Conserved temporal ordering of promoter activation implicates common mechanisms governing the immediate early response across cell types and stimuli

The promoters of immediate early genes (IEGs) are rapidly activated in response to an external stimulus. These genes, also known as primary response genes, have been identified in a range of cell types, under diverse extracellular signals and using varying experimental protocols. Whereas genomic dissection on a case-by-case basis has not resulted in a comprehensive catalogue of IEGs, a rigorous meta-analysis of eight genome-wide FANTOM5 CAGE (cap analysis of gene expression) time course datasets reveals successive waves of promoter activation in IEGs, recapitulating known relationships between cell types and stimuli: we obtain a set of 57 (42 protein-coding) candidate IEGs possessing promoters that consistently drive a rapid but transient increase in expression over time. These genes show significant enrichment for known IEGs reported previously, pathways associated with the immediate early response, and include a number of non-coding RNAs with roles in proliferation and differentiation. Surprisingly, we also find strong conservation of the ordering of activation for these genes, such that 77 pairwise promoter activation orderings are conserved. Using the leverage of comprehensive CAGE time series data across cell types, we also document the extensive alternative promoter usage by such genes, which is likely to have been a barrier to their discovery until now. The common activation ordering of the core set of early-responding genes we identify may indicate conserved underlying regulatory mechanisms. By contrast, the considerably larger number of transiently activated genes that are specific to each cell type and stimulus illustrates the breadth of the primary response.

Prefrontal Cortex Oxygenation Evoked by Convergence Load Under Conflicting Stimulus-to-Accommodation and Stimulus-to-Vergence Eye-Movements Measured by NIRS

Purpose: To extend our knowledge of the functional linkages between visual fatigue and regional cerebral prefrontal cortex (PFC) oxygenation, we measured time related hemodynamic changes over the right dorsolateral prefrontal cortex (dlPFC) during convergence load under conflicting stimulus-to-accommodation and stimulus-to-vergence eye movements with and without concurrent mental load.

Methods: Twenty healthy participants with a median age of 28 years (range: 18–44 years) fixated upon a vertical bar presented separately to the left and right eyes, using polarized filters, during four counterbalanced 10-min periods: (i) no accommodation/vergence conflict (Control, Ctrl); (ii) added convergence load and accommodation/vergence conflict (Conv); (iii) added cognitive load only (Cog) and; (iv) a combination of added cognitive and convergence load and accommodation/vergence conflict (Cc). Viewing distance was 65 cm. Non-invasive measurements of hemodynamic activity over the dlPFC were quantified by functional near-infrared spectroscopy (fNIRS). During the two-convergence load conditions, the horizontal disparity of the two bars varied dynamically from no disparity to a disparity set 20% below the individual threshold for diplopia. Cognitive load was induced by the n-back-2 test which required the subject to memorize and recall the changing colors of the horizontal bars and decide when a given color was the same as that occurring two colors previously. fNIRS data were averaged over 10-s windows centered at 0, 2, 4, 6, 8, and 10 min of each task, subtracted from a 20-s baseline window immediately preceding the visual task, and then represented as changes in oxygenated hemoglobin (ΔHbO₂)_, deoxygenated hemoglobin (ΔHHb) and total hemoglobin (ΔtHb).

Results: Linear mixed model analyses showed that hemodynamic activity was systematically influenced by time (p < 0.001). The group-averaged time-related level of change across the viewing conditions did not differ when compared with one another (p > 0.05). Larger convergence eye-movement responses under conflicting stimulus-to-accommodation, and stimulus-to-vergence over time, increased ΔHbO₂ and ΔtHb only in condition Cc and after 8 min of task time (p < 0.10 for min^-6 and min^-8: p < 0.05 for min^-10).

Discussion: Collectively, our data suggest that HbO₂, HHb, and tHb, recorded over the dlPFC with fNIRS, can be used to assay the degree to which supervisory oculomotor control processes are activated during visually deficient near work.

