87,195 research outputs found
Human ribosomal protein L4: Cloning and sequencing of the cDNA and primary structure of the protein
No full textThe cloning and sequencing of a cDNA for human ribosomal protein L4 is reported. The corresponding mRNA has a very short 5' untranslated region initiating with a sequence of 12 pyrimidines, characteristic of all vertebrate ribosomal protein mRNAs. The deduced amino acid sequence shows that human ribosomal protein L4 has 425 amino acid residues and a calculated molecular mass of 47821 Da. Comparison with the homologous counterparts of Xenopus, Drosophila and yeast shows that this protein has a very conserved amino-terminus region and an extremely divergent carboxyl-terminus portion
HUMAN RIBOSOMAL-PROTEIN L4 - CLONING AND SEQUENCING OF THE CDNA AND PRIMARY STRUCTURE OF THE PROTEIN
No full textXENOPUS-LAEVIS RIBOSOMAL-PROTEIN S11 - CLONING AND SEQUENCING OF THE CDNA AND PRIMARY STRUCTURE OF THE PROTEIN
No full textISOLATION AND NUCLEOTIDE-SEQUENCES OF CDNAS FOR XENOPUS-LAEVIS RIBOSOMAL PROTEIN-S8 - SIMILARITIES IN THE 5' AND 3' UNTRANSLATED REGIONS OF MESSENGER-RNAS FOR VARIOUS R-PROTEINS
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Structure of Xenopus laevis ribosomal protein L32 and its expression during development
No full textcDNA clones for Xenopus laevis ribosomal protein L32 have been isolated and sequenced. The deduced amino acid sequence indicates that L32 is a basic protein of 110 amino acids, has a molecular weight of 12,603 and is homologous to the rat ribosomal protein L35. Using the cDNA clone as a probe to follow the expression of this gene during Xenopus development, it has been shown that the pattern of accumulation of this mRNA follows the one previously described for other ribosomal protein mRNAs during oogenesis and embryogenesis. The analysis of the utilization of L32 mRNA during embryogenesis shows that this is controlled by the translational regulation typical of other ribosomal protein mRNAs
Pleistocene refugial areas for two species of water shrew, Neomys anomalus and N. fodiens, in the Italian peninsula as revealed by mtDNA analysis.
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Short-Training Damage Detection Method for Axially Loaded Beams Subject to Seasonal Thermal Variations
No full textVibration-based damage features are widely adopted in the field of structural health monitoring (SHM), and particularly in the monitoring of axially loaded beams, due to their high sensitivity to damage-related changes in structural properties. However, changes in environmental and operating conditions often cause damage feature variations which can mask any possible change due to damage, thus strongly affecting the effectiveness of the monitoring strategy. Most of the approaches proposed to tackle this problem rely on the availability of a wide training dataset, accounting for the most part of the damage feature variability due to environmental and operating conditions. These approaches are reliable when a complete training set is available, and this represents a significant limitation in applications where only a short training set can be used. This often occurs when SHM systems aim at monitoring the health state of an already existing and possibly already damaged structure (e.g., tie-rods in historical buildings), or for systems which can undergo rapid deterioration. To overcome this limit, this work proposes a new damage index not affected by environmental conditions and able to properly detect system damages, even in case of short training set. The proposed index is based on the principal component analysis (PCA) of vibration-based damage features. PCA is shown to allow for a simple filtering procedure of the operating and environmental effects on the damage feature, thus avoiding any dependence on the extent of the training set. The proposed index effectiveness is shown through both simulated and experimental case studies related to an axially loaded beam-like structure, and it is compared with a Mahalanobis square distance-based index, as a reference. The obtained results highlight the capability of the proposed index in filtering out the temperature effects on a multivariate damage feature composed of eigenfrequencies, in case of both short and long training set. Moreover, the proposed PCA-based strategy is shown to outperform the benchmark one, both in terms of temperature dependency and damage sensitivity
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