scholarly journals Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection

2021 ◽  
Vol 8 ◽  
Author(s):  
Yu-Hang Zhang ◽  
Hao Li ◽  
Tao Zeng ◽  
Lei Chen ◽  
Zhandong Li ◽  
...  
Keyword(s):  
Upper Airway ◽  
Vaccine Design ◽  
Classification Algorithms ◽  
Respiratory Illnesses ◽  
Incremental Feature Selection ◽  
Machine Learning Methods ◽  
Diagnostic Models ◽  
The World ◽  
Feature List ◽  
Transcriptomics Data

The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.

Aftermath of Covid -19 Pandemic in the community

2021 ◽  
pp. 498-500
Author(s):  
Sholly. CK
Keyword(s):  
World Health Organization ◽  
Antiviral Treatment ◽  
World Health ◽  
Health Concern ◽  
Severe Illness ◽  
Respiratory Illnesses ◽  
The World ◽  
Novel Coronavirus ◽  
Health Organization ◽  
Respiratory Droplets

Novel corona virus (COVID-19) is an infectious condition, which can be spread directly or indirectly from one person to another and causes respiratory illnesses, range from common cold to acute respiratory syndrome. The first cases of this virus were found in Wuhan, China. According to the World Health Organization, COVID-19 is serious health concern and has higher risk for severe illness and spreading rapidly all over the world.This novel coronavirus was named Coronavirus Disease 2019 (COVID-19) by WHO in February 2020. The World Health Organization (WHO) has declared the coronavirus disease 2019 a pandemic, in the year2020 March. A global coordinated effort is needed to stop the further spread of the virus. Among all cases about 92% of the confirmed cases were recorded from China. Initial reports suggest that death rate ranges from 1% to 2% which varies in the study and country. The most of the death have occurred in patients over 50 years of age followed by young children. For the confirmed cases which included both laboratory and clinically diagnosed till now there is no specific antiviral treatment recommended but there is vaccine currently available. Once the virus develops in people, corona viruses can be spread from person to person through respiratory droplets. The viral material hangs out in these droplets and can be breathed into the respiratory tract, where the virus can then lead to an infection. Repercussions of Covid -19 on individuals, families and on front line warriors are countless1.

scholarly journals Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dennie te Molder ◽  
Wasin Poncheewin ◽  
Peter J. Schaap ◽  
Jasper J. Koehorst
Keyword(s):  
Machine Learning ◽  
Plant Pathogens ◽  
De Novo ◽  
Classification Algorithms ◽  
Learning Approaches ◽  
Enabling Factors ◽  
Essential Step ◽  
The World ◽  
Genome Content

Abstract Background The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of important crops around the world, there is a need to distinguish between these plant-associated phenotypes. To date a large number of Xanthomonas genomes have been sequenced, which enables the application of machine learning (ML) approaches on the genome content to predict this phenotype. Until now such approaches to the pathogenomics of Xanthomonas strains have been hampered by the fragmentation of information regarding pathogenicity of individual strains over many studies. Unification of this information into a single resource was therefore considered to be an essential step. Results Mining of 39 papers considering both plant-associated phenotypes, allowed for a phenotypic classification of 578 Xanthomonas strains. For 65 plant-pathogenic and 53 non-pathogenic strains the corresponding genomes were available and de novo annotated for the presence of Pfam protein domains used as features to train and compare three ML classification algorithms; CART, Lasso and Random Forest. Conclusion The literature resource in combination with recursive feature extraction used in the ML classification algorithms provided further insights into the virulence enabling factors, but also highlighted domains linked to traits not present in pathogenic strains.

scholarly journals Features of machine learning in the study of the main factors of development of countries of the world

2021 ◽  
Vol 110 ◽  
pp. 02006
Author(s):  
Ludmila Borisova ◽  
Galina Zhukova ◽  
Anna Kuznetsova ◽  
Julie Martin
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Life Expectancy ◽  
Infant Mortality Rate ◽  
Live Births ◽  
Goods And Services ◽  
Machine Learning Methods ◽  
The World

The paper analyzes the socio-economic and demographic indicators of life expectancy in the countries of the world. Methods of regression analysis and machine learning are used. Statistically significant indicators that affect life expectancy around the world have been identified. When analyzing the data using machine learning methods, 13 of the 14 analyzed indicators were statistically significant. Significant indicators, in addition to those selected in the regression analysis, were 3: the under-five infant mortality rate (per 1,000 live births), the Net Barter Terms of Trade Index (2000 = 100), and Imports of goods and services (in % of GDP) (in the regression analysis, only the infant death rate was significant). In addition, it should be noted that there is a significant decrease in the under-five infant mortality rate (per 1,000 live births) for the EU, CIS and South-East Asian countries compared to the border set in the study for all countries: 4.65 vs. 34.9, a decrease in the birth rate from 2.785 to 1.85, a sharp increase in exports of goods and services: from 23.17 to 80.59, a halving in imports of goods and services, a drop in population growth from 2.105 to 0.85. The performed statistical analysis strongly supports the use of machine learning methods in identifying statistically significant relationships between various indicators that characterize the development of countries, if there are gaps in the data.

