An inference model gives insights into innate immune adaptation and repertoire diversity.

The innate immune system is the body's first line of defense against infection. Natural killer (NK) cells, a vital part of the innate immune system, help to control infection and eliminate cancer. Studies have identified a vast array of receptors that NK cells use to discriminate between healthy and unhealthy cells. However, at present, it is difficult to explain how NK cells will respond to novel stimuli in different environments. In addition, the expression of different receptors on individual NK cells is highly stochastic, but the reason for these variegated expression patterns is unclear. Here, we studied the recognition of unhealthy target cells as an inference problem, where NK cells must distinguish between healthy targets with normal variability in ligand expression and ones that are clear "outliers." Our mathematical model fits well with experimental data, including NK cells' adaptation to changing environments and responses to different target cells. Furthermore, we find that stochastic, "sparse" receptor expression profiles are best able to detect a variety of possible threats, in agreement with experimental studies of the NK cell repertoire. While our study was specifically motivated by NK cells, our model is general and could also apply more broadly to explain principles of target recognition for other immune cell types.

Dielectric Property and Breakdown Strength Performance of Long-Chain Branched Polypropylene for Metallized Film Capacitors.

At high temperatures, the insulation performance of polypropylene (PP) decreases, making it challenging to meet the application requirements of metallized film capacitors. In this paper, the dielectric performance of PP is improved by long-chain branching modification and adding different kinds of nucleating agents. The added nucleating agents are organic phosphate nucleating agent (NA-21), sorbitol nucleating agent (DMDBS), rare earth nucleating agent (WBG-Ⅱ) and acylamino nucleating agent (TMB-5). The results show that the long-chain branches promote heterogeneous nucleation and inhibit the motion of molecular chains, thereby enhancing the dielectric properties at high temperatures. Nucleating agents modulate the crystalline morphology of long-chain branched polypropylene (LCBPP), which leads to a decrease in the mean free path of carriers and an increase in trap energy level and trap density. Therefore, the conductivity is reduced and the breakdown strength is improved. Among the added nucleating agents, NA-21 showed a significant improvement in the electrical properties of LCBPP films. At 125 °C, compared with PP, the breakdown strength of the modified film is increased by 26.3%, and the energy density is increased by 66.1%. This method provides a reference for improving the dielectric properties of PP.

Assessing 1,9-Decadiene/Ethylene Copolymerization with Ziegler-Natta Catalyst to Access Long Chain-Branched Polyethylene.

1,9-Decadiene/ethylene copolymerization is assessed as a way for Ziegler-Natta catalysts to access long chain-branched polyethylene (PE). A MgCl2/9,9-bis-(methoxymethyl)fluorine/TiCl4 catalyst with triethylaluminium as a cocatalyst is exemplified for the task. 1,9-Decadiene was found to induce a substantial comonomer effect on catalyst activity and continuing decreases of PE molecular weight. Both the double bonds of 1,9-decadiene were poorly reactive during polymerization, of which the polymer chain-attached was even much less reactive than the original one. As a consequence, at decreased feeds, 1,9-decadiene gave small amounts (<0.1 mol %) of pendant vinyl groups to PE without prompting the formation of long-chain branches. Long chain-branching was realized at increased 1,9-decadiene feeds, which was however accompanied by proportional gelation.

Towards a Fuzzy Bayesian Network Based Approach for Safety Risk Analysis of Tunnel-Induced Pipeline Damage.

Tunneling excavation is bound to produce significant disturbances to surrounding environments, and the tunnel-induced damage to adjacent underground buried pipelines is of considerable importance for geotechnical practice. A fuzzy Bayesian networks (FBNs) based approach for safety risk analysis is developed in this article with detailed step-by-step procedures, consisting of risk mechanism analysis, the FBN model establishment, fuzzification, FBN-based inference, defuzzification, and decision making. In accordance with the failure mechanism analysis, a tunnel-induced pipeline damage model is proposed to reveal the cause-effect relationships between the pipeline damage and its influential variables. In terms of the fuzzification process, an expert confidence indicator is proposed to reveal the reliability of the data when determining the fuzzy probability of occurrence of basic events, with both the judgment ability level and the subjectivity reliability level taken into account. By means of the fuzzy Bayesian inference, the approach proposed in this article is capable of calculating the probability distribution of potential safety risks and identifying the most likely potential causes of accidents under both prior knowledge and given evidence circumstances. A case concerning the safety analysis of underground buried pipelines adjacent to the construction of the Wuhan Yangtze River Tunnel is presented. The results demonstrate the feasibility of the proposed FBN approach and its application potential. The proposed approach can be used as a decision tool to provide support for safety assurance and management in tunnel construction, and thus increase the likelihood of a successful project in a complex project environment.

Fabrication of nanofillers into a granular "nanosupport" for Ziegler-Natta catalysts: towards scalable in situ preparation of polyolefin nanocomposites.

This communication reports a strategy for scale-up of an in situ polymerization technique for polyolefin-based nanocomposites preparation, taking layered silicate (clay) and multi-walled carbon nanotubes (MWCNTs) as examples of nanofillers. The strategy is realized by transforming the nanofillers into granular "nanosupports" for Ziegler-Natta catalysts. With a catalyst to polymer replication effect on particle morphology, the in situ prepared nanocomposites are of controlled granular particle morphology. With the polymer particle morphology controlled, the in situ polymerization technique becomes suitable for industrial olefin polymerization processes for mass production of polyolefin nanocomposites.