A Multi-Opinion Based Metric for Quantifying Polarization on Social Networks: A Case Study from India

Social media has been known to be a hotbed of political and social communications and analyzing the polarization of opinions has been gaining attention. In this study, we have proposed a measure for quantifying polarization on social networks. The proposed metric, unlike state-of-the-art methods, does not assume a two-opinion scenario and applies to multiple opinions. Our metric was tested on both binary opinion based benchmark networks as well as synthetically opinion-labeled social networks with a multi-opinion scenario and varying degrees of polarization. The metric showed promising results for social networks with different levels of polarization. The method was then employed to study polarization using data obtained from Twitter concerning the tri-opinion ("pro", "anti" and "neutral") based communications regarding the implementation of the Citizenship Amendment Act (CAA) in India. We have measured the polarization on a variety of social networks such as communication networks based on retweets or mentions, a social relationships network based on follower-followee connections and finally hybrid networks by combining the communication networks with the social relationships network. The proposed method suggested a high level of polarization among the users with respect to sharing posts on Twitter, thereby indicating the highly contentious nature of the issue. We also obtained a high polarization score for a social relationships network. Thus indicating the presence of homophily among users i.e. opinion on CAA is in-line with their social relationships on the platform. For a retweet based hybrid network, the scores returned by our polarization metric highlighted opinion to be the key driver in retweeting behaviour irrespective of the social relationships among users. On the contrary, the mention based communications among the users were not polarized in nature.Comment: 17 pages, 7 figures and 2 table

A Step towards Solving Olfactory Stimulus-Percept Problem

Odours are highly complex, relying on hundreds of receptors, and people are known to disagree in their linguistic descriptions of smells. It is partly due to these facts that, it is very hard to map the domain of odour molecules or their structure to that of perceptual representations, a problem that has been referred to as the Structure-Odour-Relationship. We collected a number of diverse open domain databases of odour molecules having unorganised perceptual descriptors, and developed a graphical method to find the similarity between perceptual descriptors; which is intuitive and can be used to identify perceptual classes. We then separately projected the physico-chemical and perceptual features of these molecules in a non-linear dimension and clustered the similar molecules. We found a significant overlap between the spatial positioning of the clustered molecules in the physico-chemical and perceptual spaces. We also developed a statistical method of predicting the perceptual qualities of a novel molecule using its physico-chemical properties with high receiver operating characteristics(ROC)

More than smell - COVID-19 is associated with severe impairment of smell, taste, and chemesthesis

