The measurement of GLUT4 translocation in 3T3-L1 adipocytes

Type 2 diabetes (T2D) is one of the fastest growing threats to human health in westernised and developing countries and is associated with central obesity, atherosclerosis, dyslipidaemia, hyperinsulinaemia and  hypertension. Insulin resistance, defined as a diminished response to ordinary levels of circulating insulin in one or more peripheral tissues, is an integral feature of T2D pathophysiology. This includes an impairment of insulin to inhibit hepatic glucose output and to stimulate glucose disposal into muscle and fat. While insulin is responsible for a number of specific biological responses, stimulation of glucose transport is critical for the maintenance of glucose homeostasis. The primary mechanism for insulin stimulation of glucose uptake into muscle and fat is the translocation of glucose transporter 4 (GLUT4) to the cell surface from intracellular storage vesicles within the cell. A major advantage in focussing on insulin regulation of glucose transport is that this represents the endpoint of multiple upstream signalling pathways. This chapter describes the measurement of GLUT4 translocation in cultured cells and its potential application for both  mechanistic and therapeutic studies.

Suppressive actions of eicosapentaenoic acid on lipid droplet formation in 3T3-L1 adipocytes

Background : Lipid droplet (LD) formation and size regulation reflects both lipid influx and efflux, and is central in the regulation of adipocyte metabolism, including adipokine secretion. The length and degree of dietary fatty acid (FA) unsaturation is implicated in LD formation and regulation in adipocytes. The aims of this study were to establish the impact of eicosapentaenoic acid (EPA; C20:5n-3) in comparison to SFA (STA; stearic acid, C18:0) and MUFA (OLA; oleic acid, C18:1n-9) on 3T3-L1 adipocyte LD formation, regulation of genes central to LD function and adipokine responsiveness. Cells were supplemented with 100 μM FA during 7-day differentiation.

Results : EPA markedly reduced LD size and total lipid accumulation, suppressing PPARγ, Cidea and D9D/SCD1 genes, distinct from other treatments. These changes were independent of alterations of lipolytic genes, as both EPA and STA similarly elevated LPL and HSL gene expressions. In response to acute lipopolysaccharide exposure, EPA-differentiated adipocytes had distinct improvement in inflammatory response shown by reduction in monocyte chemoattractant protein-1 and interleukin-6 and elevation in adiponectin and leptin gene expressions.

Conclusions : This study demonstrates that EPA differentially modulates adipogenesis and lipid accumulation to suppress LD formation and size. This may be due to suppressed gene expression of key proteins closely associated with LD function. Further analysis is required to determine if EPA exerts a similar influence on LD formation and regulation in-vivo.

Regularized nonnegative shared subspace learning

Joint modeling of related data sources has the potential to improve various data mining tasks such as transfer learning, multitask clustering, information retrieval etc. However, diversity among various data sources might outweigh the advantages of the joint modeling, and thus may result in performance degradations. To this end, we propose a regularized shared subspace learning framework, which can exploit the mutual strengths of related data sources while being immune to the effects of the variabilities of each source. This is achieved by further imposing a mutual orthogonality constraint on the constituent subspaces which segregates the common patterns from the source specific patterns, and thus, avoids performance degradations. Our approach is rooted in nonnegative matrix factorization and extends it further to enable joint analysis of related data sources. Experiments performed using three real world data sets for both retrieval and clustering applications demonstrate the benefits of regularization and validate the effectiveness of the model. Our proposed solution provides a formal framework appropriate for jointly analyzing related data sources and therefore, it is applicable to a wider context in data mining.

Effects of multiple laser shock processing (LSP) impacts on mechanical properties and wear behaviors of AISI 8620 steel

The aim of this paper was to address the effects of multiple laser shock processing (LSP) impacts with different pulse energy on mechanical properties and wear behaviors of AISI 8620 steel. Wear analyses were conducted by means of calculation of volume loss and scanning electron microscope (SEM) of the wear surface. Surface profiles, roughness and micro-hardness were measured. The micro-structures in the surface layer of the untreated and LSPed samples (treated by multiple LSP impacts) were investigated by using transmission electron microscopy (TEM) observations. Experimental results and analyses indicated that multiple LSP impacts can remarkably improve the wear resistance of AISI 8620 steel, and the wear mechanism of multiple LSP impacts on AISI 8620 steel was also entirely revealed. The wear process of the unpolished sample subjected to multiple LSP impacts can be described as follows: the wear rate was big at the beginning of sliding dry wear, but then decreased after the micro-indention in the sample surface was polished to the disappear. This phenomenon can be attributed to the fact that multiple LSP impacts generate many micro-indents in the sample surface.

