Compression test assembly

A compression test assembly is described which prevents buckling of small diameter rigid specimens undergoing compression testing and permits attachment of extensometers for strain measurements. The test specimen is automatically aligned and laterally supported when compressive force is applied to the end caps and transmitted to the test specimen during testing

Development of a lightweight cryogenic insulating system Final report, 30 Jun. 1964 - 31 May 1966

Lightweight external panel insulation systems for thermal protection of cryogenic launch vehicle propellant tank

Program for the evaluation of structural reinforced plastic materials at cryogenic temperatures, phase ii annual and fourth quarterly report, 29 jun. 1964 - 30 jun. 1965

Evaluation of procedures, test specimens, and test techniques for application to structural reinforced plastic materials at cryogenic temperature

Omada: robust clustering of transcriptomes through multiple testing

Background Cohort studies increasingly collect biosamples for molecular profiling and are observing molecular heterogeneity. High-throughput RNA sequencing is providing large datasets capable of reflecting disease mechanisms. Clustering approaches have produced a number of tools to help dissect complex heterogeneous datasets, but selecting the appropriate method and parameters to perform exploratory clustering analysis of transcriptomic data requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent. To address this, we have developed Omada, a suite of tools aiming to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning–based functions. Findings The efficiency of each tool was tested with 7 datasets characterized by different expression signal strengths to capture a wide spectrum of RNA expression datasets. Our toolkit’s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Within datasets with less clear biological distinctions, our tools either formed stable subgroups with different expression profiles and robust clinical associations or revealed signs of problematic data such as biased measurements. Conclusions In conclusion, Omada successfully automates the robust unsupervised clustering of transcriptomic data, making advanced analysis accessible and reliable even for those without extensive machine learning expertise. Implementation of Omada is available at http://bioconductor.org/packages/omada/

Vicinal Surface with Langmuir Adsorption: A Decorated Restricted Solid-on-solid Model

We study the vicinal surface of the restricted solid-on-solid model coupled with the Langmuir adsorbates which we regard as two-dimensional lattice gas without lateral interaction. The effect of the vapor pressure of the adsorbates in the environmental phase is taken into consideration through the chemical potential. We calculate the surface free energy $f$, the adsorption coverage $\Theta$, the step tension $\gamma$, and the step stiffness $\tilde{\gamma}$ by the transfer matrix method combined with the density-matrix algorithm. Detailed step-density-dependence of $f$ and $\Theta$ is obtained. We draw the roughening transition curve in the plane of the temperature and the chemical potential of adsorbates. We find the multi-reentrant roughening transition accompanying the inverse roughening phenomena. We also find quasi-reentrant behavior in the step tension.Comment: 7 pages, 12 figures (png format), RevTeX 3.1, submitted to Phys. Rev.

The Pathway Coexpression Network: Revealing pathway relationships.

A goal of genomics is to understand the relationships between biological processes. Pathways contribute to functional interplay within biological processes through complex but poorly understood interactions. However, limited functional references for global pathway relationships exist. Pathways from databases such as KEGG and Reactome provide discrete annotations of biological processes. Their relationships are currently either inferred from gene set enrichment within specific experiments, or by simple overlap, linking pathway annotations that have genes in common. Here, we provide a unifying interpretation of functional interaction between pathways by systematically quantifying coexpression between 1,330 canonical pathways from the Molecular Signatures Database (MSigDB) to establish the Pathway Coexpression Network (PCxN). We estimated the correlation between canonical pathways valid in a broad context using a curated collection of 3,207 microarrays from 72 normal human tissues. PCxN accounts for shared genes between annotations to estimate significant correlations between pathways with related functions rather than with similar annotations. We demonstrate that PCxN provides novel insight into mechanisms of complex diseases using an Alzheimer's Disease (AD) case study. PCxN retrieved pathways significantly correlated with an expert curated AD gene list. These pathways have known associations with AD and were significantly enriched for genes independently associated with AD. As a further step, we show how PCxN complements the results of gene set enrichment methods by revealing relationships between enriched pathways, and by identifying additional highly correlated pathways. PCxN revealed that correlated pathways from an AD expression profiling study include functional clusters involved in cell adhesion and oxidative stress. PCxN provides expanded connections to pathways from the extracellular matrix. PCxN provides a powerful new framework for interrogation of global pathway relationships. Comprehensive exploration of PCxN can be performed at http://pcxn.org/

Biological heterogeneity in idiopathic pulmonary arterial hypertension identified through unsupervised transcriptomic profiling of whole blood

Idiopathic pulmonary arterial hypertension (IPAH) is a rare but fatal disease diagnosed by right heart catheterisation and the exclusion of other forms of pulmonary arterial hypertension, producing a heterogeneous population with varied treatment response. Here we show unsupervised machine learning identification of three major patient subgroups that account for 92% of the cohort, each with unique whole blood transcriptomic and clinical feature signatures. These subgroups are associated with poor, moderate, and good prognosis. The poor prognosis subgroup is associated with upregulation of the ALAS2 and downregulation of several immunoglobulin genes, while the good prognosis subgroup is defined by upregulation of the bone morphogenetic protein signalling regulator NOG, and the C/C variant of HLA-DPA1/DPB1 (independently associated with survival). These findings independently validated provide evidence for the existence of 3 major subgroups (endophenotypes) within the IPAH classification, could improve risk stratification and provide molecular insights into the pathogenesis of IPAH

Emerging Health Data Platforms: From Individual Control to Collective Data Governance

Health data have enormous potential to transform healthcare, health service design, research, and individual health management. However, health data collected by institutions tend to remain siloed within those institutions limiting access by other services, individuals or researchers. Further, health data generated outside health services (e.g., from wearable devices) may not be easily accessible or useable by individuals or connected to other parts of the health system. There are ongoing tensions between data protection and the use of data for the public good (e.g., research). Concurrently, there are a number of data platforms that provide ways to disrupt these traditional health data siloes, giving greater control to individuals and communities. Through four case studies, this paper explores platforms providing new ways for health data to be used for personal data sharing, self-health management, research, and clinical care. The case-studies include data platforms: PatientsLikeMe, Open Humans, Health Record Banks, and unforgettable.me. These are explored with regard to what they mean for data access, data control, and data governance. The case studies provide insight into a shift from institutional to individual data stewardship. Looking at emerging data governance models, such as data trusts and data commons, points to collective control over health data as an emerging approach to issues of data control. These shifts pose challenges as to how “traditional” health services make use of data collected on these platforms. Further, it raises broader policy questions regarding how to decide what public good data should be put towards