Search Results (17,556)

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R² = 0.75 and RMSE = 8.16 m³/hm², highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R² = 0.71 and RMSE = 6.96 m³/hm²; for Quercus mongolica, R² = 0.74 and RMSE = 6.90 m³/hm²; and for Populus davidiana, R² = 0.51 and RMSE = 9.29 m³/hm². The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m³/hm². (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R² of 0.86, lowest RMSE of 9.69 m³/hm², and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems. Full article

Oncology physiotherapists frequently provide care for patients experiencing severe immunosuppression. Exercise immunology, the science that studies the effects of exercise on the immune system, is a rapidly evolving field with direct relevance to oncology physiotherapists. Understanding oncology physiotherapists’ perspectives on the subject of immune functioning is essential to explore its possible integration into clinical reasoning. This study aimed to assess the perspectives of oncology physiotherapists concerning immune functioning in oncology physiotherapy. For this qualitative research, semi-structured interviews were performed with Dutch oncology physiotherapists. Results were analyzed via inductive thematic analysis, followed by a validation step with participants. Fifteen interviews were performed. Participants’ ages ranged from 30 to 63 years. Emerging themes were (1) the construct ‘immune functioning’ (definition, and associations with this construct in oncology physiotherapy), (2) characteristics related to decreased immune functioning (in oncology physiotherapy), (3) negative and positive influences on immune functioning (in oncology physiotherapy), (4) tailored physiotherapy treatment, (5) treatment outcomes in oncology physiotherapy, (6) the oncology physiotherapist within cancer care, and (7) measurement and interpretation of immune functioning. In conclusion, oncology physiotherapists play an important role in the personalized and comprehensive care of patients with cancer. They are eager to learn more about immune functioning with the goal of better informing patients about the health effects of exercise and to tailor their training better. Future exercise-immunology research should clarify the effects of different exercise modalities on immune functioning, and how physiotherapists could evaluate these effects. Full article

Diabetes mellites (DM) is a chronic disease with increasing prevalence worldwide and multiple health implications. Among them, sarcopenia is a metabolic disorder characterized by loss of muscle mass and function. The two age-related diseases, DM and sarcopenia, share underlying pathophysiological pathways. This narrative literature review aims to provide an overview of the existing evidence on metabolomic studies evaluating DM associated with sarcopenia. Advancements in targeted and untargeted metabolomics techniques could provide better insight into the pathogenesis of sarcopenia in DM and describe their entangled and fluctuating interrelationship. Recent evidence showed that sarcopenia in DM induced significant changes in protein, lipid, carbohydrate, and in energy metabolisms in humans, animal models of DM, and cell cultures. Newer metabolites were reported, known metabolites were also found significantly modified, while few amino acids and lipids displayed a dual behavior. In addition, several therapeutic approaches proved to be promising interventions for slowing the progression of sarcopenia in DM, including physical activity, newer antihyperglycemic classes, D-pinitol, and genetic USP21 ablation, although none of them were yet validated for clinical use. Conversely, ceramides had a negative impact. Further research is needed to confirm the utility of these findings and to provide potential metabolomic biomarkers that might be relevant for the pathogenesis and treatment of sarcopenia in DM. Full article

Soil testing has long been used to optimize fertilization and crop production. More recently, soil health testing has emerged to reflect the growing interest in soil multifunctionality and ecosystem services. Soil health encompasses physical, chemical, and biological properties that support ecosystem functions and sustainable agriculture. Despite its relevance to several United Nations Sustainable Development Goals (SDGs 1, 2, 3, 6, 12, 13, and 15), comprehensive soil health testing is not widely practiced due to complexity and cost. The aim of the study presented here was to contribute to the further development, implementation, and testing of an integrated procedure for soil health assessment in practice. We developed and tested a rapid, standardized soil health assessment tool that combines near-infrared spectroscopy (NIRS) and multi-nutrient 0.01 M CaCl₂ extraction with Inductive Coupled Plasma Mass Spectroscopy analysis. The tool evaluates a wide range of soil characteristics with high accuracy (R² ≥ 0.88 for most parameters) and has been evaluated across more than 15 countries, including those in Europe, China, New Zealand, and Vietnam. The results are compiled into a soil health indicator report with tailored management advice and a five-level ABCDE score. In a Dutch test set, 6% of soils scored A (optimal), while 2% scored E (degraded). This scalable tool supports land users, agrifood industries, and policymakers in advancing sustainable soil management and evidence-based environmental policy. Full article

