A compositional statistical analysis of capital stock

Most of economic literature has presented its analysis under the assumption of homogeneous capital stock.However, capital composition differs across countries. What has been the pattern of capital compositionassociated with World economies? We make an exploratory statistical analysis based on compositional datatransformed by Aitchinson logratio transformations and we use tools for visualizing and measuring statisticalestimators of association among the components. The goal is to detect distinctive patterns in the composition.As initial findings could be cited that:1. Sectorial components behaved in a correlated way, building industries on one side and , in a lessclear view, equipment industries on the other.2. Full sample estimation shows a negative correlation between durable goods component andother buildings component and between transportation and building industries components.3. Countries with zeros in some components are mainly low income countries at the bottom of theincome category and behaved in a extreme way distorting main results observed in the fullsample.4. After removing these extreme cases, conclusions seem not very sensitive to the presence ofanother isolated cases

Modern statistical models for forensic fingerprint examinations : a critical review

Over the last decade, the development of statistical models in support of forensic fingerprint identification has been the subject of increasing research attention, spurned on recently by commentators who claim that the scientific basis for fingerprint identification has not been adequately demonstrated. Such models are increasingly seen as useful tools in support of the fingerprint identification process within or in addition to the ACE-V framework. This paper provides a critical review of recent statistical models from both a practical and theoretical perspective. This includes analysis of models of two different methodologies: Probability of Random Correspondence (PRC) models that focus on calculating probabilities of the occurrence of fingerprint configurations for a given population, and Likelihood Ratio (LR) models which use analysis of corresponding features of fingerprints to derive a likelihood value representing the evidential weighting for a potential source.

Laser-ultraviolet-A induced ultra weak photon emission in human skin cells: A biophotonic comparison between keratinocytes and fibroblasts.

Photons participate in many atomic and molecular interactions and processes. Recent biophysical research has discovered an ultraweak radiation in biological tissues. It is now recognized that plants, animal and human cells emit this very weak biophotonic emission which can be readily measured with a sensitive photomultiplier system. UVA laser induced biophotonic emission of cultured cells was used in this report with the intention to detect biophysical changes between young and adult fibroblasts as well as between fibroblasts and keratinocytes. With suspension densities ranging from 1-8 x 106 cells/ml, it was evident that an increase of the UVA-laser-light induced photon emission intensity could be observed in young as well as adult fibroblastic cells. By the use of this method to determine ultraweak light emission, photons in cell suspensions in low volumes (100 microl) could be detected, in contrast to previous procedures using quantities up to 10 ml. Moreover, the analysis has been further refined by turning off the photomultiplier system electronically during irradiation leading to the first measurements of induced light emission in the cells after less than 10 micros instead of more than 100 milliseconds. These significant changes lead to an improvement factor up to 106 in comparison to classical detection procedures. In addition, different skin cells as fibroblasts and keratinocytes stemming from the same donor were measured using this new highly sensitive method in order to find new biophysical insight of light pathways. This is important in view to develop new strategies in biophotonics especially for use in alternative therapies.

Applications of stochastic analysis in finance and statistical inference

First: A continuous-time version of Kyle's model (Kyle 1985), known as the Back's model (Back 1992), of asset pricing with asymmetric information, is studied. A larger class of price processes and of noise traders' processes are studied. The price process, as in Kyle's model, is allowed to depend on the path of the market order. The process of the noise traders' is an inhomogeneous Lévy process. Solutions are found by the Hamilton-Jacobi-Bellman equations. With the insider being risk-neutral, the price pressure is constant, and there is no equilibirium in the presence of jumps. If the insider is risk-averse, there is no equilibirium in the presence of either jumps or drifts. Also, it is analised when the release time is unknown. A general relation is established between the problem of finding an equilibrium and of enlargement of filtrations. Random announcement time is random is also considered. In such a case the market is not fully efficient and there exists equilibrium if the sensitivity of prices with respect to the global demand is time decreasing according with the distribution of the random time. Second: Power variations. it is considered, the asymptotic behavior of the power variation of processes of the form _integral_0^t u(s-)dS(s), where S_ is an alpha-stable process with index of stability 0&alpha&2 and the integral is an Itô integral. Stable convergence of corresponding fluctuations is established. These results provide statistical tools to infer the process u from discrete observations. Third: A bond market is studied where short rates r(t) evolve as an integral of g(t-s)sigma(s) with respect to W(ds), where g and sigma are deterministic and W is the stochastic Wiener measure. Processes of this type are particular cases of ambit processes. These processes are in general not of the semimartingale kind.

