bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Molecular Dynamics Study on Low-Pressure Carbon Dioxide in the Water/Oil Interface for Enhanced Oil Recovery
Authors: Chang Qiuhao , Huang Liangliang , Wu Xingru 
Published: October 2022

SummaryThe microscopic displacement efficiency of supercritical carbon dioxide (CO2)-based enhanced oil recovery (EOR) depends critically on the phase behavior of CO2 and residual oil. Traditionally, we assume that the main drive mechanisms of supercritical CO2 EOR are attributed to oil swelling and reduced oil viscosity, and research focuses on how the supercritical CO2 interacts with remaining oil under the reservoir conditions. However, our recent study finds that once the CO2 is introduced into the reservoir, CO2 partitions into the aqueous and oil phases, reducing the interfacial tension (IFT) between the oil and water. This is particularly important when CO2 is generated through a series of chemical reactions for in-situ CO2 EOR. In this paper, we used molecular dynamics (MD) simulations to study the interfacial properties between water and oil with different mole fractions of CO2 in pressures below the minimum miscibility pressure. Simulation results show that with the increase in CO2 mole fraction, rather than evenly distributed in phases, CO2 molecules are prone to concentrate in the water/oil interface region, which decreases IFT between the aqueous phase and oil. Furthermore, the effect of CO2 orientation on the water/oil IFT reduction was observed. The change of CO2 concentration affects CO2 orientation near the interface, which in return dominates the IFT change.
Fine mapping and characterisation of a PV-PUR mediating anthocyanin synthesis in snap bean (Phaseolus vulgaris L.)
Authors: Liu Chang , Yang Xiaoxu , He Yongheng , Chen Qifu , Huang Yucheng , Yan Zhishan , Liu Dajun , Feng Guojun 
Published: October 2022

Abstract
Anthocyanin makes snap bean (Phaseolus vulgaris L.) pods purple, which is visually attractive and protects against environmental stress. In this study, we characterised the snap bean purple mutant pv-pur, which has purple cotyledon, hypocotyl, stem, leaf vein, flower and pod tissues. Total anthocyanin, delphinidin and malvidin levels in mutant pods were significantly higher than in wild-type plants. We constructed two populations for fine mapping of the PV-PUR purple mutation gene, located in the 243.9-kb region of chromosome 06. We identified Phvul.006g018800.3, encoding F3'5'H, as a candidate gene for PV-PUR. Six single base mutations occurred in the coding region of this gene, altering protein structure. Phvul.006g018800.3 transcription was measured, and expression levels in the mutant were significantly higher than in wild-type plants in all tissues. The mutant Phvul.006g018800.3gene was transferred into Arabidopsis thaliana, and the leaf base and internodes of transgenic Arabidopsis T3 plants were purple, verifying the function of the mutant gene. The results demonstrated that PV-PUR is a crucial gene for anthocyanin biosynthesis in snap bean, resulting in purple colouration. The findings lay a foundation for future breeding and improvement of snap bean.
Using an ocean culture course to improve students’ marine life concepts and learning interest
Authors: Hsieh Fu-Pei , Chen Yun-An , Tsai Chun-Yen 
Published: October 2022

Abstract
This study aimed to determine the effectiveness of an ocean culture course using the TRIP learning model (Triggered situational interest-Reading-Inquiry-Presentation) in enhancing students’ marine life concepts and learning interests. The participants consisted of 67 first graders (6 years old). A quasi-experimental design was employed, and the mixed methods analysis was used in this study. The results indicated that the experimental group students’ concepts of marine life were improved with a large effect size (d = 1.4), and they outperformed their counterparts with a large effect size (f = 0.40). The students’ learning interest in marine ecological conservation concepts was improved with a medium effect size (d = 0.53), and they outperformed their counterparts with a large effect size (f = 0.54). The results revealed that using the TRIP learning model for marine education improved the teaching of the teachers who taught the experiment group. Moreover, the model enhanced the effectiveness of the students’ learning interest in understanding marine life and marine ecological conservation concepts. Finally, some suggestions for further teaching and research were provided.
A study on endoscopy artefact detection based on deep learning
Authors: Zhang Han , Adjei Prince , Hu Xiaoming , Wang Xiaotong , Hu Yeting , Gan Tao , Rao Nini 
Published: October 2022

