Faculty Opinions recommendation of Evolution and classification of P-loop kinases and related proteins.

2003 ◽  
Author(s):  
Nick Grishin
Keyword(s):  
Related Proteins

Neurodegenerative Disorders

2019 ◽  
pp. 195-216
Author(s):  
Mohamed M. Amin
Keyword(s):  
Dna Binding ◽  
Binding Protein ◽  
Clinical Signs ◽  
Amyloid Β ◽  
Molecular Classification ◽  
Dna Binding Protein ◽  
Fused In Sarcoma ◽  
Protein Deposits ◽  
Related Proteins

Neurodegenerative diseases (NDs) are characterized by specific dysfunction and damage of neurons related to pathologically changed proteins that deposit in the patient brain but also in peripheral organs. These proteins can be used for therapy or used as biomarkers. Except for a plethora of alterations revealed for dissimilar neurodegeneration-related proteins, amyloid-β, prion protein, TAR DNA-binding protein 43 (TDP-43, transactive response DNA binding protein 43 kDa), tau and α-synuclein, or fused in sarcoma protein (FUS), molecular classification of NDs depend on the full morphological assessment of protein deposits, their spreading in the brain, and their correspondence to clinical signs with specific genetic modifications. The current chapter represents the etiology of neurodegeneration, classification of NDs, concentrating on the maximum applicable biochemical and anatomical characteristics and most imperative NDs.

Sequence classification of water channels and related proteins in view of functional predictions

1999 ◽  
Vol 101 (1-3) ◽  
pp. 77-81
Author(s):  
B. Tallur ◽  
J. Nicolas ◽  
A. Froger ◽  
D. Thomas ◽  
C. Delamarche
Keyword(s):  
Water Channels ◽  
Sequence Classification ◽  
Related Proteins

scholarly journals Classifying the Unclassified: A Phage Classification Method

Viruses ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 195 ◽  
Author(s):  
Cynthia Maria Chibani ◽  
Anton Farr ◽  
Sandra Klama ◽  
Sascha Dietrich ◽  
Heiko Liesegang
Keyword(s):  
Comparative Genomics ◽  
Hidden Markov Models ◽  
Genome Sequence ◽  
Genomic Organization ◽  
Markov Models ◽  
Sequence Data ◽  
Hidden Markov ◽  
International Committee ◽  
Related Proteins

This work reports the method ClassiPhage to classify phage genomes using sequence derived taxonomic features. ClassiPhage uses a set of phage specific Hidden Markov Models (HMMs) generated from clusters of related proteins. The method was validated on all publicly available genomes of phages that are known to infect Vibrionaceae. The phages belong to the well-described phage families of Myoviridae, Podoviridae, Siphoviridae, and Inoviridae. The achieved classification is consistent with the assignments of the International Committee on Taxonomy of Viruses (ICTV), all tested phages were assigned to the corresponding group of the ICTV-database. In addition, 44 out of 58 genomes of Vibrio phages not yet classified could be assigned to a phage family. The remaining 14 genomes may represent phages of new families or subfamilies. Comparative genomics indicates that the ability of the approach to identify and classify phages is correlated to the conserved genomic organization. ClassiPhage classifies phages exclusively based on genome sequence data and can be applied on distinct phage genomes as well as on prophage regions within host genomes. Possible applications include (a) classifying phages from assembled metagenomes; and (b) the identification and classification of integrated prophages and the splitting of phage families into subfamilies.

Evolution and Classification of P-loop Kinases and Related Proteins

2003 ◽  
Vol 333 (4) ◽  
pp. 781-815 ◽  
Author(s):  
Detlef D. Leipe ◽  
Eugene V. Koonin ◽  
L. Aravind
Keyword(s):  
Related Proteins

Note on the spectral classification of carbon stars in the photographic infrared region

1966 ◽  
Vol 24 ◽  
pp. 21-23
Author(s):  
Y. Fujita
Keyword(s):  
Infrared Region ◽  
Spectral Classification ◽  
Carbon Stars

We have investigated the spectrograms (dispersion: 8Å/mm) in the photographic infrared region fromλ7500 toλ9000 of some carbon stars obtained by the coudé spectrograph of the 74-inch reflector attached to the Okayama Astrophysical Observatory. The names of the stars investigated are listed in Table 1.

Diagnostic Value of Electron Microscopy in Soft Tissue Tumors

1970 ◽  
Vol 28 ◽  
pp. 220-221
Author(s):  
Gerald Fine ◽  
Azorides R. Morales
Keyword(s):  
Cardiac Myxoma ◽  
Osteogenic Sarcoma ◽  
Diagnostic Value ◽  
Soft Tissue Tumors ◽  
Amelanotic Melanoma ◽  
Growth Patterns ◽  
Light Microscopic Level ◽  
Mesenchymal Tumors ◽  
Good Agreement

For years the separation of carcinoma and sarcoma and the subclassification of sarcomas has been based on the appearance of the tumor cells and their microscopic growth pattern and information derived from certain histochemical and special stains. Although this method of study has produced good agreement among pathologists in the separation of carcinoma from sarcoma, it has given less uniform results in the subclassification of sarcomas. There remain examples of neoplasms of different histogenesis, the classification of which is questionable because of similar cytologic and growth patterns at the light microscopic level; i.e. amelanotic melanoma versus carcinoma and occasionally sarcoma, sarcomas with an epithelial pattern of growth simulating carcinoma, histologically similar mesenchymal tumors of different histogenesis (histiocytoma versus rhabdomyosarcoma, lytic osteogenic sarcoma versus rhabdomyosarcoma), and myxomatous mesenchymal tumors of diverse histogenesis (myxoid rhabdo and liposarcomas, cardiac myxoma, myxoid neurofibroma, etc.)

Ultrastructure of mesotheliomas

1984 ◽  
Vol 42 ◽  
pp. 98-101
Author(s):  
Irving Dardick
Keyword(s):  
Electron Microscopy ◽  
Latent Period ◽  
Spindle Cell ◽  
Spindle Cell Sarcoma ◽  
Metastatic Adenocarcinoma ◽  
Cell Sarcoma ◽  
Cytological Features ◽  
Industrial Use ◽  
Degree Of Differentiation

With the extensive industrial use of asbestos in this century and the long latent period (20-50 years) between exposure and tumor presentation, the incidence of malignant mesothelioma is now increasing. Thus, surgical pathologists are more frequently faced with the dilemma of differentiating mesothelioma from metastatic adenocarcinoma and spindle-cell sarcoma involving serosal surfaces. Electron microscopy is amodality useful in clarifying this problem.In utilizing ultrastructural features in the diagnosis of mesothelioma, it is essential to appreciate that the classification of this tumor reflects a variety of morphologic forms of differing biologic behavior (Table 1). Furthermore, with the variable histology and degree of differentiation in mesotheliomas it might be expected that the ultrastructure of such tumors also reflects a range of cytological features. Such is the case.

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