Janice I. Glasgow

Quantitative Assessment of Limb Position Sense Following Stroke

Background. Impairment of position sense of the upper extremity (UE) may impede activities of daily living and limit motor gains after stroke. Most clinical assessments of position sense rely on categorical or ordinal ratings by clinicians that lack sensitivity to change or the ability to discriminate subtle deficits. Objective. Use robotic technology to develop a reliable, quantitative technique with a continuous scale to assess UE position sense following stroke. Methods. Forty-five patients recruited from an inpatient stroke rehabilitation service and 65 age-matched healthy controls performed an arm position matching task. Each UE was fitted in the exoskeleton of a KINARM device. One UE was passively placed in one of 9 positions, and the subject was told to match his or her position with the other UE. Patients were compared with statistical distributions of control data to identify those with deficits in UE position sense. Test—retest sessions using 2 raters established interrater reliability. Results. Two thirds of left hemiparetic and one third of right hemiparetic patients had deficits in limb position sense. Left-affected stroke subjects demonstrated significantly more trial-to-trial variability than right-affected or control subjects. The robotic assessment technique demonstrated good interrater reliability but limited agreement with the clinical thumb localizing test. Conclusions. Robotic technology can provide a reliable quantitative means to assess deficits in limb position sense following stroke.

A logic for reasoning about security

A formal framework called <italic>Security Logic</italic> (<italic>SL</italic>) is developed for specifying and reasoning about security policies and for verifying that system designs adhere to such policies. Included in this modal logic framework are definitions of <italic>knowledge, permission,</italic> and <italic>obligation</italic>. Permission is used to specify secrecy policies and obligation to specify integrity policies. The combination of policies is addressed and examples based on policies from the current literature are given.

Assessment of Upper-Limb Sensorimotor Function of Subacute Stroke Patients Using Visually Guided Reaching

Objective. Using robotic technology, we examined the ability of a visually guided reaching task to assess the sensorimotor function of patients with stroke. Methods. Ninety-one healthy participants and 52 with subacute stroke of mild to moderate severity (26 with left- and 26 with right-affected body sides) performed an unassisted reaching task using the KINARM robot. Each participant was assessed using 12 movement parameters that were grouped into 5 attributes of sensorimotor control. Results. A number of movement parameters individually identified a large number of stroke participants as being different from 95% of the controls—most notably initial direction error, which identified 81% of left-affected patients. We also found interlimb differences in performance between the arms of those with stroke compared with controls. For example, whereas only 31% of left-affected participants showed differences in reaction time with their affected arm, 54% showed abnormal interlimb differences in reaction time. Good interrater reliability (r > 0.7) was observed for 9 of the 12 movement parameters. Finally, many stroke patients deemed impaired on the reaching task had been scored 6 or less on the arm portion of the Chedoke-McMaster Stroke Assessment Scale, but some who scored a normal 7 were also deemed impaired in reaching. Conclusions. Robotic technology using a visually guided reaching task can provide reliable information with greater sensitivity about a patient’s sensorimotor impairments following stroke than a standard clinical assessment scale.

Case-based reasoning in IVF: prediction and knowledge mining

In vitro fertilization (IVF) is a medically-assisted reproduction technique, enabling infertile couples to achieve successful pregnancy. Given the unpredictability of the task, we propose to use a case-based reasoning system that exploits past experiences to suggest possible modifications to an IVF treatment plan in order to improve overall success rates. Once the systems knowledge base is populated with a sufficient number of past cases, it can be used to explore and discover interesting relationships among data, thereby achieving a form of knowledge mining. The article describes the TA3IVF system--a case-based reasoning system which relies on context-based relevance assessment to assist in knowledge visualization, interactive data exploration and discovery in this domain. The system can be used as an advisor to the physician during clinical work and during research to help determine what knowledge sources are relevant for a treatment plan.

Macromolecular crystallization in a high throughput laboratory—the search phase

Macromolecular crystallization efforts are frequently divided into a search phase, during which approximate conditions are sought, and an optimization phase, when the approximate conditions are optimized to yield crystals of sufficient quality for diffraction work. Faced with the possibility that, on a yearly basis, many hundreds of proteins might be generated, both in our laboratories and at the laboratories of our collaborators, we have recently designed and commissioned a high throughput robotics lab designed for the search phase. The lab is capable of setting up and photographically evaluating over 60,000 microbatch crystallization experiments per week. In the first four months of operation we have set up crystallization experiments for more than one hundred proteins.

