bluuebunny/crossref_metadata_2025 huggingface dataset:
Case report: Clinical and molecular features of renal gastrointestinal tumor
Authors: Makker Simran , Rammal Rayan , Gu Ping , Dalbagni Guido , Al-Ahmadie Hikmat , Agaram Narasimhan P. , Iyer Gopa , Kotecha Ritesh R.
Published: February 2025

While gastrointestinal stromal tumors (GISTs) often arise within the GI tract, it is well known that GISTs may also rarely emanate outside of the digestive system. Prior case reports have documented various primary sites in non-GI organs [extra-intestinal GIST (EGIST)], yet only one report has described a localized GIST of renal origin. Here, we describe a patient who presented with bilateral renal masses who was found to have a large unresectable renal GIST tumor treated with imatinib. We discuss treatment experience and response with systemic therapy and describe molecular data to contextualize this ultra-rare presentation within the landscape of EGIST tumors.
Exploring the relationship between social capital and hedonic well-being in sport and physical activity contexts: a scoping review
Authors: Zhang Yi , Kinoshita Keita , Sato Shintaro
Published: February 2025

This scoping review aimed to summarize the conceptualization and measurements of both social capital and hedonic well-being and to explore the links between social capital and hedonic well-being within sports and physical activity contexts. Articles were sourced from five databases, including PubMed, Scopus (Elsevier), SPORTDiscus, Web of Science, and Google Scholar. Initially, 475 papers were identified. After applying the screening process, 24 papers were included. The majority (70.8%) indicated a positive relationship between social capital and hedonic well-being, while others found no direct connection (16.7%) or presented mixed results (12.5%). The review underscored a consensus on defining and measuring hedonic well-being, but it also revealed the need for a more refined conceptualization and universally accepted measurement of social capital within sports research. The findings highlighted the positive associations between social capital and hedonic well-being in sport and physical activity contexts, suggesting future research directions including an examination of potential downsides.
Yeast cell wall derivatives as a potential strategy for modulating oral microbiota and dental plaque biofilm
Authors: Scheithauer Torsten P. M. , Fernandes de Oliveira Isabela M. , Ossendrijver Michel , Dehay Elodie , van der Wurff Michelle , Rahaoui Hakim , Ballet Nathalie , Keijser Bart J. F.
Published: February 2025

Introduction

Derivatives from Saccharomyces cerevisiae yeast including yeast extracts and yeast cell walls are sustainable sources of valuable nutrients, including dietary fibers and proteins. Previous studies have shown that certain components from these yeast derivatives can inhibit the growth of harmful intestinal bacteria and promote the growth of beneficial bacteria. However, the effects of yeast derivatives on oral health have not yet been investigated.

Methods

An in vitro oral biofilm model was employed to examine the impacts of yeast derivatives on the oral microbiota and their potential benefits for maintaining oral homeostasis. The model incorporated dental plaque donor material from both healthy and periodontitis diagnosed individuals. Biofilm formation, density, and microbial composition were quantified. Additionally, the production of short-chain fatty acids in the biofilm supernatants was measured.

Results

Yeast extracts had only minor effects on oral biofilm formation. In contrast, yeast cell wall derivatives, which are rich in polysaccharides such as beta-glucans and mannans, significantly reduced the density of the oral biofilms in vitro. This reduction in biofilm density was associated with an overall shift in the bacterial community composition, including an increase in beneficial bacteria and a decrease in the abundance of Tannerella forsythia, an important species involved in bacterial coaggregation and the development and maturation of the oral biofilm. Furthermore, the yeast cell wall derivatives decreased the production of short-chain fatty acids, including acetic and butyric acid. These findings were consistent across both healthy and periodontitis microbiomes.

Conclusion

This study has demonstrated the potential of yeast cell wall derivatives to positively impact oral health by significantly reducing biofilm density, modulating the oral microbial composition, and decreasing the production of short-chain fatty acids. The observed effects highlight the promising applications of these yeast-based compounds as an approach to managing oral diseases. Further research is needed to fully elucidate the mechanisms of action and explore the clinical potential of yeast cell wall derivatives in promoting and maintaining oral health.
Case Report: Bicondylar conjoined Hoffa fracture with incarcerated patella
Authors: Mansouri Wissem , Darnaudet Jean , Huguet Romain , Fouasson-Chailloux Alban , Crenn Vincent
Published: February 2025

Case

Hoffa fractures, uncommon injuries of the femoral condyle, sometimes involve both condyles, forming bicondylar fractures, typically from high-velocity trauma. We describe a 17-year-old male with an open conjoined bicondylar Hoffa fracture and a patellar fracture with incarceration following a road traffic accident. Emergency treatments included debridement, irrigation, and cannulated screw fixation. Postoperative care involved controlled range-of-motion exercises and specialized rehabilitation.

