Models for predicting vehicle emissions: A comprehensive review

Air pollution is a primary concern, causing around 7 million premature deaths annually, with traffic-related sources contributing 23 %-45 % of emissions. While several studies have surveyed vehicle emission models, they are either outdated or focus on specific data-driven models. This paper systematically reviews vehicle emission prediction models, comparing traditional approaches with data-driven emission models. The traditional emission models can be divided into average-speed, modal, and other models, noting their reliance on empirical assumptions and parameters that may not be universally applicable. In contrast, we delve into data-driven models utilizing dynamometer and on-road test data for time-series and spatial-temporal predictions. The application of these models is discussed across various scenarios, highlighting the progress and gap. We observed that traditional models, primarily estimating total traffic emissions in study regions, lack micro-level detail crucial for tailored decisions. The direct link between road emission model accuracy and input data quality poses challenges in disaggregating on-road vehicle emission inventories. Due to unique transportation instruments, traffic fleet components, and patterns, exploring the effects of emission-reduction policies in specific cities or regions is urgent. Vehicle characteristics, environmental conditions, traffic scenarios, and prediction scales are common effect factors, while instantaneous driving profiles prove effective in model calibration. In data-driven models, ANN outperforms in estimating emissions and performance of low-power diesel engines with errors not exceeding 5 %. However, no single data-driven method performed excellently in predicting all pollutants. Besides, integrated methods utilizing LSTM, GRU, and RNN outperform individual models. To enhance prediction accuracy considering the inherent connectivity of road networks and spatiotemporal variation patterns of vehicle emissions, GCN is an emerging approach for capturing spatial-temporal relationships based on remote sensing data. Moreover, limited data-driven studies have been performed to forecast particle matter emissions, the main contributors to urban pollution, calling for more attention for future research.

How tolerances, competition and dispersal shape benthic invertebrate colonisation in restored urban streams

Biotic communities often respond poorly to river restoration activities and the drivers of community recovery after restoration are not fully understood. According to the Asymmetric Response Concept (ARC), dispersal capacity, species tolerances to stressors, and biotic interactions are three key drivers influencing community recovery of restored streams. However, the ARC remains to be tested. Here we used a dataset on benthic invertebrate communities of eleven restored stream sections in a former open sewer system that were sampled yearly over a period of eleven years. We applied four indices that reflect tolerance against chloride and organic pollution, the community's dispersal capacity and strength of competition to the benthic invertebrate taxa lists of each year and site. Subsequently, we used generalised linear mixed models to analyse the change of these indices over time since restoration. Dispersal capacity was high directly after restoration but continuously decreased over time. The initial communities thus consisted of good dispersers and were later joined by more slowly dispersing taxa. The tolerance to organic pollution also decreased over time, reflecting continuous improvement of water quality and an associated increase of sensitive species. On the contrary, chloride tolerances did not change, which could indicate a stable chloride level throughout the sampling period. Lastly, competition within the communities, reflected by interspecific trait niche overlap, increased with time since restoration. We show that recovery follows a specific pattern that is comparable between sites. Benthic communities change from tolerant, fast dispersing generalists to more sensitive, slowly dispersing specialists exposed to stronger competition. Our results lay support to the ARC (increasing role of competition, decreasing role of dispersal) but also underline that certain tolerances may still shape communities a decade after restoration. Disentangling the drivers of macroinvertebrate colonisation can help managers to better understand recovery trajectories and to define more realistic restoration targets.

The pseudoentropy of allele frequency trajectories, the persistence of variation, and the effective population size

To concisely describe how genetic variation, at individual loci or across whole genomes, changes over time, and to follow transitory allelic changes, we introduce a quantity related to entropy, that we term pseudoentropy. This quantity emerges in a diffusion analysis of the mean time a mutation segregates in a population. For a neutral locus with an arbitrary number of alleles, the mean time of segregation is generally proportional to the pseudoentropy of initial allele frequencies. After the initial time point, pseudoentropy generally decreases, but other behaviours are possible, depending on the genetic diversity and selective forces present. For a biallelic locus, pseudoentropy and entropy coincide, but they are distinct quantities with more than two alleles. Thus for populations with multiple biallelic loci, the language of entropy suffices. Then entropy, combined across loci, serves as a concise description of genetic variation. We used individual based simulations to explore how this entropy behaves under different evolutionary scenarios. In agreement with predictions, the entropy associated with unlinked neutral loci decreases over time. However, deviations from free recombination and neutrality have clear and informative effects on the entropy's behaviour over time. Analysis of publicly available data of a natural D. melanogaster population, that had been sampled over seven years, using a sliding-window approach, yielded considerable variation in entropy trajectories of different genomic regions. These mostly follow a pattern that suggests a substantial effective population size and a limited effect of positive selection on genome-wide diversity over short time scales.

Predicting quantum emitter fluctuations with time-series forecasting models

2D materials have important fundamental properties allowing for their use in many potential applications, including quantum computing. Various Van der Waals materials, including Tungsten disulfide (WS2), have been employed to showcase attractive device applications such as light emitting diodes, lasers and optical modulators. To maximize the utility and value of integrated quantum photonics, the wavelength, polarization and intensity of the photons from a quantum emission (QE) must be stable. However, random variation of emission energy, caused by the inhomogeneity in the local environment, is a major challenge for all solid-state single photon emitters. In this work, we assess the random nature of the quantum fluctuations, and we present time series forecasting deep learning models to analyse and predict QE fluctuations for the first time. Our trained models can roughly follow the actual trend of the data and, under certain data processing conditions, can predict peaks and dips of the fluctuations. The ability to anticipate these fluctuations will allow physicists to harness quantum fluctuation characteristics to develop novel scientific advances in quantum computing that will greatly benefit quantum technologies.

Air pollution and upper respiratory diseases: an examination among medically insured populations in Wuhan, China

Multiple evidence has supported that air pollution exposure has detrimental effects on the cardiovascular and respiratory systems. However, most investigations focus on the general population, with limited research conducted on medically insured populations. To address this gap, the current research was designed to examine the acute effects of inhalable particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ground-level ozone (O3), and sulfur dioxide (SO2) on the incidence of upper respiratory tract infections (URTI), utilizing medical insurance data in Wuhan, China. Data on URTI were collected from the China Medical Insurance Basic Database for Wuhan covering the period from 2014 to 2018, while air pollutant data was gathered from ten national monitoring stations situated in Wuhan city. Statistical analysis was performed using generalized additive models for quasi-Poisson distribution with a log link function. The analysis indicated that except for ozone, higher exposure to four other pollutants (NO2, SO2, PM2.5, and PM10) were significantly linked to an elevated risk of URTI, particularly during the previous 0-3 days and previous 0-4 days. Additionally, NO2 and SO2 were found to be positively linked with laryngitis. Furthermore, the effects of air pollutants on the risk of URTI were more pronounced during cold seasons than hot seasons. Notably, females and the employed population were more susceptible to infection than males and non-employed individuals. Our findings gave solid proof of the link between ambient air pollution exposure and the risk of URTI in medically insured populations.

Physiological regulation of microalgae under cadmium stress and response mechanisms of time-series analysis using metabolomics

The investigation of heavy metal wastewater treatment utilizing microalgae adsorption has been extensively demonstrated. However, the response mechanism based on metabolomics to analyze the time-series changes of microalgae under Cd stress has not been described in detail. In this study, SEM/TEM demonstrated that Cd accumulated on the cell surface of microalgae and was bioconcentrated in the cytoplasm, vesicles, and chloroplasts. Carbonyl/quinone/ketone/carboxyl groups (OCO), membrane polysaccharides (OH), and phospholipids (PO) were involved in the interaction of Cd ions, and the chlorophyll content underwent a process of decreasing in the early stage (1.62 mg/g at 48 h) and recovering to the normal level in the late stage, and the contents of MDA, GSH, and SOD were all increased (29.7 nmol/g, 0.23 mg/g, and 30.01 u/106 cells) and then gradually returned to the steady state. The results of EPS content and fluorescent labeling showed that Cd induced the overexpression and synthesis of extracellular polysaccharides and proteins, which is one of the defense mechanisms participating in the reduction of cellular damage by complexed Cd. Metabolomics results indicated that the malate synthesis pathway was activated after Cd-20 h, and the microalgal cells began to shift the metabolic pathway to storage lipid or polysaccharide biosynthesis. In the Calvin cycle, the expression of D-Sedoheptulose 7-phosphate in Cd-20 h_vs_ck and Cd-72 h_vs_Cd-20 h firstly declined and then increased, and the photosynthesis system was suppressed at the beginning, and then gradually returned to normal to maintain the successful development of the dark reaction. The results of time series analysis revealed that the response of microalgae to Cd was categorized into fast response and slow response to regulate cell adsorption and growth metabolism.

