bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of New Risk Genes and Potential Drugs for Rheumatoid Arthritis from Multiomics Data
Authors: Birga Anteneh M. , Ren Liping , Luo Huaichao , Zhang Yang , Huang Jian
Published: January 2022

Rheumatoid arthritis (RA) is an autoimmune and inflammatory disease for which there is a lack of therapeutic options. Genome-wide association studies (GWASs) have identified over 100 genetic loci associated with RA susceptibility; however, the most causal risk genes (RGs) associated with, and molecular mechanism underlying, RA remain unknown. In this study, we collected 95 RA-associated loci from multiple GWASs and detected 87 candidate high-confidence risk genes (HRGs) from these loci via integrated multiomics data (the genome-scale chromosome conformation capture data, enhancer-promoter linkage data, and gene expression data) using the Bayesian integrative risk gene selector (iRIGS). Analysis of these HRGs indicates that these genes were indeed, markedly associated with different aspects of RA. Among these, 36 and 46 HRGs have been reported to be related to RA and autoimmunity, respectively. Meanwhile, most novel HRGs were also involved in the significantly enriched RA-related biological functions and pathways. Furthermore, drug repositioning prediction of the HRGs revealed three potential targets (ERBB2, IL6ST, and MAPK1) and nine possible drugs for RA treatment, of which two IL-6 receptor antagonists (tocilizumab and sarilumab) have been approved for RA treatment and four drugs (trastuzumab, lapatinib, masoprocol, and arsenic trioxide) have been reported to have a high potential to ameliorate RA. In summary, we believe that this study provides new clues for understanding the pathogenesis of RA and is important for research regarding the mechanisms underlying RA and the development of therapeutics for this condition.
Prediction of Blasting Fragmentation Based on GWO-ELM
Authors: Jia Zhengzhao , Song Ziling , Fan Junfu , Jiang Juyu
Published: January 2022

Aiming at the complex nonlinear relationship among factors affecting blasting fragmentation, the input weight and hidden layer threshold of ELM (extreme learning machine) were optimized by gray wolf optimizer (GWO) and the prediction model of GWO-ELM blasting fragmentation was established. Taking No. 2 open-pit coal mine of Dananhu as an example, seven factors including the rock tensile strength, compressive strength, hole spacing, row spacing, minimum resistance line, super depth, and specific charge are selected as the input factors of the prediction model. The average size of blasting fragmentation X50 is selected as the output factor of the prediction model and compared with the results of PSO-ELM and ELM. The results show that MAPE of GWO-ELM, PSO-ELM, and ELM are 1.78%, 5.40%, and 10.90%, respectively; their RMSE are 0.007, 0.022, and 0.045, respectively. The ELM model optimized by the gray wolf optimizer is more accurate and has stronger data fitting ability than PSO-ELM and ELM models, and the prediction accuracy of GWO-ELM is much higher than that of PSO-ELM and ELM.
Design of Financial Management Model Using the Forward Neural Network Based on Particle Swarm Optimization Algorithm
Authors: Pan Yilin
Published: January 2022

The financial crisis of listed companies will bring huge losses to investors, so it is very important to establish a financial early warning model for investors and other stakeholders. The forward neural network model of particle swarm optimization is used to model and analyze the financial early warning of listed companies. In terms of data selection, earnings management indicators are substituted into the model for the common phenomenon of earnings management in listed companies. The results show that the accuracy of the model considering earnings management factors is improved from 65% to 70%. In the process of modeling, this paper uses the logistic regression model to further modify the model. The empirical results show that the accuracy of the model can be improved from 70% to 75%. When using the forward neural network model based on particle swarm optimization to make an empirical analysis of financial early warning of listed companies, adding quantitative indicators of earnings management can improve the accuracy of the model. In the demonstration, the correction of logistic regression model can also improve the accuracy of the particle swarm neural network financial early warning model. This greatly reduces the risk that companies with poor financial conditions will face bankruptcy and liquidation.
Monitoring and Analysis of a High-Performance Concrete Shaft Lining: A Case Study
Authors: Quan Daolu , Ji Hongguang , Su Xiaobo , Cao Hui , Liu Juanhong
Published: January 2022

Shaft lining in ultradeep mines on the coast of eastern China under the complex external environments of “high in situ stress and high osmotic pressure” has the disadvantages of failure; to solve this problem, the field test of high-performance concrete shaft lining structure was carried out in Sha-ling gold mine. The field test study of the high-performance concrete shaft lining was carried out in a −1120 m ingate and a −1114 m to −1124 m shaft. Its stress and deformation were monitored and analyzed. The maximum compressive stress of the HPC shaft lining structure at the level of −1117.7 m of the air intake shaft is 1.91 MPa. Based on the Von-Mises yield theory and the analytical solution of thick-walled cylinder theory, the ultimate limit of the high-performance concrete shaft lining in the Sha-ling gold mine was obtained. The high-performance concrete shaft lining strain changes smoothly, and concrete strain can be divided into three stages: the rapid growth period, the slow growth period, and the stable period. The monitoring results show that the high-performance concrete shaft lining has excellent mechanical properties in the external environment of high in situ stress, which can be used as a reference for the support design of similar projects.
Account of Deep Learning-Based Ultrasonic Image Feature in the Diagnosis of Severe Sepsis Complicated with Acute Kidney Injury
Authors: Lv Yi , Huang Zhijia
Published: January 2022

