scholarly journals MTQuant: “Seeing” Beyond the Diffraction Limit in Fluorescence Images to Quantify Neuronal Microtubule Organization

2016 ◽  
Author(s):  
Roshni Cooper ◽  
Shaul Yogev ◽  
Kang Shen ◽  
Mark Horowitz
Keyword(s):  
Average Length ◽  
Cell Structure ◽  
Ground Truth ◽  
Supplementary Information ◽  
Data Sets ◽  
Microtubule Organization ◽  
Ground Truth Data ◽  
C Elegans ◽  
Supplementary Material

AbstractMotivation:Microtubules (MTs) are polarized polymers that are critical for cell structure and axonal transport. They form a bundle in neurons, but beyond that, their organization is relatively unstudied.Results:We present MTQuant, a method for quantifying MT organization using light microscopy, which distills three parameters from MT images: the spacing of MT minus-ends, their average length, and the average number of MTs in a cross-section of the bundle. This method allows for robust and rapid in vivo analysis of MTs, rendering it more practical and more widely applicable than commonly-used electron microscopy reconstructions. MTQuant was successfully validated with three ground truth data sets and applied to over 3000 images of MTs in a C. elegans motor neuron.Availability:MATLAB code is available at http://roscoope.github.io/MTQuantContact:[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?

Algorithms ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 212
Author(s):  
Youssef Skandarani ◽  
Pierre-Marc Jodoin ◽  
Alain Lalande
Keyword(s):  
Deep Learning ◽  
Cardiac Mri ◽  
Expert Knowledge ◽  
Medical Image Analysis ◽  
Ground Truth ◽  
Cine Mri ◽  
Data Sets ◽  
Mri Segmentation ◽  
Segmentation Evaluation ◽  
Ground Truth Data

Deep learning methods are the de facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application, which, like many others, requires a large number of annotated data so that a trained network can generalize well. Unfortunately, the process of having a large number of manually curated images by medical experts is both slow and utterly expensive. In this paper, we set out to explore whether expert knowledge is a strict requirement for the creation of annotated data sets on which machine learning can successfully be trained. To do so, we gauged the performance of three segmentation models, namely U-Net, Attention U-Net, and ENet, trained with different loss functions on expert and non-expert ground truth for cardiac cine–MRI segmentation. Evaluation was done with classic segmentation metrics (Dice index and Hausdorff distance) as well as clinical measurements, such as the ventricular ejection fractions and the myocardial mass. The results reveal that generalization performances of a segmentation neural network trained on non-expert ground truth data is, to all practical purposes, as good as that trained on expert ground truth data, particularly when the non-expert receives a decent level of training, highlighting an opportunity for the efficient and cost-effective creation of annotations for cardiac data sets.

scholarly journals Automatic trajectory clustering for generating ground truth data sets

2010 ◽  
Author(s):  
Julia Moehrmann ◽  
Gunther Heidemann
Keyword(s):  
Ground Truth ◽  
Data Sets ◽  
Trajectory Clustering ◽  
Ground Truth Data

scholarly journals Microfluidic Electric Egg-Laying Assay and Application to In-vivo Toxicity Screening of Microplastics using C. elegans

2021 ◽  
Author(s):  
Khaled Youssef ◽  
Daphne Archonta ◽  
Terrance J. Kubiseseki ◽  
Anurag Tandon ◽  
Pouya Rezai
Keyword(s):  
Environmental Pollutants ◽  
Egg Laying ◽  
Electric Signal ◽  
Data Sets ◽  
Toxicity Screening ◽  
C Elegans ◽  
Toxicological Studies ◽  
Individual Worm ◽  
Polystyrene Microparticles

