卡美日诺大学

Question Spatial grain size and sampling design are crucial to assess plant diversity patterns, yet their effects on alpha, beta, and gamma diversity along elevational gradients remain poorly understood.Location We investigated these effects along an elevational gradient in the Central Apennines (Italy) ranging from 1100 to 2486 m a.s.l.Methods Plant presence-absence data were recorded from 83 randomly selected nested plots, each containing seven grain sizes ranging from 0.25 m & times; 0.25 m to 16 m & times; 16 m. Alpha, beta, and gamma diversity were calculated at both the nested plot and grain size levels and analyzed along the elevational gradient. Gamma was assessed within 100-m elevational bands by aggregating species occurrences across nested plots and grain sizes within each band. Beta diversity was calculated among all nested plots and grain sizes, as well as within elevational bands, using S & oslash;rensen dissimilarity.Results Our results revealed a significant effect of grain size on elevational diversity patterns. Alpha diversity exhibited a stronger pattern at larger grain size, with species richness decreasing along the elevational gradient. Gamma diversity mirrored alpha diversity trends, increasing with grain size but decreasing with elevation. Beta diversity did not change with elevation but decreased with grain size.Conclusion These results emphasize the strong influence of grain size on plant diversity patterns along elevation gradients, highlighting its importance in biodiversity assessments. The nested sampling approach used appears to be a promising tool for testing diversity patterns along elevational gradients, offering a robust framework for future ecological studies.

Previous extended x-ray absorption fine structure (EXAFS) studies have shown evidence of anomalous contributions in the atomic background function whose origin was debated in the literature. The present study presents advanced multiple scattering calculations aimed to put to a test the existence and origin of the so-called atomic XAFS (AXAFS) contribution, discussed in details in some previous works. We show with specific examples that the muffin-tin approximation normally used in EXAFS calculations is inappropriate to study this phenomenon as spurious oscillations in the atomic background are related to the potential truncation at the border of the muffin-tin spheres. We thus employed a more sophisticated approach beyond the muffin-tin approximation based on the full-potential multiple scattering theory and self-consistent charge densities. The comparison of EXAFS calculations with previous experimental results allowed us to exclude the presence of the AXAFS as due to modulations of the charge density as previously suggested. This study confirms the importance of a proper account of the atomic background in EXAFS data analysis, which may contain effects beyond the single-electron approximation but are found not affected by AXAFS. We conclude that the contribution of AXAFS in EXAFS experiments, if existing at all, is negligible and should not be considered in EXAFS data analysis.

