本试验研究日粮阴阳离子差(dietary cation-aniondifference,DCAD)对产后奶牛酸碱平衡和生产性能的影响。试验选择16头荷斯坦和8头经产娟姗奶牛,采用完全随机试验设计,奶牛分娩后立即饲喂2种DCAD值[22或47 mEq(Na+K-C1-S)/100 g DM]日...本试验研究日粮阴阳离子差(dietary cation-aniondifference,DCAD)对产后奶牛酸碱平衡和生产性能的影响。试验选择16头荷斯坦和8头经产娟姗奶牛,采用完全随机试验设计,奶牛分娩后立即饲喂2种DCAD值[22或47 mEq(Na+K-C1-S)/100 g DM]日粮。以玉米青贮为基础的日粮粗蛋白质含量为19.0%,NDF为25.4%,ADF为15.0%,泌乳净能为1.69 Mcal/kg。分娩后前5 d额外添加2.3 kg的苜蓿干草,然后连续6周每周采集奶样、血样和尿样。采用有重复观测数据(消除牛之间的误差)的混合模型分析结果表明,DCAD对奶牛干物质采食量(分别为18.2和18.3kg/d)、产奶量(33.5和33.3 kg/d)、乳成分(乳脂肪3.96%和4.11%、乳蛋白3.11%和3.00%、非脂固体乳8.95%和8.83%)、颈静脉血液pH(7.395和7.400)、HCO3-浓度(27.3和27.6 mEq/L)和局部CO2分压(46.7和46.5 mm-Hg)没有影响。当奶牛饲喂DCAD值为22和47 m Eq/100g DM的日粮时,尾根静脉血浆支链AA(431和558 g/M)和总AA中必需AA的比率(0.390和0.434)表明瘤胃中氮的代谢受到影响,该结果可能是因为大量的微生物蛋白流入小肠所引起。尿液pH随着DCAD值(8.12和8.20)的增加呈现提高的趋势。DCAD值为22 m Eq/100 g DM时,奶牛的净酸排泄物(-24和-41 mM)与47 m Eq/100 g DM相比较高,在奶牛产后净酸排泄物可以更好的表明局部酸的负荷而非血液酸碱参数相。当DCAD值从22升高到47 m Eq/100 g DM时,产后奶牛的干物质采食量和生产性能没有被提高,可能是因为奶牛产后对变异较大的日粮作出的回应,使处理效应的研究变得困难。展开更多
Spatial objects have two types of attributes： geometrical attributes and non-geometrical attributes, which belong to two different attribute domains （geometrical and non-geometrical domains）. Although geometrically...Spatial objects have two types of attributes： geometrical attributes and non-geometrical attributes, which belong to two different attribute domains （geometrical and non-geometrical domains）. Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering： local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted clustering is obtained based on those features fective and efficient. Second, local features from each site are sent to a central site where global Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.展开更多
文摘本试验研究日粮阴阳离子差(dietary cation-aniondifference,DCAD)对产后奶牛酸碱平衡和生产性能的影响。试验选择16头荷斯坦和8头经产娟姗奶牛,采用完全随机试验设计,奶牛分娩后立即饲喂2种DCAD值[22或47 mEq(Na+K-C1-S)/100 g DM]日粮。以玉米青贮为基础的日粮粗蛋白质含量为19.0%,NDF为25.4%,ADF为15.0%,泌乳净能为1.69 Mcal/kg。分娩后前5 d额外添加2.3 kg的苜蓿干草,然后连续6周每周采集奶样、血样和尿样。采用有重复观测数据(消除牛之间的误差)的混合模型分析结果表明,DCAD对奶牛干物质采食量(分别为18.2和18.3kg/d)、产奶量(33.5和33.3 kg/d)、乳成分(乳脂肪3.96%和4.11%、乳蛋白3.11%和3.00%、非脂固体乳8.95%和8.83%)、颈静脉血液pH(7.395和7.400)、HCO3-浓度(27.3和27.6 mEq/L)和局部CO2分压(46.7和46.5 mm-Hg)没有影响。当奶牛饲喂DCAD值为22和47 m Eq/100g DM的日粮时,尾根静脉血浆支链AA(431和558 g/M)和总AA中必需AA的比率(0.390和0.434)表明瘤胃中氮的代谢受到影响,该结果可能是因为大量的微生物蛋白流入小肠所引起。尿液pH随着DCAD值(8.12和8.20)的增加呈现提高的趋势。DCAD值为22 m Eq/100 g DM时,奶牛的净酸排泄物(-24和-41 mM)与47 m Eq/100 g DM相比较高,在奶牛产后净酸排泄物可以更好的表明局部酸的负荷而非血液酸碱参数相。当DCAD值从22升高到47 m Eq/100 g DM时,产后奶牛的干物质采食量和生产性能没有被提高,可能是因为奶牛产后对变异较大的日粮作出的回应,使处理效应的研究变得困难。
基金Funded by the National 973 Program of China (No.2003CB415205)the National Natural Science Foundation of China (No.40523005, No.60573183, No.60373019)the Open Research Fund Program of LIESMARS (No.WKL(04)0303).
文摘Spatial objects have two types of attributes： geometrical attributes and non-geometrical attributes, which belong to two different attribute domains （geometrical and non-geometrical domains）. Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering： local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted clustering is obtained based on those features fective and efficient. Second, local features from each site are sent to a central site where global Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.