Conclusion

Despite its rare and severe nature, this conjoined bicondylar Hoffa fracture with patellar incarceration was successfully managed, showing excellent recovery. CT scans are vital for accurate injury definition and surgical planning. Anatomical reduction and rigid fixation enable early mobilization and excellent long-term outcomes.
Comparative Analysis on Modelling Approaches for the Simulation of Fatigue Disbonding with Cohesive Zone Models
Authors: Birnie Johan , Falaschetti Maria Pia , Troiani Enrico
Published: February 2025

Adhesively bonded joints are essential in the aeronautical industry, offering benefits such as weight reduction and enhanced sustainability. However, certifying these joints is challenging due to unreliable methods for assessing their strength and the development of predictive models for fatigue-driven disbonding remains an ongoing effort. This manuscript presents the implementation and validation of a cohesive zone model for studying high-cycle fatigue disbonding under Mode I and Mixed-Mode loading. The model was integrated into the commercial finite element analysis software Abaqus using user-defined material subroutine (UMAT). Two modelling approaches were investigated: one replacing the adhesive with a cohesive layer, and the other incorporating a cohesive layer at the adhesive’s mid-plane while modelling its entire thickness, using both 2D and 3D techniques. Validation was conducted against experimental data from the literature that examined the influence of adhesive thickness on fatigue behaviour in DCB and CLS tests. The findings of this study confirm that the model accurately predicts fatigue disbonding across all cases examined. Additionally, the analysis reveals that modelling adhesive thickness plays a critical role in the simulation’s outcomes. Variations in adhesive thickness can significantly alter the crack growth behaviour, highlighting the importance of carefully considering this parameter in future assessments and applications.
Chromium Pollution and Mitigation in a Sunflower Farmland System
Authors: Yu Renjing , Yang Liyan , Yu Huan , Li Shuangzhe , Wang Lin , Yin Yanbin
Published: February 2025

Chromium is a major heavy metal pollutant that affects the health of both plants and animals. In this study, sunflower seedlings were treated with K2Cr7O4 containing 50, 100, and 250 mg of pure chromium per kilogram of soil. It was found that the chromium was absorbed by the roots and transported within plant tissues to the stems and leaves. Chromium affected sunflower photosynthesis, seen in increased the Fv/Fm values as the chromium concentration rose. Metagenomic sequencing of rhizosphere microbial communities after treatment with 100 mg/kg pure chromium indicated that the rhizosphere microorganisms were resistant to chromium exposure; chromium was found to promote dopamine secretion and chromium complexation by the microorganisms. In addition, chromium was found to reduce microbial production of N2O reductase and increase the emission of the greenhouse gas N2O. In addressing the problem of chromium pollution in sunflower farmland, Bacillus sp. strain C8 was isolated and shown to effectively reduce soil chromium contents and chromium absorption by sunflower, thereby reducing the adverse effects of the metal. Furthermore, a gene associated with chromium resistance, LOC118480906, was identified by transcriptome sequencing of sunflower plants. In conclusion, the findings denonstrate: (1) the effect of chromium exposure on sunflower growth and development of sunflower; (2) the ecological effects of chromium exposure on sunflower farmland; (3) the regulation of soil microbes and the identification of resistance associated genes are effective ways to reduce chromium pollution.
Relationship Between Bio-Climatic and Milk Composition Data of Dairy Sheep Farms: Comparison Between THI and Multivariate Weather Index
Authors: Marras Rita , Pauciullo Alfredo , Cesarani Alberto , Natale Antonio , Oppia Paolo , Macciotta Nicolò P. P. , Gaspa Giustino
Published: February 2025

Milk yield and its composition show individual variation due to the effects of the environment. Previous studies suggest that meteorological variables exert negative effects on milk yield and composition, especially during summer. This study aimed to examine the effects of meteorological variables on bulk milk composition in the Sardinian sheep production system. In this work, a total of 218,170 records belonging to 4562 dairy sheep farms were merged with the meteorological data provided by 60 meteorological stations located on Sardinia Island (Italy). Milk composition in the late spring and summer recorded during a 5-year period was used to evaluate the impact of climate exposure on bulk milk traits. The milk quality was analyzed using a linear mixed model that included the fixed effects of the year of sampling, the flock size, the temperature humidity index (THI) and the random effect of the flock. The variability of milk composition explained by flock and management ranged from 30 to 64%. The flock size exerted a significant effect on milk composition: large flocks characterized by advanced management and feeding techniques resulted in higher milk quality (e.g., higher protein and fat, lower lactose) compared to traditionally managed small flocks. The impact of THI on milk composition was statistically significant across different milk quality traits (p < 0.001); the effect of thermal stress varied according to the month of lactation. For instance, milk fat content in May increased by +0.4% for THI > 76. In June, no relevant differences were observed, whereas a decrease in fat percentage was observed in July as THI values increased (up to −0.5% for THI > 76). While somatic cell counts remained relatively stable across different conditions, total bacterial count showed greater seasonal variability, peaking during warmer periods. In addition, using factor analysis, we developed a multivariate meteorological index (MMI), which explained 51% of the variance of the original meteorological data. MMI was highly correlated with THI (r = 0.75). The same linear mixed model applied for modeling THI was used to assess the effect of MMI on milk traits. Fat, protein fractions and lactose showed significant variation across MMI classes (p-value < 0.001) in the same direction as those based on THI. Overall, our findings underscore the impact of both flock size and environmental conditions on milk quality, with heat stress and traditional versus modern management practices leading to measurable differences in milk traits.
Numerical Modelling Assessment of the Impact of Hydrogen on the Energy and Environmental Performance of a Car Using Dual Fuel (Gasoline–Hydrogen)
Authors: Pukalskas Saugirdas , Vipartas Tadas , Rimkus Alfredas , Kriaučiūnas Donatas , Žaglinskis Justas , Stravinskas Saulius , Ušinskas Andrius , Juknelevičius Romualdas , Mejeras Gabrielius , Žuraulis Vidas , Mejeras Vilius , Narkevičius Aleksas
Published: February 2025

