Hybrid treatment of varied orthodontic appliances for a patient with skeletal class II and temporomandibular joint disorders:A case report and review of literature

BACKGROUND The relation between orthodontic treatment and temporomandibular disorders(TMDs)is under debate;the management of TMD during orthodontic treatment has always been a challenge.If TMD symptoms occur during orthodontic treatment,an immediate pause of orthodontic adjustments is recommended;the treatment can resume when the symptoms are managed and stabilized.CASE SUMMARY This case report presents a patient(26-year-old,female)with angle class I,skeletal class II and TMDs.The treatment was a hybrid of clear aligners,fixed appliances and temporary anchorage devices(TADs).After 3 mo resting and treatment on her TMD,the patient’s TMD symptom alleviated,but her anterior occlusion displayed deep overbite.Therefore,the fixed appliances with TAD were used to correct the anterior deep-bite and level maxillary and mandibular deep curves.After the levelling,the patient showed dual bite with centric relation and maximum intercuspation discrepancy on her occlusion.After careful examination of temporomandibular joints(TMJ)position,the stable bite splint and Invisible Mandibular Advancement appliance were used to reconstruct her occlusion.Eventually,the improved facial appearance and relatively stable occlusion were achieved.The 1-year follow-up records showed there was no obvious change in TMJ morphology,and her occlusion was stable.CONCLUSION TMD screening and monitoring is of great clinical importance in the TMD susceptible patients.Hybrid treatment with clear aligners and fixed appliances and TADs is an effective treatment modality for the complex cases.

World Class Elite Breaststrokers Downward Kick EMG Muscle Activity of the Gluteus Maximus, Quadriceps Femoris, Hamstring, Gastrocnemius Medialis, Rectus Abdominal, and Erector Spinae

This study analyzed the difference between using a downward breaststroke kick and a horizontal breaststroke kick in a sample of world class elite swimmers.We compared average muscle activity of the gluteus maximus,quadriceps femoris(vastus medialis and rectus femoris),hamstring/long head of the biceps femoris,gastrocnemius medialis,rectus abdominal,and erector spinae when using the downward breaststroke kick technique.We find that when this sample of swimmers utilized the downward breaststroke kick,max speed and velocity per stroke increased,measured by 12,788 EMG samples,where the results are highly correlated to duration of the aerodynamic buoyant force in breaststroke kick technique.The increases in performance observed from measuring the world class elite swimmers is highly correlated to the duration of the kick aerodynamic buoyant force.Among this sample of elite swimmers,the longer a swimmer demonstrates a buoyant force breaststroke kick,the lower the time in a 100 breaststroke.

Least Squares One-Class Support Tensor Machine

One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the one-class classification problem for second-order tensor data. Traditional vector-based one-class classification methods such as one-class support vector machine (OCSVM) and least squares one-class support vector machine (LSOCSVM) have limitations when tensor is used as input data, so we propose a new tensor one-class classification method, LSOCSTM, which directly uses tensor as input data. On one hand, using tensor as input data not only enables to classify tensor data, but also for vector data, classifying it after high dimensionalizing it into tensor still improves the classification accuracy and overcomes the over-fitting problem. On the other hand, different from one-class support tensor machine (OCSTM), we use squared loss instead of the original loss function so that we solve a series of linear equations instead of quadratic programming problems. Therefore, we use the distance to the hyperplane as a metric for classification, and the proposed method is more accurate and faster compared to existing methods. The experimental results show the high efficiency of the proposed method compared with several state-of-the-art methods.

Infl uence of altitude and tree class on climate-growth relationships in a larch plantation in subtropical China

