Timing Misalignment Research Articles

Image recognition enhances efficient monitoring of the coagulation-settling in drinking water treatment plants

Water pollution is a major issue in the context of increasing population and industrialization, yet many drinking water treatment plants (DWTPs) are not fully efficient countering it. In particular, coagulation-settling stage often faces multiple disturbances and time lags, which lower the efficiency because coagulant dosage cannot be accurately calculated in real-time based on the effluent turbidity. To address this issue, we developed a method using deep learning image recognition to monitor the coagulation-settling stage in real-time. For that we used 5761 operational data and images of flocs from the sedimentation tank of a DWTP in East China in 2022, to build an image recognition regression model that predict the turbidity of the sedimentation tank effluent. Results show that our deep learning regression model, performs better with r-square (R2) of 0.97, mean absolute error (MAE) of 0.016 and mean absolute percentage error (MAPE) of 2.74%, compared with the traditional machine learning giving R2 of 0.76, MAE of 0.045 and MAPE of 8.26%. The model also avoids misclassification at different turbidity intervals. The incorporation operational data of the sedimentation tank, prediction accuracy is improved by 79.6%. By adjusting the turbidity data to correct time misalignment, our model effectively handles the time lag caused by the hydraulic retention time of the sedimentation tank, thus enhancing the timeliness and accuracy of its practical application.

Tailored guidance to apply the Estimand framework to Trials within Cohorts (TwiCs) studies

Objective: The estimand framework offers a structured approach to define the treatment effect to be estimated in a clinical study. Defining the estimand upfront helps formulating the research question and informs study design, data collection and statistical analysis methods. Since the Trials within Cohorts (TwiCs) design has unique characteristics, the objective of this study is to describe considerations and provide guidance for formulating estimands for TwiCs studies.Methods: The key attributes of an estimand are the target population, treatments that are compared, the endpoint, intercurrent events and their handling, and the population-level summary measure. The estimand framework was applied retrospectively to two TwiCs studies: the SPONGE and UMBRELLA Fit trial. The aim is to demonstrate how the estimand framework can be implemented in TwiCs studies, thereby focusing on considerations relevant for defining the estimand. Three estimands were defined for both studies. For the SPONGE trial, estimators were derived.Results: Intercurrent events considered to occur exclusively or more frequently in TwiCs studies compared to conventional randomized trials included intervention refusal after randomization, misalignment of timing of routine cohort measurements and the intervention period, and participants in the control arm initiating treatments similar to the studied intervention. Considerations for handling refusal after randomization related to decisions on whether the target population should include all eligible participants or the subpopulation that would accept (or undergo) the intervention when offered. Considerations for handling treatment initiation in the control arm and misalignments of timing related to decisions on whether such events should be considered part of treatment policy or whether interest is in a hypothetical scenario where such events do not occur.Conclusion: The TwiCs study design has unique features that pose specific considerations when formulating an estimand. The examples in this study can provide guidance in the definition of estimands in future TwiCs studies.

Effect of statin use on long-term survival after diagnosis of colorectal cancer: Emulating hypothetical target trials using a nationwide real-world database.

82 Background: Observational studies suggest that statin use after colorectal cancer (CRC) diagnosis is associated with improved survival. However, potential bias due to misalignment of time zero in observational studies may distort associations and therefore requires validation using robust epidemiological methods. We aimed to emulate a hypothetical randomized trial—a target trial—for estimating per-protocol effects of statin initiation in patients with incident colorectal cancer on long-term survival. Methods: To emulate a target trial using real-world data, we extracted information from 103,005 patients diagnosed with CRC between 2005 and 2015 from the Korean National Health Insurance (KNHI) database, encompassing health records for the entire Korean population. We excluded individuals who had been prescribed statins for at least 180 days before the CRC diagnosis or those with missing or incomplete demographic information. We set a 180-day grace period and compared the group that initiated statin treatment within 180 days after the CRC diagnosis with the non-statin group using the “clone-censor-weight” method. Patients' demographic and socioeconomic information, along with colorectal cancer treatment, were included as baseline covariates. Additionally, the Charlson Comorbidity Index (CCI) score was included both as a baseline covariate and as a time-varying covariate. Hazard ratios (HRs) for all-cause mortality and CRC-specific mortality within 60 months of baseline were calculated using pooled logistic regression models. Results: Among the 103,005 patients diagnosed with CRC, 88,516 were finally analyzed after the exclusion process. A total of 2,071 (2.3%) individuals initiated statin treatment during the grace period and 86,445 (97.7%) patients did not. Statin initiators, compared with statin noninitiators tended to be older (mean age: 65.7 vs. 62.6) and had a higher CCI score at baseline (mean CCI score: 1.38 vs.1.26). Individuals who initiated statin treatment did not reduce the hazard of all-cause mortality (The adjusted HR 1.01, 95% CI 0.98-1.04) and CRC-specific mortality (The adjusted HR 1.01, 95% CI 0.97-1.04). Conclusions: The results of the analysis after removing potential biases showed that statin was not related to improving survival in CRC patients. The study results conflict with those of previous observational studies but are consistent with the findings of other recently conducted target trial emulations.