This study was aimed at analyzing the diagnostic value of convolutional neural network models on account of deep learning for severe sepsis complicated with acute kidney injury and providing an effective theoretical reference for the clinical use of ultrasonic image diagnoses. 50 patients with severe sepsis complicated with acute kidney injury and 50 healthy volunteers were selected in this study. They all underwent ultrasound scans. Different deep learning convolutional neural network models dense convolutional network (DenseNet121), Google inception net (GoogLeNet), and Microsoft’s residual network (ResNet) were used for training and diagnoses. Then, the diagnostic results were compared with professional image physicians’ artificial diagnoses. The results showed that accuracy and sensitivity of the three deep learning algorithms were significantly higher than professional image physicians’ artificial diagnoses. Besides, the error rates of the three algorithm models for severe sepsis complicated with acute kidney injury were significantly lower than professional physicians’ artificial diagnoses. The areas under curves (AUCs) of the three algorithms were significantly higher than AUCs of doctors’ diagnosis results. The loss function parameters of DenseNet121 and GoogLeNet were significantly lower than that of ResNet, with the statistically significant difference (

P
<
0.05

). There was no significant difference in training time of ResNet, GoogLeNet, and DenseNet121 algorithms under deep learning, as the convergence was reached after 700 times, 700 times, and 650 times, respectively (

P
>
0.05

). In conclusion, the value of the three algorithms on account of deep learning in the diagnoses of severe sepsis complicated with acute kidney injury was higher than professional physicians’ artificial judgments and had great clinical value for the diagnoses and treatments of the disease.
Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection
Authors: Krishnaraj N. , Sivakumar B. , Kuppusamy Ramya , Teekaraman Yuvaraja , Thelkar Amruth Ramesh
Published: January 2022

Due to the exponential growth of high-quality fake photos on social media and the Internet, it is critical to develop robust forgery detection tools. Traditional picture- and video-editing techniques include copying areas of the image, referred to as the copy-move approach. The standard image processing methods physically search for patterns relevant to the duplicated material, restricting the usage in enormous data categorization. On the contrary, while deep learning (DL) models have exhibited improved performance, they have significant generalization concerns because of their high reliance on training datasets and the requirement for good hyperparameter selection. With this in mind, this article provides an automated deep learning-based fusion model for detecting and localizing copy-move forgeries (DLFM-CMDFC). The proposed DLFM-CMDFC technique combines models of generative adversarial networks (GANs) and densely connected networks (DenseNets). The two outputs are combined in the DLFM-CMDFC technique to create a layer for encoding the input vectors with the initial layer of an extreme learning machine (ELM) classifier. Additionally, the ELM model’s weight and bias values are optimally adjusted using the artificial fish swarm algorithm (AFSA). The networks’ outputs are supplied into the merger unit as input. Finally, a faked image is used to identify the difference between the input and target areas. Two benchmark datasets are used to validate the proposed model’s performance. The experimental results established the proposed model’s superiority over recently developed approaches.
An Improved BP Deep Neural Network Multimedia Used in Oral English Training
Authors: Huang Lihua
Published: January 2022

In order to improve the effect of spoken English training, this paper combines multimedia information technology to reform the teaching of spoken English training, and integrates BP neural network English into spoken English training. Moreover, this paper combines the actual needs of spoken English training and the teaching framework of the multimedia system to construct the data set, clean up the data set, and implement the word vector representation of students and professionals. In addition, this paper constructs the entire system framework of the spoken English resource recommendation algorithm based on the graph convolutional neural network, and combines the BP deep neural network algorithm to construct the spoken English training system. Finally, this paper designs an experiment to evaluate the effect of this system. The experimental research results show that the multimedia based on the BP deep neural network proposed in this paper has a good effect in the application research of spoken English training, and can effectively promote the effect of spoken English training of students.
Mechanistic Insights of Qingre Jiedu Recipe Based on Network Pharmacology Approach against Heart Failure
Authors: Li Xuan , Shao Mingyan , Liu Zhen , Sun Xiaoqian , Cui Lingwen , Liu Xiangning , Wang Gang , Lu Linghui , Wu Yan , Li Chun
Published: January 2022

Qingre Jiedu (QJ) recipe exerted significant cardioprotective efficacy against heart failure (HF), which is a growing health concern that continues to endanger patients' lives. To investigate the protective properties and mechanism of the QJ recipe, we established hydrogen peroxide (H2O2)-induced H9C2 cells and HF rats. The predicted targets and significant pathways of QJ against HF were collected and screened based on network pharmacology from key ingredients and validated by in vivo and in vitro experiments. The decoction of QJ (0.823 g/kg/day) was intragastrically administered for four weeks. QJ (400 μg/mL) was cultured with H2O2 stimulated in the H9C2 cells. A total of 31 effective active compounds were screened in QJ and covered 277 targets, of which 85 were shared with HF-related targets. In vivo, the QJ recipe remarkably protected heart function and reduced serum IL-1, IL-6, PIIINP, and CIV levels. Furthermore, QJ downregulated the key proteins mediating inflammatory responses (p-IKKα/β, p-NFκB, and IL-6) and cardiac fibrosis (STAT3 and MMP-9). In vitro, QJ protected the cardiomyocytes against H2O2-stimulated reactive oxygen species (ROS) production and upregulated PI3K and AKT expressions. Further experiments demonstrate that PI3K inhibitor LY294002 remarkably compromised the effects of QJ. In conclusion, our findings indicate that QJ could exert a cardioprotective effect and inhibit fibrosis and inflammation in HF rats via the PI3K-AKT signaling pathway.
Impact of the Management and Proportion of Lost to Follow-Up Cases on Cancer Survival Estimates for Small Population-Based Cancer Registries
Authors: Gil Fabian , Miranda-Filho Adalberto , Uribe-Perez Claudia , Arias-Ortiz N. E. , Yépez-Chamorro M. C. , Bravo L. M. , de Vries Esther
Published: January 2022

