Primary coronary intervention in ST-elevation myocardial infarction

Összefoglaló. Bevezetés: Heveny myocardialis infarctusban a szívizommentés sikere, a beteg életkilátása nagymértékben függ a panasz kezdete és az elzáródott koszorúér rekanalizálása között eltelt ischaemiás időtől. Jelenleg az ér nyitása optimális esetben minden betegnél koszorúér-intervencióval történik. Célkitűzés: Annak vizsgálata, hogy öt év alatt mennyit változtak az ischaemiás idő összetevői, és miben változott az elzáródott ér nyitásának módszere ST-elevációs myocardialis infarctus (STEMI) miatt végzett primer coronariaintervencióban. Módszer: 2014. 01. 01. és 2018. 12. 31. között 1663, STEMI miatt koszorúér-intervencióval kezelt betegnél (1173 férfi és 490 nő) vizsgáltuk évenkénti bontásban a panasztól a koszorúér nyitásáig eltelt idő összetevőit és a 30 napos halálozást. Eredmények: Öt év alatt a panasztól az első egészségügyi kontaktusig medián 2:53 vs. 2:10 óra (p = 0,0132), ettől az intervenciós centrumba történt felvételig medián 1:17 vs. 1:03 óra (p = 0,009), a felvételtől a ballon nyitásáig medián 0:31 vs. 0:29 óra (p = ns) telt el. A panasztól a ballon nyitásáig eltelt idő (medián 5:29 vs. 4:07 óra, p = 0,0001) rövidült, döntően 2014 és 2015 között. A gyógyszerkibocsátó stent beültetése 15%-ról 96%-ra nőtt. A vizsgált években a légzés/keringés támogatás aránya 8,2-10,6-13,9-7,6-8,4, a 30 napos halálozásé 4,1-6,8-11,1-7,4-5,7% volt; a két érték korrelációt mutat (p = 0,827). Következtetés: Öt év alatt a panasztól az első egészségügyi kontaktusig és a kórházi beszállításig eltelt idő rövidült, de az Európai Kardiológiai Társaság ajánlásához képest hosszú; a kórházi felvételtől a ballon nyitásáig eltelt idő megfelelő. A négy órán belüli reperfúzió a betegek közel felében valósult meg. Az intervenciós centrumba való gyorsabb bekerülés javíthatna az eredményen. Orv Hetil. 2021; 162(13): 497-503.

SUMMARY

INTRODUCTION

In acute myocardial infarction, the heart muscle salvage, the patient's life expectancy is highly dependent on the elapsed ischaemic time from the onset of complaint to target vessel recanalisation. Nowadays, target vessel recanalisation is performed with coronary intervention in all patients in optimal case.

OBJECTIVE

To examine how the components of ischemic time and the opening procedure of the occluded coronary have changed over five years in primary intervention done in acute ST-elevation myocardial infarction (STEMI).

METHOD

Authors studied data of 1663 (1173 male and 480 female) STEMI patients in annual breakdowns treated with coronary intervention between 01. 01. 2014 and 31. 12. 2018, time from complaint to coronary artery opening, details of intervention and 30 days mortality rate.

RESULTS

During the five years, time intervals were as follows: from onset of complaint to first medical contact: median 2:53 vs. 2:10 hours (p = 0.0132), from this to admission in the interventional centre: median 1:17 vs. 1:03 hours (p = 0.009), from hospital admission to balloon opening: median 0:31 vs. 0:29 hours (p = ns). In total, the complaint to balloon opening time (median 5:29 vs. 4:07 hours, p = 0.0001) diminished, decisively from 2014 to 2015. Ratio of drug-eluting stent implantation increased from 15% to 96%. In the investigated years, the need of respiratory and/or circulatory device support ratio was 8.2-10.6-13.9-7.6-8.4, 30-day mortality rate between 4.1-6.8-11.1-7.4-5.7%; these two values showed a correlation (p = 0.827).

CONCLUSION

The time from complaint to first medical contact and transfer to hospital against the significant decrease is still longer than the recommendation of the European Society of Cardiology. The time from hospital admission to balloon opening is adequate. Reperfusion within four hours was achieved in half of the patients in total. Faster hospitalization may improve results. Orv Hetil. 2021; 162(13): 497-503.

Can Japan Achieve Zero Transmission of HIV? Time Series Analysis Using Bayesian Local Linear Trend Model

BACKGROUND

The number of newly diagnosed human immunodeficiency virus (HIV) infections and acquired immune deficiency syndrome (AIDS) patients in Japan appears to be decreasing. However, whether these new infections cease to occur in the future in Japan, similar to abroad, is unclear. To evaluate the feasibility of this achievement, we conducted a time series analysis using Bayesian local linear trend model to evaluate the possibility of zero new infection of HIV/AIDS in Japan.

