An examination of the carbon isotope effects associated with amino acid biosynthesis

Stable carbon isotope ratios (delta(13)C) were determined for alanine, proline, phenylalanine, valine, leucine, isoleucine, aspartate (aspartic acid and asparagine), glutamate (glutamic acid and glutamine), lysine, serine, glycine, and threonine from metabolically diverse microorganisms. The microorganisms examined included fermenting bacteria, organotrophic, chemolithotrophic, phototrophic, methylotrophic, methanogenic, acetogenic, acetotrophic, and naturally occurring cryptoendolithic communities from the Dry Valleys of Antarctica. Here we demonstrated that reactions involved in amino acid biosynthesis can be used to distinguish amino acids formed by life from those formed by nonbiological processes. The unique patterns of delta(13)C imprinted by life on amino acids produced a biological bias. We also showed that, by applying discriminant function analysis to the delta(13)C value of a pool of amino acids formed by biological activity, it was possible to identify key aspects of intermediary carbon metabolism in the microbial world. In fact, microorganisms examined in this study could be placed within one of three metabolic groups: (1) heterotrophs that grow by oxidizing compounds containing three or more carbon-to-carbon bonds (fermenters and organotrophs), (2) autotrophs that grow by taking up carbon dioxide (chemolitotrophs and phototrophs), and (3) acetoclastic microbes that grow by assimilation of formaldehyde or acetate (methylotrophs, methanogens, acetogens, and acetotrophs). Furthermore, we demonstrated that cryptoendolithic communities from Antarctica grouped most closely with the autotrophs, which indicates that the dominant metabolic pathways in these communities are likely those utilized for CO(2 )fixation. We propose that this technique can be used to determine the dominant metabolic types in a community and reveal the overall flow of carbon in a complex ecosystem.     

Amino acid distribution in meteorites: diagenesis, extraction methods, and standard metrics in the search for extraterrestrial biosignatures

The relative abundance of the protein amino acids has been previously investigated as a potential marker for biogenicity in meteoritic samples. However, these investigations were executed without a quantitative metric to evaluate distribution variations, and they did not account for the possibility of interdisciplinary systematic error arising from inter-laboratory differences in extraction and detection techniques. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and stochastic probabilistic artificial neural networks (ANNs) were used to compare the distributions for nine protein amino acids previously reported for the Murchison carbonaceous chondrite, Mars meteorites (ALH84001, Nakhla, and EETA79001), prebiotic synthesis experiments, and terrestrial biota and sediments. These techniques allowed us (1) to identify a shift in terrestrial amino acid distributions secondary to diagenesis; (2) to detect differences in terrestrial distributions that may be systematic differences between extraction and analysis techniques in biological and geological laboratories; and (3) to determine that distributions in meteoritic samples appear more similar to prebiotic chemistry samples than they do to the terrestrial unaltered or diagenetic samples. Both diagenesis and putative interdisciplinary differences in analysis complicate interpretation of meteoritic amino acid distributions. We propose that the analysis of future samples from such diverse sources as meteoritic influx, sample return missions, and in situ exploration of Mars would be less ambiguous with adoption of standardized assay techniques, systematic inclusion of assay standards, and the use of a quantitative, probabilistic metric. We present here one such metric determined by sequential feature extraction and normalization (PCA), information-driven automated exploration of classification possibilities (HCA), and prediction of classification accuracy (ANNs).     