Background. Estimation of survival requires follow-up of patients from diagnosis until death ensuring complete and good quality data. Many population-based cancer registries in low- and middle-income countries have difficulties linking registry data with regional or national vital statistics, increasing the chances of cases lost to follow-up. The impact of lost to follow-up cases on survival estimates from small population-based cancer registries (<500 cases) has been understudied, and bias could be larger than in larger registries. Methods. We simulated scenarios based on idealized real data from three population-based cancer registries to assess the impact of loss to follow-up on 1-5-year overall and net survival for stomach, colon, and thyroid cancers—cancer types with very different prognosis. Multiple scenarios with varying of lost to follow-up proportions (1-20%) and sample sizes of (100-500 cases) were carried out. We investigated the impact of excluding versus censoring lost to follow-up cases; punctual and bootstrap confidence intervals for the average bias are presented. Results. Censoring of lost to follow-up cases lead to overestimation of the overall survival, this effect was strongest for cancers with a poor prognosis and increased with follow-up time and higher proportion of lost to follow-up cases; these effects were slightly larger for net survival than overall survival. Excluding cases lost to follow-up did not generate a bias on survival estimates on average, but in individual cases, there were under- and overestimating survival. For gastric, colon, and thyroid cancer, relative bias on 5-year cancer survival with 1% of lost to follow-up varied between 6% and 125%, 2% and 40%, and 0.1% and 1.0%, respectively. Conclusion. Estimation of cancer survival from small population-based registries must be interpreted with caution: even small proportions of censoring, or excluding lost to follow-up cases can inflate survival, making it hard to interpret comparison across regions or countries.
Functional Approach for Solving Reduced Order of Index‐Four Hessenberg Differential‐Algebraic Control System
Authors: Abd Ghazwa F.
Published: January 2022

This research investigates differential‐algebraic equations with higher index (index four). Specifically, a functional analytic approach is proposed to find the solution of (index four) Hessenberg differential‐algebraic equations (DAEs). The approximate solution of the proposed functional approach for a suitable separable Hilbert space is obtained with the help of the direct optimisation technique and Ritz basis functions method. Illustrations have been ranked from an 8 × 8 test system of index‐four Hessenberg linear DAEs to a 4 × 4 DAE of rotating masses as well as an 8 × 8 differential‐algebraic generator model, where it reformulated index‐four linear Hessenberg DAEs. Their approximate solutions were obtained using the present approach with comparisons. The numerical results demonstrate the simplicity of the proposed approach and express suitable accuracy and efficiency.
A Building Energy-Saving Method of Small High-Rise Office Building Based on BIM Model
Authors: Li Junqing , Li Yongbing , Zhang Xuan
Published: January 2022

Building green energy-saving is an important area of current architectural application research. Artificial intelligence algorithm can effectively improve the design of building energy-saving digital, intelligent level. Therefore, this paper proposes the use of BIM model based on genetic algorithms to optimize the application of building energy conservation research. In this paper, the basic structure of genetic algorithm and advantages and disadvantages of the algorithm are described. According to the need of optimal design of building energy-saving integration, multiobjective optimization function is introduced and a multiobjective optimization genetic algorithm is established to improve the prediction effect of building energy efficiency model. Finally, results of analysis of some of the influencing factors and the simulation test of building energy-saving integrated optimization algorithm in small high-rise office building show that the improved genetic algorithm can effectively improve the effect of energy-saving integrated optimization and has good application prospects.
Somatic and Germline Genomic Alterations in Very Young Women with Breast Cancer
Authors: Waks Adrienne G. , Kim Dewey , Jain Esha , Snow Craig , Kirkner Gregory J. , Rosenberg Shoshana M. , Oh Coyin , Poorvu Philip D. , Ruddy Kathryn J. , Tamimi Rulla M. , Peppercorn Jeffrey , Schapira Lidia , Borges Virginia F. , Come Steven E. , Brachtel Elena F. , Warner Ellen , Collins Laura C. , Partridge Ann H. , Wagle Nikhil
Published: January 2022

Purpose:

Young age at breast cancer diagnosis correlates with unfavorable clinicopathologic features and worse outcomes compared with older women. Understanding biological differences between breast tumors in young versus older women may lead to better therapeutic approaches for younger patients.

Experimental Design:

We identified 100 patients ≤35 years old at nonmetastatic breast cancer diagnosis who participated in the prospective Young Women's Breast Cancer Study cohort. Tumors were assigned a surrogate intrinsic subtype based on receptor status and grade. Whole-exome sequencing of tumor and germline samples was performed. Genomic alterations were compared with older women (≥45 years old) in The Cancer Genome Atlas, according to intrinsic subtype.

