Erratum: Automatic classification and neurotransmitter prediction of synapses in electron microscopy - CORRIGENDUM.

[This corrects the article DOI: 10.1017/S2633903X2200006X.].

Automatic classification and neurotransmitter prediction of synapses in electron microscopy.

This paper presents a deep-learning-based workflow to detect synapses and predict their neurotransmitter type in the primitive chordate Ciona intestinalis (Ciona) electron microscopic (EM) images. Identifying synapses from EM images to build a full map of connections between neurons is a labor-intensive process and requires significant domain expertise. Automation of synapse classification would hasten the generation and analysis of connectomes. Furthermore, inferences concerning neuron type and function from synapse features are in many cases difficult to make. Finding the connection between synapse structure and function is an important step in fully understanding a connectome. Class Activation Maps derived from the convolutional neural network provide insights on important features of synapses based on cell type and function. The main contribution of this work is in the differentiation of synapses by neurotransmitter type through the structural information in their EM images. This enables the prediction of neurotransmitter types for neurons in Ciona, which were previously unknown. The prediction model with code is available on GitHub.

Fault-Tolerant Sensor Detection of sEMG signals: Quality Analysis Using a Two-Class Support Vector Machine.

The capacity to identify the contamination in surface electromyography (sEMG) signals is necessary for applying the sEMG controlled prosthesis over time. In this paper, the method for the automatic identification of commonly occurring contaminant types in sEMG signals is evaluated. The presented approach uses two-class support vector machine (SVM) trained with clean sEMG and artificially contaminated sEMG. The contaminants considered include electrocardiogram interference, motion artefact, power line interference, amplifier saturation, and electrode displacement. The results demonstrated that the sEMG signal with the contaminants could readily be distinguished, even with increase channels degraded. The SFTD detection depends on the noise type, whether the amputee or non-amputee subjects and which channel is being analysed. This method presented a suitable solution for the detection of contaminants in the sEMG signal, being able to provide the acquired signal validation before the movement intended recognition to operate in an intelligent recognition with greater reliability.

A Framework for Automatic Recognition of Cell Damage on Microscopic Images using Artificial Neural Networks.

Despite several technological advances in the past years, the vast majority of microscopy examinations continue to be performed in a very laborious, time-consuming manner, requiring highly experienced personnel to spend several hours to visually examine each microscope slide. Due to recent improvements in modern Digital Image Processing, professionals that work on microscopic exams could benefit from new tools that can apply image processing possibilities to their specific field. We propose a framework consisting of an image segmentation stage, feature extraction, and then a Shallow Neural Network related to human perception. The framework is used to classify among 5 types of animal cell damage analyzed in a case study. The case study used applies the Single Cell Gel Electrophoresis assay (SCGE, also known as comet assay) to the cells of land mollusk Helix aspersa in order to measure the DNA damage caused by mutagenic agents. To train and analyze the performance of our approach, we used a dataset manually segmented by a biologist and comprised of 130 slide samples with labeled cells. Our framework proved to be robust, achieving an average accuracy of 88.3%.