level (213 µg kg−1). To our knowledge, this is the first report on the occurrence of Fusarium mycotoxins and mycotoxigenic Fusarium spp. in food commodities in Oman.
Harnessing Beneficial Microbes for Drought Tolerance: A Review of Ecological and Agricultural Innovations
Authors: Mikiciuk Grzegorz , Miller Tymoteusz , Kisiel Anna , Cembrowska-Lech Danuta , Mikiciuk Małgorzata , Łobodzińska Adrianna , Bokszczanin Kamila
Published: December 2024

Drought is an increasingly critical global challenge, significantly impacting agricultural productivity, food security, and ecosystem stability. As climate change intensifies the frequency and severity of drought events, innovative strategies are essential to enhance plant resilience and sustain agricultural systems. This review explores the vital role of beneficial microbes in conferring drought tolerance, focusing on Plant Growth-Promoting Rhizobacteria (PGPR), mycorrhizal fungi, endophytes, actinomycetes, and cyanobacteria. These microorganisms mitigate drought stress through diverse mechanisms, including osmotic adjustment, enhancement of root architecture, modulation of phytohormones, induction of antioxidant defenses, and regulation of stress-responsive gene expression. Ecological and agricultural innovations leveraging these beneficial microbes have demonstrated significant potential in bolstering drought resilience. Strategies such as soil microbiome engineering, bioaugmentation, and the integration of microbial synergies within pest management frameworks enhance ecosystem resilience and agricultural sustainability. Additionally, advancements in agricultural practices, including seed coating, soil amendments, the development of microbial consortia, and precision agriculture technologies, have validated the effectiveness and scalability of microbial interventions in diverse farming systems. Despite promising advancements, several challenges hinder the widespread adoption of microbial solutions. Environmental variability can affect microbial performance, necessitating the development of robust and adaptable strains. Scale-up and commercialization hurdles, economic constraints, and regulatory and safety considerations also pose significant barriers. Furthermore, the complex interactions between microbes, plants, and their environments require a deeper understanding to optimize microbial benefits consistently. Future research should focus on integrating cutting-edge technologies such as genomics, synthetic biology, and precision agriculture to refine and enhance microbial interventions. Collaborative efforts among academia, industry, and government are essential to bridge the gap between research and practical implementation. By addressing these challenges and harnessing microbial innovations, it is possible to develop resilient and sustainable agricultural systems capable of thriving in an increasingly water-scarce world.
Blackberry Growth Monitoring and Feature Quantification with Unmanned Aerial Vehicle (UAV) Remote Sensing
Authors: Tagoe Akwasi , Silva Alexander , Koparan Cengiz , Poncet Aurelie , Wang Dongyi , Johnson Donald , Worthington Margaret
Published: December 2024

Efficiently managing agricultural systems necessitates accurate data collection from crops to examine phenotypic characteristics and improve productivity. Traditional data collection processes for specialty horticultural crops are often subjective, labor-intensive, and may not provide accurate information for precise management decisions in phenotypic studies and crop production. Reliable and standardized techniques to record and evaluate crop features using agricultural technology are essential for improving agricultural systems. The objective of the research was to develop a methodology for accurate measurement of blackberry flowers and vegetation coverage using UAV remote sensing and image analysis. The UAV captured 20,812 images in the visible spectrum, and ImageJ software (version 1.54k) was used for segmenting floral and vegetative coverage to calculate variety-specific flower coverage. A moderately strong positive correlation (r = 0.71) was found between flower-to-vegetation ratio (FVR) and visually estimated flower area, validating UAV-derived flower coverage as a reliable method for estimating flower density in blackberries. The regression model explained 51% of the variance in flower estimates (R2 = 0.51), with a root mean square error (RMSE) of 2.79 flower/cm2. Additionally, distinct temporal flowering patterns were observed between primocane- and floricane fruiting blackberries. Vegetative growth also exhibited stability, with strong correlations between consecutive weeks. The temporal analysis provided insight into growth phases and flowering peaks critical for time-sensitive management practices. UAV computer vision for quantifying blackberry phenotypic features is an effective tool and a unique methodology that speeds up the data collection process at high accuracy for breeding research and farm data management.
Development and Evaluation of an Affordable Variable Rate Applicator Controller for Precision Agriculture
Authors: Abdalla Ahmed , Mirzakhani Nafchi Ali
Published: December 2024

