Lag Windows Research Articles

The Long-Term Mortality Effects Associated with Exposure to Particles and NOx in the Malmö Diet and Cancer Cohort.

In this study, the long-term mortality effects associated with exposure to PM10 (particles with an aerodynamic diameter smaller than or equal to 10 µm), PM2.5 (particles with an aerodynamic diameter smaller than or equal to 2.5 µm), BC (black carbon), and NOx (nitrogen oxides) were analyzed in a cohort in southern Sweden during the period from 1991 to 2016. Participants (those residing in Malmö, Sweden, born between 1923 and 1950) were randomly recruited from 1991 to 1996. At enrollment, 30,438 participants underwent a health screening, which consisted of questionnaires about lifestyle and diet, a clinical examination, and blood sampling. Mortality data were retrieved from the Swedish National Cause of Death Register. The modeled concentrations of PM10, PM2.5, BC, and NOx at the cohort participants' home addresses were used to assess air pollution exposure. Cox proportional hazard models were used to estimate the associations between long-term exposure to PM10, PM2.5, BC, and NOx and the time until death among the participants during the period from 1991 to 2016. The hazard ratios (HRs) associated with an interquartile range (IQR) increase in each air pollutant were calculated based on the exposure lag windows of the same year (lag0), 1-5 years (lag1-5), and 6-10 years (lag6-10). Three models were used with varying adjustments for possible confounders including both single-pollutant estimates and two-pollutant estimates. With adjustments for all covariates, the HRs for PM10, PM2.5, BC, and NOx in the single-pollutant models at lag1-5 were 1.06 (95% CI: 1.02-1.11), 1.01 (95% CI: 0.95-1.08), 1.07 (95% CI: 1.04-1.11), and 1.11 (95% CI: 1.07-1.16) per IQR increase, respectively. The HRs, in most cases, decreased with the inclusion of a larger number of covariates in the models. The most robust associations were shown for NOx, with statistically significant positive HRs in all the models. An overall conclusion is that road traffic-related pollutants had a significant association with mortality in the cohort.

Self-immolative nanocapsules precisely regulate depressive neuronal microenvironment for synergistic antidepression therapy

BackgroundPharmacotherapy constitutes the first-line treatment for depression. However, its clinical use is hindered by several limitations, such as time lag, side effects, and narrow therapeutic windows. Nanotechnology can be employed to shorten the onset time by ensuring permeation across the blood brain barrier (BBB) to precisely deliver more therapeutic agents; unfortunately, formidable challenges owing to the intrinsic shortcomings of commercial drugs remain.ResultsBased on the extraordinary capability of monoamines to regulate the neuronal environment, we engineer a network nanocapsule for delivering serotonin (5-hydroxytryptamine, 5-HT) and catalase (CAT) to the brain parenchyma for synergistic antidepression therapy. The nanoantidepressants are fabricated by the formation of 5-HT polymerization and simultaneous payload CAT, following by surface modifications using human serum albumin and rabies virus glycoprotein. The virus-inspired nanocapsules benefit from the surface-modifying strategies and exhibit pronounced BBB penetration. Once nanocapsules reach the brain parenchyma, the mildly acidic conditions trigger the release of 5-HT from the sacrificial nanocapsule. Releasing 5-HT further positively regulate moods, relieving depressive symptoms. Meanwhile, cargo CAT alleviates neuroinflammation and enhances therapeutic efficacy of 5-HT.ConclusionAltogether, the results offer detailed information encouraging the rational designing of nanoantidepressants and highlighting the potential of nanotechnology in mental health disorder therapies.Graphical abstract

Subject-independent trajectory prediction using pre-movement EEG during grasp and lift task

Electroencephalogram (EEG) based motor trajectory decoding for efficient control of brain–computer interface (BCI) systems has been an active area of research. The systems include prosthesis, rehabilitation and human-power augmenting devices. In this work, three-dimensional (3D) hand kinematics is estimated using pre-movement EEG signals during grasp and lift motion. Twelve subjects’ data from the publicly available database WAY-EEG-GAL is utilized for this purpose. Multi-layer perceptron (MLP) and convolutional neural network-long short-term memory (CNN-LSTM) based deep learning frameworks are proposed that utilize the motor-neural information encoded in the EEG data preceding the actual movement execution. Frequency band features are analyzed for hand kinematics decoding using EEG data filtered in seven distinct ranges. The best performing frequency band features is taken for further analysis with different EEG window sizes and lag windows. Additionally, inter-subject hand trajectory decoding analysis is performed using leave-one-subject-out (LOSO) approach. The Pearson correlation coefficient along with hand trajectory are taken to evaluate decoding performance for the proposed neural decoders. This study explores the feasibility of inter-subject 3D hand trajectory decoding using EEG signals during reach and grasp task. The proposed CNN-LSTM decoder is able to achieve the grand correlation in three axes upto 0.730 and 0.627 in intra-subject and inter-subject settings, respectively, thus providing viable information regarding decoding hand position from pre-movement EEG signals for practical BCI applications.

