bluuebunny/crossref_metadata_2025_split huggingface dataset:
Variation in the Impact of New York on Pause on Traffic Congestion by Racialized Economic Segregation and Environmental Burden
Authors: Shearston Jenni A. , Saxena Roheeni , Casey Joan A. , Kioumourtzoglou Marianthi‐Anna , Hilpert Markus 
Published: December 2024

AbstractDuring the 2019 coronavirus pandemic, stay‐at‐home policies such as New York's (NY) NY on Pause dramatically reduced traffic congestion. Despite high traffic burden in NY's environmental justice communities, this reduction has not been evaluated through an environmental justice lens—our objective in this analysis. We obtained census tract‐level traffic congestion data from Google traffic maps hourly for 2018–2020. We defined congestion as the percent of streets in a census tract with heavy traffic (red‐ or maroon‐color). We used the Index of Concentration at the Extremes (ICE) to measure racialized economic segregation and the CDC's Environmental Justice Index (EJI) as a measure of combined environmental, social, and chronic disease burden. We divided census tracts into quintiles of ICE and EJI and used linear mixed models stratified by ICE and EJI quintile in an interrupted time series design. Prior to NY on Pause, less marginalized and burdened census tracts (Q5) tended to have higher levels of traffic congestion; during NY on Pause, this trend reversed. For both ICE and EJI, more marginalized and burdened (Q1–Q2 vs. Q4–Q5) tracts had smaller absolute decreases in percent traffic congestion. For example, percent traffic congestion in ICE Q5 decreased by 7.8% (% change: −36.6%), but in Q1, it decreased by 4.2% (% change: −51.7%). NY on Pause, while protecting residents during COVID‐19, may have resulted in inequitable reductions in traffic congestion. It is critical that such inequities are measured and acknowledged so that future policies to reduce traffic congestion and respond to pandemics can enhance equity.
Prognostic relevance of treatment deviations in children with relapsed acute lymphoblastic leukemia who were treated in the ALL-REZ BFM 2002 study
Authors: Argyriadi Eleni A. , Steffen Ingo G. , Chen-Santel Christiane , Lissat Andrej , Attarbaschi Andishe , Bourquin Jean-Pierre , Henze Guenter , von Stackelberg Arend 
Published: December 2024

Abstract
Relapsed Acute Lymphoblastic Leukemia (ALL) is among the most common causes of cancer-associated deaths in children. However, little is known about the implications of deviations from ALL treatment protocols on survival rates. The present study elucidates the various characteristics of treatment deviations in children with relapsed ALL included in the ALL-REZ BFM 2002 (i.e., Relapse Berlin-Frankfurt- Münster) trial and determines their prognostic relevance for relapse and death rates. Among 687 patients, 100 were identified with treatment deviations, further classified, and examined by occurrence time, cause and type. Protocol deviation was considered a time-dependent variable and its impact on Disease Free Survival (DFS) and Overall Survival (OS) was examined using the time-dependent model Mantel Byar. Five years after the relapse diagnosis, deviations were significantly related to both inferior DFS (38%) and OS (57%) rates compared to protocol conformed treatment (DFS = 61%; OS = 70%, P < 0.001). Based on multivariate analyses, protocol deviation proved to be an independent adverse prognostic factor of DFS. Moreover, deviations triggered by chemotherapy-induced toxicity were associated with a higher relapse rate compared to deviations due to insufficient response. Therefore, to avoid impairment of results by deviations, future clinical trials, and treatment strategies should focus on less toxic treatments and stricter protocol compliance.
Bioprinting of a multi-composition array to mimic intra-tumor heterogeneity of glioblastoma for drug evaluation
Authors: Lee Gihyun , Kim Soo Jee , Choi Yejin , Park Jongho , Park Je-Kyun 
Published: December 2024

