基于USLE模型的长株潭城市群生态绿心区水土流失研究

[目的]研究2018年长株潭(长沙市—株州市—湘潭市)城市群生态绿心区最佳植被覆盖和水土流失状况,为区域综合治理提供理论依据。[方法]运用GIS和RS技术,以长株潭区域降雨、土壤、地形、土地利用等数据为基础,采用归一化植被指数(NDVI)模型和USLE国际通用土壤流失方程为优选模型。[结果]长株潭绿心区总面积为52 287 hm~2,整体植被状况较好,高覆盖度(75%～100%)面积最大,占绿心区总面积一半以上,为26 598.40 hm~2;中低覆盖度(30%～40%)面积最小,占区域总面积的8.61%,为4 501.91 hm~2。长株潭城市群生态绿心区总侵蚀(不含微度)面积为3 654.24 hm~2,占总面积的6.99%。湘潭市侵蚀比重最高,为8.51%,长沙市次之,为6.67%,株洲市侵蚀总比例最小,为5.68%。工程建设用地在禁止开发区、限制开发区和控制建设区的侵蚀面积分别为963.92,310.74,735.11 hm~2。[结论]受人为因素、城市建设、产业分布等影响,长株潭城市群生态绿心区覆盖度空间呈现西部低,中东部高的格局,工程建设是造成绿心区土壤侵蚀的主要原因。     

土壤颗粒分布体积分形维数与数量分形维数之间的关系

土壤的体积颗粒分布(PSD)可用于同时确定数量分形维数(DN)和体积分形维数(DV)。以往的试验和理论研究均未直接比较DV和DN的关系。为建立DV和DN的关系并分析其参数的敏感程度,在中国各地取了12种不同质地土壤,利用激光粒度仪进行了测定。提出了含3个参数Rupper(颗粒半径的上限),Ri(第i个半径)和Rlower(半径下限)的DV-DN理论关系,该关系可说明为什么DN可能大于3。在未调整的幂定律范围内(PLR),DV的幂定律半径为38.6～85.8μm,DN的幂定律半径为53.2～358μm,估算的DV值(2.18～2.69)比DN值(2.38～3.19)小。调整后的PLR比未调整的窄。在调整的PLRs范围内,估算的DV值变化范围为2.11～2.56,而DN变化范围为2.28～3.02。调整和未调整PLRs得出的DV和DN的差异说明,Rupper,Rlower,Ri和Rlower/Rupper均可能影响DV与DN关系,有必要进行参数的敏感性分析。进行敏感性分析可基于敏感系数(C)来鉴别DV-DN关系中各参数改变所引起的DV和DN相对变化,从而找出对该关系影响最大的参数。就不同情况下计算的C值而言,Rupper是影响DN值的最重要参数,小的Rupper值可能导致估算DN的准确度降低。PLR范围越大,DN的估算越准确。根据DV估算DN的相对误差的绝对值在12%范围内,说明得出的DV-DN理论关系是正确的。     

协同多目标攻击空战决策的启发式粒子群优化算法

利用协同多目标攻击战术的特定知识，并结合粒子群算法，提出了一种用于空战决策的启发式粒子群算法。该算法利用粒子群算法对解空间探索能力强，容易跳出局部最优陷井及启发式算法局部搜索能力强的优点，快速、高效地对全局最优值进行搜索。该算法通过求解友机导弹对目标的最优分配来确定空战决策方案。仿真实验结果表明。本文算法对最优空战决策方案的搜索性能明显优于普通粒子群算法及其他两种遗传算法。     

籼稻背景下导入Wx~(in)等位基因改良稻米食味和理化品质

水稻Wx~b等位基因已广泛用于籼稻的品质改良,但携带该等位基因的一些籼稻米饭往往偏软,仍需进一步改良。为明确籼稻背景下导入Wxin等位基因对稻米食味品质和理化品质的效应,分别以携带Wxin的IR64和携带Wx~b的9311为供体,以携带Wxa的籼稻SIR3611 (3611)为受体,基于分子标记辅助选择,通过杂交和连续回交的方式构建了3611背景下携带Wxin和Wx~b的近等基因系。系统比较了不同近等基因系间的农艺性状以及稻米的食味和理化品质。结果表明,近等基因系与受体亲本3611的主要农艺性状基本接近,无显著差异。NIL(Wxin)稻米的表观直链淀粉含量较亲本3611极显著下降而胶稠度极显著增加。NIL(Wx~b)稻米表观直链淀粉含量最低且与之对应的胶稠度最高。近等基因系NIL(Wxin)和NIL(Wx~b)稻米的食味值较亲本极显著提高。NIL(Wx~(in))和NIL(Wx~b)稻米的GBSSI丰度与对应的表观直链淀粉含量具有明显的正相关。稻米粉的黏滞性谱、热糊化特性和晶体结构与直链淀粉含量显著相关性。本研究为在我国籼稻品种品质改良中有效利用Wxin等位基因提供了重要依据。     

