Salmonella spp. in Aquaculture: An Exploratory Analysis (Integrative Review) of Microbiological Diagnoses between 2000 and 2020

The present study aimed to characterize, through descriptive statistics, data from scientific articles selected in a systematic integrative review that performed a microbiological diagnosis of Salmonella spp. in aquaculture. Data were obtained from research articles published in the BVS, Scielo, Science Direct, Scopus and Web of Science databases. The selected studies were published between 2000 and 2020 on samples of aquaculture animal production (fish, shrimp, bivalve mollusks, and other crustaceans) and environmental samples of aquaculture activity (farming water, soil, and sediments). After applying the exclusion criteria, 80 articles were selected. Data such as country of origin, categories of fish investigated, methods of microbiological diagnosis of Salmonella spp., sample units analyzed and most reported serovars were mined. A textual analysis of the word cloud and by similarity and descending hierarchical classification with the application of Reinert's algorithm was performed using R® and Iramuteq® software. The results showed that a higher percentage of the selected articles came from Asian countries (38.75%). Fish was the most sampled category, and the units of analysis of the culture water, muscle and intestine were more positive. The culture isolation method is the most widespread, supported by more accurate techniques such as PCR. The most prevalent Salmonella serovars reported were S. Typhimurium, S. Weltevreden and S. Newport. The textual analysis showed a strong association of the terms "Salmonella", "fish" and "water", and the highest hierarchical class grouped 25.4% of the associated text segments, such as "aquaculture", "food" and "public health". The information produced characterizes the occurrence of Salmonella spp. in the aquaculture sector, providing an overview of recent years. Future research focusing on strategies for the control and prevention of Salmonella spp. in fish production are necessary and should be encouraged.

Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques

The aim of this study is to analyze the effects of lockdown using natural language processing techniques, particularly sentiment analysis methods applied at large scale. Further, our work searches to analyze the impact of COVID-19 on the university community, jointly on staff and students, and with a multi-country perspective. The main findings of this work show that the most often related words were "family", "anxiety", "house", and "life". Besides this finding, we also have shown that staff have a slightly less negative perception of the consequences of COVID-19 in their daily life. We have used artificial intelligence models such as swivel embedding and a multilayer perceptron as classification algorithms. The performance that was reached in terms of accuracy metrics was 88.8% and 88.5% for students and staff, respectively. The main conclusion of our study is that higher education institutions and policymakers around the world may benefit from these findings while formulating policy recommendations and strategies to support students during this and any future pandemics.

Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models

The novel coronavirus disease (COVID-19) is an ongoing pandemic with large global attention. However, spreading false news on social media sites like Twitter is creating unnecessary anxiety towards this disease. The motto behind this study is to analyses tweets by Indian netizens during the COVID-19 lockdown. The data included tweets collected on the dates between 23 March 2020 and 15 July 2020 and the text has been labelled as fear, sad, anger, and joy. Data analysis was conducted by Bidirectional Encoder Representations from Transformers (BERT) model, which is a new deep-learning model for text analysis and performance and was compared with three other models such as logistic regression (LR), support vector machines (SVM), and long-short term memory (LSTM). Accuracy for every sentiment was separately calculated. The BERT model produced 89% accuracy and the other three models produced 75%, 74.75%, and 65%, respectively. Each sentiment classification has accuracy ranging from 75.88-87.33% with a median accuracy of 79.34%, which is a relatively considerable value in text mining algorithms. Our findings present the high prevalence of keywords and associated terms among Indian tweets during COVID-19. Further, this work clarifies public opinion on pandemics and lead public health authorities for a better society.

Influence of the pandemic dissemination of COVID-19 on facial rejuvenation: A survey of Twitter

Background

With the pandemic dissemination of COVID‐19, attitude and sentiment surrounding facial rejuvenation have evolved rapidly.

Aims

The purpose of this study was to understanding the impact of pandemic on the attitude of people toward facial skin rejuvenation.

Methods

Twitter data related to facial rejuvenation were collected from January 1, 2020, to April 30, 2020. Sentiment analysis, frequency analysis, and word cloud were performed to analyze the data. Statistical analysis included two‐tailed t tests and chi‐square tests.

