bluuebunny/crossref_metadata_2025_split huggingface dataset:
Minimum Threshold Determination Method based on Dataset Characteristics in Association Rule Mining
Authors: Hikmawati Erna , Maulidevi Nur Ulfa , Surendro Kridanto 
Published: July 2021

Abstract
The process of extracting data to obtain useful information is known as data mining. Furthermore, one of the promising and widely used techniques for this extraction process is association rule mining. This technique is used to identify interesting relationships between sets of items in a dataset and predict associative behavior for new data. The first step in association rule mining is the determination of the frequent item set that will be involved in the rule formation process. In this step, a threshold is used to eliminate items excluded in the frequent itemset which is also known as the minimum support. Furthermore, the threshold provides an important role in determining the number of rules generated. However, setting the wrong threshold leads to the failure of the association rule mining to obtain rules. Currently, the minimum support value is determined by the user. This leads to a challenge that becomes worse for a user that is ignorant of the dataset characteristics. In this study, a method was proposed to determine the minimum support value based on the characteristics of the dataset. Furthermore, this required certain criteria to be used as thresholds which led to more adaptive rules according to the needs of the user. The results of this study showed that 6 from 8 datasets, obtained a rule with lift ratio > 1 using the minimum threshold value that was determined through this method.
Development and Identification of Four New Synthetic Hexaploid Wheat Lines with Solid Stems
Authors: Liang Dongyu , Zhang Minghu , Liu Xin , Li Hui , Jia Zhenjiao , Wang Dinghao , Peng Ting , Hao Ming , Liu Dengcai , Jiang Bo , Huang Lin , Ning Shunzong , Yuan Zhongwei , Chen Xuejiao , Zhang Lianquan 
Published: July 2021

Abstract
Stem solidness is an important agronomic trait for increasing the ability of wheat to resist lodging. In this study, four new synthetic hexaploid wheat with solid stems were developed from natural chromosome doubling of F1 hybrids between a solid-stemmed durum wheat (Triticum turgidum ssp. durum, 2n = 4x = 28, AABB) and four Aegilops tauschii (2n = 2x = 14, DD) accessions. The solid expression of the second internode at the base of the stem was stable for two synthetic hexalpoid wheat Syn-SAU-117 and Syn-SAU-119 grown in both the greenhouse and field. The lodging resistance of four synthetic solid-stem wheats is stronger than that of CS, and Syn-SAU-116 has the strongest lodging resistance, followed by Syn-SAU-119. The paraffin sections of the second internode showed that four synthetic wheat lines had large outer diameters, well-developed mechanical tissues, large number of vascular bundles, and similar anatomical characteristics with solid-stemmed durum wheat. The chromosomal composition of four synthetic hexaploid wheat was identified by FISH (fluorescence in situ hybridization) using Oligo-pSc119.2-1 and Oligo-pTa535-1. At adult stage, all four synthetic hexaploid wheat showed high resistance to mixed physiological races of stripe rust pathogen (CYR31, CYR32, CYR33, CYR34). These synthetic hexaploid wheat lines provide new materials for the improvement of common wheat.
Role of Cellulose Nanofibrils in Improving the Strength Properties of Papers: A Review
Authors: Jele Thabisile Brightwell , Lekha Prabashni , Sithole Bruce 
Published: July 2021

Abstract
The pursuit for sustainability in the papermaking industry calls for the elimination or reduction of synthetic additives and the exploration of renewable and biodegradable alternatives. Cellulose nanofibrils (CNFs), due to their inherent morphological and biochemical properties, are an excellent alternative to synthetic additives. These properties enable CNFs to improve the mechanical, functional and barrier properties of different types of paper. The nanosize diameter, micrometre length, semi-crystalline structure, high strength and modulus of CNFs has a direct influence on the mechanical properties of paper such as tensile index, burst index, Scott index, breaking length, tear index, Z-strength, E-modulus, strain at break, and tensile stiffness. This review details the role played by CNFs as an additive to improve strength properties of papers and the factors affecting the improvement in paper quality when CNFs are added as additives. The paper also includes techno-economic aspects of the process and identifies areas that need further research.
Exosomal let-7f-5p Derived from Mineralized Osteoblasts Promotes the Angiogenesis of Endothelial Cells via DUSP1 / Erk1/2 Signaling Pathway
Authors: He Yiqun , Li Hailong , Yu Zuochong , Li Linli , Chen Xujun , Yang Aolei , Lyu Feizhou , Dong Youhai 
Published: July 2021

Abstract
Background: Angiogenesis is essential for the tissue engineering bone formation, and osteoblasts (OBs) has been proved to play an important role in angiogenesis via various pro-angiogenic factors. However, whether the mineralized osteoblast derived exosomes (MOB-Exos) and containing let-7f-5p can promote the angiogenesis of endothelial cells (ECs) is still unknown.Methods: MOB-Exos, let-7f-5p mimicked MOB-Exos (miR mimic group) and let-7f-5p inhibited MOB-Exos (miR inhibitor group) were respectively harvested from mineralized osteoblasts (MOBs) and then co-cultured with bEnd.3. Besides, the Erk1/2 signaling pathway in ECs in miR mimic group was inhibited. Subsequently, CCK-8 assays, wound healing assays, transwell migration assays and tube formation assays were performed to detect the angiogenic capability of ECs. Dual luciferase reporter assays were conducted to verify the target genes of exosomal let-7f-5p. Results: The results showed that MOB-Exos could significantly promote the angiogenesis of ECs, which could be enhanced by mimicking exosomal let-7f-5p, and attenuated by inhibiting exosomal let-7f-5p. And the angiogenic capability of ECs was partly impaired after inhibiting the Erk1/2 signaling pathway despite co-cultured with let-7f-5p mimicked MOB-Exos. Moreover, let-7f-5p suppressed the luciferase activity of wide-type DUSP1, while mutation of DUSP1 abrogated the repressive ability of let-7f-5p. Conclusion: Based the results, our study concluded that exosomal let-7f-5p derived from MOBs could promote the angiogenesis of ECs via activating DUSP1/Erk1/2 signaling pathway, which might be a promising target for tissue engineering bone formation.
Cellulosic Ethanol Production by Engineered Consortia of Scheffersomyces Stipitis and Zymomonas Mobilis
Authors: Sun Lingling , Wu Bo , Zhang Zengqin , Yan Jing , Liu Panting , Song Chao , Shabbir Samina , Zhu Qili , Yang Shihui , Peng Nan , He Mingxiong , Tan Furong 
Published: July 2021

Abstract
Background: As one of the clean and sustainable energies, lignocellulosic ethanol has achieved much attention around the world. The production of lignocellulosic ethanol does not compete with people for food, while the consumption of ethanol could contribute to the carbon dioxide emission reduction. Two of the conditions that are needed to attain cost-efficient lignocellulosic ethanol production at an industrial scale are the simultaneous transformation of glucose and xylose to ethanol and a highly efficient ethanol fermentation process. Results: In this study, the consortia consisting of suspended Scheffersomyces stipitis CICC1960 and Zymomonas mobilis 8b were cultivated to successfully depress carbon catabolite repression (CCR) in 80G40XRM. With this strategy, a 5.52% more xylose consumption and a 6.52% higher ethanol titer were achieved by the consortium, in which the inoculation ratio between S. stipitis and Z. mobilis was 1:3, at the end of fermentation compared with the Z. mobilis 8b mono-fermentation. Subsequently, one copy of the xylose metabolic genes was inserted into the Z. mobilis 8b genome to construct Z. mobilis FR2, leading to the xylose final-consumption amount and ethanol titer improvement by 15.36% and 6.81%, respectively. Finally, various concentrations of corn stover hydrolysates, in which the sum of glucose and xylose concentrations in the hydrolysates were 60, 90, and 120 g/L respectively, were used to evaluate the fermentation performance of the consortium consisting of S. stipitis CICC1960 and Z. mobilis FR2. Fermentation results showed that a 1.56% - 4.59% higher ethanol titer was achieved by the consortium compared with the Z. mobilis FR2 mono-fermentation, and a 46.12% - 102.14% higher ethanol titer was observed in the consortium fermentation when compared with the S. stipitis CICC1960 mono-fermentation. Conclusions: The fermentation strategy used in this study, i.e., using a genetically modified consortium, had a superior performance in ethanol production, as compared with the S. stipitis CICC1960 mono-fermentation and the Z. mobilis FR2 mono-fermentation alone. Thus, this strategy has potential for future lignocellulosic ethanol production.
Application of AI Modeling Technology Based on Fluid Biopsy to Diagnose AD
Authors: Sh Yuan , Liu Benliang , Zhang Jianhu , Zhou Ying , Hu Zhiyuan , zhang Xiuli 
Published: July 2021

