bluuebunny/crossref_metadata_2025 huggingface dataset:
Use of intravenous cangrelor in the treatment of ruptured and unruptured cerebral aneurysms: an updated single-center analysis and pooled analysis of current studies
Authors: Entezami Pouya , Dalfino John C , Boulos Alan S , Yamamoto Junichi , Holden Devin N , Field Nicholas C , Rock Andrew K , Najera Edinson , Paul Alexandra R
Published: June 2022

Background

Intracranial stent placement for the treatment of cerebral aneurysms is increasingly utilized in both ruptured and unruptured scenarios. Intravenous (IV) cangrelor is a relatively new antiplatelet agent that was initially approved for coronary interventions. In addition to our institution, five other centers have published their results using IV cangrelor in neurointerventional procedures. This article combines the aneurysm treatment data from all prior studies to provide insight into the safety and efficacy of cangrelor for intracranial aneurysm treatment.

Methods

A prospectively maintained database was reviewed to identify all cases of IV cangrelor administration during aneurysm embolization. 20 additional patients were identified who had not been previously published. In addition, a literature search was performed to identify prior publications regarding cangrelor in neurointervention. The data from these were combined with our institutional results in a pooled-analysis.

Results

Overall, 85 patients who received IV cangrelor during aneurysm embolization were identified, including 46 ruptured and 39 unruptured cases. The asymptomatic and symptomatic intracranial hemorrhage rates were 4% (2/46) for ruptured cases and 2.6% (1/39) for unruptured cases. The rate of retroperitoneal hematoma and gastrointestinal bleeding was 0%. There were no incidents of intraprocedural thromboembolic complication or intraprocedural in-stent thrombosis in either cohort. One subject suffered an ischemic stroke at 24 hours secondary to in-stent thrombosis in a ruptured case.

Conclusions

IV cangrelor during aneurysm embolization appears to be safe, with a symptomatic intracranial hemorrhage rate of 4% in ruptured cases and 2.6% in unruptured cases. More research is needed to determine the ideal dosing regimen.
IGFBP7 as a preoperative predictor of congestive acute kidney injury after cardiac surgery
Authors: MacMillan Yannick S , Mamas Mamas A , Sun Louise Y
Published: June 2022

Objective

Congestive acute kidney injury (c-AKI) refers to AKI in the presence of right ventricular failure (RVF) and is a highly morbid complication of cardiac surgery. However, treatment has traditionally been reactive rather than proactive due to limited modalities to predict this complication. The objective of this study was to investigate the ability of insulin-like growth-factor binding protein 7 (IGFBP7), to predict c-AKI, AKI and RVF in patients undergoing cardiac surgery, as compared to N-terminal prohormone B-type natriuretic peptide (NT-pro-BNP) and pulmonary artery pulsatility index (PAPi).

Methods

This prospective nested case–control study consisted of 350 adult patients who underwent elective cardiac surgery. The outcomes were c-AKI, AKI and RVF. Unadjusted and adjusted conditional logistic regression models and areas under the receiver operating characteristic curve (AUC) were used to assess the predictive performance of each marker.

Results

For the prediction of c-AKI, the unadjusted IGPBP7 model had an AUC of 0.81, as compared with 0.51 for NT-pro-BNP and 0.61 for PAPi. The adjusted c-AKI models had AUCs of 0.90 for IGFBP7, 0.87 for NT-pro-BNP and 0.77 for PAPi. For AKI and RVF, the predictive performance of IGFBP7 was moderate and exceeded that of NT-pro-BNP and PAPi in univariable analysis. IGFBP7 remained a robust independent predictor of all outcomes in multivariable analysis, whereas the other markers did not.

Conclusions

IGFBP7 is a promising biomarker for prediction of AKI, RVF and c-AKI and could have value for preoperative optimisation and risk stratification of patients undergoing cardiac surgery.
Chronic meningitis, seizures and myoclonus
Authors: McDonald James Joseph , Jones Matthew , Smith Colin , Foley Peter L , Lee Liam , Davenport Richard
Published: June 2022

A 70-year-old retired male plumber was admitted with reduced consciousness following a recent onset of confusion and headache. He developed encephalopathy, seizures and myoclonus. He died despite treatment 10 weeks later. This article reports the clinicopathological conference presented at the Edinburgh Clinical Neurology Course 2021.
Remu-Net: Multi-Branch Net Framework for 3D Brain Tumor Segmentation
Authors: Wang Zu-Min , Dong Lei , Zhang Min , Gao Bing , Jiang Zong-Kang , Duan Yu-Cong
Published: June 2022