Precise quantifi cation of climate-growth relationships can make a major contribution to scientifi c forest management.However,whether diff erences in the response of growth to climate at diff erent altitudes remains unclear.To answer this,264 trees of Larix kaempferi from 88 plots,representing diff erent altitudinal ranges(1000-2100 m)and tree classes were sampled and used to develop tree-ring chronologies.Tree-ring growth(TRG)was either positively(dominant)or negatively(intermediate and suppressed)correlated with climate in diff erent tree classes at diff erent altitudes.TRG was strongly correlated with growing season at low altitudes,but was less sensitive to climate at middle altitudes.It was mainly limited by precipitation and was highly sensitive to climate at low altitudes.Climate-growth relationships at high altitudes were opposite compared to those at low altitudes.TRG of dominant trees was more sensitive to climate change compared to intermediate and suppressed trees.Climate factors(annual temperatures;moisture,the number of frost-free days)had diff erent eff ects on tree-ring growth of diff erent tree classes along altitudinal gradients.It was concluded that the increase in summer temperatures decreased water availability,resulting in a signifi cant decline in growth rates after 2005 at lower altitudes.L.kaempferi is suitable for planting in middle altitudes and dominant trees were the best sampling choice for accurately assessing climate-growth relationships.

Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification

Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.

An Imbalanced Dataset and Class Overlapping Classification Model for Big Data

Most modern technologies,such as social media,smart cities,and the internet of things(IoT),rely on big data.When big data is used in the real-world applications,two data challenges such as class overlap and class imbalance arises.When dealing with large datasets,most traditional classifiers are stuck in the local optimum problem.As a result,it’s necessary to look into new methods for dealing with large data collections.Several solutions have been proposed for overcoming this issue.The rapid growth of the available data threatens to limit the usefulness of many traditional methods.Methods such as oversampling and undersampling have shown great promises in addressing the issues of class imbalance.Among all of these techniques,Synthetic Minority Oversampling TechniquE(SMOTE)has produced the best results by generating synthetic samples for the minority class in creating a balanced dataset.The issue is that their practical applicability is restricted to problems involving tens of thousands or lower instances of each.In this paper,we have proposed a parallel mode method using SMOTE and MapReduce strategy,this distributes the operation of the algorithm among a group of computational nodes for addressing the aforementioned problem.Our proposed solution has been divided into three stages.Thefirst stage involves the process of splitting the data into different blocks using a mapping function,followed by a pre-processing step for each mapping block that employs a hybrid SMOTE algo-rithm for solving the class imbalanced problem.On each map block,a decision tree model would be constructed.Finally,the decision tree blocks would be com-bined for creating a classification model.We have used numerous datasets with up to 4 million instances in our experiments for testing the proposed scheme’s cap-abilities.As a result,the Hybrid SMOTE appears to have good scalability within the framework proposed,and it also cuts down the processing time.

Correlation between the Six-Minute Walk Test and Subjective Functional Class in Patients with Heart Failure

Introduction: The Six-Minute Walk Test (6MWT) is an inexpensive method to objectively evaluate physical capacity or limitation and stratify prognosis in patients with Heart Failure (HF). Since the clinical perception of symptoms may be adapted or compromised, regular evaluation from medical interviews often fails to determine functional classification. This study aimed to assess the correlation between New York Heart Association Functional Class (NYHA-FC) and the distance walked in the 6MWT. Methods: We conducted a cross-sectional observational study that included patients with HF with reduced ejection fraction followed up at an outpatient service of a teaching hospital, from August 2018 to April 2019. Patients in NYHA-FC I, II, or III were included. We compared NYHA-FC subjectively obtained during the consultation with the 6MWT performed after medical consultation, and the correlation between these two parameters was assessed. Results: The study included 70 patients with HF, 41 (58.6%) of whom were female. The mean age was 61.2 ± 12.7 years. The most prevalent etiologies were dilated idiopathic cardiomyopathy (35.7%) followed by ischemic cardiomyopathy (25.7%). The mean ejection fraction was 34.1% ± 9.8%. The average distance walked in the 6MWT by NYHA-FC I patients was 437.8 ± 95.8 meters, NYHA-FC II 360.1 ± 96.4, and NYHA-FC III 248.4 ± 98.3. Functional class measured by the 6MWT was different than that estimated by NYHA-FC in 34 patients (48.6%), 23 (32.9%) for a higher functional class and 11 (15.7%) for a lower one (p = 0.07). Pearson’s correlation coefficient between NYHA-FC and the 6MWT was -0.55. Conclusion: There was a moderate correlation between the subjective NYHA-FC and the 6MWT. The 6MWT revealed a different classification from NYHA-FC in almost half of the patients. Among those who presented discrepancies between methods, 6MWT reclassification towards a higher functional class was more common.

