Analysis of Small RNA Sequencing Data in Plants

Over the past decades, next-generation sequencing (NGS) has been employed extensively for investigating the regulatory mechanisms of small RNAs. Several bioinformatics tools are available for aiding biologists to extract meaningful information from enormous amounts of data generated by NGS platforms. This chapter describes a detailed methodology for analyzing small RNA sequencing data using different open source tools. We elaborate on various steps involved in analysis, from processing the raw sequencing reads to identifying miRNAs, their targets, and differential expression studies.

Role of Graph Theory in Computational Neuroscience

Neuroscience is the study of the brain and its impact on behavior and cognitive functions. Computational neuroscience is the subfield that deals with the study of the ability of the brain to think and compute. It also analyzes various electrical and chemical signals that take place in the brain to represent and process the information. In this chapter, a special focus will be given on the processing of signals by the brain to solve the problems. In the second section of the chapter, the role of graph theory is discussed to analyze the pattern of neurons. Graph-based analysis reveals meaningful information about the topological architecture of human brain networks. The graph-based analysis also discloses the networks in which most nodes are not neighbors of each other but can be reached from every other node by a small number of steps. In the end, it is concluded that by using the various operations of graph theory, the vertex centrality, betweenness, etc. can be computed to identify the dominant neurons for solving different types of computational problems.

EXPORT FINANCE: A TOOL FOR PROMOTING EXPORT IN INDIA

Export Finance play a crucial role in growth and development of export sector in any economy, which face a tough competition from rest of world. It is an essential and prime element for successful operation of various internal and external activities to grow the export business sector. In view of many scholars and economists, export is considered as the engine of growth and development of any country. Presently, India’s total export is contributing approx. 2% in the world’s trade. In export trading the 6 Ps concepts of export marketing are depending on the availability of export finance assistance in terms of share, cost and period of time. This study attempts to analyze and review the impact of export finance used by various exporters on their export profitability, performance on varied interest rate, cost of finance and special schemes related to pre and post shipment stage in export trading. To analyze the role and importance of various financial institutions in providing the financial assistance to the exporters in India helps and supports to export sector to grow in the right direction and also to achieve the specific goals. This research paper provides meaningful information to policy maker who want to reform their export structure, promotion schemes and increase the nation’s competitiveness in present era. KEYWORDS: Export Finance, Financial Institutions, Export Credits, Export Insurance

Tracking community infection dynamics of COVID-19 by monitoring SARS-CoV-2 RNA in wastewater, counting positive reactions by qPCR

Wastewater-based epidemiology has proved useful for monitoring the COVID-19 infection dynamics in communities. However, in some countries, low concentrations of SARS-CoV-2 RNA in wastewater make this difficult. Getting meaningful information from wastewater-based epidemiology in regions of low prevalence remains a key challenge. Here we used real-time reverse-transcription PCR (RT-qPCR) to monitor SARS-CoV-2 RNA in wastewater from October 2020 to February 2021 during the third wave of the COVID-19 outbreak in Japan. Viral RNA was below the limit of quantification in all samples. However, by counting the positive reactions in repeated qPCR of each sample, we found that the ratio of positive reactions to all tests in wastewater was significantly correlated with the number of clinically confirmed cases by the date of symptom onset during periods of both increasing and decreasing infection. Time-step analysis indicated that COVID-19 patients excreted large amounts of virus in their feces 2 days either side of symptom onset, which wastewater surveillance could detect. The positive count method is thus useful for tracing COVID-19 dynamics in regions of low prevalence.

Perfect Density Models Cannot Guarantee Anomaly Detection

Thanks to the tractability of their likelihood, several deep generative models show promise for seemingly straightforward but important applications like anomaly detection, uncertainty estimation, and active learning. However, the likelihood values empirically attributed to anomalies conflict with the expectations these proposed applications suggest. In this paper, we take a closer look at the behavior of distribution densities through the lens of reparametrization and show that these quantities carry less meaningful information than previously thought, beyond estimation issues or the curse of dimensionality. We conclude that the use of these likelihoods for anomaly detection relies on strong and implicit hypotheses, and highlight the necessity of explicitly formulating these assumptions for reliable anomaly detection.

Text Mining Data from Students to Reveal Meaningful Information for Educators

Academic institutions adopt different advising tools for various objectives. Past research used both numeric and text data to predict students’ performance. Moreover, numerous research projects have been conducted to find different learning strategies and profiles of students. Those strategies of learning together with academic profiles assisted in the advising process. This research proposes an approach to supplement these activities by text mining students’ essays to better understand different students’ profiles across different courses (subjects). Text analysis was performed on 99 essays written by undergraduate students in three different courses. The essays and terms were projected in a 20-dimensional vector space. The 20 dimensions were used as independent variables in a regression analysis to predict a student’s final grade in a course. Further analyses were performed on the dimensions found statistically significant. This study is a preliminary analysis to demonstrate a novel approach of extracting meaningful information by text mining essays written by students to develop an advising tool that can be used by educators.

We Should Have Talked About Hospice: Transforming Communication With Bereaved LGB Aging Women

Because of historical discrimination, discomfort disclosing information, and differing definitions of family, lesbian, gay, bisexual, and transgender (LGBT) older adults with serious illness need both improved palliative and end-of-life (EOL) care communication with clinicians and recognized inclusion of spouses/partners. Communicating about palliative and EOL care may improve the care goals and emotional trajectory for patients and significant others. Using a descriptive qualitative approach, this study’s aim was to analyze the communication experiences during a spouse’s/partner’s EOL care for bereaved LGB women (n=16) 60 and older. Drawing on queer gerontology, issues relating to access to resources and information and the systemic silencing of older LGB women illuminate areas where policy and practice may be improved. Semi-structured, one-on-one interviews were used to provide deep and meaningful information about palliative and EOL care communication between participants, their spouse or partner, and clinicians. While results reflect certain outcomes found in prior studies with non-LGBT adults, thematic analysis revealed three main findings with evidence specific to this population: 1) avoiding deep discussions about EOL; 2) lack of understanding about palliative or EOL care; and 3) limited communication with clinicians. Findings illuminate the need for better understanding among clinicians regarding palliative and EOL communication with LGBT dyads as well as communication strategies based on recognition and acceptance. Further dyadic communication research may improve care goals for LGBT older adults. Understanding couples’ interactions and examining different communication behaviors may lead to improved palliative and EOL care goals for older LGBT adults with serious illness and their spouses/partners.

Extraction of the Relations between Significant Pharmacological Entities in Russian-Language Internet Reviews on Medications

Nowadays, an analysis of virtual media to predict society’s reaction to any events or processes is a task of great relevance. Especially it concerns meaningful information on healthcare problems. Internet sources contain a large amount of pharmacologically meaningful information useful for pharmacovigilance purposes and repurposing drug use. An analysis of such a scale of information demands developing the methods that require the creation of a corpus with labeled relations among entities. Before, there have been no such Russian language datasets. This paper considers the first Russian language dataset where labeled entity pairs are divided into multiple contexts within a single text (by used drugs, by different users, by the cases of use, etc.), and a method based on the XLM-RoBERTa language model, previously trained on medical texts to evaluate the state-of-the-art accuracy for the task of indication of the four types of relationships among entities: ADR–Drugname, Drugname–Diseasename, Drugname–SourceInfoDrug, Diseasename–Indication. As shown based on the presented dataset from the Russian Drug Review Corpus, the developed method achieves the F1-score of 81.2% (obtained using cross-validation and averaged for the four types of relationships), which is 7.8% higher than the basic classifiers.