bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biocompatibility of variable thicknesses of a novel directly printed aligner in orthodontics
Authors: Bleilöb Maximilian , Welte-Jzyk Claudia , Knode Vanessa , Ludwig Björn , Erbe Christina 
Published: January 2025

Abstract
Direct printed aligners (DPAs) offer benefits like the ability to vary layer thickness within a single DPA and to 3D print custom-made removable orthodontic appliances. The biocompatibility of appliances made from Tera Harz TA-28 (Graphy Inc., Seoul, South Korea) depends on strict adherence to a standardized production and post-production protocol, including UV curing. Our aim was to evaluate whether design modifications that increase layer thickness require a longer UV curing time to ensure biocompatibility. Specimens with varying layer thickness were printed to high accuracy using Tera Harz TA-28 and the Asiga MAX 3D printer (Asiga SPS ™ technology, Sydney, Australia). UV curing durations were set at 20, 30 and 60 min. Cytotoxicity was evaluated using the AlamarBlue assay on human gingival fibroblasts. Cell viability decreased with increasing specimen thickness (significant for 2 mm [p < 0.001], 4 mm [p < 0.0001], and 6 mm [p < 0.01]) under the manufacturer-recommended 20-min UV curing. Extending the curing time did not improve cell viability. However, cell viability never decreased by more than 30%, meeting EN ISO 10993-5 standards for non-cytotoxicity. The standard 20-minute UV curing protocol ensures the biocompatibility and patient safety of Tera Harz TA-28 for material thicknesses up to 6 mm.
Optimization of metal polymer friction pair composition for hydrogen wear reduction through thermal stabilization analysis
Authors: Volchenko Dmytro , Kernytskyy Ivan , Royko Yuriy , Ostashuk Mykola , Fidrovska Nataliia , Skrypnyk Vasyl , Zhuravlev Dmytro , Klochko Nataliia , Rys Vasyl , Berezovetska Oksana , Dixit Saurav , Stefańska Anna , Koda Eugeniusz , Singh Subhav , Sharma Kamal , Mahadeva Rajesh 
Published: January 2025

Abstract
The composition of the metal-polymer friction pair is carefully considered for interacting with water and hydrogen, ensuring the metals electrode process potential remains below waters in a neutral medium. Simultaneously, adherence to defined chemical composition ratios for the metal-polymer materials is crucial. This analysis is conducted under conditions of thermal stabilization, characterized by a minimal temperature gradient across the rim thickness within an equivalent thermal field. Using the quasi-chemical approximation, the paper derives a concentration-dependent diffusion coefficient of hydrogen (H) in iron (Fe) across a broad spectrum. This derivation includes electronic and vibrational contributions to the chemical potential. The research establishes a correlation between the equivalent diffusion coefficient and the concentration of diffusing hydrogen atoms from the metal, such as the pulley or drum rim. These findings offer novel insights into optimizing hydrogen wear behaviour in brake friction couples, contributing to advancements in materials and design considerations in the automotive field.
Enhancing lane detection in autonomous vehicles with multi-armed bandit ensemble learning
Authors: Pandian J. Arun , Thirunavukarasu Ramkumar , Mariappan L. Thanga 
Published: January 2025

Abstract
This study introduces a novel ensemble learning technique namely Multi-Armed Bandit Ensemble (MAB-Ensemble), designed for lane detection in road images intended for autonomous vehicles. The foundation of the proposed MAB-Ensemble technique is inspired in terms of Multi-Armed bandit optimization to facilitate efficient model selection for lane segmentation. The benchmarking dataset namely TuSimple is used for training, validating and testing the proposed and existing lane detection techniques. Convolutional Neural Networks (CNNs) architecture which includes ENet, PINet, ResNet-50, ResNet-101, SqueezeNet, and VGG16Net are employed in lane detection problems to construct segmentation models and demonstrate proficiency in distinct road conditions. However, the proposed MAB-Ensemble technique overcomes the limitations of individual models by dynamically selecting the most suitable CNN model based on prevailing environmental factors. The proposed technique optimizes the segmentation accuracy and treats the attained accuracy as a reward signal in the context of reinforcement learning by interacting with the environment through CNN model selection. The MAB-Ensemble achieved an overall accuracy of 90.28% in different road conditions. The results overcome the performance of the individual CNN models and state-of-the-art ensemble techniques. Also, it demonstrates superior performance which includes daytime, night-time, and abnormal road conditions. The MAB-Ensemble technique offers a promising solution for robust lane detection by harnessing the collective strengths of diverse CNN models.
Multi-objective design of multi-material truss lattices utilizing graph neural networks
Authors: Frey Ramón , Tucker Michael R. , Afrasiabi Mohamadreza , Bambach Markus 
Published: January 2025

