Subjects: Medicine, Pharmacy >> Preventive Medicine and Hygienics submitted time 2023-12-28 Cooperative journals: 《中国全科医学》
Abstract： Background Idiopathic inflammatory myopathy（IIM） is a group of connective tissue diseases characterized by muscle inflammation and muscle weakness，lung involvement is an important factor affecting the prognosis of patients. IIM can be classified into different clinical subtypes based on myositis-specific antibodies（MSAs），there are significant differences in clinical manifestations，organ involvement，prognosis and the risk of interstitial lung disease among different clinical subtypes. Objective To explore the characteristics of IIM and its different clinical subtypes，the risk factors for the development of interstitial lung disease. Methods The clinical data of patients hospitalizedin department of rheumatology and immunology of the First Affiliated Hospital of Kunming Medical University and diagnosed with IIM from April 2018 to February 2021 were collected. The included patients were divided into four clinical subtypes based on MSAs，including anti-MDA5-positive dermatomyositis （DM），anti-MDA5-negative DM，immune-mediated necrotizing myositis （IMNM） and anti synthetase syndrome （ASS） subtypes. The general data，clinical manifestations，laboratory examination results were compared among different clinical subtypes. Multivariate Logistic regression model was established to explore the risk factors for ILD in patients with IIM. Results The 150 patients with IIM were divided into 4 clinical subtypes，including 30 patients with anti MDA5-positive DM subtype（20%），58 patients with anti-MDA5-negative DM subtype（38.7%），14 patients with IMNM subtype（9.3%），and 48 patients with ASS subtype（32.0%）. There were significant differences in the incidence of muscle weakness，myalgia，ILD，heliotrope rash，shawl sign，Gottron papules or Gottron sign，arthralgia，periungual red spot and dysphagia among the four clinical subtypes（P<0.05）. The incidence of ILD in patients with anti-MDA5-positive DM and ASS subtypes was higher than patients with anti-MDA5-negative DM and IMNM subtypes，respectively（P<0.05）；The incidences of heliotrope rash and shawl sign in patients with anti-MDA5-positive DM and anti-MDA5-negative DM subtypes were higher than patients with IMNM and ASS subtypes（P<0.05）；The incidences of arthralgia in patients with anti-MDA5-positive DM subtype was higher than patients with anti-MDA5-negative DM subtype（P<0.05）. There were significant differences in the levels of WBC，ALT，AST，serum creatinine，LDH，CK，C4，ferritin，T cell，CD8+ T cell，nature kill （NK） cell and incidence of ILD among patients with different subtypes（P<0.05）. Multivariate Logistic regression analysis showed that anti MDA5 antibody positive，anti-synthetase antibody positive，lung infection，ferritin>403.2 μg/L，IgG>14.15 g/L，LDH>359.5 IU/L were all risk factors for ILD in IIM. Conclusion The clinical manifestations of patients with different clinical subtypes differ significantly. DM patients with anti-MDA5 antibody positive are more likely to develop rash，arthralgia，ILD and leukopenia. MDA5 antibody positive，anti-synthetase antibody positive，lung infection and elevated ferritin，LDH and IgG levels are the risk factors for IIM with ILD.
Subjects: Statistics >> Social Statistics submitted time 2023-12-04 Cooperative journals: 《智慧农业（中英文）》
Abstract： Objective The root system is an important component of plant composition, and its growth and development are crucial for plants. Root image segmentation is an important method for obtaining root phenotype information and analyzing root growth patterns. Research on root image segmentation still faces difficulties, because of the noise and image quality limitations, the intricate and diverse soil environment, and the ineffectiveness of conventional techniques. This paper proposed a multi-scale feature extraction root segmentation algorithm that combined data augmentation and transfer learning to enhance the generalization and universality of the root image segmentation models in order to increase the speed, accuracy, and resilience of root image segmentation. Methods Firstly, the experimental datasets were divided into a single dataset and a mixed dataset. The single dataset acquisition was obtained from the experimental station of Hebei Agricultural University in Baoding city. Additionally, a self-made RhizoPot device was used to collect images with a resolution pixels of 10,200×14,039, resulting in a total of 600 images. In this experiment, 100 sheets were randomly selected to be manually labeled using Adobe Photoshop CC2020 and segmented into resolution pixels of 768× 768, and divided into training, validation, and test sets according to 7:2:1. To increase the number of experimental samples, an open source multi-crop mixed dataset was obtained in the network as a supplement, and it was reclassified into training, validation, and testing sets. The model was trained using the data augmentation strategy, which involved performing data augmentation operations at a set probability of 0.3 during the image reading phase, and each method did