The Oryza sativa transcriptome responds spatiotemporally to polystyrene nanoplastic stress

Nanoplastic represents an emerging abiotic stress facing modern agriculture, impacting global crop production. However, the molecular response of crop plants to this stress remains poorly understood at a spatiotemporal resolution. We therefore used RNA sequencing to profile the transcriptome expressed in rice (Oryza sativa) root and leaf organs at 1, 2, 4, and 8 d post exposure with nanoplastic. We revealed a striking similarity between the rice biomass dynamics in aboveground parts to that in belowground parts during nanoplastic stress, but transcriptome did not. At the global transcriptomic level, a total of 2332 differentially expressed genes were identified, with the majority being spatiotemporal specific, reflecting that nanoplastics predominantly regulate three processes in rice seedlings: (1) down-regulation of chlorophyll biosynthesis, photosynthesis, and starch, sucrose and nitrogen metabolism, (2) activation of defense responses such as brassinosteroid biosynthesis and phenylpropanoid biosynthesis, and (3) modulation of jasmonic acid and cytokinin signaling pathways by transcription factors. Notably, the genes involved in plant-pathogen interaction were shown to be successively modulated by both root and leaf organs, particularly plant disease defense genes (OsWRKY24, OsWRKY53, Os4CL3, OsPAL4, and MPK5), possibly indicating that nanoplastics affect rice growth indirectly through other biota. Finally, we associated biomass phenotypes with the temporal reprogramming of rice transcriptome by weighted gene co-expression network analysis, noting a significantly correlation with photosynthesis, carbon metabolism, and phenylpropanoid biosynthesis that may reflect the mechanisms of biomass reduction. Functional analysis further identified PsbY, MYB, cytochrome P450, and AP2/ERF as hub genes governing these pathways. Overall, our work provides the understanding of molecular mechanisms of rice in response to nanoplastics, which in turn suggests how rice might behave in a nanoplastic pollution scenario.

Differentiated impacts of short-term exposure to fine particulate constituents on infectious diseases in 507 cities of Chinese children and adolescents: A nationwide time-stratified case-crossover study from 2008 to 2021

This study assesses the association of short-term exposure to PM2.5 (particles ≤2.5 μm) on infectious diseases among Chinese children and adolescents. Analyzing data from 507 cities (2008-2021) on 42 diseases, it focuses on PM2.5 components (black carbon (BC), ammonium (NH4+), inorganic nitrate (NO3-), organic matter (OM), and sulfate (SO42-)). PM2.5 constituents significantly associated with incidence. Sulfate showed the most substantial effect, increasing all-cause infectious disease risk by 2.72 % per interquartile range (IQR) increase. It was followed by BC (2.04 % increase), OM (1.70 %), NO3- (1.67 %), and NH4+ (0.79 %). Specifically, sulfate and BC had pronounced impacts on respiratory diseases, with sulfate linked to a 10.73 % increase in seasonal influenza risk and NO3- to a 16.39 % rise in tuberculosis. Exposure to PM2.5 also marginally increased risks for gastrointestinal, enterovirus, and vectorborne diseases like dengue (7.46 % increase with SO42-). Sexually transmitted and bloodborne diseases saw an approximate 6.26 % increase in incidence, with specific constituents linked to diseases like hepatitis C and syphilis. The study concludes that managing PM2.5 levels could substantially reduce infectious disease incidence, particularly in China's middle-northern regions. It highlights the necessity of stringent air quality standards and targeted disease prevention, aligning PM2.5 management with international guidelines for public health protection.

Predicting intubation for intensive care units patients: A deep learning approach to improve patient management

OBJECTIVE

For patients in the Intensive Care Unit (ICU), the timing of intubation has a significant association with patients' outcomes. However, accurate prediction of the timing of intubation remains an unsolved challenge due to the noisy, sparse, heterogeneous, and unbalanced nature of ICU data. In this study, our objective is to develop a workflow for pre-processing ICU data and to develop a customized deep learning model to predict the need for intubation.

METHODS

To improve the prediction accuracy, we transform the intubation prediction task into a time series classification task. We carefully design a sequence of data pre-processing steps to handle the multimodal noisy data. Firstly, we discretize the sequential data and address missing data using interpolation. Next, we employ a sampling strategy to address data imbalance and standardize the data to facilitate faster model convergence. Furthermore, we employ the feature selection technique and propose an ensemble model to combine features learned by different deep learning models.

RESULTS

The performance is evaluated on Medical Information Mart for Intensive Care (MIMIC)-III, an ICU dataset. Our proposed Deep Feature Fusion method achieves an area under the curve (AUC) of the receiver operating curve (ROC) of 0.8953, surpassing the performance of other deep learning and traditional machine learning models.

CONCLUSION

Our proposed Deep Feature Fusion method proves to be a viable approach for predicting intubation and outperforms other deep learning and classical machine learning models. The study confirms that high-frequency time-varying indicators, particularly Mean Blood Pressure (MeanBP) and peripheral oxygen saturation (SpO2), are significant risk factors for predicting intubation.

Relationship between peak aortic jet velocity and progression of aortic stenosis in patients undergoing hemodialysis

BACKGROUND

The natural history of aortic stenosis (AS) progression, especially before severe AS development, is not well documented. We aimed to investigate the time course of peak aortic jet velocity (Vmax) and AS progression risk according to baseline Vmax, particularly whether there is a Vmax threshold.

METHODS

In a retrospective multicenter cohort study of patients on hemodialysis with aortic valve calcification, we investigated the time series of Vmax and the relationship between the baseline Vmax and progression to severe AS by analyzing longitudinal echocardiographic data.

RESULTS

Among 758 included patients (mean age, 71 years; 65% male), patients with Vmax <1.5, 1.5-1.9, 2.0-2.4, 2.5-2.9, and 3.0-3.9 m/s were 395 (52%), 216 (29%), 85 (11%), 39 (5.1%), and 23 (3.0%), respectively. The Vmax slope was gradual (mean 0.05-0.07 m/s/year) at Vmax <2 m/s, but steeper (mean 0.13-0.21 m/s/year) at Vmax ≥2 m/s. During a median 3.2-year follow-up, 52 (6.9%) patients developed severe AS. While patients with Vmax <2 m/s rarely developed severe AS, the risk of those with Vmax ≥2 m/s increased remarkably with an increasing baseline Vmax; the adjusted incidence rates in patients with Vmax <1.5, 1.5-1.9, 2.0-2.4, 2.5-2.9, and 3.0-3.9 m/s were 0.59, 0.57, 4.25, 13.8, and 56.1 per 100 person-years, respectively; the adjusted hazard ratio per 0.2 m/s increase in the baseline Vmax was 1.49 (95% confidence interval: 1.32-1.68) when Vmax ≥2 m/s.

CONCLUSIONS

The risk of progression to severe AS increased with the baseline Vmax primarily at ≥2 m/s; a Vmax threshold of 2 m/s was observed.

Immediate and delayed effects of environmental temperature on schizophrenia admissions in Liuzhou, China, 2013-2020: a time series analysis

This study aimed to investigate the associations between environmental temperature and schizophrenia admissions in Liuzhou, China. A Poisson generalized linear model combined with a distributed lag nonlinear model was used to analyze the effects of daily mean temperature on schizophrenia admissions from 2013 to 2020 in Liuzhou. Additionally, subgroup analyses were conducted to investigate possible modifications stratified by gender, marital status, and age. In this study, 10,420 schizophrenia admissions were included. The relative risks of schizophrenia admissions increased as the temperature rose, and the lag effects of high temperature on schizophrenia admissions were observed when the daily mean temperature reached 21.65°C. The largest single effect was observed at lag0, while the largest cumulative effect was observed at lag6. The single effects of high temperatures on schizophrenia admissions were statistically significant in both males and females, but the cumulative effects were statistically significant only in males, with the greatest effect at lag0-7. The single effect of high temperatures on admissions for unmarried schizophrenics was greatest at lag5, while the maximum cumulative effect for unmarried schizophrenia was observed at lag0-7. The single effects of high temperatures on schizophrenia admissions were observed in those aged 0-20, 21-40, and 41-60. The cumulative effects for schizophrenics aged 21-40 were observed from lag0-3 to lag0-7, with the maximum effect at lag0-7. In conclusion, the risk of schizophrenia admissions increased as the environmental temperature increased. The schizophrenics who were unmarried appeared to be more vulnerable to the single and cumulative effects of high temperature.

Mitral Valve Segmentation and Tracking from Transthoracic Echocardiography Using Deep Learning

OBJECTIVE

Valvular heart diseases (VHDs) pose a significant public health burden, and deciding the best treatment strategy necessitates accurate assessment of heart valve function. Transthoracic echocardiography (TTE) is the key modality to evaluate VHDs, but the lack of standardized quantitative measurements leads to subjective and time-consuming assessments. We aimed to use deep learning to automate the extraction of mitral valve (MV) leaflets and annular hinge points from echocardiograms of the MV, improving standardization and reducing workload in quantitative assessment of MV disease.

