Mixed spectral-structural classification of very high resolution images with summation kernels

In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.

Land use/cover classification in the Brazilian Amazon using satellite images

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Cluster-based active learning for compact image classification

In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the pruning of the tree that best matches the labels of the sampled points. By choosing the part of the tree to sample from according to current pruning's uncertainty, sampling is focused on most uncertain clusters. This way, large clusters for which the class membership is already fixed are no longer queried and sampling is focused on division of clusters showing mixed labels. The model is tested on a VHR image in a multiclass classification setting. The method clearly outperforms random sampling in a transductive setting, but cannot generalize to unseen data, since it aims at optimizing the classification of a given cluster structure.

Persisting atypical and cystic forms of Borrelia burgdorferi and local inflammation in Lyme neuroborreliosis

BACKGROUND: The long latent stage seen in syphilis, followed by chronic central nervous system infection and inflammation, can be explained by the persistence of atypical cystic and granular forms of Treponema pallidum. We investigated whether a similar situation may occur in Lyme neuroborreliosis. METHOD: Atypical forms of Borrelia burgdorferi spirochetes were induced exposing cultures of Borrelia burgdorferi (strains B31 and ADB1) to such unfavorable conditions as osmotic and heat shock, and exposure to the binding agents Thioflavin S and Congo red. We also analyzed whether these forms may be induced in vitro, following infection of primary chicken and rat neurons, as well as rat and human astrocytes. We further analyzed whether atypical forms similar to those induced in vitro may also occur in vivo, in brains of three patients with Lyme neuroborreliosis. We used immunohistochemical methods to detect evidence of neuroinflammation in the form of reactive microglia and astrocytes. RESULTS: Under these conditions we observed atypical cystic, rolled and granular forms of these spirochetes. We characterized these abnormal forms by histochemical, immunohistochemical, dark field and atomic force microscopy (AFM) methods. The atypical and cystic forms found in the brains of three patients with neuropathologically confirmed Lyme neuroborreliosis were identical to those induced in vitro. We also observed nuclear fragmentation of the infected astrocytes using the TUNEL method. Abundant HLA-DR positive microglia and GFAP positive reactive astrocytes were present in the cerebral cortex. CONCLUSION: The results indicate that atypical extra- and intracellular pleomorphic and cystic forms of Borrelia burgdorferi and local neuroinflammation occur in the brain in chronic Lyme neuroborreliosis. The persistence of these more resistant spirochete forms, and their intracellular location in neurons and glial cells, may explain the long latent stage and persistence of Borrelia infection. The results also suggest that Borrelia burgdorferi may induce cellular dysfunction and apoptosis. The detection and recognition of atypical, cystic and granular forms in infected tissues is essential for the diagnosis and the treatment as they can occur in the absence of the typical spiral Borrelia form.

Development of a harmonised method for the profiling of amphetamine

Résumé : Cette thèse de doctorat est le fruit d'un projet de recherche européen financé par le quatrième programme cadre de la Commission Européenne (DG XII, Standards, Measurement and Testing). Ce projet, dénommé SMT-CT98-2277, a été financé pour la partie suisse par l'Office Fédéral de l'Education et de la Science (OFES, Berne, Suisse). Le but de ce projet était de développer une méthode harmonisée et collaborativement testée pour le profilage des impuretés de l'amphétamine illicite par chromatographie capillaire en phase gazeuse. Le travail a été divisé en sept phases majeures qui concernaient la synthèse de l'amphétamine, l'identification d'impuretés, l'optimisation de la préparation de l'échantillon et du système chromatographique, la variabilité des résultats, l'investigation de méthodes mathématiques pour la classification et la comparaison de profils et finalement l'application de la méthode à des réels échantillons illicites. La méthode résultant de ce travail n'a pas seulement montré que les données étaient interchangeables entre laboratoires mais aussi qu'elle était supérieure en de nombreux points aux méthodes préalablement publiées dans la littérature scientifique. Abstract : This Ph.D. thesis was carried out in parallel to an European project funded by the fourth framework program of the European Commission (DG XII, Standards, Measurement and Testing). This project, named SMT-CT98-2277 was funded, for the Swiss part, by the Federal Office of Education and Science (OFES, Bern, Switzerland). The aim of the project was to develop a harmonised, collaboratively tested method for the impurity profiling of illicit amphetamine by capillary gas chromatography. The work was divided into seven main tasks which deal with the synthesis of amphetamine, identification of impurities, optimization of sample preparation and of the chromatographic system, variability of the results, investigation of numerical methods for the classification and comparison of profiles and finally application of the methodology to real illicit samples. The resulting method has not only shown to produce interchangeable data between different laboratories but was also found to be superior in many aspects to previously published methods.

