bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative study on fatigue evaluation of suspenders by introducing actual vehicle trajectory data
Authors: Pan Yue , Dong Yiqing , Wang Dalei , Cao Sugong , Chen Airong 
Published: March 2024

AbstractSuspenders play a crucial role in transmitting loads from the bridge deck to the main cable in a suspension bridge. They are susceptible to fatigue due to repeated dynamic loads, particularly traffic loads. Traffic Load Models (TLMs), typically created using Monte–Carlo simulation and Weigh-In-Motion (WIM) data, are employed to evaluate this fatigue. However, these models often overlook practical vehicle trajectories and spatio-temporal distribution, which compromises the precision of fatigue assessments. In this study, we introduce a novel 2D Intelligent Driver Model (2D-IDM) that incorporates actual vehicle trajectories, with a particular focus on transverse vehicle movement. This enhancement aims to improve the fidelity of existing TLMs. To provide a clear, qualitative, and quantitative understanding of the effects of fatigue evaluation with or without actual trajectory characteristics, we have structured this paper as a comparative study. We compare our proposed model, denoted as TLM S-3, with two observation-based models (O-1 and O-2) and two simulation-based models (S-1 and S-2). We conducted an experimental case study on a long-span suspension bridge, where the actual traffic load trajectory was obtained using a WIM-Vision integrated system. To calculate fatigue damage considering both longitudinal and transverse directions, we established a multi-scale Finite Element Model (FEM) using solid element types to simulate the bridge girder. This model can generate the stress influence surface of the bridge and has been verified in both static and dynamic aspects. Our detailed comparative analysis demonstrates the consistency of the proposed 2D-IDM with the actual measured traffic load trajectories. This indicates that our approach can enhance the fidelity and precision of fatigue evaluations for bridge suspenders.
The immune cell infiltration-associated molecular subtypes and gene signature predict prognosis for osteosarcoma patients
Authors: Liu Bin , Liu Xiang-Yang , Wang Guo-Ping , Chen Yi-Xin 
Published: March 2024

AbstractHost immune dysregulation involves in the initiation and development of osteosarcoma (OS). However, the exact role of immune cells in OS remains unknown. We aimed to distinguish the molecular subtypes and establish a prognostic model in OS patients based on immunocyte infiltration. The gene expression profile and corresponding clinical feature of OS patients were obtained from TARGET and GSE21257 datasets. MCP-counter and univariate Cox regression analyses were applied to identify immune cell infiltration-related molecular subgroups. Functional enrichment analysis and immunocyte infiltration analysis were performed between two subgroups. Furthermore, Cox regression and LASSO analyses were performed to establish the prognostic model for the prediction of prognosis and metastasis in OS patients. The subgroup with low infiltration of monocytic lineage (ML) was related to bad prognosis in OS patients. 435 DEGs were screened between the two subgroups. Functional enrichment analysis revealed these DEGs were involved in immune- and inflammation-related pathways. Three important genes (including TERT, CCDC26, and IL2RA) were identified to establish the prognostic model. The risk model had good prognostic performance for the prediction of metastasis and overall survival in OS patients. A novel stratification system was established based on ML-related signature. The risk model could predict the metastasis and prognosis in OS patients. Our findings offered a novel sight for the prognosis and development of OS.
Adoption of hooped-battens in cold-formed steel built-up columns for superior axial performance
Authors: Sadid A. Jamshid. , Dar Mohammad Adil , Ghowsi A. Fayeq , Aydın A. Cüneyt 
Published: March 2024

