bluuebunny/crossref_metadata_2025 huggingface dataset:
On the inverse sum indeg energy of trees
Authors: Hatefi H. , Ahangar H. Abdollahzadeh , Khoeilar R. , Sheikholeslami S. M.
Published: December 2021

Let [Formula: see text] be a graph of order [Formula: see text] and [Formula: see text] be the degree of the vertex [Formula: see text], for [Formula: see text]. The [Formula: see text] matrix of [Formula: see text] is the square matrix of order [Formula: see text] whose [Formula: see text]-entry is equal to [Formula: see text] if [Formula: see text] is adjacent to [Formula: see text], and zero otherwise. Let [Formula: see text], be the eigenvalues of [Formula: see text] matrix. The [Formula: see text] energy of a graph [Formula: see text], denoted by [Formula: see text], is defined as the sum of the absolute values of the eigenvalues of [Formula: see text] matrix. In this paper, we prove that the star has the minimum [Formula: see text] energy among trees.
Ordered power ternary semigroups
Authors: Kar S. , Roy A. , Dutta I.
Published: December 2021

We consider a power ternary semigroup [Formula: see text] associated with a ternary semigroup [Formula: see text] and study some properties of [Formula: see text] by using the corresponding properties of [Formula: see text]. After that we study the notion of ordered power ternary semigroup and our main aim is to establish some interconnection between the properties of a ternary semigroup [Formula: see text] and the associated ordered ternary semigroup [Formula: see text].
Extraction of Human Motion Information from Digital Video Based on 3D Poisson Equation
Authors: Wang Yilin , Chang Baokuan
Published: December 2021

Based on the 3D Poisson equation, this paper extracts the features of the digital video human body action sequence. By solving the Poisson equation on the silhouette sequence, the time and space features, time and space structure features, shape features, and orientation features can be obtained. First, we use the silhouette structure features in three-dimensional space-time and the orientation features of the silhouette in three-dimensional space-time to represent the local features of the silhouette sequence and use the 3D Zernike moment feature to represent the overall features of the silhouette sequence. Secondly, we combine the Bayesian classifier and AdaBoost classifier to learn and classify the features of human action sequences, conduct experiments on the Weizmann video database, and conduct multiple experiments using the method of classifying samples and selecting partial combinations for training. Then, using the recognition algorithm of motion capture, after the above process, the three-dimensional model is obtained and matched with the model in the three-dimensional model database, the sequence with the smallest distance is calculated, and the corresponding skeleton is outputted as the results of action capture. During the experiment, the human motion tracking method based on the university matching kernel (EMK) image kernel descriptor was used; that is, the scale invariant operator was used to count the characteristics of multiple training images, and finally, the high-dimensional feature space was mapped into the low-dimensional to obtain the feature space approximating the Gaussian kernel. Based on the above analysis, the main user has prior knowledge of the network environment. The experimental results show that the method in this paper can effectively extract the characteristics of human body movements and has a good classification effect for bending, one-foot jumping, vertical jumping, waving, and other movements. Due to the linear separability of the data in the kernel space, fast linear interpolation regression is performed on the features in the feature space, which significantly improves the robustness and accuracy of the estimation of the human motion pose in the image sequence.
A New Variable‐Censoring Control Chart Using Lifetime Performance Index under Exponential and Weibull Distributions
Authors: Aslam Muhammad , Jeyadurga P. , Balamurali S. , Khan Sherwani Rehan Ahmad , Albassam Mohammed , Jun Chi-Hyuck
Published: December 2021

In reliability theory or life testing, exponential distribution and Weibull distribution are frequently considered to model the lifetime of the components or systems. In this paper, we design a control chart based on the lifetime performance index using Type II censoring for exponential and Weibull distributions. Average run length helps to measure the performance of the proposed control chart. The optimal values of the number of failure items and decision criteria used to decide whether the process is in‐control or out‐of‐control based on the sample results are determined such that the in‐control average run length is as close as to the specified average run length values. We simulate the data to illustrate the performance of the proposed control chart.
Identification and External Validation of a Transcription Factor-Related Prognostic Signature in Pediatric Neuroblastoma
Authors: Wang Rujia , Wang Qian
Published: December 2021