Considerable variation in soil often occurs within and across production fields, which can significantly impact farming input management strategies. Optimizing resource utilization while enhancing crop productivity is critical for achieving Sustainable Development Goals (SDGs). This paper proposes a low-cost retrofittable Variable Rate Applicator Controller (VRAC) designed to leverage soil variability and facilitate the adoption of Variable Rate Technologies. The controller operates using a Raspberry Pi platform, RTK—Global Navigation Satellite System (GNSS), a stepper motor, and an anti-slip wheel encoder. The VRAC allows precise, on-the-fly control of the Variable Rate application of farming inputs utilizing an accurate GNSS to pinpoint geographic coordinates in real time. A wheel encoder measures accurate distance travel, providing a real-time calculation of speed with a slip-resistant wheel design for precise RPM readings. The Raspberry Pi platform processes the data, enabling dynamic adjustments of variability based on predefined maps, while the motor driver controls the motor’s RPM. It is designed to be plug-and-play, user-friendly, and accessible for a broader range of farming practices, including seeding rates, dry fertilizer, and liquid fertilizer application. Data logging is performed from various field sensors. The controller exhibits an average of 0.864 s for rate changes from 267 to 45, 45 to 241, 241 to 128, 128 to 218, and 218 to 160 kg/ha at speeds of 8, 11, 16, 19, 24, and 32 km/h. It has an average coefficient of variation of 4.59, an accuracy of 97.17%, a root means square error (RMSE) of 4.57, an R square of 0.994, and an average standard deviation of 1.76 kg for seeding discharge. The cost-effectiveness and retrofitability of this technology offer an increase in precision agriculture adoption to a broader range of farmers and promote sustainable farming practices.
TFEMRNet: A Two-Stage Multi-Feature Fusion Model for Efficient Small Pest Detection on Edge Platforms
Authors: Mu Junlin , Sun Linlin , Ma Bo , Liu Ruofei , Liu Shuangxi , Hu Xianliang , Zhang Hongjian , Wang Jinxing
Published: December 2024

Currently, intelligent pest monitoring systems transmit entire monitoring images to cloud servers for analysis. This approach not only consumes significant bandwidth and increases monitoring costs, but also struggles with accurately recognizing small-target and overlapping pests. To overcome these challenges, this paper introduces a two-stage multi-feature fusion small-target pest detection algorithm based on edge computing devices, termed TFEMRNet. The algorithm initially conducts semantic segmentation on an edge processor, followed by uploading the segmented images to a cloud server for target identification. Specifically, the semantic segmentation model TFENet incorporates a Multi-Attention Channel Aggregation (MACA) module, which integrates semantic features from EfficientNet-Pest and Swin Transformer, thereby enhancing the model’s ability to extract features of small-target pests. Experimental results demonstrate that TFEMRNet surpasses models such as YOLOv11, Fast R-CNN, and Mask R-CNN on small-target pest datasets, achieving precision of 96.75%, recall of 96.45%, and an F1 score of 95.60%. Notably, the TFENet model within TFEMRNet attains an IoU of 91.63% in semantic segmentation accuracy, outperforming other segmentation models such as U-Net and PSPNet. These findings affirm TFEMRNet’s superior efficacy in small-target pest detection, offering an effective solution for agricultural pest monitoring.
UAV-Based Multispectral Inversion of Integrated Cotton Growth
Authors: Gu Haozheng , Xue Chen , Wang Guobin , Lan Yubin , Wang Huizheng , Song Cancan
Published: December 2024

Cotton growth monitoring can provide macroscopic information for cotton field management. In this paper, we utilised UAV multispectral remote sensing to monitor cotton growth and constructed a Comprehensive Growth Index (CGI) by combining cotton multi-period Leaf Area Index (LAI) and relative chlorophyll content (Soil and plant analyser development, SPAD) through the coefficient of variation method and constructed 28 types of vegetation cover indexes by combining multispectral data. CGI combined with multispectral data to construct 28 vegetation indices and a digital surface model, selected the indexes with higher correlation through correlation analysis, and constructed a single growth model (LAI and SPAD) and CGI by using the support vector machine (SVM), random forest (RF), and gradient boosted decision tree (GBDT) model. In the GBDT model, the comprehensive growth model was better than the single growth index model, with an R2 of 0.85 and an RMSE of 0.044, and in the RF model, the comprehensive growth model was better than the single growth index model, with an R2 of 0.86 and an RMSE of 0.037. In the comprehensive analysis, the comprehensive growth index can improve the accuracy of the inversion model of the cotton growth and provide a new method for the monitoring of the cotton growth by remote sensing.
Enhancing Traffic Accident Severity Prediction Using ResNet and SHAP for Interpretability
Authors: Benfaress Ilyass , Bouhoute Afaf , Zinedine Ahmed
Published: December 2024