AbstractPrevious research on cold-formed steel (CFS) battened columns has identified the critical factors influencing their performance and accordingly, their limiting values for improved performance have been recommended. However, these studies involved connecting battens to the chords (channels) via their flanges, leaving the slenderest component disconnected from the web. This study introduces a novel hooped-batten (tubular-element) that links both webs and flanges of the chords together, thereby improving the structural integrity of the built-up system and curtailing the half-wave buckling length in the webs. As a result, axial strength and stability in these built-up columns may improve adequately. Firstly, a numerical model of a conventional CFS battened column was developed in ABAQUS and verified against test results on the same reported in literature. Afterward, the validated model was used to simulate the behaviour of CFS built-up columns with hooped-battens. Two key parameters i.e., unbraced chord slenderness and overall column slenderness were varied to explore their influence on the axial behaviour of built-up columns in terms of peak strengths, failure modes and load-displacement characteristics. The performance of the hooped-battened columns was compared with the identical conventional battened columns, which reflects that the former exhibits superior strength and stability characteristics over the latter.
Cellular mechanisms underlying carry-over effects after magnetic stimulation
Authors: Ye Hui , Dima Maria , Hall Vincent , Hendee Jenna 
Published: March 2024

AbstractMagnetic fields are widely used for neuromodulation in clinical settings. The intended effect of magnetic stimulation is that neural activity resumes its pre-stimulation state right after stimulation. Many theoretical and experimental works have focused on the cellular and molecular basis of the acute neural response to magnetic field. However, effects of magnetic stimulation can still last after the termination of the magnetic stimulation (named “carry-over effects”), which could generate profound effects to the outcome of the stimulation. However, the cellular and molecular mechanisms of carry-over effects are largely unknown, which renders the neural modulation practice using magnetic stimulation unpredictable. Here, we investigated carry-over effects at the cellular level, using the combination of micro-magnetic stimulation (µMS), electrophysiology, and computation modeling. We found that high frequency magnetic stimulation could lead to immediate neural inhibition in ganglion neurons from Aplysia californica, as well as persistent, carry-over inhibition after withdrawing the magnetic stimulus. Carry-over effects were found in the neurons that fired action potentials under a variety of conditions. The carry-over effects were also observed in the neurons when the magnetic field was applied across the ganglion sheath. The state of the neuron, specifically synaptic input and membrane potential fluctuation, plays a significant role in generating the carry-over effects after magnetic stimulation. To elucidate the cellular mechanisms of such carry-over effects under magnetic stimulation, we simulated a single neuron under magnetic stimulation with multi-compartment modeling. The model successfully replicated the carry-over effects in the neuron, and revealed that the carry-over effect was due to the dysfunction of the ion channel dynamics that were responsible for the initiation and sustaining of membrane excitability. A virtual voltage-clamp experiment revealed a compromised Na conductance and enhanced K conductance post magnetic stimulation, rendering the neurons incapable of generating action potentials and, therefore, leading to the carry over effects. Finally, both simulation and experimental results demonstrated that the carry-over effects could be controlled by disturbing the membrane potential during the post-stimulus inhibition period. Delineating the cellular and ion channel mechanisms underlying carry-over effects could provide insights to the clinical outcomes in brain stimulation using TMS and other modalities. This research incentivizes the development of novel neural engineering or pharmacological approaches to better control the carry-over effects for optimized clinical outcomes.
Deep learning solutions for smart city challenges in urban development
Authors: Wu Pengjun , Zhang Zhanzhi , Peng Xueyi , Wang Ran 
Published: March 2024

AbstractIn the realm of urban planning, the integration of deep learning technologies has emerged as a transformative force, promising to revolutionize the way cities are designed, managed, and optimized. This research embarks on a multifaceted exploration that combines the power of deep learning with Bayesian regularization techniques to enhance the performance and reliability of neural networks tailored for urban planning applications. Deep learning, characterized by its ability to extract complex patterns from vast urban datasets, has the potential to offer unprecedented insights into urban dynamics, transportation networks, and environmental sustainability. However, the complexity of these models often leads to challenges such as overfitting and limited interpretability. To address these issues, Bayesian regularization methods are employed to imbue neural networks with a principled framework that enhances generalization while quantifying predictive uncertainty. This research unfolds with the practical implementation of Bayesian regularization within neural networks, focusing on applications ranging from traffic prediction, urban infrastructure, data privacy, safety and security. By integrating Bayesian regularization, the aim is to, not only improve model performance in terms of accuracy and reliability but also to provide planners and decision-makers with probabilistic insights into the outcomes of various urban interventions. In tandem with quantitative assessments, graphical analysis is wielded as a crucial tool to visualize the inner workings of deep learning models in the context of urban planning. Through graphical representations, network visualizations, and decision boundary analysis, we uncover how Bayesian regularization influences neural network architecture and enhances interpretability.
Chemical disinfection as a simple and reliable method to control the amphibian chytrid fungus at breeding points of endangered amphibians
Authors: Thumsová Barbora , González-Miras Emilio , Rubio Ángel , Granados Ignacio , Bates Kieran A. , Bosch Jaime 
Published: March 2024

