近红外光谱监测体外循环手术中脑组织氧合状况的研究

体外循环手术中,为防止因脑氧供需失衡导致脑缺氧,就要实时监测患者局部脑组织的氧合状况,以根据其变化调整生理参数或采取应急手段。用该研究小组自行研制的近红外仪器(使用一个双波长的近红外光源和两个近红外检测器)监测心脏手术中患者的脑氧,可以求出局部脑组织血红蛋白浓度的变化,并根据稳态空间分辨光谱(SRS)算法求出局部脑组织的氧饱和度(rSO₂)。体外循环中用监护仪监测患者的混合静脉血氧饱和度(SvO₂)等生理参数。测得的血红蛋白浓度变化容易受到干扰,而rSO₂的抗干扰性能较好。rSO₂在整个手术过程中都可以监测到,而SvO₂只能在体外循环过程中监测到。多数患者rSO₂和SvO₂存在正向相关性,但二者的相关系数并不很高。这是因为SvO₂是大静脉的血氧饱和度,而测得的rSO₂反映局部脑组织的氧合状况,二者的生理意义不同。实验结果表明,体外循环手术中rSO₂可以反映患者脑组织氧合状况的变化,而仅仅监测SvO₂是不够的。     

高温退火对蓝宝石基片的表面形貌和对CeO缓冲层以及Tl-2212超导薄膜长的影响

研究了蓝宝石(1102)基片在不同温度和时间下退火时表面形貌和表面相结构的变化,以及它对CeO2缓冲层和T1-2212超导薄膜生长的影响.原子力显微镜(AFM)研究表明,在流动氧环境中1000℃温度下退火,蓝宝石(1102)的表面首先局部区域形成台阶结构,然后表面形成叠层台阶结构,随着退火时间的延长.表面发生了台阶合并现象,表面形貌最终演化为稳定的具有光滑平台的宽台阶结构.XRD测试表明,通过高温热处理可以大幅度提高蓝宝石基片表面结构的完整性.在1000℃温度下热处理20 h的蓝宝石(1102)基片上可以生长出具有面内取向的CeO2(001)缓冲层.在具有缓冲层的蓝宝石基片上可以制作出高质量c轴织构的外延11-2212超导薄膜,其临界转变温度(Tc)为104.7 K,液氮温度下临界电流密度(Jc)达到3.5 MA/cm2,微波表面电阻R(77 K,10 GHz)约为390μΩ.     

AlGaN-Based Solar-Blind Schottky Photodetectors Fabricated on AlN/Sapphire Template

We report AlGaN-based back-illuminated solar-blind Schottky-type ultraviolet photodetectors with the cutoff- wavelength from 280nm to 292nm without bias. The devices show low dark current of 2.1× 10^-6A/cm^2 at the reverse bias of 5 V. The specific detectivity D＊ is estimated to be 3.3 × 10^12cmHz^1/2 W^-1 . To guarantee the performance of the photodetectors, the optimization of AlGaN growth and annealing condition for Schottky contacts were performed. The results show that high-temperature annealing method for Ni/Pt Schottky contacts is effective for the reduction of leakage current.     

关于集合的粗相似度量的注记

举例说明文献"集合的粗相似度量"中定理3.1(2)的结论是不严谨的,从而给出一个严谨的结论。     

Comparison of the Structural Evolution of β Polypropylene during the Sequential and Simultaneous Biaxial Stretching Process

In this work, the lamellar structural evolution and microvoids variations of β polypropylene(β-PP) during the processing of two different stretching methods, sequential biaxial stretching and simultaneous biaxial stretching, were investigated in detail. It was found that different stretching methods led to significantly different lamellae deformation modes, and the microporous membranes obtained from the simultaneous biaxial stretching exhibited better mechanical properties. For the sequential biaxial stretching, abundant coarse fibers originated from the tight accumulation of the lamellae parallel to the longitudinal stretching direction, whereas the lamellae perpendicular to the stretching direction were easily deformed and separated. Those coarse fibers were difficult to be separated to form micropores during the subsequent transverse stretching process, resulting in a poor micropores distribution. However, for the simultaneous biaxial stretching, the β crystal had the same deformation mode, that is, the lamellae distributed in different directions were all destroyed, forming abundant microvoids and little coarse fibers.     

X荧光光谱法测定锡酸铅TDI还原型催化剂中锡和铅含量

报道了一种准确度高、分析步骤简单的锡酸铅TDI还原型催化剂中锡和铅含量的X射线荧光光谱方法（XRF）．设计制备了标准样品,研究了仪器测试条件下元素峰位的选择和背景扣除方法．结果表明：X荧光光谱法在不破坏样品的前提下完成对二氧化锡和铅含量的同时测定,重复实验10次,Pb实验相对标准偏差为0．37％;SnO2实验相对标准偏差0．61％。     

气相色谱-质谱联用技术用于利尿剂的快速初筛和确证

利用微波辅助衍生及宏程序技术,对反兴奋剂条例的禁用列表中的一系列利尿剂药物进行了研究。建立了同时分析尿样中9种利尿剂的气相色谱-质谱联用(GC/MS)测定以及抽检尿样的初筛、确证的新方法。在GC-MS的选择离子监测(SIM)模式下,所建立定量方法的检出限为0.3~6.6μg/L;尿样中的提取回收率为37.6%~96.8%;RSD小于9.7%。由于采用了微波衍生样品前处理技术和宏程序数据处理方法,显著提高了分析效率,不但检测灵敏度完全能满足国际奥委会的要求,而且检测时间缩短3 h以上。方法成功地应用于口服吲达帕胺后尿样的检测,探讨了人尿中吲达帕胺代谢情况,有效进行兴奋剂监控提供了依据。     

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.     

Deep Multilabel Multilingual Document Learning for Cross-Lingual Document Retrieval

Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the word level and then represent documents via word embeddings, which leads to insufficient structure information. In this work, the cross-lingual comparison at the document level is achieved through the cross-lingual semantic space. Our method, MDL (deep multilabel multilingual document learning), leverages a six-layer fully connected network to project cross-lingual documents into a shared semantic space. The semantic distances can be calculated when the cross-lingual documents are transformed into embeddings in semantic space. The supervision signals are automatically extracted from the data and then used to construct the semantic space via a linear classifier. The ambiguity of manual labels could be avoided and the multilabel supervision signals can be acquired instead of a single label. The representation of the semantic space is enriched by multilabel supervision signals, which improves the discriminative ability of the embeddings. The MDL is easy to extend to other fields since it does not depend on specific data. Furthermore, MDL is more efficient than the models training all languages jointly, since each language is trained individually. Experiments on Wikipedia data showed that the proposed method outperforms the state-of-the-art cross-lingual document retrieval methods.     

电感耦合等离子体原子发射光谱法同时测定槲寄生中的微量元素

采用硝酸-双氧水对槲寄生全药及煎煮液进行湿法消解,用电感耦合等离子体原子发射光谱法(ICP-AES)同时测定试样中Cu,Mg,Zn,Mn,Ni,Se,K,Na,Ca和Fe等元素的含量。该法的的加标回收率为91.6％～108.9％,变异系数为0.47％～4.59％,具有良好的准确度和精密度。结果表明：槲寄生中含有丰富的K和Ca元素,Na和Mg的含量也相对丰富,为进一步研究槲寄生的药理提供了科学的数据。