A Comparison of Large Deflection Analysis of Bending Plates by Dynamic Relaxation

In this paper, various dynamic relaxation methods are investigated for geometric nonlinear analysis of bending plates. Sixteen wellknown algorithms are employed. Dynamic relaxation fictitious parameters are the mass matrix, the damping matrix and the time step. The difference between the mentioned tactics is how to implement these parameters. To compare the efficiency of these strategies, several bending plates’ problems with large deflections are solved. Based on the number of iterations and analysis time, the scores of the different schemes are calculated. These scores determine the ranking of each technique. The numerical results indicate the appropriate efficiency of Underwood and Rezaiee-Pajand & Alamatian processes for the nonlinear analysis of bending plates

Multiple Sclerosis Gene Therapy Using Recombinant Viral Vectors: Overexpression of IL-4, IL-10 and Leukemia Inhibitory Factor in Wharton's Jelly Stem Cells in The EAE Mice Model

Objective: Immunotherapy and gene therapy play important roles in modern medicine. The aim of this study is to evaluate the overexpression of interleukin-4 (IL-4), IL-10 and leukemia inhibitory factor (LIF) in Wharton's jelly stem cells (WJSCs) in the experimental autoimmune encephalomyelitis (EAE) mice model. Materials and Methods: In this experimental study, a DNA construction containing IL-4, IL-10 and LIF was assembled to make a polycistronic vector (as the transfer vector). Transfer and control vectors were co-transfected into Human Embryonic Kidney 293 (HEK-293T) cells with helper plasmids which produced recombinant lentiviral viruses (rLV). WJSCs were transduced with rLV to make recombinant WJSC (rWJSC). In vitro protein and mRNA overexpression of IL-4, LIF, and IL-10 were evaluated using quantitative polymerase chain reaction (qPCR), enzyme-linked immunosorbent assay (ELISA) and western blot (WB) analysis. EAE was induced in mice by MOG-CFA and pertussis toxin. EAE mice were injected twice with 2x10(5) rWJSCs. The in vivo level of IL-4, LIF, IL-10 cytokines and IL-17 were measured by ELISA. Brain tissues were analyzed histologically for evaluation of EAE lesions. Results: Isolated WJSCs were performed to characterize by in vitro differentiation and surface markers were analyzed by flow cytometry method. Cloning of a single lentiviral vector with five genes was done successfully. Transfection of transfer and control vectors were processed based on CaPO4 method with > 90% efficiency. Recombinant viruses were produced and results of titration showed 2-3x10(7) infection-unit/ml. WJSCs were transduced using recombinant viruses. IL-4, IL-10 and LIF overexpression were confirmed by ELISA, WB and qPCR. The EAE mice treated with rWJSC showed reduction of Il-17, and brain lesions as well as brain cellular infiltration, in vivo. Weights and physical activity were improved in gene-treated group. Conclusion: These results showed that gene therapy using anti-inflammatory cytokines can be a promising approach against multiple sclerosis (MS). In addition, considering the immunomodulatory potential of WJSCs, an approach using a combination of WJSCs and gene therapy will enhance the treatment efficacy

Multiple Sclerosis Gene Therapy with Recombinant Viral Vectors: Overexpression of IL-4, Leukemia Inhibitory Factor, and IL-10 in Wharton's Jelly Stem Cells Used in EAE Mice Model.

OBJECTIVES: Immunotherapy and gene therapy play important roles in modern medicine. The aim of this study is to evaluate the overexpression of interleukin-4 (IL-4), IL-10 and leukemia inhibitory factor (LIF) in Wharton's jelly stem cells (WJSCs) in the experimental autoimmune encephalomyelitis (EAE) mice model. MATERIALS AND METHODS: In this experimental study, a DNA construction containing IL- 4, IL-10 and LIF was assembled to make a polycistronic vector (as the transfer vector). Transfer and control vectors were co-transfected into Human Embryonic Kidney 293 (HEK-293T) cells with helper plasmids which produced recombinant lentiviral viruses (rLV). WJSCs were transduced with rLV to make recombinant WJSC (rWJSC). In vitro protein and mRNA overexpression of IL-4, LIF, and IL-10 were evaluated using quantitative polymerase chain reaction (qPCR), enzyme-linked immunosorbent assay (ELISA) and western blot (WB) analysis. EAE was induced in mice by MOG-CFA and pertussis toxin. EAE mice were injected twice with 2×105 rWJSCs. The in vivo level of IL-4, LIF, IL-10 cytokines and IL-17 were measured by ELISA. Brain tissues were analyzed histologically for evaluation of EAE lesions. RESULTS: Isolated WJSCs were performed to characterize by in vitro differentiation and surface markers were analyzed by flow cytometry method. Cloning of a single lentiviral vector with five genes was done successfully. Transfection of transfer and control vectors were processed based on CaPO4 method with >90% efficiency. Recombinant viruses were produced and results of titration showed 2-3×107 infection-unit/ml. WJSCs were transduced using recombinant viruses. IL-4, IL-10 and LIF overexpression were confirmed by ELISA, WB and qPCR. The EAE mice treated with rWJSC showed reduction of Il-17, and brain lesions as well as brain cellular infiltration, in vivo. Weights and physical activity were improved in gene-treated group. CONCLUSIONS: These results showed that gene therapy using anti-inflammatory cytokines can be a promising approach against multiple sclerosis (MS). In addition, considering the immunomodulatory potential of WJSCs, an approach using a combination of WJSCs and gene therapy will enhance the treatment efficacy