长期试验土壤理化性质和微生物量的研究进展

通过分析、总结长期试验的研究进展,研究长期试验的特点、意义和研究内容,综述了长期试验对土壤理化性质、土壤微生物量的影响,有助于该学科研究的纵深发展与广泛利用.目前,长期试验通常采取2种方法,分别是“长期”和“定位”,其具有时间的长期性和气候的重复性等特点,能够克服各种生态因素差异对试验带来的影响和制约;其与土壤理化性状和微生物特性等关系密切,对研究农业生产有着重要的科学价值,为不同措施对土壤性质的影响提供研究场所.     

Serum and bronchoalveolar lavage fluid endothelin-1 concentrations as diagnostic biomarkers of canine idiopathic pulmonary fibrosis

Background: Diagnosis of canine idiopathic pulmonary fibrosis (IPF) is challenging. Endothelin‐1 (ET1) is a biomarker of IPF in humans, but whether ET1 can detect and differentiate IPF from other canine respiratory diseases is unknown. Objective: To evaluate whether measurement of the concentration of ET1 in serum and bronchoalveolar lavage fluid (BALF) can be used to distinguish canine IPF from chronic bronchitis (CB) and eosinophilic bronchopneumopathy (EBP). Animals: Twelve dogs with IPF, 10 dogs with CB, 6 dogs with EBP, 13 privately owned healthy West Highland White Terriers (WHWT), and 9 healthy Beagle dogs. Methods: Prospective, case control study. ET1 concentration was determined by ELISA in serum and in BALF. Results: No significant difference in serum ET1 concentration was detected between healthy Beagle dogs and WHWT. Serum ET1 concentration was higher in dogs with IPF (median interquartile range; 2.32 pg/mL, 2.05–3.38) than healthy Beagle dogs (1.28, 1.07–1.53; P < .001), healthy WHWT (1.56, 1.25–1.85; P < .001), dogs with EBP (0.94 0.68–1.01; P = .001), and dogs with CB (1.54 0.74–1.82; P = .005). BALF ET1 concentration was below the detection limit in healthy WHWT and in dogs with CB, whereas it was measurable in all dogs with IPF. A cut‐off serum concentration of 1.8 pg/mL had a sensitivity of 100% and a specificity of 81.2% for detection of IPF, with an area under the receiver operating characteristic curve of 0.818. Conclusions and Clinical Importance: Serum ET1 can differentiate dogs with IPF from dogs with EBP or CB. ET1 can be detected in BALF of dogs with IPF.     

Use of genomic recursions and algorithm for proven and young animals for single‐step genomic BLUP analyses – a simulation study

The purpose of this study was to examine accuracy of genomic selection via single‐step genomic BLUP (ssGBLUP) when the direct inverse of the genomic relationship matrix ( G ) is replaced by an approximation of G ^?1 based on recursions for young genotyped animals conditioned on a subset of proven animals, termed algorithm for proven and young animals (APY). With the efficient implementation, this algorithm has a cubic cost with proven animals and linear with young animals. Ten duplicate data sets mimicking a dairy cattle population were simulated. In a first scenario, genomic information for 20k genotyped bulls, divided in 7k proven and 13k young bulls, was generated for each replicate. In a second scenario, 5k genotyped cows with phenotypes were included in the analysis as young animals. Accuracies (average for the 10 replicates) in regular EBV were 0.72 and 0.34 for proven and young animals, respectively. When genomic information was included, they increased to 0.75 and 0.50. No differences between genomic EBV (GEBV) obtained with the regular G ^?1 and the approximated G ^?1 via the recursive method were observed. In the second scenario, accuracies in GEBV (0.76, 0.51 and 0.59 for proven bulls, young males and young females, respectively) were also higher than those in EBV (0.72, 0.35 and 0.49). Again, no differences between GEBV with regular G ^?1 and with recursions were observed. With the recursive algorithm, the number of iterations to achieve convergence was reduced from 227 to 206 in the first scenario and from 232 to 209 in the second scenario. Cows can be treated as young animals in APY without reducing the accuracy. The proposed algorithm can be implemented to reduce computing costs and to overcome current limitations on the number of genotyped animals in the ssGBLUP method.