Out-of-sample forecast tests robust to the choice of window size

This paper proposes new methodologies for evaluating out-of-sample forecastingperformance that are robust to the choice of the estimation window size. The methodologies involve evaluating the predictive ability of forecasting models over a wide rangeof window sizes. We show that the tests proposed in the literature may lack the powerto detect predictive ability and might be subject to data snooping across differentwindow sizes if used repeatedly. An empirical application shows the usefulness of themethodologies for evaluating exchange rate models' forecasting ability.

Prediction of cross-recognition of peptide-HLA A2 by Melan-A-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationships.

The cross-recognition of peptides by cytotoxic T lymphocytes is a key element in immunology and in particular in peptide based immunotherapy. Here we develop three-dimensional (3D) quantitative structure-activity relationships (QSARs) to predict cross-recognition by Melan-A-specific cytotoxic T lymphocytes of peptides bound to HLA A*0201 (hereafter referred to as HLA A2). First, we predict the structure of a set of self- and pathogen-derived peptides bound to HLA A2 using a previously developed ab initio structure prediction approach [Fagerberg et al., J. Mol. Biol., 521-46 (2006)]. Second, shape and electrostatic energy calculations are performed on a 3D grid to produce similarity matrices which are combined with a genetic neural network method [So et al., J. Med. Chem., 4347-59 (1997)] to generate 3D-QSAR models. The models are extensively validated using several different approaches. During the model generation, the leave-one-out cross-validated correlation coefficient (q (2)) is used as the fitness criterion and all obtained models are evaluated based on their q (2) values. Moreover, the best model obtained for a partitioned data set is evaluated by its correlation coefficient (r = 0.92 for the external test set). The physical relevance of all models is tested using a functional dependence analysis and the robustness of the models obtained for the entire data set is confirmed using y-randomization. Finally, the validated models are tested for their utility in the setting of rational peptide design: their ability to discriminate between peptides that only contain side chain substitutions in a single secondary anchor position is evaluated. In addition, the predicted cross-recognition of the mono-substituted peptides is confirmed experimentally in chromium-release assays. These results underline the utility of 3D-QSARs in peptide mimetic design and suggest that the properties of the unbound epitope are sufficient to capture most of the information to determine the cross-recognition.

Cytolytic T lymphocyte recognition of the murine cytomegalovirus nonstructural immediate-early protein pp89 expressed by recombinant vaccinia virus.

The murine immediate-early (IE) protein pp89 is a nonstructural virus-encoded phosphoprotein residing in the nucleus of infected cells, where it acts as transcriptional activator. Frequency analysis has shown that in BALB/c mice the majority of virus-specific CTL recognize IE antigens. The present study was performed to assess whether pp89 causes membrane antigen expression detected by IE-specific CTL. Site-directed mutagenesis has been used to delete the introns from gene ieI, encoding pp89, for subsequent integration of the continuous coding sequence into the vaccinia virus genome. After infection with the vaccinia recombinant, the authentic pp89 was expressed in cells that became susceptible to lysis by an IE-specific CTL clone. Priming of mice with the vaccinia recombinant sensitized polyclonal CTL that recognized MCMV-infected cells and transfected cells expressing pp89. Thus, a herpesviral IE polypeptide with essential function in viral transcriptional regulation can also serve as a dominant antigen for the specific CTL response of the host.

Recognition of major histoincompatibilities after transplantation with marrow from HLA closely matched donors.

BACKGROUND: To determine the extent to which major histoincompatibilities are recognized after bone marrow transplantation, we characterized the specificity of the cytotoxic T lymphocytes isolated during graft-versus-host disease. We studied three patients transplanted with marrow from donors who were histoincompatible for different types of HLA antigens. METHODS: Patient 1 was mismatched for one "ABDR-antigen" (HLA-A2 versus A3). Two patients were mismatched for antigens that would usually not be taken into account by standard selection procedures: patient 2 was mismatched for an "HLA-A subtype" (A*0213 versus A*0201), whereas patient 3 was mismatched for HLA-C (HLA-C*0501 versus HLA-C*0701). All three HLA class I mismatches were detected by a pretransplant cytotoxic precursor test. RESULTS: Analysis of the specificity of the cytotoxic T lymphocyte clones isolated after transplantation showed that the incompatibilities detected by the pretransplant cytotoxic precursor assay were the targets recognized during graft-versus-host disease. CONCLUSIONS: Independent of whether the incompatibility consisted of a "full" mismatch, a "subtype" mismatch, or an HLA-C mismatch, all clones recognized the incompatible HLA molecule. In addition, some of these clones had undergone antigen selection and were clearly of higher specificity than the ones established before transplantation, indicating that they had been participating directly in the antihost immune response.

