bluuebunny/crossref_metadata_2025 huggingface dataset:
Proteinuria is accompanied by intratubular complement activation and apical membrane deposition of C3dg and C5b-9 in kidney transplant recipients
Authors: Isaksson Gustaf L. , Nielsen Marie B. , Hinrichs Gitte R. , Krogstrup Nicoline V. , Zachar Rikke , Stubmark Heidi , Svenningsen Per , Madsen Kirsten , Bistrup Claus , Jespersen Bente ,   The CONTEXT Study Group, Birn Henrik , Palarasah Yaseelan , Jensen Boye L.
Published: December 2021

The present study proposes a mechanistic coupling between proteinuria and aberrant filtration of complement precursors, intratubular complement activation, and apical membrane attack in kidney transplant recipients. C3dg and C5b-9-associated C9 neoantigen associate with proximal tubular apical membranes as demonstrated in urine extracellular vesicles. The discovery suggests intratubular complement as a mediator between proteinuria and progressive kidney damage. Inhibitors of soluble and/or luminal complement activation with access to the tubular lumen may be beneficial.
Investigation and Development of a Three‐Dimensional Transmission Tower‐Line System Model Using Nonlinear Truss and Elastic Catenary Elements for Wind Loading Dynamic Simulation
Authors: Zhu Xiao , Ou Ge
Published: December 2021

The accuracy of transmission tower‐line system simulation is highly impacted by the transmission line model and its coupling with the tower. Owing to the high geometry nonlinearity of the transmission line and the complexity of the wind loading, such analysis is often conducted in the commercial software. In most commercial software packages, nonlinear truss element is used for cable modeling, whereas the initial strain condition of the nonlinear truss under gravity loading is not directly available. Elastic catenary element establishes an analytical formulation for cable structure under distributed loading; however, the nonlinear iteration to reach convergence can be computational expensive. To derive an optimal transmission tower‐line model solution with high fidelity and computational efficiency, an open‐source three‐dimensional model is developed. Nonlinear truss element and elastic catenary element are considered in the model development. The results of the study imply that both elements are suitable for the transmission line model; nevertheless, the initial strain in nonlinear truss element largely impacts the model accuracy and should be calibrated from the elastic catenary model. To cross‐validate the developed models on the coupled transmission tower and line, a one‐span eight‐line system is modeled with different elements and compared with several state‐of‐the‐art commercial packages. The results indicate that the displacement time‐history root‐mean‐square error (RMSE) of the open‐source transmission tower‐line model is less than 1% and with a 66% computational time reduction compared with the ANSYS model. The application of the open‐source package transmission tower‐line model on extreme wind speed considering the aerodynamic damping is further implemented.
Life Prediction Method of Remanufactured Machinery Equipment Based on Vibration Signal Feature Extraction
Authors: Li Bin , Kui Le , Luo Jingdong , Chen Shiyong
Published: December 2021

Mechanical equipment is a key component of mechanical equipment, and its working condition is directly related to the overall performance of mechanical equipment. Accurate evaluation and prediction of the performance degradation trend of mechanical equipment is of great significance to ensure the reliability and safety of the mechanical equipment system. Based on the data of typical faulty equipment, this paper analyzes the energy characteristic parameters of mechanical equipment under different types and degrees of failure in the time domain. Using amplitude spectrum analysis, Hilbert envelope demodulation and wavelet packet decomposition method, and other vibration signal processing methods, preliminary extraction of multiple statistical feature parameters are given. Secondly, in view of the irrelevant and redundant components of multiple statistical parameters, a new method for extracting fault features of mechanical equipment based on variance value and principal component analysis is proposed. This method can effectively classify the fault status of mechanical equipment. The effectiveness of the method is verified by actual equipment signals. After that, the value extracted from the vibration signal of the double-row roller equipment is used as the degradation feature. In order to reduce the influence of irregular characteristics in the vibration signal and simplify the complexity of the vibration signal, the wavelet transform and the support vector machine model are combined, according to the degradation after decomposition. The 95% confidence interval of the predicted value is also given. The SVM model is established based on data characteristics, and single-step and multistep prediction of equipment degradation trends are carried out. The prediction result shows that, according to the mapping position formula, the distribution of equipment degradation prediction points is obtained, and a 95% confidence interval based on the distribution of the prediction points is given. Finally, on the basis of completing feature extraction, this paper applies an unsupervised feature selection method. The sensitive characteristics of life prediction and the prediction results of a single SVM model and a neural network model are compared and analyzed at the same time.
A Three-Dimensional Anisotropic Diffusion Equation-Based Video Recognition Model for Classroom Concentration Evaluation in English Language Teaching
Authors: Liu Yanghong , Liu Jintao
Published: December 2021