Association between air pollution and emergency room admission for eye diseases in Rome, Italy: A time-series analysis

Eye diseases impose a significant burden on health services due to high case numbers. However, exposure to outdoor air pollution is seldom mentioned as potential harmful factor. We conducted a time-series analysis in Rome, Italy, to estimate the association between daily mean concentration of NO2, PM10 and PM2.5 and daily number of emergency room (ER) admissions for a selected cluster of eye diseases from 2006 to 2016. We used Poisson regression adjusted for time trend, population decrease during summer vacations and holidays, day of week, apparent temperature (hot and cold) and daily concentration of nine pollen species. We observed 581,868 ER admissions during the study period. 44.74% of cases were observed in subjects with less than 20 years, 19.50% in 51-65 age category and 13.4% among children (0-14 years). No differences between sexes were recorded. Mean values of pollutant concentrations were 54.75, 31.01 and 18.14 μg/m3 for NO2, PM10 and PM2.5 respectively. The air temperature ranged from -1 °C to 32.5 °C, with a mean value of 16 °C (SD = 6.88). The apparent temperature spaced from -3.58 °C to 34.08 °C (mean = 15.61 °C, SD = 8.5). The highest percent risk increases for 10 μg/m3 increases of the three pollutants were observed at lag0-1 day (1.3%, 0.63-1.98 for PM2.5; 1.03%, 0.56-1.51 for PM10 and 0.6%, 0.13-1.07 for NO2). Risk increased significantly also at lag0 and lag0-5 day for each pollutant. Secondary analyses showed higher effects in the elderly compared to younger subjects. No differences emerged between sexes. The dose response analysis suggested of possible effects on ER admission risk also at low-level concentrations of PM2.5. A strong confounding effect of pollen was not detected. RESULTS: of this study are coherent with previous analyses. Speculation can be done about the biological mechanisms that link air pollution to eye damage.

The impact of the COVID-19 pandemic on mortality in Sweden-Did it differ across socioeconomic groups?

The characterization of the socioeconomic profile of COVID-19 mortality is limited. Likewise, the mapping of potential indirect adverse outcomes of the pandemic, such as suicide and alcohol abuse, along socioeconomic lines is still meagre. The main aim of this paper is to (i) depict SES-differences in COVID-19 mortality, and (ii) to assess the impact of the COVID-19 pandemic on suicide and alcohol mortality across socioeconomic groups. We used Swedish monthly data spanning the period January 2016-December 2021. We chose education as indicator of socioeconomic status (SES). The following causes of deaths were included in the analysis: COVID-19, all-cause mortality excluding COVID-19, suicide and a composite index of alcohol-specific deaths. SARIMA-modelling was used to assess the impact of the pandemic on suicide and alcohol-specific mortality. Two alternative measures of the pandemic were used: (1) a dummy that was coded 1 during the pandemic (March 2020 and onwards), and 0 otherwise, and (2) the Oxford COVID-19 Government Response Tracker's Stringency Index. There was a marked SES-gradient in COVID-19 mortality in the working-age population (25-64) which was larger than for other causes of death. A SES-gradient was also found in the old-age population, but this gradient did not differ from the gradient for other causes of death. The outcome from the SARIMA time-series analyses suggested that the pandemic did not have any impact on suicide or alcohol-specific mortality in any of the educational and gender groups.

A time series driven model for early sepsis prediction based on transformer module

Sepsis remains a critical concern in intensive care units due to its high mortality rate. Early identification and intervention are paramount to improving patient outcomes. In this study, we have proposed predictive models for early sepsis prediction based on time-series data, utilizing both CNN-Transformer and LSTM-Transformer architectures. By collecting time-series data from patients at 4, 8, and 12 h prior to sepsis diagnosis and subjecting it to various network models for analysis and comparison. In contrast to traditional recurrent neural networks, our model exhibited a substantial improvement of approximately 20%. On average, our model demonstrated an accuracy of 0.964 (± 0.018), a precision of 0.956 (± 0.012), a recall of 0.967 (± 0.012), and an F1 score of 0.959 (± 0.014). Furthermore, by adjusting the time window, it was observed that the Transformer-based model demonstrated exceptional predictive capabilities, particularly within the earlier time window (i.e., 12 h before onset), thus holding significant promise for early clinical diagnosis and intervention. Besides, we employed the SHAP algorithm to visualize the weight distribution of different features, enhancing the interpretability of our model and facilitating early clinical diagnosis and intervention.

The impact of fine particulate matter on chronic obstructive pulmonary disease deaths in Pudong New Area, Shanghai, during a long period of air quality improvement

Chronic obstructive pulmonary disease (COPD) deaths attributed to fine particulate matter (with an aerodynamic equivalent diameter <2.5 μm, PM2.5) exposure are a common global public health concern. Recent improvements in air quality and the corresponding health benefits have received much attention. Thus, we have explored the trends of PM2.5 pollution improvement on COPD deaths during an important period of air pollution control (2008-2017) in Pudong New Area, Shanghai, China. Data, including daily COPD death counts, meteorological variables, and ambient air pollutants, were collected from 2008 to 2017. Generalized additive models were fitted to evaluate the percent change (%) in pollution-related COPD deaths. The results showed that the number of days with daily PM2.5 concentrations <35 μg/m3 increased from 19 days (5.19%) in 2008 to 166 days (45.48%) in 2017, and PM2.5 concentrations >75 μg/m3 decreased from 222 days (60.66%) in 2008 to 33 days (9.04%) in 2017. The associations in the overall period between 2008 and 2017 was significant. In subperiod analysis, each 10 μg/m3 increment in PM2.5 was associated with a percent change (%) of 0.89 (95% confidence interval [CI], 0.37, 1.42) at lag 5 and 0.78 (95% CI, 0.26, 1.30) at lag 6 during 2008-2013. Significant results were also found at lag 0-5 [percent change (%), 1.12 (95% CI, 0.09, 2.17)], lag 0-6 [percent change (%), 1.52 (95% CI, 0.43, 2.62)] and lag 0-7 [percent change (%), 1.72 (95% CI, 0.57, 2.88)] during 2008-2013. By contrast, no significant association was found between 2014 and 2017. In conclusion, the decreased COPD deaths associated with PM2.5 exposure were found, especially after the air quality improvement turning point in 2014.

Meteorological factors, ambient air pollution, and daily hospital admissions for depressive disorder in Harbin: A time-series study

BACKGROUND

The literature has discussed the relationship between environmental factors and depressive disorders; however, the results are inconsistent in different studies and regions, as are the interaction effects between environmental factors. We hypothesized that meteorological factors and ambient air pollution individually affect and interact to affect depressive disorder morbidity.

AIM

To investigate the effects of meteorological factors and air pollution on depressive disorders, including their lagged effects and interactions.

METHODS

The samples were obtained from a class 3 hospital in Harbin, China. Daily hospital admission data for depressive disorders from January 1, 2015 to December 31, 2022 were obtained. Meteorological and air pollution data were also collected during the same period. Generalized additive models with quasi-Poisson regression were used for time-series modeling to measure the non-linear and delayed effects of environmental factors. We further incorporated each pair of environmental factors into a bivariate response surface model to examine the interaction effects on hospital admissions for depressive disorders.

RESULTS

Data for 2922 d were included in the study, with no missing values. The total number of depressive admissions was 83905. Medium to high correlations existed between environmental factors. Air temperature (AT) and wind speed (WS) significantly affected the number of admissions for depression. An extremely low temperature (-29.0 ℃) at lag 0 caused a 53% [relative risk (RR)= 1.53, 95% confidence interval (CI): 1.23-1.89] increase in daily hospital admissions relative to the median temperature. Extremely low WSs (0.4 m/s) at lag 7 increased the number of admissions by 58% (RR = 1.58, 95%CI: 1.07-2.31). In contrast, atmospheric pressure and relative humidity had smaller effects. Among the six air pollutants considered in the time-series model, nitrogen dioxide (NO2) was the only pollutant that showed significant effects over non-cumulative, cumulative, immediate, and lagged conditions. The cumulative effect of NO2 at lag 7 was 0.47% (RR = 1.0047, 95%CI: 1.0024-1.0071). Interaction effects were found between AT and the five air pollutants, atmospheric temperature and the four air pollutants, WS and sulfur dioxide.

CONCLUSION

Meteorological factors and the air pollutant NO2 affect daily hospital admissions for depressive disorders, and interactions exist between meteorological factors and ambient air pollution.

Greenspace use during the COVID-19 pandemic: A longitudinal population mobility study in the United Kingdom

BACKGROUND

The coronavirus pandemic greatly disrupted the lives of people. Restrictions introduced worldwide to limit the spread of infection included stay-at-home orders, closure of venues, restrictions to travel and limits to social contacts. During this time, parks and outdoor greenspaces gained prominent attention as alternative location for respite. Population mobility data offers a unique opportunity to understand the impact of the pandemic on outdoor behaviour. We examine the role of the restrictions on park use throughout the full span of the pandemic while controlling for weather and region.

METHODS

This study provides a longitudinal population analysis of park visitation using Google COVID-19 Community Mobility Reports data in the UK. Daily park visitation was plotted and ANOVA analyses tested season and year effects in visitation. Then, regressions examined park visitation beyond weather (temperature and rain), according to COVID-19 restrictions, while controlling for region specificities through unit fixed effect models.

RESULTS

Time series and ANOVA analyses documented the significant decrease in park visitation in the spring of 2020, the seasonal pattern in visitation, and an overall sustained and elevated use over nearly three years. Regressions confirmed park visitation increased significantly when temperature was greater and when it rained less. More visitation was also seen when there were fewer COVID-19 cases and when the stringency level of restrictions was lower. Of special interest, a significant interaction effect was found between temperature and stringency, with stringency significantly supressing the effect of higher temperature on visitation.

