Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD.

A hybrid feature selection approach for the early diagnosis of Alzheimer's disease

Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease from EEG. However, choosing suitable measures is a challenging task. Among other measures, frequency Relative Power and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency Relative Power on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate, looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing MCI and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4± 11.5). Main Results. Using a single feature to compute classification rates we achieve a performance of 78.33% for the MCI data set and of 97.56 % for Mild AD. Results are clearly improved using the multiple feature classification, where a classification rate of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using 4 features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

Audit firm tenure and qualified opinions: New evidence from Spain

The 2010 Green Paper on Audit Policy by the European Commission has explicitly questioned the sufficiency of audit rotation rules established by European Union Members to guarantee auditor independence. In addition, the Paper clearly states that more research is needed regarding the effects of long audit tenures on independence. In this article, we have replicated the research by Ruiz-Barbadillo, Gómez-Aguilar, and Biedma (2005) about the effects of audit firm tenure on independence with more updated data. However, unlike them, we have performed panel data estimations instead of pooled regression. Our approach allows for a better control of individual unobserved heterogeneity, thus reducing potential problems caused by omitted variable bias. While Ruiz-Barbadillo et al. reported an unexpected positive effect of tenure on the likelihood of audit qualifications, we do not show any significant effect of tenure on the opinion of the audit report. Our results are robust to various sensitivity analyses.

The Role of Firm Size in Training Provision Decisions: evidence from Spain

The level of training provided by small firms to their employees is below that provided by their larger counterparts. The provision of firm-related training is believed to be associated to certain characteristics of the firm. In this paper we argue that small firms provide fewer training opportunities as they are less likely to be associated with these characteristics than large firms. The suitability of estimating training decisions as a double-decision process is examined here: first, a firm has to decide whether to provide training or not and, second, having decided to do so, the amount of training to provide. The differences in training provision between small and large firms are decomposed in order to analyse the individual contribution of these characteristics to explaining the gap. The results show that small firms face greater obstacles in accessing training and that the main reasons for that are related to their technological activity and the geographical scope of the market in which they operate.

Decomposing differences in total factor productivity across firm size

This paper investigates the extent to which the gap in total factor productivity between small and large firms is due to differences in the endowment of factors determining productivity and to the returns associated with these factors. We place particular emphasis on the contribution of differences in the propensity to innovate and in the use of skilled labor across firms of different size. Empirical evidence from a representative sample of Spanish manufacturing firms corroborates that both differences in endowments and returns to innovation and skilled labor significantly contribute to the productivity gap between small and large firms. In addition, it is observed that the contribution of innovation to this gap is caused only by differences in quantity, while differences in returns have no effect; in the case of human capital, however, most of the effect can be attributed to increasing differences in returns between small and large firms.

Threshold Selection Criteria for Quantification of Lumbosacral Cerebrospinal Fluid and Root Volumes from MRI

BACKGROUND AND PURPOSE: The high variability of CSF volumes partly explains the inconsistency of anesthetic effects, but may also be due to image analysis itself. In this study, criteria for threshold selection are anatomically defined. METHODS: T2 MR images (n = 7 cases) were analyzed using 3-dimentional software. Maximal-minimal thresholds were selected in standardized blocks of 50 slices of the dural sac ending caudally at the L5-S1 intervertebral space (caudal blocks) and middle L3 (rostral blocks). Maximal CSF thresholds: threshold value was increased until at least one voxel in a CSF area appeared unlabeled and decreased until that voxel was labeled again: this final threshold was selected. Minimal root thresholds: thresholds values that selected cauda equina root area but not adjacent gray voxels in the CSF-root interface were chosen. RESULTS: Significant differences were found between caudal and rostral thresholds. No significant differences were found between expert and nonexpert observers. Average max/min thresholds were around 1.30 but max/min CSF volumes were around 1.15. Great interindividual CSF volume variability was detected (max/min volumes 1.6-2.7). CONCLUSIONS: The estimation of a close range of CSF volumes which probably contains the real CSF volume value can be standardized and calculated prior to certain intrathecal procedures

Learning and selection processes

In this paper I defend a teleological explanation of normativity, i. e., I argue that what an organism (or device) is supposed to do is determined by its etiological function. In particular, I present a teleological account of the normativity that arises in learning processes, and I defend it from some objections

Construction and validation of a scoring system for the selection of high-quality data in a Spanish population primary care database (SIDIAP)

Background Computerised databases of primary care clinical records are widely used for epidemiological research. In Catalonia, the InformationSystem for the Development of Research in Primary Care (SIDIAP) aims to promote the development of research based on high-quality validated data from primary care electronic medical records. Objective The purpose of this study is to create and validate a scoring system (Registry Quality Score, RQS) that will enable all primary care practices (PCPs) to be selected as providers of researchusable data based on the completeness of their registers. Methods Diseases that were likely to be representative of common diagnoses seen in primary care were selected for RQS calculations. The observed/ expected cases ratio was calculated for each disease. Once we had obtained an estimated value for this ratio for each of the selected conditions we added up the ratios calculated for each condition to obtain a final RQS. Rate comparisons between observed and published prevalences of diseases not included in the RQS calculations (atrial fibrillation, diabetes, obesity, schizophrenia, stroke, urinary incontinenceand Crohn’s disease) were used to set the RQS cutoff which will enable researchers to select PCPs with research-usable data. Results Apart from Crohn’s disease, all prevalences were the same as those published from the RQS fourth quintile (60th percentile) onwards. This RQS cut-off provided a total population of 1 936 443 (39.6% of the total SIDIAP population). Conclusions SIDIAP is highly representative of the population of Catalonia in terms of geographical, age and sex distributions. We report the usefulness of rate comparison as a valid method to establish research-usable data within primary care electronic medical records

Citation auctions as a method to improve selection of scientific papers

This paper describes the basis of citation auctions as a new approach to selecting scientific papers for publication. Our main idea is to use an auction for selecting papers for publication through - differently from the state of the art - bids that consist of the number of citations that a scientist expects to receive if the paper is published. Hence, a citation auction is the selection process itself, and no reviewers are involved. The benefits of the proposed approach are two-fold. First, the cost of refereeing will be either totally eliminated or significantly reduced, because the process of citation auction does not need prior understanding of the paper's content to judge the quality of its contribution. Additionally, the method will not prejudge the content of the paper, so it will increase the openness of publications to new ideas. Second, scientists will be much more committed to the quality of their papers, paying close attention to distributing and explaining their papers in detail to maximize the number of citations that the paper receives. Sample analyses of the number of citations collected in papers published in years 1999-2004 for one journal, and in years 2003-2005 for a series of conferences (in a totally different discipline), via Google scholar, are provided. Finally, a simple simulation of an auction is given to outline the behaviour of the citation auction approach

Swarm Intelligent Selection and Optimization of Machining System Parameters for Microchannel Fabrication in Medical Devices

Current technology trends in medical device industry calls for fabrication of massive arrays of microfeatures such as microchannels on to nonsilicon material substrates with high accuracy, superior precision, and high throughput. Microchannels are typical features used in medical devices for medication dosing into the human body, analyzing DNA arrays or cell cultures. In this study, the capabilities of machining systems for micro-end milling have been evaluated by conducting experiments, regression modeling, and response surface methodology. In machining experiments by using micromilling, arrays of microchannels are fabricated on aluminium and titanium plates, and the feature size and accuracy (width and depth) and surface roughness are measured. Multicriteria decision making for material and process parameters selection for desired accuracy is investigated by using particle swarm optimization (PSO) method, which is an evolutionary computation method inspired by genetic algorithms (GA). Appropriate regression models are utilized within the PSO and optimum selection of micromilling parameters; microchannel feature accuracy and surface roughness are performed. An analysis for optimal micromachining parameters in decision variable space is also conducted. This study demonstrates the advantages of evolutionary computing algorithms in micromilling decision making and process optimization investigations and can be expanded to other applications

Horizontal Grid Size Selection and its Influence on Mesoscale Model Simulations

The use of two-dimensional spectral analysis applied to terrain heights in order to determine characteristic terrain spatial scales and its subsequent use for the objective definition of an adequate grid size required to resolve terrain forcing are presented in this paper. In order to illustrate the influence of grid size, atmospheric flow in a complex terrain area of the Spanish east coast is simulated by the Regional Atmospheric Modeling System (RAMS) mesoscale numerical model using different horizontal grid resolutions. In this area, a grid size of 2 km is required to account for 95% of terrain variance. Comparison among results of the different simulations shows that, although the main wind behavior does not change dramatically, some small-scale features appear when using a resolution of 2 km or finer. Horizontal flow pattern differences are significant both in the nighttime, when terrain forcing is more relevant, and in the daytime, when thermal forcing is dominant. Vertical structures also are investigated, and results show that vertical advection is influenced highly by the horizontal grid size during the daytime period. The turbulent kinetic energy and potential temperature vertical cross sections show substantial differences in the structure of the planetary boundary layer for each model configuration

Brain size and evolutionary diversification in birds

The role of behavior in evolution remains controversial, despite that some ideas are over 100 years old. Changes in behavior are generally believed to enhance evolution by exposing individuals to new selective pressures and by facilitating range expansions. However, this hypothesis lacks firm empirical evidence. Moreover, behavioral changes can also inhibit evolution by hiding heritable variation from natural selection. Taking advantage of the complete phylogeny of extant birds, a new species-level measure of past diversification rate and the best existing measures of brain size (n = 1326 species), I show here that relative brain size is associated (albeit weakly) with diversification rates. Assuming that brain relative size reflects behavioral flexibility, an assumption well-supported by evidence, this finding supports the idea that behavior can enhance evolutionary diversification. This view is further supported by the discovery that the most important factor influencing diversification rates is ecological generalism, which is believed to require behavioral flexibility. Thus, behavioral changes that expose animals to a variety of environments can have played an important role in the evolution of birds.