Contributors: Tejaswini Vuppu
... This is my Data Visuaization course project 2.
Contributors: Tejaswini Vuppu
... This is Visualization project 1
Parvalbumin expression in oligodendrocyte-like CG4 cells causes a reduction in mitochondrial volume, attenuation in reactive oxygen species production and a decrease in cell processes' length and branching
Contributors: Lucia Lichvarova, Walter Blum, Beat Schwaller and Viktoria Szabolcsi
... Forebrain glial cells - ependymal cells and astrocytes -acquire upon injury- a “reactive” phenotype associated with parvalbumin (PV) upregulation. Since free radicals, e.g. reactive oxygen species (ROS) play a role in the pathogenesis of multiple sclerosis, and that PV-upregulation in glial cells is inversely correlated with the level of oxidative stress, we hypothesized that PV-upregulation might also protect oligodendrocytes by decreasing ROS production. Lentiviral transduction techniques allowed for PV overexpression in CG4 oligodendrocyte progenitor cells (OPCs). Depending on the growth medium CG4 cells can be maintained in an OPC-like state, or induced to differentiate into an oligodendrocyte (OLG)-like phenotype. While increased levels of PV had no effect on cell proliferation and invasiveness in vitro, PV decreased the mitochondria volume in CG4 cell bodies, as well as the mitochondrial density in CG4 processes in both OPC-like and OLG-like states. In line with the PV-induced global decrease in mitochondrial volume, elevated PV levels reduced transcript levels of mitochondrial transcription factors involved in mitochondria biogenesis. In differentiated PV-overexpressing CG4 cells with a decreased mitochondrial volume, UV-induced ROS production was lower than in control CG4 cells hinting towards a possible role of PV in counteracting oxidative stress. Unexpectedly, PV also decreased the length of processes in undifferentiated CG4 cells and moreover diminished branching of differentiated CG4 cell processes, strongly correlated with the decreased density of mitochondria in CG4 cell processes. Thus besides conferring a protective role against oxidative stress, PV in a cell autonomous fashion additionally affects process’ growth and branching in CG4 cells.
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Contributors: Danlei Qian
... It is a school project about the death causes and risk factors in USA
Contributors: HungChun Lin
... This is the report about the Suicide rate from 1985 to 2016, the dataset was got from Kaggle website (https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016), in this dataset, it contains country, year, sex, age, suicide number, population, suicide/100k pop, country-year, HDI for year, gdp_for_year, gdp_per_capita and generation. From this suicide rate dataset, we can know the trend of the suicide number by year, and also know which generation/age group has the highest suicide number. In this report, we also compared the suicide number to the other three factors to see is there any correlation between them, we compared the suicide number to GDP (https://data.worldbank.org/indicator/NY.GDP.MKTP.CD), life expectancy (https://www.kaggle.com/kumarajarshi/life-expectancy-who) and happiness score to (http://worldhappiness.report/ed/2017/) find the correlation. Here are some conclusions for this report: •1. The suicide number of the male is higher than female. •2. The age group has the highest suicide number is 35-54 years. •3. The country which has the highest suicide number is Russian. •4. GDP, life expectancy and happiness score do not have a strong correlation with the suicide number. •5. Happiness score has a strong correlation with life expectancy. There are two files I attached, the ppt file is the result of my data mining, and the html file is about the processing I dealing with this data by Python.
... Data Source – The Organization for Economic Co-operation and Development (OECD) and World Bank Data bank. Got G-20 countries data from OECD and World Bank. Narrow down the selection to top ten countries in Healthcare, Educational and Military spending. Time Limit: I Used six years of data from 2010 to 2015 for my data analysis. Used Google API, Bar charts, Geo Maps, Stacked Bar Chart, Time series Line Chart and Pie charts to visualize my data.
Contributors: Pradip Hayu, M.S., Nima Zahadat, Ph.D.
Contributors: Srilatha Lakka
... Data Visualization Individual Project-1 Analysis of Military, Health and Education Spending
Benchmarking Octave, R and Python platforms for code prototyping in Data Analytics and Machine Learning applications programming
Contributors: Harris Georgiou
... Abstract Octave, R and Python identical codes are tested in terms of in terms of end-user execution speed, using a very low-end "embedded" hardware system and a standard office workstation. The codes include algorithmic primitives common in Data Analytics and Machine Learning, i.e., matrix manipulation (inversion, product), linear Algebra, linear regression, Singular Value Decomposition (SVD), fast Fourier transformation (FFT) and a baseline Bubblesort implementation for testing flow control structures. Description In Data Analytics and Machine Learning, code prototyping is an integral part of the Research & Development (R&D) process, especially in data exploration and algorithm design. The programming tools and platforms used for these tasks are selected for rich API/library base, high-level expression syntax, very compact code, interactive on-the-fly code input, abstract data management and best-possible execution speed. Thus, traditional programming languages are usually inappropriate for such heavily iterative and exploratory coding evolutions. Today, by far the three most popular and appropriate choices are Octave, R and Python. In this work, these three programming environments are assessed in terms of end-user execution speed. More specifically, some common algorithmic primitives are implemented and tested in each language separately, including matrix manipulation (inversion, product), linear Algebra, linear regression, Singular Value Decomposition (SVD), as well as fast Fourier transformation (FFT) as a standard procedure in a signal processing pipeline. Additionally, a baseline implementation of the Bubblesort algorithm is employed for testing the efficiency of flow control structures and execution performance in code branching. The results present the performance of the three identical source codes in terms of end-user execution speed (elapsed time) in three different hardware platforms, namely: (1) simulating very low-end processing and resources machine similar to embedded systems (Linux, 2GB RAM, N20 Atom single-core CPU), (2) a standard/enhanced office workstation (Win10, 16GB RAM, dual-core i7 CPU) and (3) a high-end workstation or small office server (Win10, 32GB RAM, quad-core i7 CPU).
Contributors: Lingyi Meng M.S., Nima Zahadat, Ph.D.
... This project is related to relationship between river’s Injury and Passenger Characteristics through analyzing on Python-Pandas and Plotly. The coding is stored in file "Lingyi Meng.ipynb" and diagrams created by coding is kept as png form. There are totally two main part of datasets: "2016OhioCrash.xlsx" is the core data for analyzing and the left mini table are designed for analyzing the overlook together.