scholarly journals Estimation of Staphylococcus aureus Growth Parameters from Turbidity Data: Characterization of Strain Variation and Comparison of Methods

2006 ◽  
Vol 72 (7) ◽  
pp. 4862-4870 ◽  
Author(s):  
R. Lindqvist
Keyword(s):  
Staphylococcus Aureus ◽  
Growth Rates ◽  
Growth Parameters ◽  
Growth Models ◽  
Viable Count ◽  
Detection Methods ◽  
Accurate Estimation ◽  
Data Set ◽  
Time To Detection ◽  
Strain Variability

ABSTRACT Turbidity methods offer possibilities for generating data required for addressing microorganism variability in risk modeling given that the results of these methods correspond to those of viable count methods. The objectives of this study were to identify the best approach for determining growth parameters based on turbidity data and use of a Bioscreen instrument and to characterize variability in growth parameters of 34 Staphylococcus aureus strains of different biotypes isolated from broiler carcasses. Growth parameters were estimated by fitting primary growth models to turbidity growth curves or to detection times of serially diluted cultures either directly or by using an analysis of variance (ANOVA) approach. The maximum specific growth rates in chicken broth at 17°C estimated by time to detection methods were in good agreement with viable count estimates, whereas growth models (exponential and Richards) underestimated growth rates. Time to detection methods were selected for strain characterization. The variation of growth parameters among strains was best described by either the logistic or lognormal distribution, but definitive conclusions require a larger data set. The distribution of the physiological state parameter ranged from 0.01 to 0.92 and was not significantly different from a normal distribution. Strain variability was important, and the coefficient of variation of growth parameters was up to six times larger among strains than within strains. It is suggested to apply a time to detection (ANOVA) approach using turbidity measurements for convenient and accurate estimation of growth parameters. The results emphasize the need to consider implications of strain variability for predictive modeling and risk assessment.

scholarly journals Age estimation and the best growth model selection of the tentacled blenny Parablennius tentacularis (Brünnich, 1768) in the southeastern Black Sea

2021 ◽  
Vol 38 (2) ◽  
pp. 229-236
Author(s):  
Ayşe Van ◽  
Aysun Gümüş ◽  
Melek Özpiçak ◽  
Serdar Süer
Keyword(s):  
Growth Model ◽  
Black Sea ◽  
Growth Parameters ◽  
Growth Models ◽  
Information Criterion ◽  
Current Data ◽  
Logistic Growth ◽  
Data Set ◽  
Frequency Distributions ◽  
Selection Of

By the study's coverage, 522 individuals of tentacled blenny (Parablennius tentacularis (Brünnich, 1768)), were caught with the bottom trawl operations (commercial fisheries and scientific field surveys) between May 2010 and March 2012 from the southeastern Black Sea. The size distribution range of the sample varied between 4.8-10.8 cm. The difference between sex length (K-S test, Z=3.729, P=0.000) and weight frequency distributions (K-S test, Z=3.605, P=0.000) was found to be statistically significant. The length-weight relationship models were defined as isometric with W = 0.009L3.034 in male individuals and positive allometric with W = 0.006L3.226 in female individuals. Otolith and vertebra samples were compared for the selection of the most accurate hard structure that can be used to determine the age. Otolith was chosen as the most suitable hard structure. The current data set was used to predict the best growth model. For this purpose, the growth parameters were estimated with the widely used von Bertalanffy, Gompertz and Logistic growth functions. Akaike's Information Criterion (AIC), Lmak./L∞ ratio, and R2 criteria were used to select the most accurate growth models established through these functions. Model averaged parameters were calculated with multi-model inference (MMI): L'∞ = 15.091 cm, S.E. (L'∞) = 3.966, K'= 0.232 year-1, S.E. (K') = 0.122.

Performance of Response Surface and Davey Model for Prediction of Staphylococcus aureus Growth Parameters under Different Experimental Conditions

2004 ◽  
Vol 67 (6) ◽  
pp. 1138-1145 ◽  
Author(s):  
G. ZURERA-COSANO ◽  
A. M. CASTILLEJO-RODRÍGUEZ ◽  
R. M. GARCÍA-GIMENO ◽  
F. RINCÓN-LEÓN
Keyword(s):  
Staphylococcus Aureus ◽  
Growth Rate ◽  
Response Surface ◽  
Growth Parameters ◽  
Growth Models ◽  
Lag Time ◽  
Anaerobic Conditions ◽  
Experimental Conditions ◽  
Aerobic And Anaerobic ◽  
Aerobic And Anaerobic Conditions

The combined effect of different temperatures (7 to 19°C), pH levels (4.5 to 8.5), sodium chloride levels (0 to 8%), and sodium nitrite levels (0 to 200 ppm) on the predicted growth rate and lag time of Staphylococcus aureus under aerobic and anaerobic conditions was studied. The two predictive models developed, response surface (RS) and the Davey model, provided reliable estimates of the two kinetic parameters studied. The RS provided better predictions of maximum specific growth rate, with bias factors of 1.06 and 1.31 and accuracy factors of 1.17 and 1.37, respectively, in aerobic and anaerobic conditions. The Davey model performed more accurately for lag time, with a bias factor of 1.12 and an accuracy factor of 1.49, for both aerobic and anaerobic conditions. Predictive growth models are a valuable tool, enabling swift determination of Staphylococcus aureus growth rate and lag time. These data are essential for ensuring staphylococcus-relatedquality and safety of food products.