CONCLUSIONS

COVID-19 restrictions negatively impacted park visitation on warm days. Given the general health, social, and wellbeing benefits of greenspace use, one should consider the collateral negative impact of restrictions on park visitation. When social distancing of contacts is required, the few remaining locations where it can safely occur should instead be promoted.

Impact of environmental factors on diabetes mortality: A comparison between inland and coastal areas

BACKGROUND

Diabetes mortality varies between coastal and inland areas in Shandong Province, China. However, evidence about the reasons for this disparity is limited. We assume that distinct environmental conditions may contribute to the disparities in diabetes mortality patterns between coastal and inland areas.

METHOD

Qingdao and Jinan were selected as typical coastal and inland cities in Shandong Province, respectively, with similar socioeconomic but different environmental characteristics. Data on diabetes deaths and environmental factors (i.e., temperature, relative humidity and air pollution particles with a diameter of 2.5 μm or less (PM2.5)) were collected from 2013 to 2020. Spatial kriging methods were used to estimate the aggregated diabetes mortality at the city level. A distributed lag non-linear model (DLNM) was used to quantify the possible cumulative and non-cumulative associations between environmental factors and diabetes mortality by age, sex and location.

RESULTS

In the coastal city (Qingdao), the maximum cumulative relative risks (RRs) of temperature and PM2.5 associated with diabetes deaths were 2.54 (95 % confidence interval (CI): 1.25-5.15), and 1.17 (95 % CI: 1.01-1.37) respectively, at lag 1 week. In the inland city (Jinan), only temperature exhibited significant cumulative associations with diabetes deaths (RR = 1.54, 95 % CI: 1.07-2.23 at 29 °C). Lower relative humidity (22 %-45 %) had a lag-specific association with diabetes deaths in inland areas at lag 3 weeks (RR = 1.33, 95 % CI: 1.03-1.70 at 22 %).

CONCLUSION

Despite the lower PM2.5 concentrations in the coastal location, diabetes mortality exhibited stronger links to environmental variables in the coastal city than in the inland city. These findings suggest that the control of air pollution could decrease the mortality burden of diabetes, even in the region with relatively good air quality. Additionally, the spatial estimation method is recommended to identify associations between environmental factors and diseases in studies with limited data.

Does Abortion Liberalisation Accelerate Fertility Decline? A Worldwide Time-Series Analysis

Since WWII, the two most important global trends in family planning have been fertility decline and abortion liberalisation. But are they related? Specifically: Does abortion liberalisation affect changes in fertility rates? The demographic literature has yet to answer this important question and instead offers two opposing predictions. Some studies argue that liberalisation of this medical procedure reduces fertility rates. By contrast, others note that such legal reforms may merely have an average, negligible effect on fertility levels. We adjudicate between the two approaches by conducting, in our view, the most comprehensive global, quantitative analysis of the relationship between those legal reforms and changing fertility rates. The analysis relies on two-way fixed models and three different indicators of abortion policy liberalism created by independent research teams to estimate the relationship between abortion liberalisation and total fertility changes. The data cover 185 independent states between 1970 and 2019. Fertility rates are significantly related to average public education levels and alternative contraceptive use. Using multiple model specifications, however, abortion reforms do not have a robust association with the outcome. Replication materials for this article can be found at https://figshare.com/s/5336a4422f47c8c39228 .

AI and semantic ontology for personalized activity eCoaching in healthy lifestyle recommendations: a meta-heuristic approach

BACKGROUND

Automated coaches (eCoach) can help people lead a healthy lifestyle (e.g., reduction of sedentary bouts) with continuous health status monitoring and personalized recommendation generation with artificial intelligence (AI). Semantic ontology can play a crucial role in knowledge representation, data integration, and information retrieval.

METHODS

This study proposes a semantic ontology model to annotate the AI predictions, forecasting outcomes, and personal preferences to conceptualize a personalized recommendation generation model with a hybrid approach. This study considers a mixed activity projection method that takes individual activity insights from the univariate time-series prediction and ensemble multi-class classification approaches. We have introduced a way to improve the prediction result with a residual error minimization (REM) technique and make it meaningful in recommendation presentation with a Naïve-based interval prediction approach. We have integrated the activity prediction results in an ontology for semantic interpretation. A SPARQL query protocol and RDF Query Language (SPARQL) have generated personalized recommendations in an understandable format. Moreover, we have evaluated the performance of the time-series prediction and classification models against standard metrics on both imbalanced and balanced public PMData and private MOX2-5 activity datasets. We have used Adaptive Synthetic (ADASYN) to generate synthetic data from the minority classes to avoid bias. The activity datasets were collected from healthy adults (n = 16 for public datasets; n = 15 for private datasets). The standard ensemble algorithms have been used to investigate the possibility of classifying daily physical activity levels into the following activity classes: sedentary (0), low active (1), active (2), highly active (3), and rigorous active (4). The daily step count, low physical activity (LPA), medium physical activity (MPA), and vigorous physical activity (VPA) serve as input for the classification models. Subsequently, we re-verify the classifiers on the private MOX2-5 dataset. The performance of the ontology has been assessed with reasoning and SPARQL query execution time. Additionally, we have verified our ontology for effective recommendation generation.

RESULTS

We have tested several standard AI algorithms and selected the best-performing model with optimized configuration for our use case by empirical testing. We have found that the autoregression model with the REM method outperforms the autoregression model without the REM method for both datasets. Gradient Boost (GB) classifier outperforms other classifiers with a mean accuracy score of 98.00%, and 99.00% for imbalanced PMData and MOX2-5 datasets, respectively, and 98.30%, and 99.80% for balanced PMData and MOX2-5 datasets, respectively. Hermit reasoner performs better than other ontology reasoners under defined settings. Our proposed algorithm shows a direction to combine the AI prediction forecasting results in an ontology to generate personalized activity recommendations in eCoaching.

CONCLUSION

The proposed method combining step-prediction, activity-level classification techniques, and personal preference information with semantic rules is an asset for generating personalized recommendations.

Impact of outdoor air pollution on the incidence of pertussis in China: a time-series study

BACKGROUND

The increasing number of pertussis cases worldwide over the past two decades has challenged healthcare workers, and the role of environmental factors and climate change cannot be ignored. The incidence of pertussis has increased dramatically in mainland China since 2015, developing into a serious public health problem. The association of meteorological factors on pertussis has attracted attention, but few studies have examined the impact of air pollutants on this respiratory disease.

METHODS

In this study, we analyzed the relationship between outdoor air pollution and the pertussis incidence. The study period was from January 2013 to December 2018, and monthly air pollutant data and the monthly incidence of patients in 31 provinces of China were collected. Distributed lag nonlinear model (DLNM) analysis was used to estimate the associations between six air pollutants and monthly pertussis incidence in China.

RESULTS

We found a correlation between elevated pertussis incidence and short-term high monthly CO2 and O3 exposure, with a 10 μg/m3 increase in NO2 and O3 being significantly associated with increased pertussis incidence, with RR values of 1.78 (95% CI: 1.29-2.46) and 1.51 (95% CI: 1.16-1.97) at a lag of 0 months, respectively. Moreover, PM2.5 and SO2 also played key roles in the risk of pertussis surged. These associations remain significant after adjusting for long-term trend, seasonality and collinearity.

CONCLUSIONS

Overall, these data reinforce the evidence of a link between incidence and climate identified in regional and local studies. These findings also further support the hypothesis that air pollution is responsible for the global resurgence of pertussis. Based on this we suggest that public health workers should be encouraged to consider the risks of the environment when focusing on pertussis prevention and control.

Short-term exposure to reduced specific-size ambient particulate matter increase the risk of cause-specific cardiovascular disease: A national-wide evidence from hospital admissions

Evidence for the health effects of ambient PM1 (particulate matter with an aerodynamic diameter ≤ 1 µm) pollution is limited, and it remains unclear whether a smaller particulate matter has a greater impact on human health. We conducted a time-series study in 184 major cities by extracting daily hospital data on admissions for ischemic heart disease, heart failure, heart rhythm disturbances, and stroke between 2014 and 2017 from a medical insurance claims database of 0.28 billion beneficiaries. City-specific associations were estimated with over-dispersed generalized additive models. A random-effects meta-analysis was used to estimate regional and national average associations. We conducted stratified and meta-regression analyses to explore potential effect modifiers of the association. We recorded 8.83 million cardiovascular admissions during the study period. At the national-average level, a 10-μg/m3 increase in same-day PM1, PM2.5(particulate matter with an aerodynamic diameter ≤ 2.5 µm) and PM10(particulate matter with an aerodynamic diameter ≤ 10 µm) concentrations corresponded to a 1.14% (95% confidence interval 0.88-1.41%), 0.55% (0.40-0.70%), and 0.45% (0.36-0.55%) increase in cardiovascular admissions, respectively. PM1 exposure was also positively associated with all cardiovascular disease subtypes, including ischemic heart disease (1.28% change; 0.99-1.56%), heart failure (1.30% change; 0.70-1.91%), heart rhythm disturbances (1.11% change; 0.65-1.58%), and ischemic stroke (1.29% change; 0.88-1.71%). The associations between PM1 and cardiovascular admissions were stronger in cities with lower PM1 levels, higher air temperatures and relative humidity, as well as in subgroups with elder age (all P < 0.05). This study provides robust evidence of short-term associations between PM1 concentrations and increased hospital admissions for all major cardiovascular diseases in China. Our findings suggest a greater short-term impact on cardiovascular risk from PM1 in comparison to PM2.5 and PM10.

