Search Results (207)

Small lakes (<10 km²) provide a range of ecosystem services but are often overlooked in both monitoring efforts and limnological studies. Remote sensing has been increasingly used to complement in situ monitoring or to provide water colour data for unmonitored inland water bodies. However, due to spatial, radiometric, and spectral constraints, it has been heavily focused on large lakes. Sentinel-2 MSI is the first sensor with the capability to consistently retrieve a wide range of essential water quality variables, such as chlorophyll-a concentration (chl-a) and water transparency, in small water bodies, and to provide long time series. Here, we provide and validate a framework for retrieving two variables, chl-a and turbidity, over lakes with diverse optical characteristics using Sentinel-2 imagery. It is based on GRS for atmospheric and sun glint correction, WaterDetect for water detection, and inversion models that were automatically selected based on two different sets of optical water types (OWTs)—one for each variable; for chl-a, we produced a blended product for improved spatial representation. To validate the approach, we compared the products with more than 600 in situ data from 108 lakes located in the Adour–Garonne river basins, ranging from 3 to ∼5000 ha, as well as remote sensing reflectance (Rrs) data collected during 10 field campaigns during the summer and spring seasons. Rrs retrieval (n = 65) was robust for bands 2 to 5, with MAPE varying from 15 to 32% and achieving correlation from 0.74 up to 0.92. For bands 6 to 8A, the Rrs retrieval was much less accurate, being influenced by adjacency effects. Glint removal significantly enhanced Rrs accuracy, with RMSE improving from 0.0067 to 0.0021 sr⁻¹ for band 4, for example. Water quality retrieval showed consistent results, with an MAPE of 56%, an RMSE of 11.4 mg m⁻³, and an r of 0.76 for chl-a, and an MAPE of 47%, an RMSE of 9.7 NTU, and an r of 0.87 for turbidity, and no significant effect of lake area or lake depth on retrieval errors. The temporal and spatial representations of the selected parameters were also shown to be consistent, demonstrating that the framework is robust and can be applied over lakes as small as 3 ha. The validated methods can be applied to retrieve time series of chl-a and turbidity starting from 2016 and with a frequency of up to 5 days, largely expanding the database collected by water agencies. This dataset will be extremely useful for studying the dynamics of these small freshwater ecosystems. Full article

Background: Starting in early 2022, SARS-CoV-2 Omicron has driven large outbreaks in China, a predominantly infection-naive population with high inactivated vaccine coverage. This unique context provided a substantially less-confounded opportunity to evaluate how vaccination, public health, and social measures influenced severity. Methods: We systematically reviewed 86 studies (224 severity estimates) published from 2022 to 2024, reporting symptom and clinical severity outcomes (fever, cough, and sore throat; symptomatic, severe/critical, and fatal illness) of Omicron infections in China. Using meta-regression, we evaluated the associations of study setting, age group, vaccination status, predominant subvariants, and Oxford COVID-19 Government Response Tracker (OxCGRT) indices, including the Government Response Index (GRI), Containment and Health Index (CHI), and the Stringency Index (SI), with infection outcomes, adjusting for key confounders. Results: We found the primary or booster series of inactivated vaccines conferred strong protection against severe/critical illness (pooled relative risk (RR) 0.17 [95% CI: 0.09–0.33]) but did not reduce symptom frequency (RR 0.99 [95% CI: 0.95–1.02]). Each 10-unit increase in GRI or CHI was associated with 7% (95% CI: 1–12%) and 6% (95% CI: 1–10%) lower odds of symptomatic infection and 3% (95% CI: 1–4%) lower odds of severe/critical illness. Later subvariants (BA.5, BF.7, and XBB) showed 24–38% higher odds of upper respiratory symptoms versus BA.1. Conclusions: The data collection context significantly impacted severity estimates, with higher estimates from emergency hospitals. Overall, inactivated vaccines provided strong protection against severe/critical outcomes while stringent public health measures were associated with lower severity. Our findings underscore the importance of consistent and standardized protocols to produce reliable estimates of SARS-CoV-2 severity in evolving epidemiological contexts. Full article