A modified structural stress method for fatigue assessment of welded structures

Fatigue life assessment of weldedstructures is commonly based on the nominal stress method, but more flexible and accurate methods have been introduced. In general, the assessment accuracy is improved as more localized information about the weld is incorporated. The structural hot spot stress method includes the influence of macro geometric effects and structural discontinuities on the design stress but excludes the local features of the weld. In this thesis, the limitations of the structural hot spot stress method are discussed and a modified structural stress method with improved accuracy is developed and verified for selected welded details. The fatigue life of structures in the as-welded state consists mainly of crack growth from pre-existing cracks or defects. Crack growth rate depends on crack geometry and the stress state on the crack face plane. This means that the stress level and shape of the stress distribution in the assumed crack path governs thetotal fatigue life. In many structural details the stress distribution is similar and adequate fatigue life estimates can be obtained just by adjusting the stress level based on a single stress value, i.e., the structural hot spot stress. There are, however, cases for which the structural stress approach is less appropriate because the stress distribution differs significantly from the more common cases. Plate edge attachments and plates on elastic foundations are some examples of structures with this type of stress distribution. The importance of fillet weld size and weld load variation on the stress distribution is another central topic in this thesis. Structural hot spot stress determination is generally based on a procedure that involves extrapolation of plate surface stresses. Other possibilities for determining the structural hot spot stress is to extrapolate stresses through the thickness at the weld toe or to use Dong's method which includes through-thickness extrapolation at some distance from the weld toe. Both of these latter methods are less sensitive to the FE mesh used. Structural stress based on surface extrapolation is sensitive to the extrapolation points selected and to the FE mesh used near these points. Rules for proper meshing, however, are well defined and not difficult to apply. To improve the accuracy of the traditional structural hot spot stress, a multi-linear stress distribution is introduced. The magnitude of the weld toe stress after linearization is dependent on the weld size, weld load and plate thickness. Simple equations have been derived by comparing assessment results based on the local linear stress distribution and LEFM based calculations. The proposed method is called the modified structural stress method (MSHS) since the structural hot spot stress (SHS) value is corrected using information on weld size andweld load. The correction procedure is verified using fatigue test results found in the literature. Also, a test case was conducted comparing the proposed method with other local fatigue assessment methods.

Prediction of crude protein and classification of the growth stage of wheat plant samples from NIR spectra

Near-infrared spectroscopy (NIRS) was used to analyse the crude protein content of dried and milled samples of wheat and to discriminate samples according to their stage of growth. A calibration set of 72 samples from three growth stages of wheat (tillering, heading and harvest) and a validation set of 28 samples was collected for this purpose. Principal components analysis (PCA) of the calibration set discriminated groups of samples according to the growth stage of the wheat. Based on these differences, a classification procedure (SIMCA) showed a very accurate classification of the validation set samples : all of them were successfully classified in each group using this procedure when both the residual and the leverage were used in the classification criteria. Looking only at the residuals all the samples were also correctly classified except one of tillering stage that was assigned to both tillering and heading stages. Finally, the determination of the crude protein content of these samples was considered in two ways: building up a global model for all the growth stages, and building up local models for each stage, separately. The best prediction results for crude protein were obtained using a global model for samples in the two first growth stages (tillering and heading), and using a local model for the harvest stage samples.

