3种细菌芽胞遗传同源性及抗力对比研究

比较细菌芽胞的遗传同源性、结构和抗力差异,为炭疽芽胞的替代试验菌的可靠评价提供依据。采用资料检索、生物信息分析、显微镜观察和微生物学技术分析不同芽胞的遗传同源性、超微结构和抗力差异。炭疽芽胞与腊样芽胞的结构和大小相似,生物遗传同源性最近,对热力、UVC和有效氯的抗力相近。腊样芽胞对炭疽芽胞的代表性最好,可以倾向性选用其替代炭疽芽胞进行试验研究。     

三种光温模型在模拟水稻生育期中的应用与比较

为准确模拟水稻发育进程,本研究根据水稻发育对光温的反应,以双季早稻‘五优157’及双季晚稻‘优选10号’为试材,于2012—2013年在南京信息工程大学农业气象试验站设计水稻大田分期播种试验,同时收集江西省南昌站点2002—2008年双季早稻‘G98-202’以及南康站点2002—2008年双季晚稻‘金优207’的生育期观测数据及同期气象数据,校正光温效应模型、水稻钟模型和有效积温模型以获取模型参数,用独立数据对模型进行检验。结果表明:光温效应模型在模拟水稻播种期-拔节期、拔节期-抽穗期、抽穗期-灌浆期、灌浆期-成熟期的预测值与实测值基于1-1线的决定系数(R2)分别为0.994、0.994、0.988、0.989,且标准回归估计误差(RMSE)均小于4 d;光温效应模型对水稻品种‘五优157’、‘G98-202’、‘优选10号’、‘金优207’从播种期-成熟期的模拟精度比有效积温分别提高了2.42%、4.55%、8.74%和3.16%。研究认为,光温效应模型在模拟江西早稻和晚稻的生育期精度略高于水稻钟模型,有效积温模型精度最差,本研究结果为水稻生育期预测方法的选择提供了参考。     

芽胞抗力快速弱化技术研究

以L-丙氨酸缓冲液为发芽剂,结合芬顿反应原理,观察发芽-氧化损伤效应对芽胞的杀灭效果,以期为新型炭疽疫源地净化方法的深入研究奠定基础。以腊样芽胞为试验菌,采用透射电镜、激光扫描共聚焦显微镜、活菌计数等方法观察芽胞发芽过程的超微结构、核酸含量变化,以及在芬顿反应的联合作用下发芽体的活性变化。在20~30 min的发芽过程中,芽胞核心密度降低,核心与皮质、皮质与外壁之间界限模糊,芽胞外壁和芽胞衣有破裂,通透性增加,进一步有皮质消失、细胞核与细胞质融合、细胞膜基本形成的现象;发芽体荧光强度不断增加,显示菌体中核酸的活性和含量不断增加;发芽体对化学因子的抗力明显下降,H2O2浓度为0.20 mol/L的Fenton反应系统作用60 min时,发芽体灭活可达到3.016个对数级。诱导发芽和反应的联合处理程序可显著提高芽胞的灭活水平。     

环介导等温扩增技术在快速检测炭疽芽胞杆菌中的应用

本文旨在建立适合国境口岸现场应用的生物恐怖防控快速检测方法,从而保障口岸安全.针对生物恐怖炭疽芽胞杆菌,选择目标菌种特异性基因片段,设计引物,运用环介导等温扩增(LAMP)技术建立一套简便、高效的检测方法,并模拟生物恐怖炭疽芽胞杆菌可能存在的基质条件,评价LAMP技术在快速筛查中的适用性.结果显示,LAMP技术排查生物恐怖炭疽芽胞杆菌简便、快速、特异,检测灵敏度为102～103 CFU/ml;且能有效检出在偏酸、偏碱及黏稠基质中的炭疽芽胞杆菌.而高盐环境对该反应影响较大,有必要采用能有效去除盐分的核酸抽提方法.     