METHODS

We annotated the MV leaflets and annulus points in 2931 images from 127 patients. We propose an approach for segmenting the annotated features using Attention UNet with deep supervision and weight scheduling of the attention coefficients to enforce saliency surrounding the MV. The derived segmentation masks were used to extract quantitative biomarkers for specific MV leaflet scallops throughout the heart cycle.

RESULTS

Evaluation performance was summarized using a Dice score of 0.63 ± 0.14, annulus error of 3.64 ± 2.53 and leaflet angle error of 8.7 ± 8.3°. Leveraging Attention UNet with deep supervision robustness of clinically relevant metrics was improved compared with UNet, reducing standard deviations by 2.7° (angle error) and 0.73 mm (annulus error). We correctly identified cases of MV prolapse, cases of stenosis and healthy references from a clinical material using the derived biomarkers.

CONCLUSION

Robust deep learning segmentation and tracking of MV morphology and motion is possible by leveraging attention gates and deep supervision, and holds promise for enhancing VHD diagnosis and treatment monitoring.

Trends in police complaints and arrests on New York City subways, 2018 to 2023: an interrupted time-series analysis

BACKGROUND

Public transportation use is influenced by perceptions of safety. Concerns related to crime on New York City (NYC) transit have risen following NYC's COVID-19 pandemic state of emergency declaration in 2020, leading to declines in subway ridership. In response, the most recent mayoral administration implemented a Subway Safety Plan in 2022. This study aimed to quantify the effects of the COVID-19 pandemic and the Subway Safety Plan on rates of complaints to and arrests by the New York City Police Department (NYPD) Transit Bureau.

METHODS

Using publicly available data on complaints and arrests, we conducted interrupted time-series analyses using autoregressive integrated moving average models applied to monthly data for the period from September 2018 to August 2023. We estimated changes in the rates of complaints to and arrests by the NYPD Transit Bureau before and after: (1) the COVID-19 pandemic state of emergency declaration (i.e., March 2020), and (2) the announcement of the Subway Safety Plan (i.e., February 2022). We also examined trends by complaint and arrest type as well as changes in proportion of arrests by demographic and geographic groups.

RESULTS

After the COVID-19 pandemic declaration, there was an 84% increase (i.e., an absolute increase of 6.07 per 1,000,000 riders, CI 1.42, 10.71) in complaints to the NYPD Transit Bureau, including a 99% increase (0.91 per 1,000,000 riders, CI 0.42, 1.41) in complaints for assault and a 125% increase in complaints for harassment (0.94 per 1,000,000 riders, CI 0.29, 1.60). Following the Subway Safety Plan there was an increase in the rate of arrests for harassment (0.004 per 1,000,000 riders, CI 0.001, 0.007), as well as decreases in the proportion of arrests for individuals racialized as White (- 0.02, CI - 0.04, - 0.01) and proportion of arrests in the borough of Manhattan (- 0.13, CI - 0.17, - 0.09).

CONCLUSIONS

The increased rates of complaints to the NYPD Transit Bureau following the onset of the COVID-19 pandemic remained elevated following the enactment of the Subway Safety Plan. Further evaluation efforts can help identify effective means of promoting safety on public transportation.

Seasonality of influenza-like illness and short-term forecasting model in Chongqing from 2010 to 2022

BACKGROUND

Influenza-like illness (ILI) imposes a significant burden on patients, employers and society. However, there is no analysis and prediction at the hospital level in Chongqing. We aimed to characterize the seasonality of ILI, examine age heterogeneity in visits, and predict ILI peaks and assess whether they affect hospital operations.

METHODS

The multiplicative decomposition model was employed to decompose the trend and seasonality of ILI, and the Seasonal Auto-Regressive Integrated Moving Average with exogenous factors (SARIMAX) model was used for the trend and short-term prediction of ILI. We used Grid Search and Akaike information criterion (AIC) to calibrate and verify the optimal hyperparameters, and verified the residuals of the multiplicative decomposition and SARIMAX model, which are both white noise.

RESULTS

During the 12-year study period, ILI showed a continuous upward trend, peaking in winter (Dec. - Jan.) and a small spike in May-June in the 2-4-year-old high-risk group for severe disease. The mean length of stay (LOS) in ILI peaked around summer (about Aug.), and the LOS in the 0-1 and ≥ 65 years old severely high-risk group was more irregular than the others. We found some anomalies in the predictive analysis of the test set, which were basically consistent with the dynamic zero-COVID policy at the time.

CONCLUSION

The ILI patient visits showed a clear cyclical and seasonal pattern. ILI prevention and control activities can be conducted seasonally on an annual basis, and age heterogeneity should be considered in the health resource planning. Targeted immunization policies are essential to mitigate potential pandemic threats. The SARIMAX model has good short-term forecasting ability and accuracy. It can help explore the epidemiological characteristics of ILI and provide an early warning and decision-making basis for the allocation of medical resources related to ILI visits.

Evaluation of liver transplantation services in Kazakhstan from 2012 to 2023

There is a scarcity of publications evaluating the performance of the national liver transplantation (LTx) program in Kazakhstan. Spanning from 2012 to 2023, it delves into historical trends in LTx surgeries, liver transplant centers, and the national cohort of patients awaiting LTx. Survival analysis for those awaiting LTx, using life tables and Kaplan-Meier, is complemented by time series analysis projecting developments until 2030. The overall per million population (pmp) LTx rate varied from 0.35 to 3.77, predominantly favoring living donor LTx. Liver transplant center rates ranged from 0.06 to 0.40. Of 474 LTx patients, 364 on the waiting list did not receive transplantation. The 30-day and 1-year survival rates on the waiting list were 87.0% and 68.0%, respectively. Viral hepatitis and cirrhosis prevalence steadily rose from 2015 to 2023, with projections indicating a persistent trend until 2030. Absent targeted interventions, stable pmp rates of LTx and liver transplant centers may exacerbate the backlog of unoperated patients. This study sheds light on critical aspects of the LTx landscape in Kazakhstan, emphasizing the urgency of strategic interventions to alleviate the burden on patients awaiting transplantation.

Monitoring jellyfish outbreaks along Israel's Mediterranean coast using digital footprints

With mounting global concerns about jellyfish outbreaks, monitoring their occurrence remains challenging. Tapping into the wealth of digital data that internet users share online, which includes reports of jellyfish sightings, may provide an alternative or complement to more conventional expert-based or citizen science monitoring. Here, we explore digital footprints as a data source to monitor jellyfish outbreaks along the Israeli Mediterranean coast. We compiled jellyfish sighting data for the period 2011-2022 from multiple platforms, including leading social media platforms, searches in the Google search engine, and Wikipedia page views. Employing time series analysis, cross-correlation, and various evaluation metrics for presence/absence data, we compared weekly data from three sources: digital footprints, citizen science, and traditional expert-based field monitoring. Consistent seasonal patterns emerge across datasets, with notable correlations, particularly in jellyfish abundance. The cross-correlation between digital footprint and citizen science data exceeds >0.7, with Twitter and Instagram showing the highest correlation. Citizen science data often precedes digital footprints by up to one week. Correlation with traditional, expert-based field monitoring is limited as a result of limited data availability. Digital footprints demonstrate substantial agreement with the other data sources regarding jellyfish presence/absence and major outbreaks, especially for data from Wikipedia, Twitter, and Instagram. Overall, we highlight digital footprint data as a reliable, cost-effective tool for passive monitoring of jellyfish outbreaks, which can aid characterization in data-scarce coastal regions, including retrospective assessment. Transferring and scaling up the proposed approach should consider data accessibility as well as platform relative popularity and usage in the regions under investigation.

Social Inequalities in Child Development: Analysis of Low-Birth-Weight Trends in Brazil, 2010-2020

INTRODUCTION

Low birth weight (LBW) is a global issue prevalent in low-income countries. Economic assessments of interventions to reduce this burden are crucial to guide health policies. However, there is a relative scarcity of research that illustrates the magnitude of LBW by country and region to support the design of public policies.

OBJECTIVE

This study aimed to analyze the temporal trend of fetal growth in newborns in Brazil between 2010 and 2020.

METHODS

A time series study was conducted using data from the Live Births Information System (SINASC), which is managed by the Department of Information and Informatics of the Unified Health System (DATASUS) of the Brazilian Ministry of Health. The Prais-Winsten linear model was applied to analyze the annual proportions of LBW. The annual percentage changes (APC) and their respective 95% confidence intervals (95%CI) were calculated. Prevalence rate averages of LBW were calculated and displayed on thematic maps to visualize the evolution dynamics in each Federation Unit (FU).

RESULTS

A total of 31,887,329 women from all Federative Units of Brazil were included in the study from 2010 to 2020. The Southeast region had the largest proportion of participants, with records from 2015 accounting for 9.5% of the total. Among the women in the study, 49.6% were between the ages of 20 and 29, and the majority (75.5%) had between 8 and 12 years of schooling. The newborns of these women were predominantly male (58.8%) and non-white (59.5%). The study found that there was a trend towards stabilization of increasing proportions of LBW in the North, Northeast, and Centre-West regions between 2010 and 2020. In Brazil and other regions, these tendencies remained stable.