Background/Objectives: The HPV vaccine is highly effective and safe in preventing HPV infection. This study explored the relationship between HPV vaccination, HPV knowledge and awareness, and oral HPV infection prevalence among Indigenous Australian adults. Methods: Data were collected from a large convenience sample in South Australia in 2018–19, with annual follow-ups through 2022–23. The primary outcome was oral infection with HPV types 6, 11, 16, 18, 31, 33, 45, 52, or 58. The main exposure was HPV vaccination uptake status, which was categorised as unvaccinated, partially vaccinated (1–2 doses), or fully vaccinated (3 doses). Covariates included sociodemographic factors, general and sexual health behaviours, and HPV knowledge scores (HPV-KT). Risk ratios (RRs) for oral HPV infection were estimated using Poisson regression models. Results: Among the 1006 participants who completed at least one questionnaire and oral HPV test by 24 months, 81% were unvaccinated, 13% partially vaccinated, and 7% fully vaccinated. Fully vaccinated individuals had the highest HPV-KT scores (mean: 3.4) and the lowest oral HPV prevalence (5%). After adjusting for covariates, unvaccinated participants had a 1.08 times higher risk of oral HPV infection (95% CI: 1.00–3.11) compared to those fully vaccinated. Conclusions: Full HPV vaccination (three doses) is associated with lower oral HPV infection and greater HPV knowledge. The protective effect appears stronger than for partial vaccination or no vaccination, underscoring the importance of completing the full vaccine series to reduce oral HPV burden. Full article

Background: The occurrence (incidence or prevalence) of delirium in brain tumors is unknown, yet delirium is associated with increased morbidity and mortality and worse quality of life. We conducted a systematic review and meta-analysis to determine the occurrence of delirium in hospitalized patients with brain tumors. Methods: PubMed, Scopus, and Web of Science were systematically searched for papers from 1 January 1999 to 12 July 2024, including references from texts. Cross-sectional, prospective, and other cohort study designs were included, and individual case reports, case series, editorials, and reviews were excluded. The included papers were scored using a validated sensitivity analysis tool and tested for quality and bias using funnel plots and Egger’s test. We used random effects models for the summary estimates. We performed subgroup analyses by tumor type, tumor location, delirium subtype, and length of stay. Results: Of the 452 studies screened, 27 were included, representing 35,958 patients. The overall occurrence of delirium was 0.17 (95% CI [0.11–0.24]). Delirium occurrence in patients with low-grade gliomas, high-grade gliomas, and brain metastases was 0.10 [0.06–0.16], 0.21 [0.10–0.40], and 0.31 [0.16–0.50], respectively. Compared to the occipital lobe, there was a higher occurrence of delirium for tumors in the frontal (RR 3.08 [1.35–8.22]) and temporal lobes (RR 2.88 [1.22–7.93]). The patients were more likely to have hypoactive (RR 1.61 [1.30; 1.98]) than hyperactive delirium. Delirium was associated with 4.62 additional hospitalized days compared to those without delirium (CI [3.23–6.01]). Discussion: We confirmed high occurrence rates of delirium in patients hospitalized with brain tumors. Patients with brain metastases had a higher occurrence of delirium compared to patients with gliomas, and delirium occurrence rates were higher in patients with frontotemporal tumors. Delirium occurrence rates in the literature are very heterogeneous and point toward a need for tailored assessments in patients with brain tumors. Full article

With the increase of CO₂ emissions caused by human activities, the development of efficient CO₂ reduction technology is crucial to help address the energy crisis and mitigate climate change. In this study, a series of Cu₃P/SnS₂ composites with varying Cu/Sn molar ratios were synthesized using a hydrothermal method to improve the activity and selectivity of the electrocatalytic reduction of CO₂ (CO₂RR). The successful synthesis and structural advantages of the composite were verified via XRD, XPS, SEM, TEM, and BET. Cu₃P/SnS₂-3 (Cu/Sn = 2:1) had the largest specific surface area (78.01 m² g⁻¹) and abundant active sites. The electrochemical performance test showed that in 0.1 M KHCO₃ electrolyte saturated with CO₂, the Faraday efficiency of Cu₃P/SnS₂-3 to CO reached 87% at −1.0 V potential, which was 29 times and 1.78 times higher than that of Cu₃P (3%) and SnS₂ (48.88%). In addition, the catalyst maintained a CO Faraday efficiency of more than 75% in a 5 h stability test. The mechanism study shows that the low Tafel slope, low charge transfer resistance, and high electrochemically active area of the composite significantly promote the CO₂RR kinetics. Full article

