scholarly journals RAMP2-AS1 Regulates Endothelial Homeostasis and Aging

2021 ◽  
Vol 9 ◽  
Author(s):  
Chih-Hung Lai ◽  
Aleysha T. Chen ◽  
Andrew B. Burns ◽  
Kiran Sriram ◽  
Yingjun Luo ◽  
...  
Keyword(s):  
Vascular Function ◽  
Cardiovascular Health ◽  
Time Series Data ◽  
Principal Component ◽  
Series Data ◽  
Integrative Approach ◽  
Protein Coding ◽  
Vascular Integrity ◽  
Functional Changes ◽  
Endothelial Homeostasis

The homeostasis of vascular endothelium is crucial for cardiovascular health and endothelial cell (EC) aging and dysfunction could negatively impact vascular function. Leveraging transcriptome profiles from ECs subjected to various stimuli, including time-series data obtained from ECs under physiological pulsatile flow vs. pathophysiological oscillatory flow, we performed principal component analysis (PCA) to identify key genes contributing to divergent transcriptional states of ECs. Through bioinformatics analysis, we identified that a long non-coding RNA (lncRNA) RAMP2-AS1 encoded on the antisense of RAMP2, a determinant of endothelial homeostasis and vascular integrity, is a novel regulator essential for EC homeostasis and function. Knockdown of RAMP2-AS1 suppressed RAMP2 expression and caused EC functional changes promoting aging, including impaired angiogenesis and increased senescence. Our study demonstrates an integrative approach to quantifying EC aging based on transcriptome changes, which also identified a number of novel regulators, including protein-coding genes and many lncRNAs involved EC functional modulation, exemplified by RAMP2-AS1.

scholarly journals Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States

2021 ◽  
Vol 18 (9) ◽  
pp. 4560
Author(s):  
Mostafa Abbas ◽  
Thomas B. Morland ◽  
Eric S. Hall ◽  
Yasser EL-Manzalawy
Keyword(s):  
Functional Data ◽  
Time Series Data ◽  
Principal Component ◽  
State Level ◽  
The United States ◽  
Functional Principal Component Analysis ◽  
Series Data ◽  
Dynamic Correlation ◽  
The Us ◽  
Google Search

We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.

scholarly journals Analysis of Electric Vehicle Charging Behavior Patterns with Function Principal Component Analysis Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Chenxi Chen ◽  
Yang Song ◽  
Xianbiao Hu ◽  
Ivan G. Guardiola
Keyword(s):  
Energy Consumption ◽  
Kansas City ◽  
Time Series Data ◽  
Principal Component ◽  
Handling Time ◽  
Series Data ◽  
Behavior Patterns ◽  
Electric Vehicle Charging ◽  
Charging Stations ◽  
Ev Charging

This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.

scholarly journals Analisis Potensi Gempa di Selatan Pulau Jawa Berdasarkan Pengamatan GPS

2020 ◽  
Vol 11 (3) ◽  
pp. 151
Author(s):  
Irwan Meilano ◽  
Agidia L. Tiaratama ◽  
Dudy D. Wijaya ◽  
Putra Maulida ◽  
S. Susilo ◽  
...  
Keyword(s):  
Time Series ◽  
Subduction Zones ◽  
Velocity Vector ◽  
Time Series Data ◽  
Principal Component ◽  
Reference Station ◽  
Tectonic Activity ◽  
Series Data ◽  
Local Data ◽  
Central Java