CONCLUSION

To improve living conditions and reduce social inequalities and health inequities, public policies and actions are necessary. Strengthening the Unified Health System (SUS), income transfer programs, quota policies for vulnerable groups, and gender equality measures such as improving access to education for women and the labor sector are among the suggested approaches.

A deep learning-based approach for predicting COVID-19 diagnosis

This paper focuses on forecasting the total count of confirmed COVID-19 cases in Saudi Arabia through a range of methodologies, including ARIMA, mathematical modeling, and deep learning network (DQN) techniques. Its primary aim is to anticipate the verified COVID-19 cases in Saudi Arabia, aiding in decision-making for life-saving interventions by enhancing awareness of COVID-19 infection. Mathematical modeling and ARIMA are employed for their efficacy in forecasting, while DQN approaches, particularly through comparative analysis, are utilized for prediction. This comparative analysis evaluates the predictive capacities of ARIMA, mathematical modeling, and DQN techniques, aiming to pinpoint the most reliable method for forecasting positive COVID-19 cases. The modeling encompasses COVID-19 cases in Saudi Arabia, the United Kingdom (UK), and Tunisia (TU) spanning from 2020 to 2021. Predicting the number of individuals likely to test positive for COVID-19 poses a challenge, requiring adherence to fundamental assumptions in mathematical and ARIMA projections. The proposed methodology was implemented on a local server. The DQN algorithm formulates a reward function to uphold target functional performance while balancing training and testing periods. The findings indicate that DQN technology surpasses conventional approaches in efficiency and accuracy for predictions.

Gas concentration prediction by LSTM network combined with wavelet thresholding denoising and phase space reconstruction

The Long Short-Term Memory neural network is a specialized architecture designed for handling time series data, extensively applied in the field of predicting gas concentrations. In the harsh conditions prevalent in coal mines, the time series data of gas concentrations collected by sensors are susceptible to noise interference. Directly inputting such noisy data into a neural network for training would significantly reduce predictive accuracy and lead to deviations from the actual values. The Empirical Mode Decomposition method, commonly employed in gas concentration prediction, faces challenges in practical engineering applications due to the substantial influence of newly acquired data on the initial decomposition subsequence values. Consequently, it is difficult to use this method as intended. Conversely, the Wavelet Threshold Denoising method does not encounter this issue. Furthermore, gas concentration sequences exhibit chaotic characteristics. Performing phase space reconstruction allows for the extraction of additional valuable hidden information. In light of these factors, a prediction model is proposed, integrating WTD, Phase Space Reconstruction, and LSTM neural networks. Initially, the gas concentration sequence itself is subjected to wavelet threshold denoising. Subsequently, phase space reconstruction is performed, and the resulting reconstructed phase space matrix serves as the input for the LSTM neural network. The outcomes from the final LSTM neural network reveal that the PS method indeed extracts more valuable information. The Mean Absolute Error and Root Mean Square Error are reduced by 35.1% and 25%, respectively. Additionally, when compared to the PS-LSTM model without utilizing the WTD method, the WTD-PS-LSTM predictive model showcases reductions of 77.1% and 80% in MAE and RMSE, respectively. Compared with the LSTM model, the MAE and RMSE of the WTD-PS-LSTM prediction model were reduced by 81.4% and 82.6%, respectively. This greatly improves the credibility of whether or not a response related to coal mine safety management is implemented.

Impacts of urban emissions and air quality in São Paulo State, Brazil

Daily violations of air quality have an impact on urban populations and cause damage to the environment. Thus, the study evaluated the violations of the daily concentrations of SO2, NO2, and PM10, in regions of the State of São Paulo (SSP), based on the National Environment Council (CONAMA) resolution no 491/2018 and the World Health Organization (WHO - World Health Organization. (2016). Ambient air pollution: a global assessment of exposure and burden of disease.) criteria. Daily SO2, NO2, and PM10data from 6 air quality stations operated by Environmental Company of the State of São Paulo CETESB (1996-2011) were organized and submitted to quality control, with data faults (gaps) being identified. The imputation of data via spline proved satisfactory in filling in the gaps (r > 0.7 and low values of Standard Error of the Estimate (SEE) and Root Mean Square Error (RMSE). The cluster analysis (CA) applied to SO2 formed only one homogeneous group (G1). Contrariwise, NO2 and PM10 formed two homogeneous groups (G1 and G2) each. The stations that showed the greatest similarity according to the CA were Cerqueira Cesar and Osasco. The cophenetic matrix generated for SO2 (0.83), NO2 (0.79), and PM10 (0.77) indicate a satisfactory adjustment of the dendrograms. The exploratory statistics applied to groups G1 and G2 point to the high variability of outliers. The WHO criteria are more restrictive than CONAMA regarding daily violations, with a reduction in SO2 and an increase in specific years for NO2 and PM10. Such variability is due to the adoption of public policies by the SSP and the influence of meteorological systems, being confirmed by the Run test that indicated oscillations in the time series, mainly in PM10, and also recognized well-defined biannual cycles.

Exposure to low humidex increases the risk of hip fracture admissions in a subtropical coastal Chinese city

OBJECTIVE

The adverse impacts of meteorological factors on human health have attracted great attention. However, no studies have investigated the nonlinear effects of humidex on hip fractures (HF), particularly in middle-aged and older adults. This study aimed to quantify the impacts of humidex, a comprehensive index of temperature and relative humidity, on HF admissions.

METHODS

Daily HF admissions, meteorological variables and air pollutants in the subtropical coastal city of Shantou, China, from 2015 to 2020 were collected. A generalized linear regression model combined with a distributed lag nonlinear model was applied to explore the exposure-lag-response relationship between humidex and HF admissions. Subgroup analyses were also conducted by gender, age and season. Attributable fractions (AF) and attributable numbers (AN) were used to represent the burden of disease.

RESULTS

A total of 6200 HF admissions were identified during the study period. Taking the median humidex (31.9) as a reference, the single-day lag effects of low humidex (13, 2.5th percentile) were significant at lag 0 [relative risk (RR) = 1.145, 95 % confidence interval (CI): 1.041-1.259] to lag 2 (RR = 1.049, 95 % CI: 1.010-1.089). The cumulative lag effects of low humidex were significant at lag 0-0 (RR = 1.145, 95 % CI: 1.041-1.259) to lag 0-6 (RR = 1.258, 95 % CI: 1.010-1.567) and reached a maximum at lag 0-3 (RR = 1.330, 95 % CI: 1.113-1.590). High humidex (44, 97.5th percentile) was not associated with the risk of HF. Females and people over the age of 75 appeared to be more susceptible to low humidex. In addition, the adverse effects of low humidex were more pronounced in the cold season. The AF and AN of low humidex on HF admissions were 24.8 % (95 % CI: 10.2-37.1 %) and 1538, respectively.

CONCLUSION

Low humidex was associated with an increased risk of HF admissions. The government should take timely measures to prevent people from being exposed to low humidex to effectively reduce HF admissions.

Analyzing the nexus between environmental sustainability and clean energy for the USA

Environmental sustainability is a key target to achieve sustainable development goals (SDGs). However, achieving these targets needs tools to pave the way for achieving SDGs and COP28 targets. Therefore, the primary objective of the present study is to examine the significance of clean energy, research and development spending, technological innovation, income, and human capital in achieving environmental sustainability in the USA from 1990 to 2022. The study employed time series econometric methods to estimate the empirical results. The study confirmed the long-run cointegrating relationship among CO2 emissions, human capital, income, R&D, technological innovation, and clean energy. The results are statistically significant in the short run except for R&D expenditures. In the long run, the study found that income and human capital contribute to further aggravating the environment via increasing CO2 emissions. However, R&D expenditures, technological innovation, and clean energy help to promote environmental sustainability by limiting carbon emissions. The study recommends investment in technological innovation, clean energy, and increasing R&D expenditures to achieve environmental sustainability in the USA.

Impacts of mining on vegetation phenology and sensitivity assessment of spectral vegetation indices to mining activities in arid/semi-arid areas

Measuring the impact of mining activities on vegetation phenology and assessing the sensitivity of vegetation indices (VIs) to it are crucial for understanding land degradation in mining areas and enhancing the carbon sink capacity following the ecological restoration of mines. To this end, we have developed a novel technical framework to quantify the impact of mining activities on vegetation, and applied it to the Bainaimiao copper mining area in Inner Mongolia. Phenological indices are extracted based on the VI time series data of Sentinel-2, and changes in phenological differences in various directions are used to quantify the impact of mining activities on vegetation. Finally, indicators such as mean difference, standard deviation, index value distribution interval, and concentration of index value distribution were selected to assess the sensitivity of the Enhanced Vegetation Index (EVI), Green Chlorophyll Index (GCI), Global Environmental Monitoring Index (GEMI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Red-Edge Chlorophyll Index (RECI), and Soil-Adjusted Vegetation Index (SAVI) to mining activities. The results of the study show that the impact of mining activities on surrounding vegetation extends to an area three times larger than the actual mining activity area. When compared with the reference and unaffected areas, the affected area experienced a delay of approximately 10 days in seasonal vegetation development. Environmental pollution caused by the tailings pond was identified as the primary factor influencing this delay. Significant variations in the sensitivity of each VI to assess mining activities in arid/semi-arid areas were observed. Notably, GCI, GNDVI and RDVI displayed relatively high sensitivity to discrepancies in the spectral attributes of vegetation within the affected area, while SAVI reflected the overall spectral stability of the vegetation in the affected area. The research findings have the potential to provide valuable technical guidance for holistic environmental management in mining areas and hold great significance in preventing further land degradation and supporting ecological restoration in mining areas.

Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

Accurate predictions of stock markets are important for investors and other stakeholders of the equity markets to formulate profitable investment strategies. The improved accuracy of a prediction model even with a slight margin can translate into considerable monetary returns. However, the stock markets' prediction is regarded as an intricate research problem for the noise, complexity and volatility of the stocks' data. In recent years, the deep learning models have been successful in providing robust forecasts for sequential data. We propose a novel deep learning-based hybrid classification model by combining peephole LSTM with temporal attention layer (TAL) to accurately predict the direction of stock markets. The daily data of four world indices including those of U.S., U.K., China and India, from 2005 to 2022, are examined. We present a comprehensive evaluation with preliminary data analysis, feature extraction and hyperparameters' optimization for the problem of stock market prediction. TAL is introduced post peephole LSTM to select the relevant information with respect to time and enhance the performance of the proposed model. The prediction performance of the proposed model is compared with that of the benchmark models CNN, LSTM, SVM and RF using evaluation metrics of accuracy, precision, recall, F1-score, AUC-ROC, PR-AUC and MCC. The experimental results show the superior performance of our proposed model achieving better scores than the benchmark models for most evaluation metrics and for all datasets. The accuracy of the proposed model is 96% and 88% for U.K. and Chinese stock markets respectively and it is 85% for both U.S. and Indian markets. Hence, the stock markets of U.K. and China are found to be more predictable than those of U.S. and India. Significant findings of our work include that the attention layer enables peephole LSTM to better identify the long-term dependencies and temporal patterns in the stock markets' data. Profitable and timely trading strategies can be formulated based on our proposed prediction model.

The impact of meteorological factors on tuberculosis incidence in Spain: a spatiotemporal analysis

Tuberculosis (TB) remains a global leading cause of death, necessitating an investigation into its unequal distribution. Sun exposure, linked to vitamin D (VD) synthesis, has been proposed as a protective factor. This study aimed to analyse TB rates in Spain over time and space and explore their relationship with sunlight exposure. An ecological study examined the associations between rainfall, sunshine hours, and TB incidence in Spain. Data from the National Epidemiological Surveillance Network (RENAVE in Spanish) and the Spanish Meteorological Agency (AEMET in Spanish) from 2012 to 2020 were utilized. Correlation and spatial regression analyses were conducted. Between 2012 and 2020, 43,419 non-imported TB cases were reported. A geographic pattern (north-south) and distinct seasonality (spring peaks and autumn troughs) were observed. Sunshine hours and rainfall displayed a strong negative correlation. Spatial regression and seasonal models identified a negative correlation between TB incidence and sunshine hours, with a four-month lag. A clear spatiotemporal association between TB incidence and sunshine hours emerged in Spain from 2012 to 2020. VD levels likely mediate this relationship, being influenced by sunlight exposure and TB development. Further research is warranted to elucidate the causal pathway and inform public health strategies for improved TB control.

Effects of ambient air pollution on the hospitalization risk and economic burden of mental disorders in Qingdao, China

OBJECTIVE

The aim of this study was to examine the impacts of short-term exposure to air pollutants on hospitalizations for mental disorders (MDs) in Qingdao, a Chinese coastal city, and to assess the corresponding hospitalization risk and economic cost.

METHODS

Daily data on MD hospitalizations and environmental variables were collected from January 1, 2015, to December 31, 2019. An overdispersed generalized additive model was used to estimate the association between air pollution and MD hospitalizations. The cost of illness method was applied to calculate the corresponding economic burden.

RESULTS

With each 10 μg/m3 increase in the concentration of fine particulate matter (PM2.5) at lag05, inhalable particulate matter (PM10) at lag0, sulfur dioxide (SO2) at lag06 and ozone (O3) at lag0, the corresponding relative risks (RRs) and 95% confidence intervals (CIs) were 1.0182 (1.0035-1.0332), 1.0063 (1.0001-1.0126), 1.0997 (1.0200-1.1885) and 1.0099 (1.0005-1.0194), respectively. However, no significant effects of nitrogen dioxide (NO2) or carbon monoxide (CO) were found. Stratified analysis showed that males were susceptible to SO2 and O3, while females were susceptible to PM2.5. Older individuals (≥ 45 years) were more vulnerable to air pollutants (PM2.5, PM10, SO2 and O3) than younger individuals (< 45 years). Taking the Global Air Quality Guidelines 2021 as a reference, 8.71% (2,168 cases) of MD hospitalizations were attributable to air pollutant exposure, with a total economic burden of 154.36 million RMB.

CONCLUSION

Short-term exposure to air pollution was associated with an increased risk of hospitalization for MDs. The economic advantages of further reducing air pollution are enormous.

Incorporating inflation rate in construction projects cost: Forecasting model

Over time, the change in the inflation rate causes cost overruns by deviating the prices of goods and services in construction projects that require practitioners to make budgeting revisions. Hence, this study aims to develop a construction rates forecasting model that can incorporate the changing impact of the inflation rate on construction rates and predict the prices in a particular year, which can be adjusted when developing the Bill of Quantities. Following the time series analysis standards, a mathematical model was developed using MATLAB for forecasting. Construction rates, building prices, labour wages and machinery rates were forecasted from 2020 to 2025 based on the data collected from 2013 to 2019. Akaike information criterion was used to validate the self-developed construction rate forecasting model. It was revealed that the model yielded better results when the construction rates were compared with the autoregressive integrated moving average time series model results. The rates forecasting model may be used for any construction project where rates are affected by the inflation effect.

Disability-related inequalities in the prevalence of loneliness across the lifespan: trends from Australia, 2003 to 2020

BACKGROUND

Experiencing loneliness can be distressing and increasing evidence indicates that being lonely is associated with poor physical and mental health outcomes. Cross-sectional studies have demonstrated that people with disability have increased risk of experiencing loneliness compared to people without disability. However, we do not know if these inequalities have changed over time. This study investigated the prevalence of loneliness for people with disability in Australia annually from 2003 to 2020 to examine whether disability-related inequalities in loneliness have changed over time, and disaggregated results for subgroups of people with disability by age group, sex, and disability group.

METHODS

We used annual data (2003-2020) from the Household, Income and Labour Dynamics in Australia Survey. Loneliness was measured by a single question assessing the subjective experience of loneliness. For each wave, we calculated population-weighted age-standardised estimates of the proportion of people experiencing loneliness for people with and without disability. We then calculated the absolute and relative inequalities in loneliness between people with and without disability for each wave. Analyses were stratified by 10-year age groups, sex, and disability group (sensory or speech, physical, intellectual or learning, psychological, brain injury or stroke, other).

RESULTS

From 2003 to 2020, the prevalence of loneliness was greater for people with disability, such that people with disability were 1.5 to 1.9 times more likely to experience loneliness than people without disability. While the prevalence of loneliness decreased for people without disability between 2003 and 2020, the prevalence of loneliness did not decrease for people with disability during this period. Inequalities in loneliness were more substantial for people with intellectual or learning disabilities, psychological disability, and brain injury or stroke.

CONCLUSION

This study confirms that people with disability have increased risk of loneliness compared to people without disability. We add to the existing evidence by demonstrating that disability-related inequalities in loneliness have persisted for two decades in Australia without improvement. Our findings indicate that addressing inequalities in loneliness for people with disability is a critical public health concern given that loneliness is associated with a wide range of poor health outcomes.

A new denoising approach based on mode decomposition applied to the stock market time series: 2LE-CEEMDAN

Time series, including noise, non-linearity, and non-stationary properties, are frequently used in prediction problems. Due to these inherent characteristics of time series data, forecasting based on this data type is a highly challenging problem. In many studies within the literature, high-frequency components are commonly excluded from time series data. However, these high-frequency components can contain valuable information, and their removal may adversely impact the prediction performance of models. In this study, a novel method called Two-Level Entropy Ratio-Based Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (2LE-CEEMDAN) is proposed for the first time to effectively denoise time series data. Financial time series with high noise levels are utilized to validate the effectiveness of the proposed method. The 2LE-CEEMDAN-LSTM-SVR model is introduced to predict the next day's closing value of stock market indices within the scope of financial time series. This model comprises two main components: denoising and forecasting. In the denoising section, the proposed 2LE-CEEMDAN method eliminates noise in financial time series, resulting in denoised intrinsic mode functions (IMFs). In the forecasting part, the next-day value of the indices is estimated by training on the denoised IMFs obtained. Two different artificial intelligence methods, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR), are utilized during the training process. The IMF, characterized by more linear characteristics than the denoised IMFs, is trained using the SVR, while the others are trained using the LSTM method. The final prediction result of the 2LE-CEEMDAN-LSTM-SVR model is obtained by integrating the prediction results of each IMF. Experimental results demonstrate that the proposed 2LE-CEEMDAN denoising method positively influences the model's prediction performance, and the 2LE-CEEMDAN-LSTM-SVR model outperforms other prediction models in the existing literature.

