scholarly journals Revealing the Systematic Nature of Coral Attachment to Reef Substrates

Author(s):  
Brett Lewis ◽  
David Suggett ◽  
Peter Prentis ◽  
Luke Nothdurft

Abstract Reef-building coral colonies propagate by periodic sexual reproduction and continuous asexual fragmentation. The latter depends on successful attachment to the reef substrate through modification of soft tissues and skeletal growth. Despite decades of research examining coral sexual and asexual propagation, the contact response, tissue motion, and cellular reorganisation responsible for attaching to the substrate via a newly formed skeleton have not been documented. Here, we correlated fluorescence and electron microscopy image data with ‘live’ microscopic time-lapse of the coral tissue biomechanics and developed a multiscale imaging approach to establish the first “coral attachment model” (CAM) - identifying three distinct phases that determine the timing and success of attachment during asexual propagation: (i) an initial immune response, followed by (ii) fragment stabilisation through anchoring by the soft tissue and (iii) formation of a “lappet appendage” structure leading to substrate bonding of the tissue for encrustation through the onset of skeletal calcification. In developing CAM, we provide new frameworks and metrics that enable reef researchers, managers and coral restoration practitioners to evaluate attachment effectiveness needed to optimise species-substrate compatibility.

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Fuyong Xing ◽  
Yuanpu Xie ◽  
Xiaoshuang Shi ◽  
Pingjun Chen ◽  
Zizhao Zhang ◽  
...  

Abstract Background Nucleus or cell detection is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed. Results We analyze the applicability of deep models specifically for nucleus detection across a wide variety of microscopy image data. More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs (or cancer diseases) and come from multiple institutions. We demonstrate that for a specific target dataset, training with images from the same types of organs might be usually necessary for nucleus detection. Although the images can be visually similar due to the same staining technique and imaging protocol, deep models learned with images from different organs might not deliver desirable results and would require model fine-tuning to be on a par with those trained with target data. We also observe that training with a mixture of target and other/non-target data does not always mean a higher accuracy of nucleus detection, and it might require proper data manipulation during model training to achieve good performance. Conclusions We conduct a systematic case study on deep models for nucleus detection in a wide variety of microscopy images, aiming to address several important but previously understudied questions. We present and extensively evaluate an end-to-end, pixel-to-pixel fully convolutional regression network and report a few significant findings, some of which might have not been reported in previous studies. The model performance analysis and observations would be helpful to nucleus detection in microscopy images.


2020 ◽  
Vol 6 (2) ◽  
pp. 44-48
Author(s):  
J. Frishons ◽  
V. Novotny ◽  
P. Rejtar ◽  
P. Hejna ◽  
M. A. Kislov ◽  
...  

Postmortem computer tomography (CT) came into practice of forensic medicine in the 1990s and has later been complemented with magnetic resonance imaging (MRI). A pioneer virtual autopsy was conducted in Germany in 1983. In the Czech Republic, this examination was first performed in 1993.A typical examination requires about 30 min, with the most resource-demanding stage being the image data rendering. CT was shown to better capture skeletal structures, while MRI contrasting is superior in terms of visualising soft tissues. In the Czech Republic, CT-based virtopsy is legislated mandatory to document deaths inflicted by gunshots, road traffic and aviation accidents, high falls, occupational and explosive-related injuries, thermal and mechanical traumas, strangulation, drowning as well as to examine unidentified or decomposing bodies, deceased children and adolescents aged under 18.CT scanning prior to conventional autopsy provides a forensic expert with guidance to reveal pathologies non-invasively in particular regions that are difficult to dissect or access. The advantage of virtopsy is the objective acquisition of data that can be re-examined, reinterpreted or juxtaposed with the results of conventional autopsy and easily recovered for possible further expertise. 


2015 ◽  
Vol 19 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Marco Tektonidis ◽  
Il-Han Kim ◽  
Yi-Chun M. Chen ◽  
Roland Eils ◽  
David L. Spector ◽  
...  

2006 ◽  
Vol 14 (3) ◽  
pp. 6-11
Author(s):  
Curtis T. Rueden ◽  
Kevin W. Eliceiri

Over the past few years there has been a dramatic improvement in microscopy acquisition techniques, in effective imaging modalities as well as raw hardware performance. As the microscopist's available tools become more sophisticated and diverse—e.g., time-lapse, Z sectioning, multispectra, lifetime, nth harmonic, polarization, and many combinations thereof—we face a corresponding increase in complexity in the software for understanding and interpreting the resultant data. With lifetime imaging, for example, it is overwhelming to study the raw numbers; instead, an exponential curve-fitting algorithm must be applied to extract meaningful lifetime values from the mass of photon counts recorded by the instrument.


