Fijiyama: a registration tool for 3D multimodal time-lapse imaging

2020 ◽  
Author(s):  
Romain Fernandez ◽  
Cédric Moisy
Keyword(s):  
Time Series Data ◽  
Time Lapse ◽  
Supplementary Information ◽  
Series Data ◽  
3D Registration ◽  
Extensive Documentation ◽  
Intractable Problems ◽  
Mri Scans ◽  
And Function ◽  
Time Lapse Imaging

Abstract Summary The increasing interest of animal and plant research communities for biomedical 3D imaging devices results in the emergence of new topics. The anatomy, structure and function of tissues can be observed non-destructively in time-lapse multimodal imaging experiments by combining the outputs of imaging devices such as X-ray CT and MRI scans. However, living samples cannot remain in these devices for a long period. Manual positioning and natural growth of the living samples induce variations in the shape, position and orientation in the acquired images that require a preprocessing step of 3D registration prior to analyses. This registration step becomes more complex when combining observations from devices that highlight various tissue structures. Identifying image invariants over modalities is challenging and can result in intractable problems. Fijiyama, a Fiji plugin built upon biomedical registration algorithms, is aimed at non-specialists to facilitate automatic alignment of 3D images acquired either at successive times and/or with different imaging systems. Its versatility was assessed on four case studies combining multimodal and time series data, spanning from micro to macro scales. Availability and implementation Fijiyama is an open source software (GPL license) implemented in Java. The plugin is available through the official Fiji release. An extensive documentation is available at the official page: https://imagej.github.io/Fijiyama Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Modeling cell proliferation in human acute myeloid leukemia xenografts

2019 ◽  
Vol 35 (18) ◽  
pp. 3378-3386 ◽  
Author(s):  
Marco S Nobile ◽  
Thalia Vlachou ◽  
Simone Spolaor ◽  
Daniela Bossi ◽  
Paolo Cazzaniga ◽  
...  
Keyword(s):  
Experimental Data ◽  
Cell Proliferation ◽  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Time Series Data ◽  
Supplementary Information ◽  
Series Data ◽  
Proliferative Potential ◽  
Proliferating Cells ◽  
Acute Myeloid

Abstract Motivation Acute myeloid leukemia (AML) is one of the most common hematological malignancies, characterized by high relapse and mortality rates. The inherent intra-tumor heterogeneity in AML is thought to play an important role in disease recurrence and resistance to chemotherapy. Although experimental protocols for cell proliferation studies are well established and widespread, they are not easily applicable to in vivo contexts, and the analysis of related time-series data is often complex to achieve. To overcome these limitations, model-driven approaches can be exploited to investigate different aspects of cell population dynamics. Results In this work, we present ProCell, a novel modeling and simulation framework to investigate cell proliferation dynamics that, differently from other approaches, takes into account the inherent stochasticity of cell division events. We apply ProCell to compare different models of cell proliferation in AML, notably leveraging experimental data derived from human xenografts in mice. ProCell is coupled with Fuzzy Self-Tuning Particle Swarm Optimization, a swarm-intelligence settings-free algorithm used to automatically infer the models parameterizations. Our results provide new insights on the intricate organization of AML cells with highly heterogeneous proliferative potential, highlighting the important role played by quiescent cells and proliferating cells characterized by different rates of division in the progression and evolution of the disease, thus hinting at the necessity to further characterize tumor cell subpopulations. Availability and implementation The source code of ProCell and the experimental data used in this work are available under the GPL 2.0 license on GITHUB at the following URL: https://github.com/aresio/ProCell. Supplementary information Supplementary data are available at Bioinformatics online.

Dendritic Spines: The Locus of Structural and Functional Plasticity

2014 ◽  
Vol 94 (1) ◽  
pp. 141-188 ◽  
Author(s):  
Carlo Sala ◽  
Menahem Segal
Keyword(s):  
Dendritic Spines ◽  
Synapse Formation ◽  
Complex Structure ◽  
Time Lapse ◽  
Spiny Neurons ◽  
Synaptic Interactions ◽  
Health And Disease ◽  
Spine Structure ◽  
And Function ◽  
Time Lapse Imaging

