Social Media: The Good, the Bad, and the Ugly

Ye

Genetic architecture and population structure of Oat Landraces (Avena sativa L.) using molecular and morphological descriptors

439-450Oat is grown as winter forage in India. It is a self-pollinated crop with less variability. However, the variation for different morphological traits in oat germplasm may be available at genotypic level. The present study was conducted to find out the genetic diversity among 24 oat landraces using 9 morphological traits and 24 SSR primers. Morphological data observed across the 24 landraces showed wide variation and grouped various landraces into two clusters. GFY and DMY were positively and significantly correlated with most of the traits studied. The molecular analysis using 24 SSR primers resulted amplification of 62 polymorphic alleles with an average of 2.58 alleles per primer. Size of amplified alleles ranged from 70 to 480 bp. Mean polymorphic information content was 0.42 showing moderate level of SSR polymorphism. Cluster analysis based on SSR data differentiated 24 oat landraces into three major clusters. Bayesian model-based STRUCTURE analysis assigned landraces into two clusters and showed the extent of admixture within individuals. Clustering pattern of oat landraces based on SSR marker profiles were different from that of morphometric traits. So, based on the pooled analysis at morphological and molecular level, the landraces IG-02-121, IG-02-129 and IG-02-113 were found superior for morphological traits as well as most distant among all the landraces under study. Hence, these landraces could be used in for future breeding programmes for genetic improvement in oats

RAPID DETECTION OF MULTI DRUG RESISTANCE AMONG MULTI DRUG RESISTANT TUBERCULOSIS SUSPECTS USING LINE PROBE ASSAY

Objective: GenoType MTBDRplus line probe assay (LPA) is developed for performing drug susceptibility testing (DST) for Rifampicin (RIF) and isoniazid in sputum specimens from smear-positive pulmonary tuberculosis (TB) patients and revised national TB control Programme (RNTCP) has endorsed LPA for the diagnosis of multi drug resistant TB (MDR-TB). This study was conducted to assess the potential utility of LPA for MDR-TB patient management.Methods: MDR-TB suspects under RNTCP PMDT criteria C referred from different districts in Delhi state were included in the study January 2013 toDecember 2014. Sputum specimens found acid-fast bacilli positive by fluorescent microscopy were processed for LPA.Results: Out of 3062 specimens, 2055 (67.1%) MDR-TB suspects were read as positive and specimens from 1007 (32.9%) suspects were read as negative in sputum smear microscopy. Out of 2019 specimens valid LPA results, 1427 were found to be pan-sensitive, 280 were MDR-TB, 40 were RIF monoresistant, 183 were Isoniazid (INH) monoresistant, and 89 specimens were found negative for Mycobacterium tuberculosis.Conclusion: Routine use of LPA can substantially reduce the time to diagnosis of RIF and/or INH-resistant TB and can hence potentially enable earlier commencement of appropriate drug therapy and thereby facilitate prevention of further transmission of drug resistant strains.Keywords: Multi drug resistant tuberculosis, Line probe assay, Rifampicin, Isoniazid

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

Digital payments adoption research: A meta-analysis for generalising the effects of attitude, cost, innovativeness, mobility and price value on behavioural intention

yesThe rapid evolution of mobile-based technologies and applications has led to the development of several different forms of digital payment methods (DPMs) but with limited enthusiasm in consumers for adopting them. Hence, several academic studies have already been conducted to examine the role of various antecedents that determines consumers’ intention to adopt DPMs. The degree of effect and significance of several antecedents found to be inconsistent across different studies. This provided us a basis for undertaking a meta-analysis of existing research for estimating the cumulative effect of such antecedents. Therefore, this study aims to perform a meta-analysis of five antecedents (i.e. attitude, cost, mobility, price value and innovativeness) for confirming their overall influence on intentions to adopt DPMs. The results of this study suggest that the cumulative effect of four out of five antecedents found to be significant while influence of price value was found insignificant on behavioural intentions. The recommendations drawn from this research would help to decide if and when to use such antecedents for predicting consumer intention to adopt DPMs

Examining the role of three sets of innovation attributes for determining adoption of the interbank mobile payment service

The interbank mobile payment service (IMPS) is a very recent technology in India that serves the very critical purpose of a mobile wallet. To account for the adoption and use of IMPS by the Indian consumers, this study seeks to compare three competing sets of attributes borrowed from three recognized pieces of work in the area of innovations adoption. This study aims to examine which of the three sets of attributes better predicts the adoption of IMPS in an Indian context. The research model is empirically tested and validated against the data gathered from 323 respondents from different cities in India. The findings are analysed using the SPSS analysis tool, which are then discussed to derive the key conclusions from this study. The research implications are stated, limitations listed and suggestions for future research on this technology are then finally made

Advances in Social Media Research:Past, Present and Future

Social media comprises communication websites that facilitate relationship forming between users from diverse backgrounds, resulting in a rich social structure. User generated content encourages inquiry and decision-making. Given the relevance of social media to various stakeholders, it has received significant attention from researchers of various fields, including information systems. There exists no comprehensive review that integrates and synthesises the findings of literature on social media. This study discusses the findings of 132 papers (in selected IS journals) on social media and social networking published between 1997 and 2017. Most papers reviewed here examine the behavioural side of social media, investigate the aspect of reviews and recommendations, and study its integration for organizational purposes. Furthermore, many studies have investigated the viability of online communities/social media as a marketing medium, while others have explored various aspects of social media, including the risks associated with its use, the value that it creates, and the negative stigma attached to it within workplaces. The use of social media for information sharing during critical events as well as for seeking and/or rendering help has also been investigated in prior research. Other contexts include political and public administration, and the comparison between traditional and social media. Overall, our study identifies multiple emergent themes in the existing corpus, thereby furthering our understanding of advances in social media research. The integrated view of the extant literature that our study presents can help avoid duplication by future researchers, whilst offering fruitful lines of enquiry to help shape research for this emerging field

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

The GCP molecular marker toolkit, an instrument for use in breeding food security crops

Crop genetic resources carry variation useful for overcoming the challenges of modern agriculture. Molecular markers can facilitate the selection of agronomically important traits. The pervasiveness of genomics research has led to an overwhelming number of publications and databases, which are, nevertheless, scattered and hence often difficult for plant breeders to access, particularly those in developing countries. This situation separates them from developed countries, which have better endowed programs for developing varieties. To close this growing knowledge gap, we conducted an intensive literature review and consulted with more than 150 crop experts on the use of molecular markers in the breeding program of 19 food security crops. The result was a list of effectively used and highly reproducible sequence tagged site (STS), simple sequence repeat (SSR), single nucleotide polymorphism (SNP), and sequence characterized amplified region (SCAR) markers. However, only 12 food crops had molecular markers suitable for improvement. That is, marker-assisted selection is not yet used for Musa spp., coconut, lentils, millets, pigeonpea, sweet potato, and yam. For the other 12 crops, 214 molecular markers were found to be effectively used in association with 74 different traits. Results were compiled as the GCP Molecular Marker Toolkit, a free online tool that aims to promote the adoption of molecular approaches in breeding activities