藏兽医牦牛外科病治疗验方选

藏兽医是以藏医基础理论中的五源学说和三要素说为理论依据,同时参考了中兽医的理论和方法;在用药上有其独特之处,除应用植物药外,也用不少动物药和矿物药,范围广泛,在炮制和剂型上也与中兽医存在差异,是藏族人民长期在畜牧业生产中总结的宝贵经验。其药采自高原、用于高原,是祖国兽医的重要组成部分和遗产。文中介绍的均是来自青海、四川、甘肃、云南广大藏区民间兽医的验方,是在大量治疗实践中总结和使用的治疗牦牛外科疾病的经验方。笔者总结先人的经验,旨在抛砖引玉,丰富藏区基层兽医工作者的对牦牛常见外科病的治疗经验,达到为藏区畜牧业生产服务的目的。

Flotation and surface modification characteristics of galena,sphalerite and pyrite in collecting-depressing-reactivating system

The flotation tests and XPS analyses on galena,sphalerite and pyrite have been carried out in a collecting-depressing-reactivating system(hereafter referred as the CDR system).In this system,sulfide minerals were first collected and activated by the collector,and then depressed strongly by Ca(OH)2 in the strong alkaline solution,and finally reactivated by H2SO4.The flotation tests of pure minerals show that in this system the flotation behaviors of sphalerite and pyrite present irreversible characteristics along with the change of pulp potential.Furthermore,through the CDR system,considerable differences in the flotabilities between galena and sphalerite/pyrite are also observed.The XPS analysis results for galena,sphalerite and pyrite in a CDR system show that in the strong alkaline solution,some of the collectors,that have been already adsorbed on the mineral surface in the collecting process,are desorbed by Ca(OH)2.The XPS analysis results also show that in H2SO4 reactivating process,the surface hydroxides of galena are desorbed again by H2SO4 and replaced by diethyl dithiocarbamate,but those of sphalerite and pyrite are not desorbed.This flotation system may be applied to the bulk-differential flotation process of sulfur-bearing low-grade lead-zinc ores.

Detecting change in process systems with immunocomputing

Proposed a novel approach to detect changes in the product quality of process systems by using negative selection algorithms inspired by the natural immune system. The most important input variables of the process system was represented by artificial immune cells, from which product quality was inferred, instead of directly using the prod- uct quality which was hard to measure online, e.g. the ash content of coal flotation con- centrate. The experiment was presented and then the result was analyzed.

Screening influencing factors of blood stasis constitution in traditional Chinese medicine

Objective To study the influencing factors of blood stasis constitution and provide a basis for treating blood stasis-related diseases by traditional Chinese medicine(TCM) constitution identification.Methods Data were collected using the self-developed TCM constitution identification platform based on B/S model by the project team. The obtained data were divided into blood stasis constitution and normal constitution groups. The differences of the categorical type influencing factors(gender, birth mode, feeding mode within four months of birth, family history, marital status, eating habits, sleeping habits, exercise habits, emotional state, stress situation, and living environment) and the quantitative type influencing factors(sleep time, age,and mother’s age at birth) on the constitution of the two groups were analyzed. In the singlefactor analysis, the Pearson’s chi-square test was selected for the categorical variable, and the independent sample t test and Mann-Whitney U nonparametric test were selected for the quantitative variables according to whether they conformed to the positive-terrestrial distribution;the binary logistic stepwise regression method was selected for the multi-factor analysis.Results The data of 318 cases were collected from the TCM composition identification platform, and 159 cases of blood stasis constitution were used as the experimental group and 159 cases of normal constitution were used as the control group. The Pearson’s chi-square test yielded significant differences(P < 0.05) in the effects of gender, pressure situation, family history, living environment, emotional state, exercise habits, and dietary habits on blood stasis constitution. The independent samples t test yielded differences in sleep duration between the blood stasis constitution and normal constitution populations(P < 0.05), which meant sleep duration of the blood stasis constitution population was less than that of the normal constitution population. The Mann-Whitney U nonparametric test results accepted the original hypothesis that there was no difference in the distribution of age and mother’s age at birth across constitution types(P > 0.05). Binary logistic regression analysis showed that gender, family history, marital status, living environment, exercise habits, and emotional state were risk factors for blood stasis constitution(P < 0.05).Conclusion Gender, family history, living environment, emotional state, and exercise habits were significant influencing factors of blood stasis constitution. Blood stasis constitution populations can pay more attention to these influencing factors in their daily life for the prevention and reconciliation of blood stasis constitution.

Exploration of SDSS stellar database by AutoClass

AutoClass is an unsupervised Bayesian classification approach which seeks a maximum posterior probability classification for determining the optimal classes in large data sets. Using stellar photometric data from the Sloan Digital Sky Survey (SDSS) data release 7 (DR7), we utilize AutoClass to select non-stellar objects from this sample in order to build a pure stellar sample. For this purpose, the differences between PSF (point spread function) magnitudes and model magnitudes in five wavebands are taken as the input of AutoClass. Through clustering analysis of this sample by AutoClass, 617 non-stellar candidates are found. These candidates are identified by NED and SIMBAD databases. Most of the identified sources (13 from SIMBAD and 28 from NED respectively) are extragalactic sources (e.g., galaxies, HII, radio sources, infrared sources), some are peculiar stars (e.g., supernovas), and very few are normal stars. The extragalactic sources and peculiar stars of the identified objects occupy 94.1%. The result indicates that this method is an effective and robust clustering algorithm to find non-stellar objects and peculiar stars from the total stellar sample.