The utilization of “green” hydrogen in transportation areas gives rise to production- and supply infrastructure-related challenges; therefore, its wider application in automotive transport would lead to higher demand with cost reduction and a faster expansion of the hydrogen refuelling network. This study presents energy and environmental performance indicators analyses of a Nissan Qashqai J10 engine during the Worldwide Harmonised Light Vehicles Test Cycle (WLTC), replacing conventional fossil gasoline with dual-fuel (D-F) gasoline and hydrogen. Numerical modelling was conducted using AVL Cruise™ (Version R2022.2) software, utilizing the torque, fuel consumption, and environmental performance data of the HR16DE engine obtained through experimental testing across a wide range of loads and speeds on an engine test bench. The experimental investigation was carried out in two stages: using pure gasoline (G100); injecting a hydrogen additive into the intake air, constituting 5% of the gasoline mass (G95H5). Following similar stages, numerical modelling was conducted using the vehicle’s technical specifications to calculate engine load and speed throughout the WLTC range. Instant fuel consumption and pollutant emissions (CO, CH, NOx) were determined for various driving modes using experimental data maps. CO2 emissions were calculated considering fuel composition and consumption. By integrating the instant values, the total and specific fuel consumption and emissions were calculated. As a result, this study identified the effect of a 5% hydrogen additive in improving engine energy efficiency, reducing incomplete combustion products and lowering greenhouse gas (CO2) emissions under various driving modes. Finally, the results were compared with the requirements of EU standards.
Study on the Law of Mine Pressure Manifestation in Three-Soft Coal Seam Isolated Working Face
Authors: Liu Hui , Sun Jiarui , Yang Tao , Zhang Jie , Liu Dong , Lin Haifei , Deng Jiayue , Zhang Yiming
Published: February 2025

The isolated working face is significantly impacted by the adjacent goaf and the mining activities of the working face itself, causing the overlying rock layers above the working face to exhibit far more intense activity compared to an ordinary working face. The stress levels are high, and the surrounding rock suffers severe damage, posing serious challenges to the safe and efficient extraction of the working face. Improving the service life of the retreating roadway in an isolated working face is a pressing technical issue that coal mining companies must address. Focusing on the characteristics of the strata and mining conditions of the 8213 isolated working face in the Yanjiahe Coal Mine, which features a three-soft coal seam, a combination of field investigation, theoretical analysis, on-site monitoring, and numerical simulation methods was employed. This approach aimed to analyze the fundamental laws of mine pressure behavior in the three-soft coal seam isolated working face as well as the deformation and failure mechanisms of the surrounding rock in the retreating roadway. Using elastic thin plate theory, it was determined that the basic roof periodic fracture step of the 8213 isolated face in the Yanjiahe Coal Mine is approximately 23 m. Field mine pressure monitoring on the 8213 isolated working face revealed that during non-periodic pressure events, the support resistance of the working face generally fluctuated stably below the rated working resistance. When the basic roof collapsed, the average working resistance of the support showed a significant increase with periodic pressure steps ranging from 16 to 27 m and an average of 22 m. Numerical simulations were further used to analyze the changes in stress and the plastic zone of the overlying rock on the 8213 isolated working face, clarifying the mechanism by which instability in the overlying rock structure leads to incidents. This analysis provides theoretical support for the safe mining of isolated working faces.
Research on Image Segmentation and Defogging Technique of Coal Gangue Under the Influence of Dust Gradient
Authors: Qin Zhenghan , Jing Judong , Li Libao , Yuan Yong , Li Yong , Li Bo
Published: February 2025

