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, and generally lacked quantitative measurements. Here, we report the development, implementation, and initial results of a multilingual, international questionnaire to assess self-reported quantity and quality of perception in 3 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, and 8 others, aged 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 ± standard deviation), 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 the lack of perceived nasal obstruction suggest that severe acute respiratory syndrome coronavirus strain 2 (SARS-CoV-2) infection may disrupt sensory-neural mechanisms.

Twitter sentiment analysis for COVID-19 associated mucormycosis.

Purpose: COVID-19-associated mucormycosis (CAM) was a serious public health problem during the second wave of COVID-19 in India. We planned to analyze public perceptions by sentiment analysis of Twitter data regarding CAM. Methods: In this observational study, the application programming interface (API) provided by the Twitter platform was used for extracting real-time conversations by using keywords related to mucormycosis (colloquially known as "black fungus"), from May 3 to August 29, 2021. Lexicon-based sentiment analysis of the tweets was done using the Vader sentiment analysis tool. To identify the overall sentiment of a user on any given topic, an algorithm to label a user "k" based on their sentiments was used. Results: A total of 4,01,037 tweets were collected between May 3 and August 29, 2021, and the peak frequency of 1,60,000 tweets was observed from May 17 to May 23, 2021. Positive sentiment tweets constituted a larger share as compared to negative sentiment tweets, with weekly variations. A temporal analysis of the demand for utilities showed that the demand was high in the initial period but decreased with time, which was associated with the availability of resources. Conclusion: Sentiment analysis using Twitter data revealed that social media platforms are gaining popularity to express one's emotions during the ongoing COVID-19 pandemic. In our study, time-based assessment of tweets showed a reduction over time in the frequency of negative sentiment tweets. The polarization in the retweet network of users, based on sentiment polarity, showed that the users were well connected, highlighting the fact that such issues bond our society rather than segregating it.

Fifty Years of Cervical Myelopathy Research: Results from a Bibliometric Analysis.

We performed bibliometric analysis of the research papers published on clinical cervical spondylotic myelopathy (CSM) in the last 50 years. We extracted bibliometric data from Scopus and PubMed from 1970 to 2020 pertaining to clinical studies of CSM. The predominant journals, top cited articles, authors, and countries were identified using performance analysis. Science mapping was also performed to reveal the emerging trends, and conceptual and social structures of the authors and countries. Bibliometrix R-package was deployed for the study. The total numbers of clinical studies available in PubMed and Scopus were 1,302 and 3,470, respectively. The most cited article was published by Hilibrand AS, as observed in Scopus. Regarding the conceptual structure of the research, two main research themes were identified, one involving symptomatology, scientific-scale-based objective evaluation of symptoms, and surgical removal of the offending culprit, while the other was based on patho-etiology, relevant diagnostic modalities, and the surgery commonly performed for CSM. In terms of emerging trends, in recent times there is an increasing trend of scale-based objective evaluations, along with investigations of advanced nonoperative management. The United States is the most productive country, whereas Canada tops the list for inter-country collaboration. The trend of research showed a shift toward noninvasive procedures.

COVID-19 and ophthalmology: A scientometric analysis.

PURPOSE: Coronavirus disease pandemic has impacted global healthcare tremendously and ophthalmology is one of the high-hit specialties. An increasing number of research items are upcoming with COVID-19-related research in ophthalmology and this report aims at performing a scientometric analysis of all the available research pertaining to COVID-19 and ophthalmology. METHODS: A Web of Science (https://webofknowledge.com) query TS = ("novel coronavirus 2019" OR "coronavirus 2019" OR "COVID 2019" OR "COVID 19" OR "nCOV" OR "SARS-CoV-2" OR "COVID-19") AND WC = ("Ophthalmology") was deployed on February 22, 2021, to retrieve all research items on the topics of interest. R software (v4.0.1) with Bibliometrix library was deployed to visualize metrics to quantify geographical distribution, source metrics, author metrics, document metrics, and keyword metrics. RESULTS: A total of 616 research items appeared in our search results that were drafted by 2398 authors and published in 63 sources. India, USA, UK, and China had the greatest number of research items among others. Indian Journal of Ophthalmology, Eye, and Graefe's Archive for Clinical and Experimental Ophthalmology were sources with greatest number of research items. Documents per author were 0.257 and authors per document were 3.89. The collaboration index was noted to be 4.28. CONCLUSION: Our scientometric analysis presents descriptive quantitative metrics for COVID-related research in the field of ophthalmology and provides evidence for the increased global collaboration that global researchers have fostered to fight this pandemic.

Morality-Based Assertion and Homophily on Social Media: A Cultural Comparison Between English and Japanese Languages.

Moral psychology is a domain that deals with moral identity, appraisals and emotions. Previous work has primarily focused on moral development and the associated role of culture. Knowing that language is an inherent element of a culture, we used the social media platform Twitter to compare moral behaviors of Japanese tweets with English tweets. The five basic moral foundations, i.e., Care, Fairness, Ingroup, Authority, and Purity, along with the associated emotional valence were compared between English and Japanese tweets. The tweets from Japanese users depicted relatively higher Fairness, Ingroup, and Purity, whereas English tweets expressed more positive emotions for all moral dimensions. Considering moral similarities in connecting users on social media, we quantified homophily concerning different moral dimensions using our proposed method. The moral dimensions Care, Authority, and Purity for English and Ingroup, Authority and Purity for Japanese depicted homophily on Twitter. Overall, our study uncovers the underlying cultural differences with respect to moral behavior in English- and Japanese-speaking users.

Evolution of Glaucoma Research: A Scientometric Review.

Ophthalmic literature has been subjected to scientometrics in the past both for specific disease pathologies, such as, age-related macular degeneration, glaucoma, and diabetic retinopathy, and specific journals to add insight to the evolving trends. This short scientometric review looks at the distribution pattern and subject domain knowledge of worldwide glaucoma research with data extracted from Web of Science (WoS, Clarivate Analytics) for the past 74 years. How to cite this article: Ichhpujani P, Kalra G, Kaur R, et al. Evolution of Glaucoma Research: A Scientometric Review. J Curr Glaucoma Pract 2020;14(3):98-105.

Consensus-Based Odor Source Localization by Multiagent Systems Under Resource Constraints.

With advancements in mobile robot olfaction, networked multiagent systems (MASs) are used in odor source localization (OSL). These MASs are often equipped with small microprocessors that have limited computing capabilities, and they usually operate in a bandwidth and energy-constrained environment. The exigent need for a faster localizing algorithm under communication and computational resource constraints invites many design challenges. In this paper, we have designed a two-level hierarchical cooperative control strategy for heterogeneous nonlinear MASs for OSL. The agents are forced toward consensus expeditiously once the information on the whereabouts of the source is attained. The synthesis of the controller occurs in a hierarchical manner-obtaining a group decision, followed by resource-efficient robust control. Odor concentration and wind information have been used in a group decision-making layer to predict a probable location of the source as a tracking reference. This reference is then fed to the control layer that is synthesized using event-triggered sliding-mode control (SMC). The advantage of using event-triggered control scheduling in conjunction with the SMC is rooted in retaining the robustness of the SMC while lowering the resource utilization burden. Numerical simulations confirm the efficiency of the scheme put forth.

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

INTRODUCTION: Recently, 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 METHODS: In 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. RESULTS: Single 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). CONCLUSION: We 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/).

Consensus-Based Odor Source Localization by Multiagent Systems.

This paper presents an investigation of the task of localizing an unknown source of an odor by heterogeneous multiagent systems. A hierarchical cooperative control strategy has been proposed as a potential candidate to solve the problem. The agents are driven into consensus as soon as the information about the location of source is acquired. The controller has been designed in a hierarchical manner of group decision making, agent path planning, and robust control. In group decision making, the particle swarm optimization algorithm has been used along with the information of the movement of odor molecules to predict the odor source location. Next, a trajectory has been mapped using this predicted location of source, and the information is passed to the control layer. A variable structure control has been used in the control layer due to its inherent robustness and disturbance rejection capabilities. Cases of movement of agents toward the source under consensus and parallel formation have been discussed. The efficacy of the proposed scheme has been confirmed by simulations.

Bacteriorhodopsin-ZnO hybrid as a potential sensing element for low-temperature detection of ethanol vapour.

Zinc oxide (ZnO) and bacteriorhodopsin (bR) hybrid nanostructures were fabricated by immobilizing bR on ZnO thin films and ZnO nanorods. The morphological and spectroscopic analysis of the hybrid structures confirmed the ZnO thin film/nanorod growth and functional properties of bR. The photoactivity results of the bR protein further corroborated the sustainability of its charge transport property and biological activity. When exposed to ethanol vapour (reducing gas) at low temperature (70 °C), the fabricated sensing elements showed a significant increase in resistivity, as opposed to the conventional n-type behaviour of bare ZnO nanostructures. This work opens up avenues towards the fabrication of low temperature, photoactivated, nanomaterial-biomolecule hybrid gas sensors.

Understanding the Odour Spaces: 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).

Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification.

The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis.

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.