Higher-order thoughts in action : Consciousness as an unconscious re-description process

Peer reviewedPostprin

Novel Schizophrenia Risk Gene TCF4 Influences Verbal Learning and Memory Functioning in Schizophrenia Patients

Background: Recently, a role of the transcription factor 4 (TCF4) gene in schizophrenia has been reported in a large genome-wide association study. It has been hypothesized that TCF4 affects normal brain development and TCF4 has been related to different forms of neurodevelopmental disorders. Schizophrenia patients exhibit strong impairments of verbal declarative memory (VDM) functions. Thus, we hypothesized that the disease-associated C allele of the rs9960767 polymorphism of the TCF4 gene led to impaired VDM functioning in schizophrenia patients. Method: The TCF4 variant was genotyped in 401 schizophrenia patients. VDM functioning was measured using the Rey Auditory Verbal Learning Test (RAVLT). Results: Carriers of the C allele were less impaired in recognition compared to those carrying the AA genotype (13.76 vs. 13.06; p = 0.049). Moreover, a trend toward higher scores in patients with the risk allele was found for delayed recall (10.24 vs. 9.41; p = 0.088). The TCF4 genotype did not influence intelligence or RAVLT immediate recall or total verbal learning. Conclusion: VDM function is influenced by the TCF4 gene in schizophrenia patients. However, the elevated risk for schizophrenia is not conferred by TCF4-mediated VDM impairment. Copyright (C) 2011 S. Karger AG, Base

The Viability of Trajectory Analysis for Diagnosing Dynamical and Chemical Influences on Ozone Concentrations in the UTLS

The viability of trajectory analysis for diagnosing the interplay between chemistry and dynamics is investigated by comparing ozone mixing ratios modelled using air-parcel pathways to values observed along flight tracks during ATTREX (Airborne Tropical TRopopause EXperiment). Trajectories are initiated at the locations of ozone observations and tracked backward in time to their sources at termini of backward trajectories. The modelled values of ozone utilize 3-dimensional analysis fields from WACCM (Whole Atmosphere Community Climate Model) (a chemical-climate model with dynamical fields nudged towards MERRA (Modern-Era Retrospective Analysis and Research Applications) reanalysis) and ERA-interim (product of ECMWF - the European Centre for Medium-Range Weather Forecasts) to determine source mixing ratios with chemical production and loss terms derived from the ozone chemistry used in WACCM. A statistical base of modelled ozone is constructed with 6 trajectory platforms (adiabatic, diabatic, and kinematic forced by ERA-interim and MERRA), two chemical models (WACCM chemistry and no chemistry), and 4 trajectory lengths (5, 10, 20, and 30 days). Linear regression is employed to separate systematic errors from random errors and to characterize the impact of source mixing ratios, path length, vertical motion, and chemistry on modelled ozone errors. Errors in the analysis ozone fields are large, if not dominant, contributors to model error. Random errors are particularly large for point-by-point comparisons, however averaging over 800 km (75 minutes) flight segments substantially reduces random error and exposes systematic errors. Of the two analysis ozone data sets, WACCM, which incorporates detailed chemistry, provides the smaller systematic errors while ERA-interim, which has crude chemistry but assimilates observational data, has the smaller random errors. Of the different trajectory platforms, adiabatic calculations produce the smaller random errors (irrespective of the use of chemistry) but both vertical motion and chemistry are required to optimally reduce systematic errors. These results suggest that meaningful analysis of dynamical and chemical interactions that control ozone mixing ratios are viable on spatial scales larger than a few reanalysis grid spaces, that errors in the analyzed ozone data sets are large but not prohibitively so, and that vertical velocities and heating rates from reanalysis data, while problematic, contain useful information [on the ozone concentrations in the UTLS (Upper Troposphere/Lower Stratosphere)]

Urine Fetuin-A is a biomarker of autosomal dominant polycystic kidney disease progression.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is a genetic disorder characterized by numerous fluid-filled cysts that frequently result in end-stage renal disease. While promising treatment options are in advanced clinical development, early diagnosis and follow-up remain a major challenge. We therefore evaluated the diagnostic value of Fetuin-A as a new biomarker of ADPKD in human urine. RESULTS: We found that renal Fetuin-A levels are upregulated in both Pkd1 and Bicc1 mouse models of ADPKD. Measurement by ELISA revealed that urinary Fetuin-A levels were significantly higher in 66 ADPKD patients (17.5 ± 12.5 μg/mmol creatinine) compared to 17 healthy volunteers (8.5 ± 3.8 μg/mmol creatinine) or 50 control patients with renal diseases of other causes (6.2 ± 2.9 μg/mmol creatinine). Receiver operating characteristics (ROC) analysis of urinary Fetuin-A levels for ADPKD rendered an optimum cut-off value of 12.2 μg/mmol creatinine, corresponding to 94% of sensitivity and 60% of specificity (area under the curve 0.74 ; p = 0.0019). Furthermore, urinary Fetuin-A levels in ADPKD patients correlated with the degree of renal insufficiency and showed a significant increase in patients with preserved renal function followed for two years. CONCLUSIONS: Our findings establish urinary Fetuin-A as a sensitive biomarker of the progression of ADPKD. Further studies are required to examine the pathogenic mechanisms of elevated renal and urinary Fetuin-A in ADPKD