The effects of exposure to NO2, PM2.5 and PM10 on health service attendances with respiratory illnesses: A time-series analysis

The associations of exposure to air-pollutants and respiratory illness remains inconsistent and studies have not adequately addressed the non-linearity and delayed effects of exposure. This is a retrospective cohort study using linked routine health and pollution data collected between January 2018 and December 2021. Participants were patients who visited General Practice (GP) or accident and emergency (A&E) services for respiratory illness. Time-series analysis, distributed lagged models, was used to address the potential non-linearity and delayed effects of exposure. There were 114,930 GP and 9878 A&E respiratory visits. For every 10 μg/m3 increase in NO2 and PM2.5 above the WHO recommended 24-hr thresholds, the immediate relative risk of GP respiratory visits was 1.09 (95% CI: 1.07 to 1.05) and 1.06 (95% CI: 1.01 to 1.10), respectively. The respective relative risk of A&E visit was 1.10 (95% CI: 1.07 to 1.14) and 1.07 (95% CI: 1.00 to 1.14). Exposure to 10-unit increases in NO2, PM2.5 and PM10 above the WHO recommended 24-hr thresholds, was associated with lagged relative risks of 1.49 (95% CI: 1.42 to 1.56), 5.26 (95% CI: 4.18 to 6.61) and 2.32 (95% CI: 1.66 to 3.26), respectively, for GP respiratory attendances. The lagged relative risk of A&E respiratory visits for same units of exposure in NO2, PM2.5, and PM10 at the peak lag days were 1.98 (95% CI: 1.82 to 2.15), 4.52 (95% CI: 3.37 to 6.07) and 3.55 (95% CI: 1.85 to 6.84). A third of GP and half of A&E respiratory visits were attributable to exposure to NO2 beyond the WHO threshold. The combined cost of these visits over the study period was 1.95 million (95% CI: 1.82 to 2.09). High pollution events are related to increased health service use for respiratory illness, with impacts persisting up to 100 days post exposure. The burden of respiratory illness related to air-pollution may be considerably higher than previously reported.

Predicting energy use in construction using Extreme Gradient Boosting

Annual increases in global energy consumption are an unavoidable consequence of a growing global economy and population. Among different sectors, the construction industry consumes an average of 20.1% of the world's total energy. Therefore, exploring methods for estimating the amount of energy used is critical. There are several approaches that have been developed to address this issue. The proposed methods are expected to contribute to energy savings as well as reduce the risks of global warming. There are diverse types of computational approaches to predicting energy use. These existing approaches belong to the statistics-based, engineering-based, and machine learning-based categories. Machine learning-based frameworks showed better performance compared to these other approaches. In our study, we proposed using Extreme Gradient Boosting (XGB), a tree-based ensemble learning algorithm, to tackle the issue. We used a dataset containing energy consumption hourly recorded in an office building in Shanghai, China, from January 1, 2015, to December 31, 2016. The experimental results demonstrated that the XGB model developed using both historical and date features worked better than those developed using only one type of feature. The best-performing model achieved RMSE and MAPE values of 109.00 and 0.24, respectively.

Decadal decline of dominant copepod species in the North Sea is associated with ocean warming: Importance of marine heatwaves

Time-series are crucial to understand the status of zooplankton communities and to anticipate changes that might affect the entire food web. Long-term time series allow us to understand impacts of multiple environmental and anthropogenic stressors, such as chemical pollution and ocean warming, on the marine ecosystems. Here, a recent time series (2018-2022) of abundance data of four dominant calanoid and one harpacticoid copepod species from the Belgian Part of the North Sea was combined with previously collected (2009-2010, 2015-2016) datasets for the same study area. The time series reveals a significant decrease (up to two orders of magnitude) in calanoid copepod abundance (Temora longicornis, Acartia clausi, Centropages spp., Calanus helgolandicus), while this was not the case for the harpacticoid Euterpina acutifrons. We applied generalized additive models to quantify the relative contribution of temperature, nutrients, salinity, primary production, turbidity and pollution (anthropogenic chemicals, i.e., polychlorinated biphenyls and polycyclic aromatic hydrocarbons) to the population dynamics of these species. Temperature, turbidity and chlorophyll a concentrations were the only variables consistently showing a relative high contribution in all models predicting the abundances of the selected species. The observed heat waves which occurred during the summer periods of the investigated years coincided with population collapses (versus population densities in non-heatwave years) and are considered the most likely cause for the observed copepod abundance decreases. Moreover, the recorded water temperatures during these heatwaves correspond to the physiological thermal limit of some of the studied species. As far as we know, this is the first study to observe ocean warming and marine heat waves having such a dramatic impact (population collapse) on the dominant zooplankton species in shallow coastal areas.

Transformer-based temporal sequence learners for arrhythmia classification

An electrocardiogram (ECG) plays a crucial role in identifying and classifying cardiac arrhythmia. Traditional methods employ handcrafted features, and more recently, deep learning methods use convolution and recursive structures to classify heart signals. Considering the time sequence nature of the ECG signal, a transformer-based model with its high parallelism is proposed to classify ECG arrhythmia. The DistilBERT transformer model, pre-trained for natural language processing tasks, is used in the proposed work. The signals are denoised and then segmented around the R peak and oversampled to get a balanced dataset. The input embedding step is skipped, and only positional encoding is done. The final probabilities are obtained by adding a classification head to the transformer encoder output. The experiments on the MIT-BIH dataset show that the suggested model is excellent in classifying various arrhythmias. The model achieved 99.92% accuracy, 0.99 precision, sensitivity, and F1 score on the augmented dataset with a ROC-AUC score of 0.999.

Global change drives phenological and spatial shifts in Central European longhorn beetles (Coleoptera, Cerambycidae) during the past 150 years

Temperature increases and land-use changes induce altered annual activity periods of arthropods. However, sufficiently resolved long-term data sets (> 100 years) are mostly missing. We use a data set of longhorn beetle records (71 species) collected in Luxembourg 1864-2014. Increase of annual temperatures was significantly correlated with an earlier annual appearance. Forty-four species present before and after 1980 appeared on average 8.2 days earlier in the year in the more recent period. Since 1950, the estimated shift was 0.26 days per year. Increase of temperature in spring (March-June) preponed the first appearance of beetles by on average 9.6 days per 1 °C. We found significant changes in the composition of beetle communities, with a net gain in species richness during the last 40 years. Eleven species recorded only after 1997 were characterized by comparatively early annual appearance. Smaller beetles tended to appear earlier in the year in comparison to large-bodied species. Shifts in phenology did not correlate with species Red List status. As also demonstrated by our data, climate change in general affects insect phenologies and changes species composition. However, land-use change has taken place in parallel with climate change. Both aspects of global change are influencing the changes in longhorn beetle occurrences in Luxemburg in their combination. This might be most clearly reflected in the strong decrease of species with continental climate niches dwelling in old-growth deciduous forests that apparently are threatened by the loss of these habitats and increasing spring temperatures.

Fractal nature of human gastrointestinal system: Exploring a new era

The morphological complexity of cells and tissues, whether normal or pathological, is characterized by two primary attributes: Irregularity and self-similarity across different scales. When an object exhibits self-similarity, its shape remains unchanged as the scales of measurement vary because any part of it resembles the whole. On the other hand, the size and geometric characteristics of an irregular object vary as the resolution increases, revealing more intricate details. Despite numerous attempts, a reliable and accurate method for quantifying the morphological features of gastrointestinal organs, tissues, cells, their dynamic changes, and pathological disorders has not yet been established. However, fractal geometry, which studies shapes and patterns that exhibit self-similarity, holds promise in providing a quantitative measure of the irregularly shaped morphologies and their underlying self-similar temporal behaviors. In this context, we explore the fractal nature of the gastrointestinal system and the potential of fractal geometry as a robust descriptor of its complex forms and functions. Additionally, we examine the practical applications of fractal geometry in clinical gastroenterology and hepatology practice.

The convergence epidemic volatility index (cEVI) as an alternative early warning tool for identifying waves in an epidemic

This manuscript introduces the convergence Epidemic Volatility Index (cEVI), a modification of the recently introduced Epidemic Volatility Index (EVI), as an early warning tool for emerging epidemic waves. cEVI has a similar architectural structure as EVI, but with an optimization process inspired by a Geweke diagnostic-type test. Our approach triggers an early warning based on a comparison of the most recently available window of data samples and a window based on the previous time frame. Application of cEVI to data from the COVID-19 pandemic data revealed steady performance in predicting early, intermediate epidemic waves and retaining a warning during an epidemic wave. Furthermore, we present two basic combinations of EVI and cEVI: (1) their disjunction cEVI + that respectively identifies waves earlier than the original index, (2) their conjunction cEVI- that results in higher accuracy. Combination of multiple warning systems could potentially create a surveillance umbrella that would result in early implementation of optimal outbreak interventions.

