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Abstract For detecting field carcinogenesis non-invasively, early technical development and case–control testing of exhaled breath condensate microRNAs was performed. In design, human lung tissue microRNA-seq discovery was reconciled with TCGA and published tumor-discriminant microRNAs, yielding a panel of 24 upregulated microRNAs. The airway origin of exhaled microRNAs was topographically “fingerprinted”, using paired EBC, upper and lower airway donor sample sets. A clinic-based case–control study (166 NSCLC cases, 185 controls) was interrogated with the microRNA panel by qualitative RT-PCR. Data were analyzed by logistic regression (LR), and by random-forest (RF) models. Feasibility testing of exhaled microRNA detection, including optimized whole EBC extraction, and RT and qualitative PCR method evaluation, was performed. For sensitivity in this low template setting, intercalating dye-based URT-PCR was superior to fluorescent probe-based PCR (TaqMan). In application, adjusted logistic regression models identified exhaled miR-21, 33b, 212 as overall case–control discriminant. RF analysis of combined clinical + microRNA models showed modest added discrimination capacity (1.1–2.5%) beyond clinical models alone: all subjects 1.1% (p = 8.7e−04)); former smokers 2.5% (p = 3.6e−05); early stage 1.2% (p = 9.0e−03), yielding combined ROC AUC ranging from 0.74 to 0.83. We conclude that exhaled microRNAs are qualitatively measureable, reflect in part lower airway signatures; and when further refined/quantitated, can potentially help to improve lung cancer risk assessment.

Abstract Background When assessing the value of an intervention in bipolar disorder, researchers and clinicians often focus on metrics that quantify improvements to core diagnostic symptoms (e.g., mania). Providers often overlook or misunderstand the impact of treatment on life quality and function. We wanted to better characterize the shared experiences and obstacles of bipolar disorder within the United States from the patient’s perspective. Methods We recruited 24 individuals diagnosed with bipolar disorder and six caretakers supporting someone with the condition. Participants were involved in treatment or support services for bipolar disorder in central Texas. As part of this qualitative study, participants discussed their everyday successes and obstacles related to living with bipolar disorder during personalized, open-ended interviews. Audio files were transcribed, and Nvivo software processed an initial thematic analysis. We then categorized themes into bipolar disorder-related obstacles that limit the patient’s capability (i.e., function), comfort (i.e., relief from suffering) and calm (i.e., life disruption) (Liu et al., FebClin Orthop 475:315–317, 2017; Teisberg et al., MayAcad Med 95:682–685, 2020). We then discuss themes and suggest practical strategies that might improve the value of care for patients and their families. Results Issues regarding capability included the struggle to maintain identity, disruptions to meaningful employment, relationship loss and the unpredictable nature of bipolar disorder. Comfort related themes included the personal perception of diagnosis, social stigma and medication issues. Calm themes included managing dismissive doctors, finding the right psychotherapist and navigating financial burdens. Conclusions Qualitative data from patients with bipolar disorder helps identify gaps in care or practical limitations to treatment. When we listen to these individuals, it is clear that treatments must also address the unmet psychosocial impacts of the condition to improve patient care, capability and calm.

Post-acute sequelae of COVID-19 (PASC), also known as Long-Covid (LC), may affect 10–30 % of COVID-infected patients, and is characterized by a variety of debilitating symptoms lasting over 3 months after the acute infection, including but not limited to dyspnea, fatigue, and musculoskeletal, cognitive, and/or mental health impairments. Vitamin D is an essential nutrient primarily recognized for its role in regulating calcium and bone health but also endowed with potent anti-inflammatory activity affecting a variety of immune cells. We retrospectively evaluated the plasmatic levels of both 1,25-dihydroxyvitamin-D (1,25 OH), and 25-hydroxyvitamin-D (25 OH), the active and storage forms of vitamin-D3, respectively, in the serum of gynecologic cancer patients affected by PASC/LC vs control cancer patients. We found elevated 1,25-dihydroxyvitamin-D levels in 5 out of 5 of the PASC/LC patients (mean ± SD = 97.2 ± 26.9 pg/mL) versus 0 out of 10 of randomly selected cancer control patients (44.9 ± 17.2 pg/mL, p = 0.0005). In contrast, no significant difference was noted in the levels of 25-dihydroxyvitamin-D in PASC/LC (mean ± SD = 48.2 ± 15.8 ng/mL) versus controls (43.0 ± 11.6 ng/mL, p = 0.48). Importantly, abnormal levels of vitamin D were found to persist for at least 2 years in patients with long covid symptoms. The active form (1,25OH) but not the storage form (25 OH) of vitamin-D is significantly elevated in PASC/LC cancer patients. Abnormally and persistently elevated 1,25OH levels, similarly to sarcoidosis patients, may represent the results of extrarenal conversion of vitamin D by activated macrophages, and a novel biomarker of persistent inflammation in gynecologic cancer patients with PASC/LC.