Background. Neuroblastoma is a common solid tumor originating from the sympathetic nervous system, commonly found in children, and it is one of the leading causes of tumor-related deaths in children. In addition to pathological features, molecular-level features, such as how much gene expression is present and the mutational profile, may provide useful information for the precise treatment of neuroblastoma. Transcription factors (TFs) play an important regulatory role in all aspects of cellular life activities. But there are currently no studies on transcription factor-based biomarkers of neuroblastoma prognosis, and this study is much needed. Methods. We downloaded RNA transcriptome data and clinical data from the TARGET database to construct a prognostic model. The prognostic model was constructed by using univariate Cox analysis, LASSO, and multivariate Cox regression. We divided the patients into low-risk and high-risk groups using the median value of the risk score as the cut-off. Then, we validated the prognostic model with the dataset GSE49710. Results. We constructed a prognostic model consisting of eight genes (SATB1, ZNF564, SOX14, EN1, IKZF2, SLC2A4RG, FOXJ2, and ZNF521). Patients in the high-risk group had a lower survival rate than those in the low-risk group. The area under the 3-year ROC curve of the model reached 0.825, suggesting a good predictive efficacy. We performed target gene prediction for the eight transcription factors in the model using six online databases and found that TUT1 may be a target gene for transcription factor EN1 and is associated with immune infiltration. Conclusion. This prognostic model consisting of eight transcription factor-associated genes demonstrated reliable predictive efficacy. This prediction model may provide new potential targets for the treatment of neuroblastoma and personalized monitoring of neuroblastoma patients with high and low risk.
Role of High-Density Lipoprotein Cholesterol (HDL-C) as a Clinical Predictor of Decompensation in Patients with Chronic Liver Disease (CLD)
Authors: Rao B Harshavardhan , Nair Priya , Koshy Anoop K. , Krishnapriya S. , Greeshma C. R. , Venu Rama P.
Published: December 2021

Introduction. Systemic inflammation triggered by bacterial products like lipopolysaccharides (LPS) in the circulation is an important factor leading to decompensation in patients with chronic liver disease (CLD). High-density lipoprotein cholesterol (HDL-C) has a significant role in innate immune response to LPS in the circulation and could therefore increase the risk for decompensation in patients with CLD. In this study, we have explored the role of HDL-C as a prognostic marker for decompensation. Methods. This was a prospective, observational, cohort study where consecutive patients with CLD were included. Patients with cholestatic liver disease and hepatocellular carcinoma were excluded. Fasting lipids were measured in all patients at the time of recruitment. Each patient was carefully followed up for development of decompensation events such as new-onset/worsening ascites, hepatic encephalopathy, or variceal bleed during follow-up. Results. A total of 170 patients were included (mean age

60
±
11.5

years,

M
:
F
=
6
:
1

). At the end of follow-up, 97/170 patients (57%) had decompensation events. Mean HDL-C levels were significantly lower among patients with decompensation (

27.5
±
15

 mg/dL vs.

43.5
±
13.9

 mg/dL;

p

value 0.004). Using ROC analysis, cut-off for HDL-C of 36.4 mg/dL was identified. On multivariate analysis, HDL-C (

OR
=
6.072

; 95% CI 2.39-15.39) was found to have an independent association with risk of decompensation. Conclusions. HDL-C level (<36.4 mg/dL) is a reliable marker for risk of decompensation and can be a useful addition to existing prognostic scoring systems in CLD. It can be a valuable tool to streamline treatment protocols and prioritise liver transplantation.
Protective Effects of Eicosapentaenoic Acid on the Glomerular Endothelium via Inhibition of EndMT in Diabetes
Authors: Yasuzawa Toshinori , Nakamura Tomomi , Ueshima Shigeru , Mima Akira
Published: December 2021

Diabetes-induced endothelial pathologies are hypothesized to lead to the progression of diabetic kidney disease (DKD). The endothelial to mesenchymal transition (EndMT) possibly induces fibrosis, leading to glomerulosclerosis in the kidney. Furthermore, this could lead to albuminuria in diabetic nephropathy due to glomerular endothelial dysfunction. Eicosapentaenoic acid (EPA), purified from fish oil, decreases inflammatory cytokine levels in glomerulonephritis. Here, we aimed at finding whether ethyl eicosapentaenoate (EPA-E) exerts renal protective effects via EndMT inhibition. To find out whether EPA inhibits EndMT in vitro, the changes in CD31 expression were studied in cultured mouse endothelial cells. The addition of the conditioned medium from the adipocyte culture significantly decreased the protein levels of CD31, while the addition of EPA-E partially reversed this inhibition. Further, EndMT inhibition by EPA-E treatment might occur via the inhibition of the protein kinase Cβ (PKCβ)/transforming growth factor-β (TGF-β)/plasminogen activator inhibitor-1 (PAI-1) signaling and not via microRNAs. Streptozotocin-induced diabetic mice fed a high-fat diet (60% from fat) exhibited mesangial expansion and albuminuria. Induction of EPA-E ameliorated the mesangial expansion and decreased albuminuria without affecting blood pressure, triglyceride and free fatty acid levels, and intraperitoneal glucose. These findings suggest that EPA-E exerts renal protective effects on endothelial cells, by normalizing EndMT followed by the PKCβ/TGF-β/PAI-1 signaling. Thus, EPA-E has the potential for imparting renal protection by regulating EndMT in DKD.
Piezoelectric Sensing Element‐Assisted Ceramic Art Process Optimization and Visual Quantitative Characterization
Authors: Yang Shuxin
Published: December 2021

