Although widely touted as a replacement for glass slides and microscopes in pathology, digital slides present major challenges in data storage, transmission, processing and interoperability. Since no universal data format is in widespread use for these images today, each vendor defines its own proprietary data formats, analysis tools, viewers and software libraries. This creates issues not only for pathologists, but also for interoperability. In this paper, we present the design and implementation of OpenSlide, a vendor-neutral C library for reading and manipulating digital slides of diverse vendor formats. The library is extensible and easily interfaced to various programming languages. An application written to the OpenSlide interface can transparently handle multiple vendor formats. OpenSlide is in use today by many academic and industrial organizations world-wide, including many research sites in the United States that are funded by the National Institutes of Health.

In a brain imaging study of children learning algebra, it is shown that the same regions are active in children solving equations as are active in experienced adults solving equations. As with adults, practice in symbol manipulation produces a reduced activation in prefrontal cortex area. However, unlike adults, practice seems also to produce a decrease in a parietal area that is holding an image of the equation. This finding suggests that adolescents' brain responses are more plastic and change more with practice. These results are integrated in a cognitive model that predicts both the behavioral and brain imaging results.T he study reported here integrates behavioral methods, functional brain imaging (functional MRI), and cognitive modeling to study how children learn to solve equations. In particular, the children were solving equations like the following: 7x ϩ 1 ϭ 29. Past research with adult college students (1) modeled algebra equation solving by the interaction of three cognitive modules in the adaptive control of thought-rational (ACT-R) cognitive architecture (2, 3). There was an imaginal module that held a representation of the equation and performed imagined transformations on the equations. There was a retrieval module that retrieved algebraic rules and arithmetic facts in the solution of this equation. Finally, there was a manual module that programmed the output of the answer by the hand. A region in the left parietal cortex, which has been associated with imagery (4-6) and spatial processing (7) in other studies, was found to correspond to the imaginal module. A region in the left prefrontal cortex, which has been associated with retrieval in other studies (8)(9)(10)(11)(12)(13)(14), was found to correspond to the retrieval module. Finally, a region in the left motor and sensory cortices, which controls the right hand, was found to correspond to the manual module.After having identified these regions in algebra equation solving, we performed a series of experiments to determine whether they were specifically involved in algebra or were also involved in nonmathematical information-processing tasks (1,15,16). Similar involvement of these regions was found in a nonmathematical isomorph of algebra (artificial algebra) (1). Subsequent research (15), in which college students practiced the isomorph, found a speed-up that could be accounted for entirely in terms of reduced retrieval time. This finding was reflected in reduced activation in the prefrontal region of interest. There was not a comparable reduction in either the motor or parietal region.The present research addresses the question of whether the brain activation patterns observed from adults will be shown in children learning algebra. Specifically, do children who are just learning equation solving show activation of the same regions as in adults' algebra (1) and will their improvement be explained in terms of reduction just in the prefrontal retrieval region shown in adults' artificial algebra learning (15)? There is reason to suspect that we...

In this functional-MRI study we examined the hypothesis that the prefrontal cortex responds differently to the extent of competition during retrieval, whereas the parietal cortex is responsible for problem representation that should not be directly related to the competition. Participants mastered arbitrary person-location pairs, and their recognition memory was tested in a functional-MRI session. The pairs were constructed such that a person was associated with one, two, or three different locations and vice versa. The recognition time increased with the number of associations, reflecting increased competition. A confirmatory analysis of imaging data with prespecified prefrontal and parietal regions showed that, although both regions were highly involved during memory retrieval, only the prefrontal region responded to the levels of competition. This result was consistent with predictions of an information-processing model as well as with an exploratory identification of regions of interest. Human memory has been regarded as an associative network of elementary concepts, and a piece of knowledge arises into awareness when an association is retrieved (1). Because the appropriateness of a particular association depends on a given context, competition is sometimes inevitable. Therefore, the ability to retrieve the correct association in the face of competing associations is critical to human cognition. The purpose of the current functional-MRI (fMRI) study is to examine neural mechanisms underlying memory retrieval from multiple associations. Particularly, our aim is to dissociate the roles served by the prefrontal and the parietal cortices during memory retrieval. A number of cognitive neuroimaging studies have found parietal as well as prefrontal activations highly involved in cognitive functions such as working memory maintenance (2, 3), taskswitching (4-7), memory retrieval (8, 9), and arithmetic problem solving (10, 11). Considering the strong prefrontal-parietal interconnection (12, 13) and their functional coactivations (14), these two areas may serve at least complementary roles in the high-level cognition. However, although there is relative consensus that the prefrontal cortex directly controls processes involved in memory retrieval, it is not clear how the prefrontal function can be dissociated from the parietal function during memory retrieval.Our hypothesis is that the prefrontal cortex increases its activity when retrieval of task-relevant information is more demanding and effortful. On the other hand, we hypothesize that the parietal cortex serves as an imaginal buffer that represents and holds task-relevant information by encoding stimuli and, if necessary, updating the changes in the stimulus representation (10). The prefrontal component of this hypothesis is consistent with the findings of greater prefrontal activation during memory retrieval compared with working memory maintenance (2, 9) or other control conditions (8). Although parietal activation has been found during memory-retrieval tasks (1...

