bluuebunny/crossref_metadata_2025_split huggingface dataset:
The association between number of oocytes retrieved and cumulative live birth rate in different female age strata
Authors: Wang Peixin , Zhao Chenqiong , Xu Wen , Jin Xiaoying , Zhang Songying , Zhu Haiyan 
Published: June 2023

Abstract
Purpose:
To evaluate the association between the number of oocytes retrieved and cumulative live birth rate (CLBR) in different female age strata.
Methods:
17,931 women undergoing their first IVF/ICSI-ET cycle in the Sir Run Run Shaw Hospital of Zhejiang University were grouped by age (A: ≤35 years; B: ≥36 years) as well as the number of oocytes retrieved (a: ≤5; b:6-9; c:10-14; d: ≥15). Multivariate regression analysis was performed to assess the OR of CLBR for the variable ‘age’ and ‘number of oocytes retrieved’.
Results:
The group ≥36 years exhibited lower cumulative pregnancy rates (CPRs) and cumulative live birth rates (CLBRs), which are proportional to the number of oocytes retrieved but opposite to increasing age. Multivariate logistic regression analysis revealed that the age and number of oocytes retrieved remainsignificant independent predictive factors. (P<0.001)
Conclusion:
Age and number of oocytes retrieved are two independent factors affecting the CLBR. The discrepancy of the minimum number of oocytes retrieved for patients with different ages to achieve the ideal CLBR is instructive for clinical practice. The practice of controlling the stimulation dose is feasible for patients ≤35 years who can achieve ideal CLBR once the number of oocytes obtained is more than 6 while additional stimulation cycles and accumulation of embryos is necessary for elderly group especially those ≥38 years old who need to retrieve more than 14 oocytes to obtain higher live birth rate.
A Model of Hydrocarbon Fuel Droplet Combustion in Supercritical Pressure Environments
Authors: Xie Zhuorong , Li Zhi , Nie Jinquan , Wu Huawei , Wang Hongnan 
Published: June 2023

Abstract
Hydrocarbon fuel as a class of petroleum derived fuels is an essential component of energy resources, and its main application is releasing energy through spray combustion. The evaporation and combustion of fuel droplet serve as the basis for spray combustion. The evaporation and combustion of the fuel spray inside the combustion chamber of direct injection diesel and gasoline engine, gas turbine and liquid-propellant rocket take place at supercritical pressure (ambient pressure is larger than the critical pressure of fuel). Accordingly, the study on the combustion of hydrocarbon fuel droplets is extremely necessary. The objectives of this paper are to theoretically provide a novelty explanation for the physical scenario that the combustion of fuel droplets in supercritical pressure environment result in the evaporation of the droplets instantly, and establish theoretical foundation for the study on the fuel droplet evaporation and combustion that take place after the transition of droplet surface respectively. This study used the equations of mass conservation, component conservation, and energy conservation to describe the physical process of gas phase and liquid phase in droplet combustion, which can be applied to establish the uniform description of the physical process of transition between gas and liquid within large density range. This paper introduces the concept of vapor-liquid equilibrium, mixture critical point and evaporation enthalpy for the heat and mass exchange of droplet surface. In our study the physical process around the transition of droplet surface is thoroughly analyzed as well. This study focuses on developing a numerical model and program for the combustion and evaporation of hydrocarbon fuel droplets. The model was validated by comparison with the data from experiment.
The Quiet Revolution Revisited: Low-Information Voting and Ballot Box Support for Public Transportation in the United States
Authors: Manville Michael 
Published: June 2023

Abstract
I use an original survey to examine support for a large, transit-oriented tax ballot in the United States. Support for increasing taxes to finance public transportation is associated with positive affect for transit, but not with a desire to drive less or concerns about congestion. Most respondents, even those who voted for the measure, knew relatively little about it, partisan affiliation was one of the strongest predictors of support. In combination these results suggest that devolving transit finance to local ballots. Votes cast with low levels of information may help explain why changes in travel behavior are often small after transit ballots win.
The prognostic value of dynamic changes in SII for the patients with STEMI undergoing PPCI
Authors: Liu Ailin , Sun Na , Gao Feiyu , Wang Xiaotong , Zhu Hong , Pan Defeng 
Published: June 2023

