bluuebunny/crossref_metadata_2025_split huggingface dataset:
Risks of organ failures and deaths associated with young-onset dementia after hospitalizations for motor vehicle crash injuries: a nationwide population-based retrospective cohort study
Authors: Liu Chien-Hui , Wang Jiun-Yi , Chang Kun-Chia , Ko Ming-Chung , Lee Pei-Chen , Liu Chih-Ching 
Published: March 2023

AbstractPatients with dementia are at increased risks of adverse consequences associated with motor vehicle crash injury (MVCI). However, studies of the association for patients with young-onset dementia (YOD) are limited. Therefore, we aim to investigate whether YOD was associated with adverse outcomes after hospitalization for MVCI. In this retrospective cohort study, we identified 2052 MVCI patients with YOD (aged 40–64 years) between 2006 and 2015 and included 10 260 matched MVCI patients without YOD (matching ratio: 1:5) from Taiwan’s National Health Insurance Research Database and the Taiwan Police-Reported Traffic Accident Registry. We evaluated the intensive care unit (ICU) admission, organ failure, in-hospital and 30-day mortalities, length of hospital stay, and hospital costs. Compared with participants without dementia, patients with YOD had higher rates of ICU admission (34.31% vs. 20.89%) and respiratory failure (6.04% vs. 2.94%), with a covariate-adjusted odds ratio of 1.50 (95% CI 1.33–1.70) and 1.63 (95% CI 1.24–2.13), respectively. The patients also exhibited higher in-hospital mortality (4.73% vs. 3.12%) and 30-day mortality (5.12% vs. 3.34%) than their non-YOD counterparts, but the risk ratio was not significant after adjusting for transport mode. Moreover, the log means of hospital stay and cost were higher among patients with YOD (0.09 days; 95% CI 0.04–0.14 and NT$0.17; 95% CI 0.11–0.23, respectively). This cohort study determined that YOD may be adversely associated with hospital outcomes among MVCI patients. However, the association between YOD and mortality risk may depend on transport mode.
Study on stress variation of advance fiberglass anchor bolts during tunnel excavation process
Authors: Feng Jimeng , Tan Yumei , Yao Shiyu , Jiang Hui , Zhang Junru , Li Hongtao 
Published: March 2023

AbstractOne of the main causes for excessive deformation within a tunnel is due to the instability of the soil or soft rock ahead of the excavation face. Fiberglass bolts have been shown to be a useful advance reinforcement method for the excavation face. In this paper, an improved ADECO-RS (Analysis of controlled deformation in rock and soils) method had been proposed for soft rock mountain tunnels, in terms of the partial (mainly the upper bench) excavation face reinforcement and also for the bench excavation method. Strain gauges were used to test the micro-strain in the fiberglass bolt to investigate how the axial force of the fiberglass bolt varied during the tunnel excavation. In addition, combined with the field tunnel deformation monitoring data, the relationship between the reinforcement parameters of the fiberglass bolts and the tunnel construction phase were discussed. The research results show that: (1) The stress state of the anchor rod is related to the reinforcement length of the anchor rod; (2) Excavation within the lap area of the fiberglass bolt leads to an increase in the axial force of the bolt, while excavation outside the lap area of the fiberglass bolt has no effect on the anchor; (3) Reducing the reinforcement area of rock mass will affect the stability of the excavation. To ensure the stability of the excavation face, the initial support construction loop should be completed as soon as possible; (4) In a future project with similar conditions, the recommended lap length of the fiberglass bolt could be 3 m utilizing the fiberglass bolt grouting face reinforcement method.
Gender disparities in prevalence by diagnostic criteria, treatment and mortality of newly diagnosed acute myocardial infarction in Korean adults
Authors: Kim So Ree , Bae SungA , Lee Ji Yoon , Kim Min Sun , Kim Mi-Na , Chung Wook-Jin , Bae Jang-Ho , Lee Juneyoung , Park Seong-Mi 
Published: March 2023

