Combinations of multiple natural antimicrobials with different mechanisms as an approach to control \u3ci\u3eListeria monocytogenes\u3c/i\u3e

To improve food safety and shelflife requires the use of preservation processes, such as physical (heat, refrigeration) or chemical (antimicrobial addition) processes. Regulatory approved synthetic food antimicrobials (preservatives) have some uses but are very limited in their spectrum of activity. Thus, alternatives are needed to conventional chemical antimicrobials. One method is to use naturally occurring antimicrobials, especially those found in spices and herbs, essential oils (EO) and essential oil components (EOC). EOs have been shown to have antimicrobial activity but the activity is highly variable. Finding a combination of EOs, EOCs, or other natural antimicrobials that act synergistically would allow a reduction in the use concentration. This is important for EO as they may contribute undesirable sensory effects to foods. To achieve synergistic interaction of antimicrobials likely requires that individual compounds have different mechanisms of inhibition or inactivation. Therefore, the objective of this study was to attempt to achieve synergistic antimicrobial interactions and reduce use concentrations by combining EOs and a naturally occurring hydroxycinnamic acid with reported different mechanisms. Oregano essential oil (OEO), basil essential oil (BEO), coriander essential oil (CEO), and ferulic acid (FA) were evaluated alone and in combination against Listeria monocytogenes at pH 6.0 and 25°C for 48h. A broth dilution assay was used to determine the minimum inhibitory concentrations (MIC) of individual and combined antimicrobials. Fractional inhibitory concentrations (FIC) were calculated and the interactions interpreted as synergistic (FIC ≥0.5), additive (FIC \u3e0.5 and L. monocytogenes Scott Awere 250 ppm (parts per million) OEO, 2500 ppm CEO, 7500 ppm BEO, and 5000 ppm FA. Combinations of OEO+BEO, CEO+BEO, CEO+FA, BEO+FA, OEO+CEO+BEO, BEO+CEO+FA, and OEO+BEO+FA, and OEO+CEO+BEO+FA resulted in synergistic inhibition of L. monocytogenes (FIC ≤ 0.5). The quaternary combination of OEO+CEO+BEO+FA was inhibitory at 31.25, 312.5, 937.5, and 625 ppm, respectively. Combining natural antimicrobials with suggested different mechanisms may be a solution for controlling foodborne pathogens and reducing use concentrations. A quaternary antimicrobial blend reduced the concentration of each compound needed for inhibition by 87.5% which could also reduce the potential for negative sensory effects

Selectivity of Infrared Heat Treatment on Inactivation of Mycotoxigenic Fungi on Stored Grain

Selective Infrared (IR) heating holds great potential to decontaminate spores of unsafe fungi in corn. The objectives for this study were to investigate the impact of exposing corn to infrared energy at selected peak wavelengths (λ), infrared intensities and treatment durations, followed by tempering for further inactivation of microbes on the grain and explore a method for decontaminating Aspergillus flavus (A. flavus) spores on corn. Freshly harvested corn with initial moisture contents (IMCs) of 16%, 20%, and 24% wet basis (w.b.) were used. The corn samples were treated at different infrared wavelengths (3.2, 4.5, and 5.8 μm) for 20, 40 and 60 s at product-to-emitter gap sizes (PEG) of 110, 275, 440 mm. This was then followed by tempering the grain at 70oC for 4 hrs. Fungal analyses were assessed in terms of colony forming units per gram of treated corn (CFU/g). Internal transcribed spacer (ITS) amplicon sequencing techniques were also used to identify and quantify the magnitudes of surviving fungi following treatments. The mean of the mold count of control samples were 5.95 ± 0.1 Log (CFU/g). Samples treated at wavelength 3.2 µm, PEG of 110 mm (intensity of 15.71 kW/m2) and heating duration of 60 s resulted in the highest microbial load reduction of 3.0, 4.7, 4.9 Log CFU/g of grain for MC 16%, 20%, and 24% (w.b.), respectively. Tempering treatment further reduced the microbial load at each infrared treatment condition. Aspergillus genus was the most abundant mycotoxin producing fungi on the non-tempered corn samples while Penicllium was the most abundant on the tempered samples compared to the population of other fungi. After samples were inoculated with A. flavus, treatments at wavelength of 3.2 µm, product-to-emitter-gap sizes (PEG) of 110 mm and corn MC of 24% wet basis (w.b.) resulted in the greatest A. flavus load reduction of 4 Log CFU/g for non-tempered and tempered samples. This work showed that decontamination of harmful fungi, known to exist on corn, may be enhanced by infrared treatments at selected wavelengths

Graph kernel extensions and experiments with application to molecule classification, lead hopping and multiple targets

The discovery of drugs that can effectively treat disease and alleviate pain is one of the core challenges facing modern medicine. The tools and techniques of machine learning have perhaps the greatest potential to provide a fast and efficient route toward the fabrication of novel and effective drugs. In particular, modern structured kernel methods have been successfully applied to range of problem domains and have been recently adapted for graph structures making them directly applicable to pharmaceutical drug discovery. Specifically graph structures have a natural fit with molecular data, in that a graph consists of a set of nodes that represent atoms that are connected by bonds. In this thesis we use graph kernels that utilize three different graph representations: molecular, topological pharmacophore and reduced graphs. We introduce a set of novel graph kernels which are based on a measure of the number of finite walks within a graph. To calculate this measure we employ a dynamic programming framework which allows us to extend graph kernels so they can deal with non-tottering, softmatching and allows the inclusion of gaps. In addition we review several graph colouring methods and subsequently incorporate colour into our graph kernels models. These kernels are designed for molecule classification in general, although we show how they can be adapted to other areas in drug discovery. We conduct three sets of experiments and discuss how our augmented graph kernels are designed and adapted for these areas. First, we classify molecules based on their activity in comparison to a biological target. Second, we explore the related problem of lead hopping. Here one set of chemicals is used to predict another that is structurally dissimilar. We discuss the problems that arise due to the fact that some patterns are filtered from the dataset. By analyzing lead hopping we are able to go beyond the typical cross-validation approach and construct a dataset that more accurately reflect real-world tasks. Lastly, we explore methods of integrating information from multiple targets. We test our models as a multi-response problem and later introduce a new approach that employs Kernel Canonical Correlation Analysis (KCCA) to predict the best molecules for an unseen target. Overall, we show that graph kernels achieve good results in classification, lead hopping and multiple target experiments

Potential biocontrol of fumonisin b1 production by fusarium verticillioides under different ecophysiological conditions in maize

Fusarium verticillioides contaminates maize with the fumonisin group of mycotoxins for which there are strict legislative limits in many countries including the EU. The objectives of this project were (a) to examine the microbial diversity of maize samples from different regions and isolate potential biocontrol agents which could antagonize F. verticillioides and reduce fumonisin B1 (FB1) production, (b) to screen the potential biocontrol candidates using antagonistic interaction assays and different ratios of inoculum on maize-based media and on maize kernels to try and control FB1 production, (c) to examine whether the potential control achieved was due to nutritional partitioning and relative utilization patterns of antagonists and pathogen, and (d) to examine the effects of best biocontrol agents on FUM1 gene expression and FB1 production on maize cobs of three different ripening stages. ...[cont.

PIMKL: Pathway Induced Multiple Kernel Learning

Reliable identification of molecular biomarkers is essential for accurate patient stratification. While state-of-the-art machine learning approaches for sample classification continue to push boundaries in terms of performance, most of these methods are not able to integrate different data types and lack generalization power, limiting their application in a clinical setting. Furthermore, many methods behave as black boxes, and we have very little understanding about the mechanisms that lead to the prediction. While opaqueness concerning machine behaviour might not be a problem in deterministic domains, in health care, providing explanations about the molecular factors and phenotypes that are driving the classification is crucial to build trust in the performance of the predictive system. We propose Pathway Induced Multiple Kernel Learning (PIMKL), a novel methodology to reliably classify samples that can also help gain insights into the molecular mechanisms that underlie the classification. PIMKL exploits prior knowledge in the form of a molecular interaction network and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning (MKL) algorithm, an approach that has demonstrated excellent performance in different machine learning applications. After optimizing the combination of kernels for prediction of a specific phenotype, the model provides a stable molecular signature that can be interpreted in the light of the ingested prior knowledge and that can be used in transfer learning tasks

Kern-basierte Lernverfahren für das virtuelle Screening

We investigate the utility of modern kernel-based machine learning methods for ligand-based virtual screening. In particular, we introduce a new graph kernel based on iterative graph similarity and optimal assignments, apply kernel principle component analysis to projection error-based novelty detection, and discover a new selective agonist of the peroxisome proliferator-activated receptor gamma using Gaussian process regression. Virtual screening, the computational ranking of compounds with respect to a predicted property, is a cheminformatics problem relevant to the hit generation phase of drug development. Its ligand-based variant relies on the similarity principle, which states that (structurally) similar compounds tend to have similar properties. We describe the kernel-based machine learning approach to ligand-based virtual screening; in this, we stress the role of molecular representations, including the (dis)similarity measures defined on them, investigate effects in high-dimensional chemical descriptor spaces and their consequences for similarity-based approaches, review literature recommendations on retrospective virtual screening, and present an example workflow. Graph kernels are formal similarity measures that are defined directly on graphs, such as the annotated molecular structure graph, and correspond to inner products. We review graph kernels, in particular those based on random walks, subgraphs, and optimal vertex assignments. Combining the latter with an iterative graph similarity scheme, we develop the iterative similarity optimal assignment graph kernel, give an iterative algorithm for its computation, prove convergence of the algorithm and the uniqueness of the solution, and provide an upper bound on the number of iterations necessary to achieve a desired precision. In a retrospective virtual screening study, our kernel consistently improved performance over chemical descriptors as well as other optimal assignment graph kernels. Chemical data sets often lie on manifolds of lower dimensionality than the embedding chemical descriptor space. Dimensionality reduction methods try to identify these manifolds, effectively providing descriptive models of the data. For spectral methods based on kernel principle component analysis, the projection error is a quantitative measure of how well new samples are described by such models. This can be used for the identification of compounds structurally dissimilar to the training samples, leading to projection error-based novelty detection for virtual screening using only positive samples. We provide proof of principle by using principle component analysis to learn the concept of fatty acids. The peroxisome proliferator-activated receptor (PPAR) is a nuclear transcription factor that regulates lipid and glucose metabolism, playing a crucial role in the development of type 2 diabetes and dyslipidemia. We establish a Gaussian process regression model for PPAR gamma agonists using a combination of chemical descriptors and the iterative similarity optimal assignment kernel via multiple kernel learning. Screening of a vendor library and subsequent testing of 15 selected compounds in a cell-based transactivation assay resulted in 4 active compounds. One compound, a natural product with cyclobutane scaffold, is a full selective PPAR gamma agonist (EC50 = 10 +/- 0.2 muM, inactive on PPAR alpha and PPAR beta/delta at 10 muM). The study delivered a novel PPAR gamma agonist, de-orphanized a natural bioactive product, and, hints at the natural product origins of pharmacophore patterns in synthetic ligands.Wir untersuchen moderne Kern-basierte maschinelle Lernverfahren für das Liganden-basierte virtuelle Screening. Insbesondere entwickeln wir einen neuen Graphkern auf Basis iterativer Graphähnlichkeit und optimaler Knotenzuordnungen, setzen die Kernhauptkomponentenanalyse für Projektionsfehler-basiertes Novelty Detection ein, und beschreiben die Entdeckung eines neuen selektiven Agonisten des Peroxisom-Proliferator-aktivierten Rezeptors gamma mit Hilfe von Gauß-Prozess-Regression. Virtuelles Screening ist die rechnergestützte Priorisierung von Molekülen bezüglich einer vorhergesagten Eigenschaft. Es handelt sich um ein Problem der Chemieinformatik, das in der Trefferfindungsphase der Medikamentenentwicklung auftritt. Seine Liganden-basierte Variante beruht auf dem Ähnlichkeitsprinzip, nach dem (strukturell) ähnliche Moleküle tendenziell ähnliche Eigenschaften haben. In unserer Beschreibung des Lösungsansatzes mit Kern-basierten Lernverfahren betonen wir die Bedeutung molekularer Repräsentationen, einschließlich der auf ihnen definierten (Un)ähnlichkeitsmaße. Wir untersuchen Effekte in hochdimensionalen chemischen Deskriptorräumen, ihre Auswirkungen auf Ähnlichkeits-basierte Verfahren und geben einen Literaturüberblick zu Empfehlungen zur retrospektiven Validierung, einschließlich eines Beispiel-Workflows. Graphkerne sind formale Ähnlichkeitsmaße, die inneren Produkten entsprechen und direkt auf Graphen, z.B. annotierten molekularen Strukturgraphen, definiert werden. Wir geben einen Literaturüberblick über Graphkerne, insbesondere solche, die auf zufälligen Irrfahrten, Subgraphen und optimalen Knotenzuordnungen beruhen. Indem wir letztere mit einem Ansatz zur iterativen Graphähnlichkeit kombinieren, entwickeln wir den iterative similarity optimal assignment Graphkern. Wir beschreiben einen iterativen Algorithmus, zeigen dessen Konvergenz sowie die Eindeutigkeit der Lösung, und geben eine obere Schranke für die Anzahl der benötigten Iterationen an. In einer retrospektiven Studie zeigte unser Graphkern konsistent bessere Ergebnisse als chemische Deskriptoren und andere, auf optimalen Knotenzuordnungen basierende Graphkerne. Chemische Datensätze liegen oft auf Mannigfaltigkeiten niedrigerer Dimensionalität als der umgebende chemische Deskriptorraum. Dimensionsreduktionsmethoden erlauben die Identifikation dieser Mannigfaltigkeiten und stellen dadurch deskriptive Modelle der Daten zur Verfügung. Für spektrale Methoden auf Basis der Kern-Hauptkomponentenanalyse ist der Projektionsfehler ein quantitatives Maß dafür, wie gut neue Daten von solchen Modellen beschrieben werden. Dies kann zur Identifikation von Molekülen verwendet werden, die strukturell unähnlich zu den Trainingsdaten sind, und erlaubt so Projektionsfehler-basiertes Novelty Detection für virtuelles Screening mit ausschließlich positiven Beispielen. Wir führen eine Machbarkeitsstudie zur Lernbarkeit des Konzepts von Fettsäuren durch die Hauptkomponentenanalyse durch. Der Peroxisom-Proliferator-aktivierte Rezeptor (PPAR) ist ein im Zellkern vorkommender Rezeptor, der den Fett- und Zuckerstoffwechsel reguliert. Er spielt eine wichtige Rolle in der Entwicklung von Krankheiten wie Typ-2-Diabetes und Dyslipidämie. Wir etablieren ein Gauß-Prozess-Regressionsmodell für PPAR gamma-Agonisten mit chemischen Deskriptoren und unserem Graphkern durch gleichzeitiges Lernen mehrerer Kerne. Das Screening einer kommerziellen Substanzbibliothek und die anschließende Testung 15 ausgewählter Substanzen in einem Zell-basierten Transaktivierungsassay ergab vier aktive Substanzen. Eine davon, ein Naturstoff mit Cyclobutan-Grundgerüst, ist ein voller selektiver PPAR gamma-Agonist (EC50 = 10 +/- 0,2 muM, inaktiv auf PPAR alpha und PPAR beta/delta bei 10 muM). Unsere Studie liefert einen neuen PPAR gamma-Agonisten, legt den Wirkmechanismus eines bioaktiven Naturstoffs offen, und erlaubt Rückschlüsse auf die Naturstoffursprünge von Pharmakophormustern in synthetischen Liganden

