scholarly journals Shape matters: the relationship between cell geometry and diversity in phytoplankton

2021 ◽  
Author(s):  
Alexey Ryabov ◽  
Onur Kerimoglu ◽  
Elena Litchman ◽  
Irina Olenina ◽  
Leonilde Roselli ◽  
...  
Keyword(s):  
Cell Geometry ◽  
The Relationship

scholarly journals Shape matters: the relationship between cell geometry and diversity in phytoplankton

2020 ◽  
Author(s):  
Alexei Ryabov ◽  
Onur Kerimoglu ◽  
Elena Litchman ◽  
Leonilde Roselli ◽  
Irina Olenina ◽  
...  
Keyword(s):  
Cell Geometry ◽  
The Relationship

scholarly journals Shape matters: the relationship between cell geometry and diversity in phytoplankton

2020 ◽  
Author(s):  
Alexei Ryabov ◽  
Onur Kerimoglu ◽  
Elena Litchman ◽  
Irina Olenina ◽  
Leonilde Roselli ◽  
...  
Keyword(s):  
Cell Geometry ◽  
The Relationship

Some red blood cell geometry

1983 ◽  
Vol 61 (6) ◽  
pp. 646-649 ◽  
Author(s):  
H. Westcott Vayo
Keyword(s):  
Blood Cell ◽  
Red Blood Cell ◽  
Model Parameters ◽  
Sectional Area ◽  
Cell Geometry ◽  
Cross Sectional ◽  
Cell Dimensions ◽  
Rectangular Form ◽  
Computer Calculations ◽  
The Relationship

Formulas are presented for the cross-sectional area, volume, surface area, and circumference of a model red blood cell based on the Oval of Cassini as the geometry of choice. The relationship between the actual cell dimensions and model parameters is given as is the criterion for the validity of the model used in the formulary. Each formula is given in rectangular form and in a form suitable for computer calculations.

scholarly journals QuantifyPolarity, a new tool-kit for measuring planar polarized protein distributions and cell properties in developing tissues

Development ◽  
2021 ◽  
Author(s):  
Su Ee Tan ◽  
Weijie Tan ◽  
Katherine Fisher ◽  
David Strutt
Keyword(s):  
Quantitative Methods ◽  
Principal Component ◽  
Asymmetric Distribution ◽  
Planar Polarity ◽  
Cell Geometry ◽  
Automated Quantification ◽  
Pca Method ◽  
User Friendly ◽  
Relationship Of ◽  
The Relationship

The coordination of cells or structures within the plane of a tissue is known as planar polarization. It is often governed by the asymmetric distribution of planar polarity proteins within cells. A number of quantitative methods have been developed to provide a readout of planar polarized protein distributions. However, previous planar polarity quantification methods can be affected by variation in cell geometry. Hence, we developed a novel planar polarity quantification method based on Principal Component Analysis (PCA) that is shape insensitive. Here, we compare this method with other state-of-the-art methods on simulated models and biological datasets. We found that the PCA method performs robustly in quantifying planar polarity independently of variation in cell geometry and other image conditions. We designed a user-friendly graphical user interface called QuantifyPolarity, equipped with three polarity methods for automated quantification of polarity. QuantifyPolarity also provides tools to quantify cell morphology and packing geometry, allowing the relationship of these characteristics to planar polarization to be investigated. This tool enables experimentalists with no prior computational expertise to perform high-throughput cell polarity and shape analysis automatically and efficiently.

Interstellar gas in the central region of the Galaxy

1967 ◽  
Vol 31 ◽  
pp. 239-251 ◽  
Author(s):  
F. J. Kerr
Keyword(s):  
Central Region ◽  
Galactic Plane ◽  
Neutral Hydrogen ◽  
Continuum Emission ◽  
Galactic Centre ◽  
Interstellar Gas ◽  
Hydrogen Recombination ◽  
The Galaxy ◽  
Centre Region ◽  
The Relationship

A review is given of information on the galactic-centre region obtained from recent observations of the 21-cm line from neutral hydrogen, the 18-cm group of OH lines, a hydrogen recombination line at 6 cm wavelength, and the continuum emission from ionized hydrogen.Both inward and outward motions are important in this region, in addition to rotation. Several types of observation indicate the presence of material in features inclined to the galactic plane. The relationship between the H and OH concentrations is not yet clear, but a rough picture of the central region can be proposed.