To address the challenges of low accuracy in coal gangue image recognition and poor segmentation performance under the influence of dust in underground coal mines, a scaled simulation platform was constructed to replicate the longwall top coal caving face. This platform utilized real coal gangue particles as the raw material and employed dust simulation to mimic the dust conditions typically found in coal mines. Images of coal gangue without dust and under varying dust concentrations were then collected for analysis. In parallel, an improved DeeplabV3+ coal gangue image segmentation model is proposed, where ResNeSt is employed as the backbone network of DeeplabV3+, thereby enhancing the model’s capability to extract features of both coal and gangue. Furthermore, two channel attention modules (ECAs) are incorporated to augment the model’s ability to recognize edge features in coal gangue images. A class-label smoothing training strategy was adopted for model training. The experimental results indicate that, compared to the original DeepLabV3+ model, the optimized model achieves improvements of 3.14%, 4.70%, and 3.83% in average accuracy, mean intersection over union (mIoU), and mean pixel accuracy, respectively. Furthermore, the number of parameters was reduced from 44.18 M to 43.86 M, the floating-point operations decreased by 8.33%, and the frames per second (FPS) increased by 45.03%. When compared to other models such as UNet, PSANet, and SegFormer, the proposed model demonstrates superior performance in coal gangue segmentation, accuracy, and parameter efficiency. A method combining dark channel prior and Gaussian weighting was employed for defogging coal gangue images under varying dust concentration conditions. The recognition performance of the coal gangue images before and after defogging was assessed across different dust concentrations. The model’s segmentation accuracy and practical applicability were validated through defogging and segmentation of both indoor and underground dust images. The recognition accuracy of coal and gangue, before and after defogging, improved by 6.8–71.8% and 5.8–45.8%, respectively, as the dust concentration increased, thereby demonstrating the model’s effectiveness in coal gangue image defogging segmentation in underground dust environments.
Role of Perinatal Stem Cell Secretome as Potential Therapy for Muscular Dystrophies
Authors: Pacilio Serafina , Lombardi Sara , Costa Roberta , Paris Francesca , Petrocelli Giovannamaria , Marrazzo Pasquale , Cenacchi Giovanna , Alviano Francesco
Published: February 2025

Inflammation mechanisms play a critical role in muscle homeostasis, and in Muscular Dystrophies (MDs), the myofiber damage triggers chronic inflammation which significantly controls the disease progression. Immunomodulatory strategies able to target inflammatory pathways and mitigate the immune-mediated damage in MDs may provide new therapeutic options. Owing to its capacity of influencing the immune response and enhancing tissue repair, stem cells’ secretome has been proposed as an adjunct or standalone treatment for MDs. In this review study, we discuss the challenging points related to the inflammation condition characterizing MD pathology and provide a concise summary of the literature supporting the potential of perinatal stem cells in targeting and modulating the MD inflammation.
Psychiatric Comorbidities in Autistic Adolescents Without Intellectual Impairment: A Focus on Parent- and Self-Reported Psychopathological Assessment
Authors: Cagiano Romina , Mancini Alice , Berni Marta , Maccarrone Federica , Arena Benedetta , Cosenza Angela , Pecini Chiara , Igliozzi Roberta , Calderoni Sara , Tancredi Raffaella
Published: February 2025

Background: Co-occurring conditions and psychiatric comorbidities are more frequently observed in autistic individuals than in typically developing populations. Objective: The present study aimed to investigate the agreement of parent- and self-reported psychopathological assessment using the Child Behavior Checklist (CBCL/6-18) and the Youth Self Report (YSR/11-18), respectively, in autistic adolescents without intellectual impairment. Methods: 54 autistic adolescents without intellectual impairment (11–18 years; M = 14.73; SD = 2.28) were assessed with a psychiatric and psychological evaluation conducted by expert clinicians also using self- and parent-reported scales and semi-structured interviews (K-SADS PL, CDI, MASC) including CBCL/6-18 and YSR/11-18. Results: According to clinical judgment, over 90% of participants had at least a comorbidity: anxiety (68.5%) and mood disorder (57.4%) were the most frequent. The results indicate significant discrepancies between parent- and self-reports across the three summary scales, which assess emotional and behavioral problems, as well as their combined presentation, often observed in youth with ASD. Specifically, differences were found in Internalizing (p < 0.001), Externalizing (p = 0.013), and Total Problems (p < 0.001) scales. Conclusions: The findings show the lack of agreement in parent- and self-reported scales in our sample. These results suggest the need for a cross- and multi-informant approach to support clinical judgment and understand psychopathological comorbidities of autistic adolescents without intellectual impairment.
Sex and Strain-Specific Variations in Motor Recovery Following Compression Spinal Cord Injury: Comparison of Sprague-Dawley and Wistar Rats
Authors: Mojarad Negin , Doyle David , Garmo Lucas Gorial , Graff Ryan , Reed Kayla , Wolbert Payton Andrew , Uprety Anusha , Stewart Brynn , Rossignol Julien , Dunbar Gary L.
Published: February 2025

Background/Objectives: Prior studies have noted varied, spontaneous motor recovery in rat strains after spinal cord injury (SCI), but systematic comparisons of different locomotor measurements across different severity and sexes are lacking. Hence, we quantified hindlimb utilization in male and female Sprague-Dawley (SD) and Wistar rats following moderate and severe SCI. Methods: Compression SCI was induced using a 15-g clip for 180 s for moderate SCI or a 50-g aneurysm clip for 60 s for severe SCI in male and female SD and Wistar rats. Measures of locomotor performance using the Basso–Beattie–Bresnahan (BBB), CatWalk gait analysis, and horizontal ladder tests were taken postoperatively and weekly for seven weeks. Results: BBB scores indicated greater spontaneous recovery in SD rats, with females showing higher scores than males following moderate and severe SCI. No sex or strain differences were observed in the horizontal ladder test. The CatWalk results indicated greater average hindlimb swing speed in SD rats following moderate SCI, but greater print area was observed in Wistar rats after severe SCI, although female SD rats had greater print area than either male SD or female Wistar rats following moderate SCI. Conclusions: The findings that SD rats, especially females, exhibited greater spontaneous motor recovery following moderate SCI indicate the need to consider the sex and strain of rats when conducting therapeutic testing following moderate SCI. The significance of these findings is that they should facilitate the use of appropriate rat models for translational research in SCI that can be applied to future clinical trials.
Electrochemical Immunosensor Prototype for N-Terminal Natriuretic Peptide Detection in Human Saliva: Heart Failure Biomedical Application
Authors: Baraket Abdoullatif , Ghedir El Kahina , Zine Nadia , Jaffrezic-Renault Nicole , Aarfane Abdellatif , Nasrellah Hamid , Belhora Fouad , Bonet Francisco Palacio , Bausells Joan , Errachid Abdelhamid
Published: February 2025