Background: RiBi is integral to cell proliferation, and its dysregulation is increasingly recognized as a hallmark of aggressive cancers. We sought to develop and validate a composite “PanRibo-515 score” reflecting RiBi activity across multiple tumor types, assess its prognostic significance, and explore its relationship with immune checkpoint therapy outcomes. Methods: We curated 515 RiBi–associated genes (PanRibo-515) and used a LASSO regression-based strategy on a training dataset (GSE202203) to select the prognostically most relevant subset of 68 genes (OncoRibo-68). Directionality (positive or negative impact on survival) was assigned based on the sign of the LASSO coefficients. We integrated a forward selection approach to identify a refined subset of genes for computing the OncoRibo-68 score. For validation, patients in The Cancer Genome Atlas (TCGA) were stratified into high or low OncoRibo-68 score groups for survival analyses. Additional validation for immunotherapy response was conducted using bioinformatic platforms used for immunotherapy response analysis. Results: A higher OncoRibo-68 score consistently correlated with poorer overall and progression-free survival across multiple cancers. Elevated OncoRibo-68 score was linked to an immunosuppressive tumor microenvironment, but interestingly to increased response to checkpoint inhibitors. Conclusions: Our findings highlight RiBi as an important determinant of tumor aggressiveness and identify the OncoRibo-68 score as a promising biomarker for risk stratification and therapy selection. Future research may evaluate whether targeting RiBi pathways could enhance treatment efficacy, particularly in combination with immunotherapy. Full article

The coastal sandy land in northeast Hainan Province is typical for this land type, also exhibiting strong sand activity. This study is based on wind speed, wind direction, and sediment transport data obtained at a field meteorological station using an omnidirectional sand accumulation instrument from 2020 to 2024, studying the coastal aeolian environment and sediment transport distribution characteristics in the region. Its findings provide a theoretical basis for comprehensively analyzing the evolution of coastal aeolian landforms and the evaluation and control of coastal aeolian hazards. The research results show the following: (1) The annual average threshold wind velocity for sand movement in the study area is 6.84 m/s, and the wind speed frequency (frequency of occurrence) is 51.54%, dominated by easterly (NE, ENE) and southerly (S, SSE) winds. (2) The drift potential (DP) refers to the potential amount of sediment transported within a certain time and spatial range, and the annual drift potential (DP) and resultant drift potential (RDP) of Mulan Bay from 2020 to 2024 were 550.82 VU and 326.88 VU, respectively, indicating a high-energy wind environment. The yearly directional wind variability index (RDP/DP) was 0.59, classified as a medium ratio and indicating blunt bimodal wind conditions. The yearly resultant drift direction (RDD) was 249.45°, corresponding to a WSW direction, indicating that the sand in Mulan Bay is generally transported in the southwest direction. (3) When the measured data extracted from the sand accumulation instrument in the study area from 2020 to 2024 were used for statistical analysis, the results showed that the total sediment transport rate (the annual sediment transport of the observation section) in the study area was 110.87 kg/m·a, with the maximum sediment transport rate in the NE direction being 29.26 kg/m·a. These results suggest that when sand fixation systems are constructed for relevant infrastructure in the region, the construction direction of protective forests and other engineering measures should be perpendicular to the net direction of sand transport. Full article

