Species-level resolution is essential to understand gastric microbiome recovery after Helicobacter pylori eradication, yet short-read 16S rRNA approaches often obscure clinically relevant changes. Gastric biopsies from 121 adults in Bayan-Ölgii, Mongolia (71 H. pylori-positive, 50 H. pylori-negative) were analyzed, including nine paired pre- and post-eradication gastric biopsy samples collected six months apart, enabling exploratory longitudinal analysis. Full-length 16S rRNA (V1-V9) sequencing was performed using the Oxford Nanopore platform with EMU taxonomic assignment (SILVA v138.1/NCBI RefSeq). Ecological changes were evaluated using diversity indices, principal coordinates analysis (PCoA) with PERMANOVA, and differential abundance testing (DESeq2, FDR < 0.05). Eradication therapy (esomeprazole-bismuth-doxycycline-levofloxacin) achieved success in 54 of 57 H. pylori-positive patients (94.7%). H. pylori-positive microbiomes were dominated by H. pylori (91.8% ± 3.9%) and exhibited markedly reduced diversity (Shannon = 0.44 ± 0.11) compared with H. pylori-negative samples (2.08 ± 0.25; p < 0.001). Six months after eradication, diversity increased significantly (2.17 ± 0.20; p = 0.0001), with enrichment of oral commensals including Streptococcus mitis (↑ 11.9×), Neisseria elongata (↑ 13.7×), and Prevotella melaninogenica (↑ 13.0×). However, post-eradication profiles at six months remained distinct from H. pylori-negative communities (PERMANOVA R² = 0.12; p = 0.02). In total, 174 amplicon sequence variants changed significantly, including persistence of Fusobacterium nucleatum. Nanopore full-length 16S sequencing reveals fine-scale, clinically relevant shifts that are masked by partial-gene assays. Eradication rapidly restores microbial diversity, but at six months, is associated with a novel ecological equilibrium rather than complete normalization. This species-resolved approach offers a practical framework for post-eradication microbiome monitoring and may inform strategies to reduce residual gastric cancer risk in high-burden populations.

Timely monitoring of corn growth at early stages is essential for food security and agricultural management, yet most existing mapping approaches depend on mature-stage spectral features, delaying operational applications. This study introduces TCBA-ViT, a hybrid framework that integrates convolutional neural networks and Vision Transformers, enhanced with dual-path Convolutional Block Attention Module (CBAM) and temporal attention, to jointly capture local spectral details and global temporal dynamics from multi-temporal Sentinel-2 imagery. Using six years (2019-2024) of data from the U.S. Corn Belt, TCBA-ViT reliably identified corn as early as June (V7 stage, four weeks after seeding) and achieved stable accuracies above 90% by late July, nearly two months before physiological maturity. Cross-year experiments demonstrated robustness to interannual variability and crop rotation, while cross-regional tests confirmed strong spatial generalization, maintaining F1-scores above 0.85 within 250 km and above 0.80 within 450 km. Compared with existing baseline models, TCBA-ViT consistently delivered earlier and more accurate classification across years and regions. Ablation analyses further highlighted the indispensable contributions of CBAM and temporal attention to performance gains. By addressing the questions of how early corn can be classified and how far models can generalize, this study provides a validated framework for early-season dynamic crop classification and large-scale agricultural monitoring, supporting sustainable decision-making.

Reliable knowledge of power transmission tower locations is fundamental for large-scale inspection and asset management in modern power grids. However, in satellite and aerial remote sensing imagery, towers typically appear as small, slender structures embedded in cluttered backgrounds, which leads to frequent missed and false detections. To address this challenge, we propose SCOPE-YOLO, an integrated super-resolution-plus-detection framework tailored for scalable transmission and distribution tower monitoring. In the first stage, low-resolution image patches are enhanced by a Real-ESRGAN ×4 super-resolution frontend, which restores high-frequency lattice details and sharpens tower boundaries. The reconstructed images are then processed by SCOPE-YOLO, a YOLOv11-based detector that incorporates a Cross-Scale Feature Aggregation (CFA) module, a Gather–Distribute (GD) routing mechanism, and a high-resolution P2 detection head, together with SAT and layered inference strategies to strengthen small-object perception under complex backgrounds. Experiments on the public SRSPTD dataset demonstrate that SCOPE-YOLO improves F1 score by 0.051 and raises mAP@0.5 by 10.2 percentage points over the YOLOv11-s baseline, while maintaining a compact model size. Compared with a broad set of state-of-the-art detectors, SCOPE-YOLO achieves the best overall performance, reaching 82.8% mAP@0.5 for power tower detection. Cross-domain evaluation on the GZ-PTD test set further confirms the effectiveness of the super-resolution–detection pipeline: Real-ESRGAN×4@2048 + SCOPE-YOLO increases Recall from 0.8621 to 0.9278 and mAP@0.5 from 0.8365 to 0.9132 relative to the low-resolution baseline, substantially reducing missed detections of small and weak tower targets in real-world scenes.

