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

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

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

Introduction

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

Methods

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

Results

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

Conclusion

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

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

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

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

CONTEXT

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

OBJECTIVE

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

DESIGN

Cross-sectional study.

SETTING

Colleges and universities in the United States.

PATIENTS OR OTHER PARTICIPANTS

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

MAIN OUTCOME MEASURE(S)

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

RESULTS

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

CONCLUSIONS

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

Individual Heterogeneity in the Relations Between Sleep, Inflammation, and Somatic Symptoms

OBJECTIVE

Poor sleep is associated with the experience of more somatic symptoms and a proinflammatory state, whereas a proinflammatory state may also result in the experience of more somatic symptoms. However, existing studies ignore individual differences in these associations. We aimed to study relations between sleep, inflammatory markers, and somatic symptoms at a within-individual level.

METHODS

Time series of daily data on sleep, somatic symptoms, and inflammation markers in 10 healthy individuals (age, 19-58 years; three men) for 63 days were analyzed. Bidirectional lagged ( t - 1) and contemporaneous ( t ) relations between sleep duration, inflammatory markers (C-reactive protein, interferon-α, interleukin 1RA), and somatic symptoms were analyzed using 24-hour urine and diary data. Unified structural equation modeling was used to analyze the association between sleep duration, the three inflammatory markers, and the amount of somatic symptoms at the individual level.

RESULTS

Associations were found between sleep and at least one of three inflammatory markers in four individuals, both positive (three associations) and negative (five associations) and contemporaneous (four associations) and lagged (four associations). Sleep was related to somatic symptoms in four individuals, both positive ( n = 2) and negative ( n = 2) and contemporaneous ( n = 3) and lagged ( n = 1). Inflammatory markers were associated with somatic symptoms in three individuals, both positive (three associations) and negative (one association) and contemporaneous (three associations) and lagged (one associations). Two individuals showed no associations between sleep, inflammatory markers, and somatic symptoms.

CONCLUSIONS

We observed a large variability in presence, strength, and direction of associations between sleep, inflammatory markers, and somatic symptoms.

Network inference from short, noisy, low time-resolution, partial measurements: Application to C. elegans neuronal calcium dynamics

Network link inference from measured time series data of the behavior of dynamically interacting network nodes is an important problem with wide-ranging applications, e.g., estimating synaptic connectivity among neurons from measurements of their calcium fluorescence. Network inference methods typically begin by using the measured time series to assign to any given ordered pair of nodes a numerical score reflecting the likelihood of a directed link between those two nodes. In typical cases, the measured time series data may be subject to limitations, including limited duration, low sampling rate, observational noise, and partial nodal state measurement. However, it is unknown how the performance of link inference techniques on such datasets depends on these experimental limitations of data acquisition. Here, we utilize both synthetic data generated from coupled chaotic systems as well as experimental data obtained from Caenorhabditis elegans neural activity to systematically assess the influence of data limitations on the character of scores reflecting the likelihood of a directed link between a given node pair. We do this for three network inference techniques: Granger causality, transfer entropy, and, a machine learning-based method. Furthermore, we assess the ability of appropriate surrogate data to determine statistical confidence levels associated with the results of link-inference techniques.

Image-based time series analysis to establish differential disease progression for two Fusarium head blight pathogens in oat spikelets with variable resistance

Oat-based value-added products have increased their value as healthy foodstuff. Fusarium head blight (FHB) infections and the mycotoxins accumulated to the oat seeds, however, pose a challenge to oat production. The FHB infections are predicted to become more prevalent in the future changing climates and under more limited use of fungicides. Both these factors increase the pressure for breeding new resistant cultivars. Until now, however, genetic links in oats against FHB infection have been difficult to identify. Therefore, there is a great need for more effective breeding efforts, including improved phenotyping methods allowing time series analysis and the identification of molecular markers during disease progression. To these ends, dissected spikelets of several oat genotypes with different resistance profiles were studied by image-based methods during disease progression by Fusarium culmorum or F. langsethiae species. The chlorophyll fluorescence of each pixel in the spikelets was recorded after inoculation by the two Fusarium spp., and the progression of the infections was analyzed by calculating the mean maximum quantum yield of PSII (F_v/F_m) values for each spikelet. The recorded values were (i) the change in the photosynthetically active area of the spikelet as percentage of its initial size, and (ii) the mean of F_v/F_m values of all fluorescent pixels per spikelet post inoculation, both indicative of the progression of the FHB disease. The disease progression was successfully monitored, and different stages of the infection could be defined along the time series. The data also confirmed the differential rate of disease progression by the two FHB causal agents. In addition, oat varieties with variable responses to the infections were indicated.

