KIF20A在大肠癌中的表达及其临床意义

目的:探讨驱动蛋白家族成员20A(kinesin family member 20A,KIF20A)在大肠癌的表达及其与预后的关系。方法:分析TCGA数据库文献中KIF20A在大肠癌组织及癌旁正常组织中mRNA的表达;收集2011年1月至2012年12月在河南大学淮河医院105例经术后病理学检测确诊为大肠癌的石蜡组织样本,采用免疫组织化学法检测KIF20A在大肠癌组织中的蛋白表达,并分析KIF20A与临床病理参数及预后的相关性。结果:TCGA数据库分析结果表明,KIF20A在大肠癌组织中高表达,在癌旁正常组织中表达阴性或低表达(P<0.001);免疫组织化学法检测表明KIF20A在105例大肠癌组织中的阳性及阴性表达率分别为64%(67/105)和 36%( 38/105),差异具有统计学意义(P<0.05)。 KIF20A高表达与肿瘤浸润深度、淋巴结转移、远处转移、TNM分期有显著相关性(P<0.05)。 Kaplan-Meier生存分析显示,KIF20A高表达的患者生存时间、无复发生存时间显著缩短(P<0.001)。 Cox回归分析显示KIF20A是影响大肠癌患者预后的独立危险因素。结论:KIF20A在大肠癌中表达上调,其可能作为预测大肠癌患者预后的分子标志物,参与大肠癌的发生发展过程。

Hot deformation behavior of FGH96 superalloys

The hot deformation behavior of FGH96 superalloys at 1070-1170℃ and 5×10^-4-2×10^-1 s^-1 were investigated by means of the isothermal compression tests at a Gleeble-1500 thermal mechanical simulator. The results show that dynamic recovery acts as the main softening mechanism below 2×10^-3 s^-1, whereas dynamic recrystallization acts as the main softening mechanism above 2× 10^-3 s^-1 during deformation; the temperature increase caused by the deformation and the corresponding softening stress is negligible; the thermal-mechanical constitutive model to describe the hot deformation behavior is given, and the value of the apparent deformation activation energy （Qdef） is determined to be 354.93 kJ/mol.

Particle size dependence of the microsegregation and microstructure in the atomized Ni-based superalloy powders:Theoretical and experimental study

The microstructure and microsegregation of atomized powder,which depend on their sizes,are of great importance to the mechanical properties of the consolidated bulk materials.Therefore,it is necessary to reveal the relationship between particle size and powder attributes.The effects of particle size on the so-lidification characterization of the atomized Ni-based superalloy powders were studied via finite element simulation.Based on the simulations,a model was developed to predict the microsegregation and mi-crostructure of atomized powders with different sizes and study the influence of thermal history on the powder attributes during the atomization processes.The radiation heat transfer and temperature gradi-ent within the rapid solidification alloy powders were taken into account in this model.For validating the accuracy of the model,the predictions of the present model were compared with the microsegregation and microstructure of the specific size powder close to the screen mesh size.The results showed that mi-crostructure depended primarily on the temperature gradient within the powder,while the solidification rate had a more significant effect on the microsegregation.The model predicted microstructure features in agreement with the experiment,and for microsegregation,the deviations of prediction for most ele-ments were less than 10%.This work provides a new model to precisely predict the microsegregation and microstructure of the atomized alloy powders and sets a foundation to control the powder features for various engineering applications.

Deep reinforcement learning and 3D physical environments applied to crowd evacuation in congested scenarios

To avoid crowd evacuation simulations depending on 2D environments and real data,we propose a framework for crowd evacuation modeling and simulation by applying deep reinforcement learning(DRL)and 3D physical environments(3DPEs).In 3DPEs,we construct simulation scenarios from the aspects of geometry,semantics and physics,which include the environment,the agents and their interactions,and provide training samples for DRL.In DRL,we design a double branch feature extraction combined actor and critic network as the DRL policy and value function and use a clipped surrogate objective with polynomial decay to update the policy.With a unified configuration,we conduct evacuation simulations.In scenarios with one exit,we reproduce and verify the bottleneck effect of congested crowds and explore the impact of exit width and agent characteristics(number,mass and height)on evacuation.In scenarios with two exits and a uniform(nonuniform)distribution of agents,we explore the impact of exit characteristics(width and relative position)and agent characteristics(height,initial location and distribution)on agent exit selection and evacuation.Overall,interactive 3DPEs and unified DRL enable agents to adapt to different evacuation scenarios to simulate crowd evacuation and explore the laws of crowd evacuation.

All-fiber laser amplification at 1948 nm based on doublecladding Tm/Al co-doped photonic crystal fiber fabricated by laser additive manufacturing

In this work,we demonstrated the double-cladding Tm/Al co-doped photonic crystal fiber(PCF)by laser additive manufacturing.The measurements show that the fiber was heavily doped with a Tm^(3+)concentration of 2.13%(mass fraction)without any crystallization.The splicing property of PCF was studied,and the integrity of the PCF air holes was maintained during the splicing process.The PCF with combiner pigtail has a splice loss of 0.23 d B.The all-fiber Tm/Al co-doped PCF amplifier system achieves a slope efficiency of 13%at 1948 nm with an output laser power of nearly 1.59 W.An upconversion process was also observed under laser excitation with a 1064 nm pulse.This method provides a new idea to deal with Tmdoped PCF fabrication and promotes the promising application of 2μm fiber lasers.