In this paper, a three-dimensional anisotropic diffusion equation is used to conduct an in-depth study and analysis of students’ concentration in video recognition in English teaching classrooms. A multifeature fusion face live detection method based on diffusion model extracts Diffusion Kernel (DK) features and depth features from diffusion-processed face images, respectively. DK features provide a nonlinear description of the correlation between successive face images and express face image sequences in the temporal dimension; depth features are extracted by a pretrained depth neural network model that can express the complex nonlinear mapping relationships of images and reflect the more abstract implicit information inside face images. To improve the effectiveness of the face image features, the extracted DK features and depth features are fused using a multicore learning method to obtain the best combination and the corresponding weights. The two features complement each other, and the fused features are more discriminative, which provides a strong basis for the live determination of face images. Experiments show that the method has excellent performance and can effectively discriminate the live nature of faces in images and resist forged face attacks. Based on the above face detection and expression recognition algorithms, the classroom concentration analysis system based on expression recognition is designed to achieve real-time acquisition and processing of classroom images, complete student classroom attendance records using face detection and face recognition methods, and analyze students’ concentration from the face integrity and facial expression of students facing the blackboard by combining face detection and expression recognition to visualize and display students’ classroom data for teachers, students, and parents with more data support and help.
Evaluation of 3D-CEUS in the Recurrence of Liver Cancer after Radiofrequency Ablation
Authors: Bai Lianjie , Wang Xinping , Shi Shenglong , Gao Jian , Li Xing , Wang Ying , Jiang Maitao , Zheng Chunlei , Liu Huilin
Published: December 2021

Background. Radiofrequency ablation (RFA) has the similar curative effects to surgery, but RFA will lead to higher postoperative local recurrence rate. 3D-CEUS is a minimally invasive examination method, which is used to analyze the sensitivity to postoperative recurrence in this study. Methods. The clinical data of 60 patients with liver cancer admitted to our hospital (February 2018-February 2020) were retrospectively analyzed. All patients were treated with RFA and were followed up with 3D-CEUS, MRI, and enhanced CT examination after surgery. The ROC curve was used to analyze the differences of different examination methods in judging postoperative recurrence. Results. For the 60 patients, 52 patients (86.7%) had a single lesion and 8 patients (13.3%) had multiple lesions, with a total of 72 lesions. After RFA, 56 lesions (77.8%) were completely inactivated and 16 lesions (22.2%) remained. Totally inactivated lesions were detected as follows: 51 (91.1%) by 3D-CEUS, 42 (75.0%) by MRI, and 50 (89.3%) by enhanced CT. During a 2-year follow-up, a total of 26 recurrent lesions were detected, 24 (92.3%) by 3D-CEUS, 12 (46.2%) by MRI, and 25 (96.2%) by enhanced CT, indicating that the sensitivity of 3D-CEUS and enhanced CT was obviously higher than that of MRI (P<0.001), without conspicuous difference between sensitivity of 3D-CEUS and enhanced CT (P>0.05). Conclusion. As a new imaging examination method based on artificial intelligence, 3D-CEUS has a high sensitivity in patients with liver cancer who underwent RFA, which can effectively judge the recurrence after surgery and should be widely used in practice.
The Effect of Temperature on Dynamic Characteristics of Frozen Clay under Principal Stress Rotation
Authors: Zhang Bin-Long , Wang Da-Yan , Zhou Zhi-Wei , Ma Wei , Lei Le-Le
Published: December 2021

The foundation soil is always subjected to complex stress, including continuous rotation of the principal stress caused by traffic and earthquake loads. To comprehend the dynamic characteristics of frozen clay under complex stress sate, including continuous rotation of the principal stress, this study investigates the effect of temperature on the dynamic characteristics of frozen clay under principal stress rotation using a frozen hollow cylinder apparatus (FHCA‐300). The test results reveal that the cumulative plastic strain of frozen clay samples exponentially increases with the rising of temperature under principal stress rotation. The influence of temperature is more profound with a high cyclic stress ratio (CSR). A decrease in temperature can improve the stiffness of the frozen clay, reduces its energy dissipation, and enhances its ability to resist dynamic loading. However, the principal stress rotation phenomenon may aggravate the damage of frozen clay and increase the energy dissipation and reduces its ability to resist dynamic loading. Based on the experimental data, an empirical expression was proposed to describe the coupling influence of CSRs and temperature on the axial resilient modulus of frozen clay, which can predict the development of axial resilient modulus under different thermal‐mechanical conditions.
A Novel Sparse Array for Localization of Mixed Near-Field and Far-Field Sources
Authors: Wang Yinsheng , Cui WeiJia , Du Yuxi , Ba Bin , Mei Fengtong
Published: December 2021

As we all know, nested array can obtain a larger array aperture and more degrees of freedom using fewer sensors. In this study, we not only designed an enhanced symmetric nested array (ESNA), which achieved more consecutive lags and more unique lags compared with a generalized nested array but also developed a special cumulant matrix, in the case of a given number of sensors, which can automatically generate the largest consecutive lags of the array. First, the direction-of-arrivals (DOAs) of mixed sources are estimated using the special cumulant matrix. Then, we can estimate the range of the near-field source in the mixed source using a one-dimensional spectral search through estimated DOAs, and in the mixed sources, the near-field and far-field sources are classified by bringing in the range parameter. The largest consecutive lags and composition method of ESNA are also given, under a given number of sensors.Our algorithm has moderate computation complexity, which provides a higher resolution and improves the parameters’ estimation accuracy. Numerical simulation results demonstrate that the proposed array showed an outstanding performance under estimation accuracy and resolution ability for both DOA and range estimation compared with existing arrays of the same physical array sensors.
Study on the Impact of Online Courses for Pregnant and Lying-In Women on Maternal and Infant Health during the Epidemic
Authors: Shi Liangfang , Yuan Ling , Zhou Lin , Zhang Shuixian , Lei Xia
Published: December 2021