In this paper, piezoelectric sensing elements are used to assist in the study and analysis of ceramic art process optimization and visual quantization characteristics. A series piezoelectric element impedance sensor is designed based on the resonant frequency characteristics of the series piezoelectric element. Combining the resonant frequency characteristics of the series piezoelectric element and the basic principle of the impedance method, a multisensing impedance method based on the series piezoelectric element impedance sensor is proposed. The feasibility of the multisensing impedance method for monitoring the grout compactness was verified experimentally, and the basic principle of the method was further investigated by finite element simulation. The vase‐type porcelain vessels were classified according to symmetry elements to find the characteristic points, the abdominal morphology was used as the basis for classification, and the screened samples were extracted from the contours to exclude the influence of other factors on the vessel shape. By the symmetrical elements of each type of ware, the classification principle of the ware type was designed and divided into six types, and each type was further subdivided into various types to establish a typological map of Qing dynasty bottle porcelain. The information entropy redundancy that describes the uniformity of the code appearance probability and the visual redundancy that describes the human eye’s sensitivity to image content or details are all entry points that can be considered for image coding. The experimental results show that the LBP‐HOG fusion features can digitally express the information of ancient ceramic ornamentation and dig and verify the evolution of ceramic ornamentation with the times from the digital quantity. The GRNN model has an excellent performance in processing small samples of ancient ceramic data.
Public Transport in Rural Roads: Measures to Increase Its Modal Share in Iran
Authors: Afkham Alireza , Afandizadeh Shahriar , Naderan Ali
Published: December 2021

The supply and demand management of rural public transport has gained an important place in Iran, given its decreasing trend over the last decade. Accordingly, an effective approach is to analyze stakeholders’ opinions in this field to identify the effective local solutions for increasing the share of this transport method. The used methodology was to design a mixed questionnaire, a part of which included fuzzy pairwise comparison, while, in its first layer, the variables were classified using rotary analysis. In the other two sections of the questionnaire, the respondents were asked to express their opinions regarding the questions in the form of qualitative and words cloud. According to the inquiries made from the users in 20 selected terminals of Iran providing services to more than 85% of rural passengers, the indicators were weighted and divided into two groups based on the rotary analysis. The weighted results obtained from the users’ opinions revealed that the safety, dynamics of incentive policies, and traffic system performance in the rural transport had the highest effects on the micro‐ and macro‐level indicators. In the second section, based on the qualitative questions, a multivariate linear estimation model of the number of rural passengers was constructed. Moreover, in the third section, the users’ suggested keywords focused on policy‐making, travel time optimization, quality of services, and safety. Both the second and third sections had an acceptable agreement with the pairwise comparisons. Given the vast area of Iran and the distance between the population centers in the country, the obtained solutions to increase the share of public rural road transport included reducing the desirability of travel with private cars in short rural distances through interaction with industrial towns around metropolises, along with providing such areas with special services to attract passengers to rural transport with occupational goals.
A Feature Selection Algorithm Integrating Maximum Classification Information and Minimum Interaction Feature Dependency Information
Authors: Zhang Li
Published: December 2021

Feature selection is the key step in the analysis of high‐dimensional small sample data. The core of feature selection is to analyse and quantify the correlation between features and class labels and the redundancy between features. However, most of the existing feature selection algorithms only consider the classification contribution of individual features and ignore the influence of interfeature redundancy and correlation. Therefore, this paper proposes a feature selection algorithm for nonlinear dynamic conditional relevance (NDCRFS) through the study and analysis of the existing feature selection algorithm ideas and method. Firstly, redundancy and relevance between features and between features and class labels are discriminated by mutual information, conditional mutual information, and interactive mutual information. Secondly, the selected features and candidate features are dynamically weighted utilizing information gain factors. Finally, to evaluate the performance of this feature selection algorithm, NDCRFS was validated against 6 other feature selection algorithms on three classifiers, using 12 different data sets, for variability and classification metrics between the different algorithms. The experimental results show that the NDCRFS method can improve the quality of the feature subsets and obtain better classification results.
[Retracted] Internet of Things and Edge Computing Model Optimization in Physical Training Status and Countermeasures in College Sports Basketball Optional Course
Authors: Chen Wengang
Published: December 2021

In recent years, people have paid special attention to their own health status and paid more and more attention to the cultivation of physical fitness. People of different ages have begun physical exercises. As a compulsory course in university, physical education has played a very positive role in the health of students. In order to promote the common development of basketball activities in many sports academies across the country and improve the educational quality of basketball majors, with a view to cultivating basketball and football talents in line with the culture and social development of the times, this article investigates and evaluates the basketball activities of sports coaches in many sports academies, discusses the pros and cons of high school sports in basketball and other related fields, and provides references and suggestions for improvement in the reform of basketball activities in some sports academies. The results of the study found that based on the Internet of Things and edge computing models, physical training in college sports basketball optional courses has been greatly improved compared with traditional physical training methods. Adding adaptive scheduling to Core 1, priority 1 and 5 was distributed on Core 2∼5. Priority 2 tasks tend to be more balanced.
Failure Mechanism of Gob‐Side Roadway under Overlying Coal Pillar Multiseam Mining
Authors: Mu Hongwei , Wang Anhu , Song Dazhao , Su Dongfang , Li Donghui
Published: December 2021

