Search Results (425)

Monitoring vegetation biomass dynamics is essential for assessing ecosystem functioning and biodiversity pressures in protected mountain areas, where reduced accessibility limits in situ data collection. This study investigates the multitemporal variation in vegetation biomass within the Cioclovina–Șura Mare–Piatra Roșie strictly protected area of the Grădiștea Muncelului–Cioclovina Natural Park (Southern Carpathians, Romania), using vegetation indices derived from Sentinel-2 imagery for the 2018–2022 period. Four complementary indices (NDVI, SAVI, MSAVI, and LAI) were computed and normalized, then integrated into an original synthetic indicator (BCIS—Biomass Change Integrated Score) for quantifying biomass changes. The results indicate an overall reduction in vegetation biomass, with 89.49% of the area classified under degradation trends, while 4.53% shows regeneration processes. Grasslands and mixed agricultural–natural lands are the most affected habitats, where degradation is linked to anthropogenic pressures and ecotonal vulnerability, whereas broadleaf forests display a high degree of resilience, maintaining substantial proportions of stable or regenerating surfaces. The multispectral integration through the BCIS indicator enabled a more robust detection of critical zones, supporting sustainable vegetation management and biodiversity monitoring in protected mountain ecosystems. Full article

Vector-borne parasites might be transmitted through transfusion, notably Plasmodium spp. and Trypanosoma cruzi. Prevention strategies include blood donor screening, deferral, and blood unit treatment by pathogen inactivation methods. At the end of 2015, in line with European guidelines, Italian legislation introduced a questionnaire to identify donors at risk and their screening by serological methods. In early 2016, the Laboratory of Parasitology at Pisa University Hospital started the serological analysis of donors at risk, referring to Transfusion Services located in northwestern Tuscany. The aim of the present study was to describe the prevalence of seropositive donors observed during 8 years of screening. Donors at risk of transmitting malaria were screened by ELISA (Enzyme Linked Immunosorbent Assay). The DRG ELISA kit was employed until 2020, when it was substituted by the Euroimmun ELISA kit based on the results of a comparative evaluation of available commercial kits. Seropositive donors were offered the possibility of Plasmodium DNA testing by Loop-Mediated AMPlification (LAMP) to exclude current infection. Donors at risk of transmitting Chagas disease were screened by ICT employing recombinant antigen until 2021, when it was substituted by ELISA employing lysate antigen because of its higher accuracy. Seropositive donors were further tested by CLIA, and WB was performed in case of discordant results, according to WHO guidelines for diagnosis of chronic Chagas disease. A total of 3754 donors were tested for anti-Plasmodium antibodies, revealing a 6.8% (95% CI = 6.1–7.7%) seroprevalence. Seropositivity was higher among donors from Sub-Saharan Africa (42.9%; 95% CI = 36.1–49.9%) and Southeast Asia (10.6%; 95% CI = 6.7–16.4%). A lower seropositivity was observed when employing Euroimmun ELISA (4.8; 95% CI = 3.8–5.9%) than DRG ELISA (8.2%; 95% CI = 7.1–9.3%). Seropositivity dropped to 3.6% (95% CI = 2.4–5.6) in 2020, likely because of travel restrictions during the COVID-19 pandemic. None of the tested seropositive donors (n = 20) tested positive for Plasmodium DNA LAMP testing. A high proportion of seroreversion was observed after one year of testing. Among 4285 donors tested for anti-T. cruzi antibodies seroprevalence was 0.7% (95% CI = 0.5–1.1%), a higher value than what was observed in a recent national survey. All seropositive donors were born in Europe or Latin America. Seropositivity was apparently lower with ELISA (0.5%, 95% CI = 0.2–1.2%) than ICT (0.8%, 95% CI = 0.6–1.2%), possibly due to ELISA’s higher specificity, although the difference is not significant. No confirmed cases of chronic Chagas disease were identified. The study emphasizes the importance of defining the serological test employed for screening and the need to confirm seropositive results with further testing. The high seroreversion observed in the study suggests repeating seropositive donor screening after a year to minimize deferral and blood unit loss. Full article

