Search Results (893)

Arctic–boreal wetlands and lakes are among the most significant and most uncertain natural sources of atmospheric methane. Rapid Arctic amplification, permafrost thaw, hydrological change, and increasing ecosystem productivity are expected to intensify methane emissions from high-latitude landscapes. Yet, significant uncertainties persist in quantifying their magnitude, seasonality, and spatial distribution. This review synthesizes the current state of the art in monitoring methane emissions from Arctic–boreal wetlands and lakes through complementary bottom-up and top-down approaches. We examine Earth observation (EO) capabilities, including optical, thermal infrared (TIR), and synthetic aperture radar (SAR) missions, as well as new emerging satellite platforms. We also assess in situ measurement networks, wetland and lake inventories, empirical and process-based models, and atmospheric inversion frameworks. Key gaps remain in representing small waterbodies, shoreline heterogeneity, winter emissions, inventory harmonization, and integration between atmospheric retrievals and surface-based flux models. Moreover, advances in multi-sensor data fusion, explainable artificial intelligence (XAI), physics-informed inversion methods, and geospatial foundation models offer strong potential to reduce these uncertainties. A coordinated integration of satellite observations, field measurements, and transparent modeling frameworks is essential to improve Arctic–boreal methane budgets and strengthen projections of climate feedback in a rapidly warming region. Full article

Since 2025, 45% of the world’s population of 8.2 billion people has lived in cities, and by 2050, that number is expected to increase to 66%. As the number of people living in cities increases, natural landscapes will be transformed into impervious surfaces, leading to serious challenges and resulting in a phenomenon named the urban heat island (UHI) effect. Although urban thermal variation has been studied globally, few studies have examined the impact of land use transitions on local surface temperatures. This study aims to address this gap by investigating the impact of LULC transitions on the land surface temperature (LST) and the urban thermal field variation index (UTFVI) in the five most populated cities in Saudi Arabia between 2000 and 2024: Riyadh, Jeddah, Makkah, Madinah, and Dammam. This study provides not only a comprehensive overview of the cities in Saudi Arabia but also a detailed analysis of each city using a novel approach that integrates thermal land use analysis. In this study, Landsat TM-5, OLI-TIRS-8, and OLI2-TIRS2-9 were used to process the LULC using random forest machine learning and thermal indices. Fifteen LULC maps were generated and assessed based on four classifications across the cities and time periods: urban area, barren land, vegetation, and water. The difference-in-difference (DiD) analytical approach was used to compute the thermal effect size and compare the specified changed pixels (barren-to-urban, vegetation-to-urban) with stable urban. Then, the relationship between the LST and the NDVI–NDBI were investigated. The results show that the overall accuracy of the 15 LULC classifications ranged from 89.00% to 97.00%. The urban area increased across all the cities, with the greatest changes being 448.84, 179.67, 177.96, 126.33, and 95.69 km² in Riyadh, Jeddah, Dammam, Madinah, and Makkah, respectively. Furthermore, the vegetation cover increased in most of the cities over time. The LST of the urban areas increased by 8.31 °C in Riyadh, 5.24 °C in Jeddah, and 1.41 °C in Makkah in 2024 compared to 2000, while those in Dammam and Madinah decreased by 2.67 °C and 0.60 °C, respectively. This study delivers robust insights into two decades of urban surface temperature dynamics across major Saudi Arabian cities, offering critical evidence to inform UHI mitigation strategies and support the long-term sustainability of urban environments. Full article