Roadway deformation and rock burst are the two key challenges faced by the safe operation of coal mines. Aiming at the issue of large deformation of the gob‐side roadway under coal pillars in multiseam mining, this study has considered the case of the 8308 panel of Xinzhouyao coal mine in China. Based upon a combination of theoretical analysis, numerical simulations, and engineering practices, the mechanical model of “stress and deformation quantitative calculation of gob‐side roadway under overlying coal pillars” was established in this study. The analytical solutions of the vertical stress distribution and the plastic zone of the gob‐side roadway under overlying coal pillars were obtained. Finally, the accuracy of the mechanical model was verified using numerical simulations. The results showed that the coal pillar, upright above the gob‐side roadway, and the cantilever roof around the gob‐side roadway were the main factors leading to stress concentration and deformation around the gob‐side roadway. For the particular cases considered in this study, the peak stress of the gob‐side roadway could reach 1.8 times of the self‐weight stress of overlying strata. The rates of the contribution of the gob‐side roadway’s overlying pillar and the cantilever roof around the gob‐side roadway to peak stress were 78.3% and 16%, respectively. The obtained results have an essential reference significance for stress calculations and rock burst prevention design of gob‐side roadway under overlying coal pillars in multiseam mining.
Research Status Quo in Traditional Mongolian Medicine: A Bibliometric Analysis on Research Documents in the Web of Science Database
Authors: Kim Tae-Hun , Kang Jung Won
Published: December 2021

Objective. In this study, the current state of research on traditional Mongolian medicine (TMM) through a bibliometric analysis of research documents located in the Web of Science (WoS) database was assessed. Methods. The WoS database was searched on September 2021 with the keywords “traditional Mongolian medicine.” Publications on TMM scientific research were included in this study, without any language limitations. Bibliometric data from such publications were retrieved from the WoS database. Full records with cited reference lists were descriptively analyzed. To assess trends in TMM research topics, authors’ keywords were analyzed. A thematic evolution map based on coword analysis was suggested. To analyze research networks among co-authors, affiliations, or countries of the authors, collaboration networks were evaluated. The Bibliometrix R package (3.1) was used for the analysis. Results. A total of 234 scientific publications were included in the analysis. The top three countries of origin of the corresponding authors were China (n = 153), Japan (n = 28), and South Korea (n = 9). The top three relevant affiliations of the authors in the included publications were “Inner Mongolia Medical University,” “Inner Mongolia University of Nationalities,” and “National University of Mongolia.” “Flavonoids,” “cytotoxicity,” “NMR,” and “Tibetan medicine” were the most frequently used keywords in the included documents. Most publications focused on the chemical analysis and mechanism of effects of Mongolian herbal medications. There were few publications on nonpharmacological interventions such as bloodletting or TMM diagnostics, which should be promoted in future publications. Conclusion. There were only a limited number of publications on TMM identified through a search of the WoS database, using the keywords “Traditional Mongolian medicine.” More improved strategy for searching for TMM publications must be established. Research publications on TMM, especially regarding nonpharmacological interventions, need to be promoted. In addition, collaboration with researchers worldwide needs to be encouraged in the future.
Research on the Construction of Intelligent Community Emergency Service Platform Based on Convolutional Neural Network
Authors: Chen Yu , Tang Zhong
Published: December 2021

Aiming at the shortcomings of the existing community emergency service platform, such as single function, poor scalability, and strong subjectivity, an intelligent community emergency service platform based on convolutional neural network was constructed. Firstly, the requirements analysis of the emergency service platform was carried out, and the functional demand of the emergency service platform was analyzed from the aspects of community environment, safety, infrastructure, health management, emergency response, and so on. Secondly, through logistics network, big data, cloud computing, artificial intelligence, and all kinds of applications, the intelligent community emergency service platform was designed. Finally, a semantic matching emergency question answering system based on convolutional neural network was developed to provide key technical support for the emergency preparation stage of intelligent community. The results show that the intelligent community emergency service platform plays an important role in preventing community emergency events and taking active and effective measures to ensure the health and safety of community residents.
A Novel Integrated Fuzzy‐Rough MCDM Model for Evaluation of Companies for Transport of Dangerous Goods
Authors: Vojinović Nikolina , Sremac Siniša , Zlatanović Dragan
Published: December 2021

The organization and execution of the transport of dangerous goods is conditioned by a series of legal, technical, technological, safety, and engineering requirements, which must be met. In this way, a complex system is created which has a large number of participants and in which optimization should be performed at each stage from all the above aspects. The main goal of this paper is to create a novel Fuzzy‐Rough MCDM (Multiple‐Criteria Decision‐Making) for the evaluation of companies engaged in the transport of dangerous goods. A group decision‐making model was created to evaluate 11 transport companies based on nine legal, technical, technological criteria. The improved fuzzy stepwise weight assessment ratio analysis (IMF SWARA) method was used to calculate the criterion weights, while transport companies were ranked based on Rough Measurement Alternatives and Ranking according to the COmpromise Solution (R‐MARCOS). The integration of these methods into a single model that combines two theories of uncertainty, fuzzy and rough, was performed for the first time in this study, which represents a significant contribution. The results show that the most significant criteria are as follows: dangerous goods are classified and permitted under ADR (Agreement Concerning the International Carriage of Dangerous Goods by Road), the prescribed documents are in the transport unit, and the equipment is in the transport unit. When it comes to the evaluation results of companies, it can be noticed that A1 and A4 show the best performance, while A8 and A9 are in the last position. In order to test the stability of the model developed, sensitivity analysis, comparative analysis, and the influence of the dynamic formation of the initial matrix were created.
Review on EEG‐Based Authentication Technology
Authors: Zhang Shuai , Sun Lei , Mao Xiuqing , Hu Cuiyun , Liu Peiyuan
Published: December 2021

