bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence Analysis of Different Factors from the Tourism Sector on Rural Gross Value Added: Cross-Section Analysis at the EU Level
Authors: Ion Raluca Andreea , Petre Ionut Laurentiu
Published: October 2024

This article investigates the impact of rural tourism on the economy and on sustainable development in rural areas of EU member states. Rural tourism is a distinct form of tourism carried out in rural areas, which attracts tourists interested in authentic experiences, nature, and local culture. The article explores the multiple ways in which rural tourism influences the economy of rural areas. This paper seeks to identify factors that can enhance economic development in rural areas through the tourism sector. Therefore, various indicators of rural tourism and businesses operating in the accommodation sector will be analysed in order to determine their influence on gross value added. The analysis used the linear multiple regression method to determine the influence of these factors on gross value added. The analysis of the data identified that the founding countries of the European Union have a developed rural tourism activity, with the most nights spent as a tourist occurring in rural areas in France and the most rural accommodation establishments occurring in Italy. At the same time, Germany recorded the highest number of employees in the accommodation sector in 2021.
Mechanical Properties and DEM-Based Simulation of Double-Fractured Sandstone Under Cyclic Loading and Unloading
Authors: Sun Lichen , Lou Peijie , Pan Cheng , Ji Penghui
Published: October 2024

In response to the challenges posed by long-term cyclic loading and unloading in underground rock engineering, this study systematically investigates the macro- and meso-mechanical response mechanisms of fractured rock masses under cyclic loading conditions. We performed graded cyclic loading–unloading tests on parallel double-fractured sandstone samples with varying spatial distribution configurations. These tests were integrated with digital image correlation (DIC) technology, fractal dimension analysis, and discrete element method (DEM) numerical simulations to analyze the mechanical properties, deformation characteristics, crack propagation features, and meso-fracture mechanisms of the fractured rock masses. The findings indicate that the diverse spatial distribution characteristics of the double fractures exert a significant influence on the loading–unloading processes, surface deformation fields, and fracture states of the rock. Cyclic loading leads to an increase in the fractal dimension of the fractured samples, resulting in more intricate and chaotic crack propagation patterns. Furthermore, DEM simulations reveal the impact of fracture spatial configurations on the force chain distribution within the rock bridges. The equivalent stress nephogram effectively represents the stress field distribution. This offers valuable insights for predicting meso-fracture trends in rocks. This paper comprehensively integrates both experimental and numerical simulation methodologies to deliver a thorough analysis of the complex mechanical behavior of fractured rock masses under cyclic loading conditions, with direct relevance to engineering applications such as mine excavation and slope stabilization.
Enhanced Production of Microalgal Metabolites Through Aeration Coupled with Stirring
Authors: Wu Yangyingdong , He Yuqing , Zhao Tuo , Zhao Yang , Yu Ze , Pei Haiyan
Published: October 2024

Adequate mixing is a key factor for microalgal cultivation to achieve high biomass production, so it is essential to clarify the comparative effects of different mixing methods on microalgal productivity, which has rarely been studied previously. This work therefore aimed to investigate the effects of different mixing methods (stirring, aeration, and aeration coupled with stirring) on the growth and metabolite composition of Chlorella sorokiniana SDEC-18, a strain with potential for large-scale application. The results showed that mixing was beneficial for carbohydrate accumulation, while dual mixing (aeration coupled with stirring) promoted growth and achieved the highest dry mass and metabolite productivities (including carbohydrates, proteins, and lipids) through enhancement of light energy capture in the entire system. The stirring speed in the dual mixing approach of aeration coupled with stirring was also considered: the optimal condition was found to be 800 rpm. The maximum biomass was 3.56 g L−1, and the carbohydrate productivity was as high as 119.45 mg L−1 d−1, which was the highest metabolite productivity (higher than proteins or lipids), obtained from aeration coupled with stirring at 800 rpm. Our study suggests that aeration coupled with stirring provides a feasible strategy for microalgal production, due to the optimal availability of CO2 and light achieved through effective mixing.
Perceptions of Artificial Intelligence and Its Impact on Academic Integrity Among University Students in Peru and Chile: An Approach to Sustainable Education
Authors: Espinoza Vidaurre Sam M. , Velásquez Rodríguez Norma C. , Gambetta Quelopana Renza L. , Martinez Valdivia Ana N. , Leo Rossi Ernesto A. , Nolasco-Mamani Marco A.
Published: October 2024

In a context where artificial intelligence (AI) is transforming higher education, this study analyzes how students’ perceptions of AI influence their academic integrity (INA), with a focus on sustainable education. Through a correlational-explanatory analysis based on Structural Equation Models (SEMs) applied to a sample of 659 students from 13 universities in Chile and Peru, it is observed that AI has a significant and direct impact on academic integrity in both countries (β = 0.44). In Peru, the most influential dimension is trust in education (λ = 0.86), followed by social, economic, security, and risk implications (λ = 0.78), while attitudes towards AI also have a direct impact on integrity factors (β = 0.15). In Chile, the dimensions of trust in education (λ = 0.83) and social and economic impact (λ = 0.79) are most relevant, and the relationships between the dimensions of academic integrity such as justice, respect, and responsibility (λ = 0.71) are stronger. The study highlights the importance of incorporating AI literacy into educational curricula and developing regulatory frameworks that promote its ethical use, linking these actions to sustainable education. The findings highlight the need for sustainable educational approaches that enhance understanding of AI and ensure that its use in academia is beneficial, ethical, and contributes to sustainable development.
All Deforestation Matters: Deforestation Alert System for the Caatinga Biome in South America’s Tropical Dry Forest
Authors: Costa Diego Pereira , Lentini Carlos A. D. , Cunha Lima André T. , Duverger Soltan Galano , Vasconcelos Rodrigo N. , Herrmann Stefanie M. , Ferreira-Ferreira Jefferson , Oliveira Mariana , da Silva Barbosa Leonardo , Cordeiro Carlos Leandro , Santos Nerivaldo Afonso , Franca Rocha Rafael Oliveira , Souza Deorgia T. M. , Franca Rocha Washington J. S.
Published: October 2024

