The impact of context on pattern category learning and representation

Context traditionally has been regarded in vision research as a determinant for the interpretation of sensory information on the basis of previously acquired knowledge. Here we propose a novel, complementary perspective by showing that context also specifically affects visual category learning. In two experiments involving sets of Compound Gabor patterns we explored how context, as given by the stimulus set to be learned, affects the internal representation of pattern categories. In Experiment 1, we changed the (local) context of the individual signal classes by changing the configuration of the learning set. In Experiment 2, we varied the (global) context of a fixed class configuration by changing the degree of signal accentuation. Generalization performance was assessed in terms of the ability to recognize contrast-inverted versions of the learning patterns. Both contextual variations yielded distinct effects on learning and generalization thus indicating a change in internal category representation. Computer simulations suggest that the latter is related to changes in the set of attributes underlying the production rules of the categories. The implications of these findings for phenomena of contrast (in)variance in visual perception are discussed.

Rigid and non-rigid point-based medical image registration

The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.

Rigid and Non-rigid Point-based Medical Image Registration

The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.

Enhancement of the Cytotoxic Effect of Anticancer Agent by Cytochrome c Functionalised Hybrid Nanoparticles in Hepatocellular Cancer Cells

Treatment of hepatocellular cancer with chemotherapeutic agents has limited successin clinical practice and their efficient IC50 concentration would require extremely highdoses of drug administration which could not be tolerated due to systemic side effects.In order to potentiate the efficacy of anticancer agents we explored the potentialof co-treatment with pro-apoptotic Cytochrome c which activates the apoptoticpathway downstream of p53 that is frequently mutated in cancer. To this end weused hybrid iron oxide-gold nanoparticles as a drug delivery system to facilitate theinternalisation of Cytochrome c into cultured HepG2 hepatocellular carcinoma cells.Our results showed that Cytochrome c can be easily conjugated to the gold shell ofthe nanoparticles which are readily taken up by the cells. We used Cytochrome cin concentration (0.2μgmL-1) below the threshold required to induce apoptosis onits own. When the conjugate was administered to cells treated by doxorubicin, itsignificantly reduced its IC50 concentration from 9μgmL-1 to 3.5μgmL-1 as detectedby cell viability assay, and the efficiency of doxorubicin on decreasing viability ofHepG2 cells was significantly enhanced in the lower concentration range between0.01μgmL-1 to 5μgmL-1. The results demonstrate the potential of the application oftherapeutic proteins in activating the apoptotic pathway to complement conventionalchemotherapy to increase its efficacy. The application of hybrid iron oxide-goldnanoparticles can also augment the specificity of drug targeting and could serve as amodel drug delivery system for pro-apoptotic protein targeting and delivery.

Functional characterisation of Schistosoma japonicum acetylcholinesterase

BACKGROUND: Acetylcholinesterase (AChE) is an important metabolic enzyme of schistosomes present in the musculature and on the surface of the blood stage where it has been implicated in the modulation of glucose scavenging from mammalian host blood. As both a target for the antischistosomal drug metrifonate and as a potential vaccine candidate, AChE has been characterised in the schistosome species Schistosoma mansoni, S. haematobium and S. bovis, but not in S. japonicum. Recently, using a schistosome protein microarray, a predicted S. japonicum acetylcholinesterase precursor was significantly targeted by protective IgG1 immune responses in S. haematobium-exposed individuals that had acquired drug-induced resistance to schistosomiasis after praziquantel treatment.

