Impacts on High-level Systems-of-Systems Figures of Merit due to Integrated Architecture Sizing and Technology Evaluation at the Subsystem-Level

Understanding the impacts on high-level system-of-systems (SOS) figures of merit (FOMs) due to the design of architectures and technologies is critical in providing decision makers sufficient information in selecting suitable alternatives in an effort to reduce costly financial and schedule overruns. Several techniques exist within academia and industry for performing SOS architecture design and technology evaluation. However, these techniques fail to solve the problem in an integrated fashion when defined at the subsystem-level. In order to understand the impacts on high-level SOS FOMs due to integrated architecture sizing and technology evaluation, a general concept exploration process is utilized to perform a notional 2033 manned Mars fly by study. The notional study draws out observation with regard to specific FOMs traditionally used during the subsystem-level sizing and technology evaluation processes which can result in misleading conclusions regarding the overall SOS design. Furthermore, these observations suggest that selection of FOMs for the subsystems of an architecture should be influenced by the desired objectives of the high-level SOS objectives and FOMs

FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Data

Secure and scalable data sharing is essential for collaborative clinical decision making. Conventional clinical data efforts are often siloed, however, which creates barriers to efficient information exchange and impedes effective treatment decision made for patients. This paper provides four contributions to the study of applying blockchain technology to clinical data sharing in the context of technical requirements defined in the "Shared Nationwide Interoperability Roadmap" from the Office of the National Coordinator for Health Information Technology (ONC). First, we analyze the ONC requirements and their implications for blockchain-based systems. Second, we present FHIRChain, which is a blockchain-based architecture designed to meet ONC requirements by encapsulating the HL7 Fast Healthcare Interoperability Resources (FHIR) standard for shared clinical data. Third, we demonstrate a FHIRChain-based decentralized app using digital health identities to authenticate participants in a case study of collaborative decision making for remote cancer care. Fourth, we highlight key lessons learned from our case study

Opposing roles of ZEB1 in the cytoplasm and nucleus control cytoskeletal assembly and YAP1 activity

Epithelial-mesenchymal transition (EMT) facilitates cancer invasion and is initiated by mesenchyme-driving transcription factors and actin cytoskeletal assembly. We show a cytoplasmic-to-nuclear transport gradient of the EMT transcription factor Zeb1 toward sites of invasion in lung adenocarcinoma (LUAD), driven by the EMT inducer Tgfb, which is expressed in M2 polarized macrophages. We show that Zeb1 binds free actin monomers and RhoA in the cytoplasm to inhibit actin polymerization, blocking cell migration and Yap1 nuclear transport. Tgfb causes turnover of the scaffold protein Rassf1a, which targets RhoA. Release of this RhoA inhibition in response to Tgfb overcomes Zeb1's block of cytoskeleton assembly and frees it for nuclear transport. A ZEB1 nuclear transport signature highlights EMT progression, identifies dedifferentiated invasive/metastatic human LUADs, and predicts survival. Blocking Zeb1 nuclear transport with a small molecule identified in this study inhibits cytoskeleton assembly, cell migration, Yap1 nuclear transport, EMT, and precancerous-to-malignant transition

Feature Extraction and Classification from Planetary Science Datasets enabled by Machine Learning

In this paper we present two examples of recent investigations that we have undertaken, applying Machine Learning (ML) neural networks (NN) to image datasets from outer planet missions to achieve feature recognition. Our first investigation was to recognize ice blocks (also known as rafts, plates, polygons) in the chaos regions of fractured ice on Europa. We used a transfer learning approach, adding and training new layers to an industry-standard Mask R-CNN (Region-based Convolutional Neural Network) to recognize labeled blocks in a training dataset. Subsequently, the updated model was tested against a new dataset, achieving 68% precision. In a different application, we applied the Mask R-CNN to recognize clouds on Titan, again through updated training followed by testing against new data, with a precision of 95% over 369 images. We evaluate the relative successes of our techniques and suggest how training and recognition could be further improved. The new approaches we have used for planetary datasets can further be applied to similar recognition tasks on other planets, including Earth. For imagery of outer planets in particular, the technique holds the possibility of greatly reducing the volume of returned data, via onboard identification of the most interesting image subsets, or by returning only differential data (images where changes have occurred) greatly enhancing the information content of the final data stream

Effect of CPOE User Interface Design on User-Initiated Access to Educational and Patient Information during Clinical Care