Abstract Post-traumatic stress disorder (PTSD) is a mental disorder diagnosed by clinical interviews, self-report measures and neuropsychological testing. Traumatic brain injury (TBI) can have neuropsychiatric symptoms similar to PTSD. Diagnosing PTSD and TBI is challenging and more so for providers lacking specialized training facing time pressures in primary care and other general medical settings. Diagnosis relies heavily on patient self-report and patients frequently under-report or over-report their symptoms due to stigma or seeking compensation. We aimed to create objective diagnostic screening tests utilizing Clinical Laboratory Improvement Amendments (CLIA) blood tests available in most clinical settings. CLIA blood test results were ascertained in 475 male veterans with and without PTSD and TBI following warzone exposure in Iraq or Afghanistan. Using random forest (RF) methods, four classification models were derived to predict PTSD and TBI status. CLIA features were selected utilizing a stepwise forward variable selection RF procedure. The AUC, accuracy, sensitivity, and specificity were 0.730, 0.706, 0.659, and 0.715, respectively for differentiating PTSD and healthy controls (HC), 0.704, 0.677, 0.671, and 0.681 for TBI vs. HC, 0.739, 0.742, 0.635, and 0.766 for PTSD comorbid with TBI vs HC, and 0.726, 0.723, 0.636, and 0.747 for PTSD vs. TBI. Comorbid alcohol abuse, major depressive disorder, and BMI are not confounders in these RF models. Markers of glucose metabolism and inflammation are among the most significant CLIA features in our models. Routine CLIA blood tests have the potential for discriminating PTSD and TBI cases from healthy controls and from each other. These findings hold promise for the development of accessible and low-cost biomarker tests as screening measures for PTSD and TBI in primary care and specialty settings.

Background: Most researchers and study participants believe that the summary, or aggregate, results of health research should be returned to study participants. However, researchers often do not return aggregate results. A better understanding of the impediments to results return could support improvements in this practice. Methods: This qualitative study convened eight virtual focus groups, four with investigators and four with patient partners from research studies funded by the Patient-Centered Outcomes Research Institute (PCORI). In total, 23 investigators and 20 partners participated. We explored perspectives, experiences, influences, and recommendations related to aggregate results return. Results: Focus group participants described the ethical importance of returning aggregate results, as well as the benefits to study participants. They also noted important impediments to results return, emphasizing IRB and logistical challenges and describing a lack of support for the practice both on the part of institutions and the field at large. Participants highlighted the value of patients and caregivers' perspectives and contributions to results return, which focused on returning the most relevant findings through effective channels and formats. They further emphasized the importance of planning and identified resources that could support results return. Conclusion: Researchers, funders, and the field can better facilitate results return by promoting standardized processes in research, such as the earmarking of funds for results return and inclusion of results returns milestones in research plans. More intentional policies, infrastructures, and resources that support results return may lead to more widespread return of study results to those who make these studies possible.

We identify the need for enhanced pedestrian–vehicle simulation tools and build such a tool to explore the interaction among pedestrian “players” and virtual human- and automated-vehicles for different scenarios taking place in an urban environment. We first present contemporary research tools and then propose the design and development of a new desktop application that facilitates pedestrian-point-of-view research. We then conduct a three-step user experience experiment, in which a small number of participants answer questions before and after using the application to interact with virtual human and automated vehicles in diverse road-crossing scenarios. Behavioral results observed in virtuality, especially when motivated by consequence, tend to simulate real life sufficiently well to inform design choices. From the simulation, we observed valuable insights into human–vehicle interactions. Upon completing this preliminary testing, we iterated the tool’s design and ultimately conducted an 89-participant study of human–vehicle interactions for three scenarios taking place in a virtual environment. Our tool raised participant awareness of autonomous vehicles and their capabilities and limitations, which is an important step in overcoming public distrust of AVs. We additionally saw that participants trust humans and technology less as drivers than in other contexts, and that pedestrians feel safer around vehicles with autonomy indicators. Further, we note that study participants increasingly feel safe with automated vehicles with increased exposure. These preliminary results, as well as the efficacy of the tool’s design, may inform future socio-technical design for automated vehicles and their human interactions.

