Predicting How People Vote From How They Tweet

In 2016 Donald Trump stunned the nation and not a single pollster predicted the outcome. For the last few decades, pollsters have relied on phone banking as their main source of information. There is reason to believe that this method does not present the complete picture it once did due to several factors--less landline usage, a younger and more active electorate, and the rise of social media. Social media specifically has grown in prominence and become a forum for political debate. This project quantitatively analyzes political twitter data and leverages machine learning techniques such as Naive-Bayes to model election results. Early results are promising, and a true evaluation of the model will come from testing in future elections

Novel glioblastoma markers with diagnostic and prognostic value identified through transcriptome analysis

Purpose: Current methods of classification of astrocytoma based on histopathologic methods are often subjective and less accurate. Although patients with glioblastoma have grave prognosis, significant variability in patient outcome is observed. Therefore, the aim of this study was to identify glioblastoma diagnostic and prognostic markers through microarray analysis. Experimental Design: We carried out transcriptome analysis of 25 diffusely infiltrating astrocytoma samples [WHO grade II - diffuse astrocytoma, grade III - anaplastic astrocytoma, and grade IV - glioblastoma (GBM)] using cDNA microarrays containing 18,981 genes. Several of the markers identified were also validated by real-time reverse transcription quantitative PCR and immunohistochemical analysis on an independent set of tumor samples (n = 100). Survival analysis was carried out for two markers on another independent set of retrospective cases (n = 51). Results: We identified several differentially regulated grade-specific genes. Independent validation by real-time reverse transcription quantitative PCR analysis found growth arrest and DNA-damage-inducible α (GADD45α) and follistatin-like 1 (FSTL1) to be up-regulated in most GBMs (both primary and secondary), whereas superoxide dismutase 2 and adipocyte enhancer binding protein 1 were up-regulated in the majority of primary GBM. Further, identification of the grade-specific expression of GADD45α and FSTL1 by immunohistochemical staining reinforced our findings. Analysis of retrospective GBM cases with known survival data revealed that cytoplasmic overexpression of GADD45α conferred better survival while the coexpression of FSTL1 with p53 was associated with poor survival. Conclusions: Our study reveals that GADD45α and FSTLI are GBM-specific whereas superoxide dismutase 2 and adipocyte enhancer binding protein 1 are primary GBM-specific diagnostic markers. Whereas GADD45α overexpression confers a favorable prognosis, FSTL1 overexpression is a hallmark of poor prognosis in GBM patients

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It can be seen that the Vinnakota and Rao converters using cascade subtractors exhibit more delay than the Andraros and Ahmad design. Note that in the Andraros and Ahmad design, the full adders needed can be reduced [10] by noting that one of the inputs f in (4) is a constant 1 or 0 for � LSB’s, which we have not considered in the above evaluation in (5a). The high-speed version of the Vinnakota and Rao converter exhibits similar delay and area requirements as the Andraros and Ahmad design. However, both the Piestrak’s costeffective and high-speed designs are superior to Vinnakota and Rao’s high-speed as well as cost-effective designs. IV. CONCLUSION It has been shown that a recently described RNS-to-binary conversion technique for the moduli set P �0IY P � Y and P � CI is a variation of the well-known MRC technique. An evaluation of the area and delay performance of this technique and comparison to the Andraros and Ahmad technique described in this brief has shown that the highspeed version of Vinnakota and Rao’s converter is comparable in performance to the Andraros and Ahmad technique. However, both the cost-effective and high-speed designs of Piestrak, which are an improvement over the Andraros and Ahmad technique, are shown to be superior to Vinnakota and Rao’s converter, regarding area as well as conversion delay. ACKNOWLEDGMENT The author wishes to thank the anonymous reviewer for his objective comments which have significantly enhanced the quality of this brief

Gliomas induce and exploit microglial MT1-MMP expression for tumor expansion

Diffuse infiltration of glioma cells into normal brain tissue is considered to be a main reason for the unfavorable outcomes of patients with malignant gliomas. Invasion of glioma cells into the brain parenchyma is facilitated by metalloprotease-mediated degradation of the extracellular matrix. Metalloproteases are released as inactive pro-forms and get activated upon cleavage by membrane bound metalloproteases. Here, we show that membrane type 1 metalloprotease (MT1-MMP) is up-regulated in glioma-associated microglia, but not in the glioma cells. Overexpression of MT1-MMP is even lethal for glioma cells. Glioma-released factors trigger the expression and activity of MT1-MMP via microglial toll-like receptors and the p38 MAPK pathway, as deletion of the toll-like receptor adapter protein MyD88 or p38 inhibition prevented MT1-MMP expression and activity in cultured microglial cells. Microglial MT1-MMP in turn activates glioma-derived pro-MMP-2 and promotes glioma expansion, as shown in an ex vivo model using MT1-MMP-deficient brain tissue and a microglia depletion paradigm. Finally, MyD88 deficiency or microglia depletion largely attenuated glioma expansion in 2 independent in vivo models