Search Results (3,345)

Climate change and air pollution are associated with a range of health outcomes, including cardiovascular and respiratory disease. Evaluation of the synergic effects of air pollution and increasing natural temperature on mortality is important for understanding their potential joint health effects. In this study, the modification effects of air temperature on the short-term association of ambient fine particulate matter (PM_2.5) and ozone (O₃) with non-accidental death (NAD) and cardiovascular disease (CVD) mortality were evaluated by using the generalized additive model (GAM) combined with the distributed lag nonlinear model (DLNM) in urban areas of Taiyuan, a representative of energy and heavy industrial cities in Northern China. The data on the daily cause-specific death numbers, air pollutants concentrations, and meteorological factors were collected from January 2013 to December 2019, and the temperature was divided into low (<25th percentile), medium (25–75th percentile), and high (>75th percentile) categories. Significant associations of PM_2.5 and O₃ with NAD and CVD mortality were observed in single-effect analysis. A statistically significant increase in the effect estimates of PM_2.5 and O₃ on NAD and CVD mortality was also observed on high-temperature days. But the associations of those were not statistically significant on medium- and low-temperature days. At the same temperature level, the effects of PM_2.5 and O₃ on the CVD mortality were larger than those on NAD (1.74% vs. 1.21%; 1.67% vs. 0.57%), and the elderly and males appeared to be more vulnerable to both higher temperatures and air pollution. The results suggest that the acute effect of PM_2.5 and O₃ on NAD and CVD mortality in urban Taiyuan was enhanced by increasing temperatures, particularly for the elderly and males. It highlights the importance of reducing PM_2.5 and O₃ exposure in urban areas to reduce the public health burden under the situation of global warming. Full article

In this study, the objective is to assess the impacts of NH₃ emissions from mink farming on human health and nature, which are sensitive to atmospheric nitrogen deposition. The impact-pathway approach is applied to follow the emissions from source to impact on human health in Europe (including Denmark) and from source to critical nitrogen load exceedances for NH₃-sensitive nature in Denmark. The Danish Eulerian Hemispheric Model (DEHM) is used for modelling the air pollution concentrations in Europe and nitrogen depositions on land and water surfaces in Denmark arising from NH₃ emissions from mink farming in Denmark. The Economic Valuation of Air (EVA) pollution model system is applied for deriving the health effects and corresponding socio-economic costs in Denmark and Europe arising from the emissions from mink farming. On a local scale in Denmark, the deposition resulting from the NH₃ emissions from mink farming is modelled using the results from the OML-DEP model at a high resolution to derive the critical nitrogen load exceedances for Danish nature areas sensitive to NH₃. From the analysis of the impacts through human exposure to the air pollutants PM_2.5, NO₂, and O₃, it is concluded that in total, ~60 premature deaths annually in Europe, including Denmark, can be attributed to the emissions of NH₃ to the atmosphere from the mink farming sector in Denmark. This corresponds to annual socio-economic costs on the order of EUR 142 million. From the analysis of critical load exceedances, it is concluded that an exceedance of the critical load of nitrogen deposition of ~14,600 hectares (ha) of NH₃-sensitive nature areas in Denmark can be attributed to NH₃ emissions from mink farming. The cost for restoring nature areas of this size, damaged by eutrophication from excess nitrogen deposition, is estimated to be ~EUR 110 million. In 2020, the mink sector in Denmark was shut down in connection with the COVID-19 pandemic. All mink were culled by order of the Danish Government, and now in 2025, the process of determining the level of financial compensation to the farmers is still ongoing. The socio-economic costs following the impacts on human health in Europe and nitrogen-sensitive nature in Denmark of NH₃ emissions from the now non-existing mink sector can therefore be viewed as socio-economic benefits. In this study, these benefits are compared with the expected level of compensation from the Danish Government to the mink farmers, and the conclusion is that the compensation to the mink farmers breaks even with the benefits from reduced NH₃ emissions over a timescale of ~20 years. Full article

