Research data infrastructure for high-throughput experimental materials science.

The High-Throughput Experimental Materials Database (HTEM-DB, htem.nrel.gov) is a repository of inorganic thin-film materials data collected during combinatorial experiments at the National Renewable Energy Laboratory (NREL). This data asset is enabled by NREL's Research Data Infrastructure (RDI), a set of custom data tools that collect, process, and store experimental data and metadata. Here, we describe the experimental data flow from the RDI to the HTEM-DB to illustrate the strategies and best practices currently used for materials data at NREL. Integration of the data tools with experimental instruments establishes a data communication pipeline between experimental researchers and data scientists. This work motivates the creation of similar workflows at other institutions to aggregate valuable data and increase their usefulness for future machine learning studies. In turn, such data-driven studies can greatly accelerate the pace of discovery and design in the materials science domain.

An open experimental database for exploring inorganic materials.

The use of advanced machine learning algorithms in experimental materials science is limited by the lack of sufficiently large and diverse datasets amenable to data mining. If publicly open, such data resources would also enable materials research by scientists without access to expensive experimental equipment. Here, we report on our progress towards a publicly open High Throughput Experimental Materials (HTEM) Database (htem.nrel.gov). This database currently contains 140,000 sample entries, characterized by structural (100,000), synthetic (80,000), chemical (70,000), and optoelectronic (50,000) properties of inorganic thin film materials, grouped in >4,000 sample entries across >100 materials systems; more than a half of these data are publicly available. This article shows how the HTEM database may enable scientists to explore materials by browsing web-based user interface and an application programming interface. This paper also describes a HTE approach to generating materials data, and discusses the laboratory information management system (LIMS), that underpin HTEM database. Finally, this manuscript illustrates how advanced machine learning algorithms can be adopted to materials science problems using this open data resource.

Biomass accessibility analysis using electron tomography.

BACKGROUND: Substrate accessibility to catalysts has been a dominant theme in theories of biomass deconstruction. However, current methods of quantifying accessibility do not elucidate mechanisms for increased accessibility due to changes in microstructure following pretreatment. RESULTS: We introduce methods for characterization of surface accessibility based on fine-scale microstructure of the plant cell wall as revealed by 3D electron tomography. These methods comprise a general framework, enabling analysis of image-based cell wall architecture using a flexible model of accessibility. We analyze corn stover cell walls, both native and after undergoing dilute acid pretreatment with and without a steam explosion process, as well as AFEX pretreatment. CONCLUSION: Image-based measures provide useful information about how much pretreatments are able to increase biomass surface accessibility to a wide range of catalyst sizes. We find a strong dependence on probe size when measuring surface accessibility, with a substantial decrease in biomass surface accessibility to probe sizes above 5-10 nm radius compared to smaller probes.

Fluorodeoxyglucose positron emission tomography of mild cognitive impairment with clinical follow-up at 3 years.

BACKGROUND: Alzheimer's disease (AD) is the most common dementing illness. Development of effective treatments directed at AD requires an early diagnosis. Mild cognitive impairment (MCI) often heralds AD. Thus, characterizing MCI is fundamental to the early diagnosis of AD. METHODS: 19 MCI patients referred from a memory loss clinic and 27 healthy subjects, all followed up for 3 years. Metabolism scans (MCI minus controls) were compared voxel-wise after anatomic normalization and were examined both visually and with a computerized classifier. RESULTS: Agreement between raters as to whether the individual scans were normal or abnormal was high. Agreement between raters of the eventual clinical diagnosis and baseline metabolic pattern was poor. A computerized classifier was unsuccessful at classifying MCI from normal; however, its performance improved when using only prototypic AD-like MCI scans, indicating the classifier worked well when shared patterns existed in the data. Outcomes on follow-up were nine of 19 AD, five of 19 remained MCI, and five of 19 developed dementias other than AD. Both MCI cases of early Lewy body dementia (LBD) showed an AD-like metabolic pattern. CONCLUSIONS: Visual inspection proved reliable in determining normal from abnormal scans, but it proved unreliable at predicting diagnosis on follow-up. Computerized classification of MCI by using an AD-like metabolic template (such as derived from the averaged MCI images) showed potential to identify patients who will develop AD. However, the metabolic pattern in early LBD did not differ from that in AD.

Internet image viewer (iiV).

BACKGROUND: Visualizing 3-dimensional (3-D) datasets is an important part of modern neuroimaging research. Many tools address this problem; however, they often fail to address specific needs and flexibility, such as the ability to work with different data formats, to control how and what data are displayed, to interact with values, and to undo mistakes. RESULTS: iiV, an interactive software program for displaying 3-D brain images, is described. This tool was programmed to solve basic problems in 3-D data visualization. It is written in Java so it is extensible, is platform independent, and can display images within web pages.iiV displays 3-D images as 2-dimensional (2-D) slices with each slice being an independent object with independent features such as location, zoom, colors, labels, etc. Feature manipulation becomes easier by having a full set of editing capabilities including the following: undo or redo changes; drag, copy, delete and paste objects; and save objects with their features to a file for future editing. It can read multiple standard positron emission tomography (PET) and magnetic resonance imaging (MRI) file formats like ECAT, ECAT7, ANALYZE, NIfTI-1 and DICOM. We present sample applications to illustrate some of the features and capabilities. CONCLUSION: iiV is an image display tool with many useful features. It is highly extensible, platform independent, and web-compatible. This report summarizes its features and applications, while illustrating iiV's usefulness to the biomedical imaging community.

Bringing functional brain image analysis to the clinician: initial assessment of an online interactive diagnostic aide.

Image database extensions for functional brain images were assessed by asking clinicians questions about (i) diagnosis confidence level before and after using the software; (ii) expected and unexpected differences between patient and control images; and (iii) an overall rating of the future usefulness of this application in an everyday clinical setting. Examining the difference image of a patient compared to a normative group affects the clinicians' initial diagnosis of the patient in two-thirds of the cases. All three clinicians stated that the interface would be a useful tool when added to the clinical workup of a patient.

Where the brain grows old: decline in anterior cingulate and medial prefrontal function with normal aging.

Even healthy adults worry about declines in mental efficiency with aging. Subjective changes in mental flexibility, self-regulation, processing speed, and memory are often cited. We show here that focal decreases in brain activity occur with normal aging as measured with fluorodeoxyglucose and positron emission tomography. The largest declines localize to a medial network including the anterior cingulate/medial prefrontal cortex, dorsomedial thalamus, and sugenual cingulate/basal forebrain. Declining metabolism in this network correlates with declining cognitive function. The medial prefrontal metabolic changes with aging are similar in magnitude to the hypometabolism found in Mild Cognitive Impairment or Alzheimer's disease. These results converge with data from healthy elderly indicating dysfunction in the anterior attention system. The interaction of attention in the anterior cingulate cortex with memory in the medial temporal lobe may explain the global impairment that defines dementia. Despite the implications for an aging population, the neurophysiologic mechanisms of these metabolic decreases remain unknown.