scholarly journals Exploring Biomolecular Literature with EVEX: Connecting Genes through Events, Homology, and Indirect Associations

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Sofie Van Landeghem ◽  
Kai Hakala ◽  
Samuel Rönnqvist ◽  
Tapio Salakoski ◽  
Yves Van de Peer ◽  
...  
Keyword(s):  
Text Mining ◽  
Web Application ◽  
State Of The Art ◽  
Relevant Literature ◽  
Life Sciences ◽  
Event Extraction ◽  
Promising Technique ◽  
Binding Partners ◽  
Exponential Increase ◽  
Family Associations

Technological advancements in the field of genetics have led not only to an abundance of experimental data, but also caused an exponential increase of the number of published biomolecular studies. Text mining is widely accepted as a promising technique to help researchers in the life sciences deal with the amount of available literature. This paper presents a freely available web application built on top of 21.3 million detailed biomolecular events extracted from all PubMed abstracts. These text mining results were generated by a state-of-the-art event extraction system and enriched with gene family associations and abstract generalizations, accounting for lexical variants and synonymy. The EVEX resource locates relevant literature on phosphorylation, regulation targets, binding partners, and several other biomolecular events and assigns confidence values to these events. The search function accepts official gene/protein symbols as well as common names from all species. Finally, the web application is a powerful tool for generating homology-based hypotheses as well as novel, indirect associations between genes and proteins such as coregulators.

scholarly journals Social Media Adoption, Usage And Impact In Business-To-Business (B2B) Context: A State-Of-The-Art Literature Review

2021 ◽  
Author(s):  
Yogesh K. Dwivedi ◽  
Elvira Ismagilova ◽  
Nripendra P. Rana ◽  
Ramakrishnan Raman
Keyword(s):  
Social Media ◽  
Media Use ◽  
State Of The Art ◽  
Relevant Literature ◽  
Comprehensive Analysis ◽  
Social Media Use ◽  
Weight Analysis ◽  
Social Media Tools ◽  
Use Of Social Media ◽  
Business To Business

AbstractSocial media plays an important part in the digital transformation of businesses. This research provides a comprehensive analysis of the use of social media by business-to-business (B2B) companies. The current study focuses on the number of aspects of social media such as the effect of social media, social media tools, social media use, adoption of social media use and its barriers, social media strategies, and measuring the effectiveness of use of social media. This research provides a valuable synthesis of the relevant literature on social media in B2B context by analysing, performing weight analysis and discussing the key findings from existing research on social media. The findings of this study can be used as an informative framework on social media for both, academic and practitioners.

scholarly journals A Sign of Things to Come: Predicting the Perception of Above-the-Fold Time in Web Browsing

2021 ◽  
Vol 13 (2) ◽  
pp. 50
Author(s):  
Hamed Z. Jahromi ◽  
Declan Delaney ◽  
Andrew Hines
Keyword(s):  
Web Application ◽  
Web Applications ◽  
State Of The Art ◽  
Influencing Factor ◽  
The State ◽  
Experimental Result ◽  
Web Page ◽  
To Come ◽  
Web Quality ◽  
The Web

Content is a key influencing factor in Web Quality of Experience (QoE) estimation. A web user’s satisfaction can be influenced by how long it takes to render and visualize the visible parts of the web page in the browser. This is referred to as the Above-the-fold (ATF) time. SpeedIndex (SI) has been widely used to estimate perceived web page loading speed of ATF content and a proxy metric for Web QoE estimation. Web application developers have been actively introducing innovative interactive features, such as animated and multimedia content, aiming to capture the users’ attention and improve the functionality and utility of the web applications. However, the literature shows that, for the websites with animated content, the estimated ATF time using the state-of-the-art metrics may not accurately match completed ATF time as perceived by users. This study introduces a new metric, Plausibly Complete Time (PCT), that estimates ATF time for a user’s perception of websites with and without animations. PCT can be integrated with SI and web QoE models. The accuracy of the proposed metric is evaluated based on two publicly available datasets. The proposed metric holds a high positive Spearman’s correlation (rs=0.89) with the Perceived ATF reported by the users for websites with and without animated content. This study demonstrates that using PCT as a KPI in QoE estimation models can improve the robustness of QoE estimation in comparison to using the state-of-the-art ATF time metric. Furthermore, experimental result showed that the estimation of SI using PCT improves the robustness of SI for websites with animated content. The PCT estimation allows web application designers to identify where poor design has significantly increased ATF time and refactor their implementation before it impacts end-user experience.

