bluuebunny/crossref_metadata_2025 huggingface dataset:
A Fuzzy Strategy to Eliminate Uncertainty in Grading Positive Tuberculosis
Authors: Samuel R. Dinesh Jackson , Kanna B. Rajesh
Published: April 2022

Sputum smear microscopic examination is an effective, fast, and low-cost technique that is highly specific in areas with a high prevalence of pulmonary tuberculosis. Since manual screening needs trained pathologist in high prevalence zones, the possibility of deploying adequate technicians during the epidemic sessions would be impractical. This condition can cause overburdening and fatigue of working technicians which may tend to reduce the potential efficiency of Tuberculosis (TB) diagnosis. Hence, automation of sputum inspection is the most appropriate aspect in TB outbreak zones to maximize the detection ability. Sputum collection, smear preparing, staining, interpreting smears, and reporting of TB severity are all part of the diagnosis of tuberculosis. This study has analyzed the risk of automating TB severity grading. According to the findings of the analysis, numerous TB-positive cases do not fit into the standard TB severity grade while applying direct rule-driven strategy. The manual investigation, on the other hand, arbitrarily labels the TB grade on those cases. To counter the risk of automation, a fuzzy-based Tuberculosis Severity Level Categorizing Algorithm (TSLCA) is introduced to eliminate uncertainty in classifying the level of TB infection. TSLCA introduces the weight factors, which are dependent on the existence of maximum number of Acid-Fast Bacilli (AFB) per microscopic Field of View (FOV). The fuzzification and defuzzification operations are carried out using the triangular membership function. In addition, the [Formula: see text]-cut approach is used to eliminate the ambiguity in TB severity grading. Several uncertain TB microscopy screening reports are tested using the proposed TSLCA. Based on the experimental results, it is observed that the TB grading by TSLCA is consistent, error-free, significant and fits exactly into the standard criterion. As a result of the proposed TSLCA, the uncertainty of grading is eliminated and the reliability of tuberculosis diagnosis is ensured when adapting automatic diagnosis.
Intuitive, Efficient and Ergonomic Tele-Nursing Robot Interfaces: Design Evaluation and Evolution
Authors: Lin Tsung-Chi , Krishnan Achyuthan Unni , Li Zhi
Published: April 2022

Tele-nursing robots provide a safe approach for patient-caring in quarantine areas. For effective nurse–robot collaboration, ergonomic teleoperation and intuitive interfaces with low physical and cognitive workload must be developed. We propose a framework to evaluate the control interfaces to iteratively develop an intuitive, efficient, and ergonomic teleoperation interface. The framework is a hierarchical procedure that incorporates general to specific assessment and its role in design evolution. We first present pre-defined objective and subjective metrics used to evaluate three representative contemporary teleoperation interfaces. The results indicate that teleoperation via human motion mapping outperforms the gamepad and stylus interfaces. The tradeoff with using motion mapping as a teleoperation interface is the non-trivial physical fatigue. To understand the impact of heavy physical demand during motion mapping teleoperation, we propose an objective assessment of physical workload in teleoperation using electromyography. We find that physical fatigue happens in the actions that involve precise manipulation and steady posture maintenance. We further implemented teleoperation assistance in the form of shared autonomy to eliminate the fatigue-causing component in robot teleoperation via motion mapping. The experimental results show that the autonomous feature effectively reduces the physical effort while improving the efficiency and accuracy of the teleoperation interface.
Personalized News Recommendation: Methods and Challenges
Authors: Wu Chuhan , Wu Fangzhao , Huang Yongfeng , Xie Xing
Published: April 2022

Personalized news recommendation is important for users to find interesting news information and alleviate information overload. Although it has been extensively studied over decades and has achieved notable success in improving user experience, there are still many problems and challenges that need to be further studied. To help researchers master the advances in personalized news recommendation, in this article, we present a comprehensive overview of personalized news recommendation. Instead of following the conventional taxonomy of news recommendation methods, in this article, we propose a novel perspective to understand personalized news recommendation based on its core problems and the associated techniques and challenges. We first review the techniques for tackling each core problem in a personalized news recommender system and the challenges they face. Next, we introduce the public datasets and evaluation methods for personalized news recommendation. We then discuss the key points on improving the responsibility of personalized news recommender systems. Finally, we raise several research directions that are worth investigating in the future. This article can provide up-to-date and comprehensive views on personalized news recommendation. We hope this article can facilitate research on personalized news recommendation as well as related fields in natural language processing and data mining.
Reconciling the Quality vs Popularity Dichotomy in Online Cultural Markets
Authors: Gaeta Rossano , Garetto Michele , Ruffo Giancarlo , Flammini Alessandro
Published: April 2022

