Bioengineering of Artificial Antigen Presenting Cells and Lymphoid Organs

The immune system protects the body against a wide range of infectious diseases and cancer by leveraging the efficiency of immune cells and lymphoid organs. Over the past decade, immune cell/organ therapies based on the manipulation, infusion, and implantation of autologous or allogeneic immune cells/organs into patients have been widely tested and have made great progress in clinical applications. Despite these advances, therapy with natural immune cells or lymphoid organs is relatively expensive and time-consuming. Alternatively, biomimetic materials and strategies have been applied to develop artificial immune cells and lymphoid organs, which have attracted considerable attentions. In this review, we survey the latest studies on engineering biomimetic materials for immunotherapy, focusing on the perspectives of bioengineering artificial antigen presenting cells and lymphoid organs. The opportunities and challenges of this field are also discussed

Expression levels of immune-genes in developing workers of Apis mellifera in response to reproductive timing and infestation level by the parasitic mite Varroa destructor

The parasitic mite Varroa destructor is one of the biggest health problems of the Western Honey Bee, Apis mellifera. It feeds from the bees' hemolymph and vectors several honey bee pathogens. V. destructor has also been reported to compromise honey bee immunity but available data are insufficient to support this claim. This study was designed to assess the effect of mite infestation on honey bee immune-gene expression during the biologically relevant host developmental stages. In my experiment, mites were manually introduced into honey bee larval cells at three different levels. Control groups were either left unmanipulated or wounded. Developing bees were collected with any retrievable mites daily from the experimental cells for ten days. Mite reproduction was assessed and bee hosts were analyzed for expression levels of ten immune genes using quantitative RT-PCR. This experiment showed effects of developmental time and experimental treatment on gene expression that generally contradict the previously hypothesized immunosuppression of bees by V. destructor. However, mites might temporarily suppress the honey bees' normal response to cuticle wounding based on reproductive timing. The artificial wounding group exhibited an increased viral load, suggesting that wounding may trigger or enable virus replication. Overall, my results indicate the importance of physical trauma caused by wounding and suggest complex temporal dynamics in the relationships between bee host, mite parasite, and vectored pathogens

The design of an evolutionary algorithm for artificial immune system based failure detector generation and optimization

The development of an evolutionary algorithm and accompanying software for the generation and optimization of artificial immune system-based failure detectors is presented in this thesis. These detectors use the Artificial Immune System-based negative selection strategy. The utility is a part of an integrated set of methodologies for the detection, identification, and evaluation of a wide variety of aircraft sub-system abnormal conditions. The evolutionary algorithm and accompanying software discussed in this document is concerned with the creation, optimization, and testing of failure detectors based on the negative selection strategy. A preliminary phase consists of processing data from flight tests for self definition including normalization, duplicate removal, and clustering. A first phase of the evolutionary algorithm produces, through an iterative process, a set of detectors that do not overlap with the self and achieve a prescribed level of coverage of the non-self. A second phase consists of a classic evolutionary algorithm that attempts to optimize the number of detectors, overlapping between detectors, and coverage of the non-self while maintaining no overlapping with the self. For this second phase, the initial population is composed of sets of detectors, called individuals, obtained in the first phase. Specific genetic operators have been defined to accommodate different detector shapes, such as hyper-rectangles, hyper-spheres, hyper-ellipsoids and hyper-rotational-ellipsoids. The output of this evolutionary algorithm consists of an optimized set of detectors which is intended for later use as a part of a detection, identification, and evaluation scheme for aircraft sub-system failure.;An interactive design environment has been developed in MATLAB that relies on an advanced user-friendly graphical interface and on a substantial library of alternative algorithms to allow maximum flexibility and effectiveness in the design of detector sets for artificial immune system-based abnormal condition detection. This user interface is designed for use with Windows and MATLAB 7.6.0, although measures have been taken to maintain compatibility with MATLAB version 7.0.4 and higher, with limited interface compatibility. This interface may also be used with UNIX versions of MATLAB, version 7.0.4 or higher.;The results obtained show the feasibility of optimizing the various shapes in 2, 3, and 6 dimensions. Hyper-spheres are generally faster than the other three shapes, though they do not necessarily exhibit the best detection results. Hyper-ellipsoids and hyper-rotational-ellipsoids generally show somewhat better detection performance than hyper-spheres, but at a higher calculation cost. Calculation time for optimization of hyper-rectangles seems to be highly susceptible to dimensionality, taking increasingly long in higher dimensions. In addition, hyper-rectangles tend to need a higher number of detectors to achieve adequate coverage of the solution space, though they exhibit very little overlapping among detectors. However, hyper-rectangles are consistently and considerably quicker to calculate detection for than the other shapes, which may make them a promising candidate for online detection schemes

T-cell epitope prediction and immune complex simulation using molecular dynamics: state of the art and persisting challenges

Atomistic Molecular Dynamics provides powerful and flexible tools for the prediction and analysis of molecular and macromolecular systems. Specifically, it provides a means by which we can measure theoretically that which cannot be measured experimentally: the dynamic time-evolution of complex systems comprising atoms and molecules. It is particularly suitable for the simulation and analysis of the otherwise inaccessible details of MHC-peptide interaction and, on a larger scale, the simulation of the immune synapse. Progress has been relatively tentative yet the emergence of truly high-performance computing and the development of coarse-grained simulation now offers us the hope of accurately predicting thermodynamic parameters and of simulating not merely a handful of proteins but larger, longer simulations comprising thousands of protein molecules and the cellular scale structures they form. We exemplify this within the context of immunoinformatics

Computer vision

The field of computer vision is surveyed and assessed, key research issues are identified, and possibilities for a future vision system are discussed. The problems of descriptions of two and three dimensional worlds are discussed. The representation of such features as texture, edges, curves, and corners are detailed. Recognition methods are described in which cross correlation coefficients are maximized or numerical values for a set of features are measured. Object tracking is discussed in terms of the robust matching algorithms that must be devised. Stereo vision, camera control and calibration, and the hardware and systems architecture are discussed

Shape-specific microfabricated particles for biomedical applications: a review

The storied history of controlled the release systems has evolved over time; from degradable drug-loaded sutures to monolithic zero-ordered release devices and nano-sized drug delivery formulations. Scientists have tuned the physico-chemical properties of these drug carriers to optimize their performance in biomedical/pharmaceutical applications. In particular, particle drug delivery systems at the micron size regime have been used since the 1980s. Recent advances in micro and nanofabrication techniques have enabled precise control of particle size and geometry–here we review the utility of microplates and discoidal polymeric particles for a range of pharmaceutical applications. Microplates are defined as micrometer scale polymeric local depot devices in cuboid form, while discoidal polymeric nanoconstructs are disk-shaped polymeric particles having a cross-sectional diameter in the micrometer range and a thickness in the hundreds of nanometer range. These versatile particles can be used to treat several pathologies such as cancer, inflammatory diseases and vascular diseases, by leveraging their size, shape, physical properties (e.g., stiffness), and component materials, to tune their functionality. This review highlights design and fabrication strategies for these particles, discusses their applications, and elaborates on emerging trends for their use in formulations. GRAPHICAL ABSTRACT: [Image: see text

Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices