Extensive postsurgical wounds remain a significant therapeutic challenge in small animal veterinary practice, particularly when reconstructive procedures fail and tissue viability is compromised. This report describes the successful management of a large postsurgical wound in an 8-year-old spayed mixed-breed female dog following wide-margin nodulectomy of a dorsal thoracic mass initially diagnosed cytologically as a suspected liposarcoma. Although primary closure using an H-plasty reconstructive technique was initially performed, progressive central necrosis developed at the surgical margins during the postoperative period. On postoperative Day 3 (POD 3), the wound exhibited tissue necrosis and serosanguineous exudation, necessitating management by second-intention healing. Following surgical debridement, the wound measured approximately 143 cm2 and was subsequently managed using a multimodal advanced wound care protocol based on the principles of the TIME framework, combined with tie-over dressing fixation, advanced wound dressings, fluorescence biomodulation, electrobioregulation, and topical regenerative therapies. Histopathological examination of the excised tissue ultimately revealed chronic-active pyogranulomatous panniculitis with abundant fibrosis, ruling out the initial suspicion of neoplasia. Progressive wound contraction, granulation tissue formation, and epithelialization were observed throughout the treatment period, with serial measurements demonstrating a reduction from 143 cm2 to complete closure over approximately 16 weeks. No evidence of local infection, recurrent tissue necrosis, wound dehiscence, or systemic complications was detected during follow-up. Complete wound healing was achieved with satisfactory functional and cosmetic outcomes, without the need for additional reconstructive surgery. This case highlights the potential clinical value of a multimodal, individualized approach to managing extensive, complicated wounds in dogs. It suggests that sequentially integrating the TIME framework, tie-over fixation, fluorescence biomodulation, and electrobioregulation may offer a useful therapeutic alternative when conventional reconstructive techniques are unsuccessful.
Sensor-based soil data libraries have proven valuable for a wide range of applications. Given the increasing adoption of portable X-ray fluorescence (pXRF) spectroscopy for soil-related studies, we aimed to create a library of soils characterized by pXRF and develop prediction models for soil particle size fractions (clay, silt, and sand contents) based on 16,989 samples collected at multiple soil horizons, from all Brazilian biomes (Amazon, Caatinga, Cerrado, Atlantic Forest, Pantanal, and Pampa), under multiple climate zones, land uses and derived from several parent materials. Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Genetic Programming (GP) were used to train the models. The dataset was split into training (13,593) and independent validation (3,396) sets. The accuracy of models was assessed via root mean square error (RMSE) and coefficient of determination (R2) values. Sand, silt and clay contents ranged from 0 to 100%, 0 to 96%, and 0 to 93%, respectively. Strong correlations were found between particle size fractions and Fe and Si contents, but some strong correlations were biome-dependent, such as those between K and silt (0.77) in Pantanal. Random Forest outperformed the other algorithms, achieving RMSE and R2 values, respectively, of 8.2% and 0.89 for sand, 5.6% and 0.71 for silt, and 6.3% and 0.89 for clay contents. This pXRF library resulted in adequate predictions for sand and clay contents. This national library may contribute to establish a global library of soils characterized via pXRF. This dataset should be continuously increased to support soil chemical characterization and develop prediction models for varied soil properties.