METHODS

We used quarterly data on HIV/AIDS from the first quarter, 2001 to the second quarter, 2020. Bayesian analyses were conducted using Markov chain Monte Carlo (MCMC) method, and a local linear trend model was constructed for number of newly diagnosed HIV infection without AIDS diagnosis, AIDS cases, and their aggregate. Predictions for the following 60 quarters until the second quarter of 2035 were also made for all models.

RESULTS

The mean aggregate cases of HIV/AIDS patients became 0 by the fourth quarter of 2031 (90% credible interval 0-535). For HIV infections alone, mean cases became 0 by the second quarter of 2030 (90%CrI 0-472). For AIDS alone mean cases were 9 at the second quarter of 2035 (90%CrI 0-231).

CONCLUSION

Our local linear trend model suggested that number of HIV/AIDS cases in Japan could decrease to zero by the first quarter of 2031, if the trend of the infections followed the local linear trend model, yet with rather wide credible interval. Achieving zero new transmission of HIV in Japan is a realistic goal but measures to make it faster may be needed.

ICU Patients' Antibiotic Exposure and Triazole-Resistance in Invasive Candidiasis: Parallel Analysis of Aggregated and Individual Data

Background: The relationship between antibiotic use and the incidence of triazole-resistant phenotypes of invasive candidiasis (IC) in critically ill patients is unclear. Different methodologies on determining this relationship may yield different results.

Methods: A retrospective multicenter observational analysis was conducted to investigate exposure to antibiotics and the incidence of non-duplicate clinical isolates of Candida spp. resistant to fluconazole, voriconazole, or both during November 2013 to April 2018, using two different methodologies: group-level (time-series analysis) and individual-patient-level (regression analysis and propensity-score adjusting).

Results: Of 393 identified Candida spp. from 388 critically ill patients, there were three phenotypes of IC identified: fluconazole-resistance (FR, 63, 16.0%); voriconazole-resistance (VR, 46, 11.7%); and cross-resistance between fluconazole and voriconazole (CR, 32, 8.1%). Exposure to several antibacterial agents with activity against the anaerobic gastrointestinal flora, especially third-generation cefalosporins (mainly cefoperazone/sulbactam and ceftriaxone), but not triazoles, have an immediate effect (time lag = 0) on subsequent ICU-acquired triazole-resistant IC in the group-level (p < 0.05). When the same patient database was analyzed at the individual-patient-level, we found that exposure to many antifungal agents was significantly associated with triazole-resistance (fluconazole [adjusted odds ratio (aOR) = 2.73] or caspofungin [aOR = 11.32] on FR, voriconazole [aOR = 2.87] on CR). Compared to the mono-triazole-resistant phenotype, CR IC has worse clinical outcomes (14-days mortality) and a higher level of resistance.

Conclusion: Group-level and individual-patient-level analyses of antibiotic-use-versus-resistance relations yielded distinct but valuable results. Antibacterials with antianaerobic activity and antifungals might have “indirect” and “direct” effect on triazole-resistant IC, respectively.

Incidence and Death Rates from COVID-19 Are Not Always Coupled: An Analysis of Temporal Data on Local, Federal, and National Levels

SARS-CoV-2 has caused a deadly pandemic worldwide, placing a burden on local health care systems and economies. Infection rates with SARS-CoV-2 and the related mortality of COVID-19 are not equal among countries or even neighboring regions. Based on data from official German health authorities since the beginning of the pandemic, we developed a case-fatality prediction model that correctly predicts COVID-19-related death rates based on local geographical developments of infection rates in Germany, Bavaria, and a local community district city within Upper Bavaria. Our data point towards the proposal that local individual infection thresholds, when reached, could lead to increasing mortality. Restrictive measures to minimize the spread of the virus could be applied locally based on the risk of reaching the individual threshold. Being able to predict the necessity for increasing hospitalization of COVID-19 patients could help local health care authorities to prepare for increasing patient numbers.

Network Analysis of Cross-Correlations on Forex Market during Crises. Globalisation on Forex Market

Within the paper, the problem of globalisation during financial crises is analysed. The research is based on the Forex exchange rates. In the analysis, the power law classification scheme (PLCS) is used. The study shows that during crises cross-correlations increase resulting in significant growth of cliques, and also the ranks of nodes on the converging time series network are growing. This suggests that the crises expose the globalisation processes, which can be verified by the proposed analysis.

