牛肉中单增李斯特菌的热失活模型

【目的】建立牛肉中单增李斯特菌的热失活动力学模型。【方法】将接种了3种不同血清型的单增李斯特菌混合菌液的牛肉分别在55℃、57.5℃、60℃、63℃、66℃和70℃进行热处理,在不同温度条件下单增李斯特菌数从109CFU/g下降至103CFU/g,其热失活曲线用修正的Gompertz模型进行了拟合;利用线性模型对单增李斯特菌的相对失活率(μ)和所持续时间(M)的自然对数值与温度(55℃-70℃)进行拟合;通过在59℃和64℃对牛肉中单增李斯特菌热处理,对所建的模型进行了验证。【结果】建立了牛肉中单增李斯特菌热失活动力学的一级模型和二级模型,经验证其准确因子和偏差因子均在可接受范围内。【结论】本研究所建立的模型能较好的模拟不同温度(55℃-70℃)对牛肉中单增李斯特菌热失活的影响。     

熟肉制品中金黄色葡萄球菌生长模型的建立

对不同温度下(10℃、15℃、25℃、30℃和37℃)熟肉制品中金黄色葡萄球菌的生长进行拟合。一级生长预测模型的建立采用SGompertz和Slogistic模型,由此获得恒定温度下的参数值(最大比生长速率:μ_(max)和延滞期:λ)。同时选用平方根模型(Square Root model)和二次多项式模型(Quadratic Polynomial model)对一级生长预测模型获得的μ_(max)和λ进行拟合,建立二级生长预测模型,并对模型的拟合度和可靠性进行评价。研究发现SGompertz模型可以更好地拟合熟肉制品中金黄色葡萄球菌在10℃~37℃下的生长;二次多项式模型是拟合温度与μ_(max)之间关系的最适模型;平方根模型是拟合温度与λ之间关系的最适模型。     

金针菇中单增李斯特菌生长预测模型的建立

为建立不同温度条件下金针菇中单增李斯特菌的生长模型,将单增李斯特菌接种到金针菇表面,并于不同温度下贮藏,获得其在10、l5、20、25和35℃下的生长数据,选用Baranyi模型进行拟合,建立初级生长模型,并拟合得到最大比生长速率。通过温度对初级模型中最大比生长速率的生长动力学拟合,分别建立Ratkowsky、Huang rate、Cardinal和Arrhenius-type二级生长模型,并进行数学检验。结果表明:Arrhenius-type模型呈现良好的线性关系,且评价结果优于其他模型,可作为次级模型对不同温度下金针菇中单增李斯特茵的生长动态进行预测。     

鲜切生菜中沙门氏菌二级生长预测模型的建立

以鲜切生菜为研究对象,比较了修正的Gompertz、Gompertz、Logistic和MMF 4种一级模型对不同温度下鲜切生菜中沙门氏菌生长曲线的拟合情况,发现在36℃、20℃和10℃时,修正的Gompertz模型均为最佳的拟合模型,4℃时沙门氏菌生长受到抑制,对失活/存活曲线进行"镜像化"处理后发现拟合程度相对较低,相关系数为0.962 7,故未用于二级模型中;采用其他温度下的修正的Gompertz模型中的最大比生长速率作为二级模型的响应值,建立平方根二级模型;准确因子和偏差因子对二级模型的准确性验证结果表明,两者均接近1.0,说明所建立的二级模型用于预测鲜切生菜中沙门氏菌生长情况。本研究为鲜切生菜的微生物安全控制提供科学依据。     

单核细胞增生李斯特氏菌PCR-DHPLC检测新技术的建立

应用PCR结合变性高效液相色谱(DHPLC)技术建立食品中单核细胞增生李斯特氏菌fimY的快速检测方法。根据单核细胞增生李斯特氏菌prfA和hlyA基因序列的特点设计特异性引物,PCR扩增的产物经DHPLC技术进行快速检测。以单核细胞增生李斯特氏菌等61株参考菌株做特异性试验;单核细胞增生李斯特氏菌菌株稀释成不同梯度,进行灵敏度试验。试验结果表明该方法具有很好的特异性,灵敏度较高,检测低限可达到为181CFU/ml。可以快速、准确检测单核细胞增生李斯特氏菌,是食品中致病菌快速检测的新技术。     