2021 ◽  
Author(s):  
Matthias Arzt ◽  
Joran Deschamps ◽  
Christopher Schmied ◽  
Tobias Pietzsch ◽  
Deborah Schmidt ◽  
...  

We present Labkit, a user-friendly Fiji plugin for the segmentation of microscopy image data. It offers easy to use manual and automated image segmentation routines that can be rapidly applied to single- and multi-channel images as well as to timelapse movies in 2D or 3D. Labkit is specifically designed to work efficiently on big image data and enables users of consumer laptops to conveniently work with multiple-terabyte images. This efficiency is achieved by using ImgLib2 and BigDataViewer as the foundation of our software. Furthermore, memory efficient and fast random forest based pixel classification inspired by the Waikato Environment for Knowledge Analysis (Weka) is implemented. Optionally we harness the power of graphics processing units (GPU) to gain additional runtime performance. Labkit is easy to install on virtually all laptops and workstations. Additionally, Labkit is compatible with high performance computing (HPC) clusters for distributed processing of big image data. The ability to use pixel classifiers trained in Labkit via the ImageJ macro language enables our users to integrate this functionality as a processing step in automated image processing workflows. Last but not least, Labkit comes with rich online resources such as tutorials and examples that will help users to familiarize themselves with available features and how to best use \Labkit in a number of practical real-world use-cases.


2020 ◽  
Vol 5 ◽  
pp. 96
Author(s):  
Johnny Hay ◽  
Eilidh Troup ◽  
Ivan Clark ◽  
Julian Pietsch ◽  
Tomasz Zieliński ◽  
...  

Tools and software that automate repetitive tasks, such as metadata extraction and deposition to data repositories, are essential for researchers to share Open Data, routinely. For research that generates microscopy image data, OMERO is an ideal platform for storage, annotation and publication according to open research principles. We present PyOmeroUpload, a Python toolkit for automatically extracting metadata from experiment logs and text files, processing images and uploading these payloads to OMERO servers to create fully annotated, multidimensional datasets. The toolkit comes packaged in portable, platform-independent Docker images that enable users to deploy and run the utilities easily, regardless of Operating System constraints. A selection of use cases is provided, illustrating the primary capabilities and flexibility offered with the toolkit, along with a discussion of limitations and potential future extensions. PyOmeroUpload is available from: https://github.com/SynthSys/pyOmeroUpload.


2019 ◽  
Vol 16 (151) ◽  
pp. 20180567 ◽  
Author(s):  
Daniel Wangpraseurt ◽  
Steven Jacques ◽  
Niclas Lyndby ◽  
Jacob Boiesen Holm ◽  
Christine Ferrier Pages ◽  
...  

Coral reefs are highly productive photosynthetic systems and coral optics studies suggest that such high efficiency is due to optimized light scattering by coral tissue and skeleton. Here, we characterize the inherent optical properties, i.e. the scattering coefficient, μ s , and the anisotropy of scattering, g , of eight intact coral species using optical coherence tomography (OCT). Specifically, we describe light scattering by coral skeletons, coenoarc tissues, polyp tentacles and areas covered by fluorescent pigments (FP). Our results reveal that light scattering between coral species ranges from μ s = 3 mm −1 ( Stylophora pistillata ) to μ s = 25 mm −1 ( Echinopora lamelosa ) . For Platygyra pini , μ s was 10-fold higher for tissue versus skeleton, while in other corals (e.g. Hydnophora pilosa ) no difference was found between tissue and skeletal scattering. Tissue scattering was threefold enhanced in coenosarc tissues ( μ s = 24.6 mm −1 ) versus polyp tentacles ( μ s = 8.3 mm −1 ) in Turbinaria reniformis . FP scattering was almost isotropic when FP were organized in granule chromatophores ( g = 0.34) but was forward directed when FP were distributed diffusely in the tissue ( g = 0.96). Our study provides detailed measurements of coral scattering and establishes a rapid approach for characterizing optical properties of photosynthetic soft tissues via OCT in vivo .


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