Abstract

Objective To investigate the dynamic changes in systemic immune inflammatory index (SII) during the perioperative period of primary percutaneous coronary intervention (PPCI) and to evaluate its predictive value for in-hospital and out-of hospital outcomes in patients with ST-segment elevation myocardial infarction (STEMI). Methods This retrospective study included 324 consecutive STEMI patients in a cardiac care unit. Blood samples were collected 12h before, 12h after(T1), 24h after, 48h after PPCI, the last time before hospital discharge (T2), and 1 month after hospital discharge. SII was calculated as (neutrophils×platelets)/lymphocytes. Based on whether the primary endpoint occurred, we divided the research sample into two groups, which were the event and the non-event group. Univariable and multi-variable logistic regression analyses were performed to detect the independent risk factors that might influence the occurrence of the primary endpoint. Dynamic curves with SII were plotted and Receiver-operating characteristic(ROC) curves were drawn for each node to calculate the optimal critical value, sensitivity and specificity to assess their predictive ability for the occurrence of in-hospital and out-of hospital course. Kaplan-Meier curves were used to analyze the differences in survival rates at different status of SII inflammation levels; Results High levels of SII were individually related with the occurrence of in-hospital period and long-term outcomes during the post-operative follow-up of STEMI patients (in-hospital SII:T1:OR 1.001,95%CI 1.001–1.001,P < 0.001;SII following hospital discharge:T1M:OR 1.008,95%CI 1.006–1.010,P < 0.001). And patients with high SII levels had lower survival rates than those with lower SII levels. The analysis showed that SII 12h after(T1) and SII 1 month(T1M) had a excellent predictive value for the occurrence of in-hospital and out-of hospital outcomes, respectively(AUC:0.896, p < 0.001; AUC:0.892, p < 0.001). Conclusion There is a significant relationship between dynamic status of SII and the prognosis in STEMI patients. This study found that SII 12h and SII 1 month had an effect on in-hospital and out-of hospital outcomes, respectively. Consequently, we should focus on the dynamic change of SII.
Xtune: An XAI-Based Hyperparameter Tuning Method for Time-series Forecasting Using Deep Learning
Authors: Sumita Shimon , Nakagawa Hiroyuki , Tsuchiya Tatsuhiro 
Published: June 2023

Abstract
Given a time-series dataset and deep learning based time-series forecasting method, how can we determine the optimal hyperparameter value efficiently? How can we determine this value without running the method with a large number of candidate values? In this study, we present \textit{Xtune}, an efficient and novel hyperparameter tuning method, utilizing explainable AI, for a time-series forecasting using deep learning. Our proposed method has the following properties: (a) It is effective: it determines the optimal hyperparameter value of deep learning based time-series forecasting methods. (b) It is efficient: it does not need to run the method on various hyperparameter values. (c) It is applicable: it can be used to tune the hyperprameter tuning for the anomaly detection method utilizing deep learning-based time-series forecasting. Extensive experiments on real datasets and time-series forecasting methods demonstrate that \textit{Xtune} does indeed determine the optimal hyperparameter value efficiently, and consistently outperforms the existing methods in terms of execution speed.
Dynamics of ST2 biomarker level under the influence of spaceflight and landing factors
Authors: Pastushkova Lyudmila , Goncharov Igor , Goncharova Anna , Kashirina Daria , Popova Olga , Larina Irina 
Published: June 2023