Results:

Ninety-three tumors from 92 patients were successfully sequenced. Median age was 32.5 years; 52.7% of tumors were hormone receptor-positive/HER2-negative, 28.0% HER2-positive, and 16.1% triple-negative. Comparison of young to older women (median age 61 years) with luminal A tumors (N = 28 young women) revealed three significant differences: PIK3CA alterations were more common in older patients, whereas GATA3 and ARID1A alterations were more common in young patients. No significant genomic differences were found comparing age groups in other intrinsic subtypes. Twenty-two patients (23.9%) in the Young Women's Study cohort carried a pathogenic germline variant, most commonly (13 patients, 14.1%) in BRCA1/2.

Conclusions:

Somatic alterations in three genes (PIK3CA, GATA3, and ARID1A) occur at different frequencies in young versus older women with luminal A breast cancer. Additional investigation of these genes and associated pathways could delineate biological susceptibilities and improve treatment options for young patients with breast cancer.

See related commentary by Yehia and Eng, p. 2209
Age-Related Changes in the Composition of Intestinal Microbiota in Elderly Chinese Individuals
Authors: Wu Jiayu , Ren Weiying , Chen Lingyan , Lou Yi , Liu Chu , Huang Yanmei , Hu Yu
Published: January 2022

Introduction: Intestinal microbiota affects human health and aging. The composition of intestinal microbiota and inflammation indices in elderly Chinese, especially centenarians, is unclear. Objective: This study aimed to explore the relationships between intestinal microbiota and inflammation in healthy housebound elders in Shanghai, China. Methods: We enrolled 156 differently aged adults and assigned them into 4 groups: those aged 35–64 years were assigned into Group AD; 65–79 years into Group YO; 80–94 years into Group MO; and 95–102 years into Group VO. Results: The diversity of intestinal microbiota in Group VO was significantly reduced compared with that of the other 3 groups. Bacteroidetes abundance in Group VO was significantly lower than that in Groups AD, YO, or MO; Proteobacteria abundance showed the opposite trend. Akkermansia, Bifidobacterium, and Lactobacillus abundance in Group VO was significantly higher than that in the other 3 groups; Anaerostipes, Butyricicoccus, and Faecalibacterium abundance showed the opposite trend. Solobacterium abundance in Group VO was significantly lower than that in the other 3 groups; Campylobacter, Porphyromonas, Escherichia, and Pseudomonas abundance showed the opposite trend. Plasma levels of tumor necrosis factor-α (TNF-α), IL-6, and IL-8 in Group VO were significantly higher than those in Groups AD, YO, and MO, while those in Group MO were significantly higher than those in Groups AD and YO. IL-1β and IL-10 plasma levels were not significantly different among the 4 groups. Proteobacteria abundance was positively correlated with TNF-α and IL-8 levels, while Campylobacter abundance was positively correlated with those of TNF-α and IL-6. Anaerostipes and Faecalibacterium abundance was negatively correlated with TNF-α and IL-6 levels. Conclusions: The diversity of intestinal microbiota in the oldest participants (centenarians) decreased significantly, with several beneficial bacterial strains showing increased or decreased abundance; harmful bacterial species showed a similar trend. Our oldest participants (centenarians) demonstrated significantly increased levels of pro-inflammatory cytokines, which may be related to inflammaging.
Characteristics of Cancers in Adolescents and Young Adults Compared with Those in Adults in Their 60s: A Single-Center Experience
Authors: Aida Yuka , Fukushima Hiroko , Bando Hiroko , Ishikawa Eiichi , Shikama Ayumi , Sekine Ikuo
Published: January 2022

Introduction: Cancer is one of the main causes of death among adolescents and young adults (AYAs) aged 15–39 years. The improvement in overall 5-year survival in AYA cancer patients was far below than that of adult cancer patients. The purpose of this study was to clarify the features of cancer in AYAs by comparing them with those of controls. Methods: Patients in the cancer registry of the University of Tsukuba Hospital between 2007 and 2017 (median age, 65 years) were included in this study. We used patients between the ages of 64 and 66 years as controls. We then obtained the age at diagnosis, sex, primary site, and pathological type. Results: Among 27,281 cancer patients in the registry between 2007 and 2017, 1,947 (7.1%) patients were categorized into the AYA group, and 2,354 into the control group. Among men in the AYA group, central nervous system (CNS) tumors accounted for 22.7% of all cancers, followed by germ cell tumors, 22.5%, and hematopoietic malignancies, 12.5%. Among women in the AYA group, cervical cancer accounted for 35.9% of all cancers, followed by breast cancer, 14.6%, and CNS tumors, 11.6%. The proportion of specific cancer types relative to all cancers in the CNS, thyroid, adrenal glands, germ cells, cervix uteri, hematopoietic tissues, and sarcomas was higher in the AYA group than that in the control group. Conclusion: The present results for AYAs were in sharp contrast to those for adult cancers and may be related to different modes of pathogenesis in AYAs. The identification of high-risk groups of these tumors in the AYA generation is crucial for prevention and early detection and will be a major topic for future research. While most of adult cancers are treated independently by each medical department, AYA cancers need to be treated in collaboration with experts from several departments. It is desirable to address the issues involved in applying treatments established for adult cancers to AYA cancers on a cancer-by-cancer basis.
Brain Perfusion Single-Photon Emission Computed Tomography Using an Easy Z-Score Imaging System Predicts Progression to Neurodegenerative Dementia in Rapid Eye Movement Sleep Behavior Disorder
Authors: Numahata Kyoko , Miyamoto Tomoyuki , Akaiwa Yasuhisa , Miyamoto Masayuki
Published: January 2022

