Search Results (53)

Atmospheric carbon dioxide (CO₂) levels are rising globally, yet their multiscale variability in remote oceanic regions remains poorly characterized. This study examines a 45-year monthly CO₂ record (1980–2024) from the Azores, a subtropical North Atlantic site, using a spectral and statistical framework. The series was decomposed into high- and low-frequency components via Butterworth filtering and analyzed with the Correlogram-Based Periodogram (CBP) and Monte Carlo significance testing. The residual component robustly recovered the expected seasonal cycle (~12 months), validating the methodology. The trend component revealed an apparent enhancement in low-frequency spectral power, largely explained by the accelerating long-term increase. Control tests with a synthetic quadratic trend and polynomial detrending indicate a weak ~11-year enhancement in low-frequency power that is not robust under a red-noise (AR(1)) null. Segmented regressions showed a sustained and accelerating increase in CO₂ accumulation over the past four decades, consistent with Mauna Loa. These results demonstrate the importance of long-term monitoring in remote regions while highlighting both the potential and limitations of spectral methods for detecting weak low-frequency signals in greenhouse gas records. Full article

To address the multipath delay estimation problem in distributed hydrophone passive localization systems, a global energy minimization-based method is proposed in this paper. In this method, correlation pulses are treated as tracking targets, and their trajectories are estimated from correlograms formed by multiple frames. Specifically, an energy function is designed to jointly encode pulse similarity, motion continuity, trajectory persistence, data fidelity, and regularization, thereby reformulating multipath delay estimation as a global optimization problem. In order to balance the discreteness of observations and the continuity of trajectories, the optimization process is implemented alternating between discrete association (solved via $\alpha$-expansion) and continuous trajectory fitting (using weighted cubic splines). Furthermore, a dynamic hypothesis space expansion strategy based on trajectory merging and splitting is introduced to improve robustness while accelerating convergence. By exploiting both the intrinsic characteristics of correlation pulses in multi-frame processing and the physical properties of motion trajectories, the proposed method achieves higher tracking accuracy without requiring prior knowledge of the number of delay trajectories in a noisy environment. Numerical simulations under various noise conditions and sea trial results validate the superiorities of the proposed multipath delay estimation method. Full article

Soil salinization is increasingly affecting agricultural areas worldwide, reducing soil quality and crop yields. Surface salinization evidences present complex spectral features, increasing in depth with increasing salt concentrations. For this reason, low salinization detection provides a complex challenge to test the capabilities of new-generation hyperspectral satellites. The aim of this study is to test the capability of the new generation of hyperspectral satellites (EnMAP) in detecting early stages and low levels of topsoil salinization and to investigate the differences between laboratory and image spectra to take into account their influence on model performance. The area of study, the Grosseto plain, located in central Italy, presented heterogeneous salinity levels ($E{C}_{max}$= 11.7 dS/m, $E{C}_{mean}$= 0.99 dS/m). We investigated the salt-affected soil spectral behaviour with both laboratory-acquired spectra ($n_{obs}$= 60) and EnMAP-derived spectra ($n_{obs}$= 20). Both datasets were pre-processed with multiple data transformation algorithms and 2D correlograms, PLSR and the Random Forest regressor were tested to identify the best model for salinity detection. Two-dimensional correlograms resulted in an $R^2$ of 0.88 for laboratory data and 0.63 for EnMAP data. PLSR proved to have the worst performance. The Random Forest regressor proved its capability in detecting complex spectral features, with $R^2$ scores of 0.72 for laboratory data and 0.60 for EnMAP. The Random Forest model provides very satisfactory mapping capabilities when tested on the whole study area. The results highlight that the EnMAP-derived dataset produces similar results to those of ASD laboratory spectra, providing evidences regarding EnMAP’s predictive capability to detect early stages of topsoil salinization. Full article

This study investigated the enhancement of the mechanical and tribological properties of MWCNT-reinforced bio-based epoxy composites through systematic experiments and analysis. Composites incorporating MWCNTs at varying weight percentages were evaluated for hardness, wear rate, interfacial shear strength, and friction coefficient under diverse load, sliding speed, and distance conditions. An optimal MWCNT content of 0.3–0.4% resulted in a maximum hardness of 4 GPa and a minimum wear rate of 0.0058 mm³/N·m, demonstrating a substantial improvement over the non-reinforced system. FTIR and XRD analyses confirmed robust interfacial bonding between the MWCNTs and epoxy matrix, while molecular dynamics simulations revealed cohesive energy density and stress distribution profiles. The Taguchi optimization identified the MWCNT weight percentage as the most influential parameter, contributing over 85% to wear rate reduction. Contour plots and correlograms further illustrate the parameter interdependencies, emphasizing the role of MWCNT dispersion in enhancing the composite properties. These findings establish that MWCNT-reinforced bio-based epoxy composites are promising candidates for high-performance and sustainable tribological applications. Full article

This study analyzes the temporal dynamics of instrumental seismicity recorded in the Pertusillo reservoir area (Southern Italy) between 2001 and 2018. The Gutenberg–Richter analysis of the frequency–magnitude distribution reveals that the seismic catalog is complete for events with magnitudes $M\ge 1.1$. The time-clustering of the sequence is at both global and local levels with a coefficient of variation $C_v$ and $L_v$ significantly beyond the 95% confidence band. The Allan Factor method, applied to the series of earthquake occurrence times, corroborates the found time-clustering, showing a bi-fractal behavior indicated by the co-existence of two scaling regimes with a cutoff time scale ${\tau }_c\approx 45$ days and two different fractal exponents, $\alpha \approx 0.3$ for time scales less than ${\tau }_c$ and $\alpha \approx 1.2$ for larger ones. The application of the correlogram-based periodogram to both the monthly number of events and the monthly mean water level of the Pertusillo reservoir identifies the yearly cycle as the most significant in both variables. The connection between seismicity and the water level is also demonstrated by the value above 0.5 of the Average Edge Overlap (AEO), a topological metric derived from the Visibility Graph method applied to both the monthly variables. Furthermore, the variation in the AEO between the monthly mean water level and the monthly number of events, along with the time delay between them, indicates that the first leads the second by 1 month. Full article

Objective: The apoptotic effects of odoroside A on lung cancer cells were studied in our project. We also supported and extended our experimentally-proven results via bioinformatics analysis on human lung cancer tissues. Materials and Methods: In vitro studies were conducted using the A549 cell line. Cell proliferation was evaluated through a CCK-8 assay. For gene expression analysis, the qRT-PCR method was used, while CASP3 protein levels were detected using Western blotting and ELISA. Moreover, the oxidant status of cells was determined by measuring TAS and TOS levels. To construct a protein–protein interaction network, STRING analysis was performed. For the determination of differential expression of apoptosis-related genes, the GEPIA tool was utilized. Kaplan–Meier plots with overall survival, disease-specific survival and progression free intervals were obtained from UCSC Xena to evaluate the prognostic value of caspases. Results: The gene expression levels of CASP3, CASP7, CASP8, CASP9, FAS, and FADD were elevated between 4–16 fold in Odo A-treated lung cancer cells compared to controls. CASP3 protein expression was significantly higher in Odo A-treated cancerous cells than the control group. Low TAS (0.5700 ± 0.0067 in Odo A vs. 0.6437 ± 0.0151 in control) and high TOS (0.82800 ± 0.0208 in Odo A vs. 0.6263 ± 0.0258 in control) levels as well as high OSI values (1.4531 ± 0.0414 in Odo A vs. 0.9748 ± 0.0539 in control) were obtained. Correlogram and protein–protein network analyses suggested strong correlations and interactions among target genes. Lastly, Kaplan–Meier analysis showed no prognostic value of caspases, but potential therapeutic targets in lung cancer. Conclusions: Anti-cancer, prooxidative and therapeutic effects of Odo A on lung cancer cells were shown in our study. These data were supported and extended via computational analyses that we performed. In conclusion, Odo A could be used in clinics to treat patients with lung cancer. Full article

