Evaluating Population-Level Interventions and Exposures for Suicide Prevention

Evaluations of interventions targeting the population level are an essential component of the policy development cycle. Pre-post designs are widespread in suicide prevention research but have several significant limitations. To inform future evaluations, our aim is to explore the three most frequently used approaches for assessing the association between population-level interventions or exposures and suicide - the pre-post design, the difference-in-difference design, and Poisson regression approaches. The pre-post design and the difference-in-difference design will only produce unbiased estimates of an association if there are no underlying time trends in the data and there is no additional confounding from other sources. Poisson regression approaches with covariates for time can control for underlying time trends as well as the effects of other confounding factors. Our recommendation is that the default position should be to model the effects of population-level interventions or exposures using regression methods that account for time effects. The other designs should be seen as fall-back positions when insufficient data are available to use methods that control for time effects.

Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks

We introduce an approach which allows detecting causal relationships between variables for which the time evolution is available. Causality is assessed by a variational scheme based on the Information Imbalance of distance ranks, a statistical test capable of inferring the relative information content of different distance measures. We test whether the predictability of a putative driven system Y can be improved by incorporating information from a potential driver system X, without explicitly modeling the underlying dynamics and without the need to compute probability densities of the dynamic variables. This framework makes causality detection possible even between high-dimensional systems where only few of the variables are known or measured. Benchmark tests on coupled chaotic dynamical systems demonstrate that our approach outperforms other model-free causality detection methods, successfully handling both unidirectional and bidirectional couplings. We also show that the method can be used to robustly detect causality in human electroencephalography data.

Radiological complexity of nuclear facilities: an information complexity approach to workplace monitoring

Nuclear energy is crucial for achieving net-zero carbon emissions. A big challenge in the nuclear sector is ensuring the safety of radiation workers and the environment, while being cost-effective. Workplace monitoring is key to protecting workers from risks of ionising radiation. Traditional monitoring involves radiological surveillance via installed radiation monitors, continuously recording measurements like radiation fields and airborne particulate radioactivity concentrations, especially where sudden radiation changes could significantly impact workers. However, this approach struggles to detect incremental changes over a long period of time in the radiological measurements of the facility. To address this limitation, we propose abstracting a nuclear facility as a complex system. We then quantify the information complexity of the facility's radiological measurements using an entropic metric. Our findings indicate that the inferences and interpretations from our abstraction have a firm basis for interpretation and can enhance current workplace monitoring systems. We suggest the implementation of a radiological complexity-based alarm system to complement existing radiation level-based systems. The abstraction synthesized here is independent of the type of nuclear facility, and hence is a general approach to workplace monitoring at a nuclear facility.

Hybrid Anomaly Detection in Time Series by Combining Kalman Filters and Machine Learning Models

Due to connectivity and automation trends, the medical device industry is experiencing increased demand for safety and security mechanisms. Anomaly detection has proven to be a valuable approach for ensuring safety and security in other industries, such as automotive or IT. Medical devices must operate across a wide range of values due to variations in patient anthropometric data, making anomaly detection based on a simple threshold for signal deviations impractical. For example, surgical robots directly contacting the patient's tissue require precise sensor data. However, since the deformation of the patient's body during interaction or movement is highly dependent on body mass, it is impossible to define a single threshold for implausible sensor data that applies to all patients. This also involves statistical methods, such as Z-score, that consider standard deviation. Even pure machine learning algorithms cannot be expected to provide the required accuracy simply due to the lack of available training data. This paper proposes using hybrid filters by combining dynamic system models based on expert knowledge and data-based models for anomaly detection in an operating room scenario. This approach can improve detection performance and explainability while reducing the computing resources needed on embedded devices, enabling a distributed approach to anomaly detection.

Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data

BACKGROUND

As depression is highly heterogenous, an increasing number of studies investigate person-specific associations of depressive symptoms in longitudinal data. However, most studies in this area of research conceptualize symptom interrelations to be static and time invariant, which may lead to important temporal features of the disorder being missed.

OBJECTIVE

To reveal the dynamic nature of depression, we aimed to use a recently developed technique to investigate whether and how associations among depressive symptoms change over time.

METHODS

Using daily data (mean length 274, SD 82 d) of 20 participants with depression, we modeled idiographic associations among depressive symptoms, rumination, sleep, and quantity and quality of social contacts as dynamic networks using time-varying vector autoregressive models.

RESULTS

The resulting models showed marked interindividual and intraindividual differences. For some participants, associations among variables changed in the span of some weeks, whereas they stayed stable over months for others. Our results further indicated nonstationarity in all participants.

CONCLUSIONS

Idiographic symptom networks can provide insights into the temporal course of mental disorders and open new avenues of research for the study of the development and stability of psychopathological processes.

Effects of COVID-19-targeted non-pharmaceutical interventions on pediatric hospital admissions in North Italian hospitals, 2017 to 2022: a quasi-experimental study interrupted time-series analysis

Background

The use of Non-Pharmaceutical Interventions (NPIs), such as lockdowns, social distancing and school closures, against the COVID-19 epidemic is debated, particularly for the possible negative effects on vulnerable populations, including children and adolescents. This study therefore aimed to quantify the impact of NPIs on the trend of pediatric hospitalizations during 2 years of pandemic compared to the previous 3 years, also considering two pandemic phases according to the type of adopted NPIs.

Methods

This is a multicenter, quasi-experimental before-after study conducted in 12 hospitals of the Emilia-Romagna Region, Northern Italy, with NPI implementation as the intervention event. The 3 years preceding the beginning of NPI implementation (in March 2020) constituted the pre-pandemic phase. The subsequent 2 years were further subdivided into a school closure phase (up to September 2020) and a subsequent mitigation measures phase with less stringent restrictions. School closure was chosen as delimitation as it particularly concerns young people. Interrupted Time Series (ITS) regression analysis was applied to calculate Hospitalization Rate Ratios (HRR) on the diagnostic categories exhibiting the greatest variation. ITS allows the estimation of changes attributable to an intervention, both in terms of immediate (level change) and sustained (slope change) effects, while accounting for pre-intervention secular trends.

Results

Overall, in the 60 months of the study there were 84,368 cases. Compared to the pre-pandemic years, statistically significant 35 and 19% decreases in hospitalizations were observed during school closure and in the following mitigation measures phase, respectively. The greatest reduction was recorded for "Respiratory Diseases," whereas the "Mental Disorders" category exhibited a significant increase during mitigation measures. ITS analysis confirms a high reduction of level change during school closure for Respiratory Diseases (HRR 0.19, 95%CI 0.08-0.47) and a similar but smaller significant reduction when mitigation measures were enacted. Level change for Mental Disorders significantly decreased during school closure (HRR 0.50, 95%CI 0.30-0.82) but increased during mitigation measures by 28% (HRR 1.28, 95%CI 0.98-1.69).

Conclusion

Our findings provide information on the impact of COVID-19 NPIs which may inform public health policies in future health crises, plan effective control and preventative interventions and target resources where needed.

Forecast of the number of nursing beds per 1000 older people from 2023 to 2025: Empirical quantitative research

AIM

This research aims to offer a reference point for relevant departments to enhance the allocation of ageing resources and formulate policies accordingly.

DESIGN

This study is designed as empirical quantitative research.

METHODS

Data from the National Bureau of Statistics and the Ministry of Civil Affairs regarding older adults (aged≥60) from 2000 to 2022 and nursing beds from 1978 to 2022 were analysed. The differential autoregressive integrated moving averages model and Monte Carlo simulation were used to predict the growth of nursing beds per 1000 older people in China for the Years 2023-2025.

RESULTS

It is projected that from 2023 to 2025, China will experience a further increase in its ageing population, with an average annual growth rate of 3.1%. By 2025, the number of older people in China is expected to surpass 300 million. Additionally, there will be a rise in the number of nursing beds, with an average annual growth rate of 1.9%, leading to a total of 8.79 million nursing beds by 2025. However, due to the rapid growth of the older population, there will be a slight decline in the number of nursing beds per 1000 older people in China, with an average annual growth rate of -1.00%.

National taxation on sugar-sweetened beverages and its association with overweight, obesity, and diabetes

BACKGROUND

Consumption of sugar-sweetened beverages (SSBs) has been linked to several adverse health outcomes, thus many countries introduced taxation to reduce it.

OBJECTIVES

To summarize national SSB taxation laws and to assess their association with obesity, overweight and diabetes.

METHODS

We conducted a systematic scoping review up to January 2022 on PubMed, Web of Science, Embase, and Google Search to identify taxes on SSBs. An interrupted time series analysis (ITSA) was conducted on 17 countries with taxation implemented in 2013 or before to evaluate the level and slope modifications in the rate of change of standardized prevalence rates of overweight, obesity, and diabetes. Random-effects meta-regression was used to assess whether year of entry into force of the law, national income, and tax design affected observed results.

