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51. Grapevine Expression Atlas Reveals a Deep Transcriptome Shift Driving the Entire Plant into a Maturation Program.

Author

Fasoli, Marianna, Santo, Silvia Dal, Zenoni, Sara, Tornielli, Giovanni Battista, Farina, Lorenzo, Zamboni, Anita, Porceddu, Andrea, Venturini, Luca, Bicego, Manuele, Murino, Vittorio, Ferrarini, Alberto, Delledonne, Massimo, and Pezzotti, Mario

Subjects
GENETIC regulation, WOODY plants, TRANSCRIPTOMES, DEVELOPMENTAL programs, GRAPES, BIOMARKERS, VITIS vinifera
Abstract

We developed a genome-wide transcriptomic atlas of grapevine (Vitis vinifera) based on 54 samples representing green and woody tissues and organs at different developmental stages as well as specialized tissues such as pollen and senescent leaves. Together, these samples expressed ∼91% of the predicted grapevine genes. Pollen and senescent leaves had unique transcriptomes reflecting their specialized functions and physiological status. However, microarray and RNA-seq analysis grouped all the other samples into two major classes based on maturity rather than organ identity, namely, the vegetative/green and mature/woody categories. This division represents a fundamental transcriptomic reprogramming during the maturation process and was highlighted by three statistical approaches identifying the transcriptional relationships among samples (correlation analysis), putative biomarkers (O2PLS-DA approach), and sets of strongly and consistently expressed genes that define groups (topics) of similar samples (biclustering analysis). Gene coexpression analysis indicated that the mature/woody developmental program results from the reiterative coactivation of pathways that are largely inactive in vegetative/green tissues, often involving the coregulation of clusters of neighboring genes and global regulation based on codon preference. This global transcriptomic reprogramming during maturation has not been observed in herbaceous annual species and may be a defining characteristic of perennial woody plants. [ABSTRACT FROM AUTHOR]

Published
2012
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52. Genetic analysis of wheat sensitivity to the ToxB fungal effector from Pyrenophora tritici-repentis, the causal agent of tan spot

Author

Corsi, Beatrice, Percival-Alwyn, Lawrence, Downie, Rowena C., Venturini, Luca, Iagallo, Elyce M., Campos Mantello, Camila, McCormick-Barnes, Charlie, See, Pao Theen, Oliver, Richard P., Moffat, Caroline S., and Cockram, James

Subjects
2. Zero hunger, food and beverages, Original Article
Abstract

Key message: Genetic mapping of sensitivity to the Pyrenophora tritici-repentis effector ToxB allowed development of a diagnostic genetic marker, and investigation of wheat pedigrees allowed transmission of sensitive alleles to be tracked. Abstract: Tan spot, caused by the necrotrophic fungal pathogen Pyrenophora tritici-repentis, is a major disease of wheat (Triticum aestivum). Secretion of the P. tritici-repentis effector ToxB is thought to play a part in mediating infection, causing chlorosis of plant tissue. Here, genetic analysis using an association mapping panel (n = 480) and a multiparent advanced generation intercross (MAGIC) population (n founders = 8, n progeny = 643) genotyped with a 90,000 feature single nucleotide polymorphism (SNP) array found ToxB sensitivity to be highly heritable (h2 ≥ 0.9), controlled predominantly by the Tsc2 locus on chromosome 2B. Genetic mapping of Tsc2 delineated a 1921-kb interval containing 104 genes in the reference genome of ToxB-insensitive variety ‘Chinese Spring’. This allowed development of a co-dominant genetic marker for Tsc2 allelic state, diagnostic for ToxB sensitivity in the association mapping panel. Phenotypic and genotypic analysis in a panel of wheat varieties post-dated the association mapping panel further supported the diagnostic nature of the marker. Combining ToxB phenotype and genotypic data with wheat pedigree datasets allowed historic sources of ToxB sensitivity to be tracked, finding the variety ‘Maris Dove’ to likely be the historic source of sensitive Tsc2 alleles in the wheat germplasm surveyed. Exploration of the Tsc2 region gene space in the ToxB-sensitive line ‘Synthetic W7984’ identified candidate genes for future investigation. Additionally, a minor ToxB sensitivity QTL was identified on chromosome 2A. The resources presented here will be of immediate use for marker-assisted selection for ToxB insensitivity and the development of germplasm with additional genetic recombination within the Tsc2 region.

