Search Results (127,488)

Mangrove ecosystems are important blue carbon systems and play a critical role in understanding carbon cycling and responses to climate change. However, accurate regional estimation of Net Ecosystem Exchange (NEE) remains challenging due to the environmental complexity and spatial heterogeneity. This study combined eddy covariance observations from four mangrove sites along China’s southeastern coast (natural and restored mangrove forests) with multi-source remote sensing and environmental reanalysis data to construct three variable schemes (site observations only, with added vegetation indices, and comprehensive multi-source variables). We compared three machine learning models for daily NEE prediction, including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM). The results showed that: (1) Restored and natural mangroves exhibited similar temporal NEE dynamics and consistently functioned as carbon sinks, restored mangrove sites showed greater cross-site variability. Among the study sites, CN-LZR exhibited the strongest cumulative carbon uptake. (2) Scheme 3 combined with the XGBoost algorithm achieved the highest predictive accuracy, reaching an R² of 0.73 across sites. Differences among machine learning models were primarily associated with their ability to capture nonlinear interactions between atmospheric and hydrological variables, with tree-based models outperforming SVM. (3) SHAP analysis indicated that radiation-related variables were the dominant drivers of NEE, while hydrological influences were site-dependent; and (4) Regional upscaling indicated that all sites consistently functioned as long-term carbon sinks, with CN-LZR exhibiting slightly higher daily mean carbon uptake than the other sites. This study presented the first machine learning framework for estimating daily-scale NEE in mangroves, providing methodological and data support for regional carbon flux assessment and blue carbon management. Full article

Dissolution of iron oxides in water plays a critical role in corrosion, mineral cycling, and surface reactivity; yet, the atomic-scale mechanisms governing Fe release remain poorly understood. Here, we employ ab initio molecular dynamics and well-tempered metadynamics simulations to investigate the stepwise dissolution of surface Fe atoms from the -Fe₂O₃(0001) surface in aqueous solution. The dissolution process initiates from a stable surface configuration in which Fe is coordinated to three lattice oxygen atoms and one water molecule. It proceeds through a series of metastable states involving additional water coordination, proton-assisted Fe-O bond weakening, and eventual detachment from the substrate. The first major transition, requiring 46.5 kJ/mol, involves breaking the hydrogen-bonding net and overcoming steric hindrance to allow adsorption of a second water molecule. Intermediate barriers (10.9–30.3 kJ/mol) are associated with further coordination and bond cleavage steps. In contrast, the final release of Fe into the solution, corresponding to a state coordinated with four water molecules and no lattice oxygen, exhibits a much higher free-energy barrier of ~ 93.0 kJ/mol. This barrier arises from the formation of a rigid hydrogen-bonded water cage and the loss of proton access to the remaining surface oxygen site, as confirmed by radial distribution function analysis. Our findings reveal why -Fe₂O₃(0001) is highly resistant to complete dissolution yet prone to surface roughening, defect formation, and adatom structures under aqueous conditions. Full article

The Wuding chicken, a renowned indigenous breed in Yunnan Province, is prized for its superior meat quality; however, its economic potential is limited by pronounced broodiness and suboptimal egg production. Central to alleviating these constraints is the precise regulation of ovarian granulosa cell (GC) proliferation and steroidogenic processes that dictate follicular development and laying performance. While Estrogen Receptor 2 (ESR2) is a known transcription factor implicated in follicular maturation, its spatiotemporal dynamics within the hypothalamic-pituitary-ovarian (HPO) axis and its specific regulatory mechanisms in Wuding chicken remain elusive. This study characterizes ESR2 expression across the HPO axis during the laying and broody periods and functionally validates its role in GCs. We observed that ESR2 expression was significantly higher throughout the HPO axis during the laying period compared to the broody period, with the most pronounced differential expression occurring in the ovary. Notably, subcellular localization analysis revealed that ESR2 is distributed in both the nucleus and the cytoplasm, indicating involvement in both nuclear transcriptional regulation and cytoplasmic signaling. Functional assays indicate that ESR2 modulates the expression of genes associated with GC proliferation, steroidogenesis, and apoptosis, potentially via the PI3K/AKT/mTOR signaling pathway. Our findings indicate that this process involves a synergistic interplay between genomic and potential non-genomic actions. Specifically, ESR2 overexpression upregulates the expression of key signaling components and steroidogenic genes, including CYP19A1, STAR, PTGS2, and FSHR, while its cytoplasmic localization suggests a potential for non-genomic interactions. Together, these coordinated mechanisms may maintain GC functional homeostasis and support follicular development. Collectively, these results suggest that ESR2 plays an important role in maintaining GC homeostasis and follicular development and may involve both genomic and non-genomic modes of action, highlighting its potential relevance for future studies on reproductive performance in indigenous poultry breeds. Full article

Background: Vesicourethral anastomotic stricture (VUAS) remains a clinically relevant complication following radical prostatectomy, with important implications for postoperative urinary function. Minimally invasive approaches may offer technical advantages; however, their impact on stricture formation in patients with microangiopathic risk factors remains incompletely defined. Objective: We aimed to compare the incidence of vesicourethral anastomotic stricture following open radical prostatectomy (ORP) and laparoscopic radical prostatectomy (LRP) in patients with microangiopathic comorbidities and to explore clinical and perioperative factors associated with stricture development. Materials and Methods: A retrospective two-centre cohort study was conducted including 115 patients who underwent radical prostatectomy for clinically localized prostate cancer between 2022 and 2024. All patients had at least one microangiopathic risk factor (diabetes mellitus, hypertension, or coronary artery disease). Seventy-two patients underwent ORP and forty-three underwent LRP. VUAS was defined by obstructive symptoms with endoscopic confirmation requiring intervention within 12 months postoperatively. Univariate analyses and exploratory logistic regression models were performed to assess factors associated with stricture formation. Results: Vesicourethral anastomotic stricture occurred in 21 patients (18.3%). The crude incidence of VUAS was lower after LRP compared with ORP (9.3% vs. 23.6%); however, this difference did not reach statistical significance. Patients who developed VUAS had a significantly higher body mass index, longer operative time, and greater intraoperative blood loss. In exploratory multivariable analyses, body mass index and operative duration were consistently associated with increased odds of stricture, whereas the effect of surgical approach was unstable and imprecise due to limited event numbers. Conclusions: In patients with microangiopathic risk factors, laparoscopic radical prostatectomy was associated with a lower crude incidence of vesicourethral anastomotic stricture compared with open surgery; however, this association was not robust after adjustment. Perioperative and technical factors appear to play a more prominent role in anastomotic healing than surgical approach alone. These findings highlight the importance of optimizing intraoperative conditions to reduce postoperative stricture risk. Full article

Fusarium verticillioides is a significant agricultural pathogen and an emerging causative agent of invasive fusariosis in clinical settings. Fusarium species frequently exhibit resistance to available antifungal agents, yet the molecular mechanisms underlying azole resistance remain poorly characterized. In this study, we identified the Zn(II)₂Cys₆ transcription factor FvADS-1 as a positive regulator of the azole stress response in F. verticillioides. The transcription of FvADS-1 was significantly induced by ketoconazole (KTC), and its deletion increased susceptibility to multiple azole compounds. Mechanistically, FvADS-1 positively regulates the KTC-induced expression of genes encoding ABC transporters and ergosterol biosynthesis enzymes, thereby modulating intracellular KTC accumulation and sterol homeostasis under azole stress. Furthermore, FvADS-1 positively regulates the transcriptional response of peroxisomal genes and contributes to fungal tolerance to oxidative stress. Notably, deletion of FvADS-1 attenuates the virulence of F. verticillioides on maize. The function of ADS-1 is evolutionarily conserved: heterologous expression of N. crassa ads-1 restored azole resistance in FvADS-1 deletion mutant, and the deletion of the F. oxysporum homolog FoADS-1 similarly increased azole susceptibility. Collectively, our study demonstrates that the conserved transcription factor ADS-1 plays a central role in regulating azole resistance and virulence in the pathogen F. verticillioides, offering new insights into antifungal resistance mechanisms in pathogenic filamentous fungi. Full article

As network environments become increasingly interconnected, ensuring robust cyber-security has become critical, particularly with the growing sophistication of modern cyber threats. Intrusion detection systems (IDSs) play a vital role in identifying and mitigating unauthorized or malicious activities; however, conventional machine learning-based IDSs often rely on handcrafted features and are limited in their ability to detect diverse attack types across disparate network domains. To address these limitations, this paper introduces a novel unified intrusion detection framework that implements “Structural Dualism” to integrate three heterogeneous benchmark datasets (CSE-CIC-IDS2018, NF-BoT-IoT-v2, and IoT-23) into a harmonized, protocol-agnostic representation. The framework employs a shared autoencoder architecture with dataset-specific projection layers to learn a unified latent manifold. This 15-dimensional space captures the underlying semantics of attack patterns (e.g., volumetric vs. signaling) across multiple domains, while dataset-specific decoders preserve reconstruction fidelity through alternating multi-domain training. To identify complex micro-signatures within this manifold, the framework utilizes a synergistic hybrid convolutional neural network–deep neural network (CNN–DNN) classifier, where the CNN extracts spatial latent patterns and the DNN performs global classification across twenty-five distinct classes. Class imbalance is addressed through resampling strategies such as adaptive synthetic sampling (ADASYN) and edited nearest neighbors (ENN). Experimental results demonstrate remarkable performance, achieving 99.76% accuracy for binary classification and 99.54% accuracy for multi-class classification on the merged dataset, with strong generalization confirmed on individual datasets. These findings indicate that the shared autoencoder-based CNN–DNN framework, through its unique feature alignment and spatial extraction capabilities, significantly strengthens intrusion detection across diverse and heterogeneous environments. Full article

Background: Exercise is a key component in managing hypertension, yet adherence remains low. Beyond deliberate decision-making, implicit attitudes also play an important role in exercise behavior as automatic and unconscious evaluative processes. Traditional studies mostly rely on reaction time measures, which are susceptible to practice effects and fail to capture dynamic neural processing. Objectives: This study aimed to examine whether the EEG entropy derived from implicit attitude processing can better predict exercise behavior than traditional reaction time measures in patients with hypertension. Methods: Fifty-seven hypertensive patients completed affective and instrumental implicit association tests (IATs) with EEG recording. Seven entropy features were extracted. Multiple machine learning algorithms were applied to compare the predictive performance of reaction time with EEG entropy features. The random forest model was used to analyze the importance ranking of features from different brain regions. Results: EEG entropy outperformed reaction times in distinguishing exercisers from non-exercisers. Affective implicit attitudes consistently demonstrated stronger accuracy than instrumental attitudes. Envelope entropy showed the most robust and significant group differences. For the random forest (RF) classifier of envelope entropy, classification accuracies were 71.9% for the affective IAT (incompatible task only), and 71.9% for the model combining affective and instrumental IAT features. Frontal and central regions contributed most to classification. Conclusions: EEG entropy, particularly envelope entropy during affective IAT-incompatible tasks, provides superior discrimination of exercise behavior than reaction time measures. This suggests that exercise behavior is closely linked to the neural complexity underlying affective conflict processing. These findings advance our understanding of the neural dynamic patterns linking implicit attitudes and exercise behavior and suggest EEG entropy as a promising tool for assessing and intervening exercise behavior. Full article

