The branch-site test of positive selection is surprisingly robust but lacks power under synonymous substitution saturation and variation in GC

Positive selection is widely estimated from protein coding sequence alignments by the nonsynonymous-to-synonymous ratio omega. Increasingly elaborate codon models are used in a likelihood framework for this estimation. Although there is widespread concern about the robustness of the estimation of the omega ratio, more efforts are needed to estimate this robustness, especially in the context of complex models. Here, we focused on the branch-site codon model. We investigated its robustness on a large set of simulated data. First, we investigated the impact of sequence divergence. We found evidence of underestimation of the synonymous substitution rate for values as small as 0.5, with a slight increase in false positives for the branch-site test. When dS increases further, underestimation of dS is worse, but false positives decrease. Interestingly, the detection of true positives follows a similar distribution, with a maximum for intermediary values of dS. Thus, high dS is more of a concern for a loss of power (false negatives) than for false positives of the test. Second, we investigated the impact of GC content. We showed that there is no significant difference of false positives between high GC (up to similar to 80%) and low GC (similar to 30%) genes. Moreover, neither shifts of GC content on a specific branch nor major shifts in GC along the gene sequence generate many false positives. Our results confirm that the branch-site is a very conservative test.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Childhood epilepsy with neuropsychological regression and continuous spike waves during sleep: epilepsy surgery in a young adult.

We describe the case of a man with a history of complex partial seizures and severe language, cognitive and behavioural regression during early childhood (3.5 years), who underwent epilepsy surgery at the age of 25 years. His early epilepsy had clinical and electroencephalogram features of the syndromes of epilepsy with continuous spike waves during sleep and acquired epileptic aphasia (Landau-Kleffner syndrome), which we considered initially to be of idiopathic origin. Seizures recurred at 19 years and presurgical investigations at 25 years showed a lateral frontal epileptic focus with spread to Broca's area and the frontal orbital regions. Histopathology revealed a focal cortical dysplasia, not visible on magnetic resonance imaging. The prolonged but reversible early regression and the residual neuropsychological disorders during adulthood were probably the result of an active left frontal epilepsy, which interfered with language and behaviour during development. Our findings raise the question of the role of focal cortical dysplasia as an aetiology in the syndromes of epilepsy with continuous spike waves during sleep and acquired epileptic aphasia.

A novel HIV vaccine adjuvanted by IC31 induces robust and persistent humoral and cellular immunity.

The HIV vaccine strategy that, to date, generated immune protection consisted of a prime-boost regimen using a canarypox vector and an HIV envelope protein with alum, as shown in the RV144 trial. Since the efficacy was weak, and previous HIV vaccine trials designed to generate antibody responses failed, we hypothesized that generation of T cell responses would result in improved protection. Thus, we tested the immunogenicity of a similar envelope-based vaccine using a mouse model, with two modifications: a clade C CN54gp140 HIV envelope protein was adjuvanted by the TLR9 agonist IC31®, and the viral vector was the vaccinia strain NYVAC-CN54 expressing HIV envelope gp120. The use of IC31® facilitated immunoglobulin isotype switching, leading to the production of Env-specific IgG2a, as compared to protein with alum alone. Boosting with NYVAC-CN54 resulted in the generation of more robust Th1 T cell responses. Moreover, gp140 prime with IC31® and alum followed by NYVAC-CN54 boost resulted in the formation and persistence of central and effector memory populations in the spleen and an effector memory population in the gut. Our data suggest that this regimen is promising and could improve the protection rate by eliciting strong and long-lasting humoral and cellular immune responses.

Partisan targeting of inter-governmental transfers & state interference in local elections: evidence from Spain

[spa] En este trabajo examinamos si, en la asignación de transferencias, los gobernantes regionales discriminan a favor de los gobiernos locales controlados por el mismo partido político, y si las perspectivas electorales de los gobiernos locales mejoran si están políticamente alineados con el gobierno regional. Con una nueva base de datos que considera 3.000 municipios españoles durante el período 2000-07 y un diseño de discontinuidad en la regresión, documentamos un efecto robusto de importante magnitud: en elecciones ajustadas, los municipios alineados con el gobierno regional reciben, en media, un 83% más de transferencias per cápita y su gobernante obtiene un 10% más de votos en las elecciones locales. También demostramos que el efecto de la alineación política es mayor: (i) si las elecciones regionales y locales se celebran el mismo día, (ii) en regiones donde las elecciones regionales son menos competidas, y (iii) en regiones con más recursos presupuestarios.

