Levels of Organization in the Biological Sciences

2021 ◽  
Keyword(s):  
Biological Sciences ◽  
Levels Of Organization

Structure-function relations in physiology education: Where’s the mechanism?

2017 ◽  
Vol 41 (2) ◽  
pp. 270-278 ◽  
Author(s):  
Matthew E. Lira ◽  
Stephanie M. Gardner
Keyword(s):  
Biological Sciences ◽  
Structure Function ◽  
Cognitive Processes ◽  
Biological Organization ◽  
Physiological Mechanisms ◽  
Levels Of Organization ◽  
Organ Systems ◽  
Narrow Definition ◽  
Multiple Levels ◽  
Shed Light

Physiology demands systems thinking: reasoning within and between levels of biological organization and across different organ systems. Many physiological mechanisms explain how structures and their properties interact at one level of organization to produce emergent functions at a higher level of organization. Current physiology principles, such as structure-function relations, selectively neglect mechanisms by not mentioning this term explicitly. We explored how students characterized mechanisms and functions to shed light on how students make sense of these terms. Students characterized mechanisms as 1) processes that occur at levels of organization lower than that of functions; and 2) as detailed events with many steps involved. We also found that students produced more variability in how they characterized functions compared with mechanisms: students characterized functions in relation to multiple levels of organization and multiple definitions. We interpret these results as evidence that students see mechanisms as holding a more narrow definition than used in the biological sciences, and that students struggle to coordinate and distinguish mechanisms from functions due to cognitive processes germane to learning in many domains. We offer the instructional suggestion that we scaffold student learning by affording students opportunities to relate and also distinguish between these terms so central to understanding physiology.

Dynamical Scattering in Crystalline Biological Specimens. I. Criteria of Validity for Scattering Approximations

1975 ◽  
Vol 33 ◽  
pp. 192-193
Author(s):  
Robert M. Glaeser ◽  
Bing K. Jap
Keyword(s):  
Biological Sciences ◽  
Electron Microscopy ◽  
Electron Microscope ◽  
Electron Diffraction ◽  
Born Approximation ◽  
Materials Science ◽  
Image Data ◽  
Dynamical Effect ◽  
Crystallographic Structure ◽  
Electron Microscope Image

The dynamical scattering effect, which can be described as the failure of the first Born approximation, is perhaps the most important factor that has prevented the widespread use of electron diffraction intensities for crystallographic structure determination. It would seem to be quite certain that dynamical effects will also interfere with structure analysis based upon electron microscope image data, whenever the dynamical effect seriously perturbs the diffracted wave. While it is normally taken for granted that the dynamical effect must be taken into consideration in materials science applications of electron microscopy, very little attention has been given to this problem in the biological sciences.

The Application of Ultra-Thin Sectioning Techniques to Non-Biological Samples

1968 ◽  
Vol 26 ◽  
pp. 332-333
Author(s):  
C. F. Oster
Keyword(s):  
Biological Sciences ◽  
Epoxy Resin ◽  
Cross Sections ◽  
Biological Samples ◽  
Industrial Applications ◽  
Photographic Film ◽  
Silver Particles ◽  
Thin Sectioning ◽  
Embedding Medium

Although ultra-thin sectioning techniques are widely used in the biological sciences, their applications are somewhat less popular but very useful in industrial applications. This presentation will review several specific applications where ultra-thin sectioning techniques have proven invaluable.The preparation of samples for sectioning usually involves embedding in an epoxy resin. Araldite 6005 Resin and Hardener are mixed so that the hardness of the embedding medium matches that of the sample to reduce any distortion of the sample during the sectioning process. No dehydration series are needed to prepare our usual samples for embedding, but some types require hardening and staining steps. The embedded samples are sectioned with either a prototype of a Porter-Blum Microtome or an LKB Ultrotome III. Both instruments are equipped with diamond knives.In the study of photographic film, the distribution of the developed silver particles through the layer is important to the image tone and/or scattering power. Also, the morphology of the developed silver is an important factor, and cross sections will show this structure.

Oleg L. Kuznetsov (to the mire scientist’s anniversary)

2012 ◽  
pp. 145-146
Author(s):  
O. V. Galanina
Keyword(s):  
Biological Sciences ◽  
Research Centre ◽  
Russian Academy Of Sciences ◽  
Academy Of Sciences

On February 18, 2012 our colleague — doctor biological Sciences, head of Laboratory for mire ecosystems of Institute of biology of Karelian research centre of Russian Academy of Sciences Oleg L. Kuznetsov 60 years old.

Artificial Intelligence for epigenetics: towards personalized medicine

2020 ◽  
Vol 27 ◽  
Author(s):  
Giulia De Riso ◽  
Sergio Cocozza
Keyword(s):  
Biological Sciences ◽  
Artificial Intelligence ◽  
Personalized Medicine ◽  
Molecular Mechanisms ◽  
Genomic Sequence ◽  
Health Care Interventions ◽  
Genome Wide ◽  
Genetic And Environmental Factors ◽  
Opening Up ◽  
Omic Data

: Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mechanisms, that is in turn orchestrated by genetic and environmental factors. The epigenetic profile captures the unique regulatory landscape and the exposure to environmental stimuli of an individual. It thus constitutes a valuable reservoir of information for personalized medicine, which is aimed at customizing health-care interventions based on the unique characteristics of each individual. Nowadays, the complex milieu of epigenomic marks can be studied at the genome-wide level thanks to massive, highthroughput technologies. This new experimental approach is opening up new and interesting knowledge perspectives. However, the analysis of these complex omic data requires to face important analytic issues. Artificial Intelligence, and in particular Machine Learning, are emerging as powerful resources to decipher epigenomic data. In this review, we will first describe the most used ML approaches in epigenomics. We then will recapitulate some of the recent applications of ML to epigenomic analysis. Finally, we will provide some examples of how the ML approach to epigenetic data can be useful for personalized medicine.

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