bluuebunny/crossref_metadata_2025_split huggingface dataset:
Process and mechanism of preparing metallized blast furnace burden from metallurgical dust and sludge
Authors: Gao Xing , Chai Yifan , Wang Yici , Luo Guoping , An Shengli , Peng Jun 
Published: April 2024

AbstractMetallurgical dust and sludge are solid waste resources with recycling value. In recent years, rotary hearth furnace has become the most important means to treat metallurgical dust and sludge because of its wide range of raw materials and strong treatment capacity. In this study blast furnace ash and converter sludge were selected as the research objects, and high-quality metallized pellets were prepared based on the rotary hearth furnace process. The strength changed of pellets, the reduction process of iron oxides and the removal process of zinc during the roasting of pellets in rotary hearth furnace were studied. To explore the reasonable roasting condition for preparing metallized pellets in rotary hearth furnace. The optimum roasting temperature of the pellets was 1250℃ and the roasting time was 25 min. The compressive strength, metallization rate and dezincification rate of metallized pellets reached 1361N, 97.44% and 95.67%, respectively. The efficient resource utilization of various metallurgical dust and sludge is realized.
A support vector machine based drought index for regional drought analysis
Authors: A Alshahrani Mohammed , Laiq Muhammad , Noor-ul-Amin Muhammad , Yasmeen Uzma , Nabi Muhammad 
Published: April 2024

AbstractThe increased global warming has increased the likelihood of recurrent drought hazards. Potential links between the frequency of extreme weather events and global warming have been suggested by earlier research. The spatial variability of meteorological factors over short distances can cause distortions in conclusions or limit the scope of drought analysis in a particular region when extreme values predominate. Therefore, it is challenging to make trustworthy judgments regarding the spatiotemporal characteristics of regional drought. This study aims to improve the quality and accuracy of regional drought characterization and the process of continuous monitoring. The new drought indicator presented in this study is called the Support Vector Machine based drought index (SVM-DI). It is created by adding different weights to an SVM-based X-bar chart that is displayed with regional precipitation aggregate data. The SVM-DI application site is located in Pakistan's northern area. Using the Pearson correlation coefficient for pairwise comparison, the study compares the SVM-DI and the Regional Standard Precipitation Index (RSPI). Interestingly, compared to RSPI, SVM-DI shows more pronounced regional characteristics in its correlations with other meteorological stations, with a significantly lower Coefficient of Variation. These results confirm that SVM-DI is a useful tool for regional drought analysis. The SVM-DI methodology offers a unique way to reduce the impact of extreme values and outliers when aggregating regional precipitation data.
Static magnetic field-induced IL-6 secretion in periodontal ligament stem cells accelerates orthodontic tooth movement
Authors: Luo Shitong , Li Zhilian , Liu Lizhiyi , Zhao Juan , Ge Wenbin , Zhang Kun , Zhou Zhi , Liu Yali 
Published: April 2024

AbstractStatic magnetic field (SMF) promoting bone tissue remodeling is a potential non-invasive therapy technique to accelerate orthodontic tooth movement (OTM). The periodontal ligament stem cells (PDLSCs), which are mechanosensitive cells, are essential for force-induced bone remodeling and OTM. However, whether and how the PDLSCs influence the process of inflammatory bone remodeling under mechanical force stimuli in the presence of SMFs remains unclear. In this study, we found that local SMF stimulation significantly enhanced the OTM distance and induced osteoclastogenesis on the compression side of a rat model of OTM. Further experiments with macrophages cultured with supernatants from force-loaded PDLSCs exposed to an SMF showed enhanced osteoclast formation. RNA-seq analysis showed that interleukin-6 (IL-6) was elevated in force-loaded PDLSCs exposed to SMFs. IL-6 expression was also elevated on the pressure side of a rat OTM model with an SMF. The OTM distance induced by an SMF was significantly decreased after injection of the IL-6 inhibitor tocilizumab. These results imply that SMF promotes osteoclastogenesis by inducing force-loaded PDLSCs to secrete the inflammatory cytokine IL-6, which accelerates OTM. This will help to reveal the mechanism of SMF accelerates tooth movement and should be evaluated for application in periodontitis patients.
Enhancing effect of natural adjuvant, panduratin A, on antibacterial activity of colistin against multidrug-resistant Acinetobacter baumannii
Authors: Thadtapong Nalumon , Chaturongakul Soraya , Napaswad Chanita , Dubbs Padungsri , Soodvilai Sunhapas 
Published: April 2024

