RESUMEN
Phylogenetic tree inference is a classic fundamental task in evolutionary biology that entails inferring the evolutionary relationship of targets based on multiple sequence alignment (MSA). Maximum likelihood (ML) and Bayesian inference (BI) methods have dominated phylogenetic tree inference for many years, but BI is too slow to handle a large number of sequences. Recently, deep learning (DL) has been successfully applied to quartet phylogenetic tree inference and tentatively extended into more sequences with the quartet puzzling algorithm. However, no DL-based tools are immediately available for practical real-world applications. In this paper, we propose Fusang (http://fusang.cibr.ac.cn), a DL-based framework that achieves comparable performance to that of ML-based tools with both simulated and real datasets. More importantly, with continuous optimization, e.g. through the use of customized training datasets for real-world scenarios, Fusang has great potential to outperform ML-based tools.
Asunto(s)
Aprendizaje Profundo , Filogenia , Algoritmos , Teorema de Bayes , Alineación de Secuencia , Funciones de VerosimilitudRESUMEN
Correction for 'Metal-free oxidative synthesis of benzimidazole compounds by dehydrogenative coupling of diamines and alcohols' by Jiaming Hu et al., Org. Biomol. Chem., 2022, DOI: 10.1039/d2ob00165a.
RESUMEN
We report a novel metal-free synthesis of benzimidazole compounds by dehydrogenative coupling of diamines and alcohols. Using NHPI as a nonmetallic catalyst combined with molecular oxygen or air as the oxidant, this transformation represents a widely applicable protocol to N-heterocycles, such as benzimidazoles, benzothiophenes, benzooxazoles and quinazolines. Flow microreactors operating under optimized conditions enabled this reaction with higher efficiency, and the total residence time was 30 min compared with the batch bubbling reactor (10 h). Moreover, a possible reaction mechanism is proposed according to the control experiments.