This study provides a comprehensive overview of Phase I of the deforestation dryland alert system. It focuses on its operation and outcomes from 2020 to 2022 in the Caatinga biome, a unique Brazilian dryland ecosystem. The primary objectives were to analyze deforestation dynamics, identify areas with highest deforestation rates, and determine regions that require prioritization for anti-deforestation efforts and conservation actions. The research methodology involved utilizing remote sensing data, including Landsat imagery, processed through the Google Earth Engine platform. The data were analyzed using spectral unmixing, adjusted Normalized Difference Fraction Index, and harmonic time series models to generate monthly deforestation alerts. The findings reveal a significant increase in deforestation alerts and deforested areas over the study period, with a 148% rise in alerts from 2020 to 2022. The Caatinga biome was identified as the second highest in detected deforestation alerts in Brazil in 2022, accounting for 18.4% of total alerts. Hexagonal assessments illustrate diverse vegetation cover and alert distribution, enabling targeted conservation efforts. The Bivariate Choropleth Map demonstrates the nuanced relationship between alert and vegetation cover, guiding prioritization for deforestation control and native vegetation restoration. The analysis also highlighted the spatial heterogeneity of deforestation, with most deforestation events occurring in small patches, averaging 10.9 ha. The study concludes that while the dryland alert system (SAD-Caatinga—Phase I) has effectively detected deforestation, ongoing challenges such as cloud cover, seasonality, and more frequent and precise monitoring persist. The implementation of DDAS plays a critical role in sustainable forestry by enabling the prompt detection of deforestation, which supports targeted interventions, helps contain the process, and provides decision makers with early insights to distinguish between legal and illegal practices. These capabilities inform decision-making processes and promote sustainable forest management in dryland ecosystems. Future improvements, including using higher-resolution imagery and artificial intelligence for validation, are essential to detect smaller deforestation alerts, reduce manual efforts, and support sustainable dryland management in the Caatinga biome.
Morphometric and Soil Erosion Characterization Based on Geospatial Analysis and Drainage Basin Prioritization of the Rabigh Area Along the Eastern Red Sea Coastal Plain, Saudi Arabia
Authors: Bashir Bashar , Alsalman Abdullah
Published: October 2024

Soil erosion is one of the most significant problems in global environmental development. Assigning, analyzing, and quantifying the main physical characteristics of drainage basins are powerful keys in identifying regions where there is a higher risk of soil erosion and where prompt mitigation actions are needed. Drainage basins and their drainage systems are ideally defined using the analysis morphometric parameters and their quantitative description. The present study aims to analyze morphometric parameters to prioritize drainage basins that are prone to erosion. Topographic sheets and remotely sensed digital elevation model (DEM) datasets have been prepared and analyzed using geospatial techniques to delineate drainage basins of different sizes and extract different ordered drainage systems. Based on the analysis of morphometric parameters, the Rabigh area was divided into 12 drainage basins, which significantly contribute to determining soil erosion priority levels. The present study selected and applied the most effective morphometric parameters to rank and prioritize the drainage basins of the study area after considering the crucial quantitative characteristics, such as linear, relief, and areal aspects. For each single basin, the compound factor was assigned from several morphometric parameters and applied to rank the Rabigh area. The results confirm that Basins 1, 4, 11, and 12 require a high level of soil erosion priority over an area of 2107 km2; however, Basins 3, 8, 9, and 10 have little degradation and a low level of soil erosion priority. Therefore, in the regions where high soil erosion is a factor, mitigation techniques such as terracing, filter strips, contouring, and other effective and useful structural and non-structural methods should be applied.
Synthesis and Properties of Novel Acrylic Fluorinated Surfactants
Authors: Lin Chao , Li Jinhua , Qin Yejun , Xing Ping , Jiang Biao
Published: October 2024

Branched fluorinated surfactants with creatively introduced acrylate in the hydrophilic group were designed and prepared by adopting perfluoro-2-methyl-2-pentene as the raw substrate. These new compounds showed excellent surface properties, and the surface tension of their aqueous solution at 25 °C could be below 20.00 mN/m at the critical micelle concentration. Compared with similar structures we have synthesized previously, these synthesized compounds exhibit a great improvement with regard to their molecular arrangement at the gas–liquid interface, their polymerizability, and the antibacterial properties of their polymer form, which can provide new ideas in the work to replace perfluorooctane sulfonate/perfluorooctanoic acid.
Development of Natural Fungicidal Agricultural Defensives Using Microbial Glycolipid and Vegetable Oil Blends
Authors: de Medeiros Anderson O. , da Silva Maria da Gloria C. , Converti Attilio , de Almeida Fabiola Carolina G. , Sarubbo Leonie A.
Published: October 2024