Endoscopic sleeve gastroplasty in class Ⅲ obesity:Efficacy, safety, and durability outcomes in 404 consecutive patients

BACKGROUND Endoscopic sleeve gastroplasty(ESG) is an effective therapy for class Ⅰ-Ⅱ obesity, but there are knowledge gaps in the published literature about its implementation in patients with class Ⅲ obesity [body mass index(BMI) ≥ 40 kg/m2].AIM To evaluate the safety, clinical efficacy, and durability of ESG in adults with class Ⅲ obesity.METHODS This was a retrospective cohort study that used prospectively collected data on adults with BMI ≥ 40 kg/m2who underwent ESG and longitudinal lifestyle counseling at two centers with expertise in endobariatric therapies from May 2018-March 2022. The primary outcome was total body weight loss(TBWL) at 12 mo. Secondary outcomes included changes in TBWL, excess weight loss(EWL) and BMI at various time points up to 36 mo, clinical responder rates at 12 and 24 mo, and comorbidity improvement. Safety outcomes were reported through the study duration. One-way ANOVA test was performed with multiple Tukey pairwise comparisons for TBWL, EWL, and BMI over the study duration.RESULTS 404 consecutive patients(78.5% female, mean age 42.9 years, mean BMI 44.8 ± 4.7 kg/m2) were enrolled. ESGs were performed using an average of 7 sutures, over 42 ± 9 min, and with 100% technical success. TBWL was 20.9 ± 6.2% at 12 mo, 20.5 ± 6.9% at 24 mo, and 20.3 ± 9.5% at 36 mo. EWL was 49.6 ± 15.1% at 12 mo, 49.4 ± 16.7% at 24 mo, and 47.1 ± 23.5% at 36 mo. There was no difference in TBWL at 12, 15, 24, and 36 mo from ESG. TBWL exceeding 10%, 15%, and 20% was achieved by 96.7%, 87.4%, and 55.6% of the cohort at 12 mo, respectively. Of the cohort with the relevant comorbidity at time of ESG, 66.1% had improvement in hypertension, 61.7% had improvement in type Ⅱ diabetes, and 45.1% had improvement in hyperlipidemia over study duration. There was one instance of dehydration requiring hospitalization(0.2% serious adverse event rate).CONCLUSION When combined with longitudinal nutritional support, ESG induces effective and durable weight loss in adults with class Ⅲ obesity, with improvement in comorbidities and an acceptable safety profile.

The Driving Strategy of Online Ideological and Political Class in Universities Based on SWOT Analysis

The epidemic provided practical opportunities for online classes and also tested its drawbacks. To analyze the internal strengths (S) and weaknesses (W) as well as the external opportunities (O) and threats (T) of the online ideological and political class with SWOT theory, based on which to explore targeted solutions, is a “symptomatic” approach to improving the quality of the online ideological and political class. The analysis results are as follows: S includes “not limited by time and space”, “comprehensive teaching monitoring data”, “diverse and innovative interactive forms”, and “abundant high-quality teaching resources”. W refers to “increased difficulty in classroom control”, “teachers’ psychological pressure caused by online teaching”, and “limited by hardware conditions”. O means the opportunity provided by the modernization of ideological and political education and epidemics. T indicates external threats, such as limitations from the network and the severe impact of wrong values on the internet. The driving strategy for above case could be “upgrading of online teaching platforms”, “building a teaching team skilled in information technology teaching”, “improving hardware facilities” and “joining forces to confront value crisis”.

Higher Education Admission Policy and Social Class of Ethnic Minority:The Case of Guangxi Zhuang Autonomous Region,China

Gaokao has been considered as the gatekeeper of intergenerational class reproduction in today’s slowing social mobility in China.In order to bolster the fairness of Gaokao,the Policy of Bonus Scores for Ethnic Minority Candidates(PBSEMC)has been issued,whose,however,effectiveness in promoting upward social class has not been adequately empirically explored.The difference-in-differences(DID)analysis based on cross-sectional data,which is distinct from the traditional DID analysis paradigm,was implemented to assess the Reform of PBSEMC in the Guangxi Zhuang Autonomous Region in 1988.The results showed that PBSEMC had a significant and positive impact on the social classes of ethnic minorities,and gender,residence type and occupation type were also relevant to social class,aligning with the previous literature.The PBSEMC is effective in improving the social classes of ethnic minorities.However,the PBSEMC policy should be made more precise,in order to benefit the candidates who are in genuine need of assistance.