AbstractEnvironmental pollutants like microplastics are posing health concerns on aquatic animals and the ecosystem. Microplastic toxicity studies using C. elegans as a model are evolving but methodologically hindered from obtaining statistically strong data sets, detecting toxicity effects based on microplastics uptake, and correlating physiological and behavioural effects at an individual-worm level. In this paper, we report a novel microfluidic electric egg-laying assay for phenotypical assessment of multiple worms in parallel. The effects of glucose and polystyrene microplastics at various concentrations on the worms’ electric egg-laying, length, diameter, and length contraction during exposure to electric signal were studied. The device contained eight parallel worm-dwelling microchannels called electric traps, with equivalent electrical fields, in which the worms were electrically stimulated for egg deposition and fluorescently imaged for assessment of neuronal and microplastic uptake expression. A new bidirectional stimulation technique was developed, and the device design was optimized to achieve a testing efficiency of 91.25%. Exposure of worms to 100mM glucose resulted in a significant reduction in their egg-laying and size. The effects of 1μm polystyrene microparticles at concentrations of 100 and 1000 mg/L on the electric egg-laying behaviour, size, and neurodegeneration of N2 and NW1229 (expressing GFP pan-neuronally) worms were also studied. Of the two concentrations, 1000 mg/L caused severe egg-laying deficiency and growth retardation as well as neurodegeneration. Additionally, using single-worm level phenotyping, we noticed intra-population variability in microplastics uptake and correlation with the above physiological and behavioural phenotypes, which was hidden in the population-averaged results. Taken together, these results suggest the appropriateness of our microfluidic assay for toxicological studies and for assessing the phenotypical heterogeneity in response to microplastics.

scholarly journals Single cell network analysis with a mixture of Nested Effects Models

2018 ◽  
Author(s):  
Martin Pirkl ◽  
Niko Beerenwinkel
Keyword(s):  
Single Cell ◽  
New Technologies ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Data Sets ◽  
Cell Network ◽  
A Cell ◽  
Supplementary Material ◽  
Cell Data

AbstractMotivationNew technologies allow for the elaborate measurement of different traits of single cells. These data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous.ResultsWe developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular sub-populations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq.AvailabilityThe mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbgethz/mnem/[email protected], [email protected] informationSupplementary data are available.online.

scholarly journals A STUDY ON PRODUCING HIGHLY RELIABILE REFERENCE DATA SETS FOR GLOBAL LAND COVER VALIDATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 1207-1211
Author(s):  
N. Soyama ◽  
K. Muramatsu ◽  
M. Daigo ◽  
F. Ochiai ◽  
N. Fujiwara
Keyword(s):  
Land Cover ◽  
Reference Data ◽  
Ground Truth ◽  
Plant Functional Type ◽  
Data Sets ◽  
Functional Type ◽  
Reference Dataset ◽  
Ground Truth Data ◽  
Global Land Cover ◽  
Global Land

Validating the accuracy of land cover products using a reliable reference dataset is an important task. A reliable reference dataset is produced with information derived from ground truth data. Recently, the amount of ground truth data derived from information collected by volunteers has been increasing globally. The acquisition of volunteer-based reference data demonstrates great potential. However information given by volunteers is limited useful vegetation information to produce a complete reference dataset based on the plant functional type (PFT) with five specialized forest classes. In this study, we examined the availability and applicability of FLUXNET information to produce reference data with higher levels of reliability. FLUXNET information was useful especially for forest classes for interpretation in comparison with the reference dataset using information given by volunteers.

scholarly journals A study on landuse and landcover classification using microwave data in Joida taluk of Uttara Kannada district, Karnataka

2020 ◽  
Vol 12 (1) ◽  
pp. 9-12
Author(s):  
Arjun G. Koppad ◽  
Syeda Sarfin ◽  
Anup Kumar Das
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Ground Truth ◽  
Land Cover Classification ◽  
Data Sets ◽  
Radiometric Calibration ◽  
Ground Truth Data ◽  
Dense Forest ◽  
Sar Data