Introduction: Longitudinal studies have reported that patients with idiopathic rapid eye movement sleep behavior disorder (IRBD) have an increased risk of developing synucleinopathies, such as Parkinson’s disease and dementia with Lewy bodies (DLB). Clinical trials of disease-modifying therapies for IRBD patients require suitable biomarkers that can predict the short-term onset of neurodegenerative dementia. Methods: We retrospectively examined if easy Z-score imaging system-specific volume-of-interest analysis (SVA) using brain perfusion single-photon emission computed tomography (SPECT) imaging or the cingulate island sign score can predict the short-term development of neurodegenerative dementia in 30 patients with IRBD. Results: Ten patients (33.3%) who exceeded the thresholds for three indicators (severity, extent, and ratio) were included in an SVA-positive group, while 20 (66.7%) were included in an SVA-negative group. Nine (30.0%) IRBD patients had phenoconversion, of which eight had DLB and one had Parkinson’s disease with dementia. In Kaplan-Meier analysis, patients in the SVA-positive group converted to neurodegenerative dementia in a significantly shorter period of time compared to patients in the SVA-negative group. Conclusions: These data suggest that SVA-positive IRBD patients have an increased short-term risk of developing neurodegenerative dementia.
Effectiveness of Calcitonin Gene-Related Peptide Receptor Antagonists for Migraine Treatment: A Meta-Analysis
Authors: Pak Kyoungjune , Kim Jiyoung , Lee Gha-Hyun , Cho Jae Wook , Kim Hyun-Woo
Published: January 2022

Introduction: The pathophysiology of migraine has been researched incessantly, and it has been suggested that calcitonin gene-related peptide (CGRP) is associated with migraine attacks. CGRP receptor blockers are attracting attention as potential agents for migraine prevention and treatment of acute episodes. This meta-analysis aimed to assess the effects of available CGRP receptor antagonists, focusing on their therapeutic doses for acute migraine treatment. Methods: We systematically searched MEDLINE and Embase from inception to March 27, 2021, for English-language publications using the keywords “migraine” and “calcitonin gene-related peptide”; the searches were limited to human studies. Results: Five studies that focused on examining the effects of CGRP receptor antagonists on acute migraine treatment met the eligibility criteria for this meta-analysis. A pooled analysis demonstrated that CGRP receptor antagonists significantly increased freedom from pain (odds ratio [OR] = 2.066, 95% confidence interval [CI] 1.766–2.418, I2 = 0%) and from bothersome symptoms in general (OR = 1.606, 95% CI = 1.408–1.830, I2 = 0%); reduced the intensity of pain (OR = 1.791, 95% CI = 1.598–2.008, I2 = 0%); and increased freedom from nausea (OR = 1.361, 95% CI = 1.196–1.548, I2 = 0%) compared to a placebo. Conclusions: CGRP receptor antagonists are effective for acute migraine treatment and are expected to be used clinically as emerging therapeutic agents.
Risk Assessment of Mortality in Elderly Individuals: A Nationwide Cohort Study
Authors: Huh Youn , Kim Do-Hoon , Jung Jin-Hyung , Park Yong-Gyu , Roh Yong-Kyun , Kim Seon Mee , Cho Kyung-Hwan
Published: January 2022

Introduction: There are several methods that are used to predict emergency room visits or rehospitalization for the elderly. However, existing risk assessment models of mortality in elderly people are limited. The purpose of this study was to ascertain the factors that affect all-cause mortality and to show the risk assessment model of mortality in elderly Koreans. Methods: This was a cohort study conducted using the health checkup data of 246,422 individuals aged ≥60 years, which was provided by the National Health Insurance Service of South Korea between January 1, 2009, and December 31, 2012. The hazard ratios and 95% confidence intervals (CIs) of several conditions and all-cause deaths were estimated using a multivariable Cox proportional hazards model. A nomogram was constructed to visualize the risk factors of mortality; a calibration plot and area under the curve (AUC) were also used to verify the nomogram. Results: Being 85 years or older (100 points) had the greatest influence on all-cause mortality, followed by being underweight (57 points), having more than five chronic diseases (49 points), and ages 78–84 years (45 points); smoking and lack of regular exercise affected mortality to a similar degree. The calibration curves showed good agreement between predictions and observations. The AUC of our nomogram was 0.73 (95% CI: 0.72–0.73). Conclusions: Our results showed the relationship between each condition and mortality rate among elderly individuals in Korea. Our nomogram showed a satisfactory performance in the assessment of the risk of all-cause mortality in elderly Korean people.
Deep Brain Stimulation for Severe and Intractable Aggressive Behavior
Authors: Escobar Vidarte Oscar Andrés , Griswold Dylan Paul , Orozco Mera Javier , Arango Uribe Gabriel José , Salcedo Juan Camilo
Published: January 2022

