scholarly journals The Impact of Linear Filter Preprocessing in the Interpretation of Permutation Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 787
Author(s):  
Antonio Dávalos ◽  
Meryem Jabloun ◽  
Philippe Ravier ◽  
Olivier Buttelli
Keyword(s):  
Moving Average ◽  
Permutation Entropy ◽  
Linear Filter ◽  
Type I ◽  
Linear Filtering ◽  
Linear Filters ◽  
Ordinal Information ◽  
Specific Frequency ◽  
The Impact ◽  
Specific Contribution

Permutation Entropy (PE) is a powerful tool for measuring the amount of information contained within a time series. However, this technique is rarely applied directly on raw signals. Instead, a preprocessing step, such as linear filtering, is applied in order to remove noise or to isolate specific frequency bands. In the current work, we aimed at outlining the effect of linear filter preprocessing in the final PE values. By means of the Wiener–Khinchin theorem, we theoretically characterize the linear filter’s intrinsic PE and separated its contribution from the signal’s ordinal information. We tested these results by means of simulated signals, subject to a variety of linear filters such as the moving average, Butterworth, and Chebyshev type I. The PE results from simulations closely resembled our predicted results for all tested filters, which validated our theoretical propositions. More importantly, when we applied linear filters to signals with inner correlations, we were able to theoretically decouple the signal-specific contribution from that induced by the linear filter. Therefore, by providing a proper framework of PE linear filter characterization, we improved the PE interpretation by identifying possible artifact information introduced by the preprocessing steps.

scholarly journals Modular preprocessing pipelines can reintroduce artifacts into fMRI data

2018 ◽  
Author(s):  
Martin A. Lindquist ◽  
Stephan Geuter ◽  
Tor D. Wager ◽  
Brian S. Caffo
Keyword(s):  
Fmri Data ◽  
Linear Filter ◽  
Linear Filtering ◽  
Linear Filters ◽  
Data Set ◽  
Temporal Filtering ◽  
Global Signal ◽  
In Series ◽  
Band Pass ◽  
Fmri Analysis

AbstractThe preprocessing pipelines typically used in both task and restingstate fMRI (rs-fMRI) analysis are modular in nature: They are composed of a number of separate filtering/regression steps, including removal of head motion covariates and band-pass filtering, performed sequentially and in a flexible order. In this paper we illustrate the shortcomings of this approach, as we show how later preprocessing steps can reintroduce artifacts previously removed from the data in prior preprocessing steps. We show that each regression step is a geometric projection of data onto a subspace, and that performing a sequence of projections can move the data into subspaces no longer orthogonal to those previously removed, reintroducing signal related to nuisance covariates. Thus, linear filtering operations are not commutative, and the order in which the preprocessing steps are performed is critical. These issues can arise in practice when any combination of standard preprocessing steps—including motion regression, scrubbing, component-based correction, global signal regression, and temporal filtering—are performed sequentially. In this work we focus primarily on rs-fMRI. We illustrate the problem both theoretically and empirically through application to a test-retest rs-fMRI data set, and suggest remedies. These include (a) combining all steps into a single linear filter, or (b) sequential orthogonalization of covariates/linear filters performed in series.

Analysis Versus Synthesis for Trending of Gas-Path Measurement Time Series

2014 ◽  
Author(s):  
S. Borguet ◽  
O. Léonard ◽  
P. Dewallef
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Synthetic Data ◽  
Noise Removal ◽  
Health Condition ◽  
Fault Detection And Isolation ◽  
Linear Filter ◽  
Linear Filters ◽  
Detection Delay ◽  
Low Pass

Gas-path measurements used to assess the health condition of an engine are corrupted by noise. Generally, a data cleaning step occurs before proceeding with fault detection and isolation. Classical linear filters such as the exponentially weighted moving average filter are traditionally used for noise removal. Unfortunately, these low-pass filters distort trend shifts indicative of faults, which increases the detection delay. The present paper investigates two new approaches to non-linear filtering of time series. On one hand, the synthesis approach reconstructs the signal as a combination of elementary signals chosen from a pre-defined library. On the other hand, the analysis approach imposes a constraint on the shape of the signal (e.g., piecewise constant). Both approaches incorporate prior information about the signal in a different way, but they lead to trend filters that are very capable at noise removal while preserving at the same time sharp edges in the signal. This is highlighted through the comparison with a classical linear filter on a batch of synthetic data representative of typical engine fault profiles.

