UNDERSTANDING SOFT MOLECULAR INTERFACES USING MULTISCALE MOLECULAR DYNAMICS

Molecular dynamics simulations have proven to be a breakthrough approach in providing molecular structure and function details of biological macromolecules. The fact that molecular simulations can provide details of individual particle motions as a function of time, opens infinite possibilities of answering specific questions about a model system with atomistic and molecular precision, often more easily than experiments on the actual system. Instead of relying solely on experiments, molecular dynamics systems are based on empirical force fields that mimic molecular interactions over time. There has been increasing interest in utilizing the multiscale molecular dynamics (MD) approach to simulate the properties of biological macromolecules for meaningful applications in areas of nanotoxicity, nanotherapy, and nanomedicine. Recent advances in nanomedicine have led to the great development of several drug-delivery platforms for targeted delivery. Polymeric drug delivery systems are designed and applied to ameliorate undesirable properties of the drug agents such as hydrophobicity, poor targeting ability, and broad scale toxicity. However, rational structural design of polymeric nanocarrier is only based on theoretical investigation, that restraint by absence of real molecular-level details. So, MD approach is used to provide those desirable details which not feasible in experiments. In our work, models of three generations of anticancer nanocarriers polymer were developed and characterized using multiscale MD simulations. The results were compared to in vitro experimental results. Observations were analyzed and found to be in good agreement with experiments results on size and morphology changes. Details shown by our systems helped us discover the reasons for the different nanocarriers’ performances. Our results show the in silico methods that can be used to contribute to drug nanocarrier design optimization work. Claudins, are critical components in building tight junctions (TJs) which could form paracellular channels or barriers for physiological functions. Recently, 27 types of claudins have been classified based on different functions and characteristics, and they are becoming potential points in developing drug delivery systems and pathological studies. However, the architecture of claudin strand in TJs is still unclear thus impeding further study and applications. So, we use MD simulation approach to replicate self-assembly process and try to find out the potential construction models. In our work, claudin-1, -2, -5, and -15 monomer models were built by homology modeling and validated. Self-assembly processes of non-mutated and mutated claudins were conducted to reproduce the construction of claudin macromolecular strands. Four classified dimer models predicted by existing research were found to be reproduced in our molecular dynamics systems, and their number distributions were calculated. Our results first time showed the potential models for TJ architectures that can be a guidance for understanding TJs

Flexible-Resolution, Arbitrary-Input and Tunable Rotman Lens Spectrum Decomposer (RL-SD)

We present an enhanced design -- in terms of resolution flexibility, input port position arbitrariness and frequency-range tunability -- of the planar Rotman lens spectrum decomposer (RL-SD). This enhancement is achieved by manipulating the output port locations through proper sampling of the frequency-position law of the RL-SD, inserting a calibration array compensating for frequency deviation induced by input modification and introducing port switching, respectively. A complete design procedure is provided and two enhanced RL-SD prototypes, with uniform port distribution and uniform frequency resolution, respectively, are numerically and experimentally demonstrated

Matching records in multiple databases using a hybridization of several technologies.

A major problem with integrating information from multiple databases is that the same data objects can exist in inconsistent data formats across databases and a variety of attribute variations, making it difficult to identify matching objects using exact string matching. In this research, a variety of models and methods have been developed and tested to alleviate this problem. A major motivation for this research is that the lack of efficient tools for patient record matching still exists for health care providers. This research is focused on the approximate matching of patient records with third party payer databases. This is a major need for all medical treatment facilities and hospitals that try to match patient treatment records with records of insurance companies, Medicare, Medicaid and the veteran\u27s administration. Therefore, the main objectives of this research effort are to provide an approximate matching framework that can draw upon multiple input service databases, construct an identity, and match to third party payers with the highest possible accuracy in object identification and minimal user interactions. This research describes the object identification system framework that has been developed from a hybridization of several technologies, which compares the object\u27s shared attributes in order to identify matching object. Methodologies and techniques from other fields, such as information retrieval, text correction, and data mining, are integrated to develop a framework to address the patient record matching problem. This research defines the quality of a match in multiple databases by using quality metrics, such as Precision, Recall, and F-measure etc, which are commonly used in Information Retrieval. The performance of resulting decision models are evaluated through extensive experiments and found to perform very well. The matching quality performance metrics, such as precision, recall, F-measure, and accuracy, are over 99%, ROC index are over 99.50% and mismatching rates are less than 0.18% for each model generated based on different data sets. This research also includes a discussion of the problems in patient records matching; an overview of relevant literature for the record matching problem and extensive experimental evaluation of the methodologies, such as string similarity functions and machine learning that are utilized. Finally, potential improvements and extensions to this work are also presented

How does the Chinese government use social media to react to social crisis: a content analysis

Professional project report submitted in partial fulfillment of the requirements for the degree of Masters of Arts in Journalism from the School of Journalism, University of Missouri--Columbia.In order to examine the Chinese government's strategies and stances reflected on its social media account during a social crisis, this research uses a content analysis of 391 Weibo posts from four official government accounts. The researcher uses one-way ANOVA, Chi-square and independent-sample t test to compare the strategies and stance reflected in different phrases and between two types of government accounts. The results reveal that the Chinese government tended to adopt an accommodative stance towards social crisis. Among four government accounts, the posts from government-controlled media accounts showed a less accommodative stance. Moreover, posts from government-controlled media accounts are more likely to try explaining the cause of crisis, while the posts government-agency accounts are making promises for the future like establishing policies to secure a better environment and clean the air. Finally discussion focuses on the speculations that might lead to the results.Includes bibliographic references