The glasses of the transept's rosette of the Cathedral of Tarragona: characterization, classification and decay.

The glasses of the rosette forming the main window of the transept of the Gothic Cathedral of Tarragona have been characterised by means of SEM/EDS, XRD, FTIR and electronic microprobe. The multivariate statistical treatment of these data allow to establish a classification of the samples forming groups having an historical significance and reflecting ancient restorations. Furthermore, the decay patterns and mechanisms have been determined and the weathering by-products characterised. It has been demonstrated a clear influence of the bioactivity in the decay of these glasses, which activity is partially controlled by the chemical composition of the glasses.

ACID-SENSING ION CHANNELS: functional and molecular characterization in putative nociceptors, and modulation by nerve injury and serine protease

Résumé Les canaux ioniques ASICs (acid-sensing ion channels) appartiennent à la famille des canaux ENaC/Degenerin. Pour l'instant, quatre gènes (1 à 4) ont été clonés dont certains présentent des variants d'épissage. Leur activation par une acidification rapide du milieu extracellulaire génère un courant entrant transitoire essentiellement sodique accompagné pour certains types d'ASICs d'une phase soutenue. Les ASICs sont exprimés dans le système nerveux, central (SNC) et périphérique (SNP). On leur attribue un rôle dans l'apprentissage, la mémoire et l'ischémie cérébrale au niveau central ainsi que dans la nociception (douleur aiguë et inflammatoire) et la méchanotransduction au niveau périphérique. Toutefois, les données sont parfois contradictoires. Certaines études suggèrent qu'ils sont des senseurs primordiaux impliqués dans la détection de l'acidification et la douleur. D'autres études suggèrent plutôt qu'ils ont un rôle modulateur inhibiteur dans la douleur. De plus, le fait que leur activation génère majoritairement un courant transitoire alors que les fibres nerveuses impliquées dans la douleur répondent à un stimulus nocif avec une adaptation lente suggère que leurs propriétés doivent être modulés par des molécules endogènes. Dans une première partie de ma thèse, nous avons abordé la question de l'expression fonctionnelle des ASICs dans les neurones sensoriels primaires afférents du rat adulte pour clarifier le rôle des ASICs dans les neurones sensoriels. Nous avons caractérisé leurs propriétés biophysiques et pharmacologiques par la technique du patch-clamp en configuration « whole-cell ». Nous avons pu démontrer que près de 60% des neurones sensoriels de petit diamètre expriment des courants ASICs. Nous avons mis en évidence trois types de courant ASIC dans ces neurones. Les types 1 et 3 ont des propriétés compatibles avec un rôle de senseur du pH alors que le type 2 est majoritairement activé par des pH inférieurs à pH6. Le type 1 est médié par des homomers de la sous-unité ASIC1 a qui sont perméables aux Ca2+. Nous avons étudié leur co-expression avec des marqueurs des nocicepteurs ainsi que la possibilité d'induire une activité neuronale suite à une acidification qui soit dépendante des ASICs. Le but était d'associer un type de courant ASIC avec une fonction potentielle dans les neurones sensoriels. Une majorité des neurones exprimant les courants ASIC co-expriment des marqueurs des nocicepteurs. Toutefois, une plus grande proportion des neurones exprimant le type 1 n'est pas associée à la nociception par rapport aux types 2 et 3. Nous avons montré qu'il est possible d'induire des potentiels d'actions suite à une acidification. La probabilité d'induction est proportionnelle à la densité des courants ASIC et à l'acidité de la stimulation. Puis, nous avons utilisé cette classification comme un outil pour appréhender les potentielles modulations fonctionnelles des ASICs dans un model de neuropathie (spared nerve injury). Cette approche fut complétée par des expériences de «quantitative RT-PCR ». En situation de neuropathie, les courants ASIC sont dramatiquement changés au niveau de leur expression fonctionnelle et transcriptionnelle dans les neurones lésés ainsi que non-lésés. Dans une deuxième partie de ma thèse, suite au test de différentes substances sécrétées lors de l'inflammation et l'ischémie sur les propriétés des ASICs, nous avons caractérisé en détail la modulation des propriétés des courants ASICs notamment ASIC1 par les sérines protéases dans des systèmes d'expression recombinants ainsi que dans des neurones d'hippocampe. Nous avons montré que l'exposition aux sérine-protéases décale la dépendance au pH de l'activation ainsi que la « steady-state inactivation »des ASICs -1a et -1b vers des valeurs plus acidiques. Ainsi, l'exposition aux serine protéases conduit à une diminution du courant quand l'acidification a lieu à partir d'un pH7.4 et conduit à une augmentation du courant quand l'acidification alleu à partir d'un pH7. Nous avons aussi montré que cette régulation a lieu des les neurones d'hippocampe. Nos résultats dans les neurones sensoriels suggèrent que certains courants ASICs sont impliqués dans la transduction de l'acidification et de la douleur ainsi que dans une des phases du processus conduisant à la neuropathie. Une partie des courants de type 1 perméables au Ca 2+ peuvent être impliqués dans la neurosécrétion. La modulation par les sérines protéases pourrait expliquer qu'en situation d'acidose les canaux ASICs soient toujours activables. Résumé grand publique Les neurones sont les principales cellules du système nerveux. Le système nerveux est formé par le système nerveux central - principalement le cerveau, le cervelet et la moelle épinière - et le système nerveux périphérique -principalement les nerfs et les neurones sensoriels. Grâce à leur nombreux "bras" (les neurites), les neurones sont connectés entre eux, formant un véritable réseau de communication qui s'étend dans tout le corps. L'information se propage sous forme d'un