Effect of dietary Moringa stem meal level on growth performance,slaughter performance and serum biochemical parameters in geese

Moringa stem meal (MSM) with a high level of crude fibre (CF) might be developed and utilized in herbivorous geese as an unconventional feedstuff. The aim of this study was to investigate the effect of the MSM level in the diet on the growth performance, slaughter performance, breast meat quality and serum biochemical parameters in geese from 22 to 70 days of age. A one-factor completely randomized design was adopted in our study. A total of one thousand eight 21-day-old geese were randomly divided into six groups, with six replicates per group and 28 birds per replicate. The geese were fed diets containing MSM levels of 0, 20, 40, 60, 80 or 100 g/kg during day 22–70. The dietary MSM level had no effect (p > .05) on the final body weight (BW), average daily gain (ADG) or average daily feed intake (ADFI). The feed/gain ratio (F/G) increased linearly (p < .001) as the dietary MSM level increased. No differences (p > .05) were observed in the slaughter performance, meat quality and the relative organ weight (except for thymus) of the geese (p > .05). The relative weight of the thymus in the geese fed diets with supplementation of MSM was higher than that in the non-supplemented MSM control group (p < .05). In addition, 100 g MSM/kg of diet decreased the serum glucose (GLU) level (p < .05) and increased the alanine transaminase (ALT) enzyme activity (p = .03). Dietary MSM levels of no more than 60 g/kg had no effects on the growth performance and slaughter performance, whereas diets with 100 g MSM/kg increased the F/G and serum ALT enzyme activity, as well as decreasing the serum GLU level. Therefore, MSM provided at a reasonable level could be developed as an unconventional feedstuff for geese at the finisher period.     

结球甘蓝抽薹性遗传规律和QTL定位分析

以早抽薹‘S-1’与晚抽薹‘G-1’为亲本，构建F1、BC1、BC2和F2群体，采用植物数量性状主基因 + 多基因混合遗传模型分析法对结球甘蓝抽薹性状进行遗传分析，并采用SLAF-BSA方法对抽薹时间进行QTL定位分析。结果显示，结球甘蓝抽薹性状是由2对加性—显性—上位性主基因 + 加性—显性多基因遗传控制；主基因 + 多基因平均遗传率是93.41%。共检测到2个QTL，分别为2号染色体上的2.31 ~ 3.09 Mb和33.57 ~ 34.40 Mb，总长度为1.61 Mb。     

卷丹转基因体系构建及岷江百合LrCCoAOMT的导入

以卷丹（Lilium lancifolium Thunb.）无菌小鳞片为试材，通过切片处理、优化农杆菌侵染浓度与时间以及重悬液和共培养基成分，构建农杆菌介导的高效遗传转化体系。结果表明，MS + 1.5 mg ? L-1 6-BA + 0.5 mg ? L-1 NAA + 30 g ? L-1蔗糖是切片芽分化的最佳培养基，添加100 mg ? L-1抗坏血酸能有效抑制褐化并促进不定芽增殖。MS + 2.0 mg ? L-1 NAA + 30 g ? L-1蔗糖是生根诱导的最佳培养基。抗生素敏感性测试发现，培养基中添加Kan 100 mg ? L-1或Hyg 75 mg ? L-1 结合Cef 400 mg ? L-1适宜抗性筛选。GUS染色分析表明，以去除大量元素的改良MS + 100 μmol ? L-1 AS和去除大量元素的改良MS + 1.0 mg ? L-1 6-BA + 1.0 mg ? L-1 NAA + 100 μmol ? L-1 AS为重悬液和共培养基，将切片在农杆菌菌液浓度OD600为0.4，侵染15 min，获得81.72% 瞬时转化率和25.2% 稳定遗传转化率。将岷江百合LrCCoAOMT转化卷丹，分子检测和GUS染色分析表明已获得转基因阳性株系。     

杉木全同胞家系的遗传分析及重要树冠因子的筛选

通过对5年生杉木6×6全双列杂交(Griffing Ⅲ)试验林的调查与分析，研究杉木若干性状的遗传与变异规律，并筛选决定理想株型的重要树冠结构因子，为杂交育种和杉木理想株型无性系选育提供科学依据。运用转化分析法来处理不平衡试验数据，结合配合力分析法、相关分析法和通径分析法,研究杉木遗传变异、基因作用方式和重要树冠因子的选择。通过对杉木若干性状进行研究，获得了不同性状遗传变异大小。结果表明，杉木材积、枝下高和饱满度等性状具有中度遗传变性，树高、胸径具有窄的遗传变异性，同时杉木改良性状具有中度以上的遗传力，因此杉木生长形质性状宜采用连续多世代改良；在杉木生长、形质性状和树冠结构性状中，12个研究性状有8个重要性状的基因作用方式以非加性遗传基因效应为主，杉木高世代育种亲本是多世代与高强度选择的产物,杂合体居多,杉木的多性状、高世代遗传改良以杂交育种为主；生长、形质和树冠结构性状间具有明显的遗传相关性，通径分析揭示:决定杉木理想株型育种中重要树冠结构性状是冠形率和树冠表面积大小。由此可见:杉木多性状、多世代连续改良以杂交育种为主，杉木无性系理想株型选择时，应注重冠形率和树冠表面积的作用。     

