Lag Window Research Articles

Long-term associations between time-varying exposure to ambient PM2.5 and mortality: an analysis of the UK Biobank.

Evidence for long-term mortality risks of PM2.5 comes mostly from large administrative studies with incomplete individual information and limited exposure definitions. Here we assess PM2.5-mortality associations in the UK Biobank cohort using detailed information on confounders and exposure. We reconstructed detailed exposure histories for 498,090 subjects by linking residential data with high-resolution PM2.5 concentrations from spatio-temporal machine learning models. We split the time-to-event data and assigned yearly exposures over a lag window of 8 years. We fitted Cox proportional hazard models with time-varying exposure controlling for contextual and individual-level factors, as well as trends. In secondary analyses, we inspected the lag structure using distributed lag models and compared results with alternative exposure sources and definitions. In fully adjusted models, an increase of 10 μg/m³ in PM2.5 was associated with hazard ratios (HRs) of 1.27 (95%CI: 1.06-1.53) for all-cause, 1.24 (1.03-1.50) for non-accidental, 2.07 (1.04-4.10) for respiratory, and 1.66 (0.86-3.19) for lung cancer mortality. We found no evidence of association with cardiovascular deaths (HR=0.88, 95%CI: 0.59-1.31). We identified strong confounding by both contextual- and individual-level lifestyle factors. The distributed lag analysis suggested differences in relevant exposure windows across mortality causes. Using more informative exposure summaries and sources resulted in higher risk estimates. We found associations of long-term PM2.5 exposure with all-cause, non-accidental, respiratory, and lung cancer mortality, but not with cardiovascular mortality. This study benefits from finely reconstructed time-varying exposures and extensive control for confounding, further supporting a plausible causal link between long-term PM2.5 and mortality.

Developing a multivariate time series forecasting framework based on stacked autoencoders and multi-phase feature

Time series forecasting across different domains has received massive attention as it eases intelligent decision-making activities. Recurrent neural networks and various deep learning algorithms have been applied to modeling and forecasting multivariate time series data. Due to intricate non-linear patterns and significant variations in the randomness of characteristics across various categories of real-world time series data, achieving effectiveness and robustness simultaneously poses a considerable challenge for specific deep-learning models. We have proposed a novel prediction framework with a multi-phase feature selection technique, a long short-term memory-based autoencoder, and a temporal convolution-based autoencoder to fill this gap. The multi-phase feature selection is applied to retrieve the optimal feature selection and optimal lag window length for different features. Moreover, the customized stacked autoencoder strategy is employed in the model. The first autoencoder is used to resolve the random weight initialization problem. Additionally, the second autoencoder models the temporal relation between non-linear correlated features with convolution networks and recurrent neural networks.Finally, the model's ability to generalize, predict accurately, and perform effectively is validated through experimentation with three distinct real-world time series datasets. In this study, we conducted experiments on three real-world datasets: Energy Appliances, Beijing PM2.5 Concentration, and Solar Radiation. The Energy Appliances dataset consists of 29 attributes with a training size of 15,464 instances and a testing size of 4239 instances. For the Beijing PM2.5 Concentration dataset, there are 18 attributes, with 34,952 instances in the training set and 8760 instances in the testing set. The Solar Radiation dataset comprises 11 attributes, with 22,857 instances in the training set and 9797 instances in the testing set. The experimental setup involved evaluating the performance of forecasting models using two distinct error measures: root mean square error and mean absolute error. To ensure robust evaluation, the errors were calculated at the identical scale of the data. The results of the experiments demonstrate the superiority of the proposed model compared to existing models, as evidenced by significant advantages in various metrics such as mean squared error and mean absolute error. For PM2.5 air quality data, the proposed model's mean absolute error is 7.51 over 12.45, about ∼40% improvement. Similarly, the mean square error for the dataset is improved from 23.75 to 11.62, which is ∼51%of improvement. For the solar radiation dataset, the proposed model resulted in ∼34.7% improvement in means squared error and ∼75% in mean absolute error. The recommended framework demonstrates outstanding capabilities in generalization and outperforms datasets spanning multiple indigenous domains.

