6% for chemo (n=957), and -71.7% and -45.3% for anti-VEGF (n=1355). The optimal AUC cutoff for differentiating PD from non-PD on first restaging was -7.5% for chemo and -62.0% for anti-VEGF, chemo, ORadj=6.51 (95%CI 3.31-12.83, P  less then  0.001); anti-VEGF, ORadj=3.45 (95%CI 1.93-6.18, P  less then  0.001). A 99% NPV clinical cutoff for prediction of non-PD would avoid CT scan at first restaging in 21.0% of chemo and 16.2% of anti-VEGF treated patients. Among patients with SD on first restaging, those with CEA decrease from baseline had statistically significantly improved progression-free and overall survival. CONCLUSIONS Change in CEA from baseline to first restaging can accurately predict non-progression and correlates with long-term outcomes in patients receiving systemic chemotherapy. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please email journals.permissions@oup.com.OBJECTIVE To assess the generalizability of the overdose or serious opioid-induced respiratory depression risk index (VHA-RIOSORD), created by Zedler et al., using claims data from a large private insurer. DESIGN A retrospective nested case-control analysis of health care claims data. SUBJECTS Commercially insured individuals with a claim for an opioid prescription between October 1, 2014, and September 30, 2016 (N = 1,431,737). METHODS An overdose or serious opioid-induced respiratory depression (OSORD) occurred in 1,097 patients. Ten controls were selected per case (N = 10,970). Items and the assignment of point values to predictors were consistent with those determined by Zedler et al. Modeling of risk index scores produced predicted probabilities of OSORD; risk classes were defined by the predicted probability distribution. RESULTS All 15 items of the VHA-RIOSORD were used to determine a member's risk of OSORD. The average predicted probability of experiencing OSORD ranged from 3% in the lowest risk decile to 90% in the highest, with excellent agreement between predicted and observed incidence across risk classes. The model's C-statistic was 0.88. CONCLUSIONS Consistent with the findings of its developers, the VHA-RIOSORD performed well in identifying members of a large private insurance company who were medical users of prescription opioids at elevated risk of overdose or life-threatening respiratory depression, those most likely to benefit from preventive interventions. © 2020 American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.The toxic alkaloid nicotine is produced in the roots of in Nicotiana species and primarily accumulates in leaves as a specialized metabolite. A series of metabolic and transport genes involved in the nicotine pathway are coordinately upregulated by a pair of jasmonate-responsive AP2/ERF-family transcription factors, NtERF189 and NtERF199, in the roots of Nicotiana tabacum (tobacco). In this study, we explore the potential of manipulating the expression of these transcriptional regulators to alter nicotine biosynthesis in tobacco. Transient overexpression of NtERF189 led to alkaloid production in the leaves of N. benthamiana and N. alata. This ectopic production was further enhanced by co-overexpressing a gene encoding a bHLH-family MYC2 transcription factor. Constitutive and leaf-specific overexpression of NtERF189 increased the accumulation of foliar alkaloids in transgenic tobacco plants but negatively affected plant growth. By contrast, in a knockout mutant of NtERF189 and NtERF199 obtained through CRISPR/Cas9-based genome editing, alkaloid levels were drastically reduced without causing major growth defects. Metabolite profiling revealed the impact of manipulating the nicotine pathway on a wide range of nitrogen- and carbon-containing metabolites. Our findings provide insights into the biotechnological applications of engineering metabolic pathways by targeting transcription factors. © The Author(s) 2020. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email journals.permissions@oup.com.BACKGROUND AND OBJECTIVES The lack of appropriate quality of life (QoL) measures is a major barrier to planning and delivering health and aged care services for older Indigenous peoples worldwide. QoL is dependent on cultural values and priorities may vary between age groups. This project aims to develop a QoL tool for older Aboriginal Australians. RESEARCH DESIGN AND METHODS The study was completed with Aboriginal Australians aged over 45 years living in Perth and Melbourne, Australia. Participatory Action Research methods were applied with an Indigenous research paradigm. Semistructured interviews were undertaken to identify the factors important to having a good life. Factors were further explored in yarning groups with older Aboriginal peoples to develop the draft QoL tool questions. Face validity of the tool was completed in two regions. RESULTS The participants preferred the term "a good life" to QoL. Having a good spirit is at the core of having a good life. The protective factors for a good life were family and friends, health, culture, Elder role, respect, Country, spirituality, services and supports, community, future plans, safety and security, and basic needs. DISCUSSION AND IMPLICATIONS Twelve factors were identified and developed into key questions for the Good Spirit, Good Life tool.  https://www.selleckchem.com/products/3-aminobenzamide.html  The draft tool will undergo quantitative validity testing, prior to embedding in service provision to inform care for older Aboriginal peoples. With local adaptation, the tool, accompanying framework, and participatory methods for development may have wider applicability to other Indigenous populations worldwide. © The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.It is regarded as best practice in phylogenetic reconstruction to perform relative model selection to determine an appropriate evolutionary model for the data. This procedure ranks a set of candidate models according to their goodness-of-fit to the data, commonly using an information theoretic criterion. Users then specify the best-ranking model for inference. While it is often assumed that better-fitting models translate to increase accuracy, recent studies have shown that the specific model employed may not substantially affect inferences. We examine whether there is a systematic relationship between relative model fit and topological inference accuracy in protein phylogenetics, using simulations and real sequences. Simulations employed site-heterogeneous mechanistic codon models that are distinct from protein-level phylogenetic inference models. This strategy allows us to investigate how protein models performs when they are misspecified to the data, as will be the case for any real sequence analysis. We broadly find that phylogenies inferred across models with vastly different fits to the data produce highly consistent topologies.