scholarly journals Automated high-throughput high-content autophagy and mitophagy analysis platform

2018 ◽  
Author(s):  
Jonathan Arias-Fuenzalida ◽  
Javier Jarazo ◽  
Jonas Walter ◽  
Gemma Gomez-Giro ◽  
Julia I. Forster ◽  
...  
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
State Of The Art ◽  
Level Of Detail ◽  
Cell Populations ◽  
Current State ◽  
Pathological Conditions ◽  
High Throughput Phenotyping ◽  
Analysis Platform ◽  
High Throughput Manner

AbstractAutophagy and mitophagy play a central role in cellular homeostasis. In pathological conditions, the flow of autophagy and mitophagy can be affected at multiple and distinct steps of the pathways. Unfortunately, the level of detail of current state of the art analyses does not allow detection or dissection of pathway intermediates. Moreover, is conducted in low-throughput manner on bulk cell populations. Defining autophagy and mitophagy pathway intermediates in a high-throughput manner is technologically challenging, and has not been addressed so far. Here, we overcome those limitations and developed a novel high-throughput phenotyping platform with automated high-content image analysis to assess autophagy and mitophagy pathway intermediates.

scholarly journals High-throughput microfluidic micropipette aspiration device to probe time-scale dependent nuclear mechanics in intact cells

Lab on a Chip ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 3652-3663 ◽  
Author(s):  
Patricia M. Davidson ◽  
Gregory R. Fedorchak ◽  
Solenne Mondésert-Deveraux ◽  
Emily S. Bell ◽  
Philipp Isermann ◽  
...  
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Time Scale ◽  
Viscoelastic Properties ◽  
Micropipette Aspiration ◽  
Automated Image Analysis ◽  
Cell Nuclei ◽  
Intact Cells ◽  
Nuclear Mechanics ◽  
Analysis Platform

We report the development, validation, and application of an easy-to-use microfluidic micropipette aspiration device and automated image analysis platform that enables high-throughput measurements of the viscoelastic properties of cell nuclei.

scholarly journals Engineered Extracellular Vesicles: Tailored-Made Nanomaterials for Medical Applications

Nanomaterials ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1838
Author(s):  
Kenny Man ◽  
Mathieu Y. Brunet ◽  
Marie-Christine Jones ◽  
Sophie C. Cox
Keyword(s):  
Extracellular Vesicles ◽  
Tissue Regeneration ◽  
State Of The Art ◽  
Medical Applications ◽  
Clinical Use ◽  
Current State ◽  
Pathological Conditions ◽  
Potential Efficacy ◽  
Biomimetic Nanoparticles ◽  
Natural Cell

Extracellular vesicles (EVs) are emerging as promising nanoscale therapeutics due to their intrinsic role as mediators of intercellular communication, regulating tissue development and homeostasis. The low immunogenicity and natural cell-targeting capabilities of EVs has led to extensive research investigating their potential as novel acellular tools for tissue regeneration or for the diagnosis of pathological conditions. However, the clinical use of EVs has been hindered by issues with yield and heterogeneity. From the modification of parental cells and naturally-derived vesicles to the development of artificial biomimetic nanoparticles or the functionalisation of biomaterials, a multitude of techniques have been employed to augment EVs therapeutic efficacy. This review will explore various engineering strategies that could promote EVs scalability and therapeutic effectiveness beyond their native utility. Herein, we highlight the current state-of-the-art EV-engineering techniques with discussion of opportunities and obstacles for each. This is synthesised into a guide for selecting a suitable strategy to maximise the potential efficacy of EVs as nanoscale therapeutics.

scholarly journals Domain Agnostic Real-Valued Specificity Prediction

2019 ◽  
Vol 33 ◽  
pp. 6610-6617 ◽  
Author(s):  
Wei-Jen Ko ◽  
Greg Durrett ◽  
Junyi Jessy Li
Keyword(s):  
Domain Adaptation ◽  
State Of The Art ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Error Reduction ◽  
Level Of Detail ◽  
Reference Distribution ◽  
Unsupervised Domain Adaptation ◽  
Current State ◽  
Prediction Systems

Sentence specificity quantifies the level of detail in a sentence, characterizing the organization of information in discourse. While this information is useful for many downstream applications, specificity prediction systems predict very coarse labels (binary or ternary) and are trained on and tailored toward specific domains (e.g., news). The goal of this work is to generalize specificity prediction to domains where no labeled data is available and output more nuanced realvalued specificity ratings.We present an unsupervised domain adaptation system for sentence specificity prediction, specifically designed to output real-valued estimates from binary training labels. To calibrate the values of these predictions appropriately, we regularize the posterior distribution of the labels towards a reference distribution. We show that our framework generalizes well to three different domains with 50%-68% mean absolute error reduction than the current state-of-the-art system trained for news sentence specificity. We also demonstrate the potential of our work in improving the quality and informativeness of dialogue generation systems.

scholarly journals SmartGrain: High-Throughput Phenotyping Software for Measuring Seed Shape through Image Analysis

2012 ◽  
Vol 160 (4) ◽  
pp. 1871-1880 ◽  
Author(s):  
Takanari Tanabata ◽  
Taeko Shibaya ◽  
Kiyosumi Hori ◽  
Kaworu Ebana ◽  
Masahiro Yano
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Seed Shape ◽  
High Throughput Phenotyping

scholarly journals Rhizoslides: paper-based growth system for non-destructive, high throughput phenotyping of root development by means of image analysis

Plant Methods ◽  
2014 ◽  
Vol 10 (1) ◽  
pp. 13 ◽  
Author(s):  
Chantal Le Marié ◽  
Norbert Kirchgessner ◽  
Daniela Marschall ◽  
Achim Walter ◽  
Andreas Hund
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Root Development ◽  
Growth System ◽  
High Throughput Phenotyping ◽  
Non Destructive

scholarly journals 227 Leveraging on high-throughput phenotyping technologies to optimize livestock genetic improvement and husbandry

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 55-55
Author(s):  
Guilherme J M Rosa ◽  
João R R Dorea ◽  
Arthur Francisco Araujo Fernandes ◽  
Tiago L Passafaro
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
High Throughput ◽  
Feed Intake ◽  
Genetic Improvement ◽  
Milk Composition ◽  
Sensor Technology ◽  
Efficient Data ◽  
High Throughput Phenotyping ◽  
Complex Relationships

Abstract The advent of fully automated data recording technologies and high-throughput phenotyping (HTP) systems has opened up a myriad of opportunities to advance breeding programs and livestock husbandry. Such technologies allow scoring large number of animals for novel phenotypes and indicator traits to boost genetic improvement, as well as for real-time monitoring of animal behavior and development for optimized management decisions. HTP tools include, for example, image analysis and computer vision, sensor technology for motion, sound and chemical composition, and spectroscopy. Applications span from health surveillance, precision nutrition, and control of meat and milk composition and quality. However, the application of HTP requires sophisticated statistical and computational approaches for efficient data management and appropriate data mining, as it involves large datasets with many covariates and complex relationships. In this talk we will discuss some of the challenges and potentials of HTP in livestock. Some examples to be presented include the utilization of automated feeders to record feed intake and to monitor feeding behavior in broilers, milk-spectra information to predict dairy cattle feed intake, and image analysis and computer vision to monitor growth and body condition in pigs and cattle. HTP and big data will become an essential component of modern livestock operations in the context of precision animal agriculture, boosting animal welfare, environmental footprint, and overall sustainability of animal production.

Descriptive Image Analysis. Foundations and Descriptive Image Algebras

2019 ◽  
Vol 33 (11) ◽  
pp. 1940018 ◽  
Author(s):  
Igor Gurevich ◽  
Vera Yashina
Keyword(s):  
Image Analysis ◽  
Mathematical Theory ◽  
State Of The Art ◽  
Mathematical Methods ◽  
Mathematical Objects ◽  
Intelligent Decision Making ◽  
Current State ◽  
Intelligent Decision ◽  
Methodological Principles ◽  
Basic Ideas

The paper is devoted to Descriptive Image Analysis (DA) — a leading line of the modern mathematical theory of image analysis. DA is a logically organized set of descriptive methods, mathematical objects, and models and representations aimed at analyzing and evaluating the information represented in the form of images, as well as for automating the extraction from images of knowledge and data needed for intelligent decision-making. The basic idea of DA consists of embedding all processes of analysis (processing, recognition, understanding) of images into an image formalization space and reducing it to (1) construction of models/representations/formalized descriptions of images; (2) construction of models/representations/formalized descriptions of transformations over models and representations of images. We briefly discuss the basic ideas, methodological principles, mathematical methods, objects, and components of DA and the basic results determining the current state of the art in the field. Image algebras (IA) are considered in the context of a unified language for describing mathematical objects and operations used in image analysis (the standard IA by Ritter and the descriptive IA by Gurevich).

scholarly journals ScreenSeed as a novel high throughput seed germination phenotyping method

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicolas Merieux ◽  
Pierre Cordier ◽  
Marie-Hélène Wagner ◽  
Sylvie Ducournau ◽  
Sophie Aligon ◽  
...  
Keyword(s):  
Arabidopsis Thaliana ◽  
Image Analysis ◽  
Seed Germination ◽  
High Throughput ◽  
Automated Image Analysis ◽  
Experimental Protocol ◽  
Visual Scoring ◽  
Radicle Protrusion ◽  
High Throughput Phenotyping ◽  
Fast Procedure

AbstractA high throughput phenotyping tool for seed germination, the ScreenSeed technology, was developed with the aim of screening genotype responsiveness and chemical drugs. This technology was presently used with Arabidopsis thaliana seeds to allow characterizing seed samples germination behavior by incubating seeds in 96-well microplates under defined conditions and detecting radicle protrusion through the seed coat by automated image analysis. This study shows that this technology provides a fast procedure allowing to handle thousands of seeds without compromising repeatability or accuracy of the germination measurements. Potential biases of the experimental protocol were assessed through statistical analyses of germination kinetics. Comparison of the ScreenSeed procedure with commonly used germination tests based upon visual scoring displayed very similar germination kinetics.

Current State of the Art of High‐Throughput Metabolomics

2021 ◽  
pp. 1-18
Author(s):  
Aihua Zhang ◽  
Qiang Yang ◽  
Hui Sun ◽  
Xijun Wang
Keyword(s):  
High Throughput ◽  
State Of The Art ◽  
Current State
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