Exploring the knowledge contained in neuroimages: Statistical discriminant analysis and automatic segmentation of the most significant changes

Objective: The aim of this article is to propose an integrated framework for extracting and describing patterns of disorders from medical images using a combination of linear discriminant analysis and active contour models. Methods: A multivariate statistical methodology was first used to identify the most discriminating hyperplane separating two groups of images (from healthy controls and patients with schizophrenia) contained in the input data. After this, the present work makes explicit the differences found by the multivariate statistical method by subtracting the discriminant models of controls and patients, weighted by the pooled variance between the two groups. A variational level-set technique was used to segment clusters of these differences. We obtain a label of each anatomical change using the Talairach atlas. Results: In this work all the data was analysed simultaneously rather than assuming a priori regions of interest. As a consequence of this, by using active contour models, we were able to obtain regions of interest that were emergent from the data. The results were evaluated using, as gold standard, well-known facts about the neuroanatomical changes related to schizophrenia. Most of the items in the gold standard was covered in our result set. Conclusions: We argue that such investigation provides a suitable framework for characterising the high complexity of magnetic resonance images in schizophrenia as the results obtained indicate a high sensitivity rate with respect to the gold standard. (C) 2010 Elsevier B.V. All rights reserved.

Fatty acid synthesis and generation of glycerol-3-phosphate in brown adipose tissue from rats fed a cafeteria diet

In vivo fatty acid synthesis and the pathways of glycerol-3-phosphate (G3P) production were investigated in brown adipose tissue (BAT) from rats fed a cafeteria diet for 3 weeks. In spite of BAT activation, the diet promoted an increase in the carcass fatty acid content. Plasma insulin levels were markedly increased in cafeteria diet-fed rats. Two insulin-sensitive processes, in vivo fatty acid synthesis and in vivo glucose uptake (which was used to evaluate G3P generation via glycolysis) were increased in BAT from rats fed the cafeteria diet. Direct glycerol phosphorylation, evaluated by glycerokinase (GyK) activity and incorporation of [U-(14)C]glycerol into triacylglycerol (TAG)-glycerol, was also markedly increased in BAT from these rats. In contrast, the cafeteria diet induced a marked reduction of BAT glyceroneogenesis, evaluated by phosphoenolpyruvate carboxykinase-C activity and incorporation of [1-(14)C]pyruvate into TAG-glycerol. BAT denervation resulted in an approximately 50% reduction of GyK activity, but did not significantly affect BAT in vivo fatty acid synthesis, in vivo glucose uptake, or glyceroneogenesis. The data suggest that the supply of G3P for BAT TAG synthesis can be adjusted independently from the sympathetic nervous system and solely by reciprocal changes in the generation of G3P via glycolysis and via glyceroneogenesis, with no participation of direct phosphorylation of glycerol by GyK.

Generation of Polyclonal Antibodies Against Recombinant Human Glucocerebrosidase Produced in Escherichia coli

Deficiency of the lysosomal glucocerebrosidase (GCR) enzyme results in Gaucher`s disease, the most common inherited storage disorder. Treatment consists of enzyme replacement therapy by the administration of recombinant GCR produced in Chinese hamster ovary cells. The production of anti-GCR antibodies has already been described with placenta-derived human GCR that requires successive chromatographic procedures. Here, we report a practical and efficient method to obtain anti-GCR polyclonal antibodies against recombinant GCR produced in Escherichia coli and further purified by a single step through nickel affinity chromatography. The purified GCR was used to immunize BALB/c mice and the induction of anti-GCR antibodies was evaluated by enzyme-linked immunosorbent assay. The specificity of the antiserum was also evaluated by western blot analysis against recombinant GCR produced by COS-7 cells or against endogenous GCR of human cell lines. GCR was strongly recognized by the produced antibodies, either as cell-associated or as secreted forms. The detected molecular masses of 59-66 kDa are in accordance to the expected size for glycosylated GCR. The GCR produced in E. coli would facilitate the production of polyclonal (shown here) and monoclonal antibodies and their use in the characterization of new biosimilar recombinant GCRs coming in the near future.

