Time-lagged Cross-correlation Research Articles

Time Series of Magnetic Field Parameters of Merged MDI and HMI Space-weather Active Region Patches as Potential Tool for Solar Flare Forecasting

Solar flare prediction studies have been recently conducted with the use of Space-Weather MDI (Michelson Doppler Imager on board Solar and Heliospheric Observatory) Active Region Patches (SMARPs) and Space-Weather HMI (Helioseismic and Magnetic Imager on board Solar Dynamics Observatory) Active Region Patches (SHARPs), which are two currently available data products containing magnetic field characteristics of solar active regions (ARs). The present work is an effort to combine them into one data product, and perform some initial statistical analyses in order to further expand their application in space-weather forecasting. The combined data are derived by filtering, rescaling, and merging the SMARP and SHARP parameters, which can then be spatially reduced to create uniform multivariate time series. The resulting combined MDI–HMI data set currently spans the period between 1996 April 4 and 2022 December 13, and may be extended to a more recent date. This provides an opportunity to correlate and compare it with other space-weather time series, such as the daily solar flare index or the statistical properties of the soft X-ray flux measured by the Geostationary Operational Environmental Satellites. Time-lagged cross correlation indicates that a relationship may exist, where some magnetic field properties of ARs lead the flare index in time. Applying the rolling-window technique makes it possible to see how this leader–follower dynamic varies with time. Preliminary results indicate that areas of high correlation generally correspond to increased flare activity during the peak solar cycle.

Spatio-temporal analysis of Permian-Cretaceous magmatic activities in the Tengchong block: Implications for tectono-magmatic evolution

Understanding the tectono-magmatic evolution history of the Tengchong block is crucial for elucidating the formation of the Eastern Tethys tectonic domain. However, the correlation and evolution of the Tengchong block with the Sibumasu and Lhasa blocks is controversial during the Permian and Cretaceous. This study explores the information contained within magmatic rocks using big data and spatio-temporal analysis, providing quantitative constraints for the discussion of the tectono-magmatic evolution of the Tengchong block. To more accurately assess true magma activities and reduce errors caused by preservation and sampling processes, we utilized local singularity analysis to obtain the singularity index time-series. Correlation analysis of zircon ages and εHf(t) (correlation coefficient ≥ 0.5) values indicates that the Tengchong block is more similar to the Sibumasu block. Results from time-lagged cross-correlation analysis indicate that the Tengchong block and Sibumasu block exhibit a shorter lag in magmatic activities (3 Myr). Wavelet analysis reveals similar periods of collision-related magmatic activities (57 Myr and 43 Myr). Integrating evidence from paleontology and ophiolite belts, we propose that the Tengchong block co-evolved more closely with the Sibumasu block than with the Lhasa block, suggesting similar tectonic processes during the Early Permian to Early Cretaceous. Approximately 250–236 Ma, in the western Tengchong block, partial melting of the lower crust occurs due to crustal thickening. Around 219–213 Ma and 198–180 Ma, after the Tengchong block collided with the Eurasian continent, the subduction of the Meso-Tethys Ocean commenced. Around 130–111 Ma, the overall tectonic feature was a scissor-like closure of the Meso-Tethys Ocean from north to south.

Wastewater Surveillance to Confirm Differences in Influenza A Infection between Michigan, USA, and Ontario, Canada, September 2022-March 2023.

Wastewater surveillance is an effective way to track the prevalence of infectious agents within a community and, potentially, the spread of pathogens between jurisdictions. We conducted a retrospective wastewater surveillance study of the 2022-23 influenza season in 2 communities, Detroit, Michigan, USA, and Windsor-Essex, Ontario, Canada, that form North America's largest cross-border conurbation. We observed a positive relationship between influenza-related hospitalizations and the influenza A virus (IAV) wastewater signal in Windsor-Essex (ρ = 0.785; p<0.001) and an association between influenza-related hospitalizations in Michigan and the IAV wastewater signal for Detroit (ρ = 0.769; p<0.001). Time-lagged cross correlation and qualitative examination of wastewater signal in the monitored sewersheds showed the peak of the IAV season in Detroit was delayed behind Windsor-Essex by 3 weeks. Wastewater surveillance for IAV reflects regional differences in infection dynamics which may be influenced by many factors, including the timing of vaccine administration between jurisdictions.

