Growth Kinetic Data Research Articles

Machine Learning Prediction of Leuconostoc spp. Growth Inducing Spoilage in Cooked Deli Foods Considering the Effect of Glycine and Sodium Acetate

To control spoilage by lactic acid bacteria (Leuconostoc spp.) in cooked deli food, various combinations of environmental and/or intrinsic factors have been employed based on hurdle technology. Since many factors and their combinations greatly influence Leuconostoc spp. growth, this study aimed to develop a machine learning model based on the experimentally obtained growth kinetic data using extreme gradient boosting tree algorithm to quantitatively and flexibly predict Leuconostoc spp. growth. In particular, the effects of sodium acetate (0–1.5%) and glycine (0–1.5%), which are frequently used food additives in the Japanese food industry, on the growth of Leuconostoc spp. in cooked deli foods were examined with a combination of temperature (5–25 °C) and pH (5.0–6.0) conditions. The developed machine learning model to predict the number of Leuconostoc spp. over time successfully demonstrates comparable accuracy in culture media to the conventional Baranyi model-based prediction. Furthermore, while the accuracy of the prediction by the machine learning model for cooked deli foods such as potato salad, Japanese simmered hijiki, and unohana evaluated by the proportion of relative error within the acceptable prediction range was 98%, the accuracy of the conventional Baranyi model-based prediction was 89%. The developed machine learning model successfully and flexibly predicted the growth of Leuconostoc spp. in various cooked deli foods incorporating the effect of food additives, with an accuracy comparable to or better than that of the conventional kinetic-based model.

Growth Kinetics of Autochthonous Lactic Acid Bacteria Isolated from Double Cream Cheese as Potential Starter Culture

Currently, there is a permanent search for native Lactic Acid Bacteria (LAB) to explore and apply their biodiversity in the development and improvement of industrial processes, with studies of growth kinetics and performance factors being a fundamental tool for biotechnological use. The objective of this study was to determine the kinetic parameters of 12 autochthonous strains (Pediococcus pentosaceus (19), Leuconostoc citreum (20), Pediococcus acidilactici (21), Leuconostoc mesenteroides subsp. Mesenteroides (22, 29), Enterococcus faecium (24, 25), Enterococcus faecalis (27), Weissella viridescens (28), Lactococcus lactis (30), Lacticaseibacillus casei (31) and Limosilactobacillus fermentum (32)) isolated from a traditional Colombian cheese Double Cream Cheese to obtain information that allows establishing the ideal conditions of the inoculum, standardizing the production of metabolites and exploring their use as starter cultures. Fermentation was carried out in UHT milk at 120 rpm and 35-37 °C until stationary phase, and samples were taken over time to determine pH and titratable acidity (TTA). Exponential and logistic models were used to fit the growth kinetics data. Validation of both models was carried out with the coefficient of determination R2, obtaining good consistency for both (R2 = 0.925 - R2 = 0.932), with slight variations in the kinetic parameters in all the strains. The genera Pediococcus, Leuconostoc, Lactococcus, and E. faecium (24) had the shortest adaptation phases (0-2 h), being P. acidilactici (21), Leu. mesenteroides (22), W. viridescens (28), and E. faecium (24) presented the lowest pH values and high acidity percentages, which shows their potential to be included in the native LAB starter culture and for technology and suitability aptitude studies to produce double cream cheese using pasteurized milk without losing the qualities of the original product.

Modeling bacteria pairwise interactions in human microbiota by Sparse Identification of Nonlinear Dynamics (SINDy).