AbstractColistin- and carbapenem-resistant Acinetobacter baumannii is a serious multidrug resistant (MDR) bacterium in clinical settings. Discovery of new antibacterial drugs against MDR is facing multiple challenges in drug development. Combination of known antibiotics with a robust adjuvant might be an alternative effective strategy for MDR treatment. In the study herein, we report an antibiotic adjuvant activity of a natural compound panduratin A from fingerroot (Boesenbergia rotunda) as a potent adjuvant to colistin. The present study investigated the antibiotic adjuvant effect of panduratin A against 10 colistin- and carbapenem-resistant A. baumannii. Antibacterial activities were tested by broth microdilution method. Biofilm assay was used to determine the efficacy of panduratin A in biofilm formation inhibition on two representative strains Aci46 and Aci44. Genomic and transcriptomic analyses of colistin- and carbapenem-resistant A. baumannii strains were used to identify potential resistance and tolerance mechanism in the bacteria. Panduratin A-colistin combination showed an increased effect on antibacterial in the A. baumannii. However, panduratin A did not improve the antibacterial activity of imipenem. In addition, panduratin A improves anti-biofilm activity of colistin against Aci44 and Aci46, the colistin- and carbapenem-resistant A. baumannii. Panduratin A markedly enhances bactericidal and anti-biofilm activity of colistin against colistin- resistant A. baumannii. Based on genome comparisons, single nucleotide polymorphism (SNP) patterns in six genes encoding biofilm and lipid A biosynthesis were shared in Aci44 and Aci46. In Aci44, we identified a partial sequence of pmrB encoding a polymyxin resistant component PmrB, whereas a full length of pmrB was observed in Aci46. RNA-seq analyses of Aci44 revealed that panduratin A-colistin combination induced expression of ribosomal proteins and oxidative stress response proteins, whereas iron transporter and MFS-type transporter systems were suppressed. Panduratin A-colistin combination could promote intracellular reactive oxygen species (ROS) accumulation could lead to the cidal effect on colistin-resistant A. baumannii. Combination of panduratin A and colistin showed a significant increase in colistin efficacy against colistin- resistant A. baumannii in comparison of colistin alone. Genomic comparison between Aci44 and Aci46 showed mutations and SNPs that might affect different phenotypes. Additionally, based on RNA-Seq, panduratin A-colistin combination could lead to ROS production and accumulation. These findings confirmed the potency of panduratin as colistin adjuvant against multidrug resistant A. baumannii.
A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction
Authors: Yakovyna Vitaliy , Shakhovska Nataliya , Szpakowska Aleksandra 
Published: April 2024

AbstractThough COVID-19 is no longer a pandemic but rather an endemic, the epidemiological situation related to the SARS-CoV-2 virus is developing at an alarming rate, impacting every corner of the world. The rapid escalation of the coronavirus has led to the scientific community engagement, continually seeking solutions to ensure the comfort and safety of society. Understanding the joint impact of medical and non-medical interventions on COVID-19 spread is essential for making public health decisions that control the pandemic. This paper introduces two novel hybrid machine-learning ensembles that combine supervised and unsupervised learning for COVID-19 data classification and regression. The study utilizes publicly available COVID-19 outbreak and potential predictive features in the USA dataset, which provides information related to the outbreak of COVID-19 disease in the US, including data from each of 3142 US counties from the beginning of the epidemic (January 2020) until June 2021. The developed hybrid hierarchical classifiers outperform single classification algorithms. The best-achieved performance metrics for the classification task were Accuracy = 0.912, ROC-AUC = 0.916, and F1-score = 0.916. The proposed hybrid hierarchical ensemble combining both supervised and unsupervised learning allows us to increase the accuracy of the regression task by 11% in terms of MSE, 29% in terms of the area under the ROC, and 43% in terms of the MPP metric. Thus, using the proposed approach, it is possible to predict the number of COVID-19 cases and deaths based on demographic, geographic, climatic, traffic, public health, social-distancing-policy adherence, and political characteristics with sufficiently high accuracy. The study reveals that virus pressure is the most important feature in COVID-19 spread for classification and regression analysis. Five other significant features were identified to have the most influence on COVID-19 spread. The combined ensembling approach introduced in this study can help policymakers design prevention and control measures to avoid or minimize public health threats in the future.
Modeling spillover dynamics: understanding emerging pathogens of public health concern
Authors: Saldaña Fernando , Stollenwerk Nico , Van Dierdonck Joseba Bidaurrazaga , Aguiar Maíra 
Published: April 2024