As one of the fatal human diseases, early detection of brain tumors can effectively save patients’ lives. Brain tumor image segmentation is of great practical importance for physicians to perform brain tumor diagnoses quickly. Due to the data complexity of 3D brain images, it is impractical to segment out tumor regions manually, so automatic and reliable methods can be utilized instead of manual work to achieve accurate segmentation of tumor regions. In this paper, we propose an end-to-end, more efficient brain tumor MRI segmentation model, REMU-Net, for the problems of multi-scale feature extraction and difficulty in small target feature extraction in 3D brain tumor image segmentation. Firstly, design and use the multi-channel parallel M-RepVGG module as a decoder to achieve multi-scale feature fusion. Secondly, embedding dilated convolution with different dilated rates in the DM-RepVGG module of the encoder to better extract features at different scales. Finally, introduce the expectation-maximizing attention in the network to better extract the features of the internal details of the tumor. The experimental results on the BraTS2018 validation dataset are Dice scores of 80.93%, 90.13%, and 86.15%, respectively. Experimental results on the BraTS2019 validation dataset can be achieved with Dice scores of 78.29%, 90.65%, and 82.77%, respectively.
Definable completeness of P-minimal fields and applications
Authors: Cubides Kovacsics Pablo , Delon Françoise
Published: June 2022

We show that every definable nested family of closed and bounded subsets of a P-minimal field K has nonempty intersection. As an application we answer a question of Darnière and Halupczok showing that P-minimal fields satisfy the “extreme value property”: for every closed and bounded subset [Formula: see text] and every interpretable continuous function [Formula: see text] (where [Formula: see text] denotes the value group), f(U) admits a maximal value. Two further corollaries are obtained as a consequence of their work. The first one shows that every interpretable subset of [Formula: see text] is already interpretable in the language of rings, answering a question of Cluckers and Halupczok. This implies in particular that every P-minimal field is polynomially bounded. The second one characterizes those P-minimal fields satisfying a classical cell preparation theorem as those having definable Skolem functions, generalizing a result of Mourgues.
Reduction games, provability and compactness
Authors: Dzhafarov Damir D. , Hirschfeldt Denis R. , Reitzes Sarah
Published: June 2022

Hirschfeldt and Jockusch (2016) introduced a two-player game in which winning strategies for one or the other player precisely correspond to implications and non-implications between [Formula: see text] principles over [Formula: see text]-models of [Formula: see text]. They also introduced a version of this game that similarly captures provability over [Formula: see text]. We generalize and extend this game-theoretic framework to other formal systems, and establish a certain compactness result that shows that if an implication [Formula: see text] between two principles holds, then there exists a winning strategy that achieves victory in a number of moves bounded by a number independent of the specific run of the game. This compactness result generalizes an old proof-theoretic fact noted by H. Wang (1981), and has applications to the reverse mathematics of combinatorial principles. We also demonstrate how this framework leads to a new kind of analysis of the logical strength of mathematical problems that refines both that of reverse mathematics and that of computability-theoretic notions such as Weihrauch reducibility, allowing for a kind of fine-structural comparison between [Formula: see text] principles that has both computability-theoretic and proof-theoretic aspects, and can help us distinguish between these, for example by showing that a certain use of a principle in a proof is “purely proof-theoretic”, as opposed to relying on its computability-theoretic strength. We give examples of this analysis to a number of principles at the level of [Formula: see text], uncovering new differences between their logical strengths.
ON OPTIMIZATION OF MANUFACTURING OF PRODUCT TO OBTAIN MAXIMAL VALUE OF PROFIT TAKING INTO ACCOUNT CHANGING OF PRICES
Authors: PANKRATOV E. L.
Published: June 2022

In this paper, we introduce a model for the prognosis of profit of an enterprise taking into account several types of products. Also, we introduce an approach for maximization of the considered profit with an analytical approach for estimation of the maximum value of profit of an enterprise taking into account several types of products. Profit maximization gives a possibility to take into account changes in the price of products on the market. As an example, we consider the estimation of profit for the accounting of three types of products. But the introduced approach gives a possibility to change a number of products.
Deep Learning for Android Malware Defenses: A Systematic Literature Review
Authors: Liu Yue , Tantithamthavorn Chakkrit , Li Li , Liu Yepang
Published: June 2022

Malicious applications (particularly those targeting the Android platform) pose a serious threat to developers and end-users. Numerous research efforts have been devoted to developing effective approaches to defend against Android malware. However, given the explosive growth of Android malware and the continuous advancement of malicious evasion technologies like obfuscation and reflection, Android malware defense approaches based on manual rules or traditional machine learning may not be effective. In recent years, a dominant research field called deep learning (DL), which provides a powerful feature abstraction ability, has demonstrated a compelling and promising performance in a variety of areas, like natural language processing and computer vision. To this end, employing DL techniques to thwart Android malware attacks has recently garnered considerable research attention. Yet, no systematic literature review focusing on DL approaches for Android malware defenses exists. In this article, we conducted a systematic literature review to search and analyze how DL approaches have been applied in the context of malware defenses in the Android environment. As a result, a total of 132 studies covering the period 2014–2021 were identified. Our investigation reveals that, while the majority of these sources mainly consider DL-based Android malware detection, 53 primary studies (40.1%) design defense approaches based on other scenarios. This review also discusses research trends, research focuses, challenges, and future research directions in DL-based Android malware defenses.
Virtualizing a Post-Moore’s Law Analog Mesh Processor: The Case of a Photonic PDE Accelerator
Authors: Anderson Jeff , Kayraklioglu Engin , Reza Imani Hamid , Shen Chen , Miscuglio Mario , Sorger Volker J. , El-Ghazawi Tarek
Published: June 2022