馒头中金黄色葡萄球菌生长预测模型的建立

以传统面制品馒头作为研究对象,采用修正的Gompertz（SGompertz）和修正的Logistic（SLogistic）作为一级生长模型,应用Origin 9.0软件分别拟合馒头中金黄色葡萄球菌在10、15、25、30和37 ℃的生长情况,获得其最大比生长速率（μ_max）和迟滞期(λ)。采用平方根模型和二次多项式模型建立馒头中金黄色葡萄球菌的二级生长模型,并对该模型进行验证。结果表明,SGompertz模型能较好地拟合馒头中金黄色葡萄球菌的生长。以μ_max建立的平方根和二次多项式模型,R²分别为0.931 5和0.932 0,偏差因子（B_f）分别为1.123 2和1.050 1,准确因子（A_f）分别为1.221 0和1.190 2,表明采用μ_max进行拟合时,二次多项式模型的拟合效果较好;以迟滞期λ建立的平方根和二次多项式模型,R²分别为0.948 4和0.969 6,B_f分别为0.890 1和0.912 2,A_f分别为1.541 1和1.180 3,表明采用迟滞期λ进行拟合时,二次多项式模型的拟合效果较好。本研究可为馒头等传统面制品的定量风险评估提供参考。     

InlA和InlB介导单核细胞增生李斯特菌入侵宿主细胞分子机制的研究进展

单核细胞增生李斯特菌(Listeria monocytogenes)是一种革兰氏阳性食源性致病菌。在造成宿主食源性感染的过程中, 单核细胞增生李斯特菌能凭借其独特的表面蛋白入侵宿主的非吞噬细胞。内化素蛋白家族(Internalins)是介导单核细胞增生李斯特菌入侵宿主非吞噬细胞的主要因子。本文根据国内外一些最新的研究成果, 结合作者近几年的工作, 综述了在侵染宿主的过程中, 单核细胞增生李斯特菌主要的内化素蛋白InlA和InlB介导细菌入侵宿主细胞的分子机制, 以期为阐明食源性致病菌致病机理、预防和治疗食源性疾病提供理论基础。     

2016-2018年辽宁省食品中单核细胞增生李斯特菌污染情况及毒力基因的检测

目的了解辽宁省食品中单核细胞增生李斯特菌毒力基因携带特点,对该省食品中单核细胞增生李斯特菌的污染情况进行调查。方法依据GB 4789.30-2016《食品安全国家标准 食品微生物学检验 单核细胞增生李斯特氏菌检验》及采用PCR扩增技术检测的方法对2016-2018年采自该省15个监测点、收集的3 310份食品检出的47株单核细胞增生李斯特菌进行9种毒力基因检测。结果食品中单核细胞增生李斯特菌检出率为1.42%(47/3310),食品中单核细胞增生李斯特菌最少携带3种毒力基因,其中携带prfA、plcA、hly、mpl、plcB、inlA、inlB和iap八种毒力基因是该省食品中单核细胞增生李斯特菌的主要毒力基因型,达到检出菌总数的65.96%。结论研究结果证实辽宁省食品中存在单核细胞增生李斯特菌污染情况,应严格监控食品中单核细胞增生李斯特菌的携带情况。     

卤鸡腿中大肠杆菌生长模型的研究

为探讨食源性大肠杆菌的生长特性,建立即食食品卤鸡腿中大肠杆菌生长模型,在20℃、25℃、30℃、35℃和40℃等不同温度下测定了大肠杆菌的生长状态。采用修正的Gompertz 方程拟合大肠杆菌的生长曲线,分析了大肠杆菌的生长参数。结果表明,修正的Gompertz函数能够很好地描述大肠杆菌在20 ℃、25 ℃、30 ℃、35 ℃和40℃贮藏条件下的生长动态（R2＞0.933 6)。应用平方根模型描述了温度对最大比生长速率(μm）和延滞时间(λ)参数的影响,结果表明,温度与最大比生长速率呈现较好的线性关系。此外,应用20 ℃、25℃、30 ℃、35 ℃和40℃条件下实际测得的数值与预测模型数据进行比较,验证了恒定温度下模型的有效性。所建立的预测模型能有效地预测大肠杆菌在卤鸡腿中的生长动态,为控制即食食品中大肠杆菌污染提供理论依据。     

单增李斯特菌在冷鲜猪肉中的生长预测模型比较

[目的]探讨单增李斯特菌(Listeria monocytogenes,LM)在冷鲜猪肉中的生长及现有预测软件GP( Growth Predictor)、PMP( Pathogen Modeling Programme)及CP (ComBase Predictor)对LM在冷鲜猪肉中生长的预测准确性.[方法]测定LM在4℃、8℃、12℃及16℃冷鲜猪肉中的生长,并用DMFit软件对生长数据进行拟合,计算各个温度点下的迟滞期(Lag phase duration,LPD)、生长率(Growth rate,GR)、最大生长密度(Maximum population density,MPD),同时用3种预测模型对相同条件下LM在猪肉中的生长进行预测,将实测值与预测值进行比较分析.[结果]冷鲜猪肉在16℃,经过2.6h LM即进入对数生长期.从8℃提高至12℃时,LM在冷鲜猪肉中的生长率从0.017l0g(cfu/g).h-1增至0.038l0g(cfu/g).h-1.在4℃ - 16℃,PMP预测的GR要比实测值低,而LPD则高于实测值.GP在8℃及以上的温度范围内,所预测LPD比实测值偏高.3种预测模型中,GP对GR的预测稍高于实测值,偏差因子(Bf)为1.01,准确因子(Af)为1.38;CP对LPD的预测值与实测值更为接近,Af及Bf分别为4.33及2.83.[结论]在冷鲜猪肉生产和销售过程中,严格控制温度尤为重要.PMP的预测较为保守,不适于冷鲜猪肉中LM生长的预测;建议用GP对GR进行预测,而CP对LPD的预测仅作为参考.     

基于非线性混合模型的红松人工林枝条生长

基于黑龙江省孟家岗林场36株红松人工林的枝解析数据,以单分子式和理查德方程作为枝条基径(BD)和枝长(BL)生长模型,分别考虑样地效应和样木效应,利用SAS软件的PROC NLMIXED模块构建了枝条基径和枝长生长的非线性混合模型.采用Akaike信息准则(AIC)、贝叶斯信息准则(BIC)、对数似然值(-2Log likelihood)和似然比检验(LRT)等评价指标对所构建模型的精度进行比较.结果表明:当考虑样地效应时,α1、α3和β1、β3分别作为随机参数时基径和枝长生长模型拟合效果最好;当考虑样木效应影响时,α2、α3和β1、β3分别作为随机参数时基径和枝长生长模型拟合效果最好.非线性混合模型不但可反映枝生长总体平均变化趋势,还能反映个体之间的差异.无论考虑样地效应还是样木效应,非线性混合模型的拟合精度都比传统回归模型的拟合精度高,并且考虑样木效应的拟合精度高于考虑样地效应的拟合精度.     

以比生长速率时间曲线为基础的生物群体生长数学模型

构建了一个描述限制性环境条件下生物群体生长规律的数学模型。模型中比生长速率(μ)是时间(t)的函数。模型可以很好地拟合多种生物或生物细胞群体生长的延迟期、指数期和稳定期。该模型参数少,模型参数生物学意义明确,计算简单。     

The influence of temperature on life history traits in the Iberian slug,Arion lusitanicus

The slug Arion lusitanicus is an invasive species which has recently become established in many European countries and is considered a serious pest, both in agriculture and private gardens. The effect of temperature on growth rate, survival, egg developmental time and hatchability of the slug are reported. The growth rate was positively correlated with temperatures up to 20°C and A. lusitanicus continued (on average) to grow until they die. A model describing how growth depends on temperature was developed, and a field survey found that the predicted growth matched the observed with reasonable accuracy. This study confirms the assumption that A. lusitanicus die after egg laying. Free‐ranging A. lusitanicus has an annual life cycle, but our data show that slugs also possess the ability to live for up to 3 years. The egg developmental time was, like growth, positively correlated with temperature and was shortest at 20°C, but the optimum temperature for egg development, when regarded as the temperature with highest hatchability was found to be 10°C. In conclusion, this study has shown that growth and the life‐history traits, egg developmental time and hatchability, of A. lusitanicus are highly influenced by temperature. However, data also show that temperatures within the range 2–20°C did not influence survival rates to any large degree, and that A. lusitanicus can quickly adjust to changes in temperature. The study provides a number of basic parameters which contribute to the understanding of the population dynamics of A. lusitanicus.     

Growth with seasonally varying temperatures: an expansion of the von Bertalanffy growth model

The von Bertalanffy growth function has limitations for describing the growth of fishes in seasonal climates. In the present work, a new equation is proposed where the growth parameter k is substituted by a function related to monthly water temperatures. The computer program GROWTHS was developed to fit and simulate the growth for seasonally varying temperatures. Examples for natural populations of Barbatula barbatula and Cottus gobio are presented.     

Comparative analysis of some models of gene regulation in mixed-substrate microbial growth