Cervical cancer prevention program in Mexico disrupted due to COVID-19 pandemic: Challenges and opportunities

Introduction

The COVID-19 pandemic disrupted the preventive services for cervical cancer (CC) control programs in Mexico, which will result in increased mortality. This study aims to assess the impact of the pandemic on the interruption of three preventive actions in the CC prevention program in Mexico.

Methods

This study is a retrospective time series analysis based on administrative records for the uninsured population served by the Mexican Ministry of Health. Patient data were retrieved from the outpatient service information system and the hospital discharge database for the period 2017-2021. Data were aggregated by month, distinguishing a pre-pandemic and a pandemic period, considering April 2020 as the start date of the pandemic. A Poisson time series analysis was used to model seasonal and secular trends. Five process indicators were selected to assess the disruption of the CC program, these were analyzed as monthly data (N=39 pre-pandemic, N=21 during the pandemic). HPV vaccination indicators (number of doses and coverage) and diagnostic characteristics of CC cases were analyzed descriptively. The time elapsed between diagnosis and treatment initiation in CC cases was modeled using restricted cubic splines from robust regression.

Results

Annual HPV vaccination coverage declined dramatically after 2019 and was almost null in 2021. The number of positive Papanicolaou smears decreased by 67.8% (90%CI: -72.3, -61.7) in April-December 2020, compared to their expected values without the pandemic. The immediate pandemic shock (April 2020) in the number of first-time and recurrent colposcopies was -80.5% (95%CI:-83.5, -77.0) and -77.9% (95%CI: -81.0, -74.4), respectively. An increasing trend was observed in the proportion of advanced stage and metastatic CC cases. The fraction of CC cases that did not receive medical treatment or surgery increased, as well as CC cases that received late treatment after diagnosis.

Conclusions

Our analyses show significant impact of the COVID-19 pandemic with declines at all levels of CC prevention and increasing inequalities. The restarting of the preventive programs against CC in Mexico offers an opportunity to put in place actions to reduce the disparities in the burden of disease between socioeconomic levels.

Characterizing the Solar Activity Using the Visibility Graph Method

In this paper, the Sun and its behavior are studied by means of complex networks. The complex network was built using the Visibility Graph algorithm. This method maps time series into graphs in which every element of the time series is considered as a node and a visibility criterion is defined in order to connect them. Using this method, we construct complex networks for magnetic field and sunspots time series encompassing four solar cycles, and various measures such as degree, clustering coefficient, mean path length, betweenness centrality, eigenvector centrality and decay exponents were calculated. In order to study the system in several time scales, we perform both a global, where the network contains information on the four solar cycles, and a local analysis, involving moving windows. Some metrics correlate with solar activity, while others do not. Interestingly, those metric which seem to respond to varying levels of solar activity in the global analysis, also do in the moving windows analysis. Our results suggest that complex networks can provide a useful way to follow solar activity, and reveal new features on solar cycles.

Development of a Soil Moisture Prediction Model Based on Recurrent Neural Network Long Short-Term Memory (RNN-LSTM) in Soybean Cultivation

