Viscoelastic properties of bone as a function of hydration state determined by nanoindentation

Spherical indentation creep testing was used to examine the effect of hydration state on bone mechanical properties. Analysis of creep data was based on the elastic-viscoelastic correspondence principle and utilized a direct solution for the finite loading-rate experimental conditions. The zero-time shear modulus was computed from the creep compliance function and compared to the indentation modulus obtained via conventional indentation analysis, based on an elastic unloading response. The method was validated using a well-known polymer material under three different loading conditions. The method was applied to bone samples prepared with different water content by partial exchange with ethanol, where 70% ethanol was considered as the baseline condition. A hydration increase was associated with a 43% decrease in stiffness, while a hydration decrease resulted in a 20% increase in bone tissue stiffness.

Nanoindentation measurements of bone viscoelasticity as a function of hydration state

Bone is an anisotropic material, and its mechanical properties are determined by its microstructure as well as its composition. Mechanical properties of bone are a consequence of the proportions of, and the interactions between, mineral, collagen and water. Water plays an important role in maintaining the mechanical integrity of the composite, but the manner in which water interacts within the ultrastructure is unclear. Dentine being an isotropic two-dimensional structure presents a homogenous composite to examine the dehydration effects. Nanoindentation methods for determining the viscoelastic properties have recently been developed and are a subject of great interest. Here, one method based on elastic-viscoelastic correspondence for 'ramp and hold' creep testing (Oyen, J. Mater. Res., 2005) has been used to analyze viscoelastic behavior of polymeric and biological materials. The method of 'ramp and hold' allows the shear modulus at time zero to be determined from fitting of the displacement during the maximum load hold. Changes in the viscoelastic properties of bone and dentine were examined as the material was systematically dehydrated in a series of water:solvent mixes. Samples of equine dentine were sectioned and cryo-polished. Shear modulus was obtained by nanoindentation using spherical indenters with a maximum load hold of 120s. Samples were tested in different solvent concentrations sequentially, 70% ethanol to 50% ethanol, 70 % ethanol to 100% ethanol, 70% ethanol to 70% methanol to 100% methanol, and 70% ethanol to 100% acetone, after storage in each condition for 24h. By selectively removing and then replacing water from the composite, insights in to the ultrastructure of the tissue can be gained from the corresponding changes in the experimentally determined moduli, as well as an understanding of the complete reversibility of the dehydration process. © 2006 Materials Research Society.

Incremental Linear Discriminant Analysis Using Sufficient Spanning Sets and Its Applications

This paper presents an incremental learning solution for Linear Discriminant Analysis (LDA) and its applications to object recognition problems. We apply the sufficient spanning set approximation in three steps i.e. update for the total scatter matrix, between-class scatter matrix and the projected data matrix, which leads an online solution which closely agrees with the batch solution in accuracy while significantly reducing the computational complexity. The algorithm yields an efficient solution to incremental LDA even when the number of classes as well as the set size is large. The incremental LDA method has been also shown useful for semi-supervised online learning. Label propagation is done by integrating the incremental LDA into an EM framework. The method has been demonstrated in the task of merging large datasets which were collected during MPEG standardization for face image retrieval, face authentication using the BANCA dataset, and object categorisation using the Caltech101 dataset. © 2010 Springer Science+Business Media, LLC.

Dissecting Arabidopsis thaliana DICER function in small RNA processing, gene silencing and DNA methylation patterning.

Small RNAs have several important biological functions. MicroRNAs (miRNAs) and trans-acting small interfering RNAs (tasiRNAs) regulate mRNA stability and translation, and siRNAs cause post-transcriptional gene silencing of transposons, viruses and transgenes and are important in both the establishment and maintenance of cytosine DNA methylation. Here, we study the role of the four Arabidopsis thaliana DICER-LIKE genes (DCL1-DCL4) in these processes. Sequencing of small RNAs from a dcl2 dcl3 dcl4 triple mutant showed markedly reduced tasiRNA and siRNA production and indicated that DCL1, in addition to its role as the major enzyme for processing miRNAs, has a previously unknown role in the production of small RNAs from endogenous inverted repeats. DCL2, DCL3 and DCL4 showed functional redundancy in siRNA and tasiRNA production and in the establishment and maintenance of DNA methylation. Our studies also suggest that asymmetric DNA methylation can be maintained by pathways that do not require siRNAs.

Semantic Discriminant mapping for classification and browsing of remote sensing textures and objects

We present a new approach based on Discriminant Analysis to map a high dimensional image feature space onto a subspace which has the following advantages: 1. each dimension corresponds to a semantic likelihood, 2. an efficient and simple multiclass classifier is proposed and 3. it is low dimensional. This mapping is learnt from a given set of labeled images with a class groundtruth. In the new space a classifier is naturally derived which performs as well as a linear SVM. We will show that projecting images in this new space provides a database browsing tool which is meaningful to the user. Results are presented on a remote sensing database with eight classes, made available online. The output semantic space is a low dimensional feature space which opens perspectives for other recognition tasks. © 2005 IEEE.