Measuring and filtering reactive inhibition is essential for assessing serial decision making and learning

Learning complex structures from stimuli requires extended exposure and often repeated observation of the same stimuli. Learning induces stimulus-dependent changes in specific performance measures. The same performance measures, however, can also be affected by processes that arise due to extended training (e.g. fatigue) but are otherwise independent from learning. Thus, a thorough assessment of the properties of learning can only be achieved by identifying and accounting for the effects of such processes. Reactive inhibition is a process that modulates behavioral performance measures on a wide range of time scales and often has opposite effects than learning. Here we develop a tool to disentangle the effects of reactive inhibition from learning in the context of an implicit learning task, the alternating serial reaction time task. Our method highlights that the magnitude of the effect of reactive inhibition on measured performance is larger than that of the acquisition of statistical structure from stimuli. We show that the effect of reactive inhibition can be identified not only in population measures but also at the level of performance of individuals, revealing varying degrees of contribution of reactive inhibition. Finally, we demonstrate that a higher proportion of behavioral variance can be explained by learning once the effects of reactive inhibition are eliminated. These results demonstrate that reactive inhibition has a fundamental effect on the behavioral performance that can be identified in individual participants and can be separated from other cognitive processes like learning

Tracking the contribution of inductive bias to individualised internal models

Internal models capture the regularities of the environment and are central to understanding how humans adapt to environmental statistics. In general, the correct internal model is unknown to observers, instead they rely on an approximate model that is continually adapted throughout learning. However, experimenters assume an ideal observer model, which captures stimulus structure but ignores the diverging hypotheses that humans form during learning. We combine non-parametric Bayesian methods and probabilistic programming to infer rich and dynamic individualised internal models from response times. We demonstrate that the approach is capable of characterizing the discrepancy between the internal model maintained by individuals and the ideal observer model and to track the evolution of the contribution of the ideal observer model to the internal model throughout training. In particular, in an implicit visuomotor sequence learning task the identified discrepancy revealed an inductive bias that was consistent across individuals but varied in strength and persistence

PPARγ controls CD1d expression by turning on retinoic acid synthesis in developing human dendritic cells

Dendritic cells (DCs) expressing CD1d, a molecule responsible for lipid antigen presentation, are capable of enhancing natural killer T (iNKT) cell proliferation. The signals controlling CD1 expression and lipid antigen presentation are poorly defined. We have shown previously that stimulation of the lipid-activated transcription factor, peroxisome proliferator-activated receptor (PPAR)γ, indirectly regulates CD1d expression. Here we demonstrate that PPARγ, turns on retinoic acid synthesis by inducing the expression of retinol and retinal metabolizing enzymes such as retinol dehydrogenase 10 and retinaldehyde dehydrogenase type 2 (RALDH2). PPARγ-regulated expression of these enzymes leads to an increase in the intracellular generation of all-trans retinoic acid (ATRA) from retinol. ATRA regulates gene expression via the activation of the retinoic acid receptor (RAR)α in human DCs, and RARα acutely regulates CD1d expression. The retinoic acid–induced elevated expression of CD1d is coupled to enhanced iNKT cell activation. Furthermore, in vivo relevant lipids such as oxidized low-density lipoprotein can also elicit retinoid signaling leading to CD1d up-regulation. These data show that regulation of retinoid metabolism and signaling is part of the PPARγ-controlled transcriptional events in DCs. The uncovered mechanisms allow the DCs to respond to altered lipid homeostasis by changing CD1 gene expression

Expression of Somatostatin Receptor Subtypes (SSTR-1–SSTR-5) in Pediatric Hematological and Oncological Disorders

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PRMT1 and PRMT8 regulate retinoic acid-dependent neuronal differentiation with implications to neuropathology.

Retinoids are morphogens and have been implicated in cell fate commitment of embryonic stem cells (ESCs) to neurons. Their effects are mediated by RAR and RXR nuclear receptors. However, transcriptional cofactors required for cell and gene-specific retinoid signaling are not known. Here we show that protein arginine methyl transferase (PRMT) 1 and 8 have key roles in determining retinoid regulated gene expression and cellular specification in a multistage neuronal differentiation model of murine ESCs. PRMT1 acts as a selective modulator, providing the cells with a mechanism to reduce the potency of retinoid signals on regulatory "hotspots." PRMT8 is a retinoid receptor target gene itself and acts as a cell type specific transcriptional coactivator of retinoid signaling at later stages of differentiation. Lack of either of them leads to reduced nuclear arginine methylation, dysregulated neuronal gene expression, and altered neuronal activity. Importantly, depletion of PRMT8 results in altered expression of a distinct set of genes, including markers of gliomagenesis. PRMT8 is almost entirely absent in human glioblastoma tissues. We propose that PRMT1 and PRMT8 serve as a rheostat of retinoid signaling to determine neuronal cell specification in a context-dependent manner and might also be relevant in the development of human brain malignancy