Influence of Time-Lag Effects between Winter-Wheat Canopy Temperature and Atmospheric Temperature on the Accuracy of CWSI Inversion of Photosynthetic Parameters.

When calculating the CWSI, previous researchers usually used canopy temperature and atmospheric temperature at the same time. However, it takes some time for the canopy temperature (Tc) to respond to atmospheric temperature (Ta), suggesting the time-lag effects between Ta and Tc. In order to investigate time-lag effects between Ta and Tc on the accuracy of the CWSI inversion of photosynthetic parameters in winter wheat, we conducted an experiment. In this study, four moisture treatments were set up: T1 (95% of field water holding capacity), T2 (80% of field water holding capacity), T3 (65% of field water holding capacity), and T4 (50% of field water holding capacity). We quantified the time-lag parameter in winter wheat using time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and gray time-lag correlation analysis. Based on the time-lag parameter, we modified the CWSI theoretical and empirical models and assessed the impact of time-lag effects on the accuracy of the CWSI inversion of photosynthesis parameters. Finally, we applied several machine learning algorithms to predict the daily variation in the CWSI after time-lag correction. The results show that: (1) The time-lag parameter calculated using time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and gray time-lag correlation analysis are 44-70, 32-44, 42-58, and 76-97 min, respectively. (2) The CWSI empirical model corrected by the time-lag mutual information method has the highest correlation with photosynthetic parameters. (3) GA-SVM has the highest prediction accuracy for the CWSI empirical model corrected by the time-lag mutual information method. Considering time lag effects between Ta and Tc effectively enhanced the correlation between CWSI and photosynthetic parameters, which can provide theoretical support for thermal infrared remote sensing to diagnose crop water stress conditions.

A novel method for quantifying affective sensitivity to endogenous ovarian hormones

Increased sensitivity to ovarian hormone changes is implicated in the etiology of reproductive mood disorders across the female lifespan, including menstrually-related mood disorders, perinatal mood disorders, and perimenopausal depression. Developing a method to accurately quantify sensitivity to endogenous hormone fluctuations may therefore facilitate the prediction and prevention of these mental health conditions. Here, we propose one such method applying a synchrony analysis to compute time-lagged cross-correlations between repeated assessments of endogenous hormone levels and self-reported affect. We apply this method to a dataset containing frequent repeated assessments of affective symptoms and the urinary metabolites of estradiol (E2) and progesterone (P4) in 94 perimenopausal females. These preliminary findings suggest that, with further refinement and validation, the proposed method holds promise as a diagnostic tool to be used in clinical practice and to advance research investigating the etiology of reproductive mood disorders.

Evaluation of data imputation approaches for multi-stream building systems data1

Increasing advancements in building digitization, smart sensing, and metering technologies have allowed large amounts of timeseries data to be collected for monitoring, analyzing, and controlling building systems. However, due to sensor or communication failures, the data collected are often incomplete and poor in quality. Data imputation approaches to replace the missing values, specifically based on either statistical or predictive models have been widely adopted for multivariate datasets in other domains. It is hence of interest to find an effective way to impute timeseries data collected from a building system. In this paper, we evaluate multiple data imputation approaches using data collected from a medium sized building situated in Stockholm, Sweden and a small commercial building from the ASHRAE RP-1312 research project. Sensors with widely varying characteristics from the case study buildings were selected to evaluate the imputation methods. The imputation accuracy and the impact of each chosen imputation method on information entropy, short-term building forecasting model performance, and fault detection strategy were evaluated. Results demonstrate that incorporating time-lagged cross correlations within a k -nearest neighbor ( k NN) model provide the most accurate imputations without affecting the quality of subsequent data analysis.

