Antioxidant and antihyperglycemic potential of methanolic extract of bark of mimusops elengi l. In mice

Ayurveda refers Mimusops elengi L. for the treatment of the diabetes. Considering the traditional claim of M. elengi in management of diabetes and the possible involvement of oxidative stress in pathogenesis of diabetes, the present study was aimed to evaluate the in vitro antioxidant and in vivo antihyperglycemic property of methanolic extract of bark of M. elengi (MEMeOH). In vitro antioxidant activity of MEMeOH was evaluated using reducing power assay, DPPH and hydroxyl radical scavenging assay. MEMeOH offered significant in vitro reducing power capacity and radical scavenging activity. In acute study in alloxan induced diabetes, MEMeOH exhibited significant (p< 0.001) antihyperglycemic effect. The onset of antihyperglycemic effect was observed at 2nd hr; peak activity was demonstrated at 6th hr. The antihyperglycemic effect of MEMeOH 400mg/kg, p.o. was persistent up to 24th hr after drug administration. MEMeOH produced significant (p < 0.01) reduction in elevated glucose levels in glucose loaded non diabetic animals. The onset of action in non diabetic oral glucose tolerance test was found to be at 60th min and peak activity was observed at 120th min after oral glucose load. MEMeOH demonstrated significant (p < 0.01) reduction in elevated glucose levels 2hr before glucose administration and 6 hr after glucose load in oral glucose tolerance test in diabetic animals. MEMeOH has demonstrated antihyperglycemic activity in diabetic as well as non diabetic glucose loaded mice. MEMeOH should be further explored against diabetes and related complications.Keywords: Mimusops elengi; antihyperglycemic, antioxidant, DPPH, diabetic OGT

Inferring Networks of Substitutable and Complementary Products

In a modern recommender system, it is important to understand how products relate to each other. For example, while a user is looking for mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers. These two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other. Here we develop a method to infer networks of substitutable and complementary products. We formulate this as a supervised link prediction task, where we learn the semantics of substitutes and complements from data associated with products. The primary source of data we use is the text of product reviews, though our method also makes use of features such as ratings, specifications, prices, and brands. Methodologically, we build topic models that are trained to automatically discover topics from text that are successful at predicting and explaining such relationships. Experimentally, we evaluate our system on the Amazon product catalog, a large dataset consisting of 9 million products, 237 million links, and 144 million reviews.Comment: 12 pages, 6 figure

Exploring demographic information in social media for product recommendation

In many e-commerce Web sites, product recommendation is essential to improve user experience and boost sales. Most existing product recommender systems rely on historical transaction records or Web-site-browsing history of consumers in order to accurately predict online users’ preferences for product recommendation. As such, they are constrained by limited information available on specific e-commerce Web sites. With the prolific use of social media platforms, it now becomes possible to extract product demographics from online product reviews and social networks built from microblogs. Moreover, users’ public profiles available on social media often reveal their demographic attributes such as age, gender, and education. In this paper, we propose to leverage the demographic information of both products and users extracted from social media for product recommendation. In specific, we frame recommendation as a learning to rank problem which takes as input the features derived from both product and user demographics. An ensemble method based on the gradient-boosting regression trees is extended to make it suitable for our recommendation task. We have conducted extensive experiments to obtain both quantitative and qualitative evaluation results. Moreover, we have also conducted a user study to gauge the performance of our proposed recommender system in a real-world deployment. All the results show that our system is more effective in generating recommendation results better matching users’ preferences than the competitive baselines

A novel blood-based biomarker for detection of autism spectrum disorders

Autism spectrum disorders (ASD) are classified as neurological developmental disorders. Several studies have been carried out to find a candidate biomarker linked to the development of these disorders, but up to date no reliable biomarker is available. Mass spectrometry techniques have been used for protein profiling of blood plasma of children with such disorders in order to identify proteins/peptides that may be used as biomarkers for detection of the disorders. Three differentially expressed peptides with mass–charge (m/z) values of 2020±1, 1864±1 and 1978±1 Da in the heparin plasma of children with ASD that were significantly changed as compared with the peptide pattern of the non-ASD control group are reported here. This novel set of biomarkers allows for a reliable blood-based diagnostic tool that may be used in diagnosis and potentially, in prognosis of ASD

A unified latent variable model for contrastive opinion mining

There are large and growing textual corpora in which people express contrastive opinions about the same topic. This has led to an increasing number of studies about contrastive opinion mining. However, there are several notable issues with the existing studies. They mostly focus on mining contrastive opinions from multiple data collections, which need to be separated into their respective collections beforehand. In addition, existing models are opaque in terms of the relationship between topics that are extracted and the sentences in the corpus which express the topics; this opacity does not help us understand the opinions expressed in the corpus. Finally, contrastive opinion is mostly analysed qualitatively rather than quantitatively. This paper addresses these matters and proposes a novel unified latent variable model (contraLDA), which: mines contrastive opinions from both single and multiple data collections, extracts the sentences that project the contrastive opinion, and measures the strength of opinion contrastiveness towards the extracted topics. Experimental results show the effectiveness of our model in mining contrasted opinions, which outperformed our baselines in extracting coherent and informative sentiment-bearing topics. We further show the accuracy of our model in classifying topics and sentiments of textual data, and we compared our results to five strong baselines

Information Processing Letters 85 (2003) 145–152 A compact execution history for dynamic slicing

A slice of a program P with respect to a slicing criterion C ≡ ({var}, c_stmt) is a subset of the program which includes all statements that directly or indirectly affect the value of variable var in c_stmt [1,10–12]