Combining classification techniques to define topo-climatic landscapes

Landscape classification tackles issues related to the representation and analysis of continuous and variable ecological data. In this study, a methodology is created in order to define topo-climatic landscapes (TCL) in the north-west of Catalonia (north-east of the Iberian Peninsula). TCLs relate the ecological behaviour of a landscape in terms of topography, physiognomy and climate, which compound the main drivers of an ecosystem. Selected variables are derived from different sources such as remote sensing and climatic atlas. The proposed methodology combines unsupervised interative cluster classification with a supervised fuzzy classification. As a result, 28 TCLs have been found for the study area which may be differentiated in terms of vegetation physiognomy and vegetation altitudinal range type. Furthermore a hierarchy among TCLs is set, enabling the merging of clusters and allowing for changes of scale. Through the topo-climatic landscape map, managers may identify patches with similar environmental conditions and asses at the same time the uncertainty involved.

Kernels semàntics per a clustering de patrons

Memòria elaborada a partir d’una estada al projecte Proteus de la New York University entre abril i juny del 2007. Les tècniques de clustering poden ajudar a reduir la supervisió en processos d’obtenció de patrons per a Extracció d’Informació. Tanmateix, és necessari disposar d’algorismes adequats a documents, i aquests algorismes requereixen mesures adequades de similitud entre patrons. Els kernels poden oferir una solució a aquests problemes, però l’aprenentatge no supervisat requereix d’estrat`egies m´es astutes que l’aprenentatge supervisat per a incorporar major quantitat d’informació. En aquesta memòria, fruit de la meva estada de mes d’Abril al de Juny de 2007 al projecte. Proteus de la New York University, es proposen i avaluen diversos kernels sobre patrons. Ini- cialment s’estudien kernels amb una família de patrons restringits, i a continuació s’apliquen kernels ja usats en tasques supervisades d’Extracció d’Informació. Degut a la degradació del rendiment que experimenta el clustering a l’afegir informació irrellevant, els kernels se simpli- fiquen i es busquen estratègies per a incorporar-hi semàntica de forma selectiva. Finalment, s’estudia quin efecte té aplicar clustering sobre el coneixement semàntic com a pas previ al clustering de patrons. Les diverses estratègies s’avaluen en tasques de clustering de documents i patrons usant dades reals.

Intraobserver and interobserver variability of ascending aorta diameter measurements as assessed by ECG-gated MDCT : automatic versus manual measurements : P15

Purpose: Revolutionary endovascular treatments are on the verge of being available for management of ascending aortic diseases. Morphometric measurements of the ascending aorta have already been done with ECG-gated MDCT to help such therapeutic development. However the reliability of these measurements remains unknown. The objective of this work was to compare the intraobserver and interobserver variability of CAD (computer aided diagnosis) versus manual measurements in the ascending aorta. Methods and materials: Twenty-six consecutive patients referred for ECG-gated CT thoracic angiography (64-row CT scanner) were evaluated. Measurements of the maximum and minimum ascending aorta diameters at mid-distance between the brachiocephalic artery and the aortic valve were obtained automatically with a commercially available CAD and manually by two observers separately. Both observers repeated the measurements during a different session at least one month after the first measurements. Intraclass coefficients as well the Bland and Altman method were used for comparison between measurements. Two-paired t-test was used to determine the significance of intraobserver and interobserver differences (alpha = 0.05). Results: There is a significant difference between CAD and manual measurements in the maximum diameter (p = 0.004) for the first observer, whereas the difference was significant for minimum diameter between the second observer and the CAD (p <0.001). Interobserver variability showed a weak agreement when measurements were done manually. Intraobserver variability was lower with the CAD compared to the manual measurements (limits of variability: from -0.7 to 0.9 mm for the former and from -1.2 to 1.3 mm for the latter). Conclusion: In order to improve reproductibility of measurements whenever needed, pre- and post-therapeutic management of the ascending aorta may benefit from follow-up done by a unique observer with the help of CAD.

