Biomedical subjects

R D Teixeira

Publications and source records attributed to R D Teixeira.

3 recordsLinked to original sources

PubMed2002

Ultrastructure of spermatozoa of the lizard Ameiva ameiva, with considerations on polymorphism within the family Teiidae (Squamata).

A detailed description of sperm ultrastructure of the lizard Ameiva ameiva (Teiidae) is provided. Mature spermatozoa are characterized by: a depressed acrosome at the anterior portion; a unilateral ridge at the anterolateral portion; an acrosome vesicle divided into cortex and medulla; medulla divided into two regions with different electron-densities; paracrystalline subacrosomal material with radial organization in transverse section; a pointed prenuclear perforatorium; a stopper-like perforatorium base plate that appears embedded in the subacrosomal material; the presence of an epinuclear lucent zone surrounded by its own membrane; a large nuclear rostrum; round nuclear shoulders; a nuclear space at the nucleus tip; a bilateral stratified laminar structure; a central dense body within the proximal centriole; a short midpiece; an axonemal midpiece axial component; peripheral fibers 3 and 8 grossly enlarged at the anterior portion of axoneme; columnar mitochondria with linear cristae; solid dense bodies arranged as rings or spirals; a triangular-shaped annulus in transverse section; a fibrous sheath into the midpiece; a thin zone of cytoplasm at the anterior portion of the principal piece; and a slight decrease in diameter of the principal piece immediately after the annulus. Comparisons with Cnemidophorus sexlineatus and Micrablepharus maximiliani failed to identify unique sperm ultrastructure traits of Teiidae or Teiioidea (Teiidae + Gymnophthalmidae). High levels of polymorphism between Ameiva and Cnemidophorus, two closely related genera of the family Teiidae, were detected, suggesting that extensive sampling within squamate families is essential if sperm ultrastructure data are to be used in phylogenetic analyses at this taxonomic level.

Acrosome↗

PubMed2002

A comparative ultrastructural study of spermatozoa of the teiid lizards Cnemidophorus gularis gularis, Cnemidophorus ocellifer, and Kentropyx altamazonica (Reptilia, Squamata, Teiidae).

The ultrastructure of the spermatozoa of Cnemidophorus gularis gularis, Cnemidophorus ocellifer, and Kentropyx altamazonica is described for the first time. Mature spermatozoa of Cnemidophorus spp. and K. altamazonica differ in the occurrence of a perforatorial base plate, the enlargement of axonemal fibers 3 and 8, and shape of mitochondria. The comparisons of the ultrastructure sperm of Cnemidophorus spp. and K. altamazonica with Ameiva ameiva [J. Morphol. (2002) in press] suggest that Ameiva and Cnemidophorus are more similar to each other than either is to Kentropyx. Statistical analyses reveal that sperm of all three species studied are significantly different in the following dimensions: head, acrosome, distal centriole length, and nuclear shoulders width. There was no variable statistically different between the Cnemidophorus spp. only. The length of the tail, midpiece, entire sperm, and nuclear rostrum are significantly different between K. altamazonica and Cnemidophorus spp. Our results indicate that sperm ultrastructure presents intra and intergeneric variability.

Animals↗

PubMed2001

Recent advances in the MOBJ algorithm for training artificial neural networks.

This paper presents a new scheme for training MLPs which employs a relaxation method for multi-objective optimization. The algorithm works by obtaining a reduced set of solutions, from which the one with the best generalization is selected. This approach allows balancing between the training error and norm of network weight vectors, which are the two objective functions of the multi-objective optimization problem. The method is applied to classification and regression problems and compared with Weight Decay (WD), Support Vector Machines (SVMs) and standard Backpropagation (BP). It is shown that the systematic procedure for training proposed results on good generalization neural models, and outperforms traditional methods.

Algorithms↗