Integrating bottom-up and top-down reasoning in COLAB

The knowledge compilation laboratory COLAB integrates declarative knowledge representation formalisms, providing source-to-source and source-to-code compilers of various knowledge types. Its architecture separates taxonomical and assertional knowledge. The assertional component consists of a constraint system and a rule system, which supports bottom-up and top-down reasoning of Horn clauses. Two approaches for forward reasoning have been implemented. The first set-oriented approach uses a fixpoint computation. It allows top-down verification of selected premises. Goal-directed bottom-up reasoning is achieved by a magic-set transformation of the rules with respect to a goal. The second tuple-oriented approach reasons forward to derive the consequences of an explicitly given set of facts. This is achieved by a transformation of the rules to top-down executable Horn clauses. The paper gives an overview of the various forward reasoning approaches, their compilation into an abstract machine and their integration into the COLAB shell

Identification of Cellular Pathogenicity Markers for SIL1 Mutations Linked to Marinesco-Sjögren Syndrome.

Background and objective: Recessive mutations in the SIL1 gene cause Marinesco-Sjögren syndrome (MSS), a rare neuropediatric disorder. MSS-patients typically present with congenital cataracts, intellectual disability, cerebellar ataxia and progressive vacuolar myopathy. However, atypical clinical presentations associated with SIL1 mutations have been described over the last years; compound heterozygosity of SIL1 missense mutations even resulted in a phenotype not fulfilling the clinical diagnostic criteria of MSS. Thus, a read-out system to evaluate reliably the pathogenicity of amino acid changes in SIL1 is needed. Here, we aim to provide suitable cellular biomarkers enabling the robust evaluation of pathogenicity of SIL1 mutations. Methods: Five SIL1 variants including one polymorphism (p.K132Q), three known pathogenic mutations (p.V231_I232del, p.G312R, and p.L457P) and one ambiguous missense variant (p.R92W) were studied along with the wild-type proteins in Hek293 in vitro models by cell biological assays, immunoprecipitation, immunoblotting, and immunofluorescence as well as electron microscopy. Moreover, the SIL1-interactomes were interrogated by tandem-affinity-purification and subsequent mass spectrometry. Results: Our combined studies confirmed the pathogenicity of p.V231_I232del, p.G312R, and p.L457P by showing instability of the proteins as well as tendency to form aggregates. This observation is in line with altered structure of the ER-Golgi system and vacuole formation upon expression of these pathogenic SIL1-mutants as well as the presence of oxidative or ER-stress. Reduced cellular fitness along with abnormal mitochondrial architecture could also be observed. Notably, both the polymorphic p.K132Q and the ambiguous p.R92W variants did not elicit such alterations. Study of the SIL1-interactome identified POC1A as a novel binding partner of wild-type SIL1; the interaction is disrupted upon the presence of pathogenic mutants but not influenced by the presence of benign variants. Disrupted SIL1-POC1A interaction is associated with centrosome disintegration. Conclusions: We developed a combination of cellular outcome measures to evaluate the pathogenicity of SIL1 variants in suitable in vitro models and demonstrated that the p. R92W missense variant is a polymorphism rather than a pathogenic mutation leading to MSS

μCAD2NC : a declarative lathe-workplanning model transforming CAD-like geometries into abstract NC programs

μCAD2NC is a knowledge-based system generating workplans for idealized lathe CNC machines. It transforms CAD-like geometries of rotational-symmetric workpieces into abstract NC programs, using declarative term representations for all processing steps. The system has been developed using COLAB, a hybrid-knowledge compilation laboratory which integrates the power of forward and backward reasoning (incI. functional programming), constraint propagation, and taxonomic classification. The focus of this work is on exemplifying techniques of the hybrid, declarative COLAB formalisms for the central subtasks of CAD-to-NC transformations