Does CBN Intervention Funds Contribute to Inflationary Pressure in Nigeria? An Empirical Investigation

The objective of this paper is to empirically evaluate the effects of the Central Bank of Nigeria’s (CBN) intervention on inflation in Nigeria from 2007M12 to 2020M8. The paper employed three-variable Vector Error Correction Model (VECM), with headline inflation examined as an endogenous function of the CBN’s intervention funds and exchange rate movements. The study finds that the CBN’s interventions through credit-easing to specific industries reduce inflation in the long term, particularly food inflation. The outcome suggests that there is divergence in the outcome of unconventional monetary policy in developed and developing countries. It also established that there is a three-month policy lag window in CBN’s response to inflation using intervention funds. This is in consistent with the claim that central banks of developing countries are more flexible in approach and rely more frequently on ‘unconventional’ monetary policy tools with proof that these tools have been successful in a stagflation economy. Nonetheless, the country still faces high supply side inflation rates, which only shows that these tools should be improved upon to increase efficiency and impact.

Short-Term Association between Sulfur Dioxide and Mortality: A Multicountry Analysis in 399 Cities.

Epidemiological evidence on the health risks of sulfur dioxide () is more limited compared with other pollutants, and doubts remain on several aspects, such as the form of the exposure-response relationship, the potential role of copollutants, as well as the actual risk at low concentrations and possible temporal variation in risks. Our aim was to assess the short-term association between exposure to and daily mortality in a large multilocation data set, using advanced study designs and statistical techniques. The analysis included 43,729,018 deaths that occurred in 399 cities within 23 countries between 1980 and 2018. A two-stage design was applied to assess the association between the daily concentration of and mortality counts, including first-stage time-series regressions and second-stage multilevel random-effect meta-analyses. Secondary analyses assessed the exposure-response shape and the lag structure using spline terms and distributed lag models, respectively, and temporal variations in risk using a longitudinal meta-regression. Bi-pollutant models were applied to examine confounding effects of particulate matter with an aerodynamic diameter of () and (), ozone, nitrogen dioxide, and carbon monoxide. Associations were reported as relative risks (RRs) and fractions of excess deaths. The average daily concentration of across the 399 cities was , with 4.7% of days above the World Health Organization (WHO) guideline limit (, 24-h average), although the exceedances occurred predominantly in specific locations. Exposure levels decreased considerably during the study period, from an average concentration of in 1980-1989 to in 2010-2018. For all locations combined, a increase in daily was associated with an RR of mortality of 1.0045 [95% confidence interval (CI): 1.0019, 1.0070], with the risk being stable over time but with substantial between-country heterogeneity. Short-term exposure to was associated with an excess mortality fraction of 0.50% [95% empirical CI (eCI): 0.42%, 0.57%] in the 399 cities, although decreasing from 0.74% (0.61%, 0.85%) in 1980-1989 to 0.37% (0.27%, 0.47%) in 2010-2018. There was some evidence of nonlinearity, with a steep exposure-response relationship at low concentrations and the risk attenuating at higher levels. The relevant lag window was 0-3 d. Significant positive associations remained after controlling for other pollutants. The analysis revealed independent mortality risks associated with short-term exposure to , with no evidence of a threshold. Levels below the current WHO guidelines for 24-h averages were still associated with substantial excess mortality, indicating the potential benefits of stricter air quality standards. https://doi.org/10.1289/EHP11112.

The Cumulative Use of Muscle Relaxants and the Risk of Alzheimer's Disease: A Nationwide Case-Control Study.

Use of pharmacological treatments is one possible modifiable risk factor for cognitive disorders. To investigate if the use of muscle relaxants is associated with the risk of Alzheimer's disease (AD). The study was performed in a nested case-control design. Altogether 70,718 community-dwelling residents of Finland who received AD diagnosis in 2005-2011 were included as cases (the MEDALZ study). Each case was matched with four controls without AD by age, sex, and region of residence (N = 282,858). Data was extracted from Prescription register (1995-2012), Special Reimbursement register (1972-2012), and Hospital Discharge register (1972-2012). Drug use periods were modeled with PRE2DUP-method. Defined daily dose (DDD) was used to quantify the use. Analyses were conducted for any muscle relaxant use, and drug specific analyses were done for orphenadrine and tizanidine. A five-year lag window prior to the diagnosis was used, and results analyzed with conditional logistic regression. The use of any muscle relaxant was associated with the risk of AD, aOR (95% CI) 1.04 (1.02-1.07). Stronger associations were observed with longer use (>366 days, aOR 1.12 (1.03-1.21)) than shorter use (1-365 days aOR, 1.04 (1.02-1.06)) compared to non-users. Dose-response was not observed. Tizanidine was not associated with AD, whereas cumulative exposure of orphenadrine (≥101 DDDs) was associated with the risk of AD, aOR 1.19 (1.07-1.32). Muscle relaxant use was associated with the risk of AD and higher exposure to orphenadrine showed increased risk. Further studies on higher doses and longer durations of use are warranted.

