bluuebunny/crossref_metadata_2025_split huggingface dataset:
Loss of viral genome with altered immune microenvironment during tumour progression of Epstein‐Barr virus‐associated gastric carcinoma
Authors: Kondo Atsushi , Shinozaki‐Ushiku Aya , Rokutan Hirofumi , Kunita Akiko , Ikemura Masako , Yamashita Hiroharu , Seto Yasuyuki , Nagae Genta , Tatsuno Kenji , Aburatani Hiroyuki , Koinuma Daizo , Ushiku Tetsuo 
Published: February 2023

AbstractEpstein‐Barr virus (EBV) is one of the major drivers of gastric carcinogenesis. EBV infection is established before tumour initiation and is generally maintained throughout tumour development; however, the significance of EBV in tumour maintenance and progression remains to be elucidated. Here, we report eight cases of EBV‐associated gastric carcinoma (EBVaGC) with intratumoural heterogenous expression of EBV‐encoded small RNA (EBER), a highly expressed latent gene of EBV, and demonstrate clinicopathological characteristics of these rare cases. By performing detailed histological assessment of EBER‐positive and ‐negative components of each case, detection of EBV genome in tumour cells by fluorescence in situ hybridisation, TP73 methylation analysis, whole exome sequencing, and targeted gene panel sequencing, we identified tumours in two patients to be collision tumours of different origins. In the other six patients, some genetic/epigenetic alterations were shared between EBER‐positive and ‐negative components, suggesting that EBV was eliminated from tumour cells during progression. Interestingly, in both tumour types, programmed death ligand 1 and intratumoural infiltration of CD8+ T lymphocytes were lower in EBER‐negative than in EBER‐positive components, suggesting an immunogenic role of EBV. To the best of our knowledge, this study is the first to demonstrate the detailed histological features and genetic/epigenetic alterations in EBVaGC with heterogenous EBER expression; the loss of EBV may benefit tumour progression and immune evasion and might be clinically important for selecting treatment strategies for such cancers. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
After school: Volunteering in community emergency services and substance use among Israeli adolescents
Authors: Gil Fire , Sharon Barak , Shlomi Hail , Tirtzha Carmi , Lilach Ben‐Meir , Ariela Giladi , Yossi Harel‐Fisch , Riki Tesler 
Published: February 2023

AbstractVolunteering can serve as a protective factor against substance abuse. Yet, it is unclear whether volunteering in specific community organizations, such as emergency services, promotes or protects against substance use. We aimed to (1) describe community volunteering characteristics among adolescents; (2) investigate differences in the prevalence of substance use according to community volunteering type; and (3) determine whether volunteering type was a predictor of substance use. We analyzed data from the 2018–2019 Health Behavior in School‐aged Children survey among Israeli adolescents aged 11–17 years (N = 3972). Most participants (N = 2452; 61.7%) did not volunteer at all, 27.1% (N = 1077) volunteered in youth movements/councils, and 11.2% (N = 443) volunteered in community emergency services. In comparison to the emergency services group, there was a higher volunteering frequency among the youth movements/councils group. Of the three groups (nonvolunteering, volunteering in youth movements/councils, and volunteering in community emergency services), those in the community emergency services group reported a significantly higher prevalence of weekly alcohol use, lifetime cannabis use, and new psychoactive drug use, while no significant between‐group differences were observed in smoking tobacco prevalence. Volunteering in the community emergency services has been linked to substance use, requiring the development of intervention programs by the school staff, before their active volunteering (e.g., guidance on emotional stress and substance abuse). Also, teachers can act as a protective factor for students, and identify emotional distress and anxiety in their students to prevent substance abuse. Furthermore, emergency services workers and instructors should also be aware of the higher risk of substance use among volunteering youth and should be given tools to better collaborate with parents and teachers in dealing with it.
Plasma catalytic technology for CH4 and CO2 conversion: A review highlighting fluidized‐bed plasma reactor
Authors: Chen Xiaozhong , Kim Hyun‐Ha , Nozaki Tomohiro 
Published: February 2023

AbstractThe plasma catalytic valorization of gases, particularly CH4 and CO2, has gained increasing attention. Value‐added chemicals, such as syngas and ethene, can be formed under mild conditions when temperature‐decoupled plasma activation and multistep feasible catalytic conversion are combined. In this sense, efficient plasma–catalyst interaction is of key importance, for which, however, plasma catalysis, as an emerging technology, is still poorly studied, where new catalyst design and investigation took up the most effort. In this perspective work, the challenging but equally important plasma–catalyst interaction is discussed, comparatively analyzing which type of plasma, catalyst bed, is the most promising. Representative plasma catalytic systems with their characteristic features are summarized, where the intrinsic capability of fluidized‐bed dielectric barrier discharge (FB‐DBD) reactor to maximize the plasma–catalyst interaction is highlighted. Furthermore, ongoing research on FB plasma catalysis is reviewed, based on which the superiority of FB‐DBD to other candidates, especially the most widely used packed‐bed DBD reactor, is critically evaluated. In addition, the perspectives of FB‐DBD, including challenges and development potential, are discussed.
“Find ways to work parenting into cystic fibrosis”: A PhotoVoice exploration of being a parent and having CF
Authors: Stransky Olivia M. , Benipal Savvy , Pam Molly , Taylor‐Cousar Jennifer L. , Documet Patricia , Kazmerski Traci M. 
Published: February 2023