The gut microbiota is a community of high complexity; its composition changes due to ecological interactions, these are studied to understand the relationship with the human health. External stimuli like the administration of probiotics, prebiotics, or drugs are known to modify these interactions. The high complexity of microbiota composition can be studied by considering pairwise interactions. Pairwise interactions in bacterial communities consider each species' directionality and impact on one another, e.g., commensalism (unidirectional positive interaction) or competition (bidirectional negative interaction). These interactions can either be interspecies or intraspecies. The Lotka-Volterra (LV) model has been implemented to characterize these bacteria interactions, considering the ecological relationship among the different species presented. One of the main challenges is determining the specific interaction parameters in LV structure from experimental data. This study implemented a novel approach based on the sparse identification of nonlinear dynamic method (SINDy). One of the assumptions in SINDy method implies the knowledge of the data derivative structure. To fulfill this requirement, a differential neural network algorithm was implemented. We assessed the performance of this approach considering both a simulated and experimental interspecies scenario. A two-species bacterial LV model was simulated in the initial validation stage, and the resulting kinetic growth data was recorded. This data was utilized for training a differential neural network algorithm, which was used to derive a time-derivative structure for the dataset. After this step, SINDy method was implemented to calculate the interaction parameters. Three conditions were evaluated in intraspecies competition, obtaining an average identification parametric error of less than 2%. For experimental data, parametric analysis results are sensitive to detect the influence of a drug presence over the intraspecies interaction with a reduction of 50% in its typical values.Clinical Relevance- In this study, we devised a strategy to determine how two species of the human gastrointestinal microbiota interact and the impact of drug administration on these interactions.

Cardinal models to describe the effect of temperature and pH on the growth of Anoxybacillus flavithermus & Bacillus licheniformis

Anoxybacillus flavithermus and Bacillus licheniformis are among the predominant spore-formers of heat-processed foods. To our knowledge, no systematic analysis of growth kinetic data of A. flavithermus or B. licheniformis is currently available. In the present study, the growth kinetics of A. flavithermus and B. licheniformis in broth at various temperature and pH conditions were studied. Cardinal models were used to model the effect of the above-mentioned factors on the growth rates. The estimated values for the cardinal parameters Tmin,Topt,Tmax,pHmin and pH1/2 for A. flavithermus were 28.70 ± 0.26, 61.23 ± 0.16 and 71.52 ± 0.32 °C, 5.52 ± 0.01 and 5.73 ± 0.01, respectively, while for B. licheniformis they were 11.68 ± 0.03, 48.05 ± 0.15, 57.14 ± 0.01 °C, 4.71 ± 0.01 and 5.670 ± 0.08, respectively. The growth behaviour of these spoilers was also investigated in a pea beverage at 62 and 49 °C, respectively, to adjust the models to this product. The adjusted models were further validated at static and dynamic conditions and demonstrated good performance with 85.7 and 97.4% of predicted populations for A. flavithermus and B. licheniformis, respectively, being within the −10%–10% relative error (RE) zone. The developed models can be useful tools in assessing the potential of spoilage of heat-processed foods including plant-based milk alternatives.

Ergothioneine yield of Rhodotorula species positively correlated with hydrogen peroxide tolerance

Ergothioneine (EGT) is a natural sulfur-containing amino acid with potent antioxidant and cytoprotective activities. In this study, the oxidation tolerance of 23 different Rhodotorula strains were screened with hydrogen peroxide (H2O2) as the inhibitory condition. By measuring their carotenoid and EGT production and correlation analysis with H2O2 tolerance, the results showed that the carotenoid synthesis capacity has no effect on H2O2 tolerance, in contrast to a strong positive correlation between EGT yield and H2O2 tolerance of Rhodotorula species. In addition, a high EGT-producing strain R. mucilaginosa DL-X01 was screened with the yield of 38.48 ± 2.71 mg/L in a shaking flask fermentation. To the best of our knowledge, this is the highest EGT yield ever reported in non-recombinant strains. Growth kinetic data showed that DL-X01 had a longer lag phase in the presence of exogenous oxidant, but a shorter generation time and higher specific growth rate after entering the exponential phase. The results of this study provided a potential simple and rapid method for screening high EGT-producing strains, which could promote the efficient production of EGT by microorganisms and further development of the red yeast in the food and biotechnology industries.

Metataxonomic and metabolic evaluation of three water kefir microbiomes cultured in sugar cane juice

AbstractThree water kefir grains (WKG) C, B, M with higher heterogeneity in the consortia C and M, and different biodiversity, dominated mainly by the genus Lactobacillus were selected from three Colombian locations and evaluated metataxonomically and metabolically using sugar cane juice (SCJ) as a culture medium, the molecular characterization was performed by sequencing in Illumina Miseq, and microbial diversity used Mothur platforms. The objective was to evaluate the SCJ on the consortium and the bacterial and fungal diversity immersed in the WKG. Biomass and secondary metabolites were monitored during 152 h. Growth kinetic data were fitted to the Logistic, Richards, and Gompertz models. WKG C rRNA 16S sequencing by Simpson's index (0.298) evidenced low species biodiversity, dominated by Lactobacillus sp. In C (0.986), the ITS1 marker indicated a more heterogeneous community than B and M, with Saccharomyces sp. as the predominating species. Parameters of the Gompertz model fitted to C biomass were: μmax (0.032 h−1), latency time (1.736 h), and adaptation (6.48 h). WKG consortia B showed higher acetic acid and ethanol production with 5.269 and 0.333 g/L, respectively. SCJ is a suitable medium for Lactobacillus sp. and Saccharomyces sp. strains in WKG.