The use of pesticides causes significant environmental problems, which drives the search for natural and non-toxic alternatives. In this study, a glycolipid biosurfactant (BS), produced by the yeast Starmerella bombicola ATCC 22214, was utilized as an active ingredient in natural agricultural defensive blends. The mixtures were tested for their fungicidal potential against phytopathogenic fungi isolated from fruits such as papaya, orange, and banana, demonstrating strong inhibition of fungal growth. The genera Penicillium, Colletotrichum, and Aspergillus were the pathogens present in the deterioration of the fruits used in the experiment. The biosurfactant was produced in a fermenter, yielding 10 g/L and reducing the surface tension to 31.56 mN/m, with a critical micelle concentration (CMC) of 366 mg/L. Blends of BS with oleic acid (T1) and lemongrass oil (T2) were found to be effective in controlling fungi. Additionally, the phytotoxicity of these formulations was assessed using Cucumis anguria (gherkin) seeds, where the blend of BS with castor oil (T4) showed the best performance, promoting seed germination. These results indicate the potential of such mixtures as natural alternatives for fungal control in plants and for application in sustainable agricultural systems.
Experimental Study on Strength and Deformation Moduli of Columnar Jointed Rock Mass—Uniaxial Compression as an Example
Authors: Xu Zhenbo , Zhu Zhende , Jiang Chao , Hu Xiaobin
Published: October 2024

The irregular joint network unique to columnar joints separates the rock mass into several irregular polygonal prisms. Similar physical model specimens of columnar jointed rock mass (CJRM) were fabricated using a rock-like material. The effect of the irregularity of the joint network was considered in the horizontal plane, and the effect of the dip angle of the joint network was considered in the vertical plane. The strength and deformation moduli of the specimen were investigated using uniaxial compression tests. A total of four failure modes of regular columnar jointed rock mass (RCJRM) and irregular columnar jointed rock mass (ICJRM) were identified through the tests. The peak stress of the irregular columnar jointed rock mass specimen is reduced by 56.65%. The strength and deformation moduli of RCJRM were greater than those of ICJRM, while the anisotropic characteristics of ICJRM were stronger. The failure mode of CJRM was determined by the dip angle. With the increase in the dip angle, the strength and deformation moduli of irregular columnar jointed rock mass are a symmetrical “V” type distribution, 45° corresponds to the minimum strength, and 30° obtains the minimum deformation modulus. With the increase in the irregularity coefficient, the strength and deformation moduli of CJRM decreased first and then increased gradually. When the irregularity coefficient is 0.1, the linear deformation modulus reaches the minimum value. When the irregularity coefficient is 0.7, the median deformation modulus reaches the minimum value. The fitting function proposed in the form of the cosine function managed to predict the strength value of CJRM and showed the strength of the anisotropic characteristics caused by the change in the dip angle. Compared with the existing physical model test results, it is determined that the strength of the specimen is positively correlated with the addition amount of rock-like material and the loading rate, and negatively correlated with the water consumption.
Joint Choice of Fresh Food Purchase Channels and Terminal Delivery Service: A Background on Major Public Health Events
Authors: Zhu Huiqi , Jiang Tianhua
Published: October 2024

The paper aims to analyze the consumer joint choice behavior on fresh food purchase channels and terminal delivery services during major public health events, with the purpose of revealing the underlying influencing factors and behavioral characteristics. First, based on random utility maximization theory, the cross-nested logit model is formulated, which takes into account the influence of socioeconomic attribute factors, service attribute factors, risk perception attribute factors and trust perception attribute factors. Second, a questionnaire survey is conducted, and the obtained data are used to estimate the model parameters and perform an elasticity analysis of the utility variables. The parameter estimation results demonstrate that in the context of major public health events, consumers consider adjusting their attitudes toward e-commerce platforms first when the utility variables are altered, and fresh food purchase channels are easily replaced for consumers who choose unmanned equipment home delivery. The elasticity analysis results suggest that consumers are more willing to buy fresh food through community group-buying channels, are more sensitive to the convenience of the purchase process and are less concerned with delivery time. Although person-to-person contact increases the risk of infection, consumers still prefer attended terminal delivery services. Furthermore, consumers least agree with the effectiveness of body temperature detection methods in public places but feel that an effective way to increase consumer trust in enterprises is to strengthen personnel protection measures.
Identifying Key Factors of Reputational Risk in Finance Sector Using a Linguistic Fuzzy Modeling Approach
Authors: Hanay Uğur , İnce Hüseyin , Işık Gürkan
Published: October 2024