Critical Assessment of the Time-to-Detection Method for Accurate Estimation of Microbial Growth Parameters

2014 ◽  
Vol 35 (2) ◽  
pp. 179-192 ◽  
Author(s):  
Maria Baka ◽  
Estefanía Noriega ◽  
Ioanna Stamati ◽  
Filip Logist ◽  
Jan F.M. Van Impe
Keyword(s):  
Microbial Growth ◽  
Detection Method ◽  
Growth Parameters ◽  
Critical Assessment ◽  
Accurate Estimation ◽  
Time To Detection

Are Growth Parameters Estimated from Tagging and Age–Length Data Comparable?

1988 ◽  
Vol 45 (6) ◽  
pp. 936-942 ◽  
Author(s):  
R. I. C. C. Francis
Keyword(s):  
Growth Rate ◽  
Growth Rates ◽  
Growth Parameters ◽  
Growth Models ◽  
Maximum Length ◽  
Fish Growth ◽  
Data Sets ◽  
Von Bertalanffy ◽  
Length Data ◽  
The Mean

The two most common ways of estimating fish growth use age–length data and tagging data. It is shown that growth parameters estimated from these two types of data have different meanings and thus are not directly comparable. In particular, the von Bertalanffy parameter l∞ means asymptotic mean length at age for age–length data, and maximum length for tagging data, when estimated by conventional methods. New parameterizations are given for the von Bertalanffy equation which avoid this ambiguity and better represent the growth information in the two types of data. The comparison between growth estimates from these data sets is shown to be equivalent to comparing the mean growth rate of fish of a given age with that of fish of length equal to the mean length at that age. How much these growth rates may differ in real populations remains unresolved: estimates for two species of fish produced markedly different results, neither of which could be reproduced using growth models. Existing growth models are shown to be inadequate to answer this question.

scholarly journals A stochastic process modelling of maize phyllochron enables to characterize environmental and genetic effects

2021 ◽  
Author(s):  
S. Plancade ◽  
E. Marchadier ◽  
S. Huet ◽  
A. Ressayre ◽  
C. Noûs ◽  
...  
Keyword(s):  
Stochastic Process ◽  
Hypothesis Testing ◽  
Flowering Time ◽  
Growth Parameters ◽  
Divergent Selection ◽  
Accurate Estimation ◽  
Vegetative Development ◽  
Data Set ◽  
Maize Plants ◽  
The Impact

AbstractWe propose a flexible statistical model for phyllochron that enables to seasonal variations analysis and hypothesis testing, and demonstrate its efficiency on a data set from a divergent selection experiment on maize.The time between appearance of successive leaves or phyllochron enables to characterize the vegetative development of maize plants which determines their flowering time. Phyllochron is usually considered as constant over the development of a given plant, even though studies have demonstrated response of growth parameters to environmental variables. In this paper, we proposed a novel statistical approach for phyllochron analysis based on a stochastic process, which combines flexibility and a more accurate modelling than existing regression models. The model enables accurate estimation of the phyllochron associated with each leaf rank and enables hypothesis testing. We applied the model on an original maize dataset collected in fields from plants belonging to closely related genotypes originated from divergent selection experiments for flowering time conducted on two maize inbred lines. We showed that the main differences in phyllochron were not observed between selection populations (Early or Late), but rather ancestral lines, years of experimentation, and leaf ranks. Finally, we showed that phyllochron variations through seasons could be related to climate variations, even if the impact of each climatic variables individually was not clearly elucidated. All script and data can be found at https://doi.org/10.15454/CUEHO6

scholarly journals Modeling the Lag Time of Listeria monocytogenes from Viable Count Enumeration and Optical Density Data

2002 ◽  
Vol 68 (12) ◽  
pp. 5816-5825 ◽  
Author(s):  
F. Baty ◽  
J. P. Flandrois ◽  
M. L. Delignette-Muller
Keyword(s):  
Listeria Monocytogenes ◽  
Optical Density ◽  
Growth Parameters ◽  
Growth Models ◽  
Growth Parameter ◽  
Viable Count ◽  
Linear Increase ◽  
Lag Phase ◽  
Parameter Estimates ◽  
Phase Duration