Objectives: To describe a structured transition model for individuals with type 1 diabetes mellitus (T1DM) from pediatric to adult care in a tertiary hospital, and to explore demographic, clinical, and psychosocial factors associated with glycemic outcomes. Research Design and Methods: We conducted an observational, cross-sectional study including all patients with T1DM who transitioned from the Pediatric Endocrinology Clinic to the Adult Endocrinology and Nutrition Unit at Virgen del Rocío University Hospital between 2021 and 2024. Demographic, clinical, biochemical, glucometric, and socioeducational variables were collected at the first adult care visit. Statistical analyses included nonparametric tests and exploratory multivariate logistic regression models. Results: A total of 73 patients (45% female) were included, with a median age of 18 years and median diabetes duration of 9 years. The 46.6% of our cohort had an HbA1c > 7.5%. Overweight and obesity were present in 25% and 8% of patients, respectively, and 11% were active smokers. Eighteen percent were receiving mental health follow-up, mainly for anxiety–depressive disorders. Those using hybrid closed-loop insulin delivery and continuous glucose monitoring achieved significantly better glycemic control (TIR 67% vs. 48%; p < 0.01) and lower glycemic variability. In exploratory multivariable analyses, continuous glucose monitoring use > 90% of the time and higher maternal educational level were associated with a lower likelihood of HbA1c > 7.5%. Conclusions: In this cross-sectional transition cohort, intensive use of diabetes technology and higher maternal educational level were associated with better glycemic control at the time of transfer to adult care. These findings should be interpreted as exploratory and hypothesis-generating, and warrant confirmation in larger, prospective studies. Full article

Metabolic control is poor in East Africa for youth with type1 diabetes (T1D). Self-monitoring of blood glucose (SMBG) by fingerstick 2–3 times daily is routine care. This randomized controlled trial (RCT) will test the hypothesis that providing continuous glucose monitoring (CGM) to Ugandan youth with T1D will improve glucose time-in-range (TIR glucose 3.9–10.0 mmol/L) and be cost effective in this setting. Ugandan youth with T1D (n = 180, age 4–26 years) will be divided into four 12-month cohorts (August 2022–August 2027). Half will receive unblinded Freestyle Libre 2 Flash CGM for 12 months. For six months, control subjects received sufficient test strips for SMBG three times daily while wearing blinded Freestyle Libre Pro CGM (for endpoint assessment), and then they switch to unblinded CGM for six months. Everyone receives monthly diabetes education. The primary endpoints are as follows: (1) the six-month change from baseline in glucose TIR, unblinded CGM versus SMBG; (2) a cost analysis of CGM versus SMBG. The TIR hypothesis will be tested by linear mixed effects models. Cost analysis assumptions include direct material and indirect costs like hospitalizations, missed school/work, and diabetes complications. The study will inform T1D management guidelines in a low resource setting using evidence-based recommendations. Full article

Due to hardware limitations of Thermal InfraRed (TIR) cameras, TIR images captured by Unmanned Aerial Vehicles (UAVs) suffer from Low Resolutions (LRs) and blurred textures. Improving the spatial resolution of TIR images is of great significance for subsequent applications. Existing image Super-Resolution (SR) methods rely on High-Resolution (HR) ground truth for supervised training, resulting in limited generalization and a lack of mechanisms to preserve the physical consistency of thermal radiation. To address these two issues, this paper proposes an unsupervised super-resolution framework for UAV TIR imagery that integrates diffusion modeling with cross-modal texture transfer. The diffusion model enables stable reconstruction of the fundamental TIR structure without requiring high-resolution supervision, while multi-scale textures extracted from visible (VIS) imagery via Multi-Stage Decomposition based on Latent Low-Rank Representation (MS-DLatLRR) compensate for missing details. To suppress temperature distortions introduced by cross-modal texture transfer, a physics-guided constraint termed Prior-Informed Emissivity-Guided Coefficient Mapping (PI-EGCM) is incorporated. Emissivity-aware guidance maps constructed via semantic classification regulate texture transfer and preserve thermal radiation consistency. Experimental results demonstrate that the proposed method improves spatial resolution and perceptual quality while effectively maintaining temperature fidelity, achieving a balanced enhancement of structural detail and physical consistency. Full article