phénomène électrique, l'influx nerveux (ou potentiels d'actions). A la base des phénomènes électriques dans les neurones il y a ce que l'on appelle les canaux ioniques. Un canal ionique est une sorte de tunnel qui traverse l'enveloppe qui entoure les cellules (la membrane) et par lequel passent les ions. La plupart de ces canaux sont normalement fermés et nécessitent d'être activés pour s'ouvrire et générer un influx nerveux. Les canaux ASICs sont activés par l'acidification et sont exprimés dans tout le système nerveux. Cette acidification a lieu notamment lors d'une attaque cérébrale (ischémie cérébrale) ou lors de l'inflammation. Les expériences sur les animaux ont montré que les canaux ASICs avaient entre autre un rôle dans la mort des neurones lors d'une attaque cérébrale et dans la douleur inflammatoire. Lors de ma thèse je me suis intéressé au rôle des ASICs dans la douleur et à l'influence des substances produites pendant l'inflammation sur leur activation par l'acidification. J'ai ainsi pu montrer chez le rat que la majorité des neurones sensoriels impliqués dans la douleur ont des canaux ASICs et que l'activation de ces canaux induit des potentiels d'action. Nous avons opéré des rats pour qu'ils présentent les symptômes d'une maladie chronique appelée neuropathie. La neuropathie se caractérise par une plus grande sensibilité à la douleur. Les rats neuropathiques présentent des changements de leurs canaux ASICs suggérant que ces canaux ont une peut-être un rôle dans la genèse ou les symptômes de cette maladie. J'ai aussi montré in vitro qu'un type d'enryme produit lors de l'inflammation et l'ischémie change les propriétés des ASICs. Ces résultats confirment un rôle des ASICs dans la douleur suggérant notamment un rôle jusque là encore non étudié dans la douleur neuropathique. De plus, ces résultats mettent en évidence une régulation des ASICs qui pourrait être importante si elle se confirmait in vivo de part les différents rôles des ASICs. Abstract Acid-sensing ion channels (ASICs) are members of the ENaC/Degenerin superfamily of ion channels. Their activation by a rapid extracellular acidification generates a transient and for some ASIC types also a sustained current mainly mediated by Na+. ASICs are expressed in the central (CNS) and in the peripheral (PNS) nervous system. In the CNS, ASICs have a putative role in learning, memory and in neuronal death after cerebral ischemia. In the PNS, ASICs have a putative role in nociception (acute and inflammatory pain) and in mechanotransduction. However, studies on ASIC function are somewhat controversial. Some studies suggest a crucial role of ASICs in transduction of acidification and in pain whereas other studies suggest rather a modulatory inhibitory role of ASICs in pain. Moreover, the basic property of ASICs, that they are activated only transiently is irreconcilable with the well-known property of nociception that the firing of nociceptive fibers demonstrated very little adaptation. Endogenous molecules may exist that can modulate ASIC properties. In a first part of my thesis, we addressed the question of the functional expression of ASICs in adult rat dorsal root ganglion (DRG) neurons. Our goal was to elucidate ASIC roles in DRG neurons. We characterized biophysical and pharmacological properties of ASIC currents using the patch-clamp technique in the whole-cell configuration. We observed that around 60% of small-diameter sensory neurons express ASICs currents. We described in these neurons three ASIC current types. Types 1 and 3 have properties compatible with a role of pH-sensor whereas type 2 is mainly activated by pH lower than pH6. Type 1 is mediated by ASIC1a homomultimers which are permeable to Ca 2+. We studied ASIC co-expression with nociceptor markers. The goal was to associate an ASIC current type with a potential function in sensory neurons. Most neurons expressing ASIC currents co-expressed nociceptor markers. However, a higher proportion of the neurons expressing type 1 was not associated with nociception compared to type 2 and -3. We completed this approach with current-clamp measurements of acidification-induced action potentials (APs). We showed that activation of ASICs in small-diameter neurons can induce APs. The probability of AP induction is positively correlated with the ASIC current density and the acidity of stimulation. Then, we used this classification as a tool to characterize the potential functional modulation of ASICs in the spared nerve injury model of neuropathy. This approach was completed by quantitative RT-PCR experiments. ASICs current expression was dramatically changed at the functional and transcriptional level in injured and non-injured small-diameter DRG neurons. In a second part of my thesis, following an initial screening of the effect of various substances secreted during inflammation and ischemia on ASIC current properties, we characterized in detail the modulation of ASICs, in particular of ASIC1 by serine proteases in a recombinant expression system as well as in hippocampal neurons. We showed that protease exposure shifts the pH dependence of ASIC1 activation and steady-state inactivation to more acidic pH. As a consequence, protease exposure leads to a decrease in the current response if ASIC1 is activated by a pH drop from pH 7.4. If, however, acidification occurs from a basal pH of 7, protease-exposed ASIC1a shows higher activity than untreated ASIC1a. We provided evidence that this bi-directional regulation of ASIC1a function also occurs in hippocampal neurons. Our results in DRG neurons suggest that some ASIC currents are involved in the transduction of peripheral acidification and pain. Furthermore, ASICs may participate to the processes leading to neuropathy. Some Ca 2+-permeable type 1 currents may be involved in neurosecretion. ASIC modulation by serine proteases may be physiologically relevant, allowing ASIC activation under sustained slightly acidic conditions.