Abstract
The dynamics of the level of the biological risk marker for heart failure and cardiophibrosis ST2 under the influence of factors of space flight and landing was analyzed. Plasma samples obtained from the venous blood of 14 cosmonauts who had flown on the Russian segment of the ISS were studied. The biological samples collected before the flight, on the first and the seventh day after landing were analyzed by means of commercial test-kit "Critical Diagnostics Presage® ST2 Assay" (USA) by enzyme immunoassay (ELISA). Analysis of variance analysis of ST2 protein levels in all cosmonauts revealed a significant increase in its concentration on the first day after the flight relative to individual and group average background levels (p-value 0.05). On the 7th day of the recovery period the ST2 protein content decreased, approaching the background value. The obtained results indicate a transient mechanical deformation of myocardium during landing and an increased risk of myocardiofibrosis in the distant period of re-adaptation after space flight.
Effects of traumatic dental injuries of the primary dentition to the developing permanent teeth: a retrospective cohort study of 14 years
Authors: Costa Vanessa Polina Pereira da , Almeida Fernanda Vieira , Demarco Giulia Tarquinio , Motta Maria Giulia Larroque Silva da , Baldissera Elaine de Fátima Zanchin , Silva Alexandre Emídio Ribeiro , Langlois Caroline de Oliveira , Goettems Marília Leão 
Published: June 2023

Abstract
Objectives
This retrospective cohort study aimed to determine the occurrence of sequelae in permanent teeth following trauma in primary dentition in children treated at a Dental Trauma Center in Brazil for 14 years.
Material and Methods
Records of 140 children, with complete information about trauma and presence of sequelae, were included. Photographs and radiographs of permanent successors were evaluated to determine presence of long-term complications. Chi-square tests and Poisson regression were used for data analyses (p ≤ 0.05).
Results
A total of 244 traumatized primary teeth were evaluated in 140 children, and 81 (33.2%) of the permanent successors presented sequelae, affecting 40% of the children. Enamel discoloration (53.1%) and enamel hypoplasia (28.4%) were the most prevalent sequelae presented and the occurrence of sequelae was higher following intrusive luxation (53.3%) and avulsion (39.4%). Risk of sequelae was higher for teeth with supporting tissue trauma (RR 2.68; 95% CI 1.32–5.42; P < 0.001) than for teeth with hard tissue trauma. Risk was lower when children had more than 4 years at the time of TDI (RR 0.41; 95%CI 0.25–0.66; P < 0.001).
Conclusions
Prevalence of sequalae in permanent teeth following trauma in primary dentition was high, especially in children with up to 2 years of age at the time of TDI and who suffered supporting tissue trauma.
Clinical relevance: Findings reinforce the importance of long-term follow-up of dental trauma in primary dentition. Close monitoring and knowledge about the risk of sequelae to permanent teeth can help dentists determine prognosis after dental trauma and provide an appropriate treatment plan.
In Silico Prediction of Potent Pan-Janus Kinase (JAK3) and CYP3A4 Inhibitors Using 2-Imidazopyridine and 2-Aminopyridone Purinones via Computer-Aided Drug Design
Authors: Faris Abdelmoujoud , Ibrahim Ibrahim M , kamaly Omkulthom Al , Saleh Asmaa , Elhallaoui Menana 
Published: June 2023

Abstract
In this study, a 3D-QSAR analysis was performed using field-based and atom-based methods with a pharmacophore hypothesis approach to understand and identify the most important features that are associated with the inhibitory activity of JAK3/STAT and CYP3A4. The results for both field-based and atom-based methods showed good performance with R2 values of 0.93 and 0.94, R2 CV values of 0.51 and 0.47 and Q2 values of 0.87 and 0.86, respectively. Additionally, a pharmacophore DHRRR model was developed with a survival score of 5.88 to guide the design of new molecules. This study employed ADMET to predict the pharmacokinetic and pharmaceutical properties of candidate ligands, including solubility, permeability, and metabolic stability, which are critical for drug efficacy and pharmacokinetics. The present investigation aimed to explore the phenomenon of covalent binding between Janus Kinase (JAK3) and ligands, specifically targeting cysteine 909 (Cys909), a crucial player in the treatment of rheumatoid conditions. The findings of this study demonstrate that the establishment of covalent bonds between the ligands and Cys909 significantly enhances the inhibitory activity of JAK3/STAT, thus highlighting the potential therapeutic benefits in the context of rheumatoid treatment. Using molecular dynamics (MD) simulations and MM-GBSA calculations, potentially potent molecules were identified for selective JAK3/STAT inhibition. Finally, retrosynthesis was used to facilitate the synthetic pathway. These findings demonstrate the effectiveness of this computational approach in identifying promising new compounds for the development of JAK3/STAT drugs.
Comparison of the LBM snowdrift model output with the observation results
Authors: Tanji Seika , Inatsu Masaru , Harada Yusuke 
Published: June 2023