The relationship of the scleractinian corals to the rugose corals

Paleobiology ◽  
1980 ◽  
Vol 6 (02) ◽  
pp. 146-160 ◽  
Author(s):  
William A. Oliver
Keyword(s):  
Critical Points ◽  
Scleractinian Corals ◽  
Convincing Evidence ◽  
Early Triassic ◽  
Rugose Corals ◽  
History Of ◽  
The Subject ◽  
Relationship Of ◽  
The Relationship ◽  
Biologic Factors

The Mesozoic-Cenozoic coral Order Scleractinia has been suggested to have originated or evolved (1) by direct descent from the Paleozoic Order Rugosa or (2) by the development of a skeleton in members of one of the anemone groups that probably have existed throughout Phanerozoic time. In spite of much work on the subject, advocates of the direct descent hypothesis have failed to find convincing evidence of this relationship. Critical points are:(1) Rugosan septal insertion is serial; Scleractinian insertion is cyclic; no intermediate stages have been demonstrated. Apparent intermediates are Scleractinia having bilateral cyclic insertion or teratological Rugosa.(2) There is convincing evidence that the skeletons of many Rugosa were calcitic and none are known to be or to have been aragonitic. In contrast, the skeletons of all living Scleractinia are aragonitic and there is evidence that fossil Scleractinia were aragonitic also. The mineralogic difference is almost certainly due to intrinsic biologic factors.(3) No early Triassic corals of either group are known. This fact is not compelling (by itself) but is important in connection with points 1 and 2, because, given direct descent, both changes took place during this only stage in the history of the two groups in which there are no known corals.

Choosing a Markov blanket

2020 ◽  
Vol 43 ◽  
Author(s):  
Thomas Parr
Keyword(s):  
Markov Blanket ◽  
Target Article ◽  
The Relationship

Abstract This commentary focuses upon the relationship between two themes in the target article: the ways in which a Markov blanket may be defined and the role of precision and salience in mediating the interactions between what is internal and external to a system. These each rest upon the different perspectives we might take while “choosing” a Markov blanket.

Tinkering with cognitive gadgets: Cultural evolutionary psychology meets active inference

2019 ◽  
Vol 42 ◽  
Author(s):  
Paul Benjamin Badcock ◽  
Axel Constant ◽  
Maxwell James Désormeau Ramstead
Keyword(s):  
Evolutionary Psychology ◽  
Research Program ◽  
Active Inference ◽  
Innovative Research ◽  
Cognitive Faculties ◽  
Cultural Evolutionary ◽  
The Relationship

Abstract Cognitive Gadgets offers a new, convincing perspective on the origins of our distinctive cognitive faculties, coupled with a clear, innovative research program. Although we broadly endorse Heyes’ ideas, we raise some concerns about her characterisation of evolutionary psychology and the relationship between biology and culture, before discussing the potential fruits of examining cognitive gadgets through the lens of active inference.

Radiation Damage, Contrast and Statistics in High Resolution Biological Electron Microscopy

1970 ◽  
Vol 28 ◽  
pp. 260-261
Author(s):  
Robert M. Glaeser
Keyword(s):  
High Resolution ◽  
Particle Flux ◽  
Unit Area ◽  
Signal To Noise ◽  
Resolution Image ◽  
High Resolution Image ◽  
Pass Through ◽  
Counting Statistics ◽  
The Relationship ◽  
Picture Element

It is well known that a large flux of electrons must pass through a specimen in order to obtain a high resolution image while a smaller particle flux is satisfactory for a low resolution image. The minimum particle flux that is required depends upon the contrast in the image and the signal-to-noise (S/N) ratio at which the data are considered acceptable. For a given S/N associated with statistical fluxtuations, the relationship between contrast and “counting statistics” is s131_eqn1, where C = contrast; r2 is the area of a picture element corresponding to the resolution, r; N is the number of electrons incident per unit area of the specimen; f is the fraction of electrons that contribute to formation of the image, relative to the total number of electrons incident upon the object.

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