Aggressive behavior in patients with intellectual disability can be resistant to pharmacological treatment and have detrimental consequences to themselves, family members, and caregivers. Hypothalamic deep brain stimulation (DBS) has been used to improve this type of behavior in severe and refractory cases. Here, we present the description and analysis of DBS of the posteromedial hypothalamus (PMH) and its long-term impact as treatment to improve severe and refractory aggressive behaviors, even with previous bilateral hypothalamotomy without improvement in patients with intellectual disability. Eleven patients underwent bilateral DBS of the PMH. Their medical records were reviewed, and the impact on behavior was measured using preoperative and postoperative Modified Overt Aggression Scale (MOAS) during the last follow-up medical visit. Nine of 11 patients presented a significant decrease in the severity of aggressive behavior, with a preoperative and postoperative MOAS average value of 50.5 and 18.7, respectively. An overall improvement of 63% was seen with a mean follow-up time of 4 years. A patient who previously underwent a bilateral hypothalamotomy via radiofrequency was included in this group. During follow-up, 3 patients presented deterioration of symptoms subsequent to pulse generator depletion but made a full clinical recovery after battery replacement. We posit that DBS of the PMH may be a safe and effective in improving severe and refractory aggressive behavior in patients with long-term intellectual disability.
Influences of Vitamin D Levels and Vitamin D-Binding Protein Polymorphisms on Nonalcoholic Fatty Liver Disease Risk in a Chinese Population
Authors: Wang Min , Wang Minxian , Zhang Ru , Shen Chao , Zhang Liuxin , Ding Yajie , Tang Zongzhe , Wang Hongliang , Zhang Wei , Chen Yue , Wang Jie
Published: January 2022

Introduction: Vitamin D-binding protein (VDBP) is correlated with nonalcoholic fatty liver disease (NAFLD) through the biological functions of regulating plasma vitamin D (VD) level and the inflammatory process. Objective: This study aims to investigate the effects of VD level and VDBP gene polymorphisms on the risk of NAFLD in a Chinese population. Methods: Plasma 25-hydroxyvitamin D3 levels were measured and seven VDBP candidate genetic variants (rs222020, rs2282679, rs4588, rs1155563, rs7041, rs16847024, rs3733359) were genotyped among participants in this case-control study. The control group was frequency-matched to the NAFLD case group by age and gender. Correlation analysis and multiple linear regressions were used to screen determinants of 25-hydroxyvitamin D3 levels. Multivariable unconditional logistic regression was performed to estimate odds ratio (OR) and 95% confidence interval (95% CI). The prediction capability of models containing independent factors was estimated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow test. Results: Age, body mass index, and triacylglycerol were independent factors influencing VD levels. Participants with low VD levels had significantly higher prevalence of NAFLD compared to subjects with normal VD levels (p < 0.001). A low VD level contributed to increased the risk of NAFLD, independent of metabolic factors known to affect VD levels (adjusted OR = 2.282, 95% CI = 1.422–3.661, p = 0.001). Logistic regression analysis showed that individuals carrying rs7041-G allele had a significantly decreased the risk of NAFLD occurrence compared to T allele (additive model: adjusted OR = 0.814, 95% CI = 0.713–0.929, p = 0.002; codominant model: adjusted OR = 0.623, 95% CI = 0.449–0.866, p = 0.005), after adjusting for age, gender, and overweight. Stratification by multiple metabolic disorders did not alter this relationship. Moreover, we developed a simple model including age, gender, metabolic disorders, and VDBP single nucleotide polymorphism (SNP) to assess NAFLD risk, an AUC of which being 0.817, significantly higher than the model not included VDBP SNP, with Hosmer-Lemeshow test fitting well (p = 0.182). Conclusions: Low plasma VD levels may increase susceptibility to NAFLD, while rs7041-G allele in VDBP contributed to a decreased NAFLD risk among Chinese population. The VDBP variant significantly improved the capability for NAFLD risk assessment, which could be used for early screening and management of NAFLD.
The Utility of Cerebral Autoregulation Indices in Detecting Severe Brain Injury Varies by Cooling Treatment Phase in Neonates with Hypoxic-Ischemic Encephalopathy
Authors: Chen May W. , Lee Jennifer K. , Vezina Gilbert , Tekes Aylin , Perin Jamie , Li Ruoying , O’Kane Alexandra , McGowan Meaghan , Chang Taeun , Parkinson Charlamaine , Krein Colleen , Al-Shargabi Tareq , Northington Frances J. , Brady Ken M. , Massaro An N. , Govindan Rathinaswamy B.
Published: January 2022