Characterization of groundwater storage changes in the Amazon River Basin based on downscaling of GRACE/GRACE-FO data with machine learning models

Groundwater storage changes in the Amazon River Basin (ARB) play an important role in the hydrological behavior of the region, with significant influence on climate variability and rainforest ecosystems. The GRACE and GRACE-FO satellite missions provide gravity anomalies from which it is possible to monitor changes in terrestrial water storage, albeit at low spatial resolution. This study downscaled GRACE and GRACE-FO data from machine learning models from 1° (110 km approx) to 0.25° (27.5 km approx). It estimated the spatiotemporal variability of terrestrial and groundwater storage anomalies between 2002 and 2021 for the Amazon River Basin. In parallel, the Random Forest and AdaBoost algorithms were compared and analyzed. The results reflected a good fit of the models with a very low error and a slight superiority in the predictions obtained by AdaBoost. On the predictions at 0.25°, spatial patterns associated with the strong influence on storage changes of some rivers and snow-capped mountains were identified, as well as an increase in the accuracy of the scaled data of the original ones. Positive long-term behavior was also obtained in terrestrial and groundwater storage of 14.26 ± 1.18 km3/yr and + 22.24 ± 1.18 km3/yr, respectively. Validation of the time series of groundwater anomalies to water levels in the monitoring wells obtained maximum correlation coefficients of 0.85 with confidence levels of 0.01. These results are promising for satellite information in water management, especially in regional monitoring of unconfined aquifers. The obtained data is stored in a dedicated repository (Satizábal-Alarcón et al., 2023).

Abiotic factors impact on oak forest decline in Lorestan Province, Western Iran

The Zagros oak forests in Iran are facing a concerning decline due to prolonged and severe drought conditions over several decades, compounded by the simultaneous impact of temperature on oak populations. This study in oak woodlands of central Zagros forests in Lorestan province analyzed abiotic factors such as climate properties, topographic features, land use, and soil properties from 1958 to 2022. We found that higher elevation areas with steeper slopes and diverse topography show significant potential for enhancing oak tree resilience in the face of climate change. Additionally, traditional land use practices like livestock keeping and dryland farming contribute to a widespread decline in oak populations. Preserving forest biodiversity and ensuring ecological sustainability requires immediate attention. Implementing effective land-use management strategies, such as protecting and regulating human-forest interaction, and considering meteorological factors to address this issue is crucial. Collaborative efforts from stakeholders, policymakers, and local communities are essential to oppose destructive suburban sprawl and other developments. Sustainable forestry practices should be implemented to improve the living standards of local communities that rely on forests and traditional livestock keeping, offer forestry-related jobs, and ensure social security. Such efforts are necessary to promote conservation awareness and sustainable practices, safeguarding this unique and vital ecosystem for future generations.

Cross-correlation and time series analysis of rabies in different animal species in Nepal from 2005 to 2018

Rabies is a fatal zoonotic disease, resulting in human and livestock deaths. In Nepal, animal rabies has posed a significant challenge to public health. Because animals are the primary source of rabies in humans, a better understanding of rabies epidemiology in animals is necessary. The objectives of this study were to determine the correlation between rabies occurrences in dogs and livestock animals and to detect the trends and change points of the disease using longitudinal data. The nationwide rabies dataset from 2005 to 2018 was analyzed using cross-correlation, multiple change points, and time series methods. Autoregressive Integrated Moving Average (ARIMA) and Neural Network Autoregression (NNAR) were applied to the time series data. The results show a positive correlation between canine rabies and livestock rabies occurrences. Three significant change points were detected in the time series data, demonstrating that the occurrences were high in the initial years but stabilized before peaking to an upward trend in the final years of the study period. Nonetheless, there was no seasonality pattern in rabies occurrences. The most suitable models were ARIMA (2,1,2) and NNAR (5,1,4) (12). Based on the study findings, both locals and tourists in Nepal need to have enhanced awareness of the potential dangers posed by rabies in canines and livestock. This study offers much-needed insight into the patterns and epidemiology of animal rabies which will be helpful for policymakers in drafting rabies control plans for Nepal.

Spatial and remote sensing monitoring shows the end of the bark beetle outbreak on Belgian and north-eastern France Norway spruce (Picea abies) stands

In 2022, Europe emerged from eight of the hottest years on record, leading to significant spruce mortality across Europe. The particularly dry weather conditions of 2018 triggered an outbreak of bark beetles (Ips typographus), causing the loss of thousands of hectares of Norway spruce stands, including in Wallonia and North-eastern France. A methodology for detecting the health status of spruce was developed based on a dense time series of satellite imagery (Sentinel-2). The time series of satellite images allowed the modelling of the spectral response of healthy spruce forests over the seasons: a decrease in photosynthetic activity of the forest canopy causes deviations from this normal seasonal vegetation index trajectory. These anomalies are caused by a bark beetle attack and are detected automatically. The method leads in the production of an annual spruce health map of Wallonia and Grand-Est. The goal of this paper is to assess the damage caused by bark beetle using the resulting spruce health maps. A second objective was to compare the influence of basic variables on the mortality of spruce trees in these two regions. Lasted 6 years (2017-2022), bark beetle has destroyed 12.2% (23,674 ha) of the spruce area in Wallonia and Grand-Est of France. This study area is composed of three bioclimatic areas: Plains, Ardennes and Vosges, which have not been equally affected by bark beetle attacks. The plains were the most affected, with 50% of spruce forests destroyed, followed by the Ardennes, which lost 11.3% of its spruce stands. The Vosges was the least affected bioclimatic area, with 5.6% of spruce stands lost. For the most problematic sites, Norway spruce forestry should no longer be considered.

Weight, temperature and humidity sensor data of honey bee colonies in Germany, 2019-2022

Humans have kept honeybees as livestock to harvest honey, wax and other products for thousands of years and still continue doing so. Today however, beekeepers in many parts of the world report unprecedented high numbers of colony losses. Sensor data from honey bee colonies can contribute to new insights about development and health factors for honey bee colonies. The data can be incorporated in smart decision support systems and warning tools for beekeepers. In this paper, we present sensor data from 78 honey bee colonies in Germany collected as part of a citizen science project. Each honey bee hive was equipped with five temperature sensors within the hive, one temperature sensor for outside measurements, a combined sensor for temperature, ambient air pressure and humidity, and a scale to measure the weight. During the data acquisition period, beekeepers used a web app to report their observations and beekeeping activities. We provide the raw data with a measurement interval of up to 5 s as well as aggregated data, with per minute, hourly or daily average values. Furthermore, we performed several preprocessing steps, removing outliers with a threshold based approach, excluding changes in weight that were induced by beekeeping activities and combining the sensor data with the most important meta-data from the beekeepers' observations. The data is organised in directories based on the year of recording. Alternatively, we provide subsets of the data structured based on the occurrence or non-occurrence of a swarming event or the death of a colony. The data can be analysed using methods from time series analysis, time series classification or other data science approaches to form a better understanding of specifics in the development of honey bee colonies.

Association between gestational exposure to solar activity and pregnancy loss using live births from a Massachusetts-based medical center

BACKGROUND

Solar activity has been linked to biological mechanisms important to pregnancy, including folate and melatonin levels and inflammatory markers. Thus, we aimed to investigate the association between gestational solar activity and pregnancy loss.

METHODS

Our study included 71,963 singleton births conceived in 2002-2016 and delivered at an academic medical center in Eastern Massachusetts. We studied several solar activity metrics, including sunspot number, Kp index, and ultraviolet radiation, with data from the NASA Goddard Space Flight Center and European Centre for Medium-Range Weather Forecasts. We used a novel time series analytic approach to investigate associations between each metric from conception through 24 weeks of gestation and the number of live birth-identified conceptions (LBICs) -the total number of conceptions in each week that result in a live birth. This approach fits distributed lag models to data on LBICs, adjusted for time trends, and allows us to infer associations between pregnancy exposure and pregnancy loss.

RESULTS

Overall, the association between solar activity during pregnancy and pregnancy loss varied by exposure metric. For sunspot number, we found that an interquartile range increase in sunspot number (78·7 sunspots) in all of the first 24 weeks of pregnancy was associated with 14·0 (95% CI: 6·5, 21·3) more pregnancy losses out of the average 92 LBICs in a week, and exposure in weeks ten through thirteen was identified as a critical window. Although not statistically significant, higher exposure to Kp index and to UV radiation across all 24 weeks of pregnancy was associated with more and less pregnancy losses, respectively.

CONCLUSION

While exposure to certain metrics of solar activity (i.e., sunspot number) throughout the first 24 weeks of pregnancy may be associated with pregnancy losses, exposure to other metrics were not. Solar activity is a complex phenomenon, and more studies are needed to clarify underlying pathways.