We propose a simple model of an idealized online cultural market in which
N
items, endowed with a hidden quality metric, are recommended to users by a ranking algorithm possibly biased by the current items’ popularity. Our goal is to better understand the underlying mechanisms of the well-known fact that popularity bias can prevent higher-quality items from becoming more popular than lower-quality items, producing an undesirable misalignment between quality and popularity rankings. We do so under the assumption that users, having limited time/attention, are able to discriminate the best-quality only within a random subset of the items. We discover the existence of a harmful regime in which improper use of popularity can seriously compromise the emergence of quality, and a benign regime in which wise use of popularity, coupled with a small discrimination effort on behalf of users, guarantees the perfect alignment of quality and popularity ranking. Our findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets.
Li[Ni0.5Mn0.3Co0.2]O2 as a Superior Alternative to LiFePO4 for Long-Lived Low Voltage Li-Ion Cells
Authors: Aiken C. P. , Logan E. R. , Eldesoky A. , Hebecker H. , Oxner J. M. , Harlow J. E. , Metzger Michael , Dahn J. R.
Published: April 2022

Single crystal Li[Ni0.5Mn0.3Co0.2]O2//graphite (NMC532) pouch cells with only sufficient graphite for operation to 3.80 V (rather than ≥4.2 V) were cycled with charging to either 3.65 V or 3.80 V to facilitate comparison with LiFePO4//graphite (LFP) pouch cells on the grounds of similar maximum charging potential and similar negative electrode utilization. The NMC532 cells, when constructed with only sufficient graphite to be charged to 3.80 V, have an energy density that exceeds that of the LFP cells and a cycle-life that greatly exceeds that of the LFP cells at 40 °C, 55 °C and 70 °C. Excellent lifetime at high temperature is demonstrated with electrolytes that contain lithium bis(fluorosulfonyl)imide (LiFSI) salt, well beyond those provided by conventional LiPF6 electrolytes. Ultra-high precision coulometry and electrochemical impedance spectroscopy are used to complement cycling results and investigate the reasons for the improved performance of the NMC cells. NMC cells, particularly those balanced and charged to 3.8 V, show better coulombic efficiency, less capacity fade and higher energy density compared to LFP cells and are projected to yield lifetimes approaching a century at 25 °C.
Renewable Polysaccharide and Biomedical Application of Nanomaterials
Authors: Kanaoujiya Rahul , Saroj Shruti Kumari , Srivastava Shekhar , Chaudhary Manoj Kumar
Published: April 2022

Nanotechnology being undoubtedly an uncut gem over the past few years has been in sighting as a new form of branch with its vigorous discoveries which have led to its divergent evolution giving emergence not only in the pathway of knowledge but also developing technological techniques. The constituting nanoparticles and its versatile properties with dynamic structures have made a major breakthrough in the past few years for its role in biotechnology arising nanobiotechnology, antipollution, renewable polymers, and its biomedical applications. Nanostructure composites forming nanomaterials on the basis of its working are pectin, cellulose, lignin, hyaluronic acid, bacterial cellulose, Arabic gum, and bacterial biosurfactants. In the recent years, it is seen that nanocomposites are giving promising results in medical technology incorporating with useful metal nanoparticles such as silver nanoparticles (AgNPs), gold nanoparticles (AuNPs), diamond nanoparticles, zinc oxide (ZnO), and titanium oxide (TiO). Some useful biomedical applications are in anticancerous, sunscreen, antiageing, and antitumorous. They have shown to be nontoxic at a certain level. Nanoparticle composites have proven with right amount of doping, and experienced techniques have given excellent results. Nanofibers of biodegradable poly(L‐lactide) (PLLA)/poly(lactide‐co‐glycol ide) (PLGA) compounds are used in drug delivery, folate redox‐responsive chitosan nanoparticles (FTC‐NPs) also as anticancer drug delivery, and mesoporous silica nanoparticles‐silver nanoparticles as a tissue growth in vivo processes. The study of a biosynthetic pathway of therapeutic drugs is still much needed. Waste management of renewable nanopolymers are an ultimate goal so that there are less haphazard elements towards the environment.
Role of STAT3 Expression in Thyroid Cancer: A Meta-Analysis and Systematic Review Based on the Chinese Population
Authors: Liang Ya-Nan , Zhang Zuwang , Song Ji , Yang Fuwei , Yang Pan
Published: April 2022

Background. Signal transduction and activator of transcription 3 (STAT3) is an oncogene with transcriptional activity. In recent years, there have been several studies concerning the clinicopathological significance of the expression of the STAT3 protein in thyroid cancer. However, the results are still inconsistent. In this study, we conducted a meta-analysis to evaluate the relationship between the expression of STAT3 protein and thyroid cancer susceptibility and its clinicopathological characteristics. Methods. We searched the China National Knowledge Infrastructure (CNKI) database, Chinese Biomedical Literature Database (CBM), Chinese Scientific and Journal Database (VIP), Wanfang, PubMed, and EMBASE. The time frame of the publication search was from the establishment of each of the databases until December 2021. We performed a meta-analysis to quantitatively evaluate the relationship between the expression of the STAT3 protein in thyroid cancer and its clinicopathological characteristics. Results. A total of eight articles were included in the meta-analysis, covering 448 thyroid cancer patients and 227 controls. Results indicated that the expression of STAT3 protein in thyroid cancer tissue is highly expressed (OR = 14.41, 95% CI (6.94, 29.91), p < 0.001). Besides, we also discovered that STAT3 protein is negatively correlated with thyroid cancer tumor diameter and TNM stage (OR = 0.13, 95% CI (0.05, 0.33), p < 0.001; OR = 0.40, 95% CI (0.24, 0.67), p < 0.001) and positively correlated with lymph node metastasis (OR = 2.83, 95% CI (1.08, 7.46), p = 0.035). However, STAT3 expression is not related to gender (OR = 0.88, 95% CI (0.54, 1.44), p = 0.609), age (OR = 0.54, 95% CI (0.21, 1.36), p = 0.191), capsular invasion (OR = 2.98, 95% CI (0.23, 38.29), p = 0.403), or tumor multiplicity (OR = 0.25, 95% CI (0.003, 19.28), p = 0.533). Conclusions. This study reveals that STAT3 protein expression is significantly related to the susceptibility and clinicopathological characteristics of thyroid cancer. It also suggests that STAT3 may be a potential predictor of the clinical progression of thyroid cancer.
Correlations between Texture Profile Analysis and Sensory Evaluation of Cured Largemouth Bass Meat (Micropterus salmoides)
Authors: Li Meijin , Huang Jiajun , Chen Yanan , Zu Xiaoyan , Xiong Guangquan , Li Hailan
Published: April 2022

