Visual-gustatory interaction: orbitofrontal and insular cortices mediate the effect of high-calorie visual food cues on taste pleasantness.

Vision provides a primary sensory input for food perception. It raises expectations on taste and nutritional value and drives acceptance or rejection. So far, the impact of visual food cues varying in energy content on subsequent taste integration remains unexplored. Using electrical neuroimaging, we assessed whether high- and low-calorie food cues differentially influence the brain processing and perception of a subsequent neutral electric taste. When viewing high-calorie food images, participants reported the subsequent taste to be more pleasant than when low-calorie food images preceded the identical taste. Moreover, the taste-evoked neural activity was stronger in the bilateral insula and the adjacent frontal operculum (FOP) within 100 ms after taste onset when preceded by high- versus low-calorie cues. A similar pattern evolved in the anterior cingulate (ACC) and medial orbitofrontal cortex (OFC) around 180 ms, as well as, in the right insula, around 360 ms. The activation differences in the OFC correlated positively with changes in taste pleasantness, a finding that is an accord with the role of the OFC in the hedonic evaluation of taste. Later activation differences in the right insula likely indicate revaluation of interoceptive taste awareness. Our findings reveal previously unknown mechanisms of cross-modal, visual-gustatory, sensory interactions underlying food evaluation.

Mutations in the DNA-binding domain of NR2E3 affect in vivo dimerization and interaction with CRX.

BACKGROUND: NR2E3 (PNR) is an orphan nuclear receptor essential for proper photoreceptor determination and differentiation. In humans, mutations in NR2E3 have been associated with the recessively inherited enhanced short wavelength sensitive (S-) cone syndrome (ESCS) and, more recently, with autosomal dominant retinitis pigmentosa (adRP). NR2E3 acts as a suppressor of the cone generation program in late mitotic retinal progenitor cells. In adult rod photoreceptors, NR2E3 represses cone-specific gene expression and acts in concert with the transcription factors CRX and NRL to activate rod-specific genes. NR2E3 and CRX have been shown to physically interact in vitro through their respective DNA-binding domains (DBD). The DBD also contributes to homo- and heterodimerization of nuclear receptors. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed NR2E3 homodimerization and NR2E3/CRX complex formation in an in vivo situation by Bioluminescence Resonance Energy Transfer (BRET(2)). NR2E3 wild-type protein formed homodimers in transiently transfected HEK293T cells. NR2E3 homodimerization was impaired in presence of disease-causing mutations in the DBD, except for the p.R76Q and p.R104W mutant proteins. Strikingly, the adRP-linked p.G56R mutant protein interacted with CRX with a similar efficiency to that of NR2E3 wild-type and p.R311Q proteins. In contrast, all other NR2E3 DBD-mutant proteins did not interact with CRX. The p.G56R mutant protein was also more effective in abolishing the potentiation of rhodospin gene transactivation by the NR2E3 wild-type protein. In addition, the p.G56R mutant enhanced the transrepression of the M- and S-opsin promoter, while all other NR2E3 DBD-mutants did not. CONCLUSIONS/SIGNIFICANCE: These results suggest different disease mechanisms in adRP- and ESCS-patients carrying NR2E3 mutations. Titration of CRX by the p.G56R mutant protein acting as a repressor in trans may account for the severe clinical phenotype in adRP patients.

Regions of mouse mammary tumor virus superantigen involved in interaction with the major histocompatibility complex class II I-A molecule.

To study the major histocompatibility complex class II I-E dependence of mouse mammary tumor virus (MMTV) superantigens, we constructed hybrids between the I-E-dependent MMTV(GR) and the I-E-independent mtv-7 superantigens and tested them in vivo. Our results suggest that, although the C-terminal third mediates I-A interaction, additional binding sites are located elsewhere in the superantigen.

Disrupting the EMMPRIN (CD147)-cyclophilin A interaction reduces infarct size and preserves systolic function after myocardial ischemia and reperfusion.

Objective-Inflammation and proteolysis crucially contribute to myocardial ischemia and reperfusion injury. The extracellular matrix metalloproteinase inducer EMMPRIN (CD147) and its ligand cyclophilin A (CyPA) may be involved in both processes. The aim of the study was to characterize the role of the CD147 and CyPA interplay in myocardial ischemia/reperfusion (I/R) injury.Methods and Results-Immunohistochemistry showed enhanced expression of CD147 and CyPA in myocardial sections from human autopsies of patients who had died from acute myocardial infarction and from mice at 24 hours after I/R. At 24 hours and 7 days after I/R, the infarct size was reduced in CD147(+/-) mice vs CD147(+/+) mice (C57Bl/6), in mice (C57Bl/6) treated with monoclonal antibody anti-CD147 vs control monoclonal antibody, and in CyPA(-/-) mice vs CyPA(+/+) mice (129S6/SvEv), all of which are associated with reduced monocyte and neutrophil recruitment at 24 hours and with a preserved systolic function at 7 days. The combination of CyPA(-/-) mice with anti-CD147 treatment did not yield further protection compared with either inhibition strategy alone. In vitro, treatment with CyPA induced monocyte chemotaxis in a CD147-and phosphatidylinositol 3-kinase-dependent manner and induced monocyte rolling and adhesion to endothelium (human umbilical vein endothelial cells) under flow in a CD147-dependent manner.Conclusion-CD147 and its ligand CyPA are inflammatory mediators after myocardial ischemia and reperfusion and represent potential targets to prevent myocardial I/R injury.

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

Receptor binding and cell entry of Old World arenaviruses reveal novel aspects of virus-host interaction.