Abstract
BackgroundThere are no obvious clinical symptoms in the early stages of Alzheimer's disease (AD). Therefore, the diagnosis of AD directly leads to serious lag. Studies have shown that most patients usually have mild cognitive impairment (MCI) before diagnosis. Therefore, the actual time of diagnosis of AD is much later than the time of onset. This brings great difficulties to the late treatment and management of patients. Therefore, early diagnosis of AD is very important. This paper mainly discusses the blood biomarkers of AD patients and uses machine learning methods to find the changes of blood transcriptome during the development of AD, and to search for potential blood biomarkers.MethodIndividualized blood mRNA expression data were downloaded from the GEO database in 711 patients, including control group (CON) (238 patients), MCI (189 patients), and AD (284 patients). Firstly, we analyzed the subcellular localization, protein types and enrichment pathways of the differentially expressed mRNAs in each group, and established an artificial intelligence individualized diagnostic model. Furthermore, Xcell tool was used to analyze the blood mRNA expression data to obtain the composition and quantitative data of blood cells. Ratio characteristics were established for mRNA and Xcell data respectively. Feature engineering operations such as collinearity and importance analysis are performed on all features to obtain the best feature solicitation. Finally, four machine learning algorithms, including linear support vector machine (SVM), Adaboost, random forest and artificial neural network, were used to model the optimal feature combinations and evaluate their classification performance in the test set.ResultA total of 5625 differential mRNAs were obtained by differential analysis of blood mRNAs. Through feature engineering screening, the best feature collection was obtained, and the artificial intelligence individualized diagnosis model established based on this method achieved a classification accuracy of 91.59% in the test set. The AUC of CON, MCI and AD were 0.9746, 0.9536 and 0.9807, respectively. ConclusionThe 181 features are composed of four dimensions, which can accurately classify CON, MCI and AD groups, suggesting that machine learning methods can capture changes in blood biomarkers in Alzheimer's patients. The results of cell homeostasis analysis suggested that the homeostasis of NTK cells might be related to AD, and the homeostasis of GMP might be one of the reasons for AD.
Stochastic Nonlinear Ground Response Analysis Considering Existing Boreholes Locations by the Geostatistical Method
Authors: Amjadi A.H. , johari Ali 
Published: July 2021

Abstract
The field and laboratory evidence of nonlinear soil behavior, even at small strains, emphasizes the ‎importance of employing nonlinear methods in seismic ground response analysis. Additionally, ‎determination of dynamic characteristics of soil layers always includes some degree of uncertainty. Most of ‎previous stochastic studies of ground response analysis have focused only on uncertainties of soil ‎parameters, and the effect of soil sample location has been mostly ignored. This study attempts to couple ‎nonlinear time-domain ground response analysis with uncertainty of soil parameters considering existing ‎boreholes’ ‎location through a geostatistical method using a program written in MATLAB. To evaluate ‎the efficiency of the proposed method, stochastic seismic ground responses at construction location were compared with those of the non-stationary random ‎field method‎ through real site data. The ‎results demonstrate that applying the boreholes’ ‎location significantly affects not only the ground ‎responses but also their Coefficient Of Variation (COV). Furthermore, the mean value of the seismic ‎responses is affected more considerably by the values of soil parameters at the vicinity of the construction location. It is also inferred that considering boreholes’ location may reduce the COV of the seismic ‎responses. Among the surface responses in the studied site, the values of Peak Ground Displacement (PGD) ‎and Peak Ground Acceleration (PGA) reflect the highest and ‎lowest dispersion due to uncertainties of soil ‎properties through both non-stationary random field and geostatistical methods.
Changes in the Excitability of Primary Hippocampal Neurons Following Exposure to 3.0 GHz Radiofrequency Electromagnetic Fields
Authors: Echchgadda Ibtissam , Cantu Jody C. , Tolstykh Gleb P. , Butterworth Joseph W. , Payne Jason A. , Ibey Bennett L. 
Published: July 2021

