抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of "word of mouth" in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target?We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform node-selection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.

/pdf/maximizing-the-spread-of-influence-through-a-social-network-3nnj0jic76.pdf

Maximizing the spread of influence through a social network

Prior research has provided valuable insights into how and why employees make a decision about the adoption and use of information technologies (ITs) in the workplace. From an organizational point of view, however, the more important issue is how managers make informed decisions about interventions that can lead to greater acceptance and effective utilization of IT. There is limited research in the IT implementation literature that deals with the role of interventions to aid such managerial decision making. Particularly, there is a need to understand how various interventions can influence the known determinants of IT adoption and use. To address this gap in the literature, we draw from the vast body of research on the technology acceptance model (TAM), particularly the work on the determinants of perceived usefulness and perceived ease of use, and: (i) develop a comprehensive nomological network (integrated model) of the determinants of individual level (IT) adoption and use; (ii) empirically test the proposed integrated model; and (iii) present a research agenda focused on potential pre- and postimplementation interventions that can enhance employees' adoption and use of IT. Our findings and research agenda have important implications for managerial decision making on IT implementation in organizations.

/pdf/technology-acceptance-model-3-and-a-research-agenda-on-3xnjvztlvt.pdf

Technology Acceptance Model 3 and a Research Agenda on Interventions

The Structural Transformation of the Public Sphere: An Inquiry into a Category of Bourgeois Society

Social life from the bottom up: Agent modeling and the New Sociology

Dependence, Selectivity, and Cooperation

„Bottom-up“ Modelle sozialer Dynamiken. Agentenbasierte Computermodellierung und methodologischer Individualismus

The emerging popularity of location-aware devices and location-based services has generated a growing archive of digital traces of people's activities and opinions in physical space. In this study, we leverage geo-referenced user-generated content from Google MyMaps to discover collective local knowledge and understand the differing perceptions of urban space. Working with the large collection of publicly available, annotation-rich MyMaps data, we propose a highly parallelizable approach in order to merge identical places, discover landmarks, and recommend places. Additionally, we conduct interviews with New York City residents/visitors to validate the quantitative findings.

/pdf/mining-collective-local-knowledge-from-google-mymaps-t8l2o47ntp.pdf

Mining collective local knowledge from Google MyMaps

These are exciting times for sociologists. Device-mediated communications enable unprecedented empirical opportunities to obtain real-time data on individual human interactions on a global scale. To realize this potential, the critical need is not a universally applicable theoretical framework but a widely shared analytical toolkit of models and methods for middle-range theorizing, including methods to collect, process, and analyze massive data, and to conduct online experiments with randomized assignment involving large numbers of participants.

Michael W. Macy

Papers

Social life from the bottom up: Agent modeling and the New Sociology

Dependence, Selectivity, and Cooperation

„Bottom-up“ Modelle sozialer Dynamiken. Agentenbasierte Computermodellierung und methodologischer Individualismus

Mining collective local knowledge from Google MyMaps

Big Theory: A Trojan Horse for Economics?