Steep declines in radioactive caesium after 30 years of monitoring alpine plants in mountain areas of central Norway

The Chernobyl accident exposed large areas of northern Europe to radiocaesium (137Cs). We investigated temporal and spatial variation in concentrations of radiocaesium among five functional groups of alpine plants at two mountain areas in central Norway over a 31-year period from 1991 to 2022. Average concentrations of radiocaesium were initially high in lichens and bryophytes at around 4600-6400 Bq/kg dry weight during 1991-1994 but then decreased dramatically over three decades to current concentrations of <200 Bq/kg for all plant groups in 2019-2022. The effective half-life of radiocaesium was estimated to be 4-6 years in lichens and mosses, 7-13 years in herbaceous plants, and 22-30 years in woody plants, which were less than the physical half-life of 30.2 years. Concentrations of radiocaesium were greater at the nutrient-poor site than at the nutrient-rich site, probably due to greater deposition levels at higher elevations and the geographical pattern of the deposition. Functional groups of plants differed with higher concentrations among non-vascular than vascular plants. Common heather Calluna vulgaris was unusual among woody plants with high concentration of radiocaesium, especially in the new shoots. Our new estimates of concentrations and dynamics of radiocaesium for alpine plants in natural environments will be useful for modelling herbivore exposure and evaluating potential impacts on wildlife and human health.

Modeling spatiotemporal domestic wastewater variability: Implications for measuring treatment efficiency

Continuously measuring the efficiency of wastewater treatment plants is crucial to progress in sanitation management. Regulations for decentralized wastewater treatment plants (WWTP) can include rudimentary specifications for sporadic sampling, unencouraging continuous monitoring, and missing crucial domestic wastewater (DW) variability, especially in low- and middle-income countries. However, few studies have focused on modeling and understanding spatiotemporal DW variability. We developed and calibrated an agent-based model (ABM) to understand spatial and temporal DW variability, its role in estimated WWTP efficiency, and provide recommendations to improve sampling regulations. We simulated DW variability at various spatial and temporal resolutions in Santa Ana Atzcapotzaltongo, Mexico, focusing on chemical oxygen demand (COD) and total suspended solids (TSS). The model results show that DW variability increases at higher spatiotemporal resolutions. Without a proper understanding of DW variability, treatment efficiency can be overestimated or underestimated by as much as 25% from sporadic sampling. Sensor measurements at 6-min intervals over 3 hours are recommended to overcome uncertainty resulting from temporal variability during heavy drinking water demand in the morning. Reporting of sewage catchment areas, population sizes, and sampling times and intervals is recommended to compare WWTP efficiencies to overcome uncertainty resulting from spatiotemporal variability. The proposed model is a useful tool for understanding DW variability. It can be used to estimate the impact of spatiotemporal variability when measuring WWTP efficiencies, support improvements to sampling regulations for decentralized sanitation, and alternatively for designing and operating WWTPs.

Does the 80 km/h speed limit save lives in France?

INTRODUCTION

Speeding is considered to be a major contributor to road fatalities and injuries worldwide. Inappropriate speeding behavior is associated with a high casualty burden. It could be responsible for at least 30% of road accidents.

METHOD

In 2018, the French authorities decided to introduce a new speed limit. They lowered the speed limit to 80 km/h on the unseparated interurban network. The aim was to reduce the number of fatalities and injuries and to implement some measures in line with international commitments. This paper uses different econometric models applied to time series for different groups of counties.

RESULTS

The results show a significant positive contribution of the new speed limit. The estimated number of lives saved is between 300 and 350. The overall reduction in the number of fatalities is 10%. The results also show a differentiated impact according to the local context and the different dynamics at play.

CONCLUSIONS AND PRACTICAL APPLICATIONS

The results of this paper are in line with the scientific literature on speed limit reductions. They represent a validation of a debated public decision, while at the same time consolidating the body of knowledge on the subject, helping the decision-maker to adopt an appropriate measure to improve road safety performance.

Identifying the spatial pattern and the drivers of the decline in the eastern English Channel chlorophyll-a surface concentration over the last two decades

It has been established from previous studies that chlorophyll-a surface concentration has been declining in the eastern English Channel. This decline has been attributed to a decrease in nutrient concentrations in the rivers. However, the decrease in river discharge could also be a cause. In our study, rivers outflows and in-situ data have been compared to time series of satellite-derived chlorophyll-a concentrations. Dynamic Linear Model has been used to extract the dynamic and seasonally adjusted trends of several environmental variables. The results showed that, for the 1998-2019 period, chlorophyll-a levels stayed significantly lower than average and satellite images revealed a coast to offshore gradient. Chlorophyll-a concentration of coastal stations appeared to be related to the declining fluxes of phosphate while offshore stations were more related to nitrate-nitrite. Therefore, we can exclude that the climate variability, through river flows alone, has a dominant effect on the decline of chlorophyll-a concentration.

Predicting COVID-19 hospitalizations: The importance of healthcare hotlines, test positivity rates and vaccination coverage

In this study, we developed a negative binomial regression model for one-week ahead spatio-temporal predictions of the number of COVID-19 hospitalizations in Uppsala County, Sweden. Our model utilized weekly aggregated data on testing, vaccination, and calls to the national healthcare hotline. Variable importance analysis revealed that calls to the national healthcare hotline were the most important contributor to prediction performance when predicting COVID-19 hospitalizations. Our results support the importance of early testing, systematic registration of test results, and the value of healthcare hotline data in predicting hospitalizations. The proposed models may be applied to studies modeling hospitalizations of other viral respiratory infections in space and time assuming count data are overdispersed. Our suggested variable importance analysis enables the calculation of the effects on the predictive performance of each covariate. This can inform decisions about which types of data should be prioritized, thereby facilitating the allocation of healthcare resources.

Comparative assessment of synthetic time series generation approaches in healthcare: leveraging patient metadata for accurate data synthesis

BACKGROUND

Synthetic data is an emerging approach for addressing legal and regulatory concerns in biomedical research that deals with personal and clinical data, whether as a single tool or through its combination with other privacy enhancing technologies. Generating uncompromised synthetic data could significantly benefit external researchers performing secondary analyses by providing unlimited access to information while fulfilling pertinent regulations. However, the original data to be synthesized (e.g., data acquired in Living Labs) may consist of subjects' metadata (static) and a longitudinal component (set of time-dependent measurements), making it challenging to produce coherent synthetic counterparts.

METHODS

Three synthetic time series generation approaches were defined and compared in this work: only generating the metadata and coupling it with the real time series from the original data (A1), generating both metadata and time series separately to join them afterwards (A2), and jointly generating both metadata and time series (A3). The comparative assessment of the three approaches was carried out using two different synthetic data generation models: the Wasserstein GAN with Gradient Penalty (WGAN-GP) and the DöppelGANger (DGAN). The experiments were performed with three different healthcare-related longitudinal datasets: Treadmill Maximal Effort Test (TMET) measurements from the University of Malaga (1), a hypotension subset derived from the MIMIC-III v1.4 database (2), and a lifelogging dataset named PMData (3).

RESULTS

Three pivotal dimensions were assessed on the generated synthetic data: resemblance to the original data (1), utility (2), and privacy level (3). The optimal approach fluctuates based on the assessed dimension and metric.

CONCLUSION

The initial characteristics of the datasets to be synthesized play a crucial role in determining the best approach. Coupling synthetic metadata with real time series (A1), as well as jointly generating synthetic time series and metadata (A3), are both competitive methods, while separately generating time series and metadata (A2) appears to perform more poorly overall.

Temperature-related emergency injury visits in Hanoi, Vietnam

BACKGROUND

The short-term association between increasing temperatures and injury has been described in high-income countries, but less is known for low-income and-middle-income countries, including Vietnam.

METHODS

We used emergency injury visits (EIV) data for 2017-2019 from 733 hospitals and clinics in Hanoi, Vietnam to examine the effects of daily temperature on EIV. Time-series analysis with quasi-Poisson models was used to estimate a linear relative risk increase (RRI) for overall populations and ones stratified by age and sex. Exposure-response curves estimated non-linear associations as an RR between daily temperature and injury. Models were adjusted for the day of week, holidays, daily relative humidity, daily particulate matter, and long-term and seasonal trends.

RESULTS AND CONCLUSIONS

A total of 39 313 EIV were recorded averaging 36 injuries daily. Injuries more likely occurred in males and those aged 15-44, and aged 44-60. For linear effects, a 5°C increase in same day mean temperature was associated with an overall increased EIV (RRI 4.8; 95% CI 2.3 to 7.3) with males (RRI 5.9; 95% CI 3.0 to 8.9) experiencing a greater effect than females (RRI 3.0; 95% CI -0.5 to 6.5). Non-linear effects showed an increase in EIV at higher temperatures compared with the threshold temperature of 15°C, with the greatest effect at 33°C (RR 1.3; 95% CI 1.2 to 1.6). Further research to investigate temperature-injury among different populations and by the cause of injury is warranted.

Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates

BACKGROUND

Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research.

RESULTS

The infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575.

CONCLUSIONS

We propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.