not affect the other. When the probability was less than 0.3, changes would be made to the image. Specific data augmentation methods included changing image attributes, randomly cropping, rotating, and flipping those images. The UNet structure was improved by designing eight different multi-scale image feature extraction modules. The module structure mainly included two aspects: Image convolution and feature fusion. The convolution improvement included convolutional block attention module (CBAM), depthwise separable convolution (DP Conv), and convolution (Conv). In terms of feature fusion methods, improvements could be divided into concatenation and addition. Subsequently, ablation tests were conducted based on a single dataset, data augmentation, and random loading of model weights, and the optimal multi-scale feature extraction module was selected and compared with the original UNet. Similarly, a single dataset, data augmentation, and random loading of model weights were used to compare and validate the advantages of the improved model with the PSPNet, SegNet, and DeeplabV3Plus algorithms. The improved model used pre-trained weights from a single dataset to load and train the model based on mixed datasets and data augmentation, further improving the model's generalization ability and root segmentation ability. Results and Discussions The results of the ablation tests indicated that Conv_ 2+Add was the best improved algorithm. Compared to the original UNet, the mIoU, mRecall, and root F1 values of the model increased by 0.37%, 0.99%, and 0.56%, respectively. And, comparative experiments indicate Unet+Conv_2+Add model was superior to the PSPNet, SegNet, and DeeplabV3Plus models, with the best evaluation results. And the values of mIoU, mRecall, and the harmonic average of root F1 were 81.62%, 86.90%, and 77.97%, respectively. The actual segmented images obtained by the improved model were more finely processed at the root boundary compared to other models. However, for roots with deep color and low contrast with soil particles, the improved model could only achieve root recognition and the recognition was sparse, sacrificing a certain amount of information extraction ability. This study used the root phenotype evaluation software Rhizovision to analyze the root images of the Unet+Conv_2+Add improved model, PSPNet, SegNet, and DeeplabV3Plu, respectively, to obtain the values of the four root phenotypes (total root length, average diameter, surface area, and capacity), and the results showed that the average diameter and surface area indicator values of the improved model, Unet+Conv_2+ Add had the smallest differences from the manually labeled indicator values and the SegNet indicator values for the two indicators. Total root length and volume were the closest to those of the manual labeling. The results of transfer learning experiments proved that compared with ordinary training, the transfer training of the improved model UNet+Conv_2+Add increased the IoU value of the root system by 1.25%. The Recall value of the root system was increased by 1.79%, and the harmonic average value of F1 was increased by 0.92%. Moreover, the overall convergence speed of the model was fast. Compared with regular training, the transfer training of the original UNet improved the root IoU by 0.29%, the root Recall by 0.83%, and the root F1 value by 0.21%, which indirectly confirmed the effectiveness of transfer learning. Conclusions The multi-scale feature extraction strategy proposed in this study can accurately and efficiently segment roots, and further improve the model's generalization ability using transfer learning methods, providing an important research foundation for crop root phenotype research.
Subjects: Medicine, Pharmacy >> Preventive Medicine and Hygienics submitted time 2023-12-04 Cooperative journals: 《中国全科医学》
ZHANG, Yuan
HOU, Qiqi
QI, Qi
JIANG, Yue
WANG, Nan
YUE, Bocheng
CHEN, Shuohua
HAN, Quanle
WU, Shouling
LI, Kangbo
Abstract： Background The prevalence of atrial fibrillation（AF）has continued to rise globally in recent years，and AF increases the risk of stroke，heart failure，myocardial infarction，chronic kidney disease，and other diseases. Studies have identified hypertension，diabetes，smoking，obstructive sleep apnea，obesity and sedentary lifestyle as risk factors for AF. And most of these factors are within the scope of the "Life's Essential 8"（LE8）proposed by the American Heart Association. Objective To investigate the relationship between cardiovascular health（CVH）score based on the LE8 and AF. Methods A prospective cohort study was conducted in which 91 131 employees of Kailuan Group in Tangshan，Hebei Province were selected for physical examination from June 2006 to October 2007，and the LE8 score was evaluated according to the algorithm developed by the American Heart Association，and combined with the actual situation of the Kailuan study to form the Kailuan study version of LE8，including 4 health behaviors（diet，physical activity，tobacco exposure，and sleep）and 4 health factors（BMI，blood lipids，blood glucose，and blood pressure）. The study subjects