抗绿脓杆菌毒素A单克隆抗体杂交瘤细胞系的建立

本文采用绿脓杆菌外毒素A免疫BALB/c小鼠取脾细胞与Sp2/0细胞融合,建立了6株分泌单克隆抗体的杂交瘤,分别命名为14F10,23B9,23F1,23C12,,24A6及24B5。其中,23F1及24A6在传代培养中逐渐丧失了分泌抗体的能力,其余4株可稳定地分泌抗体。它们的Ig亚类鉴定;14F10为IgM,23B9为IgC1,23C12为JgG2b,24B5为JgG1。注射入同系小鼠腹腔,可产生高滴度抗体,在夹心ELISA试验中己证实均为抗绿脓杆菌外毒素A特异性的。此外,文中还讨论了在间接ELISA初次筛选中出现的假阴性及假阳性的原因及其排除方法。

B细胞生长因子活性上清对人—人杂交瘤细胞生长影响的研究

本研究用金黄色葡萄球菌(Cowan 1株)死菌制剂(SAC)刺激人B淋巴细胞建立了人B细胞生长因子(BCGF)的检测系统。发现经PHA刺激的人扁桃体细胞培养上清可以促进SAC活化的B细胞的生长,~3H-TdR掺入率提高了2～3倍。同时,用~3H-TdR掺入法和有限稀释法观察了该上清对人-人B细胞杂交瘤生长和克隆率的影响,发现其可以促进人-人B细胞杂交瘤的生长、~3H-TdR掺入率提高2～3倍,可提高克隆率3倍以上。但人-人B细胞杂交瘤可吸收掉上述BCGF活性上清中的促杂交瘤细胞生长因子(HGF)活性,而对BCGF活性无明显影响。提示该上清同时具有人BCGF和HGF活性,部分BCGF活性与HGF活性可能是由不问的分子所介导。

BRINGING FORWARD OF THE CONCEPT OF COGIS AND ITS ARCHITECTURE RESEARCH

Based on the analysis of the development of GIS technology and application,this paper brought forward the concept of CoGIS, namely Cooperative GIS. CoGIS is GIS facinggroup-users and supporting human-human interaction, which makes it differ from the former GISs.Then, the characteristics of general Computer Support Cooperative Work (CSCW) applications and thecomplexity of Geographic Information Science were analyzed, and the conclusion that CoGIS was not asimple GIS layer on CSCW was reached. Further, this paper gave the hierarchical architecture ofCoGIS, and analyzed the cooperative platform in detail from the following: 1) basic elements; 2)collaboration patterns; 3) cooperation control mechanism; 4) synchronization mechanism; 5) securityand 6) group communication and so on. With those, the problems about GIS applications are discussed,such as 1) distributed multi-source GIS information and knowledge sharing platform; 2) the fusionand visualization of GIS information; 3) virtual reality cooperative modeling; 4) dynamicsimulation; 5) expert system and 6) decision-making. Finally, this paper analyzed CoGIS applicationmode in brief.

An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

Human Activity Recognition(HAR)plays an important role in life care and health monitoring since it involves examining various activities of patients at homes,hospitals,or offices.Hence,the proposed system integrates Human-Human Interaction(HHI)and Human-Object Interaction(HOI)recognition to provide in-depth monitoring of the daily routine of patients.We propose a robust system comprising both RGB(red,green,blue)and depth information.In particular,humans in HHI datasets are segmented via connected components analysis and skin detection while the human and object in HOI datasets are segmented via saliency map.To track the movement of humans,we proposed orientation and thermal features.A codebook is generated using Linde-Buzo-Gray(LBG)algorithm for vector quantization.Then,the quantized vectors generated from image sequences of HOI are given to Artificial Neural Network(ANN)while the quantized vectors generated from image sequences of HHI are given to K-ary tree hashing for classification.There are two publicly available datasets used for experimentation on HHI recognition:Stony Brook University(SBU)Kinect interaction and the University of Lincoln’s(UoL)3D social activity dataset.Furthermore,two publicly available datasets are used for experimentation on HOI recognition:Nanyang Technological University(NTU)RGB-D and Sun Yat-Sen University(SYSU)3D HOI datasets.The results proved the validity of the proposed system.

Patterns of human social contact and mask wearing in high-risk groups in China

Background:The pandemic of coronavirus disease 2019(COVID-19)has changed human behavior in areas such as contact patterns and mask-wearing frequency.Exploring human–human contact patterns and mask-wearing habits in high-risk groups is an essential step in fully understanding the transmission of respiratory infection-based diseases.This study had aims to quantify local human–human(H–H)contacts in high-risk groups in representative provinces of China and to explore the occupation-specific assortativity and heterogeneity of social contacts.Methods:Delivery workers,medical workers,preschoolers,and students from Qinghai,Shanghai,and Zhejiang were recruited to complete an online questionnaire that queried general information,logged contacts,and assessed the willingness to wear a mask in different settings.The“group contact”was defined as contact with a group at least 20 individuals.The numbers of contacts across different characteristics were assessed and age-specific contact matrices were established.A generalized additive mixed model was used to analyze the associations between the number of individual contacts and several characteristics.The factors influencing the frequency of mask wearing were evaluated with a logistic regression model.Results:A total of 611,287 contacts were reported by 15,635 participants.The frequency of daily individual contacts averaged 3.14(95%confidence interval:3.13–3.15)people per day,while that of group contacts was 37.90(95%CI:37.20–38.70).Skin-to-skin contact and long-duration contact were more likely to occur at home or among family members.Contact matrices of students were the most assortative(all contacts q-index=0.899,95%CI:0.894–0.904).Participants with larger household sizes reported having more contacts.Higher household income per capita was significantly associated with a greater number of contacts among preschoolers(P_(50,000–99,999)=0.033)and students(P_(10,000–29,999)=0.017).In each of the public places,the frequency of mask wearing was highest for delivery workers.For preschoolers and students with more contacts,the proportion of those who reported always wearing masks was lower(P<0.05)in schools/workplaces and public transportation than preschoolers and students with fewer contacts.Conclusions:Contact screening efforts should be concentrated in the home,school,and workplace after an outbreak of an epidemic,as more than 75%of all contacts,on average,will be found in such places.Efforts should be made to improve the mask-wearing rate and age-specific health promotion measures aimed at reducing transmission for the younger demographic.Age-stratified and occupation-specific social contact research in high-risk groups could help inform policy-making decisions during the post-relaxation period of the COVID-19 pandemic.

Contactless interaction recognition and interactor detection in multi-person scenes

Human interaction recognition is an essential task in video surveillance.The current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without other people.In this paper,we handle more practical but more challenging scenarios where interactive subjects are contactless and other subjects not involved in the interactions of interest are also present in the scene.To address this problem,we propose an Interactive Relation Embedding Network(IRE-Net)to simultaneously identify the subjects involved in the interaction and recognize their interaction category.As a new problem,we also build a new dataset with annotations and metrics for performance evaluation.Experimental results on this datasesthow significant improvements of the proposed method when compared with current methodsdeveloped for human interaction recognition and group activity recognition.