Modeling facial perception in group context from a serial perception perspective

By utilizing statistical properties and summary statistics, the visual system can efficiently integrate perception of spatially and temporally adjacent stimuli into perception of a given target. For instance, perception of a target face can either be biased positively toward previous faces (e.g. the serial dependence effect) or be biased negatively by surrounding faces in the same trial/space (e.g. spatial ensemble averaging). However, both aspects were investigated separately. As spatial and temporal processing share the same purpose to reduce redundancy in visual processing, if one statistical processing occurs, would the statistical processing in the other domain still exist or be discarded? We investigated this question by exploring whether serial dependence of face perception (of attractiveness and averageness) survives when the changed face perception in the group context occurs. The results of Markov Chain modeling and conventional methods suggested that serial dependence (the temporal aspect) co-occurs with changed face perception in the group context (the spatial aspect). We also utilized the Hidden Markov modeling, as a new mathematical method, to model statistical processing from both domains. The results confirmed the co-occurrence of temporal effect and changed face perception in the group context for both attractiveness and averageness, suggesting potentially different spatial and temporal compression mechanisms in high-level vision. Further modeling and cluster analysis further revealed that the detailed computation of spatially and temporally adjacent faces in the attractiveness and averageness processing were similar yet different among different individuals. This work builds a bridge to understanding mathematical principles underlying changed face perception in the group context from the serial perspective.

Interpreting Social Accounting Matrix (SAM) as an Information Channel

Information theory, and the concept of information channel, allows us to calculate the mutual information between the source (input) and the receiver (output), both represented by probability distributions over their possible states. In this paper, we use the theory behind the information channel to provide an enhanced interpretation to a Social Accounting Matrix (SAM), a square matrix whose columns and rows present the expenditure and receipt accounts of economic actors. Under our interpretation, the SAM’s coefficients, which, conceptually, can be viewed as a Markov chain, can be interpreted as an information channel, allowing us to optimize the desired level of aggregation within the SAM. In addition, the developed information measures can describe accurately the evolution of a SAM over time. Interpreting the SAM matrix as an ergodic chain could show the effect of a shock on the economy after several periods or economic cycles. Under our new framework, finding the power limit of the matrix allows one to check (and confirm) whether the matrix is well-constructed (irreducible and aperiodic), and obtain new optimization functions to balance the SAM matrix. In addition to the theory, we also provide two empirical examples that support our channel concept and help to understand the associated measures.

An analysis of whole body tracer kinetics in dynamic PET studies with application to image-based blood input function extraction

In a positron emission tomography (PET) study, the local uptake of the tracer is dependent on vascular delivery and retention. For dynamic studies the measured uptake time-course information can be best interpreted when knowledge of the time-course of tracer in the blood is available. This is certainly true for the most established tracers such as 18F-Fluorodeoxyglucose (FDG) and 15O-Water (H2O). Since direct sampling of blood as part of PET studies is increasingly impractical, there is ongoing interest in image-extraction of blood time-course information. But analysis of PET-measured blood pool signals is complicated because they will typically involve a combination of arterial, venous and tissue information. Thus, a careful appreciation of these components is needed to interpret the available data. To facilitate this process, we propose a novel Markov chain model for representation of the circulation of a tracer atom in the body. The model represents both arterial and venous time-course patterns. Under reasonable conditions equilibration of tracer activity in arterial and venous blood is achieved by the end of the PET study-consistent with empirical measurement. Statistical inference for Markov model parameters is a challenge. A penalized nonlinear least squares process, incorporating a generalized cross-validation score, is proposed. Random effects analysis is used to adaptively specify the structure of the penalty function based on historical samples of directly measured blood data. A collection of arterially sampled data from PET studies with FDG and H2O is used to illustrate the methodology. These data analyses are highly supportive of the overall modeling approach. An adaptation of the model to the problem of extraction of arterial blood signals from imaging data is also developed and promising preliminary results for cerebral and thoracic imaging studies with FDG and H2O are obtained.

Estimating lifetime healthcare costs with morbidity data

BACKGROUND

In many developed countries, the economic crisis started in 2008 producing a serious contraction of the financial resources spent on healthcare. Identifying which individuals will require more resources and the moment in their lives these resources have to be allocated becomes essential. It is well known that a small number of individuals with complex healthcare needs consume a high percentage of health expenditures. Conversely, little is known on how morbidity evolves throughout life. The aim of this study is to introduce a longitudinal perspective to chronic disease management.

METHODS

Data used relate to the population of the county of Baix Empordà in Catalonia for the period 2004-2007 (average population was N = 88,858). The database included individual information on morbidity, resource consumption, costs and activity records. The population was classified using the Clinical Risk Groups (CRG) model. Future morbidity evolution was simulated under different assumptions using a stationary Markov chain. We obtained morbidity patterns for the lifetime and the distribution function of the random variable lifetime costs. Individual information on acute episodes, chronic conditions and multimorbidity patterns were included in the model.

