The analysis of user behavior provides a large amount of useful information. After being extracted, this information is called user knowledge. User knowledge plays a guiding role in implementing user-centric updates for software platforms. A good representation and application of user knowledge can accelerate the development of a software platform and improve its quality. This paper aims to further the utilization of user knowledge by mining the user knowledge that is implicit in user behavior and then constructing a knowledge graph of this behavior. First, the association between a software bug and a software component is mined from the user knowledge. Then, the knowledge entity extraction and relationship extraction are performed from the development code and the user behavior. Finally, the knowledge is stored in the graph database, from which it can be visually retrieved. Relevant experiments on CIFLog, an integrated logging processing software platform, have proved the effectiveness of this research. Constructing a user behavior knowledge graph can improve the utilization of user knowledge as well as the quality of software platform development.
In the process of software development, users play a crucial role. User knowledge plays a role of energy driver in the software ecology, and user knowledge plays a guiding and reference role in software development. From the perspective of knowledge chain, improving the acquisition rate and utilization rate of user knowledge can better complete the upgrading work of large software platform. Based on this starting point, this paper puts forward the idea of actively acquiring user behavior information, mining association rules of software platform components, and finally applying knowledge mapping technology to the knowledge representation of user knowledge and inherent knowledge of software platform, so as to strengthen the extensibility and continuity of knowledge. Experiments show that above methods can improve the acquisition rate and utilization rate of user knowledge, quickly locate relevant entities in the software to improve the speed and quality of large software platforms.
Evaluating the IgG titers targeting the S protein of SARS-CoV-2 by ELISA
assays have been used in the development of several anti-SARS-CoV-2
vaccines in China. It is unclear whether the IgG levels against S
protein tested by ELISA can monitor immune evasion against different
Omicron variants. In the study, 88 recipients immunized with three doses
of a COVID-19-inactivated vaccine were enrolled, whose serum samples
were collected within 2 months after the third immunization. The IgG
antibody levels were detected by using three commercial ELISA kits,
which coated the S protein of the prototype, Omicron BA.1 and BA.5
variants respectively. The neutralizing activities of sera against the
pseudotyped Omicron variant, prototype was determined. We also analyzed
the correlation between the IgG titers with the neutralizing antibodies.
The results showed that, after the third dose of the homologous
inactivated vaccine, the neutralizing activity against the Omicron
variant BA.1(GMT, 60) and BA. 5(GMT, 42) were decreased significantly
compared with the prototype (GMT, 331) respectively (
P<0.05). However, the IgG titers against the S of
Omicron BA.1(GMT, 2334) and BA.5(GMT, 2447) variants showed no
significant difference with the prototype (GMT, 2797). Our results
showed different correlation levels between anti-Spike protein of
SARS-CoV-2 IgG titers and neutralizing antibodies, against SARS-CoV-2
prototype, Omicron variant BA.1 and BA.5 In summary, our result
highlight that the Omicron variant BA.1 and BA.5 escape vaccine-induced
immunity by neutralization activity test, and IgG titers against the
SARS-CoV-2 Spike protein can not predict the viral immune evasion
against different Omicron variants.
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