RESULTS: We report the full-length cDNA sequence and describe phylogenetic and molecular structural analysis to facilitate understanding of the biological function of AChE (SjAChE) in S. japonicum. The protein has high sequence identity (88 %) with the AChEs in S. mansoni, S. haematobium and S. bovis and has 25 % sequence similarity with human AChE, suggestive of a highly specialised role for the enzyme in both parasite and host. We immunolocalized SjAChE and demonstrated its presence on the surface of adult worms and schistosomula, as well as its lower expression in parenchymal regions. The relatively abundance of AChE activity (90 %) present on the surface of adult S. japonicum when compared with that reported in other schistosomes suggests SjAChE may be a more effective drug or immunological target against this species. We also demonstrate that the classical inhibitor of AChE, BW285c51, inhibited AChE activity in tegumental extracts of paired worms, single males and single females by 59, 22 and 50 %, respectively, after 24 h incubation with 200 μM BW284c51.

CONCLUSIONS: These results build on previous studies in other schistosome species indicating major differences in the enzyme between parasite and mammalian host, and provide further support for the design of an anti-schistosome intervention targeting AChE.

Face description for perceptual user interfaces

[EN]We investigate mechanisms which can endow the computer with the ability of describing a human face by means of computer vision techniques. This is a necessary requirement in order to develop HCI approaches which make the user feel himself/herself perceived. This paper describes our experiences considering gender, race and the presence of moustache and glasses. This is accomplished comparing, on a set of 6000 facial images, two di erent face representation approaches: Principal Components Analysis (PCA) and Gabor lters. The results achieved using a Support Vector Machine (SVM) based classi er are promising and particularly better for the second representation approach.

Features combination for art authentication studies: brushstroke and materials analysis of Amadeo de Souza-Cardoso

This work presents a tool to support authentication studies of paintings attributed to the modernist Portuguese artist Amadeo de Souza-Cardoso (1887-1918). The strategy adopted was to quantify and combine the information extracted from the analysis of the brushstroke with information on the pigments present in the paintings. The brushstroke analysis was performed combining Gabor filter and Scale Invariant Feature Transform. Hyperspectral imaging and elemental analysis were used to compare the materials in the painting with those present in a database of oil paint tubes used by the artist. The outputs of the tool are a quantitative indicator for authenticity, and a mapping image that indicates the areas where materials not coherent with Amadeo's palette were detected, if any. This output is a simple and effective way of assessing the results of the system. The method was tested in twelve paintings obtaining promising results.

Transient Blue Skin: Pseudochromhidrosis

Objectives: Pseudochromhidrosis is a rare condition where colours due to chromogenic microbial products or extrinsic chemicals are excreted with sweat. Chromhidrosis is the production of coloured sweat from apocrine or eccrine sweat glands. The aim of this case report is to illustrate all the steps involved in the diagnosis of pseudochromhidrosis. Materials and methods: A 17-year-old patient with pseudochromhidrosis is presented. Results: Clinical features of the patient were consistent with pseudochromhidrosis. Conclusions: The distinction between chromhidrosis and pseudochromhidrosis can be made based on a detailed history, skin biopsy and empiric treatment

Asymptotic scattering and duality in the one-dimensional three-state quantum Potts model on a lattice

We determine numerically the single-particle and the two-particle spectrum of the three-state quantum Potts model on a lattice by using the density matrix renormalization group method, and extract information on the asymptotic (small momentum) S-matrix of the quasiparticles. The low energy part of the finite size spectrum can be understood in terms of a simple effective model introduced in a previous work, and is consistent with an asymptotic S-matrix of an exchange form below a momentum scale p*. This scale appears to vanish faster than the Compton scale, mc, as one approaches the critical point, suggesting that a dangerously irrelevant operator may be responsible for the behaviour observed on the lattice.

Multi-label approach for human-face classification

Single-label classification models have been widely used for human-face classification. In this paper, we present a multi-label classification approach for human-face classification. Multi-label classification is more appropriate in the real world because a human-face can be associated with multiple labels. Demographic information can be derived and utilized along with facial expression in the field of face classification to assist with multi label classification. Gabor filters; Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) methods, are used to extract and project representative demographic information from facial images. For evaluation, five classification algorithms were used. We evaluate the proposed approach by performing experiments on Yale face images database. Results show the effectiveness of multi-label classification algorithms.