Abstract
Exposures to low intensity radiofrequency electromagnetic fields (RF-EMFs, 100 kHz to 6 GHz) have been associated with both positive and negative effects on cognitive behavior. To elucidate the mechanism of RF-EMF interaction, a few studies have examined its impact on neuronal activity and synaptic plasticity. However, there is still a need for additional basic research that verify the reported effects and further our understanding of the underlying mechanisms of RF-EMFs on the neuronal system. The present study investigated changes in neuronal activity and synaptic transmission following a 60-min exposure to 3.0 GHz RF-EMF at a low dose (specific absorption rate (SAR) < 1 W/kg). We showed that RF-EMF exposure decreased the amplitude of action potential (AP), depolarized neuronal resting membrane potential (MP), and increased neuronal excitability and synaptic transmission in cultured primary hippocampal neurons (PHNs). The results show that RF-EMF exposure can alter neuronal activity and highlight that more investigations should be performed to fully explore the RF-EMF effects and mechanisms.
Novel sites for Cathepsin L cleavage in SARS-CoV-2 spike guide treatment strategies
Authors: Yang Jin-Kui , Zhao Miao-Miao , Zhu Yun , Zhang Li , Zhong Gongxun , Tai Linhua , Liu Shuo , Yin Guoliang , Huang Weijin , Fan Changfa , Shuai Lei , Wen Zhiyuan , Wang Chong , He Xijun , Bu Zhigao , Wang Youchun , Sun Fei 
Published: July 2021

Abstract
The spike (S) protein of SARS coronavirus 2 (SARS-CoV-2) is an ideal target for the development of specific vaccines or drugs. However, treatments targeting viruses with mutant S proteins that have recently emerged in many countries are limited. Cleavage of the S protein by host proteases is essential for viral infection. Here, we discovered two novel sites (CS-1 and CS-2) in the S protein for cleavage by the protease Cathepsin L (CTSL). Both sites are highly conserved among all SARS-CoV-2 variants of concern. Cryo-electron microscopy structural studies revealed that CTSL cleavage increases the dynamics of the receptor binding domain of S and induces novel conformations. In our pseudovirus (PsV) infection experiment, alteration of the cleavage site significantly reduced the infection efficiency, and CTSL inhibitors markedly inhibited infection with PsVs of both the wild-type and emerged SARS-CoV-2 variants. Furthermore, six highly efficient CTSL inhibitors were found to effectively inhibit live virus infection in human cells in vitro, and two of these were further confirmed to prevent live virus infection in human ACE2 transgenic mice in vivo. Our work suggested that the CTSL cleavage sites in SARS-CoV-2 S are emerging new but effective targets for the development of mutation-resistant vaccines and drugs.
Adverse Childhood Experiences, Associated Stressors and Comorbidities in Children and Youth with Fetal Alcohol Spectrum Disorder across the Child Protection and Justice Settings in Western Australia
Authors: Tan Grace Kuen Yee , Symons Martyn , Fitzpatrick James , Connor Sophia G , Cross Donna , Pestell Carmela 
Published: July 2021