The study has been conducted for land use and land cover classification by using SAR data. The study included examining of ALOS 2 PALSAR L- band quad pol (HH, HV, VH and VV) SAR data for LULC classification. The SAR data was pre-processed first which included multilook, radiometric calibration, geometric correction, speckle filtering, SAR Polarimetry and decomposition. For land use land cover classification of ALOS-2-PALSAR data sets, the supervised Random forest classifier was used. Training samples were selected with the help of ground truth data. The area was classified under 7 different classes such as dense forest, moderate dense forest, scrub/sparse forest, plantation, agriculture, water body, and settlements. Among them the highest area was covered by dense forest (108647ha) followed by horticulture plantation (57822 ha) and scrub/Sparse forest (49238 ha) and lowest area was covered by moderate dense forest (11589 ha).   Accuracy assessment was performed after classification. The overall accuracy of SAR data was 80.36% and Kappa Coefficient was 0.76.  Based on SAR backscatter reflectance such as single, double, and volumetric scattering mechanism different land use classes were identified.

scholarly journals RHO-1 and the Rho GEF RHGF-1 interact with UNC-6/Netrin signaling to regulate growth cone protrusion and microtubule organization in C. elegans

2019 ◽  
Author(s):  
Mahekta R. Gujar ◽  
Aubrie M. Stricker ◽  
Erik A. Lundquist
Keyword(s):  
Axon Guidance ◽  
Growth Cone ◽  
Neural Circuit ◽  
Growth Cones ◽  
Negative Regulator ◽  
Microtubule Organization ◽  
C Elegans ◽  
Guidance Cue ◽  
Rho Gef

AbstractUNC-6/Netrin is a conserved axon guidance cue that directs growth cone migrations in the dorsal-ventral axis of C. elegans and in the vertebrate spinal cord. UNC-6/Netrin is expressed in ventral cells, and growth cones migrate ventrally toward or dorsally away from UNC-6/Netrin. Recent studies of growth cone behavior during outgrowth in vivo in C. elegans have led to a polarity/protrusion model in directed growth cone migration away from UNC-6/Netrin. In this model, UNC-6/Netrin first polarizes the growth cone via the UNC-5 receptor, leading to dorsally biased protrusion and F-actin accumulation. UNC-6/Netrin then regulates protrusion based on this polarity. The receptor UNC-40/DCC drives protrusion dorsally, away from the UNC-6/Netrin source, and the UNC-5 receptor inhibits protrusion ventrally, near the UNC-6/Netrin source, resulting in dorsal migration. UNC-5 inhibits protrusion in part by excluding microtubules from the growth cone, which are pro-protrusive. Here we report that the RHO-1/RhoA GTPase and its activator GEF RHGF-1 inhibit growth cone protrusion and MT accumulation in growth cones, similar to UNC-5. However, growth cone polarity of protrusion and F-actin were unaffected by RHO-1 and RHGF-1. Thus, RHO-1 signaling acts specifically as a negative regulator of protrusion and MT accumulation, and not polarity. Genetic interactions suggest that RHO-1 and RHGF-1 act with UNC-5, as well as with a parallel pathway, to regulate protrusion. The cytoskeletal interacting molecule UNC-33/CRMP was required for RHO-1 activity to inhibit MT accumulation, suggesting that UNC-33/CRMP might act downstream of RHO-1. In sum, these studies describe a new role of RHO-1 and RHGF-1 in regulation of growth cone protrusion by UNC-6/Netrin.Author SummaryNeural circuits are formed by precise connections between axons. During axon formation, the growth cone leads the axon to its proper target in a process called axon guidance. Growth cone outgrowth involves asymmetric protrusion driven by extracellular cues that stimulate and inhibit protrusion. How guidance cues regulate growth cone protrusion in neural circuit formation is incompletely understood. This work shows that the signaling molecule RHO-1 acts downstream of the UNC-6/Netrin guidance cue to inhibit growth cone protrusion in part by excluding microtubules from the growth cone, which are structural elements that drive protrusion.

scholarly journals emeraLD: Rapid Linkage Disequilibrium Estimation with Massive Data Sets

2018 ◽  
Author(s):  
Corbin Quick ◽  
Christian Fuchsberger ◽  
Daniel Taliun ◽  
Gonçalo Abecasis ◽  
Michael Boehnke ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Association Studies ◽  
Random Access ◽  
Supplementary Information ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genome Wide ◽  
Wide Range ◽  
Supplementary Material