Testing Jump-Diffusion in Epileptic Brain Dynamics: Impact of Daily Rhythms

Stochastic approaches to complex dynamical systems have recently provided broader insights into spatial-temporal aspects of epileptic brain dynamics. Stochastic qualifiers based on higher-order Kramers-Moyal coefficients derived directly from time series data indicate improved differentiability between physiological and pathophysiological brain dynamics. It remains unclear, however, to what extent stochastic qualifiers of brain dynamics are affected by other endogenous and/or exogenous influencing factors. Addressing this issue, we investigate multi-day, multi-channel electroencephalographic recordings from a subject with epilepsy. We apply a recently proposed criterion to differentiate between Langevin-type and jump-diffusion processes and observe the type of process most qualified to describe brain dynamics to change with time. Stochastic qualifiers of brain dynamics are strongly affected by endogenous and exogenous rhythms acting on various time scales—ranging from hours to days. Such influences would need to be taken into account when constructing evolution equations for the epileptic brain or other complex dynamical systems subject to external forcings.

Application of long-term collected data for conservation: Spatio-temporal patterns of mortality in Japanese serow

Monitoring the mortality of wildlife provides basic demographic information to support management plan preparation. The utility of mortality records for conservation measures was investigated in the Japanese serow, focusing on temporal trends and spatial distribution. Using the mortality records of Japanese serow from 2006 to 2018 in Gifu prefecture, cause-specific mortality was categorized into five groups (disease, accident, vehicle collision, parapoxvirus infection, and unknown), and the sex ratios were examined. A state space model was used to analyze the time series for the monthly mortalities, and kernel estimation was used for the spatial distribution of the parapoxvirus infection. Land cover type around the detection point was also reported. Disease, accident, and vehicle collision mortality were similar, and 30% of mortality was of anthropogenic origin. The number of mortality records for males was higher, and the larger home range of males could account for this. The state space model showed moderate increases in monthly mortalities over time and a seasonal variation with the highest level in spring and lowest in winter. Land cover analysis demonstrated a temporal increase in the proportion of human settlement areas, suggesting the change of the Japanese serow habitat. The proximity of Japanese serow to human settlements contributed to increase in mortality records. The point pattern analysis indicated spatial clustering for parapoxvirus infection in the area where an epidemic had occurred in the past. Several measures are recommended; however, mortality records can help develop improved conservation plan.

Prediction model for COVID-19 patient visits in the ambulatory setting

Objective

Healthcare systems globally were shocked by coronavirus disease 2019 (COVID-19). Policies put in place to curb the tide of the pandemic resulted in a decrease of patient volumes throughout the ambulatory system. The future implications of COVID-19 in healthcare are still unknown, specifically the continued impact on the ambulatory landscape. The primary objective of this study is to accurately forecast the number of COVID-19 and non-COVID-19 weekly visits in primary care practices.

Materials and Methods

This retrospective study was conducted in a single health system in Delaware. All patients' records were abstracted from our electronic health records system (EHR) from January 1, 2019 to July 25, 2020. Patient demographics and comorbidities were compared using t-tests, Chi square, and Mann Whitney U analyses as appropriate. ARIMA time series models were developed to provide an 8-week future forecast for two ambulatory practices (AmbP) and compare it to a naïve moving average approach.

Results

Among the 271,530 patients considered during this study period, 4,195 patients (1.5%) were identified as COVID-19 patients. The best fitting ARIMA models for the two AmbP are as follows: AmbP1 COVID-19+ ARIMAX(4,0,1), AmbP1 nonCOVID-19 ARIMA(2,0,1), AmbP2 COVID-19+ ARIMAX(1,1,1), and AmbP2 nonCOVID-19 ARIMA(1,0,0).

Discussion and Conclusion

Accurately predicting future patient volumes in the ambulatory setting is essential for resource planning and developing safety guidelines. Our findings show that a time series model that accounts for the number of positive COVID-19 patients delivers better performance than a moving average approach for predicting weekly ambulatory patient volumes in a short-term period.