Due to climate change, soil moisture may increase, and outflows could become more frequent, which will have a considerable impact on crop growth. Crops are affected by soil moisture; thus, soil moisture prediction is necessary for irrigating at an appropriate time according to weather changes. Therefore, the aim of this study is to develop a future soil moisture (SM) prediction model to determine whether to conduct irrigation according to changes in soil moisture due to weather conditions. Sensors were used to measure soil moisture and soil temperature at a depth of 10 cm, 20 cm, and 30 cm from the topsoil. The combination of optimal variables was investigated using soil moisture and soil temperature at depths between 10 cm and 30 cm and weather data as input variables. The recurrent neural network long short-term memory (RNN-LSTM) models for predicting SM was developed using time series data. The loss and the coefficient of determination (R²) values were used as indicators for evaluating the model performance and two verification datasets were used to test various conditions. The best model performance for 10 cm depth was an R² of 0.999, a loss of 0.022, and a validation loss of 0.105, and the best results for 20 cm and 30 cm depths were an R² of 0.999, a loss of 0.016, and a validation loss of 0.098 and an R² of 0.956, a loss of 0.057, and a validation loss of 2.883, respectively. The RNN-LSTM model was used to confirm the SM predictability in soybean arable land and could be applied to supply the appropriate moisture needed for crop growth. The results of this study show that a soil moisture prediction model based on time-series weather data can help determine the appropriate amount of irrigation required for crop cultivation.

An intrinsic oscillator underlies visual navigation in ants

Many insects display lateral oscillations while moving, but how these oscillations are produced and participate in visual navigation remains unclear. Here, we show that visually navigating ants continuously display regular lateral oscillations coupled with variations of forward speed that strongly optimize the distance covered while simultaneously enabling them to scan left and right directions. This pattern of movement is produced endogenously and conserved across navigational contexts in two phylogenetically distant ant species. Moreover, the oscillations' amplitude can be modulated by both innate or learnt visual cues to adjust the exploration/exploitation balance to the current need. This lower-level motor pattern thus drastically reduces the degree of freedom needed for higher-level strategies to control behavior. The observed dynamical signature readily emerges from a simple neural circuit model of the insect's conserved pre-motor area known as the lateral accessory lobe, offering a surprisingly simple but effective neural control and endorsing oscillation as a core, ancestral way of moving in insects.

Burden of occupational cancer in Brazil and federative units, 1990-2019

OBJECTIVE

To analyze the spatiotemporal distribution of the burden of occupational cancer in Brazil and federative units between 1990 and 2019.

METHODS

Data were extracted from the Global Burden of Disease (GBD) study. Deaths from cancer whose attributable risk factor was occupational carcinogens were considered. Spatial analysis was performed with the first and last years of the series (1990 and 2019). Age-adjusted mortality rates were used to estimate the global Moran's Index (Moran's I), and the local indicator of spatial association (LISA) to identify clusters in the country with the respective statistical significance. The occupational cancer mortality rate, adjusted for age, was analyzed based on its trend for Brazil and federative units, in the period between 1990 and 2019.

RESULTS

Between 1990 and 2019, occupational cancer mortality rate showed a decreasing trend (R2=0.62; p<0.001) as well as the burden of disease indicator - DALY (R2=0.84; p<0.001). However, mortality is increasing in most states, suggesting that a minority of federative units induce the country's global trend. There is also the development of a spatial pattern of autocorrelation, indicating clusters of states with low mortality and DALY rates in the Northeast and high values in the South of the country.

CONCLUSION

The overall decreasing trend in the trend of occupational cancer masks the heterogeneity across states. This scenario may be associated with the diversity of economic activities, and suggests a decentralized and equitable plan for occupational cancer surveillance.

Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression

We consider the problem of predicting how the likelihood of an outcome of interest for a patient changes over time as we observe more of the patient's data. To solve this problem, we propose a supervised contrastive learning framework that learns an embedding representation for each time step of a patient time series. Our framework learns the embedding space to have the following properties: (1) nearby points in the embedding space have similar predicted class probabilities, (2) adjacent time steps of the same time series map to nearby points in the embedding space, and (3) time steps with very different raw feature vectors map to far apart regions of the embedding space. To achieve property (3), we employ a nearest neighbor pairing mechanism in the raw feature space. This mechanism also serves as an alternative to "data augmentation", a key ingredient of contrastive learning, which lacks a standard procedure that is adequately realistic for clinical tabular data, to our knowledge. We demonstrate that our approach outperforms state-of-the-art baselines in predicting mortality of septic patients (MIMIC-III dataset) and tracking progression of cognitive impairment (ADNI dataset). Our method also consistently recovers the correct synthetic dataset embedding structure across experiments, a feat not achieved by baselines. Our ablation experiments show the pivotal role of our nearest neighbor pairing.