Background/Objectives: This paper presents a Residual Neural Network (ResNet) based framework tailored for structured traffic accident data, aiming to improve accident severity prediction. The proposed model leverages residual learning to effectively model intricate relationships between numerical and categorical variables, resulting in a notable increase in prediction accuracy. Methods: A comparative analysis was performed with other Deep Learning (DL) architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Darknet, and Extreme Inception (Xception), showing superior performance of the proposed Resnet. Key factors influencing accident severity were identified, with Shapley Additive Explanations (SHAP) values helping to address the need for transparent and explainable Artificial Intelligence (AI) in critical decision-making areas. Results: The generalizability of the ResNet model was assessed by training it, initially, on a UK road accidents dataset and validating it on a distinct dataset from India. The model consistently demonstrated high predictive accuracy, underscoring its robustness across diverse contexts, despite regional differences. Conclusions: These results suggest that the adapted ResNet model could significantly enhance traffic safety evaluations and contribute to the formulation of more effective traffic management strategies.
Microstrip Antenna Design Supported by Generative Adversarial Networks
Authors: Goncalves Silvania T. , Malheiros-Silveira Gilliard N.
Published: December 2024

We report on the effectiveness of using generative neural networks in an antenna design. We considered the modeling of microstrip antennas as they have significant advantages, such as a low profile, lightness, and ease of manufacture, which make them versatile for various applications. We designed, trained, and analyzed generative models applied to the modeling of these antennas without losing the generalizability of the application of these models to any antenna. We started with a Generative Adversarial Network (GAN), which was trained with data related to the antenna models for operation within the frequency range of 1 to 30 GHz. Using the synthetic data produced by the GAN resulted in antenna designs with dimensions and electromagnetic properties that were very close to the expected values. Next, a model was developed using a Conditional GAN (CGAN), which was trained to generate antenna characteristic data conditioned on an arbitrary central frequency, i.e., 2.4 GHz (generally used for Bluetooth, Wi-Fi, and ZigBee technologies), to enable better control over the process of generating these synthetic data. The CGAN model could satisfactorily generate synthetic data for this frequency range, simultaneously considering substrates with different dielectric permittivities. This study reveals that both generative models could produce synthetic data that were very close to the expected data, as evidenced by the low error values. Additionally, in terms of application, the models could provide both geometries and more than one antenna characteristic (resonance, bandwidth, and quality factor), which is very useful for direct application to practical designs.
Ethical Decision-Making in Artificial Intelligence: A Logic Programming Approach
Authors: Machado José , Sousa Regina , Peixoto Hugo , Abelha António
Published: December 2024

This article proposes a framework for integrating ethical reasoning into AI systems through Continuous Logic Programming (CLP), emphasizing the improvement of transparency and accountability in automated decision-making. The study highlights requirements for AI that respects human values and societal norms by examining concerns such as algorithmic bias, data privacy, and ethical dilemmas in fields like healthcare and autonomous systems. The proposed CLP-based methodology offers a systematic, elucidative framework for ethical decision-making, allowing AI systems to balance operational efficiency with ethical principles. Important contributions include strategies for the integration of ethical frameworks, stakeholder engagement, and transparency, as well as discussion on artificial moral agents and their function in addressing ethical dilemmas in AI. The paper presents practical examples that illustrate the application of CLP in ethical reasoning, highlighting its ability to bring together AI performance with responsible AI practices.
Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing
Authors: Li Shizhe , Fan Chunzhi , Kargarandehkordi Ali , Sun Yinan , Slade Christopher , Jaiswal Aditi , Benzo Roberto M. , Phillips Kristina T. , Washington Peter
Published: December 2024

Substance use disorders affect 17.3% of Americans. Digital health solutions that use machine learning to detect substance use from wearable biosignal data can eventually pave the way for real-time digital interventions. However, difficulties in addressing severe between-subject data heterogeneity have hampered the adaptation of machine learning approaches for substance use detection, necessitating more robust technological solutions. We tested the utility of personalized machine learning using participant-specific convolutional neural networks (CNNs) enhanced with self-supervised learning (SSL) to detect drug use. In a pilot feasibility study, we collected data from 9 participants using Fitbit Charge 5 devices, supplemented by ecological momentary assessments to collect real-time labels of substance use. We implemented a baseline 1D-CNN model with traditional supervised learning and an experimental SSL-enhanced model to improve individualized feature extraction under limited label conditions. Results: Among the 9 participants, we achieved an average area under the receiver operating characteristic curve score across participants of 0.695 for the supervised CNNs and 0.729 for the SSL models. Strategic selection of an optimal threshold enabled us to optimize either sensitivity or specificity while maintaining reasonable performance for the other metric. Conclusion: These findings suggest that Fitbit data have the potential to enhance substance use monitoring systems. However, the small sample size in this study limits its generalizability to diverse populations, so we call for future research that explores SSL-powered personalization at a larger scale.
Transcriptome and Proteome Analyses Revealed Differences in JEV-Infected PK-15 Cells in Response to Ferroptosis Agonists and Antagonists
Authors: Zhou Xiaolong , Chen Yiwei , Kang Xinyao , Zhao Ayong , Yang Songbai
Published: December 2024

Epidemic encephalitis B caused by Japanese encephalitis virus (JEV) is a common zoonotic disease that poses threats to both pigs and humans. The cellular defense mechanism is closely tied to the body’s resistance to viral invasion. Regulated cell death, such as ferroptosis, is a strategy employed by host cells to defend against viral invasions. To understand the effect of ferroptosis on the proliferation of JEV, experimentally infected PK15 cells were treated with a ferroptosis agonist or antagonist. The results indicated that the ferroptosis agonist can suppress JEV proliferation, whereas the ferroptosis antagonist promotes JEV proliferation. Functional enrichment analysis showed that the ferroptosis agonist Erastin and antagonist SP600125 could affect JEV proliferation through the TNF, IL-17, Toll-like receptor, PI3K–AKT, and chemokine signaling pathways, as well as ECM-receptor interactions. Combined transcriptome and proteome analyses revealed 31 important genes, which are significantly associated with ferroptosis and the inflammatory response. Our results provide a better understanding of the molecular mechanisms through which ferroptosis affects the proliferation of JEV.
An Improved Theileria parva Sporozoite Seroneutralization Assay for the Identification of East Coast Fever Immune Correlates
Authors: Chege Hannah , Githigia Samuel , Gathumbi James , Chege Naomi , Ojuok Rose , Odaba Josiah , Mwalimu Stephen , Oboge Harriet , Steinaa Lucilla , Nene Vishvanath , Lacasta Anna
Published: December 2024

Background: Immune correlates of protection are ideal tools to predict treatment or vaccine efficacy. However, the accuracy of the immune correlate and the capability to robustly predict the outcome of a vaccine candidate are determined by the performance of the in vitro immunoassay used. Several Theileria parva sporozoite seroneutralization assays have previously been used to assess antibody functional activities; however, a common limitation has been the need for fresh material, target cells and sporozoites, and operator-to-operator bias. An improved assay represents a positive step toward overcoming challenges associated with variability and it might provide a more reliable means of establishing an immune correlate with protection after sub-unit vaccine administration. Methods: Herein, we describe key improvements, among them, (1) the use of frozen parasites and target cells to avoid batch-to-batch variations and (2) the development of a new assay read-out based on the detection of infected cells through flow cytometry, instead of the use of Giemsa staining and microscopic evaluation, in order to improve the reproducibility of the results. Results: The improved seroneutralization assay is not only able to detect the individual neutralizing capacity of antibodies; it also detects the additive effect of antibody combinations. Conclusions: This effect is described for the first time in Theileria parva and is of great interest for new antigen discovery and/or the epitope discovery of already known antigens like p67, opening a new avenue for the identification of ECF immune correlates of protection and the in vitro down-selection of new Theileria parva vaccine candidates, thereby contributing to reducing the use of animals in challenge experiments.
Epidemiology and Outcomes of Infected Non-Unions: An Observational Study at an Infectious Disease Referral Centre
Authors: Tedeschi Sara , Rossi Nicolò , Zamparini Eleonora , Ambretti Simone , Mosca Massimiliano , Faldini Cesare , Zaffagnini Stefano , Maso Alessandra , Sambri Andrea , De Paolis Massimiliano , Viale Pierluigi
Published: December 2024

Objectives: The main aim of this study was to describe the epidemiology of infected non-unions (INUs) managed at an Infectious Disease (ID) referral centre and to investigate the factors associated with treatment failure. Methods: This was an observational retrospective study on adult patients with INUs managed between 2012 and 2018 at the ID Unit of the IRCCS Azienda Ospedaliero-Universitaria di Bologna, an Italian ID referral centre for bone and joint infections. Patients were observed for at least 24 months. Those who achieved clinical success were compared with those who experienced clinical failure; to identify factors associated with treatment failure, we performed a univariate and multivariate logistic regression analysis. Results: Overall, 78 patients were included. A total of 57/78 (73%) were males; their median age was 43 (IQR 34–56) years; their median Charlson index was 0 (IQR 0–2); 32/78 (41%) reported a history of an open fracture; the non-union most frequently involved the leg. Polymicrobial infection was found in 23/78 cases (29%); the most common microorganisms were coagulase-negative staphylococci (n = 47) and Staphylococcus aureus (n = 35). At 24-month follow-up from index surgery, 16/78 patients had experienced clinical failure: 13 (16.6%) presented with persistence of local signs of infection and 3 (3.8%) had undergone amputation. Logistic regression analysis of risk factors for clinical failure identified body mass index (BMI) (aOR 1.15; 95% CI 1.03–1.28, p = 0.01) and MRSA infection (aOR 5.35; 95% CI 1.06–26.92, p = 0.04) as factors associated with clinical failure. Conclusions: Given that a standardized management of antibiotic therapy is initiated by an expert ID consultant team, BMI and MRSA infection are associated with worse outcomes among patients with INUs.
From Cure to Prevention: Doxycycline’s Potential in Prophylaxis for Sexually Transmitted Infections
Authors: Bird James , Alawyia Basil , Spernovasilis Nikolaos , Alon-Ellenbogen Danny
Published: December 2024

Over the past two decades, the global incidence of sexually transmitted infections (STIs) such as gonorrhea, chlamydia, and syphilis have increased significantly, particularly among cisgender men who have sex with men (MSM) and transgender women (TGW). This rise in STIs has spurred interest in new preventive measures, including doxycycline post-exposure prophylaxis (DoxyPEP). Clinical trials in the United States and France have demonstrated the effectiveness of DoxyPEP in reducing both chlamydia and syphilis incidence among MSM and TGW; although, its efficacy against gonorrhea remains limited, and it was further found to be ineffective among cisgender women in Kenya. Due to the promising results, the CDC and the German STI Society have incorporated DoxyPEP into their guidelines for specific high-risk groups. However, the broader implementation of DoxyPEP presents several challenges and ethical concerns. Key issues involve the potential development of antimicrobial resistance, particularly among common STI pathogens like C. trachomatis, M. genitalium, and N. gonorrhoeae, as well as other bacteria such as S. aureus and K. pneumoniae. Additionally, questions concerning equitable healthcare access, the potential impact on adherence to safer sex practices, and broader public health implications warrant careful consideration. Addressing these challenges necessitates a careful balance between the benefits and risks of DoxyPEP, as well as the implementation of strategies to mitigate negative outcomes while maximizing the impact on public health. Lastly, future research should explore the integration of DoxyPEP with other preventive strategies, such as vaccines, to enhance its effectiveness and reduce the global burden of STIs.
Enzyme Inhibitory, Physicochemical, and Phytochemical Properties and Botanical Sources of Honey, Bee Pollen, Bee Bread, and Propolis Obtained from the Same Apiary
Authors: Gercek Yusuf Can , Dagsuyu Eda , Basturk Fatma Nur , Kırkıncı Seran , Yıldırım Nazlıcan , Kıskanç Gamze , Özmener Bahar , Unlu Yigit Sabri , Kalkan Seda Nur , Boztaş Kadir , Oz Gül Cevahir , Yanardağ Refiye , Bayram Nesrin Ecem , Kostić Aleksandar Ž.
Published: December 2024

Bee products are an important source of nutrients and bioactive phytochemicals. This study aimed to determine the chemical composition (proximate composition, general phytochemical composition, sugar, and phenolic profiles) of four different products (honey, bee pollen, bee bread, and propolis), obtained from the same apiary, as well as to assess their biological activity through antioxidant and enzyme inhibition assays (α-amylase, α-glucosidase, lipase, AchE, neuraminidase, angiotensin-converting enzyme, urease, trypsin, tyrosinase, carbonic anhydrase, thioredoxin reductase, adenosine deaminase). Clear differences were observed among the samples in terms of both chemical composition and biological activity. The analysis revealed that bee pollen exhibited the highest carbohydrate content (87.9%), while propolis was identified as the richest source of phenolic compounds (14,858.9 mg/kg) among the analyzed samples. Propolis exhibited the highest biological activity in all applied antioxidant assays (CUPRAC, DPPH•, and ABTS•+) and in most enzyme inhibition assays. Notably, the α-glucosidase inhibition activity of propolis was comparable to that of the reference standard. In addition, honey exhibited remarkable trypsin inhibition, also comparable to the applied standard. These findings highlight the diverse bioactivities of hive products, which could play a key role in promoting health and preventing diseases.
The Consumption of Beeswax Alcohol (BWA, Raydel®) Improved Zebrafish Motion and Swimming Endurance by Protecting the Brain and Liver from Oxidative Stress Induced by 24 Weeks of Supplementation with High-Cholesterol and D-Galactose Diets: A Comparative Analysis Between BWA and Coenzyme Q10
Authors: Cho Kyung-Hyun , Lee Yunki , Bahuguna Ashutosh , Lee Sang Hyuk , Yang Chae-Eun , Kim Ji-Eun , Kwon Hyo-Shin
Published: December 2024

