SpaNCMG: improving spatial domains identification of spatial transcriptomics using neighborhood-complementary mixed-view graph convolutional network.

The advancement of spatial transcriptomics (ST) technology contributes to a more profound comprehension of the spatial properties of gene expression within tissues. However, due to challenges of high dimensionality, pronounced noise and dynamic limitations in ST data, the integration of gene expression and spatial information to accurately identify spatial domains remains challenging. This paper proposes a SpaNCMG algorithm for the purpose of achieving precise spatial domain description and localization based on a neighborhood-complementary mixed-view graph convolutional network. The algorithm enables better adaptation to ST data at different resolutions by integrating the local information from KNN and the global structure from r-radius into a complementary neighborhood graph. It also introduces an attention mechanism to achieve adaptive fusion of different reconstructed expressions, and utilizes KPCA method for dimensionality reduction. The application of SpaNCMG on five datasets from four sequencing platforms demonstrates superior performance to eight existing advanced methods. Specifically, the algorithm achieved highest ARI accuracies of 0.63 and 0.52 on the datasets of the human dorsolateral prefrontal cortex and mouse somatosensory cortex, respectively. It accurately identified the spatial locations of marker genes in the mouse olfactory bulb tissue and inferred the biological functions of different regions. When handling larger datasets such as mouse embryos, the SpaNCMG not only identified the main tissue structures but also explored unlabeled domains. Overall, the good generalization ability and scalability of SpaNCMG make it an outstanding tool for understanding tissue structure and disease mechanisms. Our codes are available at https://github.com/ZhihaoSi/SpaNCMG.

A High-Quality Mixed Matrix Membrane with Nanosheets Assembled and Uniformly Dispersed Fillers for Ethanol Recovery.

A high-quality filler within mixed matrix membranes, coupled with uniform dispersity, endows a high-efficiency transfer pathway for the significant improvement on separation performance. In this work, a zeolite-typed MCM-22 filler is reported that is doped into polydimethylsiloxane (PDMS) matrix by ultrafast photo-curing technique. The unique structure of nanosheets assembly layer by layer endows the continuous transfer channels towards penetrate molecules because of the inter-connective nanosheets within PDMS matrix. Furthermore, an ultrafast freezing effect produced by fast photo-curing is used to overcome the key issue, namely filler aggregation, and further eliminates defects. When pervaporative separating a 5 wt% ethanol aqueous solution, the resulting MCM-22/PDMS membrane exhibits an excellent membrane flux of 1486 g m-2 h-1 with an ethanol separation factor of 10.2. Considering a biobased route for ethanol production, the gas stripping and vapor permeation through this membrane also shows a great enrichment performance, and the concentrated ethanol is up to 65.6 wt%. Overall, this MCM-22/PDMS membrane shows a high separation ability for ethanol benefited from a unique structure deign of fillers and ultrafast curing speed of PDMS, and has a great potential for bioethanol separation from cellulosic ethanol fermentation.

Design of PDMS/PAN composite membranes with ultra-interfacial stability via layer integration.

The interfacial interaction between the selective layer and porous substrate directly determines the separation performance and service lifetime of functional composite membranes. Till now, almost all reported polymeric selective layers are physically in contact with the substrate, which is unsatisfactory for long-term operation. Herein, we introduced a functional composite membrane with ultra-interfacial stability via layer integration between the polydimethylsiloxane selective layer and polyacrylonitrile substrate, where a facile light-triggered copolymerization achieved their covalent bonding. The critical load for the failure of the selective layer is 45.73 mN when testing the interfacial adhesion, i.e., 5.8 times higher than that before modification and significantly higher than previous reports. It also achieves superior pervaporation performance with a separation factor of 9.54 and membrane flux of 1245.6 g m-2 h-1 feeding a 1000 ppm phenol/water solution at 60 °C that is significantly higher than the same type of polymeric ones. Not limited to pervaporation, such a strategy sheds light on the design of highly stable composite membranes with different purposes, while the facile photo-trigged technique shows enormous scalability.