Proactive dam operation based on inflow prediction by modified long short-term memory for improving resilience

The operation of dams can have a significant downstream impact, causing major flood damage. For efficient dam operation, it is essential to accurately predict the inflow using deep learning such as a long short-term memory (LSTM). The modified LSTM (MLSTM) with new optimizers combined with a metaheuristic optimization algorithm was suggested. The MLSTM were trained using rainfall, water level, and discharge of the Yongdam Dam from 2010 to 2019 as input data and the inflow of the Daecheong Dam as output data. Normalization and time lagged cross correlation (TLCC) were applied as data preprocessing. The inflow of the Daecheong Dam in 2020 was predicted using rainfall, water level, and discharge of the Yongdam Dam. Compared to LSTM, MLSTM showed an accuracy improvement of about 63.85–83.34% based on the mean square error (MSE), which is a loss function. The results of proactive dam operation were calculated based on the predicted inflow considering TLCC. Compared to the existing operation, the peak water level of the reservoir was reduced from 77.54 m to 77.17 m, and the resilience was improved from 0.9225 to 0.9623. New dam operation can maintain low water levels of reservoir and improve resilience to prevent flood damage.

Trends in Salmonella Infantis human illness incidence and chicken carcass prevalence in the United States; 1996-2019.

The incidence of human illness due to Salmonella Infantis reported to Foodborne Diseases Active Surveillance Network and the prevalence of Infantis on chicken carcasses reported by the United States Department of Agriculture Food Safety and Inspection Service have increased significantly in the past decade. However, the trends do not appear coincident, as would be expected if the increased prevalence in chicken led to the increase in the incidence of human illness. Salmonella Infantis incidence and prevalence trends are analyzed using penalized B-spline methods for generalized additive regression models. The association between the two time series is analyzed using time-lagged rank-order cross-correlation. Geographic variations in reported incidence and trends are also explored. The increase in human incidence of Salmonella Infantis began circa 2011. The increase in chicken carcass prevalence began circa 2015. A 4-year lag on chicken carcass prevalence maximizes the rank-order cross-correlation with the incidence of illness. While chicken consumption undoubtedly contributes to the incidence of human illness due to Salmonella Infantis, the initial increase in reported illness was likely due to one or more other transmission pathways. Other potential transmission pathways include non-chicken foodborne, waterborne, person-to-person, animal contact, and environmental.

Monthly Streamflow Prediction of the Source Region of the Yellow River Based on Long Short-Term Memory Considering Different Lagged Months

Accurate and reliable monthly streamflow prediction plays a crucial role in the scientific allocation and efficient utilization of water resources. In this paper, we proposed a prediction framework that integrates the input variable selection method and Long Short-Term Memory (LSTM). The input selection methods, including autocorrelation function (ACF), partial autocorrelation function (PACF), and time lag cross-correlation (TLCC), were used to analyze the lagged time between variables. Then, the performance of the LSTM model was compared with three other traditional methods. The framework was used to predict monthly streamflow at the Jimai, Maqu, and Tangnaihai stations in the source area of the Yellow River. The results indicated that grid search and cross-validation can improve the efficiency of determining model parameters. The models incorporating ACF, PACF, and TLCC with lagged time are evidently superior to the models using the current variable as the model inputs. Furthermore, the LSTM model, which considers the lagged time, demonstrated better performance in predicting monthly streamflow. The coefficient of determination (R2) improved by an average of 17.46%, 33.94%, and 15.29% for each station, respectively. The integrated framework shows promise in enhancing the accuracy of monthly streamflow prediction, thereby aiding in strategic decision-making for water resources management.

Meta-analysis provides insights into the origin and evolution of East Asian evergreen broad-leaved forests.

Evergreen broad-leaved forests (EBLFs) are dominated by a monsoon climate and form a distinct biome in East Asia with notably high biodiversity. However, the origin and evolution of East Asian EBLFs (EAEBLFs) remain elusive despite the estimation of divergence times for various representative lineages. Using 72 selected generic-level characteristic lineages, we constructed an integrated lineage accumulation rate (LAR) curve based on their crown ages. According to the crown-based LAR, the EAEBLF origin was identified at least as the early Oligocene (c. 31.8 million years ago (Ma)). The accumulation rate of the characteristic genera peaked at 25.2 and 6.4 Ma, coinciding with the two intensification periods of the Asian monsoon at the Oligocene - Miocene and the Miocene - Pliocene boundaries, respectively. Moreover, the LAR was highly correlated with precipitation in the EAEBLF region and negatively to global temperature, as revealed through time-lag cross-correlation analyses. An early Oligocene origin is suggested for EAEBLFs, bridging the gap between paleobotanical and molecular dating studies and solving conflicts among previous estimates based on individual representative lineages. The strong correlation between the crown-based LAR and the precipitation brought about by the Asian monsoon emphasizes its irreplaceable role in the origin and development of EAEBLFs.

