Precise Construction of Nitrogen-Enriched Porous Ionic Polymers as Highly Efficient Sulfur Dioxide Adsorbent.

Porous ionic polymers with unique features have exhibited high performance in various applications. However, the fabrication of functional porous ionic polymers with custom functionality and porosity for efficient removal of low-concentration SO2 remains challenging. Herein, a novel nitrogen-enriched porous ionic polymer NH2Py-PIP is prepared featuring high-content nitrogen sites (15.9 wt.%), adequate ionic sites (1.22 mmol g-1), and a hierarchical porous structure. The proposed construction pathway relies on a tailored nitrogen-functionalized cross-linker NH2Py, which effectively introduces abundant functional sites and improves the porosity of porous ionic polymers. NH2Py-PIP with a well-engineered SO2-affinity environment achieves excellent SO2/CO2 selectivity (1165) and high SO2 adsorption capacity (1.13 mmol g-1 at 0.002 bar), as well as enables highly efficient and reversible dynamic separation performance. Modeling studies further elucidate that the nitrogen sites and bromide anions collaboratively promote preferential adsorption of SO2. The unique design in this work provides new insights into constructing functional porous ionic polymers for high-efficiency separations.

One-Step Butadiene Purification in a Sulfonate-Functionalized Metal-Organic Framework through Synergistic Separation Mechanism.

Porous materials that could recognize specific molecules from complex mixtures are of great potential in improving the current energy-intensive multistep separation processes. However, due to the highly similar structures and properties of the mixtures, the design of desired porous materials remains challenging. Herein, a sulfonate-functionalized metal-organic framework ZU-609 with suitable pore size and pore chemistry is designed for 1,3-butadiene (C4H6) purification from complex C4 mixtures. The sulfonate anions decorated in the channel achieve selective recognition of C4H6 from other C4 olefins with subtle polarity differences through C-H⋅⋅⋅O-S interactions, affording recorded C4H6/trans-2-C4H8 selectivity (4.4). Meanwhile, the shrunken mouth of the channel with a suitable pore size (4.6 Å) exhibits exclusion effect to the larger molecules cis-2-C4H8, iso-C4H8, n-C4H10 and iso-C4H10. Benefiting from the moderate C4 olefins binding affinity exhibited by sulfonate anions, the adsorbed C4H6 could be easily regenerated near ambient conditions. Polymer-grade 1,3-butadiene (99.5 %) is firstly obtained from 7-component C4 mixtures via one adsorption-desorption cycle. The work demonstrates the great potential of synergistic recognition of size-sieving and thermodynamically equilibrium in dealing with complex mixtures.

A molecular sieve with ultrafast adsorption kinetics for propylene separation.

The design of molecular sieves is vital for gas separation, but it suffers from a long-standing issue of slow adsorption kinetics due to the intrinsic contradiction between molecular sieving and diffusion within restricted nanopores. We report a molecular sieve ZU-609 with local sieving channels that feature molecular sieving gates and rapid diffusion channels. The precise cross-sectional cutoff of molecular sieving gates enables the exclusion of propane from propylene. The coexisting large channels constituted by sulfonic anions and helically arranged metal-organic architectures allow the fast adsorption kinetics of propylene, and the measured propylene diffusion coefficient in ZU-609 is one to two orders of magnitude higher than previous molecular sieves. Propylene with 99.9% purity is obtained through breakthrough experiments with a productivity of 32.2 L kg-1.

Topological Network Analysis of Early Alzheimer's Disease Based on Resting-State EEG.

