Osteoclastogenesis induced by sRANKL and M-CSF from the non-adherent cell population of human bone marrow is inhibited by rhBMP-2 alone or together with rhVEGF

During bone development and repair, angiogenesis, osteogenesis and bone remodelling are closely associated processes that share some common mediators. In the present study non-adherent human bone marrow mononuclear cells under the induction of sRANKL and M-CSF, differentiated into osteoclasts with TRAP positive staining, VNR expression, and Ca-P resorptive activity. The effects of various combinations of rhBMP-2 (0, 3, 30, 300 ng/ml) and rhVEGF (0, 25 ng/ml) on osteoclastogenesis potentials were examined in this experimental system. The percentages of TRAP-positive multiple nucleated cells represent osteoclast differentiation potential and the percentages of resorptive areas in the Ca-P coated plates resemble osteoclast resorption capability. The presence of rhBMP-2 at 30 and 300 ng/ml showed inhibitory effects on osteoclast differentiation and their resorptive capability in the human osteoclast culture system. rhVEGF (25 ng/ml) enhanced the resorptive function of osteoclast whenever it was used alone or combined with 3 ng/ml rhBMP-2. However, rhVEGF induced resorptive function was inhibited by 30 ng/ml and 300 ng/ml rhBMP-2 at a dose-dependent manner. Statistical analysis demonstrated that an interactive effect exists between rhBMP-2 and rhVEGF on human osteoclastogenesis. These findings suggested that an interactive regulation may exist between BMPs and VEGF signaling pathways during osteoclastogenesis, exact mechanisms are yet to be elucidated.

Application of ultraspherical polynomials to forced oscillations of a third-order non-linear system

In this paper the method of ultraspherical polynomial approximation is applied to study the steady-state response in forced oscillations of a third-order non-linear system. The non-linear function is expanded in ultraspherical polynomials and the expansion is restricted to the linear term. The equation for the response curve is obtained by using the linearized equation and the results are presented graphically. The agreement between the approximate solution and the analog computer solution is satisfactory. The problem of stability is not dealt with in this paper.

Natural modes of a coupled non-linear system

The natural modes of a non-linear system with two degrees of freedom are investigated. The system, which may contain either hard or soft springs, is shown to possess three modes of vibration one of which does not have any counterpart in the linear theory. The stability analysis indicates the existence of seven different modal stability patterns depending on the values of two parameters of non-linearity.

Localization induced base isolation in fractionally damped non linear system

In the present article we take up the study of nonlinear localization induced base isolation of a 3 degree of freedom system having cubic nonlinearities under sinusoidal base excitation. The damping forces in the system are described by functions of fractional derivative of the instantaneous displacements, typically linear and quadratic damping are considered here separately. Under the assumption of smallness of certain system parameters and nonlinear terms an approximate estimate of the response at each degree of freedom of the system is obtained by the Method of Multiple Scales approach. We then consider a similar system where the nonlinear terms and certain other parameters are no longer small. Direct numerical simulation is made use of to obtain the amplitude plot in the frequency domain for this case, which helps us to establish the efficacy of this method of base isolation for a broad class of systems. Base isolation obtained this way has no counterpart in the linear theory.

Human marrow mesenchymal stem Cells (MSCs) in the peplated non-adherent cell population serve as a complementary source of osteogenic cells