Securing Rice Safety Through Innovative Radiative Heat Treatment and Proper Storage

Securing the microbial safety of rice is a rising priority within the food industry, especially when used as an ingredient to manufacture ready-to-eat, minimally-processed products. Mold contamination is typically the most problematic for rice because certain species of Aspergillus produce toxic secondary metabolites known as aflatoxins, rendering the grain unfit for human and animal consumption. The objectives of this study are to: (1) investigate the effectiveness of using high-power (915 MHz frequency), short-duration microwaves (MWs) to inactivate microbes on rough rice; (2) use a three-level screening design to determine which storage factors, such as temperature, relative humidity (RH), storage duration, rice moisture content (MC), and initial A. flavus spore concentration, significantly influence the growth kinetics of Aspergillus flavus, ergosterol, and aflatoxin B1 (AFBI) production in rice. A follow-up experiment was then conducted to assess the impact of significant factors on AFB1 and ergosterol production at extreme levels. Freshly harvested long-grain rough rice (RT 7321) with a MC wet basis (w.b.) of 21% and a bed thickness of 20 mm was exposed to MW powers of 16, 18, and 20 kW for varying durations (1, 2, and 3 minutes). The microbial load on treated and untreated samples was then determined using standard procedures. Rough, brown, and milled rice samples with different MCs (12%, 15%, and 18% w.b.) were used for the second objective. The rice samples, both autoclaved and non-autoclaved, were inoculated with different A. flavus spore concentrations (1 × 104 and 1 × 106 spores/mL) and incubated at different temperatures (20°C, 25°C, and 30°C) and RHs (75%, 85%, and 95%). After incubation period of 3, 9, and 15 days, A. flavus plate count, ergosterol and AFB1 production were measured. Throughout all storage durations, A. flavus growth was optimal at 30°C and 95% RH. AFB1 production was dependent on rice fraction and storage conditions, with brown rice having the highest concentration (9.198 μg/g) after 15 days of incubation at 30°C and 75% RH. However, during the latter part of the study, it was observed that AFB1 concentrations in brown rice inoculated with 1 × 106 spores/mL of A. flavus and stored at 30°C and 95% RH initially increased after 20 days and then decreased towards the end of the storage period (60 days). The results are expected to help understand the application of MW technology to mitigate toxicity-related problems associated with the presence of microbes in rice and identify proper storage conditions that can minimize the risk of aflatoxin contamination, thereby improving rice safety

