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

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

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

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

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

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

NaV channel variants in patients with painful and nonpainful peripheral neuropathy

Objective: To examine the incidence of nonsynonymous missense variants in SCN9A (NaV1.7), SCN10A (NaV1.8), and SCN11A (NaV1.9) in patients with painful and nonpainful peripheral neuropathy. Methods: Next-generation sequencing was performed on 457 patient DNA samples provided by the Peripheral Neuropathy Research Registry (PNRR). The patient diagnosis was as follows: 278 idiopathic peripheral neuropathy (67% painful and 33% nonpainful) and 179 diabetic distal polyneuropathy (77% painful and 23% nonpainful). Results: We identified 36 (SCN9A), 31 (SCN10A), and 15 (SCN11A) nonsynonymous missense variants, with 47.7% of patients carrying a low-frequency (minor allele frequency <5%) missense variant in at least 1 gene. The incidence of previously reported gain-of-function missense variants was low (≤3%), and these were detected in patients with and without pain. There were no significant differences in missense variant allele frequencies of any gene, or SCN9A haplotype frequencies, between PNRR patients with painful or nonpainful peripheral neuropathy. PNRR patient SCN9A and SCN11A missense variant allele frequencies were not significantly different from the Exome Variant Server, European American (EVS-EA) reference population. For SCN10A, there was a significant increase in the alternate allele frequency of the common variant p.V1073A and low-frequency variant pS509P in PNRR patients compared with EVS-EA and the 1000 Genomes European reference populations. Conclusions: These results suggest that identification of a genetically defined subpopulation for testing of NaV1.7 inhibitors in patients with peripheral neuropathy is unlikely and that additional factors, beyond expression of previously reported disease “mutations,” are more important for the development of painful neuropathy than previously discussed

NaV channel variants in patients with painful and nonpainful peripheral neuropathy

Objective: To examine the incidence of nonsynonymous missense variants in SCN9A (NaV1.7), SCN10A (NaV1.8), and SCN11A (NaV1.9) in patients with painful and nonpainful peripheral neuropathy. Methods: Next-generation sequencing was performed on 457 patient DNA samples provided by the Peripheral Neuropathy Research Registry (PNRR). The patient diagnosis was as follows: 278 idiopathic peripheral neuropathy (67% painful and 33% nonpainful) and 179 diabetic distal polyneuropathy (77% painful and 23% nonpainful). Results: We identified 36 (SCN9A), 31 (SCN10A), and 15 (SCN11A) nonsynonymous missense variants, with 47.7% of patients carrying a low-frequency (minor allele frequency <5%) missense variant in at least 1 gene. The incidence of previously reported gain-of-function missense variants was low (≤3%), and these were detected in patients with and without pain. There were no significant differences in missense variant allele frequencies of any gene, or SCN9A haplotype frequencies, between PNRR patients with painful or nonpainful peripheral neuropathy. PNRR patient SCN9A and SCN11A missense variant allele frequencies were not significantly different from the Exome Variant Server, European American (EVS-EA) reference population. For SCN10A, there was a significant increase in the alternate allele frequency of the common variant p.V1073A and low-frequency variant pS509P in PNRR patients compared with EVS-EA and the 1000 Genomes European reference populations. Conclusions: These results suggest that identification of a genetically defined subpopulation for testing of NaV1.7 inhibitors in patients with peripheral neuropathy is unlikely and that additional factors, beyond expression of previously reported disease “mutations,” are more important for the development of painful neuropathy than previously discussed

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

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 áreas. In the associated talk we demónstrate representatives of several generations of these parallelizing compilers

Ranking online consumer reviews

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”

Selection of the silicon sensor thickness for the Phase-2 upgrade of the CMS Outer Tracker

During the operation of the CMS experiment at the High-Luminosity LHC the silicon sensors of the Phase-2 Outer Tracker will be exposed to radiation levels that could potentially deteriorate their performance. Previous studies had determined that planar float zone silicon with n-doped strips on a p-doped substrate was preferred over p-doped strips on an n-doped substrate. The last step in evaluating the optimal design for the mass production of about 200 m$^{2}$ of silicon sensors was to compare sensors of baseline thickness (about 300 μm) to thinned sensors (about 240 μm), which promised several benefits at high radiation levels because of the higher electric fields at the same bias voltage. This study provides a direct comparison of these two thicknesses in terms of sensor characteristics as well as charge collection and hit efficiency for fluences up to 1.5 × 10$^{15}$ n$_{eq}$/cm$^{2}$. The measurement results demonstrate that sensors with about 300 μm thickness will ensure excellent tracking performance even at the highest considered fluence levels expected for the Phase-2 Outer Tracker

Beam test performance of a prototype module with Short Strip ASICs for the CMS HL-LHC tracker upgrade

The Short Strip ASIC (SSA) is one of the four front-end chips designed for the upgrade of the CMS Outer Tracker for the High Luminosity LHC. Together with the Macro-Pixel ASIC (MPA) it will instrument modules containing a strip and a macro-pixel sensor stacked on top of each other. The SSA provides both full readout of the strip hit information when triggered, and, together with the MPA, correlated clusters called stubs from the two sensors for use by the CMS Level-1 (L1) trigger system. Results from the first prototype module consisting of a sensor and two SSA chips are presented. The prototype module has been characterized at the Fermilab Test Beam Facility using a 120 GeV proton beam