19,089 research outputs found
Transcriptome Analyses of Tumor-Adjacent Somatic Tissues Reveal Genes Co-Expressed with Transposable Elements
Background: Despite the long-held assumption that transposons are normally only expressed in the germ-line, recent evidence shows that transcripts of transposable element (TE) sequences are frequently found in the somatic cells. However, the extent of variation in TE transcript levels across different tissues and different individuals are unknown, and the co-expression between TEs and host gene mRNAs have not been examined. Results: Here we report the variation in TE derived transcript levels across tissues and between individuals observed in the non-tumorous tissues collected for The Cancer Genome Atlas. We found core TE co-expression modules consisting mainly of transposons, showing correlated expression across broad classes of TEs. Despite this co-expression within tissues, there are individual TE loci that exhibit tissue-specific expression patterns, when compared across tissues. The core TE modules were negatively correlated with other gene modules that consisted of immune response genes in interferon signaling. KRAB Zinc Finger Proteins (KZFPs) were over-represented gene members of the TE modules, showing positive correlation across multiple tissues. But we did not find overlap between TE-KZFP pairs that are co-expressed and TE-KZFP pairs that are bound in published ChIP-seq studies. Conclusions: We find unexpected variation in TE derived transcripts, within and across non-tumorous tissues. We describe a broad view of the RNA state for non-tumorous tissues exhibiting higher level of TE transcripts. Tissues with higher level of TE transcripts have a broad range of TEs co-expressed, with high expression of a large number of KZFPs, and lower RNA levels of immune genes
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A Large-Scale Study of Modern Code Review and Security in Open Source Projects.
Semantic Sentiment Analysis of Twitter Data
Internet and the proliferation of smart mobile devices have changed the way
information is created, shared, and spreads, e.g., microblogs such as Twitter,
weblogs such as LiveJournal, social networks such as Facebook, and instant
messengers such as Skype and WhatsApp are now commonly used to share thoughts
and opinions about anything in the surrounding world. This has resulted in the
proliferation of social media content, thus creating new opportunities to study
public opinion at a scale that was never possible before. Naturally, this
abundance of data has quickly attracted business and research interest from
various fields including marketing, political science, and social studies,
among many others, which are interested in questions like these: Do people like
the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about
the Brexit? Answering these questions requires studying the sentiment of
opinions people express in social media, which has given rise to the fast
growth of the field of sentiment analysis in social media, with Twitter being
especially popular for research due to its scale, representativeness, variety
of topics discussed, as well as ease of public access to its messages. Here we
present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the
Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition.
201
The Scientific Competitiveness of Nations
We use citation data of scientific articles produced by individual nations in
different scientific domains to determine the structure and efficiency of
national research systems. We characterize the scientific fitness of each
nation (that is, the competitiveness of its research system) and the complexity
of each scientific domain by means of a non-linear iterative algorithm able to
assess quantitatively the advantage of scientific diversification. We find that
technological leading nations, beyond having the largest production of
scientific papers and the largest number of citations, do not specialize in a
few scientific domains. Rather, they diversify as much as possible their
research system. On the other side, less developed nations are competitive only
in scientific domains where also many other nations are present.
Diversification thus represents the key element that correlates with scientific
and technological competitiveness. A remarkable implication of this structure
of the scientific competition is that the scientific domains playing the role
of "markers" of national scientific competitiveness are those not necessarily
of high technological requirements, but rather addressing the most
"sophisticated" needs of the society
Binary versus non-binary information in real time series: empirical results and maximum-entropy matrix models
The dynamics of complex systems, from financial markets to the brain, can be
monitored in terms of multiple time series of activity of the constituent
units, such as stocks or neurons respectively. While the main focus of time
series analysis is on the magnitude of temporal increments, a significant piece
of information is encoded into the binary projection (i.e. the sign) of such
increments. In this paper we provide further evidence of this by showing strong
nonlinear relations between binary and non-binary properties of financial time
series. These relations are a novel quantification of the fact that extreme
price increments occur more often when most stocks move in the same direction.
We then introduce an information-theoretic approach to the analysis of the
binary signature of single and multiple time series. Through the definition of
maximum-entropy ensembles of binary matrices and their mapping to spin models
in statistical physics, we quantify the information encoded into the simplest
binary properties of real time series and identify the most informative
property given a set of measurements. Our formalism is able to accurately
replicate, and mathematically characterize, the observed binary/non-binary
relations. We also obtain a phase diagram allowing us to identify, based only
on the instantaneous aggregate return of a set of multiple time series, a
regime where the so-called `market mode' has an optimal interpretation in terms
of collective (endogenous) effects, a regime where it is parsimoniously
explained by pure noise, and a regime where it can be regarded as a combination
of endogenous and exogenous factors. Our approach allows us to connect spin
models, simple stochastic processes, and ensembles of time series inferred from
partial information
Bank Networks from Text: Interrelations, Centrality and Determinants
In the wake of the still ongoing global financial crisis, bank
interdependencies have come into focus in trying to assess linkages among banks
and systemic risk. To date, such analysis has largely been based on numerical
data. By contrast, this study attempts to gain further insight into bank
interconnections by tapping into financial discourse. We present a
text-to-network process, which has its basis in co-occurrences of bank names
and can be analyzed quantitatively and visualized. To quantify bank importance,
we propose an information centrality measure to rank and assess trends of bank
centrality in discussion. For qualitative assessment of bank networks, we put
forward a visual, interactive interface for better illustrating network
structures. We illustrate the text-based approach on European Large and Complex
Banking Groups (LCBGs) during the ongoing financial crisis by quantifying bank
interrelations and centrality from discussion in 3M news articles, spanning
2007Q1 to 2014Q3.Comment: Quantitative Finance, forthcoming in 201
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