The Pan American (1988-11)

https://scholarworks.utrgv.edu/panamerican/1509/thumbnail.jp

The Pan American (1988-05)

https://scholarworks.utrgv.edu/panamerican/1504/thumbnail.jp

The Pan American (1988-12)

https://scholarworks.utrgv.edu/panamerican/1510/thumbnail.jp

The Pan American (1988-10)

https://scholarworks.utrgv.edu/panamerican/1508/thumbnail.jp

The Pan American (1988-04)

https://scholarworks.utrgv.edu/panamerican/1503/thumbnail.jp

Detecting autoreactive B cells in the peripheral blood of people with type 1 diabetes using ELISpot

Type 1 diabetes mellitus (T1D) is an autoimmune disorder where T lymphocytes damage the islet beta cells but B lymphocytes also play an important role. Although changes in peripheral B cell phenotype have been observed, little is known about the B cells that secrete the autoantibodies. We developed a sensitive B cell enzyme-linked immunospot assay (ELISpot assay) to detect individual B cell antibody responses to glutamic acid decarboxylase (GAD) and islet antigen-2 (IA-2). We found that even healthy donors have B cells that secrete antibodies in response to GAD and IA-2 in the ELISpot. There was increased B cell reactivity to autoantigens in the peripheral blood of individuals with newly-diagnosed, but not long-standing, type 1 diabetes. However, no correlation with serum autoantibody levels was found, indicating that additional factors such as antigen affinity or exposure to antigens in vivo are required for antibody secretion, and that even healthy donors have potentially autoreactive B cells

Isolating Stock Prices Variation with Neural Networks

In this study we aim to define a mapping function that relates the general index value among a set of shares to the prices of individual shares. In more general terms this is problem of defining the relationship between multivariate data distributions and a specific source of variation within these distributions where the source of variation in question represents a quantity of interest related to a particular problem domain. In this respect we aim to learn a complex mapping function that can be used for mapping different values of the quantity of interest to typical novel samples of the distribution. In our investigation we compare the performance of standard neural network based methods like Multilayer Perceptrons (MLPs) and Radial Basis Functions (RBFs) as well as Mixture Density Networks (MDNs) and a latent variable method, the General Topographic Mapping (GTM). As a reference benchmark of the prediction accuracy we consider a simple method based on the average values over certain intervals of the quantity of interest that we are trying to isolate (the so called Sample Average (SA) method). According to the results, MLPs and RBFs outperform MDNs and the GTM for this one-to-many mapping problem

A batch-service queueing model with a discrete batch Markovian arrival process

Queueing systems with batch service have been investigated extensively during the past decades. However, nearly all the studied models share the common feature that an uncorrelated arrival process is considered, which is unrealistic in several real-life situations. In this paper, we study a discrete-time queueing model, with a server that only initiates service when the amount of customers in system (system content) reaches or exceeds a threshold. Correlation is taken into account by assuming a discrete batch Markovian arrival process (D-BMAP), i.e. the distribution of the number of customer arrivals per slot depends on a background state which is determined by a first-order Markov chain. We deduce the probability generating function of the system content at random slot marks and we examine the influence of correlation in the arrival process on the behavior of the system. We show that correlation merely has a small impact on the threshold that minimizes the mean system content. In addition, we demonstrate that correlation might have a significant influence on the system content and therefore has to be included in the model

Inhibition of glutamine metabolism accelerates resolution of acute lung injury

Despite recent advances, acute respiratory distress syndrome (ARDS) remains a severe and often fatal disease for which there is no therapy able to reduce the underlying excessive lung inflammation or enhance resolution of injury. Metabolic programming plays a critical role in regulating inflammatory responses. Due to their high metabolic needs, neutrophils, macrophages, and lymphocytes rely upon glutamine metabolism to support activation and function. Additionally, during times of physiologic stress, nearly all cells, including fibroblasts and epithelial cells, require glutamine metabolism. We hypothesized that inhibiting glutamine metabolism reduces lung inflammation and promotes resolution of acute lung injury. Lung injury was induced by instilling lipopolysaccharide (LPS) intratracheally. To inhibit glutamine metabolism, we administered a glutamine analogue, 6-diazo-5-oxo-L-norleucine (DON) that binds to glutamine-utilizing enzymes and transporters, after injury was well established. Treatment with DON led to less lung injury, fewer lung neutrophils, lung inflammatory and interstitial macrophages, and lower levels of proinflammatory cytokines and chemokines at 5 and/or 7 days after injury. Additionally, DON led to earlier expression of the growth factor amphiregulin and more rapid recovery of LPS-induced weight loss. Thus, DON reduced lung inflammation and promoted resolution of injury. These data contribute to our understanding of how glutamine metabolism regulates lung inflammation and repair, and identifies a novel target for future therapies for ARDS and other inflammatory lung diseases

Guidelines for training in cardiovascular magnetic resonance (CMR)

These "Guidelines for training in Cardiovascular Magnetic Resonance" were developed by the Certification Committee of the Society for Cardiovascular Magnetic Resonance (SCMR) and approved by the SCMR Board of Trustees