Partisan targeting of inter-governmental transfers & state interference in local elections: evidence from Spain

[spa] En este trabajo examinamos si, en la asignación de transferencias, los gobernantes regionales discriminan a favor de los gobiernos locales controlados por el mismo partido político, y si las perspectivas electorales de los gobiernos locales mejoran si están políticamente alineados con el gobierno regional. Con una nueva base de datos que considera 3.000 municipios españoles durante el período 2000-07 y un diseño de discontinuidad en la regresión, documentamos un efecto robusto de importante magnitud: en elecciones ajustadas, los municipios alineados con el gobierno regional reciben, en media, un 83% más de transferencias per cápita y su gobernante obtiene un 10% más de votos en las elecciones locales. También demostramos que el efecto de la alineación política es mayor: (i) si las elecciones regionales y locales se celebran el mismo día, (ii) en regiones donde las elecciones regionales son menos competidas, y (iii) en regiones con más recursos presupuestarios.

Longitudinal analyses of renal lesions due to acute pyelonephritis in children and their impact on renal growth.

PURPOSE: Acute pyelonephritis is a common condition in children, and can lead to renal scarring. The aim of this study was to analyze the progression of renal scarring with time and its impact on renal growth. MATERIALS AND METHODS: A total of 50 children who had renal scarring on dimercapto-succinic acid scan 6 months after acute pyelonephritis underwent a repeat scan 3 years later. Lesion changes were evaluated by 3 blinded observers, and were classified as no change, partial resolution or complete disappearance. Renal size at time of acute pyelonephritis and after 3 years was obtained by ultrasound, and renal growth was assessed comparing z-score for age between the 2 measures. Robust linear regression was used to identify determinants of renal growth. RESULTS: At 6 months after acute pyelonephritis 88 scars were observed in 100 renal units. No change was observed in 27%, partial resolution in 63% and complete disappearance in 9% of lesions. Overall, 72% of lesions improved. Increased number of scars was associated with high grade vesicoureteral reflux (p = 0.02). Multivariate analysis showed that the number of scars was the most important parameter leading to decreased renal growth (CI -1.05 to -0.35, p <0.001), and with 3 or more scars this finding was highly significant on univariate analysis (-1.59, CI -2.10 to -1.09, p <0.0001). CONCLUSIONS: Even 6 months after acute pyelonephritis 72% of dimercapto-succinic acid defects improved, demonstrating that some of the lesions may be not definitive. The number of scars was significantly associated with loss of renal growth at 3 years.

Robust vaccine-elicited cellular immune responses in breast milk following systemic Simian Immunodeficiency Virus DNA prime and live virus vector boost vaccination of lactating rhesus monkeys.

Breast milk transmission of HIV remains an important mode of infant HIV acquisition. Enhancement of mucosal HIV-specific immune responses in milk of HIV-infected mothers through vaccination may reduce milk virus load or protect against virus transmission in the infant gastrointestinal tract. However, the ability of HIV/SIV strategies to induce virus-specific immune responses in milk has not been studied. In this study, five uninfected, hormone-induced lactating, Mamu A*01(+) female rhesus monkey were systemically primed and boosted with rDNA and the attenuated poxvirus vector, NYVAC, containing the SIVmac239 gag-pol and envelope genes. The monkeys were boosted a second time with a recombinant Adenovirus serotype 5 vector containing matching immunogens. The vaccine-elicited immunodominant epitope-specific CD8(+) T lymphocyte response in milk was of similar or greater magnitude than that in blood and the vaginal tract but higher than that in the colon. Furthermore, the vaccine-elicited SIV Gag-specific CD4(+) and CD8(+) T lymphocyte polyfunctional cytokine responses were more robust in milk than in blood after each virus vector boost. Finally, SIV envelope-specific IgG responses were detected in milk of all monkeys after vaccination, whereas an SIV envelope-specific IgA response was only detected in one vaccinated monkey. Importantly, only limited and transient increases in the proportion of activated or CCR5-expressing CD4(+) T lymphocytes in milk occurred after vaccination. Therefore, systemic DNA prime and virus vector boost of lactating rhesus monkeys elicits potent virus-specific cellular and humoral immune responses in milk and may warrant further investigation as a strategy to impede breast milk transmission of HIV.