Results

In the post‐declaration, the number of tweets about facial rejuvenation increased significantly, and the search volume in Google Trends decreased. Negative public emotions increased, but positive emotions still dominate. The words frequency of “discounts” and “purchase” decreased. The dominant words in word cloud were “Botox,” “facelift,” “hyaluronic,” and “skin.”

Conclusion

The public has a positive attitude toward facial rejuvenation during the pandemic. In particular, minimally invasive procedures dominate the mainstream, such as “Botox,” “Hyaluronic acid,” and “PRP.” The practitioners could understand the change of the public interest in facial rejuvenation in time and decide what to focus on.

Word cloud analysis and single word summarisation as a new paediatric educational tool: Results of a neonatal application

AIMS

To analyse the value of computerised 'word cloud' (WC) generated from spontaneously articulated 'single word summarisation' (SWS) by medical students to assist their learning during the neonatal intensive care unit placement. To highlight WC as a potential new tool in paediatric teaching, improving student engagement and reflective feedback.

METHODS

An observational study was conducted in the neonatal intensive care unit of University Maternity Hospital Limerick, Ireland for 5 years (October 2012 to September 2017). One faculty member prospectively recorded SWS in neonatology by graduate entry medical students. An online 'WC generator' under an open-source licence was used to compute the WC. Hospital audit committee approved the study.

RESULTS

A total of 268 SWS were recorded consecutively from 268 medical students towards the WC generation. Structured multi-response student feedback showed SWS and WC as stimulating, unique and creative. Powerpoint presentation of the computed WC prompted students to reflect on their chosen words and that of peers.

CONCLUSIONS

Visualisation of medical student-generated SWS as a WC could stimulate interaction, reflection and clinical discussion, thus assisting teachers to foster better student engagement. This innovative educational tool equips students to 'convey more with fewer words' and has the potential transferability to other clinical disciplines.

Trends in Genomics & Informatics: a statistical review of publications from 2003 to 2018 focusing on the most-studied genes and document clusters

Genomics & Informatics (NLM title abbreviation: Genomics Inform) is the official journal of the Korea Genome Organization. Herein, we conduct a statistical analysis of the publications of Genomics & Informatics over the 16 years since its inception, with a particular focus on issues relating to article categories, word clouds, and the most-studied genes, drawing on recent reviews of the use of word frequencies in journal articles. Trends in the studies published in Genomics & Informatics are discussed both individually and collectively.

An experiment in using open-text comments from the Australian Rural Mental Health Study on health service priorities

INTRODUCTION

Conducting research in rural and remote areas is compounded by challenges associated with accessing relatively small populations spread over large geographical areas. Open-ended questions provided in a postal survey format are an advantageous way of including rural and remote residents in research studies. This method means that it is possible to ask for in-depth perspectives, from a large sample, in a relatively resource-efficient way. Such questions are frequently included in population-based surveys; however, they are rarely analysed. The aim of this article is to explore word cloud analysis, to evaluate the utility of automated programs to supplement the analysis of open-ended survey responses.

METHODS

Participants from the Australian Rural Mental Health Study completed the open-ended question 'What health services would you like to see the local health district providing that are currently not available in your area?' A word cloud analysis was then undertaken using the program Wordle; the size of the word in the cloud illustrates how many times, in proportion to other words, a word has appeared in responses, and provides an easily interpretable visual illustration of research results.

RESULTS

In total, 388 participants provided a response to the free-text question. Using the word cloud as a visual guide, key words were identified and used to locate relevant quotes from the full open-text responses. \'Mental health\' was the most frequent request, cited by 81 people (20.8%). Following mental health, requests for more \'specialists\' (n=59) and \'services\' (n=53) were the second and third most frequent responses respectively. Visiting specialists were requested by multiple respondents (n=14). Less frequent requests illustrated in the word cloud are important when considering representatives from smaller population groups such as those with specific health needs or conditions including \'maternity\' services (n=13), \'cancer\' (n=10), \'drug and alcohol\' services (n=8), and \'aged care\' (n=7) services are all core services even though they were being called for by fewer people. This lesser frequency may suggest that these services are already considered as available in some rural and remote communities.

