scholarly journals РОЗРОБКА МОНОГРАФІЙ ДЕРЖАВНОЇ ФАРМАКОПЕЇ УКРАЇНИ НА ЛІКАРСЬКУ РОСЛИННУ СИРОВИНУ ТА ЛІКАРСЬКІ РОСЛИННІ ЗАСОБИ

2021 ◽  
pp. 23-27
Author(s):  
Вікторія КИСЛИЧЕНКО ◽  
Ірина ЖУРАВЕЛЬ ◽  
Олександра КИСЛИЧЕНКО ◽  
Вікторія КУЗНЄЦОВА
Keyword(s):  
Biological Sciences

CHEMICAL AND BIOLOGICAL SCIENCES

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.

Synthesis of 3-(Cyclohexylamino)-2-arylimidazo[1,2-a]pyridine-8- carboxylic Acids Via an Efficient Three-component Condensation Reaction between Cyclohexylisocyanide and 2-Aminopyridine-3-carboxylic Acid in the Presence of Aromatic Aldehyde

2018 ◽  
Vol 21 (4) ◽  
pp. 298-301 ◽  
Author(s):  
Ghasem Marandi
Keyword(s):  
Biological Sciences ◽  
Carboxylic Acid ◽  
Carboxylic Acids ◽  
High Performance ◽  
Condensation Reaction ◽  
Aromatic Aldehydes ◽  
New Materials ◽  
One Pot ◽  
High Yields ◽  
Benzaldehyde Derivatives

Aim and Objective: The reaction of cyclohexylisocyanide and 2-aminopyridine-3- carboxylic acid in the presence of benzaldehyde derivatives in ethanol led to 3-(cyclohexylamino)-2- arylimidazo[1,2-a]pyridine-8-carboxylic acids in high yields. In a three component condensation reaction, isocyanide reacts with 2-aminopyridine-3-carboxylic acid and aromatic aldehydes without any prior activation. Material and Methods: The synthesized products have stable structures which have been characterized by IR, 1H, 13C and Mass spectroscopy as well as CHN-O analysis. Results: In continuation of our attempts to develop simple one-pot routes for the synthesis of 3- (cyclohexylamino)-2-arylimidazo[1,2-a]pyridine-8-carboxylic acids, aromatic aldehydes with divers substituted show a high performance. Conclusion: In conclusion, this study introduces the art of combinatorial chemistry using a simple one-pot procedure for the synthesis of new materials which are interesting compounds in medicinal and biological sciences.

Deep Learning in Disease Diagnosis: Models and Datasets

2020 ◽  
Vol 15 ◽  
Author(s):  
Deeksha Saxena ◽  
Mohammed Haris Siddiqui ◽  
Rajnish Kumar
Keyword(s):  
Biological Sciences ◽  
Machine Learning ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Models ◽  
Data Types ◽  
Related Data ◽  
Abstract Level ◽  
Experimental Validations ◽  
Selection Of

Background: Deep learning (DL) is an Artificial neural network-driven framework with multiple levels of representation for which non-linear modules combined in such a way that the levels of representation can be enhanced from lower to a much abstract level. Though DL is used widely in almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually as well. DL seems to be a better platform than machine learning as the former does not require an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed fields among the scientists and researchers these days for diagnosing and solving various biological problems. However, deep learning models need some improvisation and experimental validations to be more productive. Objective: To review the available DL models and datasets that are used in disease diagnosis. Methods: Available DL models and their applications in disease diagnosis were reviewed discussed and tabulated. Types of datasets and some of the popular disease related data sources for DL were highlighted. Results: We have analyzed the frequently used DL methods, data types and discussed some of the recent deep learning models used for solving different biological problems. Conclusion: The review presents useful insights about DL methods, data types, selection of DL models for the disease diagnosis.

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