Information processing by elderly consumers: a five-decade review

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gianluigi Guido ◽  
Marco Pichierri ◽  
Cristian Rizzo ◽  
Verdiana Chieffi ◽  
George Moschis
Keyword(s):  
Information Processing ◽  
Design Methodology ◽  
State Of The Art ◽  
Relevant Literature ◽  
Academic Research ◽  
The Elderly ◽  
Marketing Strategies ◽  
Content Type ◽  
Processing Activity ◽  
Elderly Consumers

Purpose The purpose of this study is to review scholarly research on elderly consumers’ information processing and suggest implications for services marketing. Design/methodology/approach The review encompasses a five-decade period (1970–2018) of academic research and presents relevant literature in four main areas related to information processing: sensation, attention, interpretation and memory. Findings The study illustrates how each of the aforementioned phases of the information processing activity may affect how elderly individuals buy and consume products and services, emphasizing the need for a better comprehension of the elderly to develop effectual marketing strategies. Originality/value The study provides readers with detailed state-of-the-art knowledge about older consumers’ information processing, offering a comprehensive review of academic research that companies can use to improve the effectiveness of their marketing efforts that target the elderly market.

Reliability Analysis of Great Lakes Marine Diesels: State of the Art and Current Modeling

1990 ◽  
Vol 27 (04) ◽  
pp. 237-249
Author(s):  
Anastassios N. Perakis ◽  
Bahadir Inozu
Keyword(s):  
Great Lakes ◽  
Diesel Engines ◽  
Failure Modes ◽  
State Of The Art ◽  
Relevant Literature ◽  
Fault Tree Analysis ◽  
Reliability Engineering ◽  
Marine Industry ◽  
Marine Diesel ◽  
Basic Concepts

Some essential steps for the application of reliability, availability, and maintainability (RAM) techniques to marine diesel engines are presented. The paper begins with a summary of the basic concepts of reliability engineering, followed by a survey of the relevant literature on RAM applications to the marine industry and to marine diesel engines in particular. Next, the results of an informal survey of the reliability, maintenance, and replacement practices of Great Lakes operators are presented. Finally, the first two steps for a RAM application, failure modes and effects analysis and fault tree analysis, are introduced and applied for a prototype Colt-Pielstick marine diesel engine.

scholarly journals One for All: Neural Joint Modeling of Entities and Events

2019 ◽  
Vol 33 ◽  
pp. 6851-6858 ◽  
Author(s):  
Trung Minh Nguyen ◽  
Thien Huu Nguyen
Keyword(s):  
Deep Learning ◽  
Recent Work ◽  
State Of The Art ◽  
Contextual Information ◽  
Joint Modeling ◽  
Event Extraction ◽  
Event Trigger ◽  
The Individual ◽  
Novel Model ◽  
Argument Roles

The previous work for event extraction has mainly focused on the predictions for event triggers and argument roles, treating entity mentions as being provided by human annotators. This is unrealistic as entity mentions are usually predicted by some existing toolkits whose errors might be propagated to the event trigger and argument role recognition. Few of the recent work has addressed this problem by jointly predicting entity mentions, event triggers and arguments. However, such work is limited to using discrete engineering features to represent contextual information for the individual tasks and their interactions. In this work, we propose a novel model to jointly perform predictions for entity mentions, event triggers and arguments based on the shared hidden representations from deep learning. The experiments demonstrate the benefits of the proposed method, leading to the state-of-the-art performance for event extraction.

scholarly journals Graph Algorithms for Word Sense Disambiguation in Biomedicine

2015 ◽  
Author(s):  
Rodrigo Goulart ◽  
Juliano De Carvalho ◽  
Vera De Lima
Keyword(s):  
Text Mining ◽  
Graph Algorithms ◽  
State Of The Art ◽  
Word Sense Disambiguation ◽  
The State ◽  
Word Sense ◽  
New Approach ◽  
Similar Performance ◽  
Sense Disambiguation ◽  
Different Levels

Word Sense Disambiguation (WSD) is an important task for Biomedicine text-mining. Supervised WSD methods have the best results but they are complex and their cost for testing is too high. This work presents an experiment on WSD using graph-based approaches (unsupervised methods). Three algorithms were tested and compared to the state of the art. Results indicate that similar performance could be reached with different levels of complexity, what may point to a new approach to this problem.

