A Time-Varying Dynamic Partial Credit Model to Analyze Polytomous and Multivariate Time Series Data

The accessibility to electronic devices and the novel statistical methodologies available have allowed researchers to comprehend psychological processes at the individual level. However, there are still great challenges to overcome as, in many cases, collected data are more complex than the available models are able to handle. For example, most methods assume that the variables in the time series are measured on an interval scale, which is not the case when Likert-scale items were used. Ignoring the scale of the variables can be problematic and bias the results. Additionally, most methods also assume that the time series are stationary, which is rarely the case. To tackle these disadvantages, we propose a model that combines the partial credit model (PCM) of the item response theory framework and the time-varying autoregressive model (TV-AR), which is a popular model used to study psychological dynamics. The proposed model is referred to as the time-varying dynamic partial credit model (TV-DPCM), which allows to appropriately analyze multivariate polytomous data and nonstationary time series. We test the performance and accuracy of the TV-DPCM in a simulation study. Lastly, by means of an example, we show how to fit the model to empirical data and interpret the results.

Spatial and temporal analysis of juvenile blacktip reef shark (Carcharhinus melanopterus) demographies identifies critical habitats

Reef shark species have undergone sharp declines in recent decades, as they inhabit coastal areas, making them an easy target in fisheries (i.e., sharks are exploited globally for their fins, meat, and liver oil) and exposing them to other threats (e.g., being part of by-catch, pollution, and climate change). Reef sharks play a critical role in coral reef ecosystems, where they control populations of smaller predators and herbivorous fishes either directly via predation or indirectly via behavior, thus protecting biodiversity and preventing potential overgrazing of corals. The urgent need to conserve reef shark populations necessitates a multifaceted approach to policy at local, federal, and global levels. However, monitoring programmes to evaluate the efficiency of such policies are lacking due to the difficulty in repeatedly sampling free-ranging, wild shark populations. Over nine consecutive years, we monitored juveniles of the blacktip reef shark (Carcharhinus melanopterus) population around Moorea, French Polynesia, and within the largest shark sanctuary globally, to date. We investigated the roles of spatial (i.e., sampling sites) and temporal variables (i.e., sampling year, season, and month), water temperature, and interspecific competition on shark density across 10 coastal nursery areas. Juvenile C. melanopterus density was found to be stable over 9 years, which may highlight the effectiveness of local and likely federal policies. Two of the 10 nursery areas exhibited higher juvenile shark densities over time, which may have been related to changes in female reproductive behavior or changes in habitat type and resources. Water temperatures did not affect juvenile shark density over time as extreme temperatures proven lethal (i.e., 33°C) in juvenile C. melanopterus might have been tempered by daily variation. The proven efficiency of time-series datasets for reef sharks to identify critical habitats (having the highest juvenile shark densities over time) should be extended to other populations to significantly contribute to the conservation of reef shark species.

Application of Time-Series Analysis and Expert Judgment in Modeling and Forecasting Blood Donation Trends in Zimbabwe

Background. Blood cannot be artificially manufactured, and there is currently no substitute for human blood. The supply of blood in transfusion facilities requires constant and timely collection of blood from donors. Modeling and forecasting trends in blood collections are critical for determining both the current and future capacity requirements and appropriate models of adequate blood provision. Objectives. The objective of this study is to determine blood collection or donation patterns and develop time-series models that can be updated and refined in predicting future blood donations in Zimbabwe when given the historical data. Materials and Methods. Monthly blood donation data for the period 2009 to 2019 were collected retrospectively from the National Blood Service Zimbabwe database. Time-series models (i.e., the Seasonal Autoregressive Integrated Moving Average [SARIMA] and Error, Trend and Seasonal [ETS]) models were applied and compared. The models were chosen because of their ability to handle the seasonality and other time-series components evident in the blood donation data. Expert opinions and experience were used in selecting the models and in making inferences in the analysis. Results. Time-series plots of blood donations showed seasonal patterns, with significant drops in blood donations in months associated with Zimbabwe's school holidays (April, August, and December) and public holidays. During these holidays, there is a reduced number of school donors, while at about the same time, there is increasing blood demand as a result of road accidents. Model identification procedures established the SARIMA ( 1 , 1 , 2 ) ( 0 , 1 , 1 ) 12 model as the appropriate model for forecasting total blood donation in Zimbabwe. The results and forecasts show an upward trend in blood donations. According to the accuracy measures used, the SARIMA model outperforms the ETS model. Conclusions. Expert knowledge in the blood donation process, coupled with statistical models, can help explain trends exhibited in blood donation data in Zimbabwe. These findings help the blood authorities plan for blood donor campaign drives. The findings are key indicators of where to allocate more resources toward blood donation and when to collect more blood units. The increasing blood donation projections ensure a stable blood bank inventory in the near future.

Highlights

A SARIMA model can be used to predict the flow of blood donations in Zimbabwe.The seasonal blood donation pattern peaks in the months of March, June/July, and September.The donations troughs are in the months of April, August, December, and January. These are the months coinciding with school holidays in Zimbabwe.Both the SARIMA and ETS models provided similar forecasts, but measures of fit and expert knowledge gave a slight preference to the SARIMA ( 1 , 1 , 2 ) ( 0 , 1 , 1 ) 12 model in predicting the flow of blood donations in Zimbabwe.These model results are useful for guiding allocation of blood donation resources and blood donation drive timing.

In-Advance Prediction of Pressure Ulcers via Deep-Learning-Based Robust Missing Value Imputation on Real-Time Intensive Care Variables

Pressure ulcers (PUs) are a prevalent skin disease affecting patients with impaired mobility and in high-risk groups. These ulcers increase patients' suffering, medical expenses, and burden on medical staff. This study introduces a clinical decision support system and verifies it for predicting real-time PU occurrences within the intensive care unit (ICU) by using MIMIC-IV and in-house ICU data. We develop various machine learning (ML) and deep learning (DL) models for predicting PU occurrences in real time using the MIMIC-IV and validate using the MIMIC-IV and Kangwon National University Hospital (KNUH) dataset. To address the challenge of missing values in time series, we propose a novel recurrent neural network model, GRU-D++. This model outperformed other experimental models by achieving the area under the receiver operating characteristic curve (AUROC) of 0.945 for the on-time prediction and AUROC of 0.912 for 48h in-advance prediction. Furthermore, in the external validation with the KNUH dataset, the fine-tuned GRU-D++ model demonstrated superior performances, achieving an AUROC of 0.898 for on-time prediction and an AUROC of 0.897 for 48h in-advance prediction. The proposed GRU-D++, designed to consider temporal information and missing values, stands out for its predictive accuracy. Our findings suggest that this model can significantly alleviate the workload of medical staff and prevent the worsening of patient conditions by enabling timely interventions for PUs in the ICU.

A novel approach for sports injury risk prediction: based on time-series image encoding and deep learning

The rapid development of big data technology and artificial intelligence has provided a new perspective on sports injury prevention. Although data-driven algorithms have achieved some valuable results in the field of sports injury risk assessment, the lack of sufficient generalization of models and the inability to automate feature extraction have made it challenging to deploy research results in the real world. Therefore, this study attempts to build an injury risk prediction model using a combination of time-series image encoding and deep learning algorithms to address this issue better. This study used the time-series image encoding approach for feature construction to represent relationships between values at different moments, including Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP). Deep Convolutional Auto-Encoder (DCAE) learned the image-encoded data for representation to obtain features with good discrimination, and the classifier was performed using Deep Neural Network (DNN). The results from five repeated experiments show that the GASF-DCAE-DNN model is overall better in the training (AUC: 0.985 ± 0.001, Gmean: 0.930 ± 0.007, Sensitivity: 0.997 ± 0.003, Specificity: 0.868 ± 0.013) and test sets (AUC: 0.891 ± 0.026, Gmean: 0.830 ± 0.027, Sensitivity: 0.816 ± 0.039, Specificity: 0.845 ± 0.022), with good discriminative power, robustness, and generalization ability. Compared with the best model reported in the literature, the AUC, Gmean, Sensitivity, and Specificity of the GASF-DCAE-DNN model were higher by 23.9%, 27.5%, 39.7%, and 16.2%, respectively, which confirmed the validity and practicability of the model in injury risk prediction. In addition, differences in injury risk patterns between the training and test sets were identified through shapley additivity interpretation. It was also found that the training volume was an essential factor that affected injury risk prediction. The model proposed in this study provides a powerful injury risk prediction tool for future sports injury prevention practice.

