Intensive Time Series Research Articles

Leveraging an intensive time series of young children's movement to capture impulsive and inattentive behaviors in a preschool setting.

Studying within-person variability in children's behavior is frequently hindered by challenges collecting repeated observations. This study used wearable accelerometers to collect an intensive time series (2.7 million observations) of young children's movement at school (N = 62, Mage = 4.5 years, 54% male, 74% Non-Hispanic White) in 2021. Machine learning analyses indicated that children's typical forward acceleration was strongly correlated with lower teacher-reported inhibitory control and attention (r = -.69). Using forward movement intensity as a proxy for impulsivity, we partitioned the intensive time series and found that (1) children modulated their behavior across periods of the school day, (2) children's impulsivity increased across the school week, and (3) children with greater impulsivity showed greater variability in behavior across days.

An Intensive time series investigation of the relationships across eating disorder-specific fear responses and behavior urges in partially remitted anorexia nervosa

Anorexia nervosa (AN) is a serious and persistent psychiatric illness. Many individuals with AN cycle between stages of remission (i.e., relapse), with research documenting that cognitive remission generally lags behind nutritional/weight restoration. Yet, little is known about which mechanisms promote movement from partial remission in AN (defined as nutritional, but not cognitive, recovery) to full remission. Fear-based processes, including avoidance and approach behaviors, likely contribute to the persistence of cognitive-behavioral AN symptoms after nutritional restoration. The current study used intensive longitudinal data to characterize these processes during partial remission (N = 41 participants with partially remitted AN; 4306 total observations). We aimed to a) characterize frequency of fear-based processes in real-time, b) investigate associations across fear-based processes and behavioral urges, and c) test if real-time associations among symptoms differed across commonly feared stimuli (e.g., food, social situations). On average, participants endorsed moderate fear and avoidance, with weight-gain fears rated higher than other feared stimuli. Momentary fear, avoidance, approach, and distress were all positively associated with AN behavior urges at one time-point and prospectively. Central symptoms and symptom connections differed across models with different feared stimuli. These findings provide empirical support for the theorized fear-avoidance-urge cycle in AN, which may contribute to the persistence of eating pathology during partial remission. Fear approach may be associated with temporary increases in urges, which should be considered during treatment. Future research should explore these associations in large, heterogeneous samples, and test the effectiveness of exposure-based interventions during partial remission.

Snow-Dependent Biogeochemical Cycling of Polycyclic Aromatic Hydrocarbons at Coastal Antarctica.

The temporal trend of polycyclic aromatic hydrocarbons (PAHs) in coastal waters with highly dynamic sources and sinks is largely unknown, especially for polar regions. Here, we show the concurrent measurements of 73 individual PAHs and environmental data, including the composition of the bacterial community, during three austral summers at coastal Livingston (2015 and 2018) and Deception (2017) islands (Antarctica). The Livingston 2015 campaign was characterized by a larger snow melting input of PAHs and nutrients. The assessment of PAH diagnostic ratios, such as parent to alkyl-PAHs or LMW to HMW PAHs, showed that there was a larger biodegradation during the Livingston 2015 campaign than in the Deception 2017 and Livingston 2018 campaigns. The biogeochemical cycling, including microbial degradation, was thus yearly dependent on snow-derived inputs of matter, including PAHs, consistent with the microbial community significantly different between the different campaigns. The bivariate correlations between bacterial taxa and PAH concentrations showed that a decrease in PAH concentrations was concurrent with the higher abundance of some bacterial taxa, specifically the order Pseudomonadales in the class Gammaproteobacteria, known facultative hydrocarbonoclastic bacteria previously reported in degradation studies of oil spills. The work shows the potential for elucidation of biogeochemical processes by intensive field-derived time series, even in the harsh and highly variable Antarctic environment.

Novel digital methods for gathering intensive time series data in mental health research: scoping review of a rapidly evolving field.