A high-light therapy restores the circadian clock and corrects the pathological syndrome generated in restricted-fed mice

Time-restricted feeding (RF) is known to shift the phasing of gene expression in most primary metabolic tissues, whereas a time misalignment between the suprachiasmatic nucleus circadian clock (SCNCC) and its peripheral CCs (PCC's) is known to induce various pathophysiological conditions, including a metabolic syndrome. We now report that a unique "light therapy," involving different light intensities (TZT0-ZT12150-TZT0-ZT12700 lx, TZT0-ZT1275-TZT0-ZT12150 lx, and TZT0-ZT12350-TZT0-ZT12700 lx), realigns the RF-generated misalignment between the SCNCC and the PCC's. Using such high-light regime, we show that through shifting the SCNCC and its activity, it is possible in a RF and "night-shifted mouse model" to prevent/correct pathophysiologies (e.g., a metabolic syndrome, a loss of memory, cardiovascular abnormalities). Our data indicate that such a "high-light regime" could be used as a unique chronotherapy, for those working on night shifts or suffering from jet-lag, in order to realign their SCNCC and PCC's, thereby preventing the generation of pathophysiological conditions.

Code-Based Differential GNSS Ranging for Lunar Orbiters: Theoretical Review and Application to the NaviMoon Observables

In the near future, international space agencies have planned to achieve significant milestones in investigating the utilization of Global Navigation Satellite Systems (GNSS) within and beyond the current space service volume up to their application to lunar missions. These initiatives aim to demonstrate the feasibility of GNSS navigation at lunar altitudes. Based on the outcomes of such demonstrations, dozens of lunar missions will likely be equipped with a GNSS receiver to support autonomous navigation in the lunar proximity. Relying on non-invasive, consolidated differential techniques, GNSS will enable baseline estimation, thus supporting a number of potential applications to lunar orbiters such as collaborative navigation, formation flight, orbital manoeuvers, remote sensing, augmentation systems and beyond. Unfortunately, the large dynamics and the geometry of such differential GNSS scenarios set them apart from current terrestrial and low-earth orbit use cases. These characteristics result in an increased sensitivity to measurements time misalignment among orbiters. Hence, this paper offers a review of baseline estimation methods and characterizes the divergences and limitations w.r.t. to terrestrial applications. The study showcases the estimation of the baseline length between a lunar CubeSat mission, VMMO, and the communication relay Lunar Pathfinder mission. Notably, real GNSS measurements generated by an Engineering Model of the NaviMoon receiver in the European Space Agency (ESA/ESTEC) Radio Navigation Laboratory are utilized. A radio-frequency constellation simulator is used to generate the GNSS signals in these hardware-in-the-loop tests. The performed analyses showed the invalidity of common terrestrial differential GNSS ranging techniques for space scenarios due to the introduction of significant biases. Improved ranging algorithms were proposed and their potential to cancel ranging errors common to both receivers involved was confirmed.

Invited commentary: it's not all about residual confounding-a plea for quantitative bias analysis for epidemiologic researchers and educators.

Epidemiologists spend a great deal of time on confounding in our teaching, in our methods development, and in our assessment of study results. This may give the impression that uncontrolled confounding is the biggest problem observational epidemiology faces, when in fact, other sources of bias such as selection bias, measurement error, missing data, and misalignment of zero time may often (especially if they are all present in a single study) lead to a stronger deviation from the truth. Compared with the amount of time we spend teaching how to address confounding in data analysis, we spend relatively little time teaching methods for simulating confounding (and other sources of bias) to learn their impact and develop plans to mitigate or quantify the bias. Here we review the accompanying paper by Desai etal (Am J Epidemiol. 2024;193(11):1600-1608), which uses simulation methods to quantify the impact of an unmeasured confounder when it is completely missing or when a proxy of the confounder is measured. We discuss how we can use simulations of sources of bias to ensure that we generate better and more valid study estimates, and we discuss the importance of simulating realistic datasets with plausible bias structures to guide data collection. This article is part of a Special Collection on Pharmacoepidemiology.