Management of reputational risk is crucial for financial institutions to establish a solid foundation for strategic decisions, gain customer trust, and enhance resilience against environmental adversities, as they largely operate on digital platforms. Since this becomes even more significant as online transactions and digital interactions amplify the visibility and potential impact of reputational issues in the context of electronic commerce, it is essential to thoroughly investigate environmental factors to achieve a comprehensive understanding of reputational risk. However, measuring and evaluating their influence on reputational risk is challenging due to their inherent connection to human perception. This study aims to explore the factors influencing reputational risk of financial organizations to mitigate potential reputational losses by addressing uncertainties associated with concepts such as vagueness. The employed methodology integrates the Decision-Making Trial and Evaluation Laboratory and Fuzzy Cognitive Map techniques using linguistic fuzzy terms. This approach focuses on both the direct effects of factors on reputational risk and the indirect effects arising from interdependencies between factors. Linguistic fuzzy variables enable us to consider the hesitation of the experts and the vagueness of human judgment. To validate the results, factors are also weighted using the fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) method. The most influential factors identified by both methods are market value, revenue, risk culture, shareholder value, firm performance, reputation awareness, and return on equity. Additionally, factors affecting other factors include firm performance, revenue, and growth opportunities.
Ceratozamia chinantlensis (Zamiaceae): A New Cycad Species from La Chinantla, Oaxaca, Mexico
Authors: Pérez-Farrera Miguel Angel , Ramirez-Oviedo Steven M. , Martínez-Martínez Mauricio Gerónimo , Moreno Mendez Gaspar , Rocha Loredo Ana Guadalupe , Gutiérrez-Ortega José Said
Published: October 2024

Ceratozamia chinantlensis sp. nov., a new cliff-dwelling cycad species from Sierra de La Chinantla, Oaxaca, Mexico, is described and compared with its congeners. The species is morphologically similar to Ceratozamia zoquorum and Ceratozamia santillanii, two geographically distant species, yet it is effortlessly distinguishable from its most proximally geographic congeners. Ceratozamia chinantlensis can be recognized based on several qualitative traits, such as its very coriaceous, long, linear to oblanceolate leaflets. Also, it has bronze-colored emerging leaves and mature leaves with articulations of a light copper color. Compared with C. zoquorum and C. santillanii, C. chinantlensis has significantly longer and wider leaflets. C. chinantlensis should be considered a critically threatened species due to its limited number of populations and individuals. Habitat destruction—historically through the extraction of Dioscorea mexicana and Vanilla planifolia, along with ongoing coffee and corn plantations—is an ongoing threat that narrows its distribution range.
Eight Element Wideband Antenna with Improved Isolation for 5G Mid Band Applications
Authors: John Deepthi Mariam , Vincent Shweta , Pathan Sameena , Boulogeorgos Alexandros-Apostolos A. , Anguera Jaume , Ali Tanweer , David Rajiv Mohan
Published: October 2024

Modern wireless communication systems have undergone a radical change with the introduction of multiple-input multiple-output (MIMO) antennas, which provide increased channel capacity, fast data rates, and secure connections. To achieve real-time requirements, such antenna technology needs to have good gains, wider bandwidths, satisfactory radiation characteristics, and high isolation. This article presents an eight-element CPW-fed antenna for the 5G mid-band. The proposed antenna consists of eight symmetrical, modified circular monopole antennas with a connected CPW-fed ground plane that offers 24 dB isolation over the operating range. The antenna is further investigated in terms of the scattering parameters, and radiation characteristics under both the x and y-axis bending scenarios. The antenna holds a volume of 83 × 129 × 0.1 mm3 and covers a measured impedance bandwidth of 4.5–5.5 GHz (20%) with an average gain of 4 dBi throughout the operating band. MIMO diversity performance of the antenna is performed, and the antenna exhibits good performance suitable for MIMO applications. Furthermore, the channel capacity (CC) is estimated, and the antenna gives a value of 41.8–42.6 bps/Hz within the operating bandwidth, which is very close to an ideal 8 × 8 MIMO system. The antenna shows an excellent match between the simulated and measured findings.
The Role of Copper in Alzheimer’s Disease Etiopathogenesis: An Updated Systematic Review
Authors: Sabalic Angela , Mei Veronica , Solinas Giuliana , Madeddu Roberto
Published: October 2024

Background: Alzheimer’s disease (AD) is the most common cause of dementia and cognitive decline in the elderly. Although the etiology of AD is unknow, an increase in amyloid precursor protein (APP) leads to the toxic aggregation of Aβ plaques. Several factors, such as hypertension, diabetes, dyslipidemia, smoking, hormonal changes, and metal exposure, could increase the risk of developing AD. In this review, we will examine the role of copper (Cu) in the pathophysiology of AD, as well as the mechanisms involved in neurotoxicity and cognitive decline. Methods: This review was conducted in accordance with PRISMA guidelines. We performed a comprehensive literature analysis over the last ten years on AD and Cu. Only late-onset Alzheimer’s disease was considered; only studies on elderly people of both sexes were included. Results: A total of seven articles were picked for this review, three studies focused on non-ceruloplasmin-bound Copper (non-Cp-Cu) and four on ceruloplasmin-bound Copper (Cp-Cu). The results showed higher Cu concentrations in patients compared to healthy controls. Conclusions: Elevated concentrations of Cu may contribute to the progression of AD, potentially interacting with ATP7B mutations, oxidative stress (OS), and amyloid-β plaques. Future research is needed to provide more robust evidence and better characterize the relationship between AD and Cu.
Scoliidines: Neuroprotective Peptides in Solitary Scoliid Wasp Venoms
Authors: Alberto-Silva Carlos , Vieira Portaro Fernanda Calheta , Kodama Roberto Tadashi , Gomes Lais , da Silva Brenda Rufino , da Cunha e Silva Felipe Assumpção , Nihei Ken-ichi , Konno Katsuhiro
Published: October 2024

