bluuebunny/crossref_metadata_2025 huggingface dataset:
Supervised Machine Learning Algorithms for Sentiment Analysis of Bangla Newspaper
Authors: Maisha Sabrina Jahan , Nafisa Nuren , Muhammad Masum Abdul Kadar
Published: November 2021

We can state undoubtedly that Bangla language is rich enough to work with and implement various Natural Language Processing (NLP) tasks. Though it needs proper attention, hardly NLP field has been explored with it. In this age of digitalization, large amount of Bangla news contents are generated in online platforms. Some of the contents are inappropriate for the children or aged people. With the motivation to filter out news contents easily, the aim of this work is to perform document level sentiment analysis (SA) on Bangla online news. In this respect, the dataset is created by collecting news from online Bangla newspaper archive.  Further, the documents are manually annotated into positive and negative classes. Composite process technique of “Pipeline” class including Count Vectorizer, transformer (TF-IDF) and machine learning (ML) classifiers are employed to extract features and to train the dataset. Six supervised ML classifiers (i.e. Multinomial Naive Bayes (MNB), K-Nearest Neighbor (K-NN), Random Forest (RF), (C4.5) Decision Tree (DT), Logistic Regression (LR) and Linear Support Vector Machine (LSVM)) are used to analyze the best classifier for the proposed model. There has been very few works on SA of Bangla news. So, this work is a small attempt to contribute in this field. This model showed remarkable efficiency through better results in both the validation process of percentage split method and 10-fold cross validation. Among all six classifiers, RF has outperformed others by 99% accuracy. Even though LSVM has shown lowest accuracy of 80%, it is also considered as good output. However, this work has also exhibited surpassing outcome for recent and critical Bangla news indicating proper feature extraction to build up the model.
Timetable Scheduling System using Genetic Algorithm for School of Computing (tsuGA)
Authors: Kutty Mammi Hazinah , Ying Ying Lim
Published: November 2021

Current timetable scheduling system in School of Computing(SC), Universiti Teknologi Malaysia(UTM) is done manually which consumes time and human effort. In this project, a Genetic Algorithm (GA) approach is proposed to aid the timetable scheduling process. GA is a heuristic search algorithm which finds the best solution based on current individual characteristics. Using GA and scheduling info such as rooms available and timeslots needed, it is shown that scheduling can be done more efficiently, with less time, effort and errors. As a testbed, a web application is developed to maintain records needed and generate timetables. Introduction of GA helps in generating a timetable automatically based on information such as rooms, subjects, lecturers, student group and timeslot. GA reduces human error and human efforts in the timetable scheduling process.
Study on Gender Identification Based on Audio Recordings Using Gaussian Mixture Model and Mel Frequency Cepstrum Coefficient Technique
Authors: Rokanatnam Thurgeaswary , Kutty Mammi Hazinah
Published: November 2021

Speaker recognition is an ability to identify speaker’s characteristics based from spoken language. The purpose of this study is to identify gender of speakers based on audio recordings. The objective of this study is to evaluate the accuracy rate of this technique to differentiate the gender and also to determine the performance rate to classify even when using self-acquired recordings. Audio forensics uses voice recordings as part of evidence to solve cases. This study is mainly conducted to provide an easier technique to identify the unknown speaker characteristics in forensic field. This experiment is fulfilled by training the pattern classifier using gender dependent data. In order to train the model, a speech database is obtained from an online speech corpus comprising of both male and female speakers. During the testing phase, apart from the data from speech corpus, audio recordings of UTM students will too be used to determine the accuracy rate of this speaker identification experiment. As for the technique to run this experiment, Mel Frequency Cepstrum Coefficient (MFCC) algorithm is used to extract the features from speech data while Gaussian Mixture Model (GMM) is used to model the gender identifier. Noise removal was not used for any speech data in this experiment. Python software is used to extract using MFCC coefficients and model the behavior using GMM technique. Experiment results show that GMM-MFCC technique can identify gender regardless of language but with varying accuracy rate.
Investigation of Two-Phase Rimming Flow and Heat Transfer Inside Rotational Paper Cylinder Dryers Using Three Multiphase Computational Models
Authors: Majeed Hamed Abdul , Pereira Victor Barboza , Wang Ting , D’Amico Joseph V. , Kononchek Chris
Published: November 2021

