bluuebunny/crossref_metadata_2025 huggingface dataset:
HPV vaccination uptake among Somali American patients at an urban primary care clinic in Minnesota, USA: identifying and testing interventions to improve uptake using video reflexive ethnography
Authors: Pratt Rebekah , Ndagire Channelle , Oyenuga Abayomi , Xiong Serena , Carroll Katherine , Adam Patricia
Published: December 2021

Objectives

While there have been efforts to address common and culturally informed barriers to healthcare, Somali Americans have low rates of human papillomavirus (HPV) vaccination. This study aimed to use video reflexive ethnography (VRE) to identify primary care health inequities, derive interventions aimed at improving HPV vaccination rates in Somali Americans, and then test their impact on vaccination rates.

Design

The VRE methodology involves three sequential steps: data collection, reflexive discussion and identifying intervention in practice. Preintervention and postintervention vaccination uptake data were collected for Somali patients.

Setting

VRE was conducted with medical assistants (MAs) and Somali patients for 3 months (June–August 2018) in an urban primary care clinic in Minnesota, USA. HPV vaccination rates were collected and analysed pre-VRE and for a period of 6 months post the implementation of the interventions identified by VRE.

Participants

14 MAs participated in the VRE which designed the study intervention, which was tested on 324 Somali patients eligible for HPV vaccination.

Primary outcome measure

HPV vaccination uptake among Somali patients.

Results

MAs identified three practice challenges related to HPV vaccination: provider fatigue related to ongoing patient vaccine hesitancy or refusal, MAs misinterpretation of patient’s vaccination dissent language, and missed opportunities to respond to unique patient concerns as a result of following standard work procedures. Using VRE, MAs identified and developed several interventions to address these practice challenges. Adjusted for age at clinic visit (years), the difference in preintervention and postintervention HPV immunisation rates was 10.1 per 100 patient-visits (95% CI 2.97 17.3; p=0.0057).

Conclusion

VRE can engage MAs in an innovative, participatory process to identify and address concerns about health inequities. In this study, MAs designed and implemented interventions that improved HPV vaccination rates in Somali Americans. Further research is needed to more fully assess the impact of such interventions.
Financial risks of care seeking for malaria by rural households in Jimma Zone, Oromia Region, Southwest Ethiopia: a cross-sectional study
Authors: Assebe Lelisa Fekadu , Dillu Dereje , Tiru Gemu , Johansson Kjell Arne , Bolongaita Sarah , Chakrabarti Averi , Hendrix Nathaniel
Published: December 2021

Objectives

Despite major progress in the prevention and control of malaria in recent years, the disease remains a major cause of morbidity in Ethiopia. Malaria also imposes substantial socioeconomic costs on households. The aim of this study is to estimate the financial risk of seeking malaria service for rural households across socioeconomic statuses in the Jimma Zone, Oromia Region.

Design

A facility-based cross-sectional survey.

Setting

Jimma Zone, Oromia Region, Southwest Ethiopia.

Participants

A total of 221 patients with malaria from 10 public health facilities were interviewed between September 2018 and December 2019.

Primary and secondary outcome measures

The main outcome measures capture the financial risks associated with malaria services, specifically catastrophic and impoverishing health expenditures. Catastrophic health expenditure (CHE) occurs when healthcare costs reach 10% of a household’s monthly income, whereas impoverishment occurs when a household’s monthly income falls below the national poverty level after paying for health service. Descriptive statistics were used to summarise the expenditure patterns associated with malaria services. All costs were gathered in Ethiopian birr and reported in 2019 US$.

Results

The average cost of receiving malaria services was US$4.40 (bootstrap 95% CI: 3.6 to 5.3), with indirect costs accounting for 52% of total costs. Overall, at the 10% threshold, 12% (bootstrap 95% CI: 8.1% to 16.7%) of patients with malaria incurred CHE: 40% (bootstrap 95% CI: 26.7% to 55.6%) of the household in the poorest quintile experienced CHE, but none from the richest quintile did. The proportion of households living in poverty increased by more than 2-3% after spending on malaria-specific health services.

Conclusion

Healthcare seeking for malaria imposes a substantial financial risk on rural households, particularly for the poorest and most vulnerable. Malaria policies and interventions should therefore seek to alleviate both the direct costs and productivity losses associated with the disease, especially among the poor.
COVID-19 screening: use of an artificial neural network
Authors: Roustaei Narges , Allahyari Elahe
Published: December 2021

Objectives

COVID-19 is the biggest pandemic of the 21st century. The disease can be influenced by various sociodemographic factors and can manifest as clinical, pulmonary and gastrointestinal symptoms. This study used an artificial neural network (ANN) model with important sociodemographic factors as well as clinical, pulmonary and gastrointestinal symptoms to screen patients for COVID-19. Patients themselves can screen for these symptoms at home.

Methods

Data on all registered patients were extracted in autumn. The best ANN model was selected from different combinations of connections, some hidden layers and some neurons in each hidden layer. In this study, 70% of the data were used in the network training process and the remaining 30% were used to evaluate the function of the multilayer, feed-forward, back-propagation algorithm.