AbstractChytridiomycosis caused by the fungal pathogen Batrachochytrium dendrobatidis (Bd) is pushing amphibians towards extinction. Whilst mitigation methods were suggested a decade ago, we lack field trials testing their efficacy. We used the agrochemical fungicide, tebuconazole, to treat Bd infected breeding waterbodies of an endangered species that is highly susceptible to the fungus. Just two applications of tebuconazole led to a significant reduction in infection loads in the vast majority of sites, and at six sites the disinfection remained one/two-years post-application. Tebuconazole values drastically decreased in the waterbodies within a week after application, with no significant effects on their hydrochemical and hydrobiological characteristics. Although the use of chemicals in natural populations is undesirable, the growing existential threat to amphibians all over the world indicates that effective interventions in selected populations of endangered species are urgently needed.
Prediction of lncRNA and disease associations based on residual graph convolutional networks with attention mechanism
Authors: Wang Shengchang , Qiao Jiaqing , Feng Shou 
Published: March 2024

AbstractLncRNAs are non-coding RNAs with a length of more than 200 nucleotides. More and more evidence shows that lncRNAs are inextricably linked with diseases. To make up for the shortcomings of traditional methods, researchers began to collect relevant biological data in the database and used bioinformatics prediction tools to predict the associations between lncRNAs and diseases, which greatly improved the efficiency of the study. To improve the prediction accuracy of current methods, we propose a new lncRNA-disease associations prediction method with attention mechanism, called ResGCN-A. Firstly, we integrated lncRNA functional similarity, lncRNA Gaussian interaction profile kernel similarity, disease semantic similarity, and disease Gaussian interaction profile kernel similarity to obtain lncRNA comprehensive similarity and disease comprehensive similarity. Secondly, the residual graph convolutional network was used to extract the local features of lncRNAs and diseases. Thirdly, the new attention mechanism was used to assign the weight of the above features to further obtain the potential features of lncRNAs and diseases. Finally, the training set required by the Extra-Trees classifier was obtained by concatenating potential features, and the potential associations between lncRNAs and diseases were obtained by the trained Extra-Trees classifier. ResGCN-A combines the residual graph convolutional network with the attention mechanism to realize the local and global features fusion of lncRNA and diseases, which is beneficial to obtain more accurate features and improve the prediction accuracy. In the experiment, ResGCN-A was compared with five other methods through 5-fold cross-validation. The results show that the AUC value and AUPR value obtained by ResGCN-A are 0.9916 and 0.9951, which are superior to the other five methods. In addition, case studies and robustness evaluation have shown that ResGCN-A is an effective method for predicting lncRNA-disease associations. The source code for ResGCN-A will be available at https://github.com/Wangxiuxiun/ResGCN-A.
A tunable flat terahertz lens using Dirac semimetals: a simulation study
Authors: Panahianfar P. , Rezaei B. , Darafsheh A. 
Published: March 2024