Bid Expression Network Controls Neuronal Cell Fate During Avian Ciliary Ganglion Development

Avian ciliary ganglion (CG) development involves a transient execution phase of apoptosis controlling the final number of neurons, but the time-dependent molecular mechanisms for neuronal cell fate are largely unknown. To elucidate the molecular networks regulating important aspects of parasympathetic neuronal development, a genome-wide expression analysis was performed during multiple stages of avian CG development between embryonic days E6 and E14. The transcriptome data showed a well-defined sequence of events, starting from neuronal migration via neuronal fate cell determination, synaptic transmission, and regulation of synaptic plasticity to growth factor associated signaling. In particular, we extracted a neuronal apoptosis network that characterized the cell death execution phase at E8/E9 and apoptotic cell clearance at E14 by combining the gene time series analysis with network synthesis from the chicken interactome. Network analysis identified TP53 as key regulator and predicted involvement of the BH3 interacting domain death agonist (BID). A virus-based RNAi knockdown approach in vivo showed a crucial impact of BID expression on the execution of ontogenetic programmed cell death (PCD). In contrast, Bcl-XL expression did not impact PCD. Therefore, BID-mediated apoptosis represents a novel cue essential for timing within CG maturation.

[Process analysis of weekly self- and external assessments of adolescents with substance abuse disorder during long-term psychotherapy]

Process analysis of weekly self- and external assessments of adolescents with substance abuse disorder during long-term psychotherapy Abstract.

OBJECTIVE

Little is known about the course of emotional and motivational states in young patients with substance use disorder (SUD) during long-term treatment.

METHOD

We collected weekly self-reports from N = 42 adolescents, resulting in 853 questionnaires. Additionally, 708 observations were obtained from their key carers. Principal component analysis (PCA) was used to explore the dimensional structure of the questionnaires. Multi-level models were applied to test for group differences and time series models to evaluate dependencies on the person level.

RESULTS

PCA yielded four factors: negative mental state, appreciation of the setting, motivation, and addiction dynamics. Correlations between self- and (unidimensional) carer-reports were low to moderate, but differential on the individual level. Scores of all four scales decreased during the course of treatment. The level of appreciation during the initial phase was the only significant predictor for drop-outs of treatment later on: in drop-outs, appreciation decreased, whereas it increased in completers.

CONCLUSION

Appreciation was the most important predictive factor for a regular therapy ending in SUD, whereas motivation showed fluctuations typical for adolescence. Addiction dynamics were of lesser relevance than commonly expected. Therefore, programs in long-term SUD treatment should focus more on improving appreciation than on issues of addiction itself.

Measuring Statistical Asymmetries of Stochastic Processes: Study of the Autoregressive Process

We use the definition of statistical symmetry as the invariance of a probability distribution under a given transformation and apply the concept to the underlying probability distribution of stochastic processes. To measure the degree of statistical asymmetry, we take the Kullback–Leibler divergence of a given probability distribution with respect to the corresponding transformed one and study it for the Gaussian autoregressive process using transformations on the temporal correlations’ structure. We then illustrate the employment of this notion as a time series analysis tool by measuring local statistical asymmetries of foreign exchange market price data for three transformations that capture distinct autocorrelation behaviors of the series—independence, non-negative correlations and Markovianity—obtaining a characterization of price movements in terms of each statistical symmetry.

Relation Between Training Load and Recovery-Stress State in High-Performance Swimming

