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Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with multiple systemic lesions. It has a high incidence of anxiety and depression due to the prolonged course of the disease, which adversely affects the treatment, affects the quality of life of patients, and threatens the life and health of patients. Through searching and reading relevant literature, the research status of anxiety and depression, influencing factors and psychological intervention measures were summarized to guide clinical timely discovery of relevant factors and psychological intervention, so as to reduce the incidence of anxiety and depression in SLE patients and improve their quality of life. (系统性红斑狼疮(SLE)是一种具有多系统损害表现的慢性自身免疫病, 病程迁延不愈, 患者易产生焦虑抑郁的负性情绪, 不利于治疗, 且影响患者生活质量, 甚至威胁患者生命健康。疾病活动度、患者的经济水平、婚姻状况、文化程度、应对方式、社会支持以及治疗方式等均可能影响SLE患者心理情绪, 临床应做到早识别早干预, 评估风险人群, 针对高风险患者积极开展心理干预, 提高心理弹性, 降低SLE患者焦虑抑郁发生率, 提高其生活质量。)

In order to weaken the Gibbs oscillation in the passband of periodic fluid-solid superlattice structure, a gradient fluid-solid superlattice structure (GFSL) with symmetrical linear distribution of solid layer filling fraction is designed by introducing structural gradient. Based on the transfer matrix method, the acoustic transmission characteristics of GFSL structure are calculated. On this basis, the effects of different gradient parameters on the sound transmission characteristics of GFSL structure are discussed to adjust the passband performance. The sound transmission characteristics of GFSL structure are verified from the perspectives of frequency domain and time domain by finite element method. The results show that the GFSL structure effectively eliminates the Gibbs oscillation and forms a flat broadband passband. The proposed GFSL structure has potential application value in underwater high-intensity ultrasonic energy concentration, acoustic impedance matching and filtering. [ABSTRACT FROM AUTHOR]

Copyright of Clean Coal Technology is the property of Clean Coal Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An accurate and rapid detection of back-fat thickness was essential to the precise feeding of the breeding pigs. However, the current manual analysis of backfat thickness cannot fully meet the large-scale production. In this study, a more accurate and stable determination of backfat thickness was developed with an automatic tool in pig breeding. The back-fat thickness was measured from the B-scan ultrasonography (B-Scan) image of pigs, where a Fully Convolutional Networks (FCN) model was trained for this task. The best performance of the model was achieved at the 20 literation with the batch size of 32, the learning rate of 0.005, and the optimizer of Adam. A prediction validation was conducted with the testing dataset, slaughtering measured dataset, and a separate human labelled dataset. The testing dataset was then segmented for the basic model performance with the FCN for the back-fat thickness. The measurement between the prediction and expert labelled (ground truth) was not statistically significant (P>0.05), where the correlation coefficient was 0.92 (P<0.01), indicating the reliable measurement of back-fat thickness for the targeted breeds using the FCN model. The slaughtering measured dataset was obtained from five selected pigs, which were the relatively close weight and body characteristics inspected by the expert group. The measuring points were chosen and marked on and near the standard B-scan measurement points, where the 5 points along the head to tail on each side were equally distanced. The B-Scan images of the measurement points were firstly taken for every pig to measure the back-fact thickness using the conventional inspection and the prediction model. Then, the direct measurements were taken with a vernier calliper on the chosen measurement points after slaughtering these selected pigs. Further analysis was made after the conventional inspection, prediction model, and direct measurement after slaughtering. It was found that there were smaller values in the conventional and prediction model than that in the direct measurement, where the minimum was found in the prediction model. The correlation coefficient between the three methods was all above 0.92 (P<0.01). Moreover, the conventional and prediction model was not statistically significant (P>0.05), with a correlation coefficient of 0.97 (P<0.01). It infers that the prediction model was much more accurate, compared with the conventional, indicating a reliable alternative way for the back-fat measurement. Furthermore, the separate human labelling dataset included the measurements on the same set of B-Scan images from four groups of six people, indicating the different levels of professional knowledge and experience. Specifically, the Expert Group (EG) involved experts combined with depth breeding knowledge and at least five years of field experience. The Professional Group (PG) were the licensed professionals on sites with the relatively focused on practice which carried hundreds of measurements. The Student Group (SG) were the undergraduates and postgraduates who majored in animal husbandry with less hands-on experience. The Novice Group (NG) were people not familiar with the industry, who only went through a quick lesson. The results between each group were not statistically significant (P>0.05), the same as every group and the prediction model. However, the standard deviation of in-group data varied notably, where the minimum was the expert group of 0.17 mm and the maximum was the novice group of 1.67 mm. In comparison, the prediction model presented a result free of human factors. Consequently, an FCN model was trained and implemented to semantic segment the B-scan images, with emphasis on some of the popular breeds in the industry, such as Large White, Landrace, and Duroc. The prediction was compared with the conventional inspection and direct measurements after slaughtering, indicating that the measured back-fat thickness from a B-Scan image using the FCN prediction model provided practically accurate, quick, and stable values. This finding can potentially provide production benefits, in order to significantly reduce the knowledge and experience requirements, as well as the workload and the training cost of staff. [ABSTRACT FROM AUTHOR]

Copyright of West China Journal of Stomatology is the property of Sichuan University, West China College of Stomatology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Applied Mathematics & Mechanics (1000-0887) is the property of Applied Mathematics & Mechanics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Clinical Hepatology / Linchuang Gandanbing Zazhi is the property of Journal of Clinical Hepatology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The problems of medical education in colleges, such as the separation of theory teaching and practice, the imbalance of teaching and research, result in teachers and students emphasizing the ability of knowledge and neglecting the cultivation of the " people" . Combining the teaching platform of " Biochemistry Experiment Course" , the paper takes research of " Total Cholesterol Assay Kit" as a foundation to guide undergraduate students to join innovation and entrepreneurship training programs. The research gets scientific and teaching results, and also explores the application of EIP-CDIO project teaching method in medical education. [ABSTRACT FROM AUTHOR]

Shaggy-like protein kinase (SKs) plays numerous roles during the plant development, such as organ development, phytohormone conduction, abiotic stress and biotic stress. Kalanchoe daigremontiana is an attractive model system for the study of the molecular mechanisms of somatic embryogenesis and organogenesis competence because of its ability to form embryos and to acquire organogenic competence through plantlets. To better understand the molecular mechanisms of plantlet formation involved in K. daigremontiana, a shaggy-like protein kinase, KdSK, was identified using rapid amplification of cDNA end (RACE) PCR. KdSK gene consists of an ORF of 1 272 bp that was predicted to encode a 483 amino acid residue-long protein of 47.79 kD with an isoelectric point of 8.37. The sequence analysis of the KdSK revealed homology to Cucumis sativus shaggy-related protein kinase kappa. Phylogenetic analysis showed that KdSK gene was closely related to Medicage sativa protein (MSK4) and formed a subgroup with Arabidopsis thaliana (AtSK4-1, AtKSK4-2), all clustered to Clade Ⅳ of GSK3/shaggy kinases family. Conservation domain structure analysis indicated that KdSK protein had typical structure of the protein kinase domain, including ATP domain and Serine/Threonine protein kinases active-site signature. Real-time PCR analysis revealed that KdSK transcript was expressed the highest in root and upregulated under osmotic stress. This study characterized the novel KdSK gene from Kalanchoe daigremontiana for the first time and the results will be useful for further functional determination of the gene. [ABSTRACT FROM AUTHOR]

Copyright of Tianjin Medical Journal is the property of Tianjin Medical Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Modern Medicine & Health is the property of Journal of Modern Medicine & Health and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)