CONCLUSIONS

This research aimed to determine whether meaningful and informative data could be obtained from short responses from open-ended survey questions using an automated data analysis technique to supplement a more in-depth analysis. The findings showed that, while not as detailed as interview responses, the open-ended survey questions provided sufficient information to develop a broad overview of the health service priorities identified by this large rural sample. Such automated data analysis techniques are rarely employed; however, the current research provides valuable support for their utility in rural and remote health research. This research has implications for researchers interested in engaging rural and remote residents, demonstrating that meaningful information can be extracted from short survey response data, contributing a resource-efficient supplement to a more detailed analysis. Open-ended questions are often asked in population-based studies yet they are rarely analysed, posing both an opportunity and a challenge for researchers using such participant-driven responses. The lessons learned from the methodology applied can be transferred to other population-based survey studies more widely.

Seeing the "Big" Picture: Big Data Methods for Exploring Relationships Between Usage, Language, and Outcome in Internet Intervention Data

Background

Assessing the efficacy of Internet interventions that are already in the market introduces both challenges and opportunities. While vast, often unprecedented amounts of data may be available (hundreds of thousands, and sometimes millions of participants with high dimensions of assessed variables), the data are observational in nature, are partly unstructured (eg, free text, images, sensor data), do not include a natural control group to be used for comparison, and typically exhibit high attrition rates. New approaches are therefore needed to use these existing data and derive new insights that can augment traditional smaller-group randomized controlled trials.

Objective

Our objective was to demonstrate how emerging big data approaches can help explore questions about the effectiveness and process of an Internet well-being intervention.

Methods

We drew data from the user base of a well-being website and app called Happify. To explore effectiveness, multilevel models focusing on within-person variation explored whether greater usage predicted higher well-being in a sample of 152,747 users. In addition, to explore the underlying processes that accompany improvement, we analyzed language for 10,818 users who had a sufficient volume of free-text response and timespan of platform usage. A topic model constructed from this free text provided language-based correlates of individual user improvement in outcome measures, providing insights into the beneficial underlying processes experienced by users.

Results

On a measure of positive emotion, the average user improved 1.38 points per week (SE 0.01, t122,455=113.60, P<.001, 95% CI 1.36–1.41), about a 27% increase over 8 weeks. Within a given individual user, more usage predicted more positive emotion and less usage predicted less positive emotion (estimate 0.09, SE 0.01, t6047=9.15, P=.001, 95% CI .07–.12). This estimate predicted that a given user would report positive emotion 1.26 points higher after a 2-week period when they used Happify daily than during a week when they didn’t use it at all. Among highly engaged users, 200 automatically clustered topics showed a significant (corrected P<.001) effect on change in well-being over time, illustrating which topics may be more beneficial than others when engaging with the interventions. In particular, topics that are related to addressing negative thoughts and feelings were correlated with improvement over time.

Conclusions

Using observational analyses on naturalistic big data, we can explore the relationship between usage and well-being among people using an Internet well-being intervention and provide new insights into the underlying mechanisms that accompany it. By leveraging big data to power these new types of analyses, we can explore the workings of an intervention from new angles, and harness the insights that surface to feed back into the intervention and improve it further in the future.

GOsummaries: an R Package for Visual Functional Annotation of Experimental Data

Functional characterisation of gene lists using Gene Ontology (GO) enrichment analysis is a common approach in computational biology, since many analysis methods end up with a list of genes as a result. Often there can be hundreds of functional terms that are significantly associated with a single list of genes and proper interpretation of such results can be a challenging endeavour. There are methods to visualise and aid the interpretation of these results, but most of them are limited to the results associated with one list of genes. However, in practice the number of gene lists can be considerably higher and common tools are not effective in such situations.

We introduce a novel R package, 'GOsummaries' that visualises the GO enrichment results as concise word clouds that can be combined together if the number of gene lists is larger. By also adding the graphs of corresponding raw experimental data, GOsummaries can create informative summary plots for various analyses such as differential expression or clustering. The case studies show that the GOsummaries plots allow rapid functional characterisation of complex sets of gene lists. The GOsummaries approach is particularly effective for Principal Component Analysis (PCA).

By adding functional annotation to the principal components, GOsummaries improves  significantly the interpretability of PCA results. The GOsummaries layout for PCA can be effective even in situations where we cannot directly apply the GO analysis. For example, in case of metabolomics or metagenomics data it is possible to show the features with significant associations to the components instead of GO terms.  

The GOsummaries package is available under GPL-2 licence at Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/GOsummaries.html).