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

We have implemented a social media data mining system capable of forecasting events related to Latin American social unrest. Our method directly extracts a small number of tweets from publicly-available data on twitter.com, condenses similar tweets into coherent forecasts, and assembles a detailed and easily-interpretable audit trail which allows end users to quickly collect information about an upcoming event. Our system functions by continually applying multiple textual and geographic filters to a large volume of data streaming from twitter.com via the public API as well as a commercial data feed. To be specific, we search the entirety of twitter.com for a few carefully chosen keywords, search within those tweets for mentions of future dates, filter again using various logistic regression classifiers, and finally assign a location to an event by geocoding retweeters. Geocoding is done using our previously-developed in-house geocoding service which, at the time of this writing, can infer the home location for over 62M twitter.com users [1]. Additionally, we identify demographics likely interested in an upcoming event by searching retweeter's recent posts for demographic-specific keywords.

Detecting future social unrest in unprocessed Twitter data: “Emerging phenomena and big data”

Collective action can be problematic for two reasons - we may get little or no additional benefitfrom our own efforts should we choose to contribute (the "efficacy problem"), yet we will enjoy the benefits of others' efforts even if wefail to contribute (the "free-rider problem"). Movement mobilizers address the free-rider problem by issuing crisis messages that call attention to impending disaster and the urgent need for action. These messages discourage free riding by making clear that soon there may be no more public resources to enjoy for free. Mobilizers address the efficacy problem by issuing empowerment messages that emphasize the fact that positive change is possible and is, in fact, happening. Crisis messages are especially prominent in the "tragedy of the commons" because of the accelerating impact of overconsumption. The "mobilizer's dilemma" is that crisis messages may actually backfire by undermining belief in the ability to "make a difference," while empowerment messages affirm efficacy but encourage free riding. We tested the hypothesized effects of crisis and empowerment messages under controlled conditions in the laboratory. In two separate experiments, members of a large group were confronted with a resource depletion problem under conditions of uncertainty. We manipulated the content of persuasive messages and measured the effect on selfrestraint in harvesting the commons. The results confirmed the mobilizer's dilemma and pointed to a way out. Whether we realize it or not, we are now engaged in an epic battle to right the balance of the earth, and the tide of this battle will turn only when the majority of people in the world become sufficiently aroused by a shared sense of urgent danger to join an all-out effort.

The Mobilizer's Dilemma: Crisis, Empowerment, and Collective Action

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https://www.jstor.org/stable/10.1086/588795

Neighborhood Chance and Neighborhood Change: A Comment on Bruch and Mare1

People manage emotions to cope with life’s demands1,2. Previous research has identified affective patterns using self-reports3 and text analysis4,5, but these measures track the expression of affect, not affective preference for external stimuli such as music, which affects mood states and levels of emotional arousal1,6,7. We analysed a dataset of 765 million online music plays streamed by 1 million individuals in 51 countries to measure diurnal and seasonal patterns of affective preference. Findings reveal similar diurnal patterns across cultures and demographic groups. Individuals listen to more relaxing music late at night and more energetic music during normal business hours, including mid-afternoon when affective expression is lowest. However, there were differences in baselines: younger people listen to more intense music; compared with other regions, music played in Latin America is more arousing, while music in Asia is more relaxing; and compared with other chronotypes, ‘night owls’ (people who are habitually active or wakeful at night) listen to less-intense music. Seasonal patterns vary with distance from the equator and between Northern and Southern hemispheres and are more strongly correlated with absolute day length than with changes in day length. Taken together with previous findings on affective expression in text4, these results suggest that musical choice both shapes and reflects mood. Using data from 765 million online music plays chosen by 1 million individuals in 51 countries, Park et al. reveal diurnal and seasonal affective rhythms in musical intensity that are consistent across diverse cultures and demographic groups. They also report differences in baseline preferences for musical intensity across cultures and ages.

Global music streaming data reveal diurnal and seasonal patterns of affective preference.

Long-range connections that span large social networks are widely assumed to be weak, composed of sporadic and emotionally distant relationships. However, researchers historically have lacked the population-scale network data needed to verify the predicted weakness. Using data from 11 culturally diverse population-scale networks on four continents—encompassing 56 million Twitter users and 58 million mobile phone subscribers—we find that long-range ties are nearly as strong as social ties embedded within a small circle of friends. These high-bandwidth connections have important implications for diffusion and social integration.

https://science.sciencemag.org/content/sci/362/6421/1410.full.pdf

Michael W. Macy

Papers

Detecting future social unrest in unprocessed Twitter data: “Emerging phenomena and big data”

The Mobilizer's Dilemma: Crisis, Empowerment, and Collective Action

Neighborhood Chance and Neighborhood Change: A Comment on Bruch and Mare1

Global music streaming data reveal diurnal and seasonal patterns of affective preference.

The strength of long-range ties in population-scale social networks