AbstractThe emergence of infectious diseases with pandemic potential is a major public health threat worldwide. The World Health Organization reports that about 60% of emerging infectious diseases are zoonoses, originating from spillover events. Although the mechanisms behind spillover events remain unclear, mathematical modeling offers a way to understand the intricate interactions among pathogens, wildlife, humans, and their shared environment. Aiming at gaining insights into the dynamics of spillover events and the outcome of an eventual disease outbreak in a population, we propose a continuous time stochastic modeling framework. This framework links the dynamics of animal reservoirs and human hosts to simulate cross-species disease transmission. We conduct a thorough analysis of the model followed by numerical experiments that explore various spillover scenarios. The results suggest that although most epidemic outbreaks caused by novel zoonotic pathogens do not persist in the human population, the rising number of spillover events can avoid long-lasting extinction and lead to unexpected large outbreaks. Hence, global efforts to reduce the impacts of emerging diseases should not only address post-emergence outbreak control but also need to prevent pandemics before they are established.
SLKIR: A framework for extracting key information from air traffic control instructions Using small sample learning
Authors: Jiang Peiyuan , Zeng Chen , Pan Weijun , Han Boyuan , Zhang Jian 
Published: April 2024

AbstractIn air traffic control (ATC), Key Information Recognition (KIR) of ATC instructions plays a pivotal role in automation. The field's specialized nature has led to a scarcity of related research and a gap with the industry's cutting-edge developments. Addressing this, an innovative end-to-end deep learning framework, Small Sample Learning for Key Information Recognition (SLKIR), is introduced for enhancing KIR in ATC instructions. SLKIR incorporates a novel Multi-Head Local Lexical Association Attention (MHLA) mechanism, specifically designed to enhance accuracy in identifying boundary words of key information by capturing their latent representations. Furthermore, the framework includes a task focused on prompt, aiming to bolster the semantic comprehension of ATC instructions within the core network. To overcome the challenges posed by category imbalance in boundary word and prompt discrimination tasks, tailored loss function optimization strategies are implemented, effectively expediting the learning process and boosting recognition accuracy. The framework's efficacy and adaptability are demonstrated through experiments on two distinct ATC instruction datasets. Notably, SLKIR outperforms the leading baseline model, W2NER, achieving a 3.65% increase in F1 score on the commercial flight dataset and a 12.8% increase on the training flight dataset. This study is the first of its kind to apply small-sample learning in KIR for ATC and the source code of SLKIR will be available at: https://github.com/PANPANKK/ATC_KIR.
Diffusion-weighted imaging as a potential non-gadolinium alternative for immediate assessing the hyperacute outcome of MRgFUS ablation for uterine fibroids
Authors: Huang Yaoqu , Zhou Shouguo , Su Yinghua , Pang Zhuochao , Cai Shihua 
Published: April 2024

AbstractThe aim of this study was to investigate the value of diffusion-weighted imaging (DWI) as a potential non-gadolinium alternative for promptly assessing the hyperacute outcome of magnetic resonance-guided focused ultrasound (MRgFUS) treatment for uterine fibroids. In this retrospective study we included 65 uterine fibroids from 44 women, who underwent axial DWI (b-value: 800 s/mm2) and contrast-enhanced (CE) MR within 15 min post-ablation. Two blinded observers independently reviewed the DWI findings of ablated necrotic lesions and measured their volumes on DWI and CE images. The post-ablation DWI images revealed clear depiction of ablative necrotic lesions in all fibroids, which were classified into two types: the bull’s eye sign (type 1) and the bright patch sign (type 2). The inter-observer intraclass correlation coefficient for classifying DWI signal types was 0.804 (p < 0.001). Volumetric analysis of ablated necrosis using DWI and CE T1-weighted imaging showed no significant variance, nor did the non-perfused volume ratios (all p > 0.05). Bland–Altman analysis revealed a mean difference of 2.38% and 1.71% in non-perfused volume ratios between DWI and CE, with 95% limits of agreement from − 19.06 to 23.82% and − 18.40 to 21.82%, respectively. The findings of this study support the potential of DWI as a viable non-gadolinium alternative for evaluating the hyperacute outcomes of MRgFUS ablation in uterine fibroids.
Wasted efforts of elite Marathon runners under a warming climate primarily due to atmospheric oxygen reduction
Authors: Wang Shixin , Gao Meng , Xiao Xiang , Jiang Xiaodong , Luo Jingjia 
Published: April 2024