Abstract

Endoscopic images are generally corrupted by various artefacts, such as blur caused by the irregular motion of endoscopists or organ peristalsis, preventing endoscopists from performing efficient and high-accuracy diagnosis and limiting the utility of computer-aided diagnosis in endoscopy. As a result, an endoscopy artefact detection algorithm that is both robust and reliable is critical in endoscopic imaging. This study proposes an endoscopy artefact detection method based on an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) detector, simply named Faster RFD3-CNN. We introduce deformable convolutions and a feature pyramid network (FPN) to strengthen the model's adaptability and feature extraction ability. Furthermore, class-aware non-maximum suppression (NMS) and false positive elimination are utilized to filter out some bounding boxes with low confidence or overlap. The experimental results on the public access EndoCV2020 data set show that the mean average precision (mAP) can reach 42.66% and the intersection of union (IoU) is 31.61%. The mAP is increased by nearly 15% compared with the existing methods. Compared with the basic Faster R-CNN, mAP and IoU are improved by 7.39% and 11.19%, respectively. To sum up, the proposed method in this paper has superior performance in endoscopy artefact detection.
Can pilot projects for green industrial transformation mitigate environmental pollution emissions? Evidence From China
Authors: Tan Jingrong , Chen Lin , Liu Xu , Gong Chaojun 
Published: October 2022

Abstract

To achieve the goals of clean production and green development, pilot projects for green industrial transformation (PPGIT) to reduce the environmental pollution emissions from regional enterprises in China have been ongoing for more than five years. This study analyzes 283 prefecture-level cities from 2006 to 2019 using the propensity score matching difference-in-differences (PSM-DID) analysis framework to determine the effects of PPGIT policy implementation. The impacts of PPGIT policy on different pollutants are significantly negative, with the most reductions occurring for sulfur dioxide (SO2) emissions and the least for particulate matter (PM2.5) emissions. Furthermore, the effects of implementing the PPGIT policy from region to region, with the greatest policy effects of PPGIT in the eastern region. Based on the mechanism effects in different regions, the implementation of PPGIT policy nationwide significantly reduces pollution emissions through the technology effect and structure effect and in different regions, the PPGIT policies reduces emissions through different mechanisms. Overall, this study makes a unified evaluation of the environmental governance practices occurring during China's industrial green transformation process. The results of this study are of great significance for promoting the modernization of environmental governance capacity and improving the construction of an ecological civilization through China’s green development.
An eigen decomposition based closed-formsolution for the Discrete Lyapunov and SteinEquations
Authors: Patel Aaqib , Khan Mohammed Zafar Ali 
Published: October 2022

Abstract
A simple closed-form solution to the discrete Lyapunov equation(DLE) is established for certain families of matrices. This solution isexpressed in terms of the eigen decomposition (ED) for which closed-form solutions are known for all 2 × 2, 3 × 3 and certain familiesof matrices. For general matrices, the proposed ED based closed-formsolution can be used as an efficient numerical solution when the EDcan be computed. The result is then extended to give closed-formsolutions for a generalization of the DLE, called the Stein equation.The proposed explicit solution’s complexity is of the same order asiterative solutions and significantly smaller than known closed-formsolutions. These solutions may prove convenient for analysis and syn-thesis problems related to these equations due to their compact form.
Transmission of COVID-19 was slower but more sustained in warm climate
Authors: Wan Xinru , Cheng Chaoyuan , Zhang Zhibin 
Published: October 2022

Abstract
Background
The COVID-19 novel virus has caused huge damage to public health around the world. Revealing the influencing factors affecting the transmission rate of COVID-19 is essential to take effective control measures. However, the association between transmission of COVID-19 and climate factors remains elusive with high uncertainty.
Methods
By using an extensive global dataset covering 617 time series from China, USA, Europe, and the rest of the world during 1/1–31/12 2020, we estimated the transmission parameters of COVID-19 and modeled the effects of the human and climate factors on COVID-19 transmission.
Results
We demonstrate that the transmission rate of COVID-19 was lower in warm climate in China, Europe, USA and the world, and in wet climate in China and Europe after excluding the confounding factors. The maximum transmission rate of COVID-19 seemed to have a peak temperature around 11.2°C in China and the world. The control efficiency (i.e. decreasing speed of transmission rate) in China, USA and the world was lower in warm and wet condition.
Conclusions
Our study suggested that in summer seasons, the transmission risk of COVID-19 would increase in the high-latitude or high-altitude regions but decrease in low-latitude or low-altitude regions. The area with the 7.8°C isocline between October and January which overlap with the major epicenters of COVID-19 should be investigated as a priority in searching for the natural hosts of COVID-19 and their habitats and movement.
Short-term Probabilistic Trajectory Prediction Approach for Intelligent Connected Vehicle
Authors: Wang Leilei , Deng Xiaoheng , Gui Jinsong 
Published: October 2022

