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Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.

A measurement of proton inelastic scattering of 8He at 8.25A MeV at TRIUMF shows a resonance at 3.54(6) MeV with a width of 0.89(11) MeV. The energy of the state is in good agreement with coupled cluster and no-core shell model with continuum calculations, with the latter successfully describing the measured resonance width as well. Its differential cross section analyzed with phenomenological collective excitation form factor and microscopic coupled reaction channels framework consistently reveals a large deformation parameter β2 = 0.40(3), consistent with no-core shell model predictions of a large neutron deformation. This deformed double-closed shell at the neutron drip-line opens a new paradigm.

Toxoplasma gondii is among the most prevalent parasites worldwide, infecting many wild and domestic animals and causing zoonotic infections in humans. T. gondii differs substantially in its broad distribution from closely related parasites that typically have narrow, specialized host ranges. To elucidate the genetic basis for these differences, we compared the genomes of 62 globally distributed T. gondii isolates to several closely related coccidian parasites. Our findings reveal that tandem amplification and diversification of secretory pathogenesis determinants is the primary feature that distinguishes the closely related genomes of these biologically diverse parasites. We further show that the unusual population structure of T. gondii is characterized by clade-specific inheritance of large conserved haploblocks that are significantly enriched in tandemly clustered secretory pathogenesis determinants. The shared inheritance of these conserved haploblocks, which show a different ancestry than the genome as a whole, may thus influence transmission, host range and pathogenicity.

Toxoplasma gondii is among the most prevalent parasites worldwide, infecting many wild and domestic animals and causing zoonotic infections in humans. T. gondii differs substantially in its broad distribution from closely related parasites that typically have narrow, specialized host ranges. To elucidate the genetic basis for these differences, we compared the genomes of 62 globally distributed T. gondii isolates to several closely related coccidian parasites. Our findings reveal that tandem amplification and diversification of secretory pathogenesis determinants is the primary feature that distinguishes the closely related genomes of these biologically diverse parasites. We further show that the unusual population structure of T. gondii is characterized by clade-specific inheritance of large conserved haploblocks that are significantly enriched in tandemly clustered secretory pathogenesis determinants. The shared inheritance of these conserved haploblocks, which show a different ancestry than the genome as a whole, may thus influence transmission, host range and pathogenicity.

First principle calculations of extended X-ray absorption fine structure (EXAFS) data have seen widespread use in bioinorganic chemistry, perhaps most notably for modeling the Mn4Ca site in the oxygen evolving complex (OEC) of photosystem II (PSII). The logic implied by the calculations rests on the assumption that it is possible to a priori predict an accurate EXAFS spectrum provided that the underlying geometric structure is correct. The present study investigates the extent to which this is possible using state of the art EXAFS theory. The FEFF program is used to evaluate the ability of a multiple scattering-based approach to directly calculate the EXAFS spectrum of crystallographically defined model complexes. The results of these parameter free predictions are compared with the more traditional approach of fitting FEFF calculated spectra to experimental data. A series of seven crystallographically characterized Mn monomers and dimers is used as a test set. The largest deviations between the FEFF calculated EXAFS spectra and the experimental EXAFS spectra arise from the amplitudes. The amplitude errors result from a combination of errors in calculated S0(2) and Debye-Waller values as well as uncertainties in background subtraction. Additional errors may be attributed to structural parameters, particularly in cases where reliable high-resolution crystal structures are not available. Based on these investigations, the strengths and weaknesses of using first-principle EXAFS calculations as a predictive tool are discussed. We demonstrate that a range of DFT optimized structures of the OEC may all be considered consistent with experimental EXAFS data and that caution must be exercised when using EXAFS data to obtain topological arrangements of complex clusters.

First principle calculations of extended X-ray absorption fine structure (EXAFS) data have seen widespread use in bioinorganic chemistry, perhaps most notably for modeling the Mn4Ca site in the oxygen evolving complex (OEC) of photosystem II (PSII). The logic implied by the calculations rests on the assumption that it is possible to a priori predict an accurate EXAFS spectrum provided that the underlying geometric structure is correct. The present study investigates the extent to which this is possible using state of the art EXAFS theory. The FEFF program is used to evaluate the ability of a multiple scattering-based approach to directly calculate the EXAFS spectrum of crystallographically defined model complexes. The results of these parameter free predictions are compared with the more traditional approach of fitting FEFF calculated spectra to experimental data. A series of seven crystallographically characterized Mn monomers and dimers is used as a test set. The largest deviations between the FEFF calculated EXAFS spectra and the experimental EXAFS spectra arise from the amplitudes. The amplitude errors result from a combination of errors in calculated S0(2) and Debye-Waller values as well as uncertainties in background subtraction. Additional errors may be attributed to structural parameters, particularly in cases where reliable high-resolution crystal structures are not available. Based on these investigations, the strengths and weaknesses of using first-principle EXAFS calculations as a predictive tool are discussed. We demonstrate that a range of DFT optimized structures of the OEC may all be considered consistent with experimental EXAFS data and that caution must be exercised when using EXAFS data to obtain topological arrangements of complex clusters.

