Characterization of Posidonia Oceanica Seagrass Aerenchyma through Whole Slide Imaging: A Pilot Study

Characterizing the tissue morphology and anatomy of seagrasses is essential to predicting their acoustic behavior. In this pilot study, we use histology techniques and whole slide imaging (WSI) to describe the composition and topology of the aerenchyma of an entire leaf blade in an automatic way combining the advantages of X-ray microtomography and optical microscopy. Paraffin blocks are prepared in such a way that microtome slices contain an arbitrarily large number of cross sections distributed along the full length of a blade. The sample organization in the paraffin block coupled with whole slide image analysis allows high throughput data extraction and an exhaustive characterization along the whole blade length. The core of the work are image processing algorithms that can identify cells and air lacunae (or void) from fiber strand, epidermis, mesophyll and vascular system. A set of specific features is developed to adequately describe the convexity of cells and voids where standard descriptors fail. The features scrutinize the local curvature of the object borders to allow an accurate discrimination between void and cell through machine learning. The algorithm allows to reconstruct the cells and cell membrane features that are relevant to tissue density, compressibility and rigidity. Size distribution of the different cell types and gas spaces, total biomass and total void volume fraction are then extracted from the high resolution slices to provide a complete characterization of the tissue along the leave from its base to the apex

Perspektiven der Nutzung von Methanhydraten als Energieträger – Eine Bestandsaufnahme

Methane hydrates are the largest existing carbon resource, and their broad geographic distribution, especially in comparison to oil and conventional gas, make them a promising future source of energy. On the other hand, there is a danger of forcing the greenhouse effect in the event of a release of methane in the atmosphere as well as causing a destabilisation of the oceanic sediments. Also the technical difficulties in the extraction of methane are not yet fully resolved. Nevertheless, the research on methane hydrates has been forced both based on political as well as economic considerations in recent years and methane hydrates have practical advantages, which make them a noteworthy transitional solution on the way to a renewable energy based future energy supply. The knowledge of the potentials and risks of methane hydrates, however, is still poor; especially in the German-speaking public and policy. This deficiency will be solved by a focused analysis of the current state of research and an outlook, based on the most important findings.

Volumetric Cell-and-Portal Generation

International audienceWe present an algorithm to generate a cell-and-portal decomposition of general indoor scenes. The method is an adaptation of the 3D watershed transform, computed on a distance-to-geometry sampled field. The watershed is processed using a flooding analogy in the distance field space. Flooding originates from local minima, each minimum producing a region. Portals are built as needed to avoid the merging of regions during their growth. As a result, the cell it deals with parametric curves, implicit surfaces, volumetric data and polygon soups in a unified way

A New Method to Address Unmet Needs for Extracting Individual Cell Migration Features from a Large Number of Cells Embedded in 3D Volumes

Background: In vitro cell observation has been widely used by biologists and pharmacologists for screening molecule-induced effects on cancer cells. Computer-assisted time-lapse microscopy enables automated live cell imaging in vitro, enabling cell behavior characterization through image analysis, in particular regarding cell migration. In this context, 3D cell assays in transparent matrix gels have been developed to provide more realistic in vitro 3D environments for monitoring cell migration (fundamentally different from cell motility behavior observed in 2D), which is related to the spread of cancer and metastases. Methodology/Principal Findings: In this paper we propose an improved automated tracking method that is designed to robustly and individually follow a large number of unlabeled cells observed under phase-contrast microscopy in 3D gels. The method automatically detects and tracks individual cells across a sequence of acquired volumes, using a template matching filtering method that in turn allows for robust detection and mean-shift tracking. The robustness of the method results from detecting and managing the cases where two cell (mean-shift) trackers converge to the same point. The resulting trajectories quantify cell migration through statistical analysis of 3D trajectory descriptors. We manually validated the method and observed efficient cell detection and a low tracking error rate (6%). We also applied the method in a real biological experiment where the pro-migratory effects of hyaluronic acid (HA) were analyzed on brain cancer cells. Using collagen gels with increased HA proportions, we were able to evidence a dose-response effect on cell migration abilities. Conclusions/Significance: The developed method enables biomedical researchers to automatically and robustly quantify the pro- or anti-migratory effects of different experimental conditions on unlabeled cell cultures in a 3D environment. © 2011 Adanja et al.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

ENTRE PLÁTANOS [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte. Subdirección General de Coordinación Bibliotecaria, 201

Tracking cells in Life Cell Imaging videos using topological alignments

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Computational prediction of neural progenitor cell fates

Understanding how stem and progenitor cells choose between alternative cell fates is a major challenge in developmental biology. Efforts to tackle this problem have been hampered by the scarcity of markers that can be used to predict cell division outcomes. Here we present a computational method, based on algorithmic information theory, to analyze dynamic features of living cells over time. Using this method, we asked whether rat retinal progenitor cells (RPCs) display characteristic phenotypes before undergoing mitosis that could foretell their fate. We predicted whether RPCs will undergo a self-renewing or terminal division with 99% accuracy, or whether they will produce two photoreceptors or another combination of offspring with 87% accuracy. Our implementation can segment, track and generate predictions for 40 cells simultaneously on a standard computer at 5 min per frame. This method could be used to isolate cell populations with specific developmental potential, enabling previously impossible investigations.The computational aspects of this work were supported by the Center for Subsurface Sensing and Imaging Systems (NSF Grant EEC-9986821), by the Rensselaer Polytechnic Institute and by the University of Wisconsin-Milwaukee. This work was supported by grants from the Canadian Institutes of Health Research and the Foundation Fighting Blindness – Canada (to M.C). M.C. is a CIHR New Investigator and a W.K. Stell Scholar of the Foundation Fighting Blindness – Canada