53. Genetic analysis of wheat sensitivity to the ToxB fungal effector from Pyrenophora tritici-repentis, the causal agent of tan spot

Author

Corsi, Beatrice, Percival-Alwyn, Lawrence, Downie, Rowena C, Venturini, Luca, Iagallo, Elyce M, Campos Mantello, Camila, McCormick-Barnes, Charlie, See, Pao Theen, Oliver, Richard P, Moffat, Caroline S, and Cockram, James

Subjects
2. Zero hunger, Genetic Markers, Genotype, Genetic Linkage, Quantitative Trait Loci, food and beverages, Chromosome Mapping, High-Throughput Nucleotide Sequencing, Genomics, Mycotoxins, Polymorphism, Single Nucleotide, Phenotype, Ascomycota, Host-Pathogen Interactions, Triticum, Disease Resistance, Plant Diseases
Abstract

Genetic mapping of sensitivity to the Pyrenophora tritici-repentis effector ToxB allowed development of a diagnostic genetic marker, and investigation of wheat pedigrees allowed transmission of sensitive alleles to be tracked. Tan spot, caused by the necrotrophic fungal pathogen Pyrenophora tritici-repentis, is a major disease of wheat (Triticum aestivum). Secretion of the P. tritici-repentis effector ToxB is thought to play a part in mediating infection, causing chlorosis of plant tissue. Here, genetic analysis using an association mapping panel (n = 480) and a multiparent advanced generation intercross (MAGIC) population (n founders = 8, n progeny = 643) genotyped with a 90,000 feature single nucleotide polymorphism (SNP) array found ToxB sensitivity to be highly heritable (h2 ≥ 0.9), controlled predominantly by the Tsc2 locus on chromosome 2B. Genetic mapping of Tsc2 delineated a 1921-kb interval containing 104 genes in the reference genome of ToxB-insensitive variety 'Chinese Spring'. This allowed development of a co-dominant genetic marker for Tsc2 allelic state, diagnostic for ToxB sensitivity in the association mapping panel. Phenotypic and genotypic analysis in a panel of wheat varieties post-dated the association mapping panel further supported the diagnostic nature of the marker. Combining ToxB phenotype and genotypic data with wheat pedigree datasets allowed historic sources of ToxB sensitivity to be tracked, finding the variety 'Maris Dove' to likely be the historic source of sensitive Tsc2 alleles in the wheat germplasm surveyed. Exploration of the Tsc2 region gene space in the ToxB-sensitive line 'Synthetic W7984' identified candidate genes for future investigation. Additionally, a minor ToxB sensitivity QTL was identified on chromosome 2A. The resources presented here will be of immediate use for marker-assisted selection for ToxB insensitivity and the development of germplasm with additional genetic recombination within the Tsc2 region.

54. Gemello digitale di un ponte in muratura mediante analisi modale operazionale e algoritmi genetici

Author

Venturini, Luca, thesis supervisor: Marzani, Alessandro, Venturini, Luca, and thesis supervisor: Marzani, Alessandro

Abstract

Il monitoraggio strutturale è un processo che, nell’ambito ingegneristico, mira a identificare la nascita e/o l’evoluzione del danno nelle strutture. Il paradigma prevede l’installazione di sensori sulla struttura, per monitorarne nel tempo alcuni parametri, e l’utilizzo di modelli strutturali e algoritmi che a partire dai dati misurati siano in grado di rilevarne eventuali danni. In seno al monitoraggio strutturale vive il problema di identificazione strutturale, cioè l’insieme di strategie analitiche e sperimentali atte a calibrare i modelli strutturali, quindi a generare dei gemelli digitali delle strutture. Queste strategie si basano sull'estrazione di caratteristiche dai dati misurati, con tecniche di identificazione quali la Analisi Modale Sperimentale (EMA) e l’Analisi Modale Operazionale (OMA), e di schemi di ottimizzazione euristici che operano allo scopo di “aggiornare” i modelli strutturali. La tesi si è focalizzata sulla generazione di un gemello digitale, ai fini del monitoraggio strutturale, di un ponte ferroviario multi-campata in muratura, sulla linea ferroviaria Foggia-Napoli. Attraverso l’analisi statica e dinamica, condotte su due modelli a elementi finiti (FEM) realizzati con i software Abaqus e SAP2000, è stato possibile calcolare dati quali le frequenze naturali e le deformate modali (caratteristiche del ponte iniziale). Si è poi proceduto a definire possibili scenari di danno variando la rigidezza di alcune parti/porzioni del ponte, utilizzati poi per estrarre i dati dei ponti danneggiati (caratteristiche del ponte danneggiato). Attraverso uno schema agli algoritmi genetici, il modello iniziale è stato aggiornato generando il gemello digitale del ponte danneggiato. L’algoritmo genetico implementato sul software Matlab prevede un processo iterativo volto a minimizzare una funzione obiettivo basata sulla differenza tra le frequenze del ponte iniziale e quelle del ponte danneggiato, al variale di alcuni parametri fisici del modello.