Innovative processor architectures aim to play a critical role in future sustainment of performance improvements under severe limitations imposed by the end of Moore’s Law. The Reconfigurable Optical Computer (ROC) is one such innovative, Post-Moore’s Law processor. ROC is designed to solve partial differential equations in one shot as opposed to existing solutions, which are based on costly iterative computations. This is achieved by leveraging physical properties of a mesh of optical components that behave analogously to lumped electrical components. However, virtualization is required to combat shortfalls of the accelerator hardware. Namely, (1) the infeasibility of building large photonic arrays to accommodate arbitrarily large problems and (2) underutilization brought about by mismatches in problem and accelerator mesh sizes due to future advances in manufacturing technology. In this work, we introduce an architecture and methodology for lightweight virtualization of ROC that exploits advantages borne from optical computing technology. Specifically, we apply temporal and spatial virtualization to ROC and then extend the accelerator scheduling tradespace with the introduction of spectral virtualization. Additionally, we investigate multiple resource scheduling strategies for a system-on-chip (SoC)-based PDE acceleration architecture and show that virtual configuration management offers a speedup of approximately 2×. Finally, we show that overhead from virtualization is minimal, and our experimental results show two orders of magnitude increased speed as compared to microprocessor execution while keeping errors due to virtualization under 10%.
Structural Mimicry in Microbial and Antimicrobial Amyloids
Authors: Golan Nimrod , Engelberg Yizhaq , Landau Meytal
Published: June 2022

The remarkable variety of microbial species of human pathogens and microbiomes generates significant quantities of secreted amyloids, which are structured protein fibrils that serve diverse functions related to virulence and interactions with the host. Human amyloids are associated largely with fatal neurodegenerative and systemic aggregation diseases, and current research has put forward the hypothesis that the interspecies amyloid interactome has physiological and pathological significance. Moreover, functional and molecular-level connections between antimicrobial activity and amyloid structures suggest a neuroimmune role for amyloids that are otherwise known to be pathological. Compared to the extensive structural information that has been accumulated for human amyloids, high-resolution structures of microbial and antimicrobial amyloids are only emerging. These recent structures reveal both similarities and surprising departures from the typical amyloid motif, in accordance with their diverse activities, and advance the discovery of novel antivirulence and antimicrobial agents. In addition, the structural information has led researchers to postulate that amyloidogenic sequences are natural targets for structural mimicry, for instance in host–microbe interactions. Microbial amyloid research could ultimately be used to fight aggressive infections and possibly processes leading to autoimmune and neurodegenerative diseases.
Properties of Deiters’ neurons and inhibitory synaptic transmission in the mouse lateral vestibular nucleus
Authors: Stitt I. M. , Wellings T. P. , Drury H. R. , Jobling P. , Callister R. J. , Brichta A. M. , Lim R.
Published: June 2022

Deiters’ neurons within the lateral vestibular nucleus project the length of the spinal cord and activate antigravity extensor muscles. Deiters’ neurons were characterized anatomically and physiologically in mice. Deiters’ neurons are tonically active, have homogeneous intrinsic membrane properties, including low input resistance, and receive significant GABAAergic synaptic inputs. Deiters’ neurons show little modulation in response to current injection. These features are consistent with Deiters’ neurons responding to perturbations to maintain posture and balance.
[Retracted] Exposure to Ambient Air Pollutant PM10 in the Second Trimester of Pregnancy Is Associated with Preterm Birth: A Birth‐Based Health Information Cohort Study
Authors: Xiao Pengying , Wang Lijun , Yao Yingjie , Chang Yu , Hou Jianghou
Published: June 2022

Objectives. We evaluated the effects of exposure to high concentrations of particulate matter (PM)10 on preterm birth (PTB) and identified a critical concentration of PM10 that could lead to PTB via a birth‐based health information cohort study. Methods. We conducted a birth‐based cohort study consisting of nonanomalous singleton births at 22‐42 weeks. PTB was defined as babies born alive before 37 weeks of pregnancy. Pregnancy period exposure averages were estimated for PM10 based on the China National Environmental Monitoring Centre (CNEMC). Pregnant women who lived within 50 km of the monitor station were recruited into this study. Logistic regression analyses were performed to determine the association between PTB and exposure to PM10 at different pregnancy periods with adjustment for confounding factors. Results. The relative frequency of PTB was 8.7% in the study cohort of 5,291 singleton live births. A total of 1137 women had a high level of PM10 exposure (≥60 μg/m3) in the second trimester of pregnancy. The average concentrations of PM10 in the first, second, and third trimesters of pregnancy and throughout pregnancy were 53.8 μg/m3, 54.2 μg/m3, 55.6 μg/m3, and 54.3 μg/m3, respectively. The generalized additive model (GAM) analysis showed that there was a nonlinear correlation between PM10 and PTB in the second trimester of pregnancy (P < 0.001). The adjusted odds ratio between PTB and low concentration PM10 exposure (PM10 < 60  μg/m3) in the second trimester of pregnancy was 1.01 (95% CI 0.95‐1.05). However, high PM10 exposure (PM10 ≥ 60 μg/m3) in the second trimester of pregnancy had an increased PTB risk even after adjustment for coexisting risk factors with an adjusted odds ratio of 1.78 (95% CI 1.69‐1.87), and the incidence of PTB increased with an increase in PM10 exposure. Conclusions. Our research discovered that exposure to high levels of PM10 increases the risk of PTB and the second trimester is the most vulnerable gestational period to ambient air pollution exposure. PM10 concentrations more than 60 μg/m3 are detrimental to pregnant women in their second trimester. This study has implications for health informatics‐oriented healthcare decision support systems.
Design and Research of Building Indoor Environment Monitoring System Relying on 3D Modeling and Image Processing
Authors: Liu Chenguang
Published: June 2022