ABSTRAKPulau Jawa merupakan salah satu pulau yang memiliki kepadatan penduduk tinggi dengan aktivitas tektonik yang sangat aktif. Hal ini dikarenakan Pulau Jawa terletak di zona konvergensi Lempeng Indo-Australia dan Lempeng Eurasia. Aktivitas tektonik ini menghasilkan kegempaan di zona subduksi dan sesar di daratan Penelitian ini menganalisis pola vektor kecepatan yang dihasilkan melalui pengolahan data stasiun pengamatan GPS (Global Positioning System) CORS (Continuously Operating Reference Station) BIG (Badan Informasi Geospasial) di wilayah Pulau Jawa bagian selatan. Data koordinat harian dianalisis dengan metode PCA (Principal Component Analysis) untuk memisahkan sinyal tektonik berupa data deret waktu global dan non-tektonik berupa data deret waktu lokal dengan penerapan aturan pemilihan varian dominan nilai eigen dalam pembetukan PC (Principal Component) dan orthogonal vektor eigen sebagai bobot dalam meminimalkan korelasi. Hasil dari data deret waktu global dan lokal digunakan untuk menghitung besar kecepatan pergeseran dari tahun 2011 sampai 2018. Hasil pengolahan menunjukkan besar resultan vektor kecepatan pada data awal berselang 0,06 sampai 10,46 mm/tahun, pada data global antara 0,06 mm/ tahun sampai 10,39 mm/tahun, dan data lokal sebesar 0,0037 sampai 1,99 mm/tahun. Variasi spasial vektor kecepatan pengamatan GPS data domain PCA menunjukkan variasi pergeseran horizontal di wilayah Banten bergerak ke arah timur laut; Jawa Barat, Daerah Istimewa Yogyakarta, dan Jawa Tengah bergerak ke arah tenggara; dan Jawa Timur bergerak ke arah timur laut. Hasil dari inversi data pergeseran terhadap slip pada zona subduksi, menunjukkan terjadinya kekurangan slip atau terjadi coupling pada zona subduksi Jawa bagian timur dan barat, sementara terjadi kelebihan slip pada bagian tengah yang merupakan efek postseismic dari gempa Pangandaran 2006.Kata kunci: GPS, PCA, potensi gempa, vektor kecepatanABSTRACTJava is one of the island that has a high population density with very active tectonic activity. This is because Java Island is located in the convergence zone of the Indo-Australian Plate and the Eurasian Plate. This tectonic activity produces seismicity in subduction zones and inland faults. This study analyzes the velocity vector patterns generated through data processing of the GPS (Global Positioning System) CORS (Continuously Operating Reference Station) BIG (Geospatial Information Agency) observation station in the southern part of Java. Daily coordinate data were analyzed using PCA (Principal Component Analysis) method to separate time series of tectonic signals as global data and non-tectonic time series data as local data by applying the rules for selecting dominant variants of eigen values for PC formation and orthogonal eigen vectors as weights in minimizing correlations. The results from global and local time series data were used to calculate the magnitude of the displacement velocity from 2011 until 2018. The processing results show the resultant velocity vector in the initial data intermittent 0.06 to 10.46 mm/year, global data from 0.06 to 10.39 mm/year, and local data of 0.0037 to 1.99 mm/year. The spatial variation of the velocity vector in PCA domain data shows the horizontal displacement in the Banten region to the northeast; West Java, Yogyakarta Special Region, Central Java to southeast; and East Java moving to northeast. The results of the inversion of the surface displacement to slip data in the subduction zone show that there is a slip deficiency or coupling occurs in the subduction zones of Eastern and Western Java, while there is excess slip in the Central Java which is a post-seismic effect of the 2006 Pangandaran earthquake.Keywords: earthquake potential, GPS, PCA, velocity vector

scholarly journals Challenges in estimating the impact of vaccination with sparse data

2018 ◽  
Author(s):  
Kayoko Shioda ◽  
Cynthia Schuck-Paim ◽  
Robert J. Taylor ◽  
Roger Lustig ◽  
Lone Simonsen ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Simulation Analysis ◽  
Principal Component ◽  
Population Level ◽  
Series Data ◽  
Synthetic Control ◽  
Vaccine Impact ◽  
Pca Method ◽  
The Impact

ABSTRACTBackgroundThe synthetic control (SC) model is a powerful tool to quantify the population-level impact of vaccines, because it can adjust for trends unrelated to vaccination using a composite of control diseases. Because vaccine impact studies are often conducted using smaller subnational datasets, we evaluated the performance of SC models with sparse time series data. To obtain more robust estimates of vaccine effects from noisy time series, we proposed a possible alternative approach, “STL+PCA” method (seasonal-trend decomposition plus principal component analysis), which first extracts smoothed trends from the control time series and uses them to adjust the outcome.MethodsUsing both the SC and STL+PCA models, we estimated the impact of 10-valent pneumococcal conjugate vaccine (PCV10) on pneumonia hospitalizations among cases <12 months and 80+ years of age during 2004-2014 at the subnational level in Brazil. The performance of these models was also compared using simulation analyses.ResultsThe SC model was able to adjust for trends unrelated to PCV10 in larger states but not in smaller states. The simulation analysis confirmed that the SC model failed to select an appropriate set of control diseases when the time series were sparse and noisy, thereby generating biased estimates of the impact of vaccination when secular trends were present. The STL+PCA approach decreased bias in the estimates for smaller populations.ConclusionsEstimates from the SC model might be biased when data are sparse. The STL+PCA model provides more accurate evaluations of vaccine impact in smaller populations.