A comprehensive LC-MS study examined the venom components of the solitary scoliid wasp Scolia oculata. Online mass fingerprinting showed that crude venom contains 25 small molecules (amino acids, biogenic amines, and nucleosides/nucleotides) and 45 peptides with MW 400-2700. The small molecules were identified by elemental composition analysis, and peptide sequences were determined by ESI-MS/MS and MALDI-TOF/TOF MS analyses. As major peptide components, a known peptide, β-scoliidine (DYVTVKGFSPLRKA), and three new peptides, γ-scoliidine (YVTVKGFSPLR), δ-scoliidine (YVTVKGFSPLREP) and ε-scoliidine (DYVTVKGFSPLREP) were identified, all of which are closely homologous to each other. Once the neuroprotective effects of β-scoliidine have already been described, the other three new scoliidine peptides were analyzed against oxidative stress-induced toxicity in PC12 neuronal cells by mitochondrial metabolism assay, and the structure-activity relationship was evaluated. Interestingly, pre-treatment with ε-scoliidine increased the mitochondrial metabolism of PC12 cells (106 ± 3.6%; p = 0.007) exposed to H2O2-induced oxidative stress in contrast to γ- and δ-scoliidines (77.6 ± 4.8 and 68.5 ± 4.1%, respectively) in compared to cells treated only H2O2 (75.8 ± 2.4%). These new peptides were also analyzed for enzyme inhibitor/substrate assays with angiotensin-converting enzyme (ACE), neprilysin (NEP), and acetylcholinesterase (AChE). In these assays, only δ- and ε-scoliidines increased the AChE activity (128.7 ± 3.8%; p = 0.01; and 116.8 ± 3.8% p = 0.03; respectively) in relation to basal activity (100.1 ± 1.6%). In addition, the four peptides were analyzed through in silico analysis, and none of them demonstrated possible hemolytic and toxic activities. In our study, the comprehensive LC-MS and MS/MS analyses of Scolia oculate venom identified four major peptide components of the venom β-, γ-, δ- and ε-scoliidines, and small differences in their primary structures are important to their neuroprotective properties.
Elucidation of Medusozoan (Jellyfish) Venom Constituent Activities Using Constellation Pharmacology
Authors: Yanagihara Angel A. , Giglio Matías L. , Hurwitz Kikiana , Kadler Raechel , Espino Samuel S. , Raghuraman Shrinivasan , Olivera Baldomero M.
Published: October 2024

Within the phylum Cnidaria, sea anemones (class Anthozoa) express a rich diversity of ion-channel peptide modulators with biomedical applications, but corollary discoveries from jellyfish (subphylum Medusozoa) are lacking. To bridge this gap, bioactivities of previously unexplored proteinaceous and small molecular weight (~15 kDa to 5 kDa) venom components were assessed in a mouse dorsal root ganglia (DRG) high-content calcium-imaging assay, known as constellation pharmacology. While the addition of crude venom led to nonspecific cell death and Fura-2 signal leakage due to pore-forming activity, purified small molecular weight fractions of venom demonstrated three main, concentration-dependent and reversible effects on defined heterogeneous cell types found in the primary cultures of mouse DRG. These three phenotypic responses are herein referred to as phenotype A, B and C: excitatory amplification (A) or inhibition (B) of KCl-induced calcium signals, and test compound-induced disturbances to baseline calcium levels (C). Most notably, certain Alatina alata venom fractions showed phenotype A effects in all DRG neurons; Physalia physalis and Chironex fleckeri fractions predominantly showed phenotype B effects in small- and medium-diameter neurons. Finally, specific Physalia physalis and Alatina alata venom components induced direct excitatory responses (phenotype C) in glial cells. These findings demonstrate a diversity of neuroactive compounds in jellyfish venom potentially targeting a constellation of ion channels and ligand-gated receptors with broad physiological implications.
Activation-Induced Marker Assay to Identify and Isolate HCV-Specific T Cells for Single-Cell RNA-Seq Analysis
Authors: Eisa Mohamed , Flores Nicol , Khedr Omar , Gomez-Escobar Elsa , Bédard Nathalie , Abdeltawab Nourtan F. , Bruneau Julie , Grakoui Arash , Shoukry Naglaa H.
Published: October 2024

