An object simulation model for modeling hypothetical disease epidemics – EpiFlex

Background  

EpiFlex is a flexible, easy to use computer model for a single computer, intended to be operated by one user who need not be an expert. Its purpose is to study in-silico the epidemic behavior of a wide variety of diseases, both known and theoretical, by simulating their spread at the level of individuals contracting and infecting others. To understand the system fully, this paper must be read together in conjunction with study of the software and its results. EpiFlex is evaluated using results from modeling influenza A epidemics and comparing them with a variety of field data sources and other types of modeling.     

Image understanding system for histopathology

An image understanding machine vision system for histological diagnoses is based on three interacting expert systems: a diagnostic expert system utilizing terms familiar to pathologists, an interpretive expert system relating human diagnostic concepts to computable histometric features and a scene segmentation expert system which extracts the diagnostic information from the imagery. The control software for the image understanding system resides on a multiprocessor computer. This article details measures to maintain system efficiency and to accommodate the requirements of interprocess communication and processing task scheduling.     

The design and synthesis of mimetics of peptide beta-turns

The synthesis of an 11 membered ring bis-lactam, a system which is designed as a conformationally restricted mimetic of type I and type II beta-turns is described. Computer assisted molecular modeling was used to compare the predicted low energy conformers of the turn mimetic with idealized type I and type II turn structures. Initial computational analysis indicates that the basic ring structure will provide an excellent foundation for the development of a varieity of beta-turn mimetics.     

Real-time unconstrained object recognition: a processing pipeline based on the mammalian visual system

The mammalian visual system is still the gold standard for recognition accuracy, flexibility, efficiency, and speed. Ongoing advances in our understanding of function and mechanisms in the visual system can now be leveraged to pursue the design of computer vision architectures that will revolutionize the state of the art in computer vision.     

Automatic counting of birds in a bird deterrence field trial

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Expert systems in treating substance abuse.

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Expert system for aiding diagnosis in hearing tests]

For expert systems intended to aid diagnosis, a structure with five levels is proposed. These levels are the original area, the parameter and a reduced parameter layer, the classification and the final-decision layer. On the basis of this structures, an expert system was developed specifically for neonatal hearing screening with transitory evoked otoacoustic emissions (TEOAE). In a second step, this system was investigated for its suitability to classify emissions, regardless of patient age. For the comparison measurements in 252 mainly adult patients, some with an acquired hearing impairment, were used. To adapt the pass/fail decision to the extended evaluation criteria, the false classifications from a first run with the new data were used for training. Thereafter, the expert system, working with a wider data basis, classified the new data with a sensitivity that was increased by 4.8% to 97.2%, and a 2.0% improvement in specificity to 95.5% when classifying new data, These results, together with those of 97.3% and 94.3% achieved with exclusively neonatal TEOAE classification, clearly show the advantage of the expert system structures chosen, and document evidence of the practical applicability of the method.     

The use of inference strategies in the differential diagnosis of microcytic anemia

A new expert system developed on a Macintosh personal computer using a commercially available artificial intelligence shell was compared with four different discriminant functions (DFs) for the differentiation of microcytic anemia into etiologic categories. Several databases were used with a different composition but all contained at least some samples from thalassemic individuals and from patients with iron deficiency anemia. The DFs analyzed were those proposed by England and Fraser, Green and colleagues, Mentzer, and by Shine and Lal. None of the databases performed satisfactorily when used singly, whereas very high false-positive rates were obtained by one of them. The diagnostic efficiency was somewhat improved by combining several DFs. An expert system using an artificial intelligence "shell" with an "interference engine" was developed using cluster analysis and a set of learning examples. The input necessary for the system to achieve a conclusion consists of MCV, RBC, and RDW as well as a statement as to whether the patient has anemia. Based upon the values of these parameters, the expert system will give an "advice" regarding the probabilities for thalassemia, iron deficiency, and/or other probabilities such as previous transfusions, anemia of chronic disease, laboratory error, etc. In a prospective trial, the system functioned with an accuracy of better than 85%.     

Integrating expert knowledge and ecological niche models to estimate Mexican primates’ distribution

Ecological niche modeling is used to estimate species distributions based on occurrence records and environmental variables, but it seldom includes explicit biotic or historical factors that are important in determining the distribution of species. Expert knowledge can provide additional valuable information regarding ecological or historical attributes of species, but the influence of integrating this information in the modeling process has been poorly explored. Here, we integrated expert knowledge in different stages of the niche modeling process to improve the representation of the actual geographic distributions of Mexican primates (Ateles geoffroyi, Alouatta pigra, and A. palliata mexicana). We designed an elicitation process to acquire information from experts and such information was integrated by an iterative process that consisted of reviews of input data by experts, production of ecological niche models (ENMs), and evaluation of model outputs to provide feedback. We built ENMs using the maximum entropy algorithm along with a dataset of occurrence records gathered from a public source and records provided by the experts. Models without expert knowledge were also built for comparison, and both models, with and without expert knowledge, were evaluated using four validation metrics that provide a measure of accuracy for presence-absence predictions (specificity, sensitivity, kappa, true skill statistic). Integrating expert knowledge to build ENMs produced better results for potential distributions than models without expert knowledge, but a much greater improvement in the transition from potential to realized geographic distributions by reducing overprediction, resulting in better representations of the actual geographic distributions of species. Furthermore, with the combination of niche models and expert knowledge we were able to identify an area of sympatry between A. palliata mexicana and A. pigra. We argue that the inclusion of expert knowledge at different stages in the construction of niche models in an explicit and systematic fashion is a recommended practice as it produces overall positive results for representing realized species distributions.