Immunotherapies for uro-genital cancers

Les cancers du col utérin et de la vessie prennent tous deux leur origine dans les sites muqueux et peuvent évoluer lentement de lésions superficielles (lésions squameuses intra-épithéliales de bas à haut grade (HSIL) et carcinomes in situ du col utérin (CIS); ou tumeurs non musculo-invasives de la vessie (NMIBC)) à des cancers invasifs plus avancés. L'éthiologie de ces deux cancers est néanmoins très différente. Le cancer du col utérin est, à l'échelle mondiale, le deuxième cancer le plus mortel chez la femme. Ce cancer résulte de l'infection des cellules basales de l'épithélium stratifié du col utérin par le papillomavirus humain à haut risque (HPV). Les vaccins prophylactiques récemment développés contre le HPV (Gardasil® et Cervarix®) sont des moyens de prévention efficaces lorsqu'ils sont administrés chez les jeunes filles qui ne sont pas encore sexuellement actives; cependant ces vaccins ne permettent pas la régression des lésions déjà existantes. Malgré un développement actif, les vaccins thérapeutiques ciblant les oncogènes viraux E6/E7 n'ont montré qu'une faible efficacité clinique jusqu'à présent. Nous avons récemment démontré qu'une immunisation sous-cutanée (s.c.) était capable de faire régresser les petites tumeurs génitales chez 90% des souris, mais chez seulement 20% des souris présentant de plus grandes tumeurs. Dans cette étude, nous avons développé une nouvelle stratégie où la vaccination est associée à une application locale (intra-vaginale (IVAG)) d'agonistes de TLR. Celle-ci induit une augmentation des cellules T CD8 totales ainsi que T CD8 spécifiques au vaccin, mais pas des cellules T CD4. L'attraction sélective des cellules T CD8 est permise par leur expression des récepteurs de chemokines CCR5 et CXCR3 ainsi que par les ligants E-selectin. La vaccination, suivie de l'application IVAG de CpG, a conduit, chez 75% des souris, à la régression de grandes tumeurs établies. Le cancer de la vessie est le deuxième cancer urologique le plus fréquente. La plupart des tumeurs sont diagnostiquées comme NMIBC et sont restreintes à la muqueuse de la vessie, avec une forte propension à la récurrence et/ou progression après une résection locale. Afin de développer des vaccins contre les antigènes associés à la tumeur (TAA), il est nécessaire de trouver un moyen d'induire une réponse immunitaire CD8 spécifique dans la vessie. Pour ce faire, nous avons comparé différentes voies d'immunisation, en utilisant un vaccin composé d'adjuvants et de l'oncogène de HPV (E7) comme modèle. Les vaccinations s.c. et IVAG ont toutes deux induit un nombre similaire de cellules T CD8 spécifiques du vaccin dans la vessie, alors que l'immunisation intra-nasale fut inefficace. Les voies s.c. et IVAG ont induit des cellules T CD8 spécifiques du vaccin exprimant principalement aL-, a4- et le ligand d'E-selectin, suggérant que ces intégrines/sélectines sont responsables de la relocalisation des cellules T dans la vessie. Une unique immunisation avec E7 a permis une protection tumorale complète lors d'une étude prophylactique, indépendemment de la voie d'immunisation. Dans une étude thérapeutique, seules les vaccinations s.c. et IVAG ont efficacement conduit, chez environ 50% des souris, à la régression de tumeurs de la vessie établies, alors que l'immunisation intra-nasale n'a eu aucun effet. La régression de la tumeur est correlée avec l'infiltration dans la tumeur des cellules T CD8 spécifiques au vaccin et la diminution des cellules T régulatrices (Tregs). Afin d'augmenter l'efficacité de l'immunisation avec le TAA, nous avons testé une vaccination suivie de l'instillation d'agonistes de TLR3 et TLR9, ou d'un vaccin Salmonella Typhi (Ty21a). Cette stratégie a entraîné une augmentation des cellules T CD8 effectrices spécifiques du vaccin dans la vessie, bien qu'à différentes échelles. Ty21a étant l'immunostimulant le plus efficace, il mérite d'être étudié de manière plus approfondie dans le contexte du NMIBC. - Both cervical and bladder cancer originates in mucosal sites and can slowly progress from superficial lesions (low to high-grade squamous intra-epithelial lesions (HSIL) and carcinoma in