Optimal Universal and Categorical Benefits with Classification Errors and Imperfect Enforcement

We determine he optimal combination of a universal benefit, B, and categorical benefit, C, for an economy in which individuals differ in both their ability to work - modelled as an exogenous zero quantity constraint on labour supply - and, conditional on being able to work, their productivity at work. C is targeted at those unable to work, and is conditioned in two dimensions: ex-ante an individual must be unable to work and be awarded the benefit, whilst ex-post a recipient must not subsequently work. However, the ex-ante conditionality may be imperfectly enforced due to Type I (false rejection) and Type II (false award) classification errors, whilst, in addition, the ex-post conditionality may be imperfectly enforced. If there are no classification errors - and thus no enforcement issues - it is always optimal to set C>0, whilst B=0 only if the benefit budget is sufficiently small. However, when classification errors occur, B=0 only if there are no Type I errors and the benefit budget is sufficiently small, while the conditions under which C>0 depend on the enforcement of the ex-post conditionality. We consider two discrete alternatives. Under No Enforcement C>0 only if the test administering C has some discriminatory power. In addition, social welfare is decreasing in the propensity to make each type error. However, under Full Enforcement C>0 for all levels of discriminatory power. Furthermore, whilst social welfare is decreasing in the propensity to make Type I errors, there are certain conditions under which it is increasing in the propensity to make Type II errors. This implies that there may be conditions under which it would be welfare enhancing to lower the chosen eligibility threshold - support the suggestion by Goodin (1985) to "err on the side of kindness".

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

Introduction: As part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints. Methods: We used gene-expression data from 230 breast cancers (grouped into training and independent validation sets), and we examined 40 predictors (five univariate feature-selection methods combined with eight different classifiers) for each of the three endpoints. Their classification performance was estimated on the training set by using two different resampling methods and compared with the accuracy observed in the independent validation set. Results: A ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set. The bootstrapping estimates were closer to the validation performance than were the cross-validation estimates. The required sample size for each endpoint was estimated, and both gene-level and pathway-level analyses were performed on the obtained models. Conclusions: We showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.

Aspects of classification of Hemiptera hemocytes from six triatomine species

The objective of this work was to characterize, and compare different morphological types of hemocytes of Rhodnius prolixus, Rhodnius, Rhodnius neglectus, Triatoma infestans, Panstrongylus megistus, and Dipetalogaster maximus. This information provides the basis for studying the cellular immune systems of these insects. Seven morphological hemocyte types wereidentified by phase-contrast microscopy: prohemocytes, plasmatocytes, granular cells, cytocytes, oenocytoids, adipohemocytes and giant cells. All seven types of hemocytes are not present in every species. For example, adipohemocytes and oenocytoids were not observed in P. megistus and P. infestans, and giant cells were rarely found in any of the species studied. The hemocytes of rhodnius and Dipetalogaster are more similar to each other than those from Triatoma and Panstrongylus which in turn closely resemble each other. Emphasis is placed on methodological problems arising in this work wicah are discussed in detail.

II Workshop on the use of Computed Tomography (CT) in pig carcass classification. Other CT applications: live animals and meat technology, 16-17th April 2009, Monells (Girona), Spain

Es va realitzar el II Workshop en Tomografia Computeritzada (TC) a Monells. El primer dia es va dedicar íntegrament a la utilització del TC en temes de classificació de canals porcines, i el segon dia es va obrir a altres aplicacions del TC, ja sigui en animals vius o en diferents aspectes de qualitat de la carn o els productes carnis. Al workshop hi van assistir 45 persones de 12 països de la UE. The II workshop on the use of Computed Tomography (CT) in pig carcass classification. Other CT applications: live animals and meat technology was held in Monells. The first day it was dedicated to the use of CT in pig carcass classification. The segond day it was open to otehr CT applications, in live animals or in meat and meat products quality. There were 45 assistants of 12 EU countries.