Exercise stress testing remains one of the most widely used and cost-effective diagnostic tools in clinical cardiology. Beyond the traditional evaluation of induced ischemia, it provides valuable information on functional capacity, blood pressure response and arrhythmic behavior during exercise. In particular, the test plays a crucial role in assessing and interpreting exercise-induced arrhythmias, including tachyarrhythmias, such as premature ventricular beats (PVBs) and bradyarrhythmias, as well as corroborating the suspicion of some ion channel diseases. The usefulness of exercise testing is also highlighted in patients with devices, where it can help evaluate their function and exercise adaptation, as well as in specific conduction disorders, such as Wolff–Parkinson–White syndrome. This practical guide summarizes the key aspects of performing and interpreting the exercise stress test, focusing on hemodynamic and arrhythmic findings and their clinical implications, and includes several illustrative clinical cases. Full article

Autophagy plays a key role in the development and homeostasis of the cochlear organ. Alterations in the autophagic pathways have been associated with damage to auditory cell types and hearing impairment caused by an array of factors like age, ototoxicity, exposure to high levels of noise, or genetic mutations. Cochlear damage frequently entails mitochondrial dysfunction, impaired mitophagy and the accumulation of high concentrations of free radicals. This review summarizes the observations made to date on the autophagic function in response to cochlear damage and the results of either activating or inhibiting these processes. The data demonstrate that autophagic activity is cell context-dependent and varies according to the cochlear cell type, the toxic agent, its levels and the length and timing of its administration; other factors that influence the autophagic response may be external to the auditory system or related to epigenetic changes or the expression of genetic variants. Modulation of the autophagic status has an effect on auditory cell loss and the progression to hearing impairment and this approach has thus become a promising avenue towards the protection of the hearing function. Nonetheless, this is no easy task and it will require the identification of reliable biomarkers to evaluate the dynamics of autophagic activity as well as the development of specific autophagy modulators that do not exert toxicity. Full article

Oxidative stress and redox (REDOX) imbalance play a key role in the development of many chronic and degenerative disorders, including cardiovascular diseases, neurodegenerative conditions, cancer, and age-related illnesses. Beyond causing direct damage to macromolecules, disrupted REDOX signaling affects cellular homeostasis, alters inflammatory responses, and modifies metabolic control, leading to disease onset and progression. Therefore, targeting oxidative pathways offers a promising therapeutic approach for managing chronic diseases. Naturally derived antioxidants, especially phytochemicals such as polyphenols, flavonoids, and carotenoids, have been identified as novel REDOX modulators with diverse biological effects that extend beyond simple free-radical scavenging. This review provides a detailed overview of the molecular mechanisms through which these phytochemicals influence oxidative pathways and exert protective effects on cells. We discuss their relevance in oxidative stress–related diseases, evaluate current clinical evidence regarding their efficacy, and highlight key challenges that limit their clinical application. Special attention is given to the roles of bioavailability, metabolism, and gut microbiota in shaping health outcomes associated with phytochemical consumption. Additionally, we outline emerging strategies to enhance phytochemical efficacy, including synergistic combinations and advanced delivery systems. Overall, this article underscores the potential of phytochemicals as active modulators of REDOX biology, supporting their role in precision nutrition and modern therapeutic approaches. Full article

