The elastic constant tensor of an inorganic compound provides a complete description of the response of the material to external stresses in the elastic limit. It thus provides fundamental insight into the nature of the bonding in the material, and it is known to correlate with many mechanical properties. Despite the importance of the elastic constant tensor, it has been measured for a very small fraction of all known inorganic compounds, a situation that limits the ability of materials scientists to develop new materials with targeted mechanical responses. To address this deficiency, we present here the largest database of calculated elastic properties for inorganic compounds to date. The database currently contains full elastic information for 1,181 inorganic compounds, and this number is growing steadily. The methods used to develop the database are described, as are results of tests that establish the accuracy of the data. In addition, we document the database format and describe the different ways it can be accessed and analyzed in efforts related to materials discovery and design.

Despite the widening use of combination anti-retroviral therapy (ART), neurocognitive impairment remains common among HIV-infected (HIV+) individuals. Associations between HIV-related neuromedical variables and magnetic resonance imaging indices of brain structural integrity may provide insight into the neural bases for these symptoms. A diverse HIV+ sample (n=251) was studied through the CNS HIV Antiretroviral Therapy Effects Research initiative. Multi-channel image analysis produced volumes of ventricular and sulcal cerebrospinal fluid (CSF), cortical and subcortical gray matter, total cerebral white matter, and abnormal white matter. Cross-sectional analyses employed a series of multiple linear regressions to model each structural volume as a function of severity of prior immunosuppression (CD4 nadir), current CD4 count, presence of detectable CSF HIV RNA, and presence of HCV antibodies; secondary analyses examined plasma HIV RNA, estimated duration of HIV infection, and cumulative exposure to ART. Lower CD4 nadir was related to most measures of the structural brain damage. Higher current CD4, unexpectedly, correlated with lower white and subcortical gray and increased CSF. Detectable CSF HIV RNA was related to less total white matter. HCV coinfection was associated with more abnormal white matter. Longer exposure to ART was associated with lower white matter and higher sulcal CSF. HIV neuromedical factors, including lower nadir, higher current CD4 levels, and detectable HIV RNA, were associated with white matter damage and variability in subcortical volumes. Brain structural integrity in HIV likely reflects dynamic effects of current immune status and HIV replication, superimposed on residual effects associated with severe prior immunosuppression.

Materials scientists increasingly employ machine or statistical learning (SL) techniques to accelerate materials discovery and design. Such pursuits benefit from pooling training data across, and thus being able to generalize predictions over, k-nary compounds of diverse chemistries and structures. This work presents a SL framework that addresses challenges in materials science applications, where datasets are diverse but of modest size, and extreme values are often of interest. Our advances include the application of power or Hölder means to construct descriptors that generalize over chemistry and crystal structure, and the incorporation of multivariate local regression within a gradient boosting framework. The approach is demonstrated by developing SL models to predict bulk and shear moduli (K and G, respectively) for polycrystalline inorganic compounds, using 1,940 compounds from a growing database of calculated elastic moduli for metals, semiconductors and insulators. The usefulness of the models is illustrated by screening for superhard materials.

The aggregation of imaging, clinical, and behavioral data from multiple independent institutions and researchers presents both a great opportunity for biomedical research as well as a formidable challenge. Many research groups have well-established data collection and analysis procedures, as well as data and metadata format requirements that are particular to that group. Moreover, the types of data and metadata collected are quite diverse, including image, physiological, and behavioral data, as well as descriptions of experimental design, and preprocessing and analysis methods. Each of these types of data utilizes a variety of software tools for collection, storage, and processing. Furthermore sites are reluctant to release control over the distribution and access to the data and the tools. To address these needs, the Biomedical Informatics Research Network (BIRN) has developed a federated and distributed infrastructure for the storage, retrieval, analysis, and documentation of biomedical imaging data. The infrastructure consists of distributed data collections hosted on dedicated storage and computational resources located at each participating site, a federated data management system and data integration environment, an Extensible Markup Language (XML) schema for data exchange, and analysis pipelines, designed to leverage both the distributed data management environment and the available grid computing resources.