This study evaluates the responses of soil properties to lithology, topography, and LULC in the Galyan watershed using soil data (n = 417) collected from LULC classes overlying different lithological units (Çatak, Hamurkesen, and Kaçkar), together with topographic variables. The study also investigates the combined effects of anthropogenic gradients, including cropland and hazelnut cultivation (Corylus avellana), and natural vegetation gradients (grassland, Alnus glutinosa, Fagus orientalis, and Picea orientalis) across lithology. In addition, LULC changes (2006–2023) were analyzed to evaluate potential risks to watershed sustainability. Soil samples were analyzed for particle size distribution, pH, electrical conductivity, organic matter, total nitrogen, total phosphorus, and exchangeable base cations. MANOVA indicated statistically substantial multivariate effects of lithology, LULC, and their interaction on soil properties. CATPCA revealed structured relationships among soil, topography, and LULC classes, while correlation analysis evaluated the relationships between topography and soil properties. Çatak soils had higher levels of clay, silt, pH, EC, Ca2+, and Mg2+. Kaçkar and Hamurkesen soils had higher sand, organic matter, total nitrogen, and total phosphate. Natural lands exhibited a vegetation gradient in response to topographical conditions. Alnus glutinosa was associated with lower silt, clay, pH, EC, TN, and exchangeable base cations, whereas Fagus orientalis and Picea orientalis were associated with higher values. Topography further structured these patterns: lower, warmer areas were associated with finer textures and higher base cation concentrations, while higher, cooler areas were associated with higher concentrations of organic matter, nitrogen, and phosphorus. Despite being based on a single soil sampling campaign, the spatial differences emerging from the combined effects of lithology, topography, and LULC provide valuable insights into potential soil responses and have important implications for sustainable watershed management. LULC analysis indicated a notable expansion of agricultural and impervious areas, suggesting potential implications for nutrient dynamics.
Climate change and anthropogenic disturbances have complicated vegetation-rainfall relationships, making it crucial to understand their coupling mechanisms, especially in water-limited ecosystems. Within this context, we examined the dynamics and drivers of the vegetation-rainfall relationships in the Yellow River Basin (YRB) since 2000 using multi-source remote sensing data, Mann-Kendall trend detection, and an attribution framework integrating Principal Component Analysis (PCA) and Hierarchical Partitioning (HP). Results demonstrated that Net Primary Productivity (NPP) and Rainfall Use Efficiency (RUE) exhibited a significant increasing trend (80.43% and 42.57% of the YRB respectively), particularly in mid-basin forest areas (95.48% and 67.47% of the midstream respectively). However, Vegetation Sensitivity to Rainfall (VSR) declined across 25.43% of the basin, indicating that vegetation growth is becoming progressively less dependent on natural rainfall, with the most pronounced decline observed in the middle basin (31.9% of its area). Human activity intensity and ecological response index (HEI) accounted for 73.5% of the variance in the drivers of RUE and VSR, while climate-water stress index (CWI) explained 10.9%. Specifically, RUE was predominantly governed by natural factors and vegetation structure (e.g., leaf area index), whereas VSR was primarily driven by human activities (e.g., population density). These divergent drivers reveal an efficiency-resilience trade-off, suggesting that regional ecosystem management should emphasize both vegetation cover and rainfall use efficiency alongside ecosystem resilience to rainfall variability-a dimension essential for ecological security and sustainable development.
Ecological restoration is widely implemented to recover degraded lands, yet the microbial mechanisms underpinning soil organic carbon (SOC) sequestration during this process remain poorly understood, particularly regarding how nutrient availability shapes microbial community traits and necromass accumulation. In this study, we utilized a space-for-time substitution method across a tropical ecological restoration chronosequence (comprising rubber monoculture, near-natural rainforest, and primary rainforest) and integrated analyses of soil properties, microbial biomarkers, and molecular sequencing to investigate the linkages between microbial nutrient limitation, community structural and functional traits, and soil carbon fractions. Our results showed that ecological restoration significantly improved litter and soil nutrient stoichiometry, alleviating microbial carbon (C) and phosphorus (P) limitation. This variation drove a shift in microbial life-history strategies from high growth yield strategies to resource-acquisition/stress-tolerance strategies, increasing microbial carbon use efficiency (19–60%), richness (27–79%), biomass (10–64%), and network complexity (1.9–3.3-fold) while reducing competitive interactions. Concurrently, stochastic processes became more dominant in community assembly. Metagenomic analysis revealed enhanced expression of genes involved in C fixation, recalcitrant C degradation (e.g., lignin degradation), and P cycling. Consequently, microbial necromass C, particularly fungal-derived C, increased significantly (13–61%) and emerged as a stronger direct driver of SOC accumulation than plant-derived C, despite a decrease in its proportional contribution to the SOC pool. These results suggest that alleviated nutrient limitation is linked to coordinated shifts in microbial metabolism and community characteristics, which in turn promote necromass accumulation and contribute to SOC sequestration. This study underscores the necessity of integrating microbial metabolic traits into soil carbon management strategies for degraded tropical plantations.
