bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel SARS-CoV-2 Beta RBD DNA vaccine directly targeted to antigen-presenting cells induces strong humoral and T cell responses
Authors: Kuczkowska Katarzyna , Bjerkan Louise , Stubsrud Elisabeth , Husbyn Hannah Cuthbertson , Chellappa Stalin , Hauge Anette , Skarshaug Renate , Torgersen Maria Lyngaas , Heim Joel Benjamin , Jørgensen Marthe Jøntvedt , Wold Christian Winther , Schleimann Mariane Høgsbjerg , Tolstrup Martin , Granum Stine , Fredriksen Agnete Brunsvik , Pedersen Mikkel Wandahl , Norheim Gunnstein 
Published: November 2023

AbstractThroughout the COVID-19 pandemic, several variants of concern (VoC) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have evolved, affecting the efficacy of the approved COVID-19 vaccines. To address the need for vaccines that induce strong and persistent cross-reactive neutralizing antibodies and T cell responses, we developed a prophylactic SARS-CoV-2 vaccine candidate based on our easily and rapidly adaptable plasmid DNA vaccine platform. The vaccine candidate, referred to here as VB2129, encodes a protein homodimer consisting of the receptor binding domain (RBD) from lineage B.1.351 (Beta) of SARS-CoV-2, a VoC with a severe immune profile, linked to a targeting unit (human LD78β/CCL3L1) that binds chemokine receptors on antigen-presenting cells (APCs) and a dimerization unit (derived from the hinge and CH3 exons of human IgG3). Immunogenicity studies in mice demonstrated that the APC-targeted vaccine induced strong antibody responses to both homologous Beta RBD and heterologous RBDs derived from Wuhan, Alpha, Gamma, Delta, and Omicron BA.1 variants, as well as cross-neutralizing antibodies against these VoC. Overall, preclinical data justify the exploration of VB2129 as a potential booster vaccine that induces broader antibody- and T cell-based protection against current and future SARS-CoV-2 VoC.
Glucocorticoids alleviate particulate matter-induced COX-2 expression and mitochondrial dysfunction through the Bcl-2/GR complex in A549 cells
Authors: Park Yeon-Ji , Heo June , Kim Yonghyeon , Cho Hyeseong , Shim Myeongkuk , Im Kyunghyun , Lim Wonchung 
Published: November 2023

AbstractExposure to particulate matter (PM) causes mitochondrial dysfunction and lung inflammation. The cyclooxygenase-2 (COX-2) pathway is important for inflammation and mitochondrial function. However, the mechanisms by which glucocorticoid receptors (GRs) suppress COX-2 expression during PM exposure have not been elucidated yet. Hence, we examined the mechanisms underlying the dexamethasone-mediated suppression of the PM-induced COX-2/prostaglandin E2 (PGE2) pathway in A549 cells. The PM-induced increase in COX-2 protein, mRNA, and promoter activity was suppressed by glucocorticoids; this effect of glucocorticoids was antagonized by the GR antagonist RU486. COX-2 induction was correlated with the ability of PM to increase reactive oxygen species (ROS) levels. Consistent with this, antioxidant treatment significantly abolished COX-2 induction, suggesting that ROS is involved in PM-mediated COX-2 induction. We also observed a low mitochondrial membrane potential in PM-treated A549 cells, which was reversed by dexamethasone. Moreover, glucocorticoids significantly enhanced Bcl-2/GR complex formation in PM-treated A549 cells. Glucocorticoids regulate the PM-exposed induction of COX-2 expression and mitochondrial dysfunction and increase the interaction between GR and Bcl-2. These findings suggest that the COX-2/PGE2 pathway and the interaction between GR and Bcl-2 are potential key therapeutic targets for the suppression of inflammation under PM exposure.
Evaluation of COVID-19 intervention policies in South Korea using the stochastic individual-based model
Authors: Chae Min-Kyung , Hwang Dong-Uk , Nah Kyeongah , Son Woo-Sik 
Published: November 2023