RESULTS

We included 76 tax laws issued between 1940 and 2020 by 43 countries, which were heterogeneous in terms of tax design, amount, and taxed products. Among children and adolescents, ITSA showed level or slope reduction for prevalence of overweight and obesity in 5 (Brazil, Samoa, Palau, Panama, Tonga) and 6 (El Salvador, Uruguay, Nauru, Norway, Palau, Tonga) countries out of 17, respectively. No clear pattern of modification of results according to investigated factors emerged from the meta-regression analysis.

CONCLUSIONS

Taxation is highly heterogeneous across countries in terms of products and design, which might influence its effectiveness. Our findings provide some evidence regarding a deceleration of the increasing prevalence rates of overweight and obesity among children occurring in some countries following introduction of taxation. PROSPERO registration number: CRD42021233309.

Spatiotemporal analysis and forecasting of lumpy skin disease outbreaks in Ethiopia based on retrospective outbreak reports

Introduction

Lumpy skin disease is a viral disease that affects cattle belonging to genus Capripoxvirus (Poxviridae) and lead to significant economic losses.

Objective

The objective of this study was to evaluate the distribution of lumpy skin disease (LSD) outbreaks and predict future patterns based on retrospective outbreak reports in Ethiopia.

Methods

Data were collected through direct communication with regional laboratories and a hierarchical reporting system from the Peasant Associations to Ministry of Agriculture. Time-series data for the LSD outbreaks were analyzed using classical additive time-series decomposition and STL decomposition. Four models (ARIMA, SARIMA, ETS, STLF) were also used to forecast the number of LSD outbreaks that occurred each month for the years (2021-2025) after the models' accuracy test was performed. Additionally, the space-time permutation model (STP) were also used to study retrospective space-time cluster analysis of LSD outbreaks in Ethiopia.

Results

This study examined the geographical and temporal distribution of LSD outbreaks in Ethiopia from 2008 to 2020, reporting a total of 3,256 LSD outbreaks, 14,754 LSD-positive cases, 7,758 deaths, and 289 slaughters. It also covered approximately 68% of Ethiopia's districts, with Oromia reporting the highest LSD outbreaks. In the LSD's temporal distribution, the highest peak was reported following the rainy season in September to December and its lowest peak in the dry months of April and May. Out of the four models tested for forecasting, the SARIMA (3, 0, 0) (2, 1, 0) [12] model performed well for the validation data, while the STLF+Random Walk had a robust prediction for the training data. Thus, the SARIMA and STLF+Random Walk models produced a more accurate forecast of LSD outbreaks between 2020 and 2025. From retrospective Space-Time Cluster Analysis of LSD, eight possible clusters were also identified, with five of them located in central part of Ethiopia.

Conclusion

The study's time series and ST-cluster analysis of LSD outbreak data provide valuable insights into the spatial and temporal dynamics of the disease in Ethiopia. These insights can aid in the development of effective strategies to control and prevent the spread of the disease and holds great potential for improving efforts to combat LSD in the country.

Rapid outpatient transient ischemic attack clinic and stroke service activity during the SARS-CoV-2 pandemic: a multicenter time series analysis

Background and aim

Rapid outpatient evaluation and treatment of TIA in structured clinics have been shown to reduce stroke recurrence. It is unclear whether short-term downtrends in TIA incidence and admissions have had enduring impact on TIA clinic activity. This study aims to measure the impact of the pandemic on hospitals with rapid TIA clinics.

Methods

Relevant services were identified by literature search and contacted. Three years of monthly data were requested - a baseline pre-COVID period (April 2018 to March 2020) and an intra-COVID period (April 2020 to March 2021). TIA presentations, ischemic stroke presentations, and reperfusion trends inclusive of IV thrombolysis (IVT) and endovascular thrombectomy (EVT) were recorded. Pandemic impact was measured with interrupted time series analysis, a segmented regression approach to test an effect of an intervention on a time-dependent outcome using a defined impact model.

Results

Six centers provided data for a total of 6,231 TIA and 13,191 ischemic stroke presentations from Australia (52.1%), Canada (35.0%), Italy (7.6%), and England (5.4%). TIA clinic volumes remained constant during the pandemic (2.9, 95% CI -1.8 to 7.6, p = 0.24), as did ischemic stroke (2.9, 95% CI -7.8 to 1.9, p = 0.25), IVT (-14.3, 95% CI -36.7, 6.1, p < 0.01), and EVT (0, 95% CI -16.9 to 16.9, p = 0.98) counts. Proportion of ischemic strokes requiring IVT decreased from 13.2 to 11.4% (p < 0.05), but those requiring EVT did not change (16.0 to 16.7%, p = 0.33).

Conclusion

This suggests that the pandemic has not had an enduring effect on TIA clinic or stroke service activity for these centers. Furthermore, the disproportionate decrease in IVT suggests that patients may be presenting outside the IVT window during the pandemic - delays in seeking treatment in this group could be the target for public health intervention.

Treatment Prediction in the ICU Using a Partitioned, Sequential, Deep Time Series Analysis

We have developed a time-oriented machine-learning tool to predict the binary decision of administering a medication and the quantitative decision regarding the specific dose. We evaluated our tool on the MIMIC-IV ICU database, for three common medical scenarios. We use an LSTM based neural network, and considerably extend its use by introducing several new concepts. We partition the common 12-hour prediction horizon into three sub-windows. Partitioning models the treatment dynamics better, and allows the use of previous sub-windows' data as additional training data with improved performance. We also introduce a sequential prediction process, composed of a binary treatment-decision model, followed, when relevant, by a quantitative dose-decision model, with improved accuracy. Finally, we examined two methods for including non-temporal features, such as age, within the temporal network. Our results provide additional treatment-prediction tools, and thus another step towards a reliable and trustworthy decision-support system that reduces the clinicians' cognitive load.

Associations of prevalence of e-cigarette use with quit attempts, quit success, use of smoking cessation medication, and the overall quit rate among smokers in England: a time-series analysis of population trends 2007-2022

INTRODUCTION

This study aimed to: (i) provide up-to-date estimates of how changes in prevalence of e-cigarette use have been associated with changes in smoking cessation activities and use of licensed treatments among smokers in England; and (ii) explore any changes in these associations over time.

METHODS

Data were aggregated quarterly on 67,548 past-year smokers between Q1-2007 and Q4-2022. Explanatory variables were prevalence of (i) current e-cigarette use among smokers and (ii) e-cigarette use during a quit attempt. Outcomes were rates of quit attempts and overall quits among past-year smokers, and the quit success rate and use of licensed treatments among those who made a quit attempt.

RESULTS

The success rate of quit attempts increased by 0.040% (95%CI 0.019; 0.062) for every 1% increase in the prevalence of e-cigarette use during a quit attempt. No clear evidence was found for an association between current e-cigarette use and the quit attempt rate (Badj=0.008 [95%CI -0.045; 0.061]) or overall quit rate (Badj=0.063 [-0.031; 0.158]); or between use of e-cigarettes during a quit attempt and the overall quit rate (Badj=0.030 [-0.054; 0.114]), use of prescription medication (varenicline/bupropion/NRT: Badj=-0.036 [-0.175; 0.102]), or use of over-the-counter NRT (Badj=-0.052 [-0.120; 0.015]). There was no clear evidence this pattern of associations has changed substantially over time.

CONCLUSIONS

Changes in prevalence of e-cigarette use in England through to 2022 have been positively associated with the success rate of quit attempts but not clearly associated with the quit attempt rate, overall quit rate, or use of licensed smoking cessation treatments.

IMPLICATIONS

If the association between the increase in e-cigarette use and the quit success rate is causal, then the use of e-cigarettes in quit attempts has helped in the region of 30,000 to 50,000 additional smokers in England to successfully quit each year since they became popular in 2013, over and above the number who were quitting before the advent of e-cigarettes.

Vaccine-preventable Pediatric Acute Bacterial Meningitis in France: A Time Series Analysis of a 19-Year Prospective National Surveillance Network

BACKGROUND

In France, vaccination has been implemented against Hi serotype b (Hib), pneumococcus with pneumococcal conjugate vaccines (PCV), and Neisseria meningitidis serogroup C (MenC). These interventions with different coverage and uptake have disrupted the epidemiology of vaccine-preventable acute bacterial meningitis (ABM).

METHODS

We analyzed data from a French prospective surveillance network of ABM in children ≤15 years old enrolled by 259 pediatric wards (estimated national coverage: 61%). From 2001 to 2020, the effect of vaccine implementation was estimated with segmented linear regression.

