Nonrigid Motion Compensation of Free Breathing Acquired Myocardial Perfusion Data

In this work, we present a novel method to compensate the movement in images acquired during free breathing using first-pass gadolinium enhanced, myocardial perfusion magnetic resonance imaging (MRI). First, we use independent component analysis (ICA) to identify the optimal number of independent components (ICs) that separate the breathing motion from the intensity change induced by the contrast agent. Then, synthetic images are created by recombining the ICs, but other then in previously published work (Milles et al. 2008), we omit the component related to motion, and therefore, the resulting reference image series is free of motion. Motion compensation is then achieved by using a multi-pass non-rigid image registration scheme. We tested our method on 15 distinct image series (5 patients) consisting of 58 images each and we validated our method by comparing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration. The average correlation to the manually obtained curves before registration 0:89 0:11 was increased to 0:98 0:02

Mapping wide row crops with video sequences acquired from a tractor moving at treatment speed

This paper presents a mapping method for wide row crop fields. The resulting map shows the crop rows and weeds present in the inter-row spacing. Because field videos are acquired with a camera mounted on top of an agricultural vehicle, a method for image sequence stabilization was needed and consequently designed and developed. The proposed stabilization method uses the centers of some crop rows in the image sequence as features to be tracked, which compensates for the lateral movement (sway) of the camera and leaves the pitch unchanged. A region of interest is selected using the tracked features, and an inverse perspective technique transforms the selected region into a bird’s-eye view that is centered on the image and that enables map generation. The algorithm developed has been tested on several video sequences of different fields recorded at different times and under different lighting conditions, with good initial results. Indeed, lateral displacements of up to 66% of the inter-row spacing were suppressed through the stabilization process, and crop rows in the resulting maps appear straight

Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis

Images acquired during free breathing using first-pass gadolinium-enhanced myocardial perfusion magnetic resonance imaging (MRI) exhibit a quasiperiodic motion pattern that needs to be compensated for if a further automatic analysis of the perfusion is to be executed. In this work, we present a method to compensate this movement by combining independent component analysis (ICA) and image registration: First, we use ICA and a time?frequency analysis to identify the motion and separate it from the intensity change induced by the contrast agent. Then, synthetic reference images are created by recombining all the independent components but the one related to the motion. Therefore, the resulting image series does not exhibit motion and its images have intensities similar to those of their original counterparts. Motion compensation is then achieved by using a multi-pass image registration procedure. We tested our method on 39 image series acquired from 13 patients, covering the basal, mid and apical areas of the left heart ventricle and consisting of 58 perfusion images each. We validated our method by comparing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration of 13 patient data sets (39 distinct slices). We compared linear, non-linear, and combined ICA based registration approaches and previously published motion compensation schemes. Considering run-time and accuracy, a two-step ICA based motion compensation scheme that first optimizes a translation and then for non-linear transformation performed best and achieves registration of the whole series in 32 ± 12 s on a recent workstation. The proposed scheme improves the Pearsons correlation coefficient between manually and automatically obtained time?intensity curves from .84 ± .19 before registration to .96 ± .06 after registration

Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

Clustering techniques for patients suffering acquired brain injury inneuro personal trainer

The study of the effectiveness of the cognitive rehabilitation processes and the identification of cognitive profiles, in order to define comparable populations, is a controversial area, but concurrently it is strongly needed in order to improve therapies. There is limited evidence about cognitive rehabilitation efficacy. Many of the trials conclude that in spite of an apparent clinical good response, differences do not show statistical significance. The common feature in all these trials is heterogeneity among populations. In this situation, observational studies on very well controlled cohort of studies, together with innovative methods in knowledge extraction, could provide methodological insights for the design of more accurate comparative trials. Some correlation studies between neuropsychological tests and patients capacities have been carried out -1---2- and also correlation between tests and morphological changes in the brain -3-. The procedures efficacy depends on three main factors: the affectation profile, the scheduled tasks and the execution results. The relationship between them makes up the cognitive rehabilitation as a discipline, but its structure is not properly defined. In this work we present a clustering method used in Neuro Personal Trainer (NPT) to group patients into cognitive profiles using data mining techniques. The system uses these clusters to personalize treatments, using the patients assigned cluster to select which tasks are more suitable for its concrete needs, by comparing the results obtained in the past by patients with the same profile.

Intelligent Therapy Assistant (ITA) for cognitive rehabilitation in patients with Acquired Brain Injury

This paper presents the design, development and first evaluation of an algorithm, named Intelligent Therapy Assistant (ITA), which automatically selects, configures and schedules rehabilitation tasks for patients with cognitive impairments after an episode of Acquired Brain Injury. The ITA is integrated in "Guttmann, Neuro Personal Trainer" (GNPT), a cognitive tele-rehabilitation platform that provides neuropsychological services.

Differential patterns of acquired virulence genes distinguish Salmonella strains

Analysis of several Salmonella typhimurium in vivo-induced genes located in regions of atypical base composition has uncovered acquired genetic elements that cumulatively engender pathogenicity. Many of these regions are associated with mobile elements, encode predicted adhesin and invasin-like functions, and are required for full virulence. Some of these regions distinguish broad host range from host-adapted Salmonella serovars and may contribute to inherent differences in host specificity, tissue tropism, and disease manifestation. Maintenance of this archipelago of acquired sequence by selection in specific hosts reveals a fossil record of the evolution of pathogenic species.

