Chinese Classification Research Articles

The Effect of China-U.S. Trade Dispute on Chinese Stock Market: New Evidence from the Event Study Analysis

In 2018, the China-U.S. trade dispute started, which brings heterogeneous impacts on the global economy. The purpose of this paper is to examine the effects of tariffs targeting Chinese exporting commodities imposed by the U.S. on the Chinese stock market by utilizing the event study analysis. 10 industries' stock returns between Jan. 3rd , 2017, and Apr. 3rd , 2020 were selected as the research objectives from the WIND database, according to the Chinese Shen Wan's classification standard. Results based on event study analysis show that: First, the China-U.S. trade dispute causes significant fluctuations to Chinese stock returns. Second, the impacts of the trade dispute are mainly negative, showing by the negative cumulative average abnormal returns in the export-oriented sectors when they are encountered with new tariffs imposed by the United States. However, the effects can also be positive because of the various situations of targeted industries, and the defensive measures taken by China. Third, the trade dispute also affects investors' views on the macro economy, in which the impact on the real economy can be transferred to other non-export-oriented industries, such as the banking sector. This study provides empirical evidence for China's policymakers to take measures in strengthening the independence of innovation, protecting intellectual property rights. Investors also need to equip themselves with more financial knowledge.

Traditional Chinese medicine classification of knee osteoarthritis with proteomics analysis

Osteoarthritis (OA) is a heterogeneous disease caused by the pathology of the synovial joint. About 10-12% of adults have symptomatic OA. In this study, the proteomics method was used to find differentially expressed proteins and to explore the material basis of traditional Chinese medicine (TCM) classification in knee OA patients. Fifteen patients of the treatment group with knee OA of three different classifications were divided into three groups according to the dialectical classification of TCM: kidney deficiency type (5 cases), Yang deficiency type (5 cases), blood stasis type (5 cases). Also, five patients with traumatic synovitis were enrolled as the control group. The joint fluids were drawn separately. The joint fluids before treatment were Aa, Ba, and Ca groups. After one week of medication, the three joint fluid types were drawn again, and they were Ab group, Bb, and Cb groups. Liquid from the control group was named group D. Quantitative analysis of protein expression was conducted to find out the differently expressed proteins. The treatment group was treated with prescriptions for syndromes according to the TCM classification. The proteomics analysis identified 251 differentially expressed protein groups, and the number of groups with quantitative information for all seven channels was 246. The three treatment groups and the control group had 32 differential proteins (P<0.01), of which nine might be differential proteins between OA and traumatic synovitis. Histone H4, histone H2A, S100 calbindin A8, fibrinogen γ, fiber protein α, cDNA (FLJ92148), C4b binding protein, and partial transketolase variants were down-regulation, and basement membrane glycan was up-regulation in the treatment group compared with the control group (P<0.01). HIST1H2BC and myeloperoxidase levels in the Aa group were less than those in the D and Ab groups. The serum amyloid P and apolipoprotein CI variants in the Ba group were less than those in the D and Bb groups. Histone H2A and C-reactive protein, Fibrinogen α levels in the Ca group were less than those in the D and Cb groups. Nine proteins are closely related to OA of different TCM classification.

Research on sentiment classification of Chinese short text

Sentiment classification can be widely used in various fields because of the value it has gained, and has received extensive attention in recent years. No matter the traditional methods based on language rules, machine statistical learning, or the current popular deep learning have achieved great success in this respect, but up to now, because of the particularity of Chinese, there are still few methods for Chinese text sentiment classification. Moreover, the traditional method is time-consuming and labor-consuming, with poor versatility. The neural network method saves resources and is generally applicable, but the application of language knowledge is not sufficient. For this reason, we propose a method combining traditional method with neural network, which combines sentiment lexicon, attention mechanism and stacked BILSTM. Word vectors are generated using word2vec, and the context semantics are captured multiple times by stacked BILSTM to get richer semantic information, for sentiment classification, sentiment words often have more influence on the result of classification, so we use sentiment lexicon which contains rich sentiment word to monitor the attention mechanism, and make the attention model pay more attention to sentiment words. Combining the advantages of traditional methods and neural networks, we use the proposed method to do the sentiment binary classification experiments on two data sets. The experimental results show that our proposed method is superior to the previous method.