Detecting Face Touching Using Smartwatches to Mitigate the Spread of COVID-19: Pilot Study (Preprint)

2021 ◽  
Author(s):  
Chen Bai ◽  
Yu-Peng Chen ◽  
Adam Wolach ◽  
Lisa Anthony ◽  
Mamoun Mardini
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Respiratory Diseases ◽  
Window Size ◽  
Support Vector ◽  
Accelerometer Data ◽  
Respiratory Illnesses ◽  
Motion Data ◽  
Machine Learning Methods

BACKGROUND Frequent spontaneous facial self-touches, predominantly during outbreaks, have the theoretical potential to be a mechanism of contracting and transmitting diseases. Despite the recent advent of vaccines, behavioral approaches remain an integral part of reducing the spread of COVID-19 and other respiratory illnesses. Real-time biofeedback of face touching can potentially mitigate the spread of respiratory diseases. The gap addressed in this study is the lack of an on-demand platform that utilizes motion data from smartwatches to accurately detect face touching. OBJECTIVE The aim of this study was to utilize the functionality and the spread of smartwatches to develop a smartwatch application to identifying motion signatures that are mapped accurately to face touching. METHODS Participants (n=10, 50% women, aged 20-83) performed 10 physical activities classified into: face touching (FT) and non-face touching (NFT) categories, in a standardized laboratory setting. We developed a smartwatch application on Samsung Galaxy Watch to collect raw accelerometer data from participants. Then, data features were extracted from consecutive non-overlapping windows varying from 2-16 seconds. We examined the performance of state-of-the-art machine learning methods on face touching movements recognition (FT vs NFT) and individual activity recognition (IAR): logistic regression, support vector machine, decision trees and random forest. RESULTS Machine learning models were accurate in recognizing face touching categories; logistic regression achieved the best performance across all metrics (Accuracy: 0.93 +/- 0.08, Recall: 0.89 +/- 0.16, Precision: 0.93 +/- 0.08, F1-score: 0.90 +/- 0.11, AUC: 0.95 +/- 0.07) at the window size of 5 seconds. IAR models resulted in lower performance; the random forest classifier achieved the best performance across all metrics (Accuracy: 0.70 +/- 0.14, Recall: 0.70 +/- 0.14, Precision: 0.70 +/- 0.16, F1-score: 0.67 +/- 0.15) at the window size of 9 seconds. CONCLUSIONS Wearable devices, powered with machine learning, are effective in detecting facial touches. This is highly significant during respiratory infection outbreaks, as it has a great potential to refrain people from touching their faces and potentially mitigate the possibility of transmitting COVID-19 and future respiratory diseases.

Face masks are an essential tool to mitigate the ongoing SARS-CoV-2 pandemic: a call to action

2020 ◽  
Vol 3 (3) ◽  
pp. 157-159
Author(s):  
P. Dehgani-Mobaraki ◽  
A. Kamber Zaidi ◽  
J.M. Levy ◽  
Keyword(s):  
Upper Airway ◽  
World Health ◽  
Contact Tracing ◽  
The Novel ◽  
Call To Action ◽  
Public Health Emergencies ◽  
Current Spread ◽  
The World ◽  
Health And Disease ◽  
Novel Coronavirus

Over the past several months, an increasing volume of infor- mation has expanded awareness regarding the transmission of SARS-CoV-2, the novel coronavirus associated with COVID-19. Following the pandemic declaration by the World Health Orga- nization (WHO), global authorities immediately took measures to reduce the transmission and subsequent morbidity associa- ted with this highly contagious disease. However, despite initial success in “flattening the curve” of viral transmission, many areas of the world are currently experiencing an increase in com- munity transmission, threatening to replicate the early public health emergencies experienced by Italy (1,2). In addition, the possibility of contact tracing through geosocial applications and public service platforms have been met with variable interest (3). Given current spread and the upcoming influenza season, it is essential that we use our voices as experts in upper airway health and disease to educate and encourage all communities to adopt appropriate protective measures, including the routine use of facemasks.