Dynamic modelling of feed assimilation, growth, lipid accumulation, and CO2 production in black soldier fly larvae.

The black soldier fly (BSF) is becoming a novel farm animal. BSF larvae can be reared on different substrates. Their performance is important but highly variable and different models have been employed to analyze their growth, so far without considering that metabolic rates, growth, and biochemical composition of the larvae are interrelated. This work develops a dynamic model, which describes general growth patterns of BSF larvae and predicts observed variability in larval performances. The model was tested against data from literature, which combines kinetic growth data with measurements of lipid or dry weight content, and CO2 production. The model combines the kinetics of the logistic model with principles from differential energy budget models and considers key events in larval life history, moulting and metamorphosis. Larvae are compartmentised into structural biomass, storage lipids, and a pool of assimilates. Feed assimilation is considered the overall rate limiting process and is reduced in relation to larval weight by a logistic function. A second logistic function further reduces the specific growth rate of structural biomass, causes imbalance between and feed assimilation and growth rates, and leaves a surplus of assimilates to be stored as lipids. Fluxes between compartments consider cost of synthesis of structural biomass and lipids, as well as maintenance. When assimilation falls below maintenance needs, storage lipids are recycled. The model is able to describe growth and lipid contents of BSF larvae reared on chicken feed, growth of feed limited BSF larvae, as well as growth, dry weight content, and CO2 production of BSF larvae reared on different substrate qualities and moisture contents. The model may be used for the analysis of growth and performance of BSF larvae under variable rearing conditions. It can deepen the analyses of experimental data and provide insight into the causes of variability of larval performances.

An Insight into Efflux-Mediated Arsenic Resistance and Biotransformation Potential of Enterobacter Cloacae RSC3 from Arsenic Polluted Area.

Indiscriminate discharge of heavy metals/metalloids from different sources into the sustainable agro-ecosystem is a major global concern for food security and human health. Arsenic (As), categorized as group one human carcinogen is a quintessential toxic metalloid that alters the microbial compositions and functions, induce physiological and metabolic changes in plants and contaminate surface/ground water. The management of arsenic toxicity, therefore, becomes imminent. Acknowledging the arsenic threat, the study was aimed at identifying arsenic resistant bacteria and evaluating its arsenic removal/detoxification potential. Of the total 118 bacterial isolates recovered from arsenic rich environment, the bacterial strain RSC3 demonstrating highest As tolerance was identified as Enterobacter cloacae by 16S rRNA gene sequence analysis. Enterobacter cloacae tolerated high concentration (6000ppm) of As and exhibited 0.55h-1 of specific growth rate as calculated from growth kinetics data. Strain RSC3 also displayed varying level of resistance to other heavy metals and many antibacterial drugs in plate bioassay. The bacterial strain RSC3 possessed gene (arsC) which causes transformation of arsenate to arsenite. The arsenate uptake and efflux of the bacterial cells was revealed by high throughput techniques such as AAS, SEM/TEM and EDX. The simultaneous As reducing ability, and multi metal/multi-antibiotics resistance potentials of E. cloacae provides a promising option in the microbes based remediation of As contaminated environments.

Development and implementation of a simultaneous fatigue crack growth test setup for polymeric hybrid laminates