Texture is an important factor in evaluating the quality of aquatic products. To evaluate the texture properties of cured large mouth bass, edible sodium chloride (0, 1.0, 2.0, and 3.0%) was smeared to the bass meat. Texture profile analysis (TPA) and sensory evaluation were performed to evaluate the quality of the cured samples, and the correlations of the indexes in the two methods were analyzed. Two principal components were obtained from the TPA indexes and sensory evaluation indexes, and the cumulative variance contribution rates were 73.87% and 72.99%, respectively. Results from the principal component analysis showed that the main indicators that affected the TPA were gumminess and springiness, while those that affected sensory evaluation were chewiness and adhesiveness. The TPA index and sensory evaluation could be effectively improved when the sodium chloride added to the bass meat was 1%. In the correlation analysis, sensory springiness was negatively correlated with TPA hardness (

P
<
0.05

, r = −0.553) but positively correlated with TPA chewiness (

P
<
0.05

, r = 0.596). After stepwise regression analysis, the prediction equation between the sensory springiness and TPA hardness was obtained as SSp=5.770−0.002Ha. These results provide a basis for predicting the quality of large mouth bass cured products.
Evaluation of the Efficacy of Different Pterygium Surgeries in Polish Caucasian Population
Authors: Palewski Marcin , Budnik Agnieszka , Lisowski Łukasz , Konopińska Joanna
Published: April 2022

The aim of the study was to compare the efficacy of the two most commonly used surgical methods for pterygium removal in the Polish population, conjunctival autograft versus amniotic membrane transplantation, and to evaluate the postoperative recurrence rate. A retrospective analysis of the medical records was conducted, including 65 patients who underwent surgery for primary or recurrent pterygium at an ophthalmology clinic in Bialystok, Poland, between 2016 and 2020. Surgical success (no regrowth) was achieved in almost half of the amniotic membrane patients (44%) and in most of the conjunctival autograft patients (79%), with statistical significance. The odds of successful surgery were 79% lower for subjects with amniotic membranes than for those with conjunctival autografts (OR with 95% CI = 0.21 (0.05; 0.94);

p
=
0.045

). Our study confirms that the conjunctival autograft surgeries present more favorable success rates in Polish Caucasian population when compared to procedures involving amniotic membranes.
Atherogenesis, Transcytosis, and the Transmural Cholesterol Flux: A Critical Review
Authors: Goldberg Doron , Khatib Soliman
Published: April 2022

The recently described phenomenon of cholesterol-loaded low-density lipoproteins (LDL) entering the arterial wall from the lumen by transcytosis has been accepted as an alternative for the long-held concept that atherogenesis involves only passive LDL movement across an injured or dysfunctional endothelial barrier. This active transport of LDL can now adequately explain why plaques (atheromas) appear under an intact, uninjured endothelium. However, the LDL transcytosis hypothesis is still questionable, mainly because the process serves no clear physiological purpose. Moreover, central components of the putative LDL transcytosis apparatus are shared by the counter process of cholesterol efflux and reverse cholesterol transport (RCT) and therefore can essentially create an energy-wasting futile cycle and paradoxically be pro- and antiatherogenic simultaneously. Hence, by critically reviewing the literature, we wish to put forward an alternative interpretation that, in our opinion, better fits the experimental evidence. We assert that most of the accumulating cholesterol (mainly as LDL) reaches the intima not from the lumen by transcytosis, but from the artery’s inner layers: the adventitia and media. We have named this directional cholesterol transport transmural cholesterol flux (TCF). We suggest that excess cholesterol, diffusing from the avascular (i.e., devoid of blood and lymph vessels) media’s smooth muscle cells, is cleared by the endothelium through its apical membrane. A plaque is formed when this cholesterol clearance rate lags behind its rate of arrival by TCF.
Intelligent Clothing Design and Data Acquisition under the Scientific Graphics Programming Combined with Wearable Multisensor
Authors: Ding Mengshu , Abdullah Qistina Donna Lee , Hamid Salmiah bt Abdul
Published: April 2022