Broader understanding of waste management has the potential to bring about broad societal change impacting the climate crisis and public health. We present existing waste management tools and commercially-available games involving waste management, highlighting the strengths and opportunities left unaddressed by these tools in educational contexts and planning use cases. A survey motivates the need for enhanced interactive tools providing clear feedback through quick-visibility performance indicators. After identifying an opportunity to build upon highly-detailed multi-criteria simulation tools, we explore the need for easy-to-read performance metrics that will bring to the field of waste management easily identifiable and measurable key performance indicators (KPIs) that vary alongside factors affecting waste management policies. Such metrics are introduced and detailed as part of a unified waste management model. We then develop a representative gamified educational tool based upon this model to be used by students, decision makers planning real-world policies, and the public. This simulator is built upon the Unity Game Engine and emulates waste management techniques and resulting KPIs within the context of a virtual city.

Objectives: Individuals with bipolar disorder commonly report cognitive impairment which is associated with several psychosocial factors (e.g., mood symptoms). Within this study, we investigated the relationship between these psychosocial factors and the perception of cognitive impairment in individuals with bipolar disorder. Methods: We measured the relationship between subjective cognitive impairment and mood symptoms, quality of life, age, gender, bipolar disorder subtype, anxiety and sleep disturbance in 140 individuals with bipolar disorder with a mixed linear regression model. Our primary outcome measures were obtained via National Institute of Health (NIH)-sponsored PROMIS cognition scores. Results: Results from the model suggest that both the PROMIS Cognitive Function and Cognitive Function-Abilities scores were significantly negatively correlated with sleep disturbance and depression symptoms (p≤0.05). PROMIS Cognitive Function was also significantly negatively correlated with anxiety (p≤0.05). Limitations: Limitations of this study include the absence of a healthy control group, limited demographic diversity, and cross-sectional study design. Conclusions: Perceived cognitive impairment in individuals with bipolar disorder is associated with increased sleep disturbance, depression, and anxiety. Future studies with objective cognitive measures combined with PROMIS self-report scores might further clarify the expression of cognitive impairment in bipolar disorder.

Water temperature plays a primary role in driving ecological processes in streams due to its direct impact on biogeochemical cycles and the physiological processes of stream fauna, such as growth, development, and the timing of life history events. Streams influenced by snowpack melt are generally cooler in the summer and demonstrate less sensitivity to climate variability in what is commonly referred to as “climate buffering”. Despite the substantial influence of snowpack on stream temperature and expected changes in snowpack accumulation and melt timing with climate change, methods for representing snowpack in statistical models for stream temperature have not been well explored. In this investigation, we quantified the extent of stream temperature buffering in free-flowing streams across a geographically diverse region in the Pacific Northwest USA. We demonstrated that statistical models of daily mean stream temperature can be improved by explicitly accounting for temporal variability in a small number of climate covariates believed to be mechanistically related to stream temperature. Our novel statistical approach included as predictors combinations and interactions between the following variables: (1) air temperature, (2) lagged air temperature (where the lag duration varied according to its relationship with flow on a given day at that site), (3) flow, (4) snowpack in the upstream catchment, and (5) day of year. We found that sites with substantial snow influence were associated with increased air temperature buffering during the warm season and longer air temperature lags (>30 days during spring high flows and ∼ 10 days during late summer low flows) compared to sites where precipitation predominantly fell as rain (

Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective point-to-point navigation, and the optimal algorithm depends on the robot geometry as well as the computing constraints, including static/holonomic and dynamic/non-holonomically-constrained systems, and requires a comprehensive understanding of contemporary solutions. The goal of this paper is to help novice practitioners gain an awareness of the classes of path planning algorithms used today and to understand their potential use cases—particularly within automated or unmanned systems. To that end, we provide broad, rather than deep, coverage of key and foundational algorithms, with popular algorithms and variants considered in the context of different robotic systems. The definitions, summaries, and comparisons are relevant to novice robotics engineers and embedded system developers seeking a primer of available algorithms.

