Search Results (6,258)

Appendiceal mucinous neoplasms (AMNs) are a rare but clinically significant category of gastrointestinal tumors, ranging from low-grade appendiceal mucinous neoplasm (LAMN), the main precursor of pseudomyxoma peritonei (PMP), to high-grade appendiceal mucinous neoplasm (HAMN), poorly differentiated and signet-ring-cell adenocarcinomas, and goblet cell adenocarcinoma. Although current WHO and PSOGI classifications provide well established diagnostic criteria, controversies persist regarding the biological behavior and prognostic significance of the most aggressive subtypes and the relationship between HAMN and mucinous adenocarcinoma. While appendectomy is sufficient for localized LAMN, cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) is the treatment of choice for peritoneal dissemination This review integrates the histopathological and molecular classification of AMN and PMP with the evolution of intraperitoneal chemotherapy. Key findings indicate that KRAS and GNAS mutations are central drivers of mucin overproduction and peritoneal spread, that tumor grade and mucin cellularity remain the strongest prognostic determinants, and that the evidence supporting HIPEC and PIPAC derives largely from observational rather than randomized data. As a novel insight, we highlight the emerging role of patient-derived organoids as translational models for functional drug testing. Progress will depend on integrating molecular characterization, critical appraisal of intraperitoneal therapies, and organoid-based testing to advance individualized treatment for peritoneal surface malignancies. Full article

The rapid transition toward renewable energy requires transparent and spatially explicit methods for identifying suitable photovoltaic (PV) development areas. This study develops a geospatial artificial intelligence (GeoAI) decision-support framework for PV site suitability assessment in Rajasthan, India. Eleven harmonized predictors were used: global horizontal irradiance (GHI), photovoltaic power output (PVOUT), temperature, wind speed, aerosol optical depth (AOD), elevation, slope, albedo, land use/land cover (LULC), distance to roads, and distance to power lines. Reference labels were generated from an explicit rule-derived suitability index, class thresholds, and exclusion logic; therefore, the machine-learning task was to reproduce a transparent suitability framework rather than to predict observed PV yield or project-level performance. Extreme Gradient Boosting (XGBoost) was compared with simpler baseline models, evaluated using random and spatial-block validation, and interpreted using SHapley Additive exPlanations (SHAP). Independent overlays with known solar-installation records, presence-background robustness testing, and uncertainty/sensitivity analysis were used to examine spatial plausibility, spatial autocorrelation, deterministic label effects, and parameter uncertainty. The resulting outputs include pixel-level suitability zones, contiguous candidate polygons, district-level capacity-oriented summaries, and planning-priority classes. The framework is intended as a risk-aware regional screening tool: high model agreement indicates consistency with the constructed suitability labels, while final project decisions require parcel-scale land, grid, environmental, social, and economic assessment. Full article

We present the results of applications of ATLAS2.0 to experimental data in this final part of our series of publications. ATLAS2.0 retrieves particle microphysical parameters from multiwavelength Raman and high-spectral-resolution lidar measurements of backscatter (β) coefficients at three wavelengths, i.e., λ = 355, 532, and 1064 nm, extinction (α) coefficients at two wavelengths, i.e., 355 and 532 nm, and particle linear depolarization ratios (PLDR, δ) at three wavelengths, i.e., 355, 532, and 1064 nm, so-called 3β + 2α + 3δ datasets. The explicit use of PLDRs is a novel feature compared to all previously developed lidar data retrieval algorithms. For the tests of ATLAS2.0, we use data that were taken with NASA Langley Research Center’s airborne high-spectral-resolution lidar 2 (HSRL-2). We show the results of two case studies. We compare the particle microphysical parameters and single-scattering albedo (SSA) retrieved with ATLAS2.0 to results obtained with the first version of ATLAS, our Tikhonov regularization algorithm (TiARA), and in situ observations carried out aboard an aircraft that followed the airborne HSRL-2 instrument. The solutions converge within the retrieval uncertainties of these techniques. The discrepancy between the measured and backcalculated, i.e., retrieved 3β + 2α + 3δ data on average stays below 10%. The difference between the retrieved and measured PLDRs is, on average, even less. This comparably good convergence of the optical datasets (experimental versus backcalculated) of both measurement cases can only be achieved if the investigated aerosol particles are analyzed on the basis of a sphere-spheroid mixture. Full article

