A Comprehensive Review of Genetically Engineered Mouse Models for Prader-Willi Syndrome Research

Prader-Willi syndrome (PWS) is a neurogenetic multifactorial disorder caused by the deletion or inactivation of paternally imprinted genes on human chromosome 15q11-q13. The affected homologous locus is on mouse chromosome 7C. The positional conservation and organization of genes including the imprinting pattern between mice and men implies similar physiological functions of this locus. Therefore, considerable efforts to recreate the pathogenesis of PWS have been accomplished in mouse models. We provide a summary of different mouse models that were generated for the analysis of PWS and discuss their impact on our current understanding of corresponding genes, their putative functions and the pathogenesis of PWS. Murine models of PWS unveiled the contribution of each affected gene to this multi-facetted disease, and also enabled the establishment of the minimal critical genomic region (PWScr) responsible for core symptoms, highlighting the importance of non-protein coding genes in the PWS locus. Although the underlying disease-causing mechanisms of PWS remain widely unresolved and existing mouse models do not fully capture the entire spectrum of the human PWS disorder, continuous improvements of genetically engineered mouse models have proven to be very powerful and valuable tools in PWS research.     

Machining accuracy improvement for a dual-spindle ultra-precision drum roll lathe based on geometric error analysis and calibration

This paper performs a comprehensive analysis and calibration on the geometric error of the ultra-precision drum roll lathe with dual-spindle symmetrical structure and cross slider layout. Firstly, the volumetric error model which contains all geometric errors of the dual-spindle ultra-precision drum roll lathe (DSUPDRL) is developed based on the combination of the homogenous transfer matrix (HTM) and multi-body system (MBS) theory. Secondly, sensitivity analysis for the volumetric error model is conducted to identify the sensitive geometric error components of the DSUPDRL using an improved Sobol method based on the quasi-Monte Carlo algorithm. The result of sensitivity analysis laid the foundation for the subsequent geometric error calibration. Then, some sensitive error components along the X and Z directions are calibrated using a laser interferometer and a pair of inductance displacement probes. Besides the volumetric error model, the concentricity error caused by dual-spindle symmetrical structure is proposed and calibrated by the on-machine measurement using a classic reversal method. Finally, a large-scale roller mold with a diameter of 250 mm and a length of 600 mm is machined using the DSUPDRL after calibration. The experimental result shows that 1.4 μm/600 mm generatrix accuracy is obtained, which validate the effectiveness of the geometric error analysis and calibration.     

门架式堆取料机在铜冶炼中的生产实践

针对铜冶炼工艺的特点,从门架式堆取料机对前后工序衔接、结构、原理出发,介绍了其在熔炼炉系统和吹炼炉系统中的应用情况。经过试生产期的生产实践证明,门架式堆取料机具有场地利用率高、取料量大,自动化程度高和安全环保等优点。     

Motion control of underactuated linkage robot based on gymnastic skill

In this paper, a reproduction of a swing‐up and a giant swing motion of underactuated robots based on technique of the horizontal bar gymnast is discussed with focusing on an equivalent center of mass (ECM) of underactuated robots and the gymnast. At first, the behaviors of the ECM of the gymnast (ECMG) are analyzed by using a motion capturing technique and an efficient motion of the ECMG for the swing‐up and for the giant swing motion are identified from analysis results, respectively. Next, a partial linearization method, which can realize that the ECM of the Acrobot (ECMA) replicates this efficient motion, is designed and reproduces the underactuated robots the swing‐up and giant swing motion like the gymnast. Finally, an effectiveness of the proposed controller is shown by numerical simulations.     

A mechanism informed neural network for predicting machining deformation of annular parts

Controlling machining deformation of annular parts is crucial for ensuring the performance of high value products and equipment. For example, during manufacturing of critical parts in aircrafts and spacecrafts, accurate prediction of machining deformation is the basis for guiding the formulation of deformation control strategies. However, due to the complexity of the machining deformation of annular parts, existing methods still have limitations in accurate prediction. To this end, this paper proposes a mechanism informed neural network (MINN) to predict machining deformation of annular parts. MINN is realized by establishing the dual sub-networks structure and using enhanced loss functions with the consideration of the deformation mechanism model characteristics of annular parts. The deformation was decomposed into the axisymmetric portion and the non-axisymmetric portion according to the deformation superposition principle, and modeled separately based on the thin-shell theory and Fourier series. Experiment results showed that the proposed method could predict the machining deformation of annular parts more accurately and stably with a small amount of training data, compared with previous methods.     

基于多尺度双线性卷积神经网络的多角度下车型精细识别

针对多角度下车辆出现一定的尺度变化和形变导致很难被准确识别的问题，提出基于多尺度双线性卷积神经网络（MS-B-CNN）的车型精细识别模型。首先，对双线性卷积神经网络（B-CNN）算法进行改进，提出MS-B-CNN算法对不同卷积层的特征进行了多尺度融合，以提高特征表达能力；此外，还采用基于中心损失函数与Softmax损失函数联合学习的策略，在Softmax损失函数基础上分别对训练集每个类别在特征空间维护一个类中心，在训练过程中新增加样本时，网络会约束样本的分类中心距离，以提高多角度情况下的车型识别的能力。实验结果显示，该车型识别模型在CompCars数据集上的正确率达到了93.63%，验证了模型在多角度情况下的准确性和鲁棒性。     

Use of cryogenic machining to improve the adhesion of sphene bioceramic coatings on titanium substrates for dental and orthopaedic applications

Rods of commercially pure titanium were machined using standard oil-based emulsion and cryogenic cooling, and were then coated with sphene (CaTiSiO₅) bioceramic by spray coating using an automatic airbrush. The sphene bioceramic was synthesized in-situ starting from a suspension of polysiloxane that used as SiO₂ precursor, CaCO₃ and TiO₂ nanoparticles. The suspension was deposited on the machined substrates, which were heat treated up to 950?°C in order to promote the formation of sphene ceramic. The produced coated prototypes were characterized to evaluate the effect of the machining conditions on surface roughness and microstructure of the substrate, and thereby their effect on coating adhesion. Nanoindentation tests were employed to determine the hardness and elastic modulus of the coating through its thickness. Results showed that the reduced amount of defects on the surface of the cryo-machined substrates, contributed to increase the hardness, elastic modulus and adhesion strength of the coating-substrate interfaces compared to standard machined samples, therefore improving adhesion of the coating to the underlying substrate.     

培训中心网络安全建设的思考

本文分析了在网络安全的严峻局面下,培训中心作为开放性的网络访问环境,面临的支撑业务与安全矛盾难题远比企业大得多。本文以网络建设的多维度分析模型方法为基础,详细分析了培训中心网络建设的难点,提出了具体落地的网络建设思路;说明培训中心网络安全建设是长期的、技术难度很高的、有价值的工作。     

数据中心内服务器能耗数据采集及特征分析

数据中心的高能耗低能效问题正受到广泛关注与研究，但目前没有公开的数据中心内服务器能耗数据集供研究人员使用，且现有过滤式特征选择并不能满足运维人员的需求。为此，提出了一套模拟数据中心内服务器运行状态的仿真环境架构，基于该架构采集了服务器运行各类任务时的多项性能指标和能耗数据。然后将基于因果关系的特征选择应用于能耗数据集的特征分析中，构建出可解释的特征子集和能耗预测结果。实验结果表明，因果特征子集大小约为过滤式特征子集大小的1/3到1/6，并且使用因果特征子集训练的模型在75%的情况下都取得了最优预测精度。