AbstractSummaryEstimating linkage disequilibrium (LD) is essential for a wide range of summary statistics-based association methods for genome-wide association studies (GWAS). Large genetic data sets, e.g. the TOPMed WGS project and UK Biobank, enable more accurate and comprehensive LD estimates, but increase the computational burden of LD estimation. Here, we describe emeraLD (Efficient Methods for Estimation and Random Access of LD), a computational tool that leverages sparsity and haplotype structure to estimate LD orders of magnitude faster than existing tools.Availability and ImplementationemeraLD is implemented in C++, and is open source under GPLv3. Source code, documentation, an R interface, and utilities for analysis of summary statistics are freely available at http://github.com/statgen/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Methods for Automatic Reference Trees and Multilevel Phylogenetic Placement

2018 ◽  
Author(s):  
Lucas Czech ◽  
Alexandros Stamatakis
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Sequence Similarity ◽  
Computational Effort ◽  
Supplementary Information ◽  
Data Sets ◽  
Metagenomic Sequencing ◽  
Sequencing Studies ◽  
Manual Selection ◽  
Supplementary Material

AbstractMotivationIn most metagenomic sequencing studies, the initial analysis step consists in assessing the evolutionary provenance of the sequences. Phylogenetic (or Evolutionary) Placement methods can be employed to determine the evolutionary position of sequences with respect to a given reference phylogeny. These placement methods do however face certain limitations: The manual selection of reference sequences is labor-intensive; the computational effort to infer reference phylogenies is substantially larger than for methods that rely on sequence similarity; the number of taxa in the reference phylogeny should be small enough to allow for visually inspecting the results.ResultsWe present algorithms to overcome the above limitations. First, we introduce a method to automatically construct representative sequences from databases to infer reference phylogenies. Second, we present an approach for conducting large-scale phylogenetic placements on nested phylogenies. Third, we describe a preprocessing pipeline that allows for handling huge sequence data sets. Our experiments on empirical data show that our methods substantially accelerate the workflow and yield highly accurate placement results.ImplementationFreely available under GPLv3 at http://github.com/lczech/[email protected] InformationSupplementary data are available at Bioinformatics online.

scholarly journals Measurement of the Persistence Length of Cytoskeletal Filaments using Curvature Distributions

2018 ◽  
Author(s):  
Pattipong Wisanpitayakorn ◽  
Keith J. Mickolajczyk ◽  
William O. Hancock ◽  
Luis Vidali ◽  
Erkan Tüzel
Keyword(s):  
Mechanical Properties ◽  
Actin Filaments ◽  
Imaging System ◽  
Average Length ◽  
Persistence Length ◽  
Small Data ◽  
Data Sets ◽  
Apparent Stiffness

AbstractCytoskeletal filaments such as microtubules and actin filaments play important roles in the mechanical integrity of cells and the ability of cells to respond to their environment. Measuring the mechanical properties of cytoskeletal structures is crucial for gaining insight into intracellular mechanical stresses and their role in regulating cellular processes. One of the ways to characterize these mechanical properties is by measuring their persistence length, the average length over which filaments stay straight. There are several approaches in the literature for measuring filament deformations, including Fourier analysis of images obtained using fluorescence microscopy. Here, we show how curvature distributions can be used as an alternative tool to quantify bio-filament deformations, and investigate how the apparent stiffness of filaments depends on the resolution and noise of the imaging system. We present analytical calculations of the scaling curvature distributions as a function of filament discretization, and test our predictions by comparing Monte Carlo simulations to results from existing techniques. We also apply our approach to microtubules and actin filaments obtained fromin vitrogliding assay experiments with high densities of non-functional motors, and calculate the persistence length of these filaments. The presented curvature analysis is significantly more accurate compared to existing approaches for small data sets, and can be readily applied to bothin vitroorin vivofilament data through the use of an ImageJ plugin we provide.

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