Elimination Theory for Nonlinear Parameter Estimation

The work presented here exploits elimination theory (solving systems of polynomial equations in several variables) [1][2] to perform nonlinear parameter identification. In particular show how this technique can be used to estimate the rotor time constant and the stator resistance values of an induction machine. Although the example here is restricted to an induction machine, parameter estimation is applicable to many practical engineering problems. In [3], L. Ljung has outlined many of the challenges of nonlinear system identification as well as its particular importance for biological systems. In these types of problems, the model developed for analysis is typically a nonlinear state space model with unknown parameter values. The typical situation is that only a few of the state variables are measurable requiring that the system be reformulated as a nonlinear input-output model. In turn, resulting the nonlinear input-output model is almost always nonlinear in the parameters. Towards that end, differential algebra tools for analysis of nonlinear systems have been developed by Michel Fliess [4][5] and Diop [6]. Moreover, Ollivier [7] as well as Ljung and Glad [8] have developed the use of the characteristic set of an ideal as a tool for identification problems. The use of these differential algebraic methods for system identification have also been considered in [9], [10]. The focus of their research has been the determination of a priori identifiability of a given system model. However, as stated in [10], the development of an efficient algorithm using these differential algebraic techniques is still unknown. Here, in contrast, a method for which one can actually numerically obtain the numerical value of the parameters is presented. We also point out that [11] has also done work applying elimination theory to systems problems

A42F-03: Small-Scale Variability in Tropical Tropopause Layer Humidity

Recent advances in statistical parameterizations of cirrus cloud processes for use in global models are highlighting the need for information about small-scale fluctuations in upper tropospheric humidity and the physical processes that control the humidity variability. To address these issues, we have analyzed high-resolution airborne water vapor measurements obtained in the Airborne Tropical TRopopause EXperiment (ATTREX) over the tropical Pacific between 14 and 20 km. Using accurate and precise 1-Hz water vapor measurements along approximately-level aircraft flight legs, we calculate structure functions spanning horizontal scales ranging from about 0.2 to 50 km, and we compare the water vapor variability in the lower (about 14 km) and upper (16-19 km) Tropical Tropopause Layer (TTL). We also compare the magnitudes and scales of variability inside TTL cirrus versus in clear-sky regions. The measurements show that in the upper TTL, water vapor concentration variance is stronger inside cirrus than in clear-sky regions. Using simulations of TTL cirrus formation, we show that small variability in clear-sky humidity is amplified by the strong sensitivity of ice nucleation rate to supersaturation, which results in highly-structured clouds that subsequently drive variability in the water vapor field. In the lower TTL, humidity variability is correlated with recent detrainment from deep convection. The structure functions indicate approximately power-law scaling with spectral slopes ranging from about minus 5 divided by 3, to minus 2

Metal-Insulator Transition in a Disordered Two-Dimensional Electron Gas in GaAs-AlGaAs at zero Magnetic Field

A metal-insulator transition in two-dimensional electron gases at B=0 is found in Ga(Al)As heterostructures, where a high density of self-assembled InAs quantum dots is incorporated just 3 nm below the heterointerface. The transition occurs at resistances around h/e^2 and critical carrier densities of 1.2 10^11cm^-2. Effects of electron-electron interactions are expected to be rather weak in our samples, while disorder plays a crucial role.Comment: 4 pages, 3 figures, 21 reference

Super Weyl invariance: BPS equations from heterotic worldsheets

It is well-known that the beta functions on a string worldsheet correspond to the target space equations of motion, e.g. the Einstein equations. We show that the BPS equations, i.e. the conditions of vanishing supersymmetry variations of the space-time fermions, can be directly derived from the worldsheet. To this end we consider the RNS-formulation of the heterotic string with (2,0) supersymmetry, which describes a complex torsion target space that supports a holomorphic vector bundle. After a detailed account of its quantization and renormalization, we establish that the cancellation of the Weyl anomaly combined with (2,0) finiteness implies the heterotic BPS conditions: At the one loop level the geometry is required to be conformally balanced and the gauge background has to satisfy the Hermitean Yang-Mills equations.Comment: 1+31 pages LaTeX, 5 figures; final version, discussion relation Weyl invariance and (2,0) finiteness extended, typos correcte