Background Congenital adrenal hyperplasia (CAH) treatment is complicated by hormonal imbalances, necessitating a dual therapeutic approach to both correct cortisol deficiency and manage androgen overproduction. Unfortunately, hospitals with limited resources lack some necessary standard laboratory tests to manage patients with CAH. Objective To investigate the interrelation between different monitoring strategies in clinical practice for managing patients with CAH. Methods This prospective cross-sectional study involved children with CAH caused by 21-hydroxylase deficiency (21-OHD) treated at King Faisal Specialist Hospital and Research Centre. KFSHRC is not resource-limited; the proposed recommendations are intended for settings that lack full biochemical panels. Univariable, bivariable, and multivariable logistic regression were done for association testing. Results The cohort included 96 children with 21-OHD, predominantly female (61.5%), with a median age of 6 years. Adrenal crises occurred in 20.8% of patients. Most participants were treated with hydrocortisone (97.9%) and fludrocortisone (88.5%), with high reported treatment compliance (90.6%). Biochemical abnormalities were observed in 26% for ACTH, 21.9% for 17-OHP, and 17.7% for testosterone. Biochemical control was significantly associated with treatment compliance (OR 7.6, p = 0.03). In adjusted analyses, androstenedione, ACTH, and testosterone control were strongly associated with 17-OHP control (all p &amp;lt; 0.01). Regarding skeletal outcomes, older age was inversely associated with bone age control, whereas clinical control (OR 11.1, p &amp;lt; 0.01) and controlled androstenedione levels (OR 3.0, p = 0.04) were independent predictors of optimal bone age. Conclusion Based on these findings, we recommend integrating growth velocity monitoring and androstenedione testing into routine visits as valuable indicators for assessing clinical control in 21-OHD children. Yet, larger studies are needed to validate simplified monitoring frameworks for resource-limited hospitals.

Atlas cedar (Cedrus atlantica) forests in Morocco’s Middle Atlas are experiencing an accelerated decline due to combined climatic and human pressures. Building on previous work on forest transition modeling, this study presents a deep-learning–based framework designed to predict and monitor the ecological resilience of Atlas cedar ecosystems. Multi-sensor satellite images from Landsat-8 and Sentinel-2, combined with field inventory data from the Ain Leuh–Sidi M’Guild massif, were processed to evaluate vegetation health, canopy density, and regeneration potential from 2013 to 2024. A hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) model was built to capture both spatial and temporal patterns of forest loss and recovery. Spectral indices such as NDVI, NBR, NDMI, and SAVI were extracted and standardized, while terrain features (altitude, slope, aspect) and bioclimatic variables (temperature seasonality, precipitation during the driest quarter) were included in the model. The hybrid CNN–BiLSTM architecture achieved an overall prediction accuracy of 94.7%, surpassing traditional machine learning methods (Random Forest, SVM, and Gradient Boosting). The spatio-temporal projections reveal a notable decline (−62%) of high-density cedar stands in low-elevation areas, while upper-slope refugia show partial stability and higher regeneration likelihoods. These results demonstrate the potential of deep learning combined with high-resolution Earth observation data for real-time forest health monitoring and adaptive management. The developed framework provides an operational foundation for Morocco’s Forest Strategy 2020–2030, enabling proactive decision-making for climate-resilient reforestation and ecological restoration in Mediterranean mountain ecosystems.

Mechanization-driven farmland consolidation has become a key component of China's efforts to raise grain productivity and optimize rural labor allocation. We used a survey of 630 grain-producing households in Shandong Province, and combined a Tobit model with propensity-score matching to identify the causal effects of consolidation on farm-household labor decisions. Consolidation reduced on-farm labor input by 8.4 percentage points through labor-saving technological substitution, yet the magnitude differed sharply between two mechanization pathways. Where households purchased their own machinery, on-farm labor rose by 5.3 percentage points, consistent with specialization incentives. By contrast, the use of custom mechanization services lowered on-farm labor by 7.5 percentage points. Labor-saving effects were strongest among ageing households, smallholders and farmers in hilly areas, suggesting enhanced overall efficiency in constrained settings. Policy implications include expanding service markets, coupling consolidation with vocational training for off-farm employment, and establishing a long-run monitoring framework to ensure sustainable transformation. However, this study relies on cross-sectional data, which limits its ability to capture dynamic change processes. Future research could conduct longitudinal tracking studies to evaluate the sustained effects and sustainability of policies.

The Southern Ocean is central to global heat and carbon cycling, connecting all the major ocean basins and regulating Earth’s climate system, and hence providing ecosystem services of global significance. However, its ecosystems are increasingly vulnerable to climate change and localized human-induced pressures, such as (biological) resource extraction, pollution, ship traffic, and tourism. Effective conservation and management require systematic and reliable monitoring frameworks. The Essential Variables concept offers a robust approach to integrate fragmented data, to standardize data collection, and to generate policy-relevant data products enabling informed responses to rapid environmental change. This paper synthesizes the key outcomes of a workshop held in Hobart, Australia, alongside the Southern Ocean Observing System Symposium, in 2023. To advance the adoption, development, and operationalization of Essential Variables tailored to the Southern Ocean, researchers with diverse expertise came together to assess current data gaps in ocean observations and to establish monitoring priorities for marine ecosystems. The workshop provided a dedicated forum to identify key Southern Ocean-specific candidate variables, address methodological challenges, and design pathways for developing a systematic, open, and adaptable framework suited to the region’s unique ecological and environmental conditions. In this paper, we propose Essential Biodiversity Variables that are tailored to the Southern Ocean and are intended to monitor changes in sea ice, planktonic, benthic, and top predator systems. The adoption of Essential Biodiversity Variables specific to the Southern Ocean can enhance our capacity to track biodiversity trends, assess ecosystem health, and inform policy by transforming fragmented data into a cohesive, policy-relevant framework. However, the success of these efforts is only possible by securing sustained funding and enhancing interoperability and collaborations across research groups. This paper as well as the Hobart 2023 workshop are activities endorsed by the UN Decade of Ocean Science for Sustainable Development.

ABSTRACT Cropland abandonment poses a significant threat to global food security, especially in ecologically fragile arid regions, where it compromises both agricultural sustainability and ecosystem integrity. Despite its importance, the spatial distribution of abandoned croplands across large geographic areas remains poorly understood. This study focuses on the Hexi Corridor, a representative oasis agricultural zone in northwestern China. Combining multi-dimensional Sentinel-2 features (phenological, spectral, textural, and physiographic) with feature optimisation, we employed a Random Forest-based post-classification change detection approach to map abandoned croplands from 2021 to 2023, achieving high accuracy (overall accuracy: 92.08%; Kappa: 0.84). (1) Landscape pattern indices and kernel density analysis revealed key spatial trends: abandonment rates declined from 7.05% in 2022 to 5.38% in 2023, while persistent abandonment covered 11,170 ha and recultivation reached 34,403 ha. (2) Spatial heterogeneity was pronounced, with core abandonment zones concentrated in Jiuquan (Yumen, Guazhou) and Zhangye (Shandan County), whereas Jinchang (Yongchang County) and Wuwei (Minqin County) underwent significant rehabilitation. (3) Abandoned croplands exhibited increased fragmentation, driven by a 21% rise in patch density, whereas recultivation was characterized by smaller, scattered plots (patch density > 109), indicating a shift from contiguous to fragmented cultivation. Kernel density analysis revealed an ‘oasis-edge aggregation’ pattern in abandonment, characterized by high-density clusters in Shandan County following remediation efforts and a distinct recultivation hotspot in Yongchang County. Our multi-feature RF approach establishes a robust framework for large-scale abandonment monitoring and delivers critical insights to support sustainable land management in arid regions.