An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning

The main purpose of an application performance monitoring/management (APM) software is to ensure the highest availability, efficiency and security of applications. An APM software accomplishes the main goals through automation, measurements, analysis and diagnostics. Gartner specifies the three crucial capabilities of APM softwares. The first is an end-user experience monitoring for revealing the interactions of users with application and infrastructure components. The second is application discovery, diagnostics and tracing. The third key component is machine learning (ML) and artificial intelligence (AI) powered data analytics for predictions, anomaly detection, event correlations and root cause analysis. Time series metrics, logs and traces are the three pillars of observability and the valuable source of information for IT operations. Accurate, scalable and robust time series forecasting and anomaly detection are the requested capabilities of the analytics. Approaches based on neural networks (NN) and deep learning gain an increasing popularity due to their flexibility and ability to tackle complex nonlinear problems. However, some of the disadvantages of NN-based models for distributed cloud applications mitigate expectations and require specific approaches. We demonstrate how NN-models, pretrained on a global time series database, can be applied to customer specific data using transfer learning. In general, NN-models adequately operate only on stationary time series. Application to nonstationary time series requires multilayer data processing including hypothesis testing for data categorization, category specific transformations into stationary data, forecasting and backward transformations. We present the mathematical background of this approach and discuss experimental results based on implementation for Wavefront by VMware (an APM software) while monitoring real customer cloud environments.

The impact of a minimum unit price on wholesale alcohol supply trends in the Northern Territory, Australia

OBJECTIVE

The Northern Territory (NT) Government introduced a minimum unit price (MUP) of $1.30 per standard drink (10g pure alcohol) explicitly aimed at reducing the consumption of cheap wine products from October 2018. We aimed to assess the impact of the NT MUP on estimates of beverage-specific population-adjusted alcohol consumption using wholesale alcohol supply data.

METHODS

Interrupted time series analyses were conducted to examine MUP effects on trends in estimated per capita alcohol consumption (PCAC) for cask wine, total wine and total alcohol, across the NT and in the Darwin/Palmerston region.

RESULTS

Significant step decreases were found for cask wine and total wine PCAC in Darwin/Palmerston and across the Northern Territory. PCAC of cask wine decreased by 50.6% in the NT, and by 48.8% in Darwin/Palmerston compared to the prior year. PCAC for other beverages (e.g. beer) were largely unaffected by MUP. Overall, PCAC across the Territory declined, but not in Darwin/Palmerston.

CONCLUSION

With minimal implementation costs, the Northern Territory Government's MUP policy successfully targeted and reduced cask wine and total wine consumption. Cask wine, in particular, almost halved in Darwin/Palmerston where the impact of the MUP was able to be determined and considering other interventions. Implications for public health: Implementation of a minimum unit price for retail alcohol sales is a cost-effective way to reduce the consumption of high alcohol content and high-risk products, such as cheap cask wine.

Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences

Plants adapt to continual changes in environmental conditions throughout their life spans. High-throughput phenotyping methods have been developed to noninvasively monitor the physiological responses to abiotic/biotic stresses on a scale spanning a long time, covering most of the vegetative and reproductive stages. However, some of the physiological events comprise almost immediate and very fast responses towards the changing environment which might be overlooked in long-term observations. Additionally, there are certain technical difficulties and restrictions in analyzing phenotyping data, especially when dealing with repeated measurements. In this study, a method for comparing means at different time points using generalized linear mixed models combined with classical time series models is presented. As an example, we use multiple chlorophyll time series measurements from different genotypes. The use of additional time series models as random effects is essential as the residuals of the initial mixed model may contain autocorrelations that bias the result. The nature of mixed models offers a viable solution as these can incorporate time series models for residuals as random effects. The results from analyzing chlorophyll content time series show that the autocorrelation is successfully eliminated from the residuals and incorporated into the final model. This allows the use of statistical inference.

Analysis of Air Mean Temperature Anomalies by Using Horizontal Visibility Graphs

The last decades have been successively warmer at the Earth’s surface. An increasing interest in climate variability is appearing, and many research works have investigated the main effects on different climate variables. Some of them apply complex networks approaches to explore the spatial relation between distinct grid points or stations. In this work, the authors investigate whether topological properties change over several years. To this aim, we explore the application of the horizontal visibility graph (HVG) approach which maps a time series into a complex network. Data used in this study include a 60-year period of daily mean temperature anomalies in several stations over the Iberian Peninsula (Spain). Average degree, degree distribution exponent, and global clustering coefficient were analyzed. Interestingly, results show that they agree on a lack of significant trends, unlike annual mean values of anomalies, which present a characteristic upward trend. The main conclusions obtained are that complex networks structures and nonlinear features, such as weak correlations, appear not to be affected by rising temperatures derived from global climate conditions. Furthermore, different locations present a similar behavior and the intrinsic nature of these signals seems to be well described by network parameters.

Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks

Gait patterns are a result of the complex kinematics that enable human two-legged locomotion, and they can reveal a lot about a person’s state and health. Analysing them is useful for researchers to get new insights into the course of diseases, and for physicians to track the progress after healing from injuries. When a person walks and is interfered with in any way, the resulting disturbance can show up and be found in the gait patterns. This paper describes an experimental setup for capturing gait patterns with a capacitive sensor floor, which can detect the time and position of foot contacts on the floor. With this setup, a dataset was recorded where 42 participants walked over a sensor floor in different modes, inter alia, normal pace, closed eyes, and dual-task. A recurrent neural network based on Long Short-Term Memory units was trained and evaluated for the classification task of recognising the walking mode solely from the floor sensor data. Furthermore, participants were asked to do the Unilateral Heel-Rise Test, and their gait was recorded before and after doing the test. Another neural network instance was trained to predict the number of repetitions participants were able to do on the test. As the results of the classification tasks turned out to be promising, the combination of this sensor floor and the recurrent neural network architecture seems like a good system for further investigation leading to applications in health and care.

Multivariate Correlation Measures Reveal Structure and Strength of Brain-Body Physiological Networks at Rest and During Mental Stress

In this work, we extend to the multivariate case the classical correlation analysis used in the field of network physiology to probe dynamic interactions between organ systems in the human body. To this end, we define different correlation-based measures of the multivariate interaction (MI) within and between the brain and body subnetworks of the human physiological network, represented, respectively, by the time series of δ, θ, α, and β electroencephalographic (EEG) wave amplitudes, and of heart rate, respiration amplitude, and pulse arrival time (PAT) variability (η, ρ, π). MI is computed: (i) considering all variables in the two subnetworks to evaluate overall brain-body interactions; (ii) focusing on a single target variable and dissecting its global interaction with all other variables into contributions arising from the same subnetwork and from the other subnetwork; and (iii) considering two variables conditioned to all the others to infer the network topology. The framework is applied to the time series measured from the EEG, electrocardiographic (ECG), respiration, and blood volume pulse (BVP) signals recorded synchronously via wearable sensors in a group of healthy subjects monitored at rest and during mental arithmetic and sustained attention tasks. We find that the human physiological network is highly connected, with predominance of the links internal of each subnetwork (mainly η-ρ and δ-θ, θ-α, α-β), but also statistically significant interactions between the two subnetworks (mainly η-β and η-δ). MI values are often spatially heterogeneous across the scalp and are modulated by the physiological state, as indicated by the decrease of cardiorespiratory interactions during sustained attention and by the increase of brain-heart interactions and of brain-brain interactions at the frontal scalp regions during mental arithmetic. These findings illustrate the complex and multi-faceted structure of interactions manifested within and between different physiological systems and subsystems across different levels of mental stress.

GENDIS: Genetic Discovery of Shapelets

In the time series classification domain, shapelets are subsequences that are discriminative of a certain class. It has been shown that classifiers are able to achieve state-of-the-art results by taking the distances from the input time series to different discriminative shapelets as the input. Additionally, these shapelets can be visualized and thus possess an interpretable characteristic, making them appealing in critical domains, where longitudinal data are ubiquitous. In this study, a new paradigm for shapelet discovery is proposed, which is based on evolutionary computation. The advantages of the proposed approach are that: (i) it is gradient-free, which could allow escaping from local optima more easily and supports non-differentiable objectives; (ii) no brute-force search is required, making the algorithm scalable; (iii) the total amount of shapelets and the length of each of these shapelets are evolved jointly with the shapelets themselves, alleviating the need to specify this beforehand; (iv) entire sets are evaluated at once as opposed to single shapelets, which results in smaller final sets with fewer similar shapelets that result in similar predictive performances; and (v) the discovered shapelets do not need to be a subsequence of the input time series. We present the results of the experiments, which validate the enumerated advantages.

Two centuries for an almost complete community turnover from native to non-native species in a riverine ecosystem