The influence of gameplay on autonomic nervous system activity was investigated by comparing electrocardiogram (ECG) data during seated rest and gameplay. A total of 13 participants (6 in the gameplay group and 7 in the control group) were analyzed. RR interval time series (2 Hz) and heart-rate variability (HRV) indices, including mean RR, SDRR, VLF, LF, HF, LF/HF, and HF peak frequency, were extracted from ECG signals over 5 min and 10 min segments. HRV indices were calculated using fast Fourier transform (FFT). The classification was performed using Logistic Regression (LGR), Random Forest (RF), XGBoost (XGB, v2.9.2), One-Class SVM (OCS), Isolation Forest (ILF), and Local Outlier Factor (LOF). A balanced dataset of 5 min and 10 min segments was evaluated using k-fold cross-validation (k = 3, 4, 5). Performance metrics, including recall, F-score, and PR-AUC, were computed for each classifier. Grid search was applied to optimize parameters for LGR, RF, and XGB, while default settings were used for the other classifiers. Among all models, OCS with k = 3 achieved the highest classification accuracy for both 5 min and 10 min data. These findings suggest that machine learning-based classification can effectively distinguish ECG patterns between gameplay and rest. Full article

Background: Physical activity is vital for promoting health and rehabilitation, and ensuring cardiovascular safety during such activities is paramount. Electrocardiography (ECG) and its longitudinal monitoring remain crucial for the early detection of cardiac diseases. Recent advancements in nonlinear RR analysis and machine learning offer promising approaches to identifying subtle precursors of cardiac pathologies in monitoring systems using simple heart rate (HR) wearable sensors. Therefore, using HR sensors in human activity recognition (HAR) is recommendable. After defining fatigue in a cardiological context, and focusing on an AI-based methods suite for HAR, the main research question of this scoping review is as follows: “Can RR time series be successfully used as physiological biomarkers for the early detection of cardiac fatigue?” The reported data on assessment of fatigue are focused on the last two decades. The aim of this scoping review was to collect, present and discuss the existing literature on the effectiveness of AI-based methods for processing RR time series as a predictive biomarker for cardiac fatigue compared to commonly used questionnaires for this outcome in adult populations. Methods: Queries were conducted in the PubMed, Scopus and Google Scholar databases for the time period 2005–2025. Only research articles and review papers were considered suitable candidates. Results: Data from 10 papers were considered, related to the information researched. Conclusions: Information on HRV-based objective measures is quite scarce and there is an urgent need to adopt a multidisciplinary approach and to improve advanced AI-based nonlinear analyses to differentiate cardiac physiological status from cardiac pathological status. Full article

We assessed the test validity of the Apple Watch series 6 measure of heart rate variability (HRV) by comparing it with the reference measure assessed via a Biopac 3-lead electrocardiogram (ECG). We recruited 78 healthy adults (aged 20–75 years). HRV was measured using an in-lab protocol while resting, talking, watching a movie, before walking, and after walking. We conducted a synchronized countdown for each condition to guarantee that the recordings would be aligned between the two devices by using event markers in the Biopac at the exact time that the Apple Watch Breathe app began and ended. We assessed test validity using the Bland–Altman method, and both precision and accuracy were estimated using Lin’s concordance correlation coefficient. The highest level of agreement and concordance between devices occurred during rest. We observed near-perfect agreement for R-R intervals and beats per minute (BPM) measures, with mean absolute percentage errors (MAPE) of 1.15% during resting conditions. We observed moderate levels of agreement and concordance for N-N intervals at rest with a MAPE of 31.31% during resting conditions. The Apple Watch provides a high level of validity for measuring R-R intervals and BPM in healthy adults. Further research is needed to determine if HRV measures with the Apple Watch offer a significant opportunity for the surveillance of CVD risk. Full article

This paper examines extreme precipitation events (EXPEs) and their trends based on daily precipitation values observed at 14 stations in Serbia for the period 1961–2020. The following EXPEs were investigated: RR10mm (heavy precipitation days), RR20mm (very heavy precipitation days), Rx1day (highest 1-day precipitation amount), Rx3day (highest 3-day precipitation amount), Rx5day (highest 5-day precipitation amount), R95p (very wet days) and R99p (extremely wet days). A positive trend for all EXPEs was dominant in Serbia from 1961 to 2020. All annual Rx1day time series show a positive trend, which is significant at 12 out of 14 stations. The highest values of all EXPEs were observed in 2014, when the annual precipitation totals were the highest at almost all stations in Serbia. To examine the potential influence of global warming, the mean values of the EXPEs were calculated for two periods: 1961–1990 and 1991–2020. In the second period, higher values were determined for all EXPEs than in the first period. The large-scale variability modes, such as the North Atlantic Oscillation (NAO), the East Atlantic Oscillation (EA), and the East Atlantic–West Russia (EAWR) pattern, were correlated with the EXPEs. A negative correlation was found between the EXPEs and the NAO and the EAWR, and a positive correlation between the EXPEs and the EA pattern. For future research, the contribution of high-resolution data will be examined. Full article

