Overt and covert effects of cognitive fatigue on attention networks

Background and aims Cognitive fatigue refers to a variation of the psychophysiological state during or after prolonged periods of mental activity that requires work efficiency, and could lead to temporary deterioration of attentional functioning, especially top-down attention and cognitive control. The present study aims to verify the effects of cognitive fatigue on attention in the context of the three attentional networks described by Posner, by using behavioral and psychophysiological measures, to detect variations in overt and covert responses respectively. Methods Thirty young healthy subjects were enrolled in the study, 15 in the “fatigue” and 15 in the control group. Cognitive fatigue was provoked by a continuous arithmetic task lasting 1 h, and the EEG recordings were conducted before and after the task, while subjects were performing the attention network test. The N1, N2 and P3 components were analyzed for the alerting, orienting and conflict networks, in conformity with behavioral analysis. Results No difference emerged between groups in networks' efficiency scores and in N1 and P3 amplitudes related to the alerting network. As regards the orienting network, P3 amplitude was significantly reduced in the fatigue group alone (p = 0.02), while no differences emerged in N1 amplitude. As regards the conflict network, both N2 and P3 amplitudes were significantly reduced in the fatigue group alone and selectively for the incongruent target (p < 0.001; p = 0.001 respectively). Conclusions Our results suggest that, in young healthy subjects, cognitive fatigue interferes with goal-driven attention especially when the task demand is higher, sparing the bottom-up attention control mechanisms and in absence of any overt observable effect

Cerebellum in timing control: Evidence from contingent negative variation after cerebellar tDCS

Background and aims Timing control is defined as the ability to quantify time. The temporal estimation of supra-seconds range is generally seen as a conscious cognitive process, while the sub-seconds range is a more automatic cognitive process. It is accepted that cerebellum contributes to temporal processing, but its function is still debated. The aim of this research was to better explore the role of cerebellum in timing control. We transitorily inhibited cerebellar activity and studied the effects on CNV components in healthy subjects. Methods Sixteen healthy subjects underwent a S1-S2 duration discrimination motor task, prior and after cathodal and sham cerebellar tDCS, in two separate sessions. In S1-S2 task they had to judge whether the duration of a probe interval trial was shorter (Short-ISI-trial:800 ms), longer (long-ISI-trail:1600 ms), or equal to the Target interval of 1200 ms. For each interval trial for both tDCS sessions, we measured: total and W2-CNV areas, the RTs of correct responses and the absolute number of errors prior and after tDCS. Results After cathodal tDCS a significant reduction in total-CNV and W2-CNV amplitudes selectively emerged for Short (p < 0.001; p = 0.003 respectively) and Target-ISI-trial (total-CNV: p < 0.001; W2-CNV:p = 0.003); similarly, a significant higher number of errors emerged for Short (p = 0.004) and Target-ISI-trial (p = 0.07) alone. No differences were detected for Longer-ISI-trials and after sham stimulation. Conclusions These data indicate that cerebellar inhibition selectively altered the ability to make time estimations for second and sub-second intervals. We speculate that cerebellum regulates the attentional mechanisms of automatic timing control by making predictions of interval timing

Mental flexibility in Parkinson's disease with central fatigue: Data from the frontal assessment battery

Background and aims Central fatigue is defined as a reduced energy level or an increased perception of effort, often associated to a failure in initiating and maintaining tasks that require self-motivation. It is common in Parkinson's disease population and it has been hypothesized to be related to a dysfunction in the striato-talamo- prefrontal loop. The aim of the present study was to explore the association between fatigue and executive functions as index of integrity of the striato-thalamo-prefrontal loop. Methods Twenty-nine non-demented PD patients without fatigue - PDnF, 28 non-demented PD patients with fatigue - PDF and 26 age and sex- matched controls underwent an evaluation with the Frontal Assessment Battery (FAB), MMSE, PSQI, BDI, STAI Y1-2, PDQ-39. Differences between groups in FAB scores (total and subitems) were analyzed by means of Kruskal-Wallis test. Moreover, a correlation between fatigue and FAB was also analyzed. Results Overall parkinsonian population displayed worse performance than controls in frontal scores especially inhibitory control (p = 0.008) and sensitivity to interference (p = 0.014). PDF displayed significantly worse than PDnF in verbal fluency (p = 0.05). Fatigue severity inversely correlated with executive performance (p b 0.001). Conclusions Phonemic fluency tasks are thought to reflect the simultaneous engagement of several executive functions such as attention, working memory, retrieval, information processing. The association of central fatigue with a deficit in mental flexibility, could support the hypothesis that central fatigue is a reliable index of the impairment of higher executive functions needed in order to effectively assess costs and benefits related to adaptive decision- making behavior

Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics

<p>Abstract</p> <p>Background</p> <p>High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination with database searching algorithms. A major problem with existing methods lies within the significant number of false positive and false negative annotations. So far, standard algorithms for protein identification do not use the information gained from separation processes usually involved in peptide analysis, such as retention time information, which are readily available from chromatographic separation of the sample. Identification can thus be improved by comparing measured retention times to predicted retention times. Current prediction models are derived from a set of measured test analytes but they usually require large amounts of training data.</p> <p>Results</p> <p>We introduce a new kernel function which can be applied in combination with support vector machines to a wide range of computational proteomics problems. We show the performance of this new approach by applying it to the prediction of peptide adsorption/elution behavior in strong anion-exchange solid-phase extraction (SAX-SPE) and ion-pair reversed-phase high-performance liquid chromatography (IP-RP-HPLC). Furthermore, the predicted retention times are used to improve spectrum identifications by a <it>p</it>-value-based filtering approach. The approach was tested on a number of different datasets and shows excellent performance while requiring only very small training sets (about 40 peptides instead of thousands). Using the retention time predictor in our retention time filter improves the fraction of correctly identified peptide mass spectra significantly.</p> <p>Conclusion</p> <p>The proposed kernel function is well-suited for the prediction of chromatographic separation in computational proteomics and requires only a limited amount of training data. The performance of this new method is demonstrated by applying it to peptide retention time prediction in IP-RP-HPLC and prediction of peptide sample fractionation in SAX-SPE. Finally, we incorporate the predicted chromatographic behavior in a <it>p</it>-value based filter to improve peptide identifications based on liquid chromatography-tandem mass spectrometry.</p

A systems approach to prion disease

Prions cause transmissible neurodegenerative diseases and replicate by conformational conversion of normal benign forms of prion protein (PrPC) to disease-causing PrPSc isoforms. A systems approach to disease postulates that disease arises from perturbation of biological networks in the relevant organ. We tracked global gene expression in the brains of eight distinct mouse strain–prion strain combinations throughout the progression of the disease to capture the effects of prion strain, host genetics, and PrP concentration on disease incubation time. Subtractive analyses exploiting various aspects of prion biology and infection identified a core of 333 differentially expressed genes (DEGs) that appeared central to prion disease. DEGs were mapped into functional pathways and networks reflecting defined neuropathological events and PrPSc replication and accumulation, enabling the identification of novel modules and modules that may be involved in genetic effects on incubation time and in prion strain specificity. Our systems analysis provides a comprehensive basis for developing models for prion replication and disease, and suggests some possible therapeutic approaches

Global Systems-Level Analysis of Hfq and SmpB Deletion Mutants in Salmonella: Implications for Virulence and Global Protein Translation

Using sample-matched transcriptomics and proteomics measurements it is now possible to begin to understand the impact of post-transcriptional regulatory programs in Enterobacteria. In bacteria post-transcriptional regulation is mediated by relatively few identified RNA-binding protein factors including CsrA, Hfq and SmpB. A mutation in any one of these three genes, csrA, hfq, and smpB, in Salmonella is attenuated for mouse virulence and unable to survive in macrophages. CsrA has a clearly defined specificity based on binding to a specific mRNA sequence to inhibit translation. However, the proteins regulated by Hfq and SmpB are not as clearly defined. Previous work identified proteins regulated by hfq using purification of the RNA-protein complex with direct sequencing of the bound RNAs and found binding to a surprisingly large number of transcripts. In this report we have used global proteomics to directly identify proteins regulated by Hfq or SmpB by comparing protein abundance in the parent and isogenic hfq or smpB mutant. From these same samples we also prepared RNA for microarray analysis to determine if alteration of protein expression was mediated post-transcriptionally. Samples were analyzed from bacteria grown under four different conditions; two laboratory conditions and two that are thought to mimic the intracellular environment. We show that mutants of hfq and smpB directly or indirectly modulate at least 20% and 4% of all possible Salmonella proteins, respectively, with limited correlation between transcription and protein expression. These proteins represent a broad spectrum of Salmonella proteins required for many biological processes including host cell invasion, motility, central metabolism, LPS biosynthesis, two-component regulatory systems, and fatty acid metabolism. Our results represent one of the first global analyses of post-transcriptional regulons in any organism and suggest that regulation at the translational level is widespread and plays an important role in virulence regulation and environmental adaptation for Salmonella

Phonon distributions of a single bath mode coupled to a quantum dot

The properties of an unconventional, single mode phonon bath coupled to a quantum dot, are investigated within the rotating wave approximation. The electron current through the dot induces an out of equilibrium bath, with a phonon distribution qualitatively different from the thermal one. In selected transport regimes, such a distribution is characterized by a peculiar selective population of few phonon modes and can exhibit a sub-Poissonian behavior. It is shown that such a sub-Poissonian behavior is favored by a double occupancy of the dot. The crossover from a unequilibrated to a conventional thermal bath is explored, and the limitations of the rotating wave approximation are discussed.Comment: 21 Pages, 7 figures, to appear in New Journal of Physics - Focus on Quantum Dissipation in Unconventional Environment

Evolving neural network optimization of cholesteryl ester separation by reversed-phase HPLC

Cholesteryl esters have antimicrobial activity and likely contribute to the innate immunity system. Improved separation techniques are needed to characterize these compounds. In this study, optimization of the reversed-phase high-performance liquid chromatography separation of six analyte standards (four cholesteryl esters plus cholesterol and tri-palmitin) was accomplished by modeling with an artificial neural network–genetic algorithm (ANN-GA) approach. A fractional factorial design was employed to examine the significance of four experimental factors: organic component in the mobile phase (ethanol and methanol), column temperature, and flow rate. Three separation parameters were then merged into geometric means using Derringer’s desirability function and used as input sources for model training and testing. The use of genetic operators proved valuable for the determination of an effective neural network structure. Implementation of the optimized method resulted in complete separation of all six analytes, including the resolution of two previously co-eluting peaks. Model validation was performed with experimental responses in good agreement with model-predicted responses. Improved separation was also realized in a complex biological fluid, human milk. Thus, the first known use of ANN-GA modeling for improving the chromatographic separation of cholesteryl esters in biological fluids is presented and will likely prove valuable for future investigators involved in studying complex biological samples