Abstract
Background: Individuals with Fetal Alcohol Spectrum Disorder (FASD) are at risk of having adverse childhood experiences (ACEs), especially those with child protection or justice system involvement. The complex relationship between FASD and psychosocial vulnerabilities in the affected individual is an important clinical risk factor for comorbidity. This study (1) explored the ACEs and associated stressors in individuals with FASD; (2) compared the ACEs profiles of those who had been involved with the child protection system only, justice system only, both or neither; (3) examined the relationship between ACEs and comorbid conditions such as mood and neurodevelopmental disorders. Methods: Data were collected retrospectively via file review from diagnostic clinics in Western Australia. Life adversity was coded using a standardised ACEs questionnaire. 211 participants (71.6% males) with FASD with a mean age of 11.12 (range = 2-21) were included in the total sample.Results: Exposure to drinking/substance misuse at home (70.1%) and domestic violence (51.7%) were the two most common ACEs across the total sample. In the entire cohort, 39.3% had four or more ACEs. Additional stressors reported were involvement with child protection system (69.7%), disengagement from school (43.1%), trouble with the law (40.3%), transiency (19.0%), documented victims of bullying (12.3%), traumatic brain injury (9.0%) and homelessness (5.2%). Further, individuals who had been involved with both the child protection and justice system were four to eleven times more likely than those without any involvement to report parental drinking/substance misuse at home, parental incarceration, exposure to domestic violence and emotional/physical neglect. Higher rates of life adversity in this clinical population were associated with an increased number of comorbidities, p <.001. Specifically, those with FASD who had comorbidities such as attachment disorder (p =.001), substance use disorder (p =.006), and PTSD (p <.001) also reported higher ACEs score.Conclusion: ACEs were common in this clinical population, particularly those who had been involved with both the child protection and justice system. Increased ACEs in this clinical population were associated with increased comorbidities. This highlights that prevention, intervention and early diagnosis of FASD are important for at risk children to reduce the negative effects of ACEs.
Advanced Artificial Intelligence Assisted Learning Model for Smart Educational Learning Strategies for Teachers and Students
Authors: Yu Xiaobing 
Published: July 2021

Abstract
A smart classroom is a technology-based learning approach that helps to improve students ' skills in vocational colleges. It allows educators to create an interactive experience during teaching, which makes the system even more appealing and collaborative in learning strategies.In this paper, Advanced Artificial Intelligence assisted Learning model (AAILM)is used to design a smart interactive education system for vocational education.A qualitative approach is used on 40 vocational colleges around Chinato analyze thefindings, interviews, and focus group dialogue based on the learning strategies followed by students and teachers. Furthermore, the results of this analysis showed many improvements in the positions of teachers and students in the smart learning program.The study consisted of 102 students, and the results have been checkedbased on F-Test and T-Test. The result findings revealed that the academic successes of students in traditional and vocational colleges vary significantly and computed with graphical illustration as well as case study. The use of smart instruction has dramatically increased the success of teachers and students has been firmly researched in this paper. The simulation results illustrate that the suggestedmodelhas a high student's performance ratio of 98.6%, a prediction ratio of 95.7%, a student engagement ratio of 94.6%, a task analysis ratio of 96.8%, an efficiency ratio of 97.8% when compared to other existing methods.
Estimation of the critical points of an epidemic by means of a logistic growth model
Authors: Allaman Ivan Bezerra , Jelihovschi Enio Galinkin 
Published: July 2021

Abstract
Epidemiological models have become a very important tool in understanding an epidemic’s development, mainly because they help researchers find more efficient strategies in their fight against its spread. Several models have been proposed up to now: some use fractional calculus to solve differential equations while others use applications from other areas such as predatorprey models. The SIR and SEIR models, among others, mainly focus on the variable response and on epidemiological parameters such as the basic reproduction number (R0) and infection rate per unit of time, nevertheless they do not focus on the variable ‘time’. We propose the use of the variable time, as the main variable, by using a reparametrization in the logistic model since it will lead to the understanding of the epidemic as it goes along the time. Moreover, this model is important because it allows the estimation of the points of acceleration and deceleration, the point of maximum growth and the asymptotic point of the epidemic. This is only possible by getting an stable epidemic curve with an ‘S’ shape. In this work we use the variable ‘accumulated cases’ of COVID-19 of China and Italy and point out the main socioeconomic facts that occurred in each period of the estimated critical points from the logistic growth model.