Recent technological advances enable the collection of intensive longitudinal data. This scoping review aimed to provide an overview of methods for collecting intensive time series data in mental health research as well as basic principles, current applications, target constructs, and statistical methods for this type of data.In January 2021, the database MEDLINE was searched. Original articles were identified that (1) used active or passive data collection methods to gather intensive longitudinal data in daily life, (2) had a minimum sample size of N ⩾ 100 participants, and (3) included individuals with subclinical or clinical mental health problems.In total, 3799 original articles were identified, of which 174 met inclusion criteria. The most widely used methods were diary techniques (e.g. Experience Sampling Methodology), various types of sensors (e.g. accelerometer), and app usage data. Target constructs included affect, various symptom domains, cognitive processes, sleep, dysfunctional behaviour, physical activity, and social media use. There was strong evidence on feasibility of, and high compliance with, active and passive data collection methods in diverse clinical settings and groups. Study designs, sampling schedules, and measures varied considerably across studies, limiting the generalisability of findings.Gathering intensive longitudinal data has significant potential to advance mental health research. However, more methodological research is required to establish and meet critical quality standards in this rapidly evolving field. Advanced approaches such as digital phenotyping, ecological momentary interventions, and machine-learning methods will be required to efficiently use intensive longitudinal data and deliver personalised digital interventions and services for improving public mental health.

Response of vegetation variation to climate change and human activities in semi-arid swamps.

Vegetation is a sensitive factor in marsh ecosystems, which can provide nesting sites, foraging areas, and hiding places for waterfowl and can affect their survival environment. The Jilin Momoge National Nature Reserve, which consists of large areas of marshes, is located in the semi-arid region of northeast China and is an important stopover site for the critically endangered species of the Siberian Crane (Grus leucogeranus). Global climate change, extreme droughts and floods, and large differences in evaporation and precipitation in this region can cause rapid vegetation succession. In recent years, increased grain production and river-lake connectivity projects carried out in this area to increase grain outputs and restore wetlands have caused significant changes in the hydrological and landscape patterns. Therefore, research on the response of variation trends in vegetation patterns to the main driving factors (climate change and human activities) is critical for the conservation of the Siberian Crane. Based on the Google Earth Engine (GEE) platform, we obtained and processed the Normalized difference vegetation index (NDVI) data of the study area during the peak summer vegetation period for each year from 1984 to 2020, estimated the annual vegetation cover using Maximum value composites (MVC) method and the image dichotomy method, calculated and analyzed the spatial and temporal trends of vegetation cover, explored the response of vegetation cover change in terms of climate change and human activities, and quantified the relative contribution of both. The results revealed that first, from the spatial and temporal changes, the average annual growth rate of regional vegetation was 0.002/a, and 71.14% of the study area was improved. The vegetation cover showed a trend of degradation and then recovery, in which the percentage of high vegetation cover area decreased from 51.22% (1984–2000) to 28.33% (2001–2005), and then recovered to 55.69% (2006–2020). Second, among climate change factors, precipitation was more correlated with the growth of vegetation in the study area than temperature, and the increase in precipitation during the growing season could promote the growth of marsh vegetation in the Momoge Reserve. Third, overall, human activities have contributed to the improvement of vegetation cover in the study area with the implementation of important ecological projects, such as the return of farmland to wetlands, the return of grazing to grass, and the connection of rivers and lakes. Fourth, climate change and human activities jointly drive vegetation change, but the contribution of human activities in both vegetation improvement and degradation areas (85.68% and 78.29%, respectively) is higher than that of climate change (14.32% and 21.71%, respectively), which is the main reason for vegetation improvement or degradation in the study area. The analysis of vegetation pattern change within an intensive time series in semi-arid regions can provide a reference and basis for studying the driving factors in regions with rapid changes in vegetation and hydrological conditions.