Abstract
The rapid advancements in additive manufacturing (AM) across different scales and material classes have enabled the creation of architected materials with highly tailored properties. Beyond geometric flexibility, multi-material AM further expands design possibilities by combining materials with distinct characteristics. While machine learning has recently shown great potential for the fast inverse design of lattice structures, its application has largely been limited to single-material systems. In this work, we propose a novel approach that incorporates material properties as edge features within the graph representation of multi-material truss lattices, utilizing graph neural networks (GNNs) to develop a fast and efficient inverse design framework. We validate this framework by designing lattices with tunable thermal expansion and stiffness properties, showcasing its ability to explore a broad and flexible design space. We showcase the framework’s inverse design capabilities for both single and multi-objective optimization tasks and assess its limitations. Additionally, we demonstrate the superior capacity of GNNs in capturing structure-property relationships for multi-material systems. We anticipate that the continued advancement of GNN-assisted inverse design will play a key role in unlocking the full potential of multi-material truss lattices.
Characterizing the omics landscape based on 10,000+ datasets
Authors: Brombacher Eva , Schilling Oliver , Kreutz Clemens 
Published: January 2025

Abstract
The characteristics of data produced by omics technologies are pivotal, as they critically influence the feasibility and effectiveness of computational methods applied in downstream analyses, such as data harmonization and differential abundance analyses. Furthermore, variability in these data characteristics across datasets plays a crucial role, leading to diverging outcomes in benchmarking studies, which are essential for guiding the selection of appropriate analysis methods in all omics fields. Additionally, downstream analysis tools are often developed and applied within specific omics communities due to the presumed differences in data characteristics attributed to each omics technology. In this study, we investigate over ten thousand datasets to understand how proteomics, metabolomics, lipidomics, transcriptomics, and microbiome data vary in specific data characteristics. We were able to show patterns of data characteristics specific to the investigated omics types and provide a tool that enables researchers to assess how representative a given omics dataset is for its respective discipline. Moreover, we illustrate how data characteristics can impact analyses at the example of normalization in the presence of sample-dependent proportions of missing values. Given the variability of omics data characteristics, we encourage the systematic inspection of these characteristics in benchmark studies and for downstream analyses to prevent suboptimal method selection and unintended bias.
Plant root carbon inputs drive methane production in tropical peatlands
Authors: Girkin N. T. , Siegenthaler A. , Lopez O. , Stott A. , Ostle N. , Gauci V. , Sjögersten S. 
Published: January 2025

Abstract
Tropical peatlands are carbon-dense ecosystems that are significant sources of atmospheric methane (CH4). Recent work has demonstrated the importance of trees as an emission pathway for CH4 from the peat to the atmosphere. However, there remain questions over the processes of CH4 production in these systems and how they relate to substrate supply. Principally, these questions relate to the relative contribution of recent photosynthetically fixed carbon, released as root exudates, versus carbon substrate supply from the slowly decomposing peat matrix to CH4 emissions within these ecosystems. Here, we examined the role of root inputs in regulating CH4 production inferred from soil emissions using a combination of in situ tree girdling, in situ13C natural abundance labelling via stem injections, and a 13CO2 labelling of transplanted plants of two contrasting plant functional types, a broadleaved evergreen tree, and a canopy palm. Girdling of broadleaved evergreen trees reduced CH4 fluxes by up to 67%. Stem injections of trees and palms with a natural abundance label resulted in significant isotopic enrichment of CH4 fluxes, reinforcing the link between root carbon inputs and peat CH4 fluxes. Ex situ
13CO2 labelling of plants resulted in significant 13C enrichment of peat CH4 fluxes. Taken together, our results demonstrate for the first time that plant root exudates make a substantial contribution to CH4 production in tropical peatlands.
Temporal transcriptional profiling of host cells infected by a veterinary alphaherpesvirus using nanopore sequencing
Authors: Tombácz Dóra , Maróti Zoltán , Oláh Péter , Dörmő Ákos , Gulyás Gábor , Kalmár Tibor , Csabai Zsolt , Boldogkői Zsolt 
Published: January 2025

