scholarly journals nanoTRON: a Picasso module for MLP-based classification of super-resolution data

2020 ◽  
Vol 36 (11) ◽  
pp. 3620-3622
Author(s):  
Alexander Auer ◽  
Maximilian T Strauss ◽  
Sebastian Strauss ◽  
Ralf Jungmann
Keyword(s):  
Super Resolution ◽  
Biological Data ◽  
Supplementary Information ◽  
Graphic User Interface ◽  
Classification Of Images ◽  
Super Resolution Microscopy ◽  
Level Analysis ◽  
Initial Classification ◽  
Resolution Data

Abstract Motivation Classification of images is an essential task in higher-level analysis of biological data. By bypassing the diffraction limit of light, super-resolution microscopy opened up a new way to look at molecular details using light microscopy, producing large amounts of data with exquisite spatial detail. Statistical exploration of data usually needs initial classification, which is up to now often performed manually. Results We introduce nanoTRON, an interactive open-source tool, which allows super-resolution data classification based on image recognition. It extends the software package Picasso with the first deep learning tool with a graphic user interface. Availability and implementation nanoTRON is written in Python and freely available under the MIT license as a part of the software collection Picasso on GitHub (http://www.github.com/jungmannlab/picasso). All raw data can be obtained from the authors upon reasonable request. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

Understanding Image Classification Using TensorFlow Deep Learning - Convolution Neural Network

2019 ◽  
Vol 3 (2) ◽  
pp. 19-37
Author(s):  
Vinit Kumar Gunjan ◽  
Rashmi Pathak ◽  
Omveer Singh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolution Neural Network ◽  
Fine Tuning ◽  
Learning Technology ◽  
The Neural Network ◽  
Learning Technique ◽  
Classification Of Images ◽  
Initial Classification

This article describes how to establish the neural network technique for various image groupings in a convolution neural network (CNN) training. In addition, it also suggests initial classification results using CNN learning characteristics and classification of images from different categories. To determine the correct architecture, we explore a transfer learning technique, called Fine-Tuning of Deep Learning Technology, a dataset used to provide solutions for individually classified image-classes.

scholarly journals Super-Resolution Microscopy Reveals Diversity of Plant Centromere Architecture

2020 ◽  
Vol 21 (10) ◽  
pp. 3488 ◽  
Author(s):  
Veit Schubert ◽  
Pavel Neumann ◽  
André Marques ◽  
Stefan Heckmann ◽  
Jiri Macas ◽  
...  
Keyword(s):  
Super Resolution ◽  
Structural Diversity ◽  
Chromosome Length ◽  
Holocentric Chromosomes ◽  
Metaphase Chromosomes ◽  
Daughter Cells ◽  
Attachment Sites ◽  
Specific Proteins ◽  
Super Resolution Microscopy

Centromeres are essential for proper chromosome segregation to the daughter cells during mitosis and meiosis. Chromosomes of most eukaryotes studied so far have regional centromeres that form primary constrictions on metaphase chromosomes. These monocentric chromosomes vary from point centromeres to so-called “meta-polycentromeres”, with multiple centromere domains in an extended primary constriction, as identified in Pisum and Lathyrus species. However, in various animal and plant lineages centromeres are distributed along almost the entire chromosome length. Therefore, they are called holocentromeres. In holocentric plants, centromere-specific proteins, at which spindle fibers usually attach, are arranged contiguously (line-like), in clusters along the chromosomes or in bands. Here, we summarize findings of ultrastructural investigations using immunolabeling with centromere-specific antibodies and super-resolution microscopy to demonstrate the structural diversity of plant centromeres. A classification of the different centromere types has been suggested based on the distribution of spindle attachment sites. Based on these findings we discuss the possible evolution and advantages of holocentricity, and potential strategies to segregate holocentric chromosomes correctly.

scholarly journals FORUM: Building a Knowledge Graph from public databases and scientific literature to extract associations between chemicals and diseases

2021 ◽  
Author(s):  
M Delmas ◽  
O Filangi ◽  
N Paulhe ◽  
F Vinson ◽  
C Duperier ◽  
...  
Keyword(s):  
Scientific Literature ◽  
Semantic Representation ◽  
Enrichment Analysis ◽  
Biological Data ◽  
Supplementary Information ◽  
Knowledge Graph ◽  
Web Interface ◽  
Levels Of Abstraction ◽  
Statistical Relevance

Abstract Motivation Metabolomics studies aim at reporting a metabolic signature (list of metabolites) related to a particular experimental condition. These signatures are instrumental in the identification of biomarkers or classification of individuals, however their biological and physiological interpretation remains a challenge. To support this task, we introduce FORUM: a Knowledge Graph (KG) providing a semantic representation of relations between chemicals and biomedical concepts, built from a federation of life science databases and scientific literature repositories. Results The use of a Semantic Web framework on biological data allows us to apply ontological based reasoning to infer new relations between entities. We show that these new relations provide different levels of abstraction and could open the path to new hypotheses. We estimate the statistical relevance of each extracted relation, explicit or inferred, using an enrichment analysis, and instantiate them as new knowledge in the KG to support results interpretation/further inquiries. Availability A web interface to browse and download the extracted relations, as well as a SPARQL endpoint to directly probe the whole FORUM knowledge graph, are available at https://forum-webapp.semantic-metabolomics.fr. The code needed to reproduce the triplestore is available at https://github.com/eMetaboHUB/Forum-DiseasesChem. Supplementary information Supplementary data are available at Bioinformatics online.

Super-Resolution Microscopy in Studying the Structure and Function of the Cell Nucleus

Acta Naturae ◽  
2017 ◽  
Vol 9 (4) ◽  
pp. 42-51
Author(s):  
S. S. Ryabichko ◽  
◽  
A. N. Ibragimov ◽  
L. A. Lebedeva ◽  
E. N. Kozlov ◽  
...  
Keyword(s):  
Cell Nucleus ◽  
Super Resolution ◽  
Structure And Function ◽  
And Function ◽  
Super Resolution Microscopy

Structural Evidence of Photoisomerization Pathways in Fluorescent Proteins

2019 ◽  
Author(s):  
Jeffrey Chang ◽  
Matthew Romei ◽  
Steven Boxer
Keyword(s):  
Rational Design ◽  
Fluorescent Protein ◽  
Fluorescent Proteins ◽  
Super Resolution ◽  
Unit Cell Size ◽  
Chlorine Substituent ◽  
Gfp Chromophore ◽  
The One ◽  
Green Fluorescent ◽  
Super Resolution Microscopy

<p>Double-bond photoisomerization in molecules such as the green fluorescent protein (GFP) chromophore can occur either via a volume-demanding one-bond-flip pathway or via a volume-conserving hula-twist pathway. Understanding the factors that determine the pathway of photoisomerization would inform the rational design of photoswitchable GFPs as improved tools for super-resolution microscopy. In this communication, we reveal the photoisomerization pathway of a photoswitchable GFP, rsEGFP2, by solving crystal structures of <i>cis</i> and <i>trans</i> rsEGFP2 containing a monochlorinated chromophore. The position of the chlorine substituent in the <i>trans</i> state breaks the symmetry of the phenolate ring of the chromophore and allows us to distinguish the two pathways. Surprisingly, we find that the pathway depends on the arrangement of protein monomers within the crystal lattice: in a looser packing, the one-bond-flip occurs, whereas in a tighter packing (7% smaller unit cell size), the hula-twist occurs.</p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p> <p> </p>

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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