Mixed-substrate microbial growth is of fundamental interest in microbiology and bioengineering. Several mathematical models have been developed to account for the genetic regulation of such systems, especially those resulting in diauxic growth. In this work, we compare the dynamics of three such models (Narang, 1998a. The dynamical analogy between microbial growth on mixtures of substrates and population growth of competing species. Biotechnol. Bioeng. 59, 116-121; Thattai and Shraiman, 2003. Metabolic switching in the sugar phosphotransferase system of Escherichia coli. Biophys. J. 85(2), 744-754; Brandt et al., 2004. Modelling microbial adaptation to changing availability of substrates. Water Res. 38, 1004-1013). We show that these models are dynamically similar--the initial motion of the inducible enzymes in all the models is described by the Lotka-Volterra equations for competing species. In particular, the prediction of diauxic growth corresponds to "extinction" of one of the enzymes during the first few hours of growth. The dynamic similarity occurs because in all the models, the inducible enzymes possess properties characteristic of competing species: they are required for their own synthesis, and they inhibit each other. Despite this dynamic similarity, the models vary with respect to the range of dynamics captured. The Brandt et al. model always predicts the diauxic growth pattern, whereas the remaining two models exhibit both diauxic and non-diauxic growth patterns. The models also differ with respect to the mechanisms that generate the mutual inhibition between the enzymes. In the Narang model, mutual inhibition occurs because the enzymes for each substrate enhance the dilution of the enzymes for the other substrate. The Brandt et al. model superimposes upon this dilution effect an additional mechanism of mutual inhibition. In the Thattai and Shraiman model, the mutual inhibition is entirely due to competition for the phosphoryl groups. For quantitative agreement with the data, all models must be modified to account for specific mechanisms of mutual inhibition, such as inducer exclusion.     

Growth and food consumption of 0+ Arctic charr fed pelleted or natural food at six different temperatures

The effects of temperature and diet on the specific growth rate and food consumption of 1-summer-old Arctic charr Salvelinus alpinus were studied. Fish were reared singly in aquaria at six different constant temperatures (5, 9, 13, 16, 18 and 20°C). They were fed Neomysis integer or commercial pelleted food for 2 weeks and growth and food consumption were measured. In both experiments, growth rate increased to an optimum at 15°C. Growth rates were high in the range 13–18°C, with no significant ( P >0·05) differences between temperatures. No significant ( P> 0·05) differences in growth were found between fish at 9 and 20°C. There were no effects of diet on size-adjusted growth rates. The growth efficiency decreased with increasing temperature in both treatments, but the decrease was faster in the Neomysis treatment. Charr seemed to compensate for the high water content (79·5%) of Neomysis by having a higher food intake.     

中华乌塘鳢的生长、生长模型和生活史类型

采用胸鳍第三支鳍骨作为研究湛江沿海中华乌塘鳢的年龄鉴定材料。胸鳍第三支鳍骨（远侧部）的关径（R）与体长（L）的关系为L＝6.1145 51.1288R。用特殊von Bertalanffy生长方程、一般von Bertalanffy生长方程、逻辑斯谛生长方程、Gompertz生长方程和灰色动态生长模型等5种生长模型拟合了中华乌塘鳢的生长,根据各模型拟合残差平方和大小判断,灰色动态生长模型对中华乌塘鳢生长的拟合效果最好,其次是一般von Bertalanffy生长方程。根据r-选择和κ－选择的典型行征以及渐近体长（L∞）、渐近体重（W∞）、生长系数（κ）、初次生殖年龄（Tm）、最大年龄（Tmax）、瞬时自然死亡率（M）和性腺指数（GI）等7个生态学参数值,可以判断中华乌塘鳢偏向r-选择。应用单位补充量产量模型计算改变起捕年龄（Tc）和瞬时捕捞死亡率（F）的产量,分析产量变化曲线同样证实中华乌塘鳢生活史偏向r-选择。作为渔业管理对策,中华乌塘鳢的起捕年龄应定为2龄。     

Choosing the right sigmoid growth function using the unified‐models approach

Growth of the young is an important part of the life history in birds. However, modelling methods have paid little attention to the choice of regression model used to describe its pattern. The aim of this study was to evaluate whether a single sigmoid model with an upper asymptote could describe avian growth adequately. We compared unified versions of five growth models of the Richards family (the four‐parameter U‐Richards and the three‐parameter U‐logistic, U‐Gompertz, U‐Bertalanffy and U4‐models) for three traits (body mass, tarsus‐length and wing‐length) for 50 passerine species, including species with varied morphologies and life histories. The U‐family models exhibit a unified set of parameters for all models. The four‐parameter U‐Richards model proved a good choice for fitting growth curves to various traits – its extra d‐parameter allows for a flexible placement of the inflection point. Which of the three‐parameter U‐models was the best performing varied greatly between species and between traits, as each three‐parameter model had a different fixed relative inflection value (fraction of the upper asymptote), implying a different growth pattern. Fixing the asymptotes to averages for adult trait value generally shifted the model preference towards one with lower relative inflection values. Our results illustrate an overlooked difficulty in the analysis of organismal growth, namely, that a single traditional three‐parameter model does not suit all growth data. This is mostly due to differences in inflection placement. Moreover, some biometric traits require more attention when estimating growth rates and other growth‐curve characteristics. We recommend fitting either several three‐parameter models from the U‐family, where the parameters are comparable between models, or only the U‐Richards model.