The SARS-CoV-2 spike (S1) protein facilitates viral entry through binding to angiotensin-converting enzyme 2 (ACE2), but it also contains an Arg–Gly–Asp (RGD) motif that may enable interactions with RGD-binding integrins on ACE2-negative cells. Here, we provide quantitative evidence for this alternative binding pathway using a live-cell, label-free resonant waveguide grating (RWG) biosensor. RWG technology allowed us to monitor real-time adhesion kinetics of live cells to RGD-displaying substrates, as well as cell adhesion to S1-coated surfaces. To characterize the strength of the integrin–S1 interaction, we determined the dissociation constant using two complementary approaches. First, we performed a live-cell competitive binding assay on RGD-displaying surfaces, where varying concentrations of soluble S1 were added to cell suspensions. Second, we recorded the adhesion kinetics of cells on S1-coated surfaces and fitted the data using a kinetic model based on coupled ordinary differential equations. By comparing the results from both methods, we estimate that approximately 33% of the S1 molecules immobilized on the Nb₂O₅ biosensor surface are capable of initiating integrin-mediated adhesion. These findings support the existence of an alternative integrin-dependent entry route for SARS-CoV-2 and highlight the effectiveness of label-free RWG biosensing for quantitatively probing virus–host interactions under physiologically relevant conditions without the need of the isolation of the interaction partners from the cells. Full article

In this study, we present analysis of TESS photometry, spectral energy distribution (SED), high-resolution spectroscopy, and spot modeling of the ${\alpha }^2$ CVn-type star AL Col (HD 46462). The primary objective is to determine its fundamental physical parameters and investigate its surface activity characteristics. Using TESS short-cadence (120 s) SAP flux, we identified a rotational frequency of 0.09655 ${\mathrm{d}}^{-1}$ ($P_{\mathrm{rot}}=10.35733$ d). Wavelet analysis reveals that while the amplitudes of the harmonic components vary over time, the strength of the primary rotational frequency remains stable. A SED analysis of multi-band photometric data yields an effective temperature ($T_{\mathrm{eff}}$) of 11,750 K. High-resolution spectroscopic observations covering wavelengthrange 4500–7000 Å provide refined estimates of $T_{\mathrm{eff}}$ = 13,814 ± 400 K, $logg$ = 4.09 ± 0.08 dex, and $\upsilon sini$ = 16 ± 1 km s⁻¹. Abundance analysis shows solar-like composition of O ii, Mg ii, S ii, and Ca ii, while helium is under-abundant by 0.62 dex. Rare earth elements (REEs) exhibit over-abundances of up to 5.2 dex, classifying the star as an Ap/Bp-type star. AL Col has a radius of $R=3.74\pm 0.48{\mathrm{R}}_{\odot }$, with its H–R diagram position estimating a mass of $M=4.2\pm 0.2{\mathrm{M}}_{\odot }$ and an age of $0.12\pm 0.01$ Gyr, indicating that the star has slightly evolved from the main sequence. The TESS light curves were modeled using a three-evolving-spot configuration, suggesting the presence of differential rotation. This star is a promising candidate for future investigations of magnetic field diagnostics and the vertical stratification of chemical elements in its atmosphere. Full article