Abstract
In this work, realistic snowdrift experiments are performed by the snowdrift model. The model consisted of the computational fluid dynamics part of the large-eddy simulation with the lattice Boltzmann method and the drifting snow part of the conventional advection algorithm for representative Lagrangian particles. The observed vertical wind profile of a 4-h drifting snow event in Teshikaga Town was used as the inflow boundary conditions in the model to compare the results of the snowdrift estimated by the model and the observed snowdrift distribution. Parallelization enabled us to simulate the snowdrift distribution in a realistic domain and on the time scale of a single drifting snow event. We demonstrated that the upgraded model could quantitatively reproduce the height and position of the observed snowdrift around a three-dimensional fence.
Irreversibility Analysis through Neural Networking of the Hybrid Nanofluid for the Solar Collector Optimization
Authors: Alharbi Sayer Obaid , Gul Taza , Khan Ilyas , Khan Mohd Shakir , Alzahrani Saleh 
Published: June 2023

Abstract
Advanced techniques are used to increase the efficiency of the energy assets and maximize the appliance efficiency of the main resources. In the recent study, the focus is paid to the solar collector to cover thermal radiation through optimization and enhance the performance of the solar panel. Hybrid nanofluids (HNs) consist of a base liquid (C3H8O2) glycol whereas copper (Cu), and aluminum oxide (Al2O3) are used as nanomaterials for formation (HNs). The flow of the stagnation point is considered in the presence of the Riga plate. The state of the solar thermal system is termed viva stagnation to control the additional heating through the flow variation in the collector loop. The inclusion of entropy generation and Bejan number formation is primarily conceived under the influence of physical parameters for energy optimization. The computational analysis was carried out utilizing the control volume finite element method (CVFEM), and Runge–Kutta 4 (RK-4) methods. The results are further validated through a machine learning neural networking procedure. The conclusions showed that the heat transfer rate is greatly upgraded with a variation of the nanoparticle's volume fraction. We expect this improvement to progress the stability of heat transfer in the solar power system.
Synthesis and activity study of novel theophylline derivatives as IDO1 inhibitors
Authors: Hou Xixi , Wu Zi-yuan , Lou Chaoxuan , Gong Xiaoqing , Shi Wei-chen , Wang Huili , Yang Jian-xue 
Published: June 2023

Abstract
Indoleamine 2,3-dioxygenase 1 (IDO1), an immunomodulatory enzyme associated with tumor immune escape. Numerous studies have shown that it is an attractive therapeutic target for cancer diseases. To identify holo-IDO1 inhibitors with novel skeleton structures, a series of theophylline derivatives with 1,2,3-triazole groups were designed and synthesized by condensingtheophylline acetic acid and 4-aminophenylacetylene.In the IDO1 enzymatic activity assay, we found that compound 3c exhibited the best inhibitory effect(IC50 = 10.07µM). Molecular docking and affinity prediction analysis further elucidated the binding ability and mode of the dominant compounds. Our study suggested that the theophylline derivatives are potential holo-IDO1 inhibitors for future development.
Applied research of deep learning technology in the classification of earthquake and blasting event
Authors: Zhou Shaohui , Li Jian , Zhou Qijie , Liu Yang , Wang Chong , Lin Bing , Zhang Han , Peng Limei , Li Dongya 
Published: June 2023

