Adipose tissue gene expression of factors related to lipid processing in obesity.

BACKGROUND Adipose tissue lipid storage and processing capacity can be a key factor for obesity-related metabolic disorders such as insulin resistance and diabetes. Lipid uptake is the first step to adipose tissue lipid storage. The aim of this study was to analyze the gene expression of factors involved in lipid uptake and processing in subcutaneous (SAT) and visceral (VAT) adipose tissue according to body mass index (BMI) and the degree of insulin resistance (IR). METHODS AND PRINCIPAL FINDINGS VLDL receptor (VLDLR), lipoprotein lipase (LPL), acylation stimulating protein (ASP), LDL receptor-related protein 1 (LRP1) and fatty acid binding protein 4 (FABP4) gene expression was measured in VAT and SAT from 28 morbidly obese patients with Type 2 Diabetes Mellitus (T2DM) or high IR, 10 morbidly obese patients with low IR, 10 obese patients with low IR and 12 lean healthy controls. LPL, FABP4, LRP1 and ASP expression in VAT was higher in lean controls. In SAT, LPL and FABP4 expression were also higher in lean controls. BMI, plasma insulin levels and HOMA-IR correlated negatively with LPL expression in both VAT and SAT as well as with FABP4 expression in VAT. FABP4 gene expression in SAT correlated inversely with BMI and HOMA-IR. However, multiple regression analysis showed that BMI was the main variable contributing to LPL and FABP4 gene expression in both VAT and SAT. CONCLUSIONS Morbidly obese patients have a lower gene expression of factors related with lipid uptake and processing in comparison with healthy lean persons.

Footprints of a trypanosomatid RNA world: pre-small subunit rRNA processing by spliced leader addition trans-splicing

The addition of a capped mini-exon [spliced leader (SL)] through trans-splicing is essential for the maturation of RNA polymerase (pol) II-transcribed polycistronic pre-mRNAs in all members of the Trypanosomatidae family. This process is an inter-molecular splicing reaction that follows the same basic rules of cis-splicing reactions. In this study, we demonstrated that mini-exons were added to precursor ribosomal RNA (pre-rRNA) are transcribed by RNA pol I, including the 5' external transcribed spacer (ETS) region. Additionally, we detected the SL-5'ETS molecule using three distinct methods and located the acceptor site between two known 5'ETS rRNA processing sites (A' and A1) in four different trypanosomatids. Moreover, we detected a polyadenylated 5'ETS upstream of the trans-splicing acceptor site, which also occurs in pre-mRNA trans-splicing. After treatment with an indirect trans-splicing inhibitor (sinefungin), we observed SL-5'ETS decay. However, treatment with 5-fluorouracil (a precursor of RNA synthesis that inhibits the degradation of pre-rRNA) led to the accumulation of SL-5'ETS, suggesting that the molecule may play a role in rRNA degradation. The detection of trans-splicing in these molecules may indicate broad RNA-joining properties, regardless of the polymerase used for transcription.

The connectome mapper: an open-source processing pipeline to map connectomes with MRI.

Researchers working in the field of global connectivity analysis using diffusion magnetic resonance imaging (MRI) can count on a wide selection of software packages for processing their data, with methods ranging from the reconstruction of the local intra-voxel axonal structure to the estimation of the trajectories of the underlying fibre tracts. However, each package is generally task-specific and uses its own conventions and file formats. In this article we present the Connectome Mapper, a software pipeline aimed at helping researchers through the tedious process of organising, processing and analysing diffusion MRI data to perform global brain connectivity analyses. Our pipeline is written in Python and is freely available as open-source at www.cmtk.org.

Evaluation of four molecular methods for the diagnosis of tuberculosis in pulmonary and blood samples from immunocompromised patients