The sudden outbreak of the new crown pneumonia has brought online learning from a supporting role to the center of the teaching stage in an instant. On the basis of the feasibility analysis and demand analysis of the microcourse learning system, this paper uses Sina cloud server to build the WeChat public platform learning online course and designs and implements the microcourse learning system function based on the microcourse public platform. We completed the recording, editing, publishing, and testing of microclass courses and provided services for teachers and pregnant women's microclass learning in order to achieve better learning results. A total of 151 people regularly participate in maternity school courses, accounting for 30.4%. There are 190 people who have never attended the maternity school course, accounting for 38.2%. There are 156 people who occasionally participate in maternity school courses, accounting for 31.4%. The top five sources of health information during pregnancy are books, maternity schools, experience of elders, the Internet, and television. The results of one-way analysis of variance showed that pregnant women of different ages had statistically different scores in the dimensions of knowledge and ideas (

P

 < 0.05). There are statistical differences in the scores of pregnant women with different economic incomes in this dimension (

P

 < 0.05). The women with economic income ≥5000 yuan/month have the highest scores, and those with economic income ≥5000 yuan/month have the lowest scores. The scores of pregnant women who participated in the maternity school were significantly higher than those who did not participate in the maternity school (

P

 < 0.05). There are statistical differences in the scores of maternal and child health basic skills among pregnant women of different age groups (

P

 < 0.05). The women aged ≥35 years old have the highest scores, and those aged 20–24 years old have the lowest scores. The differences in the scores of pregnant women with different economic incomes in this dimension are statistically significant (

P

＜0.05). The women with economic income ≥5000 yuan/month have the highest score, and those with economic income<2000 yuan/month have the lowest score. Participation in maternity schools has an impact on the scores of this dimension. Pregnant women who regularly participate in maternity schools have the highest average scores, and those who do not participate in maternity schools have the lowest average scores.
Association between Negative Life Events on Mental Health and College Student Adjustment: A Mediated Moderating Effect
Authors: Cao Liu
Published: December 2021

Objective: To explore the association between negative life events and college student adjustment and to explore the mediating and moderating effects of social support and grade level in the relationship between the two. Methods. The research was conducted with 1717 college students using the Adolescent Self-Rating Life Events Checklist (ASLEC), China College Student Adjustment Scale (CCSAS), and the Social Support Rating Scale (SSRS). Results. (1) Negative life events were significantly negative in correlation with adjustment and social support (r = −0.373, −0.174, Ps < 0.001), while social support was significantly positive in correlation with adjustment (r = 0.359,

P
<
0.001

). (2) The main effects of negative life events, social support, and grade on adjustment were significant (effect = −0.190,

P
<
0.001

, 95% CI [−0.288∼−0.092]; effect = 0.307,

P
<
0.001

, 95% CI [0.265∼0.348]; effect = 0.163,

P
<
0.001

, 95% CI [0.126∼0.200]). (3) In the relationship between negative life events and adjustment, social support played a mediating role (effect = −0.054, 95% CI [−0.071∼−0.037]) and grade level played a moderating role (effect = −0.049,

P
=
0.009

, 95% CI [−0.085∼−0.012]). Conclusion. Negative life events, social support, and grade level affected college student adjustment, and social support networks for college students should be actively constructed and targeted education should be conducted according to different grade levels, which can promote college student adjustment.
Hemosiderin-Laden Macrophages in Bronchoalveolar Lavage: Predictive Role for Acute Exacerbation of Idiopathic Interstitial Pneumonias
Authors: Arai Toru , Kagawa Tomoko , Sasaki Yumiko , Sugawara Reiko , Sugimoto Chikatoshi , Tachibana Kazunobu , Fujita Yoshiki , Hayashi Seiji , Inoue Yoshikazu
Published: December 2021

Background. Hemosiderin-laden macrophages (HLMs) have been identified in the bronchoalveolar lavage fluid (BALF) of patients with idiopathic pulmonary fibrosis (IPF). This retrospective study examined the ability of HLMs in BALF to predict the acute exacerbation (AE) of chronic idiopathic interstitial pneumonias (IIPs). Methods. Two hundred and twenty-one patients with IIP diagnosed by bronchoscopy were enrolled in the study (IPF, n = 87; IIPs other than IPF, n = 134). Giemsa stain was used to detect HLMs in BALF specimens. Prussian blue stain was used to quantify HLMs in BALF, and a hemosiderin score (HS) was given to the specimens containing HLMs. Results. Twenty-four patients had a positive HS (range: 7‒132). The receiver-operating characteristic curve analysis identified the cutoff HS value for predicting the AE of IIPs to be 61.5. Seven cases had a higher HS (≥61.5) and 214 had a lower HS. AE occurred significantly earlier in the higher HS group (4/7 cases) than in the lower HS group (41/214 cases) during a median observation period of 1239 days (log-rank test,

p

 = 0.026). Multivariate Cox proportional hazard regression analysis showed that a higher HS was a significant predictor of AE in addition to IPF, percent predicted forced vital capacity, and modified Medical Research Council score. The C-statistics for the prediction of AE did not significantly improve by all the above parameters with HS as compared without HS. Conclusions. A higher HS was a significant predictor of AE in IIPs but did not significantly improve the predictive ability of other parameters.
Adoption of Modern Hive Beekeeping Technology: The Case of Kacha-Birra Woreda, Kembata Tembaro Zone, Southern Ethiopia
Authors: Mulatu Akililu , Marisennayya Senapathy , Bojago Elias
Published: December 2021