Delamination is a common and highly relevant in-service failure mode of laminated structures. Cyclic mechanical loads superimposed with environmental factors, such as hot-humid air, are most critical. To evaluate the cyclic debonding resistance of polymeric hybrid laminates, fracture mechanics approaches based on double cantilever beam (DCB) specimens are well established. However, conventional fatigue test setups allow for measurement of just one specimen which is rather time consuming. The main objective of this paper was to develop and implement a novel fatigue test setup for simultaneous characterization of multiple DCB specimens. To validate the simultaneous fatigue testing system, steel/epoxy laminates of different number of steel plies and epoxy adhesive layers with or without filler were characterized using the novel fatigue test setup and a conventional electrodynamic test machine. Fatigue crack growth data was fitted by a form of the Hartman-Schijve crack growth equation. To derive a statistically significant threshold value a fitting procedure was employed. A good agreement of the crack growth kinetics data at ambient conditions was obtained confirming the high quality and reliability of the novel simultaneous fatigue test setup. Moreover, reliable data were gathered at the beginning of the displacement–controlled experiments in the instable crack propagation regime, as the displacement amplitude was reached from the first cycle onward. The novel test methodology is of special relevance for development and screening of adhesive formulations.

Antibacterial Effect of Colloidal Suspensions Varying in Silver Nanoparticles and Ions Concentrations.

A lot of effort has been dedicated recently to provide a better insight into the mechanism of the antibacterial activity of silver nanoparticles (AgNPs) colloidal suspensions and their released silver ionic counterparts. However, there is no consistency regarding whether the antibacterial effect displayed at cellular level originates from the AgNPs or their ionic constitutes. To address this issue, three colloidal suspensions exhibiting different ratios of AgNPs/silver ions were synthesized by a wet chemistry method in conjunction with tangential flow filtration, and were characterized and evaluated for their antimicrobial properties against two gram-negative, Escherichia coli (E. coli) and Pseudomonas aeruginosa (P. aeruginosa), and two gram-positive, Staphylococcus aureus (S. aureus) and Staphylococcus epidermidis (S. epidermidis), bacterial strains. The produced samples contained 25% AgNPs and 75% Ag ions (AgNP_25), 50% AgNPs and 50% Ag ions (AgNP_50), and 100% AgNPs (AgNP_100). The sample AgNP_100 demonstrated the lowest minimum inhibitory concentration values ranging from 4.6 to 15.6 ppm for all four bacterial strains, while all three samples indicated minimum bactericidal concentration (MBC) values ranging from 16.6 ppm to 62.5 ppm against all strains. An increase in silver ions content results in higher bactericidal activity. All three samples were found to lead to a significant morphological damage by disruption of the bacterial cell membranes as analyzed by means of scanning electron microscopy (SEM). The growth kinetics demonstrated that all three samples were able to reduce the bacterial population at a concentration of 3.1 ppm. SEM and growth kinetic data underline that S. epidermidis is the most sensitive among all strains against the investigated samples. Our results showed that all three AgNPs colloidal suspensions exhibited strong antibacterial properties and, thus, they can be applied in medical devices and antimicrobial control systems.

Accounting for the Presence of Molecular Clusters in Modeling and Interpreting Nucleation and Growth.

The effect of molecular cluster formation on the estimation of kinetic parameters for primary nucleation and growth in different systems has been studied using computationally generated data and three sets of experimental data in the literature. It is shown that the formation of molecular clusters decreases the concentration of monomers and hence the thermodynamic driving force for crystallization, which consequently affects the crystallization kinetics. For a system exhibiting a strong tendency to form molecular clusters, accounting for cluster formation in a kinetic model is critical to interpret kinetic data accurately, for instance, to estimate the specific surface energy γ from a set of primary nucleation rates. On the contrary, for a system with negligible cluster formation, a consideration of cluster formation does not affect parameter estimation outcomes. Moreover, it is demonstrated that using a growth kinetic model that accounts for cluster formation allows the estimation of γ from typical growth kinetic data (i.e., de-supersaturation profiles of seeded batch crystallization), which is a novel method of estimating γ developed in this work. The applicability of the novel method to different systems is proven by showing that the estimated values of γ are closely comparable to the actual values used for generating the kinetic data or the corresponding estimates reported in the literature.

Bayesian Generalized Linear Model for Simulating Bacterial Inactivation/Growth Considering Variability and Uncertainty.