Abstract
In order to quickly and accurately identify the types of natural earthquake and non-natural earthquake events, this paper proposes a lightweight convolutional neural network model. Since generally an event can be recorded by several stations, data preprocessing and classification based on the event shall be carried out in advance in order to avoid the occurrence of different station waveforms of the same event in any 2 of the training set, validation set and test set. With the three component waveforms recorded by stations after preprocessing as the input, the network model and hypoparameters are optimized by analyzing the average and variance of the accuracy and loss values of the verification set in the five-fold cross-validation, as well as the accuracy and loss curves in the training process. Finally, the classification results of all stations that achieve a certain signal-to-noise ratio for each event are taken as the output of this event type based on the principle that the minority is subordinate to the majority. In this study, 2190 natural earthquake and non-natural earthquake events recorded by the Hainan Seismic Network before August 2022 which contains 53067 waveforms are used to train and test the effect of the model. 20% of those events are selected randomly as the test set. The results showed that 427 of 438 randomly selected events were correctly identified, witch means that the accuracy rate is 97.48%.Among them, the accuracy rate of seismic events was 95.59%, the recall rate was 89.04%, the accuracy rate of blasting events was 97.84%, and the recall rate was 99.18%. To sum up, the convolution neural network model constructed in this paper can quickly and accurately identify the types of natural and non-natural earthquakes in Hainan.
Design and validation of primer sets for the detection and quantification of antibiotic resistance genes in environmental samples by quantitative PCR
Authors: Perez-Bou Lizandra , Gonzalez-Martinez Alejandro , Cabrera Juan J. , Rodelas Belen , Gonzalez-Lopez Jesus , Correa-Galeote David 
Published: June 2023

Abstract
The high prevalence of antibiotic resistant bacteria (ARB) in several environments is a great concern threatening human health. Hence, it is vital to dispose of molecular tools that allow proper monitoring of antibiotic resistant genes (ARGs) encoding resistances to these important therapeutic compounds. For an accurate quantification of ARGs, there is a need for sensitive and robust qPCR assays supported by a good design of primers and validated protocols. In this study, eleven relevant ARGs were selected as targets, including aadA and aadB (conferring resistance to aminoglycosides), ampC, blaTEM, blaSHV, and mecA (resistance to beta-lactams); dfrA1 (resistance to trimethoprim); ermB (resistance to macrolides); fosA (resistance to fosfomycin); qnrS (resistance to quinolones); and tetA(A) (resistance to tetracyclines). The in silico design of the new primer sets was performed based on the alignment of all the sequences of the target ARGs (orthology grade > 70%) deposited in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, allowing higher coverages of the ARG’s biodiversity than those of several primers described to date. The adequate design and well performance of the new molecular tools were validated in vivo in six samples, retrieved from both natural and engineered environments. The hallmarks of the optimized qPCR assays were high amplification efficiency (> 90%), good linearity of the standard curve (R2 > 0.980), consistency across replicate experiments, and a wide dynamic range. The new methodology described here provide valuable tools to upgrade the monitorization of the abundance and emergence of the targeted ARGs in the environment by qPCR.
Spatial Downscaling of Streamflow Data with Attention Based Spatio-Temporal Graph Convolutional Networks
Authors: Sit Muhammed , Demiray Bekir , Demir Ibrahim 
Published: June 2023

Abstract
Accurate streamflow data is vital for various climate modeling applications, including flood forecasting. However, many streams lack sufficient monitoring due to the high operational costs involved. To address this issue and promote enhanced disaster preparedness, management, and response, our study introduces a neural network-based method for estimating historical hourly streamflow in two spatial downscaling scenarios. The method targets two types of ungauged locations: (1) those without sensors in sparsely gauged river networks, and (2) those that previously had a streamflow sensor, but the gauge is no longer available. For both cases, we propose the ScaleGNN, a graph neural network architecture. We evaluate the performance of ScaleGNN against a Long Short-Term Memory (LSTM) baseline and spatial persistence in estimating discharge values over a 36-hour period. Our findings indicate that ScaleGNN surpasses spatial persistence in the first scenario, while both neural network approaches demonstrate their effectiveness compared to spatial persistence in the second scenario.