AbstractThe COVID-19 pandemic has swept the globe, and countries have responded with various intervention policies to prevent its spread. In this study, we aim to analyze the effectiveness of intervention policies implemented in South Korea. We use a stochastic individual-based model (IBM) with a synthetic population to simulate the spread of COVID-19. Using statistical data, we make the synthetic population and assign sociodemographic attributes to each individual. Individuals go about their daily lives based on their assigned characteristics, and encountering infectors in their daily lives stochastically determines whether they are infected. We reproduce the transmission of COVID-19 using the IBM simulation from November 2020 to February 2021 when three phases of increasingly stringent intervention policies were implemented, and then assess their effectiveness. Additionally, we predict how the spread of infection would have been different if these policies had been implemented in January 2022. This study offers valuable insights into the effectiveness of intervention policies in South Korea, which can assist policymakers and public health officials in their decision-making process.
Whole-lake acoustic telemetry to evaluate survival of stocked juvenile fish
Authors: Koeberle Alexander L. , Pearsall Webster , Hammers Brad E. , Mulhall Daniel , McKenna James E. , Chalupnicki Marc , Sethi Suresh A. 
Published: November 2023

AbstractEstimates of juvenile survival are critical for informing population dynamics and the ecology of fish, yet these demographic parameters are difficult to measure. Here, we demonstrate that advances in animal tracking technology provide opportunities to evaluate survival of juvenile tagged fish. We implemented a whole-lake telemetry array in conjunction with small acoustic tags (including tags < 1.0 g) to track the fate of stocked juvenile cisco (Coregonus artedi) as part of a native species restoration effort in the Finger Lakes region of New York, USA. We used time-to-event modeling to characterize the survival function of stocked fish, where we infer mortality as the cessation of tag detections. Survival estimates revealed distinct stages of juvenile cisco mortality including high immediate post-release mortality, followed by a period of elevated mortality during an acclimation period. By characterizing mortality over time, the whole-lake biotelemetry effort provided information useful for adapting stocking practices that may improve survival of stocked fish, and ultimately the success of the species reintroduction effort. The combination of acoustic technology and time-to-event modeling to inform fish survival may have wide applicability across waterbodies where receiver arrays can be deployed at scale and where basic assumptions about population closure can be satisfied.
Functional Magnetic Resonance Imaging Activation During Unscripted Discourse in People With Poststroke Aphasia
Authors: Maloney Thomas C. , Dietz Aimee , Vannest Jennifer , Wilkinson Krista , Szaflarski Jerzy P. , Stall Cassandra , Mamlekar Chitrali R. 
Published: November 2023

Purpose:
The purpose of this project was to determine the feasibility of employing a functional magnetic resonance imaging (fMRI) task that captured activation associated with overt, unscripted (or free) discourse of people with aphasia (PWA), using a continuous scan paradigm.

Method:
Seven participants (six females, ages 48–70 years) with chronic poststroke aphasia underwent two fMRI scanning sessions that included a discourse fMRI paradigm that consisted of five 1-min picture description tasks, using personally relevant photographs, interspersed with two 30-s control periods where participants looked at a fixation cross. Audio during the continuous fMRI scan was collected and marked with speaking times and coded for correct information units. Activation maps from the fMRI data were generated for the contrast between speaking and control conditions. In order to show the effects of the multi-echo data analysis, we compared it to a single-echo analysis by using only the middle echo (echo time of 30 ms).

Results:
Through the implementation of the free discourse fMRI task, we were able to elicit activation that included bilateral regions in the planum polare, central opercular cortex, precentral gyrus, superior temporal gyrus, middle temporal gyrus, superior temporal gyrus, Crus I of the cerebellum, as well as bilateral occipital regions

Conclusions:
We describe a new tool for assessing discourse recovery in PWA. By demonstrating the feasibility of a natural language paradigm in patients with chronic, poststroke aphasia, we open a new area for future research.
Ebola-Virus- und Marburg-Viruserkrankung
Authors: Schmiedel Stefan , Wolf Timo 
Published: November 2023