The present study analysed the concordance among four different molecular diagnostic methods for tuberculosis (TB) in pulmonary and blood samples from immunocompromised patients. A total of 165 blood and 194 sputum samples were collected from 181 human immunodeficiency virus (HIV)-infected patients with upper respiratory complaints, regardless of suspicious for TB. The samples were submitted for smear microscopy, culture and molecular tests: a laboratory-developed conventional polymerase chain reaction (PCR) and real-time quantitative PCR (qPCR) and the Gen-Probe and Detect-TB Ampligenix kits. The samples were handled blindly by all the technicians involved, from sample processing to results analysis. For sputum, the sensitivity and specificity were 100% and 96.7% for qPCR, 81.8% and 94.5% for Gen-Probe and 100% and 66.3% for Detect-TB, respectively. qPCR presented the best concordance with sputum culture [kappa (k) = 0.864)], followed by Gen-Probe (k = 0.682). For blood samples, qPCR showed 100% sensitivity and 92.3% specificity, with a substantial correlation with sputum culture (k = 0.754) and with the qPCR results obtained from sputum of the corresponding patient (k = 0.630). Conventional PCR demonstrated the worst results for sputa and blood, with a sensitivity of 100% vs. 88.9% and a specificity of 46.3% vs. 32%, respectively. Commercial or laboratory-developed molecular assays can overcome the difficulties in the diagnosis of TB in paucibacillary patients using conventional methods available in most laboratories.

Advances in Sequence Analysis: Theory, Methods, Applications

This book gives a general view of sequence analysis, the statistical study of successions of states or events. It includes innovative contributions on life course studies, transitions into and out of employment, contemporaneous and historical careers, and political trajectories. The approach presented in this book is now central to the life-course perspective and the study of social processes more generally. This volume promotes the dialogue between approaches to sequence analysis that developed separately, within traditions contrasted in space and disciplines. It includes the latest developments in sequential concepts, coding, atypical datasets and time patterns, optimal matching and alternative algorithms, survey optimization, and visualization. Field studies include original sequential material related to parenting in 19th-century Belgium, higher education and work in Finland and Italy, family formation before and after German reunification, French Jews persecuted in occupied France, long-term trends in electoral participation, and regime democratization. Overall the book reassesses the classical uses of sequences and it promotes new ways of collecting, formatting, representing and processing them. The introduction provides basic sequential concepts and tools, as well as a history of the method. Chapters are presented in a way that is both accessible to the beginner and informative to the expert.

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

Processing of tumor-associated antigen by the proteasomes of dendritic cells controls in vivo T-cell responses.

Dendritic cells are unique in their capacity to process antigens and prime naive CD8(+) T cells. Contrary to most cells, which express the standard proteasomes, dendritic cells express immunoproteasomes constitutively. The melanoma-associated protein Melan-A(MART1) contains an HLA-A2-restricted peptide that is poorly processed by melanoma cells expressing immunoproteasomes in vitro. Here, we show that the expression of Melan-A in dendritic cells fails to elicit T-cell responses in vitro and in vivo because it is not processed by the proteasomes of dendritic cells. In contrast, dendritic cells lacking immunoproteasomes induce strong anti-Melan-A T-cell responses in vitro and in vivo. These results suggest that the inefficient processing of self-antigens, such as Melan-A, by the immunoproteasomes of professional antigen-presenting cells prevents the induction of antitumor T-cell responses in vivo.

Auditory motion affects visual biological motion processing.

The processing of biological motion is a critical, everyday task performed with remarkable efficiency by human sensory systems. Interest in this ability has focused to a large extent on biological motion processing in the visual modality (see, for example, Cutting, J. E., Moore, C., & Morrison, R. (1988). Masking the motions of human gait. Perception and Psychophysics, 44(4), 339-347). In naturalistic settings, however, it is often the case that biological motion is defined by input to more than one sensory modality. For this reason, here in a series of experiments we investigate behavioural correlates of multisensory, in particular audiovisual, integration in the processing of biological motion cues. More specifically, using a new psychophysical paradigm we investigate the effect of suprathreshold auditory motion on perceptions of visually defined biological motion. Unlike data from previous studies investigating audiovisual integration in linear motion processing [Meyer, G. F. & Wuerger, S. M. (2001). Cross-modal integration of auditory and visual motion signals. Neuroreport, 12(11), 2557-2560; Wuerger, S. M., Hofbauer, M., & Meyer, G. F. (2003). The integration of auditory and motion signals at threshold. Perception and Psychophysics, 65(8), 1188-1196; Alais, D. & Burr, D. (2004). No direction-specific bimodal facilitation for audiovisual motion detection. Cognitive Brain Research, 19, 185-194], we report the existence of direction-selective effects: relative to control (stationary) auditory conditions, auditory motion in the same direction as the visually defined biological motion target increased its detectability, whereas auditory motion in the opposite direction had the inverse effect. Our data suggest these effects do not arise through general shifts in visuo-spatial attention, but instead are a consequence of motion-sensitive, direction-tuned integration mechanisms that are, if not unique to biological visual motion, at least not common to all types of visual motion. Based on these data and evidence from neurophysiological and neuroimaging studies we discuss the neural mechanisms likely to underlie this effect.