Results

The sensitivity and specificity of the ANN model in diagnosing patients with COVID-19 were 94.5% and 17.4%. In order of priority, clinical symptoms, sociodemographic factors, pulmonary symptoms and gastrointestinal symptoms were important predictive factors for COVID-19 using the ANN model. Screening patients for COVID-19 using clinical symptoms and sociodemographic factors (80% importance) remains essential.

Conclusions

Home monitoring of oxygen saturation and body temperature as well as old age and drug addiction can be helpful in self-screening symptoms of COVID-19 at home, thereby preventing unnecessary visits to medical centres and reducing burden on medical services.
US adults’ preferences for race-based and place-based prioritisation for COVID-19 vaccines
Authors: Schmidt Harald , Shaikh Sonia Jawaid , Sadecki Emily , Gollust Sarah
Published: December 2021

Implementing equity principles in resource allocation is challenging. In one approach, some US states implemented race-based prioritisation of COVID-19 vaccines in response to vast racial inequities in COVID-19 outcomes, while others used place-based allocation. In a nationally representative survey of n=2067 US residents, fielded in mid-April 2021 (before the entire US population became eligible for vaccines), we explored the public acceptability of race-based prioritisation compared with place-based prioritisation, by offering vaccines to harder hit zip codes before residents of other zip codes. We found that in general, a majority of respondents supported the place-based approach, and a substantial proportion supported the race-based plan. Support was higher among Democrats compared with Republicans. All US residents became eligible for vaccines on 19 April 2021 but as of this writing, equitable uptake of vaccines remains urgent not only for first doses for adults but also for boosters and for children. Our findings also provide a benchmark for future pandemic planning that racial and social justice in vaccine allocation are salient considerations for the public. The findings may furthermore be of interest to policy makers designing vaccine allocation frameworks in countries with comparable health disparities across social, ethnic and racial groups, and more broadly, for those exploring ways of promoting equity in resource allocation outside of a pandemic setting.
Significance of initial hemoglobin levels in severe trauma patients without prehospital fluid administration: a single-center study in Japan
Authors: Kawai Yasuyuki , Fukushima Hidetada , Asai Hideki , Takano Keisuke , Okuda Akinori , Tada Yusuke , Maegawa Naoki , Bolstad Francesco
Published: December 2021

Objectives

Hemoglobin (Hb) levels have been considered to remain stable in the early stages of bleeding due to trauma. However, several studies have reported that rapid compensatory fluid shifts cause Hb dilution earlier than previously thought. These reports are from Western countries where it is standard protocol to administer fluids during an emergency, making it almost impossible to eliminate the effect of prehospital intravenous fluid administration on Hb levels. This study aimed to determine the relationship between Hb levels and severity of injury on arrival at the hospital in severe trauma patients without prehospital intravenous fluid administration.

Methods

This single-center observational retrospective study included patients with Abbreviated Injury Scale scores of 3 or above between 2008 and 2014. In Japan, prehospital life-saving technicians were not allowed to administer intravenous fluids until 2014. We investigated whether the difference between the measured blood Hb level at arrival and the corresponding standard blood Hb level for each age group and sex reported in the national survey was associated with the severity of injury and the need for hemostasis.

Results

In total, 250 patients were included in this study (median age, 46 years; male patients, 183). The median time from injury to arrival at the hospital was 45 min, and there was no statistical correlation with the initial Hb level on arrival (ρ=0.092, p=0.14). When the study subjects were stratified into four groups according to the initial Hb levels, lower Hb levels correlated with higher rates of requirement for hemostatic interventions (p=0.02) and mortality (p=0.02). In addition, lower Hb levels were associated with the need for hemostasis.

Conclusion

In severe trauma patients without prehospital intravenous fluid administration, decreased Hb levels on arrival may be associated with the severity of trauma and with the need for hemostasis.

Level of evidence

Level IV.
Mode spectra of magnetic and dielectric plasmon spheres obtained from Mie scattering and free oscillation theories
Authors: Krupka Jerzy , Salski Bartlomiej , Pacewicz Adam , Kopyt Pawel
Published: December 2021

This paper presents Mie scattering theory as compared to rigorous electromagnetic theory of free oscillations in magnetic and electric plasmon spheres. It is shown that the maxima of Mie scattering and absorption spectra well correspond to resonance frequencies of plasmon modes occurring in dielectric and magnetic spheres, similarly as it takes place for ordinary dielectric resonator modes. Mie theory is well applicable to determine resonance frequencies and scattering parameters of spherical plasmons. However, this theory cannot be applied to determine intrinsic properties of modes induced in the object by the incident plane wave, like quality factors. On the contrary, rigorous electromagnetic theory of free oscillations allows one to determine the complex resonance frequency of each mode that can occur in a given object, and the corresponding quality factor accounting for various kind of losses, including medium and radiation losses. The advantage of the free oscillations theory, as shown in this paper, is in the determination of the quality factors of modes occurring in magnetic plasmon spheres made of a strongly dispersive magnetic medium.