Development of a shelling method to recover whole kernels of the Cutia nut (Couepia edulis)

The kernel of the cutia nut (castanha-de-cutia, Couepia edulis (Prance) Prance) of the western Amazon, which is consumed by the local population, has traditionally been extracted from the nut with a machete, a dangerous procedure that only produces kernels cut in half. A shelling off machine prototype, which produces whole kernels without serious risks to its operator, is described and tested. The machine makes a circular cut in the central part of the fruit shell, perpendicular to its main axis. Three ways of conditioning the fruits before cutting were compared: (1) control; (2) oven drying immediately prior to cutting; (3) oven drying, followed by a 24-hour interval before cutting. The time needed to extract and separate the kernel from the endocarp and testa was measured. Treatment 3 produced the highest output: 63 kernels per hour, the highest percentage of whole kernels (90%), and the best kernel taste. Kernel extraction with treatment 3 required 50% less time than treatment 1, while treatment 2 needed 38% less time than treatment 1. The proportion of kernels attached to the testa was 93%, 47%, and 8% for treatments 1, 2, and 3, respectively, and was the main reason for extraction time differences.

Molecular method for the characterization of Coxiella burnetii from clinical and environmental samples: variability of genotypes in Spain

Background: Coxiella burnetii is a highly clonal microorganism which is difficult to culture, requiring BSL3 conditions for its propagation. This leads to a scarce availability of isolates worldwide. On the other hand, published methods of characterization have delineated up to 8 different genomic groups and 36 genotypes. However, all these methodologies, with the exception of one that exhibited limited discriminatory power (3 genotypes), rely on performing between 10 and 20 PCR amplifications or sequencing long fragments of DNA, which make their direct application to clinical samples impracticable and leads to a scarce accessibility of data on the circulation of C. burnetii genotypes. Results: To assess the variability of this organism in Spain, we have developed a novel method that consists of a multiplex (8 targets) PCR and hybridization with specific probes that reproduce the previous classification of this organism into 8 genomic groups, and up to 16 genotypes. It allows for a direct haracterization from clinical and environmental samples in a single run, which will help in the study of the different genotypes circulating in wild and domestic cycles as well as from sporadic human cases and outbreaks. The method has been validated with reference isolates. A high variability of C. burnetii has been found in Spain among 90 samples tested, detecting 10 different genotypes, being those adaA negative associated with acute Q fever cases presenting as fever of intermediate duration with liver involvement and with chronic cases. Genotypes infecting humans are also found in sheep, goats, rats, wild boar and ticks, and the only genotype found in cattle has never been found among our clinical samples. Conclusions: This newly developed methodology has permitted to demonstrate that C. burnetii is highly variable in Spain. With the data presented here, cattle seem not to participate in the transmission of C. burnetii to humans in the samples studied, while sheep, goats, wild boar, rats and ticks share genotypes with the human population.

Classification-Based compression of Multispectral Images

The purpose of this thesis is to present a new approach to the lossy compression of multispectral images. Proposed algorithm is based on combination of quantization and clustering. Clustering was investigated for compression of the spatial dimension and the vector quantization was applied for spectral dimension compression. Presenting algo¬rithms proposes to compress multispectral images in two stages. During the first stage we define the classes' etalons, another words to each uniform areas are located inside the image the number of class is given. And if there are the pixels are not yet assigned to some of the clusters then it doing during the second; pass and assign to the closest eta¬lons. Finally a compressed image is represented with a flat index image pointing to a codebook with etalons. The decompression stage is instant too. The proposed method described in this paper has been tested on different satellite multispectral images from different resources. The numerical results and illustrative examples of the method are represented too.

Traditional local medicines in the republic of Palau and non-communicable diseases (NCD), signs of effectiveness.

ETHNOPHARMACOLOGICAL RELEVANCE: The aim of this survey was to describe which traditional medicines (TM) are most commonly used for non-communicable diseases (NCD - diabetes, hypertension related to excess weight and obesity) in Pacific islands and with what perceived effectiveness. NCD, especially prevalent in the Pacific, have been subject to many public health interventions, often with rather disappointing results. Innovative interventions are required; one hypothesis is that some local, traditional approaches may have been overlooked. MATERIALS AND METHODS: The method used was a retrospective treatment-outcome study in a nation-wide representative sample of the adult population (about 15,000 individuals) of the Republic of Palau, an archipelago of Micronesia. RESULTS: From 188 respondents (61% female, age 16-87, median 48,), 30 different plants were used, mostly self-prepared (69%), or from a traditional healer (18%). For excess weight, when comparing the two most frequent plants, Morinda citrifolia L. was associated with more adequate outcome than Phaleria nishidae Kaneh. (P=0.05). In case of diabetes, when comparing Phaleria nishidae (=Phaleria nisidai) and Morinda citrifolia, the former was statistically more often associated with the reported outcome "lower blood sugar" (P=0.01). CONCLUSIONS: Statistical association between a plant used and reported outcome is not a proof of effectiveness or safety, but it can help select plants of interest for further studies, e.g. through a reverse pharmacology process, in search of local products which may have a positive impact on population health.