Recent anecdotal and scientific reports have provided evidence of a link between COVID-19 and chemosensory impairments such as anosmia. However, these reports have downplayed or failed to distinguish potential effects on taste, ignored chemesthesis, generally lacked quantitative measurements, were mostly restricted to data from single countries. Here, we report the development, implementation and initial results of a multi-lingual, international questionnaire to assess self-reported quantity and quality of perception in three distinct chemosensory modalities (smell, taste, and chemesthesis) before and during COVID-19. In the first 11 days after questionnaire launch, 4039 participants (2913 women, 1118 men, 8 other, ages 19-79) reported a COVID-19 diagnosis either via laboratory tests or clinical assessment. Importantly, smell, taste and chemesthetic function were each significantly reduced compared to their status before the disease. Difference scores (maximum possible change+/-100) revealed a mean reduction of smell (-79.7+/- 28.7, mean+/- SD), taste (-69.0+/- 32.6), and chemesthetic (-37.3+/- 36.2) function during COVID-19. Qualitative changes in olfactory ability (parosmia and phantosmia) were relatively rare and correlated with smell loss. Importantly, perceived nasal obstruction did not account for smell loss. Furthermore, chemosensory impairments were similar between participants in the laboratory test and clinical assessment groups. These results show that COVID-19-associated chemosensory impairment is not limited to smell, but also affects taste and chemesthesis. The multimodal impact of COVID-19 and lack of perceived nasal obstruction suggest that SARS-CoV-2 infection may disrupt sensory-neural mechanisms.Additional co-authors: Veronica Pereda-Loth, Shannon B Olsson, Richard C Gerkin, Paloma Rohlfs Domínguez, Javier Albayay, Michael C. Farruggia, Surabhi Bhutani, Alexander W Fjaeldstad, Ritesh Kumar, Anna Menini, Moustafa Bensafi, Mari Sandell, Iordanis Konstantinidis, Antonella Di Pizio, Federica Genovese, Lina Öztürk, Thierry Thomas-Danguin, Johannes Frasnelli, Sanne Boesveldt, Özlem Saatci, Luis R. Saraiva, Cailu Lin, Jérôme Golebiowski, Liang-Dar Hwang, Mehmet Hakan Ozdener, Maria Dolors Guàrdia, Christophe Laudamiel, Marina Ritchie, Jan Havlícek, Denis Pierron, Eugeni Roura, Marta Navarro, Alissa A. Nolden, Juyun Lim, KL Whitcroft, Lauren R. Colquitt, Camille Ferdenzi, Evelyn V. Brindha, Aytug Altundag, Alberto Macchi, Alexia Nunez-Parra, Zara M. Patel, Sébastien Fiorucci, Carl M. Philpott, Barry C. Smith, Johan N Lundström, Carla Mucignat, Jane K. Parker, Mirjam van den Brink, Michael Schmuker, Florian Ph.S Fischmeister, Thomas Heinbockel, Vonnie D.C. Shields, Farhoud Faraji, Enrique Enrique Santamaría, William E.A. Fredborg, Gabriella Morini, Jonas K. Olofsson, Maryam Jalessi, Noam Karni, Anna D'Errico, Rafieh Alizadeh, Robert Pellegrino, Pablo Meyer, Caroline Huart, Ben Chen, Graciela M. Soler, Mohammed K. Alwashahi, Olagunju Abdulrahman, Antje Welge-Lüssen, Pamela Dalton, Jessica Freiherr, Carol H. Yan, Jasper H. B. de Groot, Vera V. Voznessenskaya, Hadar Klein, Jingguo Chen, Masako Okamoto, Elizabeth A. Sell, Preet Bano Singh, Julie Walsh-Messinger, Nicholas S. Archer, Sachiko Koyama, Vincent Deary, Hüseyin Yanik, Samet Albayrak, Lenka Martinec Novákov, Ilja Croijmans, Patricia Portillo Mazal, Shima T. Moein, Eitan Margulis, Coralie Mignot, Sajidxa Mariño, Dejan Georgiev, Pavan K. Kaushik, Bettina Malnic, Hong Wang, Shima Seyed-Allaei, Nur Yoluk, Sara Razzaghi, Jeb M. Justice, Diego Restrepo, Julien W Hsieh, Danielle R. Reed, Thomas Hummel, Steven D Munger, John E Haye

Human opinion dynamics: An inspiration to solve complex optimization problems

Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making

Expression based biomarkers and models to classify early and late-stage samples of Papillary Thyroid Carcinoma.

IntroductionRecently, the rise in the incidences of thyroid cancer worldwide renders it to be the sixth most common cancer among women. Commonly, Fine Needle Aspiration biopsy predominantly facilitates the diagnosis of the nature of thyroid nodules. However, it is inconsiderable in determining the tumor's state, i.e., benign or malignant. This study aims to identify the key RNA transcripts that can segregate the early and late-stage samples of Thyroid Carcinoma (THCA) using RNA expression profiles.Materials and methodsIn this study, we used the THCA RNA-Seq dataset of The Cancer Genome Atlas, consisting of 500 cancer and 58 normal (adjacent non-tumorous) samples obtained from the Genomics Data Commons (GDC) data portal. This dataset was dissected to identify key RNA expression features using various feature selection techniques. Subsequently, samples were classified based on selected features employing different machine learning algorithms.ResultsSingle gene ranking based on the Area Under the Receiver Operating Characteristics (AUROC) curve identified the DCN transcript that can classify the early-stage samples from late-stage samples with 0.66 AUROC. To further improve the performance, we identified a panel of 36 RNA transcripts that achieved F1 score of 0.75 with 0.73 AUROC (95% CI: 0.62-0.84) on the validation dataset. Moreover, prediction models based on 18-features from this panel correctly predicted 75% of the samples of the external validation dataset. In addition, the multiclass model classified normal, early, and late-stage samples with AUROC of 0.95 (95% CI: 0.84-1), 0.76 (95% CI: 0.66-0.85) and 0.72 (95% CI: 0.61-0.83) on the validation dataset. Besides, a five protein-coding transcripts panel was also recognized, which segregated cancer and normal samples in the validation dataset with F1 score of 0.97 and 0.99 AUROC (95% CI: 0.91-1).ConclusionWe identified 36 important RNA transcripts whose expression segregated early and late-stage samples with reasonable accuracy. The models and dataset used in this study are available from the webserver CancerTSP (http://webs.iiitd.edu.in/raghava/cancertsp/)