Investigating the impact of COVID-19 on patients with cancer from areas of conflict within the MENA region treated at King Hussein Cancer Center

Background

There is a paucity of evidence regarding the impact of COVID-19 on cancer care among refugees or patients from areas of conflict. Cancer care for these populations remains fragmented due to resource scarcity and limited infrastructure.

Aims

To explore the effect of COVID-19 on cancer care among patients from areas of conflict treated at King Hussein Cancer Center (KHCC).

Methodology

We performed a retrospective chart review of all patients from areas of conflict, treated at KHCC from 2018 to 2021. Patients' demographics and clinical characteristics are presented in the form of descriptive statistics. Interrupted Time Series (ITS) analysis was utilized to investigate the impact of COVID-19 on the number of admissions throughout the study's period.

Results

A total of 3317 patients from areas of conflict were included in the study. Among these, 1546 were males (46.6%) while 1771 (53.4%) were female. Libyans (34.6%), Palestinians (24.8%), Iraqis (24.5%), Syrians (15.3%), and Sudanese patients (0.9%) constituted our study sample. ITS analysis demonstrated that the start of the COVID-19 lockdown significantly decreased admissions by 44.0% (p = 0.020), while the end of the COVID-19 restriction significantly improved admissions by 43.0% (p = 0.023). Among those with available SEER stages, more than a quarter of patients had distant metastasis (n = 935, 28.2%) irrespective of age and biological sex. Advanced presentations during 2020 had approximately a 16% and 6% increase compared to 2018 and 2019, respectively. Breast cancer (21.4%), hematolymphoid cancers (18.1%), and cancers of the digestive system (16.5%) were the most common cancers among our cohort.

Conclusion

Restrictions associated with COVID-19 had a significant effect on the number of admissions of patients from areas of conflict. In the long term, this effect may impact the survival outcomes of affected patients.

Continuous Glucose Monitoring Time Series Data Analysis: A Time Series Analysis Package for Continuous Glucose Monitoring Data

The R package Continuous Glucose Monitoring Time Series Data Analysis (CGMTSA) was developed to facilitate investigations that examine the continuous glucose monitoring (CGM) data as a time series. Accordingly, novel time series functions were introduced to (1) enable more accurate missing data imputation and outlier identification; (2) calculate recommended CGM metrics as well as key time series parameters; (3) plot interactive and three-dimensional graphs that allow direct visualizations of temporal CGM data and time series model optimization. The software was designed to accommodate all popular CGM devices and support all common data processing steps. The program is available for Linux, Windows, and Mac at GitHub.

A systematic comparison of deep learning methods for EEG time series analysis

Analyzing time series data like EEG or MEG is challenging due to noisy, high-dimensional, and patient-specific signals. Deep learning methods have been demonstrated to be superior in analyzing time series data compared to shallow learning methods which utilize handcrafted and often subjective features. Especially, recurrent deep neural networks (RNN) are considered suitable to analyze such continuous data. However, previous studies show that they are computationally expensive and difficult to train. In contrast, feed-forward networks (FFN) have previously mostly been considered in combination with hand-crafted and problem-specific feature extractions, such as short time Fourier and discrete wavelet transform. A sought-after are easily applicable methods that efficiently analyze raw data to remove the need for problem-specific adaptations. In this work, we systematically compare RNN and FFN topologies as well as advanced architectural concepts on multiple datasets with the same data preprocessing pipeline. We examine the behavior of those approaches to provide an update and guideline for researchers who deal with automated analysis of EEG time series data. To ensure that the results are meaningful, it is important to compare the presented approaches while keeping the same experimental setup, which to our knowledge was never done before. This paper is a first step toward a fairer comparison of different methodologies with EEG time series data. Our results indicate that a recurrent LSTM architecture with attention performs best on less complex tasks, while the temporal convolutional network (TCN) outperforms all the recurrent architectures on the most complex dataset yielding a 8.61% accuracy improvement. In general, we found the attention mechanism to substantially improve classification results of RNNs. Toward a light-weight and online learning-ready approach, we found extreme learning machines (ELM) to yield comparable results for the less complex tasks.