A Process for Increasing the Samples of Coffee Rust Through Machine Learning Methods

2018 ◽  
Vol 9 (2) ◽  
pp. 32-52
Author(s):  
Jhonn Pablo Rodríguez ◽  
David Camilo Corrales ◽  
Juan Carlos Corrales
Keyword(s):  
Machine Learning ◽  
Agricultural Product ◽  
American Phytopathological Society ◽  
Learning Methods ◽  
Coffee Rust ◽  
Machine Learning Methods ◽  
The World ◽  
Supervised Learning Algorithms ◽  
Coffee Farmers ◽  
Reliable Representation

This article describes how coffee rust has become a serious concern for many coffee farmers and manufacturers. The American Phytopathological Society discusses its importance saying this: “…the most economically important coffee disease in the world…” while “…in monetary value, coffee is the most important agricultural product in international trade…” The early detection has inspired researchers to apply supervised learning algorithms on predicting the disease appearance. However, the main issue of the related works is the small number of samples of the dependent variable: Incidence Percentage of Rust, since the datasets do not have a reliable representation of the disease, which will generate inaccurate predictions in the models. This article provides a process about coffee rust to select appropriate machine learning methods to increase rust samples.

scholarly journals Pandemic Influenza and Canada's Children

2006 ◽  
Vol 17 (4) ◽  
pp. 213-215
Author(s):  
Joanne M Langley
Keyword(s):  
Infectious Disease ◽  
Public Interest ◽  
Pandemic Influenza ◽  
Influenza Pandemic ◽  
Contingency Planning ◽  
Respiratory Illnesses ◽  
Material Resources ◽  
Annual Visit ◽  
The World ◽  
Disease Note

Paediatricians and others who care for children are familiar with the regular epidemic of respiratory illnesses that accompanies the annual visit of influenza virus each winter. In recent years, media interest in new strains of influenza has generated much public interest in, and often anxiety about, the threat of an influenza pandemic. Around the world, local, regional and national jurisdictions are engaged in contingency planning for the inevitable surge of illness, shortage of human and material resources, and societal disruption that is expected to accompany this event. In the present Paediatric Infectious Disease Note, we review briefly the potential implications of pandemic influenza for Canadian children, and the actions that paediatricians and others who care for children can take to prepare for this inevitable event.

scholarly journals A Survey of the Application of Artifical Intellegence on COVID-19 Diagnosis and Prediction

2021 ◽  
Vol 11 (6) ◽  
pp. 7824-7835
Author(s):  
H. Alalawi ◽  
M. Alsuwat ◽  
H. Alhakami
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Future Research ◽  
Classification Algorithms ◽  
Classification Methods ◽  
Research Directions ◽  
The World ◽  
Modern Methods ◽  
Future Research Directions ◽  
Class Labels

The importance of classification algorithms has increased in recent years. Classification is a branch of supervised learning with the goal of predicting class labels categorical of new cases. Additionally, with Coronavirus (COVID-19) propagation since 2019, the world still faces a great challenge in defeating COVID-19 even with modern methods and technologies. This paper gives an overview of classification algorithms to provide the readers with an understanding of the concept of the state-of-the-art classification algorithms and their applications used in the COVID-19 diagnosis and detection. It also describes some of the research published on classification algorithms, the existing gaps in the research, and future research directions. This article encourages both academics and machine learning learners to further strengthen the basis of classification methods.

scholarly journals Upper airway gene expression differentiates COVID-19 from other acute respiratory illnesses and reveals suppression of innate immune responses by SARS-CoV-2

2020 ◽  
Author(s):  
Eran Mick ◽  
Jack Kamm ◽  
Angela Oliveira Pisco ◽  
Kalani Ratnasiri ◽  
Jennifer M Babik ◽  
...  
Keyword(s):  
Gene Expression ◽  
Direct Detection ◽  
Transcriptional Response ◽  
Upper Airway ◽  
Innate Immune ◽  
Lymphoid Cells ◽  
Metagenomic Sequencing ◽  
Respiratory Illnesses ◽  
Expression Of Genes ◽  
Neutrophil Degranulation

We studied the host transcriptional response to SARS-CoV-2 by performing metagenomic sequencing of upper airway samples in 238 patients with COVID-19, other viral or non-viral acute respiratory illnesses (ARIs). Compared to other viral ARIs, COVID-19 was characterized by a diminished innate immune response, with reduced expression of genes involved in toll-like receptor and interleukin signaling, chemokine binding, neutrophil degranulation and interactions with lymphoid cells. Patients with COVID-19 also exhibited significantly reduced proportions of neutrophils and macrophages, and increased proportions of goblet, dendritic and B-cells, compared to other viral ARIs. Using machine learning, we built 26-, 10- and 3-gene classifiers that differentiated COVID-19 from other acute respiratory illnesses with AUCs of 0.980, 0.950 and 0.871, respectively. Classifier performance was stable at low viral loads, suggesting utility in settings where direct detection of viral nucleic acid may be unsuccessful. Taken together, our results illuminate unique aspects of the host transcriptional response to SARS-CoV-2 in comparison to other respiratory viruses and demonstrate the feasibility of COVID-19 diagnostics based on patient gene expression.

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