The paper industry uses rotating cylinder dryers that employ steam to heat the paper web moving over the cylinder outer walls. As steam condenses, the condensate is accumulated inside the dryers and evacuated using siphons. The form of condensate motion occurring inside a rotating dryer consists of three modes: puddling, cascading, or rimming. To help improve the drying performance, it is important to understand the fundamental thermal-fluid physics in the rotational dryer. Thus, the objectives of this study are to (a) investigate the dynamic two-phase flow and heat transfer behavior inside the rotational dryer at different rotational speeds; (b) employ three different multiphase computational models, the Volume of Fluid (VOF) model, the Mixture model, and the Eulerian–Eulerian (E–E) model; and compare their results. The results show that the E–E model better captures the physics of condensate behavior inside the dryer. It also predicts very well the rimming speed in comparison with the empirical correlation although it takes longer computational time than the VOF model. The mixture model does not adequately capture the cascade and rimming physics due to excessive liquid dispersion. Based on the results, the categorization of the thermal-flow behavior of the liquid layer is expanded from the traditional three phases to five phases: puddling, transitional cascading, cascading, transitional rimming, and steady rimming. Generally, the heat transfer increases during the initial puddling period, followed by oscillatory attenuation during the cascade period, and finally reaches the steady-state after rimming is achieved.
Acoustofluidic dynamic interfacial tensiometry
Authors: Farooq Umar , Liu Yuanting , Li Pengqi , Deng Zhiting , Liu Xiufang , Zhou Wei , Yi Shasha , Rong Ning , Meng Long , Niu Lili , Zheng Hairong
Published: November 2021

The interfacial tension (IFT) of fluids plays an essential role in industrial, biomedical, and synthetic chemistry applications; however, measuring IFT at ultralow volumes is challenging. Here, we report a novel method for sessile drop tensiometry using surface acoustic waves (SAWs). The IFT of the fluids was determined by acquiring the silhouette of an axisymmetric sessile drop and applying iterative fitting using Taylor's deformation equation. Owing to physiochemical differences, upon interacting with acoustic waves, each microfluid has a different streaming velocity. This streaming velocity dictates any subsequent changes in droplet shape (i.e., height and width). We demonstrate the effectiveness of the proposed SAW-based tensiometry technique using blood plasma to screen for high leptin levels. The proposed device can measure the IFT of microscale liquid volumes (up to 1 μL) with an error margin of only ±5% (at 25 °C), which deviates from previous reported results. As such, this method provides pathologists with a solution for the pre-diagnosis of various blood-related diseases.
An Aniline-Substituted Bile Salt Analog Protects both Mice and Hamsters from Multiple Clostridioides difficile Strains
Authors: Phan Jacqueline R. , Do Dung M. , Truong Minh Chau , Ngo Connie , Phan Julian H. , Sharma Shiv K. , Schilke Angel , Mefferd Chrisabelle C. , Villarama Jacob V. , Lai Dengxun , Consul Amber , Hedlund Brian P. , Firestine Steven M. , Abel-Santos Ernesto
Published: November 2021

Clostridioides difficile
infection (CDI) is the major identifiable cause of antibiotic-associated diarrhea. The emergence of hypervirulent
C. difficile
strains has led to increases in both hospital- and community-acquired CDI.
Conserved Tandem Arginines for PbgA/YejM Allow Salmonella Typhimurium To Regulate LpxC and Control Lipopolysaccharide Biogenesis during Infection
Authors: Giordano Nicole P. , Mettlach Joshua A. , Dalebroux Zachary D.
Published: November 2021

Enterobacteriaceae use the periplasmic domain of the conserved inner membrane protein, PbgA/YejM, to regulate lipopolysaccharide (LPS) biogenesis.
Salmonella enterica
serovar Typhimurium (
S.
Typhimurium) relies on PbgA to cause systemic disease in mice and this involves functional interactions with LapB/YciM, FtsH, and LpxC.
Escherichia coli
PbgA interacts with LapB, an adaptor for the FtsH protease, via the transmembrane segments.
U-Pb (ID-TIMS) Geochronological Studies of High-Uranium Metamict Zircons: New Opportunities of Familiar Approaches
Authors: Ivanova A. A. , Salnikova E. B. , Kotov A. B. , Plotkina Yu. V. , Tolmacheva E. V. , Syritso L. F. , Bocharov V. N.
Published: November 2021

The possibility of the application of high-uranium self-irradiation-damaged metamict zircon for U-Pb geochronological studies (ID-TIMS) after preliminary high-temperature annealing and acid treatment was demonstrated using zircon from the Li–F granites of the Turga massif (Eastern Transbaikalia). Optimal conditions of high-temperature annealing and acid treatment of zircon with high self-irradiation α-dose can be selected to remove completely a metamict phase and preserve sufficient quantities of the mineral for U-Pb (ID-TIMS) studies.
Regression models of carbon and CO2 sequestration of hybrid poplar plantations in northern Serbia
Authors: Čomić Dragan R.
Published: November 2021