This study investigates the spatiotemporal variability of ambient methane (CH₄) using a drone-deployable Aeris Technologies MIRA Strato LDS midwave-infrared analyzer. Laboratory calibration with NOAA-certified gas standards and Standard Reference Material (SRM) for CH₄ demonstrated high measurement precision across a range of concentrations (R² = 0.9986, slope = 0.9678). Field validation conducted during a two-week intercomparison with a Picarro G2301 in September 2023 confirmed the MIRA Strato’s reliability under ambient conditions (R² = 0.9845; slope = 0.9438), indicating strong agreement with the reference analyzer. Diurnal patterns revealed peak CH₄ concentrations (~2.2 ppm) between 04:00–08:00 LT and minima (~2.1 ppm) between 13:00–17:00 LT, consistent with nocturnal boundary-layer stability and daytime convective mixing. Across 14 midday UAV flights from October 2023 to September 2024, CH₄–altitude slopes ranged from −3.05 × 10⁻⁴ to +1.41 × 10⁻⁴ ppm/m, reflecting variable stratification and uplift regimes. The highest flight concentration (2.23 ppm) was observed on 19 October under stable conditions, while the lowest (2.03 ppm) was observed on 14 August under elevated vertical mixing. These extremes reflect seasonal background accumulation and convective transport effects. Temperature was the most consistent predictor, with regression coefficients ranging from −0.021 to +0.008 ppm/°C, while ethane (C₂H₆) coefficients were significant but confounded due to measurements below detection limits. The analyzer maintained strong signal stability throughout (mean CV ≈ 0.0066; max = 0.0114), and remote sensing validation with TROPOMI supported observed seasonal accumulation trends. These results demonstrate the MIRA Strato’s capability to resolve near-surface CH₄ dynamics and characterize convective transport in complex atmospheric environments. Full article

Accurate diagnosis of nitrogen status is essential for precision fertilization in winter wheat. Single-modal or single-temporal remote sensing often fails to capture the multidimensional crop responses to nitrogen stress. In this study, we propose a hybrid framework based on CNN-LSTM-XGBoost for interpretable classification of wheat nitrogen stress gradients using multimodal unmanned aerial vehicle (UAV) multispectral and thermal infrared (TIR) imagery. Field experiments were conducted at the Xinxiang base in Henan Province during the 2023–2024, following a randomized block design involving 10 cultivars, four nitrogen levels, and four water treatments. Multisource UAV images acquired at jointing, heading, and filling stages were used to construct a multimodal feature set consisting of manual features (spectral bands, vegetation indices (VIs), TIR, and their interaction terms) and seven temporal statistical features. A deep learning model (CNN-LSTM) was utilized to further extract deep spatiotemporal features, and its performance was systematically compared with traditional machine learning models. The results show that multimodal feature fusion significantly enhanced classification performance. The CNN-LSTM model achieved an accuracy of 89.38% with fused multimodal features, outperforming all traditional machine learning models. Incorporating multi-temporal features improved the F1_macro of the XGBoost model to 0.9131, a 9.42 percentage-point increase over using the single heading stage alone. The hybrid model (CNN-LSTM-XGBoost) achieved the highest overall performance (Accuracy = 0.9208; F1_macro = 0.9212; AUC_macro = 0.9879; Kappa = 0.8944). SHAP analysis identified TIR × NDRE as the most influential indicator, reflecting the coupled physiological response of reduced chlorophyll content and increased canopy temperature under nitrogen deficiency. The proposed multimodal, multi-temporal, and interpretable framework provides a robust technical foundation for UAV-assisted precision nitrogen management. Full article