Conventional regression analysis using the least-squares method has been applied to describe bacterial behavior logarithmically. However, only the normal distribution is used as the error distribution in the least-squares method, and the variability and uncertainty related to bacterial behavior are not considered. In this paper, we propose Bayesian statistical modeling based on a generalized linear model (GLM) that considers variability and uncertainty while fitting the model to colony count data. We investigated the inactivation kinetic data of Bacillus simplex with an initial cell count of 105 and the growth kinetic data of Listeria monocytogenes with an initial cell count of 104. The residual of the GLM was described using a Poisson distribution for the initial cell number and inactivation process and using a negative binomial distribution for the cell number variation during growth. The model parameters could be obtained considering the uncertainty by Bayesian inference. The Bayesian GLM successfully described the results of over 50 replications of bacterial inactivation with average of initial cell numbers of 101, 102, and 103 and growth with average of initial cell numbers of 10–1, 100, and 101. The accuracy of the developed model revealed that more than 90% of the observed cell numbers except for growth with initial cell numbers of 101 were within the 95% prediction interval. In addition, parameter uncertainty could be expressed as an arbitrary probability distribution. The analysis procedures can be consistently applied to the simulation process through fitting. The Bayesian inference method based on the GLM clearly explains the variability and uncertainty in bacterial population behavior, which can serve as useful information for risk assessment related to food borne pathogens.

Antigenic Drift of the Hemagglutinin from an Influenza A (H1N1) pdm09 Clinical Isolate Increases its Pathogenicity In Vitro.

The influenza A (H1N1) pdm09 virus emerged in 2009 and has been continuously circulating in humans for over ten years. Here, we analyzed a clinical influenza A (H1N1) pdm09-infected patient case hospitalized for two months in Guangdong (from December 14, 2019 to February 15, 2020). This isolate, named A/Guangdong/LCF/2019 (LCF/19), was genetically sequenced, rescued by reverse genetics, and phylogenetically analyzed in the context of other relevant pdm09 isolates. Compared with earlier isolates, this pdm09 virus's genetic sequence contains four substitutions, S186P, T188I, D190A, and Q192E, of the hemagglutinin (HA) segment at position 186–192 (H3 numbering) in the epitope Sb, and two of which are located at the 190-helix. Phylogenetic analysis indicated that the epitope Sb started undergoing a rapid antigenic change in 2018. To characterize the pathogenicity of this novel substitution motif, a panel of reassortant viruses containing the LCF/2019 HA segment or the chimeric HA segment with the four substitutions were rescued. Kinetic growth data revealed that the reassortant viruses, including the LCF/2019 with the PTIAAQE substitution, propagated faster than those rescued ones having the STTADQQ motif in the epitope Sb in Madin-Darby Canine Kidney (MDCK) cells. The HI test showed that the binding activity of escape mutant to 2018 pdm09 sera was weaker than GLW/2018, suggesting that old vaccines might not effectively protect people from infection. Due to the difference in the selection of vaccine strains, people vaccinated in the southern hemisphere could still suffer a severe infection if infected with this antigenic drift pdm09 virus.

Toward Understanding the Growth of Cefradine in Aqueous Solution

Crystal growth, which is one of the fundamental steps in the crystallization process, can affect the morphology of crystals which will further affect physicochemical properties of products and downstream processing. In this study, crystal growth of cefradine in aqueous solution was systematically investigated by combining experimental methods and molecular dynamics simulations. Single crystal growth experiments were adopted to obtain the growth kinetics data of cefradine in water. Growth kinetics models considering the bulk diffusion step and surface integration step were applied to evaluate the experimental data. Surface chemistry, Connolly surface, radial distribution function, and electrostatic potential were calculated through molecular dynamics simulation to further explore the growth kinetics from the molecular level. The results revealed that water molecules preferred to be adsorbed onto the (1 0 1) crystal face than the (0 1 1) and (0 1̅ 1) surfaces due to the favored hydrophilicity and solvent adsorption sites. In addition, the stronger interaction between water molecules and the (1 0 1) crystal face by electrostatic and hydrogen-bonding interactions would further highly hinder the crystal growth along the radial direction. Finally, a possible growth mechanism based on blocking effect of solvent molecules was proposed to interpret the different growth kinetics along the axial and radial directions.

Crystallization of paracetamol from mixtures of ethanol and water in a planar oscillatory flow crystallizer: effect of the oscillation conditions on the crystal growth kinetics

Crystal growth kinetic data is reported for a planar oscillatory flow crystallizer.

