When plants produce not enough or at all: metabolic engineering of flavonoids in microbial hosts

As a result of the discovery that flavonoids are directly or indirectly connected to health, flavonoid metabolism and its fascinating molecules that are natural products in plants, have attracted the attention of both the industry and researchers involved in plant science, nutrition, bio/chemistry, chemical bioengineering, pharmacy, medicine, etc. Subsequently, in the past few years, flavonoids became a top story in the pharmaceutical industry, which is continually seeking novel ways to produce safe and efficient drugs. Microbial cell cultures can act as workhorse bio-factories by offering their metabolic machinery for the purpose of optimizing the conditions and increasing the productivity of a selective flavonoid. Furthermore, metabolic engineering methodology is used to reinforce what nature does best by correcting the inadequacies and dead-ends of a metabolic pathway. Combinatorial biosynthesis techniques led to the discovery of novel ways of producing natural and even unnatural plant flavonoids, while, in addition, metabolic engineering provided the industry with the opportunity to invest in synthetic biology in order to overcome the currently existing restricted diversification and productivity issues in synthetic chemistry protocols. In this review, is presented an update on the rationalized approaches to the production of natural or unnatural flavonoids through biotechnology, analyzing the significance of combinatorial biosynthesis of agricultural/pharmaceutical compounds produced in heterologous organisms. Also mentioned are strategies and achievements that have so far thrived in the area of synthetic biology, with an emphasis on metabolic engineering targeting the cellular optimization of microorganisms and plants that produce flavonoids, while stressing the advances in flux dynamic control and optimization. Finally, the involvement of the rapidly increasing numbers of assembled genomes that contribute to the gene- or pathway-mining in order to identify the gene(s) responsible for producing species-specific secondary metabolites is also considered herein.National Strategic Reference Framework. THALES-TEI CRETE, MIS 380210 Progra

The effect of progesterone on the anesthetic and analgesic requirements for ovariohysterectomy in the dog

Η παρούσα μελέτη διερεύνησε την επίδραση της συγκέντρωσης της προγεστερόνης, είτε ενδογενούς κατά τον ωοθηκικό κύκλο και την κυοφορία, είτε εξωγενούς, καθώς και του ενεργού μεταβολίτη της αλλοπρεγνανολόνη, στις απαιτήσεις σε αναισθητικά και αναλγητικά φάρμακα και στην ένταση του μετεγχειρητικού πόνου για τη διεξαγωγή ωοθηκυστερεκτομής στον σκύλο. Περιελήφθησαν 150 υγιείς, θηλυκοί σκύλοι, οι οποίο προσκομίστηκαν στην Κλινική Ζώων Συντροφιάς του ΑΠΘ για ωοθηκυστερεκτομή και οι οποίοι κατανεμήθηκαν σε 6 ομάδες με βάση το στάδιο του ωοθηκικού κύκλου και την ενδεχόμενη ορμονική αγωγή που έλαβαν. Συγκεκριμένα, σχηματίστηκαν 6 ομάδες από ζώα στο στάδιο του ανοίστρου (ομάδα Α), ζώα σε άνοιστρο που έλαβαν προγεστερόνη ενδομυϊκώς (Απ), ζώα στο στάδιο του διοίστρου (Δ), ζώα σε δίοιστρο που έλαβαν υποδορίως αγλεπριστόνη (Δα), ζώα σε δίοιστρο που έλαβαν κάψουλες τριλοστάνης (Δτ) και ζώα σε κυοφορία 28-42 ημερών (Ε). Σε κάθε ζώο καταγράφηκαν οι απαιτήσεις σε προποφόλη για εγκατάσταση της γενικής αναισθησίας, οι απαιτήσεις σε ισοφλουράνιο για διατήρηση, οι απαιτήσεις σε επιπλέον αναλγητικά φάρμακα είτε διεγχειρητικά είτε μετεγχειρητικά και έγιναν μετεγχειρητικές εκτιμήσεις πόνου. Επίσης έγιναν αιμοληψίες σε συγκεκριμένες χρονικές στιγμές για ορμονολογικές αναλύσεις. Μετά τη στατιστική επεξεργασία των αποτελεσμάτων δε βρέθηκε στατιστικώς σημαντική διαφορά στις απαιτήσεις σε αναισθητικά ή σε αναλγητικά φάρμακα μεταξύ των ομάδων, ούτε στατιστικώς σημαντική συσχέτιση μεταξύ των απαιτήσεων αυτών και των συγκεντρώσεων στο αίμα της προγεστερόνης ή της αλλοπρεγνανολόνης, εκτός από 2 συσχετίσεις εντός συγκεκριμένων ομάδων. Συμπεραίνεται ότι οι συγκεντρώσεις προγεστερόνης ή αλλοπρεγνανολόνης στο αίμα δεν επηρεάζουν τις απαιτήσεις σε αναισθητικά ή αναλγητικά φάρμακα για τη διεξαγωγή ωοθηκυστερεκτομής στο σκύλο ή ότι ενδεχόμενη τέτοια επιρροή επικαλύπτεται από τη χορήγηση προαναισθητικής αγωγής και αναλγητικών φαρμάκων.The objective of the current study was to investigate the effect of serum progesterone concentration, either endogenous, during the ovarian cycle and pregnancy, or exogenous, when administered during anestrus, and of its active metabolite allopregnanolone, on anesthetic and analgesic requirements, as well as post-operative pain intensity, for the performance of ovariohysterectomy in dogs. One hundred and fifty healthy female dogs, which were admitted to our clinic for elective ovariohysterectomy, were included in the present study. They were allocated to 6 groups according to the stage of the ovarian cycle and the corresponding serum progesterone co ncentration. The six groups consisted of dogs in anestrus (group A), in anestrus which received intramuscular progesterone injections prior to surgery (group Ap), dogs in diestrus (group D), in diestrus which received subcutaneous aglepristone injections prior to surgery (group Da), in diestrus which received oral trilostane prior to surgery (group Dt) and dogs in pregnancy of duration of 28-42 days (group P). Serum progesterone concentrations were measured in all dogs before and after any hormonal treatment and serum allopregnanolone concentrations were measured in selected dogs from all groups. The required dose of propofol for induction of anesthesia and the required isoflurane concentration for maintenance of anesthesia and the need for intraoperative fentanyl administration and extra postoperative pethidine analgesia were recorded. After statistical analysis, there were no significant differences between groups, regarding their anesthetic or analgesic requirements, that could be attributed to serum progesterone and/or allopregnanolone concentration. However, moderate correlations within certain groups were noted. Serum progesterone or allopregnanolone concentrations do not seem to have an effect on anesthetic and analgesic requirements for ovariohysterectomy in the dog or any potential effect is weak enough to be masked by the action of anesthetic premedication and/or analgesic and/or anaesthetic drugs used