Abstract We sought to find clinical subtypes of posttraumatic stress disorder (PTSD) in veterans 6–10 years post-trauma exposure based on current symptom assessments and to examine whether blood biomarkers could differentiate them. Samples were males deployed to Iraq and Afghanistan studied by the PTSD Systems Biology Consortium: a discovery sample of 74 PTSD cases and 71 healthy controls (HC), and a validation sample of 26 PTSD cases and 36 HC. A machine learning method, random forests (RF), in conjunction with a clustering method, partitioning around medoids, were used to identify subtypes derived from 16 self-report and clinician assessment scales, including the clinician-administered PTSD scale for DSM-IV (CAPS). Two subtypes were identified, designated S1 and S2, differing on mean current CAPS total scores: S2 = 75.6 (sd 14.6) and S1 = 54.3 (sd 6.6). S2 had greater symptom severity scores than both S1 and HC on all scale items. The mean first principal component score derived from clinical summary scales was three times higher in S2 than in S1. Distinct RFs were grown to classify S1 and S2 vs. HCs and vs. each other on multi-omic blood markers feature classes of current medical comorbidities, neurocognitive functioning, demographics, pre-military trauma, and psychiatric history. Among these classes, in each RF intergroup comparison of S1, S2, and HC, multi-omic biomarkers yielded the highest AUC-ROCs (0.819–0.922); other classes added little to further discrimination of the subtypes. Among the top five biomarkers in each of these RFs were methylation, micro RNA, and lactate markers, suggesting their biological role in symptom severity.

As we confront novel environmental challenges, a full understanding of the physical and biological processes that govern species responses to climate change will help maintain biodiversity and support conservation measures that are more robust to irreducible uncertainty. However, climate impacts are so complex, and the literature on salmon and trout is so vast that researchers and decision makers scramble to make sense of it all. Therefore, we conducted a systematic literature review of climate impacts on salmon and anadromous trout as a resource for stakeholders, managers, and researchers. We reviewed studies published from 2010 to 2021 that address climate impacts on these fish and organized them in a database of 1169 physical and 1853 biological papers. Papers are labeled with keywords across eight categories related to subject matter and study methods. We compared the literature by biological process and life stage and used these comparisons to assess strengths and weaknesses. We then summarized expected phenotypic and genetic responses and management actions by life stage. Overall, we found the largest research gaps related to species interactions, behavioral responses, and effects that carry over across life stages. With this collection of the literature, we can better apply scarce conservation resources, fill knowledge gaps, and make informed decisions that do not ignore uncertainty.

In a world dependent on road-based transportation, it is essential to understand automobiles. We propose an acoustic road vehicle characterization system as an integrated approach for using sound captured by mobile devices to enhance transparency and understanding of vehicles and their condition for non-expert users. We develop and implement novel deep learning cascading architectures, which we define as conditional, multi-level networks that process raw audio to extract highly granular insights for vehicle understanding. To showcase the viability of cascading architectures, we build a multi-task convolutional neural network that predicts and cascades vehicle attributes to enhance misfire fault detection. We train and test these models on a synthesized dataset reflecting more than 40 hours of augmented audio. Through cascading fuel type, engine configuration, cylinder count and aspiration type attributes, our cascading CNN achieves 87.0% test set accuracy on misfire fault detection which demonstrates margins of 8.0% and 1.7% over naïve and parallel CNN baselines. We explore experimental studies focused on acoustic features, data augmentation, and data reliability. Finally, we conclude with a discussion of broader implications, future directions, and application areas for this work.

Background: Plain language translation may facilitate the public's ability to understand and use results of scientific research. Brief description of activity: This article describes the Patient-Centered Outcomes Research Institute's (PCORI) approach to and lessons learned from developing plain language summaries of PCORI-funded research for the lay public. Implementation: We developed and tested a standard template for the summaries, incorporating feedback in the template design from focus groups with members of the public. Between February 2017 and March 2020, we completed translation of 272 plain language summaries of PCORI-funded studies, covering topics including cardiovascular disease, obesity, cancer, mental health, asthma, HIV/AIDS, and comparative effectiveness research methods. Results: Templates use a question-and-answer format, with sections on the rationale, methods, results, limitations, and how the research will help inform decisions. In addition to feedback on template heading wording and order, focus group participants stressed the importance of establishing relevance and conveying credibility and limitations. Lessons learned: Lessons learned relate to supporting consistency across individual summaries, carefully prioritizing content to include in the summaries, and balancing plain language and reading level with precision. These lessons learned from template development and implementation may be useful to other organizations or publishers contemplating similar efforts to make scientific research results more accessible. [HLRP: Health Literacy Research and Practice. 2021;5(2):e155–e161.] Plain Language Summary: The Patient-Centered Outcomes Research Institute (PCORI) funds comparative effectiveness research. This research compares the benefits and harms of two or more health care choices. In this article, we describe lessons learned from PCORI's efforts to develop plain language summaries of results from the research it funds. These lessons may help other organizations that want to share research results in plain language.

