Selective accumulation of virus-specific CD8+ T cells within the peripheral blood stem cell compartment

The absence of cellular immunity is central to the pathogenesis of herpesvirus-mediated diseases after allogeneic hemopoietic stem cell transplantation (HSCT). For both bone marrow (BM)– and granulocyte-colony stimulating factor–mobilized peripheral blood stem cells (PBSCs) HSCT, donor-derived Epstein-Barr virus (EBV) and cytomegalovirus (CMV) peptide–specific CD8+ T cells clones undergo early expansion and persist long-term, with additional diversification arising from novel antigen-specific clones from donor-derived progenitors. Whether BM or PBSC is the superior source of antiviral CD8+ T cells is unclear. Given that PBSC has largely replaced BM as a source of stem cells for HSCT, it is unlikely that herpesvirus effector T-cell reconstitution will ever be compared prospectively. PBSC grafts contain 10 to 30 times more T cells than BM and a randomized study found proven viral infections were more frequent in BM than PBSC recipients, suggesting viral-specific T-cell immunity is enhanced in PBSC. Recently Moss showed in lung cancer patients that herpesvirus-specific BM-derived CD8+ T cells have unique homing properties relative to herpesvirus-specific CD8+ T cells present in unmobilized peripheral blood (PB). Immunodominant EBV-lytic peptide–specific CD8+ T cells were enriched in BM but were reduced for CMV peptide–specific CD8+ T cells relative to PB. EBV-latent peptide–specific CD8+ T cells were equivalent, which has relevance in the context of posttransplantation lymphoproliferative disorder for which impaired EBV-latent CD8+ T-cell immunity is a risk-factor. A comparison of herpesvirus-specific cellular immunity in PBSC versus PB has yet to be performed.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

Estimation of parameters for macroparasite population evolution using approximate Bayesian computation

We estimate the parameters of a stochastic process model for a macroparasite population within a host using approximate Bayesian computation (ABC). The immunity of the host is an unobserved model variable and only mature macroparasites at sacrifice of the host are counted. With very limited data, process rates are inferred reasonably precisely. Modeling involves a three variable Markov process for which the observed data likelihood is computationally intractable. ABC methods are particularly useful when the likelihood is analytically or computationally intractable. The ABC algorithm we present is based on sequential Monte Carlo, is adaptive in nature, and overcomes some drawbacks of previous approaches to ABC. The algorithm is validated on a test example involving simulated data from an autologistic model before being used to infer parameters of the Markov process model for experimental data. The fitted model explains the observed extra-binomial variation in terms of a zero-one immunity variable, which has a short-lived presence in the host.

Workplace Equality in International Organizations: A Way Forward

In this contribution, I am interested in how discrimination issues are manifested in employment relations in the United Nations (UN), a public forum to all states political leaders to advance their concerns, the World Bank, a financial organization that promotes economic development, mainly in developing countries, and the Consultative Group on International Agricultural Research (CGIAR), the eldest and largest global public program of the World Bank with a strategic network of diverse stakeholders that harnesses the best in science to produce more and better food, reduce poverty and sustain environments. Considering the immunity and privileges granted to international organizations, what are the current available legal procedures, at the national or international level, for workplace equality? How accountable and transparent are they, based on the practice of these organizations? Can discrimination biases that go beyond the known individual-based discrimination claims be identified? If so, how can they be challenged and changed? Based of the special position of international civil servants in international organizations and the duty to protect their fundamental rights, I claim that the limitation of opportunity by discriminatory biases and the psychic burden on the individual staff member, on daily basis, qualify for a workplace wrong and call for independent and impartial legal procedures that would ensure due process and fair treatment.

Chlamydia muridarum major outer membrane protein-specific antibodies inhibit in vitro infection but enhance pathology in vivo

PROBLEM Chlamydia trachomatis is a significant worldwide health problem, and the often-asymptomatic disease can result in infertility. To develop a successful vaccine, a complete understanding of the immune response to chlamydial infection and development of genital tract pathology is required. METHOD OF STUDY We utilized the murine genital model of chlamydial infection. Mice were immunized with chlamydial major outer membrane protein, and vaginal lavage was assessed for the presence of neutralizing antibodies. These samples were then pre-incubated with Chlamydia muridarum and administered to the vaginal vaults of immune-competent female BALB/c mice to determine the effect on infection. RESULTS The administration of C. muridarum in conjunction with neutralizing antibodies reduced the numbers of mice infected, but a surprising finding was that this accelerated the development of severe oviduct pathology. CONCLUSION Antibodies play an under-recognized role in chlamydial infection and pathology development, which possibly involves interaction with Th1 immunity.

Rodent blood-stage Plasmodium survive in dendritic cells that infect naive mice.

Plasmodium spp. parasites cause malaria in 300 to 500 million individuals each year. Disease occurs during the blood-stage of the parasite’s life cycle, where the parasite is thought to replicate exclusively within erythrocytes. Infected individuals can also suffer relapses after several years, from Plasmodium vivax and Plasmodium ovale surviving in hepatocytes. Plasmodium falciparum and Plasmodium malariae can also persist after the original bout of infection has apparently cleared in the blood, suggesting that host cells other than erythrocytes (but not hepatocytes) may harbor these blood-stage parasites, thereby assisting their escape from host immunity. Using blood stage transgenic Plasmodium berghei-expressing GFP (PbGFP) to track parasites in host cells, we found that the parasite had a tropism for CD317+ dendritic cells. Other studies using confocal microscopy, in vitro cultures, and cell transfer studies showed that blood-stage parasites could infect, survive, and replicate within CD317+ dendritic cells, and that small numbers of these cells released parasites infectious for erythrocytes in vivo. These data have identified a unique survival strategy for blood-stage Plasmodium, which has significant implications for understanding the escape of Plasmodium spp. from immune-surveillance and for vaccine development.