电磁污染

雷达本身虽是一个高度复杂的系统，但它通常也是含有很多其他无源或有源电子设备的更大系统的一部分。因此，雷达各组成部分必须全部兼容，没有任何内部干扰。而且，雷达不得干扰平台上的其他设备。当然，对于其他类型的设备来讲也是一样的，特别是像电子干扰（ECM）、通信和识别类有源设备更是如此。     

合成孔径雷达光学处理的新成果

本研究项目构建了一个SAR算法分析试验台，并验证了合成孔径数据获取期间对非线性运动目标的聚焦能力。试验台由连接到低端SGI工作站上的ImSyn光电处理机组成。为了支持动目标聚焦，并为成保处理提供必要的用户交互，开发了一套软件工具。用各种各样的模拟数据集和真实SAR数据研究并测试了动目标算法。文中详细描述了已成功应用于SAR与ISAR数据收集处理的方法、算法、有些方法，算法对目标辨别与非合作目标识别也具有意义。     

通过时间拉伸预处理克服ADC的输入带宽限制时间拉伸预处理：第一部分

数字信号处理（ＤＳＰ）已经开始在现代通讯和雷达系统设计中起主导作用，不幸的是，商用模拟－数字变换器（ＡＤＣ０的速度尚不能满足通讯和雷达系统的数字化要求（尤其在微波段），可喜的是，我们已经发展了一种新的ＡＤＣ机理－在ＡＤ变换之前放慢模拟信号的速度，通过卫低输入信号（载波及其调制）的带宽的方法，ＡＤＣ的性能得到极大的改进，本文由于几个部分组成，还将研究时间压缩及其在高速数字－模拟变换器（ＤＡＣ）中的应用。     

用氮化铝基片制成的高功率小型开关

固态ＰＩＮ二极管开关比等效机电式或铁氧体式开关更快、更小，可靠性也更高。因此，在低功率和高功率运用中，它们被广泛用作控制元件。     

分布式序列最近邻多目标跟踪算法

本文提出了一种有效的分布式多目标跟踪算法。分布式跟踪器主要由本地传感器级跟踪器和一和航迹融合器组成。在航迹融合器中，首先将传感器的本地航迹转换为一般坐标系，并用线性卡尔曼滤波器使其同步。     

使用遗传算法的仅相位自适应零化

介绍一种新的相控阵自适应相位零化方法。遗传算法调整一些波束控制移相器的最小有效位来使总的输出功率减至最小。除限制了遗传算法的搜索空间外，利用较小自适应相位还减少了波束控制方向的偏差和旁瓣电平中的扰动，得出的不同结果说明了该方法的优点和局限性。总结上讲，遗传算法优于以前的相位电适应算法。     

低压低功率SiGe BiCMOS X波段低噪声放大器与IEEE 802．11a LNA的设计比较研究

介绍了一种采用0．25um SiGe BiCMOS工艺集成的低压低功率X波段低噪声放大器（LNA），比较了此种放大器与IEEE 802．11a LNA的设计。X波段LNA和IEEE 802．11a LNA的工作频率分别为10GHz和5．8GHz。所设计的LNA都采用了相同的结构和电压，并耗费同量的电流。两种LNA都只需要1．5V的电压，消耗1．5mW的直流功率。两种电路的差异是它们有不同的输入与输出匹配和负载。本文介绍的LNA在10GHz时的电压增益为11．49dB，噪声系数（NF）为3．84dB，输入反射损失为-15.37dB，输出反射损失为-17dB，P1dB为-3．75dBm。在5．8GHz时的电压增益为16．07dB，噪声系数为3．07dB，输入反射损失为-18．1dB，输出反射损失为-15．23dB，P1dB为-6．54dBm。两电路的关键特征是：低压、低功率和良好的噪声匹配。频率为IOGHz和5．8GHz时，噪声系数与最小噪声系数之差分别只有0．03dB和0．05dB。验证了一种高频（X波段）低成本设计，与其他技术（如GaAs、SiBJT、JFET、PHMET和MESFET等）相比，它是在SiGe BiCMOS中设计的。     

数字波束形成合成孔径雷达

阐述了最近在Astrium完成的两项SAR研究，简述了大地测量合成孔径成像雷达X型（TSX）卫星的能力。两项研究是在SAR成像模式时采用新颖天线波束形成概念的高分辨率宽条带成像雷达（HRWS）和LCMA（低成本多个馈电天线）。分析了它们的设计、性能和样机框图，并将它们与采用有源相控阵天线的TSX—SAR进行了比较。