Long-term prognostic value of single-photon emission computed tomography myocardial perfusion imaging in diabetic patients with and without coronary artery disease

Keywords Diabetes mellitus; coronary artery disease; myocardial ischemia; prognostic value; single-photon emission computed tomography myocardial perfusion imaging Summary Aim: To determine the long-term prognostic value of SPECT myocardial perfusion imaging (MPI) for the occurrence of cardiovascular events in diabetic patients. Methods: SPECT MPI of 210 consecutive Caucasian diabetic patients were analysed using Kaplan-Meier event-free survival curves and independent predictors were determined by Cox multivariate analyses. Results: Follow-up was complete in 200 (95%) patients with a median period of 3.0 years (0.8-5.0). The population was composed of 114 (57%) men, age 65±10 years, 181 (90.5%) type 2 diabetes mellitus, 50 (25%) with a history of coronary artery disease (CAD) and 98 (49%) presenting chest pain prior to MPI. The prevalence of abnormal MPI was 58%. Patients with a normal MPI had neither cardiac death, nor myocardial infarction, independently of a history of coronary artery disease or chest pain. Among the independent predictors of cardiac death and myocardial infarction, the strongest was abnormal MPI (p<.0001), followed by history of CAD (Hazard Ratio (HR)= t 5.9, p=0.0001), diabetic retinopathy (HR=10.0, p=0.001) and inability to exercise (HR=7.7, p=0.02). Patients with normal 1VIPI had a low revascularisation rate of 2.4% during the follow-up period. Compared to normal MPI, cardiovascular events increased 5.2 fold for reversible defects, 8.5 fold for fixed defects and 20.1 fold for the association of both defects. Conclusion: Diabetic patients with normal MPI had an excellent prognosis independently of history of CAD. On the opposite, an abnormal MPI led to a > 5 fold increase in cardiovascular events. This emphasizes the value of SPECT MPI in predicting and risk-stratifying cardiovascular events in diabetic patients. Mots-Clés Diabète; maladie coronarienne; ischémie myocardique; valeur pronostique; tomoscintigraphie myocardique de perfusion par émission monophotonique Résumé Objectifs: Déterminer la valeur pronostique à long terme de la tomoscintigraphie myocardique de perfusion (TSMP) chez les patients diabétiques pour prédire les événements cardiovasculaires (ECV). Méthodes: Etude de 210 diabétiques caucasiens consécutifs référés pour une TSMP. Les courbes de survie ont été déterminées par Kaplan-Meier et les facteurs prédictifs indépendants par analyses multivariées de type Cox. Résultats: Le suivi a été complet chez 200 (95%) patients avec une durée médiane de 3.0 ans (0.8-50). La population était composée de 114 (57%) hommes, âge moyen 65±10 ans, avec 181 (90.5%) diabète de type 2, 50 (25%) antécédents de maladie coronarienne (AMC) et 98 (49%) patients connus pour un angor avant la TSMP. La prévalence de TSMP anormales était de 58%. Aucun décès d'origine cardiaque ou infarctus du myocarde n'est survenu chez les patients avec une TSMP normale, ceci indépendamment de leurs AMC et des douleurs thoraciques. Les facteurs prédictifs indépendants pour les ECV sont une TSMP anormale (p<.0001), les AMC (Hazard Ratio (HR)=15.9, p-0.0001), suivi de la rétinopathie diabétique (HR-10.0, p=0.001) et de l'incapacité à effectuer un exercice (HR=7.7, p=0.02). Les patients avec une TSMP normale ont présenté un taux de revascularisations de 2.4%. La présence de défauts mixtes accroît le risque d'ECV de 20.1 fois, les défauts fixes de 8.5 fois et les défauts réversibles de 5.2 fois comparés aux sujets avec une TSMP normale. Conclusion: Les patients diabétiques, coronariens ou non, avec une tomoscintigraphie myocardique de perfusion normale ont un excellent pronostique. A l'opposé, une TSMP anormale est associée à une augmentation du risque d'ECV de plus de 5 fois. Ceci confirme l'utilité de la TSMP dans la stratification du risque chez les patients diabétiques.