Abstract
In our research, we performed temporal transcriptomic profiling of host cells infected with Equid alphaherpesvirus 1 (EHV-1) by utilizing direct cDNA sequencing based on nanopore MinION technology. The sequencing reads were harnessed for transcript quantification at various time points. Viral infection-induced differential gene expression was identified through the edgeR package. The identified genes were segmented into six groups based on their kinetic characteristics. The initial three clusters encompass immediate-early response genes, typically transcription factors and elements of antiviral signaling pathways. These genes were either upregulated (cluster 1) or downregulated (clusters 2 and 3) during the early infection phase. The remaining three clusters include late response genes. In these categories, it is challenging to determine whether changes in gene expression are directly connected to the viral infection or merely side effects of the infection. A study of gene associations using the STRINGDB software revealed several gene networks that might be directly impacted by the virus. We also explored whether gene co-expression could be a result of their collective regulation by upstream transcription factors using the Gene Regulatory Network database. Finally, our differential transcript usage (DTU) analysis identified a number of genes that exhibited altered proportions of transcript isoforms in comparison to non-infected cells. Thus, our analysis revealed that EHV-1 infection not only alters host gene expression but also leads to differential use of transcript isoforms, particularly splice variants.
Personality traits of women with hereditary risk for breast/ovarian cancer versus obstetric history and cancer preventive behaviors
Authors: Żurawska Joanna , Pięta Beata , Wilczak Maciej , Wojciechowska Małgorzata , Rzymski Paweł , Pieczykolan Agnieszka , Krysa Justyna , Bień Agnieszka 
Published: January 2025

Abstract
The aim of the study is to analyze the relationship between personality traits of women with hereditary predisposition to breast/ovarian cancer and their obstetric history and cancer-preventive behaviors. A total of 357 women, participants of ‘The National Program for Families With Genetic/Familial High Risk for Cancer’, were included in the study. The Neo Five-Factor Inventory (NEO-FFI) and a standardized original questionnaire designed for the purpose of the study were used. Breast ultrasound examination at a younger age was associated with Extraversion. Openness to Experience was linked with lower number of children, more frequent use of hormonal contraceptives, and younger age at first breast ultrasound examination. Women with higher Agreeableness scores were less likely to use contraceptives and underwent their first breast ultrasound later in life. Conscientiousness was associated with more frequent use of hormonal contraceptives and younger age at first breast ultrasound examination. Women at increased risk for developing breast/ovarian cancer who used hormonal contraceptives underwent breast ultrasound examinations earlier in life, while those who had breastfed their children chose to have their first mammogram earlier in life. Personality traits affect health-related behaviors and should be taken into account when designing theoretical models as well as interventions regarding health habits.
Ultrasonic emulsification of Cananga odorata nanoemulsion formulation for enhancement of herbicidal potential
Authors: Laosinwattana Chamroon , Somala Naphat , Dimak Jantra , Teerarak Montinee , Chotsaeng Nawasit 
Published: January 2025