Background/Objectives: Dementia represents a complex syndrome in which biological, psychological, social, and cultural dimensions intersect. While its clinical features are well documented, less is known about how lived experiences, caregiving contexts, and cultural beliefs shape the trajectory of illness. This study explored clinical, behavioral, and socio-cultural dimensions related to the quality of life of people living with dementia from an anthropological perspective, focusing on the interaction between comorbidities, cognition, lifestyle, and caregiving environments as reported by their informal caregivers. Methods: We conducted a single-center, observational cross-sectional study including 73 family caregivers of patients with clinically diagnosed dementia who accessed care at the Neurology–Psychiatry Department of the C.F.2 Clinical Hospital (Bucharest, Romania) between November 2023 and April 2024. Caregivers provided socio-demographic, behavioral, lifestyle, and cultural information using a newly developed anthropological questionnaire. Descriptive and exploratory inferential analyses were performed to examine relationships between cognitive performance, comorbidities, lifestyle factors, and socio-cultural variables. Results: People with dementia had a mean age of 79.2 ± 7.5 years (range 66–95) and were predominantly female (71.2%). Multimorbidity was common, averaging 2.22 ± 1.03 chronic conditions, mainly neurological (84.9%) and cardiovascular (68.5%). The mean BMI was 26.1 ± 3.8 kg/m². Cognitive impairment was substantial (MMSE mean 11.47 ± 7), with descriptively lower scores among older individuals and those with lower education or income, although inferential tests were underpowered. Appetite and sleep disturbances were frequent and tended to co-occur with lower activity levels. Disclosure of diagnosis occurred in 74% of cases; reactions varied widely, ranging from acceptance to denial, confusion, anxiety, and sadness. Family responses likewise reflected a heterogeneous and often ambivalent adjustment process. Cultural beliefs and spirituality played a salient role in shaping explanatory models and coping strategies, with many caregivers attributing importance to religious practices and, to a lesser extent, alternative treatments. Conclusions: In this Romanian cohort, dementia was shaped not only by age-related multimorbidity and cognitive decline but also by caregiving practices, socioeconomic constraints and culturally grounded interpretations of illness. These findings highlight the relevance of integrative approaches to dementia care that consider medical, behavioral, and socio-cultural dimensions and that incorporate caregiver perspectives to improve the quality of life of both patients and families. Full article

Background: Tubulin plays a pivotal role in cell division and other essential cellular processes, making it a key pharmacological target for cancer therapy, antiparasitic treatments, and neurodegenerative diseases. Numerous compounds have been developed to regulate microtubule polymerization through tubulin binding; however, most have shown significant limitations, including adverse side effects, poor bioavailability and limited specificity. In recent years, peptide-based therapies have gained considerable attention, particularly for their ability to modulate protein–protein interaction while offering improved selectivity and safety profiles. Methods: In this study, we employed an integrated computational–experimental approach combining molecular docking, molecular dynamics simulations, and MM-GBSA free energy calculations to design and evaluate 14 peptides derived from the αβ-tubulin dimer interface. Results: The peptide NH₂-P14-COOH emerged as the most promising candidate, displaying the stronger inhibition of tubulin polymerization activity (IC₅₀ = 11.24 ± 3.82 μM), selective cytotoxicity against NCI-H1299 lung carcinoma cells (IC₅₀ = 45.64 ± 3.20 μM), and no significant toxicity toward non-cancerous EA.hy926 endothelial cells (IC₅₀ > 100 μM). Flow cytometry analysis confirmed that NH₂-P14-COOH induces apoptosis, supporting a mechanism of action based on microtubule disruption. Conclusions: These findings highlight NH₂-P14-COOH as a selective antimitotic peptide with a favorable therapeutic index and demonstrate the potential of structure-guided peptide design for the development of novel microtubule-targeting agents with reduced off-target toxicity. Full article

Coal continues to play a significant role in Poland’s electricity generation system, making the sustainable management of environmental impacts from hard coal mining a critical challenge during the ongoing energy transition. In line with the European Green Deal and circular economy principles, reducing and managing mining-related waste emissions is an important component of sustainable development in regions undergoing a gradual phase-out of fossil fuel extraction. This study analyzes rock mass and dust emissions associated with underground hard coal mining in Poland over the period 2017–2025 using the most recent statistical data, including estimates for 2025 based on the first three quarters of the year. The scale, structure, and trends of emissions are examined to assess their implications for environmental sustainability, resource efficiency, and long-term land use. Particular attention is paid to the relationship between declining coal production and the relatively slower reduction in waste rock emissions, which indicates increasing contamination of extracted material and poses challenges for sustainable mining practices. The results show that while total coal output has decreased substantially, reductions in rock mass emissions have been less dynamic, highlighting the need for improved waste management strategies from a sustainability perspective. The study demonstrates that increasing the utilization of mining waste, through underground use and circular economy applications, can reduce environmental pressure, support compliance with sustainability policies, and mitigate long-term impacts on post-mining regions. Although the analysis focuses on Poland, the findings provide transferable insights for other countries seeking to balance energy security, mining sector restructuring, and sustainable development objectives during the transition away from fossil fuels. Full article