Breath aerosol analysis requires low-cost sensing substrates capable of capturing aerosolized biomolecular components while preserving chemically interpretable readouts. Here, methylcellulose–phenol red (MCPR) films are evaluated as multimodal sensing substrates using model bioaerosols consisting of sodium sulfate, bovine serum albumin (BSA), and polystyrene latex particles under acidic, neutral, and alkaline pH conditions. ATR-FTIR spectroscopy revealed inverse pH-dependent trends in sulfate (1000–1100 cm⁻¹) and protein amide (1500–1700 cm⁻¹) spectral regions. A sulfate-to-protein AUC ratio increased from 0.86 ± 0.01 at pH 4 to 3.56 ± 0.32 at pH 10, demonstrating ratiometric compositional discrimination of ionic and proteinaceous aerosol fractions. UV–Vis spectroscopy showed pH-dependent λmax shifts from 432 to 556 nm, confirming the preservation of phenol red optical responsiveness after aerosol exposure. FTIR-derived ratio metrics correlated linearly with optical responses, indicating coupled vibrational and optical sensing behavior. SEM-EDS analysis of methylcellulose capture films confirmed deposition of sulfate, proteinaceous, and particulate aerosol components, supporting the platform’s suitability for multimodal spectroscopic sensing. These findings establish MCPR films as integrated capture-and-sensing substrates capable of coupling optical pH responsiveness with label-free vibrational analysis, supporting future development of low-cost breath-relevant aerosol sensing platforms. Full article

This study compared the temporal effects of traditional cigarettes and e-cigarettes on lung health in male Rattus norvegicus over 8- and 12-week periods. Thirty rats were evaluated for tracheal/alveolar histopathology and systemic markers (IL-6, TNF-α, SOD-3, MDA). Chronic cigarette exposure (12 weeks) and nicotine aerosol (8 weeks) significantly suppressed weight gain, while ascorbic acid aerosol caused less growth inhibition. At 8 weeks, cigarette exposure (K3) induced adaptive tracheal mucosal thickening (66.88 ± 17.92 µm vs. 52.40 ± 2.63 µm in control K1), increased goblet cells (4.2 ± 2.44 N/mm), elevated SOD-3 (12.75 ± 1.10 pg/mL), and initiated emphysematous alveolar expansion (469.77 ± 91.31 µm vs. 202.03 ± 29.38 µm in K1 in K1). Conversely, 12-week cigarette smoke (K4) triggered epithelial exhaustion, significantly thinning the tracheal mucosa (34.65 ± 6.55 µm) and elevating systemic IL-6 (11.45 ± 1.17 pg/mL vs. 8.43 ± 0.88 pg/mL in control K2). Notably, chronic electronic ascorbic acid aerosolization (K6) preserved localized alveolar structural layouts and limited septal thickening compared with nicotine groups. However, it failed to suppress systemic inflammation, as evidenced by elevated IL-6 levels. In conclusion, while ascorbic acid aerosols moderate localized parenchymal destruction compared to nicotine, chronic aerosol exposure accelerates systemic immune activation. Full article

To address the limited constraints of ground-based lidar with few channels in retrieving aerosol microphysical parameters in urban atmospheres, this study developed a method to retrieve aerosol volume size distribution and effective radius from a 355/532/1064 nm triple-wavelength elastic-scattering, single-polarization lidar system. The method uses 3β + 2α optical quantities as input constraints, applies Mie scattering theory as the forward model, parameterizes the volume size distribution with B-spline functions, and achieves stable solutions through Tikhonov regularization and cross-validation. To reduce uncertainties in prior parameters, including the complex refractive index, particle size range, and lidar ratio, an optimization strategy based on parameter search, retrieval reconstruction, and error minimization was introduced. Numerical simulations showed that the method reproduced the main features of a bimodal lognormal aerosol volume size distribution with good feasibility and stability. Two case studies further showed fine-mode dominance and decreasing extinction coefficient, depolarization ratio, and effective radius with height under good air quality conditions, but enhanced coarse-mode contribution and effective radius in the upper cloud-influenced layer under lightly polluted conditions, as inferred from the combined variations in RSCS, extinction coefficient, depolarization ratio, and effective radius. Full article