AbstractMicroextrusion printing is widely used to precisely manufacture microdevices, microphysiological systems, and biological constructs that feature micropatterns and microstructures consisting of various materials. This method is particularly useful for creating biological models that recapitulate in vivo-like cellular microenvironments. Although there is a recent demand for high-throughput data from a single in vitro system, it remains challenging to fabricate multiple models with a small volume of bioinks in a stable and precise manner due to the spreading and evaporation issues of the extruded hydrogel. As printing time increases, the extruded bioink spreads and evaporates, leading to technical problems that decrease printing resolution and stability, as well as biological problems that affect 3D culture space and cell viability. In this study, we describe a novel microextrusion bioprinting technique to stably fabricate a multi-composition array consisting of massive and nanoliter-scale hydrogel dots by using multi-bioink printing and aerosol-based crosslinking techniques to prevent spreading and evaporation issues. We confirmed that the crosslinking aerosol effectively prevented spreading and evaporation by analyzing the morphological changes of the extruded hydrogel. By adjusting the extruding ratio of the bioinks, we were able to print a multi-composition array. This stable and massive array printing technique allowed us to improve the replicates of biological models and provide various data from a single culture system. The array printing technique was applied to recapitulate the intra-tumor heterogeneity of glioblastoma and assess temozolomide efficacy on the array model.
Mitochondrial oxidant stress promotes α-synuclein aggregation and spreading in mice with mutated glucocerebrosidase
Authors: La Vitola Pietro , Szegö Eva M. , Pinto-Costa Rita , Rollar Angela , Harbachova Eugenia , Schapira Anthony HV , Ulusoy Ayse , Di Monte Donato A. 
Published: December 2024

AbstractIn this study, heterozygous expression of a common Parkinson-associated GBA1 variant, the L444P mutation, was found to exacerbate α-synuclein aggregation and spreading in a mouse model of Parkinson-like pathology targeting neurons of the medullary vagal system. These neurons were also shown to become more vulnerable to oxidative and nitrative stress after L444P expression. The latter paralleled neuronal formation of reactive oxygen species and led to a pronounced accumulation of nitrated α-synuclein. A causal relationship linked mutation-induced oxidative/nitrative stress to enhanced α-synuclein aggregation and spreading that could indeed be rescued by neuronal overexpression of mitochondrial superoxide dismutase 2. Further evidence supported a key involvement of mitochondria as sources of reactive oxygen species as well as targets of oxidative and nitrative damage within L444P-expressing neurons. These findings support the conclusion that enhanced vulnerability to mitochondrial oxidative stress should be considered an important mechanism predisposing to pathology conversion in carriers of GBA1 mutations.
The Role of Soft Skills in Dental Education: Challenges and Importance
Authors: Kodali Murali Venkata Rama Mohan , Kodali Unati Sai , Gadicherla Srikanth , Smriti Komal , Singh Anupam , Khurshid Zohaib 
Published: December 2024

AbstractSoft skills encompass interpersonal abilities and values that enable individuals to adapt to diverse circumstances. In dentistry, a combination of soft and hard skills is crucial for successful practice and for achieving health care organization goals. However, dental schools face significant challenges in teaching and evaluating soft skills, including the subjective nature of assessment, variability in student engagement, and the lack of standardized curricula. The development of ethical and critical thinking skills is essential for students to balance competing interests in the profession while maintaining professionalism, such as dedication, accountability, competence, dependability, and respect for others. Health care professionals, including dentists, must cultivate soft skills to effectively guide and treat patients. This study aims to raise awareness of the importance of soft skills in dental education, specifically highlighting challenges in instruction and evaluation. Key conclusions include the need for a more structured approach to teaching soft skills, integrating them into the broader curriculum, and developing more objective assessment tools. By addressing these issues, dental education can better equip future dentists with the necessary skills for efficient patient care.
Association of Chronic Periodontitis with Hemorrhagic Stroke: A Systematic Review and Meta-Analysis
Authors: Tsimpiris Athanasios , Tsolianos Ioannis , Grigoriadis Andreas , Tsimtsiou Zoi , Goulis Dimitrios G. , Grigoriadis Nikolaos 
Published: December 2024

