Exploring the fatty acids of vernix caseosa in form of their methyl esters by off-line coupling of non-aqueous reversed phase high performance liquid chromatography and gas chromatography coupled to mass spectrometry

Vernix caseosa is a greasy biofilm formed on the skin of the human fetus in the last period of pregnancy. This matrix is known to contain a range of uncommon branched chain fatty acids. In this study, we studied the fatty acid composition of vernix caseosa by non-aqueous reversed phase high performance liquid chromatography (RP-HPLC) fractionation followed by gas chromatography-electron ionization mass spectrometry (GC/EI-MS) of the fractions. For this purpose the fatty acids from vernix caseosa were converted into the corresponding methyl esters. These were fractionated by non-aqueous RP-HPLC using three serially connected C(18)-columns and pure methanol as the eluent. Aliquots of the HPLC fractions were directly analyzed by GC/EI-MS in the selected ion monitoring mode. Data analysis and visualization were performed by the creation of a two dimensional (2D) contour plot, in which GC retention times were plotted against the HPLC fractions. Inspection of the plot resulted in the detection of 133 different fatty acids but only 16 of them contributed more than 1% to the total fatty acids detected. Identification was based on HPLC and GC retention data, GC/MS-SIM and full scan data, as well as plotting the logarithmic retention times against the longest straight carbon chain. In selected cases, aliquots of the HPLC fractions were hydrogenated or studied by means of the picolinyl esters. Using these techniques, the number of double bonds could be unequivocally assigned to all fatty acids. Moreover, the number of methyl branches, and in many cases the positions of methyl branches could be determined. The enantioselective analysis of chiral anteiso-fatty acids resulted in the dominance of the S-enantiomers. However, high proportions of R-a13:0, R-a15:0, and R-a17:1 were also detected while a17:0 was virtually S-enantiopure.     

原始工件的完工时间限制下的最小化最大延误时间的重新排序

考虑由两个代理引起的重新排序问题,其中每个代理都在公共的加工资源下完成各自的不可中断加工的工件.每个代理要求在仅依赖工件的完工时间时最小化某一个特定的目标函数.考虑在原始工件的完工时间限制下的两个代理的单机最小化最大延误时间的重新排序问题.证明了该问题能在多项式时间或者拟多项式时间内解决.     

A novel content-based active contour model for brain tumor segmentation

Brain tumor segmentation is a crucial step in surgical and treatment planning. Intensity-based active contour models such as gradient vector flow (GVF), magneto static active contour (MAC) and fluid vector flow (FVF) have been proposed to segment homogeneous objects/tumors in medical images. In this study, extensive experiments are done to analyze the performance of intensity-based techniques for homogeneous tumors on brain magnetic resonance (MR) images. The analysis shows that the state-of-art methods fail to segment homogeneous tumors against similar background or when these tumors show partial diversity toward the background. They also have preconvergence problem in case of false edges/saddle points. However, the presence of weak edges and diffused edges (due to edema around the tumor) leads to oversegmentation by intensity-based techniques. Therefore, the proposed method content-based active contour (CBAC) uses both intensity and texture information present within the active contour to overcome above-stated problems capturing large range in an image. It also proposes a novel use of Gray-Level Co-occurrence Matrix to define texture space for tumor segmentation. The effectiveness of this method is tested on two different real data sets (55 patients - more than 600 images) containing five different types of homogeneous, heterogeneous, diffused tumors and synthetic images (non-MR benchmark images). Remarkable results are obtained in segmenting homogeneous tumors of uniform intensity, complex content heterogeneous, diffused tumors on MR images (T1-weighted, postcontrast T1-weighted and T2-weighted) and synthetic images (non-MR benchmark images of varying intensity, texture, noise content and false edges). Further, tumor volume is efficiently extracted from 2-dimensional slices and is named as 2.5-dimensional segmentation.     

