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Featured submissions

May 2022

April 2022

March 2022

  • Rangelands, Volume 41 (2019), is now publicly available in the repository.


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  • Predicting Medication Nonadherence in Older Adults With Difficult-to-Treat Depression in the IRL-GRey Randomized Controlled Trial

    Altmann, Helene M.; Kazan, Joseph; Gebara, Marie Anne; Blumberger, Daniel M.; Karp, Jordan F.; Lenze, Eric J.; Mulsant, Benoit H.; Reynolds, Charles F.; Stahl, Sarah T.; Department of Psychiatry, College of Medicine, University of Arizona (Elsevier BV, 2022-03)
    Objective: Nonadherence to antidepressants interferes with optimal treatment of late-life depression. This analysis examines clinical and treatment factors predicting medication nonadherence in difficult-to-treat late-life depression. Methods: Secondary analysis of data from a clinical trial of antidepressant pharmacotherapy for Major Depressive Disorder in 468 adults aged 60+ years. All participants received venlafaxine XR for 12 weeks. Nonremitters were randomized to augmentation with either aripiprazole or placebo for 12 additional weeks. Medication adherence was assessed 14 times over 24 weeks. The analyses examined sociodemographic, clinical, and treatment factors that may predict antidepressant nonadherence during early (weeks 1–6), late (weeks 7–12), and augmentation (weeks 13-–24) treatment. Results: Poor cognitive function and early response were predictive of early nonadherence. Poor cognitive function and prior nonadherence were predictive of late nonadherence. Living alone was associated with nonadherence both late and during augmentation treatment. Conclusion: Future studies should consider the role of early response and cognitive function to improve antidepressant adherence, particularly among older adults who live alone.
  • Implications of a “Null” Randomized Controlled Trial of Mindfulness and Compassion Interventions in Healthy Adults

    Kaplan, Deanna M.; Mehl, Matthias R.; Pace, Thaddeus W. W.; Negi, Lobsang Tenzin; Silva, Brendan Ozawa-de; Lavelle, Brooke D.; Sivilli, Teri; Williams, Allison; Comstock, Tom; Price, Bryan; et al. (Springer Science and Business Media LLC, 2022-04-21)
    Objectives: Extensive research suggests that short-term meditation interventions may hold therapeutic promise for a wide range of psychosocial outcomes. In response to calls to subject these interventions to more methodologically rigorous tests, a randomized controlled trial tested the effectiveness of a mindfulness meditation intervention and a compassion meditation intervention against an active control in a demographically diverse sample of medically and psychiatrically healthy adults. Methods: Two hundred and four participants completed a battery of questionnaires to assess psychological experience, participated in a laboratory stress test to measure their biological stress reactivity, and wore the Electronically Activated Recorder (EAR) to assess daily behaviors before and after an eight-week intervention (mindfulness meditation intervention, compassion meditation intervention, or health education discussion group). Results: Neither meditation intervention reliably impacted participants’ subjective psychological experience, biological stress reactivity, or objectively assessed daily behaviors. Furthermore, post hoc moderation analyses found that neither baseline distress nor intervention engagement significantly moderated effects. Conclusions: Results from this trial—which was methodologically rigorous and powered to detect all but small effects—were essentially null. These results are an important data point for the body of research about meditation interventions. Implications of these non-significant effects are discussed in the context of prior studies, and future directions for contemplative intervention research are recommended. Clinical Trial Registry: Registry Number: NCT01643369.
  • Less Than Fully Honest: Financial Deception in Emerging Adult Romantic Relationships

    Saxey, Matthew T.; LeBaron-Black, Ashley B.; Dew, Jeffrey P.; Curran, Melissa A.; University of Arizona (SAGE Publications, 2022-04-26)
    Emerging adults lack many basic financial capabilities. To avoid conflict that may come from these deficiencies, some emerging adults may financially deceive their romantic partner. However, little is known about financial deception in emerging adult romantic relationships. Through the lenses of two theoretical frameworks, we test whether financial deception intervenes the associations of couple financial communication, financial socialization, and similarity of financial values with romantic relationship flourishing in a sample of 1,950 U.S. emerging adults. Results indicate that couple financial communication, similarity of financial values, and financial socialization may contribute positively toward romantic relationship flourishing. However, financial socialization and financial deception may contribute negatively toward romantic relationship flourishing. Findings are discussed in light of the theoretical frameworks utilized, implications for clinicians and educators are identified, and directions for future research are presented. In summary, being less than fully honest about finances may have implications for emerging adults in romantic relationships.
  • Counteracting Dark Web Text-Based CAPTCHA with Generative Adversarial Learning for Proactive Cyber Threat Intelligence

    Zhang, Ning; Ebrahimi, Mohammadreza; Li, Weifeng; Chen, Hsinchun; University of Arizona (Association for Computing Machinery (ACM), 2022-06-30)
    Automated monitoring of dark web (DW) platforms on a large scale is the first step toward developing proactive Cyber Threat Intelligence (CTI). While there are efficient methods for collecting data from the surface web, large-scale dark web data collection is often hindered by anti-crawling measures. In particular, text-based CAPTCHA serves as the most prevalent and prohibiting type of these measures in the dark web. Text-based CAPTCHA identifies and blocks automated crawlers by forcing the user to enter a combination of hard-to-recognize alphanumeric characters. In the dark web, CAPTCHA images are meticulously designed with additional background noise and variable character length to prevent automated CAPTCHA breaking. Existing automated CAPTCHA breaking methods have difficulties in overcoming these dark web challenges. As such, solving dark web text-based CAPTCHA has been relying heavily on human involvement, which is labor-intensive and time-consuming. In this study, we propose a novel framework for automated breaking of dark web CAPTCHA to facilitate dark web data collection. This framework encompasses a novel generative method to recognize dark web text-based CAPTCHA with noisy background and variable character length. To eliminate the need for human involvement, the proposed framework utilizes Generative Adversarial Network (GAN) to counteract dark web background noise and leverages an enhanced character segmentation algorithm to handle CAPTCHA images with variable character length. Our proposed framework, DW-GAN, was systematically evaluated on multiple dark web CAPTCHA testbeds. DW-GAN significantly outperformed the state-of-the-art benchmark methods on all datasets, achieving over 94.4% success rate on a carefully collected real-world dark web dataset. We further conducted a case study on an emergent Dark Net Marketplace (DNM) to demonstrate that DW-GAN eliminated human involvement by automatically solving CAPTCHA challenges with no more than three attempts. Our research enables the CTI community to develop advanced, large-scale dark web monitoring. We make DW-GAN code available to the community as an open-source tool in GitHub.
  • Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

    Cramer, Estee Y; Ray, Evan L; Lopez, Velma K; Bracher, Johannes; Brennen, Andrea; Castro Rivadeneira, Alvaro J; Gerding, Aaron; Gneiting, Tilmann; House, Katie H; Huang, Yuxin; et al. (National Academy of Sciences, 2022-04-08)
    Significance: This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

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