类似S-层蛋白的苏云金芽胞杆菌伴胞晶体蛋白基因的克隆

芽胞杆菌CTC菌被鉴定为苏云金芽胞杆菌,鞭毛血清型H2,幕虫亚种;产生卵圆形伴胞晶体,伴胞晶体蛋白为100kD;测定了该蛋白 N－末端序列,该序列与炭疽芽胞杆菌的细胞表面S－层蛋白具92－93％相似性,根据Southern杂交制作了该晶体蛋白基因ctc所在位置的限制性酶切图谱,分别克隆了该基因5’和3’端所在2．9kb XbaI片段和3．1kb Cla I DNA片段,彼此间具0．6kb重叠,通过拼接获得含完整ctc基因的克隆,含该基因的大肠杆菌与表达S－层蛋白的大肠杆菌具相似生长特征,初步表明CTC菌株的伴胞晶体由细胞表面S－层蛋白组成,苏云金芽胞杆菌区别于蜡状芽胞杆菌和炭疽芽胞杆菌的唯一标准是能形成伴胞晶体,由于S－层是细胞表面的结构成分,本文对CTC菌株鉴定为苏云金芽胞杆菌以及伴胞晶体作为苏云金芽胞杆菌鉴别的唯一标准提出了质疑。     

应用BP神经网络对荒漠啮齿动物种群数量的预测研究

群落的格局与动态是群落生态学研究的核心内容,种群数量预测是研究群落动态的主要途径之一。本研究尝试采用2006~2014年阿拉善荒漠区啮齿动物数量数据建立BP神经网络模型,对啮齿动物群落全部组成物种的总个体数量进行模拟与预测。BP神经网络通过模拟学习,建立模型,能够实现对啮齿动物群落数量动态规律进行模拟与预测。本研究以阿拉善荒漠为试验区,以啮齿动物个体数量为研究对象,采用标志重捕法,监测2006~2014年每年4~10月的数量,建立BP神经网络预测模型,利用2006~2013年的数据建立训练网络,以2014年的数据进行验证与测试,比较单层隐含层、双层隐含层和三层隐含层BP神经网络模型。结果表明:单隐含层模型的隐含层节点数为6时,最大误差百分比为16.13%,决定系数0.998 0(P=0.006 0)。双隐含层模型的两层隐含层节点数均为6时,最大误差百分比为8.58%,决定系数0.999 5(P=0.002 3)。三层隐含层模型的三层隐含层节点数分别为1、10和7时,最大误差百分比为5.87%,决定系数0.999 2(P0.000 1)。不同隐含层网络模型的预测效果均取得了满意效果,通过比较最大误差百分比、平均误差百分比、决定系数及拟合优度,三层隐含层优于单隐含层及双隐含层的BP神经网络模型。本文认为三层隐含层的BP神经网络模型更适合于阿拉善荒漠区啮齿动物群落全部组成物种的总个体数量的预测研究。     

基于文献挖掘的巨大芽胞杆菌代谢网络模型的构建与分析

【目的】通过挖掘实验性文献,建立巨大芽胞杆菌事实型代谢网络模型,以详尽解析生理特性,优化其生理功能。【方法】从PubMed、Derwent Innovations Index、中国知网等公共文献(专利)数据库中获取与巨大芽胞杆菌(Bacillus megaterium)相关的实验性文献建立本地文献数据库。采用文献挖掘工具获取功能基因、酶、代谢物和生化反应等信息,以其为基础构建代谢网络粗模型,进一步借助KEGG等数据库修正以及Matlab程序的模拟得到精细模型(系统生物学标记语言的形式)。【结果】最终的精细模型共有292个生化反应、378个代谢物、220个酶和217个基因。以1.62 mmol/g cell/h的葡萄糖底物吸收速率为限制性条件,模拟的菌体比生长速率为0.089 h-1,略低于实验值0.11 h-1。此外,嘧啶代谢途径的单基因敲除模拟结果表明,准确率为90%。【结论】该代谢网络模型涵盖了中心代谢途径、维生素B12合成途径和氨基酸代谢途径,并在一定程度上反映了营养底物与基因对巨大芽胞杆菌生长性能的影响。     