were divided into the three groups of the low CVH group（n=8 407）with a LE8 score less than 50，the medium CVH group（n=73 493）with a LE8 score of 50 or more but less than 80，and the high CVH group（n=9 231）with a LE8 score of 80 or more. The follow-up visit was performed per year with the time of the study subject's first Kailuan physical examination as the starting point，the occurrence of AF as the endpoint event，the end of AF and follow-up（2020-12-31）as the endpoint time. Kaplan Meier survival curve was used to analyze the cumulative incidence of new-onset AF in different groups，and log rank test was used to compare the differences between groups；Cox proportional hazards regression analysis was used to investigate the impact of different LE8 score groups and single factor scores on the risk of new-onset AF. Results There were significant differences in age，gender，education level，family income，history of alcohol consumption，and LE8 scores among the three groups of subjects（P<0.001）. During follow-up，1088 cases of new-onset AF were identified，including 133 cases（1.58%）in the low CVH group，882 cases（1.20%）in the medium CVH group，and 72 cases（0.78%）in the high CVH group. The median follow-up time was 15.0（14.7，15.2）years；there was statistically significant difference in the comparison of cumulative incidence rate of new-onset AF in the three groups （P<0.001）. Cox proportional hazards regression analysis after adjusting for age，gender，education level，household income，and history of alcohol consumption showed that，compared with the low CVH group，both the medium CVH group（HR=0.697，95%CI=0.579-0.841，P<0.001）and the high CVH group（HR=0.609，95%CI=0.454-0.816，P=0.001）reduced the risk of new-onset AF. An increase in LE8 score could reduce the risk of new-onset AF（HR=0.859，95%CI=0.804-0.918，P<0.001）. The individual factors of LE8，including BMI score（HR=0.762，95%CI=0.717-0.809，P<0.001）and blood pressure score（HR=0.824，95%CI=0.776-0.876，P<0.001），were negatively correlated with the risk of new-onset AF. Conclusion The LE8 score of CVH is negatively correlated with the risk of new-onset AF，and the individual factors of LE8，including BMI score and blood pressure score，are negatively correlated with the risk of new-onset AF.
Subjects: Library Science，Information Science >> Library Science submitted time 2023-10-08 Cooperative journals: 《知识管理论坛》
Abstract： [Purpose/significance] Suggestions for the further deepening and development of the future open access publication of library and information science journals in China. [Method/process] By the methods of literature research and network research, this article investigated the status of open access publication of 24 kinds of journals of Library and Information Science in CSSCI, analyzes its deficiencies and problems in operation model, OA publication policy statement, OA publication content, and the construction of additional functions of journals. [Result/conclusion] Suggestions are made on the future development of open access publishing in Chinese Library and Information Science journals: try to change the mode of running journals, strive for the right to speak in cooperation with foreign publishers, supply the OA publishing policy statement and standardize the OA publication content, make full use of new media and new technologies to expand OA journal service capabilities.
Subjects: Psychology >> Cognitive Psychology submitted time 2023-09-12
Abstract： Self-reference (or self-referential processing) refers to the cognitive processes underlying self-related information processing. It is widely studied in cognitive neuroscience to better understand the neural basis of self-cognition of human beings. However, does the term “self-reference” mean the same psychological processes across studies? This fundamental question has been largely disregarded and has not received the attention it deserves. To fill the gap, we built an ontological dataset based on neuroimaging studies of self-reference. We searched the literature and screened the articles following a standard protocol. Then, two independent coders extracted data and standardized operationalizations of self-reference on both behavioral and neural levels, resulting in a cognitive ontological dataset for neuroimaging studies of self-reference. This dataset consists of operationalizations of self-reference (in CSV file format) from 66 neuroimaging articles, coordinates data of brain areas activated by self-reference (in BrainMap format), and corresponding codebooks. The inter-rater reliability analysis indicates that the coding process exhibits an exceptional level of quality. Compared with automatic meta-analytical platforms, i.e., Neurosynth, the current dataset provides a fine-grained granularity in article selection, which allows the comparison of brain regions activated by different operationalizations of self-reference. This dataset lays a foundation for the understanding of neural mechanisms underlying self-cognition. It may also facilitate the study of cognitive ontology by serving as an exemplary model for the creation of similar metascience datasets.