3T3-L1 adipocytes display phenotypic characteristics of multiple adipocyte lineages

Differentiated 3T3-L1 adipocytes are a widely used in vitro model of white adipocytes. In addition to classical white and brown adipocytes that are derived from different cell lineages, beige adipocytes have also been identified, which have characteristics of both white and brown adipocytes. Here we show that 3T3-L1 adipocytes display features of multiple adipocytes lineages. While the gene expression profile and basal bioenergetics of 3T3-L1 adipocytes was typical of white adipocytes, they responded acutely to catecholamines by increasing oxygen consumption in an UCP1-dependent manner, and by increasing the expression of genes enriched in brown but not beige adipocytes. Chronic exposure to catecholamines exacerbated this phenotype. However, a beige adipocyte differentiation procedure did not induce a beige adipocyte phenotype in 3T3-L1 fibroblasts. These multiple lineage features should be considered when interpreting data from experiments utilizing 3T3-L1 adipocytes.

Fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images via a learning-based method

In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM) to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI) for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1) 3D T2-weighted spine MR images from 23 patients and 2) 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5), we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively.

Stable feature selection with support vector machines

The support vector machine (SVM) is a popular method for classification, well known for finding the maximum-margin hyperplane. Combining SVM with l1-norm penalty further enables it to simultaneously perform feature selection and margin maximization within a single framework. However, l1-norm SVM shows instability in selecting features in presence of correlated features. We propose a new method to increase the stability of l1-norm SVM by encouraging similarities between feature weights based on feature correlations, which is captured via a feature covariance matrix. Our proposed method can capture both positive and negative correlations between features. We formulate the model as a convex optimization problem and propose a solution based on alternating minimization. Using both synthetic and real-world datasets, we show that our model achieves better stability and classification accuracy compared to several state-of-the-art regularized classification methods.

Stabilizing l1-norm prediction models by supervised feature grouping

Emerging Electronic Medical Records (EMRs) have reformed the modern healthcare. These records have great potential to be used for building clinical prediction models. However, a problem in using them is their high dimensionality. Since a lot of information may not be relevant for prediction, the underlying complexity of the prediction models may not be high. A popular way to deal with this problem is to employ feature selection. Lasso and l1-norm based feature selection methods have shown promising results. But, in presence of correlated features, these methods select features that change considerably with small changes in data. This prevents clinicians to obtain a stable feature set, which is crucial for clinical decision making. Grouping correlated variables together can improve the stability of feature selection, however, such grouping is usually not known and needs to be estimated for optimal performance. Addressing this problem, we propose a new model that can simultaneously learn the grouping of correlated features and perform stable feature selection. We formulate the model as a constrained optimization problem and provide an efficient solution with guaranteed convergence. Our experiments with both synthetic and real-world datasets show that the proposed model is significantly more stable than Lasso and many existing state-of-the-art shrinkage and classification methods. We further show that in terms of prediction performance, the proposed method consistently outperforms Lasso and other baselines. Our model can be used for selecting stable risk factors for a variety of healthcare problems, so it can assist clinicians toward accurate decision making.

Application of ionospheric corrections in the equatorial region for L1 GPS users

In the absence of the selective availability, which was turned off on May 1, 2000, the ionosphere can be the largest source of error in GPS positioning and navigation. Its effects on GPS observable cause a code delays and phase advances. The magnitude of this error is affected by the local time of the day, season, solar cycle, geographical location of the receiver and Earth's magnetic field. As it is well known, the ionosphere is the main drawback for high accuracy positioning, when using single frequency receivers, either for point positioning or relative positioning of medium and long baselines. The ionosphere effects were investigated in the determination of point positioning and relative positioning using single frequency data. A model represented by a Fourier series type was implemented and the parameters were estimated from data collected at the active stations of RBMC (Brazilian Network for Continuous Monitoring of GPS satellites). The data input were the pseudorange observables filtered by the carrier phase. Quality control was implemented in order to analyse the adjustment and to validate the significance of the estimated parameters. Experiments were carried out in the equatorial region, using data collected from dual frequency receivers. In order to validate the model, the estimated values were compared with ground truth. For point and relative positioning of baselines of approximately 100 km, the values of the discrepancies indicated an error reduction better than 80% and 50% respectively, compared to the processing without the ionospheric model. These results give an indication that more research has to be done in order to provide support to the L1 GPS users in the Equatorial region.