地衣芽胞杆菌dif序列的功能鉴定

地衣芽胞杆菌是重要的工业菌株,如何为其建立一种有效的基因删除技术是对该菌株进行遗传改良的基础。枯草芽胞杆菌difB8序列已经被成功用于枯草芽胞杆菌多基因的删除。在分析地衣芽胞杆菌基因组序列并获得与枯草芽胞杆菌difB8以。序列十分相似的一段序列difBLi的基础上,构建了在庆大霉素抗性基因两侧具有difBLi的重组质粒pMD19-difGm和pHY-XI’：：difGm,通过电击转化法将质粒pHY-XI’：：difGm导入B．1icheniformis ATCC14580中,筛选获得了具有庆大霉素抗性的转化子。在转化子的传代过程中,重组质粒的庆大霉素抗性基因在体内Xer／dif／f位点特异性重组系统的介导下通过其侧翼的dif位点进行同源重组而被准确删除。确证了地衣芽胞杆菌中dif序列的功能,为地衣芽胞杆菌基因组中多基因的删除提供了一种新的实验途径。     

样本量与研究范围变化对MaxEnt模型准确度的影响——以黑白仰鼻猴为例

MaxEnt模型是过去几年最为流行的物种分布预测模型之一。针对一些濒危物种、入侵种和模拟数据的研究表明,MaxEnt模型均能在小样本的分布数据下得到较准确的预测结果。此外,研究范围的变化也会影响MaxEnt模型的构建。 然而,基于动物的实际分布数据来评估MaxEnt模型的研究甚少。 我们以黑白仰鼻猴 (Rhinopithecus bieti)为例,以11个猴群的分布数据为训练数据（样本量从1到10个猴群）,在不同研究范围内构建MaxEnt模型,通过其它5个的猴群分布数据验证,分析样本量和研究范围变化对模型准确度产生的影响。 结果表明,随样本量和研究范围增大,MaxEnt模型准确度及稳定性都有增加。 此外,研究范围变化对模型准确度有一定影响。 应用Maxent进行物种分布预测时,训练数据应尽可能涵盖该物种可能出现的全部环境梯度。构建模型所需的背景数据点选择,应与建模使用的物种出现点形成有效对照。     

以CotX为分子载体在枯草芽胞杆菌芽胞表面展示绿色荧光蛋白

芽胞衣壳蛋白CotB、CotC、CotG等可作为芽胞表面展示外源蛋白的分子载体,制备口服重组疫苗或具有催化活性的重组酶。CotX为枯草芽胞杆菌Bacillussubtilis芽胞衣壳中的另一种结构蛋白。为证明CotX能否作为分子载体将外源蛋白展示在芽胞表面,本研究将cotX基因与绿色荧光蛋白基因gfp的编码序列进行基因重组,构建融合表达CotX-GFP的整合型重组质粒,将该质粒转化枯草芽胞杆菌,筛选重组菌株并诱导产生芽胞,观察到重组芽胞表面具有GFP绿色荧光。结果表明枯草芽胞杆菌的芽胞衣壳蛋白CotX位于芽胞衣壳外层,可作为芽胞表面展示外源蛋白的载体分子。     

Artificial neural network models of relationships between <Emphasis Type="Italic">Alternaria</Emphasis> spores and meteorological factors in Szczecin (Poland)