Discovering technological opportunities by identifying dynamic structure-coupling patterns and lead-lag distance between science and technology

Technological opportunities are bred in intricate and interactive connections between science and technology (S&T). To identify these potential opportunities, lexical- or topic-based similarity approaches have been extensively applied to quantify S&T linkages; however, these lack consideration of different interaction patterns and lead-lag relationships between S&T. To this end, this study proposes a novel approach to detect technological opportunities within specific S&T topics by incorporating their structure-coupling patterns and temporal lead-lag distance. By transforming S&T knowledge systems into knowledge networks, a network coupling approach is employed to elaborate dynamic interaction patterns of S&T, and a time-lagged cross-correlation analysis is conducted to calculate their lead-lag distance under different time shifts. An evidence analysis from the energy conservation field demonstrates the feasibility and reliability of the proposed methodology in identifying technological opportunities implicit in S&T shared (exists in both S&T) and private topics (exists only in science or technology) from a topical dimension.

Beyond linear regression: Modeling COVID-19 clinical cases with wastewater surveillance of SARS-CoV-2 for the city of Athens and Ohio University campus

Wastewater-based surveillance has emerged as a detection tool for population-wide infectious diseases, including coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infected individuals shed the virus, which can be detected in wastewater using molecular techniques such as reverse transcription-digital polymerase chain reaction (RT-dPCR). This study examined the association between the number of clinical cases and the concentration of SARS-CoV-2 in wastewater beyond linear regression and for various normalizations of viral loads. Viral loads were measured in a total of 446 wastewater samples during the period from August 2021 to April 2022. These samples were collected from nine different locations, with 220 samples taken from four specific sites within the city of Athens and 226 samples from five sites within Ohio University. The correlation between COVID-19 cases and wastewater viral concentrations, which was estimated using the Pearson correlation coefficient, was statistically significant and ranged from 0.6 to 0.9. In addition, time-lagged cross correlation was applied to identify the lag time between clinical and wastewater data, estimated 4 to 7 days. While we also explored the effect on the correlation coefficients of various normalizations of viral loads accounting for procedural loss or amount of fecal material and of estimated lag times, these alternative specifications did not change our substantive conclusions. Additionally, several linear and non-linear regression models were applied to predict the COVID-19 cases given wastewater data as input. The non-linear approach was found to yield the highest R-squared and Pearson correlation and lowest Mean Absolute Error values between the predicted and actual number of COVID-19 cases for both aggregated OHIO Campus and city data. Our results provide support for previous studies on correlation and time lag and new evidence that non-linear models, approximated with artificial neural networks, should be implemented for WBS of contagious diseases.

Electrical coupling of superparamagnetic tunnel junctions mediated by spin-transfer-torques

In this work, the effect of electrical coupling on stochastic switching of two in-plane superparamagnetic tunnel junctions (SMTJs) is studied, using experimental measurements as well as simulations. The coupling mechanism relies on the spin-transfer-torque effect, which enables the manipulation of the state probability of an SMTJ. Through the investigation of time-lagged cross-correlation, the strength and direction of the coupling are determined. In particular, the characteristic state probability transfer curve of each SMTJ leads to the emergence of a similarity or dissimilarity effect. The cross-correlation as a function of applied source voltage reveals that the strongest coupling occurs for high positive voltages for our SMTJs. In addition, we show state tunability as well as coupling control by the applied voltage. The experimental findings of the cross-correlation are in agreement with our simulation results.