The Impacts of Household Behaviors and Housing Choice on Residential Energy Consumption

Thesis (Ph.D.)--University of Washington, 2014Despite efforts made in the past decade to curb excessive energy consumption and the corresponding greenhouse gas (GHG) emissions, both energy consumption and GHG emissions are expected to increase in coming years. Not only does such increasing trends epitomize the escalating, enduring human contribution to global warming, it verifies that our current policies are not working, at least not as well as expected or hoped. Globally, approximately a quarter of our total energy consumption is in the home, almost as much as in any other sector. Yet an understanding of the processes, determinants, and consequences of household energy consumption remains elusive. Conventional research on residential energy consumption has often applied linear methodologies and overwhelmingly focused on physical attributes of the housing stocks and systems. This approach, therefore, has failed: 1) to provide a coherent perspective of energy consumption processes, and 2) to account for the role of household behaviors. Accordingly, conventional energy policy has been left without the essential understanding of the phenomenon that would allow it to take effective action. To rectify issues with conventional research and policy, this research applies a non-linear and interdisciplinary approach to household energy consumption as an outcome of housing consumption and choice behaviors. Using data from the latest Residential Energy Consumption Survey, I use a set of Structural Equation Models to estimate the direct, indirect, and total effects of household and housing characteristics on energy use. Outcomes demonstrate that household characteristics have an indirect effect on energy consumption by influencing housing unit attributes, the housing choice effect on energy consumption. That is, a household's choice of housing unit has a permanent effect on the household's energy consumption, as an outcome, up until they relocate. Results of this study show that, accounting for the housing choice effects, the overall effect of household characteristics on energy consumption is almost twice as important as anticipated by conventional research. This study's findings highlight the role of housing choice and consumption behaviors in shaping residential energy consumption patterns. Energy consumption is expected to increase due to inevitable sociodemographic and economic changes. In addition to investing in improved building efficiencies and technologies, smart energy policies aimed at reducing energy consumption should promote more sustainable housing consumption behaviors and provide better housing choices

Replication Data for: kluster: An Efficient Scalable Procedure for Approximating the Number of Clusters in Unsupervised Learning

182 simulated datasets (first set contains small datasets and second set contains large datasets) with different cluster compositions – i.e., different number clusters and separation values – generated using clusterGeneration package in R. Each set of simulation datasets consists of 91 datasets in comma separated values (csv) format (total of 182 csv files) with 3-15 clusters and 0.1 to 0.7 separation values. Separation values can range between (−0.999, 0.999), where a higher separation value indicates cluster structure with more separable clusters. Size of the dataset, number of clusters, and separation value of the clusters in the dataset is printed in file name. size_X_n_Y_sepval_Z.csv: Size of the dataset = X number of clusters in the dataset = Y separation value of the clusters in the dataset =

DQe-v: A Database-Agnostic Framework for Exploring Variability in Electronic Health Record Data Across Time and Site Location

Data variability is a commonly observed phenomenon in Electronic Health Records (EHR) data networks. A common question asked in scientific investigations of EHR data is whether the cross-site and -time variability reflects an underlying data quality error at one or more contributing sites versus actual differences driven by various idiosyncrasies in the healthcare settings. Although research analysts and data scientists have commonly used various statistical methods to detect and account for variability in analytic datasets, self service tools to facilitate exploring cross-organizational variability in EHR data warehouses are lacking and could benefit from meaningful data visualizations. DQe-v, an interactive, database-agnostic tool for visually exploring variability in EHR data provides such a solution. DQe-v is built on an open source platform, R statistical software, with annotated scripts and a readme document that makes it fully reproducible. To illustrate and describe functionality of DQe-v, we describe the DQe-v’s readme document which includes a complete guide to installation, running the program, and interpretation of the outputs. We also provide annotated R scripts and an example dataset as supplemental materials. DQe-v offers a self service tool to visually explore data variability within EHR datasets irrespective of the data model. GitHub and CIELO offer hosting and distribution of the tool and can facilitate collaboration across any interested community of users as we target improving usability, efficiency, and interoperability

Different Regions, Differences in Energy Consumption: Do regions account for the variability in household energy consumption?

Sciences. The authors would like to acknowledge Adrian Dobra for his instructions and supervision throughout this research. This research aims to discover between-region variability in household energy consumption behaviors, using multilevel modeling with data from Residential Energy Consumption Survey in 2009. Where past research focuses on the physical characteristics of housing, our effort in this research centers on between-region variability and micro-level determinants of residential energy usage. We found significant between-region variability with household energy usage across the U.S. Results suggest that the association between heating degree days and energy consumption varies depending on the type of climate that a household lives in. Further, household energy usage is significantly associated with the race of the householder and income. In the discussion section, we connect this outcome to racial disparities between whites and non-whites in location choice, access to housing, and residential stratification. Moreover, the structure of our research reveals the necessity of multilevel modeling in gaining an accurate picture of residential energy consumption for more efficient energy conservation policy