Abstract
A nanoemulsion was fabricated from Cananga odorata essential oil (EO) and stabilized by incorporation of Tween 80 using ultrasonication. The major constituents of the EO were benzyl benzoate, linalool, and phenylmethyl ester. Differing sonication amplitude (20–60%) and time (2–10 min) were assessed for effects on nanoemulsion droplet size and polydispersity index (PI). The smallest droplet size of 43.98 nm (PI 0.222) was obtained using 40% amplitude for 8 min; this nanoemulsion was evaluated for its droplet characteristics and pre-emergence herbicidal activities on Amaranthus tricolor. FT-IR confirmed ultrasonic emulsification to not affect the EO components. Regarding stability, storage at 4 °C was determined appropriate, with droplet size changing slightly after five weeks. Assays of herbicidal potential showed the coarse emulsion and nanoemulsion to both reduce A. tricolor germination and growth, with the nanoemulsion being more effective at a given concentration and the difference in effectivity correlating to droplet size. Remarkably, treatment with 250 ppm nanoemulsion and coarse emulsion respectively resulted in 100% and 63.75% germination inhibition. Both emulsions decreased seed imbibition and alpha-amylase activity. The highest relative electrolyte leakage was achieved in seed treated with the nanoemulsion. Therefore, this ultrasonic-based nanoemulsion may have utility as bioherbicide alternative.
Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007–2016
Authors: Zhang Yu-zhen , Wu Hai-ying , Ma Run-wei , Feng Bo , Yang Rui , Chen Xiao-gang , Li Min-xiao , Cheng Li-ming 
Published: January 2025

Abstract
Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of cardiovascular disease and mental health problems. Early detection and intervention are crucial but often hindered by complex diagnostic requirements. This study aims to develop a predictive model using NHANES data, excluding biochemical indicators, to provide a simple, cost-effective tool for large-scale, non-medical screening and early prevention of adolescent MetS. After excluding adolescents with missing diagnostic variables, the dataset included 2,459 adolescents via NHANES data from 2007–2016. We used LASSO regression and 20-fold cross-validation to screen for the variables with the greatest predictive value. The dataset was divided into training and validation sets in a 7:3 ratio, and SMOTE was used to expand the training set with a ratio of 1:1. Based on the training set, we built eight machine learning models and a multifactor logistic regression model, evaluating nine predictive models in total. After evaluating all models using the confusion matrix, calibration curves and decision curves, the LGB model had the best predictive performance, with an AUC of 0.969, a Youden index of 0.923, accuracy of 0.978, F1 score of 0.989, and Kappa value of 0.800. We further interpreted the LGB model using SHAP, the SHAP hive plot showed that the predictor variables were, in descending order of importance, BMI age sex-specific percentage, weight, upper arm circumference, thigh length, and race. Finally, we deployed it online for broader accessibility. The predictive models we developed and validated demonstrated high performance, making them suitable for large-scale, non-medical primary screening and early warning of adolescent Metabolic syndrome. The online deployment of the model allows for practical use in community and school settings, promoting early intervention and public health improvement.
Adding checkpoint inhibitors to first-line chemotherapy for NUT carcinoma patients
Authors: Haebe Sarah , Schuebbe Gesa , Jurmeister Philipp , von Bergwelt-Baildon Michael , Westphalen C. Benedikt , Lauer Ulrich M. , Kunz Wolfgang G. , Subklewe Marion , Weigert Oliver 
Published: January 2025

Abstract
Rare cancers present significant challenges in diagnosis, treatment, and research, accounting for up to 25% of global cancer cases. Due to their rarity and atypical presentations, they are often misdiagnosed, resulting in late-stage detection and poor outcomes. Here, we describe a patient case with advanced metastatic nasopharynx NUT carcinoma, one of the rarest and most aggressive cancers. We conducted a comprehensive analysis of this patient’s tumor, including Tumor Mutation Burden, Microsatellite Instability, and genetic profiling to explore further putative druggable targets. The tumor exhibited high PD-L1 expression and showed a notable response to immune checkpoint inhibitors when combined with platinum-based radio-chemotherapy. Our findings indicate that checkpoint inhibitors could play a critical role in treating NUT carcinoma, offering new therapeutic avenues and hope for patients with this challenging diagnosis. Whether PD-L1 expression may be a useful predictor of immune checkpoint inhibitor efficacy warrants further research.
The Hsf1-sHsp cascade has pan-antiviral activity in mosquito cells
Authors: Qu Jieqiong , Schinkel Michelle , Chiggiato Lisa , Rosendo Machado Samara , Overheul Gijs J. , Miesen Pascal , van Rij Ronald P. 
Published: January 2025