MAG-D: A multivariate attention network based approach for cloud workload forecasting

The Coronavirus pandemic and the work-from-home have drastically changed the working style and forced us to rapidly shift towards cloud-based platforms & services for seamless functioning. The pandemic has accelerated a permanent shift in cloud migration. It is estimated that over 95% of digital workloads will reside in cloud-native platforms. Real-time workload forecasting and efficient resource management are two critical challenges for cloud service providers. As cloud workloads are highly volatile and chaotic due to their time-varying nature; thus classical machine learning-based prediction models failed to acquire accurate forecasting. Recent advances in deep learning have gained massive popularity in forecasting highly nonlinear cloud workloads; however, they failed to achieve excellent forecasting outcomes. Consequently, demands for designing more accurate forecasting algorithms exist. Therefore, in this work, we propose 'MAG-D', a Multivariate Attention and Gated recurrent unit based Deep learning approach for Cloud workload forecasting in data centers. We performed an extensive set of experiments on the Google cluster traces, and we confirm that MAG-DL exploits the long-range nonlinear dependencies of cloud workload and improves the prediction accuracy on average compared to the recent techniques applying hybrid methods using Long Short Term Memory Network (LSTM), Convolutional Neural Network (CNN), Gated Recurrent Units (GRU), and Bidirectional Long Short Term Memory Network (BiLSTM).

Temperature modifies the effects of air pollutants on respiratory diseases

Increasing studies have reported temperature modification effects on air pollutants-induced respiratory diseases. In the current study, daily data of respiratory emergency room visits (ERVs), meteorological factors, and concentrations of air pollutants were collected from 2013 to 2016 in Lanzhou, a northwest city in China. Daily average temperature was stratified into low (≤ 25 percentile, P₂₅), medium (25-75 percentile, P₂₅-P₇₅) and high (≥ 75 percentile, P₇₅) to explore how temperature modifies the effects of air pollutants (PM_2.5, PM₁₀, SO₂, and NO₂) on respiratory ERVs by using generalized additive Poisson regression model (GAM). Seasonal modification was also investigated. Results showed that (a) PM₁₀, PM_2.5, and NO₂ had the strongest effects on respiratory ERVs in low temperature; (b) males and 15-and-younger were more vulnerable in low temperature while females and those older than 46 years were highly affected in high temperature; (c) PM₁₀, PM_2.5, and NO₂ were mostly associated with the total and both males and females in winter, while SO₂ resulted in the highest risk for the total and males in autumn and females in spring. In conclusion, this study found significant temperature modification effects and seasonal differences on the risks of respiratory ERVs due to air pollutants in Lanzhou, China.

Long-term changes in body size of green turtles nesting on Trindade Island, Brazil: Signs of recovery?

Trindade Island is an important wildlife refuge in the South Atlantic Ocean and hosts the largest nesting population of green turtles (Chelonia mydas) in Brazil, about which temporal ecological dynamics are still not well understood. The present study examines 23 years of nesting for green turtles at this remote island to evaluate annual mean nesting size (MNS) changes and post-maturity somatic growth rates. Our results show a significant decrease in annual MNS over the study; Whereas MNS during the first three consecutively monitored years (1993-1995) was 115.1 ± 5.4 cm, during the last three years (2014-2016) it was 111.2 ± 6.3 cm. There was no significant change in post-maturity somatic growth rate over the course of the study; the mean annual growth rate was 0.25 ± 0.62 cm/year. These findings suggest an increase in the relative proportion of smaller, presumptive neophyte nesters appearing in Trindade during the study period.

Exploring the impact of particulate matter on mortality in coastal Mediterranean environments

Air pollution is one of the most important problems the world is facing nowadays, adversely affecting public health and causing millions of deaths every year. Particulate matter is a criteria pollutant that has been linked to increased morbidity, as well as all-cause and cause-specific mortality. However, this association remains under-investigated in smaller-size cities in the Eastern Mediterranean, which are also frequently affected by heat waves and dust storms. This study explores the impact of particulate matter with an aerodynamic diameter ≤ 10 μm (PM₁₀) and ≤ 2.5 μm (PM_2.5) on mortality (all-cause, cardiovascular, respiratory) in two coastal cities in the Eastern Mediterranean; Thessaloniki, Greece and Limassol, Cyprus. Generalized additive Poisson models were used to explore overall and gender-specific associations, controlling for long- and short-term patterns, day of week and the effect of weather variables. Moreover, the effect of different lags, season, co-pollutants and dust storms on primary associations was investigated. A 10 μg/m³ increase in PM_2.5 resulted in 1.10 % (95 % CI: -0.13, 2.34) increase in cardiovascular mortality in Thessaloniki, and in 3.07 % (95 % CI: -0.90, 7.20) increase in all-cause mortality in Limassol on the same day. Additionally, significant positive associations were observed between PM_2.5 as well as PM₁₀ and mortality at different lags up to seven days. Interestingly, an association with dust storms was observed only in Thessaloniki, having a protective effect, while the gender-specific analysis revealed significant associations only for the males in both cities. The outcome of this study highlights the need of city- or county-specific public health interventions to address the impact of climate, population lifestyle behaviour and other socioeconomic factors that affect the exposure to air pollution and other synergistic effects that alter the effect of PM on population health.

Identification of riverbank filtration sites at watershed scale: A geochemical and isotopic framework

Identifying groundwater wells performing riverbank filtration (RBF) is crucial to ensure safe drinking water through vulnerability assessment plans adapted to these hybrid water sources. Nonetheless, RBF is often unintentional or insufficiently documented and official inventories are scarce. We developed a user-friendly geochemical and isotopic framework for the in-situ identification of RBF facilities. It includes an interpretation abacus for non-specialists. While most studies using tracers are site-specific and/or based on discrete samples, we propose a novel multi-site characterization where time-series of EC, δ²H and δ¹⁸O are directly used as proxies of surface water infiltration at the watershed-scale. The basic statement is that time varying signal of raw water from a groundwater pumping facility reveals a significant induced infiltration of surface water. The framework was applied on nearly 2000 samples from 40 pumping wells and 4 neighboring rivers (<500 m), collected through collaborative sampling on a weekly to monthly basis for 18 months. Despite proximity to surface water, two-third of the complete dataset (19 facilities) were revealed not to benefit from significant contribution of surface water, demonstrating location criteria to be insufficient to identify RBF sites. Permanent RBF was evidenced at 5 facilities, where year-long seasonal variation of tracers in raw groundwater highlighted a continuous high proportion of infiltrated surface water. Unexpectedly, time-series also unveiled a third category: occasional RBF, where induced infiltration occurred only when specific hydrodynamic conditions were met (4 facilities). This study also provided concrete illustrations on how climate change may impact the efficiency of RBF to naturally attenuate microbiological contaminants and how geochemical and isotopic time-series considerably help at anticipating the evolution of contaminant attenuation capacity of RBF sites. Finally, by highlighting the existence of occasional RBF, this study tackles the common oversimplification that groundwater facilities can be binarily and classified either as RBF or groundwater.

Associations of cold exposure with hospital admission and mortality due to acute kidney injury: A nationwide time-series study in Korea

BACKGROUND

Emerging evidence supports an association between heat exposure and acute kidney injury (AKI). However, there is a paucity of studies on the association between cold exposure and AKI.

OBJECTIVE

We aimed to investigate the associations of cold exposure with hospital admission and mortality due to AKI and to explore whether these associations were influenced by age and sex.

METHODS

Information on daily counts of hospital admission and mortality due to AKI in 16 regions of Korea during the cold seasons (2010-2019) was obtained from the National Health Insurance Service (a single national insurer providing universal health coverage) and Statistics Korea. Daily mean temperature and relative humidity were calculated from hourly data obtained from 94 monitoring systems operated by the Korean Meteorological Administration. Associations of low temperatures (<10th percentile of daily mean temperature) and cold spells (≥2 consecutive days with <5th percentile of daily mean temperature) up to 21 days with AKI were estimated using quasi-Poisson regression models adjusted for potential confounders (e.g., relative humidity and air pollutants) with distributed lag models and univariate meta-regression models.

RESULTS

Low temperatures were associated with hospital admission due to AKI [relative risk (RR) = 1.12, 95 % confidence interval (CI): 1.09, 1.16]. Cold spells were associated with hospital admission (RR = 1.87, 95 % CI: 1.46, 2.39) and mortality due to AKI (RR = 4.84, 95 % CI: 1.30, 17.98). These associations were stronger among individuals aged ≥65 years than among those aged <65 years.

CONCLUSION

Our results underscore the need for the general population, particularly the elderly, physicians, and other healthcare providers to be more vigilant to cold exposure, given the risk of AKI. Government agencies need to develop specific strategies for the prevention and early detection of cold exposure-related AKI.

[Research on migraine time-series features classification based on small-sample functional magnetic resonance imaging data]

The extraction of neuroimaging features of migraine patients and the design of identification models are of great significance for the auxiliary diagnosis of related diseases. Compared with the commonly used image features, this study directly uses time-series signals to characterize the functional state of the brain in migraine patients and healthy controls, which can effectively utilize the temporal information and reduce the computational effort of classification model training. Firstly, Group Independent Component Analysis and Dictionary Learning were used to segment different brain areas for small-sample groups and then the regional average time-series signals were extracted. Next, the extracted time series were divided equally into multiple subseries to expand the model input sample. Finally, the time series were modeled using a bi-directional long-short term memory network to learn the pre-and-post temporal information within each time series to characterize the periodic brain state changes to improve the diagnostic accuracy of migraine. The results showed that the classification accuracy of migraine patients and healthy controls was 96.94%, the area under the curve was 0.98, and the computation time was relatively shorter. The experiments indicate that the method in this paper has strong applicability, and the combination of time-series feature extraction and bi-directional long-short term memory network model can be better used for the classification and diagnosis of migraine. This work provides a new idea for the lightweight diagnostic model based on small-sample neuroimaging data, and contributes to the exploration of the neural discrimination mechanism of related diseases.