NT-proBNP is the gold standard biomarker for early diagnostics of heart failure, disease prevention, and stratified and individualized patient care. In this work, we aim to develop a novel ultra-sensitive immunosensor for direct NT-proBNP detection in human artificial saliva (AS), which represents an intriguing biological matrix potentially rich in biomarkers. The immunosensor will enhance the sensitivity of detection, reduce measurement time, and enable the simultaneous detection of various biomarkers. The developed biosensor, based on gold working microelectrodes (WEs), was biofunctionalized using 4-carboxymethyl aryl diazonium (CMA) to immobilize anti-NT-proBNP antibodies. The deposition of CMA onto the gold surface of the microelectrodes was accomplished using cyclic voltammetry (CV). The binding between NT-proBNP antibodies and NT-proBNP antigens was tracked using electrochemical impedance spectroscopy (EIS) in conjunction with the standard addition method. A linear detection response within the range of 1–20 pg/mL for NT-proBNP detection in PBS and artificial saliva was demonstrated, with good selectivity in the presence of other potential interfering biomarkers (interleukin 6 (IL-6), interleukin 10 (IL-10), and interleukin 1 β (IL-1β)). The developed immunosensor shows great promise for rapid and accurate analysis in biomedical applications.
Unbiased Finite Element Mesh Delaunay Constrained Triangulation Applied to 2D Images with High Morphological Complexity Using Mathematical Morphology Tools Part 1: Binary Images
Authors: N’Guyen Franck , Kanit Toufik , Imad Abdellatif
Published: February 2025

We propose a method for establishing a Constrained Delaunay Triangulation CDT applied to 2D binary images of high morphological complexity. A prerequisite for CDT is the unbiased definition of the Planar Straight-Line Graph PSLG, which must respect the injective nature of Jordan’s Curve whatever the topology of the image objects. Mathematical morphology provides tools for extracting the image contour, on which points will be judiciously placed at particular points to construct the vector path of the PSLG. Finally, these tools will enable us to implement a judicious pointing process in the image to guarantee the relative equivalence of triangles. The deterministic and rigorous procedure detailed in this article will be generalized in a second article, Part 2, to the case of labeled images for which the definition of the PSLG is more complex to define, since the contour of objects in the image is defined by the set of contours of adjacent objects.
Low-Cost Foamed Ceramics with Enhanced Mechanical Performance and Uniform Pore Size Structure
Authors: Weng Junchi , Shen Xiulin , Yang Yixian , Zhang Xuejia , Fan Mengke , Gao Ge , Guo Zeming , Lv Zhenfei , Feng Xiujuan
Published: February 2025

Due to the lack of effective utilization, fly ash and red mud accumulate in large quantities and cause serious harm to the environment. In this experiment, a low-cost preparation of foamed ceramics was realized by applying the foaming agent addition method using fly ash and red mud. The results indicated that temperature and foaming agent content significantly affected the macrostructure, microstructure, crystalline phases, and properties of the foamed ceramics. Specifically, a formulation comprising 45 wt.% fly ash, 45 wt.% red mud, 10 wt.% clay, and 1 wt.% SiC (addition), sintered at 1210 °C, yielded a compressive strength of 8.2 MPa, a bulk density of 1.17 g/cm3, a water absorption rate of 32.05%, and an apparent porosity of 37.59%. The as-prepared materials demonstrate potential as cost-effective building materials, putting forward an effective approach for the high-value utilization of fly ash and red mud.
Revolutionizing Dentistry: Preclinical Insights and Future Applications of mRNA Vaccines in Dentistry—A Narrative Review
Authors: Koren Luciana , Koren Andro , Likić Robert , Katanec Tomislav
Published: February 2025