With the rapid development of brain‐computer interface technology, as a new biometric feature, EEG signal has been widely concerned in recent years. The safety of brain‐computer interface and the long‐term insecurity of biometric authentication have a new solution. This review analyzes the biometrics of EEG signals, and the latest research is involved in the authentication process. This review mainly introduced the method of EEG‐based authentication and systematically introduced EEG‐based biometric cryptosystems for authentication for the first time. In cryptography, the key is the core basis of authentication in the cryptographic system, and cryptographic technology can effectively improve the security of biometric authentication and protect biometrics. The revocability of EEG‐based biometric cryptosystems is an advantage that traditional biometric authentication does not have. Finally, the existing problems and future development directions of identity authentication technology based on EEG signals are proposed, providing a reference for the related studies.
A Case of Primary Signet-Ring Cell Cervical Carcinoma Treated with Chemoradiation, Brachytherapy, and Adjuvant Hysterectomy
Authors: Salmen Nicole , LaBella Dominic , Strumpf Kenneth , Bunn Wiley Douglas , Aridgides Paul
Published: December 2021

Primary signet-ring cell carcinoma of the uterine cervix is a rare subtype of cervical mucinous adenocarcinoma. Approximately 20 cases of primary signet-ring cell carcinoma of the cervix have been reported. Pathologic examination shows that adenocarcinomas with mucin accumulation in intracytoplasmic vacuoles displacing the nucleus indicate signet-ring cell carcinoma. A thorough metastatic workup is needed both for staging and to rule out gastrointestinal tract origin. Due to the rarity of the disease, both the true incidence and optimal management are unknown. Herein, the authors present a case of stage 1B3 primary signet-ring cell cervical carcinoma treated with combined chemotherapy and radiation (including external beam radiation and brachytherapy), followed by resection for residual disease. This case is consistent with limited reports where all surviving patients received surgery as well as 1 surviving patient with bulky disease required with chemoradiation and adjuvant hysterectomy.
Design of Remote Environmental Monitoring Network Based on Intelligent Sensor Network Address Allocation and Addressing
Authors: Liu Jinyong
Published: December 2021

Based on the wireless sensor network, this paper combines node monitoring data with intelligent network address management. Users can view real‐time environmental data through a computer or mobile phone and can manually remotely manage the environmental adjustment equipment of the network address through the mobile phone. This article first discusses the research background of the subject, introduces the current domestic and foreign research status of WSN in environmental detection, and analyzes the reasons for choosing ZigBee network as the wireless transmission environment of the intelligent monitoring system. Secondly, the structure, layered model, and key technologies of wireless sensor networks are introduced, and it is pointed out that ZigBee technology, which has the characteristics of low power consumption, reliable communication, self‐organization of the network, strong self‐healing ability, and low cost, is very suitable for application in the environment. Then, it analyzes TI’s protocol stack Z‐Stack based on the ZigBee2006 standard and analyzes the network address assignment and addressing in Z‐Stack, the process and steps of node binding, the routing mechanism and routing maintenance, and channel configuration. The realization of other functions was discussed in depth. During the simulation experiment, in the hardware design of the intelligent monitoring system, the network node was divided into two parts: the core board and the backplane. The crystal oscillator, power supply, antenna, and I/O port circuits of the core board were designed, and the data acquisition, relay, and power supply of the backplane were designed. Finally, this paper studies the data security issues in the environmental monitoring network and proposes two solutions to control network access and data encryption. Experimental results show that in terms of low‐power design, the energy of the entire system is calculated to determine the factors that affect the power consumption of the system and methods such as increasing the node sleep time to ensure that the system can work for a long time.
Assessment of Effective Network Connectivity among MEG None Contaminated Epileptic Transitory Events
Authors: Hadriche Abir , Behy Ichrak , Necibi Amal , Kachouri Abdennaceur , Amar Chokri Ben , Jmail Nawel
Published: December 2021

