Building Trustworthiness of User-Feedbacks on Products in Online Shopping Environment

Lack of Trust is the main issue why people fear using E-Commerce for their regular shopping purpose, whereas E-Commerce is very much cost-effective as compared to traditional shopping. But people fear using E-Commerce just because there is no personal contact with the vendor. So it is a biggest challenge to remove this fear from the minds of buyers. The only solution to remove this fear is building Trust in their minds. There are many factors which should be focussed upon while building this trust, they are broadly classified as Knowledge-Based Trust which includes familiarity, reputation, brand, size, website quality, and customer service. We in this thesis have considered reputation as a factor to enhance the website quality. In our implementation we make sure that users give genuine feedbacks on the products. We use the concept of Sentiment Analysis to know the sentiment behind the given semantic feedback and check its concordance with rating given by the same user. If concordance is satisfactory user has to like or dislike five prefabricated feedbacks. Using both the concordance and like- dislike results, Trust Degree of the user is calculated. If the Trust Degree is above certain threshold then the feedback of that user is submitted to our website else the user is blocked. This is how we improve website quality by our implementation. DOI: 10.17762/ijritcc2321-8169.16040

Trustworthiness and Analysis of Sentiment of user’s Semantic Feedbacks in E-Commerce

While Shopping Online Buyers mostly depend on reviews from users available on various websites. Trust is an important factor in any sort of relationship. A buyer can often see both the seller and the product, verify its quality, negotiate and bargain with the seller in traditional commerce. But in the context of online shopping, there is a absence of this face to face trust assessment. Absence of trust is always considered as an hurdle in online transactions. Ratings are available online. However those ratings are not always truthful. Then, they can falsify the weight and the scores .Semantic feedbacks make more sense than single scores. Our System aims at creating trust in online communities while giving action taking results .Those results such as trust weight, scores and the results of Sentiment Analysis help users to make a decision about purchasing particular product or not from an e-commerce application. Proposed design will use both ratings and semantic feedbacks to calculate trust weight and to classify comments and users. DOI: 10.17762/ijritcc2321-8169.15027

Taxonomy of breast cancer based on normal cell phenotype predicts outcome

Accurate classification is essential for understanding the pathophysiology of a disease and can inform therapeutic choices. For hematopoietic malignancies, a classification scheme based on the phenotypic similarity between tumor cells and normal cells has been successfully used to define tumor subtypes; however, use of normal cell types as a reference by which to classify solid tumors has not been widely emulated, in part due to more limited understanding of epithelial cell differentiation compared with hematopoiesis. To provide a better definition of the subtypes of epithelial cells comprising the breast epithelium, we performed a systematic analysis of a large set of breast epithelial markers in more than 15,000 normal breast cells, which identified 11 differentiation states for normal luminal cells. We then applied information from this analysis to classify human breast tumors based on normal cell types into 4 major subtypes, HR0–HR3, which were differentiated by vitamin D, androgen, and estrogen hormone receptor (HR) expression. Examination of 3,157 human breast tumors revealed that these HR subtypes were distinct from the current classification scheme, which is based on estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Patient outcomes were best when tumors expressed all 3 hormone receptors (subtype HR3) and worst when they expressed none of the receptors (subtype HR0). Together, these data provide an ontological classification scheme associated with patient survival differences and provides actionable insights for treating breast tumors

Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial

Background: Glucagon-like peptide 1 receptor agonists differ in chemical structure, duration of action, and in their effects on clinical outcomes. The cardiovascular effects of once-weekly albiglutide in type 2 diabetes are unknown. We aimed to determine the safety and efficacy of albiglutide in preventing cardiovascular death, myocardial infarction, or stroke. Methods: We did a double-blind, randomised, placebo-controlled trial in 610 sites across 28 countries. We randomly assigned patients aged 40 years and older with type 2 diabetes and cardiovascular disease (at a 1:1 ratio) to groups that either received a subcutaneous injection of albiglutide (30–50 mg, based on glycaemic response and tolerability) or of a matched volume of placebo once a week, in addition to their standard care. Investigators used an interactive voice or web response system to obtain treatment assignment, and patients and all study investigators were masked to their treatment allocation. We hypothesised that albiglutide would be non-inferior to placebo for the primary outcome of the first occurrence of cardiovascular death, myocardial infarction, or stroke, which was assessed in the intention-to-treat population. If non-inferiority was confirmed by an upper limit of the 95% CI for a hazard ratio of less than 1·30, closed testing for superiority was prespecified. This study is registered with ClinicalTrials.gov, number NCT02465515. Findings: Patients were screened between July 1, 2015, and Nov 24, 2016. 10 793 patients were screened and 9463 participants were enrolled and randomly assigned to groups: 4731 patients were assigned to receive albiglutide and 4732 patients to receive placebo. On Nov 8, 2017, it was determined that 611 primary endpoints and a median follow-up of at least 1·5 years had accrued, and participants returned for a final visit and discontinuation from study treatment; the last patient visit was on March 12, 2018. These 9463 patients, the intention-to-treat population, were evaluated for a median duration of 1·6 years and were assessed for the primary outcome. The primary composite outcome occurred in 338 (7%) of 4731 patients at an incidence rate of 4·6 events per 100 person-years in the albiglutide group and in 428 (9%) of 4732 patients at an incidence rate of 5·9 events per 100 person-years in the placebo group (hazard ratio 0·78, 95% CI 0·68–0·90), which indicated that albiglutide was superior to placebo (p<0·0001 for non-inferiority; p=0·0006 for superiority). The incidence of acute pancreatitis (ten patients in the albiglutide group and seven patients in the placebo group), pancreatic cancer (six patients in the albiglutide group and five patients in the placebo group), medullary thyroid carcinoma (zero patients in both groups), and other serious adverse events did not differ between the two groups. There were three (<1%) deaths in the placebo group that were assessed by investigators, who were masked to study drug assignment, to be treatment-related and two (<1%) deaths in the albiglutide group. Interpretation: In patients with type 2 diabetes and cardiovascular disease, albiglutide was superior to placebo with respect to major adverse cardiovascular events. Evidence-based glucagon-like peptide 1 receptor agonists should therefore be considered as part of a comprehensive strategy to reduce the risk of cardiovascular events in patients with type 2 diabetes. Funding: GlaxoSmithKline

Game Theoretic approach applied in Cybersecurity Information Exchange Framework

In CYBersecurity Information Exchange (CYBEX) framework, Cyber Threat Intelligence (CTI) is shared among multiple organizations with a view of creating situationalawareness. But there is a possibility of malicious organizations to coexist with regular ones in this framework, which can get hold of the threat data shared by otherorganizations and can use it for carrying out malicious activities. We formulate theaforementioned problem as an incomplete information game assuming that wheneverCYBEX receives any information, it processes the information for anomaly detection.We find the mixed strategy Nash equilibrium and corresponding Bayesian belief updates. We simulate the game to find the best response strategies with which regularand malicious organizations can play to increase their payoffs. Based on the bestresponse strategies of organizations, we analyze that achieving more anomaly detection rate while keeping the processing rate minimum is the best action strategy withwhich CYBEX can play to increase gain of both CYBEX and regular organizationsover malicious organizations. We also find the approximate average minimum processing rate and anomaly detection rate with which CYBEX can play in order toreduce the negative impact of malicious organizations over the framework as a whole