Estimation of Periodically Occurring Faults for Dynamic Systems With Timing Misalignment Between Faults and Observations

Estimation of Periodically Occurring Faults for Dynamic Systems With Timing Misalignment Between Faults and Observations

Learning Selective Sensor Fusion for State Estimation.

Autonomous vehicles and mobile robotic systems are typically equipped with multiple sensors to provide redundancy. By integrating the observations from different sensors, these mobile agents are able to perceive the environment and estimate system states, e.g., locations and orientations. Although deep learning (DL) approaches for multimodal odometry estimation and localization have gained traction, they rarely focus on the issue of robust sensor fusion--a necessary consideration to deal with noisy or incomplete sensor observations in the real world. Moreover, current deep odometry models suffer from a lack of interpretability. To this extent, we propose SelectFusion, an end-to-end selective sensor fusion module that can be applied to useful pairs of sensor modalities, such as monocular images and inertial measurements, depth images, and light detection and ranging (LIDAR) point clouds. Our model is a uniform framework that is not restricted to specific modality or task. During prediction, the network is able to assess the reliability of the latent features from different sensor modalities and to estimate trajectory at both scale and global pose. In particular, we propose two fusion modules--a deterministic soft fusion and a stochastic hard fusion--and offer a comprehensive study of the new strategies compared with trivial direct fusion. We extensively evaluate all fusion strategies both on public datasets and on progressively degraded datasets that present synthetic occlusions, noisy and missing data, and time misalignment between sensors, and we investigate the effectiveness of the different fusion strategies in attending the most reliable features, which in itself provides insights into the operation of the various models.

Prospective study of the association between chronotype and cardiometabolic risk among Chinese young adults

BackgroundThe association of evening chronotype with cardiometabolic disease has been well established. However, the extent to which circadian rhythm disturbances independently result in risk remains unclear. This study aimed to investigate the cross-sectional and prospective longitudinal associations between chronotype and cardiometabolic risk among Chinese young adults.MethodsFrom April to May 2019, a total of 1 135 young adults were selected to complete the self-administered questionnaire, and 744 fasting blood samples were collected to quantify cardiometabolic parameters. From April to May 2021, 340 fasting blood samples were collected to quantify cardiometabolic parameters. The Morning and Evening Questionnaire 5 (MEQ-5) was used to assess chronotype. The cardiometabolic (CM)-risk score was the sum of standardized Z scores based on gender for the 5 indicators: waist circumference (WC), mean arterial pressure (MAP), triglyceride (TG), homeostasis model assessment for insulin resistance (HOMA-IR), and high-density lipoprotein cholesterol (HDL-C), where the HDL-C is multiplied by-1. The generalized linear model was used to determine the cross-sectional and prospective longitudinal associations between chronotype and each cardiometabolic parameter.ResultsCross-sectional association analysis showed that lower MEQ-5 scores were correlated with higher fasting insulin (β=-1.420, 95%CI: -2.386~-0.453), higher HOMA-IR (β=-0.301, 95%CI: -0.507~-0.095), and higher CM risk score (β=-0.063, 95%CI: -0.122~-0.003), even after adjustment for covariates. Prospective longitudinal association analysis also showed that lower MEQ-5 scores were associated with 2 years later higher fasting glucose (β=-0.018, 95%CI: -0.034~-0.003), higher fasting insulin (β=-0.384, 95%CI: -0.766~-0.003), higher HOMA-IR (β=-0.089, 95%CI: -0.176~-0.002), and higher CM-risk score (β=-0.109, 95%CI: -0.214~-0.003) after adjustment for covariates.ConclusionsEvening chronotype was significantly correlated with higher CM risk among young adults. Our findings suggest that biologically and socially affected sleep timing misalignment is a contributing factor to cardiovascular disease risk.