Maize silage can play a key role in policies aimed at stabilising local energy systems, as it constitutes a critical renewable feedstock for European biogas plants. By providing a dense and predictable source of chemical energy, it supports balance and reliability in the agricultural energy sector. To convert this potential into stable energy production, operators require kinetic models that translate routine silage quality indicators into concrete guidance for digester operation and control. Therefore, the aim of this article was to evaluate the batch kinetics of anaerobic digestion (AD) of maize silage and to select an adequate model for describing biochemical methane potential (BMP) profiles and associated energy recovery in the context of start-up, organic loading rate (OLR), hydraulic retention time (HRT) and feedstock preparation. Ten batches of silage (A–J) were examined, covering a realistic range of pH, electrical conductivity (EC), dry and volatile solids, ash, protein–fat–fibre fractions, fibre composition (NDF, ADF and ADL), derived fractions (hemicellulose, cellulose, and residual organic matter (OM)), C/N ratio and macro-/micronutrient profiles, including trace elements relevant to methanogenesis (Ni, Co, Mo, and Se). BMP tests were carried out in batch mode, and the resulting curves were fitted using the modified Gompertz and a first-order kinetic model. Methane yields of approx. 100–120 m³ CH₄/Mg fresh matter (FM) and 336–402 m³ CH₄/Mg volatile solids (VS), with CH₄ contents of 52–57% v/v, were typical for energy-grade maize silage. Kinetic and energetic behaviours were governed mainly by residual OM and hemicellulose (shortening the lag phase and increasing the maximum methane production rate), the ADL/cellulose ratio (controlling the slower hydrolytic tail), EC and Na/Cl/S (extending the lag phase), and C/N together with Ni/Co/Mo/Se (stabilising methanogenesis). The modified Gompertz model reproduced BMP curves with a pronounced lag phase and asymmetry more accurately (lower error and better information criterion values), and its parameters directly support start-up design, OLR ramp-up and energetic performance optimisation in bioenergy reactors. The novelty of this work lies in combining batch BMP tests, comparative kinetic modelling and detailed silage characterisation to establish quantitative links between kinetic parameters and routine maize silage quality indicators that are directly relevant for biogas plant operation and renewable energy production. Full article

Regional epidemic risk prediction requires holistic modeling of heterogeneous data sources such as demographic structure, health capacity, geographical features and human mobility. In this study, a unique and multi-modal epidemiological data set integrating demographic, health, geographic and mobility indicators of Türkiye and its neighboring countries was collected. Türkiye’s neighboring countries are Greece, Bulgaria, Georgia, Armenia, Iran, and Iraq. This dataset, created by combining raw data from these neighboring countries, provides a comprehensive regional representation that allows for both quantitative classification and spatial mapping of epidemiological risk. To address the class imbalance problem, Conditional GAN (CGAN), a class-conditional synthetic example generation approach that enhances high-risk category representation was used. In this study, we proposed a multi-view deep learning model named MV-RiskNet, which effectively models the multi-dimensional data structure by processing each view into independent subnetworks and integrating the representations with an attention-based fusion mechanism for regional epidemic risk prediction. Experimental studies were compared using Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Autoencoder classifier, and Graph Convolutional Network (GCN) models. The proposed MV-RiskNet with CGAN model achieved better results compared to other models, with 97.22% accuracy and 97.40% F1-score. The generated risk maps reveal regional clustering patterns in a spatially consistent manner, while attention analyses show that demographic and geographic features are the dominant determinants, while mobility plays a complementary role, especially in high-risk regions. Full article