Avaliação de estratégias de detecção e correção de perdas de ciclos na portadora GPS L1

To prevent large errors in the GPS positioning, cycle slips should be detected and corrected. Such procedure is not trivial, mainly for single frequency receivers, but normally it is not noticed by the users. Thus, it will be discussed some practical and more used methods for cycle slips detection and correction using just GPS single-frequency observations. In the detection, the triple (TD) and tetra differences were used. In relation to the correction, in general, each slip is corrected in the preprocessing. Otherwise, other strategies should be adopted during the processing. In this paper, the option was to the second option, and two strategies were tested. In one of them, the elements of the covariance matrix of the involved ambiguities are modified and new ambiguity estimation starts. In the one, a new ambiguity is introduced as additional unknown when a cycle slip is detected. These possibilities are discussed and compared in this paper, as well as the aspects related to the practicity, implementation and viability of each one. Some experiments were carried out using simulated data with cycle slips in different satellites and epochs of the data. This allowed assessing and comparing the results of different occurrence of cycle slip and correction in several conditions.

On the periodic orbits and the integrability of the regularized Hill lunar problem

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Temperature dependence and magnetocrystalline anisotropy studies of self-assembled L1(0)-Fe55Pt45 ferromagnetic nanocrystals

Temperature dependence and uniaxial magnetocrystalline anisotropy properties of the chemically synthesized 4 nm L1(0)-Fe55Pt45 nanoparticle assembly by a modified polyol route are reported. As-prepared nanoparticles are superparamagnetic presenting fcc structure, and annealing at 550 degrees C converts the assembly into ferromagnetic nanocrystals with large coercivity (H-C>1 T) in an L1(0) phase. Magnetic measurements showed an increasing in the ferromagnetically ordered fraction of the nanoparticles with the annealing temperature increases, and the remanence ratio, S=M-R/M-S congruent to 0.76, suggests an (111) textured film. A monotonic increase of the blocking temperature T-B, the uniaxial magnetocrystalline anisotropy constant K-U, and the coercivity H-C with increasing annealing temperature was observed. Magnetic parameters indicate an enhancement in the magnetic properties due to the improved Fe55Pt45 phase stabilizing, and the room-temperature stability parameter of 67, which indicates that the magnetization should be stable for more than ten years, makes this material suitable for ultrahigh-density magnetic recording application.(c) 2007 American Institute of Physics.

Numerical study about natural escape and capture routes by the Moon via Lagrangian points L1 and L2

The lunar sphere of influence, whose radius is some 66,300 km, has regions of stable orbits around the Moon and also regions that contain trajectories which, after spending some time around the Moon, escape and are later recaptured by lunar gravity. Both the escape and the capture occur along the Lagrangian equilibrium points L1 and L2. In this study, we mapped out the region of lunar influence considering the restricted three-body Earth-Moon-particle problem and the four-body Sun-Earth-Moon-particle (probe) problem. We identified the stable trajectories, and the escape and capture trajectories through the L I and L2 in plots of the eccentricity versus the semi-major axis as a function of the time that the energy of the osculating lunar trajectory in the two-body Moon-particle problem remains negative. We also investigated the properties of these routes, giving special attention to the fact that they supply a natural mechanism for performing low-energy transfers between the Earth and the Moon, and can thus be useful on a great number of future missions. (C) 2007 Published by Elsevier Ltd on behalf of COSPAR.

Self-assembled FePt nanocrystals with large coercivity: Reduction of the fcc-to-L1(0) ordering temperature

Herein we report the synthesis and properties of Fe55Pt45 nanoparticles, both monodisperse and self-assembled into hexagonal close-packed and cubic arrays of 4.0 +/- 0.2 nm size in an L1(0) structure, obtained by a modified polyol process. The new synthetic route improved the control over the particle composition, thereby reducing the temperature required to convert from face-centered cubic (fcc) to face-centered tetragonal (fct) phase by some 30-50 degrees C without additives. Annealing at 550 degrees C for 30 min converts the self-assembled nanoparticles into ferromagnetic nanocrystals with large coercivity, H-C = 11.1 kOe. Reducing the fcc-to-fct (L1(0)) ordering temperature avoided particle coalescence and decreased the loss in particle positional order without compromising the magnetic properties, as is generally observed when additives are used.