Prospective serial proteomic analysis uncovers mechanistic pathways of chemotherapy resistance in advanced non-small cell lung cancer.

Background/Objectives: As part of the European Union’s harmonized monitoring framework, Belgium conducts antimicrobial resistance (AMR) monitoring in commensal bacteria from livestock. The aim of this study was to conduct a cost analysis of the national AMR monitoring in livestock, and to explore sampling size scenarios in relation to their associated costs and statistical performance (power and confidence) of monitoring. Methods: To our knowledge, this is the first published cost evaluation using unit cost aggregation of a national AMR monitoring program in animals. Results: The testing of the different sample size scenarios showed that if the sample size increases, the costs increase linearly. A sample size increase of 10 samples/isolates (e.g., from 170 to 180) can increase the yearly total costs per animal species by 5.2%. Moreover, the testing of the different scenarios showed that if the sample size increases, the power and the confidence level also increase, providing a higher level of trust in the results of the monitoring program. The highest total monitoring costs per animal category were estimated for fattening pigs, broilers and veal calves (over 18% of total costs each, using 2024 data). Among the various monitoring activities, antimicrobial susceptibility testing emerged as the costliest component, representing 50.2% of the total monitoring costs. Conclusions: The approach presented allows it to be used by other countries aiming to estimate the cost of their national AMR monitoring in animals or other similar activities. This economic and scenario testing analysis can be used to suggest informed suggestions to improve AMR monitoring in animals.

ABSTRACTBackgroundRecent research has challenged the viewpoint that pancreatic islets operate independently of surrounding exocrine tissues, revealing a bidirectional blood flow between the endocrine and exocrine pancreas. However, a methodology for simultaneous evaluation of pancreatic microhemodynamics and oxygen profiles remains elusive.MethodsTo generate the common microcirculatory framework, we employed laser Doppler and diffuse reflectance spectroscopy to assess pancreatic microcirculation with concurrent acquisition of microhemodynamic and oxygen data as time‐series measurements. The framework's analytical pipeline, featuring outlier adjustment using the boxplot algorithm and comparative normalization strategies (Z‐score, min–max, L2, and median scaling), was subsequently validated in a T2DM mouse model with insulin and liraglutide‐administered groups. Heat maps and chord plots were used to reveal the integrated dynamics of the associations between microcirculatory blood perfusion and oxygen saturation.ResultsThe established common microcirculatory framework effectively characterized integrated microhemodynamics and oxygen profiles, with min–max normalizing the microhemodynamic and oxygen. T2DM mice exhibited decreased blood perfusion, reduced red blood cell tissue fraction, diminished oxygen saturation, and lower hemoglobin concentration within the pancreatic microcirculation. Treatment with liraglutide significantly ameliorated these microcirculatory impairments, partially restoring the balance between blood perfusion and oxygen saturation and normalizing the disrupted coherence between oxygenated hemoglobin and speed‐resolved blood perfusion.ConclusionsThe common microcirculatory framework provides a novel methodology for monitoring, visualizing, and assessing integrated pancreatic microcirculatory function, with liraglutide demonstrating enhanced efficacy in ameliorating microcirculatory dysfunction in T2DM.