Beekeeping is one of the livelihood options available to Ethiopian farmers. The objectives of this study were to analyze the level of adoption of modern hive technology by farmers and to identify the variables influencing the adoption of modern beekeeping hive technology in Kacha-Birra Woreda. Primary data were collected from 89 respondents chosen using a multistage sampling process, while qualitative data were collected through focus group discussion and key informant interviews. Data were analyzed using a binary logit regression model and descriptive statistics. According to the results of the model, several factors, such as the educational level of the respondents, the size of the land, the extension, the contact, and the access to financing and market, had a substantial impact on the adoption of modern hive beekeeping technology. It is suggested that the Livestock and Fish Resource Development office develops a strategy to help the community's illiterate members benefit more from the use of contemporary hive beekeeping technologies, develops a strategy to benefit farmers who have large land sizes with modern hive beekeeping technology, establishes extension contact with farmers before technology innovation leads to better adoption of technology, and strongly advises to link a strategy with micro-enterprises. Promotional activities focused on preventing the access to the market of respondents that must ensure their active participation in adoption.
Accuracy of Robot-Assisted Percutaneous Pedicle Screw Placement under Regional Anesthesia: A Retrospective Cohort Study
Authors: Gao Shangju , Wei Jingchao , Li Wenyi , Zhang Long , Cao Can , Zhai Jinshuai , Gao Bo
Published: December 2021

Background. Robot-assisted pedicle screw placement is usually performed under general anesthesia to keep the body still. The aim of this study was to compare the accuracy of the robot-assisted technique under regional anesthesia with that of conventional fluoroscopy-guided percutaneous pedicle screw placement under general anesthesia in minimally invasive lumbar fusion surgery. Methods. This study recruited patients who underwent robot-assisted percutaneous endoscopic lumbar interbody fusion (PELIF) or fluoroscopy-guided minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) between December 2017 and February 2020 at a single center. Based on the method of percutaneous pedicle screw placement used, patients were divided into the robot-assisted under regional anesthesia (group RE-RO) and fluoroscopy-guided under general anesthesia (group GE-FLU) groups. The primary outcome measures were screw accuracy and the incidence of facet joint violation (FJV). Secondary outcome measures included X-ray and visual analogue scale (VAS) scores which were used to evaluate the degree of the postoperative pain at 4 hours and on postoperative days 1, 2, and 3. Intraoperative adverse events were also recorded. Results. Eighteen patients were included in group RE-RO, and 23 patients were included in group GE-FLU. The percentages of clinically acceptable screws (Gertzbein and Robbins grades A and B) were 94.4% and 91.5%, respectively. There was no significant difference in the percentages of clinically acceptable screws (

p
=
0.44

) or overall Gertzbein and Robbins screw accuracy grades (

p
=
0.35

). Only the top screws were included in the analysis of FJVs. The percentages of FJV (Babu grades 1, 2, and 3) were 5.6% and 28.3%, respectively. This difference was statistically significant (

p
=
0.01

). Overall, the FJV grades in group RE-RO were significantly better than those in group GE-FLU (

p
=
0.009

). The mean fluoroscopy time for each screw in group RE-RO was significantly shorter than that in group GE-FLU (group RE-RO: 5.4 ± 1.9 seconds and group GE-FLU: 6.8 ± 2.0 seconds;

p
=
0.03

). The postoperative pain between the RE-RO and GE-FLU groups was not statistically significant. The intraoperative adverse events included 1 case of registration failure and 1 case of guide-wire dislodgment in group RE-RO, as well as 2 cases of screw misplacement in group GE-FLU. No complications related to anesthesia were observed. Conclusion. Robot-assisted pedicle screw placement under regional anesthesia can be performed effectively and safely. The accuracy is comparable to the conventional technique. Moreover, this technique has the advantage of fewer FJVs and a lower radiation time.
Analytical Analysis of Fractional-Order Physical Models via a Caputo-Fabrizio Operator
Authors: Mofarreh Fatemah , Zidan A. M. , Naeem Muhammad , Shah Rasool , Ullah Roman , Nonlaopon Kamsing
Published: December 2021

This paper investigates a modified analytical method called the Adomian decomposition transform method for solving fractional-order heat equations with the help of the Caputo-Fabrizio operator. The Laplace transform and the Adomian decomposition method are implemented to obtain the result of the given models. The validity of the proposed method is verified by considering some numerical problems. The solution achieved has shown that the better accuracy of the suggested method. Furthermore, due to the straightforward implementation, the proposed method can solve other nonlinear fractional-order problems.
Bifurcation and Stabillity Analysis of HIV Transmission Model with Optimal Control
Authors: Cheneke Kumama Regassa , Rao Koya Purnachandra , Edessa Geremew Kenassa
Published: December 2021