AbstractPeriodontitis is a chronic, multifactorial inflammatory condition linked to dysbiotic plaque biofilms and characterized by the gradual destruction of the structures supporting the teeth owing to compromised immune system function. Hemorrhagic stroke, which primarily occurs within the brain tissue or in the subarachnoid space as a blood leak of ruptured vessels, is a sudden neurological impairment caused by vascular damage in the central nervous system, resulting in focal neurological deficits. Chronic periodontitis (CP) and hemorrhagic stroke may share common pathogenic features involving inflammation and immune system activation, prompting researchers to investigate their potential connection. The aim of the study is to systematically review the literature on the epidemiological association between CP and hemorrhagic stroke in adults. The study protocol adhered to the PRISMA 2020 guidelines, and the design followed the Cochrane methodology. A thorough literature search encompassing PubMed, Scopus, and Web of Science databases and a manual search and evaluation of gray literature was conducted. Meta-analysis was performed using Review Manager (RevMan) 5.4, with the effect size represented by the odds ratio (OR) and a 95% confidence interval (CI). Heterogeneity was assessed using the chi-squared and I
2 statistics. The selected articles, written in English without time constraints, focused on observational studies involving patients and controls and included disease diagnostic criteria. Duplicate entries were eliminated. The reliability of each study's results was evaluated using the Newcastle-Ottawa Scale and GRADE tools. Two reviewers conducted the assessments, and a third reviewer resolved any disagreements. The meta-analysis comprised four observational studies involving 1,882 individuals. It revealed that individuals diagnosed with hemorrhagic stroke were notably more likely to have concurrent CP (OR: 6.32; 95% CI: 1.35–29.49; p = 0.02) or severe CP (OR: 3.08; 95% CI: 1.56–6.06; p = 0.001) compared with healthy controls. A notable occurrence of CP was detected in patients with hemorrhagic stroke compared with controls. Health care professionals need to acknowledge the connection between the two conditions, as it allows them to provide optimal holistic care through a thorough approach to diagnosis and treatment.
Sellar Neurocysticercosis: A Literature Review
Authors: Rodrigues Mateus de Sousa , Coelho Camila Maciel Martins , Sousa Camila Rodrigues de , Oliveira Echeley Islany da Silva , Sales Atalana Sofia da Silva , Cerqueira Filho Renato Bispo de , Leite Antonio Vinicius Ramalho , Moura Samuel Miranda de , Moura José Carlos de 
Published: December 2024

AbstractThe objective of this study was to carry out a literature review on neurocysticercosis. In this sense, a literature review was performed based on articles published on Bireme and Pubmed in the period 2018–2024. The descriptor was used: “intrasellar cysticercosis.” Five studies met the eligibility criteria. Headache was the main symptom observed. The prevalence of the disease by age profile occurred mainly in young adults. The lack of general knowledge and the lack of resources for prevention, diagnosis, and early treatment may be factors that contribute to the persistence of the disease in the population. Furthermore, the lack of resources, such as neuroimaging exams and neurological care, makes it difficult to diagnose and treat the disease; thus, delaying diagnosis contributes to the spread of the infection.
Therapy Options in the Management of Brain Abscess. Literature Review
Authors: Pereira Carlos Umberto , Setton Lauro Roberto de Azevedo 
Published: December 2024

Abstract
Introduction Despite the advancements in modern neurosurgical techniques, new antibiotics, neuroimaging technologies, anesthesia practices, and microbial isolation methods, cerebral abscess remains a potentially fatal infection of the central nervous system. Its treatment remains controversial to this day. The combination of clinical and surgical interventions has been widely accepted, yielding satisfactory outcomes.
Methods The literature review process primarily relied on data obtained from the Pubmed database, Bireme (Lilacs, Medline, Scielo, Medicaribe, Cochrane). Key search terms included: central nervous system infection, cerebral abscess, and treatment. After a thorough selection analysis, 103 articles covering the period from 1980 to 2023 were included in this work.
Results The ideal treatment is surgical; however, clinical treatment has been employed in selected cases. Simple aspiration, stereotactic-guided aspiration, and endoscopy have been performed with efficient results. The outcomes of clinical or surgical treatment depend on factors such as the patient's age, neurological status, microbial isolation, primary cause of the abscess, number of infectious foci, location, and stage of abscess development. Corticosteroids have been recommended for cases of vasogenic cerebral edema, while anticonvulsants are indicated for supratentorial abscesses.
Conclusion The combination of aspiration or excision, tailored to the specific cause, number, location, and developmental stage, and intravenous antibiotics has consistently yielded satisfactory results.
Laser-driven betatron x rays for high-throughput imaging of additively manufactured materials
Authors: Senthilkumaran V. , Beier N. F. , Fourmaux S. , Shabaninezhad P. , Stinehart J. , Zhou L. , Moore J. A. , Hussein A. E. 
Published: December 2024