AbstractAcute myocardial infarction (AMI) is highly prevalent and remains the leading cause of mortality. Particularly in women, under-recognition and management of AMI have been raised. The aim of this study was to investigate the long-term trends of prevalence, treatment methodologies, and mortality of AMI by gender. The subjects of this study were patients hospitalized for AMI according to the Korean National Health Insurance Claims Database from 2002 to 2018. Total 633,097 AMI patients were hospitalized, 40% women. The incidence of AMI has been increasing since 2011, with a lower incidence in women. Overall, 53.1% of patients underwent CAG, with a lower tendency in women than in men (39.8% vs. 62.3%). Furthermore, fewer women underwent PCI than men (77.5% vs. 85.8% in 2018, p < 0.0001). Of the 336,463 AMI patients undergoing CAG, women were undertreated with a lower prescription rate of beta-blockers or statins at discharge. When adjusted for age, women showed higher 7-day mortality but lower 1-year mortality relative to men. According to the Korean National Health Insurance Claims Database, women with AMI have been under-recognized, underdiagnosed, and undertreated in terms of revascularization or medical therapy for years suggesting that efforts to close the gender gap are necessary.
Spatial–temporal distribution of incidence, mortality, and case-fatality ratios of coronavirus disease 2019 and its social determinants in Brazilian municipalities
Authors: Raymundo Carlos Eduardo , Oliveira Marcella Cini , de Araujo Eleuterio Tatiana , de Arruda Santos Junior Édnei César , da Silva Marcele Gonçalves , André Suzana Rosa , Sousa Ana Inês , de Andrade Medronho Roberto 
Published: March 2023

AbstractThe COVID-19 pandemic caused impact on public health worldwide. Brazil gained prominence during the pandemic due to the magnitude of disease. This study aimed to evaluate the spatial–temporal dynamics of incidence, mortality, and case fatality of COVID-19 and its associations with social determinants in Brazilian municipalities and epidemiological week. We modeled incidence, mortality, and case fatality rates using spatial–temporal Bayesian model. “Bolsa Família Programme” (BOLSAFAM) and “proportional mortality ratio” (PMR) were inversely associated with the standardized incidence ratio (SIR), while “health insurance coverage” (HEALTHINSUR) and “Gini index” were directly associated with the SIR. BOLSAFAM and PMR were inversely associated with the standardized mortality ratio (SMR) and standardized case fatality ratio (SCFR). The highest proportion of excess risk for SIR and the SMR started in the North, expanding to the Midwest, Southeast, and South regions. The highest proportion of excess risk for the SCFR outcome was observed in some municipalities in the North region and in the other Brazilian regions. The COVID-19 incidence and mortality in municipalities that most benefited from the cash transfer programme and with better social development decreased. The municipalities with a higher proportion of non-whites had a higher risk of becoming ill and dying from the disease.
Consequences of reprogramming acetyl-CoA metabolism by 2,3,7,8-tetrachlorodibenzo-p-dioxin in the mouse liver
Authors: Cholico Giovan N. , Orlowska Karina , Fling Russell R. , Sink Warren J. , Zacharewski Nicholas A. , Fader Kelly A. , Nault Rance , Zacharewski Tim 
Published: March 2023

Abstract2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a persistent environmental contaminant that induces the progression of steatosis to steatohepatitis with fibrosis in mice. Furthermore, TCDD reprograms hepatic metabolism by redirecting glycolytic intermediates while inhibiting lipid metabolism. Here, we examined the effect of TCDD on hepatic acetyl-coenzyme A (acetyl-CoA) and β-hydroxybutyrate levels as well as protein acetylation and β-hydroxybutyrylation. Acetyl-CoA is not only a central metabolite in multiple anabolic and catabolic pathways, but also a substrate used for posttranslational modification of proteins and a surrogate indicator of cellular energy status. Targeted metabolomic analysis revealed a dose-dependent decrease in hepatic acetyl-CoA levels coincident with the phosphorylation of pyruvate dehydrogenase (E1), and the induction of pyruvate dehydrogenase kinase 4 and pyruvate dehydrogenase phosphatase, while repressing ATP citrate lyase and short-chain acyl-CoA synthetase gene expression. In addition, TCDD dose-dependently reduced the levels of hepatic β-hydroxybutyrate and repressed ketone body biosynthesis gene expression. Moreover, levels of total hepatic protein acetylation and β-hydroxybutyrylation were reduced. AMPK phosphorylation was induced consistent with acetyl-CoA serving as a cellular energy status surrogate, yet subsequent targets associated with re-establishing energy homeostasis were not activated. Collectively, TCDD reduced hepatic acetyl-CoA and β-hydroxybutyrate levels eliciting starvation-like conditions despite normal levels of food intake.
New insights into APCVD grown monolayer MoS2 using time-domain terahertz spectroscopy
Authors: Sharma Saloni , Chauhan Pooja , Rane Shreeya , Raj Utkarsh , Srivastava Shubhda , Ansari Z. A. , Roy Chowdhury Dibakar , Gupta Bipin Kumar 
Published: March 2023

