Case-Based Learning: Predictive Features in Indexing

Interest in psychological experimentation from the Artificial Intelligence community often takes the form of rigorous post-hoc evaluation of completed computer models. Through an example of our own collaborative research, we advocate a different view of how psychology and AI may be mutually relevant, and propose an integrated approach to the study of learning in humans and machines. We begin with the problem of learning appropriate indices for storing and retrieving information from memory. From a planning task perspective, the most useful indices may be those that predict potential problems and access relevant plans in memory, improving the planner's ability to predict and avoid planning failures. This “predictive features” hypothesis is then supported as a psychological claim, with results showing that such features offer an advantage in terms of the selectivity of reminding because they more distinctively characterize planning situations where differing plans are appropriate.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46907/1/10994_2004_Article_422599.pd

The Sound Generated by Mid-Ocean Ridge Black Smoker Hydrothermal Vents

Hydrothermal flow through seafloor black smoker vents is typically turbulent and vigorous, with speeds often exceeding 1 m/s. Although theory predicts that these flows will generate sound, the prevailing view has been that black smokers are essentially silent. Here we present the first unambiguous field recordings showing that these vents radiate significant acoustic energy. The sounds contain a broadband component and narrowband tones which are indicative of resonance. The amplitude of the broadband component shows tidal modulation which is indicative of discharge rate variations related to the mechanics of tidal loading. Vent sounds will provide researchers with new ways to study flow through sulfide structures, and may provide some local organisms with behavioral or navigational cues

Case-based learning: Predictive features in indexing

Interest in psychological experimentation from the Artificial Intelligence community often takes the form of rigorous post-hoc evaluation of completed computer models. Through an example of our own collaborative research, we advocate a different view of how psychology and AI may be mutually relevant, and propose an integrated approach to the study of learning in humans and machines. We begin with the problem of learning appropriate indices for storing and retrieving information from memory. From a planning task perspective, the most useful indices may be those that predict potential problems and access relevant plans in memory, improving the planner's ability to predict and avoid planning failures. This “predictive features” hypothesis is then supported as a psychological claim, with results showing that such features offer an advantage in terms of the selectivity of reminding because they more distinctively characterize planning situations where differing plans are appropriate.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46928/1/10994_2004_Article_BF00993173.pd

Environmental risk factors for Toxoplasma gondii infections and the impact of latent infections on allostatic load in residents of Central North Carolina

Abstract Background Toxoplasma gondii infection can be acquired through ingestion of infectious tissue cysts in undercooked meat or environmental oocysts excreted by cats. This cross-sectional study assessed environmental risk factors for T. gondii infections and an association between latent infections and a measure of physiologic dysregulation known as allostatic load. Methods Serum samples from 206 adults in the Durham-Chapel Hill, North Carolina area were tested for immunoglobulin (IgG) responses to T. gondii using commercial ELISA kits. Allostatic load was estimated as a sum of 15 serum biomarkers of metabolic, neuroendocrine and immune functions dichotomized at distribution-based cutoffs. Vegetated land cover within 500 m of residences was estimated using 1 m resolution data from US EPA’s EnviroAtlas. Results Handling soil with bare hands at least weekly and currently owning a cat were associated with 5.3 (95% confidence limits 1.4; 20.7) and 10.0 (2.0; 50.6) adjusted odds ratios (aOR) of T. gondii seropositivity, respectively. There was also a significant positive interaction effect of handling soil and owning cats on seropositivity. An interquartile range increase in weighted mean vegetated land cover within 500 m of residence was associated with 3.7 (1.5; 9.1) aOR of T. gondii seropositivity. Greater age and consumption of undercooked pork were other significant predictors of seropositivity. In turn, T. gondii seropositivity was associated with 61% (13%; 130%) greater adjusted mean allostatic load compared to seronegative individuals. In contrast, greater vegetated land cover around residence was associated with significantly reduced allostatic load in both seronegative (p < 0.0001) and seropositive (p = 0.004) individuals. Conclusions Residents of greener areas may be at a higher risk of acquiring T. gondii infections through inadvertent ingestion of soil contaminated with cat feces. T. gondii infections may partially offset health benefits of exposure to the natural living environment

The Stabilization of Environments

this paper we focus on this sort of stabilization, first of all because it has been neglected (in comparison with learning and more straighforward considerations in agent design), secondly because we believe that the notion can play a central role in understanding the successes of habitual human behavior, and, finally, because it seems like an unexploited research area in the analysis and design of agents that have a long-term interaction with their environments. For the rest of the paper, we explore the idea of stabilization, and its use in the design of agents and in the analysis of agent-environment interaction. Finally we describe FixPoint, an program that implements some of these ideas, and some performance improvement results due to the stabilization o

Understanding the Significance of Biochemistry in the Storage, Handling, Purification, and Sampling of Amphiphilic Mycolactone

Mycolactone, the amphiphilic macrolide toxin secreted by Mycobacterium ulcerans, plays a significant role in the pathology and manifestations of Buruli ulcer (BU). Consequently, it follows that the toxin is a suitable target for the development of diagnostics and therapeutics for this disease. Yet, several challenges have deterred such development. For one, the lipophilic nature of the toxin makes it difficult to handle and store and contributes to variability associated with laboratory experimentation and purification yields. In this manuscript, we have attempted to incorporate our understanding of the lipophilicity of mycolactone in order to define the optimal methods for the storage, handling, and purification of this toxin. We present a systematic correlation of variability associated with measurement techniques (thin-layer chromatography (TLC), mass spectrometry (MS), and UV-Vis spectrometry), storage conditions, choice of solvents, as well as the impact of each of these on toxin function as assessed by cellular cytotoxicity. We also compared natural mycolactone extracted from bacterial culture with synthesized toxins in laboratory (solvents, buffers) and physiologically relevant (serum) matrices. Our results point to the greater stability of mycolactone in organic, as well as detergent-containing, solvents, regardless of the container material (plastic, glass, or silanized tubes). They also highlight the presence of toxin in samples that may be undetectable by any one technique, suggesting that each detection approach captures different configurations of the molecule with varying specificity and sensitivity. Most importantly, our results demonstrate for the very first time that amphiphilic mycolactone associates with host lipoproteins in serum, and that this association will likely impact our ability to study, diagnose, and treat Buruli ulcers in patients