Background/Objectives: Type 2 diabetes (T2D) affects more than 38 million Americans and remains a leading public health challenge. Behavioral self-management is central to glycemic control but is often undermined by dysregulated and addictive-like eating behaviors. Continuous glucose monitoring (CGM) offers immediate feedback that may strengthen self-regulation, yet the psychological processes linking CGM use, food addiction (FA), and behavior change are poorly understood. This secondary mixed-methods study examined how CGM-supported group medical visits (GMVs) influence glycemic outcomes and FA symptoms in adults with diabetes. Methods: Adults with T2D participated in a 14-week GMV program integrating CGM review with education on nutrition, physical activity, sleep, stress, and intermittent fasting. Thirteen participants had paired CGM summaries and psychosocial data. Quantitative outcomes included mean glucose, glycemic variability, time-in-range (TIR), and symptoms of food addiction using the modified Yale Food Addiction Scale 2.0 (mYFAS 2.0). Qualitative data came from open-ended surveys analyzed using reflexive thematic analysis. Integration followed a convergent design, merging individual change trajectories with thematic interpretations and case vignettes. Results: Mean glucose decreased by 21 mg/dL and TIR improved by 9 percentage points. Among six participants with baseline FA symptoms, all showed reductions in self-reported mYFAS 2.0 symptom counts. Four moved from mild to no symptoms, one from moderate to no symptoms, and one from severe to no symptoms. Across the full sample, the mean change was a reduction of 1.2 in the mYFAS 2.0 symptom counts per participant. Thematic analysis identified four interrelated psychological mechanisms: enhanced awareness of food–glucose relationships, increased accountability through shared tracking, motivation via gamified self-monitoring, and relief from cognitive burden associated with dietary uncertainty. Conclusions: Integrating CGM feedback into GMVs was associated with improvements in glycemic metrics and reductions in addictive-like eating symptoms in this pilot sample. These findings position CGM as a behavioral intervention tool that complements its traditional monitoring role and highlight the value of combining real-time biofeedback with group-based support in diabetes care. Full article

Leveraging thermal infrared imagery to complement RGB spatial information is a key technology in industrial sensing. This technology enables mobile devices to perform scene understanding through RGB-T semantic segmentation. However, existing networks conduct only limited information interaction between modalities and lack specific designs to exploit the thermal aggregation entropy of the thermal modality, resulting in inefficient feature complementarity within bilateral structures. To address these challenges, we propose Wavelet-CNet for RGB-T semantic segmentation. Specifically, we design a Wavelet Cross Fusion Module (WCFM) that applies wavelet transforms to separately extract four types of low- and high-frequency information from RGB and thermal features, which are then fed back into attention mechanisms for dual-modal feature reconstruction. Furthermore, a Cross-Scale Detail Enhancement Module (CSDEM) introduces cross-scale contextual information from the TIR branch into each fusion stage, aligning global localization through contour information from thermal features. Wavelet-CNet achieves competitive mIoU scores of 58.3% and 85.77% on MFNet and PST900, respectively, while ablation studies on MFNet further validate the effectiveness of the proposed WCFM and CSDEM modules. Full article

Toll-like receptors (TLRs) are essential pattern recognition receptors of the innate immune system and play critical roles in pathogen invasion in teleosts. In this study, we identified and characterized full-length open reading frames of three TLRs belonging to the TLR11 subfamily from Schizothorax prenanti, termed spTLR19 (2868 bp), spTLR20 (2835 bp), and spTLR21 (2946 bp), encoding 955, 944, and 981 amino acids, respectively. All three proteins exhibited the conserved domain architecture typical of TLRs, comprising a leucine-rich repeat (LRR) domain, a transmembrane region, and a Toll/IL-1 receptor (TIR) domain. Phylogenetic and homology analyses revealed that spTLR19 and spTLR20 clustered most closely with their homologues from Cyprinus carpio, while spTLR21 showed the highest similarity to Onychostoma macrolepis TLR21. Expression profiling showed that these TLRs were ubiquitously expressed across examined tissues, with relatively higher expression in immune-related tissues such as spleen and gills. Furthermore, challenge with Streptococcus agalactiae and Aeromonas veronii significantly up-regulated the expression of spTLR19, spTLR20, and spTLR21 in spleen, liver, and gills, suggesting their involvement in antibacterial immune responses. These findings enhance the functional understanding of the teleost TLR11 subfamily and provide a foundation for elucidating disease resistance and immune regulation in S. prenanti. Full article