RESULTS

The probability of having a specific health status in the future (healthy, acute process or different combinations of chronic illness) and the distribution function of healthcare costs for the individual lifetime were obtained for the sample population. The mean lifetime cost for women was €111,936, a third higher than for men, at €81,566 (all amounts calculated in 2007 Euros). Healthy life expectancy at birth for females was 46.99, lower than for males (50.22). Females also spent 28.41 years of life suffering from some type of chronic disease, a longer period than men (21.9).

CONCLUSIONS

Future morbidity and whole population costs can be reasonably predicted, combining stochastic microsimulation with a morbidity classification system. Potential ways of efficiency arose by introducing a time perspective to chronic disease management.

Neutral theory for life histories and individual variability in fitness components

Individuals within populations can differ substantially in their life span and their lifetime reproductive success but such realized individual variation in fitness components need not reflect underlying heritable fitness differences visible to natural selection. Even so, biologists commonly argue that large differences in fitness components are likely adaptive, resulting from and driving evolution by natural selection. To examine this argument we use unique formulas to compute exactly the variance in life span and in lifetime reproductive success among individuals with identical (genotypic) vital rates (assuming a common genotype for all individuals). Such individuals have identical fitness but vary substantially in their realized individual fitness components. We show by example that our computed variances and corresponding simulated distribution of fitness components match those observed in real populations. Of course, (genotypic) vital rates in real populations are expected to differ by small but evolutionarily important amounts among genotypes, but we show that such differences only modestly increase variances in fitness components. We conclude that observed differences in fitness components may likely be evolutionarily neutral, at least to the extent that they are indistinguishable from distributions generated by neutral processes. Important consequences of large neutral variation are the following: Heritabilities for fitness components are likely to be small (which is in fact the case), small selective differences in life histories will be hard to measure, and the effects of random drift will be amplified in natural populations by the large variances among individuals.

Examining future adolescent human papillomavirus vaccine uptake, with and without a school mandate

PURPOSE

To develop a model of adolescent (HPV) human papillomavirus vaccine utilization that explored future HPV vaccination rates, with and without a school mandate, for the vaccine at middle school entry.

METHODS

A dynamic, population-based, compartmental model was developed that estimated over a 50-year time horizon HPV vaccine uptake among female adolescents living in the United States. The model incorporated data on parental attitudes about this vaccine and adolescent health care utilization levels.

RESULTS

Without a mandate, our model predicted that 70% coverage, a lower threshold value used in many previous modeling studies of HPV vaccination, would not be achieved until a mean of 23 years after vaccine availability. Maximal coverage of 79% was achieved after 50 years. With a school mandate in place, utilization increased substantially, with 70% vaccination coverage achieved by year 8 and maximal vaccination coverage, 90%, achieved by year 43.

CONCLUSIONS

Our results suggest that vaccine utilization is likely to be low for several years, though strong school mandates might improve HPV vaccine uptake. These results affect the interpretation of previous modeling studies that estimated the potential clinical effects of HPV vaccination under assumptions of very high vaccine utilization rates.

Cost-effectiveness and healthcare budget impact in Italy of inhaled corticosteroids and bronchodilators for severe and very severe COPD patients

Current practice guidelines for the treatment of COPD recommend the use of combined inhaled corticosteroids and long-acting bronchodilators in severe and very severe patients (GOLD stages III and IV). The aim of this study was to evaluate, through a simulation model, the economic consequences of this recommendation in Italy. We developed a cost-effectiveness analysis (CEA) on five alternative therapeutic strategies (salmeterol/fluticasone, SF; formoterol! budesonide, FB; salmeterol alone, S; fluticasone alone, F; control, C). Published data on the Italian COPD population and efficacy data from international reference trials were fitted in a disease progression model based on a Markov chain representing severity stages and death. The yearly total direct costs of treating COPD patients in Italy was estimated at approximately Euro 7 billion, with a mean cost per patient per year of around Euro 2450. Mean survival of the cohort is 11.5 years. The C and F strategies were dominated (ie, are associated with worse outcomes and higher costs) by all alternatives. SF and FB were the most effective strategies, with a slight clinical superiority of SF, but they were also marginally more expensive than S. Incremental cost-effectiveness of SF vs S was Euro 679.5 per avoided exacerbation and Euro 3.3 per symptom-free day. Compared with current practice, the recommended use of combined inhaled corticosteroids and long-acting bronchodilators for severe and very severe COPD patients has the potential for improving clinical outcomes without increasing healthcare costs.

Statistical Signal Processing and the Motor Cortex

Over the past few decades, developments in technology have significantly improved the ability to measure activity in the brain. This has spurred a great deal of research into brain function and its relation to external stimuli, and has important implications in medicine and other fields. As a result of improved understanding of brain function, it is now possible to build devices that provide direct interfaces between the brain and the external world. We describe some of the current understanding of function of the motor cortex region. We then discuss a typical likelihood-based state-space model and filtering based approach to address the problems associated with building a motor cortical-controlled cursor or robotic prosthetic device. As a variation on previous work using this approach, we introduce the idea of using Markov chain Monte Carlo methods for parameter estimation in this context. By doing this instead of performing maximum likelihood estimation, it is possible to expand the range of possible models that can be explored, at a cost in terms of computational load. We demonstrate results obtained applying this methodology to experimental data gathered from a monkey.