COVID-19 Epidemic Situation, the Outbreak Peak and Cycle Centered on China's No-Drug Control Actions: A Modeling Study

Background: New characteristics have created significant challenges in non-drug regulation on the 2019 Corona Virus Disease (COVID-19) outbreak while there are few related research articles in recent months. In view of these recent developments, a more comprehensive analysis on the role of non-drug control measures (NDC) will provide a further guide for the work on disease prevention faced with a range of changes. Methods: This paper measures and divides the intensity of the NDC into eleven tiers according to the World Health Organization (WHO) and Chinese emergency classification criteria. The paper then proposes a new model for control-measures-susceptible-exposed-infected-removed-dead (CM-SEIRD), based upon the standard susceptible-exposed-infected-removed (SEIR) model, which parameterizing the new characteristics of COVID-19 infection and NDC. The start time and intensity of implementation of the NDC were taken as variables to obtain the special impact of NDC on the COVID-19 epidemic. The theoretical study on variable discretization and continuity was carried out for the Wuhan model system. Findings: NDC can efficiently minimize deaths, minimize infection risk, and delay the outbreak peak for 1-2 months. The highest outbreak time discussed in this study was around 16-23 days. If NDC can be carried out within the time threshold and intensity level, more than 10% of the total death toll will be reduced, more than 8% of the peak infected number will be reduced, and more than 90% of the population will be able to prevent infection. In contrast to the NDC starting time, the intensity of NDC impacts the outbreak more directly and has a good impact on the overall number of people exposed, sick and dead from the beginning to the end. This study also found that it would take 23 days to clear new cases in the blocked areas based on the strength of Chinese control measures. Interpretation: Despite great pressure due to the new characteristics and situation of the COVID-19 outbreak, at this point, NDC is still the most effective prevention and control step. Different countries and regions need to implement strict NDCs as quickly as possible to contain the outbreak and to promote development and economic growth as soon as possible.Funding Statement: Guangxi University.Declaration of Interests: All authors declare no competing interests.

Research on the Development Direction of Smart Substations Under the Background of Power Internet of things Technology

As a key support for smart grids, the smart substation has formed a mature technical solutions after long-term development. In recent years, the internet of things technology has been applied in the whole aspects of power system transmission and distribution. Based on the extensive data collection and fusion, it has improved the state awareness, intelligent decision-making, and asset management capabilities of the power system. With the application of the internet of things technology, the development direction and technical architecture changes of smart substations are important topics that need to be considered at present. Based on the analysis of the current substation development route, this paper studies the applicability of electric power internet of things technology in smart substations, and proposes a smart substation technology solution that combines traditional technology routes and internet of things technologies, wishing to provide some reference for the research of smart substations. Chinese Library Classification Number: TM715

Potential Application of Anti-Müllerian Hormone in Polycystic Ovary Syndrome According to Chinese Classification Criteria: A Retrospective Analysis