EMS mutagenesis in mature seed-derived rice calli as a new method for rapidly obtaining tilling mutant populations.

Background: TILLING (Targeting Induced Local Lesions IN Genomes) is a reverse genetic method that combines chemical mutagenesis with high-throughput genome-wide screening for point mutation detection in genes of interest. However, this mutation discovery approach faces a particular problem which is how to obtain a mutant population with a sufficiently high mutation density. Furthermore, plant mutagenesis protocols require two successive generations (M1, M2) for mutation fixation to occur before the analysis of the genotype can begin. Results: Here, we describe a new TILLING approach for rice based on ethyl methanesulfonate (EMS) mutagenesis of mature seed-derived calli and direct screening of in vitro regenerated plants. A high mutagenesis rate was obtained (i.e. one mutation in every 451 Kb) when plants were screened for two senescence-related genes. Screening was carried out in 2400 individuals from a mutant population of 6912. Seven sense change mutations out of 15 point mutations were identified. Conclusions: This new strategy represents a significant advantage in terms of time-savings (i.e. more than eight months), greenhouse space and work during the generation of mutant plant populations. Furthermore, this effective chemical mutagenesis protocol ensures high mutagenesis rates thereby saving in waste removal costs and the total amount of mutagen needed thanks to the mutagenesis volume reduction.

Spatial radial maze procedures and setups to dissociate local and distal relational spatial frameworks in humans.

BACKGROUND: Radial maze tasks have been used to assess optimal foraging and spatial abilities in rodents. The spatial performance was based on a capacity to rely on a configuration of local and distant cues. We adapted maze procedures assessing the relative weight of local cues and distant landmarks for arm choice in humans. NEW METHOD: The procedure allowed testing memory of places in four experimental setups: a fingertip texture-groove maze, a tactile screen maze, a virtual radial maze and a walking size maze. During training, the four reinforced positions remained fixed relative to local and distal cues. During subsequent conflict trials, these frameworks were made conflictive in the prediction of reward locations. RESULTS: Three experiments showed that the relative weight of local and distal relational cues is affected by different factors such as cues' nature, visual access to the environment, real vs. virtual environment, and gender. A fourth experiment illustrated how a walking maze can be used with people suffering intellectual disability. COMPARISON WITH EXISTING METHODS: In our procedure, long-term (reference) and short-term (working) memory can be assessed. It is the first radial task adapted to human that enables dissociating local and distal cues, to provides an indication as to their relative salience. Our mazes are moveable and easily used in limited spaces. Tasks are performed with realistic and spontaneous though controlled exploratory movements. CONCLUSION: Our tasks enabled highlighting the use of different strategies. In a clinical perspective, considering the use of compensatory strategies should orient towards adapted behavioural rehabilitation.

Feature Selection, Ranking of Each Feature and Classification for the Diagnosis of Community Acquired Legionella Pneumonia

Diagnosis of community acquired legionella pneumonia (CALP) is currently performed by means of laboratory techniques which may delay diagnosis several hours. To determine whether ANN can categorize CALP and non-legionella community-acquired pneumonia (NLCAP) and be standard for use by clinicians, we prospectively studied 203 patients with community-acquired pneumonia (CAP) diagnosed by laboratory tests. Twenty one clinical and analytical variables were recorded to train a neural net with two classes (LCAP or NLCAP class). In this paper we deal with the problem of diagnosis, feature selection, and ranking of the features as a function of their classification importance, and the design of a classifier the criteria of maximizing the ROC (Receiving operating characteristics) area, which gives a good trade-off between true positives and false negatives. In order to guarantee the validity of the statistics; the train-validation-test databases were rotated by the jackknife technique, and a multistarting procedure was done in order to make the system insensitive to local maxima.

Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units.

PURPOSE: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. METHOD: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). RESULTS: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. CONCLUSION: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information.