The prolonged consumption of D-galactose (Gal) has been associated with severe damage in the liver and brain via exacerbation of oxidative stress, non-enzymatic glycation, and the aging process. The current study was initiated for a comparative assessment of beeswax alcohol (BWA, final 0.5% and 1.0% w/w) and coenzyme Q10 (CoQ10, final 0.5% and 1.0% w/w) against high-cholesterol (HC, final 4%, w/w) and -galactose (Gal, final 30%, w/w)-induced adverse events in zebrafish during 24 weeks of consumption. The survivability of zebrafish decreased to 82.1% due to HC+Gal exposure, but this was substantially improved (91.0%) with the consumption of 0.5% and 1.0% BWA. In contrast, no protective effect of CoQ10 consumption (1.0%) was observed on the survivability of zebrafish. Nevertheless, both BWA and CoQ10 displayed a significant (p < 0.001) preventive effect against HC+Gal-induced body weight enhancement. The HC+Gal-induced cognitive changes, marked by staggered and confused swimming behavior, and retarded swimming speed and motion patterns (restricted to the bottom of the tank), were efficiently restored by BWA. A significantly higher residence time in the upper half of the tank, 3.1-and 4.5-fold reduced latency time along with 3.5-fold and 4.1-fold higher swimming distance, was logged in the 0.5% and 1.0% BWA groups, respectively, than the zebrafish that consumed HC+Gal. In addition, BWA effectively enhanced plasma ferric ion reduction (FRA) and paraoxonase (PON) activity and alleviated the total cholesterol (TC), triglyceride (TG), and blood glucose levels disrupted by the consumption of HC+Gal. Also, the HC+Gal-alleviated plasma high-density lipoprotein-cholesterol (HDL-C) was 2.6-fold (p < 0.001) enhanced in the group that consumed 1.0% BWA, which was significantly 1.5-fold (p < 0.001) better than the effect of 1.0% CoQ10. Similarly, BWA displayed a superior impact over CoQ10 to mitigate HC+Gal-induced plasma AST and ALT levels, hepatic IL-6 production, generation of oxidized species, cellular senescence, and fatty liver changes. Moreover, BWA protects the brain against HC+Gal-induced oxidative stress, apoptosis, and myelin sheath degeneration. Conclusively, compared to CoQ10, BWA efficiently can the HC+Gal-impaired brain and liver functionality to subside and improves the dyslipidemia and cognitive behavior of zebrafish.
The C-X-C Motif Chemokine Ligand 5, Which Exerts an Antioxidant Role by Inducing HO-1 Expression, Is C-X-C Motif Chemokine Receptor 2-Dependent in Human Prostate Stroma and Cancer Cells
Authors: Chang Kang-Shuo , Chen Syue-Ting , Hsu Shu-Yuan , Sung Hsin-Ching , Lin Wei-Yin , Tsui Ke-Hung , Lin Yu-Hsiang , Hou Chen-Pang , Juang Horng-Heng
Published: December 2024

While the C-X-C motif chemokine ligand 5 (CXCL5) is recognized as an inflammatory mediator and a potent attractant for immune cells, its functions within the human prostate remain unclear. This study explored the expression, functions, and regulatory mechanisms of CXCL5 in prostate stroma and cancer cells. CXCL5 secreted from prostate cancer cells enhanced neutrophil migration. CXCL5 induced cell proliferation and invasion of prostate cancer cells in vitro and tumorigenesis in a xenograft animal model. C-X-C motif chemokine receptor 2 (CXCR2) has been identified on the surface of prostate fibroblasts and cancer cells. The supernatant of LNCaP cells or CXCL5 overexpression enhanced the migration and contraction of prostate myofibroblast WPMY-1 cells; however, pretreatment with SB225002, a CXCR2 inhibitor, can reverse these effects. CXCL5 evinces antioxidant properties by upregulating heme oxygenase-1 (HO-1) to counteract H2O2-induced reactive oxygen species (ROS) in a CXCR2-dependent manner in WPMY-1 and prostate cancer cells. Our findings illustrate that CXCL5, through HO-1, plays a role in antioxidation, and determine that the CXCL5/CXCR2/HO-1 pathway facilitates antioxidative communication between fibroblasts and cancer cells in the prostate. Therefore, targeting the CXCL5/CXCR2 signaling pathway could provide a new strategy for managing oxidative stress within the prostate.
Characterization and Stability Analysis of Rock Mass Discontinuities in Layered Slopes: A Case Study from Fushun West Open-Pit Mine
Authors: Li Ming , Xiu Zhanguo , Han Jianhua , Meng Fanzhen , Wang Feili , Ji Hongguang
Published: December 2024

