Synchronic Curation for Assessing Reuse and Integration Fitness of Multiple Data Collections

Data driven applications often require using data integrated from different, large, and continuously updated collections. Each of these collections may present gaps, overlapping data, have conflicting information, or complement each other. Thus, a curation need is to continuously assess if data from multiple collections are fit for integration and reuse. To assess different large data collections at the same time, we present the Synchronic Curation (SC) framework. SC involves processing steps to map the different collections to a unifying data model that represents research problems in a scientific area. The data model, which includes the collections' provenance and a data dictionary, is implemented in a graph database where collections are continuously ingested and can be queried. SC has a collection analysis and comparison module to track updates, and to identify gaps, changes, and irregularities within and across collections. Assessment results can be accessed interactively through a web-based interactive graph. In this paper we introduce SC as an interdisciplinary enterprise, and illustrate its capabilities through its implementation in ASTRIAGraph, a space sustainability knowledge system

On Novices\u27 Interaction with Compiler Error Messages: A Human Factors Approach

The difficulty in understanding compiler error messages can be a major impediment to novice student learning. To alleviate this issue, multiple researchers have run experiments enhancing compiler error messages in automated assessment tools for programming assignments. The conclusions reached by these published experiments appear to be conflicting. We examine these experiments and propose five potential reasons for the inconsistent conclusions concerning enhanced compiler error messages: (1) students do not read them, (2) researchers are measuring the wrong thing, (3) the effects are hard to measure, (4) the messages are not properly designed, (5) the messages are properly designed, but students do not understand them in context due to increased cognitive load. We constructed mixed-methods experiments designed to address reasons 1 and 5 with a specific automated assessment tool, Athene, that previously reported inconclusive results. Testing student comprehension of the enhanced compiler error messages outside the context of an automated assessment tool demonstrated their effectiveness over standard compiler error messages. Quantitative results from a 60 minute one-on-one think-aloud study with 31 students did not show substantial increase in student learning outcomes over the control. However, qualitative results from the one-on-one thinkaloud study indicated that most students are reading the enhanced compiler error messages and generally make effective changes after encountering them

High-throughput chemogenetic drug screening reveals PKC-RhoA/PKN as a targetable signaling vulnerability in GNAQ-driven uveal melanoma

Uveal melanoma (UM) is the most prevalent cancer of the eye in adults, driven by activating mutation of GNAQ/GNA11; however, there are limited therapies against UM and metastatic UM (mUM). Here, we perform a high-throughput chemogenetic drug screen in GNAQ-mutant UM contrasted with BRAF-mutant cutaneous melanoma, defining the druggable landscape of these distinct melanoma subtypes. Across all compounds, darovasertib demonstrates the highest preferential activity against UM. Our investigation reveals that darovasertib potently inhibits PKC as well as PKN/PRK, an AGC kinase family that is part of the "dark kinome." We find that downstream of the Gαq-RhoA signaling axis, PKN converges with ROCK to control FAK, a mediator of non-canonical Gαq-driven signaling. Strikingly, darovasertib synergizes with FAK inhibitors to halt UM growth and promote cytotoxic cell death in vitro and in preclinical metastatic mouse models, thus exposing a signaling vulnerability that can be exploited as a multimodal precision therapy against mUM.</p

Transparency and Accountability in Space Domain Awareness: Demonstrating ASTRIAGraph’s Capabilities with the United Nations Registry Data

Near-earth space is geopolitically and commercially contested, and in need of environmental protection. To achieve space safety, security, and sustainability, we are developing ASTRIAGraph (http://astria.tacc.utexas.edu/AstriaGraph/), a framework that enables monitoring, assessment, and verification of space actor behavior in the context of legal and policy instruments. In this presentation given at the 7th Annual Conference on Space Traffic Management 2021, we demonstrate how the ASTRIAGraph toolset can improve space management accountability for the United Nations Office for Outer Space Affairs (UNOOSA). We first describe the ASTRIAGraph data model and our information extraction and curation processes, as well as discuss methods for measuring and reporting the reliability of these processes. We then demonstrate several key features and analysis results using ASTRIAGraph with the United Nations space object registration data (https://www.unoosa.org/oosa/en/spaceobjectregister/index.html). For example, by combining fields from different sources such as the UN registration documents and USSPACECOM data, we can visualize space objects registered with the UNOOSA, identify ASO Launch States’ liability, assess trends in the registration patterns of these Launch States, and even compute ASO resolution amidst mismatched or missing different information from provided by the different sources. This data is then able to be analyzed and communicated through graphs and plots, which highlight the liability and compliance of a nation or organization, whether it is done by evaluating the lag (difference between the date of launch and the initial registration submission date) and plotting its spread for that given state or entity, or if you were to analyze their trends in compliance over time. In addition, using different registration information fields, users are able to query the ranking of Launch States in relation to registration promptness. These rankings, set between one and five stars, are first given to individual objects, but countries with many registered ASOs often have outliers with large registration lag. As such, the rankings are scaled higher for states which register larger numbers of ASOs to prevent undue punishment of these countries for their compliance. In this submission we include the video presentation and the conference abstract.Aerospace Engineering and Engineering Mechanic

High-throughput chemogenetic drug screening reveals PKC-RhoA/PKN as a targetable signaling vulnerability in GNAQ-driven uveal melanoma

Uveal melanoma (UM) is the most prevalent cancer of the eye in adults, driven by activating mutation of GNAQ/GNA11; however, there are limited therapies against UM and metastatic UM (mUM). Here, we perform a high-throughput chemogenetic drug screen in GNAQ-mutant UM contrasted with BRAF-mutant cutaneous melanoma, defining the druggable landscape of these distinct melanoma subtypes. Across all compounds, darovasertib demonstrates the highest preferential activity against UM. Our investigation reveals that darovasertib potently inhibits PKC as well as PKN/PRK, an AGC kinase family that is part of the "dark kinome." We find that downstream of the Gαq-RhoA signaling axis, PKN converges with ROCK to control FAK, a mediator of non-canonical Gαq-driven signaling. Strikingly, darovasertib synergizes with FAK inhibitors to halt UM growth and promote cytotoxic cell death in&nbsp;vitro and in preclinical metastatic mouse models, thus exposing a signaling vulnerability that can be exploited as a multimodal precision therapy against mUM