Qualitative Spatial Interpretation of Course-of-Action Diagrams

Abstract This paper demonstrates qualitative spatial reasonin g techniques in a real-world diagrammatic reasoning task: Course-of-Action (COA) diagrams . COA diagrams are military planning diagrams that depict unit movements an d tasks in a given region . COA diagrams are a useful test be d for researching diagram understanding due to their composable symbology, their intrinsically spatial task, an d their use across many types of military planning . W e constructed two COA diagram interpreters using ou r qualitative spatial reasoning engine, GeoRep . The firs t system uses GeoRep to interpret individual COA glyphs . The second system, building upon the first, takes preclassified symbol input and then uses GeoRep to describ e geographic relationships implied by the symbol arrangements . This latter system, in a recent DARPA initiative , answered dozens of geographic queries about many different COA diagrams . This research shows that qualitative spatial reasoning, through tools like GeoRep, provides a useful substrate for complex diagrammatic reasoning

Graphical means for inspecting qualitative models of system behaviour

This article presents the design and evaluation of a tool for inspecting conceptual models of system behaviour. The basis for this research is the Garp framework for qualitative simulation. This framework includes modelling primitives, such as entities, quantities and causal dependencies, which are combined into model fragments and scenarios. Given a library of model fragments and a scenario describing an initial situation, the qualitative simulation engine generates predictions in the form of a state-transition graph. This rich knowledge representation has potential for educational purposes. However, communicating the contents of simulation models effectively to learners is not trivial. The predicate logic format used by Garp is not easy for non-experts to understand, and a simulation often contains so much information that it is difficult to get an overview while still having access to detailed information. To address these problems, a tool has been developed that generates graphical representations of the information contained in a qualitative simulation. This tool, named VisiGarp, incorporates a vocabulary of graphical elements for model ingredients and relationships, and combines these into interactive diagrams. VisiGarp has been evaluated by thirty students, with promising results, using a setup which included simulation results and exercises about Brazilian Cerrado ecology

From proteomic analysis to potential therapeutic targets: functional profile of two lung cancer cell lines, A549 and SW900, widely studied in pre-clinical research

Lung cancer is a serious health problem and the leading cause of cancer death worldwide. The standard use of cell lines as in vitro pre-clinical models to study the molecular mechanisms that drive tumorigenesis and access drug sensitivity/effectiveness is of undisputable importance. Label-free mass spectrometry and bioinformatics were employed to study the proteomic profiles of two representative lung cancer cell lines and to unravel the specific biological processes. Adenocarcinoma A549 cells were enriched in proteins related to cellular respiration, ubiquitination, apoptosis and response to drug/hypoxia/oxidative stress. In turn, squamous carcinoma SW900 cells were enriched in proteins related to translation, apoptosis, response to inorganic/organic substances and cytoskeleton organization. Several proteins with differential expression were related to cancer transformation, tumor resistance, proliferation, migration, invasion and metastasis. Combined analysis of proteome and interactome data highlighted key proteins and suggested that adenocarcinoma might be more prone to PI3K/Akt/mTOR and topoisomerase IIα inhibitors, and squamous carcinoma to Ck2 inhibitors. Moreover, ILF3 overexpression in adenocarcinoma, and PCNA and NEDD8 in squamous carcinoma shows them as promising candidates for therapeutic purposes. This study highlights the functional proteomic differences of two main subtypes of lung cancer models and hints several targeted therapies that might assist in this type of cancer.publishe

Variants in ADRB1 and CYP2C9: Association with Response to Atenolol and Losartan in Marfan Syndrome

Objective: To test whether variants in ADRB1 and CYP2C9 genes identify subgroups of individuals with differential response to treatment for Marfan syndrome through analysis of data from a large, randomized trial. Study design: In a subset of 250 white, non-Hispanic participants with Marfan syndrome in a prior randomized trial of atenolol vs losartan, the common variants rs1801252 and rs1801253 in ADRB1 and rs1799853 and rs1057910 in CYP2C9 were analyzed. The primary outcome was baseline-adjusted annual rate of change in the maximum aortic root diameter z-score over 3 years, assessed using mixed effects models. Results: Among 122 atenolol-assigned participants, the 70 with rs1801253 CC genotype had greater rate of improvement in aortic root z-score compared with 52 participants with CG or GG genotypes (Time × Genotype interaction P = .005, mean annual z-score change ± SE -0.20 ± 0.03 vs -0.09 ± 0.03). Among participants with the CC genotype in both treatment arms, those assigned to atenolol had greater rate of improvement compared with the 71 of the 121 assigned to losartan (interaction P = .002; -0.20 ± 0.02 vs -0.07 ± 0.02; P < .001). There were no differences in atenolol response by rs1801252 genotype or in losartan response by CYP2C9 metabolizer status. Conclusions: In this exploratory study, ADRB1-rs1801253 was associated with atenolol response in children and young adults with Marfan syndrome. If these findings are confirmed in future studies, ADRB1 genotyping has the potential to guide therapy by identifying those who are likely to have greater therapeutic response to atenolol than losartan

A Cognitive Approach to Sketch Understanding

... drawings and linguistic information to convey spatial and conceptual material. Our work on a computational model of sketching has the goal of both explaining human sketching and creating software that can be a human-like partner in sketching interactions. This focus has lead us to explore a very different set of tradeoffs from those typically chosen in multimodal interfaces. We highlight some results of our approach, including research performed using GeoRep, our diagrammatic reasoning architecture, and sKEA, a multimodal sketching tool used for knowledge acquisition in spatial domains

Building Qualitative Models of Thermodynamic Processes

This paper describes a qualitative domain theory for core phenomena in engineering thermodynamics, expressed in Qualitative Process theory. It represents many of the best features of domain models developed by our group over the past five years. It focuses on supporting system-level qualitative analyses of typical fluid and thermal systems, such as refrigerators and power plants. We use explicit modeling assumptions [3] to control the level of detail used in building models of specific scenarios. We begin by outlining the primitives of the specific QP modeling language. The bulk of the paper describes the domain model itself, highlighting our design choices, simplifications, and use of modeling assumptions. Next we demonstrate how this domain model can be used to build models of a variety of specific scenarios, including simplified versions of a refrigerator, a steam plant, and a thermal control system. Finally, we describe some planned extensions to the model. Contents 1 Introducti..

GeoRep: A Flexible Tool for Spatial Representation of Line Drawings

A key problem in diagrammatic reasoning is understanding how people reason about qualitative relationships in diagrams. We claim that progress in diagrammatic reasoning is slowed by two problems: (1) researchers tend to start from scratch, creating new spatial reasoners for each new problem area, and (2) constraints from human visual processing are rarely considered. To address these problems, we created GeoRep, a spatial reasoning engine that generates qualitative spatial descriptions from line drawings. GeoRep has been successfully used in several research projects, including cognitive simulation studies of human vision. In this paper, we outline GeoRep&apos;s architecture, explain the domainindependent and domain-specific aspects of its processing, and motivate the representations it produces. We then survey how GeoRep has been used in three different projects--a model of symmetry, a model of understanding juxtaposition diagrams of physical situations, and a system for re..