Advances in Diagnosis and Treatment of Lupus Nephritis(Class V)

Membranous lupus nephritis(MLN),class V,is a distinct LN characterized by immune complex deposition on subepithelial kidney biopsy.MLN is often associated with nephrotic syndrome.The histology of MLN is very similar to idiopathic(primary)membranous nephropathy(pMN).However,MLN usually has abundant mesa-glomerular deposits absent in primary membranous nephropathy.The clinical manifestations,management,and prognosis of MLN differ from other types of LN(type III,IV,or mixed type III/IV+V).Although immunosuppressive therapy is often necessary for MLN,the optimal treatment regimen is yet to be determined.This review summarizes the progress in the diagnosis and treatment of MLN and discusses the selection of immunosuppressants for MLN.

Building Detection and Counting in Convoluted Areas Using Multiclass Datasets with Unmanned Aerial Vehicles (UAVs) Imagery

This paper studies the effect of breaking single-class building data into multi-class building data for semantic segmentation under end-to-end architecture such as UNet, UNet++, DeepLabV3, and DeepLabv3+. Although, the already existing semantic segmentation methods for building detection work on the imagery of developed world, where the buildings are highly structured and there is a clearly distinguishable space present between the building instances, the same methods do not work as effectively on the developing world where there is often no clear differentiable spaces between instances of building thus reducing the number of detected instances. Hence as a noble approach, we have added building contours as new class along with building segmentation data, and detected the building contours and the inner building regions, hence giving the precise number of buildings existing in the input imagery especially in the convoluted areas where the boundary between the buildings are often hard to determine even for human eyes. Breaking down the building data into multi-class data increased the building detection precision and recall. This is useful in building detection where building instances are convoluted and are difficult for bare instance segmentation to detect all the instances.

PAD Class教学模式对英语专业学生写作焦虑影响的实证研究

本研究以英语专业大一120名学生为实验对象,进行了为期16周的实证研究,以探讨PAD class教学模式对英语专业学生写作焦虑的影响。基于《二语写作焦虑量表》调查问卷结果表明:英语专业学生总体焦虑处于中等焦虑水平,PAD Class能有效降低学生的写作总体焦虑,其中课堂焦虑、构思焦虑、自信焦虑明显降低,但回避焦虑没有达到显著水平;PAD Class模式对高中低不同写作水平学生的写作焦虑都有影响,对中、低水平学生的写作焦虑缓解比对高水平学生的影响效果更明显。

Visualization for Explanation of Deep Learning-Based Defect Detection Model Using Class Activation Map

Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however,the predicted results have uncertainty in providing accurate informa-tion to users because of the“black box”problem in the deep learning model.Therefore,this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification.The visual repre-sentative gradient-weights class activation mapping(Grad-CAM)method is adopted to provide visually explainable information.A visualizing evaluation index is proposed to quantitatively analyze visual representations;this index reflects a rough estimate of the concordance rate between the visualized heat map and intended defects.In addition,an ablation study,adopting three-branch combinations with the VGG16,is implemented to identify perfor-mance variations by visualizing predicted results.Experiments reveal that the proposed model,combined with hybrid pooling,batch normalization,and multi-attention modules,achieves the best performance with an accuracy of 97.77%,corresponding to an improvement of 2.49%compared with the baseline model.Consequently,this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models.

Effects of After-class Thesis Reading on English Major Post-graduates' Reading Ability and Autonomous Learning

Original thesis-reading capability is of great importance to English major post-graduates' research ability. This paper focuses on the influence of after-class thesis reading on postgraduates' reading and writing ability. By means of both doing an interview and making field-notes, data analysis provides convincing evidences to illustrate research questions. The interview covers the selection of reading materials, strategies of coping with new expressions and reading habits, which are key factors in influence of postgraduates' reading efficiency and in turn will influence their reading and writing capability. The result is helpful for English major postgraduates to cultivate their autonomous learning capability and improve their thesis- reading efficiency,thus developing their research ability.