Alternaria is an airborne fungal spore type known to trigger respiratory allergy symptoms in sensitive patients. Aiming to reduce the risk for allergic individuals, we constructed predictive models for the fungal spore circulation in Szczecin, Poland. Monthly forecasting models were developed for the airborne spore concentrations of Alternaria, which is one of the most abundant fungal taxa in the area. Aerobiological sampling was conducted over 2004–2007, using a Lanzoni trap. Simultaneously, the following meteorological parameters were recorded: daily level of precipitation; maximum and average wind speed; relative humidity; and maximum, minimum, average, and dew point temperature. The original factors as well as with lags (up to 3 days) were used as the explaining variables. Due to non-linearity and non-normality of the data set, the modelling technique applied was the artificial neural network (ANN) method. The final model was a split model with classification (spore presence or absence) followed by regression for spore seasons and log(x+1) transformed Alternaria spore concentration. All variables except maximum wind speed and precipitation were important factors in the overall classification model. In the regression model for spore seasons, close relationships were noted between Alternaria spore concentration and average and maximum temperature (on the same day and 3 days previously), humidity (with lag 1) and maximum wind speed 2 days previously. The most important variable was humidity recorded on the same day. Our study illustrates a novel approach to modelling of time series with short spore seasons, and indicates that the ANN method provides the possibility of forecasting Alternaria spore concentration with high accuracy.     

一种展示SARS冠状病毒受体结合区的重组枯草杆菌芽孢的制备及免疫原性分析

目的：利用枯草杆菌芽孢呈递技术制备表达SARS冠状病毒S蛋白受体结合区（RBD）的重组芽孢。方法：将枯草杆菌 CotB 基因构建到基因组整合质粒pDG1664中,再将 RBD 基因连接到 CotB 基因的下游,构建成重组质粒pDG1664-CotB-RBD,通过同源重组整合到PY-79枯草杆菌基因组中;利用红霉素抗性筛选重组菌并进行PCR和DNA测序鉴定,Western印迹鉴定重组菌芽孢表面RBD蛋白的表达情况;用表达RBD的重组芽孢以口服方式免疫小鼠,通过ELISA和流式细胞术检测重组芽孢的免疫原性。结果：制备出枯草杆菌基因组整合了RBD抗原基因的重组菌株RS1931,形成的重组芽孢表达相对分子质量约62×103的CotB-RBD融合蛋白;重组芽孢免疫的小鼠血清RBD抗原特异性IgG抗体滴度在末次免疫后2周可达1∶10880,重组芽孢初免后18周的小鼠脾细胞中IFN-γ＋CD4^＋、IL-4＋CD4^＋和IFN-γ＋CD8^＋T细胞比例上调,表明重组芽孢经口服免疫产生良好的体液免疫和细胞免疫应答。结论：针对SARS冠状病毒S蛋白RBD建立了枯草杆菌芽孢呈递技术方法,制备出在枯草杆菌芽孢表面稳定表达外源RBD蛋白的重组株,获得的重组芽孢具有良好的免疫原性,为开发芽孢呈递型SARS疫苗奠定了基础。     

基于人工神经网络-遗传算法的樟芝发酵培养基优化

采用优化模型对药用丝状真菌樟芝的复杂发酵过程进行建模,并获得最优发酵培养基组成.对樟芝发酵过程中的形态变化过程进行了观察,并分别采用人工神经网络(ANN)和响应面法(RSM)对樟芝发酵过程进行建模,同时采用遗传算法(GA)优化了发酵培养基组成.结果表明,ANN模型比RSM模型具有更好的实验数据拟合能力和预测能力,GA计算得到樟芝生物量理论最大值为6.2 g/L,并获得发酵最佳接种量及培养基组成:孢子浓度1.76× 105个/mL,葡萄糖29.1 g/L,蛋白胨9.4 g/L,黄豆粉2.8 g/L.在最佳培养条件下,樟芝生物量为(6.1±0.2)g/L.基于ANN-GA的优化方法可用于优化其他丝状真菌的复杂发酵过程,从而获得生物量或活性代谢产物.     

