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The issue of temperature rise and heat dissipation becomes crucial to enhancing motor performance as permanent magnet synchronous motors (PMSMs) for electric vehicles (EVs) advance toward high power densities and heat load densities. A unique frame-rotor dual-waterway cooling system is developed to address the heat dissipation problem of high-power-density permanent magnet synchronous machines (HPDPMSMs). The frame contains the outside water circuit, whilst the rotor support cylinder is fitted with an inner water circuit. The dual water circuits constitute a cooling circulation system via a water-cooled bearing chamber and the spinning shaft. Through thermodynamic calculations of the waterway, the structural characteristics are ascertained, and a fluid-structure interaction temperature field simulation model is developed. The efficiency of the dual waterway cooling structure is validated by comparing its cooling impact to that of the single waterway structure. The analysis of temperature distribution in the motor with a dual-waterway cooling structure, under different coolants, extreme operational conditions, and variable water velocities, validates the efficacy of the developed frame-rotor dual-waterway cooling system.

Object detection is dedicated to finding objects in an image and estimate their categories and locations. Recently, object detection algorithms suffer from a loss of semantic information in the deeper feature maps due to the deepening of the backbone network. For example, when using complex backbone networks, existing feature fusion methods cannot fuse information from different layers effectively. In addition, anchor-free object detection methods fail to accurately predict the same object due to the different learning mechanisms of the regression and centrality of the prediction branches. To address the above problem, we propose a multi-scale fusion and interactive learning method for fully convolutional one-stage anchor-free object detection, called MFIL-FCOS. Specifically, we designed a multi-scale fusion module to address the problem of local semantic information loss in high-level feature maps which strengthen the ability of feature extraction by enhancing the local information of low-level features and fusing the rich semantic information of high-level features. Furthermore, we propose an interactive learning module to increase the interactivity and more accurate predictions by generating a centrality-position weight adjustment regression task and a centrality prediction task. Following these strategic improvements, we conduct extensive experiments on the COCO and DIOR datasets, demonstrating its superior capabilities in 2D object detection tasks and remote sensing image detection, even under challenging conditions.

The prolyl hydroxylation of hypoxia-inducible factor 1α (HIF-1α) mediated by the EGLN–pVHL pathway represents a classic signalling mechanism that mediates cellular adaptation under hypoxia. Here we identify RIPK1, a known regulator of cell death mediated by tumour necrosis factor receptor 1 (TNFR1), as a target of EGLN1–pVHL. Prolyl hydroxylation of RIPK1 mediated by EGLN1 promotes the binding of RIPK1 with pVHL to suppress its activation under normoxic conditions. Prolonged hypoxia promotes the activation of RIPK1 kinase by modulating its proline hydroxylation, independent of the TNFα–TNFR1 pathway. As such, inhibiting proline hydroxylation of RIPK1 promotes RIPK1 activation to trigger cell death and inflammation. Hepatocyte-specific Vhl deficiency promoted RIPK1-dependent apoptosis to mediate liver pathology. Our findings illustrate a key role of the EGLN–pVHL pathway in suppressing RIPK1 activation under normoxic conditions to promote cell survival and a model by which hypoxia promotes RIPK1 activation through modulating its proline hydroxylation to mediate cell death and inflammation in human diseases, independent of TNFR1. Zhang, Xu, Liu, Wang et al. identify an inhibitory mechanism for RIPK1 kinase through EGLN1/pVHL-mediated proline hydroxylation, which is disrupted upon prolonged hypoxia that activates RIPK1 activity to promote cell death and inflammation.

Due to the uncertainty of decision environment and differences of decision makers’ culture and knowledge background, multi-granular HFLTSs are usually elicited by decision makers in a multi-attribute group decision making (MAGDM) problem. In this paper, a novel consensus model is developed for MAGDM based on multi-granular HFLTSs. First, it is defined the group consensus measure based on the fuzzy envelope of multi-granular HFLTSs. Afterwards, an optimization model which aims to minimize the overall adjustment amount of decision makers’ preference is established. Based on the model, an iterative algorithm is devised to help decision makers reach consensus in MAGDM with multi-granular HFLTSs. Numerical results demonstrate the characteristics of the proposed consensus model.

Elucidating the conformational heterogeneity of proteins is essential for understanding protein function and developing exogenous ligands. With the rapid development of experimental and computational methods, it is of great interest to integrate these approaches to illuminate the conformational landscapes of target proteins. SETD8 is a protein lysine methyltransferase (PKMT), which functions in vivo via the methylation of histone and nonhistone targets. Utilizing covalent inhibitors and depleting native ligands to trap hidden conformational states, we obtained diverse X-ray structures of SETD8. These structures were used to seed distributed atomistic molecular dynamics simulations that generated a total of six milliseconds of trajectory data. Markov state models, built via an automated machine learning approach and corroborated experimentally, reveal how slow conformational motions and conformational states are relevant to catalysis. These findings provide molecular insight on enzymatic catalysis and allosteric mechanisms of a PKMT via its detailed conformational landscape. Our cells contain thousands of proteins that perform many different tasks. Such tasks often involve significant changes in the shape of a protein that allow it to interact with other proteins or ligands. Understanding these shape changes can be an essential step for predicting and manipulating how proteins work or designing new drugs. Some changes in protein shape happen quickly, whereas others take longer. Existing experimental approaches generally only capture some, but not all, of the different shapes an individual protein adopts. A family of proteins known as protein lysine methyltransferases (PKMTs) help to regulate the activities of other proteins by adding small tags called methyl groups to specific positions on their target proteins. PKMTs play important roles in many life processes including in activating genes, maintaining stem cells and controlling how organs develop. It is important for cells to properly control the activity of PKMTs because too much, or too little, activity can promote cancers and neurological diseases. For example, genetic mutations that increase the levels of a PKMT known as SETD8 appear to promote the progression of some breast cancers and childhood leukemia. There is a pressing need to develop new drugs that can inhibit SETD8 and other PKMTs in human patients. However, these efforts are hindered by the lack of understanding of exactly how the shape of PKMT proteins change as they operate in cells. Chen, Wiewiora et al. used a technique called X-ray crystallography to generate structural models of the human SETD8 protein in the presence or absence of native or foreign ligands. These models were used to develop computer simulations of how the shape of SETD8 changes as it operates. Further computational analysis and laboratory experiments revealed how slow changes in the shape of SETD8 contribute to the ability of the protein to attach methyl groups to other proteins. This work is a significant stepping-stone to developing a complete model of how the SETD8 protein works, as well as understanding how genetic mutations may affect the protein’s role in the body. The next step is to refine the model by integrating data from other approaches including biophysical models and mathematical calculations of the energy associated with the shape changes, with a long-term goal to better understand and then manipulate the function of SETD8.

