ICA based steganography

A domain independent ICA-based watermarking method is introduced and studied by numerical simulations. This approach can be used either on images, music or video to convey a hidden message. It relies on embedding the information in a set of statistically independent sources (the independent components) as the feature space. For the experiments the medium has arbritraly chosen to be digital images.

ICA for watermarking digital images

A domain independent ICA-based approach to watermarking is presented. This approach can be used on images, music or video to embed either a robust or fragile watermark. In the case of robust watermarking, the method shows high information rate and robustness against malicious and non-malicious attacks, while keeping a low induced distortion. The fragile watermarking scheme, on the other hand, shows high sensitivity to tampering attempts while keeping the requirement for high information rate and low distortion. The improved performance is achieved by employing a set of statistically independent sources (the independent components) as the feature space and principled statistical decoding methods. The performance of the suggested method is compared to other state of the art approaches. The paper focuses on applying the method to digitized images although the same approach can be used for other media, such as music or video.

Initiation and progression of ossification of the posterior longitudinal ligament of the cervical spine in the hereditary spinal hyperostotic mouse (twy/twy)

Ossification of the posterior longitudinal ligament (OPLL) is a significantly critical pathology that can eventually cause serious myelopathy. Ossification commences in the vertebral posterior longitudinal ligaments, and intensifies and spreads with the progression of the disease, resulting in osseous projections and compression of the spinal cord. However, the paucity of histological studies the underlying mechanisms of calcification and ossification processes remain obscure. The pathological process could be simulated in the ossifying process of the ligament in mutant spinal hyperostotic mouse (twy/twy). The aim of this study is to observe that enlargement of the nucleus pulposus followed by herniation, disruption and regenerative proliferation of annulus fibrosus cartilaginous tissues participated in the initiation of ossification of the posterior longitudinal ligament of twy/twy mice.

Apoptosis of neurons and oligodendrocytes in the spinal cord of spinal hyperostotic mouse (twy/twy):possible pathomechanism of human cervical compressive myelopathy

Cervical compressive myelopathy is the most serious complication of cervical spondylosis or ossification of the posterior longitudinal ligament (OPLL) and the most frequent cause of spinal cord dysfunction. There is little information on the exact pathophysiological mechanism responsible for the progressive loss of neural tissue in the spinal cord of such patients. In this study, we used the spinal hyperostotic mouse (twy/twy) as a suitable model of human spondylosis, and OPLL to investigate the cellular and molecular changes in the spinal cord. Mutant twy/twy mouse developed ossification of the ligamentum flavum at C2-C3 and exhibited progressive paralysis.

The prevalence and phenotype of activated microglia/macrophages within the spinal cord of the hyperostotic mouse (twy/twy) changes in response to chronic progressive spinalcord compression:implications for human cervical compressive myelopathy

Background:Cervical compressive myelopathy, e.g. due to spondylosis or ossification of the posterior longitudinal ligament is a common cause of spinal cord dysfunction. Although human pathological studies have reported neuronal loss and demyelination in the chronically compressed spinal cord, little is known about the mechanisms involved. In particular, the neuroinflammatory processes that are thought to underlie the condition are poorly understood. The present study assessed the localized prevalence of activated M1 and M2 microglia/macrophages in twy/twy mice that develop spontaneous cervical spinal cord compression, as a model of human disease.Methods:Inflammatory cells and cytokines were assessed in compressed lesions of the spinal cords in 12-, 18- and 24-weeks old twy/twy mice by immunohistochemical, immunoblot and flow cytometric analysis. Computed tomography and standard histology confirmed a progressive spinal cord compression through the spontaneously development of an impinging calcified mass.Results:The prevalence of CD11b-positive cells, in the compressed spinal cord increased over time with a concurrent decrease in neurons. The CD11b-positive cell population was initially formed of arginase-1- and CD206-positive M2 microglia/macrophages, which later shifted towards iNOS- and CD16/32-positive M1 microglia/macrophages. There was a transient increase in levels of T helper 2 (Th2) cytokines at 18 weeks, whereas levels of Th1 cytokines as well as brain-derived neurotrophic factor (BDNF), nerve growth factor (NGF) and macrophage antigen (Mac) -2 progressively increased.Conclusions:Spinal cord compression was associated with a temporal M2 microglia/macrophage response, which may act as a possible repair or neuroprotective mechanism. However, the persistence of the neural insult also associated with persistent expression of Th1 cytokines and increased prevalence of activated M1 microglia/macrophages, which may lead to neuronal loss and demyelination despite the presence of neurotrophic factors. This understanding of the aetiopathology of chronic spinal cord compression is of importance in the development of new treatment targets in human disease. © 2013 Hirai et al.