situ (CIS) in the cervix; or non-muscle invasive tumors in the bladder (NMIBC)), to more advanced invasive cancers. The etiology of these two cancers is however very different. Cervical cancer is the second most common cause of cancer death in women worldwide. This cancer results from the infection of the basal cells of the stratified epithelium of the cervix by high-risk human papillomavirus (HPV). The recent availability of prophylactic vaccines (Gardasil® and Cervarix®) against HPV is an effective strategy to prevent this cancer when administered to young girls before sexual activity; however, these vaccines do not induce regression of established lesions. Despite active development, therapeutic vaccines targeting viral oncogenes E6/E7 had limited clinical efficacy to date. We recently reported that subcutaneous (s.c.) immunization was able to regress small genital tumors in 90% of the mice, but only 20% of mice had regression of larger tumors. Here, we developed a new strategy where vaccination is combined with the local (intravaginal (IVAG)) application of TLR agonists. This new strategy induced an increase of both total and vaccine-specific CD8 T cells in cervix-vagina, but not CD4 T cells. The selective attraction of CD8 T cells is mediated by the expression of CCR5 and CXCR3 chemokine receptors and E-selectin ligands in these cells. Vaccination followed by IVAG application of CpG resulted in tumor regression of large established tumors in 75% of the mice. Bladder cancer is the second most common urological malignancy. Most tumors are diagnosed as NMIBC, and are restricted to the mucosal bladder with a high propensity to recur and/or progress after local resection. Aiming to develop vaccines against tumor associated antigens (TAA) it is necessary to investigate how to target vaccine-specific T-cell immune responses to the bladder. Here we thus compared using an adjuvanted HPV oncogene (E7) vaccine, as a model, different routes of immunization. Both s.c. and IVAG vaccination induced similar number of vaccine-specific CD8 T-cells in the bladder, whereas intranasal (i.n.) immunization was ineffective. S.c. and IVAG routes induced predominantly aL-, a4- and E-selectin ligand-expressing vaccine-specific CD8 T-cells suggesting that these integrin/selectin are responsible for T-cell homing to the bladder. A single E7 immunization conferred full tumor protection in a prophylactic setting, irrespective of the immunization route. In a therapeutic setting, only ivag and s.c. vaccination efficiently regressed established bladder-tumors in ca. 50 % of mice, whereas i.n. immunization had no effect. Tumor regression correlated with vaccine- specific CD8 T cell tumor-infiltration and decrease of regulatory T cells (Tregs). To increase efficacy of TAA immunization, we tested vaccination followed by the local instillation of TLR3 or TLR9 agonist or of a Salmonella Typhi vaccine (Ty21a). This strategy resulted in an increase of vaccine-specific effector CD8 T cells in the bladder, although at different magnitudes. Ty21a being the most efficient, it deserves further investigation in the context of NMIBC. We further tested another strategy to improve therapies of NMIBC. In the murine MB49 bladder tumor model, we replaced the intravesical (ives) BCG therapy by another vaccine strain the Salmonella Ty21a. Ives Ty21a induced bladder tumor regression at least as efficiently as BCG. Ty21a bacteria did not infect nor survive neither in healthy nor in tumor-bearing bladders, suggesting its safety. Moreover, Ty21a induced a transient inflammatory response in healthy bladders, mainly through infiltration of neutrophils and macrophages that rapidly returned to basal levels, confirming its potential safety. The tumor regression was associated to a robust infiltration of immune cells, and secretion of cytokines in urines. Infection of murine tumor cell lines by Ty21a resulted in cell apoptosis. The infection of both murine and human urothelial cell lines induced secretion of in vitro inflammatory cytokines. Ty21a may be an attractive alternative for the ives treatment of NMIBC after transurethral resection and thus deserves more investigation.