Exploring links between climatic predictability and the evolution of within- and transgenerational plasticity

In variable environments, phenotypic plasticity can increase fitness by providing tight environment-phenotype matching. However, adaptive plasticity is expected to evolve only when the future selective environment can be predicted based on the prevailing conditions. That is, the juvenile environment should be predictive of the adult environment (within-generation plasticity) or the parental environment should be predictive of the offspring environment (transgenerational plasticity). Moreover, the environmental predictability can also shape transient responses such as stress response in an adaptive direction. Here, we test links between environmental predictability and the evolution of adaptive plasticity by combining time series analyses and a common garden experiment using temperature as a stressor in a temperate butterfly (Melitaea cinxia). Time series analyses revealed that across season fluctuations in temperature over 48 years are overall predictable. However, within the growing season, temperature fluctuations showed high heterogeneity across years with low autocorrelations and the timing of temperature peaks were asynchronous. Most life-history traits showed strong within-generation plasticity for temperature and traits such as body size and growth rate broke the temperature-size rule. Evidence for transgenerational plasticity, however, was weak and detected for only two traits each in an adaptive and non-adaptive direction. We suggest that the low predictability of temperature fluctuations within the growing season likely disfavors the evolution of adaptive transgenerational plasticity but instead favors strong within-generation plasticity.

Wearable-Measured Sleep and Resting Heart Rate Variability as an Outcome of and Predictor for Subjective Stress Measures: A Multiple N-of-1 Observational Study

The effects of stress may be alleviated when its impact or a decreased stress-resilience are detected early. This study explores whether wearable-measured sleep and resting HRV in police officers can be predicted by stress-related Ecological Momentary Assessment (EMA) measures in preceding days and predict stress-related EMA outcomes in subsequent days. Eight police officers used an Oura ring to collect daily Total Sleep Time (TST) and resting Heart Rate Variability (HRV) and an EMA app for measuring demands, stress, mental exhaustion, and vigor during 15-55 weeks. Vector Autoregression (VAR) models were created and complemented by Granger causation tests and Impulse Response Function visualizations. Demands negatively predicted TST and HRV in one participant. TST negatively predicted demands, stress, and mental exhaustion in two, three, and five participants, respectively, and positively predicted vigor in five participants. HRV negatively predicted demands in two participants, and stress and mental exhaustion in one participant. Changes in HRV lasted longer than those in TST. Bidirectional associations of TST and resting HRV with stress-related outcomes were observed at a weak-to-moderate strength, but not consistently across participants. TST and resting HRV are more consistent predictors of stress-resilience in upcoming days than indicators of stress-related measures in prior days.

Short-term effects of air pollutants on outpatients with psoriasis in a Chinese city with a subtropical monsoon climate

Introduction

Psoriasis is a common skin disease that seriously affects patients' quality of life. The association of air pollutants with psoriasis, and the extent of their effects remains unclear.

Methods

Based on a distributed lag non-linear model, this study explored the short-term effects of air pollutants on outpatients with psoriasis in Hefei, China, between 2015 and 2019 by analyzing the exposure-lag-response relationship, after controlling for confounding influences such as meteorological factors, long-term trends, day of the week, and holidays. Stratified analyses were performed for patients of different ages and genders.

Results

The maximum relative risks of psoriasis outpatients' exposure to SO₂, NO₂, and O₃ were 1.023 (95% confidence intervals (CI): 1.004-1.043), 1.170 (95% CI: 1.046-1.307), and 1.059 (95% CI: 1.030-1.090), respectively. An increase of 10 μg/m ³ of NO₂ was associated with a 2.1% (95% CI: 0.7-3.5%) increase in outpatients with psoriasis, and a decrease of 10 μg/m ³ of O₃ was associated with an 0.8% (95% CI: 0.4-1.2%) increase in outpatients with psoriasis. Stratified analyses showed that male subjects were more sensitive to a change in meteorological factors, while female subjects and outpatients with psoriasis aged 0-17 years old were more sensitive to a change in air pollutants.