AbstractBackgroundAs more people with cystic fibrosis (CF) consider their reproductive futures, the impact of parenthood on CF must be better understood. In the context of chronic disease, deciding if, when, and how to become a parent is complex. Little research has investigated how parents with CF balance their role as parents with its associated health impacts and demands of CF.MethodsPhotoVoice is a research methodology that utilizes photography to generate discussion about community issues. We recruited parents with CF with at least 1 child <10 years old and divided them into three cohorts. Each cohort met five times. Cohorts developed photography prompts, took photographs between sessions, and reflected on the photos at subsequent meetings. At the final meeting, participants selected 2−3 pictures, wrote captions, and as a group organized the photographs into thematic groups. Secondary thematic analysis identified metathemes.ResultsParticipants (n = 18) generated a total of 202 photographs. Each cohort identified 3−4 themes (n = 10) which secondary analysis grouped into 3 metathemes: (1) It is important for parents with CF to pay attention to the joyful aspects of parenting and to cultivate positive experiences; (2) Parenting with CF requires balancing your own needs with those of your child, and creativity and flexibility can be key; (3) Parenting with CF consists of competing priorities and expectations often with no clear “correct” choice.ConclusionsParents with CF identified unique challenges to their existence as both parents and patients as well as ways in which parenting has enhanced their lives.
Prediction of Solid Propellant Burning Rate Characteristics Using Machine Learning Techniques
Authors: Klinger Daniel , Casey Alex , Manship Tim , Son Steven , Strachan Alejandro 
Published: February 2023

AbstractWhen formulating a new solid propellant, one of the most important aspects of its performance is the burning rate's response to a change in pressure. For this reason, it is useful to be able to predict the burning rate response of a given propellant before the propellant formulation is created such that experimental trade studies are minimized or reduced in scale. While many theoretical and phenomenological models have been proposed to explain various aspects of energetic material combustion, little work has been made publicly available in the application of machine learning models to predicting solid propellant burning rates. To facilitate model creation, the material formulation and burning rate parameters for over 600 publicly available propellant formulations have been collected into a coherent data set. This work utilizes the large amount of publicly available data to inform a random forest machine learning (ML) model in the prediction of solid propellant burning rate parameters. This ML model operates over a large parameter space including ammonium perchlorate composite, plastic bonded high explosive, and double‐base propellants. The model's accuracy, adaptability, and prediction capabilities are presented and discussed. The effects of different materials on a propellant's expected burning rate are examined.
Artificial Intelligence Approaches for Energetic Materials by Design: State of the Art, Challenges, and Future Directions
Authors: Choi Joseph B , Nguyen Phong C. H. , Sen Oishik , Udaykumar H. S. , Baek Stephen 
Published: February 2023

AbstractArtificial intelligence (AI) is rapidly emerging as a enabling tool for solving complex materials design problems. This paper aims to review recent advances in AI‐driven materials‐by‐design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro‐morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials‐by‐design, namely representation learning of microstructure morphology (i. e., shape descriptors), structure‐property‐performance (S−P−P) linkage estimation, and optimization/design exploration. We leave out “process” as much work remains to be done to establish the connectivity between process and structure. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materials‐by‐design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials‐by‐design, such as meta‐learning, active learning, Bayesian learning, and semi‐/weakly‐supervised learning, to bridge the gap between machine learning research and EM research.
A New Parametrization of Hubble Parameter in f(Q)$f(Q)$ Gravity
Authors: Koussour M. , Pacif S. K. J. , Bennai M. , Sahoo P.K. 
Published: February 2023

AbstractIn this paper, we examine the accelerated expansion of the Universe at late‐time in the framework of gravity theory in which the non‐metricity scalar Q describes the gravitational interaction. To this, we propose a new parametrization of the Hubble parameter using a model‐independent way and apply it to the Friedmann equations in the FLRW Universe. Then we estimate the best fit values of the model parameters by using the combined datasets of updated consisting of 57 points, the Pantheon consisting of 1048 points, and BAO datasets consisting of six points with the Markov Chain Monte Carlo (MCMC) method. The evolution of deceleration parameter indicates a transition from the deceleration to the acceleration phase of the Universe. In addition, we investigate the behavior of statefinder analysis and Om diagnostic parameter, Further, to discuss other cosmological parameters, we consider a model, specifically, , where m and n are free parameters. Finally, we find that the model supports the present accelerating Universe, and the EoS parameter behaves like the quintessence model.
Monte‐Carlo simulation‐based analysis for structural reliability of the crane rail beam under stochastic crane movements and irradiation conditions
Authors: Qian Cheng , Li Wenjuan , Ren Yi , Hu Ziyang , Yang Lei , Zhao Xiaolong , Wang Xiaoli 
Published: February 2023