Ecosystem services (ESs) are extremely important, specifically in urban areas. Urban forests, even representing a pivotal role in global sustainability, have been converted into different human-modified landscapes. This paper aims to analyze the ES provided by the urban areas of 25 cities of the Atlantic Forest in Brazil. We used i-Tree Canopy v.7.1 to classify the land use. We quantified the monetary benefits of the urban vegetation and used socioeconomic variables (i.e., total population, population density, Human Development Index (HDI), and Gross Domestic Product (GDP) per capita) to analyze if the ecosystem services or the land uses are associated with this. Our data reveal that together, the cities studied sequester a significant total of 235.3 kilotonnes of carbon and a substantial 864.82 kilotonnes of CO₂ Equivalent (CO₂ Equiv.) annually. Furthermore, together, they also store a total of 4861.19 kilotonnes of carbon and 17,824.32 kilotonnes of CO₂ Equiv. We found out that the average monetary estimate of annual carbon sequestration was USD 3.57 million, while the average stored estimate was USD 73.76 million. Spearman’s correlogram showed a strong positive correlation between density and the percentage of impervious cover non-plantable no trees (IN) in urban areas (p < 0.001). IN was also positively correlated with HDI (p = 0.01), indicating that urban areas with higher HDI tend to have larger impervious areas. Our data suggest essential insights about the ecosystem services provided by urban areas and can serve as significant findings to drive policymakers’ attention to whether they want to provide more ecosystem services in cities. Full article

Predetermining the risk of possible future droughts enables proactive measures to be taken in key areas such as agriculture, water management, and food security. Through these predictions, governments, non-governmental organizations, and farmers can develop water-saving strategies, encourage more efficient use of water, and minimize economic losses that may occur due to drought. Thus, future drought forecasts stand out as a strategic planning tool for the protection of natural resources. To achieve this aim, forecasted drought conditions for the next decade (2024–2034) at nine meteorological stations in the Sakarya basin, located northwest of Türkiye, are examined, using historical monthly precipitation data from 1991 to 2023. This study uses the Standardized Precipitation Index (SPI) and Long Short-Term Memory (LSTM) deep learning methods to investigate future meteorological droughts. The research confirms the compatibility and reliability of the LSTM method for forecasting meteorological droughts by comparing historical and forecasted SPI values’ correlograms and trends. In addition, drought maps are created to visually represent the spatial distribution of the most severe droughts expected in the coming years, and areas at risk of drought in the Sakarya Basin are determined. The study contributes to the limited literature on forward-looking drought forecasts in the Sakarya Basin and provides valuable information for long-term water resource planning and drought management in the region. Full article

Pancreatic ductal adenocarcinoma (PDAC) is an extremely lethal tumor with increasing incidence, presenting numerous clinical challenges. The histopathological examination of novel, unexplored biomarkers offers a promising avenue for research, with significant translational potential for improving patient outcomes. In this study, we evaluated the prognostic significance of ferroptosis markers (TFRC, ALOX-5, ACSL-4, and GPX-4), circadian clock regulators (CLOCK, BMAL1, PER1, PER2), and KLOTHO in a retrospective cohort of 41 patients deceased by PDAC. Immunohistochemical techniques (IHC) and multiple statistical analyses (Kaplan–Meier curves, correlograms, and multinomial linear regression models) were performed. Our findings reveal that ferroptosis markers are directly associated with PDAC mortality, while circadian regulators and KLOTHO are inversely associated. Notably, TFRC emerged as the strongest risk marker associated with mortality (HR = 35.905), whereas CLOCK was identified as the most significant protective marker (HR = 0.01832). Correlation analyses indicate that ferroptosis markers are positively correlated with each other, as are circadian regulators, which also positively correlate with KLOTHO expression. In contrast, KLOTHO and circadian regulators exhibit inverse correlations with ferroptosis markers. Among the clinical variables examined, only the presence of chronic pathologies showed an association with the expression patterns of several proteins studied. These findings underscore the complexity of PDAC pathogenesis and highlight the need for further research into the specific molecular mechanisms driving disease progression. Full article