The impact of fentanyl on state- and county-level psychostimulant and cocaine overdose death rates by race in Ohio from 2010 to 2020: a time series and spatiotemporal analysis

BACKGROUND

Over the past decade in the USA, increases in overdose rates of cocaine and psychostimulants with opioids were highest among Black, compared to White, populations. Whether fentanyl has contributed to the rise in cocaine and psychostimulant overdoses in Ohio is unknown. We sought to measure the impact of fentanyl on cocaine and psychostimulant overdose death rates by race in Ohio.

METHODS

We conducted time series and spatiotemporal analyses using data from the Ohio Public Health Information Warehouse. Primary outcomes were state- and county-level overdose death rates from 2010 to 2020 for Black and White populations. Measures of interest were overdoses consisting of four drug involvement classes: (1) all cocaine overdoses, (2) cocaine overdoses not involving fentanyl, (3) all psychostimulant overdoses, and (4) psychostimulant overdoses not involving fentanyl. We fit a time series model of log standardized mortality ratios (SMRs) using a Bayesian generalized linear mixed model to estimate posterior median rate ratios (RR). We conducted a spatiotemporal analysis by modeling the SMR for each drug class at the county level to characterize county-level variation over time.

RESULTS

In 2020, the greatest overdose rates involved cocaine among Black (24.8 deaths/100,000 people) and psychostimulants among White (10.1 deaths/100,000 people) populations. Annual mortality rate ratios were highest for psychostimulant-involved overdoses among Black (aRR = 1.71; 95% CI (1.43, 2.02)) and White (aRR = 1.60, 95% CI (1.39, 1.80)) populations. For cocaine not involving fentanyl, annual mortality rate ratios were similar among Black (aRR = 1.04; 95% CI (0.96,1.16)) and White (aRR = 1.02; 95% CI (0.87, 1.20)) populations. Within each drug category, change over time was similar for both racial groups. The spatial models highlighted county-level variation for all drug categories.

CONCLUSIONS

Without the involvement of fentanyl, cocaine overdoses remained constant while psychostimulant overdoses increased. Tailored harm reduction approaches, such as distribution of fentanyl test strips and the removal of punitive laws that influence decisions to contact emergency services, are the first steps to reduce cocaine overdose rates involving fentanyl among urban populations in Ohio. In parallel, harm reduction policies to address the increase in psychostimulant overdoses are warranted.

Population adaptation to heat as seen through the temperature-mortality relationship, in the context of the impact of global warming on health: A scoping review

Climate change is the greatest threat to human health, with one of its direct effects being global warming and its impact on health. Currently, the world is experiencing an increase in the mean global temperature, but this increase affects different populations to different degrees. This is due to the fact that individual, demographic, geographical and social factors influence vulnerability and the capacity to adapt. Adaptation is the process of adjusting to the current or envisaged climate and its effects, with the aim of mitigating harm and taking advantage of the beneficial opportunities. There are different ways of measuring the effectiveness of adaptation, and the most representative indicator is via the time trend in the temperature-mortality relationship. Despite the rise in the number of studies that have examined the temperature-mortality relationship in recent years, there are very few that have analysed whether a particular population has or has not adapted to heat. We conducted a scoping review that met the following criteria, namely: including all persons; considering the heat adaptation concept; and covering the context of the impact of global warming on health and mortality. A total of 23 studies were selected. This review found very few studies targeting adaptation to heat in the human population and a limited number of countries carrying out research in this field, something that highlights the lack of research in this area. It is therefore crucial for political decision-makers to support studies that serve to enhance our comprehension of long-term adaptation to heat and its impact on the health of the human population.

What's next? Forecasting scientific research trends

Scientific research trends and interests evolve over time. The ability to identify and forecast these trends is vital for educational institutions, practitioners, investors, and funding organizations. In this study, we predict future trends in scientific publications using heterogeneous sources, including historical publication time series from PubMed, research and review articles, pre-trained language models, and patents. We demonstrate that scientific topic popularity levels and changes (trends) can be predicted five years in advance across 40 years and 125 diverse topics, including life-science concepts, biomedical, anatomy, and other science, technology, and engineering topics. Preceding publications and future patents are leading indicators for emerging scientific topics. We find the ratio of reviews to original research articles informative for identifying increasing or declining topics, with declining topics having an excess of reviews. We find that language models provide improved insights and predictions into temporal dynamics. In temporal validation, our models substantially outperform the historical baseline. Our findings suggest that similar dynamics apply across other scientific and engineering research topics. We present SciTrends, a user-friendly webtool for predicting future publication trends for any topic covered in PubMed.

Heat wave exposure and increased heat-related hospitalizations in young children in South Korea: A time-series study

BACKGROUND

Numerous studies have investigated the association between heat wave exposure increased heat-related hospitalizations in the general population. However, little is known about heat-related morbidity in young children who are more vulnerable than the general population. Therefore, we aimed to evaluate the association between hospitalization for heat-related illness in children and heat wave exposure in South Korea.

METHODS

We used the National Health Insurance Service (NHIS) database, which provides medical records from 2015 to 2019 in South Korea. We defined daily hospitalizations for heat-related illness of children younger than five years during the summer period (June to August). We considered the definition of heat waves considering the absolute temperature and percentile. A total of 12 different heat waves were used. A time-series analysis was used to investigate the association between heat wave exposure and heat-related hospitalization among children younger than five years. We used a two-stage design involving a meta-analysis after modeling by each region.

RESULTS

We included 16,879 daily heat-related hospitalizations among children younger than five years. Overall, heat wave exposure within two days was most related for heat-related hospitalizations in young children. The relative risk (RR) due to heat wave exposure within two days (lag2) (12 definitions: 70th to 90th percentile of maximum temperature) ranged from 1.038 (95% confidence interval (CI): 0.971, 1.110) to 1.083 (95% CI: 1.036, 1.133). We found that boys were more vulnerable to heat exposure than girls. In addition, we found that urban areas were more vulnerable to heat exposure than rural areas.

CONCLUSIONS

In our study, heat wave exposure during summer was found to be associated with an increased risk of hospitalization for heat-related illness among children younger than five years. Our findings suggest the need for summer heat wave management and prevention for children.

An improved equation of latitude and a global system of graticule distance coordinates

Two innovations are presented for coordinate time-series computation. First, an improved solution is given to a century-old problem, that is the non-iterative computation of conventional geodetic (CG: latitude, longitude, height) coordinates from geocentric Cartesian (GC: x, y, z) coordinates. The accuracy is 1 nm for heights < 500 km and < 10-15 rad for latitude at any point, terrestrial or outer space. This can be 3 orders of magnitude more accurate than published non-iterative methods. Secondly, CG time series are transformed into a practical system of "graticule distance" (GD: easting, northing, height) curvilinear coordinates that, unlike the commonly used system of topocentric Cartesian (TC: east, north, up) coordinates, is global in nature without arbitrary specification of GC reference coordinates for every geodetic station. Since 2011, Nevada Geodetic Laboratory has publicly produced time series for 20,000 GPS stations in GD form that have been cited by hundreds of studies. The GD system facilitates direct comparison of positions for co-located stations. Users of GD time series are able: (1) to resolve different historical station names that have been assigned to the same physical benchmark and (2) to resolve different physical benchmarks that have been assigned the same name. This benefits historical reconstruction of benchmark occupation and local site tie analysis for reference frame integrity. GD coordinates have archival value, in that inversion back to GC coordinates is practically exact. For geodetic stations, GD time series closely emulate TC time series with rates agreeing to 0.01 mm/yr, and so can be used interchangeably.

Respiratory and nervous system effects of a hydrogen sulfide crisis in Carson, California

BACKGROUND

In October 2021, many residents in Carson, California experienced malodors, headaches, and respiratory symptoms. Hydrogen sulfide (H2S), a toxic odorous gas, was measured in Carson at concentrations up to 7000 parts per billion (ppb) and remained above California's acute air quality standard of 30 ppb for about a month. Research on how low- and medium-level H2S exposure affects the respiratory and nervous systems has yielded conflicting results, and few studies have examined the effects of subacute H2S exposure.

METHODS

We calculated daily rates of emergency department (ED) visits with various respiratory and nervous systems diagnosis codes in Carson area ZIP codes (≤6 km from event's epicenter) and in Los Angeles County ZIP codes >15 km from event's epicenter (control area). Using controlled interrupted time series, we compared ED visit rates during the month of the H2S crisis in Carson to the predicted rates had the incident not occurred, based on 2018-2021 ED trends, and controlling for ED visit rate changes in the control area.

RESULTS

We observed a 24 % increase in ED visit rate for all respiratory system diseases (rate ratio = 1.24, 95 % CI: 1.16, 1.32), a 38 % increase for asthma (RR = 1.38, 95 % CI: 1.26, 1.50), a 26 % increase for acute upper respiratory infections (RR = 1.26, 95 % CI: 1.13, 1.38), a 21 % increase for dizziness (RR = 1.21, 95 % CI: 1.04, 1.38), and a 25 % increase for migraines and headaches (RR = 1.25, 95 % CI: 1.13, 1.36) in the Carson area during the first month of the H2S event compared to the expected rates.