Gap Filling Cloudy Sentinel-2 NDVI and NDWI Pixels with Multi-Frequency Denoised C-Band and L-Band Synthetic Aperture Radar (SAR), Texture, and Shallow Learning Techniques

Multispectral imagery provides unprecedented information on Earth system processes: however, data gaps due to clouds and shadows are a major limitation. Normalized-Difference Vegetation Index (NDVI) and Normalized-Difference Water Index (NDWI) are two spectral indexes employed for monitoring vegetation phenology, land-cover change and more. Synthetic Aperture Radar (SAR) with its cloud-penetrating abilities can fill data gaps using coincident imagery. In this study, we evaluated C-band Sentinel-1, L-band Uninhabited Aerial Vehicle SAR (UAVSAR) and texture for gap filling using efficient machine learning regression algorithms across three seasons. Multiple models were evaluated including Support Vector Machine, Random Forest, Gradient Boosted Trees and an ensemble of models. The Gap filling ability of SAR was evaluated with Sentinel-2 imagery from the same date, 3 days and 8 days later than both SAR sensors in September. Sentinel-1 and Sentinel-2 imagery from winter and spring seasons were also evaluated. Because SAR imagery contains noise, we compared two robust de-noising methods and evaluated performance against a refined lee speckle filter. Mean Absolute Error (MAE) rates of the cloud gap-filling model were assessed across different dataset combinations and land covers. The results indicated de-noised Sentinel-1 SAR and UAVSAR with GLCM texture provided the highest predictive abilities with random forest R2 = 0.91 (±0.014), MAE = 0.078 (±0.003) (NDWI) and R2 = 0.868 (±0.015), MAE = 0.094 (±0.003) (NDVI) during September. The highest errors were observed across bare ground and forest, while the lowest errors were on herbaceous and woody wetland. Results on January and June imagery without UAVSAR were less strong at R2 = 0.60 (±0.036), MAE = 0.211 (±0.005) (NDVI), R2 = 0.61 (±0.043), MAE = 0.209 (±0.005) (NDWI) for January and R2 = 0.72 (±0.018), MAE = 0.142 (±0.004) (NDVI), R2 = 0.77 (±0.022), MAE = 0.125 (±0.004) (NDWI) for June. Ultimately, the results suggest de-noised C-band SAR with texture metrics can accurately predict NDVI and NDWI for gap-filling clouds during most seasons. These shallow machine learning models are rapidly trained and applied faster than intensive deep learning or time series methods.

Space-time modeling of intensive binary time series eye-tracking data using a generalized additive logistic regression model.

Eye-tracking has emerged as a popular method for empirical studies of cognitive processes across multiple substantive research areas. Eye-tracking systems are capable of automatically generating fixation-location data over time at high temporal resolution. Often, the researcher obtains a binary measure of whether or not, at each point in time, the participant is fixating on a critical interest area or object in the real world or in a computerized display. Eye-tracking data are characterized by spatial-temporal correlations and random variability, driven by multiple fine-grained observations taken over small time intervals (e.g., every 10 ms). Ignoring these data complexities leads to biased inferences for the covariates of interest such as experimental condition effects. This article presents a novel application of a generalized additive logistic regression model for intensive binary time series eye-tracking data from a between- and within-subjects experimental design. The model is formulated as a generalized additive mixed model (GAMM) and implemented in the mgcv R package. The generalized additive logistic regression model was illustrated using an empirical data set aimed at understanding the accommodation of regional accents in spoken language processing. Accuracy of parameter estimates and the importance of modeling the spatial-temporal correlations in detecting the experimental condition effects were shown in conditions similar to our empirical data set via a simulation study. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Visual Analytics of Multivariate Intensive Care Time Series Data

AbstractWe present an approach for visual analysis of high‐dimensional measurement data with varying sampling rates as routinely recorded in intensive care units. In intensive care, most assessments not only depend on one single measurement but a plethora of mixed measurements over time. Even for trained experts, efficient and accurate analysis of such multivariate data remains a challenging task. We present a linked‐view post hoc visual analytics application that reduces data complexity by combining projection‐based time curves for overview with small multiples for details on demand. Our approach supports not only the analysis of individual patients but also of ensembles by adapting existing techniques using non‐parametric statistics. We evaluated the effectiveness and acceptance of our approach through expert feedback with domain scientists from the surgical department using real‐world data: a post‐surgery study performed on a porcine surrogate model to identify parameters suitable for diagnosing and prognosticating the volume state, and clinical data from a public database. The results show that our approach allows for detailed analysis of changes in patient state while also summarizing the temporal development of the overall condition.

Rapid and automatic classification of intertidal wetlands based on intensive time series Sentinel-2 images and Google Earth Engine

Rapid and automatic classification of intertidal wetlands based on intensive time series Sentinel-2 images and Google Earth Engine

Monitoring Coastline Changes of the Malay Islands Based on Google Earth Engine and Dense Time-Series Remote Sensing Images

The use of remote sensing to monitor coastlines with wide distributions and dynamic changes is significant for coastal environmental monitoring and resource management. However, most current remote sensing information extraction of coastlines is based on the instantaneous waterline, which is obtained by single-period imagery. The lack of a unified standard is not conducive to the dynamic change monitoring of a changeable coastline. The tidal range observation correction method can be used to correct coastline observation to a unified climax line, but it is difficult to apply on a large scale because of the distribution of observation sites. Therefore, we proposed a coastline extraction method based on the remote sensing big data platform Google Earth Engine and dense time-series remote sensing images. Through the instantaneous coastline probability calculation system, the coastline information could be extracted without the tidal range observation data to achieve a unified tide level standard. We took the Malay Islands as the experimental area and analyzed the consistency between the extraction results and the existing high-precision coastline thematic products of the same period to achieve authenticity verification. Our results showed that the coastline data deviated 10 m in proportion to a reach of 40% and deviated 50 m within a reach of 89%. The overall accuracy was kept within 100 m. In addition, we extracted 96 additional islands that have not been included in public data. The obtained multi-phase coastlines showed the spatial distribution of the changing hot regions of the Malay Islands’ coastline, which greatly supported our analysis of the reasons for the expansion and retreat of the coastline in this region. These research results showed that the big data platform and intensive time-series method have considerable potential in large-scale monitoring of coastline dynamic change and island reef change monitoring.

Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest

Urban areas represent the primary source region of greenhouse gas emissions. Mapping urban areas is essential for understanding land cover change, carbon cycles, and climate change (urban areas also refer to impervious surfaces, i.e., artificial cover and structures). Remote sensing has greatly advanced urban areas mapping over the last several decades. At present, we have entered the era of big data. Long time series of satellite data such as Landsat and high-performance computing platforms such as Google Earth Engine (GEE) offer new opportunities to map urban areas. The objective of this research was to determine how annual time series images from Landsat 8 Operational Land Imager (OLI) can effectively be composed to map urban areas in three cities in China in support of GEE. Three reducer functions, ee.Reducer.min(), ee.Reducer.median(), and ee.Reducer.max() provided by GEE, were selected to construct four schemes to synthesize the annual intensive time series Landsat 8 OLI data for three cities in China. Then, urban areas were mapped based on the random forest algorithm and the accuracy was evaluated in detail. The results show that (1) the quality of annual composite images was improved significantly, particularly in reducing the impact of cloud and cloud shadows, and (2) the annual composite images obtained by the combination of multiple reducer functions had better performance than that obtained by a single reducer function. Further, the overall accuracy of urban areas mapping with the combination of multiple reducer functions exceeded 90% in all three cities in China. In summary, a suitable combination of reducer functions for synthesizing annual time series images can enhance data quality and ensure differences between characteristics and higher precision for urban areas mapping.

