Concurrent Bursty Behavior of Social Sensors in Sporting Events

The advent of social media expands our ability to transmit information and connect with others instantly, which enables us to behave as "social sensors." Here, we studied concurrent bursty behavior of Twitter users during major sporting events to determine their function as social sensors. We show that the degree of concurrent bursts in tweets (posts) and retweets (re-posts) works as a strong indicator of winning or losing a game. More specifically, our simple tweet analysis of Japanese professional baseball games in 2013 revealed that social sensors can immediately react to positive and negative events through bursts of tweets, but that positive events are more likely to induce a subsequent burst of retweets. We also show that these findings hold true across cultures by analyzing tweets related to Major League Baseball games in 2015. Furthermore, we demonstrate active interactions among social sensors by constructing retweet networks during a baseball game. The resulting networks commonly exhibited user clusters depending on the baseball team, with a scale-free connectedness that is indicative of a substantial difference in user popularity as an information source. While previous studies have mainly focused on bursts of tweets as a simple indicator of a real-world event, the temporal correlation between tweets and retweets implies unique aspects of social sensors, offering new insights into human behavior in a highly connected world.Comment: 17 pages, 8 figure

Multifaceted Analyses of Epidermal Serine Protease Activity in Patients with Atopic Dermatitis

The serine proteases kallikrein-related peptidase (KLK) 5 and KLK7 cleave cell adhesion molecules in the epidermis. Aberrant epidermal serine protease activity is thought to play an important role in the pathogenesis of atopic dermatitis (AD). We collected the stratum corneum (SC) from healthy individuals (n = 46) and AD patients (n = 63) by tape stripping and then measuring the trypsin- and chymotrypsin-like serine protease activity. We also analyzed the p.D386N and p.E420K of SPINK5 variants and loss-of-function mutations of FLG in the AD patients. The serine protease activity in the SC was increased not only in AD lesions but also in non-lesions of AD patients. We found, generally, that there was a positive correlation between the serine protease activity in the SC and the total serum immunoglobulin E (IgE) levels, serum thymus and activation-regulated chemokine (TARC) levels, and peripheral blood eosinophil counts. Moreover, the p.D386N or p.E420K in SPINK5 and FLG mutations were not significantly associated with the SC's serine protease activity. Epidermal serine protease activity was increased even in non-lesions of AD patients. Such activity was found to correlate with a number of biomarkers of AD. Further investigations of serine proteases might provide new treatments and prophylaxis for AD

Significant contribution of subseafloor microparticles to the global manganese budget

Ferromanganese minerals are widely distributed in subseafloor sediments and on the seafloor in oceanic abyssal plains. Assessing their input, formation and preservation is important for understanding the global marine manganese cycle and associated trace elements. However, the extent of ferromanganese minerals buried in subseafloor sediments remains unclear. Here we show that abundant (108–109 particles cm−3) micrometer-scale ferromanganese mineral particles (Mn-microparticles) are found in the oxic pelagic clays of the South Pacific Gyre (SPG) from the seafloor to the ~100 million-year-old sediments above the basement. Three-dimensional micro-texture, and major and trace element compositional analyses revealed that these Mn-microparticles consist of poorly crystalline ferromanganese oxides precipitating from bottom water. Based on our findings, we extrapolate that 1.5–8.8 × 1028 Mn-microparticles, accounting for 1.28–7.62 Tt of manganese, are globally present in oxic subseafloor sediments. This estimate is at least two orders of magnitude larger than the manganese budget for nodules and crusts on the seafloor. Subseafloor Mn-microparticles thus contribute significantly to the global manganese budget.This study was supported in part by the Japan Society for the Promotion of Science (JSPS) Strategic Fund for Strengthening Leading-Edge Research and Development (to JAMSTEC and F.I.), the JSPS Funding Program for Next Generation World-Leading Researchers (GR102 to F.I.), JSPS Grant-in-Aid for Scientific Research (24687004 and 15H05608 to Y.M., 25871219 to G.-I.U., 15H02810 to R.W., 18H04134, 17H06458 and 17H04582 to Y.T., and 26251041 to F.I.), JSPS Grant-in-Aid for JSPS Fellows (14J00199 to G.-I.U.), and Ministry of Education, Culture, Sports, Science, and Technology (MEXT) Fund Leading Initiative for Excellent Young Researchers (to Kochi University and G.-I.U.)