A mathematical model of HIV transmission is built and studied in this paper. The system’s equilibrium is calculated. A next-generation matrix is used to calculate the reproduction number. The novel method is used to examine the developed model’s bifurcation and equilibrium stability. The stability analysis result shows that the disease-free equilibrium is locally asymptotically stable if

0
<

R

0

 
<
1

but unstable if

R

0

>
1

. However, the endemic equilibrium is locally and globally asymptotically stable if

R

0

>
1

and unstable otherwise. The sensitivity analysis shows that the most sensitive parameter that contributes to increasing of the reproduction number is the transmission rate

β

2

of HIV transmission from HIV individuals to susceptible individuals and the parameter that contributes to the decreasing of the reproduction number is identified as progression rate

η

of HIV-infected individuals to AIDS individuals. Furthermore, it is observed that as we change

η

from

0.1

to

1

, the reproduction number value decreases from 1.205 to 1.189, where the constant value of

β

2

=
0.1

. On the other hand, as we change the value of

β

2

from

0.1

to

1

, the value of the reproduction number increases from 0.205 to 1.347, where the constant value of

η
=
0.1

. Further, the developed model is extended to the optimal control model of HIV/AIDS transmission, and the cost-effectiveness of the control strategy is analyzed. Pontraygin’s Maximum Principle (PMP) is applied in the construction of the Hamiltonian function. Moreover, the optimal system is solved using forward and backward Runge–Kutta fourth-order methods. The numerical simulation depicts the number of newly infected HIV individuals and the number of individuals at the AIDS stage reduced as a result of taking control measures. The cost-effectiveness study demonstrates that when combined and used, the preventative and treatment control measures are effective. MATLAB is used to run numerical simulations.
Ancient Stone Inscription Image Denoising and Inpainting Methods Based on Deep Neural Networks
Authors: Zhang Haoming , Qi Yue , Xue Xiaoting , Nan Yahui
Published: December 2021

Chinese ancient stone inscriptions contain Chinese traditional calligraphy culture and art information. However, due to the long history of the ancient stone inscriptions, natural erosion, and poor early protection measures, there are a lot of noise in the existing ancient stone inscriptions, which has adverse effects on reading these stone inscriptions and their aesthetic appreciation. At present, digital technologies have played important roles in the protection of cultural relics. For ancient stone inscriptions, we should obtain more perfect digital results without multiple types of noise, while there are few deep learning methods designed for processing stone inscription images. Therefore, we propose a basic framework for image denoising and inpainting of stone inscriptions based on deep learning methods. Firstly, we collect as many images of stone inscriptions as possible and preprocess these images to establish an inscriptions image dataset for image denoising and inpainting. In addition, an improved GAN with a denoiser is used for generating more virtual stone inscription images to expand the dataset. On the basis of these collected and generated images, we designed a stone inscription image denoising model based on multiscale feature fusion and introduced Charbonnier loss function to improve this image denoising model. To further improve the denoising results, an image inpainting model with the coherent semantic attention mechanism is introduced to recover some effective information removed by the former denoising model as much as possible. The experimental results show that our image denoising model achieves better results on PSNR, SSIM, and CEI. The final results have obvious visual improvement compared with the original stone inscription images.
Application of the Nursing Model Based on Acceptance and Commitment Therapy (ACT) in Improving Mental Health and Quality of Life after Colorectal Cancer Drug Chemotherapy
Authors: Shi Yali , Yu Hongwei , Miao Jiangyong , Wang Lihui
Published: December 2021

According to the most current cancer impact statistics, third most commonly diagnosed cancer worldwide is colorectal cancer. Colon cancer, in addition to its physical symptoms, has been linked to mental health issues in patients, according to the study. Dealing with colorectal cancer drug chemotherapy may lead to depression and anxiety in some people. Others are affected by the physical and mental condition of undergoing many therapies at the same time. Throughout the process of diagnosis, a large number of colorectal cancer patients report clinically relevant degrees as well as a decline in overall mental wellness. In the majority of cases, colon cancer patients are cured following therapy, but those who have survived the disease confront a medical range, physical, and challenges in society, for a variety of mental and physical problems such as anxiety and depression. First, meditation therapy is to urge patients to address their issues and feelings instead of dismissing them, but in the dispassionate and unbiased manner that defines the attentive state. Both the patient and the treating professional may benefit from this treatment method, since it appears to be a very effective therapeutic strategy. After colorectal cancer treatment, in studies, it has been demonstrated that ACT improves mental health, and Internet search engines such as Web of Science and Google Scholar as well as Dialnet were utilized to conduct a systematic literature There were 19 articles that fit the criteria. This includes a discussion of the ACT’s philosophical and theoretical basis, as well as the treatment itself. On the other hand, the study on ACT for enhancing mental health and quality of life is examined. Several of the available trials had serious flaws, making it impossible to establish reliable conclusions about the effectiveness of ACT for improving mental health and quality of life. The study determined that there is only a small amount of data supporting the use of ACT for improving mental health. The aim of this study is the application of the nursing model on improving the mental health of the colorectal patients. In addition, the limits of the current empirical state of ACT are acknowledged, and the importance of further research is highlighted.
Real-Time Vehicle Trajectory Prediction for Traffic Conflict Detection at Unsignalized Intersections
Authors: Cao Qianxia , Zhao Zhongxing , Zeng Qiaoqiong , Wang Zhengwu , Long Kejun
Published: December 2021