To determine the association between FGFR4 (rs351855 and rs7708357) gene variants, serum levels, and immunohistochemical markers (Ki-67 and p53) in pituitary adenoma (PA), a case-control study was conducted involving 300 subjects divided into two groups: the control group (n = 200) and a group of PA (n = 100). The genotyping of FGFR4 rs351855 and rs7708357 was carried out using the real-time polymerase chain reaction (RT-PCR) method. The serum FGFR4 levels were measured using the ELISA method. Immunohistochemical analysis (Ki-67 and p53) was conducted. Statistical analysis of the data was performed using IBM SPSS Statistics 30.0 software. There were no statistically significant differences after analyzing the genotypes and alleles of FGFR4 rs351855 and rs7708357 in patients with PA and control groups (all p > 0.05). After evaluating the distribution of genotypes and alleles of FGFR4 rs351855 and rs7708357 in micro/macro, invasiveness, activity, and recurrence of PA and the control groups, the analysis showed no statistically significant differences between the groups (p > 0.05). Similarly, no significant differences in FGFR4 levels were observed between PA patients and control group (median (IQR): 3642.41 (1755.08) pg/mL vs. 3126.24 (1334.15) pg/mL, p = 0.121). Immunohistochemistry for Ki-67 revealed a labeling index (LI) of <1% in 25.5% of patients with PA, an LI of 1% in 10.9%, and an LI of >1% in 63.6% of patients. Further analyses showed no statistically significant associations with tumor size, invasiveness, activity, or recurrence. Immunohistochemistry for p53 revealed that macroadenomas had a significantly higher p53 H-score compared to microadenomas (median (IQR): 30.33 (28.68) vs. 18.34 (17.65), p = 0.005). Additionally, a moderate, statistically significant positive correlation between the Ki-67 LI and the p53 expression was found (Spearman’s ρ = 0.443, p = 0.003, n = 43). FGFR4 variants and serum protein levels were not significantly associated with PA risk or tumor features. Conversely, immunohistochemical markers Ki-67 and p53 were more informative, with higher p53 expression in macroadenomas and a moderate positive correlation between Ki-67 and p53, highlighting their potential relevance in tumor growth assessment. Full article

This article addresses the task of building personalized educational recommendations based on a heterogeneous knowledge graph that integrates data from university curricula, job vacancies, and online courses. To solve the problem of course recommendations by their relevance to a user’s competencies, a graph neural network (GNN)-based approach is proposed, specifically utilizing and comparing the Heterogeneous Graph Transformer (HGT) architecture, Graph Sample and Aggregate network (GraphSAGE), and Heterogeneous Graph Attention Network (HAN). Experiments were conducted on a heterogeneous graph comprising various node and relation types. The models were evaluated using regression and ranking metrics. The results demonstrated the superiority of the HGT-based recommendation model as a link regression task, especially in terms of ranking metrics, confirming its suitability for generating accurate and interpretable recommendations in educational systems. The proposed approach can be useful for developing adaptive learning recommendations aligned with users’ career goals. Full article

With the advance of digital transformation, the assessment of the environmental impact of digital tools and technologies grows more relevant. Considering the inflated expectations of environmental responsibility in higher education, this study analyses how websites of Polish universities conform to sustainable web design criteria. The sustainability audit employed a methodology encompassing carbon emissions measurement, technical website analysis, and SEO evaluation. The author analysed 63 websites of public universities in Poland using seven independent audit tools, including an original AI Custom GPT agent preconfigured in the ChatGPT ecosystem. The results revealed a substantial differentiation in CO₂ emissions and website optimisation, with an average EcoImpact Score of 66.41/100. Nearly every fourth website exhibited a significant carbon footprint and excessive component sizes, which indicates poor asset optimisation and energy-intensive design techniques. The measurements exposed considerable variability in emission intensities and resource intensity among the university websites, suggesting the need for standardised digital sustainability practices. Regulations on the carbon footprint of public institutions’ websites and mobile applications could become vital strategic components for digital climate neutrality. Promoting green hosting, “Green SEO” practices, and sustainability audits could help mitigate the environmental impact of digital technologies and advance sustainable design standards for the public sector. The proposed auditing methodology can effectively support the institutional transition towards sustainable management of digital infrastructure by integrating technical, sustainability, and organisational aspects. Full article