Platinum-based drugs play a pivotal role in contemporary cancer treatment, but their therapeutic utility is often limited by acquired resistance. The diiodido analog, cis-[PtI₂(NH₃)₂] is a promising derivative that has demonstrated the ability to overcome cisplatin resistance in vitro. To establish the molecular basis for this superior activity, we integrated experimental (NMR) spectroscopy with computational density functional theory (DFT) methods to precisely and comparatively understand the drug activation mechanisms. Comparative ¹⁴N NMR experiments elucidated the initial ligand substitution step, confirming halide displacement and a markedly higher tendency for ammonia release from cis-[PtI₂(NH₃)₂], particularly when reacting with sulfur-containing amino acids. Complementary DFT calculations determined the substitution energy values, revealing that the superior leaving-group ability of iodide results in a thermodynamically more favorable activation. Conceptual DFT parameters (softness, hardness, and Fukui indices) further demonstrated that initial substitution induces a strong trans effect, leading to the electronic sensitization of the remaining iodide ligand. This strong agreement between computational predictions and experimental data establishes a coherent molecular activation mechanism for cis-[PtI₂(NH₃)₂], demonstrating that iodide substitution promotes both thermodynamic and electronic activation of the platinum center, which is the key to its distinct pharmacological profile and ability to circumvent resistance. Full article

Humans with chronic biliary tract disease (CBTD) have low serum liposoluble vitamins (A, D, E). Few studies have been performed in veterinary medicine to evaluate whether vitamins vary in canine CBTD. This study aimed to compare liposoluble vitamin between CBTD and healthy dogs. A total of 84 client-owned dogs with CBTD and 50 healthy dogs were included. CBTD diagnosis was based on clinical, blood biochemistry and abdominal ultrasound. Dogs with CBTD were divided into subgroups according to their cholestasis ultrasound severity. To measure vitamin concentrations, leftover serum samples were used. The 25-hydroxyvitamin D, α-tocopherol, and retinol, respectively, vitamin D, E, and A metabolites, were measured with HPLC. Both, 25-hydroxyvitamin D and α-tocopherol were significatively lower in CBTD than in healthy dogs. In contrast, retinol was higher in CBTD dogs. In CBTD dogs, no significant differences in vitamin concentrations considering ultrasound severity were found. Presence of biliary disease in dogs results in lower blood vitamins D and E, and higher vitamin A concentration. Lower vitamins D and E concentration could reflect a possible lipid malabsorption. The higher concentration of vitamin A could be in line with recent human studies, where retinol increases as an expression of dysregulated homeostasis during chronic liver disease. Full article

This study assessed the effectiveness of vegetation index change metrics (ΔVI = Post − Pre) derived from Sentinel-2 imagery for detecting landslide-affected areas and evaluating their relationship with rainfall intensity, thereby enhancing the early-warning potential. The analysis focused on Sancheong-gun, Gyeongsangnam-do, South Korea, where intense rainfall in July 2025 triggered multiple landslides. Pre- and post-event Sentinel-2 Level-2A images (10 m spatial resolution) were used to compute changes in the Normalized Difference Vegetation Index (ΔNDVI), Soil-Adjusted Vegetation Index (ΔSAVI), Modified Soil-Adjusted Vegetation Index (ΔMSAVI), Normalized Difference Moisture Index (ΔNDMI), and Global Vegetation Moisture Index (ΔGVMI) over the landslide-affected post-disaster (PD) and non-damaged (ND) areas. Sensitivity was assessed based on the differences in mean ΔVI between the PD and ND areas, Welch’s t-statistics, and Cohen’s d values. All indices exhibited significant differences between the PD and ND areas (p < 0.001), with ΔMSAVI showing the highest sensitivity (MSAVI > GVMI ≈ SAVI > NDVI > NDMI). Correlation analysis revealed that ΔMSAVI had the strongest positive association with rainfall accumulation (72 h: r = 0.54; 7 days: r = 0.49), indicating that greater rainfall corresponded to stronger vegetation degradation signals. These findings highlight ΔMSAVI as a robust and responsive indicator of rainfall-triggered landslides, supporting its integration into satellite-based early-warning and rapid damage detection systems for improved landslide monitoring and response. Full article