Spider monkeys (Ateles spp.) have traditionally been described as strictly diurnal primates, with only low levels of activity during the night. Consequently, little attention has been given to the possibility of nocturnal movements and social dynamics occurring at sleeping sites. Recent advances in technologies, such as drone-based thermal infrared imaging (TIR), provide new opportunities to explore behavioral patterns that were previously undetectable through ground-based observations. In this study, we aimed to evaluate whether Geoffroy’s spider monkeys (Ateles geoffroyi) change their subgroup size once they are at their sleeping sites by comparing the numbers of monkeys detected after sunset with those detected before sunrise using TIR drone surveys. We conducted TIR drone flights over four sleeping sites of well-habituated Geoffroy’s spider monkey groups in Los Árboles Tulum in the Yucatán Peninsula, Mexico. We carried out 18 flight pairs—18 flights at sunset when the majority of individual spider monkeys were expected to have arrived at the sleeping sites, and 18 flights the next following morning at sunrise—before the monkeys began their daily movements. Our results revealed that in 12 out of the 18 flight pairs (67%), the number of monkeys counted at sunset differed from the number counted at sunrise. In 58% of these 12 flight pairs, more monkeys were counted at sunrise than at sunset. Furthermore, when changes in subgroup size occurred, they were more frequent (67%) when the subgroups at sleeping sites were larger (>10 monkeys). These changes in subgroup size are consistent with the occurrence of fissions and fusions continuing after dark. This study provides preliminary evidence that Geoffroy’s spider monkeys are more active during the night than generally assumed. Furthermore, our results highlight the value of TIR drones as an effective tool for studying primate social dynamics under low-light conditions. Unlike traditional ground-based observations, which depend on natural light, TIR drones allow for accurate and reliable monitoring throughout the night. By providing access to behavioral information that would otherwise remain hidden, this technology opens new possibilities for understanding the full temporal range of activity of diurnal species. Full article

Accurate identification and characterization of subcutaneous tumors are essential for improving breast tumor detection and treatment. This study introduces an innovative Tactile-Sensation Imaging System (TSIS) designed, implemented, and tested to detect and characterize subcutaneous inclusions simulating breast tumors. The system employs a multilayered polydimethylsiloxane (PDMS) optical waveguide that mimics the tactile structure of the human fingertip. By introducing light at a critical angle, the design enables continuous total internal reflection (TIR) within the flexible, transparent waveguide. When external pressure is applied, deformation of the contact area causes light scattering, which is recorded using a high-definition camera and processed as tactile images. Analysis of these images allows estimation of inclusion characteristics such as size, depth, and mechanical properties, including Young’s modulus. Analytical modeling and numerical simulations validated the optical performance of the waveguide, while experimental evaluations using realistic tissue phantoms confirmed the system’s ability to accurately detect and quantify embedded inclusions. The results demonstrated reliable estimations of inclusion dimensions, depths, and stiffness, verifying the system’s sensitivity and precision. The TSIS offers a noninvasive, portable, and cost-efficient solution for quantitative breast tumor assessment, bridging the gap between manual palpation and advanced imaging, with future enhancements aimed at improving resolution and diagnostic accuracy. Full article