Carboxypeptidase-M is regulated by lipids and CSFs in macrophages and dendritic cells and expressed selectively in tissue granulomas and foam cells

Granulomatous inflammations, characterized by the presence of activated macrophages (MAs) forming epithelioid cell (EPC) clusters, are usually easy to recognize. However, in ambiguous cases the use of a MA marker that expresses selectively in EPCs may be needed. Here, we report that carboxypeptidase-M (CPM), a MA-differentiation marker, is preferentially induced in EPCs of all granuloma types studied, but not in resting MAs. As CPM is not expressed constitutively in MAs, this allows utilization of CPM-immunohistochemistry in diagnostics of minute granuloma detection when dense non-granulomatous MAs are also present. Despite this rule, hardly any detectable CPM was found in advanced/active tubercle caseous disease, albeit in early tuberculosis granuloma, MAs still expressed CPM. Indeed, in vitro both the CPM-protein and -mRNA became downregulated when MAs were infected with live mycobacteria. In vitro, MA-CPM transcript is neither induced remarkably by interferon-γ, known to cause classical MA activation, nor by IL-4, an alternative MA activator. Instead, CPM is selectively expressed in lipid-laden MAs, including the foam cells of atherosclerotic plaques, xanthomatous lesions and lipid pneumonias. By using serum, rich in lipids, and low-density lipoprotein (LDL) or VLDL, CPM upregulation could be reproduced in vitro in monocyte-derived MAs both at transcriptional and protein levels, and the increase is repressed under lipid-depleted conditions. The microarray analyses support the notion that CPM induction correlates with a robust progressive increase in CPM gene expression during monocyte to MA maturation and dendritic cell (DC) differentiation mediated by granulocyte–MA-colony-stimulating factor+IL-4. M-CSF alone also induced CPM. These results collectively indicate that CPM upregulation in MAs is preferentially associated with increased lipid uptake, and exposure to CSF, features of EPCs, also. Therefore, CPM-immunohistochemistry is useful for granuloma and foam MA detections in tissue sections. Furthermore, the present data offer CPM for the first time to be a novel marker and cellular player in lipid uptake and/or metabolism of MAs by promoting foam cell formation

Analyses of association between PPAR gamma and EPHX1 polymorphisms and susceptibility to COPD in a Hungarian cohort, a case-control study

<p>Abstract</p> <p>Background</p> <p>In addition to smoking, genetic predisposition is believed to play a major role in the pathogenesis of chronic obstructive pulmonary disease (COPD). Genetic association studies of new candidate genes in COPD may lead to improved understanding of the pathogenesis of the disease.</p> <p>Methods</p> <p>Two proposed casual single nucleotide polymorphisms (SNP) <it>(rs1051740, rs2234922) </it>in microsomal epoxide hydrolase (<it>EPHX1</it>) and three SNPs <it>(rs1801282, rs1800571, rs3856806) </it>in peroxisome proliferator-activated receptor gamma (<it>PPARG</it>), a new candidate gene, were genotyped in a case-control study (272 COPD patients and 301 controls subjects) in Hungary. Allele frequencies and genotype distributions were compared between the two cohorts and trend test was also used to evaluate association between SNPs and COPD. To estimate the strength of association, odds ratios (OR) (with 95% CI) were calculated and potential confounding variables were tested in logistic regression analysis. Association between haplotypes and COPD outcome was also assessed.</p> <p>Results</p> <p>The distribution of imputed <it>EPHX1 </it>phenotypes was significantly different between the COPD and the control group (P = 0.041), OR for the slow activity phenotype was 1.639 (95% CI = 1.08- 2.49; P = 0.021) in our study. In logistic regression analysis adjusted for both variants, also age and pack-year, the rare allele of His447His of <it>PPARG </it>showed significant association with COPD outcome (OR = 1.853, 95% CI = 1.09-3.14, P = 0.0218). In haplotype analysis the GC haplotype of <it>PPARG </it>(OR = 0.512, 95% CI = 0.27-0.96, P = 0.035) conferred reduced risk for COPD.</p> <p>Conclusions</p> <p>The "slow" activity-associated genotypes of <it>EPHX1 </it>were associated with increased risk of COPD. The minor His447His allele of <it>PPARG </it>significantly increased; and the haplotype containing the minor Pro12Ala and the major His447His polymorphisms of <it>PPARG </it>decreased the risk of COPD.</p

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Penilaian Kinerja Keuangan Koperasi di Kabupaten Pelalawan

This paper describe development and financial performance of cooperative in District Pelalawan among 2007 - 2008. Studies on primary and secondary cooperative in 12 sub-districts. Method in this stady use performance measuring of productivity, efficiency, growth, liquidity, and solvability of cooperative. Productivity of cooperative in Pelalawan was highly but efficiency still low. Profit and income were highly, even liquidity of cooperative very high, and solvability was good