RESULTS

We analyzed 7,186 cases, mainly due to meningococcus (35.0%), pneumococcus (29.8%), and Hi (3.7%). MenC ABM incidence decreased (-0.12%/month, 95% CI: -0.17 to -0.07, P < 0.001) with no change for the overall meningococcal ABM when comparing the pre-MenC vaccination and the post-MenC vaccination trends. Despite a decreasing MenB ABM incidence without a vaccination program (-0.43%/month, 95% CI: -0.53 to -0.34, P < 0.001), 68.3% of meningococcal ABM involved MenB. No change in pneumococcal ABM incidence was observed after the PCV7 recommendation. By contrast, this incidence significantly decreased after the switch to PCV13 (-0.9%/month, 95% CI: -1.6 to -0.2%, P = 0.01). After May 2014, a rebound occurred (0.5%/month, 95% CI: 0.3-0.8%, P < 0.001), with 89.5% of non-PCV13 vaccine serotypes. Hib ABM incidence increased after June 2017.

CONCLUSIONS

PCV7 and MenC vaccine introduction in France, with slow vaccine uptake and low coverage, had no to little impact as compared to the switch from PCV7 to PCV13, which occurred when coverage was optimal. Our data suggest that MenB and next-generation PCVs could prevent a large part of the ABM incidence in France.

Temporal Trends of Age-Adjusted Mortality Rates for Rheumatic Heart Disease in Brazil From 2000 to 2021

Background Rheumatic heart disease (RHD) is a chronic cardiovascular condition stemming from an infectious origin, posing a substantial health burden, particularly in economically disadvantaged regions. It starts with acute rheumatic fever (ARF), a complication following group A Streptococcus infection, leading to heart valve damage and, over time, structural heart abnormalities. RHD contributes to premature deaths, especially in low-middle-income countries. Although the incidence and prevalence have generally reduced globally due to antibiotics and improved healthcare, it remains a significant public health concern in Brazil, echoing its prevalence in many developing nations around the world. RHD stands as a poignant testament to the intersection of socio-economic disparities and healthcare challenges within Brazil's diverse population. In Brazil, despite advancements in healthcare, RHD continues to impact communities, highlighting the urgent need for enhanced prevention strategies, access to quality healthcare services, and heightened awareness to combat this preventable, yet persistent, cardiac condition. Understanding the epidemiological landscape and socio-cultural factors influencing RHD in Brazil is crucial for developing targeted interventions aimed at mitigating its burden on individuals, families, and the healthcare system at large. Thus, our study focuses on analyzing age-related mortality rates linked to ARF and chronic RHD (ARHD) in Brazil from 2000 to 2021, particularly examining gender disparities. Materials and methods This retrospective cohort study employed a descriptive time-series approach, utilizing comprehensive nationwide data from Brazil spanning from 2000 to 2021 to assess trends in diverse age groups, among both sexes, enabling a detailed analysis of temporal patterns. Mortality data, extracted and categorized meticulously, were subjected to Joinpoint statistical analyses enabling comparative assessments, with average annual percent change (AAPC) and annual percent change (APC) serving as key metrics to quantify and interpret trends over the analyzed period. Results The acute RHD (ARHD)-related mortality declined over the analyzed years supported by AAPC, with higher mortality reduction in females. The age-adjusted mortality rate for "males and females" decreased from 78 to 67 deaths/100,000 from 2000 to 2021. Female mortality dropped from 85 to 69/100,000, and male mortality decreased from 73 to 63/100,000 over the same period. For ARHD, male age groups (20-29, 60-69, 70-79, 80+) showed declining mortality, while the 30-59 age group exhibited an upward. Females AAMR for chronic RHD (CRHD) decreased across all age groups, with significant reductions in the 80 years and above age group from 2000-2002 (APC: -11.94*) and steadily from 2002 onwards (APC: -1.33). Conclusions Our study revealed an overall decline in mortality rates for both acute and CRHD across both sexes. Females consistently exhibited higher mortality rates and a more pronounced reduction compared to males in both acute and CRHD. In ARHD, males experience the highest mortality in the 50-59 age group, while females have a peak in the 40-49 age group. The 60-69 age group had the highest mortality in CRHD for both sexes. Conversely, the 20-29 age group displayed the lowest mortality in CRHD, and the 80-89 age group had the lowest mortality in ARHD.

Unique Changes in the Incidence of Acute Chest Syndrome in Children With Sickle Cell Disease Unravel the Role of Respiratory Pathogens: A Time Series Analysis

BACKGROUND

Acute chest syndrome (ACS) is a life-threatening complication of sickle cell disease (SCD). Although respiratory pathogens are frequently detected in children with ACS, their respective role in triggering the disease is still unclear. We hypothesized that the incidence of ACS followed the unprecedented population-level changes in respiratory pathogen dynamics after COVID-19-related nonpharmaceutical interventions (NPIs).

RESEARCH QUESTION

What is the respective role of respiratory pathogens in ACS epidemiology?

STUDY DESIGN AND METHODS

This study was an interrupted time series analysis of patient records from a national hospital-based surveillance system. All children aged < 18 years with SCD hospitalized for ACS in France between January 2015 and May 2022 were included. The monthly incidence of ACS per 1,000 children with SCD over time was analyzed by using a quasi-Poisson regression model. The circulation of 12 respiratory pathogens in the general pediatric population over the same period was included in the model to assess the fraction of ACS potentially attributable to each respiratory pathogen.

RESULTS

Among the 55,941 hospitalizations of children with SCD, 2,306 episodes of ACS were included (median [interquartile range] age, 9 [5-13] years). A significant decrease was observed in ACS incidence after NPI implementation in March 2020 (-29.5%; 95% CI, -46.8 to -12.2; P = .001) and a significant increase after lifting of the NPIs in April 2021 (24.4%; 95% CI, 7.2 to 41.6; P = .007). Using population-level incidence of several respiratory pathogens, Streptococcus pneumoniae accounted for 30.9% (95% CI, 4.9 to 56.9; P = .02) of ACS incidence over the study period and influenza 6.8% (95% CI, 2.3 to 11.3; P = .004); other respiratory pathogens had only a minor role.

INTERPRETATION

NPIs were associated with significant changes in ACS incidence concomitantly with major changes in the circulation of several respiratory pathogens in the general population. This unique epidemiologic situation allowed determination of the contribution of these respiratory pathogens, in particular S pneumoniae and influenza, to the burden of childhood ACS, highlighting the potential benefit of vaccine prevention in this vulnerable population.

Estimation of physiological exercise thresholds based on dynamical correlation properties of heart rate variability

Aerobic and anaerobic thresholds of the three-zone exercise model are often used to evaluate the exercise intensity and optimize the training load. Conventionally, these thresholds are derived from the respiratory gas exchange or blood lactate concentration measurements. Here, we introduce and validate a computational method based on the RR interval (RRI) dynamics of the heart rate (HR) measurement, which enables a simple, yet reasonably accurate estimation of both metabolic thresholds. The method utilizes a newly developed dynamical detrended fluctuation analysis (DDFA) to assess the real-time changes in the dynamical correlations of the RR intervals during exercise. The training intensity is shown to be in direct correspondence with the time- and scale-dependent changes in the DDFA scaling exponent. These changes are further used in the definition of an individual measure to estimate the aerobic and anaerobic threshold. The results for 15 volunteers who participated in a cyclo-ergometer test are compared to the benchmark lactate thresholds, as well as to the ventilatory threshods and alternative HR-based estimates based on the maximal HR and the conventional detrended fluctuation analysis (DFA). Our method provides the best overall agreement with the lactate thresholds and provides a promising, cost-effective alternative to conventional protocols, which could be easily integrated in wearable devices. However, detailed statistical analysis reveals the particular strengths and weaknessess of each method with respect to the agreement and consistency with the thresholds-thus underlining the need for further studies with more data.

Machine learning identifies ferroptosis-related gene ANXA2 as potential diagnostic biomarkers for NAFLD

Introduction

Non-alcoholic fatty liver disease (NAFLD), a major cause of chronic liver disease, still lacks effective therapeutic targets today. Ferroptosis, a type of cell death characterized by lipid peroxidation, has been linked to NAFLD in certain preclinical trials, yet the exact molecular mechanism remains unclear. Thus, we analyzed the relationship between ferroptosis genes and NAFLD using high-throughput data.

Method

We utilized a total of 282 samples from five datasets, including two mouse ones, one human one, one single nucleus dataset and one single cell dataset from Gene Expression Omnibus (GEO), as the data basis of our study. To filter robust treatment targets, we employed four machine learning methods (LASSO, SVM, RF and Boruta). In addition, we used an unsupervised consensus clustering algorithm to establish a typing scheme for NAFLD based on the expression of ferroptosis related genes (FRGs). Our study is also the first to investigate the dynamics of FRGs throughout the disease process by time series analysis. Finally, we validated the relationship between core gene and ferroptosis by in vitro experiments on HepG2 cells.