Real-time prediction of vehicle trajectory at unsignalized intersections is important for real-time traffic conflict detection and early warning to improve traffic safety at unsignalized intersections. In this study, we propose a robust real-time prediction method for turning movements and vehicle trajectories using deep neural networks. Firstly, a vision-based vehicle trajectory extraction system is developed to collect vehicle trajectories and their left-turn, go straight, and right-turn labels to train turning recognition models and multilayer LSTM deep neural networks for the prediction task. Then, when performing vehicle trajectory prediction, we propose the vehicle heading angle change trend method to recognize the future move of the target vehicle to turn left, go straight, and turn right based on the trajectory data characteristics of the target vehicle before passing the stop line. Finally, we use the trained multilayer LSTM models of turning left, going straight, and turning right to predict the trajectory of the target vehicle through the intersection. Based on the TensorFlow-GPU platform, we use Yolov5-DeepSort to automatically extract vehicle trajectory data at unsignalized intersections. The experimental results show that the proposed method performs well and has a good performance in both speed and accuracy evaluation.
Score and Correlation Coefficient-Based Feature Selection for Predicting Heart Failure Diagnosis by Using Machine Learning Algorithms
Authors: Senan Ebrahim Mohammed , Abunadi Ibrahim , Jadhav Mukti E. , Fati Suliman Mohamed
Published: December 2021

Cardiovascular disease (CVD) is one of the most common causes of death that kills approximately 17 million people annually. The main reasons behind CVD are myocardial infarction and the failure of the heart to pump blood normally. Doctors could diagnose heart failure (HF) through electronic medical records on the basis of patient’s symptoms and clinical laboratory investigations. However, accurate diagnosis of HF requires medical resources and expert practitioners that are not always available, thus making the diagnosing challengeable. Therefore, predicting the patients’ condition by using machine learning algorithms is a necessity to save time and efforts. This paper proposed a machine-learning-based approach that distinguishes the most important correlated features amongst patients’ electronic clinical records. The SelectKBest function was applied with chi-squared statistical method to determine the most important features, and then feature engineering method has been applied to create new features correlated strongly in order to train machine learning models and obtain promising results. Optimised hyperparameter classification algorithms SVM, KNN, Decision Tree, Random Forest, and Logistic Regression were used to train two different datasets. The first dataset, called Cleveland, consisted of 303 records. The second dataset, which was used for predicting HF, consisted of 299 records. Experimental results showed that the Random Forest algorithm achieved accuracy, precision, recall, and F1 scores of 95%, 97.62%, 95.35%, and 96.47%, respectively, during the test phase for the second dataset. The same algorithm achieved accuracy scores of 100% for the first dataset and 97.68% for the second dataset, while 100% precision, recall, and F1 scores were reached for both datasets.
A Study of Spatial Attention and Squeeze Excitation Block Fusion Improved ResNet for Identifying Bank Notes
Authors: Huo Junjun
Published: December 2021

Based on deep learning and digital image processing algorithms, we design and implement an accurate automatic recognition system for bank note text and propose an improved recognition method based on ResNet for the problems of difficult image text extraction and insufficient recognition accuracy. Firstly, a deep hyperparameterized convolution (DO-Conv) is used instead of the traditional convolution in the network to improve the recognition rate while reducing the model parameters. Then, the spatial attention model (SAM) and the squeezed excitation block (SE-Block) are fused and applied to a modified ResNet to extract detailed features of bank note images in the channel and spatial domains. Finally, the label-smoothed cross-entropy (LSCE) loss function is used to train the model to automatically calibrate the network to prevent classification errors. The experimental results demonstrate that the improved model is not easily affected by the image quality, and the model in this paper has good performance in text detection and recognition in specific business ticket scenarios.
Dispatching Design for Customized Bus of Hybrid Vehicles Based on Reservation Data
Authors: Zhou Xuemei , Xi Jiaojiao , Guan Zhen , Ji Xiangfeng
Published: December 2021

Proper vehicle operation and route planning are critical for achieving the best match between bus operation and passenger services. In order to enhance the attractiveness of public transportation, a new type of the public transportation dispatching method based on passenger reservation data is proposed. This mode can meet the requirements of multiple lines in urban centers during peak hours, which can realize direct service between two stations. Then, taking the lowest operating cost of the enterprise and the lowest passenger waiting cost as the optimization goal, a customized dynamic dispatching model of multiline and hybrid vehicles was established. Finally, a calculation example is designed and the genetic algorithm is used to solve the model. The results show that the hybrid vehicle solution is more reasonable than the traditional single-vehicle solution and reveal that the model is feasible to optimize scheduling plan. The conclusions obtained in this research lay a theoretical foundation for APP setup and operation plan formulation.
The Application Value of Spiral CT Lung Densitometry Software in the Diagnosis of Radiation-Induced Lung Injury
Authors: Tao Qingsong , Zhu Ting , Ge Xiaoqin , Gong Shengping , Guo Jianxin
Published: December 2021

