Sharp gene pool transition in a population affected by phenotype-based selective hunting

We use a microscopic model of population dynamics, a modified version of the well known Penna model, to study some aspects of microevolution. This research is motivated by recent reports on the effect of selective hunting on the gene pool of bighorn sheep living in the Ram Mountain region, in Canada. Our model finds a sharp transition in the structure of the gene pool as some threshold for the number of animals hunted is reached.Comment: 5 pages, 4 figure

Assessing candidate preference through web browsing history

Predicting election outcomes is of considerable interest to candidates, political scientists, and the public at large. We propose the use of Web browsing history as a new indicator of candidate preference among the electorate, one that has potential to overcome a number of the drawbacks of election polls. However, there are a number of challenges that must be overcome to effectively use Web browsing for assessing candidate preference—including the lack of suitable ground truth data and the heterogeneity of user populations in time and space. We address these challenges, and show that the resulting methods can shed considerable light on the dynamics of voters’ candidate preferences in ways that are difficult to achieve using polls.Accepted manuscrip

Central European foreign exchange markets: a cross-spectral analysis of the 2007 financial crisis

This paper investigates co-movements between currency markets of Czech Republic, Poland, Hungary, Slovakia and the Euro in the year following the drying up of money markets in August 2007. The paper shows that assessing the degree of foreign currency co-movement by correlation can lead to concluding, erroneously, that financial contagion has not occurred. Using cross-spectral methods, the paper shows that defining contagion as changes in the structure of co-movements of asset prices encompasses more of the complex nature of exchange rate dynamics. What is shown is that, following August 2007, there is increased in the intensity of co-movements, but non-linearly. Focusing on the activities of a mix of banks and currency managers, it is suggested that changes in the structure of currency interaction present an unfavourable view of the contagion experienced by at least three of these currencies

Uropathogenic Escherichia coli superinfection enhances the severity of mouse bladder infection

Urinary tract infections (UTIs) afflict over 9 million women in America every year, often necessitating long-term prophylactic antibiotics. One risk factor for UTI is frequent sexual intercourse, which dramatically increases the risk of UTI. The mechanism behind this increased risk is unknown; however, bacteriuria increases immediately after sexual intercourse episodes, suggesting that physical manipulation introduces periurethral flora into the urinary tract. In this paper, we investigated whether superinfection (repeat introduction of bacteria) resulted in increased risk of severe UTI, manifesting as persistent bacteriuria, high titer bladder bacterial burdens and chronic inflammation, an outcome referred to as chronic cystitis. Chronic cystitis represents unchecked luminal bacterial replication and is defined histologically by urothelial hyperplasia and submucosal lymphoid aggregates, a histological pattern similar to that seen in humans suffering chronic UTI. C57BL/6J mice are resistant to chronic cystitis after a single infection; however, they developed persistent bacteriuria and chronic cystitis when superinfected 24 hours apart. Elevated levels of interleukin-6 (IL-6), keratinocyte cytokine (KC/CXCL1), and granulocyte colony-stimulating factor (G-CSF) in the serum of C57BL/6J mice prior to the second infection predicted the development of chronic cystitis. These same cytokines have been found to precede chronic cystitis in singly infected C3H/HeN mice. Furthermore, inoculating C3H/HeN mice twice within a six-hour period doubled the proportion of mice that developed chronic cystitis. Intracellular bacterial replication, regulated hemolysin (HlyA) expression, and caspase 1/11 activation were essential for this increase. Microarrays conducted at four weeks post inoculation in both mouse strains revealed upregulation of IL-1 and antimicrobial peptides during chronic cystitis. These data suggest a mechanism by which caspase-1/11 activation and IL-1 secretion could predispose certain women to recurrent UTI after frequent intercourse, a predisposition predictable by several serum biomarkers in two murine models

Structure-based discovery of glycomimetic FmlH ligands as inhibitors of bacterial adhesion during urinary tract infection

Significance The emergence of multidrug-resistant bacteria, including uropathogenic Escherichia coli (UPEC), makes the development of targeted antivirulence therapeutics a critical focus of research. During urinary tract infections (UTIs), UPEC uses chaperone–usher pathway pili tipped with an array of adhesins that recognize distinct receptors with sterochemical specificity to facilitate persistence in various tissues and habitats. We used an interdisciplinary approach driven by structural biology and synthetic glycoside chemistry to design and optimize glycomimetic inhibitors of the UPEC adhesin FmlH. These inhibitors competitively blocked FmlH in vitro, in in vivo mouse UTI models, and in ex vivo healthy human kidney tissue. This work demonstrates the utility of structure-driven drug design in the effort to develop antivirulence therapeutic compounds. </jats:p