Quantifying teenagers’ sleep patterns and sex differences in social jetlag using at-home sleep monitoring

Sleep plays an important role in the formative developmental processes occurring during the teenage years. At the same time, teenagers' changing bioregulatory mechanisms and psychosocial factors converge into the so-called social jetlag, a sleep timing misalignment between weekdays and weekends. The aim of this study was to quantify the course of day-to-day changes in sleep/wake patterns and sleep stage distributions, and the sex differences in social jetlag among teenagers. We observed the sleep of 156 teenagers (58.3% girls, 15–16 years) using a novel sleep monitor over the course of up to 10 consecutive days. 1323 nights of data were analyzed using multilevel modeling. On average, participants went to bed at 23:41, woke up at 07:48, slept for 7.7 h and had 85.5% sleep efficiency. Sleep stage distributions were in line with normative data. We found later sleep onset and offset, longer time in bed, sleep duration, and sleep onset latency (p = .001), greater proportion of light sleep and lower proportion of deep sleep, and poorer sleep efficiency (all p < .001) on weekend nights starting on Friday and Saturday. On Friday nights, girls had longer time awake after sleep onset (p = .020) than boys. On Friday and Saturday nights, girls fell asleep earlier (p < .001 and p = .006, respectively). On Saturday nights, girls had shorter sleep latency (p = .024), and better sleep efficiency (p = .019) than boys. In sum, teenagers’ sleep patterns reflected healthy, albeit somewhat short sleep. There was convincing evidence of social jetlag, and girls exhibited less severe social jetlag than boys.

Accurate and Efficient Digital Twin Construction Using Concurrent End-to-End Synchronization and Multi-Attribute Data Resampling

Accurate and efficient digital twin construction through real-time multi-attribute sensing and remote concurrent data analysis is essential in supporting complex connected industrial applications. Given the unsynchronized nature and heterogeneous sampling rates of distributed sensing processes, the varying time misalignment among different attributes will inevitably deteriorate the remote correlation analysis and digital twin construction. Furthermore, application-agnostic digital twin construction approaches could potentially involve high communication and computation overhead for comprehensive digital twin construction. In this article, a concurrent end-to-end time synchronization and multi-attribute data resampling scheme is proposed to enable accurate and efficient digital twin construction at the remote end. Specifically, digital clocks are concurrently established at the remote end, with each of them associated with a sampling rate of a unique sensing attribute. To tackle the temporal misalignment among multiple sensing attributes, raw data are accurately resampled according to the same reference frequency, with attribute-specific synchronized digital clocks providing cohesively aligned time information. An edge-centric platform is established to efficiently guide the multidimensional data processing during digital twin construction. Simulation results demonstrate that the proposed scheme can achieve more accurate and efficient digital twin construction than existing modeling methods. In the end, the digital twin-driven predictive maintenance is presented as a case study, aiming at illustrating the potential applications and benefits expected of the proposed scheme in industrial environments.

An online temporal calibration method for nonlinear optimization-based stereo visual-inertial odometry

Mobile robots have been widely used in geological exploration, accident rescue, and other fields. As the core technology of these robots, simultaneous localization and mapping is responsible for estimating the robot’s position and building a scene map. With the rapid development of simultaneous localization and mapping technology in multi-sensor fusion, many visual-inertial odometry systems based on camera and inertial measurement unit fusion have been proposed. Although visual-inertial odometry has advantages over visual simultaneous localization and mapping in accuracy and robustness, they still cannot meet the requirements of robots to work stably for a long time. The time misalignment (time offset) between visual and inertial on the timeline is one of the main reasons for that problem. Therefore, this article proposes an online temporal calibration method based on nonlinear optimization, suitable for low-cost, self-assembled stereo visual-inertial odometry systems. We take full advantage of stereo camera, which constructs a new error factor concerning time offset using the epipolar constraint in the stereo camera. The proposed method could reduce the negative influence of features’ velocity errors during temporal calibration and improve the system’s robustness and accuracy. Experiments on the EuRoC data set show that our method’s temporal calibration results are closer to the actual value than the state-of-the-art calibration methods. Our method can significantly improve the system’s accuracy and robustness.

Learning From Sparse Demonstrations

In this article, we develop the method of continuous Pontryagin differentiable programming (Continuous PDP), which enables a robot to learn an objective function from a few sparsely demonstrated keyframes. The keyframes, labeled with some time stamps, are the desired task-space outputs, which a robot is expected to follow sequentially. The time stamps of the keyframes can be different from the time of the robot's actual execution. The method jointly finds an objective function and a time-warping function such that the robot's resulting trajectory sequentially follows the keyframes with minimal discrepancy loss. The Continuous PDP minimizes the discrepancy loss using projected gradient descent by efficiently solving the gradient of the robot trajectory with respect to the unknown parameters. The method is first evaluated on a simulated robot arm and then applied to a 6-DoF quadrotor to learn an objective function for motion planning in unmodeled environments. The results show the efficiency of the method, its ability to handle time misalignment between keyframes and robot execution, and the generalization of objective learning into unseen motion conditions.