Spectral indices are widely used to assess vegetation fire severity following wildland fires. Although essential, ground-based assessments of how such indices change due to varying fire intensities remain limited, especially with deciduous tree species that exhibit resprouting. In this paper, we evaluate the efficacy of detecting post-fire physiological change and top kill in quaking aspen (Populus tremuloides) saplings using differenced spectral indices. Saplings (n = 64) were burned under controlled conditions over a range of discrete fire intensity levels from 0 to 4.0 MJ m⁻², and reflectance was collected pre-fire and at six post-fire intervals up to 16 weeks. Ten spectral indices (CCI, CSI, MIRBI, NDVIL8, NBR, NBRL8, PRI, SAVI, SW-NIR_ratio, and SW-SW_ratio) were calculated, differenced from pre-fire, and related to the change in net photosynthesis and top kill. Fire intensity most strongly influenced the observed spectral changes at weeks 1–2 post-fire, especially for ΔCSI, ΔCCI, and ΔPRI. Pre- to post-fire change in net photosynthesis was strongly related (Tjur’s R² > 0.5) with ΔCCI, ΔCSI, ΔNBRL8, and the ΔSW–NIR ratio at one week post-fire. Of the spectral indices assessed, ΔCCI and ΔPRI were most effective at predicting top kill. This study illustrates the potential of spectral indices for monitoring vegetation fire severity in deciduous tree species. Full article

Salmonella enterica is a major cause of foodborne illness globally. We analysed 41,945 food samples collected under official surveillance in Piedmont (north-western Italy) between 2013 and 2023 to characterise contamination patterns and evaluate an integrated analytical framework combining classical statistical modelling with machine-learning prediction. Overall prevalence was low (2.20%; 95% CI: 2.06–2.35) but heterogeneous across matrices, with poultry and pork displaying the highest contamination levels (11.8% and 7.14%). Risk increased at distribution/retail stages, and contamination declined markedly from 2013 to 2018, with lower levels in late autumn. Meteorological factors had minimal influence. Mixed-effects models identified food category and production stage as the main determinants of contamination, while the XGBoost algorithm showed stable predictive performance (median absolute error ≈ 0.02) and spatially coherent estimates. SHAP analyses confirmed food composition variables as the dominant predictors. These findings highlight persistent vulnerabilities within poultry and swine supply chains, particularly at post-production stages, and illustrate the complementary value of combining explanatory and predictive approaches to strengthen risk-based, One Health-aligned food-safety surveillance. Full article

The accurate estimation of grapevine biophysical parameters is important for decision support in precision viticulture. This study addresses the use of unmanned aerial vehicle (UAV) multispectral data and machine learning (ML) techniques to estimate leaf area index (LAI), pruning wood biomass, and yield, across mixed-variety vineyards in the Douro Region of Portugal. Data were collected at three phenological stages, from veraison to maturation and two modeling approaches were tested: one using only spectral features, and another combining spectral and geometric features derived from photogrammetric elevation data. Multiple linear regression (MLR) and five ML algorithms were applied, with feature selection performed using both forward and backward selection procedures. Logarithmic transformations were used to mitigate data skewness. Overall, ML algorithms provided better predictive performance than MLR, particularly when geometric features were included. At harvest-ready, Random Forest achieved the highest accuracy for LAI (R² = 0.83) and yield (R² = 0.75), while MLR produced the most accurate estimates for pruning wood biomass (R² = 0.83). Among geometric variables, canopy area was the most informative. For spectral data, the Modified Soil-Adjusted Vegetation Index (MSAVI) and the Soil-Adjusted Vegetation Index (SAVI) were the most relevant. The models performed well across grapevine varieties, indicating that UAV-based monitoring can serve as a practical, non-invasive, and scalable approach for vineyard management in heterogeneous vineyards. Full article

Wheat (Triticum aestivum L.) is a key crop in Spain, especially in Castilla and León Region. However, there are few studies evaluating predictive models based on spectral indices and multivariate analysis to estimate yield in direct seeding (DS) and conventional seeding (CS) systems. This study addresses this need by implementing a split-plot experimental design in the city of Palencia, Spain, analyzing crop physiological data and nine spectral indices derived from multispectral aerial images captured by drones. The analysis included multivariate techniques such as Principal Component Analysis (PCA) and Random Forest (RF), supplemented with statistical tests, ROC curves, and prediction analysis. The results showed that the RF model successfully classified treatments with 93.75% accuracy and a Kappa index of 0.875, highlighting performance, nitrogen, and protein as key variables. Among the vegetation indices, the Soil-Adjusted Vegetation Index (SAVI) and the Advanced Vegetation Index (AVI) were the most relevant in the flowering stage, with ROC curve values of 0.7778 and 0.8025, respectively. Spearman’s correlations confirmed a significant relationship between these indices and key physiological variables, allowing to distinguish between DS and CS systems. The RF-based prediction model for performance showed R² values above 91% in the indices with the highest correlation. However, predictive capacity was higher in DS, suggesting that conditions inherent in non-mechanized handling significantly influence model performance. This highlights the importance of using non-destructive procedures to estimate production, enabling the development of adaptive and sustainable strategies that contribute to efficient agricultural production, since it is possible to anticipate crop yields before harvest, optimizing resources such as fertilizers and water. Full article

