Face Short-Term Memory-Related Electroencephalographic Patterns can Differentiate Multi- versus Single-Domain Amnestic Mild Cognitive Impairment.

Amnestic mild cognitive impairment (aMCI) is characterized by memory deficits alone (single-domain, sd-aMCI) or associated with other cognitive disabilities (multi-domain, md-aMCI). The present study assessed the patterns of electroencephalographic (EEG) activity during the encoding and retrieval phases of short-term memory in these two aMCI subtypes, to identify potential functional differences according to the neuropsychological profile. Continuous EEG was recorded in 43 aMCI patients, whose 16 sd-aMCI and 27 md-aMCI, and 36 age-matched controls (EC) during delayed match-to-sample tasks for face and letter stimuli. At encoding, attended stimuli elicited parietal alpha (8-12 Hz) power decrease (desynchronization), whereas distracting stimuli were associated with alpha power increase (synchronization) over right central sites. No difference was observed in parietal alpha desynchronization among the three groups. For attended faces, the alpha synchronization underlying suppression of distracting letters was reduced in both aMCI subgroups, but more severely in md-aMCI cases that differed significantly from EC. At retrieval, the early N250r recognition effect was significantly reduced for faces in md-aMCI as compared to both sd-aMCI and EC. The results suggest a differential alteration of working memory cerebral processes for faces in the two aMCI subtypes, face covert recognition processes being specifically altered in md-aMCI.

Work Zone Simulation Model, 1999

To support the analysis of driver behavior at rural freeway work zone lane closure merge points, Center for Transportation Research and Education staff collected traffic data at merge areas using video image processing technology. The collection of data and the calculation of the capacity of lane closures are reported in a companion report, "Traffic Management Strategies for Merge Areas in Rural Interstate Work Zones". These data are used in the work reported in this document and are used to calibrate a microscopic simulation model of a typical, Iowa rural freeway lane closure. The model developed is a high fidelity computer simulation with an animation interface. It simulates traffic operations at a work zone lane closure. This model enables traffic engineers to visually demonstrate the forecasted delay that is likely to result when freeway reconstruction makes it necessary to close freeway lanes. Further, the model is also sensitive to variations in driver behavior and is used to test the impact of slow moving vehicles and other driver behaviors. This report consists of two parts. The first part describes the development of the work zone simulation model. The simulation analysis is calibrated and verified through data collected at a work zone in Interstate Highway 80 in Scott County, Iowa. The second part is a user's manual for the simulation model, which is provided to assist users with its set up and operation. No prior computer programming skills are required to use the simulation model.

Simulation of soybean growth and yield under northeastern Amazon climatic conditions

The objective of this work was to parameterize, calibrate, and validate a new version of the soybean growth and yield model developed by Sinclair, under natural field conditions in northeastern Amazon. The meteorological data and the values of soybean growth and leaf area were obtained from an agrometeorological experiment carried out in Paragominas, PA, Brazil, from 2006 to 2009. The climatic conditions during the experiment were very distinct, with a slight reduction in rainfall in 2007, due to the El Niño phenomenon. There was a reduction in the leaf area index (LAI) and in biomass production during this year, which was reproduced by the model. The simulation of the LAI had root mean square error (RMSE) of 0.55 to 0.82 m² m-2, from 2006 to 2009. The simulation of soybean yield for independent data showed a RMSE of 198 kg ha-1, i.e., an overestimation of 3%. The model was calibrated and validated for Amazonian climatic conditions, and can contribute positively to the improvement of the simulations of the impacts of land use change in the Amazon region. The modified version of the Sinclair model is able to adequately simulate leaf area formation, total biomass, and soybean yield, under northeastern Amazon climatic conditions.

Binding free energy differences in a TCR-peptide-MHC complex induced by a peptide mutation: a simulation analysis.

Recognition by the T-cell receptor (TCR) of immunogenic peptides presented by class I major histocompatibility complexes (MHCs) is the determining event in the specific cellular immune response against virus-infected cells or tumor cells. It is of great interest, therefore, to elucidate the molecular principles upon which the selectivity of a TCR is based. These principles can in turn be used to design therapeutic approaches, such as peptide-based immunotherapies of cancer. In this study, free energy simulation methods are used to analyze the binding free energy difference of a particular TCR (A6) for a wild-type peptide (Tax) and a mutant peptide (Tax P6A), both presented in HLA A2. The computed free energy difference is 2.9 kcal/mol, in good agreement with the experimental value. This makes possible the use of the simulation results for obtaining an understanding of the origin of the free energy difference which was not available from the experimental results. A free energy component analysis makes possible the decomposition of the free energy difference between the binding of the wild-type and mutant peptide into its components. Of particular interest is the fact that better solvation of the mutant peptide when bound to the MHC molecule is an important contribution to the greater affinity of the TCR for the latter. The results make possible identification of the residues of the TCR which are important for the selectivity. This provides an understanding of the molecular principles that govern the recognition. The possibility of using free energy simulations in designing peptide derivatives for cancer immunotherapy is briefly discussed.

