Introduction: Sarcopenia, muscle loss, often coexists with MASLD, a common liver disorder, which is more prevalent in those with sarcopenia. Sarcopenia also increases the risk of liver conditions like steatosis and fibrosis. Studies on sarcopenia in MASLD are common in developed countries, but fewer follow the EWGSOP2 guidelines in Brazil. Aim: to assess sarcopenia prevalence in MASLD patients. Methods: Cross-sectional study conducted at the Gastroen-terology/Hepatology Service of ISCMPA with patients diagnosed with MASLD. The EWGSOP2 criteria were used to evaluate sarcopenia. Categorical data presented as absolute and relative frequency; parametric continuous data ex-pressed as mean±standard deviation; non-parametric continuous data as me-dian and IQR. Gender differences in were analyzed using Fisher's Exact Test or Chi-squared tests, and for continuous variables, T Student tests (parametric) and Mann-Whitney U tests for independent samples (non-parametric). The significance level was set at 5% (p<0.05). Results: The study involved 103 MASLD patients with an average age of 60.39 years, comprising 48 (46.60%) adults and 55 (53.40%) older individuals. Concerning sarcopenia diagnosis, four individuals exhibited decreased muscle strength; two had reduced MME (sarcopenia); and one showed decreased walking speed (severe sarcopenia). Among the participants, 63 (60.6%) were physically active. 35 (62.5%) had mild to moderate steatosis, while 21 (37.5%) had severe steatosis. In terms of EHNA, 13 subjects (24.08%) had moderate to severe EHNA. Regarding fibrosis classi-fication, 68 (72.34%) individuals had undetermined or high probability based on the NAFLD score, with higher prevalence in males (n=23; 88.5%). Fibrosis assessment via liver biopsy revealed 27 (28.72%) in F1 and F2 and 15 (16.96%) in F3 and F4. Stratification of fibrosis into F3 and F4 was more com-mon among men (n=9; 47.4%). Conclusion: Most of the population was physi-cally active. The parameters indicating sarcopenia exceeded the thresholds recommended by EWGSOP2. The prevalence of sarcopenia was low in individuals with MASLD.

Gelatin industry residues are increasingly used as fertilizer and soil conditioner. However, correct residue dosage is critical for grass development and minimizing environmental impacts. This randomized block design study determined adequate dosage of Mixed Gelatin Sludge (MGS) for Marandu grass production in wet/dry seasons in Brazil. Five MGS levels (0%–200% of required nitrogen) were compared to mineral fertilizer. Agronomic/productivity characteristics, bromatological composition, macro/micro-nutrient composition of leaves, and soil chemical attributes were evaluated. Agronomic/productivity characteristics were influenced by MGS dose in both dry/rainy seasons, except for leaf blade pseudostem ratio and percentage of leaves/pseudostem. Bromatological composition was influenced by MGS doses in dry/rainy seasons except for dry/mineral material quantities. Marandu leaf tissue chemical composition was significantly influenced by MGS dose, except for potassium, boron, and iron. Chemical composition of four soil layers between 0–50 cm influenced GMS dose, except for pH, organic matter, magnesium, copper, manganese, and zinc. GMS dose for Marandu production should be 200% of nitrogen (N) requirement. GMS application increased productivity/quality of Marandu grass. Macronutrients (N, phosphorus) and micronutrients (Ca, Mg, S, Cu, and Zn) increased in Marandu grass and in the soil (Ca, S, and Na). Increased sodium (Na) level was not limiting.

Combustion power plants emit carbon dioxide (CO₂), a major contributor to climate change. Direct emissions measurement is cost-prohibitive globally, while reporting varies in detail, recency, and granularity. To fill this gap and greatly increase the number of power plants worldwide with independent emissions monitoring, we developed and applied machine learning (ML) models using power plant plumes as proxy signals to estimate electric power generation and CO₂ emissions using Landsat 8, Sentinel-2, and PlanetScope imagery. Our ML models estimated power plant activity on each image snapshot, and then an aggregation model predicted plant utilization over a 30-day period. Lastly, emissions factors specific to region, fuel, and plant technology were used to convert estimated electricity generation to CO₂ emissions. Models were trained with reported hourly generation data in the US, Europe, and Australia and were validated with additional generation and emissions data from the US, Europe, Australia, Türkiye, and India. All results with sufficiently large sample sizes indicate that our models outperformed baseline approaches. In conducting external validation to compare modeled versus reported annual generation and emissions in the countries where available, we calculated the root mean square error overall for modeled countries with validation data as respectively 1.75 TWh (236 plants across 17 countries over 4 years) and 2.04 Mt CO₂ (207 plants across 17 countries over 4 years). Ultimately, we applied our ML method to plants that comprise 32% of global power plant CO₂ emissions averaged over 2015-2022. This dataset is the most comprehensive independent and free-of-cost global power plant point-source emissions monitoring system currently known to the authors and is publicly available at climatetrace.org to support global emissions reduction.