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

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

LLM Multimodal Traffic Accident Forecasting

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

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

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

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

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

Design and Implementation of a Prototype Seismogeodetic System for Tectonic Monitoring

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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

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

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

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

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

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

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

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

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

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

Heterogeneity in Individual Network Analysis: Reality or Illusion?

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

Estimating true prevalence through questionnaire data

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

Statistical Analysis of Plasma Dynamics in Gyrokinetic Simulations of Stellarator Turbulence

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

Collective Dynamics, Diversification and Optimal Portfolio Construction for Cryptocurrencies

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

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

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

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

OBJECTIVE

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

MATERIALS AND METHOD

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

RESULTS

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

CONCLUSION

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

Impact of the COVID-19 restrictions on the epidemiology of Cryptosporidium spp. in England and Wales, 2015-2021: a time series analysis

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

Predicting antimicrobial resistance of bacterial pathogens using time series analysis

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

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

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

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

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

Playing HAVOK on the Chaos Caused by Internet Trolls

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