Betatron x rays from a laser wakefield accelerator provide a new avenue for high-resolution, high-throughput radiography of solid materials. Here, we demonstrate the optimization of betatron x rays for three-dimensional tomography of defects in additively manufactured (AM) alloys at a repetition rate of 2.5 Hz. Using the Advanced Laser Light Source in Varennes, Qc, we characterized the x-ray energy spectrum, spatial resolution, beam stability, and emission length from three different gas targets {He, N2, and He–N2 [He (99.5%) + N2 (0.5%)] mixture} to determine the conditions for optimized imaging resolution with minimized acquisition time. Mixed He–N2 produced the highest x-ray critical energy (19 ± 5) keV and average brightness (∼3.3×1010 photons/s/mm2/mrad2/0.1% BW) vs pure N2 gas (12 ± 4 keV and ∼1.6×1010 photons/s/mm2/mrad2/0.1% BW). The mixed gas demonstrated the best beam stability and pointing compared to pure He gas. The optimization of betatron sources at 2.5 Hz for high-resolution imaging of micrometer-scale defects in AM alloys will enable high-throughput data collection, accelerating the characterization of complex mechanical deformation processes in these materials.
Bandlike charge transport and electron–phonon coupling in organic molecular crystals
Authors: Chang Benjamin K , Bernardi Marco 
Published: December 2024

Abstract
Charge transport is important in organic molecular crystals (OMCs), where high carrier mobilities are desirable for a range of applications. However, modeling and predicting the mobility is challenging in OMCs due to their complex crystal and electronic structures and electron–phonon (e–ph) interactions. Here we show accurate first-principles calculations of electron and hole carrier mobility in several OMCs: benzene, anthracene, tetracene, pentacene, and biphenyl. Our calculations use the Boltzmann transport equation formalism with e–ph interactions computed from first principles. These calculations describe transport in the bandlike, weak e–ph coupling regime, and include all phonon modes and electronic bands on equal footing. In all systems studied, we predict the mobility and its temperature dependence in very good agreement with experiments between 100−400 K, where transport is phonon-limited. We show that e–ph scattering from low-frequency (LF) phonons with energy below 150 cm−1 primarily limits the mobility, even though these modes are not the ones with the strongest e–ph coupling. These LF modes are shown to consist mainly of intermolecular vibrations, with admixed long-range intramolecular character in OMCs with larger molecules. Furthermore, we find that the LF-mode scattering rates vary significantly with strain, suggesting that strain engineering can effectively modulate e–ph coupling and enhance the mobility. This work sheds light on bandlike transport mechanisms in OMCs and advances the rational design of high-mobility organic semiconductors.
A data-driven early warning method for thermal runaway during charging of lithium-ion battery packs in electric vehicles
Authors: Cheng Yuan-Ming , Gao De-Xin , Zhao Feng-Ming , Yang Qing 
Published: December 2024

Abstract
In recent years, thermal runaway during charging of lithium-ion batteries has become a critical issue. This problem has emerged as a significant barrier to the development of power batteries for electric vehicles (EVs). This paper addresses this challenge from a data-driven perspective by proposing a temperature prediction model for thermal runaway during charging of EV lithium-ion batteries. The model leverages both long short-term memory and Transformer algorithms to account for the time-series characteristics of batteries charging. The charging data under varying capacities and ambient temperatures are extracted using the Newman–Tiedemann–Gaines–Kim model for lithium-ion batteries, which is then used to optimize the accuracy of the hybrid algorithm through training. Additionally, real-world EV charging data is collected to further validate the temperature prediction model. Experimental results demonstrate that the proposed model achieves superior prediction accuracy compared to both single models and convolutional neural network hybrid models. Based on this model, a residual-based early warning method incorporating a sliding window approach is proposed. The experimental findings indicate that when the residual of the predicted charging temperature for EVs lithium-ion batteries exceeds the warning threshold, preemptive termination of charging effectively prevents thermal runaway.
A binary coding method for half-period sinusoidal stripes in focused projection
Authors: Yan Fei , Xiang Qianhuai , Zhu Rui , Ma Xingyu , Liu Yinping 
Published: December 2024