Background: Recent advances in mRNA vaccine technology, accelerated by the global COVID-19 pandemic, have generated significant interest in their applications beyond infectious diseases. Dentistry has emerged as a promising field for exploring the potential of mRNA-based therapies in preventing and treating oral diseases. Objectives: This narrative review aims to evaluate the current status of mRNA vaccine development and its preclinical applications in oral health, focusing on periodontal disease, dental caries, regenerative medicine, implantology, and oral cancer. Methods: The review synthesizes findings from preclinical studies, including research conducted in animal models and in vitro, to assess the potential of mRNA-based therapies to modulate immune responses and promote tissue regeneration in the oral cavity. Clinical trials were only mentioned in the context of broader areas of mRNA vaccine implementation such as oncology and immunotherapy. Results: The preclinical studies highlight the capacity of mRNA vaccines to enhance the body’s immune response and facilitate tissue repair processes. Despite these promising results, challenges persist in delivering mRNA vaccines effectively within the complex oral environment. These challenges include vaccine stability, delivery mechanisms, and the modulation of immune responses. Conclusions: While mRNA vaccines offer significant promise for revolutionizing oral health care, they face notable limitations concerning safety, efficacy, and clinical feasibility. Overcoming these obstacles through further research is essential to unlock their full translational potential and ensure their safe and effective integration into dental practice.
Gradient-Based Multiple Robust Learning Calibration on Data Missing-Not-at-Random via Bi-Level Optimization
Authors: Gong Shuxia , Ma Chen
Published: February 2025

Recommendation systems (RS) have become integral to numerous digital platforms and applications, ranging from e-commerce to content streaming field. A critical problem in RS is that the ratings are missing not at random (MNAR), which is due to the users always giving feedback on items with self-selection. The biased selection of rating data results in inaccurate rating prediction for all user-item pairs. Doubly robust (DR) learning has been studied in many tasks in RS, which is unbiased when either a single imputation or a single propensity model is accurate. In addition, multiple robust (MR) has been proposed with multiple imputation models and propensity models, and is unbiased when there exists a linear combination of these imputation models and propensity models is correct. However, we claim that the imputed errors and propensity scores are miscalibrated in the MR method. In this paper, we propose a gradient-based calibrated multiple robust learning method to enhance the debiasing performance and reliability of the rating prediction model. Specifically, we propose to use bi-level optimization to solve the weights and model coefficients of each propensity and imputation model in MR framework. Moreover, we adopt the differentiable expected calibration error as part of the objective to optimize the model calibration quality directly. Experiments on three real-world datasets show that our method outperforms the state-of-the-art baselines.
‘We Herders Are Often Chased About by Drought’: A Systems Analysis of Natural Resource Degradation Within the Climate–(Im)mobility–Violence–Health Nexus in Sahel
Authors: Ayeb-Karlsson Sonja , Hayward Gemma , Kniveton Dominic
Published: February 2025

This study applies a systems analysis to further our understanding of the many pathways linking climate stress to human (im)mobility and interpersonal violence via natural resource stress within eight countries (Burkina Faso, Chad, Mali, Mauritania, Niger, Nigeria, Senegal, and Sudan) across the Sahel region. To illustrate the multiple pathways within the climate–(im)mobility–violence–health nexus, contextual and conceptual systems maps were drawn out based on secondary qualitative data from 24 peer-reviewed journal articles selected from a search result of 394 publications. Even though the geography, environment, socio-political context, traditions, and cultural history were highly diverse, the overarching factors that determined people’s (im)mobility and health outcomes, in association with natural resource stress and violence, were very similar. These vulnerability pathways included gendered immobility, interpersonal conflict, and lack of social protection, which provide important lessons and offer tangible opportunities for policy interventions. The vulnerability pathways often eroded access to natural resources and positive (im)mobility and (mental) health outcomes, which ended up entrapping people in extended cycles of violence and exploitation—especially certain intersectional positions and disadvantaged groups (whether within a household, society, or country).
Enhancing EFL Writing Revision Practices: The Impact of AI- and Teacher-Generated Feedback and Their Sequences
Authors: Tran Thi Thanh Thao
Published: February 2025

The use of different generative AIs such as OpenAI’s ChatGPT, Microsoft Copilot, or Google’s Gemini has been implemented and studied in different aspects of language education. However, exploring how the combination of teacher-generated feedback and AI-generated feedback influences student revision practices in EFL academic writing remains largely unexplored. To fill in the gap, this preliminary study investigates the impact of two forms of feedback, including teacher-generated feedback and AI-generated feedback, as well as the orders in which the two types of feedback have been executed, that is, teacher-generated feedback before AI-generated feedback (TGF-AIGF) or AI-generated feedback before teacher-generated feedback (AIGF-TGF), on EFL students’ writing revision practices in a 15-week course with fourteen Vietnamese undergraduates. Using Gemini as an AI-generated feedback tool, the study analyzed student revisions in four essays, focusing on local (grammar and vocabulary) and global (content and organization) aspects. Findings revealed that AI-generated feedback consistently resulted in higher revision frequencies compared to teacher-generated feedback alone, as it provided specific, actionable, and comprehensive suggestions. The integration of teacher- and AI-generated feedback yielded the highest revision frequencies, demonstrating complementary strengths, including AI-generated feedback that addressed surface-level issues, while teacher-generated feedback focused on higher-order concerns. Although no statistically significant differences were found between the two orders in which the two types of feedback have been executed, the AIGF-TGF order showed a slightly greater quantity of revisions made by students, allowing AI-generated feedback to scaffold surface-level revisions before teacher-generated feedback addressed global issues. These results highlight the potential of combining AI- and teacher-generated feedback to enhance writing revisions and provide pedagogical insights for integrating AI tools into academic writing courses.
Energy Management Strategy of Urban Rail Energy Storage System Considering Life Assessment of Train Converter
Authors: Liu Jinyang , Guan Bo
Published: February 2025

