Three-dimensional, in-silico breast phantom for multimodality image simulations

We report on the construction of anatomically realistic three-dimensional in-silico breast phantoms with adjustable sizes, shapes and morphologic features. The concept of multiscale spatial resolution is implemented for generating breast tissue images from multiple modalities. Breast epidermal boundary and subcutaneous fat layer is generated by fitting an ellipsoid and 2nd degree polynomials to reconstructive surgical data and ultrasound imaging data. Intraglandular fat is simulated by randomly distributing and orienting adipose ellipsoids within a fibrous region immediately within the dermal layer. Cooper’s ligaments are simulated as fibrous ellipsoidal shells distributed within the subcutaneous fat layer. Individual ductal lobes are simulated following a random binary tree model which is generated based upon probabilistic branching conditions described by ramification matrices, as originally proposed by Bakic et al [3, 4]. The complete ductal structure of the breast is simulated from multiple lobes that extend from the base of the nipple and branch towards the chest wall. As lobe branching progresses, branches are reduced in height and radius and terminal branches are capped with spherical lobular clusters. Biophysical parameters are mapped onto the complete anatomical model and synthetic multimodal images (Mammography, Ultrasound, CT) are generated for phantoms of different adipose percentages (40%, 50%, 60%, and 70%) and are analytically compared with clinical examples. Results demonstrate that the in-silico breast phantom has applications in imaging performance evaluation and, specifically, great utility for solving image registration issues in multimodality imaging.

A Binary Classifier for Test Case Feasibility Applied to Automatically Generated Tests of Event-Driven Software

Modern software application testing, such as the testing of software driven by graphical user interfaces (GUIs) or leveraging event-driven architectures in general, requires paying careful attention to context. Model-based testing (MBT) approaches first acquire a model of an application, then use the model to construct test cases covering relevant contexts. A major shortcoming of state-of-the-art automated model-based testing is that many test cases proposed by the model are not actually executable. These \textit{infeasible} test cases threaten the integrity of the entire model-based suite, and any coverage of contexts the suite aims to provide. In this research, I develop and evaluate a novel approach for classifying the feasibility of test cases. I identify a set of pertinent features for the classifier, and develop novel methods for extracting these features from the outputs of MBT tools. I use a supervised logistic regression approach to obtain a model of test case feasibility from a randomly selected training suite of test cases. I evaluate this approach with a set of experiments. The outcomes of this investigation are as follows: I confirm that infeasibility is prevalent in MBT, even for test suites designed to cover a relatively small number of unique contexts. I confirm that the frequency of infeasibility varies widely across applications. I develop and train a binary classifier for feasibility with average overall error, false positive, and false negative rates under 5\%. I find that unique event IDs are key features of the feasibility classifier, while model-specific event types are not. I construct three types of features from the event IDs associated with test cases, and evaluate the relative effectiveness of each within the classifier. To support this study, I also develop a number of tools and infrastructure components for scalable execution of automated jobs, which use state-of-the-art container and continuous integration technologies to enable parallel test execution and the persistence of all experimental artifacts.