Persistence of cognitive impairment and its negative impact on psychosocial functioning in lithium-treated, euthymic bipolar patients: a 6-year follow-up study.

BACKGROUND: Previous cross-sectional studies report that cognitive impairment is associated with poor psychosocial functioning in euthymic bipolar patients. There is a lack of long-term studies to determine the course of cognitive impairment and its impact on functional outcome. Method A total of 54 subjects were assessed at baseline and 6 years later; 28 had DSM-IV TR bipolar I or II disorder (recruited, at baseline, from a Lithium Clinic Program) and 26 were healthy matched controls. They were all assessed with a cognitive battery tapping into the main cognitive domains (executive function, attention, processing speed, verbal memory and visual memory) twice over a 6-year follow-up period. All patients were euthymic (Hamilton Rating Scale for Depression score lower than 8 and Young mania rating scale score lower than 6) for at least 3 months before both evaluations. At the end of follow-up, psychosocial functioning was also evaluated by means of the Functioning Assessment Short Test. RESULTS: Repeated-measures multivariate analysis of covariance showed that there were main effects of group in the executive domain, in the inhibition domain, in the processing speed domain, and in the verbal memory domain (p<0.04). Among the clinical factors, only longer illness duration was significantly related to slow processing (p=0.01), whereas strong relationships were observed between impoverished cognition along time and poorer psychosocial functioning (p<0.05). CONCLUSIONS: Executive functioning, inhibition, processing speed and verbal memory were impaired in euthymic bipolar out-patients. Although cognitive deficits remained stable on average throughout the follow-up, they had enduring negative effects on psychosocial adaptation of patients.

Computer-simulation study of magnetic relaxation in anisotropic magnetic systems

We present a computer-simulation study of the effect of the distribution of energy barriers in an anisotropic magnetic system on the relaxation behavior of the magnetization. While the relaxation law for the magnetization can be approximated in all cases by a time logarithmic decay, the law for the dependence of the magnetic viscosity with temperature is found to be quite sensitive to the shape of the distribution of barriers. The low-temperature region for the magnetic viscosity never extrapolates to a positive no-null value. Moreover our computer simulation results agree reasonably well with some recent relaxation experiments on highly anisotropic single-domain particles.

A light in the cognitive fog?

HIV escape in the central nervous system (CNS) despite undetectable viral load in the plasma has been observed and may contribute to HIV-associated neurocognitive disorders. Favouring the use of HIV drugs with a good penetration into the CNS has been advocated, leading to the establishment of the CNS penetration-effectiveness (CPE) score. However, the relevance of this score is not fully established. Ciccarelli et al. compared two versions of the CPE scores in their capacity to predict cognitive dysfunction in HIV-infected individuals. The revised CPE score, but not the original one, showed an improved association with cognitive impairment. Prospective studies are warranted to assess the validity of the CPE score.

Electrophysiological markers of rapid cognitive decline in mild cognitive impairment

Electroencephalography (EEG) is an easily accessible and low-cost modality that might prove to be a particularly powerful tool for the identification of subtle functional changes preceding structural or metabolic deficits in progressive mild cognitive impairment (PMCI). Most previous contributions in this field assessed quantitative EEG differences between healthy controls, MCI and Alzheimer's disease(AD) cases leading to contradictory data. In terms of MCI conversion to AD, certain longitudinal studies proposed various quantitative EEG parameters for an a priori distinction between PMCI and stable MCI. However, cross-sectional comparisons revealed a substantial overlap in these parameters between MCI patients and elderly controls. Methodological differences including variable clinical definition of MCI cases and substantial interindividual differences within the MCI group could partly explain these discrepancies. Most importantly, EEG measurements without cognitive demand in both cross-sectional and longitudinal designs have demonstrated limited sensitivity and generally do not produce significant group differences in spectral EEG parameters. Since the evolution of AD is characterized by the progressive loss of functional connectivity within neocortical association areas, event-modulated EEG dynamic analysis which makes it possible to investigate the functional activation of neocortical circuits may represent a more sensitive method to identify early alterations of neuronal networks predictive of AD development among MCI cases. The present review summarizes clinically significant results of EEG activation studies in this field and discusses future perspectives of research aiming to reach an early and individual prediction of cognitive decline in healthy elderly controls.

Robust Detection of Incumbents in Cognitive Radio Networks Using Groups

Cognitive radio is a wireless technology aimed at improvingthe efficiency use of the radio-electric spectrum, thus facilitating a reductionin the load on the free frequency bands. Cognitive radio networkscan scan the spectrum and adapt their parameters to operate in the unoccupiedbands. To avoid interfering with licensed users operating on a givenchannel, the networks need to be highly sensitive, which is achieved byusing cooperative sensing methods. Current cooperative sensing methodsare not robust enough against occasional or continuous attacks. This articleoutlines a Group Fusion method that takes into account the behavior ofusers over the short and long term. On fusing the data, the method is basedon giving more weight to user groups that are more unanimous in their decisions.Simulations have been performed in a dynamic environment withinterferences. Results prove that when attackers are present (both reiterativeor sporadic), the proposed Group Fusion method has superior sensingcapability than other methods.