In order to study the application value of spiral CT lung density measurement software in the diagnosis of radioactive lung injury, the average CT values of lung apex, hilum, and diaphragm were measured by Pulmo automatic evaluation software of 16-slice spiral CT in 96 patients with different types of radiation lung injury diagnosed by conventional CT and 80 healthy subjects. The radiation lung injury on CT slices was classified, and the lung density was measured. In 96 patients with different types of radiation lung injury, 56 patients had different degrees of increase in average lung density, which was most obvious in the type of air insufficiency and chronic fibrosis. CT values of lung density in the ground glass stage and patch stage of acute radiation pneumonia had little influence due to the range and time of exposure. The lung density of 35 patients with radiation injury was measured in the normal range. There was a significant difference between normal lung density and abnormal lung density in different types of radiation lung injury (X2 = 56.718,

P
<
0.001

). The mean lung density of 68 cases was normal and that of 12 cases was abnormal. There was a significant difference in lung density between the lung injury group and the normal group (X2 = 18.027,

P
<
0.001

). Spiral CT lung density measurement can accurately evaluate the lung density values of different types of radiation lung injury and judge the correlation between lung density and different types of radiation lung injury. It is of great value to diagnose, locate, and master the radiation dose of different types of radiation lung injury.
Evaluation of the Modulatory Effect of Annona muricata Extracts on the Activity of Some Selected Antibiotics against Biofilm-Forming MRSA
Authors: Neglo David , Tettey Clement Okraku , Essuman Edward Ken , Amenu Justice Dziedzorm , Mills-Robertson Felix Charles , Sedohia Daniel , Boakye Adjoa Agyemang , Abaye Daniel A.
Published: December 2021

The study investigated the influence of Annona muricata extracts on the action of selected antibiotics against biofilm-forming MRSA. The various parts of the plant were processed into powder and extracted with ethanol or hot water and then screened for the presence of phytochemicals. The modulatory effect of the Annona muricata extract was also tested on some antibiotics against Methicillin-resistant Staphylococcus aureus (MRSA). The findings from this study revealed that the various parts of the Annona muricata extract (ethanolic and aqueous) contained different proportions of secondary metabolites. Varied antimicrobial activities were observed when the extract of the A. muricata was exposed to MRSA strain at a concentration of 100 mg/mL. The stem recorded the highest (17.00 and 18.00 mm) inhibitory activity against MRSA for both the aqueous and the ethanolic extract, respectively, and this was not different from the control, tetracycline. Again, the results on the modulatory action indicated that out of the 10 extracts of A. muricata, 4 of them antagonized the activity of ampicillin against the tested MRSA by a factor of 0.5 folds and the rest potentiated the drug within 1–4 folds, respectively. On the other hand, the various test extracts significantly potentiated the efficacy of streptomycin and tetracycline against the MRSA by a range of 1–32 folds with the aqueous root extract recording the highest synergistic effect and ethanol seed extract with the least effect. The findings of this study support the antibacterial activities of the A. muricata plant parts.
Plasticity of Extrachromosomal and IntrachromosomalBRAFAmplifications in Overcoming Targeted Therapy Dosage Challenges
Authors: Song Kai , Minami Jenna K. , Huang Arthur , Dehkordi Siavash R. , Lomeli Shirley H. , Luebeck Jens , Goodman Mark H. , Moriceau Gatien , Krijgsman Oscar , Dharanipragada Prashanthi , Ridgley Trevor , Crosson William P. , Salazar Jesus , Pazol Eli , Karin Gabriel , Jayaraman Rachana , Balanis Nikolas G. , Alhani Salwan , Sheu Kyle , ten Hoeve Johanna , Palermo Amelia , Motika Stephen E. , Senaratne T. Niroshi , Paraiso Kim H. , Hergenrother Paul J. , Rao P. Nagesh , Multani Asha S. , Peeper Daniel S. , Bafna Vineet , Lo Roger S. , Graeber Thomas G.
Published: December 2021

Focal amplifications (FA) can mediate targeted therapy resistance in cancer. Understanding the structure and dynamics of FAs is critical for designing treatments that overcome plasticity-mediated resistance. We developed a melanoma model of dual MAPK inhibitor (MAPKi) resistance that bears BRAFV600 amplifications through either extrachromosomal DNA (ecDNA)/double minutes (DM) or intrachromosomal homogenously staining regions (HSR). Cells harboring BRAFV600E FAs displayed mode switching between DMs and HSRs, from both de novo genetic changes and selection of preexisting subpopulations. Plasticity is not exclusive to ecDNAs, as cells harboring HSRs exhibit drug addiction–driven structural loss of BRAF amplicons upon dose reduction. FA mechanisms can couple with kinase domain duplications and alternative splicing to enhance resistance. Drug-responsive amplicon plasticity is observed in the clinic and can involve other MAPK pathway genes, such as RAF1 and NRAS. BRAF FA-mediated dual MAPKi–resistant cells are more sensitive to proferroptotic drugs, extending the spectrum of ferroptosis sensitivity in MAPKi resistance beyond cases of dedifferentiation.