Subjects: Astronomy >> Astrophysical processes submitted time 2021-11-12 Cooperative journals: 《天文研究与技术》
Abstract：本文利用SDO(Solar Dynamic Observatory)/HMI(Helioseismic and Magnetic Imager)观测到的矢量磁图，运用Ampere定律的积分形式计算出了2014年11月7日活动区AR12205上发生X1.6级耀斑期间的垂直电流密度，通过分析光球层上垂直电流密度的时间演化，发现它会在耀斑爆发期间表现出突然的增加，增加的区域对应于耀斑双带的辐射增强位置，然后将强电流密度轮廓叠加在SDO/AIA1600 Å波段的观测图像上，发现电流密度的形态与分布和耀斑带有强相互关系。这些观测结果与三维标准耀斑模型的理论预测一致，我们的研究为三维磁准分界面（Quasi-Separatrix Layer，QSL）重联模型提供了有力的观测证据。
Subjects: Astronomy >> Astrophysical processes submitted time 2020-11-19 Cooperative journals: 《天文研究与技术》
Abstract：太阳高能活动爆发与活动区内的电流结构有着密切的联系，安培（Ampere）定律j_z=〖1/μ_0 (∇×B)〗_z是测量活动区内视向电流密度的理论基础。由于实测的矢量磁场中不可避免地存在随机噪声，因此，应用安培定律的不同形式计算的电流密度存在显著的差异。为了比较不同形式计算结果的差异并从中探索一种实用的电流计算方法，基于太阳动力学天文台(Solar Dynamic Observatory,SDO)/HMI(Helioseismic and Magnetic Imager)在2011年2月15日测量的活动区AR11158的矢量磁图，运用安培定律的微分算法和积分算法分别计算了活动区内视向电流密度的分布图。结果显示，微分算法获得的视向电流密度j_z分布图受随机噪声的影响要远比积分算法获得的结果大，电流分布图中的电流结构没有积分算法获得的结果清晰。另外，在扩大积分环路半径的情况下，所计算的电流分布图里的噪声信号快速降低，从而使计算的视向电流分布图中的电流结构更清晰。但是当继续扩大积分环路半径时，在获得清晰电流分布图的同时，电流分布图的部分精细结构也随之失真。该研究结果论证了适当扩大积分环路计算视向电流分布图可以降低计算结果受随机噪声的影响，从而获得清晰真实的视向电流分布图，但是积分路径的半径过大在消除噪声影响的同时会丢失电流分布中的一些精细结构。因此在实际计算电流的过程中，应该利用高分辨率的矢量磁图，选定合适的积分路径运用安培定律的积分算法来计算活动区的视向电流，从而帮助我们探索耀斑爆发与活动区内电流结构的关系。
Subjects: Geosciences >> Other Disciplines of Geosciences submitted time 2019-09-10 Cooperative journals: 《干旱区研究》
Abstract：借助观测资料与FNL全球分析资料，对比研究2017年与2014年西安高温天气基本特征与有利环流形势，结果表明：① 2 a西安均发生了40 ℃以上的长时间持续高温天气，其中2014年属于常规高温年份，而2017年西安高温则提前1个月发生，高温的提前发生主要是因为南亚高压强于正常年份同期强度；② 不同于我国南方地区的闷湿高温，西安高温属于干性高温，且昼夜温差较小，由此造成24 h对人体的不适；③ 详细描述了西安高温发生的典型环流特征，即南亚高压与西太平洋副热带高压（简称副高）同时增强，并在对流层中高层相互贯通；④ 南亚高压是影响我国西北地区的主要热源基地，在较大经向度的有利环流背景下，强风速可将热气团向南向东深度输送至下游地区（即西安）；⑤ 在“上辐合、下辐散”的散度场配置下，西安500 hPa以下高空维持明显的下沉气流，加之有利的局部要素相配合，最终造成该地区高温天气的发生。
Subjects: Dynamic and Electric Engineering >> Electrical Engineering submitted time 2019-03-05 Cooperative journals: 《电气工程学报》
Abstract：铁路继电器工作中的可靠性与安全性是确保各种铁路设备正常工作的必须 要求，在列车控制方面发挥着重要的作用。本文根据加速试验原理与试验条件制定了 在振动应力下铁路继电器的加速寿命试验方案，并通过加速寿命试验获得了铁路继电 器触点接触压降的数据。选择接触压降作为继电器性能退化特征参数，利用小波阈值 法对得到的接触压降数据进行去噪处理，并建立了 EEMD-RBF 预测模型对继电器寿命 进行预测。对 EEMD-RBF 与 RBF 的误差进行对比分析，结果表明 EEMD-RBF 模型预 测精度更高。最后，根据预测出来的寿命，运用逆幂律方程预测出正常振动应力水平 下铁路继电器的寿命。
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-08-13 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the limitation that the existing big data classification methods can not meet the time and storage space in big data applications, a design method of big data parallel multi-label k-nearest neighbor classifier based on Apache Spark framework is proposed. In order to reduce the cost of the existing MapReduce scheme by using other memory operations, first, the training set is divided into several partitions in conjunction with the parallel mechanism of the Apache Spark framework. Then in the Map stage, the K nearest neighbors of each partition of the sample to be predicted are found, and in the Reduce phase, the final K nearest neighbors are determined according to the results of the Map phase. Finally, the neighboring tag sets are aggregated in parallel, and the target tag set of the sample to be predicted is output by maximizing the posterior probability. Experiments were conducted on PokerHand et al. 's four big data classification datasets. The proposed method achieved a lower Hamming loss and proved its effectiveness.
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