Aerobiology, the science that studies atmospheric biological particles (including pollen, fungal spores, bacteria, and viruses), has undergone a profound transformation from a descriptive, observational discipline into a predictive, data-driven field, thanks to advances in statistical methods and environmental sensing technologies. Early research, based on classical statistical methods such as descriptive analysis, correlation analysis, and linear regression, established a fundamental understanding of seasonal dynamics and environmental relationships. However, the inherent complexity of aerosol biological systems—characterized by nonlinear interactions, spatiotemporal variability, and multiscale processes—has spurred the adoption of modern statistical techniques. These techniques include time-series analysis, generalized linear and additive models, spatial statistics, Bayesian inference, machine learning, and data assimilation, often combined with high-resolution environmental monitoring and sensor networks. In recent years, hybrid modeling approaches have emerged, combining mechanistic understanding of atmospheric transport and biological emissions processes with data-driven learning to improve the accuracy, robustness, and interpretability of predictions. This review comprehensively compares classical, modern, and hybrid statistical methods in air biology, exploring their theoretical foundations, practical applications, and inherent limitations. Furthermore, this review highlights emerging paradigms such as uncertainty quantification, causal inference, digital twins, and AI-driven real-time prediction systems. It also discusses challenges, including data heterogeneity, model interpretability, and cross-regional portability. By treating aerobiology as a complex adaptive environmental–biological system, this study highlights statistical methods that link observations to mechanisms and advance scalable, reliable, systems-oriented prediction frameworks for future research and applications. Full article

This study explores the three-dimensional structure of dust aerosols over the Qaidam Basin using CALIPSO satellite observations from 2007 to 2022. The results show that polluted dust is the dominant aerosol type in this region. Dust activity peaks in spring, with its vertical extent reaching nearly 10 km. Dust Aerosol Optical Depth (DAOD) is relatively high in the northwest and central parts of the basin, with a spring peak of 0.25 and an autumn minimum of 0.12. DAOD has shown a notable decreasing trend over the past 16 years. In terms of vertical structure, dust aerosols are mainly concentrated below 4 km AGL, especially within the near-surface layer of 0–2 km, and their occurrence frequency declines as altitude increases. The dust layer thickness exhibits obvious seasonal variations, which are primarily controlled by changes in layer top height. The average thickness decreases from 1.53 km in spring to 0.61 km in winter, while the layer’s bottom height remains fairly stable. Analysis based on the LASSO-SHAP model indicates that potential evapotranspiration and friction velocity are the major factors affecting DAOD, highlighting the vital roles of surface dryness and near-surface dynamic forcing. Furthermore, investigation of typical dust events reveals distinct vertical stratification of dust transport. Low-level dust movement is restricted by basin terrain, whereas upper levels are governed by the westerlies. This study improves our understanding of the three-dimensional structure, seasonal evolution, and transport processes of dust aerosols in high-altitude arid basins. Full article

The new LOAC2 optical aerosol counter is designed to detect liquid and solid particulates across 19 to 30 size classes within the 0.15–90 µm size range, and to provide their main typology. The instrument can be used at ground level and on all kinds of balloons, including weather balloons, up to an altitude of about 35 km. The measurements are based on principles established for the previous version of LOAC, now incorporating improved electronics and detection geometry. Counting is performed at small scattering angles in the diffraction domain, making it insensitive to the refractive indices and the porosity of the particles, thus allowing a direct relationship between scattered intensity and aerosol size. Typology identification is now performed at three additional scattering angles, where the scattered flux is highly sensitive to the refractive index of the different aerosol families present in the atmosphere. The calibration was conducted using calibrated spherical and irregular grains, as well as different types of solid particles. Several intercomparison sessions with other counters and with reference mass-concentration air quality monitoring stations were carried out indoors, in an atmospheric simulation chamber, and in outdoor ambient air. The agreement between LOAC2 and the other instruments is good, confirming the ability of LOAC2 to be used for scientific studies and for monitoring atmospheric aerosols. Full article