Emergence of Novel Reassortant H1N1 Avian Influenza Viruses in Korean Wild Ducks in 2018 and 2019

Influenza A virus subtype H1N1 has caused global pandemics like the “Spanish flu” in 1918 and the 2009 H1N1 pandemic several times. H1N1 remains in circulation and survives in multiple animal sources, including wild birds. Surveillance during the winter of 2018–2019 in Korea revealed two H1N1 isolates in samples collected from wild bird feces: KNU18-64 (A/Greater white-fronted goose/South Korea/KNU18-64/2018(H1N1)) and WKU19-4 (A/wild bird/South Korea/WKU19-4/2019(H1N1)). Phylogenetic analysis indicated that M gene of KNU18-64(H1N1) isolate resembles that of the Alaskan avian influenza virus, whereas WKU19-4(H1N1) appears to be closer to the Mongolian virus. Molecular characterization revealed that they harbor the amino acid sequence PSIQRS↓GLF and are low-pathogenicity influenza viruses. In particular, the two isolates harbored three different mutation sites, indicating that they have different virulence characteristics. The mutations in the PB1-F2 and PA protein of WKU19-4(H1N1) indicate increasing polymerase activity. These results corroborate the kinetic growth data for WKU19-4 in MDCK cells: a dramatic increase in the viral titer after 12 h post-inoculation compared with that in the control group H1N1 (CA/04/09(pdm09)). The KNU18-64(H1N1) isolate carries mutations indicating an increase in mammal adaptation; this characterization was confirmed by the animal study in mice. The KNU18-64(H1N1) group showed the presence of viruses in the lungs at days 3 and 6 post-infection, with titers of 2.71 ± 0.16 and 3.71 ± 0.25 log10(TCID50/mL), respectively, whereas the virus was only detected in the WKU19-4(H1N1) group at day 6 post-infection, with a lower titer of 2.75 ± 0.51 log10(TCID50/mL). The present study supports the theory that there is a relationship between Korea and America with regard to reassortment to produce novel viral strains. Therefore, there is a need for increased surveillance of influenza virus circulation in free-flying and wild land-based birds in Korea, particularly with regard to Alaskan and Asian strains.

Growth-associated catabolic potential of Acetoanaerobium sticklandii DSM 519 on gelatin and amino acids.

Acetoanaerobium sticklandii DSM 519 is a hyperammonia-producing anaerobe that catabolizes proteins and amino acids into organic solvents and volatile acids via the Stickland reactions. However, the specific growth rate and metabolic capability of this organism on proteins and amino acids are not yet known. Therefore, the present study was intended to evaluate its specific growth rate and metabolic potential on gelatin and amino acids in the experimental media. We carried out metabolic assay experiments to calculate its ability to utilize pure gelatin, single amino acids, and amino acid pairs at different growth phases. The results of this study show that complete assimilation of gelatin was achieved by its log-phase culture. The subsequent fermentation of amino acids was much faster than gelatin hydrolysis. The rate of gelatin degradation was associated with the growth and catabolic rates of this organism. Many amino acids were not assimilated completely for its growth and energy conservation. A log-phase culture of this organism preferably utilized l-cysteine, l-arginine, and l-serine, and released more fraction of ammonia. As shown by our analysis, the catabolic rates of these amino acids were determined by the rates of respective enzymes involved in amino acid catabolic pathways and feedback repression of ammonia. The growth kinetic data indicated that at the initial growth stage, a metabolic shift in its solventogenesis and acidogenesis phases was associated with catabolism of certain amino acids. Thus, the results of this study provide a new insight to exploit its log-phase culture as a starter for the production of biofuel components from gelatin processing industries.

Growth kinetics of σ-phase precipitates and underlying diffusion processes in CrMnFeCoNi high-entropy alloys