Stability and adaptability of runner peanut genotypes based on nonlinear regression and AMMI analysis

The objective of this work was to estimate the stability and adaptability of pod and seed yield in runner peanut genotypes based on the nonlinear regression and AMMI analysis. Yield data from 11 trials, distributed in six environments and three harvests, carried out in the Northeast region of Brazil during the rainy season were used. Significant effects of genotypes (G), environments (E), and GE interactions were detected in the analysis, indicating different behaviors among genotypes in favorable and unfavorable environmental conditions. The genotypes BRS Pérola Branca and LViPE‑06 are more stable and adapted to the semiarid environment, whereas LGoPE‑06 is a promising material for pod production, despite being highly dependent on favorable environments.

Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks

This paper presents the general regression neural networks (GRNN) as a nonlinear regression method for the interpolation of monthly wind speeds in complex Alpine orography. GRNN is trained using data coming from Swiss meteorological networks to learn the statistical relationship between topographic features and wind speed. The terrain convexity, slope and exposure are considered by extracting features from the digital elevation model at different spatial scales using specialised convolution filters. A database of gridded monthly wind speeds is then constructed by applying GRNN in prediction mode during the period 1968-2008. This study demonstrates that using topographic features as inputs in GRNN significantly reduces cross-validation errors with respect to low-dimensional models integrating only geographical coordinates and terrain height for the interpolation of wind speed. The spatial predictability of wind speed is found to be lower in summer than in winter due to more complex and weaker wind-topography relationships. The relevance of these relationships is studied using an adaptive version of the GRNN algorithm which allows to select the useful terrain features by eliminating the noisy ones. This research provides a framework for extending the low-dimensional interpolation models to high-dimensional spaces by integrating additional features accounting for the topographic conditions at multiple spatial scales. Copyright (c) 2012 Royal Meteorological Society.

Robust Detection of Incumbents in Cognitive Radio Networks Using Groups

Cognitive radio is a wireless technology aimed at improvingthe efficiency use of the radio-electric spectrum, thus facilitating a reductionin the load on the free frequency bands. Cognitive radio networkscan scan the spectrum and adapt their parameters to operate in the unoccupiedbands. To avoid interfering with licensed users operating on a givenchannel, the networks need to be highly sensitive, which is achieved byusing cooperative sensing methods. Current cooperative sensing methodsare not robust enough against occasional or continuous attacks. This articleoutlines a Group Fusion method that takes into account the behavior ofusers over the short and long term. On fusing the data, the method is basedon giving more weight to user groups that are more unanimous in their decisions.Simulations have been performed in a dynamic environment withinterferences. Results prove that when attackers are present (both reiterativeor sporadic), the proposed Group Fusion method has superior sensingcapability than other methods.

A robust audio watermarking scheme based on MPEG 1 Layer 3 Compression

This paper describes an audio watermarking scheme based on lossy compression. The main idea is taken from an image watermarking approach where the JPEG compression algorithm is used to determine where and how the mark should be placed. Similarly, in the audio scheme suggested in this paper, an MPEG 1 Layer 3 algorithm is chosen for compression to determine the position of the mark bits and, thus, the psychoacoustic masking of the MPEG 1 Layer 3compression is implicitly used. This methodology provides with a high robustness degree against compression attacks. The suggested scheme is also shown to succeed against most of the StirMark benchmark attacks for audio.

Robust power allocation algorithms for MIMO OFDM systems with imperfect CSI

This paper presents a Bayesian approach to the design of transmit prefiltering matrices in closed-loop schemes robust to channel estimation errors. The algorithms are derived for a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Two different optimizationcriteria are analyzed: the minimization of the mean square error and the minimization of the bit error rate. In both cases, the transmitter design is based on the singular value decomposition (SVD) of the conditional mean of the channel response, given the channel estimate. The performance of the proposed algorithms is analyzed,and their relationship with existing algorithms is indicated. As withother previously proposed solutions, the minimum bit error rate algorithmconverges to the open-loop transmission scheme for very poor CSI estimates.