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).

Fluorodeoxyglucose-positron emission tomography scan-positive recurrent papillary thyroid cancer and the prognosis and implications for surgical management.

BACKGROUND To compare outcomes for patients with recurrent or persistent papillary thyroid cancer (PTC) who had metastatic tumors that were fluorodeoxyglucose-positron emission tomography (FDG-PET) positive or negative, and to determine whether the FDG-PET scan findings changed the outcome of medical and surgical management. METHODS From a prospective thyroid cancer database, we retrospectively identified patients with recurrent or persistent PTC and reviewed data on demographics, initial stage, location and extent of persistent or recurrent disease, clinical management, disease-free survival and outcome. We further identified subsets of patients who had an FDG-PET scan or an FDG-PET/CT scan and whole-body radioactive iodine scans and categorized them by whether they had one or more FDG-PET-avid (PET-positive) lesions or PET-negative lesions. The medical and surgical treatments and outcome of these patients were compared. RESULTS Between 1984 and 2008, 41 of 141 patients who had recurrent or persistent PTC underwent FDG-PET (n = 11) or FDG-PET/CT scans (n = 30); 22 patients (54%) had one or more PET-positive lesion(s), 17 (41%) had PET-negative lesions, and two had indeterminate lesions. Most PET-positive lesions were located in the neck (55%). Patients who had a PET-positive lesion had a significantly higher TNM stage (P = 0.01), higher age (P = 0.03), and higher thyroglobulin (P = 0.024). Only patients who had PET-positive lesions died (5/22 vs. 0/17 for PET-negative lesions; P = 0.04). In two of the seven patients who underwent surgical resection of their PET-positive lesions, loco-regional control was obtained without evidence of residual disease. CONCLUSION Patients with recurrent or persistent PTC and FDG-PET-positive lesions have a worse prognosis. In some patients loco-regional control can be obtained without evidence of residual disease by reoperation if the lesion is localized in the neck or mediastinum.

Peritoneal carcinomatosis due to ovarian cancer: comparison between MDCT, MRI and positron emission tomography-computed tomography with F18-fluorodeoxyglucose

Purpose: To compare MDCT, MRI and 18F-FDG PET/CT for the detection of peritoneal carcinomatosis due to ovarian cancerMethods and Materials: Fifteen women (mean age 65±) with clinical suspicion of ovarian cancer and peritoneal carcinomatosis underwent MDCT, MRI and 18F-FDG PET/CT, simultaneously and shortly performed before surgery (delay 8.1± days). According to the peritoneal cancer index nine abdominopelvic regions were defined. We applied four scores of lesion size on MDCT and MR images, while the maximal standard uptake value (SUVmax) was measured on 18F-FDG PET/CT. Three sites of lymphadenopathy and posterobasal pleural carcinomatosis were also analyzed. First, one radiologist blindly and separately read MDCT and MR images, while one nuclear physician blindly read PET/CT images grading each lesion according to four diagnostic certitudes. Secondly, all the images were reviewed jointly and compared with histopathology. Receiver operating characteristics (ROC) analysis was performed.Results: Peritoneal implants were proven in ten women (75%). Altogether, 228 abdominopelvic sites were compared. Sensitivity and specificity for MDCT was 90.2% and 90.6%, for MRI 93.5% and 86.3%, and for 18F-FDG PET/CT 92.7% and 95.7%, respectively. ROC area under the curve were 0.93 for MDCT and MRI, and 0.96 for 18F-FDG PET/CT respectively. No significant differences (p=0.11) were found between the three modalities.Conclusion: Although MRI revealed to be the most sensitive and 18F-FDG PET/CT the most specific modality, no significant differences were shown between the three techniques.