Consumption-led expansions lead to lower future output growth

When assessing future growth prospects, does the current structure of demand matter, i.e. does it affect the future growth? This question is analysed in our paper by using global and EU panel data. The result is quite striking: consumption-led growth either in terms of private or public or total consumption is slower than investment-led or exports-led growth. The same qualitative result is obtained irrespectively of the length of the past growth period (lag window), yet the more often the past is characterised by consumption-led growth, the slower the growth rate is in the future. In this context, our research provides important insights from the point of view of both structural and cyclical policies.

Emulating a target trial of proton pump inhibitors and dementia risk using claims data.

Understanding the adverse effects of proton pump inhibitors (PPIs) is important due to their widespread use, but the available evidence for an increased dementia risk amongst patients taking PPIs is inconclusive. The present study aimed to estimate the causal effect of PPIs on the risk of dementia by target trial emulation and time-varying exposure modeling. Using claims data of 2,698,176 insured people of a large German statutory health insurer, a target trial was conceptualized in which individuals aged 40years and older were classified as PPI initiators or non-initiators between 2008 and 2018, and were followed until diagnosis of dementia, death, loss to follow-up or end of study. Incidence of dementia (International Classification of Diseases 10 codes F00, F01, F03, F05.1, G30, G31.0, G31.1, G31.9 and F02.8+G31.82) was defined applying a 1-year lag window. Weighted Cox models were used to estimate the effect of PPI initiation versus non-initiation on dementia risk and weighted pooled logistic regression was used to estimate the effect of time-varying use versus non-use. In all, 29,746 PPI initiators (4.4%) and 26,830 non-initiators (1.3%) were diagnosed with dementia. Comparing PPI initiation with no initiation, the hazard ratio for dementia was 1.54 (95% confidence interval 1.51-1.58). The hazard ratio for time-dependent PPI use versus non-use was 1.56 (95% confidence interval 1.50-1.63). Differentiated subtypes, including unspecified dementia, Alzheimer's disease and vascular dementia, showed increased risk by PPI initiation and time-varying PPI use. This study suggests that PPI initiation and time-varying PPI use may increase overall dementia risk.

Stressor-elicited smoking and craving during a smoking cessation attempt.

Stressors can undermine smokers' attempts to quit smoking. Although contemporary theories and animal models support this idea, human research has struggled to demonstrate definitively the relationship between stressors and smoking. Researchers have employed more ecologically valid methods like ecological momentary assessment to address this question, but studies focusing explicitly on stressors remain sparse and findings inconsistent. The purpose of this study was to examine the effect of stressful event intensity on smoking and craving among cigarette smokers during a quit attempt. We conducted preregistered, complementary concurrent and prospective (i.e., 8-hour lag window between stressful event and outcomes) analyses to maximize statistical power and provide temporal ordering, respectively. We also conducted follow-up moderation (Lag × Stressful Event Intensity) analyses. We hypothesized that smokers would be more likely to report both smoking and craving as the intensity of stressful events increased. Cigarette smokers (N = 125; 77 male) were randomly assigned to take nicotine replacement therapy (NRT) or placebo and provided 4x daily self-reports during the first 2 weeks of a quit attempt. Stressful events increased craving and the probability of smoking in concurrent analyses, and lag moderated the effect of stressful event intensity in follow-up prospective lagged analyses. NRT reduced the probability of smoking but not craving and did not moderate the effect of stressful events on smoking or craving. This study supports a prospective relationship between stressful events and smoking/craving in situ and demonstrates that NRT does not reduce the impact of stressors on smoking or craving. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Benzodiazepine and Z-Drug Use and the Risk of Developing Dementia.

