Microbiological hygiene and biological control of leafy green vegetables

Fruit and vegetables are vital components of a healthy diet, and international strategies to encourage their consumption are in place (FAO/WHO, 2005). Ready-to-eat (RTE) leafy green vegetable products have exploded in popularity, and are a convenient and attractive way to add greens to the plate. Unfortunately, sales numbers are accompanied by increasing numbers of food-borne illness outbreaks.This thesis focuses on the microbial hygiene from a consumer’s perspective, followed by application and evaluation of biological control as a mean of reducing the risk of food-borne illness. When studying the efficacy of two different household washing methods to reduce the bacterial load on leafy green vegetables, it was seen that only after rinsing at high water velocity (8 L/min), after five repetitions, the bacterial load decreased with 90 %. The treatment disintegrated the leaves, and still left the produce with high amounts of culturable bacteria. These results highlight the inefficiency of tap water washing methods available for the consumer. Consumer habits are also important to consider when assessing the microbial hygiene of food products. Packages of RTE leafy green products were opened, stored at 7 ˚C and compared with unopened bags. The total aerobic count from different producers varied greatly and no correlation to opened bags could be made. Neither could bacterial levels be linked to a certain producer or product type.Inoculation with E. coli strains indicate that the type strain is able to survive, but not multiply, in household conditions. However, wild strains of E. coli were seen to multiply at different time-points during the shelf-life period, adapting to cold storage conditions. This varying and unpredictable bacterial status of ready to eat leafy green products calls for new strategies to reduce unwanted microorganisms and prevent food-borne illness.By the means of biological control, bacteria can be used to counteract food safety hazards. Therefore, isolates antagonistic to E. coli have been isolated and evaluated in an industrial field production setting. Selected isolates showing antagonism in vitro were coated onto spinach seeds before planting. Next generation sequencing analysis revealed that the microbiota of the plants inoculated with the selected strains was altered in a beneficial direction, and a reduction of Escherichia-Shigella could be seen during the development from seed to plant.As a tentative safety evaluation of the selected strains for biological control, an individual comparison for immunomodulating effects in mice was made. The two Bacillus coagulans strains consistently resemble the response of untreated animals, which must be considered a positive trait. The strain of Pseudomonas punonensis had a weaker influence on the immune system, while the Pseudomonas cedrina strain and the Rhocococcus cerastii strain induced inflammatory responses. The P. punonensis strain and one B. coagulans strain increased the microbiota diversity, which is correlated to host health.These results encourage the usage of bacterial antagonists as part of the solution to reduce the risk of human pathogens on leafy green vegetables

Computational models of cognition

Existing connectionist computational models of neural networks idealise the biological process in the neuron to a discrete summation, and fail to provide an efficient substrate for computation involving the spectral data that is the input to the biological perceptual process. This work presents a computational model of neural function that introduces a continuous analogue process and explores the computational uses of sub-threshold oscillations of the membrane potential. The goal of tins work is to present an in itial examination of the advantages to the practitioner that are afforded by a new computational model of the neuron that includes sub-threshold oscillations as a component on an equal footing with axonal impulses themselves. The relevant. evidence that these effects are important in a biological neural network is presented. The new resonate-and-fire model is presented and mathematically defined, and shown to be a superset of the ubiquitous integrate-and-fire model. The behaviour patterns of the model are explored initially in single neurons and then networks are examined and shown to be capable of exhibiting useful excitation patterns such as tonic oscillation, selective innervation and resonance. An unsupervised learning algorithm is defined and shown to generate networks that naturally organise to perform Fourier-style transforms central to spectral manipulations. Finally, the model is examined with respect to the current theories of computational neuroscience and cognitive science, and its p otential uses in these domains described