In order to improve the monitoring effect of building indoor environment, this paper carries out indoor environment simulation through 3D modeling, uses image processing technology to collect environmental monitoring data, and proposes a routing algorithm applied in this environment. Moreover, this paper introduces the energy consumption model of sensor nodes and the multi-attribute decision-making method and uses the multi-attribute decision-making to propose an adaptive energy balance routing algorithm based on the indoor environment of the building. In addition, this paper establishes a simulation analysis, compares it with several other routing algorithms, and verifies that the proposed algorithm has better balance in network energy consumption and longer network life cycle. Finally, this paper combines three-dimensional modeling and image processing technology to construct the building indoor environment monitoring system. It can be seen from the simulation experiments that the proposed building indoor environment monitoring system based on 3D modeling and image processing can meet the actual needs of intelligent building indoor monitoring.
A Nanoliposomal Gel Containing Cinnamomum zeylanicum Essential Oil with Effective Repellent against the Main Malaria Vector Anopheles stephensi
Authors: Osanloo Mahmoud , Firoozian Samira , Zarenezhad Elham , Montaseri Zahra , Satvati Saha
Published: June 2022

Malaria is the most important vector-borne disease; however, mosquito repellents are still a practical approach for controlling malaria, especially in endemic regions. Due to the side effects of synthetic repellents such as N, N-diethyl-meta-toluamide (DEET), the development of natural repellents has received much attention. In this study, nanoliposomes containing 0.5 and 2.5% w/v Cinnamomum zeylanicum essential oil were firstly prepared with particle sizes of 119 ± 6 and 195 ± 9 nm. Their morphologies and loading of the essential oil in the particles were then investigated using transmission electron microscopy (TEM) and attenuated total reflection-Fourier transform infrared (ATR-FTIR) analyses. The nanoliposomes were finally jellified to increase their viscosity and facilitate topical usage. The complete protection time of the nanoliposomal gel containing 2.5% C. zeylanicum essential oil was significantly longer than that of 2.5% DEET against Anopheles stephensi: 303 ± 10 > 242 ± 12 min,

p
<
0.001

. Moreover, the prepared nanoformulation was stable for at least six months at 4 and 26°C. Therefore, the prepared prototype could be considered a natural repellent against the main malaria mosquito vector in field conditions. In addition, it is suggested to be investigated against other important factors mosquitoes.
Music Sense Analysis of Bel Canto Audio and Bel Canto Teaching Based on LSTM Mixed Model
Authors: Tang Zhangcheng
Published: June 2022

As we all know, as the soul of music artists, the cultivation of music sense is an indispensable and important part of Bel Canto teaching. Traditional music classroom education lags behind the development of the information age. According to the educational method of Bel Canto teaching, the recognition experiment of Bel Canto audio is carried out, which helps students analyze the content of music sense contained in music works, and teachers cultivate students’ music sense and music theory knowledge according to effective results. In order to better let students appreciate the true meaning of music, it is necessary to add online tools to assist Bel Canto teaching. Traditional methods neither teach students in accordance with their aptitude from the actual situation, but use the rapidly developing computer technology to match resources, nor does it seriously cultivate students’ ability to appreciate music and perceive emotions. Based on the above problems, this paper starts from the field of deep learning and plans to build a hybrid model related to LSTM. The results of this paper are as follows: (1) The CNN-LSTM model has the highest recognition rate curve, and the recognition rate of some emotions is over 90%; the loss rate tends to be stable at 200 iterations, and the convergence speed is rapid. (2) After preprocessing, the emotion recognition rate is higher, and the average accuracy of audio features extracted based on spectrogram + LLDs in emotion is about 0.7. (3) According to the actual scene application, the best effect of music sense cultivation is to use the model to assist classroom teaching, and the highest score can reach 8.8 points. In addition, the error between the emotional expression identified by the model and the original work is between 0 and 0.5 points, and the emotional expression effect is excellent. (4) The model can also recognize different kinds and times of emotion in 5-minute Bel Canto works. The above experimental results show that the model basically meets the requirements of the subject, and its performance is excellent, but the details need to be optimized.
Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification
Authors: Alnabhan Mohammad , Habboush Ahmad Khader , Abu Al-Haija Qasem , Mohanty Arup Kumar , Pattnaik Saumendra , Pattanayak Binod Kumar
Published: June 2022