Abstract
Efficient, high-precision and real-time vehicle trajectory prediction contributes more feasibilities to Intelligent Connected Vehicle (ICV) applications. Existing classic time series trajectory prediction methods attempt to directly extrapolate the exact future trajectory distribution from an extensive amount of trajectory data. However, these methods are sensitive to noise, require time series linearity and they are difficult to integrate traffic rules and potential environmental constraints, which makes multimodal prediction unreliable. For this purpose, this paper proposed a Probabilistic trajectory Prediction approach for ICV (PPIV), which is mainly applied to predict the vehicle trajectory within a short-term. Based on a large amount of real vehicle trajectory data, we explored and analyzed the vehicle motion features. Specifically, we divide the vehicle motion modes into two categories: single mode and complicated mode, and construct different trajectory proposal models for the two motion modes to predict the future trajectory location of the vehicle. Furthermore, we comprehensively consider the influence of vehicle location relationships and external hidden factors on vehicle trajectory prediction to improve prediction accuracy. The advantage of PPIV algorithm is that it not only predicts the future trajectory proposal of the vehicle under different motion modes but also predicts the probability distribution of all future trajectories of the vehicle. Simulation results with extensive real trajectory datasets show that the PPIV algorithm is superior to other probabilistic trajectory prediction algorithms.
The suppression of alt-EJ in quiescent G0-phase cells is caused by enhanced APC/CCDH1-mediated CtIP degradation and low CDK levels
Authors: Li Fanghua , Mladenov Emil , Broich Anna , Soni Aashish , Stuschke Martin , Timmermann Beate , Iliakis George 
Published: October 2022

Abstract
Alt-EJ is an error-prone DNA double-strand break (DSBs) repair pathway coming to the fore when first-line repair pathways, c-NHEJ and HR, are defective or fail. It is thought to depend on DNA end-resection: a process whereby 3’ single-stranded DNA-tails are generated, initiated by the CtIP/MRE11-RAD50-NBS1 (MRN) complex and extended by EXO1 or the BLM/DNA2 complex. The connection between alt-EJ and resection remains incompletely characterized. Alt-EJ depends on cell cycle phase, is at maximum in G2-phase, substantially reduced in G1-phase and almost undetectable in quiescent, G0-phase cells. The mechanism underpinning this regulation remains uncharacterized. Here, we compare alt-EJ in G1- and G0-phase cells exposed to ionizing radiation (IR) and identify CtIP-dependent resection as the key regulator. Low levels of CtIP in G1-phase allow modest resection and alt-EJ as compared to G2-phase cells. Strikingly, CtIP is undetectable in G0-phase cells owing to APC/C mediated degradation. Suppression of CtIP degradation with bortezomib or CDH1 knockdown rescues CtIP and alt-EJ in G0-phase cells. CtIP activation in G0-phase cells also requires CDK-dependent phosphorylation by any available CDK, but is restricted to Cdk4/6 at the early stages of the normal cell cycle. We propose that suppression of alt-EJ in G0-phase enhances genomic stability in the large non-cycling cell fraction of higher eukaryotes.
Is nonlinearity needed to predict the Kumamoto Mj 6.5 and Mj 7.3 mainshocks at KUMA site?
Authors: Bordoni Paola , Giuliano Milana , Augusto Lucarelli , Giulio Giuseppe Di , Hailemikael Salomon , Vassallo Maurizio 
Published: October 2022