Unmanned aerial vehicles (UAVs) with multispectral sensors are transforming precision viticulture by enabling detailed monitoring of vineyard variability. Vegetation indices such as NDVI, NDRE, GNDVI, and SAVI are widely applied to estimate vine vigor, canopy structure, and water status. Beyond agronomic traits, UAV-derived indices can inform grape composition, including sugar content (°Brix), total phenolics, anthocyanins, titratable acidity, berry weight, and yield variables measurable in the field or laboratory to validate spectral predictions. Strengths of UAV approaches include high spatial resolution, rapid data acquisition, and flexibility across vineyard blocks, while limitations involve index saturation in dense canopies (e.g., Merlot, Cabernet Sauvignon), environmental sensitivity, and calibration requirements across varieties and phenological cycles. Integrating UAV data with ground-based measurements (leaf sampling, yield mapping, proximal or thermal sensors) improves model accuracy and stress detection. Abiotic stresses (water deficit, nutrient deficiency) can be distinguished from biotic factors (pest and fungal infections), supporting timely interventions. Compared to manned aircraft or satellite platforms, UAVs offer cost-effective, high-resolution imagery for precision vineyard management. Future directions include combining UAV indices with machine learning and data fusion to predict grape maturity and wine quality, enhancing decision-making in sustainable viticulture and precision enology. Full article

Bivalve molluscs represent an important food source and have a significant economic impact through their commercialization in many countries. As high-capacity filter feeders, they can bioaccumulate contaminants and pathogens, creating tangible consumer health risks. This study presents the first comprehensive patent landscape of bivalve mollusc decontamination technologies indexed in international patent databases (Espacenet). The survey identified 30 patents filed between 1989 and 2025. Unlike reviews based solely on scientific literature, this work provides, for the first time, a global mapping of technological developments aimed at enhancing the safety of bivalves-derived foods. The analysis highlights depuration as the predominant technology, which continues to be refined and optimized. It also reveals the emergence of disruptive approaches—such as photodynamic sterilization, the use of probiotics, immunopotentiators, natural antimicrobial compounds, and genetic hybridization—developed to preserve the viability and sensory quality of the organisms. The novelty of this study lies in providing a technological overview of innovation within the aquaculture sector, emphasizing the transition from conventional methods to cleaner, integrated, and sustainable technologies. Furthermore, the research identifies the advancement of hybrid decontamination systems that combine microbiological efficiency, environmental preservation, and commercial value, contributing to safer and more technologically advanced shellfish production. Full article

Efficient water management is essential for optimizing agricultural productivity in water-scarce regions such as the Lis Valley, Portugal. In situ measurements of soil moisture content (SMC) and electrical conductivity (EC), together with Sentinel-2-derived vegetation indices, were used to assess the crop water status and evapotranspiration dynamics of pumpkin (Cucurbita moschata ‘Butternut’) during the 2020 growing season. SMC and EC were measured at depths of 10, 20, 30, 50, and 70 cm using a TDR sensor, with strong correlations observed in the upper layers, indicating that EC can complement direct SMC measurements in characterizing near-surface moisture conditions. Sentinel-2 imagery was acquired to compute NDVI, SAVI, EVI, and GCI. In addition, NDVI values obtained from both a GreenSeeker^® sensor and Sentinel-2 imagery were compared, showing a similar temporal pattern during the season. By replacing the standard FAO-56 K_c values with those derived from each vegetation index, ET_a was recalculated to incorporate actual crop condition variability detected via satellite. ET_a estimates from RS-assisted vegetation indices agreed with those obtained using the FAO-56 method; independent ET_a measurements were not available for validation. Although such agreement is partly expected due to calibration, its confirmation for Cucurbita moschata under Mediterranean conditions—where published references are scarce—reinforces the method’s practical applicability for water management in data-limited settings. Water Productivity (WP) was estimated as 8.32 kg m⁻³, and Water Use Efficiency (WUE FAO-56) was calculated as 0.64 kg m⁻³, indicating high water use efficiency under Mediterranean smallholder irrigation conditions. These findings demonstrate that integrating high-resolution RS with continuous soil moisture monitoring can enhance precision irrigation strategies, increase crop yields, and conserve water resources in the Lis Valley. Full article

