scholarly journals Which Microbial Growth Model Best Fits to Fusarium graminearum?

2018 ◽  
Author(s):  
Edgar Cambaza
Keyword(s):  
Linear Model ◽  
Fusarium Graminearum ◽  
Growth Model ◽  
Yeast Extract ◽  
Microbial Growth ◽  
Growth Models ◽  
Head Blight ◽  
Actual Growth ◽  
Imagej Software ◽  
Yeast Extract Agar

Fusarium graminearum causes head blight in wheat and corn, and produces chemicals harmful for humans and other animals. It is important to understand how it grows in order to prevent outbreaks. There are 3 well-known growth models for microorganisms and they seem applicable to molds: linear, Gompertz and Baranyi. This study aimed to see which could better represent F. graminearum growth. Three replicates were grown in yeast extract agar (YEA) for 20 days. The Feret’s radius was measured in ImageJ software, and then related to the models. Linear model was the most closely correlated to the actual growth. Thus, considering that it was the most representative of the reality and it is easier to use, it seems to be the best logical choice for F. graminearum growth studies.

scholarly journals RGB Imaging as Tool to Analyze the Growth of Fusarium graminearum in Infected Oats (Avena sativa) and Rice (Oryza sativa)

2019 ◽  
Author(s):  
Edgar Cambaza ◽  
Shigenobu Koseki ◽  
Shuso Kawamura
Keyword(s):  
Fusarium Graminearum ◽  
Color Change ◽  
Growth Models ◽  
Economic Losses ◽  
Selection Strategy ◽  
Imaging Analysis ◽  
Current Growth ◽  
Imagej Software ◽  
Pearson Correlations ◽  
Mycelial Phase

Fusarium graminearum is a cereal pathogen responsible for economic losses worldwide every year. An understanding of its growth is key to control its infection, but current growth models are limited because their size-based approach provides little information about the mold's metabolism. Recently, a RGB (red, green and blue) imaging analysis demonstrated the predictability of F. graminearum color change as it grows in yeast extract agar (YEA). This study aimed to verify the same phenomenon in oats (aw = 0.94, 0.97 and 0.99) and rice (aw = 0.97, 0.98 and 0.99). Photos were taken using a professional camera and a smartphone (iPhone 6) after incubation and during the subsequent 16 days, and average RGB was quantified using ImageJ software. The photos showed very similar color variations, regardless of the type of grain or aw. The mold first adopted a k-selection strategy by growing as a mycelium and then a r-selection strategy, increasing spore production. All RGB channels showed positive Pearson correlations between them (p < 0.001) and it was possible to design a model showing two lag phases, the first prior to a mycelial phase and the second prior to a sporular phase at the end of the experiment.

scholarly journals Comprehensive Description of Fusarium graminearum Pigments and Related Compounds

Foods ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 165 ◽  
Author(s):  
Edgar Cambaza
Keyword(s):  
Fusarium Graminearum ◽  
Yeast Extract ◽  
Color Pattern ◽  
Size Measurement ◽  
Surface Color ◽  
Comprehensive Description ◽  
Golden Yellow ◽  
Yellow Orange ◽  
Related Compounds ◽  
Yeast Extract Agar

Several studies have explored in depth the biochemistry and genetics of the pigments present in Fusarium graminearum, but there is a need to discuss their relationship with the mold’s observable surface color pattern variation throughout its lifecycle. Furthermore, they require basic cataloguing, including a description of their major features known so far. Colors are a viable alternative to size measurement in growth studies. When grown on yeast extract agar (YEA) at 25 °C, F. graminearum initially exhibits a whitish mycelium, developing into a yellow-orange mold by the sixth day and then turning into wine-red. The colors are likely due to accumulation of the golden yellow polyketide aurofusarin and the red rubrofusarin, but the carotenoid neurosporaxanthin also possibly plays a major role in the yellow or orange coloration. Torulene might contribute to red tones, but it perhaps ends up being converted into neurosporaxanthin. Culmorin is also present, but it does not contribute to the color, though it was initially isolated in pigment studies. Additionally, there is the 5-deoxybostrycoidin-based melanin, but it mostly occurs in the teleomorph’s perithecium. There is still a need to chemically quantify the pigments throughout the lifecycle, and analyze their relationships and how much each impacts F. graminearum’s surface color.