Background/Objectives: In recent years, lipids have emerged as critical regulators of different disease processes, being involved in cancer pathogenesis, progression, and outcome. Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) has significantly expanded the technology’s reach, enabling spatially resolved profiling of lipids directly from tissue, including formalin-fixed paraffin-embedded (FFPE) specimens. In this context, MALDI matrix selection is crucial for lipid extraction and ionization, influencing key aspects such as molecular coverage and sensitivity, especially in such specimens with already depleted lipid content. Thus, in this work, we aim to explore the feasibility of mapping lipid species in FFPE clinical samples with MALDI-MSI using 6-aza-2-thiothymine (ATT) as a matrix of choice. Methods: To do so, ATT performances were first compared to those two other matrices commonly used for lipidomic analyses, 2′,5′-dihydroxybenzoic acid (DHB) and Norharmane (NOR), on lipid standards. Results: As a proof-of-concept, we then assessed ATT’s performance for the MALDI-MSI analysis of lipids in FFPE brain sections, both in positive and negative ion modes, comparing results with those obtained from other commonly used dual-polarity matrices. In this context, ATT enabled the putative annotation of 98 lipids while maintaining a well-balanced detection of glycerophospholipids (60.2%) and sphingolipids (32.7%) in positive ion mode. It outperformed both DHB and NOR in the identification of glycolipids (3%) and fatty acids (4%). Additionally, ATT exceeded DHB in terms of total lipid count (62 vs. 21) and class diversity and demonstrated performance comparable to NOR in negative ion mode. Moreover, ATT was applied to a FFPE glioblastoma tissue microarray (TMA) evaluating the ability of this matrix to reveal biologically relevant lipid features capable of distinguishing normal brain tissue from glioblastoma regions. Conclusions: Altogether, the results presented in this work suggest that ATT is a suitable matrix for pathology imaging applications, even at higher lateral resolutions of 20 μm, not only for proteomic but also for lipidomic analysis. This could enable the use of the same matrix type for the analysis of both lipids and peptides on the same tissue section, offering a unique strategic advantage for multi-omics studies, while also supporting acquisition in both positive and negative ionization modes. Full article

Pancreatic cancer (PC) is an aggressive malignancy with a poor prognosis, requiring innovative approaches to evaluate new therapies. Considering the high activity of unsymmetrical bisacridines (UAs) in PC monolayer cultures, we employed multicellular tumor spheroids (MCTS) to assess whether UAs retain pro-apoptotic activity under more physiologically relevant conditions. Ultra-low attachment plates were used to form spheroids from three PC cell lines (Panc-1, MIA PaCa-2, and AsPC-1) with different genotypes and phenotypes. The effects of UA derivatives (C-2028, C-2045, and C-2053) were evaluated using microscopy and flow cytometry (7-AAD for viability and annexin V-FITC/PI for membrane integrity). UAs altered the morphology of the spheroids and reduced their growth. Notably, Panc-1 spheroids exhibited compromised integrity. The increase in 7-AAD⁺ cells confirmed diminished cell viability, and annexin V-FITC assays showed apoptosis as the dominant death pathway. Interestingly, the exact derivative was most active against a given cell line regardless of culture conditions. These results confirm that UAs maintain anticancer activity in 3D cultures and induce apoptosis, with varying efficacy across different cell lines. This underscores the value of diverse cellular models in compound evaluation and supports UAs as promising candidates for pancreatic cancer therapy. Full article

A continuous mixing process with a twin-screw extruder was investigated for graphite-based negative electrode pastes for high-power applications. In the extrusion-based mixing process, the first kneading concentration is one of the key processing parameters for systematic optimization of relevant electrode paste properties like viscosity and particle size distribution. For different active materials at a constant electrode paste composition, a clear correlation of increasing kneading concentration with decreasing viscosity can be observed up to a certain reversal point, initiating a change in the trend and the rheological behavior, thus indicating a process limit. The fundamental effects causing this change and the associated impact on materials and battery performance were evaluated by applying further analytical methods and electrochemical characterization. It is revealed that the change in viscosity is associated with enhanced de-agglomeration of the carbon black additive and with partial particle grinding of the active material and thus a partial change in the interlayer distance of graphene layers and, correspondingly, the electrochemical behavior of the active material. Beyond this, correlations between processing parameters and product properties are presented. Furthermore, indicators are suggested with which monitoring of the machine parameters enables the detection of changes in the electrode paste characteristics. Full article