The analysis and detection of new psychoactive substances in biological matrices

New psychoactive substances (NPSs) have appeared on the recreational drug market at an unprecedented rate in recent years. Many are not new drugs but failed products of the pharmaceutical industry. The speed and variety of drugs entering the market poses a new complex challenge for the forensic toxicology community. The detection of these substances in biological matrices can be difficult as the exact compounds of interest may not be known. Many NPS are sold under the same brand name and therefore users themselves may not know what substances they have ingested. The majority of analytical methods for the detection of NPSs tend to focus on a specific class of compounds rather than a wide variety. In response to this, a robust and sensitive method was developed for the analysis of various NPS by solid phase extraction (SPE) with gas chromatography mass spectrometry (GCMS). Sample preparation and derivatisation were optimised testing a range of SPE cartridges and derivatising agents, as well as derivatisation incubation time and temperature. The final gas chromatography mass spectrometry method was validated in accordance with SWGTOX 2013 guidelines over a wide concentration range for both blood and urine for 23 and 25 analytes respectively. This included the validation of 8 NBOMe compounds in blood and 10 NBOMe compounds in urine. This GC-MS method was then applied to 8 authentic samples with concentrations compared to those originally identified by NMS laboratories. The rapid influx of NPSs has resulted in the re-analysis of samples and thus, the stability of these substances is crucial information. The stability of mephedrone was investigated, examining the effect that storage temperatures and preservatives had on analyte stability daily for 1 week and then weekly for 10 weeks. Several laboratories identified NPSs use through the cross-reactivity of these substances with existing screening protocols such as ELISA. The application of Immunalysis ketamine, methamphetamine and amphetamine ELISA kits for the detection of NPS was evaluated. The aim of this work was to determine if any cross-reactivity from NPS substances was observed, and to determine whether these existing kits would identify NPS use within biological samples. The cross- reactivity of methoxetamine, 3-MeO-PCE and 3-MeO-PCP for different commercially point of care test (POCT) was also assessed for urine. One of the newest groups of compounds to appear on the NPS market is the NBOMe series. These drugs pose a serious threat to public health due to their high potency, with fatalities already reported in the literature. These compounds are falsely marketed as LSD which increases the chance of adverse effects due to the potency differences between these 2 substances. A liquid chromatography tandem mass spectrometry (LC-MS/MS) method was validated in accordance with SWGTOX 2013 guidelines for the detection for 25B, 25C and 25I-NBOMe in urine and hair. Long-Evans rats were administered 25B-, 25C- and 25I-NBOMe at doses ranging from 30-300 µg/kg over a period of 10 days. Tail flick tests were then carried out on the rats in order to determine whether any analgesic effects were observed as a result of dosing. Rats were also shaved prior to their first dose and reshaved after the 10-day period. Hair was separated by colour (black and white) and analysed using the validated LC-MS/MS method, assessing the impact hair colour has on the incorporation of these drugs. Urine was collected from the rats, analysed using the validated LC-MS/MS method and screened for potential metabolites using both LC-MS/MS and quadrupole time of flight (QToF) instrumentation.

Segregation predictions for a binary mixture of granular material under flow conditions in a continuum mechanics framework

Abstract not available

X-Ray Absorption Spectroscopy and Computer Modelling Study of Nanocrystalline Binary Alkaline Earth Fluorides

Nanocrystalline samples of Ba1-xCaxF2 prepared by high-energy milling show an unusually high F-ion conductivity, which exhibit a maximum in the magnitude and a minimum in the activation energy at x = 0.5. Here, we report an X-ray absorption spectroscopy (XAS) at the Ca and Sr K edges and the Ba L-3 edge and a molecular dynamics (MD) simulation study of the pure and mixed fluorides. The XAS measurements on the pure binary fluorides, CaF2, SrF2 and BaF2 show that high-energy ball-milling produces very little amorphous material, in contrast to the results for ball milled oxides. XAS measurements of Ba1-xCaxF2 reveal that for 0 < x < 1 there is considerable disorder in the local environments of the cations which is highest for x = 0.5. Hence the maximum in the conductivity corresponds to the composition with the maximum level of local disorder. The MD calculations also show a highly disordered structure consistent with the XAS results and similarly showing maximum disorder at x = 0.5.

Analyse simultanée des hormones stéroïdiennes et leurs formes chimiques dans les matrices d'eau et d'urine par SPE-LC-MS/MS en ligne

Dans les dernières années, les perturbateurs endocriniens ont été observés dans les rivières qui reçoivent des entrées importantes d’eaux usées. Parmi les perturbateurs endocriniens, les hormones stéroïdiennes naturelles et synthétiques sont des composés dont le potentiel d'imiter ou d'interférer avec les fonctions hormonales normales (développement, croissance et reproduction), est reconnu même au niveau ultra-traces (ng L-1). Bien que les hormones conjuguées soient moins actives que les hormones libres, elles peuvent être clivées et redevenir libres une fois exposées aux processus microbiens avant ou pendant le traitement des eaux usées. En raison de la nécessité d'identifier et de quantifier ces composés dans l'eau, une nouvelle méthode, entièrement automatisée, a été développée pour la détermination simultanée des deux formes de plusieurs hormones stéroïdiennes (conjuguées et libres) dans les matrices d'eau et dans l’urine des femmes. La méthode est basée sur l'extraction en phase solide couplée en ligne à la chromatographie liquide et la spectrométrie de masse en tandem (SPE-LC-MS/MS). Plusieurs paramètres ont été évalués dans le but d'optimiser l'efficacité de la méthode, tels que le type et le débit de la phase mobile, des différentes colonnes de SPE et de chromatographie, ainsi que différentes sources et modes d'ionisation des échantillons pour la MS. La méthode démontre une bonne linéarité (R2 > 0.993), ainsi qu'une précision avec un coefficient de variance inférieure à 10%. Les limites de quantification varient d’un minimum de 3 à 15 ng L-1 pour un volume d'injection entre 1 mL et 5 mL et le recouvrement des composés varie de 72 % à 117 %.