Ten years ago, the first cellular receptor for the prototypic arenavirus lymphocytic choriomeningitis virus (LCMV) and the highly pathogenic Lassa virus (LASV) was identified as alpha-dystroglycan (alpha-DG), a versatile receptor for proteins of the extracellular matrix (ECM). Biochemical analysis of the interaction of alpha-DG with arenaviruses and ECM proteins revealed a strikingly similar mechanism of receptor recognition that critically depends on specific sugar modification on alpha-DG involving a novel class of putative glycosyltransferase, the LARGE proteins. Interestingly, recent genome-wide detection and characterization of positive selection in human populations revealed evidence for positive selection of a locus within the LARGE gene in populations from Western Africa, where LASV is endemic. While most enveloped viruses that enter the host cell in a pH-dependent manner use clathrin-mediated endocytosis, recent studies revealed that the Old World arenaviruses LCMV and LASV enter the host cell predominantly via a novel and unusual endocytotic pathway independent of clathrin, caveolin, dynamin, and actin. Upon internalization, the virus is rapidly delivered to endosomes via an unusual route of vesicular trafficking that is largely independent of the small GTPases Rab5 and Rab7. Since infection of cells with LCMV and LASV depends on DG, this unusual endocytotic pathway could be related to normal cellular trafficking of the DG complex. Alternatively, engagement of arenavirus particles may target DG for an endocytotic pathway not normally used in uninfected cells thereby inducing an entry route specifically tailored to the pathogen's needs.

Advances in Near-surface Seismology and Ground-penetrating Radar

Advances in Near-surface Seismology and Ground-penetrating Radar (SEG Geophysical Developments Series No. 15) is a collection of original papers by renowned and respected authors from around the world. Technologies used in the application of near-surface seismology and ground-penetrating radar have seen significant advances in the last several years. Both methods have benefited from new processing tools, increased computer speeds, and an expanded variety of applications. This book, divided into four sections ? ?Reviews,? ?Methodology,? ?Integrative Approaches,? and ?Case Studies? ? captures the most significant cutting-edge issues in active areas of research, unveiling truly pertinent studies that address fundamental applied problems. This collection of manuscripts grew from a core group of papers presented at a postconvention workshop, ?Advances in Near-surface Seismology and Ground-penetrating Radar,? held during the 2009 SEG Annual Meeting in Houston, Texas. This is the first cooperative publication effort between the near-surface communities of SEG, AGU, and EEGS. It will appeal to a large and diverse audience that includes researchers and practitioners inside and outside the near-surface geophysics community.

Iowa General Assembly Committee Meeting Rooms Ground Floor, 2013

I map of all committee meeting room during the legislative session for 2013.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

An interaction network of the mammalian COP9 signalosome identifies Dda1 as a core subunit of multiple Cul4-based E3 ligases.

The COP9 signalosome (CSN) is an evolutionarily conserved macromolecular complex that interacts with cullin-RING E3 ligases (CRLs) and regulates their activity by hydrolyzing cullin-Nedd8 conjugates. The CSN sequesters inactive CRL4(Ddb2), which rapidly dissociates from the CSN upon DNA damage. Here we systematically define the protein interaction network of the mammalian CSN through mass spectrometric interrogation of the CSN subunits Csn1, Csn3, Csn4, Csn5, Csn6 and Csn7a. Notably, we identified a subset of CRL complexes that stably interact with the CSN and thus might similarly be activated by dissociation from the CSN in response to specific cues. In addition, we detected several new proteins in the CRL-CSN interactome, including Dda1, which we characterized as a chromatin-associated core subunit of multiple CRL4 proteins. Cells depleted of Dda1 spontaneously accumulated double-stranded DNA breaks in a similar way to Cul4A-, Cul4B- or Wdr23-depleted cells, indicating that Dda1 interacts physically and functionally with CRL4 complexes. This analysis identifies new components of the CRL family of E3 ligases and elaborates new connections between the CRL and CSN complexes.

Source-wavelet estimation during full-waveform inversion of ground-penetrating radar data

A major issue in the application of waveform inversion methods to crosshole ground-penetrating radar (GPR) data is the accurate estimation of the source wavelet. Here, we explore the viability and robustness of incorporating this step into a recently published time-domain inversion procedure through an iterative deconvolution approach. Our results indicate that, at least in non-dispersive electrical environments, such an approach provides remarkably accurate and robust estimates of the source wavelet even in the presence of strong heterogeneity of both the dielectric permittivity and electrical conductivity. Our results also indicate that the proposed source wavelet estimation approach is relatively insensitive to ambient noise and to the phase characteristics of the starting wavelet. Finally, there appears to be little to no trade-off between the wavelet estimation and the tomographic imaging procedures.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Restoration of lymphoid organ integrity through the interaction of lymphoid tissue-inducer cells with stroma of the T cell zone.

The generation of lymphoid microenvironments in early life depends on the interaction of lymphoid tissue-inducer cells with stromal lymphoid tissue-organizer cells. Whether this cellular interface stays operational in adult secondary lymphoid organs has remained elusive. We show here that during acute infection with lymphocytic choriomeningitis virus, antiviral cytotoxic T cells destroyed infected T cell zone stromal cells, which led to profound disruption of secondary lymphoid organ integrity. Furthermore, the ability of the host to respond to secondary antigens was lost. Restoration of the lymphoid microanatomy was dependent on the proliferative accumulation of lymphoid tissue-inducer cells in secondary lymphoid organs during the acute phase of infection and lymphotoxin alpha(1)beta(2) signaling. Thus, crosstalk between lymphoid tissue-inducer cells and stromal cells is reactivated in adults to maintain secondary lymphoid organ integrity and thereby contributes to the preservation of immunocompetence.