White paper: A plan for cooperation between NASA and DARPA to establish a center for advanced architectures

Large, complex computer systems require many years of development. It is recognized that large scale systems are unlikely to be delivered in useful condition unless users are intimately involved throughout the design process. A mechanism is described that will involve users in the design of advanced computing systems and will accelerate the insertion of new systems into scientific research. This mechanism is embodied in a facility called the Center for Advanced Architectures (CAA). CAA would be a division of RIACS (Research Institute for Advanced Computer Science) and would receive its technical direction from a Scientific Advisory Board established by RIACS. The CAA described here is a possible implementation of a center envisaged in a proposed cooperation between NASA and DARPA

Machine Learning Identifies Digital Phenotyping Measures Most Relevant to Negative Symptoms in Psychotic Disorders: Implications for Clinical Trials

BACKGROUND: Digital phenotyping has been proposed as a novel assessment tool for clinical trials targeting negative symptoms in psychotic disorders (PDs). However, it is unclear which digital phenotyping measurements are most appropriate for this purpose. AIMS: Machine learning was used to address this gap in the literature and determine whether: (1) diagnostic status could be classified from digital phenotyping measures relevant to negative symptoms and (2) the 5 negative symptom domains (anhedonia, avolition, asociality, alogia, and blunted affect) were differentially classified by active and passive digital phenotyping variables. METHODS: Participants included 52 outpatients with a PD and 55 healthy controls (CN) who completed 6 days of active (ecological momentary assessment surveys) and passive (geolocation, accelerometry) digital phenotyping data along with clinical ratings of negative symptoms. RESULTS: Machine learning algorithms classifying the presence of a PD diagnosis yielded 80% accuracy for cross-validation in H(2)O AutoML and 79% test accuracy in the Recursive Feature Elimination with Cross Validation feature selection model. Models classifying the presence vs absence of clinically significant elevations on each of the 5 negative symptom domains ranged in test accuracy from 73% to 91%. A few active and passive features were highly predictive of all 5 negative symptom domains; however, there were also unique predictors for each domain. CONCLUSIONS: These findings suggest that negative symptoms can be modeled from digital phenotyping data recorded in situ. Implications for selecting the most appropriate digital phenotyping variables for use as outcome measures in clinical trials targeting negative symptoms are discussed

Antioxidant Carbon Nanoparticles Inhibit Fibroblast-Like Synoviocyte Invasiveness and Reduce Disease Severity in a Rat Model of Rheumatoid Arthritis

Reactive oxygen species have been involved in the pathogenesis of rheumatoid arthritis (RA). Our goal was to determine the effects of selectively scavenging superoxide (O2•−) and hydroxyl radicals with antioxidant nanoparticles, called poly(ethylene glycol)-functionalized hydrophilic carbon clusters (PEG-HCCs), on the pathogenic functions of fibroblast-like synoviocytes (FLS) from patients with rheumatoid arthritis (RA) and on the progression of an animal model of RA. We used human FLS from patients with RA to determine PEG-HCC internalization and effects on FLS cytotoxicity, invasiveness, proliferation, and production of proteases. We used the pristane-induced arthritis (PIA) rat model of RA to assess the benefits of PEG-HCCs on reducing disease severity. PEG-HCCs were internalized by RA-FLS, reduced their intracellular O2•−, and reduced multiple measures of their pathogenicity in vitro, including proliferation and invasion. In PIA, PEG-HCCs caused a 65% reduction in disease severity, as measured by a standardized scoring system of paw inflammation and caused a significant reduction in bone and tissue damage, and circulating rheumatoid factor. PEG-HCCs did not induce lymphopenia during PIA. Our study demonstrated a role for O2•− and hydroxyl radicals in the pathogenesis of a rat model of RA and showed efficacy of PEG-HCCs in treating a rat model of RA