Herein, Ca K-edge X-ray absorption spectroscopy (XAS) is developed as a means to characterize the local environment of calcium centers. The spectra for six, seven, and eight coordinate inorganic and molecular calcium complexes were analyzed and determined to be primarily influenced by the coordination environment and site symmetry at the calcium center. The experimental results are closely correlated to time-dependent density functional theory (TD-DFT) calculations of the XAS spectra. The applicability of this methodology to complex systems was investigated using structural mimics of the oxygen-evolving complex (OEC) of PSII. It was found that Ca K-edge XAS is a sensitive probe for structural changes occurring in the cubane heterometallic cluster due to Mn oxidation. Future applications to the OEC are discussed.

Herein, Ca K-edge X-ray absorption spectroscopy (XAS) is developed as a means to characterize the local environment of calcium centers. The spectra for six, seven, and eight coordinate inorganic and molecular calcium complexes were analyzed and determined to be primarily influenced by the coordination environment and site symmetry at the calcium center. The experimental results are closely correlated to time-dependent density functional theory (TD-DFT) calculations of the XAS spectra. The applicability of this methodology to complex systems was investigated using structural mimics of the oxygen-evolving complex (OEC) of PSII. It was found that Ca K-edge XAS is a sensitive probe for structural changes occurring in the cubane heterometallic cluster due to Mn oxidation. Future applications to the OEC are discussed.

This study investigated the exposure of jaguar populations and domestic animals to smooth Brucella, Leptospira spp. and Toxoplasma gondii in the Cerrado, Pantanal and Amazon biomes of Brazil. Between February 2000 and January 2010, serum samples from 31 jaguars (Panthera onca), 1,245 cattle (Bos taurus), 168 domestic dogs (Canis lupus familiaris) and 29 domestic cats (Felis catus) were collected and analysed by rose bengal test for smooth Brucella, microscopic agglutination test for Leptospira spp. and modified agglutination test for T. gondii. Cattle populations from all sites (9.88%) were exposed to smooth Brucella, but only one jaguar from Cerrado was exposed to this agent. Jaguars captured in the Cerrado (60.0%) and in the Pantanal (45.5%) were seropositive for different serovars of Leptospira spp., cattle (72.18%) and domestic dogs (13.1%) from the three sites and one domestic cat from Pantanal were also seropositive for the agent. The most prevalent serotype of Leptospira spp. identified in jaguars from the Cerrado (Grippotyphosa) and the Pantanal (Pomona) biomes were distinct from those found in the domestic animals sampled. Jaguars (100%), domestic dogs (38.28%) and domestic cats (82.76%) from the three areas were exposed to T. gondii. Our results show that brucellosis and leptospirosis could have been transmitted to jaguars by domestic animals; and jaguars probably play an important role in the maintenance of T. gondii in nature.

This study investigated the exposure of jaguar populations and domestic animals to smooth Brucella, Leptospira spp. and Toxoplasma gondii in the Cerrado, Pantanal and Amazon biomes of Brazil. Between February 2000 and January 2010, serum samples from 31 jaguars (Panthera onca), 1,245 cattle (Bos taurus), 168 domestic dogs (Canis lupus familiaris) and 29 domestic cats (Felis catus) were collected and analysed by rose bengal test for smooth Brucella, microscopic agglutination test for Leptospira spp. and modified agglutination test for T. gondii. Cattle populations from all sites (9.88%) were exposed to smooth Brucella, but only one jaguar from Cerrado was exposed to this agent. Jaguars captured in the Cerrado (60.0%) and in the Pantanal (45.5%) were seropositive for different serovars of Leptospira spp., cattle (72.18%) and domestic dogs (13.1%) from the three sites and one domestic cat from Pantanal were also seropositive for the agent. The most prevalent serotype of Leptospira spp. identified in jaguars from the Cerrado (Grippotyphosa) and the Pantanal (Pomona) biomes were distinct from those found in the domestic animals sampled. Jaguars (100%), domestic dogs (38.28%) and domestic cats (82.76%) from the three areas were exposed to T. gondii. Our results show that brucellosis and leptospirosis could have been transmitted to jaguars by domestic animals; and jaguars probably play an important role in the maintenance of T. gondii in nature.