scholarly journals Comparison of outlier detection techniques in non-stationary time series data

2021 ◽  
Vol 27 (1) ◽  
pp. 55-60
Author(s):  
Sampson Twumasi-Ankrah ◽  
Simon Kojo Appiah ◽  
Doris Arthur ◽  
Wilhemina Adoma Pels ◽  
Jonathan Kwaku Afriyie ◽  
...  
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Mahalanobis Distance ◽  
Time Series Data ◽  
Principal Component ◽  
Stationary Time Series ◽  
Series Data ◽  
Distance Method ◽  
Detection Techniques ◽  
Stationary Time

This study examined the performance of six outlier detection techniques using a non-stationary time series dataset. Two key issues were of interest. Scenario one was the method that could correctly detect the number of outliers introduced into the dataset whiles scenario two was to find the technique that would over detect the number of outliers introduced into the dataset, when a dataset contains only extreme maxima values, extreme minima values or both. Air passenger dataset was used with different outliers or extreme values ranging from 1 to 10 and 40. The six outlier detection techniques used in this study were Mahalanobis distance, depth-based, robust kernel-based outlier factor (RKOF), generalized dispersion, Kth nearest neighbors distance (KNND), and principal component (PC) methods. When detecting extreme maxima, the Mahalanobis and the principal component methods performed better in correctly detecting outliers in the dataset. Also, the Mahalanobis method could identify more outliers than the others, making it the "best" method for the extreme minima category. The kth nearest neighbor distance method was the "best" method for not over-detecting the number of outliers for extreme minima. However, the Mahalanobis distance and the principal component methods were the "best" performed methods for not over-detecting the number of outliers for the extreme maxima category. Therefore, the Mahalanobis outlier detection technique is recommended for detecting outlier in nonstationary time series data.

scholarly journals Associations Between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States

2021 ◽  
Author(s):  
Mostafa Abbas ◽  
Thomas B. Morland ◽  
Eric S. Hall ◽  
Yasser EL-Manzalawy
Keyword(s):  
Functional Data ◽  
Time Series Data ◽  
Principal Component ◽  
State Level ◽  
The United States ◽  
Functional Principal Component Analysis ◽  
Series Data ◽  
Dynamic Correlation ◽  
The Us ◽  
Google Search

ABSTRACTWe utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19 related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.

scholarly journals Dynamic autonomic nervous system patterns differentiate human emotions and manifest in resting physiology

2021 ◽  
Author(s):  
Lorenzo Pasquini ◽  
Fatemeh Noohi ◽  
Christina R. Veziris ◽  
Eena L. Kosik ◽  
Sarah R. Holley ◽  
...  
Keyword(s):  
Time Series ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Emotional Reactivity ◽  
Time Series Data ◽  
Activity Patterns ◽  
Principal Component ◽  
Series Data ◽  
Dynamic Activity ◽  
Autonomic Nervous

Whether activity in the autonomic nervous system differs during distinct emotions remains controversial. We obtained continuous multichannel recordings of autonomic nervous system activity in healthy adults during a video-based emotional reactivity task. Dimensionality reduction revealed five principal components in the autonomic time series data, and these modes of covariation differentiated periods of baseline from those of video-viewing. Unsupervised clustering of the principal component time series data uncovered separable autonomic states that distinguished among the five emotion-inducing trials. These autonomic states were also detected in baseline physiology but were intermittent and of smaller magnitude. Our results suggest the autonomic nervous system assembles dynamic activity patterns during emotions that are similar across people and are present even during undirected moments of rest.

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