马鞍列岛褐菖鲉Sebasticus marmoratus栖息地适宜性评价

为了评估趋礁鱼类在岛礁海域的生境适宜度,选取马鞍列岛的褐菖鲉(Sebasticus marmoratus)为指示物种,以2009年获取的水深、盐度、叶绿素a、浊度和底质数据作为褐菖鲉春、冬季栖息地指示因子,建立栖息地适宜度曲线,并计算各站点的栖息地适宜性指数(HSI)。结果显示:1)绿华、花鸟、嵊山沿岸站点HSI普遍较低,枸杞岛、三横山、东库山沿岸站点褐菖鲉HSI相对较高,其中最大值1.0出现在枸杞岛沿岸的站点;2)春季褐菖鲉幼鱼的适宜水深在6 m左右,成鱼适宜在8—12 m的水深处生存;冬季褐菖鲉对8—12 m的水深适宜性良好;3)春季所有褐菖鲉的适宜盐度为30PSU,冬季幼鱼的适宜盐度为27—31PSU,成鱼的适宜盐度为27PSU、31PSU;4)随着叶绿素a和浊度值的增大,褐菖鲉适宜性逐渐降低。底质类型为岩时最适合褐菖鲉生存。5)相关分析显示,褐菖鲉丰度与底质类型相关性最大,而与叶绿素a、浊度呈显著负相关。研究结果表明,初级生产力和浑浊程度越高对褐菖鲉丰度抑制越明显。底质类型是褐菖鲉丰度分布的重要影响因子,其中分布有较多大型海藻的岩礁生境是其最适宜的栖息地。利用2010年春、冬季环境调查和渔获数据进行HSI模型验证,资源丰度随HSI值升高而增加,因此构建的模型可用于趋礁鱼类在岛礁海域的栖息地适宜性分析。     

甘南高山林线岷江冷杉—杜鹃种群结构与动态

高山林线是一种典型的生态交错带,是对气候反映最敏感的地区之一。甘肃南部高山林线区域主要以原始岷江冷杉种群和杜鹃种群为优势种,通过对岷江冷杉和杜鹃种群建立静态生命表,绘制存活曲线描述其结构特征,利用种群数量动态预测时间序列分析定量研究未来的发展趋势。结果显示:(1)岷江冷杉种群幼苗比较丰富,能很好的维持种群个体的自疏死亡,存活曲线呈Deevey-Ⅲ型;杜鹃种群幼苗缺乏,存活曲线趋向于Deevey-Ⅰ型;死亡曲线和危险率曲线都随着龄级的增加而增加,杜鹃种群的死亡率在各个龄级一直大于岷江冷杉种群,危险率在Ⅱ龄级以后杜鹃种群也始终大于岷江冷杉种群。(2)岷江冷杉种群结构动态变化指数Vpi大于修正后的种群结构动态变化指数V′pi且大于0,而杜鹃种群结构动态变化指数Vpi小于修正后的种群结构动态变化指数V′pi且小于0,则岷江冷杉种群属于增长型,杜鹃种群属于衰退型,岷江冷杉、杜鹃随机干扰极大值分别为0.027、0.011,说明二者对外界干扰均比较敏感。(3)杜鹃种群时间序列预测为前期幼苗比较缺乏,中期稳定,后期衰退的动态特征,而岷江冷杉种群表现出各龄级时间变化较小,幼苗个体数较多,种群为稳定增长型,岷江冷杉更能适应甘肃南部高山林线区域当前环境。     

Estimating seed dispersal distance: A comparison of methods using animal movement and plant genetic data on two primate‐dispersed Neotropical plant species

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Evaluation of type traits in relation to production,and their importance in early selection for milk performance in dairy buffaloes

Type traits (TTs) can contribute to breeding animals with good economic traits such as production, longevity, fertility, and profitability. Dairy buffaloes are the second largest source of milk supply in the world, and their TTs should be taken into consideration in future dairy buffalo breeding programmes. However, the relationship between TTs and milk production traits in buffalo remains largely unknown. The study aimed to establish an early selection method for buffaloes with desirable milk performance by TTs. Using 1 908 records from 678 buffaloes, the relationship between TTs and milk production traits was analysed and the optimal growth curves of TTs related to milk production traits were constructed. We examined the correlations between 45 TTs (33 body structural, 12 udder and teat morphological traits) and three milk production traits (milk yield (MY), milk fat percentage (MF), and milk protein percentage (MP)). The results showed that the highest correlation was found between MY and udder circumference (r = 0.438), teat length (r = ?0.380) or heart girth (r = ?0.341). The teat distance and teat circumference exhibited a significant negative correlation with MF and MP. Rump length was the only trait that had a significant positive correlation with milk production traits, suggesting that milk performance could be comprehensively improved by including rump length in the selection procedure. Notably, we found that high milk production traits was obtained from the buffaloes with short teats (<6 cm), small heart girth (<200 cm), large udder circumference (>104 cm), long rump (>39 cm), and small distance between teats. Moreover, an early selection method for buffaloes with excellent milk performance was developed based on the non-linear models. Brody model exhibited the best fitting effect for heart girth and rump length, while the Logistic model displayed the best fitting effect for teat length. Our findings provide theoretical basis for the early selection of buffaloes with desirable milk performance.     

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Effect of physiological heterogeneity of E. coli popula-tion on antibiotic susceptivity test

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Prediction of Protein-Protein Interactions Using Protein Signature Profiling

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Prediction of protein-protein interactions using protein signature profiling

Protein domains are conserved and functionally independent structures that play an important role in interactions among related proteins. Domain-domain interactions have been recently used to predict protein-protein interactions (PPI). In general, the interaction probability of a pair of domains is scored using a trained scoring function. Satisfying a threshold, the protein pairs carrying those domains are regarded as "interacting". In this study, the signature contents of proteins were utilized to predict PPI pairs in Saccharomyces cerevisiae, Caenorhabditis elegans, and Homo sapiens. Similarity between protein signature patterns was scored and PPI predictions were drawn based on the binary similarity scoring function. Results show that the true positive rate of prediction by the proposed approach is approximately 32% higher than that using the maximum likelihood estimation method when compared with a test set, resulting in 22% increase in the area under the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs increased significantly. The predicted PPI pairs are on average 11 times more likely to interact than the random selection at a confidence level of 0.95, and on average 4 times better than those predicted by either phylogenetic profiling or gene expression profiling.