运用BP人工神经网络预测长江中下游梨黑星病发病的研究

提出了运用人工神经网络技术进行梨黑星病预测的新思路,并以梨黑星病发病的主要影响因素,即上年7月的降水量和上年8月的降水量作为训练样本模式提供给网络,按照误差逆传播网络的学习规则对网络进行训练,经过计算机2844次学习后,网络达到预先给定的收敛标准,使网络具备了预测梨树黑星病流行趋势和流行强度的功能。检验结果表明,该方法性能良好,预测准确率高,可望成为果树病早害预测预报的有效辅助手段。     

An Approach to Improve the Performance of PM Forecasters

The particulate matter (PM) concentration has been one of the most relevant environmental concerns in recent decades due to its prejudicial effects on living beings and the earth’s atmosphere. High PM concentration affects the human health in several ways leading to short and long term diseases. Thus, forecasting systems have been developed to support decisions of the organizations and governments to alert the population. Forecasting systems based on Artificial Neural Networks (ANNs) have been highlighted in the literature due to their performances. In general, three ANN-based approaches have been found for this task: ANN trained via learning algorithms, hybrid systems that combine search algorithms with ANNs, and hybrid systems that combine ANN with other forecasters. Independent of the approach, it is common to suppose that the residuals (error series), obtained from the difference between actual series and forecasting, have a white noise behavior. However, it is possible that this assumption is infringed due to: misspecification of the forecasting model, complexity of the time series or temporal patterns of the phenomenon not captured by the forecaster. This paper proposes an approach to improve the performance of PM forecasters from residuals modeling. The approach analyzes the remaining residuals recursively in search of temporal patterns. At each iteration, if there are temporal patterns in the residuals, the approach generates the forecasting of the residuals in order to improve the forecasting of the PM time series. The proposed approach can be used with either only one forecaster or by combining two or more forecasting models. In this study, the approach is used to improve the performance of a hybrid system (HS) composed by genetic algorithm (GA) and ANN from residuals modeling performed by two methods, namely, ANN and own hybrid system. Experiments were performed for PM_2.5 and PM₁₀ concentration series in Kallio and Vallila stations in Helsinki and evaluated from six metrics. Experimental results show that the proposed approach improves the accuracy of the forecasting method in terms of fitness function for all cases, when compared with the method without correction. The correction via HS obtained a superior performance, reaching the best results in terms of fitness function and in five out of six metrics. These results also were found when a sensitivity analysis was performed varying the proportions of the sets of training, validation and test. The proposed approach reached consistent results when compared with the forecasting method without correction, showing that it can be an interesting tool for correction of PM forecasters.     

Changes in concentration of Alternaria and Cladosporium spores during summer storms

Fungal spores are known to cause allergic sensitization. Recent studies reported a strong association between asthma symptoms and thunderstorms that could be explained by an increase in airborne fungal spore concentrations. Just before and during thunderstorms the values of meteorological parameters rapidly change. Therefore, the goal of this study was to create a predictive model for hourly concentrations of atmospheric Alternaria and Cladosporium spores on days with summer storms in Szczecin (Poland) based on meteorological conditions. For this study we have chosen all days of June, July and August (2004–2009) with convective thunderstorms. There were statistically significant relationships between spore concentration and meteorological parameters: positive for air temperature and ozone content while negative for relative humidity. In general, before a thunderstorm, air temperature and ozone concentration increased, which was accompanied by a considerable increase in spore concentration. During and after a storm, relative humidity increased while both air temperature ozone concentration along with spore concentrations decreased. Artificial neural networks (ANN) were used to assess forecasting possibilities. Good performance of ANN models in this study suggest that it is possible to predict spore concentrations from meteorological variables 2 h in advance and, thus, warn people with spore-related asthma symptoms about the increasing abundance of airborne fungi on days with storms.     

Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models

Background

Artificial neural networks (ANN) mimic the function of the human brain and are capable of performing massively parallel computations for data processing and knowledge representation. ANN can capture nonlinear relationships between predictors and responses and can adaptively learn complex functional forms, in particular, for situations where conventional regression models are ineffective. In a previous study, ANN with Bayesian regularization outperformed a benchmark linear model when predicting milk yield in dairy cattle or grain yield of wheat. Although breeding values rely on the assumption of additive inheritance, the predictive capabilities of ANN are of interest from the perspective of their potential to increase the accuracy of prediction of molecular breeding values used for genomic selection. This motivated the present study, in which the aim was to investigate the accuracy of ANN when predicting the expected progeny difference (EPD) of marbling score in Angus cattle. Various ANN architectures were explored, which involved two training algorithms, two types of activation functions, and from 1 to 4 neurons in hidden layers. For comparison, BayesCπ models were used to select a subset of optimal markers (referred to as feature selection), under the assumption of additive inheritance, and then the marker effects were estimated using BayesCπ with π set equal to zero. This procedure is referred to as BayesCpC and was implemented on a high-throughput computing cluster.

Results

The ANN with Bayesian regularization method performed equally well for prediction of EPD as BayesCpC, based on prediction accuracy and sum of squared errors. With the 3K-SNP panel, for example, prediction accuracy was 0.776 using BayesCpC, and ranged from 0.776 to 0.807 using BRANN. With the selected 700-SNP panel, prediction accuracy was 0.863 for BayesCpC and ranged from 0.842 to 0.858 for BRANN. However, prediction accuracy for the ANN with scaled conjugate gradient back-propagation was lower, ranging from 0.653 to 0.689 with the 3K-SNP panel, and from 0.743 to 0.793 with the selected 700-SNP panel.

Conclusions

ANN with Bayesian regularization performed as well as linear Bayesian regression models in predicting additive genetic values, supporting the idea that ANN are useful as universal approximators of functions of interest in breeding contexts.     

气候变化对物种分布影响模拟中的不确定性组分分割与制图——以油松为例

物种分布模型是预测评估气候变化对物种分布影响的主要工具。为了降低物种分布模型在预测过程中的不确定性,近期有学者提出了采用组合预测的新方法,即采用多套建模数据、模型技术,模型参数,以及环境情景数据对物种分布进行预测,构成物种分布预测集合。但是,组合预测中各组分对变异的贡献还知之甚少,因此有必要把变异组分来源进行分割,以更有效地利用组合预测方法来降低模型预测中的不确定性。以油松为例,采用8个生态位模型,9套模型训练数据,3个GCM模型和一个SRES(A2)排放情景,模型分析了油松当前(1961-1990年)和未来气候条件下3个时间段(2010-2039年,2040-2069年,2070-2099年)的潜在分布。共计得到当前分布预测数据72套,未来每个时间段分布数据216套。采用开发的ClimateChina软件进行当前和未来气候数据的降尺度处理。采用Kappa、真实技巧统计方法(TSS)和接收机工作特征曲线下的面积(AUC)对模型预测能力进行评估。结果表明,随机森林(RF)、广义线性模型(GLM),广义加法模型(GAM)、多元自适应样条函数(MARS)以及助推法(GBM)预测效果较好,几乎不受建模数据之间差异的影响。混合判别分析模型(MDA)对建模数据之间的差异非常敏感,甚至出现建模失败现象。采用三因素方差分析方法对组合预测中的不确定性来源进行变异分割,结果表明,模型之间的差异对模拟预测结果不确定性的贡献最大且所占比例极高,而建模数据之间的差异贡献最小,GCM贡献居中。研究将有助于加深对物种分布模拟预测中不确定性的认识。     

The fission yeast spore is coated by a proteinaceous surface layer comprising mainly Isp3

The spore is a dormant cell that is resistant to various environmental stresses. As compared with the vegetative cell wall, the spore wall has a more extensive structure that confers resistance on spores. In the fission yeast Schizosaccharomyces pombe, the polysaccharides glucan and chitosan are major components of the spore wall; however, the structure of the spore surface remains unknown. We identify the spore coat protein Isp3/Meu4. The isp3 disruptant is viable and executes meiotic nuclear divisions as efficiently as the wild type, but isp3∆ spores show decreased tolerance to heat, digestive enzymes, and ethanol. Electron microscopy shows that an electron-dense layer is formed at the outermost region of the wild-type spore wall. This layer is not observed in isp3∆ spores. Furthermore, Isp3 is abundantly detected in this layer by immunoelectron microscopy. Thus Isp3 constitutes the spore coat, thereby conferring resistance to various environmental stresses.