Risk of De Novo Hypertensive Disorders of Pregnancy After Exposure to PM1 and PM2.5 During the Period From Preconception to Delivery: Birth Cohort Study.

Particulate matter (PM) is detrimental to the respiratory and circulatory systems. However, no study has evaluated the lag effects of weekly exposure to fine PM during the period from preconception to delivery on the risk of hypertensive disorders of pregnancy (HDPs). We set out to investigate the lag effect windows of PM on the risk of HDPs on a weekly scale. Data from women with de novo HDPs and normotensive pregnant women who were part of the Peking University Retrospective Birth Cohort, based on the hospital information system of Tongzhou district, were obtained for this study. Meteorological data and data on exposure to fine PM were predicted by satellite remote sensing data based on maternal residential address. The de novo HDP group consisted of pregnant women who were diagnosed with gestational hypertension or preeclampsia. Fine PM was defined as PM2.5 and PM1. The gestational stage of participants was from preconception (starting 12 weeks before gestation) to delivery (before the 42nd gestational week). A distributed-lag nonlinear model (DLNM) was nested in a Cox regression model to evaluate the lag effects of weekly PM exposure on de novo HDP hazard by controlling the nonlinear relationship of exposure-reaction. Stratified analyses by employment status (employed or unemployed), education level (higher or lower), and parity (primiparity or multiparity) were performed. A total of 22,570 pregnant women (mean age 29.1 years) for whom data were available between 2013 and 2017 were included in this study. The prevalence of de novo HDPs was 6.7% (1520/22,570). Our findings showed that PM1 and PM2.5 were significantly associated with an elevated hazard of HDPs. Exposure to PM1 during the 5th week before gestation to the 6th gestational week increased the hazard of HDPs. A significant lag effect of PM2.5 was observed from the 1st week before gestation to the 6th gestational week. The strongest lag effects of PM1 and PM2.5 on de novo HDPs were observed at week 2 and week 6 (hazard ratio [HR] 1.024, 95% CI 1.007-1.042; HR 1.007, 95% CI 1.000-1.015, respectively, per 10 μg/m3 increase). The stratified analyses indicated that pregnant women who were employed, had low education, and were primiparous were more vulnerable to PM exposure for de novo HDPs. Exposure to PM1 and PM2.5 was associated with the risk of de novo HDPs. There were significant lag windows between the preconception period and the first trimester. Women who were employed, had low education, and were primiparous were more vulnerable to the effects of PM exposure; more attention should be paid to these groups for early prevention of de novo HDPs.

Effect of Fuzzy Time Series on Smoothing Estimation of the INAR(1) Process

In this paper, the effect of fuzzy time series on estimates of the spectral, bispectral and normalized bispectral density functions are studied. This study is conducted for one of the integer autoregressive of order one (INAR(1)) models. The model of interest here is the dependent counting geometric INAR(1) which is symbolized by (DCGINAR(1)). A realization is generated for this model of size n = 500 for estimation. Based on fuzzy time series, the forecasted observations of this model are obtained. The estimators of spectral, bispectral and normalized bispectral density functions are smoothed by different one- and two-dimensional lag windows. Finally, after the smoothing, all estimators are studied in the case of generated and forecasted observations of the DCGINAR(1) model. We investigate the contribution of the fuzzy time series to the smoothing of these estimates through the results.

Lugsail lag windows for estimating time-average covariance matrices

Summary Lag windows are commonly used in time series analysis, econometrics, steady-state simulation and Markov chain Monte Carlo to estimate time-average covariance matrices. In the presence of positive correlation in the underlying process, estimators of this matrix almost always exhibit significant negative bias, leading to undesirable finite-sample properties. We propose a new family of lag windows specifically designed to improve finite-sample performance by offsetting this negative bias. Any existing lag window can be adapted into a lugsail equivalent with no additional assumptions. We use these lag windows in spectral variance estimators and demonstrate their advantages in a linear regression model with autocorrelated and heteroskedastic residuals. We further employ the lugsail lag windows in weighted batch means estimators because of their computational efficiency on large simulation output. We obtain bias and variance results for these multivariate estimators and significantly weaken the mixing condition on the process. Superior finite-sample properties are demonstrated in a vector autoregressive process and a Bayesian logistic regression model.

