STDs

Batch process fault detection for multi-stage broad learning system.

In the real industrial production process, some minor faults are difficult to be detected by multivariate statistical analysis methods with mean and variance as detection indicators due to the aging equipment and catalyst deactivation. With structural characteristics, deep neural networks can better extract data features to detect such faults. However, most deep learning models contain a large num...

The usefulness of the Deep Learning method of variational autoencoder to reduce measurement noise in glaucomatous visual fields.

The aim of the study was to investigate the usefulness of processing visual field (VF) using a variational autoencoder (VAE). The training data consisted of 82,433 VFs from 16,836 eyes. Testing dataset 1 consisted of test-retest VFs from 104 eyes with open angle glaucoma. Testing dataset 2 was series of 10 VFs from 638 eyes with open angle glaucoma. A VAE model to reconstruct VF was developed usin...

Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?

Machine learning (ML) and deep learning (DL) systems, currently employed in medical image analysis, are data-driven models often considered as black b...

Machine Learning in Mass Spectrometry: A MALDI-TOF MS Approach to Phenotypic Antibacterial Screening.

Machine learning techniques can be applied to MALDI-TOF mass spectral data of drug-treated cells to obtain classification models which assign the mech...

Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer.

A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations...

Co-existence or displacement: Do street trials of intelligent vehicles test society?

This paper examines recent street tests of autonomous vehicles (AVs) in the UK and makes the case for an experimental approach in the sociology of int...

Point-of-care cervical cancer screening using deep learning-based microholography.

Most deaths (80%) from cervical cancer occur in regions lacking adequate screening infrastructures or ready access to them. In contrast, most develope...

Machine Learning Interpretation of Extended Human Papillomavirus Genotyping by Onclarity in an Asian Cervical Cancer Screening Population.

This study aimed (i) to compare the performance of the BD Onclarity human papillomavirus (HPV) assay with the Cobas HPV test in identifying cervical i...

Integrated Data-Driven Process Monitoring and Explicit Fault-Tolerant Multiparametric Control.

We propose a novel active fault-tolerant control strategy that combines machine learning based process monitoring and explicit/multiparametric model p...

Powdered coconut water (ACP 406®) as an alternative base culture medium for culture of goat preantral follicles enclosed in ovarian tissue.

This study evaluated a powdered coconut water solution (ACP 406®) as a base culture medium on the survival and development of goat preantral follicl...

Zoster patients on earth and astronauts in space share similar immunologic profiles.

BACKGROUND: On long-duration spaceflight, most astronauts experience persistent immune dysregulation and the reactivation of latent herpesviruses, inc...

Automated Muscle Segmentation from Clinical CT Using Bayesian U-Net for Personalized Musculoskeletal Modeling.

We propose a method for automatic segmentation of individual muscles from a clinical CT. The method uses Bayesian convolutional neural networks with t...

Short-term synaptic plasticity expands the operational range of long-term synaptic changes in neural networks.

The brain is highly plastic, with synaptic weights changing across a wide range of time scales, from hundreds of milliseconds to days. Changes occurri...

IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment.

Wart disease (WD) is a skin illness on the human body which is caused by the human papillomavirus (HPV). This study mainly concentrates on common and ...

Classifying intracranial stenosis disease severity from functional MRI data using machine learning.

Translation of many non-invasive hemodynamic MRI methods to cerebrovascular disease patients has been hampered by well-known artifacts associated with...

Construction and optimization of a 'NG Morbidostat' - An automated continuous-culture device for studying the pathways towards antibiotic resistance in .

To obtain a detailed picture of the dynamics of antibiotic resistance development in , we built a morbidostat according to the protocol of Toprak ., a...

In vitro anti-Trichomonas vaginalis activity of Haplophyllum myrtifolium.

INTRODUCTION: In the classic treatment of Trichomonas vaginalis infection, although metronidazole has been used since the 1960s, there has been an inc...

Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity.

In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legislation to advance chemical safety evaluations by ut...

Application of convolutional neural networks for classification of adult mosquitoes in the field.

Dengue, chikungunya and Zika are arboviruses transmitted by mosquitos of the genus Aedes and have caused several outbreaks in world over the past ten ...

Navigating Virtual Environments Using Leg Poses and Smartphone Sensors.

Realization of navigation in virtual environments remains a challenge as it involves complex operating conditions. Decomposition of such complexity is...