Discussion

Short-term air pollutant exposures were associated with outpatients having psoriasis, suggesting that patients and high-risk people with psoriasis should reduce their time spent outside and improve their skin protection gear when air quality is poor.

Differences in initial abundances reveal divergent dynamic structures in Gause's predator-prey experiments

Improved understanding of complex dynamics has revealed insights across many facets of ecology, and has enabled improved forecasts and management of future ecosystem states. However, an enduring challenge in forecasting complex dynamics remains the differentiation between complexity and stochasticity, that is, to determine whether declines in predictability are caused by stochasticity, nonlinearity, or chaos. Here, we show how to quantify the relative contributions of these factors to prediction error using Georgii Gause's iconic predator-prey microcosm experiments, which, critically, include experimental replicates that differ from one another only in initial abundances. We show that these differences in initial abundances interact with stochasticity, nonlinearity, and chaos in unique ways, allowing us to identify the impacts of these factors on prediction error. Our results suggest that jointly analyzing replicate time series across multiple, distinct starting points may be necessary for understanding and predicting the wide range of potential dynamic types in complex ecological systems.

Extreme temperature exposure and urolithiasis: A time series analysis in Ganzhou, China

Background

Ambient temperature change is a risk factor for urolithiasis that cannot be ignored. The association between temperature and urolithiasis varies from region to region. Our study aimed to analyze the impact of extremely high and low temperatures on the number of inpatients for urolithiasis and their lag effect in Ganzhou City, China.

Methods

We collected the daily number of inpatients with urolithiasis in Ganzhou from 2018 to 2019 and the meteorological data for the same period. The exposure-response relationship between the daily mean temperature and the number of inpatients with urolithiasis was studied by the distributed lag non-linear model (DLNM). The effect of extreme temperatures was also analyzed. A stratification analysis was performed for different gender and age groups.

Results

There were 38,184 hospitalizations for urolithiasis from 2018 to 2019 in Ganzhou. The exposure-response curve between the daily mean temperature and the number of inpatients with urolithiasis in Ganzhou was non-linear and had an observed lag effect. The warm effects (30.4°C) were presented at lag 2 and lag 5-lag 9 days, and the cold effects (2.9°C) were presented at lag 8 and lag 3-lag 4 days. The maximum cumulative warm effects were at lag 0-10 days (cumulative relative risk, CRR = 2.379, 95% CI: 1.771, 3.196), and the maximum cumulative cold effects were at lag 0-5 (CRR = 1.182, 95% CI: 1.054, 1.326). Men and people between the ages of 21 and 40 were more susceptible to the extreme temperatures that cause urolithiasis.

Conclusion

Extreme temperature was correlated with a high risk of urolithiasis hospitalizations, and the warm effects had a longer duration than the cold effects. Preventing urolithiasis and protecting vulnerable people is critical in extreme temperature environments.

Melatonin regulates gene expressions through activating auxin synthesis and signaling pathways

BACKGROUND

Both melatonin and indole-3-acetic acid (IAA) are derived from tryptophan. And the most interesting and unsolved puzzle in melatonin research is that what is the relationship between melatonin and auxin?

METHODS

In this study, we performed transcriptome analysis with a time series method to disclose the connection of the two metabolites in soybean.

RESULTS

Our results reveal that melatonin and IAA treatments cause substantial overlaps in gene expression changes. Common genes of melatonin and IAA treatments could be sorted into clusters with very similar expression tendency. A KEGG assay showed that exogenous applied melatonin enriched differentially expressed genes in auxin biosynthesis and signaling pathways. For details, melatonin up-regulates several YUCCA genes which participate in auxin biosynthesis; melatonin also enhances expression levels of auxin receptor coding genes, such as TIR1, AFB3 and AFB5; dozens of genes involved in auxin transport, such as AUXI and PIN, are regulated by melatonin similarly as by auxin; auxin-responsive genes, such as IAA, ARF, GH3 and SAUR-like genes, intensively respond to melatonin as well as to auxin. A DR5 promoter mediated GUS staining assay showed that low concentration of melatonin could induce auxin biosynthesis in a dosage manner, whereas high concentration of melatonin would eliminate such effect. At last, gene ontology (GO) analysis suggests that melatonin treatment has similar characteristics as auxin treatment in many processes. However, the two molecules still keep their own features respectively. For example, melatonin takes part in stress responses, while IAA treatment enriches the GO terms that related to cell growth.