To obtain enough quantity of osteogenic cells is a challenge for successful cell therapy in bone defect treatment, and cell numbers were usually achieved by culturing bone marrow cells in a relatively long duration. This study reported a simple and cost effective method to enhance the number of MSCs by collecting and replating the non-adherent cell population of marrow MSCs culture. Bone marrow MSCs were isolated from 11 patients, cultured at a density of 1×105/cm2 to 1×106/cm2 in flasks. For the first three times of media change, the floating cells were centrifuged and replated in separate flasks. The total number of cells in both the primary and replating flasks were counted at day 21. Cell proliferation rate, potentials for osteogenic, chondrognenic, and adipogenic differentiation were examined in both cell types in vitro. In-vivo osteogenic potentials of the cells were also tested in mice implantation model. The results showed that MSCs derived from non-adherent cell population of marrow cell cultures have similar cell proliferation and differentiation potentials as the originally attached MSCs in vitro. When implanted with HA-TCP materials subcutaneously in SCID mice, newly formed bony tissues were found in both cell type groups with osteocalcin expression. We have obtained 36.6% (20.70%-44.97%) more MSCs in the same culture period when the non-adherent cell populations were collected. The findings confirmed that the non-adherent cell population in the bone marrow culture is a complementary source of MSCs, collecting these cells is a simple and cost-effective way to increase MSCs numbers and reduce the time required for culturing MSCs for clinical applications.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

On Variable Selection of a Nonlinear Non-parametric System with a Limited Data Set: A Stepwise Algorithm

his paper considers a problem of identification for a high dimensional nonlinear non-parametric system when only a limited data set is available. The algorithms are proposed for this purpose which exploit the relationship between the input variables and the output and further the inter-dependence of input variables so that the importance of the input variables can be established. A key to these algorithms is the non-parametric two stage input selection algorithm.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Fractional order and non-linear system identification algorithms for biomedical applications

We discuss the modelling of dielectric responses of amorphous biological samples. Such samples are commonly encountered in impedance spectroscopy studies as well as in UV, IR, optical and THz transient spectroscopy experiments and in pump-probe studies. In many occasions, the samples may display quenched absorption bands. A systems identification framework may be developed to provide parsimonious representations of such responses. To achieve this, it is appropriate to augment the standard models found in the identification literature to incorporate fractional order dynamics. Extensions of models using the forward shift operator, state space models as well as their non-linear Hammerstein-Wiener counterpart models are highlighted. We also discuss the need to extend the theory of electromagnetically excited networks which can account for fractional order behaviour in the non-linear regime by incorporating nonlinear elements to account for the observed non-linearities. The proposed approach leads to the development of a range of new chemometrics tools for biomedical data analysis and classification.

A note on the attenuation of the sommerfeld effect of a non-ideal system taking into account a MR damper and the complete model of a DC motor

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Right-to-left shunt determination in dog lungs under inhalation anesthesia with rebreathing and non-rebreathing system

OBJETIVO: Comparar a formação de shunt venoso-arterial em pulmões de cães submetidos a anestesia geral inalatória utilizando-se sistemas de anestesia com e sem reinalação, com fração inspirada de oxigênio de 0,4 e 0,9, respectivamente. MÉTODOS: Empregaram-se 20 cães induzidos com tiopental sódico (30mg/kg) e mantidos com sevoflurano (3%) e alocados em dois grupos (n=10); os animais de GI foram ventilados com modalidade controlada em sistema semifechado, sem reinalação, F I O2 = 0,9, e os de GII, com modalidade controlada, sistema semifechado, com reinalação e F I O2 = 0,4. Os atributos analisados durante o experimento foram: freqüência cardíaca, pressão arterial média, shunt pulmonar venoso-arterial, hematócrito, hemoglobina, pressão parcial de oxigênio arterial, pressão parcial de oxigênio no sangue venoso misto, saturação de oxigênio no sangue venoso misto, pressão parcial de dióxido de carbono arterial e pressão de vapor de água nos alvéolos (P VA). RESULTADOS: A P VA foi significativamente maior em GII. A análise estatística dos valores encontrados de shunt mostrou que GI e GII apresentaram diferenças significativas, sendo que os resultados de GI são maiores que os de GII em todos os momentos avaliados. Já a análise de momentos dentro de um mesmo grupo não demonstrou diferenças. CONCLUSÃO: O sistema de anestesia sem reinalação com F I O2 = 0,9 desenvolveu maior grau de shunt pulmonar venoso-arterial que o sistema de anestesia com reinalação e F I O2 = 0,4. A umidificação dos gases em GII contribuiu para diminuir o shunt.