Who Are More Active and Influential on Twitter?:An Investigation of the Ukraine’s Conflict Episode

Twitter is an emerging form of news media with a wide spectrum of participants involving in news dissemination. Owing to their open and interactive nature, individuals, non-media, and non-commercial participants may play a greater role on this platform; thus, it is deemed to disrupt conventional media structures and introduce new ways of information flow. While this may be true in certain aspects in news dissemination such as allowing a broader range of participants, the authors' analysis of the involvement and influence of the different participant types, based on a large tweets dataset collected during the Ukraine's conflict event (2013-2014), portrays a different picture. Specifically, the results unveil that while non-commercial participants were the most “involved” in generating tweets about the news event, the retweets they attracted, a common measure of influence, were among the lowest. In contrast, mass media and sources related to journalists, professional associations and commercial organizations garnered the highest retweets

Robust Sparse Estimation of Multiresponse Regression and Inverse Covariance Matrix via the L2 distance

ABSTRACT We propose a robust framework to jointly perform two key modeling tasks involving high dimensional data: (i) learning a sparse functional mapping from multiple predictors to multiple responses while taking advantage of the coupling among responses, and (ii) estimating the conditional dependency structure among responses while adjusting for their predictors. The traditional likelihood-based estimators lack resilience with respect to outliers and model misspecification. This issue is exacerbated when dealing with high dimensional noisy data. In this work, we propose instead to minimize a regularized distance criterion, which is motivated by the minimum distance functionals used in nonparametric methods for their excellent robustness properties. The proposed estimates can be obtained efficiently by leveraging a sequential quadratic programming algorithm. We provide theoretical justification such as estimation consistency for the proposed estimator. Additionally, we shed light on the robustness of our estimator through its linearization, which yields a combination of weighted lasso and graphical lasso with the sample weights providing an intuitive explanation of the robustness. We demonstrate the merits of our framework through simulation study and the analysis of real financial and genetics data

G9a Is Essential for EMT-Mediated Metastasis and Maintenance of Cancer Stem Cell-Like Characters in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a particularly aggressive cancer with poor prognosis, largely due to lymph node metastasis and local recurrence. Emerging evidence suggests that epithelial-to-mesenchymal transition (EMT) is important for cancer metastasis, and correlated with increased cancer stem cells (CSCs) characteristics. However, the mechanisms underlying metastasis to lymph nodes in HNSCC is poorly defined. In this study, we show that E-cadherin repression correlates with cancer metastasis and poor prognosis in HNSCC. We found that G9a, a histone methyltransferase, interacts with Snail and mediates Snail-induced transcriptional repression of E-cadherin and EMT, through methylation of histone H3 lysine-9 (H3K9). Moreover, G9a is required for both lymph node-related metastasis and TGF-β-induced EMT in HNSCC cells since knockdown of G9a reversed EMT, inhibited cell migration and tumorsphere formation, and suppressed the expression of CSC markers. Our study demonstrates that the G9a protein is essential for the induction of EMT and CSC-like properties in HNSCC. Thus, targeting the G9a-Snail axis may represent a novel strategy for treatment of metastatic HNSCC

Autocrine Epiregulin Activates EGFR Pathway for Lung Metastasis Via EMT in Salivary Adenoid Cystic Carcinoma

Salivary adenoid cystic carcinoma (SACC) is characterized by invasive local growth and a high incidence of lung metastasis. Patients with lung metastasis have a poor prognosis. Treatment of metastatic SACC has been unsuccessful, largely due to a lack of specific targets for the metastatic cells. In this study, we showed that epidermal growth factor receptors (EGFR) were constitutively activated in metastatic lung subtypes of SACC cells, and that this activation was induced by autocrine expression of epiregulin (EREG), a ligand of EGFR. Autocrine EREG expression was increased in metastatic SACC-LM cells compared to that in non-metastatic parental SACC cells. Importantly, EREG-neutralizing antibody, but not normal IgG, blocked the autocrine EREG-induced EGFR phosphorylation and the migration of SACC cells, suggesting that EREG-induced EGFR activation is essential for induction of cell migration and invasion by SACC cells. Moreover, EREG-activated EGFR stabilized Snail and Slug, which promoted EMT and metastatic features in SACC cells. Of note, targeting EGFR with inhibitors significantly suppressed both the motility of SACC cells in vitro and lung metastasis in vivo. Finally, elevated EREG expression showed a strong correlation with poor prognosis in head and neck cancer. Thus, targeting the EREG-EGFR-Snail/Slug axis represents a novel strategy for the treatment of metastatic SACC even no genetic EGFR mutation