Results

We discovered ANXA2 as a central focus in NAFLD and indicated its potential to boost ferroptosis in HepG2 cells. Additionally, based on the results obtained from time series analysis, ANXA2 was observed to significantly define the disease course of NAFLD. Our results demonstrate that implementing a ferroptosis-based staging method may hold promise for the diagnosis and treatment of NAFLD.

Conclusion

Our findings suggest that ANXA2 may be a useful biomarker for the diagnosis and characterization of NAFLD.

Looking beyond vaccines: Cultural tightness-looseness moderates the relationship between immunization coverage and disease prevention vigilance

Advancements in vaccination technologies mitigate disease transmission risks but may inadvertently suppress the behavioral immune system, an evolved disease avoidance mechanism. Applying behavioral immune system theory and utilizing robust big data analytics, we examined associations between rising vaccination coverage and government policies, public mobility, and online information seeking regarding disease precautions. We tested whether cultural tightness-looseness moderates the relationship between mass immunization and disease prevention vigilance. Comprehensive time series analyses were conducted using American data (Study 1) and international data (Study 2), employing transfer function modeling, cross-correlation function analysis, and meta-regression analysis. Across both the US and global analyses, as vaccination rates rose over time, government COVID-19 restrictions significantly relaxed, community mobility increased, and online searches for prevention information declined. The relationship between higher vaccination rates and lower disease prevention vigilance was stronger in culturally looser contexts. Results provide initial evidence that mass immunization may be associated with attenuated sensitivity and enhanced flexibility of disease avoidance psychology and actions. However, cultural tightness-looseness significantly moderates this relationship, with tighter cultures displaying sustained vigilance amidst immunization upticks. These findings offer valuable perspectives to inform nuanced policymaking and public health strategies that balance prudent precautions against undue alarm when expanding vaccine coverage worldwide.

Statistical inference for dependence networks in topological data analysis

Topological data analysis (TDA) provide tools that are becoming increasingly popular for analyzing multivariate time series data. One key aspect in analyzing multivariate time series is dependence between components. One application is on brain signal analysis. In particular, various dependence patterns in brain networks may be linked to specific tasks and cognitive processes. These dependence patterns may be altered by various neurological and cognitive impairments such as Alzheimer's and Parkinson's diseases, as well as attention deficit hyperactivity disorder (ADHD). Because there is no ground-truth with known dependence patterns in real brain signals, testing new TDA methods on multivariate time series is still a challenge. Our goal here is to develop novel statistical inference procedures via simulations. Simulations are useful for generating some null distributions of a test statistic (for hypothesis testing), forming confidence regions, and for evaluating the performance of proposed TDA methods. To the best of our knowledge, there are no methods that simulate multivariate time series data with potentially complex user-specified connectivity patterns. In this paper we present a novel approach to simulate multivariate time series with specific number of cycles/holes in its dependence network. Furthermore, we also provide a procedure for generating higher dimensional topological features.

Seasonality of Acute Malnutrition in African Drylands: Evidence From 15 Years of SMART Surveys

Reduction of wasting, or low weight-for-height, is a critical target for the Zero Hunger Sustainable Development Goal, yet robust evidence establishing continuous seasonal patterns of wasting is presently lacking. The current consensus of greatest hunger during the preharvest period is based on survey designs and analytical methods, which discretize time frame into preharvest/postharvest, dry/wet, or lean/plenty seasons. We present a spatiotemporally nuanced study of acute malnutrition seasonality in African drylands using a 15-year data set of Standardized Monitoring and Assessment of Relief and Transition surveys (n = 412,370). Climatological similarity was ensured by selecting subnational survey regions with 1 rainy season and by spatially matching each survey to aridity and livelihood zones. Harmonic logit regression models indicate 2 peaks of wasting during the calendar year. Greatest wasting prevalence is estimated in April to May, coincident with the primary peak of temperature. A secondary peak of wasting is observed in August to October, coinciding with the primary peak of rainfall and secondary peak of temperature. This pattern is retained across aridity and livelihood zones and is sensitive to temperature, precipitation, and vegetation. Improved subnational estimation of acute malnutrition seasonality can thus assist decision makers and practitioners in data-sparse settings and facilitate global progress toward Zero Hunger.

Feature-based clustering of the left ventricular strain curve for cardiovascular risk stratification in the general population

Objective

Identifying individuals with subclinical cardiovascular (CV) disease could improve monitoring and risk stratification. While peak left ventricular (LV) systolic strain has emerged as a strong prognostic factor, few studies have analyzed the whole temporal profiles of the deformation curves during the complete cardiac cycle. Therefore, in this longitudinal study, we applied an unsupervised machine learning approach based on time-series-derived features from the LV strain curve to identify distinct strain phenogroups that might be related to the risk of adverse cardiovascular events in the general population.

Method

We prospectively studied 1,185 community-dwelling individuals (mean age, 53.2 years; 51.3% women), in whom we acquired clinical and echocardiographic data including LV strain traces at baseline and collected adverse events on average 9.1 years later. A Gaussian Mixture Model (GMM) was applied to features derived from LV strain curves, including the slopes during systole, early and late diastole, peak strain, and the duration and height of diastasis. We evaluated the performance of the model using the clinical characteristics of the participants and the incidence of adverse events in the training dataset. To ascertain the validity of the trained model, we used an additional community-based cohort (n = 545) as external validation cohort.

Results

The most appropriate number of clusters to separate the LV strain curves was four. In clusters 1 and 2, we observed differences in age and heart rate distributions, but they had similarly low prevalence of CV risk factors. Cluster 4 had the worst combination of CV risk factors, and a higher prevalence of LV hypertrophy and diastolic dysfunction than in other clusters. In cluster 3, the reported values were in between those of strain clusters 2 and 4. Adjusting for traditional covariables, we observed that clusters 3 and 4 had a significantly higher risk for CV (28% and 20%, P ≤ 0.038) and cardiac (57% and 43%, P ≤ 0.024) adverse events. Using SHAP values we observed that the features that incorporate temporal information, such as the slope during systole and early diastole, had a higher impact on the model's decision than peak LV systolic strain.

Conclusion

Employing a GMM on features derived from the raw LV strain curves, we extracted clinically significant phenogroups which could provide additive prognostic information over the peak LV strain.

Functional Redundancy Secures Resilience of Chain Elongation Communities upon pH Shifts in Closed Bioreactor Ecosystems

For anaerobic mixed cultures performing microbial chain elongation, it is unclear how pH alterations affect the abundance of key players, microbial interactions, and community functioning in terms of medium-chain carboxylate yields. We explored pH effects on mixed cultures enriched in continuous anaerobic bioreactors representing closed model ecosystems. Gradual pH increase from 5.5 to 6.5 induced dramatic shifts in community composition, whereas product range and yields returned to previous states after transient fluctuations. To understand community responses to pH perturbations over long-term reactor operation, we applied Aitchison PCA clustering, linear mixed-effects models, and random forest classification on 16S rRNA gene amplicon sequencing and process data. Different pH preferences of two key chain elongation species─one Clostridium IV species related to Ruminococcaceae bacterium CPB6 and one Clostridium sensu stricto species related to Clostridium luticellarii─were determined. Network analysis revealed positive correlations of Clostridium IV with lactic acid bacteria, which switched from Olsenella to Lactobacillus along the pH increase, illustrating the plasticity of the food web in chain elongation communities. Despite long-term cultivation in closed systems over the pH shift experiment, the communities retained functional redundancy in fermentation pathways, reflected by the emergence of rare species and concomitant recovery of chain elongation functions.

LLM Multimodal Traffic Accident Forecasting

With the rise in traffic congestion in urban centers, predicting accidents has become paramount for city planning and public safety. This work comprehensively studied the efficacy of modern deep learning (DL) methods in forecasting traffic accidents and enhancing Level-4 and Level-5 (L-4 and L-5) driving assistants with actionable visual and language cues. Using a rich dataset detailing accident occurrences, we juxtaposed the Transformer model against traditional time series models like ARIMA and the more recent Prophet model. Additionally, through detailed analysis, we delved deep into feature importance using principal component analysis (PCA) loadings, uncovering key factors contributing to accidents. We introduce the idea of using real-time interventions with large language models (LLMs) in autonomous driving with the use of lightweight compact LLMs like LLaMA-2 and Zephyr-7b-α. Our exploration extends to the realm of multimodality, through the use of Large Language-and-Vision Assistant (LLaVA)-a bridge between visual and linguistic cues by means of a Visual Language Model (VLM)-in conjunction with deep probabilistic reasoning, enhancing the real-time responsiveness of autonomous driving systems. In this study, we elucidate the advantages of employing large multimodal models within DL and deep probabilistic programming for enhancing the performance and usability of time series forecasting and feature weight importance, particularly in a self-driving scenario. This work paves the way for safer, smarter cities, underpinned by data-driven decision making.