Objective: Anti-Mullerian hormone (AMH) expression is elevated in patients with polycystic ovary syndrome (PCOS), however, its clinical significance is not clear. Owing to the strong correlation between AMH and polycystic ovarian morphology (PCOM), some studies believe that AMH alone can be used to diagnose PCOS. The aim of this study was to explore whether AMH can be used to diagnose PCOS and to differentiate the various PCOS subtypes. Methods: This was a retrospective study of 503 patients with PCOS. Patients were divided into eight subtypes based on the presence/absence of hyperandrogenemia (HA), insulin resistance (IR), or obesity (OB). The expression characteristics of AMH in each subtype were analyzed. Due to the small number of patients with subtypes 7 and 8, only patients with subtypes 1–6 were included in the analysis. Results: AMH showed a good positive correlation with PCOM (P = 0.000) and negative correlations with OB (P = 0.000) and IR (P = 0.003). The free testosterone index showed no correlation with AMH (P = 0.803). The percentages of patients with each subtype (excluding subtypes 7–8) and their respective AMH levels were as follows: Type 1 (HA + NIR + OB) 4.77% and 9.12 ng/mL; Type 2 (HA + IR + NOB) 20.68% and 10.34 ng/mL; Type 3 (HA + NIR + NOB) 23.66% and 9.47 ng/mL; Type 4 (HA + IR + OB) 30.82% and 8.32 ng/mL; Type 5 (NHA + NIR + NOB) 11.73% and 10.0 ng/mL; and Type 6 (NHA + IR + NOB) 6.16% and 9.76 ng/mL. The diagnostic rates of AMH (>8.09 ng/mL) and ultrasound for PCOM were 60.10% and 85.60%, respectively, suggesting that AMH did not completely predict PCOM. Conclusions: High AMH levels can be used to evaluate the incidence trend of PCOS. However, due to clinical heterogeneity, accurately evaluating the severity of PCOS and identifying the subtype of PCOS in Chinese patients are difficult. Individualized treatment should be administered based on accurate clinical subtypes and other clinical characteristics.

Simulation Analysis of Installation Process of Long stern Shaft of Large Ship

Due to the large journal and long length, the installation of the stern shaft on the large ship is very difficult. In view of this situation, the numerical method based on three bending moments is used to analyze the number and arrangement position of temporary support during the installation of the stern shaft, to solve the change of the deflection and stress of the stern shaft and the reaction force of temporary support, and to obtain the influence law of the number and arrangement position of temporary support on the deflection and stress of the stern shaft. The calculation results show that when the stern shaft is pushed into the rear end face of the fore stern bearing, the deflection of the front end of the stern shaft is too large to pass the fore stern shaft tube bearing smoothly, so that a new technology of the guide shaft fixing at the front of the stern shaft was adopted, the structure, size and hoisting mode of the guide shaft were optimized, and the smooth installation of the long stern shaft was realized. The research results can provide a theoretical reference for the installation analysis and calculation of stern shaft section of large ships. Chinese library classification no. : U662.2

An improved Chinese text multi-label classification method based on CNN

Text multi-label classification technology can accurately and quickly classify text information into related categories or topics, and help people quickly locate the required content in massive information resources, which is of great significance in application. As the traditional classification algorithm is faced with the problems of low classification accuracy due to the low correlation of data labels, unbalanced label data and few short text feature words, this paper firstly performs hierarchical pre-processing on label data to transform multi-label classification into hierarchical text multi-classification. At the same time, an improved multi-label classification algorithm Multi-label Convolutional Neural Networks (ML-CNN) is proposed. Based on the TensorFlow framework, a CNN model is designed and different training models are constructed for each level of label classification. According to the number of classification levels, the output of the upper level label is stitched to the original input tail as the next level of input. Experiments on the description information of 500,000 Chinese products with labels, show that the improved algorithm will significantly improve the classification accuracy and the accuracy of each level can reach more than 88%, which proves the feasibility and effectiveness of the algorithm.