Fisher-Shannon Investigation of the Effect of Nonlinearity of Discrete Langevin Model on Behavior of Extremes in Generated Time Series

Diverse forms of nonlinearity within stochastic equations give rise to varying dynamics in processes, which may influence the behavior of extreme values. This study focuses on two nonlinear models of the discrete Langevin equation: one with a fixed diffusion function (M1) and the other with a fixed marginal distribution (M2), both characterized by a nonlinearity parameter. Extremes are defined according to the run theory with thresholds based on percentiles. The behavior of inter-extreme times and run lengths is examined by employing Fisher's Information Measure and the Shannon Entropy. Our findings reveal a clear relationship between the entropic and informational measures and the nonlinearity of model M1-these measures decrease as the nonlinearity parameter increases. Similar relationships are evident for the M2 model, albeit to a lesser extent, even though the background data's marginal distribution remains unaffected by this parameter. As thresholds increase, both the values of Fisher's Information Measure and the Shannon Entropy also increase.

Impact of the Spanish smoke-free laws on cigarette sales by brands, 2000-2021: Evidence from a club convergence approach

INTRODUCTION

In January 2006, the Spanish government enacted a tobacco control law that banned the advertising, promotion and sponsorship of tobacco. In January 2011, further legislation on this matter was adopted to provide a more restrictive specification of the ban. In this study, we analyze the effect produced on cigarette sales by these two prohibitions. We address this problem using a cluster time-series analysis to test whether the sales of cigarettes by brands have been homogenized with the prohibition of advertising, promotion, and sponsorship.

METHODS

The data source used was the official data on legal sales of cigarettes by brands in Spain, from January 2005 to December 2021 (excluding the Canary Islands and the Autonomous Communities of the cities of Ceuta and Melilla). To achieve our objective, we used log(t) test statistics to check if there is global convergence in the three selected periods according to the regulatory changes that have occurred in Spain (2005-2021, 2005-2010 and 2011-2021). Second, once absolute convergence is rejected, we applied a clustering algorithm to test for the existence of subgroup convergence.

RESULTS

The cigarette brands that have been marketed during the period 2005-2021 (n=40), can only be grouped into three groups according to the behavior of their sales. When we focus on the period 2005-2010 (n=74), cigarette brands are grouped into five groups according to their sales behavior. Finally, the cigarette brands marketed during the period 2011-2021 (n=67) are grouped into three groups according to the temporal evolution of their sales. These results suggest a greater homogenization of cigarette sales after the application of the law of January 2011.

CONCLUSIONS

Act 42/2010 (total ban on tobacco advertising, promotion, and sponsorship actions) was associated with greater homogenization of cigarette sales than the application of Act 28/2005 (partial ban). This finding supports what is established in the previous literature that indicates that Act 42/2010 provided a more restrictive specification of the ban than Act 28/2005.

Forecasting local hospital bed demand for COVID-19 using on-request simulations

Accurate forecasting of hospital bed demand is crucial during infectious disease epidemics to avoid overwhelming healthcare facilities. To address this, we developed an intuitive online tool for individual hospitals to forecast COVID-19 bed demand. The tool utilizes local data, including incidence, vaccination, and bed occupancy data, at customizable geographical resolutions. Users can specify their hospital's catchment area and adjust the initial number of COVID-19 occupied beds. We assessed the model's performance by forecasting ICU bed occupancy for several university hospitals and regions in Germany. The model achieves optimal results when the selected catchment area aligns with the hospital's local catchment. While expanding the catchment area reduces accuracy, it improves precision. However, forecasting performance diminishes during epidemic turning points. Incorporating variants of concern slightly decreases precision around turning points but does not significantly impact overall bed occupancy results. Our study highlights the significance of using local data for epidemic forecasts. Forecasts based on the hospital's specific catchment area outperform those relying on national or state-level data, striking a better balance between accuracy and precision. These hospital-specific bed demand forecasts offer valuable insights for hospital planning, such as adjusting elective surgeries to create additional bed capacity promptly.

Response trait diversity and species asynchrony underlie the diversity-stability relationship in Romanian bird communities

Biodiversity-stability relationships have frequently been studied in ecology, with the recent integration of traits to explain community stability over time. Classical theory underlying the biodiversity-stability relationship posits that different species' responses to the environment should stabilise community-level properties (e.g. biomass or abundance) through compensatory dynamics. However, functional response traits, which aim to predict how species respond to environmental change, are still rarely integrated into studies of ecological stability. Such traits should mechanistically drive community stability, both in terms of community abundance (functional variability) and composition (compositional variability). In turn, whether and how functional or compositional stability scales to affect temporal variation in functional effect traits (a proxy for ecosystem functioning) remains largely unknown, but is key to consistent ecosystem functioning under environmental change. Here, we explore the diversity-stability relationship in bird communities using annual survey data across 98 sites in central Romania, in combination with global trait databases and structural equation models. We show that higher response trait diversity promotes compositional variability directly, and functional variability indirectly via species asynchrony. In turn, functional variability impacts the temporal stability of effect trait diversity. Multiple facets of diversity and community stability differ between natural forests and agricultural or human-dominated survey sites, and the relationship between response diversity and functional variability is mediated by land cover. Further integration of response-and-effect trait frameworks into studies of community stability will enhance understanding of the drivers of biodiversity change, allowing targeted conservation decision-making with a focus on stable ecosystem functioning in the face of global environmental change.

Trends in mental health inequalities for people with disability, Australia 2003 to 2020

OBJECTIVE

Cross-sectional studies have demonstrated that people with disability have substantial inequalities in mental health compared to people without disability. However, it is not known if these inequalities have changed over time. This study compared the mental health of people with and without disability annually from 2003 to 2020 to investigate time trends in disability-related mental health inequalities.

METHODS

We use annual data (2003-2020) of the Household, Income and Labour Dynamics in Australia Survey. Mental health was measured using the five-item Mental Health Inventory. For each wave, we calculated population-weighted age-standardised estimates of mean Mental Health Inventory scores for people with and without disability and calculated the mean difference in Mental Health Inventory score to determine inequalities. Analyses were stratified by age, sex and disability group (sensory or speech, physical, intellectual or learning, psychological, brain injury or stroke, other).

RESULTS

From 2003 to 2020, people with disability had worse mental health than people without disability, with average Mental Health Inventory scores 9.8 to 12.1 points lower than for people without disability. For both people with and without disability, Mental Health Inventory scores decreased, indicating worsening mental health, reaching the lowest point for both groups in 2020. For some subpopulations, including young females and people with intellectual disability, brain injury or stroke, mental health inequalities worsened.

CONCLUSION

This paper confirms that people with disability experience worse mental health than people without disability. We add to previous findings by demonstrating that disability-related inequalities in mental health have been sustained for a long period and are worsening in some subpopulations.

Short-term effects of air pollution exposure on the risk of preterm birth in Xi'an, China

INTRODUCTION

Long-term exposure to air pollution is known to be harmful to preterm birth (PTB), but little is known about the short-term effects. This study aims to quantify the short-term effect of particulate matter with aerodynamic diameter ≤ 2.5 μm (PM_2.5), ≤10 μm (PM₁₀) and nitrogen dioxide (NO₂) on PTB.

MATERIALS AND METHODS

A total of 18,826 singleton PTBs were collected during the study period. Poisson regression model combined with the distributed lag non-linear model was applied to evaluate the short-term effects of PTBs and air pollutants.