Significance:

Understanding the structure and dynamics of oncogene amplifications is critical for overcoming tumor relapse. BRAF amplifications are highly plastic under MAPKi dosage challenges in melanoma, through involvement of de novo genomic alterations, even in the HSR mode. Moreover, BRAF FA-driven, dual MAPKi–resistant cells extend the spectrum of resistance-linked ferroptosis sensitivity.

This article is highlighted in the In This Issue feature, p. 873
Cancer Induces a Stress Ileopathy Depending on β-Adrenergic Receptors and Promoting Dysbiosis that Contributes to Carcinogenesis
Authors: Yonekura Satoru , Terrisse Safae , Alves Costa Silva Carolina , Lafarge Antoine , Iebba Valerio , Ferrere Gladys , Goubet Anne-Gaëlle , Fahrner Jean-Eudes , Lahmar Imran , Ueda Kousuke , Mansouri Gibrail , Pizzato Eugénie , Ly Pierre , Mazzenga Marine , Thelemaque Cassandra , Fidelle Marine , Jaulin Fanny , Cartry Jérôme , Deloger Marc , Aglave Marine , Droin Nathalie , Opolon Paule , Puget Angélique , Mann Fanny , Neunlist Michel , Bessard Anne , Aymeric Laetitia , Matysiak-Budnik Tamara , Bosq Jacques , Hofman Paul , Duong Connie P.M. , Ugolini Sophie , Quiniou Valentin , Berrard Sylvie , Ryffel Bernhard , Kepp Oliver , Kroemer Guido , Routy Bertrand , Lordello Leonardo , Bani Mohamed-Amine , Segata Nicola , Yengej Fjodor Yousef , Clevers Hans , Scoazec Jean-Yves , Pasolli Edoardo , Derosa Lisa , Zitvogel Laurence
Published: December 2021

Gut dysbiosis has been associated with intestinal and extraintestinal malignancies, but whether and how carcinogenesis drives compositional shifts of the microbiome to its own benefit remains an open conundrum. Here, we show that malignant processes can cause ileal mucosa atrophy, with villous microvascular constriction associated with dominance of sympathetic over cholinergic signaling. The rapid onset of tumorigenesis induced a burst of REG3γ release by ileal cells, and transient epithelial barrier permeability that culminated in overt and long-lasting dysbiosis dominated by Gram-positive Clostridium species. Pharmacologic blockade of β-adrenergic receptors or genetic deficiency in Adrb2 gene, vancomycin, or cohousing of tumor bearers with tumor-free littermates prevented cancer-induced ileopathy, eventually slowing tumor growth kinetics. Patients with cancer harbor distinct hallmarks of this stress ileopathy dominated by Clostridium species. Hence, stress ileopathy is a corollary disease of extraintestinal malignancies requiring specific therapies.

Significance:

Whether gut dysbiosis promotes tumorigenesis and how it controls tumor progression remain open questions. We show that 50% of transplantable extraintestinal malignancies triggered a β-adrenergic receptor–dependent ileal mucosa atrophy, associated with increased gut permeability, sustained Clostridium spp.–related dysbiosis, and cancer growth. Vancomycin or propranolol prevented cancer-associated stress ileopathy.

This article is highlighted in the In This Issue feature, p. 873
Paraneoplastic Cerebellar Degeneration with Anti-CV2/CRMP5 Antibodies in Ovarian Cancer: Case Report and Review of the Literature
Authors: Juárez-Vignon Whaley Juan José , Carrera-Muiños Aurelio , Hernandez-Gutierrez Karol Gema , Rodriguez-Cid Jerónimo Rafael , Otero-Cerdeira Maria Elisa , Garcia-Montes Vanessa
Published: December 2021

Paraneoplastic neurological syndromes (PNS) are rare presentations of an underlying oncological disease and more unusual during an oncological disease. They most likely present in small-cell lung carcinomas and thymomas, but present in <1% of the gynecological neoplasms. Acknowledging the pathophysiology is essential for management, explaining its clinical presentation, and future research. We present a patient with an underlying gynecological cancer that during her disease developed a PNS with an unusual autoantibody (anti-CV2/CRMP5) mediating the disease. We report a case of a 62-year-old female diagnosed with ovarian cancer who in the course of her disease developed neurological symptoms associated with cerebellar degeneration. After ruling out differential diagnoses such as metastases, a PNS was suspected and studied, in which anti-CV2/CRMP5 antibodies were positive. With her clinical presentation, radiological features, autoantibody positivity on cerebrospinal fluid, and an underlying oncological disease, cerebellar degeneration was diagnosed. The pathophysiology of PNS is not fully understood; therefore, its diagnosis and management are complex. Diagnosis is based on clinical presentation and specific antibodies associated. Unfortunately, patients have a bad prognosis and diminished quality of life, and therefore a multidisciplinary approach is needed. It is important to mention that the presentation of PNS does not mandatorily appear before the diagnosis of cancer, and multiple cases have been reported in which patients with an underlying oncological disease develop these syndromes. As medical oncologists and neurologists, we must consider and study these syndromes as a possible etiology in cases with an underlying cancer who develop neurological symptoms in the course of their disease.