Graphical Abstract
International trade distributions and their relation with random fragmentation processes
Authors: Bustos-Guajardo R.
Published: December 2021

A study of the distribution of the value of traded goods under the Harmonized System is presented. The ramifications of this classification system are found to exhibit an approximate power law decay, indicating complexity and self-organization in the nomenclature of traded merchandises. For almost all countries with available data, log-values of annually imported and exported goods are well described by three-parameter Weibull distributions. This distribution commonly appears in particles size distributions, suggesting a connection between random fragmentation processes and the mechanisms behind the international trade of merchandises. Analysis of the resulting values for the fitting parameters from 1995 to 2018 shows a nearly constant linear relationship between the parameters of the Weibull distributions, so that, for each country, the distribution of log-values can be approximately characterized by a single shape parameter [Formula: see text]. The empirical findings of this paper suggest that specialization on trading a constant set of goods prevents the values of all traded merchandises from growing/decreasing simultaneously.
Siamese Network Object Tracking Algorithm Combining Attention Mechanism and Correlation Filter Theory
Authors: Hu Xiuhua , Liu Huan , Chen Yuan , Hui Yan , Liang Yingyu , Wu Xi
Published: December 2021

Aiming to solve the problem of tracking drift during movement, which was caused by the lack of discriminability of the feature information and the failure of a fixed template to adapt to the change of object appearance, the paper proposes an object tracking algorithm combining attention mechanism and correlation filter theory based on the framework of full convolutional Siamese neural networks. Firstly, the apparent information is processed by using the attention mechanism thought, where the object and search area features are optimized according to the spatial attention and channel attention module. At the same time, the cross-attention module is introduced to process the template branch and search area branch, respectively, which makes full use of the diversified context information of the search area. Then, the background perception correlation filter model with scale adaptation and learning rate adjustment is adopted into the model construction, using as a layer in the network model to realize the object template update. Finally, the optimal object location is determined according to the confidence map with similarity calculation. Experimental results show that the designed method in the paper can promote the object tracking performance under various challenging environments effectively; the success rate increases by 16.2%, and the accuracy rate increases by 16%.
2D Multi-Person Pose Estimation Combined with Face Detection
Authors: Li Qiming , Xu Lu , Yang Xiaoyan
Published: December 2021

Pose estimation is the basis and key of human motion recognition. In the two-dimensional human pose estimation based on image, in order to reduce the adverse effects of mutual occlusion among multiple people and improve the accuracy of motion recognition, a structurally symmetrical two-dimensional multi-person pose estimation model combined with face detection is proposed in this paper. First, transfer learning is used to initialize each sub-branch network model. Then, MTCNN is used for face detection to predict the number of people in the image. According to the number of people, the image is input into the improved two-branch OpenPose network. What is more, the double judgment algorithm is proposed to correct the false detection of MTCNN. The experimental results show that compared with TensorPose, which is the latest improved method based on OpenPose, the Average Precision (AP) (Intersection over Union [Formula: see text]) on the validation set is 8.8 higher. Furthermore, compared with OpenPose, the mean AP ([Formula: see text]) is 1.7 higher on the validation set and is 1.3 higher on the Test-dev test set.
Multimodal Biometric Person Authentication Using Face, Ear and Periocular Region Based on Convolution Neural Networks
Authors: Lohith M. S. , Manjunath Yoga Suhas Kuruba , Eshwarappa M. N.
Published: December 2021

Biometrics is an active area of research because of the increase in need for accurate person identification in numerous applications ranging from entertainment to security. Unimodal and multimodal are the well-known biometric methods. Unimodal biometrics uses one biometric modality of a person for person identification. The performance of an unimodal biometric system is degraded due to certain limitations such as: intra-class variations and nonuniversality. The person identification using more than one biometric modality of a person is multimodal biometrics. This method of identification has gained more interest due to resistance on spoof attacks and more recognition rate. Conventional methods of feature extraction have difficulty in engineering features that are liable to more variations such as illumination, pose and age variations. Feature extraction using convolution neural network (CNN) can overcome these difficulties because large dataset with robust variations can be used for training, where CNN can learn these variations. In this paper, we propose multimodal biometrics at feature level horizontal fusion using face, ear and periocular region biometric modalities and apply deep learning CNN for feature representation and also we propose face, ear and periocular region dataset that are robust to intra-class variations. The evaluation of the system is made by using proposed database. Accuracy, Precision, Recall and [Formula: see text] score are calculated to evaluate the performance of the system and had shown remarkable improvement over existing biometric system.
Effects of depth, width, and initialization: A convergence analysis of layer-wise training for deep linear neural networks
Authors: Shin Yeonjong
Published: December 2021