Background/Objectives: This study evaluated the in vitro probiotic potential of Lactiplantibacillus plantarum Probio87 (Probio87), focusing on its physiological robustness, safety, antimicrobial properties, and anticancer activity, with relevance to vaginal and cervical health. Methods: Tests included acid and bile salt tolerance, mucin adhesion, and carbohydrate utilization. Prebiotic preferences were assessed using FOS, GOS, and inulin. Antibiotic susceptibility was evaluated per EFSA standards. Antimicrobial activity of the cell-free supernatant (CFS) was tested against Staphylococcus aureus, Escherichia coli, and Candida species. Effects on Lactobacillus iners and L. crispatus were analyzed. Anticancer properties were assessed in HeLa, CaSki (HPV-positive), and C-33A (HPV-negative) cervical cancer cell lines through proliferation, apoptosis, angiogenesis, and cell cycle assays. Results: Probio87 showed strong acid and bile tolerance, efficient mucin adhesion, and broad carbohydrate utilization, favoring short-chain prebiotics like FOS and GOS over inulin. It met EFSA antibiotic safety standards. The CFS exhibited potent antimicrobial activity, including complete inhibition of Candida albicans. Probio87 selectively inhibited L. iners without affecting L. crispatus, indicating positive modulation of vaginal microbiota. In cervical cancer cells, the CFS significantly reduced proliferation and angiogenesis markers (p < 0.05), and induced apoptosis and cell cycle arrest in HPV-positive cells, with minimal effects on HPV-negative C-33A cells. Conclusions: Probio87 demonstrates strong probiotic potential, with safe, selective antimicrobial and anticancer effects. Its ability to modulate key microbial and cancer-related pathways supports its application in functional foods or therapeutic strategies for vaginal and cervical health. Full article

Introduction: This study evaluated the performance of automated dual-energy computed tomography (DECT)-based quantification of perfusion defects (PDs) in acute pulmonary embolism and examined its correlation with clinical parameters. Methods: We retrospectively analyzed data from 171 patients treated for moderate-to-severe acute pulmonary embolism, who underwent DECT imaging at two separate time points. PDs were quantified using a fully automated AI-based segmentation method that relied exclusively on iodine perfusion maps. This was compared with a semi-automatic clinician-guided segmentation, where radiologists manually adjusted thresholds to eliminate artifacts. Clinical variables including the Miller obstruction score, right-to-left ventricular diameter ratio, oxygen saturation, and patient-reported symptoms were also collected. Results: The semiautomatic method demonstrated stronger correlations with embolic burden (Miller score; r = 0.4, p < 0.001 at follow-up) and a negative correlation with oxygen saturation (r = −0.2, p = 0.04). In contrast, the fully automated AI-based quantification consistently produced lower PD values and demonstrated weaker associations with clinical parameters. Conclusions: Semiautomatic quantification of PDs currently provides superior accuracy and clinical relevance for evaluating lung PDs in acute pulmonary embolism. Future multimodal AI models that incorporate both anatomical and clinical data may further enhance diagnostic precision. Full article

Accurate detection of Alzheimer’s disease (AD) is critical yet challenging for early medical intervention. Deep learning methods, especially convolutional neural networks (CNNs), have shown promising potential for improving diagnostic accuracy using magnetic resonance imaging (MRI). This study aims to identify the most informative combination of MRI slice orientation and anatomical location for AD classification. We propose an automated framework that first selects the most relevant slices using a feature entropy-based method applied to activation maps from a pretrained CNN model. For classification, we employ a lightweight CNN architecture based on depthwise separable convolutions to efficiently analyze the selected 2D MRI slices extracted from preprocessed 3D brain scans. To further interpret model behavior, an attention mechanism is integrated to analyze which feature level contributes the most to the classification process. The model is evaluated on three binary tasks: AD vs. mild cognitive impairment (MCI), AD vs. cognitively normal (CN), and MCI vs. CN. The experimental results show the highest accuracy (97.4%) in distinguishing AD from CN when utilizing the selected slices from the ninth axial segment, followed by the tenth segment of coronal and sagittal orientations. These findings demonstrate the significance of slice location and orientation in MRI-based AD diagnosis and highlight the potential of lightweight CNNs for clinical use. Full article