CONCLUSIONS

This H2S crisis was associated with increased ED visit rates for multiple respiratory and nervous system outcomes. Reducing H2S exposure and improving to response during H2S episodes may improve public health.

The effect of temporal data aggregation to assess the impact of changing temperatures in Europe: an epidemiological modelling study

Background

Daily time-series regression models are commonly used to estimate the lagged nonlinear relation between temperature and mortality. A major impediment to this type of analysis is the restricted access to daily health records. The use of weekly and monthly data represents a possible solution unexplored to date.

Methods

We temporally aggregated daily temperatures and mortality records from 147 contiguous regions in 16 European countries, representing their entire population of over 400 million people. We estimated temperature-lag-mortality relationships by using standard time-series quasi-Poisson regression models applied to daily data, and compared the results with those obtained with different degrees of temporal aggregation.

Findings

We observed progressively larger differences in the epidemiological estimates with the degree of temporal data aggregation. The daily data model estimated an annual cold and heat-related mortality of 290,104 (213,745-359,636) and 39,434 (30,782-47,084) deaths, respectively, and the weekly model underestimated these numbers by 8.56% and 21.56%. Importantly, differences were systematically smaller during extreme cold and heat periods, such as the summer of 2003, with an underestimation of only 4.62% in the weekly data model. We applied this framework to infer that the heat-related mortality burden during the year 2022 in Europe may have exceeded the 70,000 deaths.

Interpretation

The present work represents a first reference study validating the use of weekly time series as an approximation to the short-term effects of cold and heat on human mortality. This approach can be adopted to complement access-restricted data networks, and facilitate data access for research, translation and policy-making.

Funding

The study was supported by the ERC Consolidator Grant EARLY-ADAPT (https://www.early-adapt.eu/), and the ERC Proof-of-Concept Grants HHS-EWS and FORECAST-AIR.

Low level of dengue infection and transmission risk in Hong Kong: an integrated analysis of temporal seroprevalence results and corresponding meteorological data

Hong Kong is an Asia-Pacific City with low incidence but periodic local outbreaks of dengue. A mixed-method assessment of the risk of expansion of dengue endemicity in such setting was conducted. Archived blood samples of healthy adult blood donors were tested for anti-dengue virus IgG at 2 time-points of 2014 and 2018/2019. Data on the monthly notified dengue cases, meteorological and vector (ovitrap index) variables were collected. The dengue virus (DENV) IgG seroprevalence of healthy adults in 2014 was 2.2% (95%C.I. = 1.8-2.8%, n = 3827) whereas that in 2018/2019 was 1.7% (95%C.I. = 1.2-2.3%, n = 2320). Serotyping on 42 sera in 2018/2019 showed that 22 (52.4%) were DENV-2. In 2002-2019, importation accounted for 95.3% of all reported cases. By wavelet analysis, local cases were in weak or no association with meteorological and vector variables. Without strong association between local cases and meteorological/vector variables, there was no evidence of increasing level of dengue infection in Hong Kong.

Association between temperature and mortality: a multi-city time series study in Sichuan Basin, southwest China

BACKGROUND

There are few multi-city studies on the association between temperature and mortality in basin climates. This study was based on the Sichuan Basin in southwest China to assess the association of basin temperature with non-accidental mortality in the population and with the temperature-related mortality burden.

METHODS

Daily mortality data, meteorological and air pollution data were collected for four cities in the Sichuan Basin of southwest China. We used a two-stage time-series analysis to quantify the association between temperature and non-accidental mortality in each city, and a multivariate meta-analysis was performed to obtain the overall cumulative risk. The attributable fractions (AFs) were calculated to access the mortality burden attributable to non-optimal temperature. Additionally, we performed a stratified analyses by gender, age group, education level, and marital status.

RESULTS

A total of 751,930 non-accidental deaths were collected in our study. Overall, 10.16% of non-accidental deaths could be attributed to non-optimal temperatures. A majority of temperature-related non-accidental deaths were caused by low temperature, accounting for 9.10% (95% eCI: 5.50%, 12.19%), and heat effects accounted for only 1.06% (95% eCI: 0.76%, 1.33%). The mortality burden attributable to non-optimal temperatures was higher among those under 65 years old, females, those with a low education level, and those with an alternative marriage status.

CONCLUSIONS

Our study suggested that a significant association between non-optimal temperature and non-accidental mortality. Those under 65 years old, females, and those with a low educational level or alternative marriage status had the highest attributable burden.

Fractal Time Series: Background, Estimation Methods, and Performances

Over the past 40 years, from its classical application in the characterization of geometrical objects, fractal analysis has been progressively applied to study time series in several different disciplines. In neuroscience, starting from identifying the fractal properties of neuronal and brain architecture, attention has shifted to evaluating brain signals in the time domain. Classical linear methods applied to analyzing neurophysiological signals can lead to classifying irregular components as noise, with a potential loss of information. Thus, characterizing fractal properties, namely, self-similarity, scale invariance, and fractal dimension (FD), can provide relevant information on these signals in physiological and pathological conditions. Several methods have been proposed to estimate the fractal properties of these neurophysiological signals. However, the effects of signal characteristics (e.g., its stationarity) and other signal parameters, such as sampling frequency, amplitude, and noise level, have partially been tested. In this chapter, we first outline the main properties of fractals in the domain of space (fractal geometry) and time (fractal time series). Then, after providing an overview of the available methods to estimate the FD, we test them on synthetic time series (STS) with different sampling frequencies, signal amplitudes, and noise levels. Finally, we describe and discuss the performances of each method and the effect of signal parameters on the accuracy of FD estimation.

Exploring soil radon (Rn) concentrations and their connection to geological and meteorological factors

The relationship between soil radon and meteorological parameters in a region can provide insight into natural processes occurring between the lithosphere and the atmosphere. Understanding this relationship can help models establish more realistic results, rather than depending on theoretical consequences. Radon variation can be complicated to model due to the various physical variables which can affect it, posing a limitation in atmospheric studies. To predict Rn variation from meteorological parameters, a hybrid mod el called multiANN, which is a combination of multi-regression and artificial neural network (ANN) models, is established. The model was trained with 70% of the data and tested on the remaining 30%, and its robustness was tested using the Monte-Carlo method. The regions with low performance are identified and possibly related to seismic events. This model can be a good candidate for predicting Rn concentrations from meteorological parameters and establishing the lower boundary conditions in seismo-ionospheric coupling models.

Stochasticity-driven weekly fluctuations distinguished the temporal pattern of particle-associated microorganisms from its free-living counterparts in temperate coastal seawater

Microbial community dynamics directly determine their ecosystem functioning. Despite the well-known annual recurrence pattern, little is known how different lifestyles affect the temporal variation and how community assembly mechanisms change over different temporal scales. Here, through a high-resolution observation of size fractionated samples over 60 consecutive weeks, we investigate the distinction in weekly distribution pattern and assembly mechanism between free-living (FL) and particle-associated (PA) communities in highly dynamic coastal environments. A clear pattern of annual recurrence was observed, which was more pronounced in FL compared to PA, resulting in higher temporal specificity in the former samples. Both the two size fractions displayed significant temporal distance-decay patterns, yet the PA community showed a higher magnitude of community variation between adjacent weeks, likely caused by sudden, drastic and long-lived blooms of heterotrophic bacteria. Generally, determinism (environmental selection) had a greater effect on the community assembly than stochasticity (random birth, death, and dispersal events), with significant contributions from temperature and inorganic nutrients. However, a clear shift in the temporal assembly pattern was observed, transitioning from a prevalence of stochastic processes driving short-term (within a month) fluctuations to a dominance of deterministic processes over longer time intervals. Between adjacent weeks, stochasticity was more important in the community assembly of PA than FL. This study revealed that stochastic processes can lead to rapid, dramatic and irregular PA community fluctuations, indicating weak resistance and resilience to disturbances, which considering the role of PA microbes in carbon processing would significantly affect the coastal carbon cycle. Our results provided a new insight into the microbial community assembly mechanisms in the temporal dimension.

Covariate-guided Bayesian mixture of spline experts for the analysis of multivariate high-density longitudinal data

With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging data play an important role in the advancement of science. Imaging data that measure brain function are usually multivariate high-density longitudinal data and are heterogeneous across both imaging sources and subjects, which lead to various statistical and computational challenges. In this article, we propose a group-based method to cluster a collection of multivariate high-density longitudinal data via a Bayesian mixture of smoothing splines. Our method assumes each multivariate high-density longitudinal trajectory is a mixture of multiple components with different mixing weights. Time-independent covariates are assumed to be associated with the mixture components and are incorporated via logistic weights of a mixture-of-experts model. We formulate this approach under a fully Bayesian framework using Gibbs sampling where the number of components is selected based on a deviance information criterion. The proposed method is compared to existing methods via simulation studies and is applied to a study on functional near-infrared spectroscopy, which aims to understand infant emotional reactivity and recovery from stress. The results reveal distinct patterns of brain activity, as well as associations between these patterns and selected covariates.