Deep neural networks have been used in various machine learning applications and achieved tremendous empirical successes. However, training deep neural networks is a challenging task. Many alternatives have been proposed in place of end-to-end back-propagation. Layer-wise training is one of them, which trains a single layer at a time, rather than trains the whole layers simultaneously. In this paper, we study a layer-wise training using a block coordinate gradient descent (BCGD) for deep linear networks. We establish a general convergence analysis of BCGD and found the optimal learning rate, which results in the fastest decrease in the loss. We identify the effects of depth, width, and initialization. When the orthogonal-like initialization is employed, we show that the width of intermediate layers plays no role in gradient-based training beyond a certain threshold. Besides, we found that the use of deep networks could drastically accelerate convergence when it is compared to those of a depth 1 network, even when the computational cost is considered. Numerical examples are provided to justify our theoretical findings and demonstrate the performance of layer-wise training by BCGD.
Application of Cognitive Automation to Structuring Data, Driving Existing Business Models, and Creating Value between Legacy Industries
Authors: Helm Christopher , Herberger Tim Alexander , Gerold Nicolay
Published: December 2021

To build high quality datasets and unlock the value of unstructured data, a systematic approach for data capture is necessary. Cognitive automation (CA), that is, automation of processes with artificial intelligence (AI), enables the information extraction from unstructured data to provide relevant insights and further processing with AI. This study provides an overview of this new technology and shows how it can be used to transform existing business models. Our case studies in the insurance auditing, healthcare, and banking industries show the potential managerial impact of CA, which prepares these legacy industries for their digital future’s challenges and opportunities. We present the novel data extraction pipeline for textual and visual data and demonstrate its efficiency in extracting information from the company’s unstructured data. We show its performance in quality, cost, and time compared with current industry standards and provide management insights for business applications using CA.
Laboratory for Validation of Rolling-Resistance Models
Authors: Larsen M. L. , Cesbron J. , Anfosso-Lédée F. , Ropert C. , Dyre J. C. , Hecksher T.
Published: December 2021

In this paper, a versatile drum setup for measuring rolling resistance of small wheels is presented. The purpose is to provide a flexible setup for testing of models for rolling resistance under controlled circumstances. To demonstrate this, measurements of rolling resistance with a series of sandpapers of different grit sizes representing surface textures were carried out. The measurements show a clear increase in the rolling-resistance coefficient with increasing surface roughness, rolling speed and load. Numerical calculations in the time domain for a visco-elastic contact model run on equivalent surfaces agree with the trends found experimentally. We conclude that this approach to simplifying the experiment in order to obtain a high degree of control, accuracy and repeatability is useful for validating and testing models for calculating the rolling resistance for a given surface texture.
Winter Storm Uri: A Test of Texas’ Water Infrastructure and Water Resource Resilience to Extreme Winter Weather Events
Authors: Glazer Yael R. , Tremaine Darrel M. , Banner Jay L. , Cook Margaret , Mace Robert E. , Nielsen-Gammon John , Grubert Emily , Kramer Ken , Stoner Anne M. K. , Wyatt Briana M. , Mayer Alex , Beach Timothy , Correll Rachel , Webber Michael E.
Published: December 2021

We synthesize the interconnected impacts of Texas’ water and energy resources and infrastructure including the cascading effects due to Winter Storm Uri. The government’s preparedness, communication, policies, and response as well as storm impacts on vulnerable communities are evaluated using available information and data. Where knowledge gaps exist, we propose potential research to elucidate health, environmental, policy, and economic impacts of the extreme weather event. We expect that recommendations made here — while specific to the situation and outcomes of Winter Storm Uri — will increase Texas’ resilience to other extreme weather events not discussed in this paper. We found that out of 14 million residents who were on boil water notices, those who were served by very small water systems went, on average, a minimum of three days longer without potable water. Available county-level data do not indicate vulnerable communities went longer periods of time without power or water during the event. More resolved data are required to understand who was most heavily impacted at the community or neighborhood level. Gaps in government communication, response, and policy are discussed, including issues with identifying — and securing power to — critical infrastructure and the fact that the state’s Emergency Alert System was not used consistently to update Texans during the crisis. Finally, research recommendations are made to bolster weaknesses discovered during and after the storm including (1) reliable communication strategies, (2) reducing disproportionate impacts to vulnerable communities, (3) human health impacts, (4) increasing water infrastructure resilience, and (5) how climate change could impact infrastructure resilience into the future.
Image Complexity Guided Network Compression for Biomedical Image Segmentation
Authors: Mishra Suraj , Chen Danny Z. , Hu X. Sharon
Published: December 2021

Compression is a standard procedure for making convolutional neural networks (CNNs) adhere to some specific computing resource constraints. However, searching for a compressed architecture typically involves a series of time-consuming training/validation experiments to determine a good compromise between network size and performance accuracy. To address this, we propose an image complexity-guided network compression technique for biomedical image segmentation. Given any resource constraints, our framework utilizes data complexity and network architecture to quickly estimate a compressed model which does not require network training. Specifically, we
map
the dataset complexity to the target network accuracy degradation caused by compression. Such mapping enables us to predict the final accuracy for different network sizes, based on the computed dataset complexity. Thus, one may choose a solution that meets both the network size and segmentation accuracy requirements. Finally, the mapping is used to determine the convolutional layer-wise multiplicative factor for generating a compressed network. We conduct experiments using 5 datasets, employing 3 commonly-used CNN architectures for biomedical image segmentation as representative networks. Our proposed framework is shown to be effective for generating compressed segmentation networks, retaining up to ≈95% of the full-sized network segmentation accuracy, and at the same time, utilizing ≈32x fewer network trainable weights (average reduction) of the full-sized networks.
On Hermitian Solutions of the Generalized Quaternion Matrix Equation
A
X
B
+
C
X

