The Development of Cell Processes Induced by tau Protein Requires Phosphorylation of Serine 262 and 356 in the Repeat Domain and Is Inhibited by Phosphorylation in the Proline-rich Domains

The differentiation of neurons and the outgrowth of neurites depends on microtubule-associated proteins such as tau protein. To study this process, we have used the model of Sf9 cells, which allows efficient transfection with microtubule-associated proteins (via baculovirus vectors) and observation of the resulting neurite-like extensions. We compared the phosphorylation of tau23 (the embryonic form of human tau) with mutants in which critical phosphorylation sites were deleted by mutating Ser or Thr residues into Ala. One can broadly distinguish two types of sites, the KXGS motifs in the repeats (which regulate the affinity of tau to microtubules) and the SP or TP motifs in the domains flanking the repeats (which contain epitopes for antibodies diagnostic of Alzheimer’s disease). Here we report that both types of sites can be phosphorylated by endogenous kinases of Sf9 cells, and that the phosphorylation pattern of the transfected tau is very similar to that of neurons, showing that Sf9 cells can be regarded as an approximate model for the neuronal balance between kinases and phosphatases. We show that mutations in the repeat domain and in the flanking domains have opposite effects. Mutations of KXGS motifs in the repeats (Ser262, 324, and 356) strongly inhibit the outgrowth of cell extensions induced by tau, even though this type of phosphorylation accounts for only a minor fraction of the total phosphate. This argues that the temporary detachment of tau from microtubules (by phosphorylation at KXGS motifs) is a necessary condition for establishing cell polarity at a critical point in space or time. Conversely, the phosphorylation at SP or TP motifs represents the majority of phosphate (>80%); mutations in these motifs cause an increase in cell extensions, indicating that this type of phosphorylation retards the differentiation of the cells.

Three-dimensional modeling of and ligand docking to vitamin D receptor ligand binding domain

The ligand binding domain of the human vitamin D receptor (VDR) was modeled based on the crystal structure of the retinoic acid receptor. The ligand binding pocket of our VDR model is spacious at the helix 11 site and confined at the β-turn site. The ligand 1α,25-dihydroxyvitamin D3 was assumed to be anchored in the ligand binding pocket with its side chain heading to helix 11 (site 2) and the A-ring toward the β-turn (site 1). Three residues forming hydrogen bonds with the functionally important 1α- and 25-hydroxyl groups of 1α,25-dihydroxyvitamin D3 were identified and confirmed by mutational analysis: the 1α-hydroxyl group is forming pincer-type hydrogen bonds with S237 and R274 and the 25-hydroxyl group is interacting with H397. Docking potential for various ligands to the VDR model was examined, and the results are in good agreement with our previous three-dimensional structure-function theory.

A numerical strategy for efficient modeling of the equatorial wave guide

Convection in the tropics is observed to involve a wide-ranging hierarchy of scales from a few kilometers to the planetary scales and also has a profound impact on short-term climate. The mechanisms responsible for this behavior present a major unsolved problem. A promising emerging approach to address these issues is cloud-resolving modeling. Here a family of numerical models is introduced specifically to model the feedback of small-scale deep convection on tropical planetary waves and tropical circulation in a highly efficient manner compatible with the approach through cloud-resolving modeling. Such a procedure is also useful for theoretical purposes. The basic idea in the approach is to use low-order truncation in the meriodonal direction through Gauss–Hermite quadrature projected onto a simple discrete radiation condition. In this fashion, the cloud-resolving modeling of equatorially trapped planetary waves reduces to the solution of a small number of purely zonal two-dimensional wave systems along a few judiciously chosen meriodonal layers that are coupled only by some additional source terms. The approach is analyzed in detail with full mathematical rigor for linearized equatorial primitive equations with source terms.

Dynamic modeling of gene expression data

We describe the time evolution of gene expression levels by using a time translational matrix to predict future expression levels of genes based on their expression levels at some initial time. We deduce the time translational matrix for previously published DNA microarray gene expression data sets by modeling them within a linear framework by using the characteristic modes obtained by singular value decomposition. The resulting time translation matrix provides a measure of the relationships among the modes and governs their time evolution. We show that a truncated matrix linking just a few modes is a good approximation of the full time translation matrix. This finding suggests that the number of essential connections among the genes is small.