AbstractThis study presents a Monte‐Carlo simulation‐based approach to investigate how stochastic crane movements affect the structural reliability of the rail beam considering radiation effects. In the simulation, the rail beam loaded under a stochastically moving crane is discretized into 47 equispaced structural points for calculating its strain profile. Then the structural reliability at each structural point is calculated with the consideration of irradiation creep, represented as an accumulated strain over a given period of time. And the rail beam reliability is determined by the minimum value from the reliabilities calculated at different structural points, according to the weakest link principle. In our simulations, three candidate distributions, including the normal distribution, uniform distribution, and 3‐parameter lognormal distributions respectively, are applied to describe the stochastic distances of the crane. It can be seen from the simulation results that the rail beam reliability degrades with the most rapid rate when the crane distances follow a normal distribution, compared to the other two distributions. This paper offers a possible way of using the proposed Monte‐Carlo simulation‐based method to carry out reliability analysis and improvements for the rail beam working in extreme environments, such as irradiation conditions, with random operational tasks.
Modified nonlinear cumulative damage model considering residual strength degradation and gear reliability analysis
Authors: Ding Gaohui , Yan Yinze , Si Shubin , Cai Zhiqiang 
Published: February 2023

AbstractThe fatigue damage of gear is caused by the coexistence and mutual influence of residual strength degradation and cumulative damage. The residual strength decreases continuously with the change of the loads and the increase of stress cycles. Most of the traditional cumulative damage models ignore the effect of residual strength degradation on cumulative damage. The exponential parameter in the traditional Corten‐Dolan model was defined as exponentials function related to the strength degradation coefficient, and the strength degradation coefficient was deduced by analyzing the residual strength degradation model, and the modified Corten‐Dolan model considering the residual strength degradation was established. Based on the stress‐strength interference model, a gear reliability model considering residual strength degradation was established. The effectiveness of the strength degradation model and the accuracy of the modified Corten‐Dolan model are verified by the analysis of the test data, which provides a new idea for the reliability analysis of gear. The accuracy of the gear reliability model proposed in this paper was verified by Monte‐Carlo (M‐C) simulation, which has practical significance for engineering application.
Cardiac manifestations in children with the multisystem inflammatory syndrome (MIS‐C) associated with SARS‐CoV‐2 infection: Systematic review and meta‐analysis
Authors: Arantes Junior Márcio Antonio Ferreira , Conegundes Ana Flávia , Branco Miranda Bárbara Castello , Radicchi Campos Alexia Stenner Rodrigues , França Vieira Ana Luiza , Faleiro Matheus Daniel , Campos Marco Antônio , Kroon Erna Geessien , Bentes Aline Almeida 
Published: February 2023

AbstractAccording to the World Health Organisation, as of October 2022, there have been 55,560,329 reported cases of SARS‐COV‐2 in patients under 19 years old. It is estimated that about 0.06% of these patients may develop MIS‐C, representing more than 2 million children worldwide. This systematic review and meta‐analysis examined the pooled prevalence of cardiovascular manifestation and cardiac complications in children hospitalised with MIS‐C. The PROSPERO register number is CRD42022327212. We included case‐report studies, case‐control studies, cohort studies, and cross‐sectional studies, as well as clinical trials or studies describing cardiac manifestations of MIS‐C and its sequelae in a paediatric population. Initially, 285 studies were selected, but there were 154 duplicates, and 81 were excluded because they did not fit the eligibility criteria. Thus, 50 studies were selected for review, and 30 were included in the meta‐analysis. A total sample size of 1445 children was included. The combined prevalence of myocarditis or pericarditis was 34.3% (95% CI: 25.0%–44.2%). The combined prevalence for echocardiogram anomalies was 40.8% (95% CI: 30.5%–51.5%), that of Kawasaki disease presentation was 14.8% (95% CI: 7.5%–23.7%), and that of coronary dilation was 15.2% (95% CI: 11.0%–19.8%). The rate of electrocardiogram anomalies was 5.3% (95% CI: 0.8%–12.3%), and the mortality rate was 0.5% (CI 95%: 0%–1.2%). Furthermore, 186 children still had complications at discharge, with a combined prevalence of such long‐lasting manifestations of 9.3% (95% CI: 5.6%–13.7%). Studies that assess whether these children will have an increased cardiovascular risk with a greater chance of acute myocardial infarction, arrhythmias, or thrombosis will be essential for healthcare planning.
Prevalence and clinical outcomes of COVID‐19 in patients with pre‐existing celiac disease: A systematic review and meta‐analysis
Authors: Rathore Sawai Singh , Velasquez‐Botero Felipe , Nieto‐Salazar María Alejandra , Flowers Thomas C. , Hasan Jamal , Parashar Ashwani Kumar , Tanveer Khurram , Aneis Hamam , Buremoh Ayotunde Isaac , Yusuf Keturah , Khalil Khalil , Toro‐Velandia Adriana Carolina , Sabeeh Shireen Kh. 
Published: February 2023