AbstractMeteorological conditions exert influences on sport performances via complex processes. Previous studies conventionally investigate the effects of weather conditions on marathon performance by following events held at the same places, which brings large uncertainties due to the changed participants. Via following each athlete to eliminate these uncertainties, we show that elite athletes’ marathon-running performance tends to monotonically worsen as ambient temperature rises except when it is extremely cold based on the best 16 athletes from each of the sex continents. It worsens by 0.56 (0.39 for men and 0.71 for women) min/°C when it rises beyond 15 °C. Theoretical analysis based on global marathon performance and weather observation datasets shows that more than half of this effect is mediated by reduced oxygen partial density as warmer temperature expands air and increases vapor pressure for both the men and women athletes, which is confirmed by the methods of Baron–Kenny. This atmospheric thermodynamic mechanism has not been emphasized previously. We also show that world-top athletes’ marathon performance approximately linearly worsens as ambient pressure decreases and slightly worsens as relative humidity rises if not extremely wet. These results substantially differ from the previous ones following the events instead of each athlete. Multi-variable changes in climate system are projected to slow Olympic marathon athletes by 2.51 and 1.06 min by the end of the 21st century in relative to 2020 under the high and intermediate emission scenario, respectively. Therefore, future progression of marathon performance is likely to be substantially slowed or even halted by climate changes without efficient greenhouse gas mitigation.
Evidence-based health messages increase intention to cope with loneliness in Germany: a randomized controlled online trial
Authors: Liu Shuyan , Haucke Matthias , Wegner Luisa , Gates Jennifer , Bärnighausen Till , Adam Maya 
Published: April 2024

AbstractLoneliness poses a formidable global health challenge in our volatile, post-pandemic world. Prior studies have identified promising interventions to alleviate loneliness, however, little is known about their effectiveness. This study measured the effectiveness of educational entertainment (“edutainment”) and/or evidence-based, written health messages in alleviating loneliness and increasing intention to cope with loneliness. We recruited 1639 German participants, aged 18 years or older. We compared three intervention groups who received: (A) edutainment and written health messages, (B) only edutainment, or (C) only written health messages, against (D) a control group that received nothing. The primary outcomes were loneliness and intention to cope with loneliness. Participants were also invited to leave comments about the interventions or about their perception or experiences with loneliness. We found a small (d = 0.254) but significant effect of the written messages on increased intention to cope with loneliness (b = 1.78, t(1602) = 2.91, P = 0.004), while a combination of edutainment and written messages significantly decreased loneliness scores (b = −0.25, t(1602) = −2.06, P = 0.04) when compared with the control, even after adjusting for covariables including baseline values, self-esteem, self-efficacy, and hope. We also observed significantly higher self-esteem scores after exposure to a combination of edutainment and written messages (b = 0.821, t(1609) = 1.76, one-tailed P = 0.039) and significantly higher hope scores after exposure to edutainment-only (b = 0.986, t(1609) = 1.85, one-tailed P = 0.032) when compared with the control group. Our study highlights the benefits of using written messages for increasing intention to cope with loneliness and a combination of edutainment and written messages for easing loneliness. Even in small “doses” (less than 6 min of exposure), edutainment can nurture hope, and edutainment combined with written messages can boost self-esteem.
Collimated muon beam proposal for probing neutrino charge-parity violation
Authors: Ruzi Alim , Yang Tianyi , Fu Dawei , Qian Sitian , Gao Leyun , Li Qiang 
Published: April 2024