D
=
E
Authors: Tian Yong , Liu Xin , Yuan Shi-Fang
Published: December 2021

The paper deals with the matrix equation

A
X
B
+
C
X
 
D
=
E

over the generalized quaternions. By the tools of the real representation of a generalized quaternion matrix, Kronecker product as well as vec-operator, the paper derives the necessary and sufficient conditions for the existence of a Hermitian solution and gives the explicit general expression of the solution when it is solvable and provides a numerical example to test our results. The paper proposes a unificated algebraic technique for finding Hermitian solutions to the mentioned matrix equation over the generalized quaternions, which includes many important quaternion algebras, such as the Hamilton quaternions and the split quaternions.
Evaluation of Managerial Overconfidence, Cash Holding, and Investment Efficiency in Companies
Authors: Asadi Abdorreza , Oladi Maryam , Aghel Mohammad Ghasem
Published: December 2021

Managers’ overconfidence leads to overestimating their ability to manage cash sources. Holding more cash may result in overinvestment in projects and investment inefficiency consequently. The present study aims to investigate the effect of cash holding on investment efficiency with the moderating role of managerial overconfidence in Iranian companies. All listed firms in Tehran Stock Exchange, excluding banks, insurance, pension funds, and financial intermediaries, are included in the research. We have used data from financial statements of 91 companies over the period from 2010 to 2018 and conducted multiple regression models to test the hypotheses based on pooled and panel data set with fixed effects. The results indicate a positive relationship between managerial overconfidence and cash holding. The effect of cash holding on investment efficiency turns out to be significantly negative. Furthermore, managerial overconfidence has a significant moderating effect on the relation of the variables. This study is almost the first one, which has been done in emerging markets, so the study’s findings not only contribute to the existing literature on managerial overconfidence and investment efficiency but also assist policymakers, managers, and investors in making effective decisions.
Identification of Potential Biomarkers of Depression and Network Pharmacology Approach to Investigate the Mechanism of Key Genes and Therapeutic Traditional Chinese Medicine in the Treatment of Depression
Authors: Shi Yucong , Chen Dan , Ma Shengsuo , Xu Huachong , Deng Li
Published: December 2021

Background. To explore the potential target of depression and the mechanism of related traditional Chinese medicine in the treatment of depression. Method. Differential gene expression in depression patients and controls was analyzed in the GEO database. Key genes for depression were obtained by searching the disease databases. The COREMINE Medical database was used to search for Chinese medicines corresponding to the key genes in the treatment of depression, and the network pharmacological analysis was performed on these Chinese medicines. Then, protein-protein interaction analysis was conducted. Prediction of gene phenotypes was based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment scores. Results. The total number of differentially expressed genes in the GEO database was 147. Combined with the GEO dataset and disease database, a total of 3533 depression-related genes were analyzed. After screening in COREMINE Medical, it was found that the top 4 traditional Chinese medicines with the highest frequency for depression were Paeonia lactiflora Pall., Crocus sativus L., Bupleurum chinense DC., and Cannabis sativa L. The compound target network consisted of 24 compounds and 138 corresponding targets, and the key targets involved PRKACA, NCOA2, PPARA, and so on. GO and KEGG analysis revealed that the most commonly used Chinese medicine could regulate multiple aspects of depression through these targets, related to metabolism, neuroendocrine function, and neuroimmunity. Prediction and analysis of protein-protein interactions resulted in the selection of nine hub genes (ESR1, HSP90AA1, JUN, MAPK1, MAPK14, MAPK8, RB1, RELA, and TP53). In addition, a total of four ingredients (petunidin, isorhamnetin, quercetin, and luteolin) from this Chinese medicine could act on these hub genes. Conclusions. Our research revealed the complicated antidepressant mechanism of the most commonly used Chinese medicines and also provided a rational strategy for revealing the complex composition and function of Chinese herbal formulas.
Huanglian Jiedu Decoction Exerts Antipyretic Effect by Inhibiting MAPK Signaling Pathway
Authors: Li Xing , Wei Shizhang , Ma Xiao , Li Haotian , Jing Manyi , Liu Honghong , Niu Shengqi , Tong Yuling , Chen Lisheng , Wei Ying , Ren Sichen , Zhao Yanling
Published: December 2021

Aim. The aim of this study was to explore the antipyretic effect and potential mechanism of Huanglian Jiedu Decoction (HLJDD) on LPS-induced fever in rats. Materials and Methods. The fever rat model was established by LPS. Anal temperature of rats was measured every 1 hour after modeling. TNF-α, IL-6, PGE2, and cAMP in rat serum or hypothalamus tissue were detected by ELISA kit. In order to explore the potential active ingredients and mechanism of antipyretic effect of HLJDD, we predicted the underlying antipyretic mechanism by using network pharmacology and then verified its mechanism by Western Blotting. Results. The results showed that HLJDD can alleviate LPS-induced fever in rats. The expression levels of TNF-α, IL-6, PGE2, and cAMP in the treatment group were significantly lower than those in the model group. Western Blotting results showed that the protein expression of p-ERK, p-JNK, and p-P38 was significantly inhibited. Conclusion. The findings suggest that HLJDD has a good antipyretic effect on LPS-induced fever in rats, which may be closely related to the inhibition of MAPK signaling pathway.
Numerical Approximation of Blast Loads on Confined Dry-Stacked Masonry Wall
Authors: Ishfaq Muhammad , Ullah Azmat , Ahmed Awais , Ali Sarfraz , Ali Syed Muhmmad , Uddin Marjan , Shahzada Khan
Published: December 2021