Ras Family GTPases Control Growth of Astrocyte Processes

Astrocytes in neuron-free cultures typically lack processes, although they are highly process-bearing in vivo. We show that basic fibroblast growth factor (bFGF) induces cultured astrocytes to grow processes and that Ras family GTPases mediate these morphological changes. Activated alleles of rac1 and rhoA blocked and reversed bFGF effects when introduced into astrocytes in dissociated culture and in brain slices using recombinant adenoviruses. By contrast, dominant negative (DN) alleles of both GTPases mimicked bFGF effects. A DN allele of Ha-ras blocked bFGF effects but not those of Rac1-DN or RhoA-DN. Our results show that bFGF acting through c-Ha-Ras inhibits endogenous Rac1 and RhoA GTPases thereby triggering astrocyte process growth, and they provide evidence for the regulation of this cascade in vivo by a yet undetermined neuron-derived factor.

Statistical modeling of large microarray data sets to identify stimulus-response profiles

A statistical modeling approach is proposed for use in searching large microarray data sets for genes that have a transcriptional response to a stimulus. The approach is unrestricted with respect to the timing, magnitude or duration of the response, or the overall abundance of the transcript. The statistical model makes an accommodation for systematic heterogeneity in expression levels. Corresponding data analyses provide gene-specific information, and the approach provides a means for evaluating the statistical significance of such information. To illustrate this strategy we have derived a model to depict the profile expected for a periodically transcribed gene and used it to look for budding yeast transcripts that adhere to this profile. Using objective criteria, this method identifies 81% of the known periodic transcripts and 1,088 genes, which show significant periodicity in at least one of the three data sets analyzed. However, only one-quarter of these genes show significant oscillations in at least two data sets and can be classified as periodic with high confidence. The method provides estimates of the mean activation and deactivation times, induced and basal expression levels, and statistical measures of the precision of these estimates for each periodic transcript.

Mutational analysis and molecular modeling of the nonapeptide hormone binding domains of the [Arg8]vasotocin receptor.

To identify determinants that form nonapeptide hormone binding domains of the white sucker Catostomus commersoni [Arg8]vasotocin receptor, chimeric constructs encoding parts of the vasotocin receptor and parts of the isotocin receptor have been analyzed by [(3,5-3H)Tyr2, Arg8]vasotocin binding to membranes of human embryonic kidney cells previously transfected with the different cDNA constructs and by functional expression studies in Xenopus laevis oocytes injected with mutant cRNAs. The results indicate that the N terminus and a region spanning the second extracellular loop and its flanking transmembrane segments, which contains a number of amino acid residues that are conserved throughout the nonapeptide receptor family, contribute to the affinity of the receptor for its ligand. Nonapeptide selectivity, however, is mainly defined by transmembrane region VI and the third extracellular loop. These results are complemented by a molecular model of the vasotocin receptor obtained by aligning its sequence with those of other G-protein coupled receptors as well as that of bacteriorhodopsin. The model indicates that amino acid residues of transmembrane regions II-VII that are located close to the extracellular surface also contribute to the binding of vasotocin.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

Numerical modeling of electromagnetic coupling phenomena in the vicinities of overhead power transmission lines.

Electromagnetic coupling phenomena between overhead power transmission lines and other nearby structures are inevitable, especially in densely populated areas. The undesired effects resulting from this proximity are manifold and range from the establishment of hazardous potentials to the outbreak of alternate current corrosion phenomena. The study of this class of problems is necessary for ensuring security in the vicinities of the interaction zone and also to preserve the integrity of the equipment and of the devices there present. However, the complete modeling of this type of application requires the three- -dimensional representation of the region of interest and needs specific numerical methods for field computation. In this work, the modeling of problems arising from the flow of electrical currents in the ground (the so-called conductive coupling) will be addressed with the finite element method. Those resulting from the time variation of the electromagnetic fields (the so-called inductive coupling) will be considered as well, and they will be treated with the generalized PEEC (Partial Element Equivalent Circuit) method. More specifically, a special boundary condition on the electric potential is proposed for truncating the computational domain in the finite element analysis of conductive coupling problems, and a complete PEEC formulation for modeling inductive coupling problems is presented. Test configurations of increasing complexities are considered for validating the foregoing approaches. These works aim to provide a contribution to the modeling of this class of problems, which tend to become common with the expansion of power grids.