Abstract
We present the results of 2D numerical simulation carried out using FLAC3D-7.0 for the blind predictions of the shaking induced by the 2016 Kumamoto mainshocks Mj 6.5 and Mj 7.2. The blind exercise was organized by the IASPEI/IAEE Joint Working Group on the Effects of Surface Geology on Seismic Motion (JWG-ESG6), for the 6th International ESG Symposium held in Japan in August 2021. We have drawn our subsoil model using geophysical data provided by the JWG-ESG6 and validated using a weak motion Mj 4.6 event recorded at the target site for predictions (KUMA) and at the reference site (SEVO). Prediction of events Mj 6.5 and Mj 7.2 are performed with linear elastic and nonlinear elasto-plastic simulations, the latter using the Mohr-Coulomb constitutive law to characterize some shallow layers. Their syntheticwaveforms are almost identical and fit the data satisfactorily, most likely due to the mechanical parameters and the constitutive model chosen being not able to induce important nonlinear effects. Also, and more surprisingly, a 1D linear code can reproduce the data. These results provide an important warning against the widespread use of equivalent-linear code for ground response analysis, which, if used for predictions in the Kumamoto case, would have largely underestimated the recorded shaking.
Assessment of the Effects of the Biotic and Abiotic Harm Factors on the Amount of Industrial Wood Production with Deep Learning
Authors: Sevinç Volkan 
Published: October 2022

Abstract

The protection and sustainability of forest assets is possible with the planned production of forest products with minimum loss. Among the products obtained from forests, industrial wood is the most important raw material for many sectors. For this reason, it is important to determine the factors affecting the industrial wood production amount in order to obtain this raw material in the most efficient way. This article aims to investigate and assess the effects of two biotic and two abiotic harm factors on the amount of industrial wood production by building a deep learning estimation model. These factors are forest fires, outbreaks of insects, outbreaks of diseases, and severe weather events. The study shows that the most harmful factor decreasing the industrial wood production level is outbreaks of diseases. The second effective factor, however, appears to be severe weather events. The third and the fourth factors were determined to be outbreaks of insects and burned forest areas, respectively.
Comparison of the surveillance effectiveness with different absenteeism collection methods: a pilot study, China, September 2021 to June 2022
Authors: Wang Wei Ye , Huang Xiao Liu , Yang Zhen 
Published: October 2022

Abstract
Background
The recently launched facial recognition based syndromic surveillance system (FRSSS) by a group needs to address whether the innovative method used in FASSS can result in higher surveillance accuracy, and how to construct appropriate indicators for FASSS.
Aim
To compare the surveillance effectiveness of absenteeism collected by FRSSS and school physicians and provide a theoretical basis for surveillance indexes constructed for FRSSS.
Methods
Two schools in Hangzhou, Zhejiang Province were selected (3110 and 3118 students in the first and second semesters, respectively). Grades 1–2 (DARL), 3–6 (DARH), and school-wide (DARX) all-cause absenteeism were reported by FRSSS, and all-cause absenteeism (DARY) and sickness absenteeism (DARZ) were reported by school physicians from September 1, 2021, to June 24, 2022. The sensitivity, specificity and Youden index of these indicators were analyzed through correlation, time series, control chart and event investigation.
Results
In school A, the sensitivities of DARL, DARH, DARX, DARY and DARZ were 95.0%, 92.9%, 100%, 100% and 100%, respectively; specificities were 88.0%, 92.1%, 80.6%, 78.2% and 76.8%, respectively; Youden indexes were 83.0%, 85.0%, 80.6%, 78.2% and 76.8%, respectively. In school B, the sensitivities of the same five indicators were 100%; specificities were 89.3%, 91.0%, 83.9%, 80.4% and 81.0%, respectively; Youden indexes were 89.3%, 91.0%, 83.9%, 80.4% and 81.0%, respectively.
Conclusions
The absenteeism collected by FASSS was realistic and had better accuracy of infectious disease detection than absenteeism collected by school physicians. Moreover, classifying total all-cause absenteeism as grade 1–2 and 3–6 significantly improved FASSS infectious disease situational awareness.
Outcomes of endoscopic treatment for early correction of craniosynostosis in children: A 26-year single center experience
Authors: Dominguez Leonardo , Rivas-Palacios Claudio , Barbosa Mario M , Escobar Maria Andrea , F Elvira Puello , Garcia-Ballestas Ezequiel 
Published: October 2022