The reliability of the bidirectional converter plays an important role in the energy storage system. However, the power devices that make up the converter are prone to failure under complex operating conditions. Therefore, how to extend the service life of devices in this case becomes a tricky problem. Due to the typical intermittent, random, and fluctuating nature of train regenerative braking energy, only relying on capacity allocation to extend device life is easily limited by actual operating conditions. Meanwhile, most energy management strategies tend to focus on only two factors: economic cost and energy savings. Therefore, this paper proposes an energy management strategy that considers the lifetime of the energy storage converter device. The objective function of the energy management strategy including device life and systematic evaluation indicators is then established. Taking the actual line conditions as a case, the bald eagle search (BES) is used to optimize the objective. This method can lengthen the IGBT device lifetime and reduce the economic cost while achieving a good energy saving rate and voltage stabilization rate. Its effectiveness and feasibility are verified by simulations under different arithmetic cases.
Emotion Recognition Using a Siamese Model and a Late Fusion-Based Multimodal Method in the WESAD Dataset with Hardware Accelerators
Authors: Choi Hyun-Sik
Published: February 2025

Emotion recognition, which includes stress analysis, is a critical research topic. In particular, emotion recognition using wearable applications that offer rapid feedback can significantly assist in emotion regulation. A key dataset for this purpose is the wearable stress and affect detection (WESAD) dataset, which is well-suited for wearable environments. This study aims to evaluate the accuracy and processing time of the proposed neural network using the WESAD dataset. However, data collected in wearable environments often have limitations in terms of their accuracy and channel count, making classification challenging. To address this issue, this study utilizes only wrist-measured blood volume pulse (BVP) data measured by photo-plethysmography (PPG) and electro-dermal activity (EDA) data, classifying them through a late fusion-based multimodal method. For accurate classification, the BVP data are preprocessed using the empirical mode decomposition (EMD) method, and a Siamese model is employed for learning on small datasets, with an additional structure of dense layers for detailed classification. The Siamese network generates a similarity score, which is then fed into a dense layer to classify multiple classes. Meta-classes are used to compute pairwise similarities, ultimately classifying the data into one of four emotional states: neutral, stress, amusement, and meditation. The proposed multitasking model simultaneously trains the Siamese network and multi-classifier, achieving an accuracy of 99.8% in classifying four emotion states in the WESAD dataset. Moreover, the model is designed to operate with minimal hardware resources, making it easy to implement using hardware accelerators. This study demonstrates the applicability of neural network-based wearable systems in the field of emotion recognition.
MNFF-GNN: Multi-Order Neighbor Feature Fusion Graph Neural Network
Authors: Liu Yiwen , Wen Tao , Wu Wen , Lu Bo
Published: February 2025

With the rapid development of artificial intelligence, automated artifact recognition technology has gradually replaced the traditional manual quality evaluation method. The existing samples of CT images containing artifacts are small, and the relationships between the images are of great significance. In this study, firstly, a method for CT image artifact recognition was developed by transforming the problem into a node classification framework. Secondly, the characteristics of this complex network and the features of the CT image texture were extracted. Finally, the combination of the complex network’s characteristics and CT image texture features was viewed as node attribution; the relationship between different nodes was analyzed using a graph attention network; and classification was carried out. The integration of multi-order neighbor features in the MNFF-GNN model improves the representation of motion artifact regions, targeting the limitations of traditional methods and convolutional neural networks (CNNs). The model demonstrates potential as a clinical tool, particularly in resource-constrained settings, by effectively identifying artifacts even with limited data, with an accuracy of 90.9%, which is an improvement of 9.73%. This innovative approach leverages graph neural networks (GNNs), which are particularly effective at capturing both local and global relationships within graph-structured data.
AI-Assisted Inheritance of Qinghua Porcelain Cultural Genes and Sustainable Design Using Low-Rank Adaptation and Stable Diffusion
Authors: Bao Qian , Zhao Jiajia , Liu Ziqi , Liang Na
Published: February 2025

Blue-and-white porcelain, as a representative of traditional Chinese craftsmanship, embodies rich cultural genes and possesses significant research value. Against the backdrop of the generative AI era, this study aims to optimize the creative processes of blue-and-white porcelain to enhance the efficiency and accuracy of complex artistic innovations. Traditional methods of crafting blue-and-white porcelain encounter challenges in accurately and efficiently constructing intricate patterns. This research employs grounded theory in conjunction with the KANO-AHP hybrid model to classify and quantify the core esthetic features of blue-and-white porcelain, thereby establishing a multidimensional esthetic feature library of its patterns. Subsequently, leveraging the Stable Diffusion platform and utilizing Low-Rank Adaptation (LoRA) technology, a generative artificial intelligence (AIGC)-assisted workflow was proposed, capable of accurately restoring and innovating blue-and-white porcelain patterns. This workflow enhances the efficiency and precision of pattern innovation while maintaining consistency with the original artistic style. Finally, by integrating principles of sustainable design, this study explores new pathways for digital innovation in blue-and-white porcelain design, offering viable solutions for the contemporary reinvention of traditional crafts. The results indicate that AIGC technology effectively facilitates the integration of traditional and modern design approaches. It not only empowers the inheritance and continuation of the cultural genes of blue-and-white porcelain but also introduces new ideas and possibilities for the sustainable development of traditional craftsmanship.
Correction: Li et al. An Efficient Multi-Level 2D DWT Architecture for Parallel Tile Block Processing with Integrated Quantization Modules. Electronics 2024, 13, 4668
Authors: Li Qitao , Zhang Wei , Wu Zhuolun , Dai Yuzhou , Liu Yanyan
Published: February 2025