Was ist neu?
Ebola-Viruserkrankung Während der durch die Zaire-Virusvariante hervorgerufenen Ebola-Ausbrüche wurden klinische Studien mit Impfstoffen sowie antiviralen Substanzen durchgeführt. Aktuell stehen als Therapie die monoklonalen Antikörper Atoltivimab/Maftivimab/Odesivimab (kombiniert in Inmazeb) und Ansuvimab (Ebanga) zur Verfügung. Zur präventiven Impfung und zur Prä- und Postexpositionsprophylaxe einer Ebola-Virusinfektion (Variante Zaire) sind die Ebola-Zaire-Impfstoffe rVSVΔG-ZEBOV-GP, lebend (Ervebo), Ebola-Zaire-Impfstoff Ad26.ZEBOV-GP, rekombinant (Zabdeno) und der Ebola-Impfstoff (MVA-BN-Filo, rekombinant (Mvabea) geeignet.
Marburg-Viruserkrankung Es gibt derzeit kein zugelassenes Medikament zur Behandlung der Marburg-Viruserkrankung. Trotz erheblicher logistischer Schwierigkeiten und der Tatsache, dass die meisten Marburg-Ausbrüche eher zu klein waren, um Impfstoffstudien zu ermöglichen, wurden von der WHO 3 Impfstoffkandidaten für eine mögliche Studie favorisiert.
Curriculum for Early Exposure to Clinical Informatics and Data Science for Noninformatics Trainees to Promote Interest and Inclusion in Informatics
Authors: Ravi Akshay , Weia Benjamin , Sakumoto Matthew , Oates Aris , Liu Xinran 
Published: November 2023

Abstract
Background Curricula aimed at increasing exposure to informatics and practical data analytics among medical trainees could increase their effectiveness in clinical research, quality improvement, and clinical operations.
Objectives The Clinical Informatics Data Science (CI-DS) pathway is a cross-disciplinary curriculum aimed at improving informatics exposure among medical trainees. We describe the development of this novel curriculum, the inaugural cohort, and lessons learned.
Methods The CI-DS pathway is framed around upfront informatics didactics followed by a longitudinal, experiential training focused on mentorship, clinical data extraction/machine learning, and health technology governance. The curriculum was evaluated based on pre- and postpathway surveys completed by learners and logs of the elective activities selected by learners.
Results The CI-DS pathway attracted 19 learners across 12 medical subspecialties, from medical students to fellows. Baseline surveys showed limited exposure to informatics across learners. The top three longitudinal activities completed were participating in electronic health record (EHR) governance meetings, data science supplemental courses, and designated mentorship meetings. Comparison of baseline with postpathway surveys demonstrated significant improvements in learner self-reported confidence in appraising an EHR modification ticket, accessing UCSF's deidentified data, exploring a database with basic structured query language (SQL), extracting data using SQL, and interpreting machine learning models.
Conclusion An early exposure curriculum in clinical informatics with training in data extraction and governance can successfully recruit a diverse array of learners and improve confidence in practical informatics skills. We reflect on the strengths and weaknesses of this curriculum, and summarize the lessons learned to guide others in creating similar curricula for noninformatics clinicians.
On the general classifiersgeandzàgin Hakka
Authors: Huang Han-Chun 
Published: November 2023

AbstractThis paper investigates the distribution and properties of the Hakka general classifiersgeandzàg. We focus on the [determiner/numeral + classifier + noun] construction where we observe the relations between the general classifiers and their following nouns, chosen based on their frequency in this construction. We adopt a corpus-based collostructional analysis which calculates the collocational strength values ofgeandzàgwith following nouns. A Hakka corpus was compiled for the study. The three-way distinction in the collostructional analysis (attractive, neutral, and repulsive) is directly mapped to acceptability of various degrees. The results show thatgeis highly correlated with human-denoting nouns, whereaszàgis highly correlated with animal-denoting nouns. Nouns denoting abstract entities or concrete objects without physical properties like size or shape usually lack specific classifiers, and bothgeandzàgcan collocate with them, albeit with varying degrees of preference. We argue that bothgeandzàgare general classifiers because both are more frequently used than specific classifiers and both exhibit disjointed semantic distribution and allow abstract nouns. While they show preferences for different nouns, requirements to qualify as general classifiers are equally met.
Electronic, magnetic, and structural properties of NiFeMnAl
Authors: Lukashev Pavel V , Wysong Jax , McFadden Stephen , Baker Gavin , Schmidt Brandon , Shand Paul M , Kharel Parashu 
Published: November 2023