Previous studies made progress in the early diagnosis of Alzheimer's disease (AD) using electroencephalography (EEG) without considering EEG connectivity. To fill this gap, we explored significant differences between early AD patients and controls based on frequency domain and spatial properties using functional connectivity in mild cognitive impairment (MCI) and mild AD datasets. Four global metrics, network resilience, connection-level metrics and node versatility were used to distinguish between controls and patients. The results show that the main frequency bands that are different between MCI patients and controls are the θ and low α bands, and the differently affected brain areas are the frontal, left temporal and parietal areas. Compared to MCI patients, in patients with mild AD, the main frequency bands that are different are the low and high α bands, and the main differently affected brain region is a larger right temporal area. Four LOFC bands were used as input to train the ResNet-18 model. For the MCI dataset, the average accuracy of 20 runs was 93.42% and the best accuracy was 98.33%, while for the mild AD dataset, the average accuracy was 98.54% and the best accuracy was 100%. To determine the timing of early treatment and discovering the susceptible patients, and to slow the progression of the disease, we assume that the occurrence of MCI and mild AD and their progression to more serious AD and dementia could be inferred by analyzing the topological structure of the brain network generated by EEG. Our findings provide a novel solution for connectome-based biomarker analysis to improve personalized medicine.

regBase: whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants.

Predicting the functional or pathogenic regulatory variants in the human non-coding genome facilitates the interpretation of disease causation. While numerous prediction methods are available, their performance is inconsistent or restricted to specific tasks, which raises the demand of developing comprehensive integration for those methods. Here, we compile whole genome base-wise aggregations, regBase, that incorporate largest prediction scores. Building on different assumptions of causality, we train three composite models to score functional, pathogenic and cancer driver non-coding regulatory variants respectively. We demonstrate the superior and stable performance of our models using independent benchmarks and show great success to fine-map causal regulatory variants on specific locus or at base-wise resolution. We believe that regBase database together with three composite models will be useful in different areas of human genetic studies, such as annotation-based casual variant fine-mapping, pathogenic variant discovery as well as cancer driver mutation identification. regBase is freely available at https://github.com/mulinlab/regBase.

dbCAN-seq: a database of carbohydrate-active enzyme (CAZyme) sequence and annotation.

Carbohydrate-active enzyme (CAZymes) are not only the most important enzymes for bioenergy and agricultural industries, but also very important for human health, in that human gut microbiota encode hundreds of CAZyme genes in their genomes for degrading various dietary and host carbohydrates. We have built an online database dbCAN-seq (http://cys.bios.niu.edu/dbCAN_seq) to provide pre-computed CAZyme sequence and annotation data for 5,349 bacterial genomes. Compared to the other CAZyme resources, dbCAN-seq has the following new features: (i) a convenient download page to allow batch download of all the sequence and annotation data; (ii) an annotation page for every CAZyme to provide the most comprehensive annotation data; (iii) a metadata page to organize the bacterial genomes according to species metadata such as disease, habitat, oxygen requirement, temperature, metabolism; (iv) a very fast tool to identify physically linked CAZyme gene clusters (CGCs) and (v) a powerful search function to allow fast and efficient data query. With these unique utilities, dbCAN-seq will become a valuable web resource for CAZyme research, with a focus complementary to dbCAN (automated CAZyme annotation server) and CAZy (CAZyme family classification and reference database).

dbCAN2: a meta server for automated carbohydrate-active enzyme annotation.

Complex carbohydrates of plants are the main food sources of animals and microbes, and serve as promising renewable feedstock for biofuel and biomaterial production. Carbohydrate active enzymes (CAZymes) are the most important enzymes for complex carbohydrate metabolism. With an increasing number of plant and plant-associated microbial genomes and metagenomes being sequenced, there is an urgent need of automatic tools for genomic data mining of CAZymes. We developed the dbCAN web server in 2012 to provide a public service for automated CAZyme annotation for newly sequenced genomes. Here, dbCAN2 (http://cys.bios.niu.edu/dbCAN2) is presented as an updated meta server, which integrates three state-of-the-art tools for CAZome (all CAZymes of a genome) annotation: (i) HMMER search against the dbCAN HMM (hidden Markov model) database; (ii) DIAMOND search against the CAZy pre-annotated CAZyme sequence database and (iii) Hotpep search against the conserved CAZyme short peptide database. Combining the three outputs and removing CAZymes found by only one tool can significantly improve the CAZome annotation accuracy. In addition, dbCAN2 now also accepts nucleotide sequence submission, and offers the service to predict physically linked CAZyme gene clusters (CGCs), which will be a very useful online tool for identifying putative polysaccharide utilization loci (PULs) in microbial genomes or metagenomes.