Non-native species introductions affect freshwater communities by changing community compositions, functional roles, trait occurrences and ecological niche spaces. Reconstructing such changes over long periods is difficult due to limited data availability. We collected information spanning 215 years on fish and selected macroinvertebrate groups (Mollusca and Crustacea) in the inner-Florentine stretch of the Arno River (Italy) and associated water grid, to investigate temporal changes. We identified an almost complete turnover from native to non-native fish (1800: 92% native; 2015: 94% non-native species) and macroinvertebrate species (1800: 100% native; 2015: 70% non-native species). Non-native fish species were observed ~50 years earlier compared to macroinvertebrate species, indicating phased invasion processes. In contrast, α-diversity of both communities increased significantly following a linear pattern. Separate analyses of changes in α-diversities for native and non-native species of both fish and macroinvertebrates were nonlinear. Functional richness and divergence of fish and macroinvertebrate communities decreased non-significantly, as the loss of native species was compensated by non-native species. Introductions of non-native fish and macroinvertebrate species occurred outside the niche space of native species. Native and non-native fish species exhibited greater overlap in niche space over time (62%-68%) and non-native species eventually replaced native species. Native and non-native macroinvertebrate niches overlapped to a lesser extent (15%-30%), with non-natives occupying mostly unoccupied niche space. These temporal changes in niche spaces of both biotic groups are a direct response to the observed changes in α-diversity and species turnover. These changes are potentially driven by deteriorations in hydromorphology as indicated by alterations in trait modalities. Additionally, we identified that angling played a considerable role for fish introductions. Our results support previous findings that the community turnover from native to non-native species can be facilitated by, for example, deteriorating environmental conditions and that variations in communities are multifaceted requiring more indicators than single metrics.

Predicting the detection of leprosy in a hyperendemic area of Brazil: Using time series analysis

BACKGROUND

Brazil has the second highest prevalence of leprosy worldwide. Autoregressive integrated moving average models are useful tools in surveillance systems because they provide reliable forecasts from epidemiological time series.

AIM

To evaluate the temporal patterns of leprosy detection from 2001 to 2015 and forecast for 2020 in a hyperendemic area in northeastern Brazil.

METHODS

A cross-sectional study was conducted using monthly leprosy detection from the Brazil information system for notifiable diseases. The Box-Jenkins method was applied to fit a seasonal autoregressive integrated moving average model. Forecasting models (95% prediction interval) were developed to predict leprosy detection for 2020.

RESULTS

A total of 44,578 cases were registered with a mean of 247.7 cases per month. The best-fitted model to make forecasts was the seasonal autoregressive integrated moving average ((1,1,1); (1,1,1)). It was predicted 0.32 cases/100,000 inhabitants to January of 2016 and 0.38 cases/100,000 inhabitants to December of 2020.

LIMITATIONS

This study used secondary data from Brazil information system for notifiable diseases; hence, leprosy data may be underreported.

CONCLUSION

The forecast for leprosy detection rate for December 2020 was < 1 case/100,000 inhabitants. Seasonal autoregressive integrated moving average model has been shown to be appropriate and could be used to forecast leprosy detection rates. Thus, this strategy can be used to facilitate prevention and elimination programmes.

Are networks of trophic interactions sufficient for understanding the dynamics of multi-trophic communities? Analysis of a tri-trophic insect food-web time-series

Resource-consumer interactions are considered a major driving force of population and community dynamics. However, species also interact in many non-trophic and indirect ways and it is currently not known to what extent the dynamic coupling of species corresponds to the distribution of trophic links. Here, using a 10-year data set of monthly observations of a 40-species tri-trophic insect community and nonlinear time series analysis, we compare the occurrence and strengths of both the trophic and dynamic interactions in the insect community. The matching between observed trophic and dynamic interactions provides evidence that population dynamic interactions reflect resource-consumer interactions in the many-species community. However, the presence of a trophic interaction does not always correspond to a detectable dynamic interaction especially for top-down effects. Moreover a considerable proportion of dynamic interactions are not attributable to direct trophic interactions, suggesting the unignorable role of non-trophic and indirect interactions as co-drivers of community dynamics.

A Novel Measure Inspired by Lyapunov Exponents for the Characterization of Dynamics in State-Transition Networks

The combination of network sciences, nonlinear dynamics and time series analysis provides novel insights and analogies between the different approaches to complex systems. By combining the considerations behind the Lyapunov exponent of dynamical systems and the average entropy of transition probabilities for Markov chains, we introduce a network measure for characterizing the dynamics on state-transition networks with special focus on differentiating between chaotic and cyclic modes. One important property of this Lyapunov measure consists of its non-monotonous dependence on the cylicity of the dynamics. Motivated by providing proper use cases for studying the new measure, we also lay out a method for mapping time series to state transition networks by phase space coarse graining. Using both discrete time and continuous time dynamical systems the Lyapunov measure extracted from the corresponding state-transition networks exhibits similar behavior to that of the Lyapunov exponent. In addition, it demonstrates a strong sensitivity to boundary crisis suggesting applicability in predicting the collapse of chaos.