A benchmark dataset and case study for Chinese medical question intent classification

BackgroundTo provide satisfying answers, medical QA system has to understand the intentions of the users’ questions precisely. For medical intent classification, it requires high-quality datasets to train a deep-learning approach in a supervised way. Currently, there is no public dataset for Chinese medical intent classification, and the datasets of other fields are not applicable to the medical QA system. To solve this problem, we construct a Chinese medical intent dataset (CMID) using the questions from medical QA websites. On this basis, we compare four intent classification models on CMID using a case study.MethodsThe questions in CMID are obtained from several medical QA websites. The intent annotation standard is developed by the medical experts, which includes four types and 36 subtypes of users’ intents. Besides the intent label, CMID also provides two types of additional information, including word segmentation and named entity. We use the crowdsourcing way to annotate the intent information for each Chinese medical question. Word segmentation and named entities are obtained using the Jieba and a well-trained Lattice-LSTM model. We loaded a Chinese medical dictionary consisting of 530,000 for word segmentation to obtain a more accurate result. We also select four popular deep learning-based models and compare their performances of intent classification on CMID.ResultsThe final CMID contains 12,000 Chinese medical questions and is organized in JSON format. Each question is labeled the intention, word segmentation, and named entity information. The information about question length, number of entities, and are also detailed analyzed. Among Fast Text, TextCNN, TextRNN, and TextGCN, Fast Text and TextCNN models have achieved the best results in four types and 36 subtypes intent classification, respectively.ConclusionsIn this work, we provide a dataset for Chinese medical intent classification, which can be used in medical QA and related fields. We performed an intent classification task on the CMID. In addition, we also did some analysis on the content of the dataset.

Maternal preconception body mass index and time-to-pregnancy in Shanghai Women, China

ABSTRACT Previous studies suggested an association between female fertility and body size, but most of these studies were from Western countries and focused mainly on obesity. This study investigated the association between preconception body mass index (BMI) and time to pregnancy (TTP) in women planning to conceive from Shanghai, China. A total of 1,182 women aged 24–46 years were recruited from the Shanghai Birth Cohort between 2013 and 2015 and were followed up for 12 months. Preconception BMI was categorized as underweight, normal weight, and overweight/obesity according to the Chinese classification of BMI. Fecundability (FOR) and infertility (IOR) odds ratios were estimated using Cox (n = 1,092) and Logistic (n = 820) regression models, respectively. We found no differences in fecundability between underweight and overweight/obese women and normal-weight women. Furthermore, underweight and overweight/obese women did not have a higher risk of infertility compared with normal-weight women. Our findings suggest that non-optimal preconception BMI does not appear to influence female fecundability and infertility in Chinese women. These results should be interpreted with caution as they may be applicable only to women with demographic and anthropometric characteristics similar to our study population. Our findings need to be confirmed in other populations.

Fine-grained emotion classification of Chinese microblogs based on graph convolution networks

Microblogs are widely used to express people’s opinions and feelings in daily life. Sentiment analysis (SA) can timely detect personal sentiment polarities through analyzing text. Deep learning approaches have been broadly used in SA but still have not fully exploited syntax information. In this paper, we propose a syntax-based graph convolution network (GCN) model to enhance the understanding of diverse grammatical structures of Chinese microblogs. In addition, a pooling method based on percentile is proposed to improve the accuracy of the model. In experiments, for Chinese microblogs emotion classification categories including happiness, sadness, like, anger, disgust, fear, and surprise, the F-measure of our model reaches 82.32% and exceeds the state-of-the-art algorithm by 5.90%. The experimental results show that our model can effectively utilize the information of dependency parsing to improve the performance of emotion detection. What is more, we annotate a new dataset for Chinese emotion classification, which is open to other researchers.

Application of Reyanning Mixture in evaluating combining disease with syndrome of human coronavirus pneumonia with pestilence attacking lung syndrome