Enhancing Predictability Assessment: An Overview and Analysis of Predictability Measures for Time Series and Network Links

Driven by the variety of available measures intended to estimate predictability of diverse objects such as time series and network links, this paper presents a comprehensive overview of the existing literature in this domain. Our overview delves into predictability from two distinct perspectives: the intrinsic predictability, which represents a data property independent of the chosen forecasting model and serves as the highest achievable forecasting quality level, and the realized predictability, which represents a chosen quality metric for a specific pair of data and model. The reviewed measures are used to assess predictability across different objects, starting from time series (univariate, multivariate, and categorical) to network links. Through experiments, we establish a noticeable relationship between measures of realized and intrinsic predictability in both generated and real-world time series data (with the correlation coefficient being statistically significant at a 5% significance level). The discovered correlation in this research holds significant value for tasks related to evaluating time series complexity and their potential to be accurately predicted.

Cannabis Vape Product Sales in California Following CDC's Initial Advisory About Lung Injuries

Introduction: The 2019 outbreak of e-cigarette or vaping product use-associated lung injury (EVALI) is believed to have been caused by vitamin E acetate, an additive used in some cannabis vaporizer products. Previous studies have primarily focused on changes in sales of electronic nicotine delivery systems following the initial advisory issued by the Centers for Disease Control (CDC) on August 17, 2019. The present study is intended to examine variation by age groups in sales of regulated cannabis vape products in the state of California before, during, and after the outbreak. Methods: Weekly sales revenue of cannabis vape products (from January 1, 2018, to December 31, 2020) was obtained from a sample of recreational cannabis retailers licensed in California. An interrupted time series analysis, using AutoRegressive, Integrated, Moving Average methods, was employed to estimate changes in the sales and market share of cannabis vape products in the weeks following the CDC advisory. Results: The total volume of regulated cannabis vape product sales increased substantially over the 3-year study period (2018-2020). Sales and market share of cannabis vape products, however, declined in both young and older adults immediately following the advisory, rebounding to pre-EVALI levels only for the young adults. For sales, the potential EVALI effect following the CDC's advisory equates to an 8.0% and 2.2% decline below expected levels in the older and young adults, respectively. Conclusions: The differential age effect on sales may reflect concerns regarding health effects of cannabis vaping products and greater awareness of the outbreak among older adults. Findings highlight the importance of informing consumers about health risks associated with using cannabis vape products acquired from regulated versus illicit sources.

Design and Implementation of a Prototype Seismogeodetic System for Tectonic Monitoring

This manuscript describes the design, development, and implementation of a prototype system based on seismogeodetic techniques, consisting of a low-cost MEMS seismometer/accelerometer, a biaxial inclinometer, a multi-frequency GNSS receiver, and a meteorological sensor, installed at the Doñana Biological Station (Huelva, Spain) that transmits multiparameter data in real and/or deferred time to the control center at the University of Cadiz. The main objective of this system is to know, detect, and monitor the tectonic activity in the Gulf of Cadiz region and adjacent areas in which important seismic events occur produced by the interaction of the Eurasian and African plates, in addition to the ability to integrate into a regional early warning system (EWS) to minimize the consequences of dangerous geological phenomena.

[Time series analysis of fine particulate matter and death risk among residents in an urban area of Chongqing City in 2013-2020]

OBJECTIVE

To investigate the effects of the concentration of fine particulate matter(PM_(2.5)) on the risk of death among residents in an urban area of Chongqing, China.

METHODS

Daily data on mean PM_(2.5) concentration, meteorological factors(air temperature and relative humidity), and the number of deaths from 2013 to 2020 in this urban area were collected. A generalized additive model was used to analyze the association of PM_(2.5) concentration with the number of deaths, and stratified analyses by sex and age were further performed.

RESULTS

In this area from 2013 to 2020, the median concentration of atmospheric ambient PM_(2.5) was 44.00 μg/m~3; 48 089 non-accidental deaths, 19 252 deaths from circulatory diseases, and 8753 deaths from respiratory diseases were reported. The PM_(2.5) concentration was higher in winter and spring. The number of deaths showed no obvious seasonal changes. The time series analysis showed that for every 10 μg/m~3 increase in the PM_(2.5) concentration, the risks of non-accidental death(lag03), circulatory diseases-caused death(lag3), and respiratory diseases-caused death(lag03) increased by 0.64%(95% CI 0.07%-1.21%), 0.68%(95% CI 0.05%-1.32%) and 1.72%(95% CI 0.54%-2.90%), respectively. After adjusting for several gaseous pollutants(PM_(10), NO_2, O_3, SO_2 and CO), the impact of PM_(2.5) concentration on residents' health had no significant changes. The stratified analyses by sex and age showed that when the PM_(2.5) concentration increased, the risks of non-accidental death and death from respiratory diseases were higher in women and residents aged ≥65 years than in men and higher in residents aged ≥65 years than in those aged 5-64 years, but there were no significant differences between the groups.

CONCLUSION

PM_(2.5) pollution may increase the risk of death for residents in this urban area in Chongqing.

Lamb Wave-Based Structural Damage Detection: A Time Series Approach Using Cointegration

Although Lamb waves have found extensive use in structural damage detection, their practical applications remain limited. This limitation primarily arises from the intricate nature of Lamb wave propagation modes and the effect of temperature variations. Therefore, rather than directly inspecting and interpreting Lamb wave responses for insights into the structural health, this study proposes a novel approach, based on a two-step cointegration-based computation procedure, for structural damage evaluation using Lamb wave data represented as time series that exhibit some common trends. The first step involves the composition of Lamb wave series sharing a common upward (or downward) trend of temperature. In the second step, the cointegration analysis is applied for each group of Lamb wave series, which represents a certain condition of damage. So, a cointegration analysis model of Lamb wave series is created for each damage condition. The geometrical and statistical features of Lamb wave series and cointegration residual series are used for detecting and distinguishing damage conditions. These features include the shape, peak-to-peak amplitude, and variance of the series. The validity of this method is confirmed through its application to the Lamb wave data collected from both undamaged and damaged aluminium plates subjected to temperature fluctuations. The proposed approach can find its application not only in Lamb wave-based damage detection, but also in other structural health monitoring (SHM) systems where the data can be arranged in the form of sharing common environmental and/or operational trends.

Unraveling dyadic psycho-physiology of social presence between strangers during an audio drama - a signal-analysis approach

A mere co-presence of an unfamiliar person may modulate an individual's attentive engagement with specific events or situations to a significant degree. To understand better how such social presence affects experiences, we recorded a set of parallel multimodal facial and psychophysiological data with subjects (N = 36) who listened to dramatic audio scenes alone or when facing an unfamiliar person. Both a selection of 6 s affective sound clips (IADS-2) followed by a 27 min soundtrack extracted from a Finnish episode film depicted familiar and often intense social situations familiar from the everyday world. Considering the systemic complexity of both the chosen naturalistic stimuli and expected variations in the experimental social situation, we applied a novel combination of signal analysis methods using inter-subject correlation (ISC) analysis, Representational Similarity Analysis (RSA) and Recurrence Quantification Analysis (RQA) followed by gradient boosting classification. We report our findings concerning three facial signals, gaze, eyebrow and smile that can be linked to socially motivated facial movements. We found that ISC values of pairs, whether calculated on true pairs or any two individuals who had a partner, were lower than the group with single individuals. Thus, audio stimuli induced more unique responses in those subjects who were listening to it in the presence of another person, while individual listeners tended to yield a more uniform response as it was driven by dramatized audio stimulus alone. Furthermore, our classifiers models trained using recurrence properties of gaze, eyebrows and smile signals demonstrated distinctive differences in the recurrence dynamics of signals from paired subjects and revealed the impact of individual differences on the latter. We showed that the presence of an unfamiliar co-listener that modifies social dynamics of dyadic listening tasks can be detected reliably from visible facial modalities. By applying our analysis framework to a broader range of psycho-physiological data, together with annotations of the content, and subjective reports of participants, we expected more detailed dyadic dependencies to be revealed. Our work contributes towards modeling and predicting human social behaviors to specific types of audio-visually mediated, virtual, and live social situations.