Software for Designing Custom Orbital-Craniofacial Implant Plans

Purpose: Custom cranio-orbital implants have been shown to achieve better performance than their hand-shaped counterparts by restoring skull anatomy more accurately and by reducing surgery time. Designing a custom implant involves reconstructing a model of the patient's skull using their computed tomography (CT) scan. The healthy side of the skull model, contralateral to the damaged region, can then be used to design an implant plan. Designing implants for areas of thin bone, such as the orbits, is challenging due to poor CT resolution of bone structures. This makes preoperative design time-intensive since thin bone structures in CT data must be manually segmented. The objective of this thesis was to research methods to accurately and efficiently design cranio-orbital implant plans, with a focus on the orbits, and to develop software that integrates these methods. Methods: The software consists of modules that use image and surface restoration approaches to enhance both the quality of CT data and the reconstructed model. It enables users to input CT data, and use tools to output a skull model with restored anatomy. The skull model can then be used to design the implant plan. The software was designed using 3D Slicer, an open-source medical visualization platform. It was tested on CT data from thirteen patients. Results: The average time it took to create a skull model with restored anatomy using our software was 0.33 hours ± 0.04 STD. In comparison, the design time of the manual segmentation method took between 3 and 6 hours. To assess the structural accuracy of the reconstructed models, CT data from the thirteen patients was used to compare the models created using our software with those using the manual method. When registering the skull models together, the difference between each set of skulls was found to be 0.4 mm ± 0.16 STD. Conclusions: We have developed a software to design custom cranio-orbital implant plans, with a focus on thin bone structures. The method described decreases design time, and is of similar accuracy to the manual method.

Environmental factors correlated with the metabolite profile of Vitis vinifera cv. Pinot Noir berry skins along a European latitudinal gradient

Mature berries of Pinot Noir grapevines were sampled across a latitudinal gradient in Europe, from southern Spain to central Germany. Our aim was to study the influence of latitude-dependent environmental factors on the metabolite composition (mainly phenolic compounds) of berry skins. Solar radiation variables were positively correlated with flavonols and flavanonols and, to a lesser extent, with stilbenes and cinnamic acids. The daily means of global and erythematic UV solar radiation over long periods (bud break-veraison, bud break-harvest, and veraison-harvest), and the doses and daily means in shorter development periods (5–10 days before veraison and harvest) were the variables best correlated with the phenolic profile. The ratio between trihydroxylated and monohydroxylated flavonols, which was positively correlated with antioxidant capacity, was the berry skin variable best correlated with those radiation variables. Total flavanols and total anthocyanins did not show any correlation with radiation variables. Air temperature, degree days, rainfall, and aridity indices showed fewer correlations with metabolite contents than radiation. Moreover, the latter correlations were restricted to the period veraison-harvest, where radiation, temperature, and water availability variables were correlated, making it difficult to separate the possible individual effects of each type of variable. The data show that managing environmental factors, in particular global and UV radiation, through cultural practices during specific development periods, can be useful to promote the synthesis of valuable nutraceuticals and metabolites that influence wine quality.

Experimental comparison of approaches for feature extraction of facial attributes

In this paper, we compare the effectiveness of widely used approaches for representation of facial features in face images. Feature extraction is performed on face images for representation of four facial attributes, namely gender, age, race, and expression, by using discrete wavelet transform (DWT), Gabor wavelet, scale-invariant feature transform, local binary pattern (LBP), and Eigenfaces. After feature extraction and dimension reduction, demographic and expression classification is performed to identify the most discriminating techniques for representation of facial features. Extensive experiments are performed using publicly available face databases, namely Yale, Face95 Essex, and Cohn-Kanade (CK+) databases. Experimental results show that DWT, LBP, and Gabor wavelet methods are robust to variations of illumination, facial expression, and geometric transformations. Experimental results also show that race and expression are more difficult to predict than gender and age.