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Based on adaptive control of thought-rational (ACT-R), a cognitive architecture for cognitive modeling, researchers have developed an information-processing model to predict the blood oxygenation level-dependent (BOLD) response of functional MRI in symbol manipulation tasks. As an extension of this research, the current event-related functional MRI study investigates the effect of relatively extensive practice on the activation patterns of related brain regions. The task involved performing transformations on equations in an artificial algebra system. This paper shows that the base-level activation learning in the ACT-R theory can predict the change of the BOLD response in practice in a left prefrontal region reflecting retrieval of information. In contrast, practice has relatively little effect on the form of BOLD response in the parietal region reflecting imagined transformations to the equation or the motor region reflecting manual programming. I t has been a major challenge to understand how different brain regions are involved in the performance of a complex task. Anderson et al. (1) examined the performance of two variations of symbol manipulation tasks: an algebraic transformation task studied by Anderson et al. (2) and an abstract symbol manipulation task studied by Blessing and Anderson (3). Informationprocessing models that predict the latency patterns in these tasks exist in the adaptive control of thought-rational (ACT-R) cognitive architecture (4). These models require activity in an imaginal buffer to make changes to the problem representation, in a retrieval buffer to hold information from declarative memory, and in a manual buffer to hold information about motor behavior. Activity in the imaginal buffer predicted the blood oxygenation level-dependent (BOLD) response of functional MRI (fMRI) response in a left, posterior parietal region [Brodmann areas (BA) 39/40]; activity in the retrieval buffer predicted the BOLD response in a left prefrontal region (BA 45/46); and activity in the manual buffer predicted activity in a motor region (BA 4/3).This prior study (1) looked at performance of participants over a brief period. The goal of the current study was to explore the change of the brain activation patterns with learning, especially in those three regions. Numerous studies have reported that, with practice, the participants' accuracy will increase and reaction time will decrease. Even though the accuracy reaches asymptotically high levels, the reaction time will still decrease gradually in a negatively accelerated manner; that is, each unit of practice produces a smaller and smaller improvement in performance (compare ref. On the other hand, before and after Newell and Rosenbloom (6), there were other theories to explain the negative acceleration in practice, such as exponential learning function (e.g., refs. 8 and 9) and mixtures of different power functions (e.g., refs. 10 and 11). It is beyond the scope of this article to deal with these arguments. The most important thing is that all of the...

In an attempt to solve as much of the AAAI Robot Challenge as possible, five research institutions representing academia, industry and government, integrated their research in a single robot named GRACE. This paper describes this first year effort by the GRACE team, and describes not only the various techniques each participant brought to GRACE, but also the difficult integration effort itself.

In a functional magnetic resonance imaging study, we investigated how people solve mathematically equivalent problems presented in two alternative formats: verbal, story format or symbolic, equation format. Although representation format had no effect on behavior, anterior prefrontal activation was greater in the story condition and posterior parietal activation was greater in the equation condition. These results show that there exist alternative neural pathways that implement different and yet equally efficient problem-solving strategies.

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