This research contributes to the assessment of the potential for carbon dioxide (CO2) sequestration in poplar plantations, through applying regression models of total and annual sequestration as related to stand age. The study was carried out in northern Serbia, in plantations of Populus ×euramericana (Dode) Guinier clone I-214. Based on the measured data obtained during the field research, a modeling framework for quantifying carbon (C) sequestration in forest ecosystems (CO2FIX version 3.1) was used to calculate the total carbon stored in fresh (aboveground and belowground) biomass, in soil organic matter, and in wood products. Four regression models were created as a part of the research: (i) total C sequestration model, (ii) total CO2 sequestration and carbon credit (CO2e) generation model, (iii) annual C sequestration model, and (iv) annual CO2 sequestration and annual carbon credit generation model. The stand-level research results indicated that the total sequestration of C for a 30-year production cycle was 78.58 t C·ha−1, while the average value for all years of a 30-year production cycle was 44.02 t C·ha−1. The average annual sequestration of C for all years of a 30-year production cycle was 2.62 t C·ha−1·year−1, while the average annual sequestration of CO2 or the average annual changes in CO2 stocks for all years of a 30-year production cycle was 9.60 t CO2·ha−1·year−1.
Understanding the Working Habits of GH-SO Users on GitHub Commit Activity and Stack Overflow Post Activity
Authors: Kim Jungil , Lee Eunjoo
Published: November 2021

GitHub and Stack Overflow are often used together for software development. GH-SO users, who use both GitHub and Stack Overflow, contribute to the development of various software projects in GitHub and share their knowledge and experience on software development in Stack Overflow. To widely understand the interests and working habits of GH-SO users on software development, it is important to investigate how GH-SO users utilize GitHub and Stack Overflow. In this paper, we present an exploratory study on GitHub commit and Stack Overflow post activities of GH-SO users. Specifically, we investigate the working habits of GH-SO users on GitHub commit and Stack Overflow post activities. We randomly selected 19,756 of GH-SO users as our target sample and collected 2,819,483 and 2,147,317 of commit activity data and post activity data of the GH-SO users. We then categorized the collected commit and post activity datasets into specific categories on programming languages and statistically analyzed the categorized commit and post activity datasets. As the results of our analysis, we found the following: (1) The overall commit and post activities of the GH-SO users share some similarity. (2) The commit activities gradually change while the post activities drastically change over time. (3) The commit activities of the GH-SO users are broadly distributed while the post activities are narrowly distributed and the commit activity can be better predictor for post activity. (4) The commit activity of the GH-SO users tends to be performed prior post activity. We believe that our findings can contribute to finding the ways to better support commit and post activities of GitHub and Stack Overflow users.
Embodying the Number of an Entity’s Relations for Knowledge Representation Learning
Authors: Suo Xinhua , Guo Bing , Shen Yan , Wang Wei , Chen Yaosen , Zhang Zhen
Published: November 2021

Knowledge representation learning (knowledge graph embedding) plays a critical role in the application of knowledge graph construction. The multi-source information knowledge representation learning, which is one class of the most promising knowledge representation learning at present, mainly focuses on learning a large number of useful additional information of entities and relations in the knowledge graph into their embeddings, such as the text description information, entity type information, visual information, graph structure information, etc. However, there is a kind of simple but very common information — the number of an entity’s relations which means the number of an entity’s semantic types has been ignored. This work proposes a multi-source knowledge representation learning model KRL-NER, which embodies information of the number of an entity’s relations between entities into the entities’ embeddings through the attention mechanism. Specifically, first of all, we design and construct a submodel of the KRL-NER LearnNER which learns an embedding including the information on the number of an entity’s relations; then, we obtain a new embedding by exerting attention onto the embedding learned by the models such as TransE with this embedding; finally, we translate based onto the new embedding. Experiments, such as related tasks on knowledge graph: entity prediction, entity prediction under different relation types, and triple classification, are carried out to verify our model. The results show that our model is effective on the large-scale knowledge graphs, e.g. FB15K.
STATIONARY DISTRIBUTION AND EXTINCTION OF STOCHASTIC CORONAVIRUS (COVID-19) EPIDEMIC MODEL
Authors: KHAN AMIR , ULLAH HEDAYAT , ZAHRI MOSTAFA , HUMPHRIES USA WANNASINGHA , KARITE TOURIA , YUSUF ABDULLAHI , ULLAH HAKEEM , FIZA MEHREEN
Published: November 2021

The aim of this paper is to model corona-virus (COVID-19) taking into account random perturbations. The suggested model is composed of four different classes i.e. the susceptible population, the smart lockdown class, the infectious population, and the recovered population. We investigate the proposed problem for the derivation of at least one unique solution in the positive feasible region of nonlocal solution. For one stationary ergodic distribution, the necessary result of existence is developed by applying the Lyapunov function and the condition for the extinction of the disease is also established. The obtained results show that the effect of Brownian motion and noise terms on the transmission of the epidemic is very high. If the noise is large the infection may decrease or vanish. For validation of our obtained scheme, the results for all the classes of the problem have been simulated numerically.
Existence and uniqueness results for Riemann–Stieltjes integral boundary value problems of nonlinear implicit Hadamard fractional differential equations
Authors: Abbas Mohamed I.
Published: November 2021

In this paper, we study the existence and uniqueness of solutions for Riemann–Stieltjes integral boundary value problems of nonlinear implicit Hadamard fractional differential equations. The investigation of the main results depends on Schauder’s fixed point theorem and Banach’s contraction principle. An illustrative example is given to show the applicability of theoretical results.
Global Strategies to Minimize Environmental Impacts of Ruminant Production
Authors: Du Yuanyuan , Ge Ying , Chang Jie
Published: November 2021

Demand for ruminant products (dairy products, beef, and sheep meat) is increasing rapidly with population and income growth and the acceleration of urbanization. However, ruminant animals exert the highest environmental impacts and consume the most resources in the livestock system. Increasing studies have focused on various measures to reduce ammonia, greenhouse gas emissions, and resource depletion from ruminant production to consumption. This review offers supply- and demand-side management strategies to reduce the environmental impact of ruminant products and emphasizes the mitigation potential of coupling livestock production with cultivation and renewable energy. On a global scale, more attention should be paid to the green-source trade and to strengthening global technology sharing. The success of these strategies depends on the cost effectiveness of technology, public policy, and financial support. Future studies and practice should focus on global database development for sharing mitigation strategies, thus facilitating technology innovations and socioeconomic feasibility.
Running the Female Power Grid Across Lifespan Through Brain Estrogen Signaling
Authors: Ingraham Holly A. , Herber Candice B. , Krause William C.
Published: November 2021

The role of central estrogen in cognitive, metabolic, and reproductive health has long fascinated the lay public and scientists alike. In the last two decades, insight into estrogen signaling in the brain and its impact on female physiology is beginning to catch up with the vast information already established for its actions on peripheral tissues. Using newer methods to manipulate estrogen signaling in hormone-sensitive brain regions, neuroscientists are now identifying the molecular pathways and neuronal subtypes required for controlling sex-dependent energy allocation. However, the immense cellular complexity of these hormone-sensitive brain regions makes it clear that more research is needed to fully appreciate how estrogen modulates neural circuits to regulate physiological and behavioral end points. Such insight is essential for understanding how natural or drug-induced hormone fluctuations across lifespan affect women's health.
The Power of Beliefs
Authors: Levi Margaret
Published: November 2021

My commitment to combining normative concerns with empirical social science led, perhaps a bit counterintuitively, to early adoption of rational choice political economy. However, it was a modified form of rational choice that takes into account ethical and societal concerns. This was the approach I applied to considerations of compliance and consent with government, what makes a trustworthy government, the formation of legitimating beliefs, and finally the construction of an expanded and inclusive community of fate as a building block for a new moral political economy.
Is It Good to Have a Stiff Aorta with Aging? Causes and Consequences
Authors: Pierce Gary L. , Coutinho Thais A. , DuBose Lyndsey E. , Donato Anthony J.
Published: November 2021

Aortic stiffness increases with advancing age, more than doubling during the human life span, and is a robust predictor of cardiovascular disease (CVD) clinical events independent of traditional risk factors. The aorta increases in diameter and length to accommodate growing body size and cardiac output in youth, but in middle and older age the aorta continues to remodel to a larger diameter, thinning the pool of permanent elastin fibers, increasing intramural wall stress and resulting in the transfer of load bearing onto stiffer collagen fibers. Whereas aortic stiffening in early middle age may be a compensatory mechanism to normalize intramural wall stress and therefore theoretically “good” early in the life span, the negative clinical consequences of accelerated aortic stiffening beyond middle age far outweigh any earlier physiological benefit. Indeed, aortic stiffness and the loss of the “windkessel effect” with advancing age result in elevated pulsatile pressure and flow in downstream microvasculature that is associated with subclinical damage to high-flow, low-resistance organs such as brain, kidney, retina, and heart. The mechanisms of aortic stiffness include alterations in extracellular matrix proteins (collagen deposition, elastin fragmentation), increased arterial tone (oxidative stress and inflammation-related reduced vasodilators and augmented vasoconstrictors; enhanced sympathetic activity), arterial calcification, vascular smooth muscle cell stiffness, and extracellular matrix glycosaminoglycans. Given the rapidly aging population of the United States, aortic stiffening will likely contribute to substantial CVD burden over the next 2–3 decades unless new therapeutic targets and interventions are identified to prevent the potential avalanche of clinical sequelae related to age-related aortic stiffness.
Audit Data Analysis and Application Based on Correlation Analysis Algorithm
Authors: Chen Jifan , Talha Muhammad
Published: November 2021