AbstractIn modern era, wireless communications at ultrafast speed are need of the hour and search for its solution through cutting edge sciences is a new perspective. To address this issue, the data rates in order of terabits per second (TBPS) could be a key step for the realization of emerging sixth generation (6G) networks utilizing terahertz (THz) frequency regime. In this context, new class of transition metal dichalcogenides (TMDs) have been introduced as potential candidates for future generation wireless THz technology. Herein, a strategy has been adopted to synthesize high-quality monolayer of molybdenum di-sulfide (MoS2) using indigenously developed atmospheric pressure chemical vapor deposition (APCVD) set-up. Further, the time-domain transmission and sheet conductivity were studied as well as a plausible mechanism of terahertz response for monolayer MoS2 has been proposed and compared with bulk MoS2. Hence, the obtained results set a stepping stone to employ the monolayer MoS2 as potential quantum materials benefitting the next generation terahertz communication devices.
Exposure to hate speech deteriorates neurocognitive mechanisms of the ability to understand others’ pain
Authors: Pluta Agnieszka , Mazurek Joanna , Wojciechowski Jakub , Wolak Tomasz , Soral Wiktor , Bilewicz Michał 
Published: March 2023

AbstractThe widespread ubiquity of hate speech affects people's attitudes and behavior. Exposure to hate speech can lead to prejudice, dehumanization, and lack of empathy towards members of outgroups. However, the impact of exposure to hate speech on empathy and propensity to attribute mental states to others has never been directly tested empirically. In this fMRI study, we examine the effects of exposure to hate speech on neural mechanisms of empathy towards ingroup (Poles) versus outgroup members (Arabs). Thirty healthy young adults were randomly assigned to 2 groups: hateful and neutral. During the fMRI study, they were initially exposed to hateful or neutral comments and subsequently to narratives depicting Poles and Arabs in pain. Using whole-brain and region of interest analysis, we showed that exposure to derogatory language about migrants attenuates the brain response to someone else’s pain in the right temporal parietal junction (rTPJ), irrespective of group membership (Poles or Arabs). Given that rTPJ is associated with processes relevant to perspective-taking, its reduced activity might be related to a decreased propensity to take the psychological perspective of others. This finding suggests that hate speech affects human functioning beyond intergroup relations.
Medical image captioning via generative pretrained transformers
Authors: Selivanov Alexander , Rogov Oleg Y. , Chesakov Daniil , Shelmanov Artem , Fedulova Irina , Dylov Dmitry V. 
Published: March 2023

AbstractThe proposed model for automatic clinical image caption generation combines the analysis of radiological scans with structured patient information from the textual records. It uses two language models, the Show-Attend-Tell and the GPT-3, to generate comprehensive and descriptive radiology records. The generated textual summary contains essential information about pathologies found, their location, along with the 2D heatmaps that localize each pathology on the scans. The model has been tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO, and the results measured with natural language assessment metrics demonstrated its efficient applicability to chest X-ray image captioning.
Blind spectral unmixing for characterization of plaque composition based on multispectral photoacoustic imaging
Authors: Cano Camilo , Matos Catarina , Gholampour Amir , van Sambeek Marc , Lopata Richard , Wu Min 
Published: March 2023

