5153 research outputs found
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Separation and Characterization of Cells with Varying Magnetic Nanoparticle Concentration
No full textThe activation of magnetic nanoparticles (mNPs) by an alternating magnetic field (AMF) is currently being explored as technique for targeted therapeutic heating of tumors. Engineered nanoparticles have attracted much attention during the last decade because of their size-related properties, large surface-to-volume ratios and high surface reactivity. Parameters such as the size, charge and surface chemistry are examined because they affect the interactions of the nanoparticles with living cells. The mechanism for the uptake and distribution within cells is not fully understood. Optimization of targeted cellular uptake is critical if many nanoparticle-based applications are to become utilized in the clinical and research settings. We propose to develop a method to separate cells based on intracellular mNP concentration which will enable further study and optimization of cellular uptake in tumor cells
Preliminary Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention Reports: Prediction Framework Study
No full textBackground: Social networking sites (SNSs) such as Twitter are widely used by diverse demographic populations. The amount of data within SNSs has created an efficient resource for real-time analysis. Thus, data from SNSs can be used effectively to track disease outbreaks and provide necessary warnings. Current SNS-based flu detection and prediction frameworks apply conventional machine learning approaches that require lengthy training and testing, which is not the optimal solution for new outbreaks with new signs and symptoms. Objective: The objective of this study was to propose an efficient and accurate framework that uses data from SNSs to track disease outbreaks and provide early warnings, even for newest outbreaks, accurately. Methods: We presented a framework of outbreak prediction that included 3 main modules: text classification, mapping, and linear regression for weekly flu rate predictions. The text classification module used the features of sentiment analysis and predefined keyword occurrences. Various classifiers, including FastText (FT) and 6 conventional machine learning algorithms, were evaluated to identify the most efficient and accurate one for the proposed framework. The text classifiers were trained and tested using a prelabeled dataset of flu-related and unrelated Twitter postings. The selected text classifier was then used to classify over 8,400,000 tweet documents. The flu-related documents were then mapped on a weekly basis using a mapping module. Finally, the mapped results were passed together with historical Centers for Disease Control and Prevention (CDC) data to a linear regression module for weekly flu rate predictions. Results: The evaluation of flu tweet classification showed that FT, together with the extracted features, achieved accurate results with an F-measure value of 89.9% in addition to its efficiency. Therefore, FT was chosen to be the classification module to work together with the other modules in the proposed framework, including a regression-based estimator, for flu trend predictions. The estimator was evaluated using several regression models. Regression results show that the linear regression–based estimator achieved the highest accuracy results using the measure of Pearson correlation. Thus, the linear regression model was used for the module of weekly flu rate estimation. The prediction results were compared with the available recent data from CDC as the ground truth and showed a strong correlation of 96.29%. Conclusions: The results demonstrated the efficiency and the accuracy of the proposed framework that can be used even for new outbreaks with new signs and symptoms. The classification results demonstrated that the FT-based framework improves the accuracy and the efficiency of flu disease surveillance systems that use unstructured data such as data from SNSs.https://doi.org/10.2196/1238
Multi-Walled Carbon Nanotube & Polypyrrole Nanocomposite and its Interactions with AMB-1 Bacteria
No full textFaculty Research Day 2018: Graduate Student Poster 3rd PlaceBeing able to easily manufacture pristine carbon nanotube (CNT) is a difficult problem that we face today. There are many different chirality (twists) that the carbon nanotubes orient themselves in making them either more metallic or semi-conducting in nature. Being able to separate these two types is very important to electronic industries because semi-conducting carbon nanotubes are better suited for devices such as transistors. Magnetospirillum magneticum (AMB-1) is a bacteria that can be used through magnetotaxis for controlled assembly of CNT based devices. It has been shown that there are favorable interactions between the MSP-1 surface protein and flagellin protein from AMB-1 with only semi-conducting carbon nanotubes1 through glycine and its flanking residues. In order to better study this interaction, this work is on the fabrication of a free standing nanocomposite carbon nanotube and polypyrrole (Ppy) film using cyclic voltammetry. This technique electro-polymerizes the pyrrole monomer into Ppy through a series of oxidation and reduction reactions along with multi-walled carbon nanotubes (MWNT) to be deposited on an electrode. It has been shown before that graphene, pyrrole, and carbon nanotubes can form a film together using cyclic voltammetry2. In general, carbon nanotubes have a difficult time being suspended in water because they are non-polar (hydrophobic), which challenges the process of making the films containing only CNTs. The specific aim of this project is to look at the interactions between AMB-1 and MWNT/Ppy film using scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS) and atomic force microscopy (AFM). In order to have large number of interactions between AMB-1 and CNT only, we are working to find the least amount of pyrrole needed to make the film containing the most amount of CNTs