Traditional audit data analysis algorithms have many shortcomings, such as the lack of means to mine the hidden audit clues behind the data, the difficulty of finding increasingly hidden cheating techniques caused by the electronic and networked environment, and the inability to solve the quality defects of the audited data. Correlation analysis algorithm in data mining technology is an effective means to obtain knowledge from massive data, which can complete, muffle, clean, and reduce defective data and then can analyze massive data and obtain audit trails under the guidance of expert experience or analysts. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounds the research status and significance of audit data analysis and application; elaborates the development background, current status, and future challenges of correlation analysis algorithm; introduces the methods and principles of data model and its conversion and audit model construction; conducts audit data collection and cleaning; implements audit data preprocessing and its algorithm description; performs audit data analysis based on correlation analysis algorithm; analyzes the hidden node activation value and audit rule extraction in correlation analysis algorithm; proposes the application of audit data based on correlation analysis algorithm; discusses the relationship between audit data quality and audit risk; and finally compares different data mining algorithms in audit data analysis. The findings demonstrate that by analyzing association rules, the correlation analysis algorithm can determine the significance of a huge quantity of audit data and characterise the degree to which linked events would occur concurrently or sequentially in a probabilistic manner. The correlation analysis algorithm first inputs the collected audit data through preprocessing module to filter out useless data and then organizes the obtained data into a format that can be recognized by data mining algorithm and executes the correlation analysis algorithm on the sorted data; finally, the obtained hidden data is divided into normal data and suspicious data by comparing it with the pattern in the rule base. The algorithm can conduct in-depth analysis and research on the company’s accounting vouchers, account books, and a large number of financial accounting data and other data of various natures in the company’s accounting vouchers; reveal its original characteristics and internal connections; and turn it into an audit. People need more direct and useful information. The study results of this paper provide a reference for further researches on audit data analysis and application based on correlation analysis algorithm.
Urinary Tract Infections in the First 6 Months after Renal Transplantation
Authors: Arabi Ziad , Al Thiab Khalefa , Altheaby Abdulrahman , Aboalsamh Ghaleb , Kashkoush Samy , Almarastani Mohamad , Shaheen Mohammed F. , Altamimi Abdulrahman , O’hali Wael , Bin Saad Khalid , Alnajjar Lina , Alhussein Rawan , Almuhiteb Raghad , Alqahtani Bashayr , Alotaibi Rayana , Alqahtani Marah , Tawhari Mohammed
Published: November 2021

Purpose. Urinary tract infections (UTIs) are common in the first 6 months after renal transplantation, and there are only limited data about UTIs after transplantation in Saudi Arabia in general. Methods. A retrospective study from January 2017 to May 2020 with 6-month follow-up. Results. 279 renal transplant recipients were included. Mean age was 43.4 ± 16.0 years, and114 (40.9%) were women. Urinary stents were inserted routinely during transplantation and were removed 35.3 ± 28 days postoperatively. Ninety-seven patients (35%) developed urinary tract infections (UTIs) in the first six months after renal transplantation. Of those who developed the first episode of UTI, the recurrence rates were 57%, 27%, and 14% for having one, two, or three recurrences, respectively. Late urinary stent removals, defined as more than 21 days postoperatively, tended to have more UTIs (OR: 1.43, P: 0.259, CI: 0.76–2.66). Age >40, female gender, history of neurogenic bladder, and transplantation abroad were statistically significant factors associated with UTIs and recurrence. Diabetes, level of immunosuppression, deceased donor renal transplantation, pretransplant residual urine volume, or history of vesicoureteral reflux (VUR) was not associated with a higher incidence of UTIs. UTIs were asymptomatic in 60% but complicated with bacteremia in 6% of the cases. Multidrug resistant organisms (MDROs) were the causative organisms in 42% of cases, and in-hospital treatment was required in about 50% of cases. Norfloxacin + Bactrim DD (160/800 mg) every other day was not associated with the lower risk of developing UTIs compared to the standard prophylaxis daily Bactrim SS (80/400 mg). Conclusion. UTIs and recurrence are common in the first 6 months after renal transplantation. Age >40, female gender, neurogenic bladder, and transplantation abroad are associated with the increased risk of UTIs and recurrence. MDROs are common causative organisms, and hospitalization is frequently required. Dual prophylactic antibiotics did not seem to be advantageous over the standard daily Bactrim.
Acute Toxicity, Phytochemical Screening, Analgesic, and Anti-Inflammatory Activities of Aqueous and Methanol Root Extracts of Maerua triphylla A. Rich. (Capparaceae)
Authors: Wangusi Brian Muyukani , Kanja Laetitia Wakonyu , Ole-Mapenay Isaac Mpapuluu , Onyancha Jared Misonge
Published: November 2021