荒漠草原不同时间尺度下垫面水分消耗与气象植被因子的关系

利用大型称重式蒸渗仪2012—2018年连续观测数据，系统解析了希拉穆仁荒漠草原植物生长季（4—10月）不同时间尺度蒸散发（ET)与气象植被因子之间的关系。研究表明：（1）小时、日和月尺度上，始终与ET保持高度相关的气象植被因子包括风速（P<0.01）、空气温度（P<0.01）和降水量（P<0.01）；（2）结合不同时间尺度主控因子，利用多元回归定量表征了小时、日、月尺度的下垫面ET变化特征，经验方程决定系数分别为0.94（P<0.01）、0.42（P<0.01）、0.82（P<0.01），小时和月尺度上的经验方程可较好地反映希拉穆仁荒漠草原下垫面耗水特征；（3）ET与降水差值显示，2012—2018年希拉穆仁荒漠草原植物生长季（4—10月）水汽交换以下垫面水分消耗为主，8月份发生干旱事件的概率最大。     

Precipitation forecasting by large-scale climate indices and machine learning techniques

Global warming is one of the most complicated challenges of our time causing considerable tension on our societies and on the environment. The impacts of global warming are felt unprecedentedly in a wide variety of ways from shifting weather patterns that threatens food production, to rising sea levels that deteriorates the risk of catastrophic flooding. Among all aspects related to global warming, there is a growing concern on water resource management. This field is targeted at preventing future water crisis threatening human beings. The very first stage in such management is to recognize the prospective climate parameters influencing the future water resource conditions. Numerous prediction models, methods and tools, in this case, have been developed and applied so far. In line with trend, the current study intends to compare three optimization algorithms on the platform of a multilayer perceptron (MLP) network to explore any meaningful connection between large-scale climate indices (LSCIs) and precipitation in the capital of Iran, a country which is located in an arid and semi-arid region and suffers from severe water scarcity caused by mismanagement over years and intensified by global warming. This situation has propelled a great deal of population to immigrate towards more developed cities within the country especially towards Tehran. Therefore, the current and future environmental conditions of this city especially its water supply conditions are of great importance. To tackle this complication an outlook for the future precipitation should be provided and appropriate forecasting trajectories compatible with this region's characteristics should be developed. To this end, the present study investigates three training methods namely backpropagation (BP), genetic algorithms (GAs), and particle swarm optimization (PSO) algorithms on a MLP platform. Two frameworks distinguished by their input compositions are denoted in this study: Concurrent Model Framework (CMF) and Integrated Model Framework (IMF). Through these two frameworks, 13 cases are generated: 12 cases within CMF, each of which contains all selected LSCIs in the same lead-times, and one case within IMF that is constituted from the combination of the most correlated LSCIs with Tehran precipitation in each lead-time. Following the evaluation of all model performances through related statistical tests, Taylor diagram is implemented to make comparison among the final selected models in all three optimization algorithms, the best of which is found to be MLP-PSO in IMF.     

新单系列玉米品种的遗传基础解析以及品质和产量性状变化趋势分析

为探索新单系列玉米品种籽粒品质性状之间的关系以及产量性状的变化趋势,以新乡市农科院2003年以来审定的玉米品种为材料,研究了它们的遗传基础、杂优模式、品质性状以及群体、个体生产力。结果表明：13个玉米品种利用的杂优模式主要有2种,9个品种杂优模式为改良Reid×唐四平头,4个品种为外来种质×唐四平头。品质分析中,‘新单28’、‘新单38’、‘新单66’粗淀粉含量高,‘新单61’、‘新单65’和‘新单68’的粗蛋白和赖氨酸含量高。粗脂肪含量和粗淀粉含量之间呈显著负相关,相关系数为0.610,粗蛋白含量和赖氨酸含量之间呈显著正相关,相关系数为0.491。产量分析表明,‘新单22’单株生产力最大,‘新单33’单株生产力最小,‘新单58’的群体生产力最大,‘新单28’的群体生产力最小。群体生产力随密度增加呈现上升趋势,其中密度为75000株/hm²时的群体生产力较45000株/hm²高62.9%,平均每公顷增加7500株产量增益为12.97%。单株生产力随密度增加开始呈下降趋势,增至60000株/hm²后逐渐平缓,低密度时单株生产力较高密度高5.5%。外来种质×唐四平头杂优模式选育的玉米新品种在品质上优于改良Reid×唐四平头模式选育的品种,且耐密性也显著增加,群体生产力也不断上升。     

Genetic diversity and aggressiveness of Calonectria pteridis in Eucalyptus spp.

Calonectria leaf blight, caused by Calonectria pteridis, is currently one of the main foliar diseases in eucalypt plantations in Brazil. In warm and high rainfall regions, the disease can be a limiting factor for eucalypt production when planting susceptible genotypes. The most effective method for controlling this disease in the field is the use of resistant genotypes, which requires knowledge of the genetic variability and aggressiveness of the pathogen population for effective deployment of plant resistance. This work evaluated the genetic diversity and aggressiveness of C. pteridis populations obtained from infected eucalypt plants in Monte Dourado (Pará state) and Imperatriz (Maranhão state), Brazil. To study the genetic diversity, 16 ISSR primers were tested, five of which amplified polymorphic, reproducible and informative bands. Thirty-one closely related genotypes were identified from 84 isolates studied, indicating that the population has a low genetic diversity. The aggressiveness of seven isolates, selected according to geographic origin and their clustering in the ISSR-based dendogram, was determined by inoculation of a hybrid Eucalyptus grandis × E. urophylla clone under controlled conditions. Disease severity was assessed by both measuring the percentage of plant defoliation and assigning a score according to a diagrammatic scale of symptoms. A high correlation between the two evaluation methods was observed, which revealed significant differences in aggressiveness among the isolates. The diagrammatic scale is recommended for disease evaluation because results are obtained much faster, before the occurrence of severe defoliation. No correlation between clustering in the ISSR-based phylogenetic analysis and aggressiveness was observed.