Discontinuities exist widely in high rock slopes and threaten their stability at all times. To accurately characterize the information of rock mass discontinuities in high slopes, the layered rock slope on the north side of the Fushun West Open-pit Mine was taken as a typical sample, the high-definition images were collected using unmanned aerial vehicle (UAV) photogrammetry technology, and the Agisoft PhotoScan software was then used to generate the 3D rock slope model (i.e., 3D point cloud data) in this study. After the noise reduction processing of the generated rock slope model, the orientation and dominant grouping of the rock mass discontinuities were calculated by the KNN (K-nearest neighbor) search and the PCA (principal component analysis) algorithms, and the calculated results are calibrated with manual measurements in the local slope area. Moreover, the high-definition images containing different rock layers were selected to identify the detailed information of the rock discontinuities using the MATLAB program. This study shows that the orientation information of the rock discontinuities extracted from point cloud data are close to the corresponding result of manual measurements. There are great differences in the orientation of the discontinuities at different rock layers. The small-angle discontinuities are densely distributed in brown shale, which has a serious influence on the stability of the cracked rock slope. This research can provide a reference for the stability analysis of layered rock slopes and the prevention of landslide disasters in the Fushun West Open-Pit Mine.
Gradient Descent Algorithm with Greedy Repositioning Using Power Deficit Aggregation of Wakes to Accelerate the Offshore Wind Farm Layout Optimization Problem in Irregular Concession Areas
Authors: Gonzalez-Rodriguez Angel Gaspar , Roldan-Fernandez Juan Manuel , Serrano-Gonzalez Javier , Muñoz-Díez José Vicente
Published: December 2024

Wind farm layout optimization is essential to maximize the energy production of renewable energy systems. A new layout optimization method for offshore wind farms is proposed to minimize power deficits due to the wake effect without limitation on the number of turbines, the shape, or the extension of the concession area. The main engine of the algorithm is a gradient-descent method in which throughout the optimization process, new turbines are progressively and randomly included within the concession area and quickly expand outward, looking for areas with less perturbation, in turn, pushing the previous ones. When the optimization process ends, to avoid local maxima, it enters into a process of suppression of the set of locations that cause the greatest potential (power deficit). Then, a map of potential for the entire area is created, and a greedy algorithm places new turbines to complete the layout with the final number of turbines. The overall process is completed in 25 s. To drastically speed up the search process and the creation of the potential map, a simplification has been validated and added: for turbines affected by multiple wakes, the resulting power has been calculated by using a linear aggregation of power deficits, instead of the usual linear (or quadratic) aggregation of speed deficits. Owing to this type of aggregation, an analogy is established between power deficit and repulsive non-isotropic electrostatic potential energy, which allows using the properties of conservative fields. Thanks to this, the process is 20 times faster than any other layout optimization algorithm found in the revised literature. Irregular concession areas are easily treated using Stokes’ theorem to detect outer points.
The Development of a Converter Transformer Fire Model Based on the Fire Dynamics Simulator and the Analysis of Cooling Mechanisms of Spraying and Coating
Authors: Qiao Xinhan , Wang Yijiao , Zhang Yuchang , Yu Le , Zhang Dongdong , Wang Zhi
Published: December 2024

This research develops a numerical fire model for a converter transformer utilizing the Fire Dynamics Simulator (FDS). The model’s accuracy was validated through comprehensive evaluations of temperature distribution, radiative heat transfer, and mass burning rate. Additionally, the cooling efficacy of fire-resistant coating and fine water mist with varying droplet sizes was investigated. The results indicate that fireproof coating significantly reduces the surface temperature of the transformer, thereby enhancing its fire resistance. Specifically, temperature reductions of 57.68%, 45.63%, 37.78%, and 36.78% were recorded at different facade heights. Furthermore, the cooling performance of fine water mist is strongly influenced by droplet size, primarily due to thermal buoyancy effects. Larger droplets (400 μm) exhibited the most efficient cooling effect directly beneath the spray, achieving temperature reductions of up to 67%. In contrast, smaller droplets (100 μm) showed diminished cooling performance in certain regions, owing to the compensatory buoyancy of hot air, even resulting in an 11% temperature increase in some cases. During the flame stabilization phase, the mass burning rate stabilized between 0.056 kg/(m2·s) and 0.070 kg/(m2·s), with the inhibitory effect of small particle mist becoming pronounced only after 450 s. These findings offer critical insights for optimizing fire protection strategies for converter transformers, highlighting the significance of cooling mechanisms and material properties.
Advancing a Comprehensive Model for Carbon Steel Corrosion in Weak Acids: Validation Using Valeric and Acetic Acids
Authors: Messinese Elena , Ormellese Marco , Brenna Andrea
Published: December 2024