Botulinum hemagglutinin disrupts the intercellular epithelial barrier by directly binding E-cadherin

Botulinum neurotoxin's nontoxic HA protein binds E-cadherin to disrupt cell–cell adhesion in a species-specific manner

Replication data for: Concurrent Bursty Behavior of Social Sensors in Sporting Events

Using the Twitter Search API, which allows 180 queries per 15-min window, we compiled a dataset of tweets related to Japan's 2013 Nippon Professional Baseball (NPB) games, including at least one hashtag of NPB teams such as #giants (Yomiuri Giants) and #rakuteneagles (Tohoku Rakuten Golden Eagles). This hashtag-based crawling with multiple crawlers allowed us to obtain the nearly-complete data regarding these sporting events: 528,501 tweets surrounding 19 baseball games from the Climax Series (the annual playoff series) and from the Japan Series (the annual championship series) in the 2013 NPB. We also collected tweets related to Major League Baseball (MLB) games in 2015, including at least one hashtag of the MLB teams such as #Yankees and #BlueJays. We sampled 730,142 tweets from 17 games of New York Yankees from September 11 to 27, 2015

<i>R</i>_max between tweet and retweet time series for the 2013 Japan Series (A) and the 2013 Climax Series for the Central (B) and Pacific (C) Leagues. Red letters with an underline denote the winning team and blue letters denote the losing team. G: Yomiuri Giants, E: Tohoku Rakuten Golden Eagles, T: Hanshin Tigers, C: Hiroshima Toyo Carp, M: Chiba Lotte Marines, L: Saitama Seibu Lions.

<p><i>R</i>_max between tweet and retweet time series for the 2013 Japan Series (A) and the 2013 Climax Series for the Central (B) and Pacific (C) Leagues. Red letters with an underline denote the winning team and blue letters denote the losing team. G: Yomiuri Giants, E: Tohoku Rakuten Golden Eagles, T: Hanshin Tigers, C: Hiroshima Toyo Carp, M: Chiba Lotte Marines, L: Saitama Seibu Lions.</p

Retweet networks and their cumulative in-degree distributions (<i>P</i>_cum(<i>k</i>)) in the sixth round of the 2013 Japan Series.

<p>The retweet network (A) consists of data generated during 30 min from 19:17, in which more retweets were generated with #rakuteneagles. The retweet network (B) consists of data generated during 30 min from 20:16, in which more retweets were generated with #giants. Green lines and circles denote #giants and blue lines and circles denote #rakuteneagles.</p

Example of tweet and retweet time series (counts per minute) for the Yankees vs. Blue Jays game on September 11, 2015.

<p>Red lines denote tweets and blue dashed lines denote retweets. The upper panel shows tweets for the Yankees (#Yankees) and the lower panel for the Blue Jays (#BlueJays).</p

Example of the correlation between tweet and retweet time series (<i>r</i>_<i>xy</i>(<i>τ</i>)) for the six round in the 2013 Japan Series (cf. Fig 2).

<p>Example of the correlation between tweet and retweet time series (<i>r</i>_<i>xy</i>(<i>τ</i>)) for the six round in the 2013 Japan Series (cf. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144646#pone.0144646.g002" target="_blank">Fig 2</a>).</p

<i>R</i>_max values between tweet and retweet time series for the 2015 Major League Baseball (Yankees games from September 11 to 27).

<p>(A) <i>R</i>_max values by games (<i>n</i> = 17). Red letters with an underline denote the winning team and blue letters denote the losing team. Y: New York Yankees, B: Toronto Blue Jays, M: New York Mets, R: Tampa Bay Rays, W: Chicago White Sox. (B) Boxplots of <i>R</i>_max in the winning team group and the losing team group, with a significant difference between two groups.</p