A critical comparison of ICA algorithms

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Bayesian neural network approach for determining the risk of re-intervention after endovascular aortic aneurysm repair

This article proposes a Bayesian neural network approach to determine the risk of re-intervention after endovascular aortic aneurysm repair surgery. The target of proposed technique is to determine which patients have high chance to re-intervention (high-risk patients) and which are not (low-risk patients) after 5 years of the surgery. Two censored datasets relating to the clinical conditions of aortic aneurysms have been collected from two different vascular centers in the United Kingdom. A Bayesian network was first employed to solve the censoring issue in the datasets. Then, a back propagation neural network model was built using the uncensored data of the first center to predict re-intervention on the second center and classify the patients into high-risk and low-risk groups. Kaplan-Meier curves were plotted for each group of patients separately to show whether there is a significant difference between the two risk groups. Finally, the logrank test was applied to determine whether the neural network model was capable of predicting and distinguishing between the two risk groups. The results show that the Bayesian network used for uncensoring the data has improved the performance of the neural networks that were built for the two centers separately. More importantly, the neural network that was trained with uncensored data of the first center was able to predict and discriminate between groups of low risk and high risk of re-intervention after 5 years of endovascular aortic aneurysm surgery at center 2 (p = 0.0037 in the logrank test).

Measurement of intervertebral cervical motion by means of dynamic X-ray image processing and data interpolation

Accurate measurement of intervertebral kinematics of the cervical spine can support the diagnosis of widespread diseases related to neck pain, such as chronic whiplash dysfunction, arthritis, and segmental degeneration. The natural inaccessibility of the spine, its complex anatomy, and the small range of motion only permit concise measurement in vivo. Low dose X-ray fluoroscopy allows time-continuous screening of cervical spine during patient's spontaneous motion. To obtain accurate motion measurements, each vertebra was tracked by means of image processing along a sequence of radiographic images. To obtain a time-continuous representation of motion and to reduce noise in the experimental data, smoothing spline interpolation was used. Estimation of intervertebral motion for cervical segments was obtained by processing patient's fluoroscopic sequence; intervertebral angle and displacement and the instantaneous centre of rotation were computed. The RMS value of fitting errors resulted in about 0.2 degree for rotation and 0.2 mm for displacements. © 2013 Paolo Bifulco et al.

An artificial neural network stratifies the risks of reintervention and mortality after endovascular aneurysm repair:a retrospective observational study

Background Lifelong surveillance after endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) is considered mandatory to detect potentially life-threatening endograft complications. A minority of patients require reintervention but cannot be predictively identified by existing methods. This study aimed to improve the prediction of endograft complications and mortality, through the application of machine-learning techniques. Methods Patients undergoing EVAR at 2 centres were studied from 2004-2010. Pre-operative aneurysm morphology was quantified and endograft complications were recorded up to 5 years following surgery. An artificial neural networks (ANN) approach was used to predict whether patients would be at low- or high-risk of endograft complications (aortic/limb) or mortality. Centre 1 data were used for training and centre 2 data for validation. ANN performance was assessed by Kaplan-Meier analysis to compare the incidence of aortic complications, limb complications, and mortality; in patients predicted to be low-risk, versus those predicted to be high-risk. Results 761 patients aged 75 +/- 7 years underwent EVAR. Mean follow-up was 36+/- 20 months. An ANN was created from morphological features including angulation/length/areas/diameters/ volume/tortuosity of the aneurysm neck/sac/iliac segments. ANN models predicted endograft complications and mortality with excellent discrimination between a low-risk and high-risk group. In external validation, the 5-year rates of freedom from aortic complications, limb complications and mortality were 95.9% vs 67.9%; 99.3% vs 92.0%; and 87.9% vs 79.3% respectively (p0.001) Conclusion This study presents ANN models that stratify the 5-year risk of endograft complications or mortality using routinely available pre-operative data.