BackgroundBenzodiazepines (BZDs) and Z-drugs (BZDRs) are among the most prescribed medications for anxiety and insomnia, especially among older adults. Our objective was to investigate the association between the use of BZDRs and the risk of dementia.MethodsA community-based retrospective cohort study was conducted based on the data available from 2002 to 2015 in Catalan Health Service. This cohort included all BZDR users (N = 83 138) and nonusers (N = 84 652) older than 45 years. A minimum 5-year lag window and an adjustment for psychiatric problems were applied for the data analysis.ResultsThe hazard ratio (HR) for the risk of incident dementia among BZDR users was 1.22 (95% CI = 1.15 to 1.31). This risk was not significant after adjusting the data confounding factors (HR = 1.01; 95% CI = 0.94 to 1.08). We observed a higher risk with short-to-intermediate half-life BZDs (HR = 1.11; 95% CI = 1.04 to 1.20) and Z-drugs (HR = 1.20; 95% CI = 1.07 to 1.33) than for intermediate-to-long half-life BZDs (HR = 1.01; 95% CI = 0.94 to 1.08). We demonstrated a higher risk of incident dementia (HR = 1.23; 95% CI = 1.07 to 1.41 and odds ratio = 1.38; 95% CI = 1.27 to 1.50, respectively) in patients who received 91 to 180 defined daily doses (DDDs) and >180 DDDs compared with patients who received <90 DDD. Regarding patient sex, the risk of dementia was higher in women than in men.ConclusionWe found that the incidence of dementia was not higher among all BZDR users. Short half-life BZDs and Z-drugs increased the risk of dementia at the highest doses, especially in female patients, showing a dose-response relationship.

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.

Blackman–Tukey spectral estimation and electric network frequency matching from power mains and speech recordings

Forensic applications exploit electric network frequency (ENF) as a fingerprint to determine multimedia content authenticity, as well as the time and region of multimedia recording. ENF is present at a nominal frequency of 50/60 Hz and its harmonics. Strong interference due to speech content deteriorates ENF estimation accuracy. Herein, the authors propose a non-parametric approach for ENF estimation, which incorporates a customised lag window design into the Blackman–Tukey spectral estimation method. Leakage reduction is formulated as a problem of energy maximisation within the main lobe of the spectral window. The proposed approach is compared to state-of-the-art methods for ENF estimation. Maximum correlation coefficient and minimum standard deviation of errors are employed to measure ENF estimation accuracy. Hypothesis testing is performed to determine whether the improvements in ENF estimation accuracy of the proposed approach over the state-of-the-art methods are statistically significant. Experimental results and statistical tests indicate that the proposed approach improves ENF estimation against many state-of-the-art methods.

Online State of Charge Prediction in Next Generation Vehicle Batteries Using Deep Recurrent Neural Networks and Continuous Model Size Control

This investigation presents a data-driven Long-short Term Memory battery model for predicting State of Charge for lithium-ion batteries LiFePO4 for next-generation vehicle operations. Our modified algorithm builds and updates a model using multivariate inputs that include physical properties, voltage, current, and ambient temperature during operations. The primary research goal is to improve prediction performance on future values from multiple training examples using an online learning scheme. Initial results demonstrate excellent predictions that outperform results from literature and other neural network algorithms. Due to computing constraints in on-board vehicle systems, the authors develop online training with autonomous control of lag (window width). The control algorithm embeds in the model with rules that govern and adjust lag during training. This method ensures the minimization of computational cost and prediction errors with the use of standard computing equipment during driving conditions.

Proton pump inhibitors and the risk of Alzheimer\u2019s disease and non-Alzheimer\u2019s dementias