RESULTS

Maternal exposure to NO₂ was significantly associated increased risk of PTB at Lag1 (RR: 1.025, 95%CI: 1.003-1.047). In the moving average model, maternal exposure to NO₂ significantly increased the risk of PTB at Lag01 (RR: 1.029, 95%CI: 1.004-1.054). In the cumulative model, maternal exposure to NO₂ significant increased the risk of PTB at Cum01 (RR:1.026, 95%CI: 1.002-1.051), Cum02 (RR: 1.030, 95%CI: 1.003-1.059), and Cum03 (RR: 1.033, 95%CI: 1.002-1.066). The effects of PM_2.5, PM₁₀ and NO₂ on PTB were significant and greater in the cold season than the warm season.

CONCLUSIONS

Maternal exposure to NO₂, PM_2.5 and PM₁₀ before delivery has a significant risk for PTB, particularly in the cold season.Key messagesMaternal exposure to NO₂ was significant associated with an increased risk of preterm birth at the day 1 before delivery.Particle matter (PM_2.5 and PM₁₀) showed a significant short-term effect on preterm birth in the cold season.The effects of air pollutants on preterm birth was greater in the cold season compared with the warm season.

CALDA: Improving Multi-Source Time Series Domain Adaptation With Contrastive Adversarial Learning

Unsupervised domain adaptation (UDA) provides a strategy for improving machine learning performance in data-rich (target) domains where ground truth labels are inaccessible but can be found in related (source) domains. In cases where meta-domain information such as label distributions is available, weak supervision can further boost performance. We propose a novel framework, CALDA, to tackle these two problems. CALDA synergistically combines the principles of contrastive learning and adversarial learning to robustly support multi-source UDA (MS-UDA) for time series data. Similar to prior methods, CALDA utilizes adversarial learning to align source and target feature representations. Unlike prior approaches, CALDA additionally leverages cross-source label information across domains. CALDA pulls examples with the same label close to each other, while pushing apart examples with different labels, reshaping the space through contrastive learning. Unlike prior contrastive adaptation methods, CALDA requires neither data augmentation nor pseudo labeling, which may be more challenging for time series. We empirically validate our proposed approach. Based on results from human activity recognition, electromyography, and synthetic datasets, we find utilizing cross-source information improves performance over prior time series and contrastive methods. Weak supervision further improves performance, even in the presence of noise, allowing CALDA to offer generalizable strategies for MS-UDA.

Sample sizes for estimating the sensitivity of a monitoring system that generates repeated binary outcomes with autocorrelation

Sample size formulas are provided to determine how many events and how many patient care units are needed to estimate the sensitivity of a monitoring system. The monitoring systems we consider generate time series binary data that are autocorrelated and clustered by patient care units. Our application of interest is an automated hand hygiene monitoring system that assesses whether healthcare workers perform hand hygiene when they should. We apply an autoregressive order 1 mixed effects logistic regression model to determine sample sizes that allow the sensitivity of the monitoring system to be estimated at a specified confidence level and margin of error. This model overcomes a major limitation of simpler approaches that fail to provide confidence intervals with the specified levels of confidence when the sensitivity of the monitoring system is above 90%.

Physical Activity Pattern of Adults With Metabolic Syndrome Risk Factors: Time-Series Cluster Analysis

Background

Physical activity plays a crucial role in maintaining a healthy lifestyle, and wrist-worn wearables, such as smartwatches and smart bands, have become popular tools for measuring activity levels in daily life. However, studies on physical activity using wearable devices have limitations; for example, these studies often rely on a single device model or use improper clustering methods to analyze the wearable data that are extracted from wearable devices.

Objective

This study aimed to identify methods suitable for analyzing wearable data and determining daily physical activity patterns. This study also explored the association between these physical activity patterns and health risk factors.

Methods

People aged >30 years who had metabolic syndrome risk factors and were using their own wrist-worn devices were included in this study. We collected personal health data through a web-based survey and measured physical activity levels using wrist-worn wearables over the course of 1 week. The Time-Series Anytime Density Peak (TADPole) clustering method, which is a novel time-series method proposed recently, was used to identify the physical activity patterns of study participants. Additionally, we defined physical activity pattern groups based on the similarity of physical activity patterns between weekdays and weekends. We used the χ2 or Fisher exact test for categorical variables and the 2-tailed t test for numerical variables to find significant differences between physical activity pattern groups. Logistic regression models were used to analyze the relationship between activity patterns and health risk factors.

Results

A total of 47 participants were included in the analysis, generating a total of 329 person-days of data. We identified 2 different types of physical activity patterns (early bird pattern and night owl pattern) for weekdays and weekends. The physical activity levels of early birds were less than that of night owls on both weekdays and weekends. Additionally, participants were categorized into stable and shifting groups based on the similarity of physical activity patterns between weekdays and weekends. The physical activity pattern groups showed significant differences depending on age (P=.004) and daily energy expenditure (P<.001 for weekdays; P=.003 for weekends). Logistic regression analysis revealed a significant association between older age (≥40 y) and shifting physical activity patterns (odds ratio 8.68, 95% CI 1.95-48.85; P=.007).

Conclusions

This study overcomes the limitations of previous studies by using various models of wrist-worn wearables and a novel time-series clustering method. Our findings suggested that age significantly influenced physical activity patterns. It also suggests a potential role of the TADPole clustering method in the analysis of large and multidimensional data, such as wearable data.

Long-term data reveal contrasting impacts of native versus invasive nest predators in Iceland

Bird species on islands are strongly impacted by biological invasions, with the Icelandic common eider (Somateria mollissima borealis) being particularly threatened. Down collection by local families in Breiðafjörður, West Iceland, provided long-term datasets of nests from two archipelagos, covering 95 islands over 123 years and 39 islands over 27 years, respectively. Using these exceptional datasets, we found that the arrival of the invasive semi-aquatic American mink (Neogale vison) was a more impactful driver of population dynamics than climate. This invasive predator heavily reduced eider nest numbers by ca. 60% in the Brokey archipelago. In contrast, we detected an apparently adaptive response to the return of the native fox in the Purkey archipelago, with dense nests on islands inaccessible to the fox and no apparent impact on eider populations. This difference might be due to the eiders lacking a joint evolutionary history with the mink and therefore lacking appropriate antipredator responses.

Exploring Whether the Electronic Optimization of Routine Health Assessments Can Increase Testing for Sexually Transmitted Infections and Provider Acceptability at an Aboriginal Community Controlled Health Service: Mixed Methods Evaluation

BACKGROUND

In the context of a syphilis outbreak in neighboring states, a multifaceted systems change to increase testing for sexually transmitted infections (STIs) among young Aboriginal people aged 15 to 29 years was implemented at an Aboriginal Community Controlled Health Service (ACCHS) in New South Wales, Australia. The components included electronic medical record prompts and automated pathology test sets to increase STI testing in annual routine health assessments, the credentialing of nurses and Aboriginal health practitioners to conduct STI tests independently, pathology request forms presigned by a physician, and improved data reporting.

OBJECTIVE

We aimed to determine whether the systems change increased the integration of STI testing into routine health assessments by clinicians between April 2019 and March 2020, the inclusion of syphilis tests in STI testing, and STI testing uptake overall. We also explored the understandings of factors contributing to the acceptability and normalization of the systems change among staff.

METHODS

We used a mixed methods design to evaluate the effectiveness and acceptability of the systems change implemented in 2019. We calculated the annual proportion of health assessments that included tests for chlamydia, gonorrhea, and syphilis, as well as an internal control (blood glucose level). We conducted an interrupted time series analysis of quarterly proportions 24 months before and 12 months after the systems change and in-depth semistructured interviews with ACCHS staff using normalization process theory.

RESULTS

Among 2461 patients, the annual proportion of health assessments that included any STI test increased from 16% (38/237) in the first year of the study period to 42.9% (94/219) after the implementation of the systems change. There was an immediate and large increase when the systems change occurred (coefficient=0.22; P=.003) with no decline for 12 months thereafter. The increase was greater for male individuals, with no change for the internal control. Qualitative data indicated that nurse- and Aboriginal health practitioner-led testing and presigned pathology forms proved more difficult to normalize than electronic prompts and shortcuts. The interviews identified that staff understood the modifications to have encouraged cultural change around the role of sexual health care in routine practice.