Identifying the hemodynamic range that best supports cerebral perfusion using near infrared spectroscopy (NIRS) autoregulation monitoring is a potential physiologic marker for neonatal hypoxic-ischemic encephalopathy (HIE) during therapeutic hypothermia. However, an optimal autoregulation monitoring algorithm has not been identified for neonatal clinical medicine. We tested whether the hemoglobin volume phase (HVP), hemoglobin volume (HVx), and pressure passivity index (PPI) identify changes in autoregulation that are associated with brain injury on MRI or death. The HVP measures the phase difference between a NIRS metric of cerebral blood volume, the total hemoglobin (THb), and mean arterial blood pressure (MAP) at the frequency of maximum coherence. The HVx is the correlation coefficient between MAP and THb. The PPI is the percentage of coherent MAP-DHb (difference between oxygenated and deoxygenated hemoglobin, a marker of cerebral blood flow) epochs in a chosen time period. Neonates cooled for HIE were prospectively enrolled in an observational study in two neonatal intensive care units. In analyses adjusted for study site and encephalopathy level, all indices detected relationships between poor autoregulation in the first 6 h after rewarming with a higher injury score on MRI. Only HVx and PPI during hypothermia and the PPI during rewarming identified autoregulatory dysfunction associated with a poor outcome independent of study site and encephalopathy level. Our findings suggest that the accuracy of mathematical autoregulation algorithms in detecting the risk of brain injury or death may depend on temperature and postnatal age. Extending autoregulation monitoring beyond the standard 72 h of therapeutic hypothermia may serve as a method to provide personalized care by assessing the need for and efficacy of future therapies after the hypothermia treatment phase.
Long-Term Outcomes of Left versus Right Laparoscopic Living Donor Nephrectomy with Multiple Renal Arteries
Authors: Karayagiz Abdulhak Hamit , Besli Sevval , Yilmaz Gulay , Ozdemir Ebru , Cakir Ulkem , Berber Ibrahim
Published: January 2022

Objectives: The objective of this study was to compare the long-term graft outcomes of left-versus-right donor nephrectomy with multiple renal arteries (MRAs), and therefore creating a reference for the expansion of the potential living kidney donor pool. Methods: Laparoscopic live donor nephrectomy cases between May 2010 and October 2020 were included in this retrospective cross-sectional study. The data relating to donor and recipient demographics, surgical and anatomical characteristics, recipient, and graft status were retrieved and compared using nonparametric statistical methods and multivariate regression. Analyses were fit for survival factors. Results: A total of 1,009 recipients were included in this retrospective cross-sectional study with their donors. 16.7% of the donors had been discovered to have more than one renal artery supplying the donated kidney. The acute rejection rate was 12.8%. Death-censored graft survival at postoperative year 5 for single renal artery (SRA) transplants was 89.6%, 89.5% for left-sided MRAs, and 88.2% for right-sided ones. Conclusions: Both right donor nephrectomy and left donor nephrectomy are safe procedures with no significant negatively impacted rates for neither survival nor complications of the recipients in the long-term, compared to SRA ones.
Free space optical communication system for indoor applications based on printed circuit board design
Authors: Mohammad Alsharef , Alzaidi Mohammed S. , Eid Mahmoud M. A. , Sorathiya Vishal , Lavadiya Sunil , Patel Shobhit K. , Rashed Ahmed Nabih Zaki
Published: January 2022

This study clarified an overview of wired and wireless optical communication system block diagram with practical applications. Freespace optical (FSO) communication is a trending field that is rising so fast to replace electromagnetic waves in a communication, so we have presented a theoretical circuit as an example and modified it to fit and work in communication purposes, simulation is used and then practical work is done and printed circuit board (PCB) is designed. Light emitting diode (LED) have been used as transmitter and Photo Transistor as a receiver and variable resistance to change voltage sent to the LED that indicates the change in the transmitted signal.
Chaotic elliptic map for speech encryption
Authors: Al-hazaimeh Obaida M. , Abu-Ein Ashraf A. , Nahar Khalid M. , Al-Qasrawi Isra S.
Published: January 2022

Using a new key management system and Jacobian elliptic map, a new speech encryption scheme has been developed for secure speech communication data. Jacobian elliptic map-based speech encryption has been developed as a novel method to improve the existing speech encryption methods' drawbacks, such as poor quality in decrypted signals, residual intelligibility, high computational complexity, and low-key space. Using the Jacobian elliptic map as a key management solution, a new cryptosystem was created. The proposed scheme's performance is evaluated using spectrogram analysis, histogram analysis, key space analysis, correlation analysis, key sensitivity analysis and randomness test analysis. Using the results, we can conclude that the proposed speech encryption scheme provides a better security system with robust decryption quality.
Crime prediction using a hybrid sentiment analysis approach based on the bidirectional encoder representations from transformers
Authors: Boukabous Mohammed , Azizi Mostafa
Published: January 2022

Sentiment analysis (SA) is widely used today in many areas such as crime detection (security intelligence) to detect potential security threats in realtime using social media platforms such as Twitter. The most promising techniques in sentiment analysis are those of deep learning (DL), particularly bidirectional encoder representations from transformers (BERT) in the field of natural language processing (NLP). However, employing the BERT algorithm to detect crimes requires a crime dataset labeled by the lexiconbased approach. In this paper, we used a hybrid approach that combines both lexicon-based and deep learning, with BERT as the DL model. We employed the lexicon-based approach to label our Twitter dataset with a set of normal and crime-related lexicons; then, we used the obtained labeled dataset to train our BERT model. The experimental results show that our hybrid technique outperforms existing approaches in several metrics, with 94.91% and 94.92% in accuracy and F1-score respectively.
An unsupervised generative adversarial network based-host intrusion detection system for internet of things devices
Authors: Idrissi Idriss , Azizi Mostafa , Moussaoui Omar
Published: January 2022