Using Fiber Bragg Grating (FBG) sensors to measure vertical displacements of bridges : a preliminary study

In many bridges, vertical displacements are the most relevant parameter for monitoring in the both short and long term. However, it is difficult to measure vertical displacements of bridges and yet they are among the most important indicators of structural behaviour. Therefore, it prompts a need to develop a simple, inexpensive and yet more practical method to measure vertical displacements of bridges. With the development of fiber-optics technologies, fiber Bragg grating (FBG) sensors have been widely used in structural health monitoring. The advantages of these sensors over the conventional sensors include multiplexing capabilities, high sample rate, small size and electro magnetic interference (EMI) immunity. In this paper, methods of vertical displacement measurements of bridges are first reviewed. Then, FBG technology is briefly introduced including principle, sensing system, characteristics and different types of FBG sensors. Finally, the methodology of vertical displacement measurements using FBG sensors is presented and a trial test is described. It is concluded that using FBG sensors is feasible to measure vertical displacements of bridges. This method can be used to understand global behaviour of bridge‘s span and can further develop for structural health monitoring techniques such as damage detection.

Partial protection against chlamydial reproductive tract infection by a recombinant major outer membrane protein/CpG/cholera toxin intranasal vaccine in the guinea pig Chlamydia cavaie model

Chlamydia trachomatis is a major cause of sexually transmitted diseases worldwide. There currently is no vaccine to protect against chlamydial infection of the female reproductive tract. Vaccine development has predominantly involved using the murine model, however infection of female guinea pigs with Chlamydia caviae more closely resembles chlamydial infection of the human female reproductive tract, and presents a better model to assess potential human chlamydial vaccines. We immunised female guinea pigs intranasally with recombinant major outer membrane protein (r-MOMP) combined with CpG-10109 and cholera toxin adjuvants. Both systemic and mucosal immune responses were elicited in immunised animals. MOMP-specific IgG and IgA were present in the vaginal mucosae, and high levels of MOMP-specific IgG were detected in the serum of immunised animals. Antibodies from the vaginal mucosae were also shown to be capable of neutralising C. caviae in vitro. Following immunisation, animals were challenged intravaginally with a live C. caviae infection of 102 inclusion forming units. We observed a decrease in duration of infection and a significant (p<0.025) reduction in infection load in r-MOMP immunised animals, compared to animals immunised with adjuvant only. Importantly, we also observed a marked reduction in upper reproductive tract (URT) pathology in r-MOMP immunised animals. Intranasal immunisation of female guinea pigs with r-MOMP was able to provide partial protection against C. caviae infection, not only by reducing chlamydial burden but also URT pathology. This data demonstrates the value of using the guinea pig model to evaluate potential chlamydial vaccines for protection against infection and disease pathology caused by C. trachomatis in the female reproductive tract.

Pattern recognition using invariants defined from higher order spectra : 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants

Using Fiber Bragg Grating (FBG) sensors for vertical displacement measurement of bridges

In many bridges, vertical displacements are one of the most relevant parameters for structural health monitoring in both the short and long terms. Bridge managers around the globe are always looking for a simple way to measure vertical displacements of bridges. However, it is difficult to carry out such measurements. On the other hand, in recent years, with the advancement of fiber-optic technologies, fiber Bragg grating (FBG) sensors are more commonly used in structural health monitoring due to their outstanding advantages including multiplexing capability, immunity of electromagnetic interference as well as high resolution and accuracy. For these reasons, using FBG sensors is proposed to develop a simple, inexpensive and practical method to measure vertical displacements of bridges. A curvature approach for vertical displacement measurement using curvature measurements is proposed. In addition, with the successful development of a FBG tilt sensors, an inclination approach is also proposed using inclination measurements. A series of simulation tests of a full-scale bridge was conducted. It shows that both the approaches can be implemented to determine vertical displacements for bridges with various support conditions, varying stiffness (EI) along the spans and without any prior known loading. These approaches can thus measure vertical displacements for most of slab-on-girder and box-girder bridges. Moreover, with the advantages of FBG sensors, they can be implemented to monitor bridge behavior remotely and in real time. Further recommendations of these approaches for developments will also be discussed at the end of the paper.

Pattern recognition using invariants defined from higher order spectra - one-dimensional inputs

A new approach to pattern recognition using invariant parameters based on higher order spectra is presented. In particular, invariant parameters derived from the bispectrum are used to classify one-dimensional shapes. The bispectrum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale and amplification invariant, as well. A minimal set of these invariants is selected as the feature vector for pattern classification, and a minimum distance classifier using a statistical distance measure is used to classify test patterns. The classification technique is shown to distinguish two similar, but different bolts given their one-dimensional profiles. Pattern recognition using higher order spectral invariants is fast, suited for parallel implementation, and has high immunity to additive Gaussian noise. Simulation results show very high classification accuracy, even for low signal-to-noise ratios.