Imaging plasticity and structure of cortical maps in cat and mouse visual cortex

The study reported in the first part of this thesis utilized optical imaging of intrinsic signals to visualize changes in orientation maps in cat visual cortex induced by pairing a visual stimulus with an intracortical electrical stimulation. We found that the direction of plasticity within orientation maps depends critically on the relative timing between visual and electrical stimulation on a millisecond time scale: a shift in orientation preference towards the paired orientation was observed if the cortex was first visually and then electrically stimulated. In contrast, the cortical response to the paired orientation was diminished if the electrical preceded the visual cortical stimulation. Spike-time-dependent plasticity has been observed in single cell studies; however, our results demonstrate an analogous effect at the systems level in the live animal. Thus, timing-dependent plasticity needs to be incorporated into our conception of cortical map development. While the pairing paradigm induced pronounced shifts in orientation preference, the general setup of the orientation preference map remained unaltered. In order to unravel potential factors contributing to this overall stability, we determined the distribution of plasticity across the cortical surface. We found that pinwheel centers, points were domains of all orientation meet, exhibited less plasticity than other regions of the orientation map. The resistance of pinwheel centers to changes in orientation preference may support maintenance of the general structure of the orientation map. The study that forms the second part employs optical imaging to visualize the retinotopy in mouse visual cortex. We were able to resolve the pattern of retinotopic activity with high precision and reliability in the primary visual cortex (area 17). Functional imaging of the position, size and shape of area 17 corresponded exactly to the location of this area in stained histological sections. The imaged maps were also confirmed with electrophysiological recordings. The retinotopic structure of area 17 showed very low inter-animal variability, thus allowing averaging maps across animals and therefore statistical analysis. These averaged maps greatly facilitated the identification of at least four extrastriate visual areas. In addition, we detected decreases in the intrinsic signal below baseline with a shape and location reminiscent of lateral inhibition. This decrease of the intrinsic signal was shown to be correlated with a decrease in neuronal firing rate below baseline. Both studies were facilitated by the development of a signal analysis technique (part III), which improves the quality of optical imaging data. Intrinsic signal fluctuations originating from blood vessels were minimized based on their correlation with the actual superficial blood vessel pattern. These fluctuation components were then extracted from images obtained during sensory stimulation. This method increases the reproducibility of functional maps from cat, rat, and mouse visual cortex significantly and might also be applied to high resolution imaging using voltage sensitve dyes or functional magnetic resonance

Artificial ontogenesis: a connectionist model of development

This thesis suggests that ontogenetic adaptive processes are important for generating intelligent beha- viour. It is thus proposed that such processes, as they occur in nature, need to be modelled and that such a model could be used for generating artificial intelligence, and specifically robotic intelligence. Hence, this thesis focuses on how mechanisms of intelligence are specified.A major problem in robotics is the need to predefine the behaviour to be followed by the robot. This makes design intractable for all but the simplest tasks and results in controllers that are specific to that particular task and are brittle when faced with unforeseen circumstances. These problems can be resolved by providing the robot with the ability to adapt the rules it follows and to autonomously create new rules for controlling behaviour. This solution thus depends on the predefinition of how rules to control behaviour are to be learnt rather than the predefinition of rules for behaviour themselves.Learning new rules for behaviour occurs during the developmental process in biology. Changes in the structure of the cerebral 'cortex underly behavioural and cognitive development throughout infancy and beyond. The uniformity of the neocortex suggests that there is significant computational uniformity across the cortex resulting from uniform mechanisms of development, and holds out the possibility of a general model of development. Development is an interactive process between genetic predefinition and environmental influences. This interactive process is constructive: qualitatively new behaviours are learnt by using simple abilities as a basis for learning more complex ones. The progressive increase in competence, provided by development, may be essential to make tractable the process of acquiring higher -level abilities.While simple behaviours can be triggered by direct sensory cues, more complex behaviours require the use of more abstract representations. There is thus a need to find representations at the correct level of abstraction appropriate to controlling each ability. In addition, finding the correct level of abstrac- tion makes tractable the task of associating sensory representations with motor actions. Hence, finding appropriate representations is important both for learning behaviours and for controlling behaviours. Representations can be found by recording regularities in the world or by discovering re- occurring pat- terns through repeated sensory -motor interactions. By recording regularities within the representations thus formed, more abstract representations can be found. Simple, non -abstract, representations thus provide the basis for learning more complex, abstract, representations.A modular neural network architecture is presented as a basis for a model of development. The pat- tern of activity of the neurons in an individual network constitutes a representation of the input to that network. This representation is formed through a novel, unsupervised, learning algorithm which adjusts the synaptic weights to improve the representation of the input data. Representations are formed by neurons learning to respond to correlated sets of inputs. Neurons thus became feature detectors or pat- tern recognisers. Because the nodes respond to patterns of inputs they encode more abstract features of the input than are explicitly encoded in the input data itself. In this way simple representations provide the basis for learning more complex representations. The algorithm allows both more abstract represent- ations to be formed by associating correlated, coincident, features together, and invariant representations to be formed by associating correlated, sequential, features together.The algorithm robustly learns accurate and stable representations, in a format most appropriate to the structure of the input data received: it can represent both single and multiple input features in both the discrete and continuous domains, using either topologically or non -topologically organised nodes. The output of one neural network is used to provide inputs for other networks. The robustness of the algorithm enables each neural network to be implemented using an identical algorithm. This allows a modular `assembly' of neural networks to be used for learning more complex abilities: the output activations of a network can be used as the input to other networks which can then find representations of more abstract information within the same input data; and, by defining the output activations of neurons in certain networks to have behavioural consequences it is possible to learn sensory -motor associations, to enable sensory representations to be used to control behaviour