Objective: Authors evaluated whether displaying context sensitive links to infrequently accessed educational materials and patient information via the user interface of an inpatient computerized care provider order entry (CPOE) system would affect access rates to the materials. Design: The CPOE of Vanderbilt University Hospital (VUH) included "baseline” clinical decision support advice for safety and quality. Authors augmented this with seven new primarily educational decision support features. A prospective, randomized, controlled trial compared clinicians' utilization rates for the new materials via two interfaces. Control subjects could access study-related decision support from a menu in the standard CPOE interface. Intervention subjects received active notification when study-related decision support was available through context sensitive, visibly highlighted, selectable hyperlinks. Measurements: Rates of opportunities to access and utilization of study-related decision support materials from April 1999 through March 2000 on seven VUH Internal Medicine wards. Results: During 4,466 intervention subject-days, there were 240,504 (53.9/subject-day) opportunities for study-related decision support, while during 3,397 control subject-days, there were 178,235 (52.5/subject-day) opportunities for such decision support, respectively (p = 0.11). Individual intervention subjects accessed the decision support features at least once on 3.8% of subject-days logged on (278 responses); controls accessed it at least once on 0.6% of subject-days (18 responses), with a response rate ratio adjusted for decision support frequency of 9.17 (95% confidence interval 4.6-18, p < 0.0005). On average, intervention subjects accessed study-related decision support materials once every 16 days individually and once every 1.26 days in aggregate. Conclusion: Highlighting availability of context-sensitive educational materials and patient information through visible hyperlinks significantly increased utilization rates for study-related decision support when compared to "standard” VUH CPOE methods, although absolute response rates were lo

Polyunsaturated fatty acids inhibit a pentameric ligand-gated ion channel through one of two binding sites

Polyunsaturated fatty acids (PUFAs) inhibit pentameric ligand-gated ion channels (pLGICs) but the mechanism of inhibition is not well understood. The PUFA, docosahexaenoic acid (DHA), inhibits agonist responses of the pLGIC, ELIC, more effectively than palmitic acid, similar to the effects observed in the GAB

Poke: An open-source ray-based physical optics platform

Integrated optical models allow for accurate prediction of the as-built performance of an optical instrument. Optical models are typically composed of a separate ray trace and diffraction model to capture both the geometrical and physical regimes of light. These models are typically separated across both open-source and commercial software that don't interface with each other directly. To bridge the gap between ray trace models and diffraction models, we have built an open-source optical analysis platform in Python called Poke that uses commercial ray tracing APIs and open-source physical optics engines to simultaneously model scalar wavefront error, diffraction, and polarization. Poke operates by storing ray data from a commercial ray tracing engine into a Python object, from which physical optics calculations can be made. We present an introduction to using Poke, and highlight the capabilities of two new propagation physics modules that add to the utility of existing scalar diffraction models. Gaussian Beamlet Decomposition is a ray-based approach to diffraction modeling that allows us to integrate physical optics models with ray trace models to directly capture the influence of ray aberrations in diffraction simulations. Polarization Ray Tracing is a ray-based method of vector field propagation that can diagnose the polarization aberrations in optical systems. Poke has been recently used to study the next generation of astronomical observatories, including the ground-based Extremely Large Telescopes and a 6 meter space telescope early concept for NASA's Habitable Worlds Observatory.Comment: 11 Pages, 9 Figures, Published in Proceedings of SPIE Optical Modeling and Performance Predictions XIII Paper 12664-

Operator theory and function theory in Drury-Arveson space and its quotients

The Drury-Arveson space $H^2_d$, also known as symmetric Fock space or the $d$-shift space, is a Hilbert function space that has a natural $d$-tuple of operators acting on it, which gives it the structure of a Hilbert module. This survey aims to introduce the Drury-Arveson space, to give a panoramic view of the main operator theoretic and function theoretic aspects of this space, and to describe the universal role that it plays in multivariable operator theory and in Pick interpolation theory.Comment: Final version (to appear in Handbook of Operator Theory); 42 page

Cancer of the ampulla of Vater: analysis of the whole genome sequence exposes a potential therapeutic vulnerability

BACKGROUND: Recent advances in the treatment of cancer have focused on targeting genomic aberrations with selective therapeutic agents. In rare tumors, where large-scale clinical trials are daunting, this targeted genomic approach offers a new perspective and hope for improved treatments. Cancers of the ampulla of Vater are rare tumors that comprise only about 0.2% of gastrointestinal cancers. Consequently, they are often treated as either distal common bile duct or pancreatic cancers. METHODS: We analyzed DNA from a resected cancer of the ampulla of Vater and whole blood DNA from a 63 year-old man who underwent a pancreaticoduodenectomy by whole genome sequencing, achieving 37× and 40× coverage, respectively. We determined somatic mutations and structural alterations. RESULTS: We identified relevant aberrations, including deleterious mutations of KRAS and SMAD4 as well as a homozygous focal deletion of the PTEN tumor suppressor gene. These findings suggest that these tumors have a distinct oncogenesis from either common bile duct cancer or pancreatic cancer. Furthermore, this combination of genomic aberrations suggests a therapeutic context for dual mTOR/PI3K inhibition. CONCLUSIONS: Whole genome sequencing can elucidate an oncogenic context and expose potential therapeutic vulnerabilities in rare cancers