Abstract

Aedes mosquitoes transmit pathogenic arthropod-borne (arbo) viruses, putting nearly half the world’s population at risk. Blocking virus replication in mosquitoes is a promising approach to prevent arbovirus transmission, the development of which requires in-depth knowledge of virus-host interactions and mosquito immunity. By integrating multi-omics data, we find that heat shock factor 1 (Hsf1) regulates eight small heat shock protein (sHsp) genes within one topologically associated domain in the genome of the Aedes aegypti mosquito. This Hsf1-sHsp cascade acts as an early response against chikungunya virus infection and shows pan-antiviral activity against chikungunya, Sindbis, and dengue virus as well as the insect-specific Agua Salud alphavirus in Ae. aegypti cells and against chikungunya virus and O’nyong-nyong virus in Aedes albopictus and Anopheles gambiae cells, respectively. Our comprehensive in vitro data suggest that Hsf1 could serve as a promising target for the development of novel intervention strategies to limit arbovirus transmission by mosquitoes.
Exogenous dsRNA triggers sequence-specific RNAi and fungal stress responses to control Magnaporthe oryzae in Brachypodium distachyon
Authors: Zheng Ying , Moorlach Benjamin , Jakobs-Schönwandt Desiree , Patel Anant , Pastacaldi Chiara , Jacob Stefan , Sede Ana R. , Heinlein Manfred , Poranen Minna M. , Kogel Karl-Heinz , Ladera Carmona Maria 
Published: January 2025

Abstract
In vertebrates and plants, dsRNA plays crucial roles as PAMP and as a mediator of RNAi. How higher fungi respond to dsRNA is not known. We demonstrate that Magnaporthe oryzae (Mo), a globally significant crop pathogen, internalizes dsRNA across a broad size range of 21 to about 3000 bp. Incubation of fungal conidia with 10 ng/µL dsRNA, regardless of size or sequence, induced aberrant germ tube elongation, revealing a strong sequence-unspecific effect of dsRNA in this fungus. Accordingly, the synthetic dsRNA analogue poly(I:C) and dsRNA of various sizes and sequences elicited canonical fungal stress pathways, including nuclear accumulation of the stress marker mitogen-activated protein kinase Hog1p and production of ROS. Leaf application of dsRNA to the cereal model species Brachypodium distachyon suppressed the progression of leaf blast disease. Notably, the sequence-unspecific effect of dsRNA depends on higher doses, while pure sequence-specific effects were observed at low concentrations of dsRNA ( < 0.03 ng/µL). The protective effects of dsRNA were further enhanced by maintaining a gap of at least seven days between dsRNA application and inoculation, and by stabilising the dsRNA in alginate-chitosan nanoparticles. Overall, our study opens up additional possibilities for the development and use of dsRNA pesticides in agriculture.
Free mobility across group boundaries promotes intergroup cooperation
Authors: Gross Jörg , Götz Martin , Reher Katharina , Toscano Filippo 
Published: January 2025

Abstract
Group cooperation is a cornerstone of human society, enabling achievements that surpass individual capabilities. However, groups also define and restrict who benefits from cooperative actions and who does not, raising the question of how to foster cooperation across group boundaries. This study investigates the impact of voluntary mobility across group boundaries on intergroup cooperation. Participants, organized into two groups, decided whether to create benefits for themselves, group members, or everyone. In each round, they were paired with another participant and could reward the other’s actions during an ‘enforcement stage’, allowing for indirect reciprocity. In line with our preregistered hypothesis, when participants interacted only with in-group members, indirect reciprocity enforced group cooperation, while intergroup cooperation declined. Conversely, higher intergroup cooperation emerged when participants were forced to interact solely with out-group members. Crucially, in the free-mobility treatment – where participants could choose whether to meet an in-group or an out-group member in the enforcement stage – intergroup cooperation was significantly higher than when participants were forced to interact only with in-group members, even though most participants endogenously chose to interact with in-group members. A few ‘mobile individuals’ were sufficient to enforce intergroup cooperation by selectively choosing out-group members, enabling indirect reciprocity to transcend group boundaries. These findings highlight the importance of free intergroup mobility for overcoming the limitations of group cooperation.