This research focuses on the improvement of intelligent Hanfu design system’s performance. Henceforth, in the current study, an intelligent Hanfu design data acquisition system based on scientific graphics programming and wearable multisensor is designed for the field of human body size measurement and human model reconstruction. Initially, on the basis of wearable multisensor, a design of the recognition and analysis system of human posture is presented. The error of nine-axis inertial sensor in the process of collecting data, the data of accelerometer, gyroscope, and magnetometer are preprocessed, and the processing results are observed. Moreover, MATLAB programming is used for preprocessing of collected images. It is also used for grey normalization, filtering denoising, and image sharpening. The advantages and disadvantages of different methods are compared experimentally. Experiments show that MATLAB programming is more suitable for grey normalization than histogram equalization, and local brightness will appear in histogram equalization. After denoising the image with salt-and-pepper noise and Gaussian noise, the median filter and mean filter have defects. Wiener’s adaptive filter leads to the increase of noise, and wavelet denoising has the best effect. The data acquisition system of intelligent Hanfu design established in this work provides a certain direction for the development of intelligent Hanfu design.
Genetic Association of ERCC6 rs2228526 Polymorphism with the Risk of Cancer: Evidence from a Meta‐Analysis
Authors: Lin Xiaochun , Wu Yongfu , Li Qingde , Yu Hongying , Li Xugui , Li Xiaohua , Zheng Jinkun
Published: April 2022

At present, several studies have assessed the association between ERCC6 rs2228526 polymorphism and the risk of cancer. However, the association remained controversial. To provide a more accurate estimate on the association, we performed a meta‐analysis search of case‐control studies on the associations of ERCC6 rs2228526 with susceptibility to cancer. PubMed, Embase, Google Scholar, Wanfang database, and Chinese National Knowledge Infrastructure databases (CNKI) China Biological Medicine Database (CBM) (up to August 2021) were searched to identify eligible studies. The effect summary odds ratio (OR) with 95% confidence intervals (CI) was applied to assay the association between the ERCC6 rs2228526 polymorphism and the risk of cancer. 14 studies included 15 case‐control studies which contained 5,856 cases, and 6,387 controls were finally determined as qualified studies for this meta‐analysis. Overall, based on current studies, we found significant association between ERCC6 rs2228526 polymorphism and the risk of cancer in four genetic models [the allele model G vs. A: 1.10, (1.03–1.17); the homozygous model GG vs. AA: 1.27, (1.07–1.51); heterozygote model GA vs. AA: 1.08, (1.00–1.17); the dominant model GG + GA vs. AA: 1.10, (1.02–1.19); the recessive model GG vs. GA + AA: 1.22, (1.03–1.45)]. In the stratified analysis based on ethnicity, we found significant association in two genetic models in Asians. Further, significant genetic cancer susceptibility was found under PB control on subgroup analysis by source of control. In addition, no significant association was found in lung cancer and bladder cancer patients in subgroup analyses based on cancer style. This study suggests that the ERCC6 rs2228526 polymorphism may be associated with increased cancer risk.
Unique Presentation of Septic Cavernous Sinus Thrombosis and Pulmonary Embolism in the Setting of Reusable Face Covering
Authors: Barranco-Trabi Javier , Scott Jazmin C. , Fryer Jacob M. , Byrne Matthew , Smith Adam , Song Kaoru H. , Tagawa Chelsea W. , Chi Sharon , Ngauy Viseth
Published: April 2022

Identified in December 2019, SARS-CoV-2 quickly spread worldwide with a resultant increase in global morbidity, mortality, and economic disruption on a scale not seen since the 1918 Spanish flu. Health officials recommended universal masking to further reduce human-to-human spread of SARS-CoV-2. The state of Hawaii and the Department of Defense (DOD) adopted strict mask policies early in the pandemic and is shown to be effective at reducing transmission. We report a case of Staphylococcus aureus bacteremia in an immunocompetent 21-year-old man attributed to local skin irritation with resultant infection in the setting of continuous reuse of a mask that resulted in bilateral cavernous venous thrombosis and septic pulmonary embolism.
Application of Decision Tree Algorithm in Teaching Quality Analysis of Physical Education
Authors: Zhao JiaJia
Published: April 2022

Physical education is not only an important part of national education, but also one of the important means to improve the physical quality of students and citizens. With the rapid innovation of information technology in China, the decision tree algorithm is introduced into the teaching quality analysis of physical education, which becomes the new reform thinking direction of PE teaching, and promotes the teaching method and the training way of coaches. Under the background of new era, in this paper, the basic principle and realization process of decision tree algorithm is analyzed, applying it to the data analysis of the teaching quality of physical education. The computer is used to excavate the factors that influence the teaching of physical education. And the results show that the method can classify the data well, and the acquired knowledge is of great significance to the teaching reform of the physical education class in the future. Through the rational evaluation of teachers’ overall teaching quality, teachers can optimize the teaching process according to the evaluation results, find the weak links of students’ learning in time, remind students to review and consolidate relevant knowledge, help students improve their study and examination scores, and further improve their impact on physical education teaching behavior.
Electroacupuncture Attenuates Learning and Memory Impairment via PI3K/Akt Pathway in an Amyloid β25-35-Induced Alzheimer’s Disease Mouse Model
Authors: Shao Si-Mai , Park Kyung Hye , Yuan Ye , Zhang Zijuan , You Yanwen , Zhang Zhenqiang , Hao Li
Published: April 2022

