Perfusion-distention fixation of heart specimens: A key step in immortalizing heart specimens for wax infiltration and generating 3D imaging data sets for reconstruction and printed 3D models

The history of neuroscience has tracked with the evolution of science and technology. Today, neuroscience's trajectory is heavily dependent on computational systems and the availability of high-performance computing (HPC), which are becoming indispensable for building simulations of the brain, coping with high computational demands of analysis of brain imaging data sets, and developing treatments for neurological diseases. This chapter will briefly review the current and potential future use of supercomputers in neuroscience.

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Mass spectrometry imaging and LC/MS reveal decreased cerebellar phosphoinositides in Niemann-Pick type C1-null mice

The Journal of Lipid Research ◽

10.1194/jlr.ra119000606 ◽

2020 ◽

Vol 61 (7) ◽

pp. 1004-1013

Author(s):

Koralege C. Pathmasiri ◽

Melissa R. Pergande ◽

Fernando Tobias ◽

Rima Rebiai ◽

Avia Rosenhouse-Dantsker ◽

...

Keyword(s):

Organ Damage ◽

Null Mouse ◽

Data Sets ◽

Imaging Data ◽

Spectra Analysis ◽

Niemann Pick Disease ◽

Progressive Neurodegeneration ◽

Cerebellar Tissue ◽

Niemann Pick ◽

Phosphatidylinositol 4

Niemann-Pick disease type C1 (NPC1) is a lipid storage disorder in which cholesterol and glycosphingolipids accumulate in late endosomal/lysosomal compartments because of mutations in the NPC1 gene. A hallmark of NPC1 is progressive neurodegeneration of the cerebellum as well as visceral organ damage; however, the mechanisms driving this disease pathology are not fully understood. Phosphoinositides are phospholipids that play distinct roles in signal transduction and vesicle trafficking. Here, we utilized a consensus spectra analysis of MS imaging data sets and orthogonal LC/MS analyses to evaluate the spatial distribution of phosphoinositides and quantify them in cerebellar tissue from Npc1-null mice. Our results suggest significant depletion of multiple phosphoinositide species, including PI, PIP, and PIP2, in the cerebellum of the Npc1-null mice in both whole-tissue lysates and myelin-enriched fractions. Additionally, we observed altered levels of the regulatory enzyme phosphatidylinositol 4-kinase type 2α in Npc1-null mice. In contrast, the levels of related kinases, phosphatases, and transfer proteins were unaltered in the Npc1-null mouse model, as observed by Western blot analysis. Our discovery of phosphoinositide lipid biomarkers for NPC1 opens new perspectives on the pathophysiology underlying this fatal neurodegenerative disease.

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4D cell biology: big data image analytics and lattice light-sheet imaging reveal dynamics of clathrin-mediated endocytosis in stem cell–derived intestinal organoids

Molecular Biology of the Cell ◽

10.1091/mbc.e18-06-0375 ◽

2018 ◽

Vol 29 (24) ◽

pp. 2959-2968 ◽

Cited By ~ 11

Author(s):

Johannes Schöneberg ◽

Daphné Dambournet ◽

Tsung-Li Liu ◽

Ryan Forster ◽

Dirk Hockemeyer ◽

...

Keyword(s):

Big Data ◽

Stem Cell ◽

Adaptive Optics ◽

Cell Biology ◽

Fluorescent Protein ◽

Embryonic Stem ◽

Light Sheet ◽

Data Sets ◽

Intestinal Epithelial ◽

Imaging Data

New methods in stem cell 3D organoid tissue culture, advanced imaging, and big data image analytics now allow tissue-scale 4D cell biology, but currently available analytical pipelines are inadequate for handing and analyzing the resulting gigabytes and terabytes of high-content imaging data. We expressed fluorescent protein fusions of clathrin and dynamin2 at endogenous levels in genome-edited human embryonic stem cells, which were differentiated into hESC-derived intestinal epithelial organoids. Lattice light-sheet imaging with adaptive optics (AO-LLSM) allowed us to image large volumes of these organoids (70 × 60 × 40 µm xyz) at 5.7 s/frame. We developed an open-source data analysis package termed pyLattice to process the resulting large (∼60 Gb) movie data sets and to track clathrin-mediated endocytosis (CME) events. CME tracks could be recorded from ∼35 cells at a time, resulting in ∼4000 processed tracks per movie. On the basis of their localization in the organoid, we classified CME tracks into apical, lateral, and basal events and found that CME dynamics is similar for all three classes, despite reported differences in membrane tension. pyLattice coupled with AO-LLSM makes possible quantitative high temporal and spatial resolution analysis of subcellular events within tissues.

