1,410 research outputs found

    Ti(III)-mediated radical cyclization of β-aminoacrylate containing epoxy alcohol moieties: synthesis of highly substituted azacycles

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    Ti(III)-mediated radical cyclization of β-aminoacrylate containing 2,3-epoxy alcohol moieties led to the formation of highly substituted piperidine and pyrrolidine rings. The pyrrolidine ring system was then transformed into an indolizidine framework present in many natural products

    Antimicrobial and antioxidative activities in the bark extracts of Sonneratia caseolaris, a mangrove plant

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    The present study deals with the phytochemical contents, antimicrobial and antioxidative activities of bark tissue of Sonneratia caseolaris, a mangrove plant from Sundarban estuary, India. Phytochemical analyses revealed the presence of high amounts of phenolics, flavonoids, tannins, alkaloids and saponins. Antimicrobial efficacies of various extracts of S. caseolaris were assessed by disc diffusion method against two Gram-positive (Bacillus subtilis and Bacillus coagulans), two Gram-negative (Escherichia coli and Proteus vulgaris) bacteria and one fungus (Saccharomyces cerevisiae). The methanolic extract among others showed significant minimum inhibitory concentration (MIC) values. The antioxidant activity as indicated by 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity of the bark tissue extract from the species was found to be quite appreciable. The extracts were found to retain their antimicrobial activities despite pH and thermal treatments, thus indicating the stability of their activity even at extreme conditions. The antioxidant activity was also found to be considerably stable after thermal treatments. The components of the tissue extracts were subjected to separation using thin layer chromatography (TLC). The constituents with antimicrobial and antioxidative properties were identified using TLC-bioautography by agar-overlay and DPPH spraying methods respectively. A number of bioactive constituents with antimicrobial and radical scavenging properties were observed on the developed bioautography plate. The fractions with antimicrobial properties were isolated from the reference TLC plates and subjected to gaschromatography-mass spectrometry (GC-MS) analysis for partial characterization and identification of the metabolites that might be responsible for the activities. The study suggests Sonneratia caseolaris bark as a potential source of bioactive compounds with stable antimicrobial and antioxidative properties and can be used as natural antimicrobial/antioxidative agents in clinical, pharmaceutical and food processing industries

    Analysis of community question‐answering issues via machine learning and deep learning: State‐of‐the‐art review

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    Over the last couple of decades, community question-answering sites (CQAs) have been a topic of much academic interest. Scholars have often leveraged traditional machine learning (ML) and deep learning (DL) to explore the ever-growing volume of content that CQAs engender. To clarify the current state of the CQA literature that has used ML and DL, this paper reports a systematic literature review. The goal is to summarise and synthesise the major themes of CQA research related to (i) questions, (ii) answers and (iii) users. The final review included 133 articles. Dominant research themes include question quality, answer quality, and expert identification. In terms of dataset, some of the most widely studied platforms include Yahoo! Answers, Stack Exchange and Stack Overflow. The scope of most articles was confined to just one platform with few cross-platform investigations. Articles with ML outnumber those with DL. Nonetheless, the use of DL in CQA research is on an upward trajectory. A number of research directions are proposed

    Predicting the “helpfulness” of online consumer reviews

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    YesOnline shopping is increasingly becoming people's first choice when shopping, as it is very convenient to choose products based on their reviews. Even for moderately popular products, there are thousands of reviews constantly being posted on e-commerce sites. Such a large volume of data constantly being generated can be considered as a big data challenge for both online businesses and consumers. That makes it difficult for buyers to go through all the reviews to make purchase decisions. In this research, we have developed models based on machine learning that can predict the helpfulness of the consumer reviews using several textual features such as polarity, subjectivity, entropy, and reading ease. The model will automatically assign helpfulness values to an initial review as soon as it is posted on the website so that the review gets a fair chance of being viewed by other buyers. The results of this study will help buyers to write better reviews and thereby assist other buyers in making their purchase decisions, as well as help businesses to improve their websites

    Rumour Veracity Estimation with Deep Learning for Twitter

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    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

    Automatic parallelization of irregular and pointer-based computations: perspectives from logic and constraint programming