Pancreatic cancer is a type of gastrointestinal tumor with a growing incidence and mortality worldwide. Pancreatic ductal adenocarcinoma (PDAC) constitutes 90% of cases, and late-stage diagnosis is common, leading to a 5-year survival rate of less than 10% in high-income countries. The use of biomarkers has different proven translational applications, facilitating early diagnosis, accurate prognosis and identification of potential therapeutic targets. Several studies have shown a correlation between the tissue expression levels of various molecules, measured through immunohistochemistry (IHC), and survival rates in PDAC. Following the hallmarks of cancer, epigenetic and metabolic reprogramming, together with immune evasion and tumor-promoted inflammation, plays a critical role in cancer initiation and development. In this study, we aim to explore via IHC and Kaplan–Meier analyses the prognostic value of various epigenetic-related markers (histones 3 and 4 (H3/H4), histone acetyl transferase 1 (HAT-1), Anti-Silencing Function 1 protein (ASF1), Nuclear Autoantigenic Sperm Protein (NASP), Retinol Binding Protein 7 (RBBP7), importin 4 (IPO4) and IPO5), metabolic regulators (Phosphoglycerate mutase (PGAM)) and inflammatory mediators (allograft inflammatory factor 1 (AIF-1), interleukin 10 (IL-10), IL-12A and IL-18) in patients with PDAC. Also, through a correlation analysis, we have explored the possible interconnections in the expression levels of these molecules. Our results show that higher expression levels of these molecules are directly associated with poorer survival rates in PDAC patients, except in the case of IL-10, which shows an inverse association with mortality. HAT1 was the molecule more clearly associated with mortality, with a hazard risk of 21.74. The correlogram demonstrates an important correlation between almost all molecules studied (except in the case of IL-18), highlighting potential interactions between these molecules. Overall, our study demonstrates the relevance of including different markers from IHC techniques in order to identify unexplored molecules to develop more accurate prognosis methods and possible targeted therapies. Additionally, our correlation analysis reveals potential interactions among these markers, offering insights into PDAC’s pathogenesis and paving the way for targeted therapies tailored to individual patient profiles. Future studies should be conducted to confirm the prognostic value of these components in PDAC in a broader sample size, as well as to evaluate the possible biological networks connecting them. Full article

Factors responsible for species distribution of benthic macroinvertebrates, including responses at different spatial scales, have been previously investigated. The aim of the present research was to review the most relevant factors explaining chironomid species distribution focusing on factors operating at different spatial scales, such as latitude, longitude, altitude, substrate, salinity, water temperature, current velocity, conductivity, acidity, dissolved oxygen, nutrient content etc. acting at regional levels and at a large or small water basin level. Data including chironomid species abundances from different lentic and lotic waters in Italy and other surrounding countries were analyzed using partial canonical correspondence analysis (pCCA) and multiple discriminant analysis (DISCR). Spatial analyses, including univariate Moran’s I correlograms, multivariate Mantel correlograms and Moran’s eigenvector maps (MEMs), were thereafter carried out. The results showed that habitat type, including different types of lotic waters (i.e., kryal, crenal, rhithral, potamal) and different lake types (i.e., littoral, sublittoral, profundal zones), is the most significant factor separating chironomid assemblages, while spatial factors act only as indirect influencers. Full article

In this work, an advanced 2D nonparametric correlogram method is presented to cope with output-only measurements of linear (slow) time-varying systems. The proposed method is a novel generalization of the kernel function-based regularization techniques that have been developed for estimating linear time-invariant impulse response functions. In the proposed system identification technique, an estimation method is provided that can estimate the time-varying auto- and cross-correlation function and indirectly, the time-varying auto- and cross-correlation power spectrum estimates based on real-life measurements without measuring the perturbation signals. The (slow) time-varying behavior means that the dynamic of the system changes as a function of time. In this work, a tailored regularization cost function is considered to impose assumptions such as smoothness and stability on the 2D auto- and cross-correlation function resulting in robust and uniquely determined estimates. The proposed method is validated on two examples: a simulation to check the numerical correctness of the method, and a flutter test measurement of a scaled airplane model to illustrate the power of the method on a real-life challenging problem. Full article