T38. POSTER PRESENTATION: HAMLETT: HANDLING ANTIPSYCHOTIC MEDICATION, A LONGTERM EVALUATION OF TARGETED TREATMENT A PRAGMATIC SINGLE-BLIND RANDOMISED CONTROLLED TRIAL OF CONTINUATION VERSUS DISCONTINUATION OF PSYCHOTIC MEDICATION

BackgroundWhen achieving remission after a first psychotic episode using antipsychotic medication, international guidelines generally recommend continuation of use for >1 year. However, patients often have a strong wish to stop earlier due to side-effects, affecting everyday functioning. Recently, guidelines have been questioned as one Dutch study found that more patients achieved long-term functional remission after early discontinuation. Yet, this finding has not yet been replicated. Psychiatrists, patients and family are unsure which regime to follow: to continue or not to continue?MethodsIn total 512 participants will be included who achieved remission after first-episode psychosis and use antipsychotic medication. Recruitment takes place at 24 Dutch sites. HAMLETT is a multicenter pragmatic single-blind randomized controlled trial with two conditions (1:1): maintenance treatment versus discontinuation/dose reduction of antipsychotic medication.Main research question: Is long-term general functioning better if patients reduce/discontinue antipsychotic medication at an early stage (3–6 months after remission of their first psychotic episode), than when they continue medication >1 year? General functioning is measured in two ways: with the WHO-DAS interview and with Ecological Momentary Assessments (EMA).EMA is a structured diary method in which individuals are asked in daily life to report on their current thoughts, feelings and symptoms, as well as the context (e.g. location, company, activity) and the appraisal of the context (e.g. stress). Diaries are completed via a smartphone diary app maximally 10 times daily at semi-random moments, over eight consecutive days. Momentary positive/negative affect, self-esteem, subjective well-being, paranoia, hallucinations, sleep, and frequency, type and appraisal of social company and activities are assessed on a 1–7 scale. At baseline and after 6 months, 1, 2, 3 and 4 years follow-up, patients of both arms will perform EMA. This results in an intensive time series of psychopathology, subjective well-being and social functioning in relation to antipsychotic medication and a range of contextual influences.ResultsThe study is active and currently recruiting patients (since September 2017), At present 194 patients have been included, 20% participated in EMA measurements. Results of the interim analysis and preliminary of EMA data will be presented.DiscussionThe HAMLETT study investigates the effects of maintenance treatment versus discontinuation/dose reduction of antipsychotic medication after remission of first episode psychosis on personal and social functioning, psychotic symptom severity, health-related quality of life and cognitive functioning. The HAMLETT study will offer evidence to guide patients and clinicians when evaluating optimal treatment duration for psychotic disorders. Using different types of outcome measures will provide a more in-depth analysis of effects of continuation/ discontinuation on functioning.

A Data-Intensive Approach to Address Food Sustainability: Integrating Optic and Microwave Satellite Imagery for Developing Long-Term Global Cropping Intensity and Sowing Month from 2001 to 2015

It is necessary to develop a sustainable food production system to ensure future food security around the globe. Cropping intensity and sowing month are two essential parameters for analyzing the food–water–climate tradeoff as food sustainability indicators. This study presents a global-scale analysis of cropping intensity and sowing month from 2000 to 2015, divided into three groups of years. The study methodology integrates the satellite-derived normalized vegetation index (NDVI) of 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) and daily land-surface-water coverage (LSWC) data obtained from The Advanced Microwave Scanning Radiometer (AMSR-E/2) in 1-km aggregate pixel resolution. A fast Fourier transform was applied to normalize the MODIS NDVI time-series data. By using advanced methods with intensive optic and microwave time-series data, this study set out to anticipate potential dynamic changes in global cropland activity over 15 years representing the Millennium Development Goal period. These products are the first global datasets that provide information on crop activities in 15-year data derived from optic and microwave satellite data. The results show that in 2000–2005, the total global double-crop intensity was 7.1 million km2, which increased to 8.3 million km2 in 2006–2010, and then to approximately 8.6 million km2 in 2011–2015. In the same periods, global triple-crop agriculture showed a rapid positive growth from 0.73 to 1.12 and then 1.28 million km2, respectively. The results show that Asia dominated double- and triple-crop growth, while showcasing the expansion of single-cropping area in Africa. The finer spatial resolution, combined with a long-term global analysis, means that this methodology has the potential to be applied in several sustainability studies, from global- to local-level perspectives.