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling (Preprint)

2020 ◽  
Author(s):  
Eduardo Atem De Carvalho ◽  
Rogerio Atem De Carvalho
Keyword(s):  
New York ◽  
Reproduction Number ◽  
Moving Average ◽  
Real Data ◽  
Time Dependent ◽  
Initial Value ◽  
Health Authorities ◽  
Reproduction Numbers ◽  
Effect Estimation ◽  
The Impact

BACKGROUND Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. OBJECTIVE This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. METHODS The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. RESULTS Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. CONCLUSIONS We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent, incubation period-independent Reproduction Numbers (Rt). We also demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

scholarly journals STING and cGAS gene expressions were downregulated among HIV-1-infected persons after antiretroviral therapy

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Lorena Leticia Peixoto de Lima ◽  
Allysson Quintino Tenório de Oliveira ◽  
Tuane Carolina Ferreira Moura ◽  
Ednelza da Silva Graça Amoras ◽  
Sandra Souza Lima ◽  
...  
Keyword(s):  
Gene Expression ◽  
Viral Load ◽  
T Cell ◽  
Antiretroviral Therapy ◽  
Cell Count ◽  
Enzyme Linked Immunosorbent Assay ◽  
Type I ◽  
Α Level ◽  
The Impact ◽  
Hiv 1

Abstract Background The HIV-1 epidemic is still considered a global public health problem, but great advances have been made in fighting it by antiretroviral therapy (ART). ART has a considerable impact on viral replication and host immunity. The production of type I interferon (IFN) is key to the innate immune response to viral infections. The STING and cGAS proteins have proven roles in the antiviral cascade. The present study aimed to evaluate the impact of ART on innate immunity, which was represented by STING and cGAS gene expression and plasma IFN-α level. Methods This cohort study evaluated a group of 33 individuals who were initially naïve to therapy and who were treated at a reference center and reassessed 12 months after starting ART. Gene expression levels and viral load were evaluated by real-time PCR, CD4+ and CD8+ T lymphocyte counts by flow cytometry, and IFN-α level by enzyme-linked immunosorbent assay. Results From before to after ART, the CD4+ T cell count and the CD4+/CD8+ ratio significantly increased (p < 0.0001), the CD8+ T cell count slightly decreased, and viral load decreased to undetectable levels in most of the group (84.85%). The expression of STING and cGAS significantly decreased (p = 0.0034 and p = 0.0001, respectively) after the use of ART, but IFN-α did not (p = 0.1558). Among the markers evaluated, the only markers that showed a correlation with each other were STING and CD4+ T at the time of the first collection. Conclusions ART provided immune recovery and viral suppression to the studied group and indirectly downregulated the STING and cGAS genes. In contrast, ART did not influence IFN-α. The expression of STING and cGAS was not correlated with the plasma level of IFN-α, which suggests that there is another pathway regulating this cytokine in addition to the STING–cGAS pathway.

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

scholarly journals The Dynamic Interaction between Extracellular Matrix Remodeling and Breast Tumor Progression

Cells ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1046
Author(s):  
Jorge Martinez ◽  
Patricio C. Smith
Keyword(s):  
Extracellular Matrix ◽  
Type I Collagen ◽  
Cell Metabolism ◽  
Breast Tumors ◽  
Extracellular Matrix Remodeling ◽  
Type I ◽  
Matrix Remodeling ◽  
Desmoplastic Reaction ◽  
The Impact

Desmoplastic tumors correspond to a unique tissue structure characterized by the abnormal deposition of extracellular matrix. Breast tumors are a typical example of this type of lesion, a property that allows its palpation and early detection. Fibrillar type I collagen is a major component of tumor desmoplasia and its accumulation is causally linked to tumor cell survival and metastasis. For many years, the desmoplastic phenomenon was considered to be a reaction and response of the host tissue against tumor cells and, accordingly, designated as “desmoplastic reaction”. This notion has been challenged in the last decades when desmoplastic tissue was detected in breast tissue in the absence of tumor. This finding suggests that desmoplasia is a preexisting condition that stimulates the development of a malignant phenotype. With this perspective, in the present review, we analyze the role of extracellular matrix remodeling in the development of the desmoplastic response. Importantly, during the discussion, we also analyze the impact of obesity and cell metabolism as critical drivers of tissue remodeling during the development of desmoplasia. New knowledge derived from the dynamic remodeling of the extracellular matrix may lead to novel targets of interest for early diagnosis or therapy in the context of breast tumors.