Extreme rainfall represents one of the main triggers of shallow landslides in mountainous and hilly environments; however, the rapid identification of rainfall-induced slope failures remains challenging due to the lack of systematic post-event inventories and the limited availability of field data. This gap constrains the development of reliable diagnostic and predictive frameworks, particularly in regions increasingly affected by extreme precipitation events. This study aims to evaluate the capability of satellite-derived spectral indices to detect rainfall-triggered landslides and to interpret their spectral responses within a geological and environmental geochemical context. The analysis focuses on the extreme precipitation event of 1–3 March 2011 in the Marche Region (Central Italy), an area characterized by widespread marly–clayey and Plio–Pleistocene fine-grained formations. Multitemporal Landsat 5 imagery at 30 m spatial resolution acquired between 2007 and 2011 was used to derive seven spectral indices related to vegetation cover, soil exposure, moisture conditions, and surface mineralogy (NDVI, NDWI, NBR, NDSI, BSI, SCI, and CMR). Owing to multicollinearity among predictors, each index was analyzed independently using two complementary statistical approaches: Binary Logistic Regression (BLR), representing a parametric linear framework, and the QUEST decision tree algorithm, designed to capture non-linear relationships and threshold effects. Vegetation-related indices (NDVI and NBR) also show strong predictive capability, with AUC values ranging from 0.94 to 0.96 across both models. Moisture-sensitive indices exhibit a predominantly non-linear response: NDWI performs markedly better under QUEST than BLR (AUC 0.88 vs. 0.58), while NDSI shows the opposite pattern (AUC 0.81 with BLR vs. 0.54 with QUEST), highlighting the complementary strengths of the two modeling frameworks. Importantly, both BLR and QUEST converge on consistent and robust threshold values, providing objective criteria for post-event landslide detection. The identified spectral thresholds are physically interpreted in terms of short-term geochemical weakening processes affecting clay-rich and marly materials. Intense rainfall promotes hydration of expandable clay minerals, partial dissolution of carbonate cement, and rapid soil–water–mineral interactions, leading to reduced soil cohesion and enhanced slope instability. By integrating remote sensing, statistical modeling, and environmental geochemistry, this study offers a practical and transferable approach for rapid post-event landslide mapping. The framework is intended to support hazard assessment in sedimentary terrains prone to rainfall-induced failures, with careful attention to its potential applicability in similar settings. Graphical abstract descriptions: The graphical abstract schematically illustrates the conceptual framework and methodological workflow of the study, integrating geological, geochemical, remote sensing, and statistical components into a unified representation. The upper section depicts the geomorphological and lithological setting of the Marche Region in Central Italy, highlighting marly–clayey formations and Plio–Pleistocene deposits that are highly susceptible to rainfall-triggered slope instability. Intense precipitation is shown to initiate landslide processes through infiltration into fine-grained materials, activating physico-chemical mechanisms such as clay hydration, carbonate dissolution, and cation exchange, which lead to modifications in pore-water chemistry and progressive reduction of soil shear strength. The central portion of the diagram connects these subsurface processes to surface responses detectable by satellite remote sensing, introducing key spectral indices including the Soil Composition Index (SCI), Bare Soil Index (BSI), and Normalized Difference Vegetation Index (NDVI), which capture variations in mineralogical composition, bare soil exposure, and vegetation cover associated with landslide occurrence. The lower section presents the analytical phase, where spectral information is processed through binary logistic regression and decision tree models to quantify landslide probability and identify threshold values distinguishing stable and unstable terrain. Finally, the integration of geochemical interpretation, satellite data, and predictive modeling is linked to landslide hazard assessment, emphasizing the interdisciplinary approach adopted in the study. The graphical abstract thus condenses the entire research structure into a single coherent visual scheme, enabling an immediate perception of the scientific logic, methodological sequence, and interpretative outcomes of the investigation. Rapid post-event detection of rainfall-triggered landslides using Landsat-derived spectral indices. Comparative use of Binary Logistic Regression and QUEST decision tree models. SCI and BSI identified as the most effective indices (AUC up to 0.99). Robust and consistent spectral thresholds for landslide discrimination. Spectral thresholds interpreted in terms of geochemical weakening of clay-rich and marly materials during extreme rainfall.

Introduction: Advances in pediatric oncology have increased survival rates of patients. To support the long-term care of these survivors, the Survivorship Passport (SurPass) provides a personalized, portable summary of their diagnosis, treatments, and potential sequelae. Objective: This study investigates the correlation between SurPass use and longterm knowledge gains among survivors, as measured by its impact on the accuracy of treatment recall and awareness of potential long-term effects. Methods: This observational study included long-term survivors classified into two arms: Those with SurPass (Arm A) and those without (Arm B). A structured questionnaire assessed recall of diagnosis, chemotherapy, radiotherapy, and awareness of late effects. Responses were scored for accuracy, and between-group comparisons were performed. Results: Survivors with SurPass, who are typically followed up in specialized centers, demonstrated markedly higher recall accuracy than those without it. Correct recollection of radiotherapy and chemotherapy was higher in Arm A by 64% (p=0.05) and 48% (p=0.03), respectively. Awareness of late effects and adherence to structured follow-up were also greater within Arm A. The SurPass mitigated the expected decline in recall accuracy over time, particularly for survivors treated at younger ages.

The global increase in tea consumption has led to significant amounts of tea waste, particularly spent tea leaves (STLs), which are commonly discarded despite retaining a significant quantity of fiber, proteins, and functional compounds. This study aimed to explore the reuse of STLs to produce a sustainable variant of kombucha, a beverage with health-promoting properties obtained through sweetened tea fermentation. Two kombucha formulations were prepared: a traditional one using fresh tea and an experimental formulation with the addition of post-brewing STLs directly during fermentation. The final goal was to understand the impact of STLs on fermentation dynamics, chemical characteristics, and volatile organic compounds (VOCs) profile. Microbiological analyses included total aerobic mesophilic bacteria, lactic acid bacteria, yeasts, molds, and acetic acid bacteria count, while chemical parameters such as pH, color, and °Brix were measured, alongside spectrophotometric quantification for bioactive compounds. VOCs were identified through gas chromatography-mass spectrometry (HSPME-GC/MS). The results showed that STLs effectively supported microbial fermentation, and fermentation started earlier compared to the conventional process. Distinct differences were observed in VOCs profiles especially for terpenes content, suggesting that the STLs directly influence aroma development. This research highlights the potential of STLs as a valuable ingredient in kombucha production and demonstrates how the fermentation dynamics could be modified by valorizing discarded tea residues.