Abstract
During sinusoidal streak projection measurements, the nonlinear responses inherent in projectors and cameras can significantly alter the characteristics of projection and imaging. Traditional techniques require precise out-of-focus levels to achieve accurate depth measurements, which inherently limit the depth range that can be effectively measured. Addressing this limitation, this paper introduces an innovative binarization coding method for half-period sinusoidal stripes in focused projection. Our approach begins with a binarization process that encodes the half-period sinusoidal stripes. We then capture these stripes as they are modulated by the height of the object under test. The subsequent synthesis of the half-period sinusoidal stripes is achieved through a method of superposition calculation. To enhance the experimental procedure, we employ a four-step phase-shifting technique, complemented by an assisted phase unfolding using the gray code method. For the validation of our method, a standard ball with a diameter of 50.8140 mm was measured. The results are compelling, demonstrating a root mean square error of merely 0.0285 mm. Comparative experimental results prove that the proposed method can effectively avoid the influence of nonlinearity, and also obtain high-precision reconstruction results for discontinuous scenes.
A novel Modelica-based reliability modeling approach for ship electric propulsion systems
Authors: Gai Jingbo , Fang Jianyang , Du Xuejiao , Sun Hai 
Published: December 2024

Abstract
As the primary power source for ships, the reliability of electric propulsion systems directly impacts the safety, stability, and economic efficiency of maritime operations. However, the composition of ship electric propulsion systems is complex and is continuously exposed to the dynamic and variable marine environment, which complicates their reliability modeling and analysis. This paper introduces a novel approach to reliability modeling for electric propulsion systems based on the Modelica language. The aim is to overcome the limitations of traditional reliability modeling methods by considering the heterogeneity, dynamicity, and interactivity of electric propulsion systems. The approach addresses system heterogeneity through multi-domain modeling, captures environmental dynamics through parametric modeling, and establishes device interactions using Modelica language connectors. Additionally, modeling efficiency is enhanced by reusing device model packages, which benefits system optimization. Using a specific ship’s electric propulsion system as a case study, the modeling process and simulation results are presented to demonstrate the effectiveness and flexibility of the proposed approach. This approach offers a new tool for reliability modeling of complex electromechanical systems and contributes to enhancing the accuracy and efficiency of system reliability assessments.
Roadmap for the development of machine learning-based interatomic potentials
Authors: Zhang Yong-Wei , Sorkin Viacheslav , Aitken Zachary H , Politano Antonio , Behler Jörg , P Thompson Aidan , Ko Tsz Wai , Ong Shyue Ping , Chalykh Olga , Korogod Dmitry , Podryabinkin Evgeny , Shapeev Alexander , Li Ju , Mishin Yuri , Pei Zongrui , Liu Xianglin , Kim Jaesun , Park Yutack , Hwang Seungwoo , Han Seungwu , Sheriff Killian , Cao Yifan , Freitas Rodrigo 
Published: December 2024

Abstract
An interatomic potential, traditionally regarded as a mathematical function, serves to depict atomic interactions within molecules or solids by expressing potential energy concerning atom positions. These potentials are pivotal in materials science and engineering, facilitating atomic-scale simulations, predictive material behavior, accelerated discovery, and property optimization. Notably, the landscape is evolving with machine learning transcending conventional mathematical models. Various machine learning-based interatomic potentials, such as artificial neural networks, kernel-based methods, deep learning, and physics-informed models, have emerged, each wielding unique strengths and limitations. These methods decode the intricate connection between atomic configurations and potential energies, offering advantages like precision, adaptability, insights, and seamless integration. The transformative potential of machine learning-based interatomic potentials looms large in materials science and engineering. They promise tailor-made materials discovery and optimized properties for specific applications. Yet, formidable challenges persist, encompassing data quality, computational demands, transferability, interpretability, and robustness. Tackling these hurdles is imperative for nurturing accurate, efficient, and dependable machine learning-based interatomic potentials primed for widespread adoption in materials science and engineering. This roadmap offers an appraisal of the current machine learning-based interatomic potential landscape, delineates the associated challenges, and envisages how progress in this domain can empower atomic-scale modeling of the composition-processing-microstructure-property relationship, underscoring its significance in materials science and engineering.