AbstractTo improve the assessment of carotid plaque vulnerability, a comprehensive characterization of their composition is paramount. Multispectral photoacoustic imaging (MSPAI) can provide plaque composition based on their absorption spectra. However, although various spectral unmixing methods have been developed to characterize different tissue constituents, plaque analysis remains a challenge since its composition is highly complex and diverse. In this study, we employed an adapted piecewise convex multiple-model endmember detection method to identify carotid plaque constituents. Additionally, we explore the selection of the imaging wavelengths in linear models by conditioning the coefficient matrix and its synergy with our unmixing approach. We verified our method using plaque mimicking phantoms and performed ex-vivo MSPAI on carotid endarterectomy samples in a spectral range from 500 to 1300 nm to identify the main spectral features of plaque materials for vulnerability assessment. After imaging, the samples were processed for histological analysis to validate the photoacoustic decomposition. Results show that our approach can perform spectral unmixing and classification of highly heterogeneous biological samples without requiring an extensive fluence correction, enabling the identification of relevant components to assess plaque vulnerability.
A coupled agent-based model for France for simulating adaptation and migration decisions under future coastal flood risk
Authors: Tierolf Lars , Haer Toon , Botzen W. J. Wouter , de Bruijn Jens A. , Ton Marijn J. , Reimann Lena , Aerts Jeroen C. J. H. 
Published: March 2023

AbstractIn this study, we couple an integrated flood damage and agent-based model (ABM) with a gravity model of internal migration and a flood risk module (DYNAMO-M) to project household adaptation and migration decisions under increasing coastal flood risk in France. We ground the agent decision rules in a framework of subjective expected utility theory. This method addresses agent’s bounded rationality related to risk perception and risk aversion and simulates the impact of push, pull, and mooring factors on migration and adaptation decisions. The agents are parameterized using subnational statistics, and the model is calibrated using a household survey on adaptation uptake. Subsequently, the model simulates household adaptation and migration based on increasing coastal flood damage from 2015 until 2080. A medium population growth scenario is used to simulate future population development, and sea level rise (SLR) is assessed for different climate scenarios. The results indicate that SLR can drive migration exceeding 8000 and 10,000 coastal inhabitants for 2080 under the Representative Concentration Pathways 4.5 and 8.5, respectively. Although household adaptation to flood risk strongly impacts projected annual flood damage, its impact on migration decisions is small and falls within the 90% confidence interval of model runs. Projections of coastal migration under SLR are most sensitive to migration costs and coastal flood protection standards, highlighting the need for better characterization of both in modeling exercises. The modeling framework demonstrated in this study can be upscaled to the global scale and function as a platform for a more integrated assessment of SLR-induced migration.
Utilizing photonic band gap in triangular silicon carbide structures for efficient quantum nanophotonic hardware
Authors: Saha Pranta , Majety Sridhar , Radulaski Marina 
Published: March 2023

AbstractSilicon carbide is among the leading quantum information material platforms due to the long spin coherence and single-photon emitting properties of its color center defects. Applications of silicon carbide in quantum networking, computing, and sensing rely on the efficient collection of color center emission into a single optical mode. Recent hardware development in this platform has focused on angle-etching processes that preserve emitter properties and produce triangularly shaped devices. However, little is known about the light propagation in this geometry. We explore the formation of photonic band gap in structures with a triangular cross-section, which can be used as a guiding principle in developing efficient quantum nanophotonic hardware in silicon carbide. Furthermore, we propose applications in three areas: the TE-pass filter, the TM-pass filter, and the highly reflective photonic crystal mirror, which can be utilized for efficient collection and propagating mode selection of light emission.
An efficient antenna system with improved radiation for multi-standard/multi-mode 5G cellular communications
Authors: Parchin Naser Ojaroudi , Mohamed Heba G. , Moussa Karim H. , See Chan Hwang , Abd-Alhameed Raed A. , Alwadai Norah Muhammad , Amar Ahmed S. I. 
Published: March 2023