The Detailed Forms of the LMC Cepheid PL and PLC Relations

Possible deviations from linearity of the LMC Cepheid PL and PLC relations are investigated. Two datasets are studied, respectively from the OGLE and MACHO projects. A nonparametric test, based on linear regression residuals, suggests that neither PL relation is linear. If colour dependence is allowed for then the MACHO PL relation is found to deviate more significantly from the linear, while the OGLE PL relation is consistent with linearity. These finding are confirmed by fitting "Generalised Additive Models" (nonparametric regression functions) to the two datasets. Colour dependence is shown to be nonlinear in both datasets, distinctly so in the case of the MACHO Cepheids. It is also shown that there is interaction between the period and colour functions in the MACHO data.Comment: 20 pages, 20 figures, MNRAS accepte

Partisan Asymmetries in Online Political Activity

We examine partisan differences in the behavior, communication patterns and social interactions of more than 18,000 politically-active Twitter users to produce evidence that points to changing levels of partisan engagement with the American online political landscape. Analysis of a network defined by the communication activity of these users in proximity to the 2010 midterm congressional elections reveals a highly segregated, well clustered partisan community structure. Using cluster membership as a high-fidelity (87% accuracy) proxy for political affiliation, we characterize a wide range of differences in the behavior, communication and social connectivity of left- and right-leaning Twitter users. We find that in contrast to the online political dynamics of the 2008 campaign, right-leaning Twitter users exhibit greater levels of political activity, a more tightly interconnected social structure, and a communication network topology that facilitates the rapid and broad dissemination of political information.Comment: 17 pages, 10 figures, 6 table

The topology of a discussion: the #occupy case

We analyse a large sample of the Twitter activity developed around the social movement 'Occupy Wall Street' to study the complex interactions between the human communication activity and the semantic content of a discussion. We use a network approach based on the analysis of the bipartite graph @Users-#Hashtags and of its projections: the 'semantic network', whose nodes are hashtags, and the 'users interest network', whose nodes are users In the first instance, we find out that discussion topics (#hashtags) present a high heterogeneity, with the distinct role of the communication hubs where most the 'opinion traffic' passes through. In the second case, the self-organization process of users activity leads to the emergence of two classes of communicators: the 'professionals' and the 'amateurs'. Moreover the network presents a strong community structure, based on the differentiation of the semantic topics, and a high level of structural robustness when a certain set of topics are censored and/or accounts are removed. Analysing the characteristics the @Users-#Hashtags network we can distinguish three phases of the discussion about the movement. Each phase corresponds to specific moment of the movement: from declaration of intent, organisation and development and the final phase of political reactions. Each phase is characterised by the presence of specific #hashtags in the discussion. Keywords: Twitter, Network analysisComment: 13 pages, 9 figure

Twitter-based analysis of the dynamics of collective attention to political parties

Large-scale data from social media have a significant potential to describe complex phenomena in real world and to anticipate collective behaviors such as information spreading and social trends. One specific case of study is represented by the collective attention to the action of political parties. Not surprisingly, researchers and stakeholders tried to correlate parties' presence on social media with their performances in elections. Despite the many efforts, results are still inconclusive since this kind of data is often very noisy and significant signals could be covered by (largely unknown) statistical fluctuations. In this paper we consider the number of tweets (tweet volume) of a party as a proxy of collective attention to the party, identify the dynamics of the volume, and show that this quantity has some information on the elections outcome. We find that the distribution of the tweet volume for each party follows a log-normal distribution with a positive autocorrelation of the volume over short terms, which indicates the volume has large fluctuations of the log-normal distribution yet with a short-term tendency. Furthermore, by measuring the ratio of two consecutive daily tweet volumes, we find that the evolution of the daily volume of a party can be described by means of a geometric Brownian motion (i.e., the logarithm of the volume moves randomly with a trend). Finally, we determine the optimal period of averaging tweet volume for reducing fluctuations and extracting short-term tendencies. We conclude that the tweet volume is a good indicator of parties' success in the elections when considered over an optimal time window. Our study identifies the statistical nature of collective attention to political issues and sheds light on how to model the dynamics of collective attention in social media.Comment: 16 pages, 7 figures, 3 tables. Published in PLoS ON