Statistical Filtering for Multimodal Mobility Modeling in Cyber Physical Systems

A Cyber-Physical System integrates computations and dynamics of physical processes. It is an engineering discipline focused on technology with a strong foundation in mathematical abstractions. It shares many of these abstractions with engineering and computer science, but still requires adaptation to suit the dynamics of the physical world. In such a dynamic system, mobility management is one of the key issues against developing a new service. For example, in the study of a new mobile network, it is necessary to simulate and evaluate a protocol before deployment in the system. Mobility models characterize mobile agent movement patterns. On the other hand, they describe the conditions of the mobile services. The focus of this thesis is on mobility modeling in cyber-physical systems. A macroscopic model that captures the mobility of individuals (people and vehicles) can facilitate an unlimited number of applications. One fundamental and obvious example is traffic profiling. Mobility in most systems is a dynamic process and small non-linearities can lead to substantial errors in the model. Extensive research activities on statistical inference and filtering methods for data modeling in cyber-physical systems exist. In this thesis, several methods are employed for multimodal data fusion, localization and traffic modeling. A novel energy-aware sparse signal processing method is presented to process massive sensory data. At baseline, this research examines the application of statistical filters for mobility modeling and assessing the difficulties faced in fusing massive multi-modal sensory data. A statistical framework is developed to apply proposed methods on available measurements in cyber-physical systems. The proposed methods have employed various statistical filtering schemes (i.e., compressive sensing, particle filtering and kernel-based optimization) and applied them to multimodal data sets, acquired from intelligent transportation systems, wireless local area networks, cellular networks and air quality monitoring systems. Experimental results show the capability of these proposed methods in processing multimodal sensory data. It provides a macroscopic mobility model of mobile agents in an energy efficient way using inconsistent measurements

Inflammatory Manifestations of Experimental Lymphatic Insufficiency

BACKGROUND: Sustained lymph stagnation engenders a pathological response that is complex and not well characterized. Tissue inflammation in lymphedema may reflect either an active or passive consequence of impaired immune traffic. METHODS AND FINDINGS: We studied an experimental model of acute post-surgical lymphedema in the tails of female hairless, immunocompetent SKH-1 mice. We performed in vivo imaging of impaired immune traffic in experimental, murine acquired lymphatic insufficiency. We demonstrated impaired mobilization of immunocompetent cells from the lymphedematous region. These findings correlated with histopathological alterations and large-scale transcriptional profiling results. We found intense inflammatory changes in the dermis and the subdermis. The molecular pattern in the RNA extracted from the whole tissue was dominated by the upregulation of genes related to acute inflammation, immune response, complement activation, wound healing, fibrosis, and oxidative stress response. CONCLUSIONS: We have characterized a mouse model of acute, acquired lymphedema using in vivo functional imaging and histopathological correlation. The model closely simulates the volume response, histopathology, and lymphoscintigraphic characteristics of human acquired lymphedema, and the response is accompanied by an increase in the number and size of microlymphatic structures in the lymphedematous cutaneous tissues. Molecular characterization through clustering of genes with known functions provides insights into processes and signaling pathways that compose the acute tissue response to lymph stagnation. Further study of genes identified through this effort will continue to elucidate the molecular mechanisms and lead to potential therapeutic strategies for lymphatic vascular insufficiency

A New Mouse Model to Study Acquired Lymphedema

Schneider and colleagues say that the new model, published in PLoS Medicine, promises to increase our understanding of lymphedema and hopefully accelerate the development and testing of new treatments

Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data

<p>Abstract</p> <p>Background</p> <p>Time-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differentially expressed in time-course data, measured between different biological conditions. These differentially expressed genes can reveal the changes in biological process due to the change in condition which is essential to understand differences in dynamics.</p> <p>Results</p> <p>In this paper, we propose a novel method for finding differentially expressed genes in time-course data and across biological conditions (say <it>C</it>₁and <it>C</it>₂). We model the expression at <it>C</it>₁using Principal Component Analysis and represent the expression profile of each gene as a linear combination of the dominant Principal Components (PCs). Then the expression data from <it>C</it>₂is projected on the developed PCA model and scores are extracted. The difference between the scores is evaluated using a hypothesis test to quantify the significance of differential expression. We evaluate the proposed method to understand differences in two case studies (1) the heat shock response of wild-type and HSF1 knockout mice, and (2) cell-cycle between wild-type and Fkh1/Fkh2 knockout Yeast strains.</p> <p>Conclusion</p> <p>In both cases, the proposed method identified biologically significant genes.</p

Stochastic variation of transcript abundance in C57BL/6J mice

<p>Abstract</p> <p>Background</p> <p>Transcripts can exhibit significant variation in tissue samples from inbred laboratory mice. We have designed and carried out a microarray experiment to examine transcript variation across samples from adipose, heart, kidney, and liver tissues of C57BL/6J mice and to partition variation into within-mouse and between-mouse components. Within-mouse variance captures variation due to heterogeneity of gene expression within tissues, RNA-extraction, and array processing. Between-mouse variance reflects differences in transcript abundance between genetically identical mice.</p> <p>Results</p> <p>The nature and extent of transcript variation differs across tissues. Adipose has the largest total variance and the largest within-mouse variance. Liver has the smallest total variance, but it has the most between-mouse variance. Genes with high variability can be classified into groups with correlated patterns of expression that are enriched for specific biological functions. Variation between mice is associated with circadian rhythm, growth hormone signaling, immune response, androgen regulation, lipid metabolism, and the extracellular matrix. Genes showing correlated patterns of within-mouse variation are also associated with biological functions that largely reflect heterogeneity of cell types within tissues.</p> <p>Conclusions</p> <p>Genetically identical mice can experience different individual outcomes for medically important traits. Variation in gene expression observed between genetically identical mice can identify functional classes of genes that are likely to vary in the absence of experimental perturbations, can inform experimental design decisions, and provides a baseline for the interpretation of gene expression data in interventional studies. The extent of transcript variation among genetically identical mice underscores the importance of stochastic and micro-environmental factors and their phenotypic consequences.</p

Statins promote the regression of atherosclerosis via activation of the CCR7-dependent emigration pathway in macrophages.

HMG-CoA reductase inhibitors (statins) decrease atherosclerosis by lowering low-density-lipoprotein cholesterol. Statins are also thought to have additional anti-atherogenic properties, yet defining these non-conventional modes of statin action remains incomplete. We have previously developed a novel mouse transplant model of atherosclerosis regression in which aortic segments from diseased donors are placed into normolipidemic recipients. With this model, we demonstrated the rapid loss of CD68+ cells (mainly macrophages) in plaques through the induction of a chemokine receptor CCR7-dependent emigration process. Because the human and mouse CCR7 promoter contain Sterol Response Elements (SREs), we hypothesized that Sterol Regulatory Element Binding Proteins (SREBPs) are involved in increasing CCR7 expression and through this mechanism, statins would promote CD68+ cell emigration from plaques. We examined whether statin activation of the SREBP pathway in vivo would induce CCR7 expression and promote macrophage emigration from plaques. We found that western diet-fed apoE(-/-) mice treated with either atorvastatin or rosuvastatin led to a substantial reduction in the CD68+ cell content in the plaques despite continued hyperlipidemia. We also observed a significant increase in CCR7 mRNA in CD68+ cells from both the atorvastatin and rosuvastatin treated mice associated with emigration of CD68+ cells from plaques. Importantly, CCR7(-/-)/apoE(-/-) double knockout mice failed to display a reduction in CD68+ cell content upon statin treatment. Statins also affected the recruitment of transcriptional regulatory proteins and the organization of the chromatin at the CCR7 promoter to increase the transcriptional activity. Statins promote the beneficial remodeling of plaques in diseased mouse arteries through the stimulation of the CCR7 emigration pathway in macrophages. Therefore, statins may exhibit some of their clinical benefits by not only retarding the progression of atherosclerosis, but also accelerating its regression