scholarly journals Comprehensive Description of Fusarium graminearum Pigments and Related Compounds

2018 ◽  
Author(s):  
Edgar Cambaza
Keyword(s):  
Fusarium Graminearum ◽  
Yeast Extract ◽  
Color Pattern ◽  
Size Measurement ◽  
Surface Color ◽  
Comprehensive Description ◽  
Golden Yellow ◽  
Yellow Orange ◽  
Related Compounds ◽  
Yeast Extract Agar

Several studies explore in depth the biochemistry and genetics of the pigments present in Fusarium graminearum but there is a need to discuss about their relationship with the mold’s observable surface color pattern variation throughout its lifecycle. Furthermore, they require basic cataloguing and description of their major features known so far. Colors are a viable alternative to size measurement in growth studies. When grown on yeast extract agar (YEA) at 25 °C, F. graminearum initially exhibits a whitish mycelium, developing into a yellow-orange mold by the sixth day and then turning into wine-red. The colors are likely due to accumulation of the golden yellow polyketide aurofusarin and the red rubrofusarin, but the carotenoid neurosporaxanthin possibly play also a major role in the yellow or orange coloration. Torulene might contribute for red tones but it perhaps ends up being converted into neurosporaxanthin. Culmorin is also present but it does not contribute for the color, though it was initially isolated in pigment studies, and there is the 5-deoxybostrycoidin-based melanin, but it occurs mostly in the teleomorph’s perithecium. There is still a need to chemically quantify the pigments throughout the lifecycle, analyze their relationships and how much each impacts F. graminearum surface color.

Characterization of the Growth of Corrosion Defects on Energy Pipelines Using Bayesian Dynamic Linear Model

2014 ◽  
Author(s):  
S. Zhang ◽  
W. Zhou ◽  
S. Kariyawasam ◽  
M. Al-Amin
Keyword(s):  
Linear Model ◽  
Growth Model ◽  
Growth Models ◽  
Model Parameters ◽  
Dynamic Linear Model ◽  
Average Growth ◽  
Imperfect Data ◽  
Natural Gas Pipeline ◽  
Proposed Model ◽  
Corrosion Defects

This paper describes the use of the second-order polynomial dynamic linear model (DLM) to characterize the growth of the depth of corrosion defects on energy pipelines using imperfect data obtained from multiple high-resolution in-line inspections (ILI). The growth model is formulated by incorporating the general form of the measurement error (including the biases and random scattering error) of the ILI tools as well as the correlations between the random scattering errors of different tools. The temporal variability of the corrosion growth is captured by allowing the average growth rate between two successive inspections to vary with time. The Markov Chain Monte Carlo simulation is employed to carry out the Bayesian updating of the growth model and evaluate the posterior distributions of the model parameters. An example involving real ILI data collected from an in-service natural gas pipeline is employed to illustrate and validate the growth model. The analysis results show that the defect depths predicted by the proposed model agree well with the actual depths and are more accurate than those predicted by the Gamma process-based growth models reported in the literature.

Growth model selection for juvenile blacklip abalone (Haliotis rubra): assessing statistical and biological validity

2012 ◽  
Vol 63 (1) ◽  
pp. 23 ◽  
Author(s):  
Fay Helidoniotis ◽  
Malcolm Haddon
Keyword(s):  
Linear Model ◽  
Growth Model ◽  
Growth Rates ◽  
Shell Length ◽  
Linear Models ◽  
Marine Organism ◽  
Growth Models ◽  
Von Bertalanffy ◽  
Haliotis Rubra ◽  
Selection Of

Accurate estimates of marine organism growth are important for modelling the dynamics of populations and rely on the selection of an appropriate growth model. However, there is no assurance that the statistically optimum model will also be biologically plausible. Three growth models (von Bertalanffy, Gompertz and a linear model) were fitted to a dataset consisting of two cohorts of juvenile size classes of blacklip abalone (Haliotis rubra). Results show that the non-seasonal Gompertz was statistically better than the non-seasonal von Bertalanffy and linear models. There was a persistent seasonal signal through the juvenile size range, with slow growth in winter and fast growth during summer. When a seasonal term was formally incorporated, the model fits were greatly improved, particularly for the linear and von Bertalanffy models. The seasonal-Gompertz predicted growth rates that were biologically implausible for juveniles of 2 mm shell length; 107 μm day–1 for one cohort and 24 μm day–1 for the other. These rates are inconsistent with published growth rates observed under both controlled and wild conditions. In contrast, the seasonal-linear model predicted growth rates of 60 μm day–1 for animals of 2 mm shell length, consistent with published findings. The selection of a growth model based solely on statistical criteria may not take into account the complex processes that influence growth of juveniles.

scholarly journals Screening of Durum Wheat (Triticum turgidum L. subsp. durum (Desf.) Husn.) Italian Cultivars for Susceptibility to Fusarium Head Blight Incited by Fusarium graminearum

Plants ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 68
Author(s):  
Gaetano Bentivenga ◽  
Alfio Spina ◽  
Karim Ammar ◽  
Maria Allegra ◽  
Santa Olga Cacciola
Keyword(s):  
Durum Wheat ◽  
Fusarium Head Blight ◽  
Fusarium Graminearum ◽  
Triticum Turgidum ◽  
Disease Incidence ◽  
Morphological Characteristics ◽  
Pcr Amplification ◽  
Conidial Suspension ◽  
Head Blight ◽  
Italian Origin

In 2009, a set of 35 cultivars of durum wheat (Triticum turgidum L. subsp. durum (Desf.) Husn.) of Italian origin was screened for fusarium head blight (FHB) susceptibility at CIMMYT (Mexico) and in the 2019–20 cropping season, 16 of these cultivars, which had been included in the Italian National Plant Variety Register, were tested again in southern and northern Italy. Wheat cultivars were artificially inoculated during anthesis with a conidial suspension of Fusarium graminearum sensu lato using a standard spray inoculation method. Inoculum was a mixture of mono-conidial isolates sourced in the same areas where the trials were performed. Isolates had been characterized on the basis of morphological characteristics and by DNA PCR amplification using a specific primer set and then selected for their virulence and ability to produce mycotoxins. The susceptibility to FHB was rated on the basis of the disease severity, disease incidence and FHB index. Almost all of the tested cultivars were susceptible or very susceptible to FHB with the only exception of “Duprì”, “Tiziana” and “Dylan” which proved to be moderately susceptible. The susceptibility to FHB was inversely correlated with the plant height and flowering biology, the tall and the late heading cultivars being less susceptible.

Near-infrared versus visual sorting of Fusarium-damaged kernels in winter wheat

2008 ◽  
Vol 88 (6) ◽  
pp. 1087-1089 ◽  
Author(s):  
Stephen N Wegulo ◽  
Floyd E Dowell
Keyword(s):  
Quality Assurance ◽  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Key Words ◽  
Fusarium Graminearum ◽  
Near Infrared ◽  
Strongly Correlated ◽  
Head Blight ◽  
Rater Reliability ◽  
Key Words Wheat

Fusarium head blight (scab) of wheat, caused by Fusarium graminearum, often results in shriveled and/or discolored kernels, which are referred to as Fusarium-damaged kernels (FDK). FDK is a major grain grading factor and therefore is routinely determined for purposes of quality assurance. Measurement of FDK is usually done visually. Visual sorting can be laborious and is subject to inconsistencies resulting from variability in intra-rater repeatability and/or inter-rater reliability. The ability of a single-kernel near-infrared (SKNIR) system to detect FDK was evaluated by comparing FDK sorted by the system to FDK sorted visually. Visual sorting was strongly correlated with sorting by the SKNIR system (0.89 ≤ r ≤ 0.91); however, the SKNIR system had a wider range of FDK detection and was more consistent. Compared with the SKNIR system, visual raters overestimated FDK in samples with a low percentage of Fusarium-damaged grain and underestimated FDK in samples with a high percentage of Fusarium-damaged grain. Key words: Wheat, Fusarium head blight, Fusarium-damaged kernels, single-kernel near-infrared

scholarly journals Survival of Fusarium graminearum, the causal agent of Fusarium head blight. A review

2012 ◽  
Vol 33 (1) ◽  
pp. 97-111 ◽  
Author(s):  
Johann Leplat ◽  
Hanna Friberg ◽  
Muhammad Abid ◽  
Christian Steinberg
Keyword(s):  
Fusarium Head Blight ◽  
Fusarium Graminearum ◽  
Causal Agent ◽  
Head Blight

scholarly journals Molecular Characterization and Functional Analysis of PR-1-Like Proteins Identified from the Wheat Head Blight Fungus Fusarium graminearum

2018 ◽  
Vol 108 (4) ◽  
pp. 510-520 ◽  
Author(s):  
Shunwen Lu ◽  
Michael C. Edwards
Keyword(s):  
Functional Analysis ◽  
Fusarium Graminearum ◽  
Expression Patterns ◽  
Polyacrylamide Gel Electrophoresis ◽  
Sodium Dodecyl ◽  
Transcriptional Analysis ◽  
Fungal Genome ◽  
Dimensional Structure ◽  
Head Blight ◽  
Fungal Virulence

The group 1 pathogenesis-related (PR-1) proteins originally identified from plants and their homologs are also found in other eukaryotic kingdoms. Studies on nonplant PR-1-like (PR-1L) proteins have been pursued widely in humans and animals but rarely in filamentous ascomycetes. Here, we report the characterization of four PR-1L proteins identified from the ascomycete fungus Fusarium graminearum, the primary cause of Fusarium head blight of wheat and barley (designated FgPR-1L). Molecular cloning revealed that the four FgPR-1L proteins are all encoded by small open reading frames (612 to 909 bp) that are often interrupted by introns, in contrast to plant PR-1 genes that lack introns. Sequence analysis indicated that all FgPR-1L proteins contain the PR-1-specific three-dimensional structure, and one of them features a C-terminal transmembrane (TM) domain that has not been reported for any stand-alone PR-1 proteins. Transcriptional analysis revealed that the four FgPR-1L genes are expressed in axenic cultures and in planta with different spatial or temporal expression patterns. Phylogenetic analysis indicated that fungal PR-1L proteins fall into three major groups, one of which harbors FgPR-1L-2-related TM-containing proteins from both phytopathogenic and human-pathogenic ascomycetes. Low-temperature sodium dodecyl sulfate polyacrylamide gel electrophoresis and proteolytic assays indicated that the recombinant FgPR-1L-4 protein exists as a monomer and is resistant to subtilisin of the serine protease family. Functional analysis confirmed that deletion of the FgPR-1L-4 gene from the fungal genome results in significantly reduced virulence on susceptible wheat. This study provides the first example that the F. graminearum–wheat interaction involves a pathogen-derived PR-1L protein that affects fungal virulence on the host.

scholarly journals Comparison of growth models to describe growth from birth to 6 years in a Beninese cohort of children with repeated measurements

BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e035785
Author(s):  
Shukrullah Ahmadi ◽  
Florence Bodeau-Livinec ◽  
Roméo Zoumenou ◽  
André Garcia ◽  
David Courtin ◽  
...  
Keyword(s):  
Growth Model ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Information Criterion ◽  
Height Growth ◽  
Sub Saharan Africa ◽  
Individual Growth ◽  
Secondary Outcome ◽  
Growth Trajectories ◽  
Best Fitting

ObjectiveTo select a growth model that best describes individual growth trajectories of children and to present some growth characteristics of this population.SettingsParticipants were selected from a prospective cohort conducted in three health centres (Allada, Sekou and Attogon) in a semirural region of Benin, sub-Saharan Africa.ParticipantsChildren aged 0 to 6 years were recruited in a cohort study with at least two valid height and weight measurements included (n=961).Primary and secondary outcome measuresThis study compared the goodness-of-fit of three structural growth models (Jenss-Bayley, Reed and a newly adapted version of the Gompertz growth model) on longitudinal weight and height growth data of boys and girls. The goodness-of-fit of the models was assessed using residual distribution over age and compared with the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The best-fitting model allowed estimating mean weight and height growth trajectories, individual growth and growth velocities. Underweight, stunting and wasting were also estimated at age 6 years.ResultsThe three models were able to fit well both weight and height data. The Jenss-Bayley model presented the best fit for weight and height, both in boys and girls. Mean height growth trajectories were identical in shape and direction for boys and girls while the mean weight growth curve of girls fell slightly below the curve of boys after neonatal life. Finally, 35%, 27.7% and 8% of boys; and 34%, 38.4% and 4% of girls were estimated to be underweight, wasted and stunted at age 6 years, respectively.ConclusionThe growth parameters of the best-fitting Jenss-Bayley model can be used to describe growth trajectories and study their determinants.

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