Spatio-temporal and time series analysis of bluetongue outbreaks with environmental factors extracted from Google Earth Engine (GEE) in Andhra Pradesh, India

This study describes the spatial and temporal patterns of bluetongue (BT) outbreaks with environmental factors in undivided Andhra Pradesh, India. Descriptive analysis of the reported BT outbreaks (n = 2,697) in the study period (2000-2017) revealed a higher frequency of outbreaks during monsoon and post-monsoon months. Correlation analysis of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), rainfall and relative humidity (RH) displayed a significant positive correlation with BT outbreaks (p < .05). Retrospective unadjusted space-time, adjusted temporal and spatial analysis detected two, five and two statistically significant (p < .05) clusters, respectively. Time series distribution lag analysis examined the temporal patterns of BT outbreaks with environmental, biophysical factors and estimated that a decrease in 1 unit of rainfall (mm) was associated with 0.2% increase in the outbreak at lag 12 months. Similarly, a 1°C increase in land surface temperature (LST) was associated with 6.54% increase in the outbreaks at lag 12 months. However, an increase in 1 unit of wind speed (m/s) was associated with a 16% decrease in the outbreak at lag 10 months. The predictive model indicated that the peak of BT outbreaks were from October to December, the post-monsoon season in Andhra Pradesh region. The findings suggest that environmental factors influence BT outbreaks, and due to changes in climatic conditions, we may notice higher numbers of BT outbreaks in the coming years. The knowledge of spatial and temporal clustering of BT outbreaks may assist in adopting proper measures to prevent and control the BT spread.

Case Report: Dynamic Interdependencies Between Complementary and Alternative Medicine (CAM) Practice, Urinary Interleukin-6 Levels, and Fatigue in a Breast Cancer Survivor

Background: This study investigated the influence of complementary and alternative medicine (CAM) techniques (i.e., Jin Shin Jyutsu, music, physiotherapy, Tai Chi, and energy healing) on urinary interleukin-6 (IL-6) levels and fatigue in a 49-year-old breast cancer survivor suffering from cancer-related fatigue and depression. Data were sampled under conditions of "life as it is lived." Methods: For 28 days, a female breast cancer survivor collected her full urine output in 12-h intervals from about 8 a.m. to 8 p.m. and from about 8 p.m. to 8 a.m. These urine samples were used to determine urinary IL-6 levels through ELISA and creatinine concentrations via HPLC. In 12-h intervals (every morning and evening), the patient completed the DIARI, which included fatigue measurement and notes on incidents and activities such as CAM practice. In addition, the patient was interviewed weekly to identify meaningful everyday incidents. In this context, CAM practice was also discussed. Time series analysis consisted of ARIMA modeling and cross-correlational analyses (p < 0.05). Results: When each CAM technique was considered separately in time series analysis, CAM was consistently associated with increases in urinary IL-6 release and decreases in fatigue. Furthermore, when all CAM techniques experienced as positive were included in one time series, a biphasic urinary IL-6 response pattern was found in which CAM practice was first preceded by decreases in IL-6 by 12-0 h and then followed by increases in IL-6 after 108-120 h. Finally, cross-correlations between IL-6 and fatigue showed that increases in IL-6 were followed by decreases in fatigue intensity after 48-60 h and, conversely, that decreases in fatigue intensity were followed by decreases in IL-6 after 24-36 h and 48-60 h. Conclusion: IL-6 increases and fatigue decreases highlight potential health-promoting effects of CAM practice. Moreover, a cyclic IL-6 pattern in response to all CAM activities experienced as positive underscores that CAM was meaningful to the patient. Additionally, a negative feedback circuit between IL-6 and fatigue intensity was detected. Taken together, this study confirms the necessity of integrating subjective meaning and dynamic complexity into biopsychosocial research in order to understand human functioning under real-life conditions.

Ecological forecasts reveal limitations of common model selection methods: predicting changes in beaver colony densities