This research intends to numerically study the out-of-plane behaviour of confined dry-stacked masonry (CDSM) walls against blast loading. CDSM is a mortarless interlocking masonry system consisting of Hydraform blocks laid in stretcher bond with reinforced concrete (RC) confining elements. A nonlinear numerical model is developed using advanced finite element hydrocode ANSYS-Autodyn to study the response of CDSM walls subjected to explosive loads. Four different test cases using a charge weight of 4 kg, 8 kg, 12 kg, and 16 kg of Wabox explosive are investigated numerically. The results obtained from numerical simulation are validated with the experimental tests results. The numerical results are found in good agreement with the experimental results. The ability of the numerical model is studied to correctly predict the pressure-time history in pressure gauges installed on walls and compared with experimental data. Peak incident overpressures obtained in these numerical tests ranged from 240 to 1000 kPa. Likewise, the damage patterns obtained from the numerical simulations are compared with available experimental results which show a satisfactory agreement. This study helps to check the response of CDSM structures against blast load which can be used for the construction of blast resisting design of buildings.
Prevalence and Risk Factors of Kaposi’s Sarcoma-Associated Herpesvirus Infection among Han and Uygur Populations in Xinjiang, China
Authors: Wen Zhi , Li Wenli , Fang Yuan , Zhou Chang , Lin Kang , Wu Huanwu , Zhang Yiting , Zhu Yulin , Xu Xingchen , Zeng Yan , Lu Baojing , Wang Linding
Published: December 2021

Kaposi’s sarcoma-associated herpesvirus (KSHV) is the causative agent of Kaposi’s sarcoma (KS), which is endangering human health worldwide, especially in Africa, Europe, the United States, and parts of Asia. The aim of this study was to investigate the prevalence of KSHV in Xinjiang. Three KSHV recombinant proteins (ORF65, ORF73, and K8.1) were used to detect KSHV infection. The serum samples to be tested were detected by an indirect ELISA method. The overall infection rate of KSHV in Xinjiang was 25.60%, with a higher infection rate in the Uygur population of 29.79%. After adjusting for possible confounders, Uygur (OR = 3.95, 95% CI 2.64–6.12,

P
<
0.001

), agriculture and livestock (OR = 1.60, 95% CI 1.20–2.17,

P

 = 0.002), age ≤ 50 years (OR = 1.50, 95% CI 1.13–2.00,

P

 = 0.006), and predominantly meat-based diet (OR = 1.72, 95% CI 1.11–2.78,

P

 = 0.018) were significantly associated with the odds of KSHV seropositivity correlation. Three unique sequences of KSHV were obtained in this study; genotypic analysis showed that the three unique sequences were all subtype A2.
Development and Application of Magnetically Controlled Capsule Endoscopy in Detecting Gastric Lesions
Authors: Zhang Yaoping , Zhang Yanning , Huang Xiaojun
Published: December 2021

In the past 20 years, several magnetically controlled capsule endoscopes (MCCE) have been developed for the evaluation of gastric lesions, including NaviCam (ANKON), MiroCam-Navi (Intromedic), Endocapsule MGCE (Olympus and Siemens), SMCE (JIFU), and FAMCE (Jinshan). Although limited to observing esophageal and duodenal lesions and lacking the ability of biopsy, MCCE has the advantages of comfort, safety, no anesthesia, no risk of cross-infection, and high acceptability. Several high-quality RCTs showed that the diagnostic accuracy of MCCE is comparable to the traditional gastroscopy. Due to the nonnecessity of anesthesia, MCCE may be more suitable for the elderly with obvious comorbidities as well as children. With more evidences accumulated and more innovative technologies developed, MCCE is expected to be an important tool for screening of early gastric cancer or the diagnosis of gastric diseases.
Identification and Analysis of Chemical Constituents and Rat Serum Metabolites in Gushuling Using UPLC-Q-TOF/MS Coupled with Novel Informatics UNIFI Platform
Authors: Chang Hong , Lv Shujie , Yuan Tengteng , Wu Huan , Wang Lei , Sang Ran , Zhang Caiyun , Chen Weidong
Published: December 2021