CONCLUSIONS

This study provides evidence for the first time that optimizing health assessments electronically is an effective and acceptable strategy to increase and sustain clinician integration and the completeness of STI testing among young Aboriginal people attending an ACCHS. Future strategies should focus on increasing the uptake of health assessments and promote whole-of-service engagement and accountability.

The impact of healthy pregnancy on features of heart rate variability and pulse wave morphology derived from wrist-worn photoplethysmography

Due to the association between dysfunctional maternal autonomic regulation and pregnancy complications, tracking non-invasive features of autonomic regulation derived from wrist-worn photoplethysmography (PPG) measurements may allow for the early detection of deteriorations in maternal health. However, even though a plethora of these features-specifically, features describing heart rate variability (HRV) and the morphology of the PPG waveform (morphological features)-exist in the literature, it is unclear which of these may be valuable for tracking maternal health. As an initial step towards clarity, we compute comprehensive sets of HRV and morphological features from nighttime PPG measurements. From these, using logistic regression and stepwise forward feature elimination, we identify the features that best differentiate healthy pregnant women from non-pregnant women, since these likely capture physiological adaptations necessary for sustaining healthy pregnancy. Overall, morphological features were more valuable for discriminating between pregnant and non-pregnant women than HRV features (area under the receiver operating characteristics curve of 0.825 and 0.74, respectively), with the systolic pulse wave deterioration being the most valuable single feature, followed by mean heart rate (HR). Additionally, we stratified the analysis by sleep stages and found that using features calculated only from periods of deep sleep enhanced the differences between the two groups. In conclusion, we postulate that in addition to HRV features, morphological features may also be useful in tracking maternal health and suggest specific features to be included in future research concerning maternal health.

Interrupted time series analysis of drug overdose fatalities in service-related industries versus non-service-related industries during the COVID-19 pandemic, 2018-2021

BACKGROUND

Variation among industries in the association between COVID-19-related closing or reopening orders and drug overdose deaths is unknown. The objectives of this study were to compare drug overdose decedent demographics, annual drug overdose fatality rates and monthly drug overdose fatality rates by specific industry within the service-related industry sector, and to perform an interrupted time series analysis comparing weekly drug overdose mortality counts in service-related and non-service-related industries, examining the COVID-19 pre-pandemic and pandemic phases by Kentucky closing and reopening orders.

METHODS

Kentucky drug overdose death certificate and toxicology testing data for years 2018-2021 were analysed using Χ2 and interrupted time series methods.

RESULTS

Before the pandemic, annual drug overdose fatality rates in service-related industries were higher than in non-service-related industries. However, these trends reversed during the pandemic. Both service-related and non-service-related industry groups experienced increased fatal drug overdoses at change points associated with the gubernatorial business closure orders, although the magnitude of the increase differed between the two groups. Young, female and black workers in service-related industries had higher frequencies of drug overdose deaths compared with decedents in the non-service-related industries.

CONCLUSION

Spikes in drug overdose mortality in both service-related and non-service-related industries during the pandemic highlight the need to consider and include industries and occupations, as well as worker populations vulnerable to infectious diseases, as integral stakeholder groups when developing and implementing drug overdose prevention interventions, and implementing infectious disease surveillance systems.

Time-resolved proteomic profiling reveals compositional and functional transitions across the stress granule life cycle

Stress granules (SGs) are dynamic, membrane-less organelles. With their formation and disassembly processes characterized, it remains elusive how compositional transitions are coordinated during prolonged stress to meet changing functional needs. Here, using time-resolved proteomic profiling of the acute to prolonged heat-shock SG life cycle, we identify dynamic SG proteins, further segregated into early and late proteins. Comparison of different groups of SG proteins suggests that their biochemical properties help coordinate SG compositional and functional transitions. In particular, early proteins, with high phase-separation-propensity, drive the rapid formation of the initial SG platform, while late proteins are subsequently recruited as discrete modules to further functionalize SGs. This model, supported by immunoblotting and immunofluorescence imaging, provides a conceptual framework for the compositional transitions throughout the acute to prolonged SG life cycle. Additionally, an early SG constituent, non-muscle myosin II, is shown to promote SG formation by increasing SG fusion, underscoring the strength of this dataset in revealing the complexity of SG regulation.

SPARSE 1.0: a template for databases of species inventories, with an open example of Czech birds

Here, we introduce SPARSE (acronym for "SPecies AcRoss ScalEs"), a simple and portable template for databases that can store data on species composition derived from ecological inventories, surveys and checklists, with emphasis on metadata describing sampling effort and methods. SPARSE can accommodate resurveys and time series and data from different spatial scales, as well as complex sampling designs. SPARSE focuses on inventories that report multiple species for a given site, together with sampling methods and effort, which can be used in statistical models of true probability of occurrence of species. SPARSE is spatially explicit and can accommodate nested spatial structures from multiple spatial scales, including sampling designs where multiple sites within a larger area have been surveyed and the larger area can again be nested in an even larger region. Each site in SPARSE is represented either by a point, line (for transects) or polygon, stored in an ESRI shapefile. SPARSE implements a new combination of our own field definitions with Darwin Core biodiversity data standard and its Humboldt core extension. The use of Humboldt core also makes SPARSE suitable for biodiversity data with temporal replication. We provide an example use of the SPARSE framework by digitising data on birds from the Czech Republic, from 348 sites and 524 sampling events, with 15,969 unique species-per-event observations of presence, abundance or population density. To facilitate use without the need for a high-level database expertise, the Czech bird example is implemented as MS Access .accdb file, but can be ported to other database engines. The example of Czech birds complements other bird datasets from the Czech Republic, specifically the four gridded national atlases and the breeding bird survey which cover a similar temporal extent, but different locations and spatial scales.

A semiconductor 96-microplate platform for electrical-imaging based high-throughput phenotypic screening

High-content imaging for compound and genetic profiling is popular for drug discovery but limited to endpoint images of fixed cells. Conversely, electronic-based devices offer label-free, live cell functional information but suffer from limited spatial resolution or throughput. Here, we introduce a semiconductor 96-microplate platform for high-resolution, real-time impedance imaging. Each well features 4096 electrodes at 25 µm spatial resolution and a miniaturized data interface allows 8× parallel plate operation (768 total wells) for increased throughput. Electric field impedance measurements capture >20 parameter images including cell barrier, attachment, flatness, and motility every 15 min during experiments. We apply this technology to characterize 16 cell types, from primary epithelial to suspension cells, and quantify heterogeneity in mixed co-cultures. Screening 904 compounds across 13 semiconductor microplates reveals 25 distinct responses, demonstrating the platform's potential for mechanism of action profiling. The scalability and translatability of this semiconductor platform expands high-throughput mechanism of action profiling and phenotypic drug discovery applications.

Multiple change point analysis of hepatitis B reports in Xinjiang, China from 2006 to 2021

Objective

Hepatitis B (HB) is a major global challenge, but there has been a lack of epidemiological studies on HB incidence in Xinjiang from a change-point perspective. This study aims to bridge this gap by identifying significant change points and trends.

Method

The datasets were obtained from the Xinjiang Information System for Disease Control and Prevention. Change points were identified using binary segmentation for full datasets and a segmented regression model for five age groups.

Results

The results showed four change points for the quarterly HB time series, with the period between the first change point (March 2007) and the second change point (March 2010) having the highest mean number of HB reports. In the subsequent segments, there was a clear downward trend in reported cases. The segmented regression model showed different numbers of change points for each age group, with the 30-50, 51-80, and 15-29 age groups having higher growth rates.

Conclusion

Change point analysis has valuable applications in epidemiology. These findings provide important information for future epidemiological studies and early warning systems for HB.