Novel Hormonal Combination Therapy for Triple Negative Breast Cancer

Anti-estrogen and anti-HER2 treatments have been among the first and most successful examples of targeted-therapy for breast cancers. However, around 10-20% of breast cancers lack estrogen receptor (ER) expression and HER2 amplification. Therefore, the treatment of ER-/HER2- Triple Negative Breast Cancer (TNBC) remains a major challenge due to lack of a druggable target. Currently, TNBCs are treated with non-targeted conventional chemotherapy and although they respond initially, less than 30% patients survive beyond 5 years highlighting the need for novel therapeutic strategies for this disease. We previously discovered that approximately two-thirds of TNBCs express Vitamin D Receptor (VDR) and/or Androgen Receptor (AR) and hypothesized that TNBCs that co-express both AR and VDR (HR2-av TNBC) could be treated by targeting both hormone receptors. To evaluate the feasibility of VDR/AR-targeted therapy in TNBC, we characterized 15 different breast cancer cell lines and identified two HR2-av TNBC lines. Surprisingly, we found that AR antagonist inhibited proliferation of most breast cancer cell lines in an AR-independent manner, raising questions regarding their mechanism of action. In contrast, combination treatment of the same cell lines with AR and VDR agonist hormones appeared to inhibit cell proliferation additively in a hormone receptor dependent manner. Moreover, cell viability was further decreased when AR/VDR agonist hormones were combined with chemotherapeutic drugs. The mechanisms of inhibition by AR/VDR agonist hormones included cell cycle arrest and apoptosis in TNBC cell lines. In addition, AR/VDR agonist hormones induced differentiation and inhibited cancer stem cells (CSCs) measured by reduction in tumorsphere formation efficiency, ALDH activity and CSC marker changes. Moreover, alternate AR and VDR ligands are available with enhanced or similar efficacy compared to the traditional agonists but with better side-effect profile. The combination of alternate AR and VDR ligands also showed additive decrease in cell viability in combination with each other as well as with chemotherapeutic agents in vitro. Thus, in summary, AR/VDR targeted agonist hormone therapy can inhibit HR2-av TNBC through multiple mechanisms in a receptor dependent manner and can be combined with chemotherapy

Heat shock factor 1 induces cancer stem cell phenotype in breast cancer cell lines

Heat shock factor 1 (HSF1) has long been recognized as the master transcription factor that regulates heat shock proteins (HSPs). More recently HSF1 has been associated with a broader role in regulating response to a variety of cellular stresses beyond heat-shock. We previously found that high HSF1 expression is associated with poor outcome in lung, breast and colon cancers. Importantly, however, the HSF1 signature correlated with poor outcome in these studies was not related to the heat shock response, which suggested that tumor outcome associated with high HSF expression may be due to processes other than stress response. Hence, we explored the question whether high HSF1 expression might be associated with the cancer stem cell (CSC) phenotype. To do so, we examined the association of HSF1 with CSC phenotype by FACS and immunofluorescence. In addition, we evaluated the effects of HSF1 over-expression and knock-down on sphere formation and CSC marker expression in breast cancer cell lines. Here, we report results demonstrating that high HSF1 not only correlates with CSC marker expression, but inducible HSF1 over-expression augments and HSF1 knock-down inhibits CSC phenotype. Furthermore, HSF1 expression confers resistance to chemotherapeutic drugs and increases CSC frequency. In conclusion, our study indicates that one of the potential HSP-independent HSF1 driven mechanisms that may contribute to poor outcome in human tumors involves regulation of the CSC phenotype. Hence, therapeutic inhibition of HSF1 may be one route to target CSCs in human tumors. Electronic supplementary material The online version of this article (doi:10.1007/s10549-015-3521-1) contains supplementary material, which is available to authorized users

Taxonomy of breast cancer based on normal cell phenotype predicts outcome

Accurate classification is essential for understanding the pathophysiology of a disease and can inform therapeutic choices. For hematopoietic malignancies, a classification scheme based on the phenotypic similarity between tumor cells and normal cells has been successfully used to define tumor subtypes; however, use of normal cell types as a reference by which to classify solid tumors has not been widely emulated, in part due to more limited understanding of epithelial cell differentiation compared with hematopoiesis. To provide a better definition of the subtypes of epithelial cells comprising the breast epithelium, we performed a systematic analysis of a large set of breast epithelial markers in more than 15,000 normal breast cells, which identified 11 differentiation states for normal luminal cells. We then applied information from this analysis to classify human breast tumors based on normal cell types into 4 major subtypes, HR0–HR3, which were differentiated by vitamin D, androgen, and estrogen hormone receptor (HR) expression. Examination of 3,157 human breast tumors revealed that these HR subtypes were distinct from the current classification scheme, which is based on estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Patient outcomes were best when tumors expressed all 3 hormone receptors (subtype HR3) and worst when they expressed none of the receptors (subtype HR0). Together, these data provide an ontological classification scheme associated with patient survival differences and provides actionable insights for treating breast tumors