CONCLUSION

Taken together, exogenous applied melatonin, if not exceeds the appropriate concentration, could promote auxin responses range from biosynthesis to signaling transduction. Thus, our research is a key part to explain the auxin-like roles of melatonin in regulating plant growth.

Impact of the complexity of glucose time series on all-cause mortality in patients with type 2 diabetes

CONTEXT

Previous studies suggest that the complexity of glucose time series may serve as a novel marker of glucose homeostasis.

OBJECTIVE

We aimed to investigate the relationship between the complexity of glucose time series and all-cause mortality in patients with type 2 diabetes.

METHODS

Prospective data of 6000 adult inpatients with type 2 diabetes from a single center was analyzed. The complexity of glucose time series index (CGI) based on continuous glucose monitoring (CGM) was measured at baseline with refined composite multi-scale entropy. Participants were stratified by the tertiles of CGI: < 2.15, 2.15-2.99, and ≥ 3.00. Cox proportional hazards regression models were used to assess the relationship between CGI and all-cause mortality.

RESULTS

During a median follow-up of 9.4 years, 1217 deaths were identified. A significant interaction between glycated hemoglobin A1c (HbA1c) and CGI in relation to all-cause mortality was noted (P for interaction = 0.016). The multivariable-adjusted hazard ratios for all-cause mortality at different CGI levels [≥ 3.00 (reference group), 2.15-2.99, and < 2.15] were 1.00, 0.76 (95% CI 0.52-1.12), and 1.47 (95% CI 1.03-2.09) in patients with HbA1c < 7.0%, while the association was nonsignificant in those with HbA1c ≥ 7.0%. The restricted cubic spline regression revealed a non-linear (P for non-linearity = 0.041) relationship between CGI and all-cause mortality in subjects with HbA1c < 7.0% only.

CONCLUSIONS

Lower CGI is associated with an increased risk of all-cause mortality among patients with type 2 diabetes achieving the HbA1c target. CGI may be a new indicator for the identification of residual risk of death in well-controlled type 2 diabetes.

Air pollution and pediatric respiratory hospital admissions in Bursa, Turkey: A time series study

We aimed to investigate the relation between air pollution and the number of daily hospitalizations due to pneumonia, asthma, bronchitis in children aged 0-18 in Bursa city of Turkey, between the years 2013-2018. The daily values of air pollutants (PM10, SO2, NO2, NOx, CO, and O3) from 2013 until 2018, were obtained. Adjusted Quasi-Poisson regression models including distributed lags, controlled for climate variables were used for data analysis. Increases in SO2, ozone, PMs, and nitrogen oxides were associated with pneumonia hospitalizations, increases in SO2 NOx and PMs were associated with asthma hospitalizations, and increases in SO2 and ozone were associated with bronchitis hospitalizations. Male hospitalization was related with SO2, ozone, and NOx; while female hospitalization was only related with SO2. This study showed that short-term exposure to air pollution is associated with an increased risk of pneumonia, asthma, and bronchitis hospitalization among children in Bursa.

Traffic violation analysis using time series, clustering and panel zero-truncated one-inflated mixed model

Traffic rules violations in urban areas, which can cause traffic crashes and unsafe situations, are a major issue nowadays. The present paper aims to analyze the frequency of traffic violations in Tehran city, Iran, over a five-year period (March 2016- March 2021). The data is obtained via road traffic violation monitoring system which can capture and process various traffic violations. This database, containing about 97 million violations committed by about 16 million drivers, is explored applying three statistical approaches. In the first approach, some multiplicative SARIMA and Bayesian Spatio-temporal models are fitted to the monthly violations. Also, in the second approach, the K-means clustering algorithm is applied to discover homogeneous districts of Tehran Municipality regarding their number of violations and their number of violations per camera towers meter during the study. Finally, in the third approach, a random-effect zero-truncated one-inflated Poisson model is proposed to study factors affecting driver's number of violations over time.