Over the past two decades, there have been numerous calls to make ecology a more predictive science through direct empirical assessments of ecological models and predictions. While the widespread use of model selection using information criteria has pushed ecology toward placing a higher emphasis on prediction, few attempts have been made to validate the ability of information criteria to correctly identify the most parsimonious model with the greatest predictive accuracy. Here, we used an ecological forecasting framework to test the ability of information criteria to accurately predict the relative contribution of density dependence and density-independent factors (forage availability, harvest, weather, wolf [Canis lupus] density) on inter-annual fluctuations in beaver (Castor canadensis) colony densities. We modeled changes in colony densities using a discrete-time Gompertz model, and assessed the performance of four models using information criteria values: density-independent models with (1) and without (2) environmental covariates; and density-dependent models with (3) and without (4) environmental covariates. We then evaluated the forecasting accuracy of each model by withholding the final one-third of observations from each population and compared observed vs. predicted densities. Information criteria and our forecasting accuracy metrics both provided strong evidence of compensatory density dependence in the annual dynamics of beaver colony densities. However, despite strong within-sample performance by the most complex model (density-dependent with covariates) as determined using information criteria, hindcasts of colony densities revealed that the much simpler density-dependent model without covariates performed nearly as well predicting out-of-sample colony densities. The hindcast results indicated that the complex model over-fit our data, suggesting that parameters identified by information criteria as important predictor variables are only marginally valuable for predicting landscape-scale beaver colony dynamics. Our study demonstrates the importance of evaluating ecological models and predictions with long-term data and revealed how a known limitation of information criteria (over-fitting of complex models) can affect our interpretation of ecological dynamics. While incorporating knowledge of the factors that influence animal population dynamics can improve population forecasts, we suggest that comparing forecast performance metrics can likewise improve our knowledge of the factors driving population dynamics.

A Tutorial on Estimating Time-Varying Vector Autoregressive Models

Time series of individual subjects have become a common data type in psychological research. These data allow one to estimate models of within-subject dynamics, and thereby avoid the notorious problem of making within-subjects inferences from between-subjects data, and naturally address heterogeneity between subjects. A popular model for these data is the Vector Autoregressive (VAR) model, in which each variable is predicted by a linear function of all variables at previous time points. A key assumption of this model is that its parameters are constant (or stationary) across time. However, in many areas of psychological research time-varying parameters are plausible or even the subject of study. In this tutorial paper, we introduce methods to estimate time-varying VAR models based on splines and kernel-smoothing with/without regularization. We use simulations to evaluate the relative performance of all methods in scenarios typical in applied research, and discuss their strengths and weaknesses. Finally, we provide a step-by-step tutorial showing how to apply the discussed methods to an openly available time series of mood-related measurements.

Temporal analysis of visceral leishmaniasis between 2000 and 2019 in Ardabil Province, Iran: A time-series study using ARIMA model

Background:

Visceral leishmaniasis in human (VLH) also known as kala-azar is a neglected disease of humans that mainly occurs in more than 50 countries mostly located in the Eastern Mediterranean and the Northern America.

Objective:

The purpose of this study was to determine the temporal patterns and predict of occurrence of VL in Ardabil Province, in northwestern Iran using autoregressive integrated moving average (ARIMA) models.

Methods:

This descriptive study employed yearly and monthly data of 602 cases of VLH in the province between January 2000 to December 2019, which was provided by the leishmaniasis national surveillance system. The monthly occurrences case constructed the ARIMA model of time-series model. The insignificance of the correlation in the lags of 12, 24 and 36 months, and Chi-square test showed the occurrence of VLH does not have a seasonal pattern. Eleven potential ARIMA models were examined for VLH cases. Finally, the best model was selected with the lower Akaike Information Criteria (AIC) and Bayesian information criterion (BIC) value. Then, the selected model was used to forecast frequency of monthly occurrences case. The forecasting precision was estimated by mean absolute percentage error (MAPE). Data analysis was performed using Stata14 and its package time series analysis.

Results:

ARIMA (5, 0, 1) model with AIC (25.7) and BIC (43.35) was selected. The MAPE value was 26.89% and the portmanteau test for white noise was (Q = 23.02, P = 0.98) for the residuals of the selected model showed that the data were fully modelled. The total cumulative VLH cases in the next 24 months’ in Ardabil province predicted 14 cases (95% CI: 4-54 case).

Conclusion:

The ARIMA (5, 0, 1) model can be a useful tool to predict VLH cases as early warning system and the results are helpful for policy makers and primary care physicians in the readiness of public health problems before the outbreak of the disease.

A Comprehensive Framework for Uncovering Non-Linearity and Chaos in Financial Markets: Empirical Evidence for Four Major Stock Market Indices

The presence of chaos in the financial markets has been the subject of a great number of studies, but the results have been contradictory and inconclusive. This research tests for the existence of nonlinear patterns and chaotic nature in four major stock market indices: namely Dow Jones Industrial Average, Ibex 35, Nasdaq-100 and Nikkei 225. To this end, a comprehensive framework has been adopted encompassing a wide range of techniques and the most suitable methods for the analysis of noisy time series. By using daily closing values from January 1992 to July 2013, this study employs twelve techniques and tools of which five are specific to detecting chaos. The findings show no clear evidence of chaos, suggesting that the behavior of financial markets is nonlinear and stochastic.