Constructing temporal networks with bursty activity patterns

Human social interactions tend to vary in intensity over time, whether they are in person or online. Variable rates of interaction in structured populations can be described by networks with the time-varying activity of links and nodes. One of the key statistics to summarize temporal patterns is the inter-event time, namely the duration between successive pairwise interactions. Empirical studies have found inter-event time distributions that are heavy-tailed, for both physical and digital interactions. But it is difficult to construct theoretical models of time-varying activity on a network that reproduce the burstiness seen in empirical data. Here we develop a spanning-tree method to construct temporal networks and activity patterns with bursty behavior. Our method ensures any desired target inter-event time distributions for individual nodes and links, provided the distributions fulfill a consistency condition, regardless of whether the underlying topology is static or time-varying. We show that this model can reproduce burstiness found in empirical datasets, and so it may serve as a basis for studying dynamic processes in real-world bursty interactions.

Human behavior recognition based on sparse transformer with channel attention mechanism

Human activity recognition (HAR) has recently become a popular research field in the wearable sensor technology scene. By analyzing the human behavior data, some disease risks or potential health issues can be detected, and patients' rehabilitation progress can be evaluated. With the excellent performance of Transformer in natural language processing and visual tasks, researchers have begun to focus on its application in time series. The Transformer model models long-term dependencies between sequences through self-attention mechanisms, capturing contextual information over extended periods. In this paper, we propose a hybrid model based on the channel attention mechanism and Transformer model to improve the feature representation ability of sensor-based HAR tasks. Extensive experiments were conducted on three public HAR datasets, and the results show that our network achieved accuracies of 98.10%, 97.21%, and 98.82% on the HARTH, PAMAP2, and UCI-HAR datasets, respectively, The overall performance is at the level of the most advanced methods.

Conditional Poisson Regression with Random Effects for the Analysis of Multi-site Time Series Studies

The analysis of time series studies linking daily counts of a health indicator with environmental variables (e.g., mortality or hospital admissions with air pollution concentrations or temperature; or motor vehicle crashes with temperature) is usually conducted with Poisson regression models controlling for long-term and seasonal trends using temporal strata. When the study includes multiple zones, analysts usually apply a two-stage approach: first, each zone is analyzed separately, and the resulting zone-specific estimates are then combined using meta-analysis. This approach allows zone-specific control for trends. A one-stage approach uses spatio-temporal strata and could be seen as a particular case of the case-time series framework recently proposed. However, the number of strata can escalate very rapidly in a long time series with many zones. A computationally efficient alternative is to fit a conditional Poisson regression model, avoiding the estimation of the nuisance strata. To allow for zone-specific effects, we propose a conditional Poisson regression model with a random slope, although available frequentist software does not implement this model. Here, we implement our approach in the Bayesian paradigm, which also facilitates the inclusion of spatial patterns in the effect of interest. We also provide a possible extension to deal with overdispersed data. We first introduce the equations of the framework and then illustrate their application to data from a previously published study on the effects of temperature on the risk of motor vehicle crashes. We provide R code and a semi-synthetic dataset to reproduce all analyses presented.

Retrospective analysis of enhancer activity and transcriptome history

Cell state changes in development and disease are controlled by gene regulatory networks, the dynamics of which are difficult to track in real time. In this study, we used an inducible DCM-RNA polymerase subunit b fusion protein which labels active genes and enhancers with a bacterial methylation mark that does not affect gene transcription and is propagated in S-phase. This DCM-RNA polymerase fusion protein enables transcribed genes and active enhancers to be tagged and then examined at later stages of development or differentiation. We apply this DCM-time machine (DCM-TM) technology to study intestinal homeostasis, revealing rapid and coordinated activation of enhancers and nearby genes during enterocyte differentiation. We provide new insights in absorptive-secretory lineage decision-making in intestinal stem cell (ISC) differentiation and show that ISCs retain a unique chromatin landscape required to maintain ISC identity and delineate future expression of differentiation-associated genes. DCM-TM has wide applicability in tracking cell states, providing new insights in the regulatory networks underlying cell state changes.

Trend of the Tuberculous Pleurisy Notification Rate in Eastern China During 2017-2021: Spatiotemporal Analysis

BACKGROUND

Tuberculous pleurisy (TP) presents a serious allergic reaction in the pleura caused by Mycobacterium tuberculosis; however, few studies have described its spatial epidemiological characteristics in eastern China.

OBJECTIVE

This study aimed to determine the epidemiological distribution of TP and predict its further development in Zhejiang Province.

METHODS

Data on all notified cases of TP in Zhejiang Province, China, from 2017 to 2021 were collected from the existing tuberculosis information management system. Analyses, including spatial autocorrelation and spatial-temporal scan analysis, were performed to identify hot spots and clusters, respectively. The prediction of TP prevalence was performed using the seasonal autoregressive integrated moving average (SARIMA), Holt-Winters exponential smoothing, and Prophet models using R (The R Foundation) and Python (Python Software Foundation).

RESULTS

The average notification rate of TP in Zhejiang Province was 7.06 cases per 100,000 population, peaking in the summer. The male-to-female ratio was 2.18:1. In terms of geographical distribution, clusters of cases were observed in the western part of Zhejiang Province, including parts of Hangzhou, Quzhou, Jinhua, Lishui, Wenzhou, and Taizhou city. Spatial-temporal analysis identified 1 most likely cluster and 4 secondary clusters. The Holt-Winters model outperformed the SARIMA and Prophet models in predicting the trend in TP prevalence.

CONCLUSIONS

The western region of Zhejiang Province had the highest risk of TP. Comprehensive interventions, such as chest x-ray screening and symptom screening, should be reinforced to improve early identification. Additionally, a more systematic assessment of the prevalence trend of TP should include more predictors.

Reconstructing disease dynamics for mechanistic insights and clinical benefit

Diseases change over time, both phenotypically and in their underlying molecular processes. Though understanding disease progression dynamics is critical for diagnostics and treatment, capturing these dynamics is difficult due to their complexity and the high heterogeneity in disease development between individuals. We present TimeAx, an algorithm which builds a comparative framework for capturing disease dynamics using high-dimensional, short time-series data. We demonstrate the utility of TimeAx by studying disease progression dynamics for multiple diseases and data types. Notably, for urothelial bladder cancer tumorigenesis, we identify a stromal pro-invasion point on the disease progression axis, characterized by massive immune cell infiltration to the tumor microenvironment and increased mortality. Moreover, the continuous TimeAx model differentiates between early and late tumors within the same tumor subtype, uncovering molecular transitions and potential targetable pathways. Overall, we present a powerful approach for studying disease progression dynamics-providing improved molecular interpretability and clinical benefits for patient stratification and outcome prediction.

Circadian-like patterns in electrochemical skin conductance measured from home-based devices: a retrospective study

In this study, we investigated the potential of electrochemical skin conductance (ESC) measurements gathered from home-based devices to detect circadian-like patterns. We analyzed data from 43,284 individuals using the Withings Body Comp or Body Scan scales, which provide ESC measurements. Our results highlighted a circadian pattern of ESC values across different age groups and countries. Our findings suggest that home-based ESC measurements could be used to evaluate circadian rhythm disorders associated with neuropathies and contribute to a better understanding of their pathophysiology. However, further controlled studies are needed to confirm these results. This study highlights the potential of digital health devices to generate new scientific and medical knowledge.

Incidence of Infections, Explantations, and Displacements/Mechanical Complications of Spinal Cord Stimulation During the Past Eight Years

OBJECTIVES

The overall awareness and potential of real-world data have drastically increased in the medical field, with potential implications for postmarket medical device surveillance. The goal of this study was to evaluate real-world data on incidence of infections, explantations, and displacements/mechanical complications of spinal cord stimulation (SCS) during the past eight years and to forecast point estimates for the upcoming three years on the basis of the identified patterns.

MATERIALS AND METHODS

Based on electronic health records from 80 healthcare organizations within the TriNetX data base in the USA, data of 11,934 patients who received SCS as treatment for persistent spinal pain syndrome type 2 (PSPS T2) were extracted. Events of interest were explantations and displacements/mechanical complications of both the lead and implanted pulse generator (IPG), in addition to infection rates from 2015 to 2022. Mann-Kendall tests were performed to detect monotonic trends in the time series. Forecasts were conducted for the upcoming three years for every event of interest.