Abstract
Half-metallic Heusler compounds have been extensively studied in the recent years, both experimentally and theoretically, for potential applications in spin-based electronics. Here, we present the results of a combined theoretical and experimental study of the quaternary Heusler compound NiFeMnAl. Our calculations indicate that this material is half-metallic in the ground state and maintains its half-metallic electronic structure under a considerable range of external hydrostatic pressure and biaxial strain. NiFeMnAl crystallizes in the regular cubic Heusler structure, and exhibits ferromagnetic alignment. The practical feasibility of the proposed system is confirmed in the experimental section of this work. More specifically, a bulk ingot of NiFeMnAl was synthesized in A2 type disordered cubic structure using arc melting. It shows a high Curie temperature of about 468 K and a saturation magnetization of 2.3

μ

B

/

f

.u

. The measured magnetization value is smaller than the one calculated for the ordered structure. This discrepancy is likely due to the A2 type atomic disorder, as demonstrated by our calculations. We hope that the presented results may be useful for researchers working on practical applications of spin-based electronics.
Multi-representation transferable attention network for remaining useful life prediction of rolling bearings under multiple working conditions
Authors: Shi Yabin , Cui Youchang , Cheng Han , Li Lin , Li Xiaopeng , Kong Xianguang 
Published: November 2023

Abstract
Remaining useful life (RUL) prediction improves equipment reliability by avoiding unexpected breakdowns. However, different operating conditions may cause the performance degradation of the prediction model due to the domain-shift problem. Therefore, a novel method based on the multiple representation transferable attention network (MRTAN) is proposed in this paper for RUL prediction under multiple working conditions. In the MRTAN-based method, multiple convolutional modules are first used to learn the degradation features. Next, the multi-representation adaptation module is utilized to further mine the multiple domain-invariant representations. Then, during the training process, the transferable attention module can activate the representation with high transferability dynamically. Finally, the prognostic model is optimized by multiple optimization objectives and the back-propagation algorithm. Besides, multiple cross-domain RUL prediction tasks are employed to validate the effectiveness of the MRTAN-based model. Experiments demonstrate that the proposed method can provide better prognostic performance and avoid the negative transfer problem.
Improved artificial gorilla troops optimizer with chaotic adaptive parameters - application to the parameter estimation problem of mixed additive and multiplicative random error models
Authors: Wang Leyang , Han Shuhao , Pang Ming 
Published: November 2023

Abstract
For mixed additive and multiplicative random error models (MAM models), due to the complex correlation between the parameters and the model power array, derivative operations will be inevitable in the actual calculation. When the observation equation is in nonlinear form, the operations will be more complicated. The swarm intelligence optimization algorithm (SIO) can effectively solve the derivative problem when estimating the nonlinear model parameters using conventional iterative algorithms. However, for different problems, the conventional SIO cannot effectively balance the ability of global and local behavior, resulting in the algorithm falling into prematureness and failing to output effective parameter information. To address the above problems, the improved artificial gorilla troops optimizer (CAGTO) algorithm with chaotic adaptive behavior is proposed. To address the problem that the population generated by the algorithm using pseudo-random numbers in the initialization population phase has poor traversability in the feasible domain, the chaotic sequence is applied to initialize the population instead of pseudo-random number generation to ensure that the population can traverse the feasible domain as much as possible and improve the global search capability of the algorithm. Adaptive parameters that vary linearly and nonlinearly with the algorithm process are constructed to balance the global search and local search ability, while accelerating the convergence speed. Two CAGTO algorithms with different parameter settings are constructed for different problems, and the experimental results show that both CAGTO algorithms can effectively solve the parameter estimation problem of MAM models with different nonlinear forms of observation equations compared with several other comparative algorithms.
A state detection method of offshore wind turbines’ gearbox bearing based on the transformer and GRU
Authors: Zhao Zi-han , Wang Qiang , Shao Chang-sheng , Chen Ning , Liu Xin-yao , Wang Guang-bin 
Published: November 2023