Maerua triphylla root extracts are used by Maasai and Kikuyu communities in Kenya to manage headaches, stomachaches, migraines, and rheumatism. However, scientific data on their safety and efficacy are limited. The current study aims to investigate the safety, phytochemical constituents, analgesic, and anti-inflammatory activities of M. triphylla root extracts. Aqueous and methanol M. triphylla root extracts were prepared by cold maceration, and the extracts’ safety was evaluated using Wistar rats according to the Organization for Economic Cooperation and Development (2008) guidelines. Standard qualitative phytochemical screening methods were used for the detection of various phytochemical groups in the extracts. Analgesic activity assay in Swiss albino mice was done using the acetic acid-induced writhing test, while anti-inflammatory activity was determined in Wistar rats using the acetic acid-induced paw edema method. The methanol and aqueous extracts revealed LD50 > 2000 mg/kg bw, classifying them as nontoxic. The presence of cardiac glycosides, flavonoids, alkaloids, and phenols was observed in both extracts. However, saponins were only present in the methanol extract. In the analgesic study, mice that received 100 mg/kg bw and 500 mg/kg bw of aqueous root extract of M. triphylla had significantly lower acetic acid-induced writhing than mice that received acetylsalicylic acid 75 mg (reference drug) (p<0.05). Additionally, mice that received 500 mg/kg bw of methanol root extract of M. triphylla had significantly lower acetic acid-induced writhing than mice that received the acetylsalicylic acid 75 mg (p<0.05). In the anti-inflammatory study, there was no significant difference (p<0.05) between the inhibitory activity of different doses of the aqueous root extract of M. triphylla and a 50 mg/kg dose of diclofenac sodium (reference drug) on acetic acid-induced paw edema in rats. Moreover, there was no significant difference in the inhibitory activity of 100 mg/kg bw and 500 mg/kg bw doses of the methanol root extract of M. triphylla and a 50 mg/kg dose of diclofenac sodium on acetic acid-induced paw edema (p>0.05). These findings suggest that the roots of M. triphylla may be useful in the safe mitigation of pain and inflammation and therefore support their ethnomedicinal use in the management of pain and inflammation.
Toward the Development of Personalized Syndrome Discriminant Systems: A Discriminant System for Hypertension with Liver Yang Hyperactivity Syndrome
Authors: Shang Guang-yao , Zhang Lei , Lin Lin , Jiang Hai-qiang , Li Chao , Jiang Feng , Qi Dong-mei , Li Yun-lun , Yang Wen-qing
Published: November 2021

Traditional Chinese medicine has shown promising results in treating the symptoms of hypertension, a major global health concern not yet fully managed by modern medicine. It is, therefore, of high priority to clarify the altered pathophysiology of hypertension in individuals with liver Yang hyperactivity syndrome (HLYH) in response to effective treatments to better understand this disorder. The primary aim of this study was to construct a personalized syndrome discriminant system based on data capable of informing management strategies prior to the initiation of antihypertensive therapy or the implementation of screening strategies in at-risk HLYH. Based on the successful replication of HLYH rat models, we extracted the core discriminant factors of the disorder through the integration of physical signs, biochemical indicators, and metabolic markers. Macro and micro information was correlated to construct a syndrome discriminant system. At the macroscopic level, HLYH rat models characterized by elevated blood pressure were found to be associated with significant changes in water intake, pain threshold, retention time on a rotating platform, and body surface temperature. A total of 27 potential biomarkers and 14 metabolic pathways appeared to reflect the primary metabolic characteristics. Through the integration of these data, we successfully constructed a combined macro-micro personalized syndrome discriminant system, which provides a foundation for research regarding the risk loci of HLYH. Our findings also broaden our understanding of the biological pathways involved in HLYH.
[Retracted] LncRNA PVT1 Promotes Hypoxia‐Induced Cardiomyocyte Injury by Inhibiting miR‐214‐3p
Authors: Liu Chuanliang , Zhang Jieqiong , Lun Xuejie , Li Lei
Published: November 2021

Objective. To explore the effect and related mechanism of LncRNA PVT1 on hypoxia‐induced cardiomyocyte injury. Methods. PVT1RNA and miR‐214‐3p levels were detected by quantitative real‐time polymerase chain reaction (qRT‐PCR). Cell vitality and apoptosis were, respectively, evaluated by Cell Counting Kit‐8 (CCK‐8) and flow cytometry analysis. Starbase and Dual luciferase reporter (DLR) gene assay was employed to validate the interaction between miR‐214‐3p and PVT1. Results. PVT1 was statistically upregulated, and miR‐214‐3p was statistically downregulated in hypoxia‐induced H9c2 cells. The survival rate of H9c2 cells induced by hypoxia decreased statistically, while the apoptosis rate increased statistically (P < 0.05). PVT1 knockdown upregulated the hypoxia‐induced H9c2 cell viability and inhibited apoptosis. DLR assay verified the targeting relationship between PVT1 and miR‐214‐3p. In addition, miR‐214‐3p inhibitors reversed the viability of H9c2 cells with PVT1 knockout and promoted apoptosis. Conclusion. Silencing PVT1 can enhance the hypoxia‐induced H9c2 cell viability and inhibit apoptosis, providing a potential target for the treatment of cardiovascular diseases.
Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches
Authors: Bangyal Waqas Haider , Qasim Rukhma , Rehman Najeeb ur , Ahmad Zeeshan , Dar Hafsa , Rukhsar Laiqa , Aman Zahra , Ahmad Jamil
Published: November 2021

A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost,

K

-nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate...), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.
Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique
Authors: Jais Fatin Nabihah , Che Azemin Mohd Zulfaezal , Hilmi Mohd Radzi , Mohd Tamrin Mohd Izzuddin , Kamal Khairidzan Mohd
Published: November 2021