Background: The relation between training load, especially internal load, and the recovery-stress state is of central importance for avoiding negative adaptations in high-performance sports like swimming. The aim of this study was to analyze the individual time-delayed linear effect relationship between training load and recovery-stress state with single case time series methods and to monitor the acute recovery-stress state of high-performance swimmers in an economical and multidimensional manner over a macro cycle. The Acute Recovery and Stress Scale (ARSS) was used for daily monitoring of the recovery-stress state. The methods session-RPE (sRPE) and acute:chronic workload-ratio (ACWR) were used to compare different methods for quantifying the internal training load with regard to their interrelationship with the recovery-stress state. Methods: Internal load and recovery-stress state of five highly trained female swimmers [with a training frequency of 13.6 ± 0.8 sessions per week and specializing in sprint (50 and 100 m), middle-distance (200 and 400 m), or long distance (800 and 1,500 m) events] were daily documented over 17 weeks. Two different types of sRPE were applied: RPE^∗duration (sRPE^h) and RPE^∗volume (sRPE^km). Subsequently, we calculated the ratios ACWR^h and ACWR^km (sRPE last week: 4-week exponentially weighted moving average). The recovery-stress state was measured by using the ARSS, consisting of eight scales, four of which are related to recovery [Physical Performance Capability (PPC), Mental Performance Capability (MPC), Emotional Balance (EB), Overall Recovery (OR)], and four to stress [Muscular Stress (MS), Lack of Activation (LA), Negative Emotional State (NES), Overall Stress (OS)]. To examine the relation between training load and recovery-stress state a cross correlation (CCC) was conducted with sRPE^h, sRPE^km, ACWR^h, and ACWR^km as lead and the eight ARSS-scales as lag variables. Results: A large variation of training load can be observed in the individual week-to-week fluctuations whereby the single fluctuations can significantly differ from the overall mean of the group. The range also shows that the CCC individually reaches values above 0.3, especially with sRPE^km as lead variable. Overall, there is a large range with significant differences between the recovery and stress dimensions of the ARSS and between the training load methods, with sRPE^km having the largest span (Range = 1.16). High inter-individual differences between the athletes lie in strength and direction of the correlation | 0.66|≤ CCC ≥|-0.50|. The time delayed effects (lags 0-7) are highly individual, however, clear patterns can be observed. Conclusion: The ARSS, especially the physical and overall-related scales (PPC, OR, MS, OS), is a suitable tool for monitoring the acute recovery-stress state in swimmers. MPC, EB, LA, and NES are less affected by training induced changes. Comparably high CCC and Ranges result from the four internal load methods, whereby sRPE, especially sRPE^km, shows a stronger relation to recovery-stress state than ACWR. Based on these results and the individual differences in terms of time delay in training response, we recommend for swimming to use sRPE to monitor the internal training load and to use the ARSS, with a focus at the physical and overall-scales, to monitor the recovery-stress state.

Forecasting zoonotic cutaneous leishmaniasis using meteorological factors in eastern Fars province, Iran: a SARIMA analysis

OBJECTIVES

To predict the occurrence of zoonotic cutaneous leishmaniasis (ZCL) and evaluate the effect of climatic variables on disease incidence in the east of Fars province, Iran using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model.

METHODS

The Box-Jenkins approach was applied to fit the SARIMA model for ZCL incidence from 2004 to 2015. Then the model was used to predict the number of ZCL cases for the year 2016. Finally, we assessed the relation of meteorological variables (rainfall, rainy days, temperature, hours of sunshine and relative humidity) with ZCL incidence.

RESULTS

SARIMA(2,0,0) (2,1,0)12 was the preferred model for predicting ZCL incidence in the east of Fars province (validation Root Mean Square Error, RMSE = 0.27). It showed that ZCL incidence in a given month can be estimated by the number of cases occurring 1 and 2 months, as well as 12 and 24 months earlier. The predictive power of SARIMA models was improved by the inclusion of rainfall at a lag of 2 months (β = -0.02), rainy days at a lag of 2 months (β = -0.09) and relative humidity at a lag of 8 months (β = 0.13) as external regressors (P-values < 0.05). The latter was the best climatic variable for predicting ZCL cases (validation RMSE = 0.26).

CONCLUSIONS

Time series models can be useful tools to predict the trend of ZCL in Fars province, Iran; thus, they can be used in the planning of public health programmes. Introducing meteorological variables into the models may improve their precision.

