Rumour Veracity Estimation with Deep Learning for Twitter

Part 4: Security, Privacy, Ethics and MisinformationInternational audienceTwitter has become a fertile ground for rumours as information can propagate to too many people in very short time. Rumours can create panic in public and hence timely detection and blocking of rumour information is urgently required. We proposed and compare machine learning classifiers with a deep learning model using Recurrent Neural Networks for classification of tweets into rumour and non-rumour classes. A total thirteen features based on tweet text and user characteristics were given as input to machine learning classifiers. Deep learning model was trained and tested with textual features and five user characteristic features. The findings indicate that our models perform much better than machine learning based models

Effect of Kohl-Chikni Dawa – a compound ophthalmic formulation of Unani medicine on naphthalene-induced cataracts in rats

BACKGROUND: Cataracts are the leading cause of blindness worldwide, accounting for 13-27% of cases. Kohl-Chikni Dawa (KCD) is reputed for its beneficial effects in the treatment of premature cataracts. However, its efficacy is yet to be tested. To investigate the rationality of the therapeutic use of Kohl-Chikni Dawa (KCD) in Unani medicine. METHODS: The effect of Kohl-Chikni Dawa eye drops on naphthalene-induced cataracts in rats was investigated by slit-lamp biomicroscopic analysis. The normal group of experimental animals was administered with mineral oil (orally), while other groups were given naphthalene (orally) along with local application of KCD eye drops (once and twice daily), placebo and distilled water (twice daily). Initial morphological changes of the lenses were observed twice a week for two weeks, and thereafter once a week for four weeks. RESULTS: Local application of KCD (twice daily) caused significant reduction in the lens opacification after 2 to 4 weeks of naphthalene administration. CONCLUSION: KCD eye drops may have the potential to delay progression of naphthalene-induced cataracts in rats

Radiation-Induced Bystander Effects in Cultured Human Stem Cells

The radiation-induced "bystander effect" (RIBE) was shown to occur in a number of experimental systems both in vitro and in vivo as a result of exposure to ionizing radiation (IR). RIBE manifests itself by intercellular communication from irradiated cells to non-irradiated cells which may cause DNA damage and eventual death in these bystander cells. It is known that human stem cells (hSC) are ultimately involved in numerous crucial biological processes such as embryologic development; maintenance of normal homeostasis; aging; and aging-related pathologies such as cancerogenesis and other diseases. However, very little is known about radiation-induced bystander effect in hSC. To mechanistically interrogate RIBE responses and to gain novel insights into RIBE specifically in hSC compartment, both medium transfer and cell co-culture bystander protocols were employed.Human bone-marrow mesenchymal stem cells (hMSC) and embryonic stem cells (hESC) were irradiated with doses 0.2 Gy, 2 Gy and 10 Gy of X-rays, allowed to recover either for 1 hr or 24 hr. Then conditioned medium was collected and transferred to non-irradiated hSC for time course studies. In addition, irradiated hMSC were labeled with a vital CMRA dye and co-cultured with non-irradiated bystander hMSC. The medium transfer data showed no evidence for RIBE either in hMSC and hESC by the criteria of induction of DNA damage and for apoptotic cell death compared to non-irradiated cells (p>0.05). A lack of robust RIBE was also demonstrated in hMSC co-cultured with irradiated cells (p>0.05).These data indicate that hSC might not be susceptible to damaging effects of RIBE signaling compared to differentiated adult human somatic cells as shown previously. This finding could have profound implications in a field of radiation biology/oncology, in evaluating radiation risk of IR exposures, and for the safety and efficacy of hSC regenerative-based therapies

Intelligent Monitoring and Controlling of Public Policies Using Social Media and Cloud Computing

Part 3: Government and InfrastructureInternational audienceLack of public participation in various policy making decision has always been a major cause of concern for government all around the world while formulating as well as evaluating such policies. With availability of latest IT infrastructure and the migration of government think-tank towards realizing more efficient cloud based e-government, this problem has been partially answered, but this predicament still persists. However, the exponential rise in usage of social media platforms by general public has given the government a wider insight to overcome this long pending dilemma. This paper presents a pragmatic approach that combines the capabilities of cloud computing and social media analytics towards efficient monitoring and controlling of public policies. The proposed arrangement has provided us some encouraging results, when tested for the policy of the century i.e. GST implementation by Indian government and established that proposed system can be successfully implemented for efficient policy making and implementation