The SOCS3-independent expression of IDO2 supports the homeostatic generation of T regulatory cells by human dendritic cells.

Dendritic cells (DCs) are professional APCs that have a role in the initiation of adaptive immune responses and tolerance. Among the tolerogenic mechanisms, the expression of the enzyme IDO1 represents an effective tool to generate T regulatory cells. In humans, different DC subsets express IDO1, but less is known about the IDO1-related enzyme IDO2. In this study, we found a different pattern of expression and regulation between IDO1 and IDO2 in human circulating DCs. At the protein level, IDO1 is expressed only in circulating myeloid DCs (mDCs) and is modulated by PGE2, whereas IDO2 is expressed in both mDCs and plasmacytoid DCs and is not modulated by PGE2. In healthy subjects, IDO1 expression requires the presence of PGE2 and needs continuous transcription and translation, whereas IDO2 expression is constitutive, independent from suppressor of cytokine signaling 3 activity. Conversely, in patients suffering from inflammatory arthritis, circulating DCs express both IDO1 and IDO2. At the functional level, both mDCs and plasmacytoid DCs generate T regulatory cells through an IDO1/IDO2-dependent mechanism. We conclude that, in humans, whereas IDO1 provides an additional mechanism of tolerance induced by proinflammatory mediators, IDO2 is stably expressed in steady-state conditions and may contribute to the homeostatic tolerogenic capacity of DCs.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Robotic, fluoroscopic or EMG assisted pedicle screw insertion. A CT based comparative study

Introduction: In order to improve safety of pedicle screw placement several techniques have been developed. More recently robotically assisted pedicle insertion has been introduced aiming at increasing accuracy. The aim of this study was to compare this new technique with the two main pedicle insertion techniques in our unit namely fluoroscopically assisted vs EMG aided insertion. Material and methods: A total of 382 screws (78 thoracic,304 lumbar) were introduced in 64 patients (m/f = 1.37, equally distributed between insertion technique groups) by a single experienced spinal surgeon. From those, 64 (10 thoracic, 54 lumbar) were introduced in 11 patients using a miniature robotic device based on pre operative CT images under fluoroscopic control. 142 (4 thoracic, 138 lumbar) screws were introduced using lateral fluoroscopy in 27 patients while 176 (64 thoracic, 112 lumbar) screws in 26 patients were inserted using both fluoroscopy and EMG monitoring. There was no difference in the distribution of scoliotic spines between the 3 groups (n = 13). Screw position was assessed by an independent observer on CTs in axial, sagittal and coronal planes using the Rampersaud A to D classification. Data of lumbar and thoracic screws were processed separately as well as data obtained from axial, sagittal and coronal CT planes. Results: Intra- and interobserver reliability of the Rampersaud classification was moderate, (0.35 and 0.45 respectively) being the least good on axial plane. The total number of misplaced screws (C&D grades) was generally low (12 thoracic and 12 lumbar screws). Misplacement rates were same in straight and scoliotic spines. The only difference in misplacement rates was observed on axial and coronal images in the EMG assisted thoracic screw group with a higher proportion of C or D grades (p <0.05) in that group. Recorded compound muscle action potentials (CMAP) values of the inserted screws were 30.4 mA for the robot and 24.9mA for the freehand technique with a CI of 3.8 of the mean difference of 5.5 mA. Discussion: Robotic placement did improve the placement of thoracic screws but not that of lumbar screws possibly because our misplacement rates in general near that of published navigation series. Robotically assisted spine surgery might therefore enhance the safety of screw placement in particular in training settings were different users at various stages of their learning curve are involved in pedicle instrumentation.