Magnetic carbon xerogels for the catalytic wet peroxide oxidation of sulfamethoxazole in environmentally relevant water matrices

Novel magnetic carbon xerogels consisting of interconnected carbon microspheres with iron and/or cobalt microparticles embedded in their structure were developed by a simple route. As inferred from the characterization data, materials with distinctive properties may be directly obtained upon inclusion of iron and/or cobalt precursors during the sol-gel polymerization of resorcinol and formaldehyde, followed by thermal annealing. The unique properties of these magnetic carbon xerogels were explored in the catalytic wet peroxide oxidation (CWPO) of an antimicrobial agent typically found throughout the urban water cycle – sulfamethoxazole (SMX). A clear synergistic effect arises from the inclusion of cobalt and iron in carbon xerogels (CX/CoFe),the resulting magnetic material revealing a better performance in the CWPO of SMX at the ppb level(500 microg L−1) when compared to that of monometallic carbon xerogels containing only iron or cobalt.This effect was ascribed to the increased accessibility of highly active iron species promoted by the simultaneous incorporation of cobalt.The performance of the CWPO process in the presence of CX/CoFe was also evaluated in environmentally relevant water matrices, namely in drinking water and secondary treated wastewater, considered in addition to ultrapure water. It was found that the performance decreases when applied to more complex water and wastewater samples. Nevertheless, the ability of the CWPO technology for the elimination of SMX in secondary treated wastewater was unequivocally shown, with 96.8% of its initial content being removed after 6 h of reaction in the presence of CX/CoFe, at atmospheric pressure, room temperature(T = 25◦C), pH = 3, [H2O2]0= 500 mg L−1and catalyst load = 80 mg L−1. A similar performance (97.8% SMX removal) is obtained in 30 min when the reaction temperature is slightly increased up to 60◦C in an ultra-pure water matrix. Synthetic water containing humic acid, bicarbonate, sulphate or chloride, was also tested. The results suggest the scavenging effect of the different anions considered, as well as the negative impact of dissolved organic matter typically found in secondary treated wastewater, as simulated by the presence of humic acid.An in-situ magnetic separation procedure was applied for catalyst recovery and re-use during reusability cycles performed to mimic real-scale applications. CWPO runs performed with increased SMX concentration (10 mg L−1), under a water treatment process intensification approach, allowed to evalu-ate the mineralization levels obtained, the antimicrobial activity of the treated water, and to propose adegradation mechanism for the CWPO of SMX.

Learning Binary Code Representations for Effective and Efficient Image Retrieval

The size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.

A lower bound for the energy of symmetric matrices and graphs

The energy of a symmetric matrix is the sum of the absolute values of its eigenvalues. We introduce a lower bound for the energy of a symmetric partitioned matrix into blocks. This bound is related to the spectrum of its quotient matrix. Furthermore, we study necessary conditions for the equality. Applications to the energy of the generalized composition of a family of arbitrary graphs are obtained. A lower bound for the energy of a graph with a bridge is given. Some computational experiments are presented in order to show that, in some cases, the obtained lower bound is incomparable with the well known lower bound $2\sqrt{m}$, where $m$ is the number of edges of the graph.

Calculation of sample size for stroke trials assessing functional outcome: comparison of binary and ordinal approaches

Background Many acute stroke trials have given neutral results. Sub-optimal statistical analyses may be failing to detect efficacy. Methods which take account of the ordinal nature of functional outcome data are more efficient. We compare sample size calculations for dichotomous and ordinal outcomes for use in stroke trials. Methods Data from stroke trials studying the effects of interventions known to positively or negatively alter functional outcome – Rankin Scale and Barthel Index – were assessed. Sample size was calculated using comparisons of proportions, means, medians (according to Payne), and ordinal data (according to Whitehead). The sample sizes gained from each method were compared using Friedman 2 way ANOVA. Results Fifty-five comparisons (54 173 patients) of active vs. control treatment were assessed. Estimated sample sizes differed significantly depending on the method of calculation (Po00001). The ordering of the methods showed that the ordinal method of Whitehead and comparison of means produced significantly lower sample sizes than the other methods. The ordinal data method on average reduced sample size by 28% (inter-quartile range 14–53%) compared with the comparison of proportions; however, a 22% increase in sample size was seen with the ordinal method for trials assessing thrombolysis. The comparison of medians method of Payne gave the largest sample sizes. Conclusions Choosing an ordinal rather than binary method of analysis allows most trials to be, on average, smaller by approximately 28% for a given statistical power. Smaller trial sample sizes may help by reducing time to completion, complexity, and financial expense. However, ordinal methods may not be optimal for interventions which both improve functional outcome