The main characteristic of Alzheimer’s disease (AD) is the progressive decline of learning and memory ability. Electroacupuncture (EA) may improve AD-related learning and memory ability. However, the underlying molecular mechanism of action remains unclear. The objective of the present study was to assess the effects and the molecular mechanism of EA on learning and memory in an amyloid β25-35 (Aβ25-35) induced AD mouse model. The AD model was established by intracerebroventricular (ICV) administration of Aβ25-35 oligomers. AD mice were electroacupunctured with wisdom three-needle combined with Baihui (GV20) five times per week for three consecutive weeks. The Morris water maze (MWM) and Y maze tests were applied to evaluate spatial learning and memory ability. A transmission electron microscope (TEM) was used to measure mitochondria and autophagy of hippocampal neurons, and western blot was applied to observe molecular changes in the mice hippocampus. The results suggested that EA treatment significantly alleviated learning and memory impairment related to AD, reduced mitochondria damage, improved autophagy, increased mitochondrial protein 2 (Mfn2), Beclin 1, and LC3B, and decreased the expressions of fission protein 1 (Fis1) level. Furthermore, EA further upregulated the protein expression of phosphatidylinositol 3-kinase (PI3K) and the ratio of p-Akt/Akt in the hippocampus of AD mice. This study demonstrates that EA treatment attenuates cognitive deficits, modulates mitochondrial fusion and fission, and enhances autophagy via the PI3K/Akt pathway in a mouse AD model.
Animation Costume Style Migration Based on CycleGAN
Authors: Zhang Anfeng , Lee Jonghan
Published: April 2022

The traditional style migration of animation costumes is mainly performed between two paired animation costumes. However, the generalization ability is weak, and the migration effect is not good when the gap between the training and testing costumes is large. To address the above problems, this paper proposes a style migration method for animated costumes combining full convolutional network (FCN) and CycleGAN, which enables the instance style migration between animated costumes with specific targets. It is also verified that the training dataset is not the factor that causes the poor style migration of CycleGAN. The experiments demonstrate that the animation costume style migration method combining full convolutional network and CycleGAN increases the recognition ability and can achieve the local style migration of the animation costume while maintaining the integrity of the rest of the elements, and compared with CycleGAN, the method can effectively suppress the style migration in areas outside the target.
An Optima Combination Method of Three-Frequency Real-Time Cycle Slip Detection for Non-Normal Ionospheric Variation Data
Authors: Gao Yaping , Chen Guo , Chen Xi , Ma Liangliang , Luo Tong , Xue Dongdong
Published: April 2022

Linear combinations of triple-frequency help improve the performance of cycle slip detection for high-precision positioning using a single receiver; however, the position can be easily misjudged under ionospheric scintillation conditions or low sampling rates. We propose a method, which is developed specially for the datasets under ionospheric scintillation conditions or low sampling rates, to detect the triple-frequency cycle slips in real-time based on optimal linear combination coefficients and ionospheric range delay. Detection formulas are derived from the triple-frequency geometry-free code-phase combination, and ionospheric range delay is estimated by the wide lane combination. In addition, the principle used to select an optimal linear phase combination coefficient is derived, and the optimal linear coefficient suitable under high ionospheric activity conditions is provided. Finally, the data collected from self-build stations JYPS and NQ01 are used to test the performance of the method. The results demonstrate that the improved method can be used to detect all combinations of cycle slips in real-time, even under conditions of ionospheric scintillation or a sampling period exceeding 10 s.
Reliability Analysis of the Inverse Modified Weibull Model with Applications
Authors: Kayid Mohamed , Djemili Salah
Published: April 2022

This paper presents a novel flexible inverse modified Weibull model with a concave Weibull probability diagram that describes various reliability phenomena. We investigate some essential attributes and reliability properties of this model. The new model exhibits an increasing or unimodal form of the hazard rate function, making it more suitable for simulating certain aging classes of life distributions with appropriate choices of parameter values. We used the Weibull probability paper approach and the maximum likelihood method to estimate the model's parameters. In addition, we conduct a simulation study to investigate the behavior and efficiency of these estimators. Finally, we analyze a maintenance dataset to demonstrate the usefulness of the proposed model.
Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network
Authors: Magotra Varun , Rohil Mukesh Kumar
Published: April 2022