Key mechanisms and elementary diffusion processes that control the growth kinetics of σ precipitates in high-entropy alloys were investigated in the present study. For this purpose, an off-equiatomic Cr26Mn20Fe20Co20Ni14 alloy with an initially single-phase FCC structure was subjected to isothermal heat treatments, which are known to promote the formation of σ phase, i.e., aging between 600 °C and 1000 °C for times ranging from 0.1 h to 1000 h. The growth kinetics of σ precipitates at grain boundaries of the FCC matrix and those located within the interior of the grains were analyzed separately. The latter precipitates are found to grow through direct substitutional diffusion of Cr-solutes towards and Mn, Fe, Co, and Ni away from them and the growth rate of the allotriomorphs can be rationalized by the collector plate mechanism of interfacial diffusion-aided growth. From the growth-kinetics data obtained in the present study, lattice interdiffusion coefficients as well as diffusivities along crystalline defects were obtained. Above 800 °C, the growth kinetics are dominated by lattice interdiffusion of Cr in the FCC matrix described by DL = 9.8 × 10-4 exp[(-300 kJ/mol)/(RT)] m2/s. At lower temperatures, the growth kinetics are enhanced by fast interdiffusion along dislocation pipes, which temperature dependence is given by DD = 5.0 × 10-3 exp[(-205 kJ/mol)/(RT)] m2/s. The Cr-diffusivity along σ/FCC interphase boundaries deduced from the thickening kinetics of grain boundary precipitates can be represented by the Arrhenius relationship DI = 0.5 × 10-4 exp[(-145 kJ/mol)/(RT)] m2/s, which is similar to that found for grain boundary interdiffusion in metals and alloys.

Characterization of Fungal Surface Contaminants of the Small Maltese June Pear, Pyrus communis var. bambinella

Fungal pathogens cause surface contamination and potential premature fruit spoilage of bambinella, a fruit endemic to the Maltese islands, leading to the loss of fruit during the postharvest phase. The objective of this study was to isolate, quantify, and characterize fungal contaminants of the small Maltese June Pear and describe their growth kinetics. In total, 284 fungicide-free fruits were collected over three consecutive summers (2014, 2015, 2016). The isolated fungi were identified by using forward and reverse colonial morphology. Species identification was determined using PCR-based methods. The number of CFU per square centimeter of bambinella outer skin was calculated. Mycelium diameter growth rate studies of the isolates were also carried out at seven different temperatures, ranging from 5 to 35°C. Fungi isolated from bambinella included Cladosporium ramotenellum, Alternaria arborescens, Penicillium lanosum, Penicillium expansum, and Aspergillus sydowii, listed from the most abundant to the least abundant. The Rosso model was fitted to the growth kinetic data and showed that the optimal temperatures for growth of all five fungi were in the range of 20 to 22°C, whereas growth was slower at temperatures below 10°C and above 30°C. As observed in the diameter studies, the order of highest to lowest germination rate was found to be P. expansum, A. sydowii, P. lanosum, C. ramotenellum, and A. arborescens. Germination studies showed that the highest germination rate was observed for P. lanosum, followed by A. arborescens, C. ramotenellum, P. expansum, and A. sydowii, in descending order. The highest germination lag time was observed for A. arborescens, followed by C. ramotenellum, P. expansum, P. lanosum, and A. sydowii, in ascending order.

Growth Kinetics of Σ-Phase Precipitates and Underlying Diffusion Processes in Crmnfeconi High-Entropy Alloys

Key mechanisms and elementary diffusion processes that control the growth kinetics of σ precipitates in high-entropy alloys were investigated in the present study. For this purpose, an off-equiatomic Cr26Mn20Fe20Co20Ni14 alloy with an initially single-phase FCC structure was subjected to isothermal heat treatments, which are known to promote the formation of σ phase, i.e. aging between 600 °C and 1000 °C for times ranging from 0.1 h to 1000 h. The growth kinetics of σ precipitates at grain boundaries of the FCC matrix and those located within the interior of the grains were analyzed separately. The latter precipitates are found to grow through direct substitutional diffusion of Cr-solutes towards and Mn, Fe, Co, and Ni away from them and the growth rate of the allotriomorphs can be rationalized by the collector plate mechanism of interfacial diffusion-aided growth. From the growth-kinetics data obtained in the present study, lattice interdiffusion coefficients as well as diffusivities along crystalline defects were obtained. Above 800 °C, the growth kinetics are dominated by lattice interdiffusion of Cr in the FCC matrix described by DL = 9.8 × 10-4 exp[( 300 kJ/mol)/(RT)] m2/s. At lower temperatures, the growth kinetics are enhanced by fast interdiffusion along dislocation pipes, which temperature dependence is given by DD = 2.5 × 10-3 exp[( 210 kJ/mol)/(RT)] m2/s. The Cr-diffusivity along σ/FCC interphase boundaries deduced from the thickening kinetics of grain boundary precipitates can be represented by the Arrhenius relationship DI = 0.5 × 10-4 exp[( 145 kJ/mol)/(RT)] m2/s, which is similar to that found for grain boundary interdiffusion in metals and alloys.