Interferons (IFNs) are pleiotropic cytokines involved in antiviral defense, immune regulation, and tumor suppression. In myeloproliferative neoplasms (MPNs) and related disorders—including classical BCR, ABL1-negative MPNs, chronic myeloid leukemia (CML), and rarer entities such as chronic neutrophilic leukemia and hypereosinophilic syndromes—disease pathogenesis arises from a spectrum of somatic and structural genetic abnormalities and chronic inflammation, in which IFNs play a paradoxical role. They contribute to disease pathogenesis by promoting abnormal hematopoiesis and immune dysregulation, while also representing a therapeutic option capable of inducing hematologic and molecular remissions. This review outlines the biology and classification of IFNs, focusing on their signaling pathways and downstream effects in both normal and malignant hematopoiesis. We discuss the dual impact of IFN signaling on hematopoietic stem cells, including induction of proliferation, senescence, apoptosis, and DNA damage, and how these mechanisms may both sustain clonal evolution and facilitate disease control. Clinical data supporting the efficacy and safety of IFN-α, particularly pegylated formulations, in polycythemia vera, essential thrombocythemia, myelofibrosis, and chronic myeloid leukemia are reviewed, along with insights into next-generation IFNs and combination therapies. Understanding the dichotomous effects of IFNs in MPNs not only clarifies their role in disease biology but also informs their optimal use in clinical practice. This duality highlights the need for personalized approaches to IFN-based therapies. Full article

Climate change is associated with an increase in the frequency of extreme weather that threatens emotional well-being, with some research pointing to increased vulnerability among older adults. We investigated how age relates to depression and anxiety following adversities due to extreme weather or natural disaster. Socioemotional selectivity theory (SST) posits that older age buffers against emotional distress. The strength and vulnerability integration model (SAVI) posits that this age-related advantage is attenuated during periods of acute stress. Members (n = 9761, M age = 52.22, SD = 16.36 yrs) of a nationally representative, probability-based US internet panel, the Understanding America Study (UAS), reported their experience with extreme weather or natural disaster (e.g., severe storms, tornado, flood), associated adversities (e.g., property loss), and depression and anxiety over the past month. Of the 1075 respondents experiencing extreme weather or natural disaster, 216 reported related adversity. Those experiencing adversity reported more anxiety and depression than those with no events, while extreme weather or disaster alone made no significant difference. Consistent with SST, older age was associated with less depression and anxiety. This age-related benefit was most apparent among those experiencing weather- or disaster-related adversity, even when controlling for socio-demographic correlates. Findings highlight age-related emotional resilience with implications for climate change policy and practice. Full article

The evolution of Industrial Internet of Things (IIoT) systems demands flexible and intelligent architectures capable of addressing low-latency requirements, real-time analytics, and adaptive resource management. In this context, softwarized edge computing emerges as a key enabler, supporting advanced IoT deployments through programmable infrastructures, distributed intelligence, and seamless integration with cloud environments. This paper presents an extended and publicly available proof of concept (PoC) for a softwarized, data-driven architecture designed to operate across the cloud/edge/IoT continuum. The proposed architecture incorporates containerized microservices, open standards, and ML-based inference services to enable runtime decision-making and on-the-fly network reconfiguration based on real-time telemetry from IIoT nodes. Unlike traditional solutions, our approach leverages a modular control plane capable of triggering dynamic adaptations in the system through RESTful communication with a cloud-hosted inference engine, thus enhancing responsiveness and autonomy. We evaluate the system in representative IIoT scenarios involving multi-agent collaboration, showcasing its ability to process data at the edge, minimize latency, and support real-time decision-making. This work contributes to the ongoing efforts toward building advanced IoT ecosystems by bridging conceptual designs and practical implementations, offering a robust foundation for future research and deployment in intelligent, software-defined industrial environments. Full article