Inter-Individual Differences in Multivariate Time-Series

Abstract. Theories of emotion regulation posit the existence of individual differences in emotion dynamics. Current multi-subject time-series models account for differences in dynamics across individuals only to a very limited extent. This results in an aggregation that may poorly apply at the individual level. We present the exploratory method of latent class vector-autoregressive modeling (LCVAR), which extends the time-series models to include clustering of individuals with similar dynamic processes. LCVAR can identify individuals with similar emotion dynamics in intensive time-series, which may be of unequal length. The method performs excellently under a range of simulated conditions. The value of identifying clusters in time-series is illustrated using affect measures of 410 individuals, assessed at over 70 time points per individual. LCVAR discerned six clusters of distinct emotion dynamics with regard to diurnal patterns and augmentation and blunting processes between eight emotions.

Advances in the application of artificial intelligence in critical care medicine

Artificial intelligence technology has produced a series of advanced models and algorithms in the field of critical care. Many model applications have been tested by clinical trials to support the physicians′ optimal clinical decision making. In this review, we will introduce application progresses of artificial intelligence in intensive care. This review aims to make clinicians understand how artificial intelligence serve in critical care medicine. The major contents of this review include introducing characteristics of data in intensive care unit (ICU), e.g., time series, image, text and discrete data. Early warning models in the field and research based on medical information mart for intensive care (MIMIC) database will be discussed in detail. Based on the application of artificial intelligence, clinicians will make better clinical decisions in a timely manner so as to improve clinical service ability for caring critical patients. Key words: Artificial intelligence; Big data; Critical care medicine; Intensive care unit

Constrained Fourth Order Latent Differential Equation Reduces Parameter Estimation Bias for Damped Linear Oscillator Models

Second-order linear differential equations can be used as models for regulation since under a range of parameter values they can account for a return to equilibrium as well as potential oscillations in regulated variables. One method that can estimate parameters of these equations from intensive time series data is the method of Latent Differential Equations (LDE). However, the LDE method can exhibit bias in its parameters if the dimension of the time delay embedding and thus the width of the convolution kernel is not chosen wisely. This article presents a simulation study showing that a constrained fourth-order Latent Differential Equation (FOLDE) model for the second-order system almost completely eliminates bias as long as the width of the convolution kernel is less than two-thirds the period of oscillations in the data. The FOLDE model adds two degrees of freedom over the standard LDE model but significantly improves model fit.

Bringing light into darkness: A multiple baseline mixed methods case series evaluation of Augmented Depression Therapy (ADepT)

Two core features of depression are elevations in negative valence system (NVS) functioning and reductions in positive valence system (PVS) functioning. Existing psychological treatments have focused on the NVS and neglected the PVS, which may contribute to sub-optimal outcomes. The present mixed methods multiple randomised baseline case series preliminarily evaluates Augmented Depression Therapy (ADepT), a novel depression treatment targeting PVS and NVS disturbance, that aims both to reduce depression and enhance wellbeing. Eleven clinically depressed participants were recruited. Intensive time series analyses showed that 7/11 participants improved on both wellbeing and depression. Reliable and clinically significant improvement was observed for 9/11 participants on at least one of these outcomes (and also across a range of other PVS and NVS outcomes). Group level analyses showed significant pre to post change on all outcomes. Benchmarking analyses indicated these effect sizes were at least comparable (and for some PVS outcomes superior) to existing treatments. Gains were largely sustained over one-year follow-up. Qualitative interviews indicated ADepT was feasible and acceptable. These findings provide preliminary support for ADepT as a novel depression treatment. Further evaluation, directly comparing ADepT to existing treatments using randomised controlled trial designs, is now required.

Autoregressive Generalized Linear Mixed Effect Models with Crossed Random Effects: An Application to Intensive Binary Time Series Eye-Tracking Data.