Identification and isolation of antigen-specific T cells for downstream transcriptomic analysis is key for various immunological studies. Traditional methods using major histocompatibility complex (MHC) multimers are limited by the number of predefined immunodominant epitopes and MHC matching of the study subjects. Activation-induced markers (AIM) enable highly sensitive detection of rare antigen-specific T cells irrespective of the availability of MHC multimers. Herein, we have developed an AIM assay for the detection, sorting and subsequent single-cell RNA sequencing (scRNA-seq) analysis of hepatitis C virus (HCV)-specific T cells. We examined different combinations of the activation markers CD69, CD40L, OX40, and 4-1BB at 6, 9, 18 and 24 h post stimulation with HCV peptide pools. AIM+ CD4 T cells exhibited upregulation of CD69 and CD40L as early as 6 h post-stimulation, while OX40 and 4-1BB expression was delayed until 18 h. AIM+ CD8 T cells were characterized by the coexpression of CD69 and 4-1BB at 18 h, while the expression of CD40L and OX40 remained low throughout the stimulation period. AIM+ CD4 and CD8 T cells were successfully sorted and processed for scRNA-seq analysis examining gene expression and T cell receptor (TCR) usage. scRNA-seq analysis from this one subject revealed that AIM+ CD4 T (CD69+ CD40L+) cells predominantly represented Tfh, Th1, and Th17 profiles, whereas AIM+ CD8 T (CD69+ 4-1BB+) cells primarily exhibited effector and effector memory profiles. TCR analysis identified 1023 and 160 unique clonotypes within AIM+ CD4 and CD8 T cells, respectively. In conclusion, this approach offers highly sensitive detection of HCV-specific T cells that can be applied for cohort studies, thus facilitating the identification of specific gene signatures associated with infection outcome and vaccination.
Differences in Susceptibility to SARS-CoV-2 Infection Among Transgenic hACE2-Hamster Founder Lines
Authors: Gibson Scott A. , Liu Yanan , Li Rong , Hurst Brett L. , Fan Zhiqiang , Siddharthan Venkatraman , Larson Deanna P. , Sheesley Ashley Y. , Stewart Rebekah , Kunzler Madelyn , Polejaeva Irina A. , Van Wettere Arnaud J , Moisyadi Stefan , Morrey John D. , Tarbet E. Bart , Wang Zhongde
Published: October 2024

Animal models that are susceptible to SARS-CoV-2 infection and develop clinical signs like human COVID-19 are desired to understand viral pathogenesis and develop effective medical countermeasures. The golden Syrian hamster is important for the study of SARS-CoV-2 since hamsters are naturally susceptible to SARS-CoV-2. However, infected hamsters show only limited clinical disease and resolve infection quickly. In this study, we describe development of human angiotensin-converting enzyme 2 (hACE2) transgenic hamsters as a model for COVID-19. During development of the model for SARS-CoV-2, we observed that different hACE2 transgenic hamster founder lines varied in their susceptibility to SARS-CoV-2 lethal infection. The highly susceptible hACE2 founder lines F0F35 and F0M41 rapidly progress to severe infection and death within 6 days post-infection (p.i.). Clinical signs included lethargy, weight loss, dyspnea, and mortality. Lethality was observed in a viral dose-dependent manner with a lethal dose as low as 1 × 100.15 CCID50. In addition, virus shedding from highly susceptible lines was detected in oropharyngeal swabs on days 2–5 p.i., and virus titers were observed at 105.5−6.5 CCID50 in lung and brain tissue by day 4 p.i.. Histopathology revealed that infected hACE2-hamsters developed rhinitis, tracheitis, bronchointerstitial pneumonia, and encephalitis. Mortality in highly susceptible hACE2-hamsters can be attributed to neurologic disease with contributions from the accompanying respiratory disease. In contrast, virus challenge of animals from less susceptible founder lines, F0M44 and F0M51, resulted in only 0–20% mortality. To demonstrate utility of this SARS-CoV-2 infection model, we determined the protective effect of the TLR3 agonist polyinosinic-polycytidylic acid (Poly (I:C)). Prophylactic treatment with Poly (I:C) significantly improved survival in highly susceptible hACE2-hamsters. In summary, our studies demonstrate that hACE2 transgenic hamsters differ in their susceptibility to SARS-CoV-2 infection, based on the transgenic hamster founder line, and that prophylactic treatment with Poly (I:C) was protective in this COVID-19 model of highly susceptible hACE2-hamsters.
Assessment of Nine Real-Time PCR Kits for African Swine Fever Virus Approved in Republic of Korea
Authors: Lee Siwon , Han Tae Uk , Kim Jin-Ho
Published: October 2024

The African swine fever virus (ASFV) causes severe disease in wild and domestic pigs, with high mortality rates, extensive spread, and significant economic losses globally. Despite ongoing efforts, an effective vaccine remains elusive. Therefore, effective diagnostic methods are needed to rapidly detect and prevent the further spread of ASF. This study assessed nine commercial kits based on real-time polymerase chain reaction (PCR) approved in the Republic of Korea using the synthesized ASFV plasmid, 20 food waste samples, and artificially spiked samples (ASSs). The kits were evaluated for their diagnostic sensitivity, specificity, cost per reaction, and reaction running time. In addition, the results were compared with those of the World Organization for Animal Health (WOAH) standard methods. Three commercial kits (VDx® ASFV qPCR Kit, Palm PCR™ ASFV Fast PCR Kit, and PowerChek™ ASFV Real-time PCR Detection Kit Ver.1.0) demonstrated the highest sensitivity (100 ag/μL), cost-effectiveness (less than KRW 10,000), and shortest running time (less than 70 min). These kits are suitable for the monitoring, early diagnosis, and prevention of the spread of ASF. This is the first report on the performance comparison of ASFV diagnostic kits approved in the Republic of Korea, providing valuable information for selecting kits for testing with food waste samples.
Field Trial with Vaccine Candidates Against Bovine Tuberculosis Among Likely Infected Cattle in a Natural Transmission Setting
Authors: Ferrara Muñiz Ximena , García Elizabeth , Blanco Federico Carlos , Garbaccio Sergio , Garro Carlos , Zumárraga Martín , Dellagostin Odir , Trangoni Marcos , Marfil María Jimena , Bianco Maria Verónica , Abdala Alejandro , Revelli Javier , Bergamasco Maria , Soutullo Adriana , Marini Rocío , Rocha Rosana Valeria , Sánchez Amorina , Bigi Fabiana , Canal Ana María , Eirin María Emilia , Cataldi Angel Adrián
Published: October 2024