Early growth response-1 is a regulator of DR5-induced apoptosis in colon cancer cells

BACKGROUND: Tumour necrosis factor-related apoptosis-inducing ligand (TRAIL) induces tumour cell apoptosis by binding to death receptor 4 (DR4) and DR5. DR4 and DR5 activation however can also induce inflammatory and pro-survival signalling. It is not known how these different cellular responses are regulated and what the individual role of DR4 vs DR5 is in these processes.METHODS: DNA microarray study was carried out to identify genes differentially expressed after DR4 and DR5 activation. RT-PCR and western blotting was used to examine the expression of early growth response gene-1 (Egr-1) and the proteins of the TRAIL signalling pathway. The function of Egr-1 was studied by siRNA-mediated knockdown and overexpression of a dominant-negative version of Egr-1.RESULTS: We show that the immediate early gene, Egr-1, regulates TRAIL sensitivity. Egr-1 is constitutively expressed in colon cancer cells and further induced upon activation of DR4 or DR5. Our results also show that DR4 mediates a type II, mitochondrion-dependent apoptotic pathway, whereas DR5 induces a mitochondrion-independent, type I apoptosis in HCT15 colon carcinoma cells. Egr-1 drives c-FLIP expression and the short splice variant of c-FLIP (c-FLIPS) specifically inhibits DR5 activation.CONCLUSION: Selective knockdown of c-FLIPS sensitises cells to DR5-induced but not DR4-induced apoptosis and Egr-1 exerts an effect as an inhibitor of the DR5-induced apoptotic pathway, possibly by regulating the expression of c-FLIPS. British Journal of Cancer (2010) 102, 754-764. doi:10.1038/sj.bjc.6605545 www.bjcancer.com Published online 19 January 2010 (C) 2010 Cancer Research U

A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach

E-commerce start-ups have ventured into emerging economies and are growing at a significantly faster pace. Big data has acted like a catalyst in their growth story. Big data analytics (BDA) has attracted e-commerce firms to invest in the tools and gain cutting edge over their competitors. The process of adoption of these BDA tools by e-commerce start-ups has been an area of interest as successful adoption would lead to better results. The present study aims to develop an interpretive structural model (ISM) which would act as a framework for efficient implementation of BDA. The study uses hybrid multi criteria decision making processes to develop the framework and test the same using a real-life case study. Systematic review of literature and discussion with experts resulted in exploring 11 enablers of adoption of BDA tools. Primary data collection was done from industry experts to develop an ISM framework and fuzzy MICMAC analysis is used to categorize the enablers of the adoption process. The framework is then tested by using a case study. Thematic clustering is performed to develop a simple ISM framework followed by fuzzy analytical network process (ANP) to discuss the association and ranking of enablers. The results indicate that access to relevant data forms the base of the framework and would act as the strongest enabler in the adoption process while the company rates technical skillset of employees as the most important enabler. It was also found that there is a positive correlation between the ranking of enablers emerging out of ISM and ANP. The framework helps in simplifying the strategies any e-commerce company would follow to adopt BDA in future. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature

Attention-based LSTM network for rumor veracity estimation of tweets

YesTwitter has become a fertile place for rumors, as information can spread to a large number of people immediately. Rumors can mislead public opinion, weaken social order, decrease the legitimacy of government, and lead to a significant threat to social stability. Therefore, timely detection and debunking rumor are urgently needed. In this work, we proposed an Attention-based Long-Short Term Memory (LSTM) network that uses tweet text with thirteen different linguistic and user features to distinguish rumor and non-rumor tweets. The performance of the proposed Attention-based LSTM model is compared with several conventional machine and deep learning models. The proposed Attention-based LSTM model achieved an F1-score of 0.88 in classifying rumor and non-rumor tweets, which is better than the state-of-the-art results. The proposed system can reduce the impact of rumors on society and weaken the loss of life, money, and build the firm trust of users with social media platforms