黑线仓鼠（Cricetulus barabensis Pallas）主动脉弓分支类型的初步观察

我们将我院黑线仓鼠（也叫中国地鼠）Ａ３０家系１只雌鼠（第２８代）和Ｂ５０２家系１５４只（从第１０—１５代）地鼠（其中雌性１３８只；雄性１５只），解剖观察了其主动脉弓分支的类型，共见到３个类型，Ａ型９７．４２％，Ｃ型１．２９％，Ｆ型０．６４％，并发现一例罕见畸型。而Ａ型为中国地鼠主动脉弓分支的基本类型，但基本型中又有各个亚型。     

Subset selection from multi-experiment data sets with application to milk fatty acid profiles

The development of routine analyses to allow for the handling of large amounts of samples and to avoid cost and time expensive analytical techniques is of high value. These routine analyses most often require calibration using the detailed analyses as reference values. A representative subset reflecting the complete range of the variables of interest is required for this purpose. In this paper this subset selection problem is tackled for multi-experiment data sets. Conventional techniques such as the Kennard and Stone algorithm and OptiSim are compared to a new approach based on Genetic Algorithms. The challenge here is to find an adequate objective function and to modify the standard crossover and mutation operators to keep the number of desired samples fixed. These techniques are applied on a data set containing the concentration of 45 fatty acids, determined by a simplified reference method, in 1033 milk samples, stemming from six different experiments. The objective is to select a subset of 100 samples in which each of the six different experiments is sufficiently represented. While there is no obvious way to generalize the conventional methods for multi-experiment data sets, this can quite easily be accomplished for Genetic Algorithms by modifying the objective function. Our results indicate that Genetic Algorithms are very capable of handling the subset selection problem for multi-experiment data sets.