In the previous research, our laboratory established a mouse model combining disease with syndrome of human coronavi-rus pneumonia with pestilence attacking the lung syndrome, based on the national traditional Chinese medicine clinical classification of Novel Coronavirus Infected Pneumonia Diagnosis and Treatment Plan. In this study, a mouse model combining disease with syndrome of human coronavirus pneumonia with pestilence attacking the lung syndrome was used to evaluate the effectiveness of Reyanning Mixture to provide animal experimental support for clinical application. Mice were divided into normal group, 229 E infection group, cold-dampness group, cold-dampness+229 E infection group(the model group), Reyanning high and low dose groups. The cold-dampness group, cold-dampness+229 E infection group, two Reyanning groups were given cold and damp stimulation for 7 days. On the 5 th day, the 229 E infection group, cold-dampness+229 E infection group, and two Reyanning groups were infected with HCoV-229 E virus. Reyanning was administered for 3 days, starting from the day of infection. Blood was collected on the 4 th day and the lung tissue was dissected to calculate the lung index and inhibition rate; flow cytometry was used to detect the percentage of T and B lymphocytes in peripheral blood; RT-PCR was used to detect the nucleic acid virus load in lung tissue; ELISA was used to detect motilin and gastrin in serum, and inflammatory factors TNF-α, IFN-γ, IL-6, IL-10 in lung tissue proteins. Reyanning Mixture could reduce the lung index(P<0.01) of coronavirus pneumonia mice with pestilence attacking the lung; it could significantly increase the percentage of CD8~+ T lymphocytes and CD4~+ T lymphocytes in peripheral blood of model mice(P<0.05, P<0.01). The low dose of Reyanning could effectively increase the percentage of total B lymphocytes(P<0.05), reduce virus load in lung tissue of model mice(P<0.01), reduce the levels of TNF-α, IFN-γ, IL-6, IL-10 in the lung tissue of model mice(P<0.01), reduce the content of motilin in the serum of model mice(P<0.01). Reyanning Mixture convey a better effect in treating coronavirus pneumonia mice with pestilence attacking the lung. It manifested obvious effects in improving lung lesions, enhancing the gastrointestinal function of mice, improving the autoimmune function of mice, and reducing the expression of inflammatory factors in vivo, which could provide evidences for clinical research.

Distinguish different Chinese medicine types of metabolic syndrome by combining body mass index and uric acid.

To provide clinical support for traditional Chinese medicine classification of metabolic syndrome patients. The retrospective study was conducted at Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, and Sichuan Provincial Hospital of Chinese Medicine, Chengdu, China, and comprised data of metabolic syndrome patients having visited the two hospitals between January and December 2017. The patients were divided into 2 groups based on the different types of Chinese medicine. Group1 had Qi-Yin deficiency and group 2 had phlegm-blood stasis. Various laboratory tests related to metabolic syndrome were conducted, analysed and compared in both the groups. Of the 263 patients, 173(66%) were in group 1 and 90(34%) were in group 2. Body mass index and blood uric acid level had statistical significance between the groups (p<05) .The sensitivity was over 90% and the accuracy was 60%. Body mass index and blood uric acid level were found to be the two independent factors to distinguish between different types of metabolic syndrome.

Psychiatric and neuropsychiatric presentations associated with severe coronavirus infections: a systematic review and meta-analysis with comparison to the COVID-19 pandemic