Density-dependent and density-independent drivers of population change in Barton Springs salamanders

Understanding population change is essential for conservation of imperiled species, such as amphibians. Worldwide amphibian declines have provided an impetus for investigating their population dynamics, which can involve both extrinsic (density-independent) and intrinsic (density-dependent) drivers acting differentially across multiple life stages or age classes. In this study, we examined the population dynamics of the endangered Barton Springs Salamander (Eurycea sosorum) using data from a long-term monitoring program. We were interested in understanding both the potential environmental drivers (density-independent factors) and demographic factors (interactions among size classes, negative density dependence) to better inform conservation and management activities. We used data from three different monitoring regimes and multivariate autoregressive state-space models to quantify environmental effects (seasonality, discharge, algae, and sediment cover), intraspecific interactions among three size classes, and intra-class density dependence. Results from our primary data set revealed similar patterns among sites and size classes and were corroborated by our out-of-sample data. Cross-correlation analysis showed juvenile abundance was most strongly correlated with a 9-month lag in aquifer discharge, which we suspect is related to inputs of organic carbon into the aquifer. However, sedimentation limited juvenile abundance at the surface, emphasizing the importance of continued sediment management. Recruitment from juveniles to the sub-adult size class was evident, but negative density-dependent feedback ultimately regulated each size class. Negative density dependence may be an encouraging sign for the conservation of E. sosorum because populations that can reach carrying capacity are less likely to go extinct compared to unregulated populations far below their carrying capacity. However, periodic population declines coupled with apparent migration into the aquifer complicate assessments of species status. Although both density-dependent and density-independent drivers of population change are not always apparent in time series of animal populations, both have important implications for conservation and management of E. sosorum.

Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model

Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male's emotion dynamics over time, but not in the female's-which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system.

Effects of hurricanes and climate oscillations on annual variation in reproduction in wet forest, Puerto Rico

Interannual changes in global climate and weather disturbances may influence reproduction in tropical forests. Phenomena such as the El Niño Southern Oscillation (ENSO) are known to produce interannual variation in reproduction, as do severe storms such as hurricanes. Using stationary trap-based phenology data collected fortnightly from 1993 to 2014 from a hurricane-affected (1989 Hugo, 1998 Georges) subtropical wet forest in northeastern Puerto Rico, we conducted a time series analysis of flowering and seed production. We addressed (1) the degree to which interannual variation in flower and seed production was influenced by global climate drivers and time since hurricane disturbance, and (2) how long-term trends in reproduction varied with plant lifeform. The seasonally de-trended number of species in flower fluctuated over time while the number of species producing seed exhibited a declining trend, one that was particularly evident during the second half of the study period. Lagged El Niño indices and time series hurricane disturbance jointly influenced the trends in numbers of flowering and fruiting species, suggesting complex global influences on tropical forest reproduction with variable periodicities. Lag times affecting flowering tended to be longer than those affecting fruiting. Long-term patterns of reproduction in individual lifeforms paralleled the community-wide patterns, with most groups of lifeform exhibiting a long-term decline in seed but not flower production. Exceptions were found for hemiepiphytes, small trees, and lianas whose seed reproduction increased and then declined over time. There was no long-term increase in flower production as reported in other Neotropical sites.

Impact of Florida's prescription drug monitoring program and pill mill law on high-risk patients: A comparative interrupted time series analysis

PURPOSE

We quantified the effects of Florida's prescription drug monitoring program and pill mill law on high-risk patients.

METHODS

We used QuintilesIMS LRx Lifelink data to identify patients receiving prescription opioids in Florida (intervention state, N: 1.13 million) and Georgia (control state, N: 0.54 million). The preintervention, intervention, and postintervention periods were July 2010 to June 2011, July 2011 to September 2011, and October 2011 to September 2012. We identified 3 types of high-risk patients: (1) concomitant users: patients with concomitant use of benzodiazepines and opioids; (2) chronic users: long-term, high-dose, opioid users; and (3) opioid shoppers: patients receiving opioids from multiple sources. We compared changes in opioid prescriptions between Florida and Georgia before and after policy implementation among high-risk/low-risk patients. Our monthly measures included (1) average morphine milligram equivalent per transaction, (2) total opioid volume across all prescriptions, (3) average days supplied per transaction, and (4) total number of opioid prescriptions dispensed.