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Integrative bioinformatics applications for complex human disease contexts

This thesis presents new methods for the analysis of high-throughput data from modern sources in the context of complex human diseases, at the example of a bioinformatics analysis workflow. New measurement techniques improve the resolution with which cellular and molecular processes can be monitored. While RNA sequencing (RNA-seq) measures mRNA expression, single-cell RNA-seq (scRNA-seq) resolves this on a per-cell basis. Long-read sequencing is increasingly used in genomics. With imaging mass spectrometry (IMS) the protein level in tissues is measured spatially resolved. All these techniques induce specific challenges, which need to be addressed with new computational methods. Collecting knowledge with contextual annotations is important for integrative data analyses. Such knowledge is available through large literature repositories, from which information, such as miRNA-gene interactions, can be extracted using text mining methods. After aggregating this information in new databases, specific questions can be answered with traceable evidence. The combination of experimental data with these databases offers new possibilities for data integrative methods and for answering questions relevant for complex human diseases. Several data sources are made available, such as literature for text mining miRNA-gene interactions (Chapter 2), next- and third-generation sequencing data for genomics and transcriptomics (Chapters 4.1, 5), and IMS for spatially resolved proteomics (Chapter 4.4). For these data sources new methods for information extraction and pre-processing are developed. For instance, third-generation sequencing runs can be monitored and evaluated using the poreSTAT and sequ-into methods. The integrative (down-stream) analyses make use of these (heterogeneous) data sources. The cPred method (Chapter 4.2) for cell type prediction from scRNA-seq data was successfully applied in the context of the SARS-CoV-2 pandemic. The robust differential expression (DE) analysis pipeline RoDE (Chapter 6.1) contains a large set of methods for (differential) data analysis, reporting and visualization of RNA-seq data. Topics of accessibility of bioinformatics software are discussed along practical applications (Chapter 3). The developed miRNA-gene interaction database gives valuable insights into atherosclerosis-relevant processes and serves as regulatory network for the prediction of active miRNA regulators in RoDE (Chapter 6.1). The cPred predictions, RoDE results, scRNA-seq and IMS data are unified as input for the 3D-index Aorta3D (Chapter 6.2), which makes atherosclerosis related datasets browsable. Finally, the scRNA-seq analysis with subsequent cPred cell type prediction, and the robust analysis of bulk-RNA-seq datasets, led to novel insights into COVID-19. Taken all discussed methods together, the integrative analysis methods for complex human disease contexts have been improved at essential positions.Die Dissertation beschreibt Methoden zur Prozessierung von aktuellen Hochdurchsatzdaten, sowie Verfahren zu deren weiterer integrativen Analyse. Diese findet Anwendung vor allem im Kontext von komplexen menschlichen Krankheiten. Neue Messtechniken erlauben eine detailliertere Beobachtung biomedizinischer Prozesse. Mit RNA-Sequenzierung (RNA-seq) wird mRNA-Expression gemessen, mit Hilfe von moderner single-cell-RNA-seq (scRNA-seq) sogar für (sehr viele) einzelne Zellen. Long-Read-Sequenzierung wird zunehmend zur Sequenzierung ganzer Genome eingesetzt. Mittels bildgebender Massenspektrometrie (IMS) können Proteine in Geweben räumlich aufgelöst quantifiziert werden. Diese Techniken bringen spezifische Herausforderungen mit sich, die mit neuen bioinformatischen Methoden angegangen werden müssen. Für die integrative Datenanalyse ist auch die Gewinnung von geeignetem Kontextwissen wichtig. Wissenschaftliche Erkenntnisse werden in Artikeln veröffentlicht, die über große Literaturdatenbanken zugänglich sind. Mittels Textmining können daraus Informationen extrahiert werden, z.B. miRNA-Gen-Interaktionen, die in eigenen Datenbank aggregiert werden um spezifische Fragen mit nachvollziehbaren Belegen zu beantworten. In Kombination mit experimentellen Daten bieten sich so neue Möglichkeiten für integrative Methoden. Durch die Extraktion von Rohdaten und deren Vorprozessierung werden mehrere Datenquellen erschlossen, wie z.B. Literatur für Textmining von miRNA-Gen-Interaktionen (Kapitel 2), Long-Read- und RNA-seq-Daten für Genomics und Transcriptomics (Kapitel 4.2, 5) und IMS für Protein-Messungen (Kapitel 4.4). So dienen z.B. die poreSTAT und sequ-into Methoden der Vorprozessierung und Auswertung von Long-Read-Sequenzierungen. In der integrativen (down-stream) Analyse werden diese (heterogenen) Datenquellen verwendet. Für die Bestimmung von Zelltypen in scRNA-seq-Experimenten wurde die cPred-Methode (Kapitel 4.2) erfolgreich im Kontext der SARS-CoV-2-Pandemie eingesetzt. Auch die robuste Pipeline RoDE fand dort Anwendung, die viele Methoden zur (differentiellen) Datenanalyse, zum Reporting und zur Visualisierung bereitstellt (Kapitel 6.1). Themen der Benutzbarkeit von (bioinformatischer) Software werden an Hand von praktischen Anwendungen diskutiert (Kapitel 3). Die entwickelte miRNA-Gen-Interaktionsdatenbank gibt wertvolle Einblicke in Atherosklerose-relevante Prozesse und dient als regulatorisches Netzwerk für die Vorhersage von aktiven miRNA-Regulatoren in RoDE (Kapitel 6.1). Die cPred-Methode, RoDE-Ergebnisse, scRNA-seq- und IMS-Daten werden im 3D-Index Aorta3D (Kapitel 6.2) zusammengeführt, der relevante Datensätze durchsuchbar macht. Die diskutierten Methoden führen zu erheblichen Verbesserungen für die integrative Datenanalyse in komplexen menschlichen Krankheitskontexten