AbstractThere is a scarcity of scientific evidence addressing the outcomes of COVID‐19 in celiac disease (CD) patients. This systematic review and meta‐analysis aimed to evaluate the correlation between pre‐existing CD and COVID‐19. A rigorous literature search was conducted using multiple databases. All eligible observational studies were included from around the globe. The random effect model calculated the pooled prevalence and associated 95% confidence intervals (CI). Mantel‐Haenszel odds ratios were produced to report the overall effect size using random effect models for severity and mortality outcomes. Funnel plots, Egger regression tests, and Begg‐Mazumdar's rank correlation test were used to appraise publication bias. Data from 11 articles consisting of 44,378 CD patients were obtained. Overall pooled random‐effects estimate of SARS‐CoV‐2 infection in CD patients was 4.25% (95% CI, I2 = 98%). Our findings also indicated that pre‐existing CD was not associated with an increased risk of hospitalisation with COVID‐19 illness (OR = 1.04, 95% CI 0.87–1.24, I2 = 0%) and mortality due to illness (OR = 0.92, 95% CI 0.56–1.5, I2 = 45%) compared with patients without pre‐existing CD. No significant publication bias was evident in the meta‐analysis. The preliminary data from our analysis suggest that SARS‐CoV‐2 infection in patients with pre‐existing CD is not associated with an increased risk of hospitalisation or mortality. Additional studies are required to overcome the restrictions of the limited data available at present.
Appointed‐time formation‐containment control for nonlinear multi‐agent networks using sample‐data feedback
Authors: Zhou Yuan , Liu Yongfang , Zhao Yu , Wen Guanghui 
Published: February 2023

AbstractThis paper focuses on the single‐ and second‐order nonlinear multi‐agent systems, contributing to realizing the formation‐containment control over directed communication networks, respectively. By dividing the agents into three types, that is, the tracking‐leader, formation‐leaders and followers, a class of two‐layer sample‐data formation‐containment control protocols are developed. Then, under the proposed sample mechanism, the investigated problem is transformed into a motion planning problem and the influence caused by non‐identical nonlinear dynamics is offset by integral techniques. It shows that with the time projection method, the formation‐containment behaviour emerges at an appointed time, which is regardless of any parameters and initial conditions for agents. In comparison with previous results, the main contribution of this paper is achieving the appointed‐time formation‐containment control and appointed‐time tracking to a dynamic leader with nonlinear dynamics. Moreover, the condition on dynamics is more relaxed, only requiring the nonlinearities errors to be bounded. Finally, two simulation examples are given to indicate the effectiveness of obtained results.
Learning‐based model predictive control for improved mobile robot path following using Gaussian processes and feedback linearization
Authors: Wang Jie , Fader Michael T. H. , Marshall Joshua A. 
Published: February 2023

AbstractThis paper proposes a high‐performance path following algorithm that combines Gaussian processes (GP) based learning and feedback linearization (FBL) with model predictive control (MPC) for ground mobile robots operating in off‐road terrains, referred to as GP‐FBLMPC. The algorithm uses a nominal kinematic model and learns unmodeled dynamics as GP models by using observation data collected during field experiments. Extensive outdoor experiments using a Clearpath Husky A200 mobile robot show that the proposed GP‐FBLMPC algorithm's performance is comparable to existing GP learning‐based nonlinear MPC (GP‐NMPC) methods with respect to the path following errors. The advantage of GP‐FBLMPC is that it is generalizable in reducing path following errors for different paths that are not included in the GP models training process, while GP‐NMPC methods only work well on exactly the same path on which GP models are trained. GP‐FBLMPC is also computationally more efficient than the GP‐NMPC because it does not conduct iterative optimization and requires fewer GP models to make predictions over the MPC prediction horizon loop at every time step. Field tests show the effectiveness and generalization of reducing path following errors of the GP‐FBLMPC algorithm. It requires little training data to perform GP modeling before it can be used to reduce path‐following errors for new, more complex paths on the same terrain (see video at https://youtu.be/tC09jJQ0OXM).