No association between proton pump inhibitor use and ALS risk: a nationwide nested case\u2013control study

The use of proton pump inhibitors (PPIs) has been proposed as a potential risk factor for neurodegenerative diseases, but little is known regarding its role in amyotrophic lateral sclerosis (ALS). We therefore aimed to assess the association of PPI use with the subsequent risk of ALS, and performed a register-based nationwide nested case–control study, including 2,484 ALS cases diagnosed during July 2006–December 2013 in Sweden and 10 population controls per case that were individually matched to the case by sex, age, and area of residence. Dispenses and cumulative defined daily doses (cDDDs) of PPIs were extracted from the Swedish Prescribed Drug Register. The association of PPI use with the risk of ALS was assessed using conditional logistic regression, after applying different lag windows to avoid reverse causation. ALS patients were more likely to be dispensed with PPIs before diagnosis than controls. However, previous PPI use was not associated with an increased risk of ALS (OR = 1.08, 95% CI 0.97–1.19), and there was no dose–response relationship between cDDDs of PPIs and ALS risk (p = 0.0874), after excluding dispenses during the year before ALS diagnosis. The results were similar after excluding dispenses during the 2 or 3 years before ALS diagnosis.

Machine Learning Activity Classification Models For Preschool-aged Children: The Need For Free-living Training Data.

Machine learning (ML) classification models trained on laboratory activity trials exhibit poor performance when evaluated under free-living conditions. Training models on free living data, including temporal features such as lead and lag windows, and using shorter sliding windows may improve recognition accuracy under free-living conditions. PURPOSE: To evaluate the accuracy of free-living hip and wrist Random Forest activity classifiers for pre-schoolers trained on features extracted from windows of 1s, 5s, 10s, and 15s. Performance was benchmarked against classifiers trained on laboratory-based data using a 15s window. METHODS: 31 preschool-aged children (4.0 ± 0.9 y) were video recorded during a 20-minute unstructured active play session. Participants wore an accelerometer on their right hip and non-dominant wrist. A bespoke two-stage direct observation system was used to code ground truth activity class and specific activity types occurring within each class. Data from 21 of the children were randomly selected to train the classifiers. Models were trained with and without temporal features and cross-validated in a hold sample of the remaining 10 children with overall and class-level accuracy. RESULTS: Accuracy improved as window size increased from 1 sec (73.5%-77.7%) to 10 sec (82.4%-86.0%); with only minimal improvements observed for 15s windows. Inclusion of lag and lead features increased accuracy by 1.6% to 6.6%, with the largest improvements observed for shorter duration windows (≤10s). Comparatively, the accuracy of the laboratory trained model was 56.9% and 67.5% for wrist and hip, respectively. For a 10s window, training models on free-living data and including temporal features increased recognition of sedentary from 70.6% - 74.4% to 83.3% - 90.4%; light activities and games from 57.5% - 76.9% to 88.6% - 88.8%; walking from 7.5% - 17.5% to 64.1% - 75.0%; and running from 50.0% - 77.8% to 71.4% - 85.7%. There was no improvement in recognition of mod-vig activities and games (56.3% - 62.5%). CONCLUSIONS: Unlike models trained on laboratory activity trials, ML activity classification models for pre-schoolers trained on free-living accelerometer data perform well when evaluated under true free-living conditions. Funding: Australian Research Council Discovery Project Grant: DP150100116

Increasing the precision in the estimates of noise power spectrum in digital x-ray imaging systems

The noise power spectrum (NPS) of a digital x-ray imaging device is usually estimated from the average of periodograms of ROIs in images obtained with uniform radiation fields. The purpose of this work was developing a new estimator for calculating the NPS and comparing its uncertainties with those of the smoothed periodogram.The new estimator is built by removing those addends in the summation of the periodogram that do not contain information on stochastic noise. This was carried out by applying a short-length lag window to the autocorrelation function of noise. The length of the window was obtained from the support of this function. It has to be large enough not to eliminate information on noise autocorrelation and it has be as short as possible to minimize uncertainty. In this work, this length was set to three times the support of the autocorrelation function of noise.The new truncated sum (TS) estimator is shown to be unbiased and to have a much higher precision than that of the periodogram. The combined process of applying lag windows to the autocorrelation function of noise and removing addends with null expected values from the periodogram summation has a double effect on NPS curves. On the one hand, the curves are smoothed and, on the other hand, the uncertainties in the calculated values are highly reduced.