This research utilizes metaheuristic optimization inspired by the Egyptian Vulture Optimization (EVO) technique. Biomedical image segregation is developed to reduce the complex association of hyperparameters of Convolutional Neural networks (CNN). The complex attributes of CNN include the type of kernel, size of the kernel, size of the batch, epoch counts, momentum, learning rate, activation function, convolution layer, and dropout. However, the life cycle of an Egyptian vulture influences the optimization technique to resolve complexity and increase the accuracy of CNN. The proposed CNN-based EVO model was evaluated in comparison to ANN-based and deep learning-based classifiers utilizing brain MRI image datasets. The results achieved have confirmed the efficiency and performance of the proposed CNN-based EVO model, in which the average detection accuracy and precision were 93% and 95%, respectively.
Mobile Music Recognition based on Deep Neural Network
Authors: Zhang Nan
Published: June 2022

The piano, as the king of playing instruments, is the most popular instrument for people to learn to play. Learning to play the piano, on the other hand, necessitates professional instruction and a lot of practice. People do not have enough time for systematic training because of the fast pace of life. At the same time, a lack of professional piano teachers and high tuition fees discourage piano students. If the computer can recognize and evaluate the learner’s piano music in real time, the learner will be able to identify and correct errors in real time. There are currently some music recognition technologies, but the majority of them have the following flaws: first and foremost, the recognition accuracy is poor. Second, the identification process is slow and not real-time. Based on the existing problems, this paper proposes a mobile-based music recognition method. The main work of this paper is as follows: (1) a deep neural network (DNN) is applied to the recognition of piano playing music. The use of deep learning models improves the accuracy of music recognition. (2) In order to make the identification of music easier to use, a mobile application is developed in this paper. The app can be installed on mobile phones and tablets. It can input songs in real-time or offline, outputting misplayed notes and scoring the entire composition. In order to evaluate the effect of this study on music recognition, the experimental part uses multiple models for comparison. The experimental results show that the research in this paper is feasible and effective.
Intelligent Basketball Dribble Trajectory Monitoring Based on Data-Driven Technology
Authors: Xie Xianjiao
Published: June 2022

Data-driven technology is a problem-solving method, starting from the initial data or observations, using heuristic rules to find and establish the relationship between internal features, to discover some theorems or laws. With the sharp increase in the number of basketball players, there are increasingly studies on the dribbling action of its trajectory and placement. Data-driven technology usually also refers to natural language processing methods based on large-scale statistical data. This paper proposes to use this technology to monitor the trajectory of basketball dribbling, which can more effectively reduce the pain caused by improper dribbling when teenagers play basketball, and can also more effectively improve a shooting percentage. The invention also relates to the field of Internet of Things technology, moving target detection, and the algorithm of target movement trajectory. The Internet of Things technology originated in the media field and is the third revolution in the information technology industry. The Internet of Things refers to the connection of any object with the network through the information-sensing equipment and according to the agreed protocol, and the objects exchange and communicate information through the information dissemination medium to realize intelligent identification, positioning, tracking, supervision, and other functions. After that, a lot of experiments were carried out, and the results showed that the number of breakthroughs after using this technology accounted for 30%, and the hit rate after the breakthrough was also above 70%. The TFHBase system and its optimization strategy are efficient and can effectively solve the problem of basketball trajectory monitoring, and we also do some exploration on trajectory visualization, to further control the landing point of the basketball trajectory.
Correlation between Platelet miRNA Expression and Coagulation Function in Children with Severe Pneumonia
Authors: Hao Mingming , Guo Jing , Lu Jinying , Chang Taotao , Yin Zhanru
Published: June 2022

Objective. To investigate the relationship between expression levels of platelet miRNAs and severe pneumonia (SP) in children. Methods. A randomized controlled trial was conducted in 129 children with SP hospitalized from May 2018 to May 2020. All children joined the study group and were divided into the mild infection group, moderate infection group, and severe infection group according to the diagnostic criteria, 43 cases in each group. Besides, 129 healthy children were selected as the control group. The expression levels of platelet miR-223 and miR-192 were detected by real-time quantitative polymerase chain reaction (qPCR). The prothrombin time (PT), activated partial thromboplastin time (APTT), and fibrinogen (FIB) were detected by the Sysmex CA-1500 System (Sysmex Corporation, Japan). The Pearson analysis was conducted to evaluate the correlation between coagulation function and the levels of miR-223 and miR-192. Results. Compared with the control group, miR-223 in the study group was significantly higher and miR-192 was significantly lower (

P
<
0.05

). Compared with the mild infection group, miR-223 was significantly higher and miR-192 was significantly lower in the moderate infection group and severe infection group (

P
<
0.05

). Compared with the control group, PT and APTT were significantly lower and FIB was significantly higher in the study group (

P
<
0.05

). Pearson correlation analysis revealed that miR-223 was positively correlated with PT and APTT (

P
<
0.05

) and negatively correlated with FIB (

P
<
0.05

); miR-192 was negatively correlated with PT and APTT (

P
<
0.05

) and positively correlated with FIB (

P
<
0.05

). Conclusion. miR-223 and miR-192 can reflect coagulation function in children with SP, which can provide a certain reference basis for clinical guidance and treatment and prognosis.
Study on the Correlation between Interleukin-27 and CXCL10 in Pulmonary Tuberculosis
Authors: Fan Jiahui , Yang Yefeng , Wang Liang , Shang Xiaoqian , Zhang Li , Sun Hu , Ma Yujie , Li Ying , Wang Jing , Ma Xiumin
Published: June 2022