Gushuling (GSL), a well-known hospital preparation composed of traditional Chinese medicine (TCM), has been widely used in the clinical treatment of osteoporosis (OP) for decades due to its remarkable therapeutic effect. However, the chemical constituents of GSL are still unclear so far, which limits the in-depth study of its pharmacodynamic material basis and further restricts its clinical application. In this study, we developed a strategy for qualitative analysis of the chemical constituents of GSL in vitro and in vivo. Based on the results of ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS) and the UNIFI informatics platform, the chemical constituents of GSL can be determined quickly and effectively. By comparing the retention time, accurate mass, and fragmentation spectrum of the compounds in GSL, a total of 93 compounds were identified or preliminarily identified, including flavonoids, terpenoids, phenylpropanoids, steroids, etc. Among them, nine compounds have been confirmed by standard substances, namely epimedin A, epimedin B, epimedin C, icariin, ecdysterone, calycosin, calycosin-7-glucoside, ononin, and ginsenoside Ro. Fragment patterns and characteristic ions of representative compounds with different chemical structure types were analyzed. At the same time, 20 prototype compounds and 42 metabolites were detected in rat serum. Oxidation, hydration, reduction, dehydration, glutathione S-conjugation, and acetylcysteine conjugation were the main transformation reactions of GSL in rat serum. In this research, the rapid method to characterize the in vitro and in vivo chemical constituents of GSL can not only be used for the standardization and quality control of GSL but also be helpful for further research on its pharmacodynamic material basis.
Outsourced Mutual Private Set Intersection Protocol for Edge-Assisted IoT
Authors: Zhang Jing , Qin Rongxia , Mu Ruijie , Wang Xiaojun , Tang Yongli
Published: December 2021

The development of edge computing and Internet of Things technology has brought convenience to our lives, but the sensitive and private data collected are also more vulnerable to attack. Aiming at the data privacy security problem of edge-assisted Internet of Things, an outsourced mutual Private Set Intersection protocol is proposed. The protocol uses the ElGamal threshold encryption algorithm to rerandomize the encrypted elements to ensure all the set elements are calculated in the form of ciphertext. After that, the protocol maps the set elements to the corresponding hash bin under the execution of two hash functions and calculates the intersection in a bin-to-bin manner, reducing the number of comparisons of the set elements. In addition, the introduction of edge servers reduces the computational burden of participating users and achieves the fairness of the protocol. Finally, the IND-CPA security of the protocol is proved, and the performance of the protocol is compared with other relevant schemes. The evaluation results show that this protocol is superior to other related protocols in terms of lower computational overhead.
[Retracted] Effects of Sevoflurane on Apoptosis of Myocardial Cells in IRI Rats
Authors: Zhang Shikun , Du Xiaoyan , Zhang Kun , Wang Haiyan
Published: December 2021

Background. Cardiomyocyte apoptosis functions essentially in ischemia/reperfusion‐ (I/R‐) induced myocardial injury. It is suggested that autophagy is widely implicated in the regulation of cell survival and death. Sevoflurane, as a largely used inhalational general anesthetic, has been shown to have a protective effect on cardiomyocytes. However, it was yet elusive on the underlying mechanisms. Aim. The objective of this study is to investigate the association of sevoflurane‐mediated cardioprotective effects with autophagy regulation. Methods. An in vitro hypoxia model was established in primary cardiomyocytes from fresh myocardial tissue of the rats. The apoptosis rate of myocardial cells treated with hypoxia and treated with sevoflurane was measured. Western blot and immunocytochemical assay were used to measure the protein expression. The cell proliferation rate and cell apoptosis were measured using the MTT assay and flow cytometry, respectively. Results. The expression of apoptotic proteins including B cell lymphoma‐2 (Bcl‐2), CCAAT/enhancer‐binding protein homologous protein (CHOP), glucose‐regulated protein 78 (GRP78), and Bcl‐2‐associated X protein (BAX) in myocardium treated with sevoflurane was significantly lower than that in myocardium treated with hypoxia. The expression of adhesion proteins such as intercellular adhesion molecule‐1 (ICAM‐1), vascular cell adhesion molecule‐1 (VCAM‐1), and E‐selectin in myocardium treated with sevoflurane was higher than that in myocardium treated with hypoxia, suggesting better connectivity of the myocardium. Conclusion. Sevoflurane treatment reduced the apoptosis of myocardial cells after hypoxia treatment.
Research on the Influence of Sports and Nutrition Matching on Improving Students’ Physique Based on Intelligent Sensor
Authors: Wu Yejin , Zhang Fucai
Published: December 2021

With the continuous development of social economy, students’ learning is paid more and more attention, but at the same time, their physical condition also deserves attention. In view of these problems and limitations, this paper is based on intelligent sensors, which is different from the traditional physical education teaching, adopts the corresponding prescription for sports teaching, combines teaching with fun for the corresponding health intervention, and realizes the two‐way dimensional analysis of compound sports prescription through comprehensive nutrition collocation, so as to improve students’ physical and mental health, in order to improve students’ physical fitness. The simulation results show that the intelligent sensor is effective, which can improve the systematicness and scientificity of students’ participation in exercise in sports activities and improve students’ learning and physical fitness.
A Coupled Discrete‐Finite Element Method for Shear Strength Analysis of Geogrid‐Reinforced Railway Ballast
Authors: Ji Danyang , Ma Zheng , Zhou Junjie , Li Yajun , Shao Shuai
Published: December 2021