ABSTRACT The following two factors significantly influence estimates of the maximum specific growth rate (μmax) and the lag-phase duration (λ): (i) the technique used to monitor bacterial growth and (ii) the model fitted to estimate parameters. In this study, nine strains of Listeria monocytogenes were monitored simultaneously by optical density (OD) analysis and by viable count enumeration (VCE) analysis. Four usual growth models were fitted to our data, and estimates of growth parameters were compared from one model to another and from one monitoring technique to another. Our results show that growth parameter estimates depended on the model used to fit data, whereas there were no systematic variations in the estimates of μmax and λ when the estimates were based on OD data instead of VCE data. By studying the evolution of OD and VCE simultaneously, we found that while log OD/VCE remained constant for some of our experiments, a visible linear increase occurred during the lag phase for other experiments. We developed a global model that fits both OD and VCE data. This model enabled us to detect for some of our strains an increase in OD during the lag phase. If not taken into account, this phenomenon may lead to an underestimate of λ.

scholarly journals Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector

2019 ◽  
Vol 9 (6) ◽  
pp. 1128 ◽  
Author(s):  
Yundong Li ◽  
Wei Hu ◽  
Han Dong ◽  
Xueyan Zhang
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Hurricane Sandy ◽  
Training Data ◽  
Aerial Images ◽  
Detection Methods ◽  
Single Shot ◽  
Data Set ◽  
Augmentation Strategies ◽  
Post Disaster

Using aerial cameras, satellite remote sensing or unmanned aerial vehicles (UAV) equipped with cameras can facilitate search and rescue tasks after disasters. The traditional manual interpretation of huge aerial images is inefficient and could be replaced by machine learning-based methods combined with image processing techniques. Given the development of machine learning, researchers find that convolutional neural networks can effectively extract features from images. Some target detection methods based on deep learning, such as the single-shot multibox detector (SSD) algorithm, can achieve better results than traditional methods. However, the impressive performance of machine learning-based methods results from the numerous labeled samples. Given the complexity of post-disaster scenarios, obtaining many samples in the aftermath of disasters is difficult. To address this issue, a damaged building assessment method using SSD with pretraining and data augmentation is proposed in the current study and highlights the following aspects. (1) Objects can be detected and classified into undamaged buildings, damaged buildings, and ruins. (2) A convolution auto-encoder (CAE) that consists of VGG16 is constructed and trained using unlabeled post-disaster images. As a transfer learning strategy, the weights of the SSD model are initialized using the weights of the CAE counterpart. (3) Data augmentation strategies, such as image mirroring, rotation, Gaussian blur, and Gaussian noise processing, are utilized to augment the training data set. As a case study, aerial images of Hurricane Sandy in 2012 were maximized to validate the proposed method’s effectiveness. Experiments show that the pretraining strategy can improve of 10% in terms of overall accuracy compared with the SSD trained from scratch. These experiments also demonstrate that using data augmentation strategies can improve mAP and mF1 by 72% and 20%, respectively. Finally, the experiment is further verified by another dataset of Hurricane Irma, and it is concluded that the paper method is feasible.

scholarly journals An Optimized Stacking Ensemble Model for Phishing Websites Detection

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1285
Author(s):  
Mohammed Al-Sarem ◽  
Faisal Saeed ◽  
Zeyad Ghaleb Al-Mekhlafi ◽  
Badiea Abdulkarem Mohammed ◽  
Tawfik Al-Hadhrami ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Ensemble Method ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Ensemble Model ◽  
Security Attacks ◽  
Data Set ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning

Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectively.

scholarly journals Embed2Detect: temporally clustered embedded words for event detection in social media

2021 ◽  
Author(s):  
Hansi Hettiarachchi ◽  
Mariam Adedoyin-Olowe ◽  
Jagdev Bhogal ◽  
Mohamed Medhat Gaber
Keyword(s):  
Social Media ◽  
Event Detection ◽  
High Volume ◽  
Detection Methods ◽  
Word Embeddings ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Media Data

AbstractSocial media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.

Application Research of Neural Expert System on Tree Growth Model

2014 ◽  
Vol 513-517 ◽  
pp. 3728-3731
Author(s):  
Wen Qing Zhang
Keyword(s):  
Expert System ◽  
Tree Growth ◽  
Growth And Development ◽  
Production Management ◽  
Environmental Control ◽  
Growth Parameters ◽  
Growth Models ◽  
Growth Management ◽  
Management Decision ◽  
Factors Affecting

In order to simulate growth and development process of tree, then provide services for production management and scientific research, all kinds of tree growth models are constructed. The paper firstly considers a variety of factors affecting the growth and development of tree, then studies artificial intelligence knowledge such as neural network and expert system, uses the neural expert system to solve the acquisition and management of tree growth parameters, and design and develop tree growth management and expert system based on growth models, the models combine morphogenesis model of tree and knowledge model to provide comprehensive environmental control and management decision-making. Practice has indicated that the growth models of tree can reflect the growth of trees under different physiological and ecological conditions, and visual effect is very good.

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