Infectious Disease

Global-and-local-structure-based neural network for fault detection.

A novel statistical fault detection method, called the global-and-local-structure-based neural network (GLSNN), is proposed for fault detection. GLSNN is a nonlinear data-driven process monitoring technique through preserving both global and local structures of normal process data. GLSNN is characterized by adaptively training a neural network which takes both the global variance information and t...

Sentiment Analysis of Social Media on Childhood Vaccination: Development of an Ontology.

BACKGROUND: Although vaccination rates are above the threshold for herd immunity in South Korea, a growing number of parents have expressed concerns about the safety of vaccines. It is important to understand these concerns so that we can maintain high vaccination rates.

An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks.

BACKGROUND AND OBJECTIVE: Chest X-ray (CXR) is one of the most used imaging techniques for detection and diagnosis of pulmonary diseases. A critical c...

Error Tolerance of Machine Learning Algorithms across Contemporary Biological Targets.

Machine learning continues to make strident advances in the prediction of desired properties concerning drug development. Problematically, the efficac...

Ascending Lipopolysaccharide-Induced Intrauterine Inflammation in Near-Term Rabbits Leading to Newborn Neurobehavioral Deficits.

BACKGROUND: Chorioamnionitis from ascending bacterial infection through the endocervix is a potential risk factor for cerebral palsy. Tetrahydrobiopte...

Electrochemical inhibition bacterial sensor array for detection of water pollutants: artificial neural network (ANN) approach.

This work reports on further development of an inhibition electrochemical sensor array based on immobilized bacteria for the preliminary detection of ...

A low-cost, automated parasite diagnostic system via a portable, robotic microscope and deep learning.

Manual hand counting of parasites in fecal samples requires costly components and substantial expertise, limiting its use in resource-constrained sett...

Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis.

BACKGROUND: The propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences their capacity to transmit pathoge...

MHCSeqNet: a deep neural network model for universal MHC binding prediction.

BACKGROUND: Immunotherapy is an emerging approach in cancer treatment that activates the host immune system to destroy cancer cells expressing unique ...

Artificial Neural Networks (ANNs) and Response Surface Methodology (RSM) Approach for Modelling the Optimization of Chromium (VI) Reduction by Newly Isolated Strain NS-MIE from Agricultural Soil.

Numerous technologies and approaches have been used in the past few decades to remove hexavalent chromium (Cr[VI]) in wastewater and the environment. ...

Cholecalciferol, Ergosterol, and Cholesterol Enhance the Antibiotic Activity of Drugs.

UNLABELLED: Background: This is the first report demonstrating the antibiotic-modifying activity of cholecalciferol.

Estimating the association between antibiotic exposure and colonization with extended-spectrum β-lactamase-producing Gram-negative bacteria using machine learning methods: a multicentre, prospective cohort study.

OBJECTIVES: The aim of the study was to measure the impact of antibiotic exposure on the acquisition of colonization with extended-spectrum β-lactamas...

Tackling the Radiological Society of North America Pneumonia Detection Challenge.

We provide overviews of deep learning approaches used by two top-placing teams for the 2018 Radiological Society of North America (RSNA) Pneumonia De...

Weekly ILI patient ratio change prediction using news articles with support vector machine.

BACKGROUND: Influenza continues to pose a serious threat to human health worldwide. For this reason, detecting influenza infection patterns is critica...

DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks.

Automated protein function prediction is critical for the annotation of uncharacterized protein sequences, where accurate prediction methods are still...

A machine learning approach for predicting urine output after fluid administration.

BACKGROUND AND OBJECTIVE: To develop a machine learning model to predict urine output (UO) in sepsis patients after fluid resuscitation.

Perceptions of drones, digital adherence monitoring technologies and educational videos for tuberculosis control in remote Madagascar: a mixed-method study protocol.

INTRODUCTION: Poor road and communication infrastructure pose major challenges to tuberculosis (TB) control in many regions of the world. TB surveilla...

Streptococcus Gallolyticus endocarditis in patient with liver cirrhosis: a case report.

Streptococcus gallolyticus (S. gallolyticus) bacteremia is commonly associated with endocarditis and diseases of gastrointestinal tract, especially wi...

Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction.

BACKGROUND: The diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of c...

Predicting the concentration of indoor culturable fungi using a kernel-based extreme learning machine (K-ELM).

Indoor fungal is of great significance for human health. The kernel-based extreme learning machine is employed to determine the most important paramet...