An abstract model of hierarchical graphs and hierarchical graph transformation

Giorgio BusattoPaderborn, Univ., Diss., 200

The Peach RGF/GLV Signaling Peptide pCTG134 Is Involved in a Regulatory Circuit That Sustains Auxin and Ethylene Actions

In vascular plants the cell-to-cell interactions coordinating morphogenetic and physiological processes are mediated, among others, by the action of hormones, among which also short mobile peptides were recognized to have roles as signals. Such peptide hormones (PHs) are involved in defense responses, shoot and root growth, meristem homeostasis, organ abscission, nutrient signaling, hormone crosstalk and other developmental processes and act as both short and long distant ligands. In this work, the function of CTG134, a peach gene encoding a ROOT GROWTH FACTOR/GOLVEN-like PH expressed in mesocarp at the onset of ripening, was investigated for its role in mediating an auxin-ethylene crosstalk. In peach fruit, where an auxin-ethylene crosstalk mechanism is necessary to support climacteric ethylene synthesis, CTG134 expression peaked before that of ACS1 and was induced by auxin and 1-methylcyclopropene (1-MCP) treatments, whereas it was minimally affected by ethylene. In addition, the promoter of CTG134 fused with the GUS reporter highlighted activity in plant parts in which the auxin-ethylene interplay is known to occur. Arabidopsis and tobacco plants overexpressing CTG134 showed abnormal root hair growth, similar to wild-type plants treated with a synthetic form of the sulfated peptide. Moreover, in tobacco, lateral root emergence and capsule size were also affected. In Arabidopsis overexpressing lines, molecular surveys demonstrated an impaired hormonal crosstalk, resulting in a re-modulated expression of a set of genes involved in both ethylene and auxin synthesis, transport and perception. These data support the role of pCTG134 as a mediator in an auxin-ethylene regulatory circuit and open the possibility to exploit this class of ligands for the rational design of new and environmental friendly agrochemicals able to cope with a rapidly changing environment

Exosome-delivered microRNAs promote IFN-α secretion by human plasmacytoid DCs via TLR7

The excessive production of type I IFNs is a hallmark and a main pathogenic mechanism of many autoimmune diseases, including systemic lupus erythematosus (SLE). In these pathologies, the sustained secretion of type I IFNs is dependent on the improper activation of plasmacytoid DCs (pDCs) by self-nucleic acids. However, the nature and origin of pDC-activating self-nucleic acids is still incompletely characterized. Here, we report that exosomes isolated from the plasma of SLE patients can activate the secretion of IFN-α by human blood pDCs in vitro. This activation requires endosomal acidification and is recapitulated by microRNAs isolated from exosomes, suggesting that exosome-delivered microRNAs act as self-ligands of innate single-stranded endosomal RNA sensors. By using synthetic microRNAs, we identified an IFN induction motif that is responsible for the TLR7-dependent activation, maturation, and survival of human pDCs. These findings identify exosome-delivered microRNAs as potentially novel TLR7 endogenous ligands able to induce pDC activation in SLE patients. Therefore, microRNAs may represent novel pathogenic mediators in the onset of autoimmune reactions and potential therapeutic targets in the treatment of type I IFN-mediated diseases

Tree Compression with Top Trees Revisited

We revisit tree compression with top trees (Bille et al, ICALP'13) and present several improvements to the compressor and its analysis. By significantly reducing the amount of information stored and guiding the compression step using a RePair-inspired heuristic, we obtain a fast compressor achieving good compression ratios, addressing an open problem posed by Bille et al. We show how, with relatively small overhead, the compressed file can be converted into an in-memory representation that supports basic navigation operations in worst-case logarithmic time without decompression. We also show a much improved worst-case bound on the size of the output of top-tree compression (answering an open question posed in a talk on this algorithm by Weimann in 2012).Comment: SEA 201

Radiation and magnetic field effects on new semiconductor power devices for HL-LHC experiments

The radiation hardness of commercial Silicon Carbide and Gallium Nitride power MOSFETs is presented in this paper, for Total Ionizing Dose effects and Single Event Effects, under gamma, neutrons, protons and heavy ions. Similar tests are discussed for commercial DC-DC converters, also tested in operation under magnetic field