AbstractThis paper introduces a multi-input multiple-output (MIMO) antenna array system that provides improved radiation diversity for multi-standard/multi-mode 5G communications. The introduced MIMO design contains four pairs of miniaturized self-complementary antennas (SCAs) fed by pairs of independently coupled structures which are symmetrically located at the edge corners of the smartphone mainboard with an overall size of 75 × 150 (mm2). Hence, in total, the design incorporates four pairs of horizontally and vertically polarized resonators. The elements have compact profiles and resonate at 3.6 GHz, the main candidate bands of the sub-6 GHz 5G spectrum. In addition, despite the absence of decoupling structures, adjacent elements demonstrate high isolation. To the best of the authors’ knowledge, it is the first type of smartphone antenna design using dual-polarized self-complementary antennas that could possess anti-interference and diversity properties. In addition to exhibiting desirable radiation coverage, the presented smartphone antenna also supports dual polarizations on different sides of the printed circuit board (PCB). It also exhibits good isolation, high-gain patterns, improved radiation coverage, low ECC/TARC, and sufficient channel capacity. The introduced antenna design was manufactured on a standard smartphone board and its main characteristics were experimentally measured. Simulations and measurement results are generally in good agreement with each other. Moreover, the presented antenna system delivers low SAR with adequate efficiency when it comes to the appearance of the user. Hence, the design could be adapted to 5G hand-portable devices. As an additional feature, a new ultra-compact phased array millimeter-wave antenna with super-wide bandwidth and end-fire radiation is being introduced for integration into the MIMO antenna systems. As a result, the proposed antenna system design with improved radiation and multi-standard operation is a good candidate for future multi-mode 5G cellular applications.
The predictive model for COVID-19 pandemic plastic pollution by using deep learning method
Authors: Nanehkaran Yaser A. , Licai Zhu , Azarafza Mohammad , Talaei Sona , Jinxia Xu , Chen Junde , Derakhshani Reza 
Published: March 2023

AbstractPandemic plastics (e.g., masks, gloves, aprons, and sanitizer bottles) are global consequences of COVID-19 pandemic-infected waste, which has increased significantly throughout the world. These hazardous wastes play an important role in environmental pollution and indirectly spread COVID-19. Predicting the environmental impacts of these wastes can be used to provide situational management, conduct control procedures, and reduce the COVID-19 effects. In this regard, the presented study attempted to provide a deep learning-based predictive model for forecasting the expansion of the pandemic plastic in the megacities of Iran. As a methodology, a database was gathered from February 27, 2020, to October 10, 2021, for COVID-19 spread and personal protective equipment usage in this period. The dataset was trained and validated using training (80%) and testing (20%) datasets by a deep neural network (DNN) procedure to forecast pandemic plastic pollution. Performance of the DNN-based model is controlled by the confusion matrix, receiver operating characteristic (ROC) curve, and justified by the k-nearest neighbours, decision tree, random forests, support vector machines, Gaussian naïve Bayes, logistic regression, and multilayer perceptron methods. According to the comparative modelling results, the DNN-based model was found to predict more accurately than other methods and have a significant predominance over others with a lower errors rate (MSE = 0.024, RMSE = 0.027, MAPE = 0.025). The ROC curve analysis results (overall accuracy) indicate the DNN model (AUC = 0.929) had the highest score among others.
Human footprint is associated with shifts in the assemblages of major vector-borne diseases
Authors: Skinner Eloise B. , Glidden Caroline K. , MacDonald Andrew J. , Mordecai Erin A. 
Published: March 2023

AbstractPredicting how increasing intensity of human–environment interactions affects pathogen transmission is essential to anticipate changing disease risks and identify appropriate mitigation strategies. Vector-borne diseases (VBDs) are highly responsive to environmental changes, but such responses are notoriously difficult to isolate because pathogen transmission depends on a suite of ecological and social responses in vectors and hosts that may differ across species. Here we use the emerging tools of cumulative pressure mapping and machine learning to better understand how the occurrence of six medically important VBDs, differing in ecology from sylvatic to urban, respond to multidimensional effects of human pressure. We find that not only is human footprint—an index of human pressure, incorporating built environments, energy and transportation infrastructure, agricultural lands and human population density—an important predictor of VBD occurrence, but there are clear thresholds governing the occurrence of different VBDs. Across a spectrum of human pressure, diseases associated with lower human pressure, including malaria, cutaneous leishmaniasis and visceral leishmaniasis, give way to diseases associated with high human pressure, such as dengue, chikungunya and Zika. These heterogeneous responses of VBDs to human pressure highlight thresholds of land-use transitions that may lead to abrupt shifts in infectious disease burdens and public health needs.