Life Cycle Assessment With Primary Data on Heavy Rare Earth Oxides From Ion-Adsorption Clays

This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Please cite this article as: Deng, H. & Kendall, A. Int J Life Cycle Assess (2019) 24: 1643. https://doi.org/10.1007/s11367-019-01582-1Heavy and light rare earth elements (REEs) are critical to clean energy technologies, and thus the environmental impacts from their production are increasingly scrutinized. Most previous LCAs of REE production focus on sites producing light REEs. This research addresses this gap by collecting primary data from sites producing heavy rare earth oxides (HREOs) from ion-adsorption clays, conducting an LCA, and providing open-source life cycle inventory (LCI) datasets of HREO production for the LCA community

Advances in Lung Stem Cells and Lung Cancer Stem Cells

Cancer stem cells (CSCs) are emerging as a hot topic for cancer research. Lung CSCs share many characteristics with normal lung stem cells (SCs), including self-renewal and multi-potency for differentiation. Many molecular markers expressed in various types of CSCs were also found in lung CSCs, such as CD133, CD44, aldehyde dehydrogenase (ALDH) and ATP-binding cassette sub-family G member 2 (ABCG2). Similarly, proliferation and expansion of lung CSCs are regulated not only by signal transduction pathways functioning in normal lung SCs, such as Notch, Hedgehog and Wnt pathways, but also by those acting in tumor cells, such as epidermal growth factor receptor (EGFR), signal transducer and activator of transcription 3 (STAT3) and phosphatidylinositol 3 kinase (PI3K) pathways. As CSC plays an critical role in tumor recurrence, metastasis and drug-resistance, understanding the difference between lung CSCs and normal lung SCs, identifying and targeting CSC markers or related signaling pathways may increase the efficacy of therapy on lung cancer and improved survival of lung cancer patients

Semantic approaches to software component retrieval with English queries

Enabling code reuse is an important goal in software engineering, and it depends crucially on effective code search interfaces. We propose to ground word meanings in source code and use such language-code mappings in order to enable a search engine for programming library code where users can pose queries in English. We exploit the fact that there are large programming language libraries which are documented both via formally specified function or method signatures as well as descriptions written in natural language. Automatically learned associations between words in descriptions and items in signatures allows us to use queries formulated in English to retrieve methods which are not documented via natural language descriptions, only based on their signatures. We show that the rankings returned by our model substantially outperforms a strong term-matching baseline

Evaluating the Citations of Information Systems Journals in Wikipedia

Journals from Information Systems field have been evaluated based on several approaches, such as expert assessment or bibliometric measures. These approaches assess the academic quality of journals and rank them accordingly. However, the quality of these journals beyond academia has not been investigated extensively. In this study, we aim to evaluate Information Systems Journals based on their number of citations on Wikipedia, which is the platform for public audience to gain knowledge. Our preliminary findings show that the citations of papers published in IS journals by Wikipedia articles are few. The best two journals, MIS Quarterly and Information Systems Research, have relatively higher number of citations compared with other journals. Academic expert assessment and bibliometric measures generally indicate similar results with Wikipedia citations

Modeling the Adoption of Full-day, Part-day and Overtime Telecommuting: An Investigation of Northern California Workers Using Non-Mean-Centered Factor Scores to Segment on Built Environment Attitudes

This paper investigates the impact of several factors on the adoption of telecommuting, using empirical data collected from 1,864 commuters in Northern California. We distinguish three types of telecommuting – full-day, part-day, and overtime – as they might affect travel behavior in very different ways. We develop binary logit models and a trivariate probit model of the adoption of each form of telecommuting, using non-centered factor scores to identify pro-high- vs. pro-low-density workers. We test the hypothesis that the impact of the built environment on the decision to adopt telecommuting differs between those two segments. The results support the hypothesis, with numerous variables significant to one of those segments but not the other; in the case of the binary logit model for overtime telecommuting, employment density in the home area even has opposite influences for pro-high-density (positive) compared to pro-low-density people (negative). Other groups of variables, including personal attitudes and demographic traits, also significantly affect individuals’ decisions on whether and how to telecommute, if at all. For example (consistent with other studies), education and household income are strong (positive) predictors of the decision to telecommute. Compared to the separate binary logit models, the trivariate probit model better predicts the joint probabilities of adopting the various forms of telecommuting, and better models the correlated adoption of the three types of telecommuting