We consider the lossless compression bound of any individual data sequence. Conceptually, its Kolmogorov complexity is such a bound yet uncomputable. According to Shannon’s source coding theorem, the average compression bound is $nH$, where n is the number of words and H is the entropy of an oracle probability distribution characterizing the data source. The quantity $nH\left({\widehat{\theta }}_n\right)$ obtained by plugging in the maximum likelihood estimate is an underestimate of the bound. Shtarkov showed that the normalized maximum likelihood (NML) distribution is optimal in a minimax sense for any parametric family. Fitting a data sequence—without any a priori distributional assumption—by a relevant exponential family, we apply the local asymptotic normality to show that the NML code length is $nH\left({\widehat{\theta }}_n\right)+{\displaystyle \frac{d}{2}}log{\displaystyle \frac{n}{2\pi }}+log{\int }_{\Theta }{\left|I\left(\theta \right)\right|}^{1/2}d\theta +o\left(1\right)$, where d is dictionary size, $\left|I\right(\theta \left)\right|$ is the determinant of the Fisher information matrix, and $\Theta$ is the parameter space. We demonstrate that sequentially predicting the optimal code length for the next word via a Bayesian mechanism leads to the mixture code whose length is given by $nH\left({\widehat{\theta }}_n\right)+{\displaystyle \frac{d}{2}}log{\displaystyle \frac{n}{2\pi }}+log{\displaystyle \frac{|I\left({\widehat{\theta }}_n\right){|}^{1/2}}{w\left({\widehat{\theta }}_n\right)}}+o\left(1\right)$, where $w\left(\theta \right)$ is a prior. The asymptotics apply to not only discrete symbols but also continuous data if the code length for the former is replaced by the description length for the latter. The analytical result is exemplified by calculating compression bounds of protein-encoding DNA sequences under different parsing models. Typically, compression is maximized when parsing aligns with amino acid codons, while pseudo-random sequences remain incompressible, as predicted by Kolmogorov complexity. Notably, the empirical bound becomes more accurate as the dictionary size increases. Full article

Onboard the MetOp satellite series, Infrared Atmospheric Sounding Interferometer (IASI) is a Fourier Transform spectrometer based on the Michelson interferometer. IASI acquires interferograms, which are processed to provide high-resolution atmospheric emission spectra. These spectra enable the derivation of temperature and humidity profiles, among other parameters, with exceptional spectral resolution. In this study, we evaluate a novel, rapid retrieval approach in the interferogram domain, aiming for near-real-time (NRT) analysis of large spectral datasets anticipated from next-generation tropospheric sounders, such as MTG-IRS. The Partially Sampled Interferogram (PSI) method, applied to trace gas retrievals from IASI, has been sparsely explored. However, previous studies suggest its potential for high-accuracy retrievals of specific gases, including CO, CO₂, CH₄, and N₂O at the resolution of a single IASI footprint. This article presents the results of a study based on retrieval in the interferogram domain. Furthermore, the optical pathway differences sensitive to the parameters of interest are studied. Interferograms are generated using a fast Fourier transform on synthetic IASI spectra. Finally, the relationship to the total column of carbon monoxide is explored using three different algorithms—from the most intuitive to a complex neural network approach. These algorithms serve as a proof of concept for interferogram classification and rapid predictions of surface temperature, as well as the abundances of H₂O and CO. IASI spectra simulations were performed using the LATMOS Atmospheric Retrieval Algorithm (LARA), a robust and validated radiative transfer model based on least squares estimation. The climatological library TIGR was employed to generate IASI interferograms from LARA spectra. TIGR includes 2311 atmospheric scenarios, each characterized by temperature, water vapor, and ozone concentration profiles across a pressure grid from the surface to the top of the atmosphere. Our study focuses on CO, a critical trace gas for understanding air quality and climate forcing, which displays a characteristic absorption pattern in the 2050–2350 ${\mathrm{cm}}^{-1}$ wavenumber range. Additionally, the study explores the potential of correlating interferogram characteristics with surface temperature and H₂O content, aiming to enhance the accuracy of CO column retrievals. Starting with intuitive retrieval algorithms, we progressively increased complexity, culminating in a neural network-based algorithm. The results of the NN study demonstrate the feasibility of fast interferogram-domain retrievals, paving the way for operational applications. Full article

We present a synthesis based on epigenetics, machine learning and polymer physics from which emerges new relationships between the thermodynamic Flory–Huggins parameter (χ), epigenetic age (eAge) and Shannon entropy. Using a framework for the estimation of χ in the nuclear environment we show that $\mathsf{\chi }\propto {\mathrm{e}\mathrm{A}\mathrm{g}\mathrm{e}}^{-1}$ and $\mathsf{\chi }\propto {\mathrm{S}\mathrm{h}\mathrm{a}\mathrm{n}\mathrm{n}\mathrm{o}\mathrm{n} \mathrm{E}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{p}\mathrm{y}}^{-1}$. As cells age, epigenetic drift results in “smoothing out” of the epigenetic landscape reducing the magnitude of χ. Epigenetic rejuvenation reverses epigenetic drift and restores χ to levels found in young cells with concomitant reduction in both eAge and Shannon entropy. Full article