The introduction of high-resolution time lapse imaging and molecular biological tools has changed dramatically the rate of progress towards the understanding of the complex structure-function relations in synapses of central spiny neurons. Standing issues, including the sequence of molecular and structural processes leading to formation, morphological change, and longevity of dendritic spines, as well as the functions of dendritic spines in neurological/psychiatric diseases are being addressed in a growing number of recent studies. There are still unsettled issues with respect to spine formation and plasticity: Are spines formed first, followed by synapse formation, or are synapses formed first, followed by emergence of a spine? What are the immediate and long-lasting changes in spine properties following exposure to plasticity-producing stimulation? Is spine volume/shape indicative of its function? These and other issues are addressed in this review, which highlights the complexity of molecular pathways involved in regulation of spine structure and function, and which contributes to the understanding of central synaptic interactions in health and disease.

scholarly journals Identification of astroglia-like cardiac nexus glia that are critical regulators of cardiac development and function

PLoS Biology ◽  
2021 ◽  
Vol 19 (11) ◽  
pp. e3001444
Author(s):  
Nina L. Kikel-Coury ◽  
Jacob P. Brandt ◽  
Isabel A. Correia ◽  
Michael R. O’Dea ◽  
Dana F. DeSantis ◽  
...  
Keyword(s):  
Heart Rate ◽  
Nervous System ◽  
Cardiac Development ◽  
Time Lapse ◽  
Zebrafish Model ◽  
Single Cell Sequencing ◽  
Parasympathetic System ◽  
And Function ◽  
Time Lapse Imaging

Glial cells are essential for functionality of the nervous system. Growing evidence underscores the importance of astrocytes; however, analogous astroglia in peripheral organs are poorly understood. Using confocal time-lapse imaging, fate mapping, and mutant genesis in a zebrafish model, we identify a neural crest–derived glial cell, termed nexus glia, which utilizes Meteorin signaling via Jak/Stat3 to drive differentiation and regulate heart rate and rhythm. Nexus glia are labeled with gfap, glast, and glutamine synthetase, markers that typically denote astroglia cells. Further, analysis of single-cell sequencing datasets of human and murine hearts across ages reveals astrocyte-like cells, which we confirm through a multispecies approach. We show that cardiac nexus glia at the outflow tract are critical regulators of both the sympathetic and parasympathetic system. These data establish the crucial role of glia on cardiac homeostasis and provide a description of nexus glia in the PNS.

scholarly journals Inferring causality in biological oscillators

2021 ◽  
Author(s):  
Jonathan Tyler ◽  
Daniel Forger ◽  
JaeKyoung Kim
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Supplementary Information ◽  
Series Data ◽  
Biological Study ◽  
Oscillatory Systems ◽  
Model Based ◽  
Model Free ◽  
Oscillatory Networks ◽  
Inference Methods

Abstract Motivation Fundamental to biological study is identifying regulatory interactions. The recent surge in time-series data collection in biology provides a unique opportunity to infer regulations computationally. However, when components oscillate, model-free inference methods, while easily implemented, struggle to distinguish periodic synchrony and causality. Alternatively, model-based methods test the reproducibility of time series given a specific model but require inefficient simulations and have limited applicability. Results We develop an inference method based on a general model of molecular, neuronal, and ecological oscillatory systems that merges the advantages of both model-based and model-free methods, namely accuracy, broad applicability, and usability. Our method successfully infers the positive and negative regulations within various oscillatory networks, e.g., the repressilator and a network of cofactors at the pS2 promoter, outperforming popular inference methods. Availability We provide a computational package, ION (Inferring Oscillatory Networks), that users can easily apply to noisy, oscillatory time series to uncover the mechanisms by which diverse systems generate oscillations. Accompanying MATLAB code under a BSD-style license and examples are available at ttps://github.com/Mathbiomed/ION. Additionally, the code is available under a CC-BY 4.0 License at https://doi.org/10.6084/m9.figshare.16431408.v1. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Achieving functional neuronal dendrite structure through sequential stochastic growth and retraction

2020 ◽  
Author(s):  
André Ferreira Castro ◽  
Lothar Baltruschat ◽  
Tomke Stürner ◽  
Amirhoushang Bahrami ◽  
Peter Jedlicka ◽  
...  
Keyword(s):  
Body Wall ◽  
Time Lapse ◽  
Membrane Curvature ◽  
Class I ◽  
List Type ◽  
Larval Body ◽  
And Function ◽  
Dendritic Branches ◽  
Time Lapse Imaging