SummaryBackgroundBefore the COVID-19 pandemic, coronaviruses caused two noteworthy outbreaks: severe acute respiratory syndrome (SARS), starting in 2002, and Middle East respiratory syndrome (MERS), starting in 2012. We aimed to assess the psychiatric and neuropsychiatric presentations of SARS, MERS, and COVID-19.MethodsIn this systematic review and meta-analysis, MEDLINE, Embase, PsycINFO, and the Cumulative Index to Nursing and Allied Health Literature databases (from their inception until March 18, 2020), and medRxiv, bioRxiv, and PsyArXiv (between Jan 1, 2020, and April 10, 2020) were searched by two independent researchers for all English-language studies or preprints reporting data on the psychiatric and neuropsychiatric presentations of individuals with suspected or laboratory-confirmed coronavirus infection (SARS coronavirus, MERS coronavirus, or SARS coronavirus 2). We excluded studies limited to neurological complications without specified neuropsychiatric presentations and those investigating the indirect effects of coronavirus infections on the mental health of people who are not infected, such as those mediated through physical distancing measures such as self-isolation or quarantine. Outcomes were psychiatric signs or symptoms; symptom severity; diagnoses based on ICD-10, DSM-IV, or the Chinese Classification of Mental Disorders (third edition) or psychometric scales; quality of life; and employment. Both the systematic review and the meta-analysis stratified outcomes across illness stages (acute vs post-illness) for SARS and MERS. We used a random-effects model for the meta-analysis, and the meta-analytical effect size was prevalence for relevant outcomes, I2 statistics, and assessment of study quality.Findings1963 studies and 87 preprints were identified by the systematic search, of which 65 peer-reviewed studies and seven preprints met inclusion criteria. The number of coronavirus cases of the included studies was 3559, ranging from 1 to 997, and the mean age of participants in studies ranged from 12·2 years (SD 4·1) to 68·0 years (single case report). Studies were from China, Hong Kong, South Korea, Canada, Saudi Arabia, France, Japan, Singapore, the UK, and the USA. Follow-up time for the post-illness studies varied between 60 days and 12 years. The systematic review revealed that during the acute illness, common symptoms among patients admitted to hospital for SARS or MERS included confusion (36 [27·9%; 95% CI 20·5–36·0] of 129 patients), depressed mood (42 [32·6%; 24·7–40·9] of 129), anxiety (46 [35·7%; 27·6–44·2] of 129), impaired memory (44 [34·1%; 26·2–42·5] of 129), and insomnia (54 [41·9%; 22·5–50·5] of 129). Steroid-induced mania and psychosis were reported in 13 (0·7%) of 1744 patients with SARS in the acute stage in one study. In the post-illness stage, depressed mood (35 [10·5%; 95% CI 7·5–14·1] of 332 patients), insomnia (34 [12·1%; 8·6–16·3] of 280), anxiety (21 [12·3%; 7·7–17·7] of 171), irritability (28 [12·8%; 8·7–17·6] of 218), memory impairment (44 [18·9%; 14·1–24·2] of 233), fatigue (61 [19·3%; 15·1–23·9] of 316), and in one study traumatic memories (55 [30·4%; 23·9–37·3] of 181) and sleep disorder (14 [100·0%; 88·0–100·0] of 14) were frequently reported. The meta-analysis indicated that in the post-illness stage the point prevalence of post-traumatic stress disorder was 32·2% (95% CI 23·7–42·0; 121 of 402 cases from four studies), that of depression was 14·9% (12·1–18·2; 77 of 517 cases from five studies), and that of anxiety disorders was 14·8% (11·1–19·4; 42 of 284 cases from three studies). 446 (76·9%; 95% CI 68·1–84·6) of 580 patients from six studies had returned to work at a mean follow-up time of 35·3 months (SD 40·1). When data for patients with COVID-19 were examined (including preprint data), there was evidence for delirium (confusion in 26 [65%] of 40 intensive care unit patients and agitation in 40 [69%] of 58 intensive care unit patients in one study, and altered consciousness in 17 [21%] of 82 patients who subsequently died in another study). At discharge, 15 (33%) of 45 patients with COVID-19 who were assessed had a dysexecutive syndrome in one study. At the time of writing, there were two reports of hypoxic encephalopathy and one report of encephalitis. 68 (94%) of the 72 studies were of either low or medium quality.InterpretationIf infection with SARS-CoV-2 follows a similar course to that with SARS-CoV or MERS-CoV, most patients should recover without experiencing mental illness. SARS-CoV-2 might cause delirium in a significant proportion of patients in the acute stage. Clinicians should be aware of the possibility of depression, anxiety, fatigue, post-traumatic stress disorder, and rarer neuropsychiatric syndromes in the longer term.FundingWellcome Trust, UK National Institute for Health Research (NIHR), UK Medical Research Council, NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust and University College London.