RESULTS

Statistically significant increasing time trends were revealed for the annual incidence of IPG and lead displacements/mechanical complications in patients with PSPS T2 over the past eight years. These time trends were visible in both male and female patients and in smokers and nonsmokers. For annual incidence of explantations and infections, no significant time effect was observed. In 2025, the incidence of displacements/mechanical complications of the lead (3.07%) is predicted to be the highest, followed by explantations of the IPG (2.67%) and lead (2.02%).

CONCLUSIONS

Based on real world data, device explantation was the most frequent event of interest, with negative peaks in the time series in 2016 and 2020, presumably due to the introduction of rechargeable pulse generators and to the COVID-19 pandemic, respectively.

TCF-Trans: Temporal Context Fusion Transformer for Anomaly Detection in Time Series

Anomaly detection tasks involving time-series signal processing have been important research topics for decades. In many real-world anomaly detection applications, no specific distributions fit the data, and the characteristics of anomalies are different. Under these circumstances, the detection algorithm requires excellent learning ability of the data features. Transformers, which apply the self-attention mechanism, have shown outstanding performances in modelling long-range dependencies. Although Transformer based models have good prediction performance, they may be influenced by noise and ignore some unusual details, which are significant for anomaly detection. In this paper, a novel temporal context fusion framework: Temporal Context Fusion Transformer (TCF-Trans), is proposed for anomaly detection tasks with applications to time series. The original feature transmitting structure in the decoder of Informer is replaced with the proposed feature fusion decoder to fully utilise the features extracted from shallow and deep decoder layers. This strategy prevents the decoder from missing unusual anomaly details while maintaining robustness from noises inside the data. Besides, we propose the temporal context fusion module to adaptively fuse the generated auxiliary predictions. Extensive experiments on public and collected transportation datasets validate that the proposed framework is effective for anomaly detection in time series. Additionally, the ablation study and a series of parameter sensitivity experiments show that the proposed method maintains high performance under various experimental settings.

State Space Mixture Modeling: Finding People with Similar Patterns of Change

Increasingly, behavioral scientists encounter data where several individuals were measured on multiple variables over numerous occasions. Many current methods combine these data, assuming all individuals are randomly equivalent. An extreme alternative assumes no one is randomly equivalent. We propose state space mixture modeling as one possible compromise. State space mixture modeling assumes that unknown groups of people exist who share the same parameters of a state space model, and simultaneously estimates both the state space parameters and group membership. The goal is to find people that are undergoing similar change processes over time. The present work demonstrates state space mixture modeling on a simulated data set, and summarizes the results from a large simulation study. The illustration shows how the analysis is conducted, whereas the simulation provides evidence of its general validity and applicability. In the simulation study, sample size had the greatest influence on parameter estimation and the dimension of the change process had the greatest impact on correctly grouping people together, likely due to the distinctiveness of their patterns of change. State space mixture modeling offers one of the best-performing methods for simultaneously drawing conclusions about individual change processes while also analyzing multiple people.

ECG Synthesis via Diffusion-Based State Space Augmented Transformer

Cardiovascular diseases (CVDs) are a major global health concern, causing significant morbidity and mortality. AI's integration with healthcare offers promising solutions, with data-driven techniques, including ECG analysis, emerging as powerful tools. However, privacy concerns pose a major barrier to distributing healthcare data for addressing data-driven CVD classification. To address confidentiality issues related to sensitive health data distribution, we propose leveraging artificially synthesized data generation. Our contribution introduces a novel diffusion-based model coupled with a State Space Augmented Transformer. This synthesizes conditional 12-lead electrocardiograms based on the 12 multilabeled heart rhythm classes of the PTB-XL dataset, with each lead depicting the heart's electrical activity from different viewpoints. Recent advances establish diffusion models as groundbreaking generative tools, while the State Space Augmented Transformer captures long-term dependencies in time series data. The quality of generated samples was assessed using metrics like Dynamic Time Warping (DTW) and Maximum Mean Discrepancy (MMD). To evaluate authenticity, we assessed the similarity of performance of a pre-trained classifier on both generated and real ECG samples.

Fluorescent Photoelectric Detection of Peroxide Explosives Based on a Time Series Similarity Measurement Method

Due to the characteristics of peroxide explosives, which are difficult to detect via conventional detection methods and have high explosive power, a fluorescent photoelectric detection system based on fluorescence detection technology was designed in this study to achieve the high-sensitivity detection of trace peroxide explosives in practical applications. Through actual measurement experiments and numerical simulation methods, the derivative dynamic time warping (DDTW) algorithm and the Spearman correlation coefficient were used to calculate the DDTW-Spearman distance to achieve time series correlation measurements. The detection sensitivity of triacetone triperoxide (TATP) and H2O2 was studied, and the detection of organic substances of acetone, acetylene, ethanol, ethyl acetate, and petroleum ether was carried out. The stability and specific detection ability of the fluorescent photoelectric detection system were determined. The research results showed that the fluorescence photoelectric detection system can effectively identify the detection data of TATP, H2O2, acetone, acetonitrile, ethanol, ethyl acetate, and petroleum ether. The detection limit of 0.01 mg/mL of TATP and 0.0046 mg/mL of H2O2 was less than 10 ppb. The time series similarity measurement method improves the analytical capabilities of fluorescence photoelectric detection technology.

Remotely sensed localised primary production anomalies predict the burden and community structure of infection in long-term rodent datasets

The increasing frequency and cost of zoonotic disease emergence due to global change have led to calls for the primary surveillance of wildlife. This should be facilitated by the ready availability of remotely sensed environmental data, given the importance of the environment in determining infectious disease dynamics. However, there has been little evaluation of the temporal predictiveness of remotely sensed environmental data for infection reservoirs in vertebrate hosts due to a deficit of corresponding high-quality long-term infection datasets. Here we employ two unique decade-spanning datasets for assemblages of infectious agents, including zoonotic agents, in rodents in stable habitats. Such stable habitats are important, as they provide the baseline sets of pathogens for the interactions within degrading habitats that have been identified as hotspots for zoonotic emergence. We focus on the enhanced vegetation index (EVI), a measure of vegetation greening that equates to primary productivity, reasoning that this would modulate infectious agent populations via trophic cascades determining host population density or immunocompetence. We found that EVI, in analyses with data standardised by site, inversely predicted more than one-third of the variation in an index of infectious agent total abundance. Moreover, in bipartite host occupancy networks, weighted network statistics (connectance and modularity) were linked to total abundance and were also predicted by EVI. Infectious agent abundance and, perhaps, community structure are likely to influence infection risk and, in turn, the probability of transboundary emergence. Thus, the present results, which were consistent in disparate forest and desert systems, provide proof-of-principle that within-site fluctuations in satellite-derived greenness indices can furnish useful forecasting that could focus primary surveillance. In relation to the well-documented global greening trend of recent decades, the present results predict declining infection burden in wild vertebrates in stable habitats; but if greening trends were to be reversed, this might magnify the already upwards trend in zoonotic emergence.

Using Dynamic Structural Equation Modeling to Examine Between- and Within-Persons Factor Structure of the DASS-21

The recent integration of traditional time series analysis and confirmatory factor analysis techniques allows researchers to evaluate the psychometric properties of measurement instruments at between- and within-persons levels while accounting for autoregressive dependencies. The current study applies a dynamic structural equation modeling (SEM) latent factor analysis (i.e., DSEM-CFA) to a sample of 333 individuals who completed the DASS-21 at their regular therapy sessions. The results of the DSEM-CFA illuminate the reliability, invariance, and structural features of each DASS-21 subscale both between and within persons. The results suggest that the DASS-21 reliably measures depression, anxiety, and stress symptoms when evaluating differences between persons, but does not reliably assess within-persons fluctuations in symptoms over time. The results also suggest that currently accepted methods of modeling sensitivity to change within an instrument are likely lacking and the DSEM-CFA provides insight into reliability within and between persons, which is extremely important for instruments used across time.