An Algebra of Hierarchical Graphs

We define an algebraic theory of hierarchical graphs, whose axioms characterise graph isomorphism: two terms are equated exactly when they represent the same graph. Our algebra can be understood as a high-level language for describing graphs with a node-sharing, embedding structure, and it is then well suited for defining graphical representations of software models where nesting and linking are key aspects

Resource-Bound Quantification for Graph Transformation

Graph transformation has been used to model concurrent systems in software engineering, as well as in biochemistry and life sciences. The application of a transformation rule can be characterised algebraically as construction of a double-pushout (DPO) diagram in the category of graphs. We show how intuitionistic linear logic can be extended with resource-bound quantification, allowing for an implicit handling of the DPO conditions, and how resource logic can be used to reason about graph transformation systems

Assimilating knowledge from neuroimages in schizophrenia diagnostics

The aim of this article is to propose an integrated framework for classifying and describing patterns of disorders from medical images using a combination of image registration, linear discriminant analysis and region-based ontologies. In a first stage of this endeavour we are going to study and evaluate multivariate statistical methodologies to identify the most discriminating hyperplane separating two populations contained in the input data. This step has, as its major goal, the analysis of all the data simultaneously rather than feature by feature. The second stage of this work includes the development of an ontology whose aim is the assimilation and exploration of the knowledge contained in the results of the previous statistical methods. Automated knowledge discovery from images is the key motivation for the methods to be investigated in this research. We argue that such investigation provides a suitable framework for characterising the high complexity of MR images in schizophrenia

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases

BACKGROUND: Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL). METHODS: We applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, 18F-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for 18F-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients. RESULTS: Compared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using 18F-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of discrimination due to a pattern of increased voxel-weight values in AD individuals was surprisingly high (ranging from approximately 20% to 40% depending on the imaging modality), located mainly in primary sensorimotor and visual cortices and subcortical nuclei. CONCLUSION: The MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to 18F-FDG-PET data. Moreover, the MKL-ROI approach demonstrates that brain regions typically spared in mild stages of AD also contribute substantially in the individual discrimination of AD patients from controls

Correlation between voxel based morphometry and manual volumetry in magnetic resonance images of the human brain

This is a comparative study between manual volumetry (MV) and voxel based morphometry (VBM) as methods of evaluating the volume of brain structures in magnetic resonance images. The volumes of the hippocampus and the amygdala of 16 panic disorder patients and 16 healthy controls measured through MV were correlated with the volumes of gray matter estimated by optimized modulated VBM. The chosen structures are composed almost exclusively of gray matter. Using a 4 mm Gaussian filter, statistically significant clusters were found bilaterally in the hippocampus and in the right amygdala in the statistical parametric map correlating with the respective manual volume. With the conventional 12 mm filter,a significant correlation was found only for the right hippocampus. Therefore,narrowfilters increase the sensitivity of the correlation procedure, especially when small brain structures are analyzed. The two techniques seem to consistently measure structural volume.Trata-se de estudo comparativo entre a volumetria manual(VM) e a morfometria baseada no vóxel (MBV), como métodos de avaliação do volume de estruturas cerebrais. Os volumes do hipocampo e da amídala de 16 pacientes de pânico e 16 controles sadios medidos através da VM foram correlacionados com os volumes de matéria cinzenta estimados pela MBV.As estruturas escolhidas são constituídas quase exclusivamente de matéria cinzenta. Utilizando um filtro Gaussiano de 4 mm, encontram-se, bilateralmente, aglomerados significativos de correlação nas duas estruturas no mapa estatístico paramétrico, correspondendo ao respectivo volume manual. Com o filtro convencional de 12 mm, apenas uma correlação significativa foi encontrada no hipocampo direito. Portanto, filtros estreitos aumentam a sensibilidade do procedimento de correlação,especialmente quando estruturas pequenas são analisadas. Ambas as técnicas parecem medir consistentemente o volume estrutural.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)(FAEPA) Hospital das Clínicas da FMRPUSP - Fundação de Apoio ao Ensino, Pesquisa e Asssistênci