Low TCR avidity and lack of tumor cell recognition in CD8(+) T cells primed with the CEA-analogue CAP1-6D peptide.

The use of "altered peptide ligands" (APL), epitopes designed for exerting increased immunogenicity as compared with native determinants, represents nowadays one of the most utilized strategies for overcoming immune tolerance to self-antigens and boosting anti-tumor T cell-mediated immune responses. However, the actual ability of APL-primed T cells to cross-recognize natural epitopes expressed by tumor cells remains a crucial concern. In the present study, we show that CAP1-6D, a superagonist analogue of a carcinoembriyonic antigen (CEA)-derived HLA-A*0201-restricted epitope widely used in clinical setting, reproducibly promotes the generation of low-affinity CD8(+) T cells lacking the ability to recognized CEA-expressing colorectal carcinoma (CRC) cells. Short-term T cell cultures, obtained by priming peripheral blood mononuclear cells from HLA-A*0201(+) healthy donors or CRC patients with CAP1-6D, were indeed found to heterogeneously cross-react with saturating concentrations of the native peptide CAP1, but to fail constantly lysing or recognizing through IFN- gamma release CEA(+)CRC cells. Characterization of anti-CAP1-6D T cell avidity, gained through peptide titration, CD8-dependency assay, and staining with mutated tetramers (D227K/T228A), revealed that anti-CAP1-6D T cells exerted a differential interaction with the two CEA epitopes, i.e., displaying high affinity/CD8-independency toward the APL and low affinity/CD8-dependency toward the native CAP1 peptide. Our data demonstrate that the efficient detection of self-antigen expressed by tumors could be a feature of high avidity CD8-independent T cells, and underline the need for extensive analysis of tumor cross-recognition prior to any clinical usage of APL as anti-cancer vaccines.

Maximum likelihood Linear Programming Data Fusion for Speaker Recognition

Biometric system performance can be improved by means of data fusion. Several kinds of information can be fused in order to obtain a more accurate classification (identification or verification) of an input sample. In this paper we present a method for computing the weights in a weighted sum fusion for score combinations, by means of a likelihood model. The maximum likelihood estimation is set as a linear programming problem. The scores are derived from a GMM classifier working on a different feature extractor. Our experimental results assesed the robustness of the system in front a changes on time (different sessions) and robustness in front a change of microphone. The improvements obtained were significantly better (error bars of two standard deviations) than a uniform weighted sum or a uniform weighted product or the best single classifier. The proposed method scales computationaly with the number of scores to be fussioned as the simplex method for linear programming.

Non-Linear and Non-Conventional Speech Processing: Alternative Techniques

This special issue aims to cover some problems related to non-linear and nonconventional speech processing. The origin of this volume is in the ISCA Tutorial and Research Workshop on Non-Linear Speech Processing, NOLISP’09, held at the Universitat de Vic (Catalonia, Spain) on June 25–27, 2009. The series of NOLISP workshops started in 2003 has become a biannual event whose aim is to discuss alternative techniques for speech processing that, in a sense, do not fit into mainstream approaches. A selected choice of papers based on the presentations delivered at NOLISP’09 has given rise to this issue of Cognitive Computation.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Improving a leaves automatic recognition process using PCA

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used, and Principal Component Analysis (PCA) is applied in order to study which is the best number of components for the classification task, implemented by means of a Support Vector Machine (SVM) System. Obtained results are satisfactory, and compared with [4] our system improves the recognition success, diminishing the variance at the same time.

Automatic Recognition of Leaves by Shape Detection Pre-Processing with Ica

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used to characterize the leaves. Independent Component Analysis (ICA) is then applied in order to study which is the best number of components to be considered for the classification task, implemented by means of an Artificial Neural Network (ANN). Obtained results with ICA as a pre-processing tool are satisfactory, and compared with some references our system improves the recognition success up to 80.8% depending on the number of considered independent components.

Empirical mode decomposition-based face recognition system

In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition.