The effects of density and artificial substrate on intensive shrimp Litopenaeus vannamei nursery production

Recirculating aquaculture systems (RAS) can be installed indoors, allowing year-round production of tropical animals in nearly any climate. A nursery phase is commonly used in Litopenaeus vannamei production since it allows for enhanced biosecurity and better quantification of animals while reducing space requirements. However, it is unclear whether animal density and inclusion of artificial substrate may improve shrimp performance during the nursery phase. In this experiment, we compared shrimp production parameters in two stocking densities with or without the use of an artificial substrate by creating four treatments: low-density LD; 1500 PL/m⁻³, low-density with substrate LDS, high-density HD; 3000 PL/m⁻³), and high-density with substrate (HDS). The LDS and HDS treatments included 0.46-m² of high-density polyethylene 2.5-cm mesh as a substrate, which increased the tank surface area by 21 %. Each treatment was randomly assigned to four 160-l culture tanks, each with a biofilter. The shrimp had an initial weight of 4 mg and were grown for 50 days. The low-density treatments had significantly higher dissolved oxygen (DO) and pH than the high-density treatments (P ≤ 0.001). Specifically, LDS had the highest DO and pH followed by the LD, HD, and HDS treatments, respectively. High-density treatments had significantly higher NO₂-N levels than low-density treatments during week 2 of the experiment when an unusually high concentration of nitrite was observed. FCR was significantly lower in both low-density treatments than in high-density treatments. At harvest, the total biomass (kg m⁻³) was significantly higher in high-density treatments than in low-density treatments (P ≤ 0.001), and the HDS treatment had a significantly greater biomass output than HD. Producers should consider artificial substrate and higher densities during nursery production to maximize shrimp production; however, the effects on water quality should also be taken into account.     

基于BP神经网络的人机联合率定SWMM研究

为解决雨洪管理模型(SWMM)在率定过程中参数复杂、过程繁琐等问题。以西宁市某地块为例建立SWMM模型,利用Morris筛选法进行参数灵敏度分析,并根据灵敏度分析的结果进行人工率定;另外利用BP神经网络对模型进行率定,并结合参数灵敏度对其进行优化。对3种率定方案进行分析,结果表明:水文水力模块参数的相对灵敏度基本一致,其中灵敏度较大的参数为子汇水区面积(Area)、不透水率(Imperv)和不透水区洼地蓄积量(Destore-Imperv),并且不同降雨条件下模型参数的灵敏度存在差异。经过优化后的BP神经网络参数率定方法的模型模拟效果最好,纳什系数最大。结合灵敏度优化BP神经网络的人机联合率定方法一方面能提高BP神经网络率定的准确性,另一方面又能提高传统人工率定的效率。     

国内杜鹃属植物组培快繁技术的研究进展

本文从外植体选择、外植体的灭菌方式、基本培养基的选择、诱导培养基的选择、生根培养基的选择、生根苗的移栽这几个方面归纳总结了国内有关杜鹃属植物组培快繁应用的研究进展,并提出了目前存在问题与发展利用前景。     

运用植被指数时序特征对落叶松人工林分类

为探究植被指数时序特征是否有利于落叶松人工林提取,以孟家岗林场为研究试验区域,根据落叶松人工林季相和物候特征,利用Landsat8OLI影像数据提取研究区内5种植被的归一化植被指数(I NDV)、差值植被指数(I DV)、比值植被指数(I RV)、增强型植被指数(I EV),构建相应的植被指数时序特征。采用最大似然和随机森林两种方法对单一时相影像和加入植被指数时序特征的影像进行对比试验。结果表明:影像中加入植被指数时序特征后,最大似然算法的分类总体精度为89.53%,Kappa系数为0.87,比单一时序特征的影像分类精度提高了13.35%;随机森林算法的森林类型分类总体精度为93.22%,Kappa系数为0.92,比单一时序特征的影像分类精度提高了19.8%。因此,加入植被指数时序特征后能得到更高的落叶松人工林提取精度。     

蝴蝶兰‘大辣椒’组培快繁技术体系的优化

探索优化蝴蝶兰组培快繁技术体系，以蝴蝶兰‘大辣椒’半木质化花梗为试验材料，研究消毒剂、基础培养基、植物生长调节剂配比及浓度对蝴蝶兰不定芽诱导、增殖和生根过程的影响。结果表明:1）先用2% H₂O₂溶液处理15 min，再用10% NaClO溶液处理10 min，消毒效果良好，花梗污染率低，为26.6%；2）不定芽诱导最适培养基为花宝1号+花宝2号+ 6-BA 3 mg/L+NAA 0.05 mg/L，出芽率为78.9%；3）不定芽增殖最适培养基为花宝1号+6-BA 5 mg/L+NAA 0.05 mg/L，增殖系数为3.60；4）幼苗生根最适培养基为1/2MS+NAA 0.2mg/L，生根率为73.7%。本研究旨在优化‘大辣椒’品种的组培快繁技术体系，为进一步建立蝴蝶兰再生体系和工厂化生产提供技术支持。     

番茄斑萎病毒病抗性种质筛选及人工接种鉴定方法优化

对154份番茄材料(包括普通番茄76份,樱桃番茄78份)进行两年两次田间番茄斑萎病毒病的病情指数调查,筛选出14份对番茄斑萎病毒病具有稳定抗性的番茄材料,利用sw-5-2共显性SCAR标记对田间表现抗病的材料进行分子标记鉴定,发现3份抗病材料携带抗番茄斑萎病毒病的Sw-5基因。为缩短番茄斑萎病毒病人工接种鉴定周期,以含有sw-5的抗病材料H8和感病材料M82为研究对象,设置4、6、8、10片真叶4个接种时期,分别在接种后14、21、28 d进行病情指数调查和抗性分级,结果表明,6片真叶期接种,接种后28 d进行病情调查即可有效鉴别植株番茄斑萎病毒病抗性,与8、10片真叶期接种效果相同,人工接种抗病性鉴定效率显著提升。     

Prediction of baking results from farinograph measurements by using stepwise linear regression and artificial neuronal networks

In an effort to extract additional data from farinograph experiments a model was developed to simulate the measurements and correlate the parameters of the model with results from baking tests. This additional information can be used in bakeries to predict the baking properties of the flours and adjust the recipes to maintain a constant product quality. For this eight different flours were characterized with a farinograph and 13 different results from baking experiments. An approach with five nonlinear differential equations was able to model the farinograph measurements very well (average R² = 0.995 ± 0.005). While a stepwise multilinear regression only showed weak correlations in cross validation between a single parameter of the model and the baking volume (R² = 0.745) and the volume yield (R² = 0.796) respectively, the artificial neuronal network was more successful. For the baking weight (R² = 0.926), the dough yield gross (R² = 0.909) and net (R² = 0.913) strong correlations were found. A good correlation for the baking volume (R² = 0.853) was also determined, while the volume yield showed comparable results to the linear regression (R² = 0.792).     

Artificial neural network modeling of microbial community structures in the Atlantic Forest of Brazil

Microbial communities vary across the landscape in forest soils, but prediction of their biomass and composition is a difficult challenge due to the large numbers of variables that influence their community structures. Here we examine the use of artificial neural network (ANN) models for extraction of patterns among soil chemical variables and microbial community structures in forest soils from three regions of the Atlantic Forest of Brazil. At each location, variations in soil chemical properties and FAME profiles of microbial community structures were mapped at 20 × 20 m intervals within 10 ha parcels. Geostatistical analyses showed that spatial variability in soil physical and chemical variables could be mapped at scale distances of 20 m, but that FAME profiles representing the microbial communities were highly variable and had no spatial dependence at the same scale in most cases. RDA analysis showed that FAME signatures representing different microbial groups were positively associated with soil pH, OM, P and base cations concentrations, whereas microbial biomass was negatively associated with the same environmental factors. In contrast, ANN models revealed clear relationships between microbial community structures at each parcel location, and generated verifiable predictions of variations in FAME profiles in relation to soil pH, texture, and the relative abundances of base cations. The results suggest that ANN modeling provides a useful approach for describing the relationships between microbial community structures and soil properties in tropical forest soils that were not able to be captured using geostatistical and RDA analyses.     

插条粗度和萌芽度对降香黄檀扦插成活率的影响

在江门市林科所苗圃选择不同粗细度、不同萌芽度的降香黄檀插条进行扦插繁殖试验,每10 d进行1次成活率调查。结果表明:插条的萌芽度和粗细度均极显著影响扦插的成活率,萌芽度和粗细度的交互作用对扦插的成活率也有极显著影响;未萌芽插条扦插成活率明极显高于已萌芽插条的扦插成活率;3种粗细度的枝条中以粗细度为0.7~1.2 cm的插条扦插成活率最高;在枝条萌芽度及粗细度交互作用下,粗细度为0.7~1.2 cm的未萌芽插条扦插成活率最高,粗细度小于0.7 cm的已萌芽插条扦插成活率最低。     

Polymerisation of gluten proteins in developing wheat grain as affected by desiccation

The breadmaking quality of wheat is affected by the composition of gluten proteins and the polymerisation of subunits that are synthesised and accumulated in developing wheat grain. The biological mechanisms and time course of these events during grain development are documented, but not widely confirmed. Therefore, the aim of this study was to monitor the accumulation of gluten protein subunits and the size distribution of protein aggregates during grain development. The effect of desiccation on the polymerisation of gluten proteins and the functional properties of gluten were also studied. The results showed that the size of glutenin polymers remained consistently low until yellow ripeness (YR), while it increased during grain desiccation after YR. Hence, this polymerisation process was presumed to be initiated by desiccation. A similar polymerisation event was also observed when premature grains were dried artificially. The composition of gluten proteins, the ratios of glutenin to gliadin and high molecular weight-glutenin subunits to low molecular weight-glutenin subunits, in premature grain after artificial desiccation showed close association with the size of glutenin polymers in artificially dried grain. Functional properties of gluten in these samples were also associated with polymer size after artificial desiccation.