Generation of eigenstates using the phase-estimation algorithm

The phase estimation algorithm is so named because it allows an estimation of the eigenvalues associated with an operator. However, it has been proposed that the algorithm can also be used to generate eigenstates. Here we extend this proposal for small quantum systems, identifying the conditions under which the phase-estimation algorithm can successfully generate eigenstates. We then propose an implementation scheme based on an ion trap quantum computer. This scheme allows us to illustrate two simple examples, one in which the algorithm effectively generates eigenstates, and one in which it does not.

Automatic construction of explicit R matrices for the one-parameter families of irreducible typical highest weight (0(m)vertical bar alpha(n)) representations of U-q[gl(m vertical bar n)]

We detail the automatic construction of R matrices corresponding to (the tensor products of) the (O-m\alpha(n)) families of highest-weight representations of the quantum superalgebras Uq[gl(m\n)]. These representations are irreducible, contain a free complex parameter a, and are 2(mn)-dimensional. Our R matrices are actually (sparse) rank 4 tensors, containing a total of 2(4mn) components, each of which is in general an algebraic expression in the two complex variables q and a. Although the constructions are straightforward, we describe them in full here, to fill a perceived gap in the literature. As the algorithms are generally impracticable for manual calculation, we have implemented the entire process in MATHEMATICA; illustrating our results with U-q [gl(3\1)]. (C) 2002 Published by Elsevier Science B.V.

Generation of CTL responses using Kunjin replicon RNA

The Kunjin replicon was used to express a polytope that consisted of seven hepatitis C virus cytotoxic T lymphocyte epitopes and one influenza cytotoxic T lymphocyte epitope for vaccination studies. The self-replicating nature of, and expression from, the ribonucleic acid was confirmed in vitro . Initial vaccinations with one dose of Kun-Poly ribonucleic acid showed that an influenza-specific cytotoxic T lymphocyte response was elicited more efficiently by intradermal inoculation compared with intramuscular delivery. Two micrograms of ribonucleic acid delivered in the ear pinnae of mice was sufficient to elicit a detectable cytotoxic T lymphocyte response 10 days post-vaccination. Further vaccination studies showed that four of the seven hepatitis C virus cytotoxic T lymphocyte epitopes were able to elicit weak cytotoxic T lymphocyte responses whereas the influenza epitope was able to elicit strong, specific cytotoxic T lymphocyte responses following three doses of Kun-Poly ribonucleic acid. These studies vindicate the use of the Kunjin replicon as a vector to deliver encoded proteins for the development of cell-mediated immune responses.

Generation of aldehyde-derived protein modifications in ethanol-exposed heart

Background: Although excessive ethanol consumption is known to lead to a variety of adverse effects in the heart, the molecular mechanisms of such effects have remained poorly defined. We hypothesized that posttranslational covalent binding of reactive molecular species to proteins occurs in the heart in response to acute ethanol exposure. Methods: The generation of protein adducts with several aldehydic species was examined by using monospecific antibodies against adducts with malondialdehyde (MDA), acetaldehyde (AA), MDA-AA hybrids, and hydroxyethyl radicals. Specimens of heart tissue were obtained from rats after intraperitoneal injections with alcohol (75 mmol/kg body weight) with or without pretreatment with cyanamide (0.05 mmol/kg body weight), an aldehyde dehydrogenase inhibitor. Results: The amounts of MDA and unreduced AA adducts were found to be significantly increased in the heart of the rats treated with ethanol, cyanamide, or both, whereas no other adducts were detected in statistically significant quantities. Immunohistochemical studies for characterization of adduct distribution revealed sarcolemmal adducts of both MDA and AA in the rats treated with ethanol and cyanamide in addition to intracellular adducts, which were also present in the group treated with ethanol alone. Conclusions: These findings support the role of enhanced lipid peroxidation and the generation of protein-aldehyde condensates in vivo as a result of excessive ethanol intake. These findings may have implications in the molecular mechanisms of cardiac dysfunction in alcoholics.