Objective. To investigate the correlation between interleukin-27 and CXCL10 and other cytokines in pulmonary tuberculosis and to further explore the related miRNAs through bioinformatics. Methods. Collect the lesion tissue and peripheral blood of pulmonary tuberculosis patients and the peripheral blood of healthy controls. Immunohistochemical staining and qRT-PCR were used to observe the expression of interleukin-27, CXCL9, CXCL10, and CXCL11. Then, predict the key miRNA, qRT-PCR was used to verify the expression of miRNA in the peripheral blood and evaluated the correlation between them. Results. Both immunohistochemical staining and qRT-PCR indicated that the expressions of IL-27, CXCL9, CXCL10, and CXCL11 were significantly increased in tuberculosis patients, and IL-27 was significantly correlated with CXCL10 (

r
=
0.68

). Key molecules such as has-let-7b-5p, has-miR-30a-3p, and has-miR-320b were screened out. Among them, has-let-7b-5p was significantly downregulated, and has-miR-30a-3p was significantly upregulated; they were related to interleukin-27 and CXCL10. Conclusion. Our data shows that interleukin-27 and CXCL10 are significantly related in pulmonary tuberculosis, and has-let-7b-5p and has-miR-30a-3p are also related to interleukin-27 and CXCL10. It laid the foundation for subsequently exploiting the potential biomarkers in tuberculosis disease.
Construction of Internal and External Environmental Management System of Human Resources in Civil Aviation Institutions Based on System Dynamics
Authors: Tang Zhe
Published: June 2022

The basic investment needed for the development of civil aviation transportation has a long period, large investment, and strong exclusivity. The quantity of fixed investment should match the scale of the stage of civil aviation development. Scientific demand forecasting is the basis of reasonable planning of civil aviation infrastructure and fleet construction. Based on the principle of system dynamics, this paper analyzes the internal and external influencing factors of air passenger transport demand, and establishes a system dynamics model between civil aviation passenger transport market demand forecast and human resource management system. Using the model, based on the level of economic and social development, it is predicted that the demand for air passenger transport will reach 1 billion passengers around 2024. The demand system dynamics model of civil aviation passenger transport market has universal applicability and long-term effectiveness for the derived long-term demand forecast and analysis of civil aviation transportation. The results show that based on the derivative characteristics of air transport demand, the demand influencing factors of air passenger transport market can be divided into external and internal factors, which are mainly affected by population, economy, residents’ consumption level, air-rail competition, and other factors. Internal factors mainly include ticket price and service level.
Boron Isotope Geochemistry of the Lakkor Co Salt Lake (Tibet) and Its Geological Significance
Authors: Li B. K. , Cheng H. D. , Ma H. Z.
Published: June 2022

The origin of boron in modern salt lakes has long been a controversial subject. This study focuses on the boron geochemistry of Lakkor Co salt lake, located on the Shiquanhe-Lakkor Co-Yongzhu-Jiali Ophiolite Mélange Zone (SYMZ). Lakkor Co is one of the main boron-rich salt lakes in the Tibetan plateau where the boron concentration ranges from 401 to 849 mg L-1. The boron isotope value (

δ

11

B

) of the carbonate deposit within the Lakkor Co region is -28.08‰. The boron isotope value of the Somme river decreases from -9.97‰ to -11.15‰ after it flows through the carbonate deposit region, indicating that the carbonate deposit in this region releases boron, which has a more negative isotope value. However, the boron isotope value of Bai Kok river increases from -15.63‰ to -13.76‰, when it flows through the Quaternary clay region in Ganga Co, indicating that the carbonate deposit undergoes a process of boron adsorption. It can be concluded that the Quaternary carbonate deposits may be a source of boron and also serve as a temporary boron reservoir, thus playing an important role in the formation of modern salt lake boron deposits in Tibet.
Construction of Applied Undergraduate Course Evaluation System Based on BP Neural Network
Authors: Huo Xiuzhen
Published: June 2022

The curriculum standard is a programmatic document that integrates the content of curriculum objectives, curriculum resource construction, students’ academic performance, and important reference and basis for teachers’ curriculum teaching and curriculum management evaluation. The purpose of this paper is to study how to analyze and study the evaluation of applied undergraduate courses based on the BP neural network and describe the construction of the system. This paper proposes the construction of the course evaluation system, which is based on the BP neural network and then elaborates on its concept and related algorithms and designs and analyzes the construction of the application-oriented undergraduate course evaluation system. The experimental results show that 90.74% of the currently used curriculum standards meet the requirements of applied talents training, 88.89% indicated that the course content was well aligned with professional standards, and 5.19% believed that the design of practical teaching links had a great effect on improving students’ skills. The curriculum is the basic way of education, teachers are the main body of curriculum implementation, and students are the carriers of curriculum effects. Taking teachers, students, and courses as evaluation elements in curriculum evaluation can highlight the core position of curriculum evaluation in curriculum construction.
A Traceable and Anonymous Data Aggregation Scheme with Fog Computing Devices for Smart Grid
Authors: Wu Fan , Li Xiong
Published: June 2022