The role of awareness, information gathering and processing in school choice

This paper studies the determinants of school choice, focusing on the role of information. Weconsider how parents' search efforts and their capacity to process information (i.e., tocorrectly assess schools) affect the quality of the schools they choose for their children. Usinga novel dataset, we are able to identify parents' awareness of schools in their neighborhoodand measure their capacity to rank the quality of the school with respect to the officialrankings. We find that parents education and wealth are important factors in determiningtheir level of school awareness and information gathering. Moreover, these search effortshave important consequences in terms of the quality of school choice.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

Comparison of Energy Minimization Methods for 3-D Brain Tissue Classification

This paper presents 3-D brain tissue classificationschemes using three recent promising energy minimizationmethods for Markov random fields: graph cuts, loopybelief propagation and tree-reweighted message passing.The classification is performed using the well knownfinite Gaussian mixture Markov Random Field model.Results from the above methods are compared with widelyused iterative conditional modes algorithm. Theevaluation is performed on a dataset containing simulatedT1-weighted MR brain volumes with varying noise andintensity non-uniformities. The comparisons are performedin terms of energies as well as based on ground truthsegmentations, using various quantitative metrics.

Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods

Usual image fusion methods inject features from a high spatial resolution panchromatic sensor into every low spatial resolution multispectral band trying to preserve spectral signatures and improve spatial resolution to that of the panchromatic sensor. The objective is to obtain the image that would be observed by a sensor with the same spectral response (i.e., spectral sensitivity and quantum efficiency) as the multispectral sensors and the spatial resolution of the panchromatic sensor. But in these methods, features from electromagnetic spectrum regions not covered by multispectral sensors are injected into them, and physical spectral responses of the sensors are not considered during this process. This produces some undesirable effects, such as resolution overinjection images and slightly modified spectral signatures in some features. The authors present a technique which takes into account the physical electromagnetic spectrum responses of sensors during the fusion process, which produces images closer to the image obtained by the ideal sensor than those obtained by usual wavelet-based image fusion methods. This technique is used to define a new wavelet-based image fusion method.

Integration of ground-penetrating radar and microgravimetric methods to map shallow caves

Ground-penetrating radar (GPR) and microgravimetric surveys have been conducted in the southern Jura mountains of western Switzerland in order to map subsurface karstic features. The study site, La Grande Rolaz cave, is an extensive system in which many portions have been mapped. By using small station spacing and careful processing for the geophysical data, and by modeling these data with topographic information from within the cave, accurate interpretations have been achieved. The constraints on the interpreted geologic models are better when combining the geophysical methods than when using only one of the methods, despite the general limitations of two-dimensional (2D) profiling. For example, microgravimetry can complement GPR methods for accurately delineating a shallow cave section approximately 10 X 10 mt in size. Conversely, GPR methods can be complementary in determining cavity depths and in verifying the presence of off-line features and numerous areas of small cavities and fractures, which may be difficult to resolve in microgravimetric data.

Respiration, metabolic balance, and attention in affective picture processing

The respiratory behavior during affective states is not completely understood. We studied breathing pattern responses to picture series in 37 participants. We also measured end-tidal pCO2 (EtCO2) to determine if ventilation is in balance with metabolic demands and spontaneous eye-blinking to investigate the link between respiration and attention. Minute ventilation (MV) and inspiratory drive increased with self-rated arousal. These relationships reflected increases in inspiratory volume rather than shortening of the time parameters. EtCO2 covaried with pleasantness but not arousal. Eye-blink rate decreased with increasing unpleasantness in line with a negativity bias in attention. This study confirms that respiratory responses to affective stimuli are organized to a certain degree along the dimensions of valence and arousal. It shows, for the first time, that during picture viewing, ventilatory increases with increasing arousal are in balance with metabolic activity and that inspiratory volume is modulated by arousal. MV emerges as the most reliable respiratory index of self-perceived arousal