AbstractWe propose and design a flat and tunable terahertz lens achieved through a two-dimensional photonic crystal composed of an array of rods made of a Dirac semimetal placed in air as the background medium. The structure of interest is a graded index photonic crystal, made possible by the slight variations in the rods’ radii in a direction perpendicular to the direction of the light propagation. Dirac semimetals' ability to respond to variations in their Fermi energy level manifested as a change in the refractive index provides the tunability of our proposed lens. The interaction of electromagnetic waves with the designed structure is investigated for both transverse magnetic and transverse electric polarizations using two-dimensional finite-difference time-domain method.
UV-light-responsive Ag/TiO2/PVA nanocomposite for photocatalytic degradation of Cr, Ni, Zn, and Cu heavy metal ions
Authors: Dehghani Mohammad Taha , Delnavaz Mohammad 
Published: March 2024

AbstractThe rapid growth of industrialization has led to the uncontrolled pollution of the environment, and rapid action is needed. This study synthesized Ag/TiO2/polyvinyl alcohol (PVA) nano photocatalyst for promising light-derived photocatalytic removal of heavy metal ions. The design of experiment (DOE) was used to study the effect of important factors (pH, reaction time, and photocatalyst dosage) to maximize the final performance of the photocatalyst. In the optimized condition, the Ag/TiO2/PVA nano-photocatalyst removed more than 94% of Cr6+ in 180 min, and the efficiency was more than 70% for Cu2+, Zn2+, and Ni2+ metal ions. The adsorption of the heavy metal ions on the photocatalyst was described well with the Langmuir isotherm, while the pseudo-second-order linear kinetic model fitted with the experimental data. The nano-photocatalyst's stability was confirmed after maintaining its performance for five successive runs. The enhanced photocatalytic activity for the heavy metal ions removal can be attributed to the presence of metallic silver nanoparticles (electron transfer and plasmonic fields mechanisms) and PVA, which delayed the recombination of electron–hole. The synthesized ternary Ag/TiO2/PVA nano-photocatalyst showed promising performance for the elimination of heavy metal ions and can be used for environmental remediation purposes.
Impact of industrial versus biomass burning aerosols on the Atlantic Meridional Overturning Circulation
Authors: Allen Robert J. , Vega Claire , Yao Eva , Liu Wei 
Published: March 2024

AbstractThe ocean’s major circulation system, the Atlantic Meridional Overturning Circulation (AMOC), is slowing down. Such weakening is consistent with warming associated with increasing greenhouse gases, as well as with recent decreases in industrial aerosol pollution. The impact of biomass burning aerosols on the AMOC, however, remains unexplored. Here, we use the Community Earth System Model version 1 Large Ensemble to quantify the impact of both aerosol types on the AMOC. Despite relatively small changes in North Atlantic biomass burning aerosols, significant AMOC evolution occurs, including weakening from 1920 to ~1970 followed by AMOC strengthening. These changes are largely out of phase relative to the corresponding AMOC evolution under industrial aerosols. AMOC responses are initiated by thermal changes in sea surface density flux due to altered shortwave radiation. An additional dynamical mechanism involving the North Atlantic sea-level pressure gradient is important under biomass-burning aerosols. AMOC-induced ocean salinity flux convergence acts as a positive feedback. Our results show that biomass-burning aerosols reinforce early 20th-century AMOC weakening associated with greenhouse gases and also partially mute industrial aerosol impacts on the AMOC. Recent increases in wildfires suggest biomass-burning aerosols may be an important driver of future AMOC variability.
TP53 gain-of-function mutations promote osimertinib resistance via TNF-α–NF-κB signaling in EGFR-mutated lung cancer
Authors: Ibusuki Ritsu , Iwama Eiji , Shimauchi Atsushi , Tsutsumi Hirono , Yoneshima Yasuto , Tanaka Kentaro , Okamoto Isamu 
Published: March 2024

