Classification of urban multi-angular image sequences by aligning their manifolds

When dealing with multi-angular image sequences, problems of reflectance changes due either to illumination and acquisition geometry, or to interactions with the atmosphere, naturally arise. These phenomena interplay with the scene and lead to a modification of the measured radiance: for example, according to the angle of acquisition, tall objects may be seen from top or from the side and different light scatterings may affect the surfaces. This results in shifts in the acquired radiance, that make the problem of multi-angular classification harder and might lead to catastrophic results, since surfaces with the same reflectance return significantly different signals. In this paper, rather than performing atmospheric or bi-directional reflection distribution function (BRDF) correction, a non-linear manifold learning approach is used to align data structures. This method maximizes the similarity between the different acquisitions by deforming their manifold, thus enhancing the transferability of classification models among the images of the sequence.

Serrated polyps of the colon: how reproducible is their classification?

For several years, the lack of consensus on definition, nomenclature, natural history, and biology of serrated polyps (SPs) of the colon has created considerable confusion among pathologists. According to the latest WHO classification, the family of SPs comprises hyperplastic polyps (HPs), sessile serrated adenomas/polyps (SSA/Ps), and traditional serrated adenomas (TSAs). The term SSA/P with dysplasia has replaced the category of mixed hyperplastic/adenomatous polyps (MPs). The present study aimed to evaluate the reproducibility of the diagnosis of SPs based on currently available diagnostic criteria and interactive consensus development. In an initial round, H&E slides of 70 cases of SPs were circulated among participating pathologists across Europe. This round was followed by a consensus discussion on diagnostic criteria. A second round was performed on the same 70 cases using the revised criteria and definitions according to the recent WHO classification. Data were evaluated for inter-observer agreement using Kappa statistics. In the initial round, for the total of 70 cases, a fair overall kappa value of 0.318 was reached, while in the second round overall kappa value improved to moderate (kappa = 0.557; p < 0.001). Overall kappa values for each diagnostic category also significantly improved in the final round, reaching 0.977 for HP, 0.912 for SSA/P, and 0.845 for TSA (p < 0.001). The diagnostic reproducibility of SPs improves when strictly defined, standardized diagnostic criteria adopted by consensus are applied.

Determination of four sequential stages during microautophagy in vitro.

Microautophagy is the transfer of cytosolic components into the lysosome by direct invagination of the lysosomal membrane and subsequent budding of vesicles into the lysosomal lumen. This process is topologically equivalent to membrane invagination during multivesicular body formation and to the budding of enveloped viruses. Vacuoles are lysosomal compartments of yeasts. Vacuolar membrane invagination can be reconstituted in vitro with purified yeast vacuoles, serving as a model system for budding of vesicles into the lumen of an organelle. Using this in vitro system, we defined different reaction states. We identified inhibitors of microautophagy in vitro and used them as tools for kinetic analysis. This allowed us to characterize four biochemically distinguishable steps of the reaction. We propose that these correspond to sequential stages of vacuole invagination and vesicle scission. Formation of vacuolar invaginations was slow and temperature-dependent, whereas the final scission of the vesicle from a preformed invagination was fast and proceeded even on ice. Our observations suggest that the formation of invaginations rather than the scission of vesicles is the rate-limiting step of the overall reaction.

Cost-Benefit Analysis of Sequential Warning Lights in Nighttime Work Zone Tapers, TPF-5(081), 2011