Vegetation Dynamics and Its Trends Associated with Extreme Climate Events in the Yellow River Basin, China

As a vital ecological barrier in China, Yellow River Basin (YRB) is strategically significant for China’s national development and modernization. However, YRB has fragile ecosystems, and is sensitive to climatic change. Extreme climate events (e.g., heavy precipitation, heatwaves, and extreme hot and cold) occur frequently in this basin, but the implications (positive and negative effects) of these events on vegetation dynamics remains insufficiently understood. Combing with net primary productivity (NPP), the normalized difference vegetation index (NDVI) and extreme climate indexes, we explored the spatio–temporal characteristics of plants’ growth and extreme climate, together with the reaction of plants’ growth to extreme climate in the Yellow River Basin. This study demonstrated that annual NPP and NDVI of cropland, forest, and grassland in the study region all revealed a climbing tendency. The multi-year monthly averaged NPP and NDVI were characterized by a typical unimodal distribution, with the maximum values of NPP (66.18 gC·m−2) and NDVI (0.54) occurring in July and August, respectively. Spatially, multi–year averaged of vegetation indicators decreased from southeast to northwest. During the study period, carbon flux (NPP) and vegetation index (NDVI) both exhibited improvement in most of the YRB. The extreme precipitation indexes and extreme high temperature indexes indicated an increasing tendency; however, the extreme low temperature indexes reduced over time. NPP and NDVI were negatively associated with extreme low temperature indexes and positively correlated with extreme high temperature indexes, and extreme precipitation indicators other than consecutive dry days. Time lag cross–correlation analysis displayed that the influences of extreme temperature indexes on vegetation indexes (NPP and NDVI) were delayed by approximately six months, while the effects of extreme precipitation indexes were immediate. The study outcomes contribute to our comprehension of plants’ growth, and also their reaction to extreme climates, and offer essential support for evidence–based ecological management practices in the Yellow River Basin.

Inertial effects in sedimenting suspensions of solid spheres in a liquid

Particle-resolved Direct Numerical Simulations have been performed on the gravitational settling of mono-disperse solid spheres in a viscous fluid and triply periodic domain. In a comprehensive study, the bulk solid volume concentration was varied from ϕ=0.5 to 30%. To study the effect of inertia, three different Galileo numbers were considered in the inertial regime, Ga=144, 178 and 210, for which a single settling sphere exhibits distinctly different wake and path characteristics. The particle/fluid mass density ratio was fixed at 1.5. We find that for ϕ=2−30% the suspension microstructure and dynamics depend predominantly on the bulk concentration. In qualitative agreement with previous studies in literature, three different sedimentation regimes can be distinguished: (1) the dilute concentration regime for ϕ≲2% with preferential settling of particles in vertical trains, (2) the moderate concentration regime for 2%≲ϕ≲10% with preferential settling of particles in horizontal pairs with an interparticle distance of ∼ 1.5 particle diameters, and (3) the dense concentration regime for ϕ≳10% with a nearly random (“hard-sphere”) distribution of the particles in space. The clustering of particles is dictated by, respectively, trapping of particles in the wake of other particles, a drafting–kissing–tumbling (DKT) instability by which two vertically aligned particles quickly reorient themselves into a horizontally aligned particle pair, and short-range multiparticle interactions through viscous lubrication and to a lesser extent collisions between particles. In all cases, hindered settling at a reduced speed is observed as compared to a single settling sphere. The well-known Richardson–Zaki relation for the mean sedimentation velocity appears valid only for the dense concentration regime. We provide ample evidence that in the dense regime the characteristic velocity and time scales of particle motion are proportional to gDp and Dp/g, respectively, with g the gravitational acceleration and Dp the particle diameter. We also observe an ω−3 scaling of the particle velocity spectra for ωDp/g≳0.4 and we propose a model to explain this scaling behavior, based on the inertial response of the particles to small-scale flow perturbations. Kinematic waves, i.e., vertically propagating plane waves in the local concentration field, are observed in all cases, though unrelated particle motions are responsible for significant loss of the spatio-temporal coherence of the waves. The wave speed was determined from repeated space–time autocorrelations of the local concentration field and appears in reasonable agreement with Kynch sedimentation theory using the Richardson–Zaki relation. The passage of kinematic waves causes perturbations in the particle velocity at a frequency that matches well with peak frequencies in the particle velocity spectra for concentrations up to ϕ≈10%. The time-lagged cross-correlation of the vertical and horizontal particle velocity suggests that kinematic waves may trigger DKT instabilities, while conversely DKT instabilities may be responsible for the onset of kinematic waves. Finally, we suggest that obstruction and perturbation of the particle wake by neighboring particles could offer an explanation for the small influence of the Galileo number on the suspension behavior for ϕ=2−30%.