基于CLASS促进幼儿教师提升师幼互动能力的策略

师幼互动是学前教育课程实施的主要过程,也是学前儿童在幼儿园一日活动中的生活体验、学习体验和情感体验的重要载体。师幼互动的质量对学前教育的质量起决定性作用。CLASS(课堂评价评分系统)不仅可以用来评价课堂师幼互动的质量,还可以用于提升教师师幼互动的能力。基于CLASS,文章提出了一系列提升教师师幼互动能力的相关策略。

On Students' Self-Evaluation and Class Performance——An Empirical Study in College English Class

This paper aims to demonstrate whether students'self-evaluations influence their class performance.A questionnaire is designed and data is collected from 30 sophomore students in English class.The data analysis shows that students who have positive self-evaluation perform better and get higher class performance scores than students who have negative self-evaluation.

ASARA,a prediction model based on Child-Pugh class in hepatocellular carcinoma patients undergoing transarterial chemoembolization

Background:Due to the high heterogeneity among hepatocellular carcinoma(HCC)patients receiving transarterial chemoembolization(TACE),the prognosis of patients varies significantly.The decisionmaking on the initiation and/or repetition of TACE under different liver functions is a matter of concern in clinical practice.Thus,we aimed to develop a prediction model for TACE candidates using risk stratification based on varied liver function.Methods:A total of 222 unresectable HCC patients who underwent TACE as their only treatment were included in this study.Cox proportional hazards regression was performed to select the independent risk factors and establish a predictive model for the overall survival(OS).The model was validated in patients with different Child-Pugh class and compared to previous TACE scoring systems.Results:The five independent risk factors,including alpha-fetoprotein(AFP)level,maximal tumor size,the increase of albumin-bilirubin(ALBI)grade score,tumor response,and the increase of aspartate aminotransferase(AST),were used to build a prognostic model(ASARA).In the training and validation cohorts,the OS of patients with ASARA score≤2 was significantly higher than that of patients with ASARA score>2(P<0.001,P=0.006,respectively).The ASARA model and its modified version“AS(ARA)”can effectively distinguish the OS(P<0.001,P=0.004)between patients with Child-Pugh class A and B,and the C-index was 0.687 and 0.706,respectively.For repeated TACE,the ASARA model was superior to Assessment for Retreatment with TACE(ART)and ALBI grade,maximal tumor size,AFP,and tumor response(ASAR)among Child-Pugh class A patients.For the first TACE,the performance of AS(ARA)was better than that of modified hepatoma arterial-embolization prognostic(mHAP),mHAP3,and ASA(R)models among Child-Pugh class B patients.Conclusions:The ASARA scoring system is valuable in the decision-making of TACE repetition for HCC patients,especially Child-Pugh class A patients.The modified AS(ARA)can be used to screen the ideal candidate for TACE initiation in Child-Pugh class B patients with poor liver function.

实现一流技能 领跑世界经济——英国白皮书World Class Skills:Implementing the Leitch Review of Skillsin England

英国新的创新、大学与技能部于2007年发布了白皮书World Class Skills:Implementing the Leitch Review of Skills in England,从世界一流技能的目标、支持个人改善技能、增加雇主的发言权、创设新型的技术发展伙伴关系和重视年轻人工作和生活所需技能的培训等五个方面,阐述了英国提高全社会劳动力技能的计划及措施,以确保英国在2020年成为世界经济的领跑者。

A review of addressing class noise problems of remote sensing classification

The development of image classification is one of the most important research topics in remote sensing. The prediction accuracy depends not only on the appropriate choice of the machine learning method but also on the quality of the training datasets. However, real-world data is not perfect and often suffers from noise. This paper gives an overview of noise filtering methods. Firstly, the types of noise and the consequences of class noise on machine learning are presented. Secondly, class noise handling methods at both the data level and the algorithm level are introduced. Then ensemble-based class noise handling methods including class noise removal, correction, and noise robust ensemble learners are presented. Finally, a summary of existing data-cleaning techniques is given.