Improving safety at nighttime work zones is important because of the extra visibility concerns. The deployment of sequential lights is an innovative method for improving driver recognition of lane closures and work zone tapers. Sequential lights are wireless warning lights that flash in a sequence to clearly delineate the taper at work zones. The effectiveness of sequential lights was investigated using controlled field studies. Traffic parameters were collected at the same field site with and without the deployment of sequential lights. Three surrogate performance measures were used to determine the impact of sequential lights on safety. These measures were the speeds of approaching vehicles, the number of late taper merges and the locations where vehicles merged into open lane from the closed lane. In addition, an economic analysis was conducted to monetize the benefits and costs of deploying sequential lights at nighttime work zones. The results of this study indicates that sequential warning lights had a net positive effect in reducing the speeds of approaching vehicles, enhancing driver compliance, and preventing passenger cars, trucks and vehicles at rural work zones from late taper merges. Statistically significant decreases of 2.21 mph mean speed and 1 mph 85% speed resulted with sequential lights. The shift in the cumulative speed distributions to the left (i.e. speed decrease) was also found to be statistically significant using the Mann-Whitney and Kolmogorov-Smirnov tests. But a statistically significant increase of 0.91 mph in the speed standard deviation also resulted with sequential lights. With sequential lights, the percentage of vehicles that merged earlier increased from 53.49% to 65.36%. A benefit-cost ratio of around 5 or 10 resulted from this analysis of Missouri nighttime work zones and historical crash data. The two different benefitcost ratios reflect two different ways of computing labor costs.

Randomised trial of oral versus sequential intravenous/oral cephalosporins in children with pyelonephritis.

The hypothesis was tested that oral antibiotic treatment in children with acute pyelonephritis and scintigraphy-documented lesions is equally as efficacious as sequential intravenous/oral therapy with respect to the incidence of renal scarring. A randomised multi-centre trial was conducted in 365 children aged 6 months to 16 years with bacterial growth in cultures from urine collected by catheter. The children were assigned to receive either oral ceftibuten (9 mg/kg once daily) for 14 days or intravenous ceftriaxone (50 mg/kg once daily) for 3 days followed by oral ceftibuten for 11 days. Only patients with lesions detected on acute-phase dimercaptosuccinic acid (DMSA) scintigraphy underwent follow-up scintigraphy. Efficacy was evaluated by the rate of renal scarring after 6 months on follow-up scintigraphy. Of 219 children with lesions on acute-phase scintigraphy, 152 completed the study; 80 (72 females, median age 2.2 years) were given ceftibuten and 72 (62 females, median age 1.6 years) were given ceftriaxone/ceftibuten. Patients in the intravenous/oral group had significantly higher C-reactive protein (CRP) concentrations at baseline and larger lesion(s) on acute-phase scintigraphy. Follow-up scintigraphy showed renal scarring in 21/80 children treated with ceftibuten and 33/72 with ceftriaxone/ceftibuten (p = 0.01). However, after adjustment for the confounding variables (CRP and size of acute-phase lesion), no significant difference was observed for renal scarring between the two groups (p = 0.2). Renal scarring correlated with the extent of the acute-phase lesion (r = 0.60, p < 0.0001) and the grade of vesico-ureteric reflux (r = 0.31, p = 0.03), and was more frequent in refluxing renal units (p = 0.04). The majority of patients, i.e. 44 in the oral group and 47 in the intravenous/oral group, were managed as out-patients. Side effects were not observed. From this study, we can conclude that once-daily oral ceftibuten for 14 days yielded comparable results to sequential ceftriaxone/ceftibuten treatment in children aged 6 months to 16 years with DMSA-documented acute pyelonephritis and it allowed out-patient management in the majority of these children.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.

Classification and Pay Plans FY2014, June 2013

This document Classifications and Pay Plans is produced by the State of Iowa Executive Branch, Department of Administrative Services. Informational document about the pay plan codes and classification codes, how to use them.

Characterization and classification of matrix effects in biological samples analyses.

An exhaustive classification of matrix effects occurring when a sample preparation is performed prior to liquid-chromatography coupled to mass spectrometry (LC-MS) analyses was proposed. A total of eight different situations were identified allowing the recognition of the matrix effect typology via the calculation of four recovery values. A set of 198 compounds was used to evaluate matrix effects after solid phase extraction (SPE) from plasma or urine samples prior to LC-ESI-MS analysis. Matrix effect identification was achieved for all compounds and classified through an organization chart. Only 17% of the tested compounds did not present significant matrix effects.

Integration of local-scale hydrological and regional-scale geophysical based on a nonlinear Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed a downscaling procedure based on a non-linear Bayesian sequential simulation approach. The basic objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity, which is available throughout the model space. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariate kernel density function. This method is then applied to the stochastic integration of low-resolution, re- gional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this downscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the downscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.