Assessment of possible impact of a health promotion program in Korea from health risk trends in a longitudinally observed cohort

BACKGROUND: Longitudinally observed cohort data can be utilized to assess the potential for health promotion and healthcare planning by comparing the estimated risk factor trends of non-intervened with that of intervened. The paper seeks (1) to estimate a natural transition (patterns of movement between states) of health risk state from a Korean cohort data using a Markov model, (2) to derive an effective and necessary health promotion strategy for the population, and (3) to project a possible impact of an intervention program on health status. METHODS: The observed transition of health risk states in a Korean employee cohort was utilized to estimate the natural flow of aggregated health risk states from eight health risk measures using Markov chain models. In addition, a reinforced transition was simulated, given that a health promotion program was implemented for the cohort, to project a possible impact on improvement of health status. An intervened risk transition was obtained based on age, gender, and baseline risk state, adjusted to match with the Korean cohort, from a simulated random sample of a US employee population, where a health intervention was in place. RESULTS: The estimated natural flow (non-intervened), following Markov chain order 2, showed a decrease in low risk state by 3.1 percentage points in the Korean population while the simulated reinforced transition (intervened) projected an increase in low risk state by 7.5 percentage points. Estimated transitions of risk states demonstrated the necessity of not only the risk reduction but also low risk maintenance. CONCLUSIONS: The frame work of Markov chain efficiently estimated the trend, and captured the tendency in the natural flow. Given only a minimally intense health promotion program, potential risk reduction and low risk maintenance was projected.

Comparison of two Bayesian approaches to dose-individualization for once-daily aminoglycoside regimens

AIMS

Bayesian dose-individualization methods have been shown to have good predictive performance using minimal data points, and are now used widely in clinical practice. This study was designed to compare two computerised Bayesian dose-individualization methods, ABBOTTBASE and SeBA-GEN, in once-daily dosing of aminoglycosides.

METHODS

ABBOTTBASE uses the maximum a posteriori estimator (MAP) algorithm which analyses all available serum drug concentration data for individual patients simultaneously, while the prior model remains unchanged. SeBA-GEN analyses each data set sequentially while continually modifying the individual patient's prior model, allowing within-patient variability to be modelled. One hundred consecutive patients who received once-daily dosing of aminoglycosides were prospectively dose-individualized using either of these methods. Retrospectively the alternative dosing method was used to provide comparative data. The ability of the methods to predict subsequent serum aminoglycoside concentration data was assessed in terms of their predictive performance, bias and precision.

RESULTS

From the 100 patients, 277 serum aminoglycoside concentrations were available. Ninety-eight patients had serum concentrations available from the first dose and 55 from the second dose. Gentamicin was used in 96 patients. There was no significant bias when predicting peak concentrations from the prior model using either SeBA-GEN or ABBOTTBASE. The prior model used by ABBOTTBASE did, however, significantly underpredict the mid-dose concentration (mean bias = -0.79 mg l-1, 95% Confidence Interval [CI]: -1.3 to -0.3). When using the Bayesian algorithms for prediction of the second set of concentrations neither method was biased when predicting the peak concentration. ABBOTTBASE significantly overpredicted the mid-dose concentration (mean bias = 0.38 mg l-1, 95% CI: 0.03 to 0.74). The prior model used by SeBA-GEN was more precise at predicting both peak and mid-dose concentrations (P < 0.01), indicating better use of covariates. There was no difference between the methods in terms of estimation of the value of volume of distribution, but they differed significantly in the estimation of clearance (mean difference = 0.24 l h-1 for SeBA-GEN-ABBOTTBASE, 95% CI: 0.05 to 0.43).

CONCLUSIONS

Bayesian techniques appear to work well with once-daily aminoglycoside dosing. The method of incorporation of individual patient information into the prior model appears to be important in the optimum choice of the first dose. SeBA-GEN has an advantage in this and in the lack of bias related to predicting low concentrations compared with ABBOTTBASE.

Over- and underrepresentation of short DNA words in herpesvirus genomes

The relative abundance and rarity of DNA words have been recognized in previous biological studies to have implications for the regulation, repair, and evolutionary mechanisms of a genome. In this paper, we review several different measures of abundance and rarity of DNA words, including z-scores, representation ratios, and cross-ratios, that have appeared in the recent literature, and examine the concordance among them using the human cytomegalovirus genome sequence. We then rank all words of length k = 2, ..., 5 of seven herpesvirus genomes according to their abundance, as measured by one of the z-scores based upon a stationary Markov model of order k-2. Using a simple metric on the ranks of 2-words of the seven herpesvirus sequences, we construct an evolutionary tree. Several 3-words are observed to be consistently over- or underrepresented in all seven herpesviruses. Furthermore, clusters of some of the most over- and underrepresented 4- and 5-words in the genomes are identified with functional sites such as the origins of replication and regulatory signals of individual viruses.