Characterizing epileptogenic zones EZ (sources responsible of excessive discharges) would assist a neurologist during epilepsy diagnosis. Locating efficiently these abnormal sources among magnetoencephalography (MEG) biomarker is obtained by several inverse problem techniques. These techniques present different assumptions and particular epileptic network connectivity. Here, we proposed to evaluate performances of distributed inverse problem in defining EZ. First, we applied an advanced technique based on Singular Value Decomposition (SVD) to recover only pure transitory activities (interictal epileptiform discharges). We evaluated our technique’s robustness in separation between transitory and ripples versus frequency range, transitory shapes, and signal to noise ratio on simulated data (depicting both epileptic biomarkers and respecting time series and spectral properties of realistic data). We validated our technique on MEG signal using detector precision on 5 patients. Then, we applied four methods of inverse problem to define cortical areas and neural generators of excessive discharges. We computed network connectivity of each technique. Then, we confronted obtained noninvasive networks to intracerebral EEG transitory network connectivity using nodes in common, connection strength, distance metrics between concordant nodes of MEG and IEEG, and average propagation delay. Coherent Maximum Entropy on the Mean (cMEM) proved a high matching between MEG network connectivity and IEEG based on distance between active sources, followed by Exact low-resolution brain electromagnetic tomography (eLORETA), Dynamical Statistical Parametric Mapping (dSPM), and Minimum norm estimation (MNE). Clinical performance was interesting for entire methods providing in an average of 73.5% of active sources detected in depth and seen in MEG, and vice versa, about 77.15% of active sources were detected from MEG and seen in IEEG. Investigated problem techniques succeed at least in finding one part of seizure onset zone. dSPM and eLORETA depict the highest connection strength among all techniques. Propagation delay varies in this range [18, 25]ms, knowing that eLORETA ensures the lowest propagation delay (18 ms) and the closet one to IEEG propagation delay.
Cytotoxic and Antimigration Activity of Etlingera alba (A.D.) Poulsen Rhizome
Authors: Wahyuni W. , Diantini Ajeng , Ghozali Mohammad , Subarnas Anas , Julaeha Euis , Amalia Riezki , Sahidin I.
Published: December 2021

Etlingera alba is one of the Etlingera plants that might have anticancer activity. This study aims to investigate the cytotoxic and antimetastatic activity of E. alba rhizome fractions and migration cell assay against MDA-MB-231 cell lines, which are used for triple-negative breast cancer (TNBC) treatment assay. The cytotoxic activity was assayed using CCK-8 assay, while the antimetastatic was assayed using migration cell assay for the fractions A–F. They were followed by LCMS/MS profiling to determine the chemical contents in the most active fraction. According to results obtained, fraction B was the most active fraction for cytotoxic activity with an IC50 value of 65.43 μg/mL, while fraction E was the most active fraction for antimetastasis activity against migration rate doses of 50, 100, and 200 ppm which were 6.80, 3.66, and 3.00%, respectively. Several compounds in fraction B, such as rengyolone, licochalcone A, sugiol, and spinasterol, might have been known to have activity against cancer cells, as well as aschantin and lirioresinol B dimethyl ether from fraction E. In conclusion, the chemical components from E. alba rhizome fractions provided potency for discovering new agents for cancer treatment, specifically for TNBC.
Model and Verification of Medical English Machine Translation Based on Optimized Generalized Likelihood Ratio Algorithm
Authors: Yu Peng , Zhu Youyu
Published: December 2021

Phrase identification plays an important role in medical English machine translation. However, the phrases in medical English are complicated in internal structure and semantic relationship, which hinders the identification of machine translation and thus affects the accuracy of translation results. With the aim of breaking through the bottleneck of machine translation in medical field, this paper designed a machine translation model based on the optimized generalized likelihood ratio (GLR) algorithm. Specifically, the model in question established a medical phrase corpus of 250,000 English and 280,000 Chinese words, applied the symbol mapping function to the identification of the phrase’s part of speech, and employed the syntactic function of the multioutput analysis table structure to correct the structural ambiguity in the identification of the part of speech, eventually obtaining the final identification result. According to the comprehensive verification, the translation model employing the optimized GLR algorithm was seen to improve the speed, accuracy, and update performance of machine translation and was seen to be more suitable for machine translation in medical field, therefore providing a new perspective for the employment of medical machine translation.
Efficacy and Safety of Emergent Transcatheter Aortic Valve Implantation in Patients with Acute Decompensated Aortic Stenosis: Systematic Review and Meta-Analysis
Authors: Shao Ruochen , Li Junli , Qu Tianyi , Fu Xiaoying , Liao Yanbiao , Chen Mao
Published: December 2021

Introduction. The aim of this systematic review and meta-analysis was to investigate the efficacy and safety of emergent transcatheter aortic valve implantation (TAVI) in patients with decompensated aortic stenosis (AS) by comparing the clinical outcomes with the patients who had received the elective TAVI. Methods. By searching PubMed, EMBASE, and Cochrane databases, we obtained the studies comparing the clinical outcomes of emergent TAVI and elective TAVI. Finally, 14 studies were included. Results. A total of 14 eligible articles with 73,484 patients were included in this meta-analysis. Emergent TAVI was associated with a higher mortality during hospitalization (HR 2.09, 95% CI [1.39 to 3.14]), 30 days (HR 2.29, 95% CI [1.69 to 3.10]), and 1 year (HR 1.96, 95% CI [1.55 to 2.49]). Consistently, the incidence of acute kidney injury (AKI) (RR 2.48, 95% CI [1.85 to 3.32]), dialysis (RR 2.37, 95% CI [1.95 to 2.88]), bleeding (RR 1.62, 95% CI [1.27 to 2.08]), major bleeding (RR 1.05, 95% CI [1.00 to 1.10]), and 30-day rehospitalization (RR 1.30, 95% CI [1.07, 1.58]) were more common in patients receiving emergent TAVI. No statistical differences were found in the occurrence rate of vascular complications (RR 1.11, 95% CI [0.90, 1.36]), major vascular complications (RR 1.14, 95% CI [0.52, 2.52]), permanent pacemaker (PPM) placement (RR 1.05, 95% CI [0.99, 1.11]), cerebrovascular events (RR 1.11, 95% CI [0.98, 1.25]), moderate to severe paravalvular leakage (PVL) (RR 1.23, 95% [CI 0.94 to 1.61]), and device success (RR 0.99, 95% CI [0.97, 1.01]). Conclusion. Emergent TAVI is associated with some postoperative complications and increased mortality compared with elective TAVI. Emergent TAVI should be implemented cautiously and individually.
Long-Term In Vitro Passaging Had a Negligible Effect on Extracellular Vesicles Released by Leishmania amazonensis and Induced Protective Immune Response in BALB/c Mice
Authors: Dupin Talita Vieira , Reis Natasha Ferraz de Campos , Perez Elizabeth Cristina , Soares Rodrigo Pedro , Torrecilhas Ana Claudia , Xander Patricia
Published: December 2021