Longitudinal Plant Health Monitoring via High-Resolution Mass Spectrometry Screening Workflows: Application to a Fertilizer Mediated Tomato Growth Experiment

Significant efforts have been spent in the modern era towards implementing environmentally friendly procedures like composting to mitigate the negative effects of intensive agricultural practices. In this context, a novel fertilizer was produced via the hydrolysis of an onion-derived compost, and has been previously comprehensively chemically characterized. In order to characterize its efficacy, the product was applied to tomato plants at five time points to monitor plant health and growth. Control samples were also used at each time point to eliminate confounding parameters due to the plant's normal growth process. After harvesting, the plant leaves were extracted using aq. MeOH (70:30, v/v) and analyzed via UPLC-QToF-MS, using a C18 column in both ionization modes (±ESI). The data-independent (DIA/bbCID) acquisition mode was employed, and the data were analyzed by MS-DIAL. Statistical analysis, including multivariate and trend analysis for longitudinal monitoring, were employed to highlight the differentiated features among the controls and treated plants as well as the time-point sequence. Metabolites related to plant growth belonging to several chemical classes were identified, proving the efficacy of the fertilizer product. Furthermore, the efficiency of the analytical and statistical workflows utilized was demonstrated.

BioNetGMMFit: estimating parameters of a BioNetGen model from time-stamped snapshots of single cells

Mechanistic models are commonly employed to describe signaling and gene regulatory kinetics in single cells and cell populations. Recent advances in single-cell technologies have produced multidimensional datasets where snapshots of copy numbers (or abundances) of a large number of proteins and mRNA are measured across time in single cells. The availability of such datasets presents an attractive scenario where mechanistic models are validated against experiments, and estimated model parameters enable quantitative predictions of signaling or gene regulatory kinetics. To empower the systems biology community to easily estimate parameters accurately from multidimensional single-cell data, we have merged a widely used rule-based modeling software package BioNetGen, which provides a user-friendly way to code for mechanistic models describing biochemical reactions, and the recently introduced CyGMM, that uses cell-to-cell differences to improve parameter estimation for such networks, into a single software package: BioNetGMMFit. BioNetGMMFit provides parameter estimates of the model, supplied by the user in the BioNetGen markup language (BNGL), which yield the best fit for the observed single-cell, time-stamped data of cellular components. Furthermore, for more precise estimates, our software generates confidence intervals around each model parameter. BioNetGMMFit is capable of fitting datasets of increasing cell population sizes for any mechanistic model specified in the BioNetGen markup language. By streamlining the process of developing mechanistic models for large single-cell datasets, BioNetGMMFit provides an easily-accessible modeling framework designed for scale and the broader biochemical signaling community.

Use of recurrent neural networks considering maintenance to predict urban road performance in Beijing, China

A correct understanding of the pavement performance change law forms the premise of the scientific formulation of maintenance decisions. This paper aims to develop a predictive model taking into account the costs of different types of maintenance works that reflects the continuous true usage performance of the pavement. The model proposed in this study was trained on a dataset containing five-year maintenance work data on urban roads in Beijing with pavement performance indicators for the corresponding years. The same roads were matched and combined to obtain a set of sequences of pavement performance changes with the features of the current year; with the recurrent-neural-network-based long short-term memory (LSTM) network and gate recurrent unit (GRU) network, the prediction accuracy of highway pavement performance on the test set was significantly increased. The prediction result indicates that the generalization ability of the improved recurrent neural network model is satisfactory, with the R² achieving 0.936, and of the two models the GRU model is more efficient, with an accuracy that reaches almost the same level as LSTM but with the training convergence time reduced to 25 s. This study demonstrates that data generated by the work of maintenance units can be used effectively in the prediction of pavement performance. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Decline of seagrass (Posidonia oceanica) production over two decades in the face of warming of the Eastern Mediterranean Sea

The response of Posidonia oceanica meadows to global warming of the Eastern Mediterranean Sea, where the increase in sea surface temperature (SST) is particularly severe, is poorly investigated. Here, we reconstructed the long-term P. oceanica production in 60 meadows along the Greek Seas over two decades (1997-2018), using lepidochronology. We determined the effect of warming on production by reconstructing the annual and maximum (i.e. August) SST, considering the role of other production drivers related to water quality (i.e. Chla, suspended particulate matter, Secchi depth). Grand mean (±SE) production across all sites and the study period was 48 ± 1.1 mg DW per shoot yr-1 . Production over the last two decades followed a trajectory of decrease, which was related to the concurrent increase in annual SST and SSTaug . Annual SST > 20°C and SSTaug  > 26.5°C was related to production decline (GAMM, P < 0.05), while the rest of the tested factors did not help explain the production pattern. Our results indicate a persistent and increasing threat for Eastern Mediterranean meadows, drawing attention to management authorities, highlighting the necessity of reducing local impacts to enhance the resilience of seagrass meadows to global change threats.

The COVID-19 pandemic and seeking information about condoms online: an infodemiology approach

Objectives: As condoms are effective tools for pathogen-avoidance in sexual intercourse, seeking information about condoms online may be a reactive response to the COVID-19 according to the behavioral immune system theory.Design: Taking an infodemiology perspective, this research employed multilevel analyses to examine how COVID-19 online query data (i.e., Google topic search terms Coronavirus and COVID-19) and coronavirus epidemiological data (i.e., COVID-19 cases per million and case fatality rate) would predict condom information seeking behavior online (i.e., Google topic search term Condom) throughout the pandemic across American states (Study 1) and 102 countries/territories (Study 2), after accounting for death-thought accessibility (i.e., illness-related searches), interest in birth control (i.e., birth-control-related searches), COVID-19 control policy, stay at home behavior, season, religious holidays, yearly trends, autocorrelation, and contextual variables such as HIV prevalence rate and socioeconomic development indicators (GINI index, urbanization, etc.).Results: When there were high levels of COVID-19 concerns in cyberspace in a given week, search volume for condoms increased from the previous week across American states and different countries/territories. By contrast, the effect of actual coronavirus threat was non-significant.Conclusion: Seeking information about condoms online could be a reactive response to high levels of COVID-19 concerns across different populations.

A time series analysis of milk productivity in US dairy states

As US dairy cow production evolves, it is important to characterize trends and seasonal patterns to project amounts and fluctuations in milk and milk components by states or regions. Hence, this study aimed to (1) quantify historical trends and seasonal patterns of milk and milk components production associated with calving date by parities and states; (2) classify parities and states with similar trends and seasonal patterns into clusters; and (3) summarize the general pattern for each cluster for further application in simulation models. Our data set contained 9.18 million lactation records from 5.61 million Holstein cows distributed in 17 states during the period January 2006 to December 2016. Each record included a cow's total milk, fat, and protein yield during a lactation. We used time series decomposition to obtain each state's annual trend and seasonal pattern in milk productivity for each parity. Then, we classified states and parities with agglomerative hierarchical clustering into groups according to 2 methods: (1) dynamic time warping on the original time series and (2) Euclidean distance on extracted features of trend and seasonality from the decomposition. Results showed distinguishable trends and seasonality for all states and lactation numbers for all response variables. The clusters and cluster centroid pattern showed a general upward trend for all yields [energy-corrected milk (ECM), milk, fat, and protein] and a steady trend for fat and protein percent for all states except Texas. We also found a larger seasonality amplitude for all yields (ECM, milk, fat, and protein) from higher lactation numbers and a similar amplitude for fat and protein percent across lactation numbers. The results could be used for advising management decisions according to farm productivity goals. Furthermore, the trend and seasonality patterns could be used to adjust the production level in a specific state, year, and season for farm simulations to accurately project milk and milk components production.

Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics

COVID-19 pandemic has questioned the way healthcare operations take place globally as the healthcare professionals face an unprecedented task of controlling and treating the COVID-19 infected patients with a highly straining and draining facility due to the erratic admissions of infected patients. However, COVID-19 is considered as a white swan event. Yet, the impact of the COVID-19 pandemic on healthcare operations is highly uncertain and disruptive making it as a black swan event. Therefore, the study explores the impact of the COVID-19 outbreak on healthcare operations and develops machine learning-based forecasting models using time series data to foresee the progression of COVID-19 and further using predictive analytics to better manage healthcare operations. The prediction error of the proposed model is found to be 0.039 for new cases and 0.006 for active COVID-19 cases with respect to mean absolute percentage error. The proposed simulated model further could generate predictive analytics and yielded future recovery rate, resource management ratios, and average cycle time of a patient tested COVID-19 positive. Further, the study will help healthcare professionals to devise better resilience and decision-making for managing uncertainty and disruption in healthcare operations.