Background: Periodic limb movements in sleep (PLMS) are characterised by repetitive, involuntary limb movements that occur during sleep and are often associated with autonomic nervous system dysregulation. While it is known that PLMS influence cardiovascular parameters, the exact role of heart rate variability (HRV) and the balance between sympathetic and parasympathetic activity remains unclear. Previous studies have suggested that longer PLMS events may trigger more pronounced autonomic responses, but the relationship between the duration of PLMS and autonomic dynamics has yet to be fully explored. This study aims to investigate the influence of PLMS duration on autonomic co-activation and its potential cardiovascular implications. Methods: A retrospective analysis was conducted on polysomnographic, demographic, and medical data from five patients, encompassing a total of 1348 PLMS events. We measured heart rate (HR), high-frequency HRV (HF-HRV), systolic blood pressure (SBP), and diastolic blood pressure (DBP) for 10 heartbeats before and 10 heartbeats after each PLMS series. A time–frequency approach was used, employing 10 RR interval segments to analyse HF-HRV dynamics. Statistical analysis was performed using IBM SPSS Statistics (v. 28.0.0.0), and the Kruskal–Wallis test was used to assess statistically significant deviations from baseline. Results: HF-HRV increased during PLMS, indicating enhanced parasympathetic activation. No significant changes in mean DBP or SBP were observed with leg movements of <2.1 s. However, with movements of >2.1 s, significant increases in DBP and SBP were noted, suggesting sympathetic activation. Longer PLMS events were associated with greater parasympathetic activity, while the absence of HR changes indicates concurrent sympathetic activation, supporting autonomic co-activation. Conclusions: Our study indicates that PLMS events lasting >2.1 s are linked to increased parasympathetic activity, likely accompanied by sympathetic activation. This simultaneous activation of both branches of the autonomic nervous system, referred to as autonomic co-activation, could lead to autonomic dysregulation and an increased risk of cardiovascular instability, including potentially life-threatening events. Full article

The generalized linear autoregressive moving-average model (GLARMA) has been used in epidemiology to evaluate the impact of pollutants on health. These effects are quantified through the relative risk (RR) measure, which inference can be based on the asymptotic properties of the maximum likelihood estimator. However, for small series, this can be troublesome. This work studies different types of bootstrap confidence intervals (CIs) for the RR. The simulation study revealed that the model parameter related to the data’s autocorrelation could influence the intervals’ coverage. Problems could arise when covariates present an autocorrelation structure. To solve this, using the vector autoregressive (VAR) filter in the covariates is suggested. Full article

Background: Ozone pollution has increased alongside China’s economic development, contributing to public health issues such as cardiovascular and cerebrovascular diseases. At present, the problem of an aging population is aggravated, which is worth more attention in terms of the health problems of elderly people. Methods: This study employed a distributional lag nonlinear model (DLNM) with Poisson regression to analyze the impact of ozone on cardiovascular and cerebrovascular disease mortality among the elderly in Shenyang, China, from 2014 to 2018. In addition, a time-series generalized additive regression model (GAM) was used to analyze the joint effect between PM_2.5 and ozone. Results: We found a positive correlation between ozone and mortality from cardiovascular and cerebrovascular diseases in the elderly. The maximum relative risk (RR) of mortality from cardiovascular and cerebrovascular diseases for every 10 μg/m³ increase in ozone was 1.005 (95% CI: 1.002–1.008). Males (RR: 1.018, 95% CI: 1.007–1.030), individuals in unconventional marital status (RR: 1.024, 95% CI: 1.011–1.038), and outdoor workers (RR: 1.017, 95% CI: 1.002–1.031) were more vulnerable to ozone pollution. This study did not find significant differences in the impact of ozone pollution on cardiovascular and cerebrovascular disease mortality risks among different educational groups. Additionally, a joint effect between ozone and PM_2.5 was observed. Conclusion: This study confirms that ozone exposure is positively associated with increased mortality from cardiovascular and cerebrovascular diseases. It emphasizes the joint effect of ozone and PM_2.5 in exacerbating cardiovascular and cerebrovascular disease mortality. Full article