Abstract
Through the analysis of data from the SCADA system of a wind turbine unit in a specific offshore wind farm located in Zhanjiang, it was observed that the most prevalent type of fault is bearing alarms on the gearbox’s generator side. Considering the growing need for intelligent offshore wind turbine maintenance, this study employed GRA on SCADA data collected over a significant duration from a representative wind turbine unit. Relevant features were extracted, with temperature serving as the target parameter. To address the challenge of long-term dependencies in long-term time series forecasting tasks, this study uniquely combined the GRU with the advanced Transformer neural network which incorporates attention mechanisms, to predict the temperature trend of the gearbox’s generator-side bearing. Based on the prediction residuals obtained during normal operation and their subsequent analysis, the study devised an effective anomaly detection process to identify early abnormal states of the gearbox’s generator-side bearing. Comparative performance evaluations were conducted, comparing the combined model with its individual component models, as well as the traditional LightGBM, in terms of temperature time series prediction and their application in anomaly detection. The results unequivocally demonstrate that the combined model outperforms both the individual models and LightGBM in terms of time series prediction accuracy and anomaly detection effectiveness, indicating an enhanced ability to handle long-term memory challenges. Furthermore, the combined model exhibits great potential of practical application for the early warning of gearbox bearing anomalies during actual wind turbine daily operation and maintenance, providing a valuable solution for the offshore wind turbine industry.
Filament extrusion state monitoring in fused filament fabrication using Bhattacharyya distance and K-nearest neighbor
Authors: Li Feng , Yang Zhensheng , wang Chaoge , Huang Yuanyuan , Li Hao 
Published: November 2023

Abstract
Fused filament fabrication (FFF) is one of the most popular techniques of additive manufacturing. However, product quality issues still limit the further application of FFF technology. Filament extrusion state has a great influence on the quality of FFF fabricated products, since both under-extrusion and over-extrusion can lead to the deterioration of product quality. Therefore, monitoring the filament extrusion states is vital and essential. This paper aims to monitor the filament extrusion state by acoustic emission (AE). To achieve this goal, experiments are conducted on a desktop FFF machine, where the states of under-extrusion and over-extrusion are induced by different extrusion speeds. Original AE signals are collected during the experiments. Confronted with the challenge posed by the susceptibility of AE signals to noise during the complex extrusion process and different conditions, one calculates the statistical distribution of the features defined on the raw AE signals, without the need for noise reduction steps. The k-nearest neighbor algorithm is then adopted to identify the different extrusion states, where the Bhattacharyya distance is employed to measure the distances or similarities of the calculated distributions. The findings demonstrate the successful identification of various extrusion states induced by different extrusion speeds through the presented method. The outcomes of this study pave the way for the development of an affordable in-situ FFF monitoring system with comprehensive capabilities.
Research on dynamic characteristics of the turnover system of rail belt conveyor
Authors: Hao Nini , Yi Xiaoting , Zhang Yuan , Chang Xin , Liu Jinshan , Wang Zhen , Zhang Junwei 
Published: November 2023

Abstract
The rail belt conveyor is a new bulk material conveying system that combines the advantages of traditional belt conveyors and railway systems. However, the turnover system, which is a key part of the carriage’s direction-changing mechanism, experiences severe vibration and impact problems that affect its performance and reliability. In this study, a dynamic model of the carriage-rail coupling in the turnover system is established, and the vibration characteristics of the rail are analyzed. The influence of carriage speed, wheelbase, and wheel diameter on the vibration acceleration of the rail is investigated. The results show that higher carriage speeds intensify the vibration response of the rail when the carriage leaves the turnover wheel. The wheel diameter has a significant effect on the vibration acceleration, especially at high speeds. Additionally, the wheelbase affects the vibration acceleration, with smaller wheelbases resulting in more prominent effects. The established dynamic model is validated using measured data, providing insights into the dynamic behavior of the turnover system in rail belt conveyors.