Automatic traditional Chinese painting classification: A benchmarking analysis

SummaryIn the recent years, there is a growing trend toward digitization of cultural heritage for better accessibility and preservation. For instance, the development of image processing techniques in traditional Chinese painting (TCP) has begun to attract researchers' attention in the computer vision field. TCP is one of the representative of Chinese traditional arts. Evidenced by the successes of development in image processing techniques in various applications, this article aim to apply the deep learning approach on TCP for several purposes, which include automatic establishment of unified image library, facilitating update‐to‐date data in the database, reduction of cost required for image classification and retrieval. First, a unified database is established, that consists of more than a thousand of images from six major TCP themes. Then, several deep learning algorithms that are based on mathematical models are applied to examine the classification performance. In addition, the salient regions that denote significant features are identified, by adopting the instance segmentation technique. As a result, the modified pretrained neural network is capable to achieve 99.66% recognition accuracy. Qualitative results are also presented to demonstrate the effectiveness of the proposed method. We also note that this is the first work that performs multiclass classification on six categories in this domain. Furthermore, a 10‐class classification result of 96% is obtained when performing on one of the painting types, namely, ghost‐and‐god.

Treatment Rate of Schizophrenia in China: a Meta-Analysis of Epidemiological Studies.

Effective and prompt medication treatment is essential for schizophrenia patients to alleviate psychotic symptoms and improve prognosis. The treatment rate of schizophrenia usually varies greatly across studies. This is a meta-analysis that examined the overall treatment rate of schizophrenia in China. Both international (PubMed, EMBASE, PsycINFO, Web of Science) and Chinese (CNKI, WanFang and Sinomed) databases were searched. The random effects model was used to analyze the data. Fifteen studies with 1,219,472 patients were included. This meta-analysis found that 73% (95%CI: 66%, 80%) of schizophrenia patients received treatment in any type of medical institutions, while 31.0% (95%CI = 21.0%, 41.0%) received treatment in psychiatric institutions. Studies conducted in middle region of China (76.8% for any type of medical institutions; 42.6% for psychiatric institutions) and those published before 2007 (74.6% for any type of medical institutions; 31.3% for psychiatric institutions) reported significantly higher treatment rates. Studies using combined diagnostic instruments had a higher treatment rate of schizophrenia (72.0%) in any type of medical institutions, while using the Chinese classification of Mental Disorders (47.7%) and including both rural and urban areas (35.7%) had higher treatment rates of schizophrenia in psychiatric institutions. The treatment rate of schizophrenia was relatively high in China, but most treatments were delivered in non-psychiatric medical institutions. Greater efforts should be made to improve the provision of accessible mental health services for schizophrenia patients in this part of the world.

Classification of Invariant Algebraic Curves and Nonexistence of Algebraic Limit Cycles in Quadratic Systems from Family (I) of the Chinese Classification

We give the complete classification of irreducible invariant algebraic curves in quadratic systems from family [Formula: see text] of the Chinese classification, that is, of differential system [Formula: see text] with [Formula: see text]. In addition, we provide a complete and correct proof of the nonexistence of algebraic limit cycles for these equations.

Influence of body mass index on the clinicopathological features of 13,995 papillary thyroid tumors.

This retrospective study aimed to assess the association between obesity, gender, and specific clinicopathological features in patients with papillary thyroid cancer (PTC) and whether diagnostic ultrasonography (US) is adversely affected by obesity in these patients. This study retrospectively analyzed 13,995 adult patients with PTC from a single medical center in China. Data stratification was performed to assess the association of obesity with US features and aggressive clinicopathological features in different models according to the World Health Organization Body Mass Index (WHO-BMI) and Chinese BMI classification (CN-BMI). The odds ratio (OR) of malignant US features and aggressive clinicopathological features was calculated from binary logistic regression models. The BMI, obesity ratio, malignant US features, and aggressive pathological characteristics of males were significantly higher than those of females. After adjusting for confounding factors, the association of obesity with malignant US features and aggressive pathological characteristics was found to be sex-dependent. Next, obesity (WHO-BMI) was found to have an "interfering effect" on the US assessment of PTC (OR = 0.754, 95% CI 0.609-0.932, P = 0.009) in women. Among both sexes, obesity (WHO-BMI) increased the risk of tumor size (ORmale = 1.539 and ORfemale = 1.521) and multifocality (ORmale = 1.659 and ORfemale = 1.449). However, obesity did not increase the risk of capsular invasion or tumor staging in males. The above results are consistent with the CN-BMI. In addition, age was found to have an "interfering effect" on the US evaluation of malignant nodules in both sexes. The results of our study confirm that higher BMI is significantly associated with aggressive clinicopathological features of PTC. Gender differences were present with obesity ratios and aggressive clinicopathological features being significantly higher in men.

Clinical practice guideline of integrated traditional Chinese and western medicine：atlantoaxial dislocation (AAD) (2019)

EDITOR'S NOTE In 2019, the clinical practice guideline of integrated traditional Chinese and western medicine atlantoaxial dislocation (ADD) was developed by the professional committee of spine medicine of the Chinese Association for Integration of Chinese and Western medicine, aiming at the major clinical problems of atlantoaxial dislocation (AAD) endangering the life center of the medulla oblongata in the field of spine surgery. More than 40 experts at home and abroad in the field of cervical spine surgery organized the first guide for the diagnosis and treatment of AAD. Guided by the principles of evidence based medicine and the consensus of experts, this guideline is based on the theory of "combination of disease and syndrome, from supervision to treatment" for the diagnosis and treatment of AAD；based on the treatment principles of "spinal cord decompression, reconstruction of atlantoaxial stability"；based on Tan technology, Goel technology, Abumi technology and other core technologies for surgical treatment. The main content includes four parts：AAD diagnosis standard, TCM syndrome differentiation, clinical classification, treatment strategy and method. It provides academic guidance to solve the clinical situation of AAD, which is "unclear definition of diagnosis and classification, confused choice of treatment strategy". Clinical Practice Guideline of Integrated Traditional Chinese and Western Medicine：Atlantoaxial Dislocation (AAD) was enacted by the academic committee of spine surgery of integrated traditional Chinese and Western medicine. The guideline's recommendations forms by evidence-based medicine and the expert consensus of Integrative Medicine. The guidement principles is "spinal cord decompression, reconstruction of atlantoaxial stability". The objective of the guideline is providing an academic suggestion. The main contents include diagnostic criteria, traditional Chinese medicine's (TCM) dialectical method, clinical classification, treatment strategies and methods.

Patent Automatic Classification Based on Symmetric Hierarchical Convolution Neural Network

With the rapid growth of patent applications, it has become an urgent problem to automatically classify the accepted patent application documents accurately and quickly. Most previous patent automatic classification studies are based on feature engineering and traditional machine learning methods like SVM, and some even rely on the knowledge of domain experts, hence they suffer from low accuracy problem and have poor generalization ability. In this paper, we propose a patent automatic classification method via the symmetric hierarchical convolution neural network (CNN) named PAC-HCNN. We use the title and abstract of the patent as the input data, and then apply the word embedding technique to segment and vectorize the input data. Then we design a symmetric hierarchical CNN framework to classify the patents based on the word embeddings, which is much more efficient than traditional RNN models dealing with texts, meanwhile keeping the history and future information of the input sequence. We also add gated linear units (GLUs) and residual connection to help realize the deep CNN. Additionally, we equip our model with a self attention mechanism to address the long-term dependency problem. Experiments are performed on large-scale datasets for Chinese short text patent classification. Experimental results prove our proposed model’s effectiveness, and it performs better than other state-of-the-art models significantly and consistently on both fine-grained and coarse-grained classification.