scholarly journals Remote Gait Type Classification System Using Markerless 2D Video

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1824
Author(s):  
Pedro Albuquerque ◽  
João Pedro Machado ◽  
Tanmay Tulsidas Verlekar ◽  
Paulo Lobato Correia ◽  
Luís Ducla Soares
Keyword(s):  
Gait Analysis ◽  
Learning Strategies ◽  
Web Application ◽  
State Of The Art ◽  
The State ◽  
Remote Healthcare ◽  
Visual Artifacts ◽  
Analysis System ◽  
Private Network ◽  
User Friendly

Several pathologies can alter the way people walk, i.e., their gait. Gait analysis can be used to detect such alterations and, therefore, help diagnose certain pathologies or assess people’s health and recovery. Simple vision-based systems have a considerable potential in this area, as they allow the capture of gait in unconstrained environments, such as at home or in a clinic, while the required computations can be done remotely. State-of-the-art vision-based systems for gait analysis use deep learning strategies, thus requiring a large amount of data for training. However, to the best of our knowledge, the largest publicly available pathological gait dataset contains only 10 subjects, simulating five types of gait. This paper presents a new dataset, GAIT-IT, captured from 21 subjects simulating five types of gait, at two severity levels. The dataset is recorded in a professional studio, making the sequences free of background camouflage, variations in illumination and other visual artifacts. The dataset is used to train a novel automatic gait analysis system. Compared to the state-of-the-art, the proposed system achieves a drastic reduction in the number of trainable parameters, memory requirements and execution times, while the classification accuracy is on par with the state-of-the-art. Recognizing the importance of remote healthcare, the proposed automatic gait analysis system is integrated with a prototype web application. This prototype is presently hosted in a private network, and after further tests and development it will allow people to upload a video of them walking and execute a web service that classifies their gait. The web application has a user-friendly interface usable by healthcare professionals or by laypersons. The application also makes an association between the identified type of gait and potential gait pathologies that exhibit the identified characteristics.

scholarly journals Brain organoids and insights on human evolution

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 760 ◽  
Author(s):  
Alysson R. Muotri
Keyword(s):  
Stem Cells ◽  
Human Brain ◽  
Human Health ◽  
Human Evolution ◽  
Pluripotent Stem Cells ◽  
Disease Modeling ◽  
State Of The Art ◽  
Future Perspectives ◽  
Promising Technique ◽  
Current State

Human brain organoids, generated from pluripotent stem cells, have emerged as a promising technique for modeling early stages of human neurodevelopment in controlled laboratory conditions. Although the applications for disease modeling in a dish have become routine, the use of these brain organoids as evolutionary tools is only now getting momentum. Here, we will review the current state of the art on the use of brain organoids from different species and the molecular and cellular insights generated from these studies. Besides, we will discuss how this model might be beneficial for human health and the limitations and future perspectives of this technology.

scholarly journals PRESa2i: incremental decision trees for prediction of Adenosine to Inosine RNA editing sites

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 262
Author(s):  
Alif Choyon ◽  
Ashiqur Rahman ◽  
Md. Hasanuzzaman ◽  
Dewan Md Farid ◽  
Swakkhar Shatabda
Keyword(s):  
Rna Editing ◽  
Web Application ◽  
State Of The Art ◽  
Independent Set ◽  
Prediction Tool ◽  
Decision Tree Algorithm ◽  
Rna Sequences ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Protein Encoding

RNA editing is a very crucial cellular process affecting protein encoding and is sometimes correlated with the cause of fatal diseases, such as cancer. Thus knowledge about RNA editing sites in a RNA sequence is very important. Adenosine to Inosine (A-to-I) is the most common of the RNA editing events. In this paper,we present PRESa2i, a computation prediction tool for identification of A-to-I RNA editing sites in given RNA sequences. PRESa2i uses a simple, yet effective set of sequence based features generated from RNA sequences and a novel feature selection technique. It uses an incremental decision tree algorithm as the classification algorithm. On a standard benchmark dataset and independent set, it achieves 86.48% accuracy and 90.67% sensitivity and significantly outperforms state-of-the-art methods. We have also implemented a web application based on PRESa2i and made it available freely at: http://brl.uiu.ac.bd/presa2i/index.php. The materials for this paper are also available to use from: https://github.com/swakkhar/RNA-Editing/.

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