Zinc is an essential trace mineral for livestock, but excessive use can contribute to ecotoxicity and antimicrobial resistance. The objective of this study was to assess the impact of different zinc oxide (ZnO) levels in diets for weaned pigs on growth performance, mortality, dietary zinc flow, and environmental impacts. A 6-week feeding trial with 432 weaned pigs assessed three dietary treatments: high ZnO (pharmaceutical levels), intermediate ZnO, and low ZnO (EU recommendation). Growth performance for the growing–finishing period was modeled using the NRC (2012), and dietary Zn intake and fecal Zn excretion were estimated. Environmental impacts were analyzed via life cycle assessment (LCA) using SimaPro LCA software. High ZnO improved growth performance and reduced mortality (p < 0.05), but increased nursery fecal zinc excretion, resulting in a total fecal Zn excretion per pig of 54,125 mg, 59,485 mg, and 106,043 mg for low-, intermediate-, and high-ZnO treatments, respectively. In the nursery phase, high-ZnO treatment had the greatest impact on environmental footprint, increasing freshwater ecotoxicity and marine ecotoxicity indicators by 59.6% and 57.9%, respectively. However, high-ZnO-fed pigs had a greater body weight at the end of the nursery phase and were predicted to achieve a higher growth rate per 130 kg market pig, with fewer days to market and by sparing feed. Therefore, high-ZnO-fed pigs had reduced environmental burdens, including global warming potential, ozone depletion, land use, and mineral resource depletion. These findings demonstrate how livestock nutritionists can apply integrated modeling approaches to link animal performance with environmental outcomes within a One Health framework. Full article

In the context of the current global transition toward low-carbon energy, the issue of $C{O}_2$ utilization has become increasingly important. One of the most promising natural targets for $C{O}_2$ sequestration is the terrigenous sedimentary formations found in oil, gas, and coal basins. It is generally assumed that $C{O}_2$ injected into such formations can be stored indefinitely in a stable form. However, the dissolution of $C{O}_2$ into subsurface water leads to a reduction in pH, which may cause partial dissolution of the host formation, altering the structure of the subsurface in the injection zone. This process is relatively slow, potentially unfolding over decades or even centuries, and its long-term consequences require careful investigation through mathematical modeling. The geological formation is treated as a partially soluble porous medium, where the dissolution rate is governed by surface chemical reactions occurring at the pore boundaries. In this study, we present an applied mathematical model that captures the coupled processes of mass transport, surface chemical reactions, and the resulting microscopic changes in the pore structure of the formation. To ensure the model remains grounded in realistic geological conditions, we based it on exploration data characterizing the composition and microstructure of the pore space typical of the Cenomanian suite in northern Western Siberia. The model incorporates the dominant geochemical reactions involving calcium carbonate (calcite, $CaC{O}_3$), characteristic of Cenomanian reservoir rocks. It describes the dissolution of $C{O}_2$ in the pore fluid and the associated evolution of ion concentrations, specifically $H^{+}$, $C{a}^{2+}$, and $HC{O}_3^{-}$. The input parameters are derived from experimental data. While the model focuses on calcite-based formations, the algorithm can be adapted to other mineralogies with appropriate modifications to the reaction terms. The simulation domain is defined as a cubic region with a side length of 1 $\mathsf{\mu }$m, representing a fragment of the geological formation with a porosity of 0.33. The pore space is initially filled with a mixture of liquid $C{O}_2$ and water at known saturation levels. The mathematical framework consists of a system of diffusion–reaction equations describing the dissolution of $C{O}_2$ in water and the subsequent mineral dissolution, coupled with a model for surface evolution of the solid phase. This model enables calculation of surface reaction rates within the porous medium and estimates the timescales over which significant changes in pore structure may occur, depending on the relative saturations of water and liquid $C{O}_2$. Full article