Differential Evolution: A Survey and Analysis
No full textDifferential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. In particular, we present a state-of-the-art survey of the literature on DE and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques.http://dx.doi.org/10.3390/app810194
Visualization and Analysis of Air Pollution in US East Coast Cities
No full textAir pollution has negative impact on human health and leads to many chronic diseases. U.S. Environmental Protection Agency (EPA) has been closely monitoring the air pollution using its ground stations in various locations around the nation. The collected data has been included in its air pollution database and made publically available in its website. The detailed daily air pollutant concentrations (e.g. PM2.5, PM10, SO2, CO, Pb, NO2, Ozone) can be downloaded in Excel format. In this poster, we visualize and analyze the air pollution in the US East Coast in the past years using Tableau software. Such visualization allows us to observe the trend of air pollution and its transmission pattern in major cities of the east coast. The correlations between air pollution and various conditions (e.g. traffic, season, location) are discussed. The influence of various terrain conditions to the PM2.5 pollutant diffusion is explored. The visualization and analysis of air pollution data helps better understand its mechanism and distribution pattern
Comparison and Analysis of Project Management software (Tools) available in the Market
No full textProject management software is software that provides an overview of the goals, resources, and processes for projects. Project management software is often used as a tool for project communication, creating workflows, and aiding with specific project management techniques, like Agile or Waterfall. It is a software which is dedicated to keeping projects under budget, in scope, and on time. It’s often used by project managers and stakeholders. Project management software typically includes resource management, file sharing, and task management. Research has shown that these tools improve overall team communication, final project quality, and customer satisfaction. In addition, project management software is effective at creating deliverables on budget and on time. As a system that’s designed to systematize a business’s overall communication and product strategy, some form of project management software is often present in business and not-for-profit enterprises. Studies show that organizations are completing their projects on time and on budget and with higher quality when implementing project management software. Teams using project management software are also likely to communicate better. The biggest cause of dissatisfaction among the minority who don't care for their project management software is cost, followed by demoing too many products
Professional Development For Culturally And Linguistically Responsive Classrooms: An Action Research Study
No full textThis action research study evaluated and sought to improve teacher professional development (PD) strategies designed to instill culturally and linguistically responsive pedagogies in classrooms at an elementary school. The study assessed participants’ practical application and perceptions of the applicability of English learner instruction strategies and culturally responsive pedagogy in mainstream classrooms. The first phase of the study included piloting the survey instrument with participants who attended a conference on bilingual education, and conducting a thorough literature review. The action research study was then designed so that 29 teachers in a public elementary school received professional development (PD). Nine of the teachers that participated in the PD met the sampling criteria and were willing to participate in the study - four in the first iteration and five in the second. Observations, pre- and post-PD surveys, and participant interviews provided insight into the teachers’ PD needs, the impact of the
intervention, and provided feedback on the opportunities and challenges throughout the teachers’ learning experience. Teacher-participants were observed prior to their participation in the PD intervention using a protocol with observable components of sheltered instruction and culturally responsive teaching. A two sample t-test for means determined that three observable elements were statistically significant on the post-intervention observations: language objectives clearly defined, displayed, and reviewed with students; concepts explicitly linked to students' background experiences; and instruction is scaffolded to promote culturally and linguistically diverse (CLD)
student learning.
In the first iteration, 15 themes emerged from the interviews during which the four participants discussed the content received in the PD and how they would apply it in their classrooms. Second iteration interviews further supported that participants felt the professional development and strategies were applicable to their practice. Surveys administered before and after each iteration supported that the PD helped participants become more comfortable with teaching culturally and linguistically diverse students after receiving professional development (PD) on the topic. The researcher was also a participant and facilitator relying on deep reflection to improve the PD modules. The results from iterations one and two were carefully analyzed to complete the
third iteration – a practical guide to planning professional development for creating culturally and linguistically responsive classrooms. Although strategies should be adapted to meet the needs of each school’s population, academic offerings, and themes (as in the case of magnet schools), the professional development guide resulting from this research can help administrators and faculty collect baseline data, plan and deliver PD, prioritize and implement strategies, and collect post-PD data to determine if the PD has impacted instruction