Background/Objectives: Vaccines may improve the control and eradication of bovine tuberculosis. However, the evaluation of experimental candidates requires the assessment of the protection, excretion, transmission and biosafety. A natural transmission trial among likely infected animals was conducted. Methods: Seventy-four male heifers were randomly distributed (five groups) and vaccinated subcutaneously with attenuated strains (M. bovis Δmce2 or M. bovis Δmce2-phoP), a recombinant M. bovis BCG Pasteur (BCGr) or M. bovis BCG Pasteur. Then, they cohoused with a naturally infected bTB cohort under field conditions exposed to the infection. Results: A 23% of transmission of wild-type strains was confirmed (non-vaccinated group). Strikingly, first vaccination did not induce immune response (caudal fold test and IFN-gamma release assay). However, after 74 days of exposure to bTB, animals were re-vaccinated. Although their sensitization increased throughout the trial, the vaccines did not confer significant protection, when compared to the non-vaccinated group, as demonstrated by pathology progression of lesions and confirmatory tools. Besides, the likelihood of acquiring the infection was similar in all groups compared to the non-vaccinated group (p > 0.076). Respiratory and digestive excretion of viable vaccine candidates was undetectable. To note, the group vaccinated with M. bovis Δmce2-phoP exhibited the highest proportion of animals without macroscopic lesions, compared to the one vaccinated with BCG, although this was not statistically supported. Conclusions: This highlights that further evaluation of these vaccines would not guarantee better protection. The limitations detected during the trial are discussed regarding the transmission rate of M. bovis wild-type, the imperfect test for studying sensitization, the need for a DIVA diagnosis and management conditions of the trials performed under routine husbandry conditions. Re-vaccination of likely infected bovines did not highlight a conclusive result, even suggesting a detrimental effect on those vaccinated with M. bovis BCG.
ASIA Syndrome: State-of-the-Art and Future Perspectives
Authors: Caldarelli Mario , Rio Pierluigi , Giambra Vincenzo , Gasbarrini Antonio , Gambassi Giovanni , Cianci Rossella
Published: October 2024

The expression “Autoimmune/inflammatory syndrome induced by adjuvants (ASIA)” was coined by Shoenfeld and colleagues in 2011. It defines a group of immune-mediated disorders that arise in people, with a genetic predisposition, following exposure to adjuvant agents. This syndrome has been reported after contact with silicone implants, medications, infections, metals, vaccines, and other substances. It typically occurs in individuals with a genetic predisposition, particularly involving genes, such as HLA-DRB1 (major histocompatibility complex, class II, DR beta 1) and PTPN22 (protein tyrosine phosphatase non-receptor type 22). Some stimuli lead to an overactivation of the immune system, prompt the production of autoantibodies, and finally cause autoimmune disorders. This narrative review aims to provide an overview of the ASIA syndrome with a special focus on the role of adjuvants in different vaccines, especially after the COVID-19 pandemic, and insights into development of new treatments.
Early Detection of Lumpy Skin Disease in Cattle Using Deep Learning—A Comparative Analysis of Pretrained Models
Authors: Senthilkumar Chamirti , C Sindhu , Vadivu G. , Neethirajan Suresh
Published: October 2024

Lumpy Skin Disease (LSD) poses a significant threat to agricultural economies, particularly in livestock-dependent countries like India, due to its high transmission rate leading to severe morbidity and mortality among cattle. This underscores the urgent need for early and accurate detection to effectively manage and mitigate outbreaks. Leveraging advancements in computer vision and artificial intelligence, our research develops an automated system for LSD detection in cattle using deep learning techniques. We utilized two publicly available datasets comprising images of healthy cattle and those with LSD, including additional images of cattle affected by other diseases to enhance specificity and ensure the model detects LSD specifically rather than general illness signs. Our methodology involved preprocessing the images, applying data augmentation, and balancing the datasets to improve model generalizability. We evaluated over ten pretrained deep learning models—Xception, VGG16, VGG19, ResNet152V2, InceptionV3, MobileNetV2, DenseNet201, NASNetMobile, NASNetLarge, and EfficientNetV2S—using transfer learning. The models were rigorously trained and tested under diverse conditions, with performance assessed using metrics such as accuracy, sensitivity, specificity, precision, F1-score, and AUC-ROC. Notably, VGG16 and MobileNetV2 emerged as the most effective, achieving accuracies of 96.07% and 96.39%, sensitivities of 93.75% and 98.57%, and specificities of 97.14% and 94.59%, respectively. Our study critically highlights the strengths and limitations of each model, demonstrating that while high accuracy is achievable, sensitivity and specificity are crucial for clinical applicability. By meticulously detailing the performance characteristics and including images of cattle with other diseases, we ensured the robustness and reliability of the models. This comprehensive comparative analysis enriches our understanding of deep learning applications in veterinary diagnostics and makes a substantial contribution to the field of automated disease recognition in livestock farming. Our findings suggest that adopting such AI-driven diagnostic tools can enhance the early detection and control of LSD, ultimately benefiting animal health and the agricultural economy.
First Overview of Zoological Medicine on Iberian Countries
Authors: Cardoso-Freitas Fábio , Silva Vanessa , Martinez-Silvestre Albert , Martins Ângela , Poeta Patrícia
Published: October 2024

The role of veterinary doctors, particularly those dedicated to Zoological Medicine, is becoming increasingly relevant and essential within the One Health framework. Educational and employment opportunities in this field vary widely across Europe, prompting us to characterise the sector in Portugal and Spain. A survey was conducted, resulting in 169 valid responses from veterinarians. Respondents were characterised based on gender, age, work environment, the highest level of education, additional training, whether they attended zoological medicine classes at university and their perception of the adequacy of that training. Further factors included years of experience, the percentage of their work involving zoological medicine, sectors they work in, the types of animals they treat, their confidence in treating these animals, sources of information they use, and membership in relevant professional associations. Relationships between these variables were examined. The results describe Iberian professionals working with non-traditional pets, wildlife, and zoo animals. Notably, only half of these professionals had taken zoological medicine classes at university; among those, the majority felt they inadequately prepared them for their work. Given the findings, enhancing the training and resources available to these professionals is crucial to prepare them for their roles better.
Development of a Modular Adjustable Wearable Haptic Device for XR Applications
Authors: Najm Ali , Banakou Domna , Michael-Grigoriou Despina
Published: October 2024

Current XR applications move beyond audiovisual information, with haptic feedback rapidly gaining ground. However, current haptic devices are still evolving and often struggle to combine key desired features in a balanced way. In this paper, we propose the development of a high-resolution haptic (HRH) system for perception enhancement, a wearable technology designed to augment extended reality (XR) experiences through precise and localized tactile feedback. The HRH system features a modular design with 58 individually addressable actuators, enabling intricate haptic interactions within a compact wearable form. Dual ESP32-S3 microcontrollers and a custom-designed system ensure robust processing and low-latency performance, crucial for real-time applications. Integration with the Unity game engine provides developers with a user-friendly and dynamic environment for accurate, simple control and customization. The modular design, utilizing a flexible PCB, supports a wide range of actuators, enhancing its versatility for various applications. A comparison of our proposed system with existing solutions indicates that the HRH system outperforms other devices by encapsulating several key features, including adjustability, affordability, modularity, and high-resolution feedback. The HRH system not only aims to advance the field of haptic feedback but also introduces an intuitive tool for exploring new methods of human–computer and XR interactions. Future work will focus on refining and exploring the haptic feedback communication methods used to convey information and expand the system’s applications.
Machine Learning-Based Water Quality Classification Assessment
Authors: Chen Wenliang , Xu Duo , Pan Bowen , Zhao Yuan , Song Yan
Published: October 2024

Water is a vital resource, and its quality has a direct impact on human health. Groundwater, as one of the primary water sources, requires careful monitoring to ensure its safety. Although manual methods for testing water quality are accurate, they are often time-consuming, costly, and inefficient when dealing with large and complex data sets. In recent years, machine learning has become an effective alternative for water quality assessment. However, current approaches still face challenges, such as the limited performance of individual models, minimal improvements from optimization algorithms, lack of dynamic feature weighting mechanisms, and potential information loss when simplifying model inputs. To address these challenges, this paper proposes a hybrid model, BS-MLP, which combines GBDT (gradient-boosted decision tree) and MLP (multilayer perceptron). The model leverages GBDT’s strength in feature selection and MLP’s capability to manage nonlinear relationships, enabling it to capture complex interactions between water quality parameters. We employ Bayesian optimization to fine-tune the model’s parameters and introduce a feature-weighting attention mechanism to develop the BS-FAMLP model, which dynamically adjusts feature weights, enhancing generalization and classification accuracy. In addition, a comprehensive parameter selection strategy is employed to maintain data integrity. These innovations significantly improve the model’s classification performance and efficiency in handling complex water quality environments and imbalanced datasets. This model was evaluated using a publicly available groundwater quality dataset consisting of 188,623 samples, each with 15 water quality parameters and corresponding labels. The BS-FAMLP model shows strong classification performance, with optimized hyperparameters and an adjusted feature-weighting attention mechanism. Specifically, it achieved an accuracy of 0.9616, precision of 0.9524, recall of 0.9655, F1 Score of 0.9589, and an AUC score of 0.9834 on the test set. Compared to single models, classification accuracy improved by approximately 10%, and when compared to other hybrid models with additional attention mechanisms, BS-FAMLP achieved an optimal balance between classification performance and computational efficiency. The core objective of this study is to utilize the acquired water quality parameter data for efficient classification and assessment of water samples, with the aim of streamlining traditional laboratory-based water quality analysis processes. By developing a reliable water quality classification model, this research provides robust technical support for water safety management.
Theory and Technology for the Prevention of Mine Water Disasters
Authors: Gu Helong , Shang Xueyi , Zhao Huatao
Published: October 2024

The complex hydrogeological conditions in deep mining areas, coupled with large-scale and high-intensity mining activities, have triggered a series of mine water disasters and significant resource waste issues [...]