Reliable observation of water resources is a major challenge for sustainable development, particularly in the river-centric deltaic countries like Bangladesh, where the data is generally scarce. Leveraging operational satellites, this study presents a real-time capable water level (WL), discharge (Q), and floodplain monitoring framework implemented for the Brahmaputra River in Bangladesh. The multi-satellite approach presented here combined satellite altimetry, synthetic aperture radar (SAR), and optical imagery. A set of WL time series is obtained first from Jason-2/3 and Sentinel-3 altimetry, while a combination of Sentinel-1 SAR and Sentinel-2 optical images is used to extract the floodplain extent. Seasonal Rating Curve (RC) models are then developed to estimate Q from the river WL (altimetry) and width (imagery). The altimetry WL measurement is further complemented by the width-based Q utilizing an inverse RC. Furthermore, the water level is combined with a floodplain map to extract floodplain topography and its evolution. The proposed framework provides consistent and reliable observations in the Brahmaputra River, with a bias, root mean-squared errors (RMSEs), and correlation coefficient of 0.03 m, 0.68 m, and 0.96 for WL, and −168.22 m3/s, 4161.46 m3/s, and 0.97 for Q, respectively, relative to a mean discharge of approximately 30,000 m3/s. The locations of high erosion–accretion across the river reach are also well-captured in the evolving floodplain maps. By integrating multiple satellite altimetry missions with SAR and optical imagery, the multi-satellite approach reduces the effective monitoring interval for both water level and discharge from approximately 10 days (single-mission altimetry) to about 4 days, enabling improved capture of extreme events such as floods. As the operational satellites used in this study are expected to provide long-term observations, the proposed framework supports sustainable monitoring of floodplain dynamics in Bangladesh and other similar data-poor environments, towards informed water management under ongoing climatic and anthropogenic changes.

Rosacea is a common chronic inflammatory dermatosis with complex pathophysiology and heterogeneous clinical manifestations. Despite its prevalence, no specific serological biomarkers exist for reliable diagnosis or disease monitoring. Current reliance on subjective clinical assessment underscores the need for objective and quantifiable evaluation methods. This comprehensive review examines the current applications and research progress of non-invasive skin imaging modalities-including computer-aided imaging analyzers, dermoscopy, reflectance confocal microscopy (RCM), optical coherence tomography (OCT), high-frequency ultrasound (HFUS), and laser speckle contrast imaging (LSCI)-in rosacea management. We also discuss the emerging potential of image-based artificial intelligence (AI) for enhancing diagnostic accuracy and clinical decision-making. The integration of multimodal imaging with AI provides a more comprehensive and objective approach to rosacea management, enabling precise subtype classification, accurate severity assessment, and improved treatment monitoring. Multimodal non-invasive imaging combined with AI offers a more objective and comprehensive framework for rosacea diagnosis, subtype stratification, and treatment monitoring, supporting personalized management strategies. However, clinical adoption remains limited by insufficient evidence. Future efforts should focus on large-scale validation, standardization of imaging protocols, and development of AI models that integrate multimodal data to facilitate clinical decision-making.

TFE3 translocation renal cell carcinoma (tRCC), an aggressive kidney cancer driven by TFE3 gene fusions, is frequently misdiagnosed owing to morphologic overlap with other kidney cancer subtypes. Conventional liquid biopsy assays that detect tumor DNA via somatic mutations or copy number alterations are unsuitable for tRCC since it often lacks recurrent genetic alterations and because fusion breakpoints are highly variable between patients. We reasoned that epigenomic profiling could more effectively detect tRCC because the driver fusion constitutes an oncogenic transcription factor that alters gene regulation. By defining a TFE3-driven epigenomic signature in tRCC cell lines and detecting it in patient plasma using ChIP-seq, we distinguished tRCC from clear-cell RCC (AUC = 0.86) and samples of individuals without evidence of cancer (AUC = 0.92) at low tumor fractions (<1%). This work establishes a framework for noninvasive epigenomic detection, diagnosis, and monitoring of tRCC, with implications for other mutationally quiet, fusion-driven cancers.

Development of a hybrid first principles-machine learning adaptive modeling framework for health monitoring of power plant boiler superheaters

From hormones to habitat: A new framework for assessing maturity in the Central-Eastern Mediterranean blue shark (Prionace glauca) population.

A spatially-informed interpretable deep learning framework for high-resolution nutrient monitoring in complex coastal waters

Objectivization of an expert assessment framework for drought monitoring

SmartEars: A practical framework for poultry respiratory monitoring via spectrogram-based audio classification and AI-assisted labeling

ABSTRACT This paper introduces Continuous Artificial Intelligence (AI)-based Reporting, Monitoring, and Assurance (CAIBRMA), extending traditional Continuous Auditing/Continuous Monitoring (CACM) frameworks through AI integration. To provide clarity for future research and practice, we establish distinct boundaries between reporting, monitoring, and assurance functions. By addressing implementation barriers that have limited CACM adoption, AI tools enable us to outline specific integration opportunities across the reporting, monitoring, and assurance functions. JEL Classifications: M41; M42; D83