Incorporating Human Readiness Levels at Sandia National Laboratories

Since 2010, the concept of human readiness levels has been under development as a possible supplement to the existing technology readiness level (TRL) scale. The intent is to provide a mechanism to address safety and performance risks associated with the human component in a system that parallels the TRL structure already familiar to the systems engineering community. Sandia National Laboratories in Albuquerque, New Mexico, initiated a study in 2015 to evaluate options to incorporate human readiness planning for Sandia processes and products. The study team has collected the majority of baseline assessment data and has conducted interviews to understand staff perceptions of four different options for human readiness planning. Preliminary results suggest that all four options may have a vital role, depending on the type of work performed and the phase of product development. Upon completion of data collection, the utility of identified solutions will be assessed in one or more test cases

Asset liability modelling and pension schemes: the application of robust optimization to USS

This paper uses a novel numerical optimization technique - robust optimization - that is well suited to solving the asset-liability management (ALM) problem for pension schemes. It requires the estimation of fewer stochastic parameters, reduces estimation risk and adopts a prudent approach to asset allocation. This study is the first to apply it to a real-world pension scheme, and the first ALM model of a pension scheme to maximise the Sharpe ratio. We disaggregate pension liabilities into three components - active members, deferred members and pensioners, and transform the optimal asset allocation into the scheme’s projected contribution rate. The robust optimization model is extended to include liabilities and used to derive optimal investment policies for the Universities Superannuation Scheme (USS), benchmarked against the Sharpe and Tint, Bayes-Stein, and Black-Litterman models as well as the actual USS investment decisions. Over a 144 month out-of-sample period robust optimization is superior to the four benchmarks across 20 performance criteria, and has a remarkably stable asset allocation – essentially fix-mix. These conclusions are supported by six robustness checks

Sensitivity analysis for lexicographic ordering in radiation therapy treatment planning

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134937/1/mp0218.pd

A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts

Cycling of the signaling protein phospholipase D through cilia requires the BBSome only for the export phase

The BBSome is a complex of seven proteins, including BBS4, that is cycled through cilia by intraflagellar transport (IFT). Previous work has shown that the membrane-associated signaling protein phospholipase D (PLD) accumulates abnormally in cilia of Chlamydomonas reinhardtii bbs mutants. Here we show that PLD is a component of wild-type cilia but is enriched approximately 150-fold in bbs4 cilia; this accumulation occurs progressively over time and results in altered ciliary lipid composition. When wild-type BBSomes were introduced into bbs cells, PLD was rapidly removed from the mutant cilia, indicating the presence of an efficient BBSome-dependent mechanism for exporting ciliary PLD. This export requires retrograde IFT. Importantly, entry of PLD into cilia is BBSome and IFT independent. Therefore, the BBSome is required only for the export phase of a process that continuously cycles PLD through cilia. Another protein, carbonic anhydrase 6, is initially imported normally into bbs4 cilia but lost with time, suggesting that its loss is a secondary effect of BBSome deficiency

Exploring social media recruitment strategies and preliminary acceptability of an mHealth tool for teens with eating disorders

(1) Background: The current study leveraged social media to connect with teens with EDs to identify population specific characteristics and to gather feedback on an mHealth intervention. (2) Methods: We recruited teens with EDs from social media in two phases: (1) Discovery Group, (2) Testing Group. The Discovery Group

Mediators of the relationship between thin-ideal internalization and body dissatisfaction in the natural environment

Social comparisons (i.e., body, eating, exercise) and body surveillance were tested as mediators of the thin-ideal internalization-body dissatisfaction relationship using ecological momentary assessment (EMA). Participants were 232 college women who completed a 2-week EMA protocol, responding to questions three times per day. Multilevel path analysis was used to examine a 2-1-1 mediation model (thin-ideal internalization assessed as trait; between-person effects examined) and a 1-1-1 model (component of thin-ideal internalization [thin-ideal importance] assessed momentarily; within- and between-person effects examined). For the 2-1-1 model, only body comparison and body surveillance were significant specific mediators of the between-person effect. For the 1-1-1 model, all four variables were significant specific mediators of the within-person effect. Only body comparison was a significant specific mediator of the between-person effect. At the state level, many processes explain the thin-ideal internalization-body dissatisfaction relationship. However, at the trait level, body comparison and body surveillance are more important explanatory factors