Volcanic super-eruptions can perturb atmospheric composition and climate-relevant radiative properties in ways that are not captured by simple scaling from Pinatubo-like events. This study presents a reduced-order regime theory for the coupled evolution of stratospheric sulfur, sulfate aerosol burden, reactive halogens, ozone loss, stratospheric thermal adjustment, and aerosol residence time. The analysis is intended as an interpretive tool for organizing sulfur-rich volcanic scenarios, comparing literature-based benchmark classes, and designing chemistry–climate model experiments, rather than as an event-specific calibration or a substitute for three-dimensional models. Four control parameters structure the response: sulfur loading relative to microphysical saturation, effective halogen strength, ash-uptake efficiency, and dynamical lifetime sensitivity, with hemispheric asymmetry treated diagnostically. An external consistency check against published Pinatubo-like, idealized 10–40 teragrams of sulfur (Tg S), Toba-like, and Los Chocoyos-like responses is used to evaluate whether the reduced theory reproduces the expected rank ordering of aerosol saturation, forcing-efficiency decline, ozone-loss amplification, ash-driven sulfur suppression, and residence-time sensitivity. This comparison does not assign pointwise error margins against three-dimensional model output; it evaluates regime membership, sign of response, rank ordering, and broad magnitude behavior. The main conclusion is that volcanic super-eruption impacts are governed by interacting regime transitions rather than by sulfur mass alone. Microphysical saturation can limit forcing efficiency, halogens can shift the system toward chemically amplified ozone depletion, ash uptake can reduce the effective sulfur burden during the early phase, and dynamical state can control persistence and hemispheric expression. By separating these mechanisms, the study provides a compact basis for interpreting large volcanic perturbations to atmospheric chemistry and for designing targeted model experiments on extreme eruption scenarios. Full article

Aerosols emitted by biomass burning represent one of the largest sources of uncertainty in our current understanding of the Earth’s radiative balance. We investigate the climatic influence of biomass burning aerosols emitted from six key regions of biomass burning by using GEOS-Chem coupled with the rapid radiative transfer model. We evaluate our model using AERONET observation, with the model reproducing data with 87% observed spatial and seasonal variability with a low negative bias of 7%. The radiation sensitivity is generally highest for North Asia (NAS) and for North America (NCC); lowest for South America (SAM) and South and Southeast Asia (SSA); and moderate for Africa (AFR) and Oceania (OCE). These regional differences are related to the main burning types of the regions. When we consider the global radiation influence, AFR dominates the global picture due to the comparatively large biomass burned. We estimate the global mean radiation influence of biomass burning aerosol is −0.116 W m⁻². For monthly features, in summer, due to higher incident energy obtained in NAS and NCC, high negative radiation sensitivity of biomass burning, biomass burning aerosols, and biomass burning organic aerosol are shown in these regions. Meanwhile, the radiation sensitivity peak of black carbon for these two regions occurs earlier in late spring (NAS) or early summer (NCC), when large incident energy and large high reflectance snow cover coexist in these two high-latitude regions. A significant yearly difference in radiation influence, rather than radiation sensitivity, is found, with the relative difference between the maximum year and minimum year reaching 90% of the maximum radiation influence year. Specifically, two regions affected by El Niño (OCE and SSA) have the most significant yearly variation in all factors, with anomalies occurring in El Niño years. Full article

This study examined how duct inclination influences particle removal in a hybrid acoustic–electrostatic filtration system operating at low airflow velocities. The experiments were carried out in a 150 mm diameter air duct at airflow speeds of 0.50 and 0.75 m/s, with duct inclinations of 45° and 90°. Aerosol particles with properties similar to marine diesel exhaust, spanning a size range of 0.2–10 µm, were introduced at stable concentrations. Electrostatic voltages of 17.5 and 20 kV were applied, together with acoustic voltages between 100 and 200 V. Particle removal was evaluated using both size-resolved and overall collection efficiencies. The results show that duct inclination mainly affects the removal of fine and medium-sized particles. The largest differences were observed for particles around 1 µm in diameter, where the vertical duct increased collection efficiency by up to 27 percentage points at an airflow speed of 0.75 m/s. For larger particles in the 5–10 µm size range, high removal efficiency was achieved under all tested conditions, and duct orientation had a smaller influence on collection performance. Overall, the results confirm that duct inclination has a clear and measurable effect on the performance of hybrid acoustic–electrostatic filtration systems operating at low airflow velocities. Full article