Use of ordinal outcomes in vascular prevention trials: comparison with binary outcomes in published trials

Background and Purpose—Vascular prevention trials mostly count “yes/no” (binary) outcome events, eg, stroke/no stroke. Analysis of ordered categorical vascular events (eg, fatal stroke/nonfatal stroke/no stroke) is clinically relevant and could be more powerful statistically. Although this is not a novel idea in the statistical community, ordinal outcomes have not been applied to stroke prevention trials in the past. Methods—Summary data on stroke, myocardial infarction, combined vascular events, and bleeding were obtained by treatment group from published vascular prevention trials. Data were analyzed using 10 statistical approaches which allow comparison of 2 ordinal or binary treatment groups. The results for each statistical test for each trial were then compared using Friedman 2-way analysis of variance with multiple comparison procedures. Results—Across 85 trials (335 305 subjects) the test results differed substantially so that approaches which used the ordinal nature of stroke events (fatal/nonfatal/no stroke) were more efficient than those which combined the data to form 2 groups (P0.0001). The most efficient tests were bootstrapping the difference in mean rank, Mann–Whitney U test, and ordinal logistic regression; 4- and 5-level data were more efficient still. Similar findings were obtained for myocardial infarction, combined vascular outcomes, and bleeding. The findings were consistent across different types, designs and sizes of trial, and for the different types of intervention. Conclusions—When analyzing vascular events from prevention trials, statistical tests which use ordered categorical data are more efficient and are more likely to yield reliable results than binary tests. This approach gives additional information on treatment effects by severity of event and will allow trials to be smaller. (Stroke. 2008;39:000-000.)

Modificación de las propiedades de matrices cementantes mediante la adición de nanopartículas de sílice

En la presente investigación, se evaluó el efecto de adicionar nanopartículas de sílice a dos matrices cementes buscando establecer su influencia en las propiedades finales de dichos materiales. Para llevar a cabo esta evaluación, se adicionaron las nanopartículas en dosificaciones desde 0.30% - 5.0% en peso, con la finalidad de determinar la dosificación a la cual se obtienen los mejores resultados. Una de las conclusiones obtenidas de la presente investigación señala que al utilizar este tipo de materiales, el mayor reto a superar, es la correcta dispersión de las nanopartículas en las matrices cementantes, ya que de no asegurar dicha dispersión y debido a la alta energía superficial que poseen las nanopartículas, se presenta una tendencia natural a aglomerarse conformando así materiales de tamaños muy superiores a los que presentan en su estado original, perdiendo así la ventaja de su uso. Para prevenir este efecto y como resultado de la primera etapa de esta investigación, se propuso un método de mezclado en el cual se utilizó como dispersante un aditivo superplastificante (en adelante llamado SP) de base policarboxilato de calcio en dosificaciones de 0.5% en peso que se adicionó a ambas matrices cementantes. Los resultados obtenidos sugieren que algunas propiedades tales como la resistencia mecánica a la compresión, la resistencia a la flexión y sobre todo, la resistencia al ataque químico por sulfatos, se ven mejoradas mediante la adición de nanopartículas de sílice. Dicha mejoría se observó tanto en la matriz de cemento Portland como en la matriz de cemento sulfoaluminoso. Los resultados finales de la investigación mostraron que la resistencia mecánica a la compresión para un cemento Portland ordinario se mejoró hasta en un 40% a edad de 24 h cuando se adicionaron las nanopartículas en porcentaje de 0.50% en peso, sin embargo, los resultados al resto de las edades no presentaron mejoría. Para la matriz de cemento sulfoaluminoso (en adelante CSA), los mejores resultados se obtuvieron también con la dosificación de 0.50% en peso con relación al cemento, sin embargo, la mejoría no fue de la misma magnitud a la obtenida con la matriz de cemento Portland. Por otro lado, la resistencia al ataque químico por sulfatos presentó una mejoría de hasta un 80% para la matriz de cemento sulfoaluminoso cuando se adicionaron las nanopartículas de sílice. Este resultado sugiere que la matriz con nanopartículas presenta una mayor densificación propiciando por ende, un menor grado de porosidad.