In the original publication [...]
Demonstration of Grid-Forming Controls in Hybrid AC/DC Grid in a Real-Time PHiL Environment
Authors: Richter Michael , Kuri Ananya , Richter Julian , Wagner Timo , Henninger Stefan , Mehlmann Gert
Published: February 2025

The transition to inverter-dominated power systems with novel control strategies has created weak grid scenarios, prompting extensive research into grid-forming (GFM) converters and advanced GFM control schemes. This study evaluates two GFM control strategies for their stabilizing effects in weakly interconnected and islanded power systems: the established Virtual Synchronous Machine (VSM) control and the newly developed Phase-Restoring Principle (PRP). The evaluation is conducted using a real-time electromagnetic transient (EMT) simulation testbed with Hardware-in-the-Loop (HiL) capabilities. The system includes a multi-terminal medium-voltage (MVDC) and high-voltage direct current (HVDC) network weakly coupled to a network equivalent, represented by a scaled synchronous machine (SynM) to replicate grid inertia. Modular multilevel converters (MMCs) model the converters, while Power Hardware-in-the-Loop (PHiL) experiments integrate real power hardware. Dynamic interactions, including islanded conditions, are emulated to assess the performance of GFM controls and their interactions in HV/MV and AC/DC systems. The results demonstrate the compatibility of diverse GFM schemes with grid-following controls and synchronous machines, emphasizing their positive contributions to system stability. This modular demonstrator, as a realistic prototype of future power systems, provides a flexible platform for testing emerging GFM strategies and hardware, supporting the development of robust and stable inverter-based grids.
TCTFusion: A Triple-Branch Cross-Modal Transformer for Adaptive Infrared and Visible Image Fusion
Authors: Zhang Liang , Jiang Yueqiu , Yang Wei , Liu Bo
Published: February 2025

Infrared-visible image fusion (IVIF) is an important part of multimodal image fusion (MMF). Our goal is to combine useful information from infrared and visible sources to produce strong, detailed, fused images that help people understand scenes better. However, most existing fusion methods based on convolutional neural networks extract cross-modal local features without fully utilizing long-range contextual information. This limitation reduces performance, especially in complex scenarios. To address this issue, we propose TCTFusion, a three-branch cross-modal transformer for visible–infrared image fusion. The model includes a shallow feature module (SFM), a frequency decomposition module (FDM), and an information aggregation module (IAM). The three branches specifically receive input from infrared, visible, and concatenated images. The SFM extracts cross-modal shallow features using residual connections with shared weights. The FDM then captures low-frequency global information across modalities and high-frequency local information within each modality. The IAM aggregates complementary cross-modal features, enabling the full interaction between different modalities. Finally, the decoder generates the fused image. Additionally, we introduce pixel loss and structural loss to significantly improve the model’s overall performance. Extensive experiments on mainstream datasets demonstrate that TCTFusion outperforms other state-of-the-art methods in both qualitative and quantitative evaluations.
Ethanol Reduction in Montepulciano Wine: Starmerella bombicola Sequential Fermentation at Pilot Scale Under Aeration Conditions
Authors: Canonico Laura , Gattucci Silvia , Moretti Laura , Agarbati Alice , Comitini Francesca , Ciani Maurizio
Published: February 2025

One of the most relevant challenges in winemaking is the increase in the alcohol content of wine, mainly due to climate change. The use of selected non-Saccharomyces yeasts in sequential fermentation with Saccharomyces cerevisiae is one of the effective strategies for dealing with this issue, even if it has been poorly confirmed at the winery level. This work evaluated the use of Starmerella bombicola and commercial S. cerevisiae strains in sequential fermentation at pilot scale in winery conditions to reduce the ethanol content and obtain a wine with enhanced aroma complexity. The results showed that the sequential S. bombicola/S. cerevisiae fermentation in aeration conditions (20 mL/L/min for the first three days) resulted in a reduction in ethanol of 0.80% (v/v) compared to pure S. cerevisiae fermentation. The aeration conditions of sequential fermentation did not affect the fermentation performance of yeasts. The winery conditions determined, in the sequential fermentation modalities, an enhancement of wild yeasts’ presence. At the same time, the inoculation of S. bombicola determined an enhancement of glycerol and lactic acid, which positively influences the structure and body of the wine as well as specific aromatic notes. In winery conditions, better control of fermentation is needed to achieve potential ethanol reduction and favorable by-product formation using S. bombicola.