Simple Clock Drift Estimation and Compensation for Packet-Level Index Modulation and Its Implementation in LoRaWAN

Low-power wide-area network (LPWAN), which includes the long-range wide-area network (LoRaWAN) protocol, is an enabling technology that satisfy low power consumption and long-range communication. In many applications of LPWAN, an end node (EN) transmits data at regular intervals. Based on this, we had previously proposed the concept of packet-level index modulation (PLIM) to increase the number of information bits transmitted by one data packet. In PLIM, the generation interval between two consecutive packets is split into multiple time slots. Each EN transmits its packet in a specific combination of time slot and frequency channel, which represents the index, to convey additional information bits. To retrieve additional information, the gateway (GW) detects the time slot in which the data packet is being transmitted. Therefore, accurate synchronization between EN and GW is essential. However, clock drift occurs due to the inexpensive real-time clock oscillator on each EN, which results in timing misalignment between each node and the GW. This article proposes a simple clock drift estimation and compensation method for the PLIM. An experimental measurement is performed to model the clock drift. The numerical results obtained using the clock drift model show that it can accurately detect the time slot index under the influence of clock drift. Furthermore, PLIM is implemented on a commercial LoRaWAN node and GW to demonstrate its practicability.

Development of a workflow for processing ground-penetrating radar data from multiconcurrent receivers

Ground-penetrating radar (GPR) systems with multiconcurrent sampling receivers can rapidly acquire dense multioffset GPR data, which are not feasible using typical common-offset (CO) GPR systems with a single fixed offset transmitter-receiver pair. Multioffset GPR data from these new multiconcurrent receiver systems have the potential to be used to create detailed subsurface velocity models and enhanced reflection sections. These are important features that can improve qualitative and quantitative interpretation of GPR data. To realize these benefits and to deal with the large amount of multioffset data generated by these new systems, we have developed an automated and customized data processing workflow. There are three key algorithms that we have developed as part of our workflow, which is crucial for processing large volume, multioffset GPR data so as: first, to efficiently correct and manage time misalignments from multiconcurrent receivers; second, to carry out trace balancing of common-midpoint data for semblance analysis; and third, to automate the velocity analysis step. We showcase our processing workflow using two field data sets acquired using a multiconcurrent sampling receiver GPR system consisting of one transmitter and seven receivers. The field data were collected at two different locations: a site using a system with a 500 MHz center frequency and another site using a system with a 1000 MHz center frequency. We have determined, with both data sets, that our processing workflow could produce automated stacking velocity fields and enhanced zero-offset reflection cross sections. These benefits increase the information that can be used for interpretation (compared with conventional CO data) and can form the basis of further processing steps such as migration. As the cost of these multiconcurrent sampling receiver systems decreases over time, we anticipate their use, and the acquisition of dense multioffset GPR data, to become much more commonplace.

Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19.

Estimating the changes of epidemiological parameters, such as instantaneous reproduction number, Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to estimate the instantaneous reproduction number Rt during emerging epidemics, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in describing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for making accurate and timely estimation for transmission dynamics based on reported data.

Evaluation of intraperitoneal vancomycin in peritoneal dialysis-associated peritonitis.

Intraperitoneal (IP) vancomycin is recommended as one of the treatment options for gram-positive coverage in the management of peritoneal dialysis (PD)-associated peritonitis. There is a lack of literature supporting the optimal dose and approach to vancomycin therapeutic drug-level monitoring. A retrospective chart review was conducted using the BC Renal Agency PROMIS Database and our hospital records from 1 June 2011 to 1 July 2019. Adult patients with PD-associated peritonitis who received IP vancomycin and had at least one serum vancomycin level drawn were included. All patients received a loading dose of 30 mg/kg, which was repeated every 3-5 days depending on PD modality. Serum vancomycin levels were drawn prior to the second vancomycin dose, then at the discretion of the prescriber. The primary end point was the rate of therapeutic serum vancomycin levels ≥15 mg/L. Twenty-three episodes of PD-associated peritonitis in 20 patients met the eligibility criteria. Only 15/23 serum vancomycin levels were drawn appropriately after the first dose. Sixty per cent of these levels were subtherapeutic at <15 mg/L. All subsequent serum vancomycin levels were above the therapeutic target. Most peritonitis episodes (78%) achieved resolution of infection. Residual kidney function was not significantly correlated with serum vancomycin levels (p = 0.19). An IP vancomycin regimen of 30 mg/kg every 3-5 days resulted in subtherapeutic serum vancomycin levels in most patients following the loading dose but therapeutic levels thereafter. A large percentage of vancomycin levels were drawn inappropriately due to misalignment of outpatient follow-up visits and timing of blood work.