The applications of AI in the healthcare sector are increasing day by day. The application of convolutional neural network (CNN) and mask-region-based CNN (Mask-RCCN) to the medical domain has really revolutionized medical image analysis. CNNs have been prominently used for identification, classification, and feature extraction tasks, and they have delivered a great performance at these tasks. In our study, we propose a lightweight CNN, which requires less time to train, for identifying malaria parasitic red blood cells and distinguishing them from healthy red blood cells. To compare the accuracy of our model, we used transfer learning on two models, namely, the VGG-19 and the Inception v3. We train our model in three different configurations depending on the proportion of data being fed to the model for training. For all three configurations, our proposed model is able to achieve an accuracy of around 96%, which is higher than both the other models that we trained for the same three configurations. It shows that our model is able to perform better along with low computational requirements. Therefore, it can be used more efficiently and can be easily deployed for detecting malaria cells.
[Retracted] Adoption of Magnetic Resonance Image Features under Segmentation Algorithm in Effect Evaluation of Ginkgo Diterpenoid Lactone Glucamine Injection in Treatment of Cerebral Infarction
Authors: Lei Aidi , Zhang Yanbo , Liang Fulong , Zhang Jianli , Cai Jinle
Published: April 2022

Magnetic resonance imaging (MRI) image segmentation based on a segmentation algorithm was performed to assess neurological function in patients with acute cerebral infarction, to investigate the efficacy evaluation of Ginkgo diterpene lactones meglumine injection (GDLI) in the treatment of cerebral infarction and the efficiency of MRI image segmentation algorithm. First, the results of the fast semisupervised segmentation algorithm (algorithm group) and traditional processing (control group) were compared and analyzed. The recall rate, accuracy, recognition accuracy, and segmentation time of the two groups were compared. The control group was given conventional treatment, while the algorithm group was given GDLI based on conventional treatment. Finally, the difference in serum vascular endothelial growth factor (VEGF), hypoxia‐inducible factor‐la (HIF‐la), angiotensin (Ang)‐1, Ang‐2, and interleukin (IL)‐6 protein concentration was analyzed after treatment. The algorithm evaluation results showed that the accuracy and recall rate of MRI images recognized by the algorithm group fluctuate at 90%. In the control group, the accuracy and recall rate of MRI image results fluctuated at 80%, and the data were statistically different (p < 0.05). The clinical index test results showed that the serum VEGF content of the test group was higher than that of the control group, and the data was statistically different (p < 0.05). In addition, the cerebral blood flow (CBF) and cerebral blood volume (CBV) of the lesion side of the algorithm group were greatly higher than those of the control group on the 30th day, and the differences were significant (p < 0.05). There was little difference between the method presented in this study and the manual delineation by a physician. Compared with traditional manual segmentation, this method greatly reduced the time required for the segmentation of lesions. The diagnostic specificity, sensitivity, and accuracy of the images segmented by the fast semisupervised algorithm were higher than those of the conventional method, and the diagnostic accuracy of acute cerebral infarction was high. In addition, it was sensitive and accurate to detect acute cerebral infarction, which provided a reliable reference for early diagnosis and condition judgment of patients.
Study on Discrete Dynamic Modeling of College Students’ Innovative Employment Security Mechanism under the Environment of Internet of Things
Authors: Gu Liming , Chen Xue
Published: April 2022

The innovative mechanism of college students’ employment security is a series of security measures implemented by the state and society to solve the mismatch between the scale growth of college graduates and the jobs provided by the society. In order to promote the development of emerging Internet of things technologies, users can find interesting and valuable information from a large number of data sets and use this information to meet the needs of users. This article mainly studies the historical development trend and discrete dynamic modeling and analysis technology of college students’ innovative employment security mechanism under the environment of Internet of things. Using two different modeling methods of Bayesian network and BP neural network, this article makes discrete dynamic modeling on the influencing factors and employment security mechanism of college students’ employment, so as to improve the college biological network innovation employment security mechanism and better help college students’ employment.
Network Pharmacology Deciphers the Action of Bioactive Polypeptide in Attenuating Inflammatory Osteolysis via the Suppression of Oxidative Stress and Restoration of Bone Remodeling Balance
Authors: Cui Zichen , Feng Changgong , Chen Jiazheng , Wang Yi , Meng Qi , Zhao Shihao , Zhang Yuanji , Feng Dianjie , Li Ziqing , Sun Shui
Published: April 2022

Oxidative stress involves enormously in the development of chronic inflammatory bone disease, wherein the overproduction of reactive oxygen species (ROS) negatively impacts the bone remodeling via promoting osteoclastogenesis and inhibiting osteogenesis. Lacking effective therapies highlights the importance of finding novel treatments. Our previous study screened a novel bioactive peptide D7 and demonstrated it could enhance the cell behaviors and protect bone marrow mesenchymal stem cells (BMSCs). Since BMSCs are progenitor cells of osteoblast (OB), we therefore ask whether D7 could also protect against the progress of inflammatory osteolysis. To validate our hypothesis and elucidate the underlying mechanisms, we first performed network pharmacology-based analysis according to the molecule structure of D7, and then followed by pharmacological evaluation on D7 by in vitro lipopolysaccharide(LPS)-induced models. The result from network pharmacology identified 20 candidate targets of D7 for inflammatory osteolysis intervention. The further analysis of Gene Ontology (GO)/KEGG pathway enrichment suggested the therapeutic effect of D7 may primarily affect osteoclast (OC) differentiation and function during the inflammatory osteolysis. Through validating the real effects of D7 on OC and OB as postulated, results demonstrated suppressive effects of D7 on LPS-stimulated OC differentiation and resorption, via the inhibition on OC marker genes. Contrarily, by improving the expression of OB marker genes, D7 displayed promotive effects on OB differentiation and alleviated LPS-induced osteogenic damage. Further mechanism study revealed that D7 could reduce LPS-induced ROS formation and strengthen antioxidants expressions in both OC and OB precursors, ameliorating LPS-triggered redox imbalance in bone remodeling. Taken together, our findings unveiled therapeutic effects of D7 against LPS-induced inflammatory osteolysis through the suppression of oxidative stress and the restoration of the bone remodeling process, providing a new therapeutic candidate for chronic inflammatory bone diseases.
Proteomic Profiling and Functional Analysis of B Cell-Derived Exosomes upon Pneumocystis Infection
Authors: Ma Dan , Zhang Qian-Yu , Rong Heng-Mo , Zhai Kan , Tong Zhao-Hui
Published: April 2022