55. Gemello digitale di un ponte in muratura mediante analisi modale operazionale e algoritmi genetici

Author

Venturini, Luca, thesis supervisor: Marzani, Alessandro, Venturini, Luca, and thesis supervisor: Marzani, Alessandro

Abstract

Il monitoraggio strutturale è un processo che, nell’ambito ingegneristico, mira a identificare la nascita e/o l’evoluzione del danno nelle strutture. Il paradigma prevede l’installazione di sensori sulla struttura, per monitorarne nel tempo alcuni parametri, e l’utilizzo di modelli strutturali e algoritmi che a partire dai dati misurati siano in grado di rilevarne eventuali danni. In seno al monitoraggio strutturale vive il problema di identificazione strutturale, cioè l’insieme di strategie analitiche e sperimentali atte a calibrare i modelli strutturali, quindi a generare dei gemelli digitali delle strutture. Queste strategie si basano sull'estrazione di caratteristiche dai dati misurati, con tecniche di identificazione quali la Analisi Modale Sperimentale (EMA) e l’Analisi Modale Operazionale (OMA), e di schemi di ottimizzazione euristici che operano allo scopo di “aggiornare” i modelli strutturali. La tesi si è focalizzata sulla generazione di un gemello digitale, ai fini del monitoraggio strutturale, di un ponte ferroviario multi-campata in muratura, sulla linea ferroviaria Foggia-Napoli. Attraverso l’analisi statica e dinamica, condotte su due modelli a elementi finiti (FEM) realizzati con i software Abaqus e SAP2000, è stato possibile calcolare dati quali le frequenze naturali e le deformate modali (caratteristiche del ponte iniziale). Si è poi proceduto a definire possibili scenari di danno variando la rigidezza di alcune parti/porzioni del ponte, utilizzati poi per estrarre i dati dei ponti danneggiati (caratteristiche del ponte danneggiato). Attraverso uno schema agli algoritmi genetici, il modello iniziale è stato aggiornato generando il gemello digitale del ponte danneggiato. L’algoritmo genetico implementato sul software Matlab prevede un processo iterativo volto a minimizzare una funzione obiettivo basata sulla differenza tra le frequenze del ponte iniziale e quelle del ponte danneggiato, al variale di alcuni parametri fisici del modello.

56. TP53Alterations Including Missense Mutations, Aberrant Exon-Junctions and Internal Intron Retentions Are Frequent and May Contribute to MDM2 Antagonist-Resistance in B-Acute Lymphoblastic leukemia

Author

Iacobucci, Ilaria, Ferrari, Anna, Trino, Stefania, Lonetti, Annalisa, Papayannidis, Cristina, Ferrarini, Alberto, Venturini, Luca, Abbenante, Maria Chiara, Parisi, Sarah, Cattina, Federica, Soverini, Simona, Paolini, Stefania, Russo, Domenico, Vignetti, Marco, Pane, Fabrizio, Musto, Pellegrino, Delledonne, Massimo, Baccarani, Michele, and Martinelli, Giovanni

Abstract

Abstract 1484

Published
2011
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57. A Planning and Control System for Self-Driving Racing Vehicles

Author

Adriano Fagiolini, Francesco Amerotti, Federico Massa, Alessandro Settimi, Stefano De Caro, Andrea Biondo, Andrea Corti, Lucia Pallottino, Danio Caporale, Luca Venturini, Caporale, Danilo, Fagiolini, Adriano, Pallottino, Lucia, Settimi, Alessandro, Biondo, Andrea, Amerotti, Francesco, Massa, Federico, De Caro, Stefano, Corti, Andrea, and Venturini, Luca

Subjects
Operations research, Renewable Energy, Sustainability and the Environment, Computer science, Control (management), Energy Engineering and Power Technology, Computer Science Applications1707 Computer Vision and Pattern Recognition, Autonomous robots, self-driving vehicles, racing, robotics challenge, Pedestrian, Industrial and Manufacturing Engineering, Competition (economics), self-driving vehicles, Autonomous robot, racing, Computer Networks and Communication, Settore ING-INF/04 - Automatica, Artificial Intelligence, Autonomous robots, Control system, self-driving vehicle, Trajectory, Key (cryptography), Robot, robotics challenge, Everyday life, Instrumentation
Abstract

Autonomous robots will soon enter our everyday life as self-driving cars. These vehicles are designed to behave according to certain sets of cooperative rules, such as traffic ones, and to respond to events that might be unpredictable in their occurrence but predictable in their nature, such as a pedestrian suddenly crossing a street, or another car losing control. As civilian autonomous cars will cross the road, racing autonomous cars are under development, which will require superior Artificial Intelligence Drivers to perform in structured but uncertain conditions. We describe some preliminary results obtained during the development of a planning and control system as key elements of an Artificial Intelligence driver for the competition scenario.

Published
2018
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