The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

PatternLab for proteomics: a tool for differential shotgun proteomics

<p>Abstract</p> <p>Background</p> <p>A goal of proteomics is to distinguish between states of a biological system by identifying protein expression differences. Liu <it>et al</it>. demonstrated a method to perform semi-relative protein quantitation in shotgun proteomics data by correlating the number of tandem mass spectra obtained for each protein, or "spectral count", with its abundance in a mixture; however, two issues have remained open: how to normalize spectral counting data and how to efficiently pinpoint differences between profiles. Moreover, Chen <it>et al</it>. recently showed how to increase the number of identified proteins in shotgun proteomics by analyzing samples with different MS-compatible detergents while performing proteolytic digestion. The latter introduced new challenges as seen from the data analysis perspective, since replicate readings are not acquired.</p> <p>Results</p> <p>To address the open issues above, we present a program termed PatternLab for proteomics. This program implements existing strategies and adds two new methods to pinpoint differences in protein profiles. The first method, ACFold, addresses experiments with less than three replicates from each state or having assays acquired by different protocols as described by Chen <it>et al</it>. ACFold uses a combined criterion based on expression fold changes, the AC test, and the false-discovery rate, and can supply a "bird's-eye view" of differentially expressed proteins. The other method addresses experimental designs having multiple readings from each state and is referred to as nSVM (natural support vector machine) because of its roots in evolutionary computing and in statistical learning theory. Our observations suggest that nSVM's niche comprises projects that select a minimum set of proteins for classification purposes; for example, the development of an early detection kit for a given pathology. We demonstrate the effectiveness of each method on experimental data and confront them with existing strategies.</p> <p>Conclusion</p> <p>PatternLab offers an easy and unified access to a variety of feature selection and normalization strategies, each having its own niche. Additionally, graphing tools are available to aid in the analysis of high throughput experimental data. PatternLab is available at <url>http://pcarvalho.com/patternlab</url>.</p

Neutrophils in cancer: neutral no more

Neutrophils are indispensable antagonists of microbial infection and facilitators of wound healing. In the cancer setting, a newfound appreciation for neutrophils has come into view. The traditionally held belief that neutrophils are inert bystanders is being challenged by the recent literature. Emerging evidence indicates that tumours manipulate neutrophils, sometimes early in their differentiation process, to create diverse phenotypic and functional polarization states able to alter tumour behaviour. In this Review, we discuss the involvement of neutrophils in cancer initiation and progression, and their potential as clinical biomarkers and therapeutic targets

A Novel RNA Transcript with Antiapoptotic Function Is Silenced in Fragile X Syndrome

Several genome-wide transcriptomics efforts have shown that a large percentage of the mammalian genome is transcribed into RNAs, however, only a small percentage (1–2%) of these RNAs is translated into proteins. Currently there is an intense interest in characterizing the function of the different classes of noncoding RNAs and their relevance to human disease. Using genomic approaches we discovered FMR4, a primate-specific noncoding RNA transcript (2.4 kb) that resides upstream and likely shares a bidirectional promoter with FMR1. FMR4 is a product of RNA polymerase II and has a similar half-life to FMR1. The CGG expansion in the 5′ UTR of FMR1 appears to affect transcription in both directions as we found FMR4, similar to FMR1, to be silenced in fragile X patients and up-regulated in premutation carriers. Knockdown of FMR4 by several siRNAs did not affect FMR1 expression, nor vice versa, suggesting that FMR4 is not a direct regulatory transcript for FMR1. However, FMR4 markedly affected human cell proliferation in vitro; siRNAs knockdown of FMR4 resulted in alterations in the cell cycle and increased apoptosis, while the overexpression of FMR4 caused an increase in cell proliferation. Collectively, our results demonstrate an antiapoptotic function of FMR4 and provide evidence that a well-studied genomic locus can show unexpected functional complexity. It cannot be excluded that altered FMR4 expression might contribute to aspects of the clinical presentation of fragile X syndrome and/or related disorders

Atypical ductal hyperplasia is a multipotent precursor of breast carcinoma

The current model for breast cancer progression proposes independent “low‐grade (LG) like” and “high‐grade (HG) like” pathways but lacks a known precursor to HG cancer. We applied low coverage whole genome sequencing to atypical ductal hyperplasia (ADH) with and without carcinoma to shed light on breast cancer progression. 14/20 isolated ADH cases harboured at least one copy number alteration (CNA), but had fewer aberrations than LG or HG ductal carcinoma in situ (DCIS). ADH carried more HG‐like CNA than LG DCIS (eg. 8q gain). Correspondingly, 64% (7/11) of ADH cases with synchronous HG carcinoma were clonally related, similar to LG carcinoma (67%, 6/9). This study represents a significant shift in our understanding of breast cancer progression, with ADH as a common precursor lesion to the independent “low‐grade like” and “high‐grade like” pathways. These data suggest that ADH can be a precursor of HG breast cancer and that LG and HG carcinomas can evolve from a similar ancestor lesion. We propose that although LG DCIS may be committed to a LG molecular pathway, ADH may remain multipotent, progressing to either LG or HG carcinoma. This multipotent nature suggests that some ADH could be more clinically significant than LG DCIS, requiring biomarkers for personalising management