Using data on the children of the respondents who participated in Wave IV (2008) and Wave V (2016–2018) of the National Longitudinal Study of Adolescent to Adult Health, we estimate the effect of long-term exposure to ozone (O₃) and particulate matter 2.5 (PM_2.5) on diagnoses of allergies and asthma in adolescence and young adulthood. Estimates from individual-level fixed-effect models with time-varying controls show that exposure to PM_2.5 and O₃ is associated with higher likelihood of asthma and allergies in females at younger ages (10–12 years old) and allergies in males at older ages (13 years old and above). These findings are novel and contribute to the growing body of literature exploring gender and age differences in susceptibility to asthma and allergies. Full article

Reactive oxygen species (ROS) are fundamental components found in cells that exist in an oxygen environment. While they are often viewed as detrimental metabolic byproducts that can harm cells, leading to aging and cell death, they can also play a role in cellular regulatory processes and have beneficial effects. One of the main ROS present in all cells is hydrogen peroxide (H₂O₂), which can function as a signaling molecule in extra- and intracellular signaling. To enhance our understanding of how various enzymes regulate cellular H₂O₂ levels, we created a mathematical model of H₂O₂ neutralization and performed computer simulations to estimate the neutralization efficiency in various types of cells. Data on gene expression for genes participating in this process were incorporated into the calculations, along with the regulation of enzymes in oxidation and reduction processes. The conducted simulations demonstrate that cells originating from different tissues utilize systems neutralizing H₂O₂ variously, which results in differences in H₂O₂ cellular levels. The simulation findings suggest that the differences in radiosensitivity seen in various cancer cell types may be linked to their effectiveness in scavenging H₂O₂. Analysis of results from model simulations for colorectal, lung, and breast cancer cell lines indicated that radiosensitive cell lines exhibited elevated levels of H₂O₂, attributed to the reduced efficiency of neutralizing enzymes. By highlighting cell-type-specific differences in H₂O₂ neutralization, our findings may contribute to a deeper understanding of redox regulation in cancer cells and reveal new potential correlations with radioresistance. Full article

Portable near-infrared reflectance spectroscopy (NIRS) offers the opportunity of a rapid measurement of forage dry matter concentration, allowing producers to make faster adjustments in real time. This study compared dry matter (DM) concentration predictions of three units of a portable near-infrared reflectance spectrometer (pNIRS) with conventional forced-air oven drying (48 h at 60 °C) using corn forage and silage samples. Moreover, a common on-farm method (Koster tester) was also compared. The reflectance curve used by pNIRS to predict DM was obtained by scanning WPCS samples and developed by SciO. A total of 113 whole-plant corn forage (WPCF) and 27 whole-plant corn silage (WPCS) samples from 66 corn hybrids were obtained from three separate experiments conducted between 2018 and 2019. These three experiments were completely independent of each other, with sample collections over different periods. In Experiment 1, all treatments were tested in WPCF, and the DM concentration of the forced-air oven differed from Koster testers (35.4 vs. 34.3% DM, on average, respectively) and all three pNIRS units (35.4 vs. 30.7% DM, on average, respectively), with no differences among pNIRS. All treatments were positively correlated with the forced-air oven treatment DM values. Experiment 2 evaluated the Koster tester and pNIRS in WPCF on farms, and the Koster tester differed from pNIRS (37.2 vs. 33.3% DM, on average, respectively) treatments. All pNIRS were positively correlated with Koster tester treatment. In Experiment 3, all treatments were tested in WPCS, and the DM concentration of the forced-air oven was greater than other treatments (35.3 vs. 32.8% DM, on average, respectively). Overall, Koster tester predictions for both Experiments 1 and 3 were better correlated with the forced-air oven than pNIRS. Additionally, pNIRS showed a lower mean bias, although low coefficients of determination and concordance correlation were observed in Experiment 3 compared to Experiments 1 and 2, which might be related to the prediction curve. Further calibrations of the predictive curve with forage samples would be needed to reasonably estimate the DM concentration of WPCF, whereas a greater number of samples could account for the variations in WPCS due to large heterogeneity in particle composition (e.g., leaves, stem, and kernel), size, and distribution. Full article