Time-varying information measures: an adaptive estimation of information storage with application to brain-heart interactions

Network Physiology is a rapidly growing field of study that aims to understand how physiological systems interact to maintain health. Within the information theory framework the information storage (IS) allows to measure the regularity and predictability of a dynamic process under stationarity assumption. However, this assumption does not allow to track over time the transient pathways occurring in the dynamical activity of a physiological system. To address this limitation, we propose a time-varying approach based on the recursive least squares algorithm (RLS) for estimating IS at each time instant, in non-stationary conditions. We tested this approach in simulated time-varying dynamics and in the analysis of electroencephalographic (EEG) signals recorded from healthy volunteers and timed with the heartbeat to investigate brain-heart interactions. In simulations, we show that the proposed approach allows to track both abrupt and slow changes in the information stored in a physiological system. These changes are reflected in its evolution and variability over time. The analysis of brain-heart interactions reveals marked differences across the cardiac cycle phases of the variability of the time-varying IS. On the other hand, the average IS values exhibit a weak modulation over parieto-occiptal areas of the scalp. Our study highlights the importance of developing more advanced methods for measuring IS that account for non-stationarity in physiological systems. The proposed time-varying approach based on RLS represents a useful tool for identifying spatio-temporal dynamics within the neurocardiac system and can contribute to the understanding of brain-heart interactions.

Daylight Saving Time transitions and Cardiovascular Disease in Andalusia: Time Series Modeling and Analysis Using Visibility Graphs

The present study aimed to determine whether transitions both to and from daylight saving time (DST) led to an increase in the incidence of hospital admissions for major acute cardiovascular events (MACE). To support the analysis, natural visibility graphs (NVGs) were used with data from Andalusian public hospitals between 2009 and 2019. We calculated the incidence rates of hospital admissions for MACE, and specifically acute myocardial infarction and ischemic stroke during the 2 weeks leading up to, and 2 weeks after, the DST transition. NVG were applied to identify dynamic patterns. The study included 157 221 patients diagnosed with MACE, 71 992 with AMI (42 975 ST-elevation myocardial infarction (STEMI) and 26 752 non-ST-elevation myocardial infarction (NSTEMI)), and 51 420 with ischemic stroke. Observed/expected ratios shown an increased risk of AMI (1.06; 95% CI (1.00-1.11); P = .044), NSTEMI (1.12; 95% CI (1.02-1.22); P = .013), and acute coronary syndrome (1.05; 95% CI (1.00-1.10); P = .04) around the autumn DST. The NVG showed slight variations in the daily pattern of pre-DST and post-DST hospitalization admissions for all pathologies, but indicated that the increase in the incidence of hospital admissions after the DST is not sufficient to change the normal pattern significantly.

A user-friendly guide to using distance measures to compare time series in ecology

Time series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification (e.g., assigning species to individual bird calls); clustering (e.g., clustering similar responses in population dynamics to abrupt changes in the environment or management interventions); prediction (e.g., accuracy of model predictions to original time series data); and anomaly detection (e.g., detecting possible catastrophic events from population time series). These common tasks in ecological research all rely on the notion of (dis-) similarity, which can be determined using distance measures. A plethora of distance measures have been described, predominantly in the computer and information sciences, but many have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures for time-series-related tasks. Therefore, many potential applications remain unexplored. Here, we describe 16 properties of distance measures that are likely to be of importance to a variety of ecological questions involving time series. We then test 42 distance measures for each property and use the results to develop an objective method to select appropriate distance measures for any task and ecological dataset. We demonstrate our selection method by applying it to a set of real-world data on breeding bird populations in the UK and discuss other potential applications for distance measures, along with associated technical issues common in ecology. Our real-world population trends exhibit a common challenge for time series comparisons: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well suited to comparing noisy time series and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit-for-purpose but are consistent in their rankings of the population trends. The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing ecological time series and help us answer new ecological questions.

Who is calling? Optimizing source identification from marmoset vocalizations with hierarchical machine learning classifiers

With their highly social nature and complex vocal communication system, marmosets are important models for comparative studies of vocal communication and, eventually, language evolution. However, our knowledge about marmoset vocalizations predominantly originates from playback studies or vocal interactions between dyads, and there is a need to move towards studying group-level communication dynamics. Efficient source identification from marmoset vocalizations is essential for this challenge, and machine learning algorithms (MLAs) can aid it. Here we built a pipeline capable of plentiful feature extraction, meaningful feature selection, and supervised classification of vocalizations of up to 18 marmosets. We optimized the classifier by building a hierarchical MLA that first learned to determine the sex of the source, narrowed down the possible source individuals based on their sex and then determined the source identity. We were able to correctly identify the source individual with high precisions (87.21%-94.42%, depending on call type, and up to 97.79% after the removal of twins from the dataset). We also examine the robustness of identification across varying sample sizes. Our pipeline is a promising tool not only for source identification from marmoset vocalizations but also for analysing vocalizations of other species.

Opioid Injection-Associated Bacterial Infections in England, 2002-2021: A Time Series Analysis of Seasonal Variation and the Impact of Coronavirus Disease 2019

BACKGROUND

Bacterial infections cause substantial pain and disability among people who inject drugs. We described time trends in hospital admissions for injecting-related infections in England.

METHODS

We analyzed hospital admissions in England between January 2002 and December 2021. We included patients with infections commonly caused by drug injection, including cutaneous abscesses, cellulitis, endocarditis, or osteomyelitis, and a diagnosis of opioid use disorder. We used Poisson regression to estimate seasonal variation and changes associated with coronavirus disease 2019 (COVID-19) response.

RESULTS

There were 92 303 hospital admissions for injection-associated infections between 2002 and 2021. Eighty-seven percent were skin, soft-tissue, or vascular infections; 72% of patients were male; and the median age increased from 31 years in 2002 to 42 years in 2021. The rate of admissions reduced from 13.97 per day (95% confidence interval [CI], 13.59-14.36) in 2003 to 8.94 (95% CI, 8.64-9.25) in 2011, then increased to 18.91 (95% CI, 18.46-19.36) in 2019. At the introduction of COVID-19 response in March 2020, the rate of injection-associated infections reduced by 35.3% (95% CI, 32.1-38.4). Injection-associated infections were also seasonal; the rate was 1.21 (95% CI, 1.18-1.24) times higher in July than in February.

CONCLUSIONS

This incidence of opioid injection-associated infections varies within years and reduced following COVID-19 response measures. This suggests that social and structural factors such as housing and the degree of social mixing may contribute to the risk of infection, supporting investment in improved social conditions for this population as a means to reduce the burden of injecting-related infections.

The temporal event-based model: Learning event timelines in progressive diseases

Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer's disease (AD) and Huntington's disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of 80 % with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions.

Heterogeneity in Individual Network Analysis: Reality or Illusion?

The use of idiographic research techniques has gained popularity within psychological research and network analysis in particular. Idiographic research has been proposed as a promising avenue for future research, with differences between idiographic results highlighting evidence for radical heterogeneity. However, in the quest to address the individual in psychology, some classic statistical problems, such as those arising from sampling variation and power limitations, should not be overlooked. This article aims to determine to what extent current tools to compare idiographic networks are suited to disentangle true from illusory heterogeneity in the presence of sampling error. To this end, we investigate the performance of tools to inspect heterogeneity (visual inspection, comparison of centrality measures, investigating standard deviations of random effects, and GIMME) through simulations. Results show that power limitations hamper the validity of conclusions regarding heterogeneity and that the power required to assess heterogeneity adequately is often not realized in current research practice. Of the tools investigated, inspecting standard deviations of random effects and GIMME proved the most suited. However, all tools evaluated leave the door wide open to misinterpret all observed variability in terms of individual differences. Hence, the current paper calls for caution in the use and interpretation of new time-series techniques when it comes to heterogeneity.

Estimating true prevalence through questionnaire data

We present a general analytical method for obtaining unbiased prevalence estimates based on data from regional or national testing programs, where individual participation in the testing program is voluntary but where additional questionnaire data is collected regarding the individual-level reason/motivation for being tested. The approach is based on re-writing the conditional probabilities for being tested, being infected, and having symptoms, so that a series of equations can be defined that relate estimable quantities (from test data and questionnaire data) to the result of interest (an unbiased estimate of prevalence). The final estimates appear to be robust based on prima-facie examination of the temporal dynamics estimated, as well as agreement with an independent estimate of prevalence. Our approach demonstrates the potential strength of incorporating questionnaires when testing a population during an outbreak, and can be used to help obtain unbiased estimates of prevalence in similar settings.

Statistical Analysis of Plasma Dynamics in Gyrokinetic Simulations of Stellarator Turbulence

A geometrical method for assessing stochastic processes in plasma turbulence is investigated in this study. The thermodynamic length methodology allows using a Riemannian metric on the phase space; thus, distances between thermodynamic states can be computed. It constitutes a geometric methodology to understand stochastic processes involved in, e.g., order-disorder transitions, where a sudden increase in distance is expected. We consider gyrokinetic simulations of ion-temperature-gradient (ITG)-mode-driven turbulence in the core region of the stellarator W7-X with realistic quasi-isodynamic topologies. In gyrokinetic plasma turbulence simulations, avalanches, e.g., of heat and particles, are often found, and in this work, a novel method for detection is investigated. This new method combines the singular spectrum analysis algorithm with a hierarchical clustering method such that the time series is decomposed into two parts: useful physical information and noise. The informative component of the time series is used for the calculation of the Hurst exponent, the information length, and the dynamic time. Based on these measures, the physical properties of the time series are revealed.