AbstractThe phenomenon of neutrino oscillation is of great theoretical and experimental interest for our understand of the nature of the neutrino and its implication for physics beyond the standard Model. Currently available neutrino oscillation experiments can already constrain neutrino mixing parameters with a confidence level up to 3 standard deviations (σ). However, it remains challenging to provide a deterministic constraint on the Charge-Parity (CP) violation phase of the neutrino mixing matrix. Here, we propose an experimental setup that exploits collimated muon beams to probe neutrino CP-violation. In our proposed acceleration experiment, a 45 GeV positron source with additional muon collimation, interfaces with near-future neutrino detectors like DUNE and T2K, to probe neutrino CP-violation phase with a significantly higher sensitivity than obtained with the neutrino detectors alone, and to determine tau neutrino properties. Simulations estimate the collection of 104 tau (anti-) neutrino in 5 years, and a sensitivity of over 7 standard deviations for δCP = ∣π/2∣ in 5 years. Collecting ντ appearance events from μ− and μ+ beams over 10 years can attain a 3-4 standard deviation sensitivity. This proposal may serve as a tau factory.
Deep learning detects premalignant lesions in the Fallopian tube
Authors: Bogaerts Joep M. A. , Bokhorst John-Melle , Simons Michiel , van Bommel Majke H. D. , Steenbeek Miranda P. , de Hullu Joanne A. , Linmans Jasper , Bart Joost , Bentz Jessica L. , Bosse Tjalling , Bulten Johan , Chien Yen-Wei , Desouki Mohamed Mokhtar , Lastra Ricardo R. , Numan Tricia A. , Schoolmeester J. Kenneth , Schwartz Lauren E. , Shih Ie-Ming , Soong T. Rinda , Turashvili Gulisa , Vang Russell , Volchek Mila , van der Laak Jeroen A. W. M. 
Published: April 2024

AbstractTubo-ovarian high-grade serous carcinoma is believed to originate in the fallopian tubes, arising from precursor lesions like serous tubal intraepithelial carcinoma (STIC) and serous tubal intraepithelial lesion (STIL). Adequate diagnosis of these precursors is important, but can be challenging for pathologists. Here we present a deep-learning algorithm that could assist pathologists in detecting STIC/STIL. A dataset of STIC/STIL (n = 323) and controls (n = 359) was collected and split into three groups; training (n = 169), internal test set (n = 327), and external test set (n = 186). A reference standard was set for the training and internal test sets, by a panel review amongst 15 gynecologic pathologists. The training set was used to train and validate a deep-learning algorithm (U-Net with resnet50 backbone) to differentiate STIC/STIL from benign tubal epithelium. The model’s performance was evaluated on the internal and external test sets by ROC curve analysis, achieving an AUROC of 0.98 (95% CI: 0.96–0.99) on the internal test set, and 0.95 (95% CI: 0.90–0.99) on the external test set. Visual inspection of all cases confirmed the accurate detection of STIC/STIL in relation to the morphology, immunohistochemistry, and the reference standard. This model’s output can aid pathologists in screening for STIC, and can contribute towards a more reliable and reproducible diagnosis.
Glucose hypometabolism prompts RAN translation and exacerbates C9orf72-related ALS/FTD phenotypes
Authors: Nelson Andrew T , Cicardi Maria Elena , Markandaiah Shashirekha S , Han John YS , Philp Nancy J , Welebob Emily , Haeusler Aaron R , Pasinelli Piera , Manfredi Giovanni , Kawamata Hibiki , Trotti Davide 
Published: April 2024

AbstractThe most prevalent genetic cause of both amyotrophic lateral sclerosis and frontotemporal dementia is a (GGGGCC)nnucleotide repeat expansion (NRE) occurring in the first intron of theC9orf72gene (C9). Brain glucose hypometabolism is consistently observed in C9-NRE carriers, even at pre-symptomatic stages, but its role in disease pathogenesis is unknown. Here, we show alterations in glucose metabolic pathways and ATP levels in the brains of asymptomatic C9-BAC mice. We find that, through activation of the GCN2 kinase, glucose hypometabolism drives the production of dipeptide repeat proteins (DPRs), impairs the survival of C9 patient-derived neurons, and triggers motor dysfunction in C9-BAC mice. We also show that one of the arginine-rich DPRs (PR) could directly contribute to glucose metabolism and metabolic stress by inhibiting glucose uptake in neurons. Our findings provide a potential mechanistic link between energy imbalances and C9-ALS/FTD pathogenesis and suggest a feedforward loop model with potential opportunities for therapeutic intervention.