Random Permutation Statistics and An Improved Slide-Determine Attack on KeeLoq

KeeLoq is a lightweight block cipher which is extensively used in the automotive industry. Its periodic structure, and overall simplicity makes it vulnerable to many different attacks. Only certain attacks are considered as really "practical" attacks on KeeLoq: the brute force, and several other attacks which require up to 2p16 known plaintexts and are then much faster than brute force, developed by Courtois et al., and (faster attack) by Dunkelman et al. On the other hand, due to the unusually small block size, there are yet many other attacks on KeeLoq, which require the knowledge of as much as about 2p32 known plaintexts but are much faster still. There are many scenarios in which such attacks are of practical interest, for example if a master key can be recovered, see Section 2 in [11] for a detailed discussion. The fastest of these attacks is an attack by Courtois, Bard and Wagner from that has a very low complexity of about 2p28 KeeLoq encryptions on average. In this paper we will propose an improved and refined attack which is faster both on average and in the best case. We also present an exact mathematical analysis of probabilities that arise in these attacks using the methods of modern analytic combinatorics

Mouse anatomy ontologies:enhancements and tools for exploring and integrating biomedical data

Mouse anatomy ontologies provide standard nomenclature for describing normal and mutant mouse anatomy, and are essential for the description and integration of data directly related to anatomy such as gene expression patterns. Building on our previous work on anatomical ontologies for the embryonic and adult mouse, we have recently developed a new and substantially revised anatomical ontology covering all life stages of the mouse. Anatomical terms are organized in complex hierarchies enabling multiple relationships between terms. Tissue classification as well as partonomic, developmental, and other types of relationships can be represented. Hierarchies for specific developmental stages can also be derived. The ontology forms the core of the eMouse Atlas Project (EMAP) and is used extensively for annotating and integrating gene expression patterns and other data by the Gene Expression Database (GXD), the eMouse Atlas of Gene Expression (EMAGE) and other database resources. Here we illustrate the evolution of the developmental and adult mouse anatomical ontologies toward one combined system. We report on recent ontology enhancements, describe the current status, and discuss future plans for mouse anatomy ontology development and application in integrating data resources. Mamm Genome 2015 Oct; 26(9-10):422-3

Logical Development of the Cell Ontology

<p>Abstract</p> <p>Background</p> <p>The Cell Ontology (CL) is an ontology for the representation of <it>in vivo </it>cell types. As biological ontologies such as the CL grow in complexity, they become increasingly difficult to use and maintain. By making the information in the ontology computable, we can use automated reasoners to detect errors and assist with classification. Here we report on the generation of computable definitions for the hematopoietic cell types in the CL.</p> <p>Results</p> <p>Computable definitions for over 340 CL classes have been created using a genus-differentia approach. These define cell types according to multiple axes of classification such as the protein complexes found on the surface of a cell type, the biological processes participated in by a cell type, or the phenotypic characteristics associated with a cell type. We employed automated reasoners to verify the ontology and to reveal mistakes in manual curation. The implementation of this process exposed areas in the ontology where new cell type classes were needed to accommodate species-specific expression of cellular markers. Our use of reasoners also inferred new relationships within the CL, and between the CL and the contributing ontologies. This restructured ontology can be used to identify immune cells by flow cytometry, supports sophisticated biological queries involving cells, and helps generate new hypotheses about cell function based on similarities to other cell types.</p> <p>Conclusion</p> <p>Use of computable definitions enhances the development of the CL and supports the interoperability of OBO ontologies.</p

The RICORDO approach to semantic interoperability for biomedical data and models: strategy, standards and solutions.

BACKGROUND: The practice and research of medicine generates considerable quantities of data and model resources (DMRs). Although in principle biomedical resources are re-usable, in practice few can currently be shared. In particular, the clinical communities in physiology and pharmacology research, as well as medical education, (i.e. PPME communities) are facing considerable operational and technical obstacles in sharing data and models. FINDINGS: We outline the efforts of the PPME communities to achieve automated semantic interoperability for clinical resource documentation in collaboration with the RICORDO project. Current community practices in resource documentation and knowledge management are overviewed. Furthermore, requirements and improvements sought by the PPME communities to current documentation practices are discussed. The RICORDO plan and effort in creating a representational framework and associated open software toolkit for the automated management of PPME metadata resources is also described. CONCLUSIONS: RICORDO is providing the PPME community with tools to effect, share and reason over clinical resource annotations. This work is contributing to the semantic interoperability of DMRs through ontology-based annotation by (i) supporting more effective navigation and re-use of clinical DMRs, as well as (ii) sustaining interoperability operations based on the criterion of biological similarity. Operations facilitated by RICORDO will range from automated dataset matching to model merging and managing complex simulation workflows. In effect, RICORDO is contributing to community standards for resource sharing and interoperability.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are