Time-Lag Response of Landslide to Reservoir Water Level Fluctuations during the Storage Period: A Case Study of Baihetan Reservoir

Fluctuations in reservoir water levels exert a strong triggering effect on landslides along reservoir banks, constituting a long-term concern in the safe operation of hydroelectric projects and in the prevention and management of geological disasters. While existing research has investigated the impact of periodic water level changes on the deformation of reservoir bank landslides, observation and detection of such deformation are challenging, with noticeable gaps in understanding how these deformations respond to water level changes during the water impoundment period. To address this, our study targets the Baihetan Reservoir, leveraging 567 ascending and descending LiCSAR data and LiCSBAS (the small-baseline subset within LiCSAR) technology to construct a time series of ground deformations in the study area from 2019 to 2023. The TLCC (Time Lag Cross Correlation) model was employed to examine the time-lag response pattern of reservoir bank landslide deformations to reservoir water level changes during the impoundment period. Our findings indicate a clear time-lag response in reservoir bank landslide deformations to water level changes during the impoundment process. The rise in water levels emerged as a primary factor influencing the instability of reservoir bank landslides. During the half-year impoundment period of the Baihetan Reservoir, a time lag of 5–7 days was observed between landslide deformations and increases in water levels, with landslides on the eastern and western banks exhibiting differing time-lag response patterns. Our study illuminates the time-lag effect between water level changes during reservoir impoundment and reservoir bank landslide deformation monitoring. By proposing a quantitative analysis methodology utilizing LiCSBAS technology and the TLCC model, our findings can inform decision-making in the field of disaster prevention and reduction in reservoir engineering.

Targeting a free viral fraction enhances the early alert potential of wastewater surveillance for SARS-CoV-2: a methods comparison spanning the transition between delta and omicron variants in a large urban center.

Wastewater surveillance has proven to be a valuable approach to monitoring the spread of SARS-CoV-2, the virus that causes Coronavirus disease 2019 (COVID-19). Recognizing the benefits of wastewater surveillance as a tool to support public health in tracking SARS-CoV-2 and other respiratory pathogens, numerous wastewater virus sampling and concentration methods have been tested for appropriate applications as well as their significance for actionability by public health practices. Here, we present a 34-week long wastewater surveillance study that covers nearly 4 million residents of the Detroit (MI, United States) metropolitan area. Three primary concentration methods were compared with respect to recovery of SARS-CoV-2 from wastewater: Virus Adsorption-Elution (VIRADEL), polyethylene glycol precipitation (PEG), and polysulfone (PES) filtration. Wastewater viral concentrations were normalized using various parameters (flow rate, population, total suspended solids) to account for variations in flow. Three analytical approaches were implemented to compare wastewater viral concentrations across the three primary concentration methods to COVID-19 clinical data for both normalized and non-normalized data: Pearson and Spearman correlations, Dynamic Time Warping (DTW), and Time Lagged Cross Correlation (TLCC) and peak synchrony. It was found that VIRADEL, which captures free and suspended virus from supernatant wastewater, was a leading indicator of COVID-19 cases within the region, whereas PEG and PES filtration, which target particle-associated virus, each lagged behind the early alert potential of VIRADEL. PEG and PES methods may potentially capture previously shed and accumulated SARS-CoV-2 resuspended from sediments in the interceptors. These results indicate that the VIRADEL method can be used to enhance the early-warning potential of wastewater surveillance applications although drawbacks include the need to process large volumes of wastewater to concentrate sufficiently free and suspended virus for detection. While lagging the VIRADEL method for early-alert potential, both PEG and PES filtration can be used for routine COVID-19 wastewater monitoring since they allow a large number of samples to be processed concurrently while being more cost-effective and with rapid turn-around yielding results same day as collection.