Proceedings of the Flat-plate Solar Array Project Research Forum on the High-speed Growth and Characterization of Crystals for Solar Cells

Theoretical and experimental phenomena, applications, and characterization including stress/strain and other problem areas that limit the rate of growth of crystals suitable for processing into efficient, cost-effective solar cells are discussed. Melt spinning, ribbon growth, rapid solidification, laser recrystallization, and ignot growth of silicon and metals are also discussed

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Exploration of a novel non-lytic viral transmission mechanism utilized by a non-enveloped positive-sense RNA virus

While enteroviruses, including poliovirus, are conventionally released upon cell lysis, recent studies show that phosphatidylserine-enriched infectious extracellular vesicles (IEVs) shed by infected cells can transport clusters of enteroviruses from cell to cell, resulting in increased infectivity. Combining structural and biochemical analyses, we focused on IEVs shed from poliovirus-infected cells, a classical prototype for studying enteroviruses. Transmission cryo-electron microscopy, cryo-electron tomography and computational reconstruction, present the first three-dimensional structures of well-preserved IEVs and purified exosomes. We observed that single-membraned IEVs present a wide size range in diameter. Clusters of virions can be either densely packed within a protein-coated irregularly shaped IEV, or concentrated at one or both ends of an IEV, forming a polar structure. In addition to virions, IEVs often contain internal vesicles, “ramen-noodle”-like structures with strong density, and partially assembled virion-like structures. Viral replication complex components, including viral proteins polymerase 3D, 3CD, 3A, 3AB, 2BC, 2C and (+) and (-) stranded RNAs were detected in IEVs. Furthermore, (-) stranded RNA templates are protected by the IEVs, not packed in viral capsids. The transported viral replication components (viral proteins and RNAs) and virions within IEVs initiate a stronger and faster viral replication in recipient cells than free virions. Both cryo-electron tomographic and mass spectrometry data also showed that virions and “ramen-noodle”-like structures were also observed in purified CD9 positive exosomes from poliovirus-infected cells. Viral protein 3AB, detected on the membrane of IEVs, can invaginate membranous structures to engulf large proteins into a closed lumen. Our study demonstrates that IEVs can transport viral replication complex components to initiate a rapid onset of viral replication, as part of a novel viral transmission mechanism. Viral protein 3AB may contribute to forming IEVs throughout the infection.2019-06-12T00:00:00