Behaviorally and environmentally induced non-24-hour sleep-wake rhythm disorder in sighted patients.

To determine whether there was evidence of circadian or sleep-regulatory dysfunction in sighted individuals with non-24-hour sleep-wake rhythm disorder. Three sighted individuals with signs and/or symptoms of non-24-hour sleep-wake rhythm disorder were studied. Thirty-five- to 332-day laboratory and home-based assessments of sleep-wake and circadian timing, endogenous circadian period, photic input to the circadian pacemaker, and/or circadian and sleep-wake-dependent regulation of sleep were conducted. No evidence of circadian dysfunction was found in these individuals. Instead, sleep-wake timing appeared to dissociate from the circadian timing system, and/or self-selected sleep-wake and associated light/dark timing shifted the circadian pacemaker later, rather than the circadian pacemaker determining sleep-wake timing. These findings suggest that the etiology of this disorder may be light- and/or behaviorally induced in some sighted people, which has implications for the successful treatment of this disorder. Emens JS, St Hilaire MA, Klerman EB, etal. Behaviorally and environmentally induced non-24-hour sleep-wake rhythm disorder in sighted patients. J Clin Sleep Med. 2022;18(2):453-459.

Why meals during resting time cause fat accumulation in mammals? Mathematical modeling of circadian regulation on glucose metabolism.

Synchronization of metabolic rhythms regulated by circadian clock and meal timing is essential for maintaining nutrient homeostasis in response to fluctuating food intake in animals. Despite numerous experimental findings on the involvement of circadian regulation of glucose and lipid metabolism, the optimal regulatory strategy for the maintenance of energy homeostasis remains poorly defined. A mathematical framework is useful to assess the circadian regulation of glycogen production/breakdown and de novo lipogenesis/lipolysis by evaluating the contribution of time of the day-dependent activation or the repression of each metabolic process in the maintenance of energy homeostasis. Here, we present a mathematical model that describes the dynamics of glycogen and triglyceride contents, two major forms of energy storage in the body that provide the fuel needed during different phases of food deprivation. By changing peak phases of glycogenesis and fat synthesis, we searched for the optimal phase set that minimizes the risks of two types of possible metabolic dysfunctions: (1) high blood glucose and (2) energy exhaustion. Based on the optimal phase set, we compared the level of fat accumulation between meal timing in the active and resting periods. Our results showed that an increased fat accumulation by food intake in the resting period can be the byproduct of minimizing energy homeostasis risks in the synchronized feeding schedule that animals adopt in nature. Our finding will be useful to schedule an optimal meal timing to prevent metabolic diseases caused by misalignment of biological and social time in modern society.

Common genetic variation in circadian clock genes are associated with cardiovascular risk factors in an African American and Hispanic/Latino cohort.

Misalignment of the internal circadian time with external physical time due to environmental factors or due to genetic variantion in circadian clock genes has been associated with increased incidence of cardiovascular risk factors. Common genetic variation in circadian genes in the United States have been identified predominantly in European ancestry individuals. We therefore examined the association between circadian clock single nucleotide polymorphisms (SNPs) in Clock, Cry1, Cry2, Bmal1 and Per3 genes and cardiovascular risk factors in African Americans and Hispanic/Latinos. We analyzed 17 candidate circadian SNPs in 1,166 subjects who self-identified as African-American or Hispanic/Latino and were enrolled in the UIC Cohort of Patients, Family and Friends. We found significant differences in the minor allele frequencies between African American and Hispanic/Latino subjects. Our analyses also established ethnic-specific SNPs that are associated with cardiovascular risk factors. In Hispanic/Latinos, the rs6850524 in Clock was associated with increased risk for hypertension, meanwhile rs12649507, rs4864546, and rs4864548 reduced the risk, also rs8192440 (Cry1) reduced the risk for type 2 diabetes. In African Americans, the Clock rs1801260 and rs6850524 were negatively associated with the presence of obesity; Bmal1 rs11022775 reduced the risk for dyslipidemia; and the Cry2 rs2292912 increased the risk for dyslipidemia and diabetes. Genetic variations in candidate circadian-clock genes are associated with risk factors for cardiovascular disease in African-Americans and Hispanic/Latinos. Our findings may help to improve cardiovascular risk assessment as well as better understand how circadian misalignment impacts cardiovascular risk in diverse populations.