Additive manufacturing alumina components with lattice structures by digital light processing technique

Digital light processing technique was applied to manufacture alumina ceramic parts with two types of lattice structure units, i.e. vertex interconnect structure and edge structure. The internal porosity of the unit is 40%. The printed parts were sintered and the grain size is about 1.1 μm. The bending strength of the vertex interconnect structure is much larger than that of the edge structure. Materials genome initiative(MGI) aims to digital design and intelligent manufacture for advanced components. This research shows us an example to achieve this goal.

Winter wheat mapping using a random forest classifier combined with multi-temporal and multi-sensor data

Wheat is a major staple food crop in China.Accurate and cost-effective wheat mapping is exceedingly critical for food production management,food security warnings,and food trade policy-making in China.To reduce confusion between wheat and non-wheat crops for accurate growth stage wheat mapping,we present a novel approach that combines a random forest(RF)classifier with multi-sensor and multi-temporal image data.This study aims to(1)determine whether an RF combined with multi-sensor and multi-temporal imagery can achieve accurate winter wheat mapping,(2)to find out whether the proposed approach can provide improved performance over the traditional classifiers,and(3)examine the feasibility of deriving reliable estimates of winter wheat-growing areas from medium-resolution remotely sensed data.Winter wheat mapping experiments were conducted in Boxing County.The experimental results suggest that the proposed method can achieve good performance,with an overall accuracy of 92.9%and a kappa coefficient(κ)of 0.858.The winter wheat acreage was estimated at 33,895.71 ha with a relative error of only 9.3%.The effectiveness and feasibility of the proposed approach has been evaluated through comparison with other image classification methods.We conclude that the proposed approach can provide accurate delineation of winter wheat areas.

廊坊临空经济中心项目大跨度室外连廊结构设计

为实现建筑造型与结构受力的完美结合,避免现有结构形式单一、繁琐、成本高等缺点,廊坊临空经济中心项目中的大跨度室外连廊创新性地采用两端刚接鱼腹式钢桁架形式。针对此结构形式的受力分布情况、桁架整体稳定、节点局部稳定及结构抗震性能问题,采用盈建科2.0.3软件进行整体计算,并考虑恒、活荷载及温度荷载;采用SAUSAGE 2020软件通过直接分析法,考虑几何非线性和材料非线性,按第一阶线弹性屈曲模态设置初始缺陷,考虑活荷载不利布置,对有无混凝土楼板的钢桁架分别进行整体稳定分析;采用盈建科2.0.3软件进行抗震性能化设计;采用SAUSAGE 2020软件进行罕遇地震弹塑性时程分析,选取两组天然波及一组人工波,研究结构在强震下的变形及塑性损伤情况,寻找结构的薄弱部位;采用ANSYS R19.2软件对应力比最大的节点区域进行有限元静力分析,采用实体单元,考虑几何非线性和材料非线性。结果表明:两端刚接鱼腹型桁架弯矩图与桁架高度及桁架杆件截面变化规律相吻合;混凝土楼板对增强桁架整体稳定的作用显著,混凝土楼板对桁架侧向变形的约束使稳定控制因素由桁架平面外侧向变形转变为框架柱平面外压弯失稳,稳定系数均满足GB 50017—2017《钢结构设计标准》的要求;结构整体指标、钢构件应力比及混凝土构件配筋均满足GB 50017—2017的要求,不存在明显的薄弱部位,罕遇地震下损伤程度有限;部分节点区域虽存在应力集中但应力数值未超过钢材屈服强度且衰减很快,节点承载力性能存有一定的富裕度且局部稳定满足设计要求。针对不同方面的分析结果,在施工图设计中分别采取有效的加强措施。

Machine learning and a computational fluid dynamic approach to estimate phase composition of chemical vapor deposition boron carbide

Chemical vapor deposition is an important method for the preparation of boron carbide.Knowledge of the correlation between the phase composition of the deposit and the deposition conditions (temperature,inlet gas composition,total pressure,reactor configuration,and total flow rate) has not been completely determined.In this work,a novel approach to identify the kinetic mechanisms for the deposit composition is presented.Machine leaning (ML) and computational fluid dynamic (CFD) techniques are utilized to identify core factors that influence the deposit composition.It has been shown that ML,combined with CFD,can reduce the prediction error from about 25% to 7%,compared with the ML approach alone.The sensitivity coefficient study shows that BHCl_(2 )and BCl_(3) produce the most boron atoms,while C_(2)H_(4) and CH_(4) are the main sources of carbon atoms.The new approach can accurately predict the deposited boron-carbon ratio and provide a new design solution for other multi-element systems.