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PynPoint: a modular pipeline architecture for processing and analysis of high-contrast imaging data

Astronomy and Astrophysics ◽

10.1051/0004-6361/201834136 ◽

2019 ◽

Vol 621 ◽

pp. A59 ◽

Cited By ~ 15

Author(s):

T. Stolker ◽

M. J. Bonse ◽

S. P. Quanz ◽

A. Amara ◽

G. Cugno ◽

...

Keyword(s):

Data Reduction ◽

Direct Detection ◽

Principal Component ◽

Monte Carlo Analysis ◽

Data Sets ◽

High Contrast ◽

Imaging Data ◽

Pipeline Architecture ◽

Contrast Imaging ◽

High Contrast Imaging

Context. The direct detection and characterization of planetary and substellar companions at small angular separations is a rapidly advancing field. Dedicated high-contrast imaging instruments deliver unprecedented sensitivity, enabling detailed insights into the atmospheres of young low-mass companions. In addition, improvements in data reduction and point spread function (PSF)-subtraction algorithms are equally relevant for maximizing the scientific yield, both from new and archival data sets. Aims. We aim at developing a generic and modular data-reduction pipeline for processing and analysis of high-contrast imaging data obtained with pupil-stabilized observations. The package should be scalable and robust for future implementations and particularly suitable for the 3–5 μm wavelength range where typically thousands of frames have to be processed and an accurate subtraction of the thermal background emission is critical. Methods. PynPoint is written in Python 2.7 and applies various image-processing techniques, as well as statistical tools for analyzing the data, building on open-source Python packages. The current version of PynPoint has evolved from an earlier version that was developed as a PSF-subtraction tool based on principal component analysis (PCA). Results. The architecture of PynPoint has been redesigned with the core functionalities decoupled from the pipeline modules. Modules have been implemented for dedicated processing and analysis steps, including background subtraction, frame registration, PSF subtraction, photometric and astrometric measurements, and estimation of detection limits. The pipeline package enables end-to-end data reduction of pupil-stabilized data and supports classical dithering and coronagraphic data sets. As an example, we processed archival VLT/NACO L′ and M′ data of β Pic b and reassessed the brightness and position of the planet with a Markov chain Monte Carlo analysis; we also provide a derivation of the photometric error budget.

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Detecting outliers in astronomical images with deep generative networks

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa1647 ◽

2020 ◽

Vol 496 (2) ◽

pp. 2346-2361 ◽

Cited By ~ 2

Author(s):

Berta Margalef-Bentabol ◽

Marc Huertas-Company ◽

Tom Charnock ◽

Carla Margalef-Bentabol ◽

Mariangela Bernardi ◽

...

Keyword(s):

Generative Models ◽

Test Cases ◽

Data Sets ◽

Proof Of Concept ◽

Imaging Data ◽

Training Set ◽

Merging Galaxies ◽

Hydrodynamic Simulations ◽

Astronomical Images ◽

Automated Tools

ABSTRACT With the advent of future big-data surveys, automated tools for unsupervised discovery are becoming ever more necessary. In this work, we explore the ability of deep generative networks for detecting outliers in astronomical imaging data sets. The main advantage of such generative models is that they are able to learn complex representations directly from the pixel space. Therefore, these methods enable us to look for subtle morphological deviations which are typically missed by more traditional moment-based approaches. We use a generative model to learn a representation of expected data defined by the training set and then look for deviations from the learned representation by looking for the best reconstruction of a given object. In this first proof-of-concept work, we apply our method to two different test cases. We first show that from a set of simulated galaxies, we are able to detect ${\sim}90{{\ \rm per\ cent}}$ of merging galaxies if we train our network only with a sample of isolated ones. We then explore how the presented approach can be used to compare observations and hydrodynamic simulations by identifying observed galaxies not well represented in the models. The code used in this is available at https://github.com/carlamb/astronomical-outliers-WGAN.

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