Ambient temperature variability and hospital admissions for pneumonia: A nationwide study

Few investigations have assessed the impact of short-term ambient temperature change on pneumonia risk. We aimed to study the relation of temperature variability (TV) with daily hospitalizations for pneumonia in China. We conducted a time-series study in 184 major cities by extracting daily hospital data between 2014 and 2017 from a medical insurance claims database of 0.28 billion beneficiaries. TV was calculated as standard deviation of daily minimum and maximum temperatures over exposure days. We estimated associations of pneumonia admissions with TV for each city using over-dispersed generalized linear models controlling for weather conditions and ambient air pollution, and pooled city-specific estimates using random effects meta-analyses. We also investigated exposure-response relationship curve and potential effect modifiers. We identified 4.2 million pneumonia hospitalizations during the study period. TV was positively related to daily pneumonia admissions. At the national-average level, each 1-°C increase in TV at 0-6 days' exposure corresponded to a 0.65 % (95 % CI: 0.34 %-0.96 %) increase in pneumonia admissions. An approximately linear exposure-response curve for the relation of TV with pneumonia admission was noted. The relations were more evident in cities with larger average age (P = 0.038). As the first study in China to assess the impact of temperature change on pneumonia on a national scale, our results indicated that acute TV exposure was related to higher admissions for pneumonia. Our findings should provide new insight into the health impacts associated with climate change.

Association between ambient fine particulate matter and adult outpatient visits for rheumatoid arthritis in Beijing, China

The association between fine particulate matter (PM_2.5) and rheumatoid arthritis (RA) is currently unclear, especially in Beijing, a city with severe air pollution. Our study aimed to explore the relationship between short-term outdoor exposure to PM_2.5 and RA outpatient visits using a time-series analysis in Beijing. We used the Beijing's Medical Claims for Employees database to identify patients with RA in 2010-2012. A generalized additive model with a Poisson link was used to estimate the percentage change in RA outpatient visits after the PM_2.5 concentration increased by 10 μg/m³. From January 1, 2010, to June 30, 2012, a total of 541,061 RA outpatient visits were identified. During the study period, the average daily (standard deviation) concentration of PM_2.5 was 99.5 (75.3) µg/m³. A 10 µg/m³ increase in PM_2.5 concentration was correlated with a 0.21% (95% CI, 0.18-0.23%) increase in outpatient visits for RA on the same day. A significant association for the cumulative effect of PM_2.5 was found, and the largest significant association was observed for a lag of 0-3 days (0.26%; 95% CI, 0.23-0.29%). Stratified analyses revealed that females (0.29%, 95% CI: 0.26-0.33%) and 18-65 years old patients (0.29%, 95% CI: 0.25-0.32%) were most susceptible to the effects of PM_2.5 exposure. The current findings showed that short-term exposure to PM_2.5 was followed by an increase in the number of outpatient visits for RA in Beijing. Future studies should investigate the mechanisms underlying this association.

Association between extreme temperature exposure and occupational injuries among construction workers in Italy: An analysis of risk factors

BACKGROUND/AIM

Extreme temperatures have impact on the health and occupational injuries. The construction sector is particularly exposed. This study aims to investigate the association between extreme temperatures and occupation injuries in this sector, getting an insight in the main accidents-related parameters.

METHODS

Occupational injuries in the construction sector, with characteristic of accidents, were retrieved from Italian compensation data during years 2014-2019. Air temperatures were derived from ERA5-land Copernicus dataset. A region based time-series analysis, in which an over-dispersed Poisson generalized linear regression model, accounting for potential non-linearity of the exposure- response curve and delayed effect, was applied, and followed by a meta-analysis of region-specific estimates to obtain a national estimate. The relative risk (RR) and attributable cases of work-related injuries for an increase in mean temperature above the 75th percentile (hot) and for a decrease below the 25th percentile (cold) were estimated, with effect modifications by different accidents-related parameters.

RESULTS

The study identified 184,936 construction occupational injuries. There was an overall significant effect for high temperatures (relative risk (RR) 1.216 (95% CI: (1.095-1.350))) and a protective one for low temperatures (RR 0.901 (95% CI: 0.843-0.963)). For high temperatures we estimated 3,142 (95% CI: 1,772-4,482) attributable cases during the studied period. RRs from 1.11 to 1.30 were found during heat waves days. Unqualified workers, as well as masons and plumbers, were found to be at risk at high temperatures. Construction, quarry and industrial sites were the risky working environments, as well as specific physical activities like working with hand-held tools, operating with machine and handling of objects. Contact with sharp, pointed, rough, coarse 'Material Agent' were the more risky mode of injury in hot conditions.

CONCLUSIONS

Prevention policies are needed to reduce the exposure to high temperatures of construction workers. Such policies will become a critical issue considering climate change.

50 years of the water-flow variance in Tucuruí reservoir related with Brazilian energy consumption

Dammed rivers lose its natural characteristics of the flow cycle and becomes controlled by the energy demands of the hydroelectric plants. With the connection of the energy-producing plants to a central station in Brazil the situation is aggravated since demands in different regions of the country affect the water flow. Using downstream flow data from the Tucuruí dam over a 50-year period, we tested whether the variation in water flow has changed. We observed an increase of the annual variation of the water flow and the extreme events of flooding at downstream of the dam, indicating the operation of the dam intensified the control of water passage. The study reveals an increase in the variation of water flow in the dam's downstream section following the interconnection of the Tucurui dam with the Central System in 1997. Management strategies for the dam should be considered integrated with the national electricity demand, since distant demands may affect the local environment in question.

Gut microbiota and holobiont metabolome composition of the medaka fish (Oryzias latipes) are affected by a short exposure to the cyanobacterium Microcystis aeruginosa

Blooms of toxic cyanobacteria are a common stress encountered by aquatic fauna. Evidence indicates that long-lasting blooms affect fauna-associated microbiota. Because of their multiple roles, host-associated microbes are nowadays considered relevant to ecotoxicology, yet the respective timing of microbiota versus functional changes in holobionts response needs to be clarified. The response of gut microbiota and holobiont's metabolome to exposure to a dense culture of Microcystis aeruginosa was investigated as a microcosm-simulated bloom in the model fish species Oryzias latipes (medaka). Both gut microbiota and gut metabolome displayed significant composition changes after only 2 days of exposure. A dominant symbiont, member of the Firmicutes, plummeted whereas various genera of Proteobacteria and Actinobacteriota increased in relative abundance. Changes in microbiota composition occurred earlier and faster compared to metabolome composition. Liver and muscle metabolome were much less affected than guts, supporting that the gut and associated microbiota are in the front row upon exposure. This study highlights that even short cyanobacterial blooms, that are increasingly frequent, trigger changes in microbiota composition and holobiont metabolome. It emphasizes the relevance of multi-omics approaches to explore organism's response to an ecotoxicological stress.

A new alcohol-related traffic law, a further reduction in traffic fatalities? Analyzing the case of Turkey

BACKGROUND

In June 2013, an alcohol-related traffic law took effect in Turkey. The law 6487 introduced administrative fines for not respecting blood alcohol concentration limits, health warning messages on alcohol containers (bottles, cans), and prohibited the sale of alcohol beverages in retail facilities between 10 p.m. and 6 a.m.. This article examines how this law is associated with traffic fatality variation.

METHODS

Data from the Turkish Statistical Institute for the 2008-2019 period were analyzed. Outcomes were traffic fatality rates per 100,000 population and 10,000 motor vehicles. Exposure variable was the presence of law 6487. Alcohol, tobacco, and related beverages' household expenditure, unemployment rate, number of health professionals, number of crashes, and lags of the outcomes represented control variables. A time-series cross-regional fixed effect model was applied.

RESULTS

Empirical estimates suggest that the law 6487 was associated with a reduction of 15% (Incidence Rate Ratio (IRR) 0.85, 95% Confidence Interval (CI): 082, 0.94) in the traffic fatality per population rate and with a reduction of 14% (IRR: 0.86 (95% CI: 0.78, 0.92) in the traffic fatality per motor-vehicle rate. After 6 years of its implementation, this intervention was associated with an absolute reduction of 1519 (95% reduction interval: 1177, 1810) traffic fatalities.

CONCLUSIONS

Our research emphasizes that legislation with direct and indirect measures targeting driving under the influence of alcohol (DUIA) may be related to traffic fatalities reduction.

PRACTICAL APPLICATIONS

This finding has important implications for policy and future research in contexts in which alcohol consumption is low such is in Turkey. Future research should seek to identify mechanisms that explain how laws are ultimately associated with DUIA variation.

Short-term effect of ambient ozone pollution on respiratory diseases in western China

Ambient air pollution has been regarded as an important cause of the morbidity and mortality of respiratory diseases. In the current work, a total of 469,490 respiratory emergency room (ER) visits in Lanzhou, China from Jan 1, 2013 to Dec 31, 2016 were collected. A generalized additive model (GAM) was used to investigate the association between O₃ and respiratory ER visits for the different gender and age subgroups. The results showed that: (a) with per inter-quartile range (IQR) (31 µg/m³) increase in O₃, the greatest relative risk (RR) of respiratory ER visits for the total was 1.014 (95% CI 1.008-1.020) at lag 4 days. Females and 16-to-45-year-olds were relatively more sensitive to O₃; (b) the significant lag effects were found in single-day lag models, with the highest RR values for different groups were observed at lag 3-lag 5 days. The multi-day cumulative lag effects were stronger for the total; (c) in the multiple-pollutant models, the effects of O₃ were generally increased when introducing other pollutants (PM₁₀, PM_2.5, SO₂ and NO₂) for adjustment. This study demonstrated that short-term exposure to O₃ increased the RR of respiratory ER visits in Lanzhou, China.