Impact of energy efficiency interventions in public housing buildings on cold-related mortality: a case-crossover analysis.

Interventions to mitigate fuel poverty and particularly energy efficiency façade retrofitting (EEFR) have demonstrated positive impacts on health but the impacts of EEFR interventions on cold-related mortality have not been studied in depth. We evaluated the impact of EEFR interventions in Barcelona on the association between cold outdoor temperatures and mortality (from all natural causes and from neoplasms, circulatory system and respiratory system causes) from 1986 to 2012. A time-stratified case-crossover analysis was used. Relative risks (RR) for death related to extreme cold (lowest fifth percentile) in the no-intervention and intervention groups were obtained for temperature lag windows covering the day of the death and the previous 20 days (0-2, 3-5, 6-8, 9-11, 12-14, 15-17, 18-20). The statistical significance of the observed changes was evaluated using the RR for the cold temperature-intervention interaction. In men, interventions significantly increased the extreme cold-death association for the lag window 15-17 [interaction RR 2.23, 95% confidence interval (CI) 1.14-4.36]. The impacts were stronger for respiratory system causes and in men aged 75 or older. In women, on lag window 0-2, the extreme cold-death association was not significantly reduced when analysing all natural causes of death (interaction RR 0.46, 95% CI 0.21-1.01), but it was reduced significantly when analysing only deaths from neoplasms, circulatory system and respiratory system causes together. The impacts were stronger in women who died from circulatory system causes, in women with no education and in those aged 75 or older. EEFR interventions had differentiated effects on cold-related mortality in men and women. Differentiated effects were also observed by cause, educational level and age.

Time series analysis to predict link quality of wireless community networks

Community networks have emerged under the mottos “break the strings that are limiting you”, “don't buy the network, be the network” or “a free net for everyone is possible”. Such networks create a measurable social impact as they provide to the community the right and opportunity of communication. As any other network that mixes wired and wireless links, the routing protocol must face several challenges that arise from the unreliable nature of the wireless medium. Link quality tracking helps the routing layer to select links that maximize the delivery rate and minimize traffic congestion. Moreover, link quality prediction has proved to be a technique that surpasses link quality tracking by foreseeing which links are more likely to change its quality. In this work, we focus on link quality prediction by means of a time series analysis. We apply this prediction technique in the routing layer of large-scale, distributed, and decentralized networks. We demonstrate that it is possible to accurately predict the link quality in 98% of the instances, both in the short and the long terms. Particularly, we analyse the behaviour of the links globally to identify the best prediction algorithm and metric, the impact of lag windows in the results, the prediction accuracy some time steps ahead into the future, the degradation of prediction over time, and the correlation of prediction with topological features. Moreover, we also analyse the behaviour of links individually to identify the variability of link quality prediction between links, and the variability of link quality prediction over time. Finally, we also present an optimized prediction method that considers the knowledge about the expected link quality values.

On lag windows connected with Jacobi polynomials

Lag windows whose corresponding spectral windows are Jacobi polynomials or sums of Jacobi polynomials are introduced. The bias and variance of their spectral density estimators are investigated and their window bandwidth and characteristic exponent are determined.

Spectral Matched Filters in Coherent Laser Radar

Abstract Insight into the properties of maximum likelihood Doppler frequency shift estimators, which function as spectral domain matched filters, is often obtained more readily from examination of their spectral correlating functions, and lag windows than from simulated performance data. These functions are compared and contrasted with reference to lidar applications for the three principal estimators.

LAG WINDOW ESTIMATION OF THE DEGREE OF DIFFERENCING IN FRACTIONALLY INTEGRATED TIME SERIES MODELS

Abstract. In this paper we consider the estimation of the degree of differencing d in the fractionally integrated autoregressive moving‐average time series model ARFIMA (p, d, q). Using lag window spectral density estimators we develop a regression type estimator of d which is easy to calculate and does not require prior knowledge of p and q. Some large sample properties of the estimator are studied and the performance of the estimator for small samples is investigated using the simulation method for a range of commonly used lag windows. Some practical recommendations on the choice of lag windows and the choice of the window parameters are provided.