Engineering the thermal emission of macroporous silicon

Todos los cuerpos emiten luz espontaneamente al ser calentados. El espectro de radiacion es una funcion de la temperatura y el material. Sin embargo, la mayoria de los materiales irradia, en general, en una banda espectral amplia. Algunas matereiales, por el contrario, son capaces de concentrar la radiacion termica en una banda espectral mucho mas estrecha. Estos materiales se conocen como emisores selectivos y su uso tiene un profundo impacto en la eficiencia de sistemas sistemas tales como iluminacion y conversion de energia termofotovoltaica. De los emisores selectivos se espera que sean capaces de operar a altas temperaturas y que emitan en una banda espectral muy concisa. Uno de los metodos mas prometedores para controlar y disenar el espectro de emision termico es la utilizacion de cristales fotonicos. Los cristales fotonicos son estructuras periodicas artificiales capaces de controlar y confinar la luz de formas sin precedentes. Sin embargo, la produccion de dichas estructuras con grandes superficies y capaces de soportar altas temperaturas sigue siendo una dificil tarea. Este trabajo esta dedicada al estudio de las propiedades de emision termica de estructuras 3D de silicio macroporoso en el rango espectral mid-IR (2-30 m). En particular, este trabajo se enfoca en reducir la elevada emisividad del silicio cristalino. Las muestras estudiadas en este trabajo tienen una periodicidad de 4 m, lo que limitan los resultados obtenidos a la banda del infrarrojo medio, aunque estructuras mucho mas pequenas son tecnologicamente realizables con el metodo de fabricacion utilizado. Hemos demostrado que el silicio macroporoso 3D puede inhibir completamente la emision termica en su superficie. Mas aun, esta banda se puede ajustar en un amplio margen mediante pequenos cambios durante la formacion de los macroporos. Tambien hemos demostrado que tanto el ancho como la frecuencia de la banda de inhibicion se puede doblar mediante la aplicacion de tecnicas de postprocesado adecuadas. Finalmente hemos mostrado que es posible crear bandas de baja emisividad arbitrariamente anchas mediante estructuras macroporosas aperiodicas.

Increased audiovisual integration in cochlear-implanted deaf patients: independent components analysis of longitudinal positron emission tomography data.

It has been demonstrated in earlier studies that patients with a cochlear implant have increased abilities for audio-visual integration because the crude information transmitted by the cochlear implant requires the persistent use of the complementary speech information from the visual channel. The brain network for these abilities needs to be clarified. We used an independent components analysis (ICA) of the activation (H2 (15) O) positron emission tomography data to explore occipito-temporal brain activity in post-lingually deaf patients with unilaterally implanted cochlear implants at several months post-implantation (T1), shortly after implantation (T0) and in normal hearing controls. In between-group analysis, patients at T1 had greater blood flow in the left middle temporal cortex as compared with T0 and normal hearing controls. In within-group analysis, patients at T0 had a task-related ICA component in the visual cortex, and patients at T1 had one task-related ICA component in the left middle temporal cortex and the other in the visual cortex. The time courses of temporal and visual activities during the positron emission tomography examination at T1 were highly correlated, meaning that synchronized integrative activity occurred. The greater involvement of the visual cortex and its close coupling with the temporal cortex at T1 confirm the importance of audio-visual integration in more experienced cochlear implant subjects at the cortical level.

Structural and functional brain changes in middle-aged type 2 diabetic patients: a cross-sectional study.