Collective Dynamics, Diversification and Optimal Portfolio Construction for Cryptocurrencies

Since its conception, the cryptocurrency market has been frequently described as an immature market, characterized by significant swings in volatility and occasionally described as lacking rhyme or reason. There has been great speculation as to what role it plays in a diversified portfolio. For instance, is cryptocurrency exposure an inflationary hedge or a speculative investment that follows broad market sentiment with amplified beta? We have recently explored similar questions with a clear focus on the equity market. There, our research revealed several noteworthy dynamics such as an increase in the market's collective strength and uniformity during crises, greater diversification benefits across equity sectors (rather than within them), and the existence of a "best value" portfolio of equities. In essence, we can now contrast any potential signatures of maturity we identify in the cryptocurrency market and contrast these with the substantially larger, older and better-established equity market. This paper aims to investigate whether the cryptocurrency market has recently exhibited similar mathematical properties as the equity market. Instead of relying on traditional portfolio theory, which is grounded in the financial dynamics of equity securities, we adjust our experimental focus to capture the presumed behavioral purchasing patterns of retail cryptocurrency investors. Our focus is on collective dynamics and portfolio diversification in the cryptocurrency market, and examining whether previously established results in the equity market hold in the cryptocurrency market and to what extent. The results reveal nuanced signatures of maturity related to the equity market, including the fact that correlations collectively spike around exchange collapses, and identify an ideal portfolio size and spread across different groups of cryptocurrencies.

Increasing positive rate of IgG against hepatitis E virus with steady IgM positivity and clinical incidence: A retrospective seroprevalence time series analysis of HEV from 2012 to 2021 in Chongqing, China

China is an epidemic area of hepatitis E, and the serum prevalence data is very important for formulating prevention and control strategies. However, almost all related research in the past decade are cross-sectional studies. In this study, we analyzed the serological data from 2012 to 2021 in Chongqing for 10 consecutive years. We found that the positive rate of hepatitis E IgG antibody increased gradually, from 1.61% in January 2012 to 50.63% in December 2021. The autoregressive integrated moving average model was used to predict the trend, and it was found that it will continue to show an upward trend in the recent future. In contrast, the positive rate of IgM and clinical incidence of hepatitis E showed a relatively stable trend. Although the positive rate of antibodies gradually increased with age, there was no significant difference in the age distribution of the subjects each year. Therefore, these results suggest that the accumulated infection of hepatitis E in Chongqing may be gradually increasing, but the clinical incidence rate remains unchanged, which provides a new concern for formulating prevention and control strategies.

The Hypothetical Psychological Impact of the COVID-19 Pandemic on Pediatrics and Pediatric Emergency Admissions: Evidence from Autoregressive Distributed Lag Model Method

OBJECTIVE

The COVID-19 pandemic and related deaths affected the number of admissions of patients to hospitals. However, no study has been found that examines the short and long-term psychological effects of children or their possible psychiatric admissions to hospitals during the pandemic period. In this context, the study aims to analyze the behaviors of individuals under the age of 18 in their health service utilization during the COVID-19 pandemic period.

MATERIALS AND METHOD

For the study, whether the pandemic and psychiatry department (PSY) admissions affect the pediatrics department (PD) and pediatric emergency department (PED) admissions of children was investigated. The sample was taken from hospitals in Sivas between 2019 and 2021. Autoregressive dis- tributed lag (ARDL) model is applied. The ARDL is an econometric method that can estimate the existence of the long-term correlations (cointegration) of variables and the short and long-term effects of explanatory variables on the dependent variable.

RESULTS

In the PED application model, the number of deaths, representing the impact of the pandemic, decreased the number of PED applications, while the number of vaccinations increased. On the other hand, applications to the PSY decreased in the short term, but increased in the long term. In the model of pediatric department admissions, in the long term, the number of new COVID-19 cases has decreased the number of PD admissions, while the number of vaccines has increased. While applications made to PSY in the short term have decreased the applications of PD, they have increased in the long term. As a result, the pandemic decreased both children's department admissions. In addition, admissions to PSY, which had greatly decreased in the short term, increased rapidly in the long term.

CONCLUSION

Providing psychological support to both children and adolescents and their guardians during and after the pandemic should be included in planning.

in England and Wales, 2015-2021: a time series analysis

Introduction. In England and Wales, cryptosporidiosis cases peak in spring and autumn, associated with zoonotic/environmental exposures (Cryptosporidium parvum, spring/autumn) and overseas travel/water-based activities (Cryptosporidium hominis, autumn). Coronavirus disease 2019 (COVID-19) restrictions prevented social mixing, overseas travel and access to venues (swimming pools/restaurants) for many months, potentially increasing environmental exposures as people sought alternative countryside activities.Hypothesis. COVID-19 restrictions reduced incidence of C. hominis cases and potentially increased incidence of C. parvum cases.Aim. To inform/strengthen surveillance programmes, we investigated the impact of COVID-19 restrictions on the epidemiology of C. hominis and C. parvum cases.Methodology. Cases were extracted from the Cryptosporidium Reference Unit (CRU) database (1 January 2015 to 31 December 2021). We defined two periods for pre- and post-COVID-19 restrictions implementation, corresponding to before and after the first UK-wide lockdown on 23 March 2020. We conducted a time series analysis, assessing differences in C. parvum and C. hominis incidence, trends and periodicity between these periods.Results. There were 21 304 cases (C. parvum=12 246; C. hominis=9058). Post-restrictions implementation incidence of C. hominis dropped by 97.5 % (95 % CI: 95.4-98.6 %; P<0.001). The decreasing incidence trend pre-restrictions was not observed post-restrictions implementation due to lack of cases. No periodicity change was observed post-restrictions implementation. There was a strong social gradient; there was a higher proportion of cases in deprived areas. For C. parvum, post-restrictions implementation incidence fell by 49.0 % (95 % CI: 38.4-58.3 %; P<0.001). There was no pre-restrictions incidence trend but an increasing incidence trend post-restrictions implementation. A periodicity change was observed post-restriction implementation, peaking 1 week earlier in spring and 2 weeks later in autumn. The social gradient was the inverse of that for C. hominis. Where recorded, 22 % of C. hominis and 8 % of C. parvum cases had travelled abroad.Conclusion. C. hominis cases almost entirely ceased post-restrictions implementation, reinforcing that foreign travel seeds infections. C. parvum incidence fell sharply but recovered post-restrictions implementation, consistent with relaxation of restrictions. Future exceedance reporting for C. hominis should exclude the post-restriction implementation period but retain it for C. parvum (except the first 6 weeks post-restrictions implementation). Infection prevention and control advice should be improved for people with gastrointestinal illness (GI) symptoms to ensure hand hygiene and swimming pool avoidance.

Predicting antimicrobial resistance of bacterial pathogens using time series analysis

Antimicrobial resistance (AMR) is arguably one of the major health and economic challenges in our society. A key aspect of tackling AMR is rapid and accurate detection of the emergence and spread of AMR in food animal production, which requires routine AMR surveillance. However, AMR detection can be expensive and time-consuming considering the growth rate of the bacteria and the most commonly used analytical procedures, such as Minimum Inhibitory Concentration (MIC) testing. To mitigate this issue, we utilized machine learning to predict the future AMR burden of bacterial pathogens. We collected pathogen and antimicrobial data from >600 farms in the United States from 2010 to 2021 to generate AMR time series data. Our prediction focused on five bacterial pathogens (Escherichia coli, Streptococcus suis, Salmonella sp., Pasteurella multocida, and Bordetella bronchiseptica). We found that Seasonal Auto-Regressive Integrated Moving Average (SARIMA) outperformed five baselines, including Auto-Regressive Moving Average (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA). We hope this study provides valuable tools to predict the AMR burden not only of the pathogens assessed in this study but also of other bacterial pathogens.

Spatio-Temporal Cluster Mapping System in Smart Beds for Patient Monitoring

Innovative technological solutions are required to improve patients' quality of life and deliver suitable treatment. Healthcare workers may be able to watch patients from a distance using the Internet of Things (IoT) by using big data algorithms to analyze instrument outputs. Therefore, it is essential to gather information on use and health problems in order to improve the remedies. To ensure seamless incorporation for use in healthcare institutions, senior communities, or private homes, these technological tools must first and foremost be easy to use and implement. We provide a network cluster-based system known as smart patient room usage in order to achieve this. As a result, nursing staff or caretakers can use it efficiently and swiftly. This work focuses on the exterior unit that makes up a network cluster, a cloud storage mechanism for data processing and storage, as well as a wireless or unique radio frequency send module for data transfer. In this article, a spatio-temporal cluster mapping system is presented and described. This system creates time series data using sense data collected from various clusters. The suggested method is the ideal tool to use in a variety of circumstances to improve medical and healthcare services. The suggested model's ability to anticipate moving behavior with high precision is its most important feature. The time series graphic displays a regular light movement that continued almost the entire night. The last 12 h' lowest and highest moving duration numbers were roughly 40% and 50%, respectively. When there is little movement, the model assumes a normal posture. Particularly, the moving duration ranges from 7% to 14%, with an average of 7.0%.