Automatic modeling of an orthotic bracing for nonoperative correction of PectusCarinatum

Pectus Carinatum is a deformity of the chest wall, characterized by an anterior protrusion of the sternum, often corrected surgically due to cosmetic motivation. This work presents an alternative approach to the current open surgery option, proposing a novel technique based on a personalized orthosis. Two different processes for the orthosis’ personalization are presented. One based on a 3D laser scan of the patient chest, followed by the reconstruction of the thoracic wall mesh using a radial basis function, and a second one, based on a computer tomography scan followed by a neighbouring cells algorithm. The axial position where the orthosis is to be located is automatically calculated using a Ray-Triangle intersection method, whose outcome is input to a pseudo Kochenek interpolating spline method to define the orthosis curvature. Results show that no significant differences exist between the patient chest physiognomy and the curvature angle and size of the orthosis, allowing a better cosmetic outcome and less initial discomfort

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Automatic modeling of an orthotic bracing for nonoperative correction of pectus carinatum

Pectus Carinatum is a deformity of the chest wall, characterized by an anterior protrusion of the sternum, often corrected surgically due to cosmetic motivation. This work presents an alternative approach to the current open surgery option, proposing a novel technique based on a personalized orthosis. Two different processes for the orthosis’ personalization are presented. One based on a 3D laser scan of the patient chest, followed by the reconstruction of the thoracic wall mesh using a radial basis function, and a second one, based on a computer tomography scan followed by a neighbouring cells algorithm. The axial position where the orthosis is to be located is automatically calculated using a Ray-Triangle intersection method, whose outcome is input to a pseudo Kochenek interpolating spline method to define the orthosis curvature. Results show that no significant differences exist between the patient chest physiognomy and the curvature angle and size of the orthosis, allowing a better cosmetic outcome and less initial discomfort.

Artificial neural networks for automatic modelling of the pectus excavatum corrective prosthesis

Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82±5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7±4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.

Automatic assignment of absolute configuration from 1D NMR data

Opposite enantiomers exhibit different NMR properties in the presence of an external common chiral element, and a chiral molecule exhibits different NMR properties in the presence of external enantiomeric chiral elements. Automatic prediction of such differences, and comparison with experimental values, leads to the assignment of the absolute configuration. Here two cases are reported, one using a dataset of 80 chiral secondary alcohols esterified with (R)-MTPA and the corresponding 1H NMR chemical shifts and the other with 94 13C NMR chemical shifts of chiral secondary alcohols in two enantiomeric chiral solvents. For the first application, counterpropagation neural networks were trained to predict the sign of the difference between chemical shifts of opposite stereoisomers. The neural networks were trained to process the chirality code of the alcohol as the input, and to give the NMR property as the output. In the second application, similar neural networks were employed, but the property to predict was the difference of chemical shifts in the two enantiomeric solvents. For independent test sets of 20 objects, 100% correct predictions were obtained in both applications concerning the sign of the chemical shifts differences. Additionally, with the second dataset, the difference of chemical shifts in the two enantiomeric solvents was quantitatively predicted, yielding r2 0.936 for the test set between the predicted and experimental values.

Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization

Liver steatosis is mainly a textural abnormality of the hepatic parenchyma due to fat accumulation on the hepatic vesicles. Today, the assessment is subjectively performed by visual inspection. Here a classifier based on features extracted from ultrasound (US) images is described for the automatic diagnostic of this phatology. The proposed algorithm estimates the original ultrasound radio-frequency (RF) envelope signal from which the noiseless anatomic information and the textural information encoded in the speckle noise is extracted. The features characterizing the textural information are the coefficients of the first order autoregressive model that describes the speckle field. A binary Bayesian classifier was implemented and the Bayes factor was calculated. The classification has revealed an overall accuracy of 100%. The Bayes factor could be helpful in the graphical display of the quantitative results for diagnosis purposes.

Orchestration of e-learning services for automatic evaluation of programming exercises

Managing programming exercises require several heterogeneous systems such as evaluation engines, learning objects repositories and exercise resolution environments. The coordination of networks of such disparate systems is rather complex. These tools would be too specific to incorporate in an e-Learning platform. Even if they could be provided as pluggable components, the burden of maintaining them would be prohibitive to institutions with few courses in those domains. This work presents a standard based approach for the coordination of a network of e-Learning systems participating on the automatic evaluation of programming exercises. The proposed approach uses a pivot component to orchestrate the interaction among all the systems using communication standards. This approach was validated through its effective use on classroom and we present some preliminary results.