Acidic corrosion in industrial environments represents a serious threat that requires an active prevention and management strategy. In this context, weak acids can create a severe corrosion environment for metallic surfaces, sometimes exceeding the severity observed in strongly acidic solutions under similar conditions. While most of the research efforts of the last decades in the field of the predictive modeling of acidic corrosion have been focused on the specific case of sweet corrosion caused by carbonic acid, the goal of this work is to describe and validate a predictive model to be used as a more transversal tool for acidic corrosion. The model, called the Tafel–Piontelli model, leverages Tafel law to mechanistically describe the electrochemical behavior of carbon steel in acidic aqueous environments. Two different acids, acetic and valeric, were used to experimentally evaluate the performance of the model in weakly acidic solutions, varying the pH and the temperature conditions. Potentiodynamic polarization tests and mass loss tests were performed, allowing us to assess the kinetic parameters (the Tafel slope and the exchange current density of the cathodic and anodic reactions) and corrosion rates of the corrosion process. The promising results suggest that the Tafel–Piontelli model is able to adapt to different scenarios and its intrinsically theoretical nature allows us to extend its predictions outside the range of experimental conditions used to validate it.
Effect of the Storage Conditions on the Microbiological Quality and Selected Bioactive Compound Content in Fruit Mousses for Infants and Young Children
Authors: Purkiewicz Aleksandra , Wiśniewski Patryk , Tańska Małgorzata , Goksen Gulden , Pietrzak-Fiećko Renata
Published: December 2024

Fruit mousses, as low-processed products, are highly susceptible to external conditions, and storage leads to the degradation of bioactive compounds, particularly phenolic compounds and vitamins, as well as promoting the growth of yeasts and molds. This study investigated the impact of storage conditions on the microbiological quality and degradation of selected bioactive compounds in fruit mousses from various producers (from apples, pears, and multi-components). Total phenolic (TPC) and total flavonoid (TFC) contents, vitamin C level, antioxidant capacity (AC, measured by the DPPH assay), and concentrations of macro- and microminerals were evaluated in fresh mousses and those stored for 48 h at 23 °C and 4 °C. Changes in total aerobic mesophilic bacteria (TAMB), yeast and mold counts, and selected microbial groups were also checked. It was found that the analyzed compounds varied depending on the components of the mousses. Multi-component mousses contained the highest levels of TPC, TFC, and vitamin C, and had 2–5 times higher AC values compared to apple and pear mousses. Storage at room temperature resulted in TFC lowering of up to 25% in apple mousses and vitamin C reductions of up to 22% in multi-component mousses. During refrigerated storage, the highest losses were observed in pear mousses, with TPC decreasing by up to 13% and vitamin C by up to 11%. Among the minerals, magnesium and zinc levels decreased most significantly in apple mousses stored at 23 °C (up to 33% and up to 29%, respectively). Microbiological analysis revealed variability in TAMB, yeast, and mold counts, with refrigeration (4 °C) generally limiting microbial growth compared to room temperature (23 °C). Notably, no pathogenic bacteria were detected under any storage conditions, and the mousses retained a high microbiological quality even after room-temperature storage.
An Interactive Virtual Reality Approach to Understanding Cultural Heritage Through Storyliving: A Case Study of Seoul City Wall (Hanyangdoseong) in South Korea
Authors: Choi Jin Woo , Park Jong Jin , Jun Han Jong
Published: December 2024

In this study, we developed and evaluated a nonlinear narrative virtual reality approach to enhancing the understanding of cultural heritage. The system was designed to convey the historical and cultural context of Seoul City Wall (Hanyangdoseong) while engaging users in a personalized experience. A novel “storyliving” structure was used, in which users choose the progress of the narrative. To achieve this, the methodology employed three key components: evaluating criteria to assess Hanyangdoseong’s sense-of-place, clustering to create distinct scenario segments, and sticker mapping to identify spatial preferences. Our approach maximizes user immersion through selective pathways and branching stories. Initial validation results suggest that this virtual reality system can greatly deepen users’ understanding of Hanyangdoseong and demonstrate the system’s potential as an innovative tool for cultural heritage education. This study contributes to the digital transformation of cultural heritage and provides important direction for the development of virtual reality content with richer and more personalized educational experiences.