The electrocatalytic reduction of CO₂ (ECO₂RR) to syngas with tunable CO/H₂ ratios offers a promising route for sustainable energy conversion and chemical production. Here, we report a series of N-doped carbon black (NCBx) catalysts with tailored nitrogen species that enable precise control over the composition of syngas. Among the catalysts, NCB3 exhibits the optimal performance, achieving high CO selectivity (64.14%) and activity (1.9 mA cm⁻²) in an H-type cell at −0.9 V. Furthermore, NCB3 produces syngas with a wide range of CO/H₂ ratios (0.52 to 4.77) across the applied potentials (−0.5 to −1.0 V). Stability tests confirm the robust durability of NCB3, which maintains consistent activity and selectivity over prolonged electrolysis. This work demonstrates the critical role of nitrogen species in tuning ECO₂RR pathways and establishes a strategy for designing efficient and stable carbon-based catalysts for CO₂ utilization and syngas production. Full article

A sustainable reaction of electrocatalytic nitrate conversion in ammonia production (NO₃RR) occurring under ambient conditions is currently of prime interest, as well as urgent research due to the real potential replacement of the environmentally unfavorable Haber–Bosch process. Herein, a series of electrocatalysts based on two-component cobalt alloys was synthesized using low-cost non-noble metals Co, Fe, Cr, and also Si. The samples of electrocatalysts were characterized and studied by the following methods: SEM, EDX, XRD (both transmission and reflection), UV–VIS spectroscopy, optical microscopy, linear (and cyclic) voltammetry, chronoamperometry, and electrochemical impedance spectroscopy. Beyond that, the determination of electrochemically active surface area was also carried out for all samples of electrocatalysts. Unexpectedly, the sample having an intermetallic compound (IMC) of the composition Co₂Si turned out to be the most highly effective. The highest Faradaic efficiency (FE) of 80.8% at E = −0.585 V (RHE) and an ammonia yield rate of 22.3 µmol h⁻¹ cm⁻² at E = −0.685 V (RHE) indicate the progressive role of IMC as the main active component of the electrocatalyst. Thus, this study demonstrates the promise and enormous potential of IMC as the main component of highly efficient electrocatalysts for NO₃RR. This work can serve primarily as a starting point for future studies of electrocatalytic conversion reactions in the production of ammonia using IMC catalysts containing non-noble metals. Full article

Climate change and related events such as temperature increase over time and more frequent extreme weather events constitute a risk to the population and wellbeing. This study contributes to the knowledge on this subject by analyzing changes in mortality in Portugal using the most recent historical and future climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6). A time-series distributed lag non-linear model (DLNM) was used to estimate the temperature-related mortality burdens in Portugal in the historical period (or reference, 1995–2014), the mid-century period (2046–2065), and the end of the century period (2081–2100) under moderate (SSP2-4.5) and extreme (SSP5-8.5) climate change scenarios. The findings show that winter periods of the contemporary climate (1995–2014) showed a significantly elevated risk of deaths from cold temperatures (RR = 2.23 (95% CI: 1.07, 4.64) at a minimum value of −3 °C), while at the maximum value (35.9 °C), the RR of 1.69 (95% CI: 1.01, 2.82) in the summer period indicated a moderate increase in risk. In terms of future projections, heat-related and extreme-heat-related mortality are higher under SSP5-8.5, while cold-related and extreme-cold-related mortality are generally higher under SSP2-4.5. Under the SSP2-4.5 scenario, the future periods of 2046–2065 and 2081–2100 showed a small net change in heat-related mortality. However, there is projected to be an increase in heat-related mortality due to increased heat, ranging from 0.13% to 0.14%. The impact of extreme heat is expected to result in a mortality increase of 0.03% to 0.04%, while extreme cold is expected to decrease mortality by −0.10%. Under the SSP5-8.5 scenario, the net change in mortality during the future period of 2046–2065 is estimated to decrease by −0.13%, with some uncertainty in the estimate. From 2081 to 2100, there is expected to be an estimated increase of 0.06% in mortality. The specific impact of increased heat shows an increase in heat-related mortality ranging from 0.15% to 0.17%, while extreme heat has an estimated increase of 0.04% to 0.05%. The developed framework provides a comprehensive assessment of excess mortality attributed to varying non-optimum temperatures for designing public health policies in Portugal. Full article