Proton pump inhibitors (PPIs) are among the most prescribed medications. Previous epidemiological studies have presented contradictory results about PPIs and the risk of dementia. Our objective was to investigate the association between the use of PPIs and an increasing risk of incident AD or non-AD dementias. A community-based retrospective cohort study was conducted based on the data available from 1st January 2002 to 31st December 2015 in the Catalan health service (CatSalut) system. This cohort included all PPI users (N = 36,360) and non-users (N = 99,362). A lag window of 5 years was considered between the beginning of the PPI treatment and the diagnosis of dementia. PPI use was not associated with the risk of AD (adjusted odds ratio (OR) 1.06) (95% CI 0.93–1.21; p = 0.408). A weakly but significantly increased risk of non-AD dementias was observed among PPI users (adjusted OR 1.20, 95% CI 1.05–1.37; p = 0.007). A higher dose of PPIs was not associated with an increased risk of either AD or non-AD dementias (OR 1.20; 95% CI 0.91–1.61 and OR 0.95; 95% CI 0.74–1.22, respectively). Regarding the number of PPIs used, we observed an increased risk of AD (OR 1.47; 95% CI 1.18–1.83) and non-AD dementias (OR 1.38; 95% CI 1.12–1.70) in users of two types of PPIs compared with those who used only one type. We did not find a higher incidence of AD among PPI users, but a weak increase in the risk of non-AD dementias among PPI users was observed.

Reporting and variability of constructing medication treatment episodes in pharmacoepidemiology studies: A methodologic systematic review using the case study of DPP-4 inhibitors and cardiovascular outcomes.

In pharmacoepidemiologic studies, estimating medication adherence, persistence, and exposure patterns is critical. Constructing medication treatment episodes from prescription claims data involves assumptions related to grace period, carry-over, and lag effect, but there are no guidelines for these assumptions. We evaluated reporting and variability of these parameters in pharmacoepidemiology studies, using a case study of antihyperglycemic medications and major adverse cardiovascular events (MACE). We conducted a systemic review using MEDLINE and EMBASE for studies published prior to January 2, 2020 comparing the risk of MACE between dipeptidyl peptidase 4 (DPP-4) inhibitors and active comparators. We extracted study characteristics and results, including grace period, carry-over, and lag effect. Risk of bias was assessed by the Newcastle-Ottawa scale, and assessments for prevalent user, immortal time, time lag, and time window biases. A total of 14/1850 studies identified were included. Grace period was not reported in 5 (35.7%) studies and ranged from 0 days to 180 days when reported. Carry-over was not reported in 10 studies (71.4%). Lag effect was not reported in nine (71.4%) studies and ranged from 0 days to 180 days when reported. No studies conducted sensitivity analyses examining the effects of these assumptions on study findings. Predominant biases were inadequate follow-up time, comparability of cohorts, prevalent use, and lag time bias. Use of grace period, carry-over, and lag effect were poorly reported and highly variable. Future pharmacoepidemiology studies should improve reporting, justify ranges for these parameters, and conduct sensitivity analyses to evaluate effects of these assumptions.

Technical note: Random forests prediction of daily eating time of dairy cows from 3-dimensional accelerometer and radiofrequency identification

Feed intake and time spent eating at the feed bunk are important predictors of dairy cows' productivity and animal welfare, and deviations from normal eating behavior may indicate subclinical or clinical disease. In the current study, we developed a random forests algorithm to predict dairy cows' daily eating time (of a total mixed ration from a common feed bunk) using data from a 3-dimensional accelerometer and a radiofrequency identification (RFID) prototype device (logger) mounted on a neck collar. Models were trained on continuous focal animal observations from a total of 24 video recordings of 18 dairy cows at the Danish Cattle Research Centre (Foulum, Tjele, Denmark). Each session lasted from 21 to 48 h. The models included both the present time signal and observations several seconds back in time (lag window). These time-lagged signals were included with the purpose of capturing changes over time. Because of the high costs of installing an RFID antenna in the feed bunk, we also investigated a model based solely on 3-dimensional accelerometer data. Furthermore, to address the trade-off between prediction accuracy and reduced model complexity and its implications for battery longevity, we investigated the importance of including observations back in time using lag window sizes between 8 and 128 s. Performance was evaluated by internal leave-one-cow-out cross-validation. The results indicated that we obtained accurate predictions of daily eating time. For the most complex model (a lag window size of 128 s), the median of the balanced accuracy was 0.95 (interquartile interval: 0.93 to 0.96), and the median daily eating time deviation was 7 min 37 s (interquartile interval: -6 to 15 min). The median of the average daily eating time during sessions was 3 h 41 min with an interquartile interval of 2 h 56 min to 4 h 16 min. Exclusion of RFID data resulted in a considerable decrease in prediction accuracy, mainly due to a decreased sensitivity of locating the cow at the feed bunk (median balanced accuracy of 0.87 at a lag window size of 128 s). In contrast, prediction accuracy only slightly decreased with decreasing lag window size (median balanced accuracy of 0.94 at a lag window size of 8 s). We suggest a lag window size of 64 s for further development of the prototype logger. The methodology presented in this paper may be relevant for future automatic recordings of eating behavior in commercial dairy herds.