AbstractClass I ventral posterior dendritic arborisation (c1vpda) proprioceptive sensory neurons respond to contractions in the Drosophila larval body wall during crawling. Their dendritic branches run along the direction of contraction, possibly a functional requirement to maximise membrane curvature during crawling contractions. Although the molecular machinery of dendritic patterning in c1vpda has been extensively studied, the process leading to the precise elaboration of their comb-like shapes remains elusive. Here, to link dendrite shape with its proprioceptive role, we performed long-term, non-invasive, in vivo time-lapse imaging of c1vpda embryonic and larval morphogenesis to reveal a sequence of differentiation stages. We combined computer models and dendritic branch dynamics tracking to propose that distinct sequential phases of targeted growth and stochastic retraction achieve efficient dendritic trees both in terms of wire and function. Our study shows how dendrite growth balances structure–function requirements, shedding new light on general principles of self-organisation in functionally specialised dendrites.In briefAn optimal wire and function trade-off emerges from noisy growth and stochastic retraction during Drosophila class I ventral posterior dendritic arborisation (c1vpda) dendrite development.HighlightsC1vpda dendrite outgrowth follows wire constraints.Stochastic retraction of functionally suboptimal branches in a subsequent growth phase.C1vpda growth rules favour branches running parallel to larval body wall contraction.Comprehensive growth model reproduces c1vpda development in silico.

scholarly journals JMJD6 Dysfunction Due to Iron Deficiency in Preeclampsia Disrupts Fibronectin Homeostasis Resulting in Diminished Trophoblast Migration

2021 ◽  
Vol 9 ◽  
Author(s):  
Sruthi Alahari ◽  
Abby Farrell ◽  
Leonardo Ermini ◽  
Chanho Park ◽  
Julien Sallais ◽  
...  
Keyword(s):  
Time Lapse ◽  
Lysosomal Degradation ◽  
Related Disorder ◽  
Novel Target ◽  
And Function ◽  
Time Lapse Imaging ◽  
Fibronectin Assembly ◽  
Degradation Time ◽  
Trophoblast Migration

The mechanisms contributing to excessive fibronectin in preeclampsia, a pregnancy-related disorder, remain unknown. Herein, we investigated the role of JMJD6, an O2- and Fe2+-dependent enzyme, in mediating placental fibronectin processing and function. MALDI-TOF identified fibronectin as a novel target of JMJD6-mediated lysyl hydroxylation, preceding fibronectin glycosylation, deposition, and degradation. In preeclamptic placentae, fibronectin accumulated primarily in lysosomes of the mesenchyme. Using primary placental mesenchymal cells (pMSCs), we found that fibronectin fibril formation and turnover were markedly impeded in preeclamptic pMSCs, partly due to impaired lysosomal degradation. JMJD6 knockdown in control pMSCs recapitulated the preeclamptic FN phenotype. Importantly, preeclamptic pMSCs had less total and labile Fe2+ and Hinokitiol treatment rescued fibronectin assembly and promoted lysosomal degradation. Time-lapse imaging demonstrated that defective ECM deposition by preeclamptic pMSCs impeded HTR-8/SVneo cell migration, which was rescued upon Hinokitiol exposure. Our findings reveal new Fe2+-dependent mechanisms controlling fibronectin homeostasis/function in the placenta that go awry in preeclampsia.

scholarly journals MOSim: Multi-Omics Simulation in R

2018 ◽  
Author(s):  
Carlos Martínez-Mira ◽  
Ana Conesa ◽  
Sonia Tarazona
Keyword(s):  
Time Series Data ◽  
Simulated Data ◽  
R Package ◽  
Experimental Designs ◽  
Supplementary Information ◽  
Series Data ◽  
Data Sets ◽  
Expression Data ◽  
Supplementary Material ◽  
Omic Data

AbstractMotivationAs new integrative methodologies are being developed to analyse multi-omic experiments, validation strategies are required for benchmarking. In silico approaches such as simulated data are popular as they are fast and cheap. However, few tools are available for creating synthetic multi-omic data sets.ResultsMOSim is a new R package for easily simulating multi-omic experiments consisting of gene expression data, other regulatory omics and the regulatory relationships between them. MOSim supports different experimental designs including time series data.AvailabilityThe package is freely available under the GPL-3 license from the Bitbucket repository (https://bitbucket.org/ConesaLab/mosim/)[email protected] informationSupplementary material is available at bioRxiv online.

