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Research on resistance against polymyxins induced by the mcr-1 gene is gaining interest. In this study, using agar dilution method, polymerase chain reaction, and comparative genomic analysis, we investigated the colistin resistance mechanism of clinical E. coli isolates. The minimum inhibitory concentration (MIC) analysis results revealed that of the 515 isolates tested, bacteria with significantly increased MIC levels against colistin were isolated in 2019. Approximately one-fifth (17.14% to 19.65%) of the isolates showed MIC values ≥1 mg/L against colistin in 2015, 2016, and 2017. However, in 2019, up to three-quarters (74.11%, 146/197) of the isolates showed MIC values ≥1 mg/L against colistin indicating an increase in colistin resistance. Six isolates (EC7518, EC4968, EC3769, EC16, EC117, EC195, 1.13%, 6/515) were found to carry the mcr-1 gene and a novel mcr-1 variant with Met2Ile mutation was identified in EC3769. All six strains showed higher MIC levels (MIC=4 mg/L) than any mcr-1-negative strains (MIC ≤ 2 mg/L). Whole-genome sequencing of the six mcr-1-positive isolates revealed that EC195 carried the highest number of resistance genes (n = 28), nearly a half more than those of the following EC117 (n = 19). Thus, EC195 showed a wider resistance spectrum and higher MIC levels against the antimicrobials tested than the other five isolates. Multi-locus sequence typing demonstrated that these mcr-1-positive strains belonged to six different sequence types. The six mcr-1 genes were located in three different incompatibility group plasmids (IncI2, IncHI2 and IncX4). The genetic context of mcr-1 was related to a sequence derived from Tn6330 (ISApl1-mcr-1-pap2-ISApl1). Investigations into the colistin resistance mechanism and characterization of the molecular background of the mcr genes may help trace the development and spread of colistin resistance in clinical settings.

Lithium-sulfur (Li-S) batteries are considered to be one of the most promising alternatives for next-generation high energy-density storage systems. Nevertheless, the notorious "shuttle effect" and sluggish kinetic conversion in actual operation seriously hamper its practical application. Herein, inspired by the action mechanism of vulcanization accelerators, dipentamethylenethiuram tetrasulfide (DPTT) is employed as a novel electrolyte additive. Just like a scavenger, DPTT sweeps lithium polysulfide by a spontaneous instant chemical reaction between them, and the latter is quickly converted to Li

Graph convolutional networks (GCNs) have shown the powerful ability in text structure representation and effectively facilitate the task of text classification. However, challenges still exist in adapting GCN on learning discriminative features from texts due to the main issue of graph variants incurred by the textual complexity and diversity. In this paper, we propose a dual-attention GCN to model the structural information of various texts as well as tackle the graph-invariant problem through embedding two types of attention mechanisms, i.e. the connection-attention and hop-attention, into the classic GCN. To encode various connection patterns between neighbour words, connection-attention adaptively imposes different weights specified to neighbourhoods of each word, which captures the short-term dependencies. On the other hand, the hop-attention applies scaled coefficients to different scopes during the graph diffusion process to make the model learn more about the distribution of context, which captures long-term semantics in an adaptive way. Extensive experiments are conducted on five widely used datasets to evaluate our dual-attention GCN, and the achieved state-of-the-art performance verifies the effectiveness of dual-attention mechanisms.

Lithium–Sulfur (Li–S) battery is a promising device in the field of electrochemical energy storage since its high specific capacity and low cost. However, the low insoluble Li2S conversion efficiency and uncontrollable deposition on the electrodes lead to low sulfur utilization, poor reversibility and rapid capacity decay. Consequently, the practical application of Li–S battery is limited. Herein, phenyl diselenide (PDSe) is added into the conventional electrolyte try to improve the batteries performance. The results show that the Se–Se bond in PDSe is broken and the Li–S bond in Li2S is elongated induced by PDSe. Moreover, PDSe promotes Li2S conversion and reduces Li2S deposition on the electrodes. As a result, the batteries using the electrolyte with PDSe exhibit enhanced discharge capacity and improved rate performance. Particularly, the batteries with 5.0 wt% PDSe additive exhibit a high initial discharge capacity of 1585.6 mAh g−1, even after 300 cycles at 0.5 C, the batteries still maintain 916.5 mAh g−1, which is much higher than conventional batteries of 618.7 mA h g−1.

Lithium-sulfur batteries are considered to be the most promising high-energy-density storage system, yet the dissolution of polysulfides during the charging process limits the utilization, and cycle stability of active materials, which affects the practical application of lithium-sulfur batteries. Herein, a poly(N-methylaniline) (PMAn) coating film is formed in situ on the cathode surface by electrochemically polymerizing the monomer of N-methylaniline(MAn) contained in the electrolyte during film formation process. The PMAn coating film on the surface of the cathode can prevent the dissolution of polysulfide, then reduce the loss of active material, and slow the oxidation of polysulfide by lithium nitrate, resulting in improved cycle stability. As a result, the lithium-sulfur battery with 0.5 wt% MAn render an initial discharge specific capacity of 1224 mAh/g and a capacity retention of 63.79% for 200 cycles, which is better than the performance of the battery without MAn.

A new method for the big data analysis - multi-granularity generalized functions data model (referred to as MGGF for short) is put forward. This method adopts the dynamic adaptive multi-granularity clustering technique, transforms the grid like "Hard partitioning" to the input data space by the generalized functions data model (referred to as GFDM for short) into the multi-granularity partitioning, and identifies the multi-granularity pattern class in the input data space. By defining the type of the mapping relationship between the multi-granularity model class and the decision-making category ftype:Ci→y, and the concept of the Degree of Fulfillment (referred to as DoF (x)) of the input data to the classification rules of the various pattern classes, the corresponding MGGF model is established. Experimental test results of different data sets show that, compared with the GFDM method, the method proposed in this paper has better data summarization ability, stronger noise data processing ability and higher searching efficiency.

The paper presents some extensions and improvements of an earlier general formulation to deal with operational constraints and plants involving continuous operations. The concept of Unit Usability is introduced to give a unified formulation for a variety of operational constraints. A modified task model for continuous operations enables us to linearize cross-products of one integer variable and two continuous variables. These ideas are illustrated by application to a realistic case-study.