BACKGROUND Type 2 diabetes mellitus (T2DM) is an emerging risk factor for cognitive impairment. Whether this impairment is a direct effect of this metabolic disorder on brain function, a consequence of vascular disease, or both, remains unknown. Structural and functional neuroimaging studies in patients with T2DM could help to elucidate this question. OBJECTIVE We designed a cross-sectional study comparing 25 T2DM patients with 25 age- and gender-matched healthy control participants. Clinical information, APOE genotype, lipid and glucose analysis, structural cerebral magnetic resonance imaging including voxel-based morphometry, and F-18 fluorodeoxyglucose positron emission tomography were obtained in all subjects. METHODS Gray matter densities and metabolic differences between groups were analyzed using statistical parametric mapping. In addition to comparing the neuroimaging profiles of both groups, we correlated neuroimaging findings with HbA1c levels, duration of T2DM, and insulin resistance measurement (HOMA-IR) in the diabetic patients group. Results: Patients with T2DM presented reduced gray matter densities and reduced cerebral glucose metabolism in several fronto-temporal brain regions after controlling for various vascular risk factors. Furthermore, within the T2DM group, longer disease duration, and higher HbA1c levels and HOMA-IR were associated with lower gray matter density and reduced cerebral glucose metabolism in fronto-temporal regions. CONCLUSION In agreement with previous reports, our findings indicate that T2DM leads to structural and metabolic abnormalities in fronto-temporal areas. Furthermore, they suggest that these abnormalities are not entirely explained by the role of T2DM as a cardiovascular risk factor.

Use of DNA profiles for investigation using a simulated national DNA database: Part II. Statistical and ethical considerations on familial searching.

Familial searching consists of searching for a full profile left at a crime scene in a National DNA Database (NDNAD). In this paper we are interested in the circumstance where no full match is returned, but a partial match is found between a database member's profile and the crime stain. Because close relatives share more of their DNA than unrelated persons, this partial match may indicate that the crime stain was left by a close relative of the person with whom the partial match was found. This approach has successfully solved important crimes in the UK and the USA. In a previous paper, a model, which takes into account substructure and siblings, was used to simulate a NDNAD. In this paper, we have used this model to test the usefulness of familial searching and offer guidelines for pre-assessment of the cases based on the likelihood ratio. Siblings of "persons" present in the simulated Swiss NDNAD were created. These profiles (N=10,000) were used as traces and were then compared to the whole database (N=100,000). The statistical results obtained show that the technique has great potential confirming the findings of previous studies. However, effectiveness of the technique is only one part of the story. Familial searching has juridical and ethical aspects that should not be ignored. In Switzerland for example, there are no specific guidelines to the legality or otherwise of familial searching. This article both presents statistical results, and addresses criminological and civil liberties aspects to take into account risks and benefits of familial searching.

JULIDE: a software tool for 3D reconstruction and statistical analysis of autoradiographic mouse brain sections.

In this article we introduce JULIDE, a software toolkit developed to perform the 3D reconstruction, intensity normalization, volume standardization by 3D image registration and voxel-wise statistical analysis of autoradiographs of mouse brain sections. This software tool has been developed in the open-source ITK software framework and is freely available under a GPL license. The article presents the complete image processing chain from raw data acquisition to 3D statistical group analysis. Results of the group comparison in the context of a study on spatial learning are shown as an illustration of the data that can be obtained with this tool.

Statistical discrimination of steroid profiles in doping control with support vector machines.

Due to their performance enhancing properties, use of anabolic steroids (e.g. testosterone, nandrolone, etc.) is banned in elite sports. Therefore, doping control laboratories accredited by the World Anti-Doping Agency (WADA) screen among others for these prohibited substances in urine. It is particularly challenging to detect misuse with naturally occurring anabolic steroids such as testosterone (T), which is a popular ergogenic agent in sports and society. To screen for misuse with these compounds, drug testing laboratories monitor the urinary concentrations of endogenous steroid metabolites and their ratios, which constitute the steroid profile and compare them with reference ranges to detect unnaturally high values. However, the interpretation of the steroid profile is difficult due to large inter-individual variances, various confounding factors and different endogenous steroids marketed that influence the steroid profile in various ways. A support vector machine (SVM) algorithm was developed to statistically evaluate urinary steroid profiles composed of an extended range of steroid profile metabolites. This model makes the interpretation of the analytical data in the quest for deviating steroid profiles feasible and shows its versatility towards different kinds of misused endogenous steroids. The SVM model outperforms the current biomarkers with respect to detection sensitivity and accuracy, particularly when it is coupled to individual data as stored in the Athlete Biological Passport.