Machine learning (ML) and deep learning (DL) have achieved amazing progress in diverse disciplines. One of the most efficient approaches is unsupervised learning (UL), a sort of algorithms for analyzing and clustering unlabeled data; it allows identifying hidden patterns or performing data clustering over provided data without the need for human involvement. There is no prior knowledge of actual abnormalities when using UL methods in anomaly detection (AD); hence, a DL-intrusion detection system (IDS)- based on AD depends intensely on their assumption about the distribution of anomalies. In this paper, we propose a novel unsupervised AD Host-IDS for internet of things (IoT) based on adversarial training architecture using the generative adversarial network (GAN). Our proposed IDS, called “EdgeIDS”, targets mostly IoT devices because of their limited functionality; IoT devices send and receive only specific data, not like traditional devices, such as servers or computers that exchange a wide range of data. We benchmarked our proposed “EdgeIDS” on the message queuing telemetry transport (MQTTset) dataset with five attack types, and our obtained results are promising, up to 0.99 in the ROC-AUC metric, and to just 0.035 in the ROC-EER metric. Our proposed technique could be a solution for detecting cyber abnormalities in the IoT.
Predicting students' learning styles using regression techniques
Authors: Altamimi Ahmad Mousa , Azzeh Mohammad , Albashayreh Mahmoud
Published: January 2022

Traditional learning systems have responded quickly to the COVID pandemic and moved to online or distance learning. Online learning requires a personalization method because the interaction between learners and instructors is minimal, and learners have a specific learning method that works best for them. One of the personalization methods is detecting the learners' learning style. To detect learning styles, several works have been proposed using classification techniques. However, the current detection models become ineffective when learners have no dominant style or a mix of learning styles. Thus, the objective of this study is twofold. Firstly, constructing a prediction model based on regression analysis provides a probabilistic approach for inferring the preferred learning style. Secondly, comparing regression models and classification models for detecting learning style. To ground our conceptual model, a set of machine learning algorithms have been implemented based on a dataset collected from a sample of 72 students using visual, auditory, reading/writing, and kinesthetic (VARK's) inventory questionnaire. Results show that regression techniques are more accurate and representative for real-world scenarios than classification algorithms, where students might have multiple learning styles but with different probabilities. We believe that this research will help educational institutes to engage learning styles in the teaching process.
Analysis and comparison of a proposed mutation operator and its effects on the performance of genetic algorithm
Authors: Ullah Sami , Salam Abdus , Masood Mohsin
Published: January 2022

Genetic algorithms (GAs) are dependent on various operators and parameters. The most common evolutionary operators are parent selection, crossover, and mutation. Each operator has broad implementations with its pros and cons. A successful GA is highly dependent on genetic diversity which is the main driving force that steers a GA towards an optimal solution. Mutation operator implements the idea of exploration to search for uncharted areas and introduces diversity in a population. Thus, increasing the probability of GA to converge to a globally optimum solution. In this paper, a new variant of mutation operator is proposed, and its functions are studied and compared with the existing operators. The proposed mutation operator as well as others such as m-mutation, shuffle, swap, and inverse are tested for their ability to introduce diversity in population and hence, their effects on the performance of GA. All these operators are applied to Max one problem. The results concluded that the proposed variant is far more superior to the existing operators in terms of introducing diversity and hence early convergence to an optimum solution.
A review on power quality issues in electric vehicle interfaced distribution system and mitigation techniques
Authors: Harish Basaralu Nagasiddalingaiah , Surendra Usha
Published: January 2022

Electric vehicles (EV) penetration in the distribution systems is evident and intended to grow day by day. Power quality issues pop up in the distribution system with an increase in EV penetration. Distribution networks need to consider the power quality issues developed due to the penetration of EVs for planning and designing the system. The power quality issues, including voltage imbalance, total harmonic distortion, distribution transformer failure, and related issues, are anticipated due to EV penetration in distribution systems. Detailed review of power quality issues and mitigation techniques are detailed in this paper. Discussion on the effect of these power quality issues on the distribution systems and corresponding mitigation measures are detailed. Power quality impact mitigation techniques have been discussed recently, which exploits the bidirectional power flow of vehicle to grid vehicle to grid (V2G) and grid to vehicle grid-to-vehicle (G2V). Methods and methodologies that mitigate power quality problems in the EV penetrated distribution system is discussed. Bidirectional power flow during EV charging and discharging and power quality issues in this topology is detailed in this review paper. A discussion on future trends and different possible future research paradigms is discussed as the review's conclusion.
A novel pipelined carry adder design based on half adder
Authors: Alkurwy Salah Hasan , Hameed Isam Salah
Published: January 2022

A new design of binary parallel adder circuit is presented in this paper. The pipeline technique is applied to implement a group of a half adder (HA) blocks to architect the proposed adder. The pipelined carry adder (PCA) method is suitable for carrying out the desired adder by using the HA circuits of XOR and AND gates. The applied technique reduces the critical path delay by 27% compared with the ripple carry adder (RCA) and relatively lowers logic gates by 55% compared with the carry look-ahead adder (CLA). The coded design of the proposed circuit is implemented and simulated on the Cyclone IV FPGA kit platform. Results show that the circuit needs a 7.69 ƞ Sec delay time to provide the output values. The suggested PCA circuit is more attractive than the conventional ripple carry adder for future electronic applications. 