RESULTS

Among opioid-receiving individuals in Florida, 6.62% were concomitant users, 1.96% were chronic users, and 0.46% were opioid shoppers. Following policy implementation, Florida's high-risk patients experienced relative reductions in morphine milligram equivalent (opioid shoppers: -1.08 mg/month, 95% confidence interval [CI] -1.62 to -0.54), total opioid volume (chronic users: -4.58 kg/month, CI -5.41 to -3.76), and number of dispensed opioid prescriptions (concomitant users: -640 prescriptions/month, CI -950 to -340). Low-risk patients generally did not experience statistically significantly relative reductions.

CONCLUSIONS

Compared with Georgia, Florida's prescription drug monitoring program and pill mill law were associated with large relative reductions in prescription opioid utilization among high-risk patients.

Evaluation of the "Angelina Jolie Effect" on Screening Mammography Utilization in an Academic Center

PURPOSE

The aim of this study was to understand the impact on screening mammography at our institution, comparing weekly utilization in the 2 years before and the 2 years after Ms Angelina Jolie disclosed in the New York Times on May 13, 2013, that she had had a prophylactic double mastectomy.

METHODS

All 48,110 consecutive screening mammograms conducted at our institution between May 16, 2011, and May 16, 2015, were selected from our electronic medical record system. We used interrupted time series statistical models and graphical methods on utilization data to understand utilization changes before and after Ms Jolie's news.

RESULTS

The graphed trend of weekly screening mammogram utilization failed to show changes around the time of interest. Analytical models and statistical tests also failed to show a step change increase or acceleration of utilization around May 2013. However, graphical and time series analyses showed a flattening of utilization in the middle of 2014.

CONCLUSIONS

In our well-powered analysis in a large regional breast imaging center, we found no support for the hypothesis that this celebrity news drove increased screening.

Dynamical Pattern Representation of Cardiovascular Couplings Evoked by Head-up Tilt Test

Shannon entropy (ShE) is a recognised tool for the quantization of the temporal organization of time series. Transfer entropy (TE) provides insight into the dependence between coupled systems. Here, signals are analysed that were produced by the cardiovascular system when a healthy human underwent a provocation test using the head-up tilt (HUT) protocol. The information provided by ShE and TE is evaluated from two aspects: that of the algorithmic stability and that of the recognised physiology of the cardiovascular response to the HUT test. To address both of these aspects, two types of symbolization of three-element subsequent values of a signal are considered: one, well established in heart rate research, referring to the variability in a signal, and a novel one, revealing primarily the dynamical trends. The interpretation of ShE shows a strong dependence on the method that was used in signal pre-processing. In particular, results obtained from normalized signals turn out to be less conclusive than results obtained from non-normalized signals. Systematic investigations based on surrogate data tests are employed to discriminate between genuine properties—in particular inter-system coupling—and random, incidental fluctuations. These properties appear to determine the occurrence of a high percentage of zero values of TE, which strongly limits the reliability of the couplings measured. Nevertheless, supported by statistical corroboration, we identify distinct timings when: (i) evoking cardiac impact on the vascular system, and (ii) evoking vascular impact on the cardiac system, within both the principal sub-systems of the baroreflex loop.

Testing Process Factor Analysis Models Using the Parametric Bootstrap

Process factor analysis (PFA) is a latent variable model for intensive longitudinal data. It combines P-technique factor analysis and time series analysis. The goodness-of-fit test in PFA is currently unavailable. In the paper, we propose a parametric bootstrap method for assessing model fit in PFA. We illustrate the test with an empirical data set in which 22 participants rated their effects everyday over a period of 90 days. We also explore Type I error and power of the parametric bootstrap test with simulated data.

