Access to sufficient financial resources is vital for effective biodiversity conservation. Although the importance of biodiversity conservation is widely recognized, lack of funding has been a significant impediment to achieving conservation goals. Yet, information on the allocation of conservation funding remains limited. This study addresses this gap by mapping conservation funding flows in Bhutan over the past four decades. We identified 249 projects totaling US$ 239.4 million allocated for biodiversity conservation in Bhutan from 1980 to 2019. Most of this funding derived from bilateral and multilateral aid agencies, with domestic trust fund and private foundations also contributing. Funding for projects with coupled conservation and development objectives and gender components was relatively high, particularly for funds allocated by multilateral and bilateral organizations. By contrast, domestic funding typically did not include development or gender components. Private foundations and domestic sources emphasized capacity development interventions. Despite relatively limited funding flows, the socio-political context in Bhutan, which favors environmentally friendly practices, may have been key to the country's widely recognized conservation success. Evidence on trends and patterns in conservation finance, as presented here for Bhutan, can advance conservation science and practice by shedding new light on historical and current conservation priorities and helping inform future allocation.

A bstract Introduction Spontaneous breathing trial (SBT) is always successful in mechanically ventilated patients. This study was conducted to assess the prediction of successful SBT and extubation of trachea by bedside lung ultrasound in mechanically ventilated patients. Methodology This was a prospective observational study for 1 year conducted at a tertiary teaching hospital ICU on 102 patients with age more than 18 years and who were mechanically ventilated for more than 24 hours. Bedside lung ultrasound was used to assess the lung ultrasound score (LUS) and lung profiles in patients who clinically met the criteria for SBT. The LUS at the beginning of SBT and 30 minutes after SBT were used to predict the successful SBT and tracheal extubation. Result Spontaneous breathing trial and tracheal extubation were successful in 73 (71.6%) and 57 (55.8%) of the patients. The AUC for lung ultrasound in predicting successful SBT at the beginning and 30 minutes of SBT were 0.781 (CI 95% 0.674–0.888, p < 0.001) and 0.841 (CI 95% 0.742–0.941, p < 0.001) with a cut-off value of 17.5 and 19.5, respectively. Similarly, AUC for LUS in relation to tracheal extubation was 0.786 (CI 95% 0.694–0.879, p < 0.001) and 0.841(CI 95% 0.756–0.925, p < 0.001) at 0 and 30 minutes. About 57.5% of the patients with A profiles tolerated successful SBT while 48.3% of the patients having C profile had failed SBT ( p < 0.001). COPD, lung ultrasound, higher SOFA score, and longer duration of mechanical ventilation had a statistically significant negative correlation with successful SBT. Conclusion Lower LUS and A profiles lung ultrasound are associated with more successful weaning and tracheal extubation in mechanically ventilated patients. How to cite this article Rajbanshi LK, Bajracharya A, Devkota D. Prediction of Successful Spontaneous Breathing Trial and Extubation of Trachea by Lung Ultrasound in Mechanically Ventilated Patients in Intensive Care Unit. Indian J Crit Care Med 2023;27(7):482–487.

Automated text categorization methods are of broad relevance for domain experts since they free researchers and practitioners from manual labeling, save their resources (e.g., time, labor), and enrich the data with information helpful to study substantive questions. Despite a variety of newly developed categorization methods that require substantial amounts of annotated data, little is known about how to build models when (a) labeling texts with categories requires substantial domain expertise and/or in-depth reading, (b) only a few annotated documents are available for model training, and (c) no relevant computational resources, such as pretrained models, are available. In a collaboration with environmental scientists who study the socio-ecological impact of funded biodiversity conservation projects, we develop a method that integrates deep domain expertise with computational models to automatically categorize project reports based on a small sample of 93 annotated documents. Our results suggest that domain expertise can improve automated categorization and that the magnitude of these improvements is influenced by the experts' understanding of categories and their confidence in their annotation, as well as data sparsity and additional category characteristics such as the portion of exclusive keywords that can identify a category.