As an extension of the Internet of Things (IoT), smart city is a new aim for applications used in different industrial aspects. Intelligent IoT devices are used everywhere in smart cities to implement the functions such as monitoring and managing. Smart grid is one of the critical parts. Obtaining the quantity and cost of electricity usage is the most critical task of the smart grid. But such data in plaintext may leak the user privacy. So, it is an emergent aim to protect the private information of the user. Thus, we present a secure scheme for the smart grid, which not only protects the user’s information including identity and power consumption in the communications but also tracks the correct electricity cost for each user. Also, electricity consumption data from users could be aggregated in fog devices and then analyzed by the utility provider. The formal proof shows that the message transmission reaches the security level of the chosen plaintext attack (CPA). Also, the security properties of the scheme express the robustness. Finally, the performance study demonstrates that the proposed protocol is acceptable in practice.
The Combined Clinical Diagnosis of TNF-α, TSH, and p185 Protein in Breast Cancer
Authors: Jiang Junjie , Zhang Wei , Liu Hui , Yang Yunyun , Zhang Wei , Zang Chunxia
Published: June 2022

Objective. To study the concentrations of tumor necrosis factor (TNF-α), thyroid-stimulating hormone (TSH), and c-erbB-2 oncogene protein product P185 in different pathological stages of breast cancer and to analyze their combined clinical diagnosis of breast cancer significance. Methods. 67 breast cancer patients who were treated in our hospital from January 2018 to September 2020 were set as the breast cancer group and were divided into stages I, II, III, and IV according to clinicopathology. In addition, 55 patients with benign breasts who were admitted to the hospital at the same time were selected as the benign breast group, and 60 healthy people in our hospital during the same period were selected as the healthy group. The differences between serum TNF-α, TSH, and p185 protein positive rate in 3 groups and the levels of TNF-α and TSH and p185 protein positive rate in patients with different pathological characteristics were compared and analyzed, and the differences between the combined detection and the single detection were analyzed. Results. Compared with the benign breast group and the healthy group, the serum levels of TNF-α (

44.61
±
12.54

versus

29.75
±
10.19

versus

56.87
±
15.36

versus

102.37
±
15.36

), TSH (

0.98
±
0.13

versus

0.94
±
0.17

versus

1.17
±
0.24

versus

1.22
±
0.15

) and p185 protein positive rate were higher in the I-II and III-IV groups, and the difference was statistically significant (

P
<
0.05

). TNF-α detection sensitivity was 44.74%, specificity was 62.06%, which was higher than p185 sensitivity of 31.01%, specificity of 49.78%, higher than TSH sensitivity of 27.51%, specificity of 39.77%. At the same time, the sensitivity and specificity of combined detection of TNF-α, TSH, and p185 protein were 67.35% and 70.41%, which were significantly higher than the sensitivity and specificity of single detection, and the difference was statistically significant (

P
<
0.05

). Conclusion. TNF-α, TSH, and p185 protein are expected to be used as auxiliary basis for diagnosis in the future. But in general, the serum indexes in this study had low sensitivity and specificity for the diagnosis of breast cancer, which limited their diagnostic function in clinical use.
Development and Performance Evaluation of a New Energy-Efficient Double Cluster-Head Routing (EEDCR) Protocol for Wireless Sensor Networks
Authors: Deepa C. , Latha B. , Jenish I.
Published: June 2022

Utilization of energy and the lifetime increment are the big issues in designing of routing algorithms for wireless sensor networks (WSNs). Many routing algorithms have been developed by various researchers to achieve energy efficiency and to improve the lifetime of the network. But, the way to route the information from the sensor node (SN) to the base station (BS) and vice versa is an important issue, because of resource constraints. In this paper, we have proposed a low energy consumed, cluster-based routing protocol named an energy-efficient and double cluster-head routing (EEDCR) protocol, to increase the network lifetime and minimize the end-to-end delay. Selection of the cluster head (CH) is depending on random selection method, energy method, the total number of nodes, and its energy level. The performances of developed protocol were assessed using simulations and found that it provides successful outcomes in terms of energy-efficient and lifetime increment in routing for WSNs.
Applying Real-Time Dynamic Scaffolding Techniques during Tutoring Sessions Using Intelligent Tutoring Systems
Authors: Anwar Aamir , Haq Ijaz Ul , Mian Imdad Ahmad , Shah Fadia , Alroobaea Roobaea , Hussain Saddam , Ullah Syed Sajid , Umar Fazlullah
Published: June 2022

An intelligent tutoring system (ITS) is a computer system or software application that is built to replicate human tutors by supporting the theory of “learning by doing.” Even though ITSs have been proven to be successful in academic studies, they still have not found large adoption by the industry due to the complexities of building such systems due to the high technical expertise and domain knowledge requirements. Attempts have been made to build authoring tools that can provide assistance in building tutoring systems; however, most of these tools are targeted toward authors that have considerable programming experience. This research proposes an authoring tool for ITS, which is targeted at novice authors with minimum technical/programming experience and provides real-time scaffolding to learner’s incomplete/incorrect answers using the best scaffolding techniques. Two evaluation techniques were applied for the evaluation of the performance of the proposed authoring tool, e.g., paired t-test analysis and postexperiment survey. The learning gains obtained from paired t-test contend a significant learning gain and improvement in the learning process with enhanced learning performance with multiple scaffolding techniques as compared to single scaffolding technique experience. The postexperiment survey has a notable result that shows the effectiveness of the tutor model that ensures a very user-friendly interface, deploying scaffolding techniques and adequate control of selecting and deploying scaffolding techniques and making the authoring process easy.
Transcatheter Mitral Valve Replacement in High-Surgical Risk Patients: A Single-Center Experience and Outcome
Authors: Taha Fatma A. , Naeim Hesham , Alnozha Fareed , Amoudi Osama , Abuelatta Reda
Published: June 2022