Abstract
The authors have requested that this preprint be removed from Research Square.
What are the current diabetic foot assessment methods in private podiatry practices in Belgium: A mixed methods study.
Authors: Vansteenland Irene , Forss Rachel 
Published: October 2022

Abstract
Background
Diabetic foot (DF) assessments detect patients’ risk of developing a diabetic foot ulcer and can significantly reduce the risk of amputation. In order to organize this assessment effectively, DF assessment guidelines are required according to the International Working Group of the Diabetic Foot.
However, these international guidelines have not been adapted into a national guidelines for podiatrists in Flanders, Belgium. This study aims to explore podiatrists’ views and to identify which methods and guidelines are used to assess the DF in private podiatry practices in Belgium.
Methods
This mixed methods study was composed of an anonymous online survey comprising of open- and closed-ended questions followed by 1:1 semi-structured interviews. Participants were recruited via e-mail and a closed private Facebook group of podiatry alumni. Data was analyzed using SPSS statistics and thematic analysis described by Braun and Clarke.
Results
This study showed that the vascular assessment of the diabetic foot exists solely of a medical history and palpation of the pedal pulses. Non-invasive tests such as Doppler or ABPI are seldom used. Only 66% reported to use a guideline for the DF assessment. There was a variety of reported guidelines and risk stratification systems in use. This study raised the podiatrists’ concern of inconsistencies in the interpretation of the risk stratification of the DF between podiatrists. Other reported difficulties in assessing the diabetic foot were the lack of referral pathways and the current DF risk stratification system and reimbursement by the NIDHI in Belgium.
Conclusion
There is a need to develop a national uniform guideline for the DF assessment in Flanders, Belgium. Further research should focus on implementing and improving the adherence to this guideline to improve the diabetic foot care.
Bioinformatic analysis and experimental validation of six cuproptosis-associated genes as prognostic signatures in breast cancer
Authors: Chen Xiang , Sun Hening , Yang Changcheng , Wang Wei , Lyu Wenzhi , Zou Kejian , Zhang Fan , Dai Zhijun , Dong Huaying , He Xionghui 
Published: October 2022

Abstract

Background
Breast carcinoma (BRCA) is the life-threatening malignancy in women with poor prognosis. Cuproptosis is a novel mode of cell death, and its relationship with BRCA is unclear. This study endeavored to develop the cuproptosis-relevant prognostic genes and signature for BRCA.
Methods
Cuproptosis-relevant subtypes of BRCA patients were derived by consistent clustering. Disparate expression analysis was implemented in the ‘limma’ package. The univariate Cox and multivariate Cox analysis were executed to determine the cuproptosis-relevant prognostic signature. The signature was created and affirmed in distinct datasets. The Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) were also conducted to uncover the molecular mechanisms involved in the prognostic signature. ESTIMATE and CIBERSORT algorithm were applied to probe the linkage between the gene signature and tumor microenviroment (TME). Immunotherapy responsiveness were projected by Tumor Immune Dysfunction and Exclusion (TIDE) website. Detection of the expression of cuproptosis-revelant prognostic genes in breast cancer cell lines was implemented by Real Time Quantitative PCR (RT-qPCR).
Results
A grand total of 38 cuproptosis-associated differentially expressed genes (DEGs) in BRCA were mined by consistent clustering and disparate expression analysis. Based on univariate Cox and multivariate Cox analysis, six cuproptosis-revelant prognostic genes, namely SAA1, KRT17, VAV3, IGHG1, TFF1 and CLEC3A, were mined to establish a cuproptosis-revelant signature. Then, we affirmed the signature by external validation set. GSVA and GSEA manifested that multiple cell cycle-linked and immune-related pathways and biological processes were connected to the signature. The ESTIMATE and CIBERSORT results revealed significantly different TMEs for the two Cusig score subgroups. Finally, the result of RT-qPCR of cell lines further affirmed the expression trend of SAA1, KRT17, IGHG1 and CLEC3A.
Conclusion
Taken together, this study authenticated the cuproptosis-revelant prognostic genes and developed a signature for the overall survival projection of BRCA, which will provide the basis for developing prognostic molecular biomarkers and in-depth understanding of the relationship between cuproptosis and BRCA.