scholarly journals In situ quantification of supraglacial cryoconite morphodynamics using time-lapse imaging: an example from Svalbard

2011 ◽  
Vol 57 (204) ◽  
pp. 651-657 ◽  
Author(s):  
Tristram D.L. Irvine-Fynn ◽  
Jonathan W. Bridge ◽  
Andrew J. Hodson
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Time Series Data ◽  
Time Lapse ◽  
Random Motion ◽  
Biologically Active ◽  
Series Data ◽  
Small Scale ◽  
Meteorological Forcing ◽  
Ice Surface

AbstractThere is growing recognition of the significance of biologically active supraglacial dust (cryoconite) for glacial mass balance and ecology. Nonetheless, the processes controlling the distribution, transport and fate of cryoconite particles in the glacial system remain somewhat poorly understood. Here, using a 216 hour time series of plot-scale (0.04 m2) images, we quantify the small-scale dynamics of cryoconite on Longyearbreen, Svalbard. We show significant fluctuations in the apparent cryoconite area and dispersion of cryoconite over the plot, within the 9 day period of observations. However, the net movement of cryoconite across the ice surface averaged only 5.3 mm d−1. High-resolution measurements of cryoconite granule motion showed constant, random motion but weak correlation with meteorological forcing factors and no directional trends for individual particle movement. The high-resolution time-series data suggest that there is no significant net transport of dispersed cryoconite material across glacier surfaces. The areal coverage and motion of particles within and between cryoconite holes appears to be a product of differential melting leading to changes in plot-scale microtopography, local meltwater flow dynamics and weather-dependent events. These subtle processes of cryoconite redistribution may be significant for supraglacial albedo and have bearing on the surface energy balance at the glacier scale.

Observations of Seven Atmospheric Density Current Fronts in Dixon, California

2011 ◽  
Vol 139 (5) ◽  
pp. 1338-1351 ◽  
Author(s):  
Shane D. Mayor
Keyword(s):  
Time Series Data ◽  
Empirical Relationship ◽  
Time Lapse ◽  
Series Data ◽  
Density Current ◽  
Atmospheric Density ◽  
Air Mass ◽  
The North ◽  
Water Vapor Mixing Ratio ◽  
Aerosol Backscatter

Seven atmospheric density current fronts are identified in sequences of ground-based, scanning aerosol backscatter lidar images and in situ micrometeorological time series data that were collected simultaneously and nearly continuously between 15 March and 11 June of 2007 in Dixon, California. The fronts, observed on different days, had the following features in common: 1) an increase in aerosol backscatter intensity, 2) a decrease in air temperature, 3) an increase in water vapor mixing ratio, 4) movement toward the north, 5) airflow from the south in the denser air mass, and 6) occurrence within a 3.5-h time span in the afternoon. The observations support the hypothesis that the fronts are the leading edges of shallow marine air masses advancing northward from the Sacramento–San Joaquin River Delta. The observations are used to test an empirical relationship between front speed, airmass density difference, depth of the dense air mass, and speed of the opposing flow. Prominent features of the fronts such as lobe and cleft structure, billows, and nose and head structure are described. Time-lapse animations of the lidar scans are available in the online version of this article.

scholarly journals Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 800
Author(s):  
Xiaodong Zhang ◽  
Suhui Liu ◽  
Xin Zheng
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Data Representation ◽  
Attention Mechanism ◽  
Supplementary Information ◽  
Series Data ◽  
Price Movement ◽  
Factorization Machine

The prediction of stock price movement is a popular area of research in academic and industrial fields due to the dynamic, highly sensitive, nonlinear and chaotic nature of stock prices. In this paper, we constructed a convolutional neural network model based on a deep factorization machine and attention mechanism (FA-CNN) to improve the prediction accuracy of stock price movement via enhanced feature learning. Unlike most previous studies, which focus only on the temporal features of financial time series data, our model also extracts intraday interactions among input features. Further, in data representation, we used the sub-industry index as supplementary information for the current state of the stock, since there exists stock price co-movement between individual stocks and their industry index. The experiments were carried on the individual stocks in three industries. The results showed that the additional inputs of (a) the intraday interactions among input features and (b) the sub-industry index information effectively improved the prediction accuracy. The highest prediction accuracy of the proposed FA-CNN model is 64.81%. It is 7.38% higher than that of traditional LSTM, and 3.71% higher than that of the model without sub-industry index as additional input features.

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