Chemometric assisted RP-HPLC for fingerprinting of Indian orthodox black tea

In view of the large variation in tea quality, this study aims to fingerprint the best quality of Indian orthodox black tea depending upon the production region, variation of seasons, mechanical grading and processing with different fermentation times. A HPLC method reported by us earlier has been adapted to separate biochemical constituents in various tea samples, in which Thermo Hypersil ODS column has been found to be more efficient in terms of resolution and retention time. Even black tea processed from the same region but processed in different seasons or mechanically graded resulted in significant compositional changes. The average data from HPLC was subjected to chemometric analysis i.e. principal component analysis (PCA). Different clusters were obtained for each type of tea which can act as a fingerprint for further analysis and interpretation of tea quality for its ranking. Thus, HPLC integrated with chemometrics can be explored as a routine diagnostic tool for online monitoring of black tea quality

A novel approach using Dynamic Social Impact Theory for optimization of impedance-Tongue (iTongue)

This paper presents a novel multiobjective wrapper approach using Dynamic Social Impact Theory based optimizer (SITO). A Fuzzy Inference System in conjunction with support vector machines classifier has been used for the optimization of an impedance-Tongue for the classification of samples collected from single batch production of Kangra orthodox black tea. Impedance spectra of the tea samples have been measured in the range of 20 Hz to 1 MHz using a two electrode setup employing platinum and gold electrodes. The proposed approach has been compared, for its robustness and validity using various intra and inter measures, against Genetic Algorithm and binary Particle Swarm Optimization. Feature subset selection methods based on the first and second order statistics have also been employed for comparisons. The proposed approach outperforms the Genetic Algorithm and binary Particle Swarm Optimization

A Simple Electronic Tongue

In this work a novel electronictongue (ET) using Fourier transform impedance spectroscopy has been demonstrated. Odd random phase multisine waveform has been used as an excitation signal. Texas Instruments’ PCM2900B USB audio CODEC chip has been used as a signal generation and an acquisition module for the ET. The acquired impedance features have been further subjected to Principal Component Analysis (PCA) for dimensionality reduction and Support Vector Machines (SVM) for pattern classification. A good classification accuracy has been achieved for single specie samples (taste samples), multi-species samples (water) and complex samples (tea). Also, the performance of the proposed system has been measured in terms of qualitative performance parameters namely, false positive rate, false negative rate, sensitivity rate and specificity rate

Enhancing electronic nose performance: A novel feature selection approach using dynamic social impact theory and moving window time slicing for classification of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze)

This paper presents a novel multiobjective wrapper approach using dynamic social impact theory based optimizer (SITO) and moving window time slicing (MWTS) for the performance enhancement of an electronic nose (EN). SITO, in conjunction with principal component analysis (PCA) and support vector machines (SVMs) classifier, has been used for the classification of samples collected from the single batch production of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze). The work employs a novel SITO assisted MWTS (SITO-MWTS) technique for identifying the optimum time intervals of the EN sensor array response, which give the maximum classification rate. Results show that, by identifying the optimum time slicing window positions for each sensor response, the performance of an EN can be improved. Also, the sensor response variability is time dependent in a sniffing cycle, and hence good classification can be obtained by selecting different time intervals for different sensors. The proposed method has also been compared with other established techniques for EN feature extraction. The work not only demonstrates the efficacy of SITO for feature selection owing to its simplicity in terms of few control parameters, but also the capability of an EN to differentiate Kangra orthodox black tea samples at different production stages