The Association between Air Pollution and Outpatient and Inpatient Visits in Shenzhen, China

Nowadays, air pollution is a severe environmental problem in China. To investigate the effects of ambient air pollution on health, a time series analysis of daily outpatient and inpatient visits in 2015 were conducted in Shenzhen (China). Generalized additive model was employed to analyze associations between six air pollutants (namely SO₂, CO, NO₂, O₃, PM₁₀, and PM_2.5) and daily outpatient and inpatient visits after adjusting confounding meteorological factors, time and day of the week effects. Significant associations between air pollutants and two types of hospital visits were observed. The estimated increase in overall outpatient visits associated with each 10 µg/m³ increase in air pollutant concentration ranged from 0.48% (O₃ at lag 2) to 11.48% (SO₂ with 2-day moving average); for overall inpatient visits ranged from 0.73% (O₃ at lag 7) to 17.13% (SO₂ with 8-day moving average). Our results also suggested a heterogeneity of the health effects across different outcomes and in different populations. The findings in present study indicate that even in Shenzhen, a less polluted area in China, significant associations exist between air pollution and daily number of overall outpatient and inpatient visits.

Listeria monocytogenes contamination of ready-to-eat foods and the risk for human health in the EU

Food safety criteria for Listeria monocytogenes in ready-to-eat (RTE) foods have been applied from 2006 onwards (Commission Regulation (EC) 2073/2005). Still, human invasive listeriosis was reported to increase over the period 2009-2013 in the European Union and European Economic Area (EU/EEA). Time series analysis for the 2008-2015 period in the EU/EEA indicated an increasing trend of the monthly notified incidence rate of confirmed human invasive listeriosis of the over 75 age groups and female age group between 25 and 44 years old (probably related to pregnancies). A conceptual model was used to identify factors in the food chain as potential drivers for L. monocytogenes contamination of RTE foods and listeriosis. Factors were related to the host (i. population size of the elderly and/or susceptible people; ii. underlying condition rate), the food (iii. L. monocytogenes prevalence in RTE food at retail; iv. L. monocytogenes concentration in RTE food at retail; v. storage conditions after retail; vi. consumption), the national surveillance systems (vii. improved surveillance), and/or the bacterium (viii. virulence). Factors considered likely to be responsible for the increasing trend in cases are the increased population size of the elderly and susceptible population except for the 25-44 female age group. For the increased incidence rates and cases, the likely factor is the increased proportion of susceptible persons in the age groups over 45 years old for both genders. Quantitative modelling suggests that more than 90% of invasive listeriosis is caused by ingestion of RTE food containing > 2,000 colony forming units (CFU)/g, and that one-third of cases are due to growth in the consumer phase. Awareness should be increased among stakeholders, especially in relation to susceptible risk groups. Innovative methodologies including whole genome sequencing (WGS) for strain identification and monitoring of trends are recommended.

Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets

Statistical and machine learning (ML)-based methods have recently advanced in construction of gene regulatory network (GRNs) based on high-throughput biological datasets. GRNs underlie almost all cellular phenomena; hence, comprehensive GRN maps are essential tools to elucidate gene function, thereby facilitating the identification and prioritization of candidate genes for functional analysis. High-throughput gene expression datasets have yielded various statistical and ML-based algorithms to infer causal relationship between genes and decipher GRNs. This review summarizes the recent advancements in the computational inference of GRNs, based on large-scale transcriptome sequencing datasets of model plants and crops. We highlight strategies to select contextual genes for GRN inference, and statistical and ML-based methods for inferring GRNs based on transcriptome datasets from plants. Furthermore, we discuss the challenges and opportunities for the elucidation of GRNs based on large-scale datasets obtained from emerging transcriptomic applications, such as from population-scale, single-cell level, and life-course transcriptome analyses.