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    Irregular computations pose some of the most interesting and challenging problems in automatic parallelization. Irregularity appears in certain kinds of numerical problems and is pervasive in symbolic applications. Such computations often use dynamic data structures which make heavy use of pointers. This complicates all the steps of a parallelizing compiler, from independence detection to task partitioning and placement. In the past decade there has been significant progress in the development of parallelizing compilers for logic programming and, more recently, constraint programming. The typical applications of these paradigms frequently involve irregular computations, which arguably makes the techniques used in these compilers potentially interesting. In this paper we introduce in a tutorial way some of the problems faced by parallelizing compilers for logic and constraint programs. These include the need for inter-procedural pointer aliasing analysis for independence detection and having to manage speculative and irregular computations through task granularity control and dynamic task allocation. We also provide pointers to some of the progress made in these ĂĄreas. In the associated talk we demĂłnstrate representatives of several generations of these parallelizing compilers

    Ranking online consumer reviews

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    YesProduct reviews are posted online by the hundreds and thousands for popular products. Handling such a large volume of continuously generated online content is a challenging task for buyers, sellers and researchers. The purpose of this study is to rank the overwhelming number of reviews using their predicted helpfulness scores. The helpfulness score is predicted using features extracted from review text, product description, and customer question-answer data of a product using the random-forest classifier and gradient boosting regressor. The system classifies reviews into low or high quality with the random-forest classifier. The helpfulness scores of the high-quality reviews are only predicted using the gradient boosting regressor. The helpfulness scores of the low-quality reviews are not calculated because they are never going to be in the top k reviews. They are just added at the end of the review list to the review-listing website. The proposed system provides fair review placement on review listing pages and makes all high-quality reviews visible to customers on the top. The experimental results on data from two popular Indian e-commerce websites validate our claim, as 3–4 newer high-quality reviews are placed in the top ten reviews along with 5–6 older reviews based on review helpfulness. Our findings indicate that inclusion of features from product description data and customer question-answer data improves the prediction accuracy of the helpfulness score.Ministry of Electronics and Information Technology (MeitY), Government of India for financial support during research work through “Visvesvaraya PhD Scheme for Electronics and IT”

    High-Risk Histopathological Features of Retinoblastoma following Primary Enucleation: A Global Study of 1426 Patients from 5 Continents

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    Purpose: To evaluate high-risk histopathological features (HRHF) following primary enucleation of eyes with retinoblastoma (RB) and assess the patient outcomes across continents // Methods: Retrospective study of 1426 primarily enucleated RB eyes from five continents // Results: Of all, 923 (65%) were from Asia (AS), 27 (2%) from Australia (AUS), 120 (8%) from Europe (EUR), 162 (11%) from North America (NA), and 194 (14%) from South America (SA). Based on the continent (AS vs. AUS vs. EUR vs. NA vs. SA), the histopathology features included massive choroidal invasion (31% vs. 7% vs. 13% vs. 19% vs. 27%, p=0.001), post-laminar optic nerve invasion (27% vs. 0% vs. 16% vs. 21% vs. 19%, p=0.0006), scleral infiltration (5% vs. 0% vs. 4% vs. 2% vs. 7%, p=0.13), and microscopic extrascleral infiltration (4% vs. 0% vs. <1% vs. <1% vs. 4%, p=0.68). Adjuvant chemotherapy with/without orbital radiotherapy was given in 761 (53%) patients. Based on Kaplan-Meier estimates in different continents (AS vs. AUS vs. EUR vs. NA vs. SA), the 6-year risk of orbital tumor recurrence was 5% vs. 2% vs. 0% vs. 0% vs. 12% (p<0.001), systemic metastasis was reported in 8% vs. 5% vs. 2% vs. 0% vs. 13% (p=0.001), and death in 10% vs. 3% vs. 2% vs. 0% vs. 11% (p<0.001) patients. // Conclusion: There is a wide variation in the infiltrative histopathology features of RB across continents, resulting in variable outcomes. SA and AS had a higher risk of orbital tumor recurrence, systemic metastasis, and death compared to AUS, EUR, and NA
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