scholarly journals Treadmill Running Changes Endothelial Lipase Expression: Insights from Gene and Protein Analysis in Various Striated Muscle Tissues and Serum

Biomolecules ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 906
Author(s):  
Agnieszka Mikłosz ◽  
Bartłomiej Łukaszuk ◽  
Adrian Chabowski ◽  
Jan Górski
Keyword(s):  
Protein Expression ◽  
Density Lipoprotein ◽  
Treadmill Running ◽  
Endothelial Lipase ◽  
Type I ◽  
Fiber Types ◽  
Gene And Protein Expression ◽  
Single Bout ◽  
The Impact ◽  
White Gastrocnemius

Endothelial lipase (EL) is an enzyme capable of HDL phospholipids hydrolysis. Its action leads to a reduction in the serum high-density lipoprotein concentration, and thus, it exerts a pro-atherogenic effect. This study examines the impact of a single bout exercise on the gene and protein expression of the EL in skeletal muscles composed of different fiber types (the soleus—mainly type I, the red gastrocnemius—mostly IIA, and the white gastrocnemius—predominantly IIX fibers), as well as the diaphragm, and the heart. Wistar rats were subjected to a treadmill run: 1) t = 30 [min], V = 18 [m/min]; 2) t = 30 [min], V = 28 [m/min]; 3) t = 120 [min], V = 18 [m/min] (designated: M30, F30, and M120, respectively). We established EL expression in the total muscle homogenates in sedentary animals. Resting values could be ordered with the decreasing EL protein expression as follows: endothelium of left ventricle > diaphragm > red gastrocnemius > right ventricle > soleus > white gastrocnemius. Furthermore, we observed that even a single bout of exercise was capable of inducing changes in the mRNA and protein level of EL, with a clearer pattern observed for the former. After 30 min of running at either exercise intensity, the expression of EL transcript in all the cardiovascular components of muscles tested, except the soleus, was reduced in comparison to the respective sedentary control. The protein content of EL varied with the intensity and/or duration of the run in the studied whole tissue homogenates. The observed differences between EL expression in vascular beds of muscles may indicate the muscle-specific role of the lipase.

scholarly journals Relationship between Changes over Time in Factors, Including the Impact of Meteorology on Photochemical Oxidant Concentration and Causative Atmospheric Pollutants in Kawasaki

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 446
Author(s):  
Akinori Fukunaga ◽  
Takaharu Sato ◽  
Kazuki Fujita ◽  
Daisuke Yamada ◽  
Shinya Ishida ◽  
...  
Keyword(s):  
Moving Average ◽  
Atmospheric Pollutants ◽  
Important Indicator ◽  
Photochemical Oxidant ◽  
Oxidant Concentration ◽  
Using Data ◽  
Changes Over Time ◽  
The Impact ◽  
The Relationship ◽  
The City

To clarify the relationship between changes in photochemical oxidants’ (Ox) concentrations and their precursors in Kawasaki, a series of analyses were conducted using data on Ox, their precursors, nitrogen oxides (NOx) and volatile organic compounds (VOCs), and meteorology that had been monitored throughout the city of Kawasaki for 30 years from 1990 to 2019. The trend in air temperature was upward, wind speed was downward, and solar radiation was upward, indicating an increasing trend in meteorological factors in which Ox concentrations tend to be higher. Between 1990 and 2013, the annual average Ox increased throughout Kawasaki and remained flat after that. The three-year moving average of the daily peak increased until 2015, and after that, it exhibited a slight decline. The amount of generated Ox is another important indicator. To evaluate this, a new indicator, the daytime production of photochemical oxidant (DPOx), was proposed. DPOx is defined by daytime averaged Ox concentrations less the previous day’s nighttime averaged Ox concentrations. The trend in DPOx from April to October has been decreasing since around 2006, and it was found that this indicator reflects the impact of reducing emissions of NOx and VOCs in Kawasaki.

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