Abstract
The fitting of physical dynamical models to stimulus-response data such as the chemical concentration measured after a gas has been released to the environment, or the plasma concentration measured after an intravenous or oral input of a drug, are important problems in the area of system identification. Using models of different structures, one can obtain relevant statistical information on the parameters of the model from an array of software packages available in the literature. A meaningful interpretation of these parameters requires that in the presence of error-free data and an error-free model structure, a unique solution for the model parameters is guaranteed. This problem is known as a priori identifiability. Once the model is deemed identifiable, the parameters are then obtained, usually via a nonlinear least squares technique. In addition to identifiability, there is the problem of convergence of the parameters to the true values. It is a known fact that nonlinear parameter estimation algorithms do not always converge to the true parameter set. This is due to the fact that estimating the parameters of a nonlinear model can at times be an ill-conditioned problem. In this paper we use the same state space analysis techniques used to determine identifiability, to estimate the model parameters in a linear fashion. We approach the problem from a system identification point of view and then take advantage of the similarity transformation between the physical model and the identified model. We formulate the similarity relations and then transform them into a null space problem whose solution leads to the physical parameters. The novelty of our approach is in the use of a state space system identification algorithm to identify a black-box system, followed by a physical parameter extraction step using robust numerical tools such as the singular value decomposition.

Abstract
Meiotic and early-embryonic cell divisions in vertebrates take place in the absence of transcription and rely on the translational regulation of stored maternal messenger RNAs. Most of these mRNAs are regulated by the cytoplasmic-polyadenylation-element-binding protein (CPEB), which mediates translational activation and repression through cytoplasmic changes in their poly(A) tail length. It was unknown whether translational regulation by cytoplasmic polyadenylation and CPEB can also regulate mRNAs at specific points of mitotic cell-cycle divisions. Here we show that CPEB-mediated post-transcriptional regulation by phase-specific changes in poly(A) tail length is required for cell proliferation and specifically for entry into M phase in mitotically dividing cells. This translational control is mediated by two members of the CPEB family of proteins, CPEB1 and CPEB4. We conclude that regulation of poly(A) tail length is not only required to compensate for the lack of transcription in specialized cell divisions but also acts as a general mechanism to control mitosis.

Abstract
Skeletal muscle presents an internal fibrous structure. The existence of muscle fibers surrounded by interstitial fluid originates an internal step refractive index profile that causes light scattering. One way to minimize this effect inside a muscle is to perform an optical clearing treatment, using an adequate solution that presents a refractive index higher than the interstitial fluid. We have studied muscle spectral transmittance during sample immersion in propylene glycol. With the collection of transmittance spectra registered during a period of 20 minutes of immersion we could represent spectral transmittance evolution for several wavelengths and verify that the tissue samples have become more translucent. The optical clearing effect created in the tissue samples was characterized by an increase of 45% above the natural transmittance and the variations observed in tissue mass, pH and global refractive index. We also identified the initial mechanisms of agent diffusion into the tissue and consequent tissue dehydration from the spectral transmittance evolution. The histological analysis of variations caused in the internal structure of the tissues permitted to better explain the optical clearing effect created. Considering a mathematical model developed in previous studies, we could estimate the amount of agent that was inserted into the tissue samples.

Abstract
It is known that the fibrous structure of muscle causes light scattering. This phenomenon occurs due to the refractive index discontinuities located between muscle fibers and interstitial fluid. To study the possibility of reducing light scattering inside muscle, we consider its spectral transmittance evolution during an immersion treatment with an optical clearing solution containing ethanol, glycerol, and distilled water. Our methodology consists of registering spectral transmittance of muscle samples while immersed in that solution. With the spectral data collected, we represent the transmittance evolution for some wavelengths during the treatment applied. Additionally, we study the variations that the treatment has caused on the samples regarding tissue refractive index and mass. By analyzing microscopic photographs of tissue cross section, we can also verify changes in the internal arrangement of muscle fibers caused by the immersion treatment. Due to a mathematical model that we develop, we can explain the variations observed in the studied parameters and estimate the amount of optical clearing agent that has diffused into the tissue samples during the immersion treatment. At the end of the study, we observe and explain the improvement in tissue spectral transmittance, which is approximately 65% after 20 min. (C) 2010 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3486539]

Abstract
Background: Within Rosaceae, the RNase based gametophytic self-incompatibility (GSI) system has been studied at the molecular level in Maloideae and Prunus species that have been diverging for, at least, 32 million years. In order to understand RNase based GSI evolution within this family, comparative studies must be performed, using similar methodologies. Result: It is here shown that many features are shared between the two species groups such as levels of recombination at the S-RNase ( the S-pistil component) gene, and the rate at which new specificities arise. Nevertheless, important differences are found regarding the number of ancestral lineages and the degree of specificity sharing between closely related species. In Maloideae, about 17% of the amino acid positions at the S-RNase protein are found to be positively selected, and they occupy about 30% of the exposed protein surface. Positively selected amino acid sites are shown to be located on either side of the active site cleft, an observation that is compatible with current models of specificity determination. At positively selected amino acid sites, non-conservative changes are almost as frequent as conservative changes. There is no evidence that at these sites the most drastic amino acid changes may be more strongly selected. Conclusions: Many similarities are found between the GSI system of Prunus and Maloideae that are compatible with the single origin hypothesis for RNase based GSI. The presence of common features such as the location of positively selected amino acid sites and lysine residues that may be important for ubiquitylation, raise a number of issues that, in principle, can be experimentally addressed in Maloideae. Nevertheless, there are also many important differences between the two Rosaceae GSI systems. How such features changed during evolution remains a puzzling issue.

Abstract
Modern greenhouse climate controllers are based on models in order to simulate and predict the greenhouse environment behaviour. These models must be able to describe indoor climate process dynamics, which are a function of both the control actions taken and the outside climate. Moreover, if predictive or feedforward control techniques are to be applied, it is necessary to employ models to describe and predict the weather. From all the climate variables, solar radiation is the one with greater impact in the greenhouse heat load. Hence, making good predictions of this physical quantity is of extreme importance. In this paper, the solar radiation is represented as a time-series and a support vector regression model is used to make long term predictions. Results are compared with the ones achieved by using other type of models, both linear and non-linear.