Depending on Leishmania species and the presence/absence of virulence factors, Leishmania extracellular vesicles (EVs) can differently stimulate host immune cells. This work is aimed at characterizing and evaluating the protective role of EVs released by Leishmania amazonensis promastigotes under different maintenance conditions. Initially, using a control strain, we standardized 26°C as the best release temperature to obtain EVs with a potential protective role in the experimental leishmaniasis model. Then, long-term (LT-P) promastigotes of L. amazonensis were obtained after long-term in vitro culture (100 in vitro passages). In vivo-derived (IVD-P) promastigotes of L. amazonensis were selected after 3 consecutive experimental infections in BALB/c mice. Those strains developed similar lesion sizes except for IVD-P at 8 weeks post infection. No differences in EV production were detected in both strains. However, the presence of LPG between LT-P and IVD-P EVs was different. Groups of mice immunized with EVs emulsified in the adjuvant and challenged with IVD-P parasites showed decreased lesion size and parasitic load compared with the nonimmunized groups. The immunization regimen with two doses showed high IFN-γ and IgG2a titers in challenged mice with either IVD-P or LT-P EVs. IL-4 and IL-10 were detected in immunized mice, suggesting a mixed Th1/Th2 profile. EVs released by either IVD-P or LT-P induced a partial protective effect in an immunization model. Thus, our results uncover a potential protective role of EVs from L. amazonensis for cutaneous leishmaniasis. Moreover, long-term maintenance under in vitro conditions did not seem to affect EV release and their immunization properties in mice.
Contribution of Gut Microbiota to Immune Tolerance in Infants
Authors: Méndez Constanza S. , Bueno Susan M. , Kalergis Alexis M.
Published: December 2021

The prevalence of food allergy has increased in recent years, especially among the pediatric population. Differences in the gut microbiota composition between children with FA and healthy children have brought this topic into the spotlight as a possible explanation for the increase in FA. The gut microbiota characteristics are acquired through environmental interactions starting early in life, such as type of delivery during birth and breastfeeding. The microbiota features may be shaped by a plethora of immunomodulatory mechanisms, including a predominant role of Tregs and the transcription factor FOXP3. Additionally, a pivotal role has been given to vitamin A and butyrate, the main anti-inflammatory metabolite. These observations have led to the study and development of therapies oriented to modifying the microbiota and metabolite profiles, such as the use of pre- and probiotics and the determination of their capacity to induce tolerance to allergens that are relevant to FA. To date, evidence supporting these approaches in humans is scarce and inconclusive. Larger cohorts and dose-titration studies are mandatory to evaluate whether the observed changes in gut microbiota composition reflect medical recovery and increased tolerance in pediatric patients with FA. In this article, we discuss the establishment of the microbiota, the immunological mechanisms that regulate the microbiota of children with food allergies, and the evidence in research focused on its regulation as a means to achieve tolerance to food allergens.
A Hybrid Butterfly Optimization Algorithm for Numerical Optimization Problems
Authors: Zhou Huan , Cheng Hao-Yu , Wei Zheng-Lei , Zhao Xin , Tang An-Di , Xie Lei
Published: December 2021

The butterfly optimization algorithm (BOA) is a swarm‐based metaheuristic algorithm inspired by the foraging behaviour and information sharing of butterflies. BOA has been applied to various fields of optimization problems due to its performance. However, BOA also suffers from drawbacks such as diminished population diversity and the tendency to get trapped in local optimum. In this paper, a hybrid butterfly optimization algorithm based on a Gaussian distribution estimation strategy, called GDEBOA, is proposed. A Gaussian distribution estimation strategy is used to sample dominant population information and thus modify the evolutionary direction of butterfly populations, improving the exploitation and exploration capabilities of the algorithm. To evaluate the superiority of the proposed algorithm, GDEBOA was compared with six state‐of‐the‐art algorithms in CEC2017. In addition, GDEBOA was employed to solve the UAV path planning problem. The simulation results show that GDEBOA is highly competitive.
IoT Workflow Scheduling Using Intelligent Arithmetic Optimization Algorithm in Fog Computing
Authors: Abd Elaziz Mohamed , Abualigah Laith , Ibrahim Rehab Ali , Attiya Ibrahim
Published: December 2021

