Incidence and characterisation of mycoviruses from Aspergillus fumigatus

This study investigated the incidence and characterisation of mycoviruses in a range of different fungi including Phytophthora spp. Phlebiopsis gigantea and Aspergillus fumigatus and their effects on their hosts. The investigation developed methodology for rapid molecular characterisation of dsRNA elements from different fungi which were applied in detail to a range of isolates of A. fumigatus. DsRNA elements or mycoviruses are present in almost all major classes of fungi where they lack an extracellular phase and are transmitted intracellulary via anastomosis or spores. Mycoviruses can confer a range of phenotypes on their fungal hosts ranging from symptomless to debilitating and hypovirulence to hypervirulence. In most cases most fungal mycovirus infections are symptomless and the effects of A. fumigatus mycoviruses on fitness and growth of infected isolates were also investigated. A screen of thirty nine clinical isolates of A. fumigatus, which is an opportunistic human pathogen and causes aspergillosis in immunocompromised patients, revealed the presence of dsRNA elements which were hitherto unknown. Two mycoviruses were identified including a chrysovirus (isolate A-56) and an unclassified, triripartite dsRNA-containing mycovirus (isolate A-54). All four dsRNA segments of the A-56 chrysovirus were sequenced in their entirety using the methodologies developed earlier in the investigation and the sequences analysed and compared to other members of the family Chrysoviridae and other characterised mycovirus families. Also the effects of the chrysovirus on the fitness of the host fungus were studied as was transfer of the purified chrysovirus to cured strains of A. fumigatus via protoplast fusion and direct transfection by different methods. It is intended to develop the use of dsRNA elements for gene silencing in A. fumigatus and towards this aim a full-length clone of the smallest A-56 dsRNA has been constructed and characterised and used to produce dsRNA transcripts for infection and amplification in the fungus

Belief Revision, Minimal Change and Relaxation: A General Framework based on Satisfaction Systems, and Applications to Description Logics

Belief revision of knowledge bases represented by a set of sentences in a given logic has been extensively studied but for specific logics, mainly propositional, and also recently Horn and description logics. Here, we propose to generalize this operation from a model-theoretic point of view, by defining revision in an abstract model theory known under the name of satisfaction systems. In this framework, we generalize to any satisfaction systems the characterization of the well known AGM postulates given by Katsuno and Mendelzon for propositional logic in terms of minimal change among interpretations. Moreover, we study how to define revision, satisfying the AGM postulates, from relaxation notions that have been first introduced in description logics to define dissimilarity measures between concepts, and the consequence of which is to relax the set of models of the old belief until it becomes consistent with the new pieces of knowledge. We show how the proposed general framework can be instantiated in different logics such as propositional, first-order, description and Horn logics. In particular for description logics, we introduce several concrete relaxation operators tailored for the description logic \ALC{} and its fragments \EL{} and \ELext{}, discuss their properties and provide some illustrative examples

Streaming Binary Sketching based on Subspace Tracking and Diagonal Uniformization

In this paper, we address the problem of learning compact similarity-preserving embeddings for massive high-dimensional streams of data in order to perform efficient similarity search. We present a new online method for computing binary compressed representations -sketches- of high-dimensional real feature vectors. Given an expected code length $c$ and high-dimensional input data points, our algorithm provides a $c$-bits binary code for preserving the distance between the points from the original high-dimensional space. Our algorithm does not require neither the storage of the whole dataset nor a chunk, thus it is fully adaptable to the streaming setting. It also provides low time complexity and convergence guarantees. We demonstrate the quality of our binary sketches through experiments on real data for the nearest neighbors search task in the online setting