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

Social network sites SNSs are increasingly attracting the attention of academic and industry researchers intrigued by their affordances and reach This special theme section of the Journal of Computer-Mediated Communication brings together scholarship on these emergent phenomena In this introductory article, we describe features of SNSs and propose a comprehensive definition We then present one perspective on the history of such sites, discussing key changes and developments After briefly summarizing existing scholarship concerning SNSs, we discuss the articles in this special section and conclude with considerations for future research

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1083-6101.2007.00393.x

Social Network Sites: Definition, History, and Scholarship

This study examines the relationship between use of Facebook, a popular online social network site, and the formation and maintenance of social capital. In addition to assessing bonding and bridging social capital, we explore a dimension of social capital that assesses one’s ability to stay connected with members of a previously inhabited community, which we call maintained social capital. Regression analyses conducted on results from a survey of undergraduate students (N = 286) suggest a strong association between use of Facebook and the three types of social capital, with the strongest relationship being to bridging social capital. In addition, Facebook usage was found to interact with measures of psychological well-being, suggesting that it might provide greater benefits for users experiencing low self-esteem and low life satisfaction.

/pdf/the-benefits-of-facebook-friends-social-capital-and-college-2j90l44gqu.pdf

The Benefits of Facebook “Friends:” Social Capital and College Students’ Use of Online Social Network Sites

Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous/pervasive computing. The particle filter works better than other comparable methods for estimating the centers of earthquakes and the trajectories of typhoons. As an application, we construct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (96% of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and sends e-mails to registered users. Notification is delivered much faster than the announcements that are broadcast by the JMA.

/pdf/earthquake-shakes-twitter-users-real-time-event-detection-by-1tue7cvj9p.pdf

Earthquake shakes Twitter users: real-time event detection by social sensors

The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Given the rise of Big Data as a socio-tech...

/pdf/critical-questions-for-big-data-4wditehe4v.pdf

Critical questions for big data

Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamic aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given URL. We also present a dynamic model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.

/pdf/usage-patterns-of-collaborative-tagging-systems-42t00kyp9o.pdf

Usage patterns of collaborative tagging systems

Twitter - a microblogging service that enables users to post messages ("tweets") of up to 140 characters - supports a variety of communicative practices; participants use Twitter to converse with individuals, groups, and the public at large, so when conversations emerge, they are often experienced by broader audiences than just the interlocutors. This paper examines the practice of retweeting as a way by which participants can be "in a conversation." While retweeting has become a convention inside Twitter, participants retweet using different styles and for diverse reasons. We highlight how authorship, attribution, and communicative fidelity are negotiated in diverse ways. Using a series of case studies and empirical data, this paper maps out retweeting as a conversational practice.

/pdf/tweet-tweet-retweet-conversational-aspects-of-retweeting-on-119j7dh0rd.pdf

Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter

Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content In this paper we analyze the structure of collaborative tagging systems as well as their dynamical aspects Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given url We also present a dynamical model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge

http://www.hpl.hp.com/research/idl/papers/tags/tags.pdf

The structure of Collaborative Tagging Systems

We identified individual-level diurnal and seasonal mood rhythms in cultures across the globe, using data from millions of public Twitter messages. We found that individuals awaken in a good mood that deteriorates as the day progresses—which is consistent with the effects of sleep and circadian rhythm—and that seasonal change in baseline positive affect varies with change in daylength. People are happier on weekends, but the morning peak in positive affect is delayed by 2 hours, which suggests that people awaken later on weekends.

https://science.sciencemag.org/content/sci/333/6051/1878.full.pdf

Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures

College students spend a significant amount of time using online social net- work services for messaging, sharing information, and keeping in touch with one another (eg [3, 10]) As these services represent a plentiful source of electronic data, they provide an opportunity to study dynamic patterns of social interactions quickly and exhaustively In this paper, we study the social net- work service Facebook, which began in early 2004 in select universities, but grew quickly to encompass a very large number of universities Studies have shown that, as of 2006, Facebook use is nearly ubiquitous among U S college students with over 90% active participation among undergraduates [5, 16]

Scott A. Golder

Papers

Usage patterns of collaborative tagging systems

Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter

The structure of Collaborative Tagging Systems

Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures

Rhythms of Social Interaction: Messaging Within a Massive Online Network