The time dynamics of the instrumental seismicity recorded in the area of the Lai Chau reservoir (Vietnam) between 2015 and 2021 were analyzed in this study. The Gutenberg–Richter analysis of the frequency–magnitude distribution has revealed that the seismic catalog is complete for events with magnitudes larger or equal to 0.6. The fractal method of the Allan Factor applied to the series of the occurrence times suggests that the seismic series is characterized by time-clustering behavior with rather large degrees of clustering, as indicated by the value of the fractal exponent $\alpha \approx 0.55$. The time-clustering of the time distribution of the earthquakes is also confirmed by a global coefficient of variation value of 1.9 for the interevent times. The application of the correlogram-based periodogram, which is a robust method used to estimate the power spectrum of short series, has revealed three main cycles with a significance level of $p<0.01$ (of 10 months, 1 year, and 2 years) in the monthly variation of the mean water level of the reservoir, and two main periodicities with a significance level of $p<0.01$ (at 6 months and 2 years) in the monthly number of earthquakes. By decomposing the monthly earthquake counts into intrinsic mode functions (IMFs) using the empirical decomposition method (EMD), we identified two IMFs characterized by cycles of 10 months and 2 years, significant at the 1% level, and one cycle of 1 year, significant at the 5% level. The cycles identified in these two IMFs are consistent with those detected in the water level, showing that, in a rigorously statistical manner, the seismic process occurring in the Lai Chau area might be triggered by the loading–unloading operational cycles of the reservoir. Full article

Restoring current ponderosa pine (Pinus ponderosa Dougl. Ex P. and C. Laws)-dominated forests (also known as “dry forests”) to spatially resilient stand structures requires an adequate understanding of the overstory spatial variation of forests least impacted by Euro-American settlers (also known as “reference conditions”) and how much contemporary forests (2016) deviate from reference conditions. Because of increased tree density, dry forests are more spatially homogeneous in contemporary conditions compared to reference conditions, forests minimally impacted by Euro-American settlers. Little information is available that can be used by managers to accurately depict the spatial variation of reference conditions and the differences between reference and contemporary conditions. Especially, forest managers need this information as they are continuously designing management treatments to promote contemporary dry forest resiliency against fire, disease, and insects. To fill this knowledge gap, our study utilized field inventory data from reference conditions (1934) along with light detection and ranging and ground-truthing data from contemporary conditions (2016) associated with various research units of Blacks Mountain Experimental Forest, California, USA. Our results showed that in reference conditions, above-ground biomass—a component of overstory stand structure—was more spatially heterogeneous compared to contemporary forests. Based on semivariogram analyses, the 1934 conditions exhibited spatial variation at a spatial scale < 50 m and showed spatial autocorrelation at shorter ranges (150–200 m) compared to those observed in contemporary conditions (>250 m). In contemporary conditions, prescribed burn with high structural diversity treatment enhanced spatial heterogeneity as indicated by a greater number of peaks in the correlograms compared to the low structural diversity treatment. High structural diversity treatment units exhibited small patches of above-ground biomass at shorter ranges (~120 to 440 m) compared to the low structural diversity treatment units (~165 to 599 m). Understanding how spatial variation in contemporary conditions deviates from reference conditions and identifying specific management treatments that can be used to restore spatial variation observed in reference conditions will help managers to promote spatial variation in stand structure that has been resilient to wildfire, insects, and disease. Full article

The comparative phylogeography of marine species with contrasting dispersal potential across the southern Caribbean Sea was evaluated by the presence of two putative barriers: the Magdalena River plume (MRP) and the combination of the absence of a rocky bottom and the almost permanent upwelling in the La Guajira Peninsula (ARB + PUG). Three species with varying biological and ecological characteristics (i.e., dispersal potentials) that inhabit shallow rocky bottoms were selected: Cittarium pica (PLD < 6 days), Acanthemblemaria rivasi (PLD < 22 days), and Nerita tessellata (PLD > 60 days). We generated a set of SNPs for the three species using the ddRad-seq technique. Samples of each species were collected in five locations from Capurganá to La Guajira. For the first time, evidence of a phylogeographic break caused by the MRP is provided, mainly for A. rivasi (AMOVA: Φ_CT = 0.420). The ARB + PUG barrier causes another break for A. rivasi (Φ_CT = 0.406) and C. pica (Φ_CT = 0.224). Three populations (K = 3) were identified for A. rivasi and C. pica, while N. tessellata presented one population (K = 1). The Mantel correlogram indicated that A. rivasi and C. pica fit the hierarchical population model, and only the A. rivasi and C. pica comparisons showed phylogeographic congruence. Our results demonstrate how the biological traits of these three species and the biogeographic barriers have influenced their phylogeographic structure. Full article