Mathematical modeling of drying process of unripe banana slices

Unripe banana flour (UBF) production employs bananas not submitted to maturation process, is an interesting alternative to minimize the fruit loss reduction related to inappropriate handling or fast ripening. The UBF is considered as a functional ingredient improving glycemic and plasma insulin levels in blood, have also shown efficacy on the control of satiety, insulin resistance. The aim of this work was to study the drying process of unripe banana slabs (Musa cavendishii, Nanicão) developing a transient drying model through mathematical modeling with simultaneous moisture and heat transfer. The raw material characterization was performed and afterwards the drying process was conducted at 40 ºC, 50 ºC e 60 ºC, the product temperature was recorded using thermocouples, the air velocity inside the chamber was 4 m·s-1. With the experimental data was possible to validate the diffusion model based on the Fick\'s second law and Fourier. For this purpose, the sorption isotherms were measured and fitted to the GAB model estimating the equilibrium moisture content (Xe), 1.76 [g H2O/100g d.b.] at 60 ºC and 10 % of relative humidity (RH), the thermophysical properties (k, Cp, ?) were also measured to be used in the model. Five cases were contemplated: i) Constant thermophysical properties; ii) Variable properties; iii) Mass (hm), heat transfer (h) coefficient and effective diffusivity (De) estimation 134 W·m-2·K-1, 4.91x10-5 m-2·s-1 and 3.278?10-10 m·s-2 at 60 ºC, respectively; iv) Variable De, it presented a third order polynomial behavior as function of moisture content; v) The shrinkage had an effect on the mathematical model, especially in the 3 first hours of process, the thickness experienced a contraction of about (30.34 ± 1.29) % out of the initial thickness, finding two decreasing drying rate periods (DDR I and DDR II), 3.28x10-10 m·s-2 and 1.77x10-10 m·s-2, respectively. COMSOL Multiphysics simulations were possible to perform through the heat and mass transfer coefficient estimated by the mathematical modeling.

A grammatical approach to the modeling of an autonomous robot

Virtual Worlds Generator is a grammatical model that is proposed to define virtual worlds. It integrates the diversity of sensors and interaction devices, multimodality and a virtual simulation system. Its grammar allows the definition and abstraction in symbols strings of the scenes of the virtual world, independently of the hardware that is used to represent the world or to interact with it. A case study is presented to explain how to use the proposed model to formalize a robot navigation system with multimodal perception and a hybrid control scheme of the robot. The result is an instance of the model grammar that implements the robotic system and is independent of the sensing devices used for perception and interaction. As a conclusion the Virtual Worlds Generator adds value in the simulation of virtual worlds since the definition can be done formally and independently of the peculiarities of the supporting devices.

Integration of different models in the design of chemical processes: Application to the design of a power plant

With advances in the synthesis and design of chemical processes there is an increasing need for more complex mathematical models with which to screen the alternatives that constitute accurate and reliable process models. Despite the wide availability of sophisticated tools for simulation, optimization and synthesis of chemical processes, the user is frequently interested in using the ‘best available model’. However, in practice, these models are usually little more than a black box with a rigid input–output structure. In this paper we propose to tackle all these models using generalized disjunctive programming to capture the numerical characteristics of each model (in equation form, modular, noisy, etc.) and to deal with each of them according to their individual characteristics. The result is a hybrid modular–equation based approach that allows synthesizing complex processes using different models in a robust and reliable way. The capabilities of the proposed approach are discussed with a case study: the design of a utility system power plant that has been decomposed into its constitutive elements, each treated differently numerically. And finally, numerical results and conclusions are presented.

Optimization of Chemical Processes Using Surrogate Models Based on a Kriging Interpolation

Superstructure approaches are the solution to the difficult problem which involves the rigorous economic design of a distillation column. These methods require complex initialization procedures and they are hard to solve. For this reason, these methods have not been extensively used. In this work, we present a methodology for the rigorous optimization of chemical processes implemented on a commercial simulator using surrogate models based on a kriging interpolation. Several examples were studied, but in this paper, we perform the optimization of a superstructure for a non-sharp separation to show the efficiency and effectiveness of the method. Noteworthy that it is possible to get surrogate models accurate enough with up to seven degrees of freedom.

Phrase-based statistical machine translation: explanation of its processes and statistical models and evaluation of the English to Spanish translations produced

Statistical machine translation (SMT) is an approach to Machine Translation (MT) that uses statistical models whose parameter estimation is based on the analysis of existing human translations (contained in bilingual corpora). From a translation student’s standpoint, this dissertation aims to explain how a phrase-based SMT system works, to determine the role of the statistical models it uses in the translation process and to assess the quality of the translations provided that system is trained with in-domain goodquality corpora. To that end, a phrase-based SMT system based on Moses has been trained and subsequently used for the English to Spanish translation of two texts related in topic to the training data. Finally, the quality of this output texts produced by the system has been assessed through a quantitative evaluation carried out with three different automatic evaluation measures and a qualitative evaluation based on the Multidimensional Quality Metrics (MQM).