Instead of the cloud, the Internet of things (IoT) activities are offloaded into fog computing to boost the quality of services (QoSs) needed by many applications. However, the availability of continuous computing resources on fog computing servers is one of the restrictions for IoT applications since transmitting the large amount of data generated using IoT devices would create network traffic and cause an increase in computational overhead. Therefore, task scheduling is the main problem that needs to be solved efficiently. This study proposes an energy‐aware model using an enhanced arithmetic optimization algorithm (AOA) method called AOAM, which addresses fog computing’s job scheduling problem to maximize users’ QoSs by maximizing the makespan measure. In the proposed AOAM, we enhanced the conventional AOA searchability using the marine predators algorithm (MPA) search operators to address the diversity of the used solutions and local optimum problems. The proposed AOAM is validated using several parameters, including various clients, data centers, hosts, virtual machines, tasks, and standard evaluation measures, including the energy and makespan. The obtained results are compared with other state‐of‐the‐art methods; it showed that AOAM is promising and solved task scheduling effectively compared with the other comparative methods.
Current Recommendations on the Management of Pediatric Asthma and Allergic Rhinitis during the COVID-19 Pandemic versus Real-Life Practice
Authors: Gokmirza Ozdemir Pinar , Celik Velat
Published: December 2021

Introduction: Several statements and position papers on the management of childhood asthma and allergies during the COVID-19 pandemic have been published of late. The aim of this study was to evaluate the knowledge and awareness of pediatricians and family physicians regarding the management of asthma and allergic rhinitis during the pandemic according to recently published updated guidelines. Method: We conducted an online survey among pediatricians and family physicians in Turkey, using a questionnaire designed to evaluate 4 items: (1) the relationship between COVID-19 infection risk and pediatric asthma/allergic rhinitis and medications used in treatment; (2) the follow-up and management of asthma/allergic rhinitis according to published updated recommendations; (3) pediatricians’ and family physicians’ observations and perceptions of treatment compliance and the attitudes of their pediatric asthma patients; and (4) pediatricians and family physicians’ attitudes to using telehealth in the follow-up and management of pediatric asthma patients during the pandemic. Results: A total of 346 participants responded to the survey. The relationship between the risk of COVID-19 and asthma was known by less than 25% of the participants. More than 33% of family physicians and 20% of pediatricians were unaware that asthma medication does not lead to a susceptibility to COVID-19 infection; 55% of family physicians and 48% of pediatricians thought that patients showed better compliance with asthma controller medication; over 33% of pediatricians and approximately 50% of family physicians stated that they could not distinguish between an asthma attack and lung involvement in COVID-19 infection; of the respondents, over 75% stated that they prefer face-to-face visits with patients, even in situations that do not require a physical examination. Conclusion: The overall knowledge and awareness of pediatricians and, especially, family physicians regarding the management of pediatric asthma/allergic rhinitis during the pandemic is not at a satisfactory level. There is an urgent need to inform them about updated recommendations appearing in recent guidelines published by allergy organizations.
Presumed Melanoma of Unknown Primary Origin Metastatic to the Choroid Mimics Primary Uveal Melanoma
Authors: Rieth John M. , Bowen Randy Chris , Milhem Mohammed M. , Boldt H. Culver , Binkley Elaine M.
Published: December 2021

We describe the case of a 69-year-old woman who presented with a decline in vision in the left eye and was found to have a choroidal lesion with clinical and echographic features concerning for primary uveal melanoma. Systemic imaging identified numerous metastases to the liver, kidneys, paratracheal lymph nodes, lung, and brain. The hepatic lesion was biopsied, and genetic analysis identified a Val600Glu (c.1799T>A) BRAF mutation, consistent with a cutaneous primary malignancy, although no primary tumor was identified. This case highlights that metastasis to the choroid is a rare presentation of nonuveal melanoma that can mimic primary uveal melanoma. Genetic analysis of tumor tissue can identify the origin of the melanoma and guide treatment options. Systemic imaging should be performed prior to intervention for choroidal neoplasms.
Associations between Prolactin, Diabetes, and Cognitive Impairment: A Literature Review
Authors: Duc Nguyen Hai , Oh Hojin , Yu Byung Pal , Hoang Ngoc Minh Hong , Jo Won Hee , Young Chung Hae , Kim Min-Sun
Published: December 2021

Background: Converging evidence indicates prolactin (PRL) and diabetes play an important role in the pathophysiology of cognitive impairment. However, little is known about the mechanisms responsible for the effects of PRL and diabetes on cognitive impairment. Summary: We summarize and review the available literature and current knowledge of the association between PRL and diabetes on aspects of cognitive impairment. Key Messages: The phosphatidylinositol 3-kinase/protein kinase B pathway is central to the molecular mechanisms underlying how PRL and diabetes interact in cognitive impairment. Further work is needed to identify the interaction between PRL and diabetes, especially in the molecular aspects of cognitive impairment, which can suggest novel strategies for cognitive dysfunction treatment.