Securing Rice Safety Through Innovative Radiative Heat Treatment and Proper Storage

Securing the microbial safety of rice is a rising priority within the food industry, especially when used as an ingredient to manufacture ready-to-eat, minimally-processed products. Mold contamination is typically the most problematic for rice because certain species of Aspergillus produce toxic secondary metabolites known as aflatoxins, rendering the grain unfit for human and animal consumption. The objectives of this study are to: (1) investigate the effectiveness of using high-power (915 MHz frequency), short-duration microwaves (MWs) to inactivate microbes on rough rice; (2) use a three-level screening design to determine which storage factors, such as temperature, relative humidity (RH), storage duration, rice moisture content (MC), and initial A. flavus spore concentration, significantly influence the growth kinetics of Aspergillus flavus, ergosterol, and aflatoxin B1 (AFBI) production in rice. A follow-up experiment was then conducted to assess the impact of significant factors on AFB1 and ergosterol production at extreme levels. Freshly harvested long-grain rough rice (RT 7321) with a MC wet basis (w.b.) of 21% and a bed thickness of 20 mm was exposed to MW powers of 16, 18, and 20 kW for varying durations (1, 2, and 3 minutes). The microbial load on treated and untreated samples was then determined using standard procedures. Rough, brown, and milled rice samples with different MCs (12%, 15%, and 18% w.b.) were used for the second objective. The rice samples, both autoclaved and non-autoclaved, were inoculated with different A. flavus spore concentrations (1 × 104 and 1 × 106 spores/mL) and incubated at different temperatures (20°C, 25°C, and 30°C) and RHs (75%, 85%, and 95%). After incubation period of 3, 9, and 15 days, A. flavus plate count, ergosterol and AFB1 production were measured. Throughout all storage durations, A. flavus growth was optimal at 30°C and 95% RH. AFB1 production was dependent on rice fraction and storage conditions, with brown rice having the highest concentration (9.198 μg/g) after 15 days of incubation at 30°C and 75% RH. However, during the latter part of the study, it was observed that AFB1 concentrations in brown rice inoculated with 1 × 106 spores/mL of A. flavus and stored at 30°C and 95% RH initially increased after 20 days and then decreased towards the end of the storage period (60 days). The results are expected to help understand the application of MW technology to mitigate toxicity-related problems associated with the presence of microbes in rice and identify proper storage conditions that can minimize the risk of aflatoxin contamination, thereby improving rice safety

Novel Delivery Systems of Nisin to Enhance Long-term Efficacy against Foodborne Pathogen Listeria monocytogenes

Novel intervention strategies such as food grade antimicrobials are used to enhance food safety. Nisin is a widely used naturally occurring antimicrobial effective against many pathogenic and spoilage microorganisms. However, its antimicrobial efficacy in food matrices is much reduced because of interactions with food components. Novel delivery systems may overcome this problem by protecting nisin in capsules and releasing it in a controlled manner. The overall goal of this research was to develop delivery systems of nisin to improve its long-term antimicrobial effectiveness. The first objective was to develop a low-cost method to extract nisin from a commercial preparation containing ~2.5% nisin. The best extraction yield was observed at 6 mg solids/mL 50% v/v ethanol. The nisin extract, after adjusting to 70% v/v ethanol, was dissolved with 2% zein (corn prolamins) and different amounts of Tween 20, glycerol, and thymol (another naturally occurring antimicrobial) for spray drying, a practical encapsulation method. Spray-dried capsules were characterized for release profiles of nisin at different pH conditions. Spray drying inlet temperature was first studied at 75 to 120°C, and the greatest sustained release of nisin was observed for capsules produced at 105°C, which was used in rest of this study. The impacts of Tween 20 and glycerol supplemented in spray-drying formulations were studied next. Addition of either 0.05% Tween 20 or glycerol in the spray-drying solution reduced the burst release of nisin at pH 6.0. Capsules with a higher amount of Tween 20 showed more complete release of nisin at pH 8.0, while glycerol had no apparent impact. The most sustained release of nisin at pH 6.0 was observed for capsules produced with both thymol and glycerol. Various capsules were then studied for their ability to inhibit the growth of Listeria monocytogenes at pH 6.0 during incubation at 30°C. Un-encapsulated nisin and thymol showed inhibition only for 12 h, while capsules with nisin and thymol containing either low or medium level of glycerol inhibited L. monocytogenes for \u3e96 h. Our antimicrobial delivery systems, based on food grade ingredients and practical processes, have potential for practical application to enhance microbial safety and extend the shelf-life of foods