Methylation in the CHH context allows to predict recombination in rice

DNA methylation is the most studied epigenetic trait. It is considered a key factor in regulating plant development and physiology, and has been associated with the regulation of several genomic features, including transposon silencing, regulation of gene expression, and recombination rates. Nonetheless, understanding the relation between DNA methylation and recombination rates remains a challenge. This work explores the association between recombination rates and DNA methylation for two commercial rice varieties. The results show negative correlations between recombination rates and methylated cytosine counts for all contexts tested at the same time, and for CG and CHG contexts independently. In contrast, a positive correlation between recombination rates and methylated cytosine count is reported in CHH contexts. Similar behavior is observed when considering only methylated cytosines within genes, transposons, and retrotransposons. Moreover, it is shown that the centromere region strongly affects the relationship between recombination rates and methylation. Finally, machine learning regression models are applied to predict recombination using the count of methylated cytosines in the CHH context as the entrance feature. These findings shed light on the understanding of the recombination landscape of rice and represent a reference framework for future studies in rice breeding, genetics, and epigenetics

Characterization of cassava ORANGE proteins and their capability to increase provitamin A carotenoids accumulation

Cassava (Manihot esculenta Crantz) biofortification with provitamin A carotenoids is an ongoing process that aims to alleviate vitamin A deficiency. The moderate content of provitamin A carotenoids achieved so far limits the contribution to providing adequate dietary vitamin A levels. Strategies to increase carotenoid content focused on genes from the carotenoids biosynthesis pathway. In recent years, special emphasis was given to ORANGE protein (OR), which promotes the accumulation of carotenoids and their stability in several plants. The aim of this work was to identify, characterize and investigate the role of OR in the biosynthesis and stabilization of carotenoids in cassava and its relationship with phytoene synthase (PSY), the rate-limiting enzyme of the carotenoids biosynthesis pathway. Gene and protein characterization of OR, expression levels, protein amounts and carotenoids levels were evaluated in roots of one white (60444) and two yellow cassava cultivars (GM5309-57 and GM3736-37). Four OR variants were found in yellow cassava roots. Although comparable expression was found for three variants, significantly higher OR protein amounts were observed in the yellow varieties. In contrast, cassava PSY1 expression was significantly higher in the yellow cultivars, but PSY protein amount did not vary. Furthermore, we evaluated whether expression of one of the variants, MeOR_X1, affected carotenoid accumulation in cassava Friable Embryogenic Callus (FEC). Overexpression of maize PSY1 alone resulted in carotenoids accumulation and induced crystal formation. Co-expression with MeOR_X1 led to greatly increase of carotenoids although PSY1 expression was high in the co-expressed FEC. Our data suggest that posttranslational mechanisms controlling OR and PSY protein stability contribute to higher carotenoid levels in yellow cassava. Moreover, we showed that cassava FEC can be used to study the efficiency of single and combinatorial gene expression in increasing the carotenoid content prior to its application for the generation of biofortified cassava with enhanced carotenoids levels

South Green bioinformatics platform : Plateforme collaborative de bioinformatique verte héraultaise

Drivers and other road users often encounter situations where priority is unclear or ambiguous, but must be resolved, for example, after arriving at an intersection nearly simultaneously. The participants in such scenarios reach agreement by communicating; while instinctive to humans, this is a significant challenge for autonomous vehicles. Currently, the nature of interaction for resolving ambiguous road situations between pedestrians and autonomous vehicles remains mostly in the realm of speculation, for which no direct means for expressing intent and acknowledgment has yet been established. This thesis approaches the challenge by contributing a model and approach for planning that can produce actions that are expressive and encode certain aspects of intent; the result is communicative in that vehicle-pedestrian coordination arises via a negotiation of intent in a prototypical unsignalized intersection crossing scenario. We deliberately construct a prototypical crossing setting with a vehicle and one pedestrian at an unsignalized intersection such that there is substantial ambiguity in crossing order. A decision-theoretic model is then used for capturing this scenario along with its ambiguity as uncertainty arising from non-determinism and partial observability. We solve the problem by first proposing a Markov decision process to express the interaction at the intersection. Next, we focus on the partial-observability and include it in the model to generate a sequence of vehicle actions by solving via a state-of-the-art online solver. We implement the approach on a self-driving Ford Lincoln MKZ platform and examine an experimental setting involving real-time interaction. The experiment shows that the method achieves safe and efficient navigation. We analyze the resulting policy in detail in simulation and examine the coupled behavior of the vehicle and pedestrian, interpreting evidence for implicit communication that emerges as the two resolve ambiguity to achieve safe and efficient navigation

Text classification

The fast growing of the Internet and the continuous rising of the available material in digital form, make ergent the need for classification algorithms. Older techniques of the Knowledge Engineering back at the '80s, have been replaced by Machine Learning techniques. The last 15 years a large number of methods have been studied and developed, which have succeed in solving this problem. Such techniques include Decision Tress, Naive Bayes, Neural Networks, Linear Classifiers, Logistic Regression, Perceptron, Support Vector Machines etc. An important group of classifiers is Linear Classifiers, which try to classify data by defining separating hyperplanes. Types of Linear Classifiers are Centroid Classifier, Rocchio Classifier and Perceptron Classifier. By combining characteristics of those three simple classifiers, a new, fast and accurate classifier is defined, under the symbolic name Modified Perceptron, due to its resemblence with the classic Perceptron Classifier. This new classifier is proved that it converges and it is shown experimentally that it converges much faster than other Linear Classifiers. By evaluating its performance on classifying international text corpuses and corpuses from conference challenges, it turned that its performance is comparable, if not better, than the state of the art in the field of Text Classification, such as SVMs. Note that it took the first place in the international challenge on spam detection on social bookmarking networks of ECML 2008. Evaluation is performed by the classic model of flat classification, where each category is considered independent from others. This technique, one versus all, has some limitations, such as how it scales when the number of categories is very large or the number of examples is very large. This limitation refers in space requirements, beacause all classifiers have to be stored in memory. As for time requirements, time complexity is O(NM), where N is the number classifiers and M is the dimensionality of each example. To overcome those limitation, a hierarchical model for classification has been developed. Relations among categories where defined and an experimental search was done on the sampling for creating negative sets for each category. The proposed algorithm was succesfully applied on very large classification problems and new issues for further research and improvements where opened.Η ταχεία εξάπλωση του διαδικτύου και η συνεχώς αυξανόμενη διάθεση υλικού σε ηλεκτρονική μορφή καθιστά επιτακτική την ανάγκη εύρωστων αλγορίθμων ταξινόμησης (κατηγοριοποίησης) του υλικού αυτού. Παλαιότερες τεχνικές της Μηχανικής Γνώσης (Knowledge Engineering) του '80, έχουν δώσει τη θέση τους σε τεχνικές Μηχανικής Μάθησης (Machine Learning). Μία πληθώρα μεθόδων έχουν μελετηθεί και αναπτυχθεί τα τελευταία 15 χρόνια, οι οποίες άλλες λιγότερο και άλλες περισσότερο, σημειώνουν επιτυχία στην επίλυση του προβλήματος. Τέτοιες τεχνικές για παράδειγμα είναι, τα Δένδρα Αποφάσεων, Naive Bayes, Νευρωνικά Δίκτυα, Γραμμικοί Κατηγοριοποιητές, Λογιστική Παλινδρόμηση, Perceptron, Μηχανές Διανυσμάτων Υποστήριξης κ.α. Μία σημαντική ομάδα κατηγοριοποιητών, είναι οι Γραμμικοί Κατηγοριοποιητές, οι οποίοι επιδιώκουν την κατηγοριοποίηση των παραδειγμάτων, ορίζοντας διαχωριστικά υπερεπίπεδα μεταξύ τους. Είδη τέτοιων κατηγοριοποιητών αποτελούν ο κατηγοριοποιητής κεντροειδών (centroid classifier), ο κατηγοριοποιητής Rocchio και ο κατηγοριοποιητής Perceptron. Συνδυάζοντας στοιχεία και χαρακτηριστικά των τριών αυτών απλών κατηγοριοποιητών, ορίζεται ένας νέος γρήγορος και ακριβής γραμμικός κατηγοριοποιητής, παίρνοντας το συμβολικό όνομα Modified Perceptron, εξαιτίας της ομοιότητάς του με τον κλασικό κατηγοριοποιητή Perceptron. Ο νέος αυτός κατηγοριοποιητής αποδεικνύεται ότι συγκλίνει και δείχνεται πειραματικά ότι συγκλίνει αρκετά γρηγορότερα από άλλους γραμμικούς κατηγοριοποιητές. Αξιολογώντας την επίδοσή του στην κατηγοριοποίηση διεθνών συλλογών κειμένων και συλλογών διαγωνισμών, φαίνεται ότι επιτυγχάνει επιδόσεις συγκρίσιμες και τις περισσότερες φορές καλύτερες με τις κορυφαίες τεχνικές κατηγοριοποίησης κειμένων, όπως για παράδειγμα είναι τα SVMs. Σημειωτέον ότι στη συμμετοχή μας στο ECML challenge 2008 απέσπασε την πρώτη θέση σε πρόβλημα ‘link spamming” σε κοινωνικά δίκτυα. Η αξιολόγηση του αλγόριθμου γίνεται με το κλασικό μοντέλο της επίπεδης κατηγοριοποίησης, όπου κάθε κατηγορία θεωρείται ανεξάρτητη από κάθε άλλη. Η τεχνική αυτή του «ενός έναντι όλων» έχει όμως τους περιορισμούς της όπως είναι η κλιμάκωση του αλγόριθμου όταν το πλήθος το κατηγοριών είναι αρκετά μεγάλο ή τα προς ταξινόμηση παραδείγματα είναι πολλά. Οι περιορισμοί αναφέρονται στο χώρο αφού όλοι οι ταξινομητές πρέπει να φυλάσσονται στη μνήμη. Χαρακτηριστικό παράδειγμα του προβλήματος περιλαμβάνει 20,000 ταξινομητές μεγέθους 800,000 χαρακτηριστικών ο καθένας. Ως προς το χρόνο η πολυπλοκότητα του προβλήματος είναι Ο(ΝΜ) όπου Ν είναι το πλήθος των ταξινομητών και Μ το μέγεθος των διανυσμάτων των προς ταξινόμηση κειμένων. Για την υπέρβαση των περιορισμών αυτών υλοποιήθηκε ένα μοντέλο ιεραρχικής κατηγοριοποίησης. Επίσης ορίστηκαν οι σχέσεις εξάρτησης μεταξύ των κατηγοριών και πραγματοποιήθηκε μια πειραματική διερεύνηση όσο αφορά την δειγματοληψία για την δημιουργία των παραδειγμάτων εκπαίδευσης ιδιαίτερα των αρνητικών παραδειγμάτων. Ο αλγόριθμος εφαρμόστηκε σε πολύ μεγάλα προβλήματα κατηγοριοποίησης με επιτυχία και άνοιξε νέα θέματα για περαιτέρω βελτιώσεις