Intramolecularly enhanced receptors with unusual binding and molecularly imprinted nanoparticles as nanoreactors and sensors

The conformational flexibility of a receptor is usually considered unfavorable to binding, due to the entropy loss upon binding. Inspired by the biological receptor with intra-receptor secondary binding sites which can strengthen guest binding, two series of artificial receptors with similar structures were constructed: pseudo crown ethers with aromatic donor/acceptor groups and aromatic tweezer molecules with carboxylic acids. When the guest binding was weakened by the solvents, i.e., pseudo crown ethers bound with cations in polar solvents and aromatic tweezers bound with counterparts in non-polar solvents, intra-receptor interactions became stronger and contributed to the guest binding. The overall binding showed a reversed solvent effect and unusually strong affinity. Molecular imprinting with cross-linked micelles and functional templates created binding sites after removal of templates. The binding site complementary to the template can be used to study the chemical reactions in the confined nanospace, which can be different from those in homogeneous solution. Imine formation between amines and the aldehyde inside the binding site was studied. It was found the binding influenced reaction reactivity more than the electronic property of the amine. Besides the study of reaction reactivity, the binding site can be further functionalized with a fluorescent group to achieve sensitive and selective sensing

Complementarity of Systemic Functional Grammar and Constructional Grammar

Construction Grammar (CG) as developed by Fillmore, Goldberg and others is a recent development in syntactic theory, which has become more and more influential. Its central claim is that in a language there are a large number of grammatical units, called constructions, which are the basic forms for the speakers to express their meanings. Systemic Functional Grammar (SFG), put forward by Halliday, also pays great attention to how the speakers generate utterances and texts to convey their intended meanings. This paper explores the relationship between CG and SFG. It argues that the concept of constructions should be introduced in SFG and reflected in the transitivity network. It also suggests that main ideas from SFG be used in CG to describe language more adequately. The objective is to make SFG and CG better theories of language, by combining their strengths

Cognitive Grounding and Its Adaptability to Chinese Noun Studies

Cognitive Grammar is a linguistic theory represented by the symbolic thesis and the usage-based thesis. Cognitive grounding theory is a newly fledged theory in CG. Studies related to grounding have been in their infancy, exhibiting a typological vigor. There have been so far no systematic studies devoted to the grounding system of the Chinese language. Chinese grammar studies applying modern Western linguistic theories have long been the pursuit of scholars from generation to generation. This paper is devoted to introduce grounding theory and then focus on its adaptability to Chinese noun studies. It is concluded that (1) grounding is a cognitive process in which the construal of entities becomes more subjective, and in which a type concept is changed into instances that are singled out by the interlocutors; (2) grounding theory and Chinese noun studies have high adaptability, so Chinese noun studies can be approached from the perspective of Chinese nominal grounding

OmiEmbed: a unified multi-task deep learning framework for multi-omics data

High-dimensional omics data contains intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data due to the large number of molecular features and small number of available samples, which is also called 'the curse of dimensionality' in machine learning. To tackle this problem and pave the way for machine learning aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed support multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy comparing to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various application of high-dimensional omics data and has a great potential to facilitate more accurate and personalised clinical decision making.Comment: 14 pages, 8 figures, 7 table