Time Series Analysis for Spatial Node Selection in Environment Monitoring Sensor Networks

Wireless sensor networks are widely used in environmental monitoring. The number of sensor nodes to be deployed will vary depending on the desired spatio-temporal resolution. Selecting an optimal number, position and sampling rate for an array of sensor nodes in environmental monitoring is a challenging question. Most of the current solutions are either theoretical or simulation-based where the problems are tackled using random field theory, computational geometry or computer simulations, limiting their specificity to a given sensor deployment. Using an empirical dataset from a mine rehabilitation monitoring sensor network, this work proposes a data-driven approach where co-integrated time series analysis is used to select the number of sensors from a short-term deployment of a larger set of potential node positions. Analyses conducted on temperature time series show 75% of sensors are co-integrated. Using only 25% of the original nodes can generate a complete dataset within a 0.5 °C average error bound. Our data-driven approach to sensor position selection is applicable for spatiotemporal monitoring of spatially correlated environmental parameters to minimize deployment cost without compromising data resolution.

Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan

OBJECTIVE

Emergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to patients. One of the crucial elements for a good management strategy is demand forecasting. Our study sought to construct an adequate model and to forecast monthly ED visits.

METHODS

We retrospectively gathered monthly ED visits from January 2009 to December 2016 to carry out a time series autoregressive integrated moving average (ARIMA) analysis. Initial development of the model was based on past ED visits from 2009 to 2016. A best-fit model was further employed to forecast the monthly data of ED visits for the next year (2016). Finally, we evaluated the predicted accuracy of the identified model with the mean absolute percentage error (MAPE). The software packages SAS/ETS V.9.4 and Office Excel 2016 were used for all statistical analyses.

RESULTS

A series of statistical tests showed that six models, including ARIMA (0, 0, 1), ARIMA (1, 0, 0), ARIMA (1, 0, 1), ARIMA (2, 0, 1), ARIMA (3, 0, 1) and ARIMA (5, 0, 1), were candidate models. The model that gave the minimum Akaike information criterion and Schwartz Bayesian criterion and followed the assumptions of residual independence was selected as the adequate model. Finally, a suitable ARIMA (0, 0, 1) structure, yielding a MAPE of 8.91%, was identified and obtained as Visit_t=7111.161+(a_t+0.37462 a_t-1).

CONCLUSION

The ARIMA (0, 0, 1) model can be considered adequate for predicting future ED visits, and its forecast results can be used to aid decision-making processes.

On the Use of Mixed Markov Models for Intensive Longitudinal Data

Markov modeling presents an attractive analytical framework for researchers who are interested in state-switching processes occurring within a person, dyad, family, group, or other system over time. Markov modeling is flexible and can be used with various types of data to study observed or latent state-switching processes, and can include subject-specific random effects to account for heterogeneity. We focus on the application of mixed Markov models to intensive longitudinal data sets in psychology, which are becoming ever more common and provide a rich description of each subject's process. We examine how specifications of a Markov model change when continuous random effect distributions are included, and how mixed Markov models can be used in the intensive longitudinal research context. Advantages of Bayesian estimation are discussed and the approach is illustrated by two empirical applications.

Network Mapping with GIMME

Network science is booming! While the insights and images afforded by network mapping techniques are compelling, implementing the techniques is often daunting to researchers. Thus, the aim of this tutorial is to facilitate implementation in the context of GIMME, or group iterative multiple model estimation. GIMME is an automated network analysis approach for intensive longitudinal data. It creates person-specific networks that explain how variables are related in a system. The relations can signify current or future prediction that is common across people or applicable only to an individual. The tutorial begins with conceptual and mathematical descriptions of GIMME. It proceeds with a practical discussion of analysis steps, including data acquisition, preprocessing, program operation, a posteriori testing of model assumptions, and interpretation of results; throughout, a small empirical data set is analyzed to showcase the GIMME analysis pipeline. The tutorial closes with a brief overview of extensions to GIMME that may interest researchers whose questions and data sets have certain features. By the end of the tutorial, researchers will be equipped to begin analyzing the temporal dynamics of their heterogeneous time series data with GIMME.

Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data

The Experience Sampling Method is a common approach in psychological research for collecting intensive longitudinal data with high ecological validity. One characteristic of ESM data is that it is often unequally spaced, because the measurement intervals within a day are deliberately varied, and measurement continues over several days. This poses a problem for discrete-time (DT) modeling approaches, which are based on the assumption that all measurements are equally spaced. Nevertheless, DT approaches such as (vector) autoregressive modeling are often used to analyze ESM data, for instance in the context of affective dynamics research. There are equivalent continuous-time (CT) models, but they are more difficult to implement. In this paper we take a pragmatic approach and evaluate the practical relevance of the violated model assumption in DT AR(1) and VAR(1) models, for the N = 1 case. We use simulated data under an ESM measurement design to investigate the bias in the parameters of interest under four different model implementations, ranging from the true CT model that accounts for all the exact measurement times, to the crudest possible DT model implementation, where even the nighttime is treated as a regular interval. An analysis of empirical affect data illustrates how the differences between DT and CT modeling can play out in practice. We find that the size and the direction of the bias in DT (V)AR models for unequally spaced ESM data depend quite strongly on the true parameter in addition to data characteristics. Our recommendation is to use CT modeling whenever possible, especially now that new software implementations have become available.

An extended data envelopment analysis for the decision-making

Based on the CCR model, we propose an extended data envelopment analysis to evaluate the efficiency of decision making units with historical input and output data. The contributions of the work are threefold. First, the input and output data of the evaluated decision making unit are variable over time, and time series method is used to analyze and predict the data. Second, there are many sample decision making units, which are divided into several ordered sample standards in terms of production strategy, and the constraint condition consists of one of the sample standards. Furthermore, the efficiency is illustrated by considering the efficiency relationship between the evaluated decision making unit and sample decision making units from constraint condition. Third, to reduce the computation complexity, we introduce an algorithm based on the binary search tree in the model to choose the sample standard that has similar behavior with the evaluated decision making unit. Finally, we provide two numerical examples to illustrate the proposed model.

On the interpretability and computational reliability of frequency-domain Granger causality

This Correspondence article is a comment which directly relates to the paper "A study of problems encountered in Granger causality analysis from a neuroscience perspective" ( Stokes and Purdon, 2017). We agree that interpretation issues of Granger causality (GC) in neuroscience exist, partially due to the historically unfortunate use of the name "causality", as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, since data from simulated systems are used, the pitfalls that are found with the used formulation are intended to be general, and not limited to neuroscience. It would be a pity if this paper, even if written in good faith, became a wildcard against all possible applications of GC, regardless of the large body of work recently published which aims to address faults in methodology and interpretation. In order to provide a balanced view, we replicate the simulations of Stokes and Purdon, using an updated GC implementation and exploiting the combination of spectral and causal information, showing that in this way the pitfalls are mitigated or directly solved.

Forecasting the Incidence of Mumps in Zibo City Based on a SARIMA Model

This study aimed to predict the incidence of mumps using a seasonal autoregressive integrated moving average (SARIMA) model, and provide theoretical evidence for early warning prevention and control in Zibo City, Shandong Province, China. Monthly mumps data from Zibo City gathered between 2005 and 2013 were used as a training set to construct a SARIMA model, and the monthly mumps in 2014 were defined as a test set for the model. From 2005 to 2014, a total of 8722 cases of mumps were reported in Zibo City; the male-to-female ratio of cases was 1.85:1, the age group of 1-20 years old accounted for 94.05% of all reported cases, and students made up the largest proportion (65.89%). The main serious endemic areas of mumps were located in Huantai County, Linzi District, and Boshan District of Zibo City. There were two epidemic peaks from April to July and from October to January in next year. The fitted model SARIMA (0, 1, 1) (0, 1, 1)₁₂ was established (AIC = 157.528), which has high validity and reasonability. The SARIMA model fitted dynamic changes of mumps in Zibo City well. It can be used for short-term forecasting and early warning of mumps.