Best Lag Window for Spectrum Estimation of Law Order MA Process

In this article, we investigate spectrum estimation of law order moving average (MA) process. The main tool is the lag window which is one of the important components of the consistent form to estimate spectral density function (SDF). We show, based on a computer simulation, that the Blackman window is the best lag window to estimate the SDF of MA1 and MA2 at the most values of parameters βi and series sizes n, except for a special case when β=−1 and n≥40 in MA1. In addition, the Hanning–Poisson window appears as the best to estimate the SDF of MA2 when β1=β2=−0.5 and n≥40.

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.

Testing discrete-valued time series for whiteness

We consider the problem of testing a univariate discrete-valued time series for whiteness in the sequency domain, using Walsh–Fourier analysis. We show that the distribution of the lag window estimator of the Walsh spectral density is a scaled chi-square distribution, where the scale and degrees of freedom, both depend on the bandwidth of the smoothing window associated with the estimator. The definition of the bandwidth is extended from the frequency to the sequency domain. To address our problem, we propose three tests: the first one is based on the cumulative Walsh periodogram, and is shown to converge to a Brownian bridge. The second test is based on applying the Cramer–von Mises functional to an estimate of the Walsh spectral density, and is shown to converge to a Normal distribution, while the last test is based on a distance to whiteness, and is shown to have an approximate scaled chi-square distribution. Simulations are reported on the performance of the tests. Finally, we apply the proposed tests to the brain functional connectivity of schizophrenic patients.

Spectral Estimation Using Multitaper Whittle Methods with a Lasso Penalty.

Spectral estimation provides key insights into the frequency domain characteristics of a time series. Naive non-parametric estimates of the spectral density, such as the periodogram, are inconsistent, and the more advanced lag window or multitaper estimators are often still too noisy. We propose an L 1 penalized quasi-likelihood Whittle framework based on multitaper spectral estimates which performs semiparametric spectral estimation for regularly sampled univariate stationary time series. Our new approach circumvents the problematic Gaussianity assumption required by least square approaches and achieves sparsity for a wide variety of basis functions. We present an alternating direction method of multipliers (ADMM) algorithm to efficiently solve the optimization problem, and develop universal threshold and generalized information criterion (GIC) strategies for efficient tuning parameter selection that outperform cross-validation methods. Theoretically, a fast convergence rate for the proposed spectral estimator is established. We demonstrate the utility of our methodology on simulated series and to the spectral analysis of electroencephalogram (EEG) data.

Intake of Meat, Fish, Fruits, and Vegetables and Long-Term Risk of Dementia and Alzheimer's Disease.

The links between diet and the risk of dementia have never been studied considering the possibility of protopathic bias (i.e., reverse causation). We aimed to examine the relationship between consumption frequency of meat, fish, fruits, and vegetables and long-term risk of dementia and Alzheimer's disease (AD), by taking into account this possibility. We analyzed data of 5,934 volunteers aged 65 and over from the Three-city study who were followed every 2 to 4 years for 12 years. Dietary habits were assessed at inclusion using a brief food frequency questionnaire. The presence of symptoms of dementia was investigated at each follow-up visit. To limit the risk of protopathic bias, a 4-year lag window between exposure and disease assessment was implemented by excluding from the analyses all dementia cases that occurred during the first four years after inclusion. Analyses were performed using a Cox proportional hazard model and were adjusted for socio-demographic, lifestyle, and health factors. The average follow-up time was 9.8 years. During this period, 662 cases of dementia, including 466 of AD, were identified. After adjustment, only low meat consumption (≤1 time/week) was associated with an increased risk of dementia and AD compared with regular consumption (≥4 times/week) (HR = 1.58 95% CI = [1.17-2.14], HR = 1.67 95% CI = [1.18-2.37], respectively). No association was found between the consumption of fish, raw fruits, or cooked fruits and vegetables and the risk of dementia or AD. These findings suggest very low meat consumption increases the long-term risk of dementia and AD, and that a protopathic bias could have impacted finding from previous studies.