Limits on WWZ and WW\gamma couplings from p\bar{p}\to e\nu jj X events at \sqrt{s} = 1.8 TeV

We present limits on anomalous WWZ and WW-gamma couplings from a search for WW and WZ production in p-bar p collisions at sqrt(s)=1.8 TeV. We use p-bar p -> e-nu jjX events recorded with the D0 detector at the Fermilab Tevatron Collider during the 1992-1995 run. The data sample corresponds to an integrated luminosity of 96.0+-5.1 pb^(-1). Assuming identical WWZ and WW-gamma coupling parameters, the 95% CL limits on the CP-conserving couplings are -0.33<lambda<0.36 (Delta-kappa=0) and -0.43<Delta-kappa<0.59 (lambda=0), for a form factor scale Lambda = 2.0 TeV. Limits based on other assumptions are also presented.Comment: 11 pages, 2 figures, 2 table

Zgamma Production in pbarp Collisions at sqrt(s)=1.8 TeV and Limits on Anomalous ZZgamma and Zgammagamma Couplings

We present a study of Z +gamma + X production in p-bar p collisions at sqrt{S}=1.8 TeV from 97 (87) pb^{-1} of data collected in the eegamma (mumugamma) decay channel with the D0 detector at Fermilab. The event yield and kinematic characteristics are consistent with the Standard Model predictions. We obtain limits on anomalous ZZgamma and Zgammagamma couplings for form factor scales Lambda = 500 GeV and Lambda = 750 GeV. Combining this analysis with our previous results yields 95% CL limits |h{Z}_{30}| < 0.36, |h{Z}_{40}| < 0.05, |h{gamma}_{30}| < 0.37, and |h{gamma}_{40}| < 0.05 for a form factor scale Lambda=750 GeV.Comment: 17 Pages including 2 Figures. Submitted to PR

GO Explorer: A gene-ontology tool to aid in the interpretation of shotgun proteomics data

<p>Abstract</p> <p>Background</p> <p>Spectral counting is a shotgun proteomics approach comprising the identification and relative quantitation of thousands of proteins in complex mixtures. However, this strategy generates bewildering amounts of data whose biological interpretation is a challenge.</p> <p>Results</p> <p>Here we present a new algorithm, termed GO Explorer (GOEx), that leverages the gene ontology (GO) to aid in the interpretation of proteomic data. GOEx stands out because it combines data from protein fold changes with GO over-representation statistics to help draw conclusions. Moreover, it is tightly integrated within the PatternLab for Proteomics project and, thus, lies within a complete computational environment that provides parsers and pattern recognition tools designed for spectral counting. GOEx offers three independent methods to query data: an interactive directed acyclic graph, a specialist mode where key words can be searched, and an automatic search. Its usefulness is demonstrated by applying it to help interpret the effects of perillyl alcohol, a natural chemotherapeutic agent, on glioblastoma multiform cell lines (A172). We used a new multi-surfactant shotgun proteomic strategy and identified more than 2600 proteins; GOEx pinpointed key sets of differentially expressed proteins related to cell cycle, alcohol catabolism, the Ras pathway, apoptosis, and stress response, to name a few.</p> <p>Conclusion</p> <p>GOEx facilitates organism-specific studies by leveraging GO and providing a rich graphical user interface. It is a simple to use tool, specialized for biologists who wish to analyze spectral counting data from shotgun proteomics. GOEx is available at <url>http://pcarvalho.com/patternlab</url>.</p

Levels of DNA methylation vary at CpG sites across the BRCA1 promoter, and differ according to triple negative and "BRCA-like" status, in both blood and tumour DNA

Triple negative breast cancer is typically an aggressive and difficult to treat subtype. It is often associated with loss of function of the BRCA1 gene, either through mutation, loss of heterozygosity or methylation. This study aimed to measure methylation of the BRCA1 gene promoter at individual CpG sites in blood, tumour and normal breast tissue, to assess whether levels were correlated between different tissues, and with triple negative receptor status, histopathological scoring for BRCA-like features and BRCA1 protein expression. Blood DNA methylation levels were significantly correlated with tumour methylation at 9 of 11 CpG sites examined (p<0.0007). The levels of tumour DNA methylation were significantly higher in triple negative tumours, and in tumours with high BRCA-like histopathological scores (10 of 11 CpG sites; p<0.01 and p<0.007 respectively). Similar results were observed in blood DNA (6 of 11 CpG sites; p<0.03 and 7 of 11 CpG sites; p<0.02 respectively). This study provides insight into the pattern of CpG methylation across the BRCA1 promoter, and supports previous studies suggesting that tumours with BRCA1 promoter methylation have similar features to those with BRCA1 mutations, and therefore may be suitable for the same targeted therapies