An integrated data repository (IDR) containing aggregations of clinical, biomedical, economic, administrative, and public health data is a key component of an overall translational research infrastructure. But most available data repositories are designed using standard data warehouse architecture that employs arbitrary data encoding standards, making queries across disparate repositories difficult. In response to these shortcomings we have designed a Health Ontology Mapper (HOM) that translates terminologies into formal data encoding standards without altering the underlying source data. We believe the HOM system promotes inter-institutional data sharing and research collaboration, and will ultimately lower the barrier to developing and using an IDR.

An integrated data repository (IDR) containing aggregations of clinical, biomedical, economic, administrative, and public health data is a key component of an overall translational research infrastructure. But most available data repositories are designed using standard data warehouse architecture that employs arbitrary data encoding standards, making queries across disparate repositories difficult. In response to these shortcomings we have designed a Health Ontology Mapper (HOM) that translates terminologies into formal data encoding standards without altering the underlying source data. We believe the HOM system promotes inter-institutional data sharing and research collaboration, and will ultimately lower the barrier to developing and using an IDR.

In this paper, I combine the ideas of attention from cognitive psychology with concept formation in machine learning. M y claim is that the use of attention can lead to a more efficient learning system, without sacrificing accuracy. Attention leads to a savings in efficiency because it focuses only on the relevant attributes, retrieves less information from the environment, and is therefore less costly than a system that uses every piece of information available. I present a working dgorithm for attention, built onto the Classit concept formation system, and describe results from three domains.'

This paper presents a method, generalization to interval, that can encode images into symbolic expressions. This method generalizes over instances of spatial patterns, and outputs a constraint program that can be used declaratively as a learned concept about spatial patterns, and procedural as a method for reasoning about spatial relations. Thus our method transforms numeric spatial patterns to symbolic declarative/procedural representations. We have implemented generalization to interval with Acorn,^ a system that acquires knowledge about spatial relations by observing 2-D raster images. We have applied this system to some layout problems to demonstrate the ability of the system and the flexibility of constraint programs for knowledge representation.

This thesis presents a careful experimental examination of the task of concept formation. This learning task involves acquiring concepts or classes that group observed instances. These classes can then be used for the recognition or classification of unseen instances and for predicting values of unknown attributes. To carry out this task, I present CLASSIT, a robust, general-purpose system for incremental, unsupervised concept learning. In order to test the system, the thesis reports on a variety of experimental results. There are two broad categories for the experiments in this thesis: experiments that test the ability of the system with different input data, and experiments that hold the input data constant and test various components of the system by changing the system itself. The latter include parametric studies and comparative studies with cluster analysis methods. The largest modification I suggest for the basic concept formation algorithm is the addition of an attention mechanism. This lets the system focus attention on only the most important features (or attributes) of an instance. With attention, instances can be more quickly recognized, since the less important attributes are simply ignored and treated as missing. Finally, although the accomplishments of the CLASSIT system are modest, I conclude by discussing the potential for using the system as part of a larger cognitive architecture.

Given a set of observations, humans acquire concepts that organize those observations and use them in classifying future experiences. This type of concept formation can occur in the absence of a tutor and it can take place despite irrelevant and incomplete information. A reasonable model of such human concept learning should be both incremental and capable of handling this type of complex experiences that people encounter in the real world. In this paper, we review three previous models of incremental concept formation and then present CLASSIT, a model that extends these earlier systems. All of the models integrate the process of recognition and learning, and all can be viewed as carrying out search through the space of possible concept hierarchies. In an attempt to show that CLASSIT is a robust concept formation system, we also present some empirical studies of its behavior under a variety of conditions.

In this paper, I describe a large variety of clustering methods within a single framework. This paper unifies work across different fields, from biology (numerical taxonomy) to machine learning (concept formation). An important objective for this paper is to show that one can benefit by a knowledge of research across different disciplines. After describing the task from a set of different viewpoints or paradigms, I begin by describing the similarity measures or evaluation functions that form the basis of any clustering technique. Next, I describe a number of different algorithms that use these measures, and I close with a brief discussion of ways to evaluate different approaches to clustering.