This paper presents a coupled discrete‐finite element method for the investigation of shear strength of geogrid‐reinforced ballast by direct shear tests and pull‐out tests. The discrete element method (DEM) and finite element method (FEM) are employed to simulate ballast and geogrid, respectively. Irregularly shaped ballast particles are modeled with clumps, and the nonlinear contact force model is used to calculate contact force between particles. Continuum geogrid is modeled by a two‐node beam element with six degrees of freedom. A contact algorithm based on the static equilibrium is proposed at the geogrid‐ballast contact surface. The simulation results indicate that shear strengths increase with the installation of geogrid. Moreover, ballast particle displacements and nominal volumetric strains are analyzed to provide a microscopic view on the mechanism of the reinforcement effect of geogrid.
Healthcare Practitioners’ Knowledge of Lymphedema
Authors: Yarmohammadi Hossein , Rooddehghan Amirhossein , Soltanipur Masood , Sarafraz Amirabbas , Mahdavi Anari Seyed Fatah
Published: December 2021

Objectives. Lymphedema is neglected in medical education, and a review on healthcare practitioners’ (HCPs) knowledge is necessary to shed light on gaps and to provide evidence for establishing educational programs on lymphedema. Methods. This systematic review was performed based on the PRISMA guideline in PubMed, Scopus, Web of Science, and Google Scholar databases. There was no limitation on the type of lymphedema or HCPs. The quality assessment was performed based on QATSDD. Data regarding study characteristics, questionnaire context, and findings of the study were summarized from each article. Results. After the screening, 16 articles were included that 12 were cross-sectional, two were qualitative, and two were interventional pilot studies. Breast cancer and other cancer-related lymphedema, lymphatic filariasis, and podoconiosis were included, and the majority of articles were focused on primary HCPs. The overall knowledge was low and average in five and 11 articles, respectively, and prior education was a significant factor related to higher knowledge of lymphedema in two studies. Conclusion. Structured education of lymphedema is needed to increase the knowledge of HCPs and to enhance their collaboration in multidisciplinary care teams. Improvement of HCPs’ knowledge may lead to better outcomes of lymphedema patients’ management which are neglected.
Deep Learning-Based Economic Forecasting for the New Energy Vehicle Industry
Authors: Cai Bowen
Published: December 2021

Recently the issues of insufficient energy and serious air pollution around the world have been rising. Henceforth, there is a need to carry out a research of new energy. Soon, new energy vehicles will be the mainstream trend, which can not only reduce the burden of consumers due to rising fuel prices but also solve the air pollution problem caused by the exhaust emissions of fuel vehicles. With the rapid development of science and technology, deep learning continues to make breakthroughs, and, in the field of economy with huge information data, we have more powerful weapons available to predict and research important economic data with infinite value, which can not only provide reference information to policy makers but also help enterprises and even economic markets to develop more healthily and sustainably. Therefore, this article uses deep learning algorithms to forecast and analyze the new energy industry, starting from the financial information released by new energy vehicle companies in their annual reports, in order to make basic judgments and help policy makers and enterprises in the new energy vehicle industry.
[Retracted] Application of Optimized GA‐BPNN Algorithm in English Teaching Quality Evaluation System
Authors: Zhu Yaowu , Xu Junnong , Zhang Sihong
Published: December 2021

The assessment of teaching quality is a very complex and fuzzy nonlinear process, which involves many factors and variables, so the establishment of the mathematical model is complicated, and the traditional evaluation method of teaching quality is no longer fully competent. In order to evaluate teaching quality effectively and accurately, an optimized GA‐BPNN algorithm based on genetic algorithm (GA) and backpropagation neural network (BPNN) is proposed. Firstly, an index system of teaching quality evaluation is established, and a questionnaire is designed according to the index system to collect data. Then, an English teaching quality evaluation system is established by optimizing model parameters. The simulation shows that the average evaluation accuracy of the GA‐BPNN algorithm is 98.56%, which is 13.23% and 5.85% higher than those of the BPNN model and the optimized BPNN model, respectively. The comparison results show that the GA‐BPNN algorithm in teaching quality evaluation can make reasonable and scientific results.
Ginsenoside Rg1 as a Potential Regulator of Hematopoietic Stem/Progenitor Cells
Authors: He Fang , Yao Guanping
Published: December 2021

Ginsenoside Rg1 (Rg1), a purified, active component of the root or stem of ginseng, exerts positive effects on mesenchymal stem cells (MSCs). Many recent studies have found that hematopoietic stem cells (HSCs), which can develop into hematopoietic progenitor cells (HPCs) and mature blood cells, are another class of heterogeneous adult stem cells that can be regulated by Rg1. Rg1 can affect HSC proliferation and migration, regulate HSC/HPC differentiation, and alleviate HSC aging, and these findings potentially provide new strategies to improve the HSC homing rate in HSC transplantation and for the treatment of graft-versus-host disease (GVHD) or other HSC/HPC dysplasia-induced diseases. In this review, we used bioinformatics methods, molecular docking verification, and a literature review to systematically explore the possible molecular pharmacological activities of Rg1 through which it regulates HSCs/HPCs.