Introduction. Early detection of visual symptoms in pterygium patients is crucial as the progression of the disease can cause visual disruption and contribute to visual impairment. Best-corrected visual acuity (BCVA) and corneal astigmatism influence the degree of visual impairment due to direct invasion of fibrovascular tissue into the cornea. However, there were different characteristics of pterygium used to evaluate the severity of visual impairment, including fleshiness, size, length, and redness. The innovation of machine learning technology in visual science may contribute to developing a highly accurate predictive analytics model of BCVA outcomes in postsurgery pterygium patients. Aim. To produce an accurate model of BCVA changes of postpterygium surgery according to its morphological characteristics by using the machine learning technique. Methodology. A retrospective of the secondary dataset of 93 samples of pterygium patients with different pterygium attributes was used and imported into four different machine learning algorithms in RapidMiner software to predict the improvement of BCVA after pterygium surgery. Results. The performance of four machine learning techniques were evaluated, and it showed the support vector machine (SVM) model had the highest average accuracy (94.44% ± 5.86%), specificity (100%), and sensitivity (92.14% ± 8.33%). Conclusion. Machine learning algorithms can produce a highly accurate postsurgery classification model of BCVA changes using pterygium characteristics.
Clinical Features and Laboratory Examination to Identify Severe Patients with COVID‐19: A Systematic Review and Meta‐Analysis
Authors: Meng Yan , Wang Jinpeng , Wen Kaicheng , Da Wacili , Yang Keda , Zhou Siming , Tao Zhengbo , Liu Hang , Tao Lin
Published: November 2021

Background. With the COVID‐19 epidemic breakout in China, up to 25% of diagnosed cases are considered to be severe. To effectively predict the progression of COVID‐19 via patients’ clinical features at an early stage, the prevalence of these clinical factors and their relationships with severe illness were assessed. Methods. In this study, electronic databases (PubMed, Embase, Web of Science, and Chinese database) were searched to obtain relevant studies, including information on severe patients. Publication bias analysis, sensitivity analysis, prevalence, sensitivity, specificity, likelihood ratio, diagnosis odds ratio calculation, and visualization graphics were achieved through software Review Manager 5.3, Stata 15, Meta‐DiSc 1.4, and R. Results. Data of 3.547 patients from 24 studies were included in this study. The results revealed that patients with chronic respiratory system diseases (pooled positive likelihood 6.07, 95% CI: 3.12‐11.82), chronic renal disease (4.79, 2.04‐11.25), cardiovascular disease (3.45, 2.19‐5.44), and symptoms of the onset of chest tightness (3.8, 1.44‐10.05), shortness of breath (3.18, 2.24‐4.51), and diarrhea (2.04, 1.38‐3.04) exhibited increased probability of progressing to severe illness. C‐reactive protein, ratio of neutrophils to lymphocytes, and erythrocyte sedimentation rate increased a lot in severe patients compared to nonsevere. Yet, it was found that clinical features including fever, cough, and headache, as well as some comorbidities, have little warning value. Conclusions. The clinical features and laboratory examination could be used to estimate the process of infection in COVID‐19 patients. The findings contribute to the more efficient prediction of serious illness for patients with COVID‐19 to reduce mortality.
Diagnostic Performance of Frequency-Domain Optical Coherence Tomography to Predict Functionally Significant Left Main Coronary Artery Stenosis
Authors: Bouki Konstantina P. , Vlad Delia I. , Goulas Nikolaos , Lambadiari Vaia A. , Dimitriadis George D. , Kotsakis Athanasios A. , Barοutsi Kyriaki , Toutouzas Konstantinos P.
Published: November 2021

Aims. The aim of this study was to assess the safety and diagnostic efficacy of frequency-domain optical coherence tomography (FD-OCT) in identifying functional severity of the left main coronary artery (LM) stenosis determined by fractional flow reserve (FFR). Methods and Results. 101 patients with LM lesion (20–70% diameter stenosis angiographically) underwent FFR measurement and FD-OCT imaging of the LM. The following parameters were measured by FD-OCT in the LM: reference lumen area (RLA), reference lumen diameter (RLD), minimum lumen area (MLA), minimum lumen diameter (MLD), % lumen area stenosis, and % diameter stenosis. The LM lesions were analyzable by FD-OCT in 88/101 (87.1%) patients. FFR at maximum hyperemia was ≤0.80 in 39/88 (44.3%) patients. FFR values were correlated significantly with FD-OCT-derived LM lumen parameters. An MLA cutoff value of 5.38 mm2 had the highest sensitivity and specificity of 82% and 81%, respectively, followed by an MLD of 2.43 mm (sensitivity 77%, specificity 72%) and AS of 60% (sensitivity 72%, specificity 72%) for predicting FFR <0.80. Conclusions. FD-OCT is a safe and feasible imaging technique for the assessment of LM stenosis. An FD-OCT-derived MLA of ≤5.38 mm2 strongly predicts the functional severity of an LM lesion.
[Retracted] Immersive Virtual Reality Physical Education Instructional Patterns on the Foundation of Vision Sensor
Authors: Zhang Xianhao , Shi Yongxiu , Bai Hua
Published: November 2021