A Multi-objective Bi-level Optimisation model for Agricultural Policy in Scotland

Agricultural policy analysis can be visualised as a multiple objective hierarchical optimisation problem whereby sequential non-cooperative interactions between the policy makers and the farmers take place. The objectives and choices of policy makers will almost always diverge from the objectives and choices of farmers. Policy makers exercise authority over some, but not all, of the variables in the total system whereas other variables affecting their multiple goals are under the direct control of myriad farmers who operate according to their own utility maximising motives. In order to advance their own objectives, the policy makers unilaterally and pre-emptively set the policy measures to influence the farmers. The farmers execute their decisions after, and in view of, the policies and make their production decisions that observe their goals best. Ultimately, the payoffs to both the policy makers and the farmers depend not only on the actions of the former, but also on the reactions of the latter. Such problems are difficult to solve due to their intrinsic nonconvexity and multiple objectives. This thesis shows how multi-objective genetic algorithms (MOGA) in conjunction with mathematical programming (MP) can be used for solving this type of problems. A MP model is developed to capture the production choices of farmers. The model is based on positive mathematical programming and its objective function parameters are estimated using the method of generalised maximum entropy. The model is nested in and controlled by a MOGA which captures the process of multi-objective optimisation of policy decisions. The approach is illustrated using a case study taken from the Scottish agricultural systems, where several socio-economic and environmental objectives for policy making are considered. Four types of policy instruments are examined: the current single payment scheme, a multi-payment scheme based on land use, an input taxation and a regulatory scheme. For a selection of scenarios alternative Pareto-optimal solutions are discovered and tradeoffs between the policy objectives are presented along with their associated production patterns. The performance of the modelling tool developed suggests that it is well suited to dealing with real-world policy issues. It offers considerable possibilities for exploring tradeoffs between non-commensurable and conflicting objectives relevant to sustainable development of Scottish agriculture

Audio-assisted movie dialogue detection

An audio-assisted system is investigated that detects if a movie scene is a dialogue or not. The system is based on actor indicator functions. That is, functions which define if an actor speaks at a certain time instant. In particular, the crosscorrelation and the magnitude of the corresponding the crosspower spectral density of a pair of indicator functions are input to various classifiers, such as voted perceptrons, radial basis function networks, random trees, and support vector machines for dialogue/non-dialogue detection. To boost classifier efficiency AdaBoost is also exploited. The aforementioned classifiers are trained using ground truth indicator functions determined by human annotators for 41 dialogue and another 20 non-dialogue audio instances. For testing, actual indicator functions are derived by applying audio activity detection and actor clustering to audio recordings. 23 instances are randomly chosen among the aforementioned 41 dialogue instances, 17 of which correspond to dialogue scenes and 6 to non-dialogue ones. Accuracy ranging between 0.739 and 0.826 is reported

Audio-assisted movie dialogue detection

An audio-assisted system is investigated that detects if a movie scene is a dialogue or not. The system is based on actor indicator functions. That is, functions which define if an actor speaks at a certain time instant. In particular, the cross-correlation and the magnitude of the corresponding the cross-power spectral density of a pair of indicator functions are input to various classifiers, such as voted perceptions, radial basis function networks, random trees, and support vector machines for dialogue/non-dialogue detection. To boost classifier efficiency AdaBoost is also exploited. The aforementioned classifiers are trained using ground truth indicator functions determined by human annotators for 41 dialogue and another 20 non-dialogue audio instances. For testing, actual indicator functions are derived by applying audio activity detection and actor clustering to audio recordings. 23 instances are randomly chosen among the aforementioned 41 dialogue instances, 17 of which correspond to dialogue scenes and 6 to non-dialogue ones. Accuracy ranging between 0.739 and 0.826 is reported. © 2008 IEEE

Towards Emotion Recognition: A Persistent Entropy Application

Emotion recognition and classification is a very active area of research. In this paper, we present a first approach to emotion classification using persistent entropy and support vector machines. A topology-based model is applied to obtain a single real number from each raw signal. These data are used as input of a support vector machine to classify signals into 8 different emotions (calm, happy, sad, angry, fearful, disgust and surprised)