AbstractEGFR tyrosine kinase inhibitors (TKIs) are effective against EGFR-mutated lung cancer, but tumors eventually develop resistance to these drugs. Although TP53 gain-of-function (GOF) mutations promote carcinogenesis, their effect on EGFR-TKI efficacy has remained unclear. We here established EGFR-mutated lung cancer cell lines that express wild-type (WT) or various mutant p53 proteins with CRISPR-Cas9 technology and found that TP53-GOF mutations promote early development of resistance to the EGFR-TKI osimertinib associated with sustained activation of ERK and expression of c-Myc. Gene expression analysis revealed that osimertinib activates TNF-α–NF-κB signaling specifically in TP53-GOF mutant cells. In such cells, osimertinib promoted interaction of p53 with the NF-κB subunit p65, translocation of the resulting complex to the nucleus and its binding to the TNF promoter, and TNF-α production. Concurrent treatment of TP53-GOF mutant cells with the TNF-α inhibitor infliximab suppressed acquisition of osimertinib resistance as well as restored osimertinib sensitivity in resistant cells in association with attenuation of ERK activation and c-Myc expression. Our findings indicate that induction of TNF-α expression by osimertinib in TP53-GOF mutant cells contributes to the early development of osimertinib resistance, and that TNF-α inhibition may therefore be an effective strategy to overcome such resistance in EGFR-mutant lung cancer with TP53-GOF mutations.
Exceptionally strong coupling of defect emission in hexagonal boron nitride to stacking sequences
Authors: Li Song , Pershin Anton , Li Pei , Gali Adam 
Published: March 2024

AbstractVan der Waals structures present a unique opportunity for tailoring material interfaces and integrating photonic functionalities. By precisely manipulating the twist angle and stacking sequences, it is possible to elegantly tune and functionalize the electronic and optical properties of layered van der Waals structures. Among these materials, two-dimensional hexagonal boron nitride (hBN) stands out for its remarkable optical properties and wide band gap, making it a promising host for solid state single photon emitters at room temperature. Previous investigations have demonstrated the observation of bright single photon emission in hBN across a wide range of wavelengths. In this study, we unveil an application of van der Waals technology in modulating their spectral shapes and brightness by carefully controlling the stacking sequences and polytypes. Our theoretical analysis reveals remarkably large variations in the Huang-Rhys factors–an indicator of the interaction between a defect and its surrounding lattice–reaching up to a factor of 3.3 for the same defect in different stacking sequences. We provide insights into the underlying mechanism behind these variations, shedding light on the design principles necessary to achieve rational and precise control of defect emission. This work paves the way for enhancing defect identification and facilitating the engineering of highly efficient single photon sources and qubits using van der Waals materials.
New perspectives on ‘Breathomics’: metabolomic profiling of non-volatile organic compounds in exhaled breath using DI-FT-ICR-MS
Authors: Malik Madiha , Demetrowitsch Tobias , Schwarz Karin , Kunze Thomas 
Published: March 2024

AbstractBreath analysis offers tremendous potential for diagnostic approaches, since it allows for easy and non-invasive sample collection. “Breathomics” as one major research field comprehensively analyses the metabolomic profile of exhaled breath providing insights into various (patho)physiological processes. Recent research, however, primarily focuses on volatile compounds. This is the first study that evaluates the non-volatile organic compounds (nVOCs) in breath following an untargeted metabolomic approach. Herein, we developed an innovative method utilizing a filter-based device for metabolite extraction. Breath samples of 101 healthy volunteers (female n = 50) were analysed using DI-FT-ICR-MS and biostatistically evaluated. The characterisation of the non-volatile core breathome identified more than 1100 metabolites including various amino acids, organic and fatty acids and conjugates thereof, carbohydrates as well as diverse hydrophilic and lipophilic nVOCs. The data shows gender-specific differences in metabolic patterns with 570 significant metabolites. Male and female metabolomic profiles of breath were distinguished by a random forest approach with an out-of-bag error of 0.0099. Additionally, the study examines how oral contraceptives and various lifestyle factors, like alcohol consumption, affect the non-volatile breathome. In conclusion, the successful application of a filter-based device combined with metabolomics-analyses delineate a non-volatile breathprint laying the foundation for discovering clinical biomarkers in exhaled breath.