Spectral decomposition of time-frequency distribution kernels

This paper addresses the general problem of approximating a given time-frequency distribution (TFD) in terms of other distributions with desired properties. It relates the approximation of two time-frequency distributions to their corresponding kernel approximation. It is shown that the singular-value decomposition (SVD) of the time-frequency (t-f) kernels allows the expression of the time-frequency distributions in terms of weighted sum of smoothed pseudo Wigner-Ville distributions or modified periodograms, which are the two basic nonparametric power distributions for stationary and nonstationary signals, respectively. The windows appearing in the decomposition take zero and/or negative values and, therefore, are different than the time and lag windows commonly employed by these two distributions. The centrosymmetry and the time-support properties of the kernels along with the fast decay of the singular values lead to computational savings and allow for an efficient reduced rank kernel approximations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Least‐squares minimization for spectral regions of combined lag windows

Least‐squares minimization for spectral regions of combined lag windows

Power spectra of sub-periodic functions

Abstract A definition of sample power spectra is given for periodic functions that are defined or known only over a subinterval of their period. The fact that a function is periodic on an interval larger than the one being analysed is in effect a constraint on the frequency content of that function. The power spectrum of this ‘sub-periodic’ function, like that of the full periodic function, must be discrete rather than continuous as in ordinary time series analysis, and must contain the same frequencies as the full periodic function. The usual definitions of data windows, covariance functions, and lag windows, and operations on them, must also be modified slightly. Because of numerical inversion considerations, ‘sub-periodic’ functions are usually analysed over the subinterval rather than over the full interval. Fourier series on a subinterval of a circle, and spherical cap harmonic analysis on a portion (or subinterval) of a sphere, are typical examples. Formulations for Fourier transforms and spherical harmonic coefficients relative to the full interval are given in terms of the transforms and coefficients relative to the subinterval. Power spectra, under the ‘sub-periodic’ definitions, can then be obtained in a straightforward manner. Power spectra are also defined for potential-gradient fields, such as the magnetic field, over a spherical cap, and a formulation is given for sub-periodic spherical cap power spectra in terms of the coefficients of spherical cap harmonic analysis.

Spectral smoothing and recursion based on the nonstationarity of the autocorrelation function

Averaging and recursion in short-time spectral analysis in view of the nature of the nonstationarity of the random process is addressed. Using the Wigner spectrum estimator as a general model, this correspondence relates the selection of the time and lag windows to the stationary intervals of the autocorrelation at different lags. The lag-dependent time averaging for two types of commonly assumed nonstationarities is presented and used to devise the recursive estimates of their time-varying spectrum. >

Computation of short periodical variations of pole coordinates using maximum entropy spectral analysis and an ormsby filter

The main purpose of this paper is the search for the optimum method of detecting weak short periodical variations of pole coordinates determined by different techniques in theMERIT Campaign. The optimum filter, length of Maximum Entropy Spectral Analysis (MESA) for these analyses was investigated on the basis of the Rovelli-Vulpiani formula. The unbiased autocovariance estimation multiplied by different lag windows was introduced into this formula for a better estimation of the optimum filter length. The optimum filter length in theMESA is discussed on the basis of the model data similar to the observed data. The model data was disturbed by white and red noises with standard deviations greater than the average amplitude of an oscillation in the model. Each short periodical oscillation in pole coordinates was calculated by a properly, defined Ormsby band pass filter. Their sum creates a short periodical signal part which subtracted from smoothed pole coordinates diminishes their standard deviations and their autocovariance estimations.

Statistical and spectral properties of some paleomagnetic/paleoclimatic time series

Power spectra were computed for the six paleomagnetic records in Wollin et al. (1977) and for composite time series formed by adding the six records sequentially. Use of the composite series reduces the noise associated with local magnetic and climatic variability. The statistical properties of the composite were found to be insensitive to the ordering of the individual records. Maximum likelihood estimates for the theoretical autocorrelation function of the composite data spaced at intervals of 10,000 years show that the data are described by a second order autoregressive process with first and second order coefficients 0.7 and −0.2. The theoretical power spectrum for these coefficients is monotonically increasing with period becoming nearly flat at periods longer than 100,000 years. Spectra with lag windows of 25 and 50 do not have peaks exceeding the 95% confidence limits. A 100 lag window spectrum has peaks exceeding the confidence limits at 105, 7.4×104 and 2.16×104 yr; however, since it is quite common for artificial paleomagnetic time series generated from the above second order autoregressive process to have significant peaks near one or more of the Earth's three orbital frequencies when using a 100 lag window, one must conclude that there is little if any reliable evidence for orbital forcing in the data studied.