The majority of Columbia River summer-run steelhead encounter high river temperatures (near or > 20°C) during their spawning migration. While some steelhead pass through the mid-Columbia River in a matter of days, others use tributary habitats as temperature refuges for periods that can last months. Using PIT tag detection data from adult return years 2004-2016, we fit 3-component mixture models to differentiate between "fast", "slow", and "overwintering" migration behaviors in five aggregated population groups. Fast fish migrated straight through the reach on average in ~7-9 days while slow fish delayed their migration for weeks to months, and overwintering fish generally took ~150-250 days. We then fit covariate models to examine what factors contributed to the probability of migration delay during summer months (slow or overwintering behaviors), and to explore how migration delay related to mortality. Finally, to account for the impact of extended residence times in the reach for fish that delayed, we compared patterns in estimated average daily rates of mortality between migration behaviors and across population groups. Results suggest that migration delay was primarily triggered by high river temperatures but temperature thresholds for delay were lowest just before the seasonal peak in river temperatures. While all populations groups demonstrated these general patterns, we documented substantial variability in temperature thresholds and length of average delays across population groups. Although migration delay was related to higher reach mortality, it was also related to lower average daily mortality rates due to the proportional increase in reach passage duration being larger than the associated increase in mortality. Lower daily mortality rates suggest that migration delay could help mitigate the impacts of harsh migration conditions, presumably through the use of thermal refuges, despite prolonged exposure to local fisheries. Future studies tracking individual populations from their migration through reproduction could help illuminate the full extent of the tradeoffs between different migration behaviors.

In 2015, the Pacific marine heat wave, low river flows, and record high water temperatures in the Columbia River Basin contributed to a near-complete failure of the adult migration of endangered Snake River sockeye salmon (Oncorhynchus nerka, NOAA Fisheries 2016). These extreme weather events may become the new normal due to anthropogenic climate change, with catastrophic consequences for endangered species. Existing anthropogenic pressures may amplify vulnerability to climate change, but these potential synergies have rarely been quantified. We examined factors affecting survival of endangered sockeye (Oncorhynchus nerka) and threatened Chinook salmon (O. tshawytscha) as they migrated upstream through eight dams and reservoirs to spawning areas in the Snake River Basin. Our extensive database included histories of 17,279 individual fish that migrated since 2004. A comparison between conditions in 2015 and daily temperatures and flows in a regulated basin forced by output from global climate models showed that 2015 did have many characteristics of projected future mean conditions. To evaluate potential salmon responses, we modeled migration timing and apparent survival under historical and future climate scenarios (2040s). For Chinook salmon, adult survival from the first dam encountered to spawning grounds dropped by 4-15%, depending on the climate scenario. For sockeye, survival dropped by ~80% from their already low levels. Through sensitivity analyses, we observed that the adult sockeye migration would need to shift more than 2 weeks earlier than predicted to maintain survival rates typical of those seen during 2008-2017. Overall, the greater impacts of climate change on adult sockeye compared with adult Chinook salmon reflected differences in life history and environmental sensitivities, which were compounded for sockeye by larger effect sizes from other anthropogenic factors. Compared with Chinook, sockeye was more negatively affected by a history of juvenile transportation and by similar temperatures and flows. The largest changes in temperature and flow were projected to be upstream from the hydrosystem, where direct mitigation through hydrosystem management is not an option. Unfortunately, Snake River sockeye have likely lost much of their adaptive capacity with the loss of the wild population. Further work exploring habitat restoration or additional mitigation actions is urgently needed.

Spatial and temporal patterns in stream temperature are primary factors determining species composition, diversity and productivity in stream ecosystems. The availability of spatially and temporally continuous estimates of stream temperature would improve the ability of biologists to fully explore the effects of stream temperature on biota. Most statistical stream temperature modeling techniques are limited in their ability to account for the influence of variables changing across spatial and temporal gradients. We identified and described important interactions between climate and spatial variables that approximate mechanistic controls on spatiotemporal patterns in stream temperature. With identified relationships we formed models to generate reach-scale basin-wide spatially and temporally continuous predictions of daily mean stream temperature in four Columbia River tributaries watersheds of the Pacific Northwest, USA. Models were validated with a testing dataset composed of completely distinct sites and measurements from different years. While some patterns in residuals remained, testing dataset predictions of selected models demonstrated high accuracy and precision (averaged RMSE for each watershed ranged from 0.85–1.54 °C) and was only 17% higher on average than training dataset prediction error. Aggregating daily predictions to monthly predictions of mean stream temperature reduced prediction error by an average of 23%. The accuracy of predictions was largely consistent across diverse climate years, demonstrating the ability of the models to capture the influences of interannual climatic variability and extend predictions to timeframes with limited temperature logger data. Results suggest that the inclusion of a range of interactions between spatial and climatic variables can approximate dynamic mechanistic controls on stream temperatures.