High penetrance of sequencing errors and interpretative shortcomings in mtDNA sequence analysis of LHON patients

For identifying mutation(s) that are potentially pathogenic it is essential to determine the entire mitochondrial DNA (mtDNA) sequences from patients suffering from a particular mitochondrial disease, such as Leber hereditary optic neuropathy (LHON). Howe

Model-based influences on humans' choices and striatal prediction errors.

The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors, and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making.

Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans.

Theories of instrumental learning are centred on understanding how success and failure are used to improve future decisions. These theories highlight a central role for reward prediction errors in updating the values associated with available actions. In animals, substantial evidence indicates that the neurotransmitter dopamine might have a key function in this type of learning, through its ability to modulate cortico-striatal synaptic efficacy. However, no direct evidence links dopamine, striatal activity and behavioural choice in humans. Here we show that, during instrumental learning, the magnitude of reward prediction error expressed in the striatum is modulated by the administration of drugs enhancing (3,4-dihydroxy-L-phenylalanine; L-DOPA) or reducing (haloperidol) dopaminergic function. Accordingly, subjects treated with L-DOPA have a greater propensity to choose the most rewarding action relative to subjects treated with haloperidol. Furthermore, incorporating the magnitude of the prediction errors into a standard action-value learning algorithm accurately reproduced subjects' behavioural choices under the different drug conditions. We conclude that dopamine-dependent modulation of striatal activity can account for how the human brain uses reward prediction errors to improve future decisions.

Static and dynamic errors in particle tracking microrheology.

Particle tracking techniques are often used to assess the local mechanical properties of cells and biological fluids. The extracted trajectories are exploited to compute the mean-squared displacement that characterizes the dynamics of the probe particles. Limited spatial resolution and statistical uncertainty are the limiting factors that alter the accuracy of the mean-squared displacement estimation. We precisely quantified the effect of localization errors in the determination of the mean-squared displacement by separating the sources of these errors into two separate contributions. A "static error" arises in the position measurements of immobilized particles. A "dynamic error" comes from the particle motion during the finite exposure time that is required for visualization. We calculated the propagation of these errors on the mean-squared displacement. We examined the impact of our error analysis on theoretical model fluids used in biorheology. These theoretical predictions were verified for purely viscous fluids using simulations and a multiple-particle tracking technique performed with video microscopy. We showed that the static contribution can be confidently corrected in dynamics studies by using static experiments performed at a similar noise-to-signal ratio. This groundwork allowed us to achieve higher resolution in the mean-squared displacement, and thus to increase the accuracy of microrheology studies.

WAVELENGTH POSITIONING ERRORS AND TRUE DETECTION LIMIT IN RELATION TO MULTICOMPONENT ANALYSIS TECHNIQUES FOR THE CORRECTION OF LINE INTERFERENCES IN INDUCTIVELY COUPLED PLASMA ATOMIC EMISSION-SPECTROMETRY

The present paper deals with the evaluation of the relative error (DELTA(A)) in estimated analyte concentrations originating from the wavelength positioning error in a sample scan when multicomponent analysis (MCA) techniques are used for correcting line interferences in inductively coupled plasma atomic emission spectrometry. In the theoretical part, a quantitative relation of DELTA(A) with the extent of line overlap, bandwidth and the magnitude of the positioning error is developed under the assumption of Gaussian line profiles. The measurements of eleven samples covering various typical line interferences showed that the calculated DELTA(A) generally agrees well with the experimental one. An expression of the true detection limit associated with MCA techniques was thus formulated. With MCA techniques, the determination of the analyte and interferent concentrations depend on each other while with conventional correction techniques, such as the three-point method, the estimate of interfering signals is independent of the analyte signals. Therefore. a given positioning error results in a larger DELTA(A) and hence a higher true detection limit in the case of MCA techniques than that in the case of conventional correction methods. although the latter could be a reasonable approximation of the former when the peak distance expressed in the effective width of the interfering line is larger than 0.4. In the light of the effect of wavelength positioning errors, MCA techniques have no advantages over conventional correction methods unless the former can bring an essential reduction ot the positioning error.

EVALUATION OF THE EFFECT OF WAVELENGTH POSITIONING ERRORS ON KALMAN FILTERING RESULTS IN INDUCTIVELY COUPLED PLASMA ATOMIC EMISSION-SPECTROMETRY

This work evaluates the effect of wavelength positioning errors in spectral scans on analytical results when the Kalman filtering technique is used for the correction of line interferences in inductively coupled plasma atomic emission spectrometry (ICP-AES). The results show that a positioning accuracy of 0.1 pm is required in order to obtain accurate and precise estimates for analyte concentrations. The positioning error in sample scans is more crucial than that in model scans. The relative bias in measured analyte concentration originating from a positioning error in a sample scan increases linearly with an increase in the magnitude of the error and the peak distance of the overlapping lines, but is inversely proportional to the signal-to-background ratio. By the use of an optimization procedure for the positions of scans with the innovations number as the criterion, the wavelength positioning error can be reduced and, correspondingly, the accuracy and precision of analytical results improved.

Correcting the errors: Volatility forecast evaluation using high-frequency data and realized volatilities

We develop general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit recent nonparametric asymptotic distributional results, are both easy-to-implement and highly accurate in empirically realistic situations. We also illustrate that properly accounting for the measurement errors in the volatility forecast evaluations reported in the existing literature can result in markedly higher estimates for the true degree of return volatility predictability.

Difficulties and errors in number reasoning development

The detailed study of difficulties and errors in young learner comprehension is a relevant and productive research field in Mathematics Education. Studies in the field are numerous although somewhat too varied. The present paper is suggesting methodological perspectives and principles applying to the field of research; we also show an example with school work. The use of figurate numbers as a representation system gives richer conceptual values, boosts visual reasoning and facilitates learner understanding.

Error sensitive historical GIS: Identifying areal interpolation errors in time series data

Historical GIS has the potential to re-invigorate our use of statistics from historical censuses and related sources. In particular, areal interpolation can be used to create long-run time-series of spatially detailed data that will enable us to enhance significantly our understanding of geographical change over periods of a century or more. The difficulty with areal interpolation, however, is that the data that it generates are estimates which will inevitably contain some error. This paper describes a technique that allows the automated identification of possible errors at the level of the individual data values.