As a method to ascertain person and item effects in psycholinguistics, a generalized linear mixed effect model (GLMM) with crossed random effects has met limitations in handing serial dependence across persons and items. This paper presents an autoregressive GLMM with crossed random effects that accounts for variability in lag effects across persons and items. The model is shown to be applicable to intensive binary time series eye-tracking data when researchers are interested in detecting experimental condition effects while controlling for previous responses. In addition, a simulation study shows that ignoring lag effects can lead to biased estimates and underestimated standard errors for the experimental condition effects.

Network Approach to Understanding Emotion Dynamics in Relation to Childhood Trauma and Genetic Liability to Psychopathology: Replication of a Prospective Experience Sampling Analysis.

Background: The network analysis of intensive time series data collected using the Experience Sampling Method (ESM) may provide vital information in gaining insight into the link between emotion regulation and vulnerability to psychopathology. The aim of this study was to apply the network approach to investigate whether genetic liability (GL) to psychopathology and childhood trauma (CT) are associated with the network structure of the emotions “cheerful,” “insecure,” “relaxed,” “anxious,” “irritated,” and “down”—collected using the ESM method.Methods: Using data from a population-based sample of twin pairs and siblings (704 individuals), we examined whether momentary emotion network structures differed across strata of CT and GL. GL was determined empirically using the level of psychopathology in monozygotic and dizygotic co-twins. Network models were generated using multilevel time-lagged regression analysis and were compared across three strata (low, medium, and high) of CT and GL, respectively. Permutations were utilized to calculate p values and compare regressions coefficients, density, and centrality indices. Regression coefficients were presented as connections, while variables represented the nodes in the network.Results: In comparison to the low GL stratum, the high GL stratum had significantly denser overall (p = 0.018) and negative affect network density (p < 0.001). The medium GL stratum also showed a directionally similar (in-between high and low GL strata) but statistically inconclusive association with network density. In contrast to GL, the results of the CT analysis were less conclusive, with increased positive affect density (p = 0.021) and overall density (p = 0.042) in the high CT stratum compared to the medium CT stratum but not to the low CT stratum. The individual node comparisons across strata of GL and CT yielded only very few significant results, after adjusting for multiple testing.Conclusions: The present findings demonstrate that the network approach may have some value in understanding the relation between established risk factors for mental disorders (particularly GL) and the dynamic interplay between emotions. The present finding partially replicates an earlier analysis, suggesting it may be instructive to model negative emotional dynamics as a function of genetic influence.

Stress and stability: applying the Anna Karenina principle to animal microbiomes.

All animals studied to date are associated with symbiotic communities of microorganisms. These animal microbiotas often play important roles in normal physiological function and susceptibility to disease; predicting their responses to perturbation represents an essential challenge for microbiology. Most studies of microbiome dynamics test for patterns in which perturbation shifts animal microbiomes from a healthy to a dysbiotic stable state. Here, we consider a complementary alternative: that the microbiological changes induced by many perturbations are stochastic, and therefore lead to transitions from stable to unstable community states. The result is an 'Anna Karenina principle' for animal microbiomes, in which dysbiotic individuals vary more in microbial community composition than healthy individuals-paralleling Leo Tolstoy's dictum that "all happy families look alike; each unhappy family is unhappy in its own way". We argue that Anna Karenina effects are a common and important response of animal microbiomes to stressors that reduce the ability of the host or its microbiome to regulate community composition. Patterns consistent with Anna Karenina effects have been found in systems ranging from the surface of threatened corals exposed to above-average temperatures, to the lungs of patients suffering from HIV/AIDs. However, despite their apparent ubiquity, these patterns are easily missed or discarded by some common workflows, and therefore probably underreported. Now that a substantial body of research has established the existence of these patterns in diverse systems, rigorous testing, intensive time-series datasets and improved stochastic modelling will help to explore their importance for topics ranging from personalized medicine to theories of the evolution of host-microorganism symbioses.