Why do creative industries cluster? An analysis of the determinants of clustering of creative industries

Creative industries tend to concentrate mainly around large- and medium-sized cities, forming creative local production systems. The text analyses the forces behind clustering of creative industries to provide the first empirical explanation of the determinants of creative employment clustering following a multidisciplinary approach based on cultural and creative economics, evolutionary geography and urban economics. A comparative analysis has been performed for Italy and Spain. The results show different patterns of creative employment clustering in both countries. The small role of historical and cultural endowments, the size of the place, the average size of creative industries, the productive diversity and the concentration of human capital and creative class have been found as common factors of clustering in both countries.

Transcriptional activation of the utrophin promoter B by a constitutively active Ets-transcription factor.

Duchenne muscular dystrophy is an X-linked genetic disease caused by the absence of functional dystrophin. Pharmacological upregulation of utrophin, the autosomal homologue of dystrophin, offers a potential therapeutic approach to treat Duchenne patients. Full-length utrophin mRNA is transcribed from two alternative promoters, called A and B. In contrast to the utrophin promoter A, little is known about the factors regulating the activity of the utrophin promoter B. Computer analysis of this second promoter revealed the presence of several conserved binding motives for Ets-transcription factors. Using electrotransfer of cDNA into mouse muscles, we demonstrate that a genetically modified beta-subunit of the Ets-transcription factor GA-binding protein potently activates a utrophin promoter B reporter construct in innervated muscle fibers in vivo. These results make the GA-binding protein and the signaling cascade regulating its activity in muscle cells, potential targets for the pharmacological modulation of utrophin expression in Duchenne patients.

Classification of immature stage habitats of Culicidae (Diptera) collected in Córdoba, Argentina

In order to classify mosquito immature stage habitats, samples were taken in 42 localities of Córdoba Province, Argentina, representing the phytogeographic regions of Chaco, Espinal and Pampa. Immature stage habitats were described and classified according to the following criteria: natural or artificial; size; location related to light and neighboring houses; vegetation; water: permanence, movement, turbidity and pH. Four groups of species were associated based on the habitat similarity by means of cluster analysis: Aedes albifasciatus, Culex saltanensis, Cx. mollis, Cx. brethesi, Psorophora ciliata, Anopheles albitarsis, and Uranotaenia lowii (Group A); Cx. acharistus, Cx. quinquefasciatus, Cx. bidens, Cx. dolosus, Cx. maxi and Cx. apicinus (Group B); Cx. coronator, Cx. chidesteri, Mansonia titillans and Ps. ferox (Group C); Ae. fluviatilis and Ae. milleri (Group D). The principal component analysis (ordination method) pointed out that the different types of habitats, their nature (natural or artificial), plant species, water movement and depth are the main characters explaining the observed variation among the mosquito species. The distribution of mosquito species by phytogeographic region did not affect the species groups, since species belonging to different groups were collected in the same region.

Clustering or scattering: the underlying reason for regulating distance among retail outlets

Concerns on the clustering of retail industries and professional services in main streets had traditionally been the public interest rationale for supporting distance regulations. Although many geographic restrictions have been suppressed, deregulation has hinged mostly upon the theory results on the natural tendency of outlets to differentiate spatially. Empirical evidence has so far offered mixed results. Using the case of deregulation of pharmacy establishment in a region of Spain, we empirically show how pharmacy locations scatter, and that there is not rationale for distance regulation apart from the underlying private interest of very few incumbents.

Structured output SVM for remote sensing image classification

In the recent years, kernel methods have revealed very powerful tools in many application domains in general and in remote sensing image classification in particular. The special characteristics of remote sensing images (high dimension, few labeled samples and different noise sources) are efficiently dealt with kernel machines. In this paper, we propose the use of structured output learning to improve remote sensing image classification based on kernels. Structured output learning is concerned with the design of machine learning algorithms that not only implement input-output mapping, but also take into account the relations between output labels, thus generalizing unstructured kernel methods. We analyze the framework and introduce it to the remote sensing community. Output similarity is here encoded into SVM classifiers by modifying the model loss function and the kernel function either independently or jointly. Experiments on a very high resolution (VHR) image classification problem shows promising results and opens a wide field of research with structured output kernel methods.