Pneumocystis is a life-threatening fungal pathogen that frequently causes fatal pneumonia (PCP) in immunocompromised individuals. Recently, B cells have been reported to play a crucial role in the pathogenesis of PCP through producing antibodies and activating CD4+ T cell response. Exosomes are nanoscale small extracellular vesicles abundant with protein cargo and can mediate immune response during infectious disease. In this study, using tandem mass tag-based quantitative proteomics coupled with bioinformatic analysis, we attempted to characterize exosomes derived from B lymphocytes in response to PCP. Several proteins were verified by parallel reaction monitoring (PRM) analysis. Also, the effects of B cell exosomes on CD4+ T cell response and phagocytic function of macrophages were clarified. Briefly, 1701 proteins were identified from B cell exosomes, and the majority of them were reported in Vesiclepedia. A total of 51 differentially expressed proteins of B cell exosomes were found in response to PCP. They were mainly associated with immune response and transcription regulation. PRM analysis confirmed the significantly changed levels of histone H1.3, vimentin, and tyrosine-protein phosphatase nonreceptor type 6 (PTPN6). Moreover, a functional study revealed the proinflammatory profile of B cell exosomes on CD4+ T cell response in PCP. Taken together, our results suggest the involvement of exosomes derived from B cells in cell-to-cell communication, providing new information on the function of B cells in response to PCP.
FreMix: Frequency-Based Mixup for Data Augmentation
Authors: Xiu Yang , Zheng Xinyi , Sun Linlin , Fang Zhuohao
Published: April 2022

Deep learning models have attracted tremendous attention in computer vision in recent years, while most of them heavily rely on massive data for training. As one of the solutions to the sparse data problem, data augmentation techniques, such as image translation and rotation, can substantially increase the model’s generalization ability and performance. However, on one hand, these approaches primarily work under the pixel domain, which is limited to fully mining and fusing picture data from the frequency viewpoint. On the other hand, the fusion weighting factors are primarily modified in a manual fashion, which increases the application costs in practice. To this end, we propose a novel method termed as frequency-based Mixup (FreMix) that allows images to be fused in the frequency domain and to improve the efficiency of data augmentation by adaptively adjusting the weighting coefficients in this paper. In FreMix, first, a fast Fourier transformation (FFT) is performed on the input image, such that the frequency information rather than raw pixel information can be extracted for further augmentation. Besides, an exploration-exploitation training paradigm is exploited, such that the FreMix can be trained periodically to facilitate learning and avoid manually hyperparameter settings. We conduct comparing experiments on three benchmark datasets including CIFAR, ImageNet, and ILSVRC2015, and the experimental results validate the effectiveness of the proposed method.
Subcellular Proteome Analysis Reveals Apoptotic Vulnerability of T‐Cell Acute Lymphoblastic Leukemia
Authors: Song Xiaolei , Wu Xiaojing , Zhang Zihan , Cui Zhangxiu , Zheng Yong , Sun Jian
Published: April 2022

Targeting death receptor‐mediated apoptosis in T‐cell acute lymphoblastic leukemia (T‐ALL), an aggressive disease with poor prognosis, is hindered by the inherent resistance of primary leukemia cells. Knowledge on therapeutic vulnerabilities in these malignant cells will provide opportunities for developing novel combinatory treatments for patients. Using label‐free quantitative mass spectrometry and subcellular fractionation techniques, we systematically compared organelle‐specific proteomes between Jurkat cells, an in vitro model for T‐ALL, and a Jurkat mutant with increased resistance to death receptor‐mediated apoptosis. By identifying several differentially regulated protein clusters, our data argued that extensive metabolic reprograming in the mitochondria, characterized by enhanced respiration and energy production, might allow cells to evade DR5‐mediated cytotoxicity. Further analysis using clinical datasets demonstrated that the elevated expression of a three‐gene signature, consisting of SDHA, IDH3A, and ANXA11, was significantly associated with poor survival of acute leukemia patients. Our analysis therefore provided a unique dataset for a mechanistic understanding of T‐ALL and for the design of novel ALL treatments.
Ethiopian Native Highlander’s Adaptation to Chronic High‐Altitude Hypoxia
Authors: Getu Ayechew
Published: April 2022

