Ruthenium-Catalyzed Electrochemical Synthesis of Indolines through Dehydrogenative [3 + 2] Annulation with H Evolution.

A dehydrogenative [3 + 2] annulation reaction of aniline derivatives and alkenes has been developed via the ruthenium-electron catalytic systems for the synthesis of versatile indolines. Electricity is used as a sustainable oxidant to regenerate the active Ru(II) catalyst and promote H evolution. This protocol is ecofriendly and easy to handle as it uses a simple undivided cell in mild conditions without the employment of metal oxidants.

Highly Enantioselective Hydrogenation of Non--Substituted 2-Pyridyl Aryl Ketones via Iridium--Diaphos Catalysis.

This work disclosed a highly enantioselective hydrogenation of non--substituted 2-pyridyl aryl ketones via Ir/-diaphos catalysis. This catalytic system allows for full control over the configuration of the stereocenter, affording two enantiomers of the desired products with extremely high enantioselectivity (up to >99% ee in most cases) and excellent reactivity (TON of up to 19600, TOF of 1633 h) under mild conditions. Density functional theory calculations and control experiments revealed that the relay hydrogen bonding among the solvent isopropanol, substrate, and ligand is crucial for high ee's.

Complex harmonic regularization with differential evolution in a memetic framework for biomarker selection.

For studying cancer and genetic diseases, the issue of identifying high correlation genes from high-dimensional data is an important problem. It is a great challenge to select relevant biomarkers from gene expression data that contains some important correlation structures, and some of the genes can be divided into different groups with a common biological function, chromosomal location or regulation. In this paper, we propose a penalized accelerated failure time model CHR-DE using a non-convex regularization (local search) with differential evolution (global search) in a wrapper-embedded memetic framework.

A novel logistic regression model combining semi-supervised learning and active learning for disease classification.

Traditional supervised learning classifier needs a lot of labeled samples to achieve good performance, however in many biological datasets there is only a small size of labeled samples and the remaining samples are unlabeled. Labeling these unlabeled samples manually is difficult or expensive. Technologies such as active learning and semi-supervised learning have been proposed to utilize the unlabeled samples for improving the model performance.

Robust sparse accelerated failure time model for survival analysis.

To identify the bio-mark genes related to disease with high dimension and low sample size gene expression data, various regression approaches with different regularization methods have been proposed to solve this problem. Nevertheless, high-noises in biological data significantly reduce the performances of methods. The accelerated failure time (AFT) modelwas designed for gene selection and survival time estimation in cancer survival analysis.

The effect of marine colloids on the growth of photosysthetic bacteria.

Marine colloids could be an important source of nitrogen for bacteria and photoplankton. But elevated concentration of colloids may stimulate algal growth and lead to red tides in coastal waters. The effects of colloidal organic carbon (COC) concentration on the growth of photosysthetic bacteria (PSB) were investigated under different colloidal treatments in the laboratory.