Social stress results in altered glucocorticoid regulation and shorter survival in simian acquired immune deficiency syndrome

From early in the AIDS epidemic, psychosocial stressors have been proposed as contributors to the variation in disease course. To test this hypothesis, rhesus macaques were assigned to stable or unstable social conditions and were inoculated with the simian immunodeficiency virus. Animals in the unstable condition displayed more agonism and less affiliation, shorter survival, and lower basal concentrations of plasma cortisol compared with stable animals. Early after inoculation, but before the emergence of group differences in cortisol levels, animals receiving social threats had higher concentrations of simian immunodeficiency virus RNA in plasma, and those engaging in affiliation had lower concentrations. The results indicate that social factors can have a significant impact on the course of immunodeficiency disease. Socially induced changes in pituitary–adrenal hormones may be one mechanism mediating this relationship.

Innate and acquired humoral immunities to influenza virus are mediated by distinct arms of the immune system

“Natural” Igs, mainly IgM, comprise part of the innate immune system present in healthy individuals, including antigen-free mice. These Igs are thought to delay pathogenicity of infecting agents until antigen-induced high affinity Igs of all isotypes are produced. Previous studies suggested that the acquired humoral response arises directly from the innate response, i.e., that B cells expressing natural IgM, upon antigen encounter, differentiate to give rise both to cells that secrete high amounts of IgM and to cells that undergo affinity maturation and isotype switching. However, by using a murine model of influenza virus infection, we demonstrate here that the B cells that produce natural antiviral IgM neither increase their IgM production nor undergo isotype switching to IgG2a in response to the infection. These cells are distinct from the B cells that produce the antiviral response after encounter with the pathogen. Our data therefore demonstrate that the innate and the acquired humoral immunities to influenza virus are separate effector arms of the immune system and that antigen exposure per se is not sufficient to increase natural antibody production.

Acquired, nonrandom chromosomal abnormalities associated with the development of acute promyelocytic leukemia in transgenic mice

We previously generated a transgenic mouse model for acute promyelocytic leukemia (APL) by expressing the promyelocytic leukemia (PML)–retinoic acid receptor (RARα) cDNA in early myeloid cells. This fusion protein causes a myeloproliferative disease in 100% of animals, but only 15–20% of the animals develop acute leukemia after a long latency period (6–13 months). PML-RARα is therefore necessary, but not sufficient, for APL development. The coexpression of a reciprocal form of the fusion, RARα-PML, increased the likelihood of APL development (55–60%), but did not shorten latency. Together, these results suggested that additional genetic events are required for the development of APL. We therefore evaluated the splenic tumor cells from 18 transgenic mice with APL for evidence of secondary genetic events, by using spectral karyotyping analysis. Interstitial or terminal deletions of the distal region of one copy of chromosome 2 [del(2)] were found in 1/5 tumors expressing PML-RARα, but in 11/13 tumors expressing both PML-RARα and RARα-PML (P < 0.05). Leukemic cells that contained a deletion on chromosome 2 often contained additional chromosomal gains (especially of 15), chromosomal losses (especially of 11 or X/Y), or were tetraploid (P ≤ 0.001). These changes did not commonly occur in nontransgenic littermates, nor in aged transgenic mice that did not develop APL. These results suggest that expression of RARα-PML increases the likelihood of chromosome 2 deletions in APL cells. Deletion 2 appears to predispose APL cells to further chromosomal instability, which may lead to the acquisition of additional changes that provide an advantage to the transformed cells.

Generation of broad-spectrum disease resistance by overexpression of an essential regulatory gene in systemic acquired resistance

The recently cloned NPR1 gene of Arabidopsis thaliana is a key regulator of acquired resistance responses. Upon induction, NPR1 expression is elevated and the NPR1 protein is activated, in turn inducing expression of a battery of downstream pathogenesis-related genes. In this study, we found that NPR1 confers resistance to the pathogens Pseudomonas syringae and Peronospora parasitica in a dosage-dependent fashion. Overexpression of NPR1 leads to enhanced resistance with no obvious detrimental effect on the plants. Thus, for the first time, a single gene is shown to be a workable target for genetic engineering of nonspecific resistance in plants.

Accumulation of Salicylic Acid and 4-Hydroxybenzoic Acid in Phloem Fluids of Cucumber during Systemic Acquired Resistance Is Preceded by a Transient Increase in Phenylalanine Ammonia-Lyase Activity in Petioles and Stems1

Cucumber (Cucumis sativa) leaves infiltrated with Pseudomonas syringae pv. syringae cells produced a mobile signal for systemic acquired resistance between 3 and 6 h after inoculation. The production of a mobile signal by inoculated leaves was followed by a transient increase in phenylalanine ammonia-lyase (PAL) activity in the petioles of inoculated leaves and in stems above inoculated leaves; with peaks in activity at 9 and 12 h, respectively, after inoculation. In contrast, PAL activity in inoculated leaves continued to rise slowly for at least 18 h. No increases in PAL activity were detected in healthy leaves of inoculated plants. Two benzoic acid derivatives, salicylic acid (SA) and 4-hydroxybenzoic acid (4HBA), began to accumulate in phloem fluids at about the time PAL activity began to increase, reaching maximum concentrations 15 h after inoculation. The accumulation of SA and 4HBA in phloem fluids was unaffected by the removal of all leaves 6 h after inoculation, and seedlings excised from roots prior to inoculation still accumulated high levels of SA and 4HBA. These results suggest that SA and 4HBA are synthesized de novo in stems and petioles in response to a mobile signal from the inoculated leaf.