Selective inhibition of protein methyltransferases is a promising new approach to drug discovery. An attractive strategy towards this goal is the development of compounds that selectively inhibit binding of the cofactor, S -adenosylmethionine, within specific protein methyltransferases. Here we report the three-dimensional structure of the protein methyltransferase DOT1L bound to EPZ004777 , the first S -adenosylmethionine-competitive inhibitor of a protein methyltransferase with in vivo efficacy. This structure and those of four new analogues reveal remodelling of the catalytic site. EPZ004777 and a brominated analogue, SGC0946 , inhibit DOT1L in vitro and selectively kill mixed lineage leukaemia cells, in which DOT1L is aberrantly localized via interaction with an oncogenic MLL fusion protein. These data provide important new insight into mechanisms of cell-active S -adenosylmethionine-competitive protein methyltransferase inhibitors, and establish a foundation for the further development of drug-like inhibitors of DOT1L for cancer therapy. Selective inhibitors of protein methyltransferases are anticancer drug candidates. Yu et al . report the structural changes that occur when selective inhibitors bind to the protein methyltransferase DOT1L.

Human TIG3 protein is a member of H-REV107 protein family which belongs to the type II tumor suppressor family. TIG3 can induce apoptosis in cancer cells, and it also possesses Ca 2+ -independent phospholipase A 1/2 activity. The NMR assignments of the N-terminal domain of TIG3 are essential for its solution structure determination.

为探索生态浮床池塘水体微生物对氮循环的贡献及其潜在驱动机制，本研究采用16S rRNA基因扩增子测序和宏基因组测序技术联合解析有无生态浮床水体中微生物群落结构及氮循环功能差异。群落结构分析结果表明，与无浮床水体相比，浮床水体中变形菌门（Proteobacteria）微生物占主要优势。微生物群落结构发生明显改变，浮床水体中C39、甲基杆菌属（Methylobacter）、栖湖菌属（Limnohabitans）、多核杆菌属（Polynucleobacter）和黄杆菌属（Flavobacterium）的相对丰度显著增加（P<0.05）。试验期间，浮床水体微生物Shannon多样性指数显著降低（P<0.05），而Chao丰富度指数显著升高（P<0.05）。甲基杆菌、热单胞菌（Caldimonas）和伯克氏菌（Bulkholderia）是浮床水体脱氮过程中关键核心微生物组。氮循环功能研究结果表明，铺设生态浮床后，浮床水体氮代谢活动能力显著增强（P<0.05），以固氮（nifH、nifD、nifK）、反硝化（narG、napA、nirS、norB和nosZ）和异化硝酸盐还原过程（nrfA、nrfH）为主的氮循环功能基因丰度显著提高（P<0.05）。核心微生物组促进了水体生物固氮、反硝化和异化硝酸盐还原过程，提升了养殖池塘水体氮循环的能力，显著降低了池塘水体中总氮、硝态氮和亚硝态氮含量（P<0.05）。研究表明，生态浮床核心微生物组介导的固氮和反硝化作用是实现池塘水体氮素转化和迁移的重要途径，促进了养殖池塘水体氮循环，有利于养殖池塘生态环境保护和含氮污染物的去除。To explore the contribution of microorganisms to the nitrogen cycle and the potential driving mechanism in ecological floating bed ponds, 16S rRNA gene amplicon sequencing and metagenomic sequencing technology were used to analyze the characteristics of microbial community structure and nitrogen cycling function. Compared with the non-f

GSP13 encoded by gene yugI is a σB-dependent general stress protein in Bacillus subtilis, which can be induced by heat shock, salt stress, ethanol stress, glucose starvation, oxidative stress and cold shock. Here we report the solution structure of GSP13 and it is the first structure of S1 domain containing protein in Bacillus subtilis. The structure of GSP13 mainly consists of a typical S1 domain along with a C-terminal 50-residue flexible tail, different from the other known S1 domain containing proteins. Comparison with other S1 domain structures reveals that GSP13 has a conserved RNA binding surface, and it may function similarly to cold shock proteins in response to cold stress.

Nature Communications 3: Article number: 1288 (2012); Published: 18 December 2012; Updated: 21 May 2013. While this Article was undergoing peer review, Basavapathruni et al. published co-crystal structures of EPZ004777 and related compounds in complex with DOT1L, which are in agreement with our results.