The Impact of Aggregate Level Alcohol Consumption on Homicide Rates: A Time Series Analysis

Consumption of alcohol has an impact on violent crimes and homicides. The study examines the association between aggregate level consumption of spirit and homicide rates in the State of Kerala in India. Time-series analyses were conducted by building Autoregressive Moving Average with Exogenous Variables (ARMAX) models and OLS Regression models to explain the relationship between the monthly rate of consumption of alcoholic spirits and homicide rates. The study concludes that consumption of alcoholic spirits has a statistically significant impact on the total homicide rates and the male and female homicide rates. The study has significant policy implications being one of the first studies examining the relationship between alcohol consumption and homicide rates in India and suggesting methods to address challenges of adverse public health consequences associated with alcohol consumption.

Playing HAVOK on the Chaos Caused by Internet Trolls

Trump supporting Twitter posting activity from right-wing Russian trolls active during the 2016 United States presidential election was analyzed at multiple timescales using a recently developed procedure for separating linear and nonlinear components of time series. Trump supporting topics were extracted with DynEGA (Dynamic Exploratory Graph Analysis) and analyzed with Hankel Alternative View of Koopman (HAVOK) procedure. HAVOK is an exploratory and predictive technique that extracts a linear model for the time series and a corresponding nonlinear time series that is used as a forcing term for the linear model. Together, this forced linear model can produce surprisingly accurate reconstructions of nonlinear and chaotic dynamics. Using the R package havok, Russian troll data yielded well-fitting models at several timescales, not producing well-fitting models at others, suggesting that only a few timescales were important for representing the dynamics of the troll factory. We identified system features that were timescale-universal versus timescale-specific. Timescale-universal features included cycles inherent to troll factory governance, which identified their work-day and work-week organization, later confirmed from published insider interviews. Cycles were captured by eigen-vector basis components resembling Fourier modes, rather than Legendre polynomials typical for HAVOK. This may be interpreted as the troll factory having intrinsic dynamics that are highly coupled to nearly stationary cycles. Forcing terms were timescale-specific. They represented external events that precipitated major changes in the time series and aligned with major events during the political campaign. HAVOK models specified interactions between the discovered components allowing to reverse-engineer the operation of Russian troll factory. Steps and decision points in the HAVOK analysis are presented and the results are described in detail.

Media Production on Smartphones: Analysis of the Timing, Content, and Context of Message Production Using Real-World Smartphone Use Data

Although media production is a critical concept in communication theory, we know surprisingly little about the timing, content, and context of individuals' production behavior. Intensive observation and analysis of 94 American adults' smartphone use over 1 week showed that although time spent in producing content was on average only about 6 percent of the amount of time spent on smartphones, the production content was more purposeful, expressive, articulate, condensed, confident, personal, and emotionally charged than consumption content. Analysis of the temporal dynamics of production suggests that the content consumed in the minute before individuals' production began to resemble the subsequently produced content. Other results suggest that content production on smartphones was fragmented, idiosyncratic, and purposeful, highlighting the impact of individuals' quick interactions with media, and the need to develop user-centric theories about how, when, and why individuals produce digital content.

The effect of vaccine mandate announcements on vaccine uptake in Canada: An interrupted time series analysis

Introduction

In 2021, the ten provinces in Canada enacted COVID-19 vaccine mandates that restricted access to non-essential businesses and services to those that could provide proof of full vaccination to decrease the risk of transmission and provide an incentive for vaccination. This analysis aims to examine the effects of vaccine mandate announcements on vaccine uptake over time by age group and province.

Methods

Aggregated data from the Canadian COVID-19 Vaccination Coverage Surveillance System (CCVCSS) were used to measure vaccine uptake (defined as the weekly proportion of individuals who received at least one dose) among those 12 years and older following the announcement of vaccination requirements. We performed an interrupted time series analysis using a quasi-binomial autoregressive model adjusted for the weekly number of new COVID-19 cases, hospitalizations, and deaths to model the effect of mandate announcements on vaccine uptake. Additionally, counterfactuals were produced for each province and age group to estimate vaccine uptake without mandate implementation.

Results

The times series models demonstrated significant increases in vaccine uptake following mandate announcement in BC, AB, SK, MB, NS, and NL. No trends in the effect of mandate announcements were observed by age group. In AB and SK, counterfactual analysis showed that announcement were followed by 8 % and 7 % (310,890 and 71,711 people, respectively) increases in vaccination coverage over the following 10 weeks. In MB, NS, and NL, there was at least a 5 % (63,936, 44,054, and 29,814 people, respectively) increase in coverage. Lastly, BC announcements were followed by a 4 % (203,300 people) increase in coverage.

Conclusion

Vaccine mandate announcements could have increased vaccine uptake. However, it is difficult to interpret this effect within the larger epidemiological context. Effectiveness of the mandates can be affected by pre-existing levels of uptake, hesitancy, timing of announcements and local COVID-19 activity.

Multifractal Analysis of the Influence of Indole-3-Acetic Acid on Fast-Activating Vacuolar (FV) Channels of Beta vulgaris L. Taproot Cells

In this paper, the multifractal properties of the ion current time series in the fast-activating vacuolar (FV) channels of Beta vulgaris L. taproot cells were investigated. These channels are permeable for only monovalent cations and mediate K⁺ at very low concentrations of cytosolic Ca²⁺ and large voltages of either polarity. Using the patch clamp technique, the currents of the FV channels in red beet taproot vacuoles were recorded and analysed by using the multifractal detrended fluctuation analysis (MFDFA) method. The activity of the FV channels depended on the external potential and was sensitive to the auxin. It was also shown that the singularity spectrum of the ion current in the FV channels is non-singular, and the multifractal parameters, i.e., the generalised Hurst exponent and the singularity spectrum, were modified in the presence of IAA. Taking into account the obtained results, it can be suggested that the multifractal properties of fast-activating vacuolar (FV) K⁺ channels, indicating the existence of long-term memory, should be taken into account in the molecular mechanism of the auxin-induced growth of plant cells.

Trends in Collegiate Student-Athlete Mental Health in the National College Health Assessment, 2011-2019

CONTEXT

Recently, the athletic training community has paid increased attention to collegiate student-athlete mental health, mental health treatment-seeking behavior, and the effects of mental health factors on athletic and academic performance. Ongoing efforts to better educate and equip athletic trainers to help student-athletes in this regard should result in improved mental health-related outcomes.

OBJECTIVE

To examine changes in the mental health of student-athletes over the past decade compared with that of nonathlete students.

DESIGN

Cross-sectional study.

SETTING

Colleges and universities in the United States.

PATIENTS OR OTHER PARTICIPANTS

Varsity athletes (athletes; n = 54 479) and nonathlete students (nonathletes; n = 448 301) who completed the National College Health Assessment between 2011 and 2019.

MAIN OUTCOME MEASURE(S)

Surveys included responses (self-reported) to questions in 5 mental health-related categories: recent mental health symptoms, recent mental health diagnosis, mental health treatment-seeking behavior, receiving mental health information from the institution, and the recent effect of mental health factors on academic performance.

RESULTS

Athletes consistently described lower symptom and diagnosis rates compared with nonathletes, except for attempted suicide, substance abuse, and eating disorders. Rates of diagnosis increased over time in both groups but remained lower in athletes. Treatment-seeking behavior and openness to future treatment increased over time in both groups but remained lower in athletes. Athletes received more information on stress reduction, substance abuse, eating disorders, and handling distress or violence compared with nonathletes. Both groups received information more frequently over time. Athletes reported fewer academic effects, especially for depression and anxiety, but these effects grew over time in both groups. The effects of injuries and extracurricular activities on academic performance were greater in athletes than in nonathletes.

CONCLUSIONS

Athletes described overall lower levels of mental health symptoms, diagnoses, and academic effects compared with nonathletes. Whereas the rates in nonathletes climbed over the past decade, the rates in athletes broadly remained flat or climbed less rapidly. Increasingly positive attitudes toward treatment were encouraging, but the deficit in athletes relative to nonathletes persisted. Ongoing efforts of athletic trainers to educate athletes and guide them to mental health resources are needed to continue, or better yet to accelerate, the observed positive trends in information dissemination and treatment-seeking behavior.