The main goal of this study was to investigate the properties of ZnO thin films, including pure films and those doped with indium (up to 8 mol%) that was deposited using a spray pyrolysis technique on glass and silicon substrates in order to prepare the position-sensitive structure, Si-SiO₂-ZnO:In. To this aim, the present work is focused on investigating the effect of indium concentration on the morphology, structure, and optical properties of the films. X-ray diffraction (XRD) analysis reveals a wurtzite polycrystalline structure. Scanning electron microscopy (SEM) images display a smooth and uniform surface characterized by closely packed nanocrystalline clusters. As the indium concentration rises to 8 mol%, the number of nuclei grows, resulting in uniformly distributed grains across the entire substrate surface. The estimated root mean square (RMS) roughness values for the thin films undoped and doped with 3 mol%, 5 mol%, and 8 mol% of ZnO measured using AFM are 6.13, 9.64, and 13.76 nm, respectively. The increase in indium concentration leads to a slight decrease in film transmittance. The measured LPV photosensitivity of about 44 mV/mm confirms the potential use of these thin films in practical applications. Full article

In the present research, a novel magnetic adsorbent was developed via the sol–gel method by coating CuFe₂O₄ nanoparticles on biochar sourced from brewed tea waste. The synthesized adsorbent was utilized for the removal of Ni(II) ions from aqueous media. The adsorption efficiency of Ni(II) ions was assessed under crucial experimental conditions such as initial solution pH, contact time, adsorbent dosage, and initial Ni(II) concentration. The adsorbent exhibited rapid adsorption kinetics, achieving equilibrium in approximately 15 min, and maintained high efficiency across a wide pH range. Adsorption experiments were conducted for Ni(II) solutions at their natural pH (5.6) to minimize chemical usage and enhance process simplicity. An impressive maximum adsorption capacity of 232.6 mg g⁻¹ was recorded, outperforming many previously reported adsorbents. Furthermore, desorption studies demonstrated nearly 100% recovery of Ni(II) ions using 1.0 M HCl solution, indicating excellent regeneration potential of the adsorbent. Additionally, the prediction performance of an artificial neural network (ANN) model was evaluated to predict Ni(II) removal efficiency based on experimental variables, showing strong agreement with experimental data. Isotherm and kinetic models were also applied to the data to estimate the adsorption mechanisms. These findings demonstrate the promise of CuFe₂O₄-modified tea waste biochar for sustainable water treatment applications. Full article

The synthesis of Fe₃O₄ and ε-Fe₂O₃ nanoparticles (hereafter referred to as Fe₃O₄ NPs and ε-Fe₂O₃ NPs, respectively) was conducted in an eco-friendly manner using FeCl₃·6H₂O as the primary reactant. The experiment was conducted by subjecting the sample to an aqueous solution of FeCl₂·4H₂O at a temperature of 80 °C for a duration of 45 min, with the inclusion of apricot kernel shell extract (AKSE) as a natural reducing agent. The synthesized Fe₃O₄ NPs and ε-Fe₂O₃ NPs were characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA). The optical properties of Fe₃O₄ NPs and ε-Fe₂O₃ NPs were examined, with the band gap energy estimated using the Kubelka–Munk formula. The results demonstrated a band gap of E_g (Fe₃O₄ NPs) = 2.59 eV and E_g (ε-Fe₂O₃ NPs) = 2.75 eV, thereby confirming their semiconductor behavior. The photoconductivity of Fe₃O₄ NPs and ε-Fe₂O₃ NPs was analyzed as a function of photon energy. For Fe₃O₄ NPs, photoconductivity exhibited an increase between 1.37 eV and 6.2 eV prior to reaching a state of stability. A comparable trend was observed for ε-Fe₂O₃ NPs, with an increase from 1.35 eV to 6.22 eV, followed by stabilization. Furthermore, the extinction coefficient (k) was determined. For Fe₃O₄ NPs, k ranged from 39 to a maximum of 300, while for ε-Fe₂O₃ NPs, it varied from 37 to a maximum of 280. A higher k value indicates strong light absorption, rendering these nanoparticles highly suitable for photothermal and sensing applications. Full article