Background. Re-operative mitral valve (MV) replacement is a high-risk procedure, therefore, transcatheter MV replacement (TMVR) is a promising therapeutic option. Aim. In this study, we aimed to evaluate the feasibility and safety of TMVR in patients with high surgical risk with degenerated mitral bioprostheses (TMViV), failed surgical rings (TMViR), and mitral annular calcification (TMViMAC). Methods. This is a retrospective cohort study that enrolled patients with high surgical risk who underwent TMVR from February 2017 to September 2020. The TMVR procedure was performed using Edwards SAPIEN-3 valves through the transseptal approach. Results. Sixty-four patients aged 62.7 ± 16.1 years with an STS score of 9.2 ± 3.7% underwent TMVR [35 (55%) TMViV, 16 (25%) TMViR, and 13 (20%) TMViMAC]. Mitral stenosis was more frequent in TMViV, mitral regurgitation was more frequent in TMViR, and combined mitral stenosis and regurgitation were more frequent in TMViMAC (

P
<
0.05

). The MV gradient was 14.3 ± 5.3 mmHg and the MV area was 1.5±0.6 cm2. The 29 mm valve was frequently used in TMViV and TMViMAC, while the 23 mm valve was frequently used in TMViR (

P
=

0.003

∗

). The procedural and fluoroscopy times were 58.7 ± 8.9 and 41.1 ± 8.2 minutes, respectively. Technical success was reported in 62 (98.4%) patients; 1 TMViR patient experienced valve embolization and salvage surgery, and 1 TMViMAC patient experienced slight valve malposition. At 3 months, 2 (3.1%) patients showed valve thrombosis (treated with anticoagulation), and 1 (1.6%) patient developed a paravalvular leak (underwent surgical MV replacement). At 6 months, 3 (4.7%) patients showed valve degeneration (underwent surgical MV replacement). Throughout follow-up, no patient exhibited mortality. Conclusions. TMVR is a feasible and safe approach in patients with high surgical risk. TMViV and TMViR are reasonable as the first treatment approaches, and TMViMAC seems encouraging.
Improvement of College English Teaching Model under the Background of Information Technology
Authors: Du Fang , Xiong Yi-xin , Ming Xian-cheng
Published: June 2022

In order to improve the effect of college English teaching reform, this paper combines information technology to improve the college English teaching mode. According to the digital processing requirements of English teaching resources, in order to make the loop establishment circuit time of automatic gain control independent of the input signal, this paper uses a logarithmic amplifier to detect it. Moreover, this paper combines the SC algorithm and the SC algorithm based on the time-domain PN sequence to propose a new frame synchronization algorithm. The algorithm has precise frame synchronization points and reduces the usage of hardware resources. Finally, this paper constructs a college English teaching system based on information technology. The experimental research results show that the college English teaching system based on information technology proposed in this paper has a good teaching effect.
ST8SIA6-AS1 Promotes the Epithelial-to-Mesenchymal Transition and Angiogenesis of Pituitary Adenoma
Authors: Li Zuowei , Zhang Chengcheng , Zong Xin , Wang Zhao , Ren Rong , Wang Lei , Sun Ping , Zhu Chunmei , Guo Mingxia , Guo Guizhen , Hu Guo , Wu Ya’nan
Published: June 2022

To investigate the effect of long noncoding RNA ST8SIA6-AS1 on the epithelial-to-mesenchymal transition (EMT) and angiogenesis of pituitary adenoma and its possible mechanism. The expression levels of ST8SIA6-AS1 and HOXA9 in noninvasive pituitary adenoma and invasive pituitary adenoma were detected using qRT-PCR. sh-ST8SIA6-AS1 transfection silenced the expression of ST8SIA6-AS1 in GH3 and GTI-1 cells. The effects of ST8SIA6-AS1 on the proliferation, invasion, angiogenesis, and EMT of GH3 and GTI-1 pituitary adenoma cells were detected. The migration ability of cells was detected through scratch assay. Dual luciferase analysis verified the targeting relationship between ST8SIA6-AS1 and miR-5195-3p. ST8SIA6-AS1 and HOXA9 were highly expressed in invasive pituitary adenoma. In pituitary adenomas, miR-5195-3p directly targeted HOXA9. miR-5195-3p is the target gene of ST8SIA6-AS1. ST8SIA6-AS1 knockdown inhibited the proliferation, invasion, angiogenesis, and EMT of pituitary adenoma. HOXA9 expression mediates the biological effect of ST8SIA6-AS1. ST8SIA6-AS1 targets miR-5195-3p to regulate the expression of HOXA9 and promote the EMT of pituitary adenomas.