The Effects of Online Health Information-Seeking Behavior on Sexually Transmitted Disease in China: Infodemiology Study of the Internet Search Queries.

Sexually transmitted diseases (STDs) are a serious issue worldwide. With the popularity of the internet, online health information-seeking behavior (OHISB) has been widely adopted to improve health and prevent disease. This study aimed to investigate the short-term and long-term effects of different types of OHISBs on STDs, including syphilis, gonorrhea, and AIDS due to HIV, based on the Baidu index. Multisource big data were collected, including case numbers of STDs, search queries based on the Baidu index, provincial total population, male-female ratio, the proportion of the population older than 65 years, gross regional domestic product (GRDP), and health institution number data in 2011-2018 in mainland China. We categorized OHISBs into 4 types: concept, symptoms, treatment, and prevention. Before and after controlling for socioeconomic and medical conditions, we applied multiple linear regression to analyze associations between the Baidu search index (BSI) and Baidu search rate (BSR) and STD case numbers. In addition, we compared the effects of 4 types of OHISBs and performed time lag cross-correlation analyses to investigate the long-term effect of OHISB. The distributions of both STD case numbers and OHISBs presented variability. For case number, syphilis, and gonorrhea, cases were mainly distributed in southeastern and northwestern areas of China, while HIV/AIDS cases were mostly distributed in southwestern areas. For the search query, the eastern region had the highest BSI and BSR, while the western region had the lowest ones. For 4 types of OHISB for 3 diseases, the BSI was positively related to the case number, while the BSR was significantly negatively related to the case number (P<.05). Different categories of OHISB have different effects on STD case numbers. Searches for prevention tended to have a larger impact, while searches for treatment tended to have a smaller impact. Besides, due to the time lag effect, those impacts would increase over time. Our study validated the significant associations between 4 types of OHISBs and STD case numbers, and the impact of OHISBs on STDs became stronger over time. It may provide insights into how to use internet big data to better achieve disease surveillance and prevention goals.

Good sleep is a mood buffer for young women during menses.

We sought to elucidate the interaction between sleep and mood considering menstrual cycle phase (menses and non-menses portions of the cycle) in 72 healthy young women (18-33 years) with natural, regular menstrual cycles and without menstrual-associated disorders. This work fills a gap in literature of examining mood in context of sleep and menstrual cycle jointly, rather than individually. Daily subjective measures of sleep and mood, and date of menses were remotely, digitally collected over a 2-month period. Each morning, participants rated their sleep on the previous night, and each evening participants rated the extent of positive and negative mood for that day. Objective sleep was tracked with a wearable (ŌURA ring) during month 2 of the study. Time-lag cross-correlation and mixed linear models were used to analyze the significance and directionality of the sleep-mood relationship, and how the interaction between menstrual cycle status and sleep impacted mood levels. We found that menstrual status alone did not impact mood. However, subjective sleep quality and menstrual status interacted to impact positive mood (p < .05). After a night of perceived poor sleep quality, participants reported lower positive mood during menses compared to non-menses portions of the cycle, while after a night of perceived good sleep quality participants reported equivalent levels of positive mood across the cycle. We suggest that the perception of good sleep quality acts as a mood equalizer, with good sleep providing a protective buffer to positive mood across the menstrual cycle.

Molecular Dynamics and Electrical Modeling of Multi-Layered Nanopores of MoS2 and Hexagonal Boron Nitride for Biosensing of DNA

Nanopore transistors made of vertically stacked monolayer MoS2–hBN in a multi-sensing electronic scheme are predicted to improve sensing robustness and noise reduction in detecting biomolecules such as DNA and proteins. Our approach based on all-atom molecular dynamics simulation coupled with a semi-classical Boltzmann formalism for electronic transport along the layers shows quenching of the conformational motion of the biomolecule translocating through the multi-layer membrane. The synchronization of electronic sensing current signatures across the successive MoS2 probes achieved by time-lagged cross-correlation is seen to enhance the signal-to-noise ratio, notably in the lower-frequency spectrum, thereby enabling the identification of homopolymers.