Temporal and contemporaneous network structures of affect and physical activity in emotional disorders

BACKGROUND

High negative affect, low positive affect, and limited physical activity figure prominently in psychopathology, but little is known about the interrelatedness of affect and physical activity in emotional disorders.

METHODS

We combined ecological momentary assessment data with a network approach to examine the dynamic relations among positive affect, negative affect, and smartphone-based estimates of physical activity in 34 participants with anxiety and depressive disorders over a 2-week period.

RESULTS

In the contemporaneous networks, the positive affect nodes exhibited greater overall strength centrality than negative affect nodes. The temporal networks indicated that the negative affect node 'sadness' exhibited the greatest out-strength centrality. Furthermore, physical activity was unconnected to the affect nodes in either the temporal or contemporaneous networks.

CONCLUSIONS

Whereas positive affect plays a greater role in the contemporaneous experience of emotions, negative affect contributes more so to future affective states.

Time-series monitoring of river hydrochemistry and multiple isotope signals in the Yarlung Tsangpo River reveals a hydrological domination of fluvial nitrate fluxes in the Tibetan Plateau

Nutrient element cycling in the Tibetan Plateau, the highest and largest plateau in the world, is sensitive to anthropogenic disturbances and climate change. Studying the spatiotemporal dynamics of reactive nitrogen (N) - predominantly in the form of nitrate (NO₃^-) - in the plateau is crucial to understand the regional and global N cycles and their feedbacks with climate change. We conducted the first weekly frequency hydro-geochemical monitoring (i.e., discharge, water chemistry, and multiple isotopes) from the upper to the lower reaches of the Yarlung Tsangpo River, the largest river in the plateau, in pronounced wet/dry cycles to reveal the biogeochemical transformations and fluvial fluxes of NO₃^- response to hydrologic condition. Relative stable NO₃^- concentration and significant linear correlations between the fluvial NO₃^- fluxes and the discharge were observed, suggesting that a significant potential NO₃^- source counterbalanced the diluting effects during the rainy season. The negative correlations between δ¹⁵N-NO₃^- and discharge/NO₃^- fluxes suggested that the increasing NO₃^- flux respond to the increasing discharge was mainly from water leaching of ¹⁵N-depleted soil sources, rather than ¹⁵N-enriched sewage. The isotopic mixing model calculation showed that NO₃^- fluxes were largely generated in the relatively densely populated middle reaches (56%), of which 74% were from soil sources. The fluxes of the soil sources showed large seasonal variation and peaked in August, with hydrological condition as the primary driver. Based on the critical findings, we put forward a NO₃^- export conceptual model that integrated anthropogenic and climatic forcings and classified NO₃^- export mechanisms in river basins into transport-limited and generation-limited regimes. In a transport-limited regime that characterized most river basins in the Tibetan Plateau, fluvial NO₃^- flux presented a linearly relationship in response to runoff variation. In contrast, in a generation-limited regime, the flux would be largely dependent on the thermodynamic of nitrification.

Genome-wide post-transcriptional regulation of bovine mammary gland response to Streptococcus uberis

MicroRNAs (miRNAs) as post-transcriptionally regulators of gene expression have been shown to be critical regulators to fine-tuning immune responses, besides their criteria for being an ideal biomarker. The regulatory role of miRNAs in responses to most mastitis-causing pathogens is not well understood. Gram-positive Streptococcus uberis (Str. uberis), the leading pathogen in dairy herds, cause both clinical and subclinical infections. In this study, a system biology approach was used to better understand the main post-transcriptional regulatory functions and elements of bovine mammary gland response to Str. uberis infection. Publicly available miRNA-Seq data containing 50 milk samples of the ten dairy cows (five controls and five infected) were retrieved for this current research. Functional enrichment analysis of predicted targets revealed that highly confident responsive miRNAs (4 up- and 19 downregulated) mainly regulate genes involved in the regulation of transcription, apoptotic process, regulation of cell adhesion, and pro-inflammatory signaling pathways. Time series analysis showed that six gene clusters significantly differed in comparisons between Str. uberis-induced samples with controls. Additionally, other bioinformatic analysis, including upstream network analysis, showed essential genes, including TP53 and TGFB1 and some small molecules, including glucose, curcumin, and LPS, commonly regulate most of the downregulated miRNAs. Upregulated miRNAs are commonly controlled by the most important genes, including IL1B, NEAT1, DICER1 enzyme and small molecules including estradiol, tamoxifen, estrogen, LPS, and epigallocatechin. Our study used results of next-generation sequencing to reveal key miRNAs as the main regulator of gene expression responses to a Gram-positive bacterial infection. Furthermore, by gene regulatory network (GRN) analysis, we can introduce the common upregulator transcription factor of these miRNAs. Such milk-based miRNA signature(s) would facilitate risk stratification for large-scale prevention programs and provide an opportunity for early diagnosis and therapeutic intervention.

Exposure-lag response of air temperature on COVID-19 incidence in twelve Italian cities: A meta-analysis

The exposure-lag response of air temperature on daily COVID-19 incidence is unclear and there have been concerns regarding the robustness of previous studies. Here we present an analysis of high spatial and temporal resolution using the distributed lag non-linear modelling (DLNM) framework. Utilising nearly two years' worth of data, we fit statistical models to twelve Italian cities to quantify the delayed effect of air temperature on daily COVID-19 incidence, accounting for several categories of potential confounders (meteorological, air quality and non-pharmaceutical interventions). Coefficients and covariance matrices for the temperature term were then synthesised using random effects meta-analysis to yield pooled estimates of the exposure-lag response with effects presented as the relative risk (RR) and cumulative RR (RR_cum). The cumulative exposure response curve was non-linear, with peak risk at 15.1 °C and declining risk at progressively lower and higher temperatures. The lowest RR_cum at 0.2 °C is 0.72 [0.56,0.91] times that of the highest risk. Due to this non-linearity, the shape of the lag response curve necessarily varied by temperature. This work suggests that on a given day, air temperature approximately 15 °C maximises the incidence of COVID-19, with the effects distributed in the subsequent ten days or more.

Exposure-lag response of air temperature on COVID-19 incidence in twelve Italian cities: A meta-analysis

The exposure-lag response of air temperature on daily COVID-19 incidence is unclear and there have been concerns regarding the robustness of previous studies. Here we present an analysis of high spatial and temporal resolution using the distributed lag non-linear modelling (DLNM) framework. Utilising nearly two years' worth of data, we fit statistical models to twelve Italian cities to quantify the delayed effect of air temperature on daily COVID-19 incidence, accounting for several categories of potential confounders (meteorological, air quality and non-pharmaceutical interventions). Coefficients and covariance matrices for the temperature term were then synthesised using random effects meta-analysis to yield pooled estimates of the exposure-lag response with effects presented as the relative risk (RR) and cumulative RR (RR_cum). The cumulative exposure response curve was non-linear, with peak risk at 15.1 °C and declining risk at progressively lower and higher temperatures. The lowest RR_cum at 0.2 °C is 0.72 [0.56,0.91] times that of the highest risk. Due to this non-linearity, the shape of the lag response curve necessarily varied by temperature. This work suggests that on a given day, air temperature approximately 15 °C maximises the incidence of COVID-19, with the effects distributed in the subsequent ten days or more.

Consumption of antidepressants and economic austerity in Brazil

OBJECTIVE

To describe consumption of antidepressants in Brazil through dispensing data from pharmacy retail outlets, in between 2011 and 2017, and explore the relationship between consumption patterns and changing economic context during this period.

METHODS

A time-series analysis of dispensing data from pharmacy retail outlets from the Brazilian Controlled Products Management System was carried out considering ten commonly used antidepressants. DDDs/1000 inhabitants/year for each drug was calculated for each quarter and time-series graphs were constructed to analyze the volumes of drugs purchased. Trends were analyzed using Prais-Winsten regression. The relationship between economic context and consumption was assessed using the following indicators: annual percent change in Gross Domestic Product (GDP), public debt (% of GDP), and annual net savings (in millions of Brazilian reais -BRL-).

RESULTS

overall consumption of antidepressants from pharmacy retail outlets increased over the study period despite a sharp fall of -3,55% in annual percent change in GDP, negative net annual savings of -53.568 BRL, and an increase in public debt exceeding 32% of the GDP during the economic crisis of 2015.

CONCLUSION

Consumption of antidepressants from pharmacy retail outlets increased even within a context of economic crisis, which may be a reflection of the disease burden in Brazil. Health budget cuts due to the economic crisis may be directing users to out-of-pocket expenses, deepening social inequalities. Segmented trend analysis is a workable approach for developing hypotheses about the possible influence of the economic context on medication consumption patterns.