Data-driven modeling of noise time series with convolutional generative adversarial networks

Random noise arising from physical processes is an inherent characteristic of measurements and a limiting factor for most signal processing and data analysis tasks. Given the recent interest in generative adversarial networks (GANs) for data-driven modeling, it is important to determine to what extent GANs can faithfully reproduce noise in target data sets. In this paper, we present an empirical investigation that aims to shed light on this issue for time series. Namely, we assess two general-purpose GANs for time series that are based on the popular deep convolutional GAN architecture, a direct time-series model and an image-based model that uses a short-time Fourier transform data representation. The GAN models are trained and quantitatively evaluated using distributions of simulated noise time series with known ground-truth parameters. Target time series distributions include a broad range of noise types commonly encountered in physical measurements, electronics, and communication systems: band-limited thermal noise, power law noise, shot noise, and impulsive noise. We find that GANs are capable of learning many noise types, although they predictably struggle when the GAN architecture is not well suited to some aspects of the noise, e.g. impulsive time-series with extreme outliers. Our findings provide insights into the capabilities and potential limitations of current approaches to time-series GANs and highlight areas for further research. In addition, our battery of tests provides a useful benchmark to aid the development of deep generative models for time series.

Connecting higher-order interactions with ecological stability in experimental aquatic food webs

Community ecology is built on theories that represent the strength of interactions between species as pairwise links. Higher-order interactions (HOIs) occur when a species changes the pairwise interaction between a focal pair. Recent theoretical work has highlighted the stabilizing role of HOIs for large, simulated communities, yet it remains unclear how important higher-order effects are in real communities. Here, we used experimental communities of aquatic protists to examine the relationship between HOIs and stability (as measured by the persistence of a species in a community). We cultured a focal pair of consumers in the presence of additional competitors and a predator and collected time series data of their abundances. We then fitted competition models with and without HOIs to measure interaction strength between the focal pair across different community compositions. We used survival analysis to measure the persistence of individual species. We found evidence that additional species positively affected persistence of the focal species and that HOIs were present in most of our communities. However, persistence was only linked to HOIs for one of the focal species. Our results vindicate community ecology theory positing that species interactions may deviate from assumptions of pairwise interactions, opening avenues to consider possible consequences for coexistence and stability.

Forecasting New Tuberculosis Cases in Malaysia: A Time-Series Study Using the Autoregressive Integrated Moving Average (ARIMA) Model

Background The application of the Box-Jenkins autoregressive integrated moving average (ARIMA) model has been widely employed in predicting cases of infectious diseases. It has shown a positive impact on public health early warning surveillance due to its capability in producing reliable forecasting values. This study aimed to develop a prediction model for new tuberculosis (TB) cases using time-series data from January 2013 to December 2018 in Malaysia and to forecast monthly new TB cases for 2019. Materials and methods The ARIMA model was executed using data gathered between January 2013 and December 2018 in Malaysia. Subsequently, the well-fitted model was employed to make projections for new TB cases in the year 2019. To assess the efficacy of the model, two key metrics were utilized: the mean absolute percentage error (MAPE) and stationary R-squared. Furthermore, the sufficiency of the model was validated via the Ljung-Box test. Results The results of this study revealed that the ARIMA (2,1,1)(0,1,0)12 model proved to be the most suitable choice, exhibiting the lowest MAPE value of 6.762. The new TB cases showed a clear seasonality with two peaks occurring in March and December. The proportion of variance explained by the model was 55.8% with a p-value (Ljung-Box test) of 0.356. Conclusions The application of the ARIMA model has developed a simple, precise, and low-cost forecasting model that provides a warning six months in advance for monitoring the TB epidemic in Malaysia, which exhibits a seasonal pattern.

Federated Learning for Predictive Maintenance and Anomaly Detection Using Time Series Data Distribution Shifts in Manufacturing Processes

In the manufacturing process, equipment failure is directly related to productivity, so predictive maintenance plays a very important role. Industrial parks are distributed, and data heterogeneity exists among heterogeneous equipment, which makes predictive maintenance of equipment challenging. In this paper, we propose two main techniques to enable effective predictive maintenance in this environment. We propose a 1DCNN-Bilstm model for time series anomaly detection and predictive maintenance of manufacturing processes. The model combines a 1D convolutional neural network (1DCNN) and a bidirectional LSTM (Bilstm), which is effective in extracting features from time series data and detecting anomalies. In this paper, we combine a federated learning framework with these models to consider the distributional shifts of time series data and perform anomaly detection and predictive maintenance based on them. In this paper, we utilize the pump dataset to evaluate the performance of the combination of several federated learning frameworks and time series anomaly detection models. Experimental results show that the proposed framework achieves a test accuracy of 97.2%, which shows its potential to be utilized for real-world predictive maintenance in the future.

Determining the relationship between dengue and vulnerability in a Brazilian city: a spatial modeling analysis

Dengue is a viral infection transmitted by the Aedes aegypti mosquito. This study aimed to assess the distribution of cases and deaths from dengue and severe dengue, and its relationship with social vulnerability in Belo Horizonte, State of Minas Gerais, Brazil, from 2010 to 2018. The incidence and lethality rates of dengue and their relationship with sex, age, education, skin color, and social vulnerability were studied using chi-square tests, Ordinary Least Squares (OLS), and Geographically Weighted Regression (GWR) analyses. The number of cases of dengue in Belo Horizonte during the study period was 324,044 dengue cases, with 1,334 cases of severe dengue and 88 deaths. During the past few decades, the incidence rate of both dengue and severe cases varied, with an average incidence rate of respectively 1515.5 and 6.2/100,000 inhabitants. The increase in dengue cases was directly related to areas with higher social vulnerability areas and more working-age people. Also, the disease is more severe in people self-declared as black, elderly, and male. The findings of this study might provide relevant information for health services in the organization of control and prevention policies for this problem, emphasizing the most vulnerable urban areas and categories.

Continual Deep Learning for Time Series Modeling

The multi-layer structures of Deep Learning facilitate the processing of higher-level abstractions from data, thus leading to improved generalization and widespread applications in diverse domains with various types of data. Each domain and data type presents its own set of challenges. Real-world time series data may have a non-stationary data distribution that may lead to Deep Learning models facing the problem of catastrophic forgetting, with the abrupt loss of previously learned knowledge. Continual learning is a paradigm of machine learning to handle situations when the stationarity of the datasets may no longer be true or required. This paper presents a systematic review of the recent Deep Learning applications of sensor time series, the need for advanced preprocessing techniques for some sensor environments, as well as the summaries of how to deploy Deep Learning in time series modeling while alleviating catastrophic forgetting with continual learning methods. The selected case studies cover a wide collection of various sensor time series applications and can illustrate how to deploy tailor-made Deep Learning, advanced preprocessing techniques, and continual learning algorithms from practical, real-world application aspects.

Survey of Time Series Data Generation in IoT

Nowadays, with the rapid growth of the internet of things (IoT), massive amounts of time series data are being generated. Time series data play an important role in scientific and technological research for conducting experiments and studies to obtain solid and convincing results. However, due to privacy restrictions, limited access to time series data is always an obstacle. Moreover, the limited available open source data are often not suitable because of a small quantity and insufficient dimensionality and complexity. Therefore, time series data generation has become an imperative and promising solution. In this paper, we provide an overview of classical and state-of-the-art time series data generation methods in IoT. We classify the time series data generation methods into four major categories: rule-based methods, simulation-model-based methods, traditional machine-learning-based methods, and deep-learning-based methods. For each category, we first illustrate its characteristics and then describe the principles and mechanisms of the methods. Finally, we summarize the challenges and future directions of time series data generation in IoT. The systematic classification and evaluation will be a valuable reference for researchers in the time series data generation field.