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Detection of specific antinuclear antibodies is very important in term of diagnosis, prognosis and management of patients with systemic autoimmune rheumatic diseases. Chemiluminescence microarray immunoassay (CLMIA) is a microdot array-based method that allows simultaneous detection of multiple antinuclear antibodies, which received increasing attention.

Objective: To achieve reasonable and effective grading of live crayfish, and improve the work of grading crayfish. Methods: The construction of crayfish image shooting platform, to obtain the original image of crayfish, and the semantic segmentation dataset which segmented the three parts of the crayfish head, crayfish pincers, and crayfish tail was created. The correlation between the actual weight of three parts and the corresponding pixel size in the dataset was analyzed, and a new grading standard for crayfish which was according to the proportion of head and pincers in the whole crayfish was summarized. The DeepLab V3+ neural network was trained using the crayfish semantic segmentation dataset, and the test set was used to test the semantic segmentation effect of the model and the accuracy of crayfish grading. Semantic segmentation evaluation criteria were mean intersection over union （MIoU）, mean pixel accuracy （MPA） and pixel accuracy （PA）. Results: The MIoU of the crayfish semantic segmentation test set was 94.35%, the MPA was 96.56%, and the PA was 99.44%. The accuracy of crayfish grading in the test set was 85.56%. Conclusion: The DeepLab V3+ model can accurately segment crayfish images and estimate the proportion of crayfish head and pincers, and the model can complete the crayfish grading task.

[Purpose/Significance] Different collaborators play different roles and assume corresponding responsibilities in scientific research collaboration. Distinguishing the different roles in research collaborators is important for the evaluation of research talent and human resources allocation. Previous studies have defined the roles of collaborators from multiple perspectives, both qualitative and quantitative, but lack a simple and efficient way to identify core collaborators. In this paper, we use the number of collaborations to identify core collaborators in scientists' collaborative relationships based on the H-index measure, which is a very easy to calculate and intuitively understandable method. [Method/Process] Using the OpenAlex database as a data source, an empirical analysis of approximately 5.05 million journal papers in the field of computing in China and G7 countries over 20 years (2000-2021) was conducted. First, the core collaborators of highly productive scientists were studied and their collaboration characteristics were analyzed from the perspective of size and share. Second, based on the H-index fitting formula proposed by previous authors, a formula for estimating the number of core collaborators based on the number of publications and the average number of collaborators per article was proposed. Finally, the formula was used to compare the differences between the theoretical and actual values of the number of core collaborators across countries. [Results/Conclusions] The study found that in terms of size and proportion of core collaborators, China had the highest average total number of collaborators among highly productive scientists, followed by the USA, Germany and the UK, while Italy had the lowest. The number of core collaborators was generally 3-7 across countries, with China and Italy having a higher rate of cooperation and the UK, France and Canada having a lower rate of cooperation. In terms of the number of core collaborators as a percentage, no country has more than 10%, with Italy having the highest percentage of core collaborators at 7.42%, followed by Japan, France and Canada, while the US has the lowest percentage of core collaborators. In terms of the total number of collaborators, there is no significant difference between China, the US and Germany, while there is a significant difference among all five other countries. In terms of the number of core collaborators, China is not significantly different from Italy and is significantly different from all other six countries. The number of core collaborators can be estimated by using the formula of the product of the number of publications and the power of the average number of collaborators per article, which has a good fit of 0.8 or more. Among China and the G7, the US, Germany and the UK have a lower proportion of core collaborators, with more frequent mobility and exchange of talent, while Italy, Japan and China have a higher proportion of core collaborators, indicating a lack of talent mobility and a relative consolidation of research collaboration.

The significance of developing upper limb assisted exoskeletons in the current epidemic situation, frequent disasters and shortage of labor resources is briefly described. The development history, classification methods of exoskeletons and the characteristics, applicable groups, application scenarios of upper limb assisted exoskeletons are introduced. The research results and products related to upper limb assisted exoskeletons at home and abroad are reviewed in detail, including their basic structure and evaluation experimental results, and the key technologies are described and analyzed such as the drive and transmission, human motion intention recognition, human-robot coordinated motion involved in human-robot interaction, etc. The existing problems in the current research are summarized and the future development trend of upper limb assisted exoskeletons is prospected.