The creation of visual settings using avatar technology is the initial implementation of a voxel in the reporting context. This article is aimed at demonstrating the important role of a virtual reality immersive physical education model in current physical education and analyzing the immersive virtual reality physical education model. Relying on the mature VR virtual reality technology to establish a virtual simulation experiment platform, the application value in the education field is also reflected in the saving of experimental teaching costs. A complete set of VR teaching courseware also satisfies the functions of teaching training and assessment and is used repeatedly to maximize the value of utilization. This research mainly introduces the content of the optimization of the teaching method of the course combined with virtual reality technology. In order to make the data more convincing, reference literature and data in recent years have been referred to around immersive teaching. The first part is the discussion of immersive teaching, which includes the research of virtual immersive classroom teaching. The second part is a separate analysis of virtual reality technology. The third part is the practical exercise based on the first two parts; that is, from the learning effect and attitude of the student as the main body, the theoretical basis of the two parts on visual sensors and immersive virtual reality physical education teaching is transformed into real practice teaching. In the experiment part, in order to demonstrate the effectiveness and support of immersive virtual teaching, on the one hand, we started with the teaching of teachers, and on the other hand, we carried out investigations from the aspects of students’ learning. The desktop virtual environment teaching method is compared with the existing classroom teaching methods. The image processing and image analysis of the virtual reality technology are combined with the image gray level of the three‐dimensional image to analyze the multitask algorithm of the vision sensor. The feasibility of immersive virtual teaching was verified. Research data shows that the 10 students participating in the experiment gave a score of 7.9 for the target attitude of immersive virtual reality physical education. Students’ interest in learning will increase, and efficiency will also be greatly improved. VR can not only provide students with a new learning experience but also be used to strengthen teachers’ teaching skills. Because VR can simulate a real teaching environment, teachers can use this set of tools to try new course materials and improve classroom management capabilities.
Abstract IA-002: Dendritic cell corner stone of tumor immunity in PDAC
Authors: DeNardo David G.
Published: November 2021

T cell-directed immunotherapies have not been effective for the majority of pancreatic cancer patients, in part, due to our limited understanding of how T cell immunity is subverted in this disease. We sought to identify mechanisms for this failure using spontaneous mouse models. We report that endogenous antigen-specific responses in PDAC are aberrant due to a scarcity of dendritic cells, which favors the expansion of tumor-promoting TH17 immunity. Restoring cDCs in pancreatic cancer can enhance CD8+ T cell and TH1 activity to ultimately help control disease. These findings expand our understanding of T cell ineffectiveness in pancreatic cancer, and propose combinatorial strategies to modulate cDCs in conjunction with existing therapies for pancreatic cancer and similar solid malignancies.

Citation Format: David G. DeNardo. Dendritic cell corner stone of tumor immunity in PDAC [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr IA-002.
Abstract PO-012: The concept of artificial intelligence against pancreatic cancer
Authors: Kumar Subash
Published: November 2021

Pancreatic cancer (PC) remains the fourth leading cause of cancer-related death in both men and women in the United States. Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Pancreatic ductal adenocarcinoma (PDAC) is on track to become the number 2 cancer killer in the United States within the next decade unless there is a major improvement in outcomes. Surgical resection remains the only reasonable hope for a cure from PDAC. The potential for early detection of asymptomatic pancreatic neoplasms in high-risk individuals using an endoscopic approach, but this approach is operator dependent and at the same time, these existing techniques are favored once patients reach the age of 75 years. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. Machine learning refers to the study of algorithms that learn their behavior from data. To see why such algorithms are important, consider the following basic task, building a program to predict if an image contains a dog or a cat. Although it is exceedingly difficult for us to manually specify the exact rules to determine that a dog is a dog, it is comparatively straightforward to prepare a reference set of images and labels. This setting, where knowledge is more easily encoded in data rather than as a descriptive set of rules, is the focus of ML algorithms. One of the most promising areas of innovation in medical imaging in the past decade has been the application of deep learning. Deep learning has the potential to impact the entire medical imaging workflow from image acquisition, image registration, to interpretation. Traditional image processing is dominated by algorithms that are based on statistical models. These statistical model-based processing algorithms carry out inference based on a complete knowledge of the underlying statistical model relating the observations at hand and the desired information and do not require data to learn their mapping. In practice, accurate knowledge of the statistical model relating the observations and the desired information is typically unavailable. The past decade has witnessed a deep learning revolution. Deep learning methods have surpassed the state of the art for many problems in signal processing, imaging, and vision with unprecedented performance gains.

Citation Format: Subash Kumar. The concept of artificial intelligence against pancreatic cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-012.