Intelligent decision support for protein crystal growth

Current structural genomics projects are likely to produce hundreds of proteins a year for structural analysis. The primary goal of our research is to speed up the process of crystal growth for proteins in order to enable the determination of protein structure using single crystal X-ray diffraction. We describe Max, a working prototype that includes a high-throughput crystallization and evaluation setup in the wet laboratory and an intelligent software system in the computer laboratory. A robotic setup for crystal growth is able to prepare and evaluate over 40 thousand crystallization experiments a day. Images of the crystallization outcomes captured with a digital camera are processed by an image-analysis component that uses the two-dimensional Fourier transform to perform automated classification of the experiment outcome. An information repository component, which stores the data obtained from crystallization experiments, was designed with an emphasis on correctness, completeness, and reproducibility. A case-based reasoning component provides support for the design of crystal growth experiments by retrieving previous similar cases, and then adapting these in order to create a solution for the problem at hand. While work on Max is still in progress, we report here on the implementation status of its components, discuss how our work relates to other research, and describe our plans for the future.

Progress in computational methods for representing geographical concepts

Over the past ten years, a subfield of GIScience has been recognized that addresses the linkage between human thought regarding geographical space, and the mechanisms for implementing these concepts in computational models. This research area has developed an identity through a series of successful international conferences and the establishment of a journal. It has also been complemented through community activities such as international standardization efforts and GIS interoperability. Historically, much of the advancement in computational methods has occurred at or close to the implementation level, as exemplified by attention to the development of spatial access methods. Significant progress has been made at the levels of spatial data models and spatial query languages, although we note the lack of a comprehensive theoretical framework comparable to the relational data model in database management systems. The difficult problems that need future research efforts are at the highly abstract level of cap...

Applications of case-based reasoning in molecular biology

Case-based reasoning (CBR) is a computational reasoning paradigm that involves the storage and retrieval of past experiences to solve novel problems. It is an approach that is particularly relevant in scientific domains, where there is a wealth of data but often a lack of theories or general principles. This article describes several CBR systems that have been developed to carry out planning, analysis, and prediction in the domain of molecular biology.

Programming Styles in Nial

The programming language Nial several styles f supports several styles of programming including imperative, procedural, applicative, and lambda-free. Nial tools can be built to illustrate relational and object-oriented styles of programming.

THE IMAGERY DEBATE REVISITED: A COMPUTATIONAL PERSPECTIVE

Mental imagery is considered by many as a major medium of thought; in approaches to problem solving people often describe the sensation of visualizing, manipulating and inspecting “pictures in their healds.” For example, subjects in psychological studies of imagery report the ability to mentally rotate and superimpose images onto one another in order to determine whether or not they are congruent. Although imagery is most often associated with visual perception, images can be generated corresponding to a variety of modalities; imagine the touch sensation of petting a kitten, the smell of fresh-ground coffee or the sound of a flute. No one seems to deny the existence of the phenomenon of mental imagery, yet there is a heated and ongoing debate concerning the structure and function of imagery in human cognition.’ Some researchers suggest that images are represented and manipulated in depictive or analog forms; others argue that mental images are structural descriptions, no different from the representations in other areas of cognition. Alternative theories have been developed and experimental data generated to support or refute these conflicting opinions. It has been proposed that the imagery debate cannot be resolved through purely empirical studies, since such studies often rely on the use of personal introspection which does not provide reliable information concerning the nature of cognition. Although the major proponents in the imagery #debate have historically been cognitive psychologists and philosophers, investigations in neuropsychology have recently contributed to resolving the conflict. Results from these studies suggest that there exist distinct visual and spatial representational subsystems involved in mental imagery, where the visual subsystem is concerned with the geometric properties of images and the spatial subsystem is concerned with understanding spatial configurations in the world (Farah et al., 1988; Kosslyn, 1987). The primary goal of this paper is to bring the imagery debate to the A1 community, where it can be addressed from a computational perspective in terms of knowledge representation and reasoning techniques. One motivation for considering the imagery debate from a computational viewpoint is to provide insight into what representation schemes and reasoning strategies may be most appropriate for problems that involve imagery if solved by humans. The appropriateness of alternative paradigms can be evaluated in terms of criteria such as programming ease, efficiency and inferential adequacy. We argue that a depictive knowledge represent ation scheme and associated reasoning strategies are demonstrably preferable, under thlese criteria, for computational imagery. A second motivation for studying imagery from a computational perspective is that it will contribute to the existing imagery debate through the development of techniques for simulating and testing the predictive power of alternative theories of cognition. It is important to clarify what is meant by imagery in the context of AI, and at what level we wish to debate the issues involved in imagery. Imagery can be considered as the