Anticancer effects of Curcuma C20-dialdehyde against colon and cervical cancer cell lines

Background: Recent attention on chemotherapeutic intervention against cancer has been focused on discovering and developing phytochemicals as anticancer agents with improved efficacy, low drug resistance and toxicity, low cost and limited adverse side effects. In this study, we investigated the effects of Curcuma C20-dialdehyde on growth, apoptosis and cell cycle arrest in colon and cervical cancer cell lines. Materials and Methods: Antiproliferative, apoptosis induction, and cell cycle arrest activities of Curcuma C20-dialdehyde were determined by WST cell proliferation assay, flow cytometric Alexa fluor 488-annexin V/propidium iodide (PI) staining and PI staining, respectively. Results: Curcuma C20 dialdehyde suppressed the proliferation of HCT116, HT29 and HeLa cells, with IC50 values of 65.4±1.74 μg/ml, 58.4±5.20 μg/ml and 72.0±0.03 μg/ml, respectively, with 72 h exposure. Flow cytometric analysis revealed that percentages of early apoptotic cells increased in a dose-dependent manner upon exposure to Curcuma C20-dialdehyde. Furthermore, exposure to lower concentrations of this compound significantly induced cell cycle arrest at G1 phase for both HCT116 and HT29 cells, while higher concentrations increased sub-G1 populations. However, the concentrations used in this study could not induce cell cycle arrest but rather induced apoptotic cell death in HeLa cells. Conclusions: Our findings suggest that the phytochemical Curcuma C20-dialdehyde may be a potential antineoplastic agent for colon and cervical cancer chemotherapy and/or chemoprevention. Further studies are needed to characterize the drug target or mode of action of the Curcuma C20-dialdehyde as an anticancer agent.

The risk of re-intervention after endovascular aortic aneurysm repair

This thesis studies survival analysis techniques dealing with censoring to produce predictive tools that predict the risk of endovascular aortic aneurysm repair (EVAR) re-intervention. Censoring indicates that some patients do not continue follow up, so their outcome class is unknown. Methods dealing with censoring have drawbacks and cannot handle the high censoring of the two EVAR datasets collected. Therefore, this thesis presents a new solution to high censoring by modifying an approach that was incapable of differentiating between risks groups of aortic complications. Feature selection (FS) becomes complicated with censoring. Most survival FS methods depends on Cox's model, however machine learning classifiers (MLC) are preferred. Few methods adopted MLC to perform survival FS, but they cannot be used with high censoring. This thesis proposes two FS methods which use MLC to evaluate features. The two FS methods use the new solution to deal with censoring. They combine factor analysis with greedy stepwise FS search which allows eliminated features to enter the FS process. The first FS method searches for the best neural networks' configuration and subset of features. The second approach combines support vector machines, neural networks, and K nearest neighbor classifiers using simple and weighted majority voting to construct a multiple classifier system (MCS) for improving the performance of individual classifiers. It presents a new hybrid FS process by using MCS as a wrapper method and merging it with the iterated feature ranking filter method to further reduce the features. The proposed techniques outperformed FS methods based on Cox's model such as; Akaike and Bayesian information criteria, and least absolute shrinkage and selector operator in the log-rank test's p-values, sensitivity, and concordance. This proves that the proposed techniques are more powerful in correctly predicting the risk of re-intervention. Consequently, they enable doctors to set patients’ appropriate future observation plan.