This study examined the chemical composition and quantitative source contributions of coarse (PM_10–2.5) and fine (PM_2.5) particles in ship-based PM₁₀ and PM_2.5 filter samples from 2020 to 2024 across the Yellow Sea. The observations were primarily conducted during the spring season, when the influence of continental air masses from East Asia is pronounced, and detailed analyses of water-soluble ions and elemental species were performed. In coarse particles, sea salt components (e.g., Na⁺ and Cl⁻) and soil-derived species (e.g., nss-Ca²⁺ and CO₃²⁻) were predominant, whereas fine particles were dominated by secondary inorganic species such as nss-SO₄²⁻, NO₃₋, and NH₄⁺. Source contributions were estimated using Dispersion Normalized Positive Matrix Factorization (DN-PMF), and eight common factors were identified, including sea salt, soil, secondary nitrate, secondary sulfate, oil combustion, biomass burning, marine biogenic emissions, and plant growth. Additionally, an industry factor was uniquely resolved in coarse particles, whereas a mobile source factor was identified in fine particles. In coarse particles, sea salt (30.9%) and soil (15.1%) were the major contributing sources, whereas fine particles were dominated by secondary nitrate (48.6%) and secondary sulfate (15.6%). Potential Source Contribution Function (PSCF) analysis indicated that the sea salt and oil combustion factors in coarse particles were associated with coastal regions of the Yellow Sea and the East China Sea, while the soil factor corresponded spatially with inland regions of northern China. In contrast, the secondary nitrate, secondary sulfate, and biomass burning factors in fine particles showed strong associations with inland regions of eastern China. Using size-resolved DN-PMF and five years of repeated observations over the same marine region, this study provides the first quantitative source apportionment analysis of interannual atmospheric composition variability and long-range transport affecting air quality over the Yellow Sea. Full article

Elevated particulate matter (PM) concentrations pose severe health risks, necessitating green infrastructure mitigation. While deposition is well documented, wind-induced remobilization remains insufficiently quantified. This study establishes a size-fractionated (PM_1–2.5 and PM_2.5–10) wind-induced resuspension and net removal values for six Central European shrub and climbing species (Parthenocissus quinquefolia, Hedera helix, Viburnum opulus, Viburnum lantana, Ligustrum ovalifolium, and Cornus mas) under controlled laboratory conditions. Following standardized aerosol chamber loading, leaves were subjected to constant, laminar airflow velocity of 3 m/s. Numerical quantification of particle counts per unit area (cm²) was performed via scanning electron microscopy with backscattered electron signal processing. Results demonstrate significant interspecific variations. Parthenocissus quinquefolia was most efficient, retaining the highest particle counts (121.6 × 10³ particles/cm² for PM_2.5–10) and achieving net removal rates of 46.3% and 60.5% for PM_1–2.5 and PM_2.5–10, respectively, relative to initial deposition. Cornus mas exhibited the lowest net removal efficiency for coarse particles (21.2% for PM_2.5–10), while Hedera helix showed the highest fractional resuspension rates (k = 1.93 × 10⁻⁴ ∙ s⁻¹ and 2.01 × 10⁻⁴ ∙ s⁻¹, respectively). These species-specific traits are vital for optimizing urban green infrastructure. Ultimately, these findings provide actionable recommendations for targeted plant selection to maximize urban air purification. Full article

High-frequency FY-4B aerosol optical depth (AOD) observations provide useful spatiotemporal constraints for dust nowcasting, but their application over bright deserts is limited by retrieval gaps and high-AOD uncertainty. This study develops a physics-informed spatiotemporal learning framework for 15–60 min FY-4B AOD nowcasting over the Taklimakan Desert. Historical FY-4B AOD, valid masks, ERA5 dynamic fields, model-level diagnostics, and surface constraints are organized on a unified 48 × 64 grid. An LSTM–TCN–Transformer temporal backbone is combined with spatial-context encoding, mask-aware observation encoding, and structured source–transport prediction heads to represent both temporal evolution and spatial plume structures. A physics encoder represents boundary-layer mixing, vertical wind shear, source-region emission, upwind transport, and deposition loss. Mask-aware encoding and structured prediction heads are used to handle missing retrievals, source and transport increments, high-AOD tails, and low-confidence regions. Results show that FY-4B AOD constrains the main dust-belt position and spatial extent within 1 h, with skill decreasing from 15 to 60 min. High-coverage samples show more stable spatial structures, whereas low-coverage and extreme high-AOD cases have larger peak underestimation and boundary errors. The proposed framework improves high-AOD event detection and spatial-structure preservation compared with persistence, advective persistence, ConvLSTM, and ST-UNet baselines. An additional case-based comparison with MODIS MAIAC AOD and MERRA-2 dust optical depth shows partial spatial colocation between predicted high-value footprints and independent aerosol-enhancement references; however, the reported skill scores should still be interpreted mainly as spatiotemporal consistency with the FY-4B AOD product field rather than direct validation of true atmospheric dust loading. Full article