With the intensification of global climate change and the frequent occurrence of extreme drought events, forest production is facing severe challenges. This study imposed drought stress and exogenous abscisic acid (ABA) treatment on Larix gmelini seedlings, evaluated their physiological characteristics, and analyzed the transcriptional response mechanism using transcriptome sequencing. The results showed that drought stress induced organ-specific changes in superoxide dismutase (SOD) and peroxidase (POD) activities, malondialdehyde (MDA) accumulation, and soluble protein content. SOD activity in leaves significantly increased, while POD activity, MDA content, and soluble protein levels in roots exhibited more dynamic changes. After ABA application, SOD activity in leaves reached its peak at 24 h, which was opposite to the situation in roots and stems, where POD activity was highest at 24 h. At 48 h, MDA accumulation was most significant in roots, while the early response in leaves was minimal. At 24 h, the soluble protein increase was most significant in stems. In addition, at this time point, ABA application significantly increased the soluble protein content in all three organs. Transcriptome sequencing analysis further identified core response genes involved in the MAPK signaling pathway, plant hormone signal transduction, starch and sucrose metabolism, and flavonoid biosynthesis pathways, including SNRK2, MAPKKK17, PYL, PP2C, XRN4, TMEM, TIR1, and TGA. In summary, Larix gmelini seedlings alleviate the inhibitory effect of drought stress on growth through a synergistic mechanism, specifically by activating the antioxidant system, initiating the MAPK signaling pathway, regulating plant hormone signal transduction, and reshaping carbon metabolism pathways, thereby enhancing stress resistance. Full article

Background/Objectives: Variation in repeatome composition is a major determinant of genome architecture and an important substrate for evolutionary change in plants. Despite the availability of genomic sequence data, repeatome-wide assessments have not been performed for Epidendrum, the largest Neotropical genus of Orchidaceae. Here, we assessed repeatome profiles across 34 Epidendrum species using publicly available genomic datasets. Methods:Epidendrum repeatomes were characterized with the RepeatExplorer2 pipeline, and patterns of repeat composition were evaluated for phylogenetic structure using a species phylogeny. Results: Repeat composition showed no clear phylogenetic structure, with closely related species often displaying divergent satDNA and TE profiles. satDNA content varied widely among species (15.5–69% of the repeatome fraction). A total of 208 satDNA families were detected, which were used to build a custom database for comparative analyses. We detected 73 satDNA clusters shared among species, whereas only three were species-specific. Regarding TEs, Class I elements were the most abundant repeats, dominated by Ty3-Gypsy LTR retrotransposons. Only two Class II TIR superfamilies were detected (EnSpm/CACTA and hAT). Conclusions: This study provides the first comprehensive characterization of the Epidendrum repeatome and establishes a resource for future work on cytogenomic diversity within this megagenus. The heterogeneous distribution of repeats among closely related species is consistent with lineage-specific amplification and loss, highlighting rapid repeatome turnover in Epidendrum. Potential drivers, as hybridization and ecological differentiation, should be tested explicitly in future analyses integrating broader genome size sampling and trait data. Full article

Clonal propagation often must incorporate heaters to warm stock plants and stabilize growth. This study investigates the impact that different temperature regimes for stock plants have on the rooting capacity of mini-cuttings derived therefrom. Experiments were conducted in growth chambers using two clones of Eucalyptus dunnii Maiden, with clone A’s rooting being moderately better that that of clone B in commercial production. Root primordia differentiation and elongation were faster in clone A than clone B. Stock plants were maintained for one month under two temperature conditions: Δ0 (26/26 °C day/night) and Δ10 (26/16 °C). The main results indicate that rooting significantly decreased with the reduction in nocturnal temperature. Clone A exhibited a 38% reduction in rooting, whereas clone B showed a more pronounced decrease of 65%. In cold nights, soluble carbohydrates at the cutting bases dropped by approximately 25% considering both clones, and overall foliar nutrients also decreased. Cutting base transcript profiles revealed that cold nights decreased the expression of efflux auxin transporter PIN1, increased expression of auxin catabolism-related enzyme DAO, and that expression of auxin nuclear receptor TIR1 remained stable. Fine management of clonal gardens by adjusting thermal conditions can optimize the physiological status of donor plants and enhance the rooting potential and establishment of the derived cuttings. Full article