Exploring the microbial influence on seasonal nitrous oxide concentration in a full-scale wastewater treatment plant using metagenome assembled genomes

Nitrous oxide is a highly potent greenhouse gas and one of the main contributors to the greenhouse gas footprint of wastewater treatment plants (WWTP). Although nitrous oxide can be produced by abiotic reactions in these systems, biological N₂O production resulting from the imbalance of nitrous oxide production and reduction by microbial populations is the dominant cause. The microbial populations responsible for the imbalance have not been clearly identified, yet they are likely responsible for strong seasonal nitrous oxide patterns. Here, we examined the seasonal nitrous oxide concentration pattern in Avedøre WWTP alongside abiotic parameters, the microbial community composition based on 16S rRNA gene sequencing and already available metagenome-assembled genomes (MAGs). We found that the WWTP parameters could not explain the observed pattern. While no distinct community changes between periods of high and low dissolved nitrous oxide concentrations were determined, we found 26 and 28 species with positive and negative correlations to the seasonal N₂O concentrations, respectively. MAGs were identified for 124 species (approximately 31% mean relative abundance of the community), and analysis of their genomic nitrogen transformation potential could explain this correlation for four of the negatively correlated species. Other abundant species were also analysed for their nitrogen transformation potential. Interestingly, only one full-denitrifier (Candidatus Dechloromonas phosphorivorans) was identified. 59 species had a nosZ gene predicted, with the majority identified as a clade II nosZ gene, mainly from the phylum Bacteroidota. A correlation of MAG-derived functional guilds with the N₂O concentration pattern showed that there was a small but significant negative correlation with nitrite oxidizing bacteria and species with a nosZ gene (N₂O reducers (DEN)). More research is required, specifically long-term activity measurements in relation to the N₂O concentration to increase the resolution of these findings.

Sleep and physical activity - the dynamics of bi-directional influences over a fortnight

Study objectives

The day-to-next day predictions between physical activity (PA) and sleep are not well known, although they are crucial for advancing public health by delivering valid sleep and physical activity recommendations. We used Big Data to examine cross-lagged time-series of sleep and PA over 14 days and nights.

Methods

Bi-directional cross-lagged autoregressive pathways over 153,154 days and nights from 12,638 Polar watch users aged 18–60 years (M = 40.1 SD = 10.1; 44.5% female) were analyzed with cross-lagged panel data modeling (RI-CPL). We tested the effects of moderate-to-vigorous physical activity (MVPA) vs. high intensity PA (vigorous, VPA) on sleep duration and quality, and vice versa.

Results

Within-subject results showed that more minutes spent in VPA the previous day was associated with shorter sleep duration the next night, whereas no effect was observed for MVPA. Longer sleep duration the previous night was associated with less MVPA but more VPA the next day. Neither MVPA nor VPA were associated with subsequent night’s sleep quality, but better quality of sleep predicted more MVPA and VPA the next day.

Conclusions

Sleep duration and PA are bi-directionally linked, but only for vigorous physical activity. More time spent in VPA shortens sleep the next night, yet longer sleep duration increases VPA the next day. The results imply that a 24-h framing for the interrelations of sleep and physical activity is not sufficient – the dynamics can even extend beyond, and are activated specifically for the links between sleep duration and vigorous activity. The results challenge the view that sleep quality can be improved by increasing the amount of PA. Yet, better sleep quality can result in more PA the next day.

Forecasting COVID19 parameters using time-series: KSA, USA, Spain, and Brazil comparative case study

Many countries are suffering from the COVID19 pandemic. The number of confirmed cases, recovered, and deaths are of concern to the countries having a high number of infected patients. Forecasting these parameters is a crucial way to control the spread of the disease and struggle with the pandemic. This study aimed at forecasting the number of cases and deaths in KSA using time-series and well-known statistical forecasting techniques including Exponential Smoothing and Linear Regression. The study is extended to forecast the number of cases in the main countries such that the US, Spain, and Brazil (having a large number of contamination) to validate the proposed models (Drift, SES, Holt, and ETS). The forecast results were validated using four evaluation measures. The results showed that the proposed ETS (resp. Drift) model is efficient to forecast the number of cases (resp. deaths). The comparison study, using the number of cases in KSA, showed that ETS (with RMSE reaching 18.44) outperforms the state-of-the art studies (with RMSE equal to 107.54). The proposed forecasting model can be used as a benchmark to tackle this pandemic in any country.

Long-term patterns of an interconnected core marine microbiota

BACKGROUND

Ocean microbes constitute ~ 70% of the marine biomass, are responsible for ~ 50% of the Earth's primary production and are crucial for global biogeochemical cycles. Marine microbiotas include core taxa that are usually key for ecosystem function. Despite their importance, core marine microbes are relatively unknown, which reflects the lack of consensus on how to identify them. So far, most core microbiotas have been defined based on species occurrence and abundance. Yet, species interactions are also important to identify core microbes, as communities include interacting species. Here, we investigate interconnected bacteria and small protists of the core pelagic microbiota populating a long-term marine-coastal observatory in the Mediterranean Sea over a decade.

RESULTS

Core microbes were defined as those present in > 30% of the monthly samples over 10 years, with the strongest associations. The core microbiota included 259 Operational Taxonomic Units (OTUs) including 182 bacteria, 77 protists, and 1411 strong and mostly positive (~ 95%) associations. Core bacteria tended to be associated with other bacteria, while core protists tended to be associated with bacteria. The richness and abundance of core OTUs varied annually, decreasing in stratified warmers waters and increasing in colder mixed waters. Most core OTUs had a preference for one season, mostly winter, which featured subnetworks with the highest connectivity. Groups of highly associated taxa tended to include protists and bacteria with predominance in the same season, particularly winter. A group of 13 highly-connected hub-OTUs, with potentially important ecological roles dominated in winter and spring. Similarly, 18 connector OTUs with a low degree but high centrality were mostly associated with summer or autumn and may represent transitions between seasonal communities.

CONCLUSIONS

We found a relatively small and dynamic interconnected core microbiota in a model temperate marine-coastal site, with potential interactions being more deterministic in winter than in other seasons. These core microbes would be essential for the functioning of this ecosystem over the year. Other non-core taxa may also carry out important functions but would be redundant and non-essential. Our work contributes to the understanding of the dynamics and potential interactions of core microbes possibly sustaining ocean ecosystem function.

Seasonal diversity dynamics of a boreal zooplankton community under climate impact

Seasonality and long-term environmental variability affect species diversity through their effects on the dynamics of species. To investigate such effects, we fitted a dynamic and heterogeneous species abundance model generating the lognormal species abundance distribution to an assemblage of freshwater zooplankton sampled five times a year (June-October) during the ice-free period over 28 years (1990-2017) in Lake Atnsjøen (Norway). By applying a multivariate stochastic community dynamics model for describing the fluctuations in abundances, we show that the community dynamics was driven by environmental variability in spring (i.e., June). In contrast, community-level ecological heterogeneity is highest in autumn. The autumn months (i.e., September and October) that rearranged the community are most likely crucial months to monitor long-term changes in community structure. Indeed, noises from early summer are filtered away, making it easier to track long-term changes. The community returned faster towards equilibrium when ecological heterogeneity was the highest (i.e., in September and October). This occurred because of stronger density-regulation in months with highest ecological heterogeneity. The community responded to the long-term warming of water temperature with decreasing species diversity and increasing abundance. Unevenness associated with variabilities in abundances might affect species interactions within the community. These can have consequences for the stability and functioning of the ecosystem.

Effects of short-term ambient particulate matter exposure on the risk of severe COVID-19

OBJECTIVES

Previous studies have suggested a relationship between outdoor air pollution and the risk of coronavirus disease 2019 (COVID-19). However, there is a lack of data related to the severity of disease, especially in China. This study aimed to explore the association between short-term exposure to outdoor particulate matter (PM) and the risk of severe COVID-19.

METHODS

We recruited patients diagnosed with COVID-19 during a recent large-scale outbreak in eastern China caused by the Delta variant. We collected data on meteorological factors and ambient air pollution during the same time period and in the same region where the cases occurred and applied a generalized additive model (GAM) to analyze the effects of short-term ambient PM exposure on the risk of severe COVID-19.

RESULTS

A total of 476 adult patients with confirmed COVID-19 were recruited, of which 42 (8.82%) had severe disease. With a unit increase in PM₁₀, the risk of severe COVID-19 increased by 81.70% (95% confidence interval [CI]: 35.45, 143.76) at a lag of 0-7 days, 86.04% (95% CI: 38.71, 149.53) at a lag of 0-14 days, 76.26% (95% CI: 33.68, 132.42) at a lag of 0-21 days, and 72.15% (95% CI: 21.02, 144.88) at a lag of 0-28 days. The associations remained significant at lags of 0-7 days, 0-14 days, and 0-28 days in the multipollutant models. With a unit increase in PM_2.5, the risk of severe COVID-19 increased by 299.08% (95% CI: 92.94, 725.46) at a lag of 0-7 days, 289.23% (95% CI: 85.62, 716.20) at a lag of 0-14 days, 234.34% (95% CI: 63.81, 582.40) at a lag of 0-21 days, and 204.04% (95% CI: 39.28, 563.71) at a lag of 0-28 days. The associations were still significant at lags of 0-7 days, 0-14 days, and 0-28 days in the multipollutant models.

CONCLUSIONS

Our results indicated that short-term exposure to outdoor PM was positively related to the risk of severe COVID-19, and that reducing air pollution may contribute to the control of COVID-19.