Iowa Roads and Streets Functional Classification Manual, 1970

This manual provides a set of procedural rules and regulations for use in functionally classifying all roads and streets in Iowa according to the character of service they are intended to provide. Functional classification is a requirement of House File 394 (Functional Highway Classification Bill) enacted by the 63rd General Assembly of the Iowa Legislature. Functional classification is defined in this Bill as: "The grouping of roads and streets into systems according to the character of service they will be expected to provide, and the assignment of jurisdiction over each class to the governmental unit having primary interest in each type of service."

Iowa Roads and Streets Functional Classification Manual, 1975

This manual provides a set of procedural rules and regulations for use in functionally classifying all roads and streets in Iowa according to the character of service they are intended to provide. Functional classification is a requirement of the 1973 Code of Iowa (Chapter 306) as amended by Senate File 1062 enacted by the 2nd session of the 65th General Assembly of Iowa. Functional classification is defined as the grouping of roads and streets into systems according to the character of service they will be expected to provide, and the assignment of jurisdiction over each class to the governmental unit having primary interest in each type of service. Stated objectives of the legislation are: "Functional classification will serve the legislator by providing an equitable basis for determination of proper source of tax support and providing for the assignment of financial resources to the governmental unit having responsibility for each class of service. Functional classification promotes the ability of the administrator to effectively prepare and carry out long range programs which reflect the transportation needs of the public." All roads and streets in legal existence will be classified. Instructions are also included in this manual for a continuous reporting to the Highway Commission of changes in classification and/or jurisdiction resulting from new construction, corporation line changes, relocations, and deletions. This continuous updating of records is absolutely essential for modern day transportation planning as it is the only possible way to monitor the status of existing road systems, and consequently determine adequacy and needs with accuracy.

Characterization and multivariate classification of grapes and wines of two Cabernet Sauvignon clones

The objective of this work was to assess and characterize two clones, 169 and 685, of Cabernet Sauvignon grapes and to evaluate the wine produced from these grapes. The experiment was carried out in São Joaquim, SC, Brazil, during the 2009 harvest season. During grape ripening, the evolution of physical-chemical properties, phenolic compounds, organic acids, and anthocyanins was evaluated. During grape harvest, yield components were determined for each clone. Individual and total phenolics, individual and total anthocyanins, and antioxidant activity were evaluated for wine. The clones were also assessed regarding the duration of their phenological cycle. During ripening, the evolution of phenolic compounds and of physical-chemical parameters was similar for both clones; however, during harvest, significant differences were observed regarding yield, number of bunches per plant and berries per bunch, leaf area, and organic acid, polyphenol, and anthocyanin content. The wines produced from these clones showed significant differences regarding chemical composition. The clones showed similar phenological cycle and responses to bioclimatic parameters. Principal component analysis shows that clone 685 is strongly correlated with color characteristics, mainly monomeric anthocyanins, while clone 169 is correlated with individual phenolic compounds.

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

Approximate polytope ensemble for one-class classification

In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, the decision whether a point belongs to the convex hull model in high dimensional spaces is approximated by means of random projections and an ensemble decision process. Finally, a tiling strategy is proposed in order to model non-convex structures. Experimental results show that the proposed strategy is significantly better than state of the art one-class classification methods on over 200 datasets.

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.