滑动焦点光声显微成像方法

在高分辨光声显微成像系统中,只有在有限焦深范围内的吸收体可以获得高分辨率高信噪比的成像,而实际的生物组织往往具有不规则的轮廓,导致成像结果分辨率和信噪比不均匀。提出了一种基于稀疏扫描轮廓的滑动焦点光声显微成像方法,首先在固定的焦点上稀疏扫描轮廓,人工选择轮廓点获取轮廓的稀疏矩阵,然后通过双调和(Biharmonic)插值获取完整的轮廓矩阵,并将轮廓矩阵转化为焦点位置移动矩阵,最后进行二次扫描,根据焦点位置移动矩阵在每次扫描之前调整焦点位置,使得在整个扫描过程中,样品始终处于焦深的范围,从而获得分辨率和信噪比均匀的光声显微图像。通过模型实验和在体皮下血管成像、颅下血管成像,证明了该方法可以有效地提高光声成像的质量。     

鲁棒惯性地形辅助导航算法研究

传统的地形轮廓匹配(terrain contour matching, TERCOM)算法在速度误差和航向误差较大时可靠性下降,基于扩展Kalman滤波的北航惯性地形辅助惯性导航(BUAA inertial terrain aided navigation, BITAN) 算法在初始位置误差或高度表测量噪声较大时,系统无法准确定位,导致系统的鲁棒性降低. 为解决上述问题,对BITAN算法进行改进,发展了鲁棒北航惯性地形辅助导航(robust BUAA inertial terrain aided navigation, RBITAN)算法. RBITAN算法根据平均绝对差、均方差和交叉相关算法的统计决策信息设计了搜索模式算法,以基于扩展Kalman滤波原理的BITAN算法作为跟踪算法, 综合了TERCOM算法和BITAN算法的优点,提高了算法的鲁棒性.利用真实的数字高程模型和试飞数据进行仿真验证, 并和BITAN算法进行比较.仿真结果验证了RBITAN算法可以在较大初始位置误差和较大高度表测量噪声时准确定位, 提高了算法的鲁棒性.     

Primal and dual algorithms for optimization over the efficient set

ABSTRACT

Optimization over the efficient set of a multi-objective optimization problem is a mathematical model for the problem of selecting a most preferred solution that arises in multiple criteria decision-making to account for trade-offs between objectives within the set of efficient solutions. In this paper, we consider a particular case of this problem, namely that of optimizing a linear function over the image of the efficient set in objective space of a convex multi-objective optimization problem. We present both primal and dual algorithms for this task. The algorithms are based on recent algorithms for solving convex multi-objective optimization problems in objective space with suitable modifications to exploit specific properties of the problem of optimization over the efficient set. We first present the algorithms for the case that the underlying problem is a multi-objective linear programme. We then extend them to be able to solve problems with an underlying convex multi-objective optimization problem. We compare the new algorithms with several state of the art algorithms from the literature on a set of randomly generated instances to demonstrate that they are considerably faster than the competitors.     

Fuzzy multi-choice goal programming

Goal programming as a well known technique has been widely used for solving multi objective decision making problems. However, in some practical cases, there may exist situations where the decision maker is interested in setting multi aspiration levels for objectives that may not be expressed in a precise manner. In this paper, a novel formulation of fuzzy multi-choice goal programming (FMCGP) is presented. The proposed approach not only improves the applicability of goal programming in real world situations but also provides useful insight about the solution of a new class of problems. To illustrate and clarify the proposed approach, a numerical example is presented.     

一种改进ICCP水下地形匹配算法

针对水下地形最近等值线迭代（ICCP）匹配算法的原理缺陷和惯性导航累积误差对匹配精度的影响,提出了一种利用粒子群算法优化估计航路的改进ICCP算法．分析了ICCP算法的不足,利用估计航路、惯性导航定位误差和数字地图确定误差椭圆和参考航路,用豪斯多夫距离表征参考航路与实际航路的位置差异,并利用粒子群优化算法实现参考航路的快速寻优．仿真结果证明改进算法具有更好的定位精度和鲁棒性,湖上试验验证了算法的工程可行性．     

学生SP的培训及其在OSCE的初步应用

为了了解学生标准化病人及客观结构化临床考试(OSCE)在教学中的应用情况,随机选取了若干大四学生及老师对有关OSCE的设计、质量及学生标准化病人(SP)的质量进行了调查。结果显示:82的学生认为提高了临床能力和培养了临床思维,72的学生认为学生SP模仿逼真,92.3的老师对OSCE的设计和质量比较满意。调查分析显示:OSCE能科学、客观、公平地评估学生的临床实践能力,有助于发现学生学习中的不足,有助于培养临床思维能力以及为毕业生的临床技能考核提供新思路。