People living in a high‐altitude environment have distinct lifelong challenges. Adaptive mechanisms have allowed high‐altitude residents to survive in a low‐oxygen environment for thousands of years. The purpose of this review was to provide a brief review of the Ethiopian native highlanders’ adaptive mechanisms to chronic hypoxia problems at high altitude. Traditionally, an elevated hemoglobin concentration has been considered as a hallmark of lifelong adaptation to high‐altitude hypoxia, though this notion has been refuted recently as a result of the establishment of the alternative adaptive responses found in Amhara highlanders living in the Simien Mountains of northern Ethiopia. These populations did not have elevated hemoglobin (no erythrocytosis) but had normal hemoglobin saturation and arterial oxygen level, which alerts researchers to explore the possibility of the presence of an alternative adaptive mechanism. Contrary to this, Oromos living in the Bale Mountains of southern Ethiopia have elevated hemoglobin. The presence of increased nitric oxide (NO) and cyclic guanosine monophosphate (cGMP) in native Amhara highlanders suggests the possibility of adaptation via vasodilation, which would improve oxygen supply to metabolic tissues. Native Amhara highlanders showed no indications of chronic mountain sickness and had a higher pulmonary blood pressure without having a higher pulmonary vascular resistance. In addition, the cerebral circulation is sensitive to NO and carbon dioxide (CO2) but not to hypoxia, which would likely promote increased cerebral blood flow and increase oxygen delivery to the brain, making Ethiopian high‐altitude natives better suited for survival at high altitudes. Further research is warranted to translate these background natural features of Ethiopian native highlanders to clinical applications.
Application of Artificial Intelligence Technology in the Teaching of Complex Situations of Folk Music under the Vision of New Media Art
Authors: Li Ning , Ismail Md Jais Bin
Published: April 2022

Enhancement in information technology has made the online teaching-learning process easier. However, this process is still a challenging task for teaching courses of theoretical type, music, dance, and arts. For the classes of these types, the traditional system of teaching and learning is well suited, but, in particularly complex situations, accomplishing the task is highly difficult. Hence, new media art technology is introduced to overcome the difficulties. In this research, Chinese folk music is taught online with the aid of new media art with the support of artificial intelligence. The analysis of the proposed work is carried out on the folk music dataset, which considers folk music of ethnic minority groups. A novel Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) art algorithm is implemented to perform the analysis. The performance is compared with the existing gradient descent, Adam, and AdaDelta algorithms. L-BFGS algorithm is essentially a particular recipe for designing and possibly executing an artwork, including algorithms, functions, facial expression, and other input that ultimately decides the structure the folk music and media art would then take. This contribution could be numerical, information processing, or formative. From the obtained results, it can be shown that the proposed system has provided 97% and 98% of accuracy on training and testing data, which is higher when compared to the existing algorithms.
Advanced Computing Approach for Modeling and Prediction COVID-19 Pandemic
Authors: Morsi Sami A. , Alzahrani Mohammad Eid
Published: April 2022

The emergence of many strains of the coronavirus, including the latest omicron strain, which is spreading at a very high speed, is leading to the World Health Organization’s (WHO) concern about the creation of this new mutation. Therefore, there is a strong motivation for modeling and predicting COVID-19 to control the number of cases of the disease. The proposed system for predicting the number of cases of COVID-19 can help governments take precautions to prevent the spread of the disease. In this paper, a statistical logistic growth model was employed to predict the spread of COVID-19 in Australia and Brazil. The datasets were collected from the surveillance systems in Australia and Brazil from March 13, 2020, to December 12, 2021, for 641 days. This proposed method used a tested logistic growth model for the complex spread of COVID-19 and forecasted future values within a time interval of six days. The results of the predicted, cumulative, confirmed cases indicate the robustness and effectiveness of the proposed system, which was categorized by time-dependent dynamics. The coefficient of determination (

R

) metric was used to evaluate the model to predict COVID-19, and the proposed system scored the highest correlation (

R

2

=
99
%

). The proposed system has the potential to contribute to public health by making decisions about how to prevent the spread of COVID-19.
Analysis on the Influence Mechanism of Employees’ Active Behavior Based on Data Mining and Mobile Edge Computing Technology
Authors: Chen Xiang , Hu Gaoxi , Luo Ping , Zhang Guang
Published: April 2022

High-level comprehensive talents are an indispensable key factor for enterprises to achieve sustainable development in a modern society with global integration, highly developed computer network technology, and information technology. Organizations must set new standards for employee job performance in the current environment, which necessitates active participation on the part of employees. The impact mechanism of employees’ active behavior is examined using data mining and mobile edge computing technology in this study. Collect data using mobile computing nodes, build a data analysis model based on data mining theory, and change the traditional data processing method of businesses by optimizing their existing business data processing processes. We can grasp the influence mechanism of employees’ active behavior and discover potential information relationships using multidimensional data analysis. The true cause of employee active behavior influence mechanism data was determined. In addition, the mediating effect of responsibility and the moderating effect of contract type are investigated, as well as the impact of psychological contract satisfaction on proactive behavior among employees.