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Artificial intelligence (AI) represents technologies with human-like cognitive abilities to learn, perform, and make decisions. AI in precision agriculture (PA) enables farmers and farm managers to deploy highly targeted and precise farming practices based on site-specific agroclimatic field measurements. The foundational and applied development of AI has matured considerably over the last 30 years. The time is now right to engage seriously with the ethics and responsible practice of AI for the well-being of farmers and farm managers. In this paper, we identify and discuss both challenges and opportunities for improving farmers' trust in those providing AI solutions for PA. We highlight that farmers' trust can be moderated by how the benefits and risks of AI are perceived, shared, and distributed. We propose four recommendations for improving farmers' trust. First, AI developers should improve model transparency and explainability. Second, clear responsibility and accountability should be assigned to AI decisions. Third, concerns about the fairness of AI need to be overcome to improve human-machine partnerships in agriculture. Finally, regulation and voluntary compliance of data ownership, privacy, and security are needed, if AI systems are to become accepted and used by farmers. National Science Foundation [2202706, 2026431] Published version ACKNOWLEDGMENTS This research is based upon work supported by the National Science Foundation under Grant Number 2202706 and 2026431. The authors thank the anonymous reviewers and journal editor for their valuable suggestions to improve the manuscript. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Centuries of human development have altered the connectivity of rivers, adversely impacting ecosystems and the services provided. Significant investments in natural resource projects are made annually with the goal to restore function to degraded rivers and floodplains and protect freshwater resources. Yet restoration projects often fall short of their objectives, in part, due to the lack of systems-based, strategic planning. To evaluate channel-floodplain (dis)connectivity and erosion/incision hazard at the regional scale, we calculate Specific Stream Power (SSP), an estimate of the energy of a river, using a topographically-based, low-complexity hydraulic model. Other basin-wide SSP modeling approaches neglect reach-specific geometric information embedded in Digital Elevation Models. Our approach leverages this information to generate reach-specific SSP-flow curves. We extract measures from these curves that describe (dis)connected floodwater storage capacity and erosion hazard at individual design storm flood stages and demonstrate how these measures may be used to identify watershed-scale patterns in connectivity. We show proof-of-concept using 25 reaches in the Mad River watershed in central Vermont and demonstrate that the SSP results have acceptable agreement with a well-calibrated process-based model (2D Hydraulic Engineering Center’s River Analysis System) across a broad range of design events. While systems-based planning of regional restoration and conservation activities has been limited largely due to computational and human resource requirements, measures derived from low complexity models can provide an overview of reach-scale conditions at the regional level and aid planners in identifying areas for further restoration and/or conservation assessments.

Background Understanding uncertainty in participatory decision-making requires scientific attention to interaction between what actually happens when patients, families and clinicians engage one another in conversation and the multi-level contexts in which these occur. Achieving this understanding will require conceptually grounded and scalable methods for use in large samples of people representing diversity in cultures, speaking and decision-making norms, and clinical situations. Discussion Here, we focus on serious illness and describe Conversational Stories as a scalable and conceptually grounded framework for characterizing uncertainty expression in these clinical contexts. Using actual conversations from a large direct-observation cohort study, we demonstrate how natural language processing and unsupervised machine learning methods can reveal underlying types of uncertainty stories in serious illness conversations. Conclusions Conversational Storytelling offers a meaningful analytic framework for scalable computational methods to study uncertainty in healthcare conversations.

Chagas disease is a lethal, neglected tropical disease. Unfortunately, aggressive insecticide-spraying campaigns have not been able to eliminate domestic infestation of Triatoma dimidiata, the native vector in Guatemala. To target interventions toward houses most at risk of infestation, comprehensive socioeconomic and entomologic surveys were conducted in two towns in Jutiapa, Guatemala. Given the exhaustively large search space associated with combinations of risk factors, traditional statistics are limited in their ability to discover risk factor interactions. Two recently developed statistical evolutionary algorithms, specifically designed to accommodate risk factor interactions and heterogeneity, were applied to this large combinatorial search space and used in tandem to identify sets of risk factor combinations associated with infestation. The optimal model includes 10 risk factors in what is known as a third-order disjunctive normal form (i.e., infested households have chicken coops AND deteriorated bedroom walls OR an accumulation of objects AND dirt floors AND total number of occupants ≥ 5 AND years of electricity ≥ 5 OR poor hygienic condition ratings AND adobe walls AND deteriorated walls AND dogs). Houses with dirt floors and deteriorated walls have been reported previously as risk factors and align well with factors currently targeted by Ecohealth interventions to minimize infestation. However, the tandem evolutionary algorithms also identified two new socioeconomic risk factors (i.e., households having many occupants and years of electricity ≥ 5). Identifying key risk factors may help with the development of new Ecohealth interventions and/or reduce the survey time needed to identify houses most at risk.

Objective Serious illness conversations are complex clinical narratives that remain poorly understood. Natural Language Processing (NLP) offers new approaches for identifying hidden patterns within the lexicon of stories that may reveal insights about the taxonomy of serious illness conversations. Methods We analyzed verbatim transcripts from 354 consultations involving 231 patients and 45 palliative care clinicians from the Palliative Care Communication Research Initiative. We stratified each conversation into deciles of "narrative time" based on word counts. We used standard NLP analyses to examine the frequency and distribution of words and phrases indicating temporal reference, illness terminology, sentiment and modal verbs (indicating possibility/desirability). Results Temporal references shifted steadily from talking about the past to talking about the future over deciles of narrative time. Conversations progressed incrementally from "sadder" to "happier" lexicon; reduction in illness terminology accounted substantially for this pattern. We observed the following sequence in peak frequency over narrative time: symptom terms, treatment terms, prognosis terms and modal verbs indicating possibility. Conclusions NLP methods can identify narrative arcs in serious illness conversations. Practice implications Fully automating NLP methods will allow for efficient, large scale and real time measurement of serious illness conversations for research, education and system re-design.

Forests play a key part in the water cycle, so both planting and removing forests can affect streamflow. In a recent Article1, Evaristo and McDonnell used a gradient-boosted-tree model to conclude that streamflow response to forest removal is predominantly controlled by the potential water storage in the landscape, and that removing the world’s forests would contribute an additional 34,098 km3 yr−1 to streamflow worldwide, nearly doubling global river flow. Here we report several problems with Evaristo and McDonnell’s1 database, their model, and the extrapolation of their results to the continental and global scale. The main results of the paper1 remain unsubstantiated, because they rely on a database with multiple errors and a model that fails validation tests.

Floodplain reconnection and wetland restoration projects are increasingly implemented to enhance flood resiliency, and these nature-based solutions can also achieve co-benefits of nutrient storage and improved habitats. Considering the multiple and sometimes incompatible objectives of stakeholders for uses of riverside lands, a decision-support tool linked to a hydraulic model would enable planners to simulate floodplain restoration scenarios while also quantifying and assessing the trade-offs between the stakeholder objectives to arrive at optimal restoration designs. We illustrate a simple ranking approach using an n-dimensional objective function to represent key stakeholders engaged in restoration. We applied our approach in a watershed in central Vermont (USA) that has been identified by regional and state-level stakeholders as an important location to mitigate flooding damages but also to improve water quality - all within a context of increasing development pressures on riparian lands and limited financial resources to accomplish restoration. Eleven different floodplain reconnection and wetland restoration modifications were combined in six scenarios and simulated with 2D Hydrologic Engineering Center's River Analysis System (2D HEC-RAS), along with a baseline (no-action) scenario. Only modest attenuation of peak flows for 2-, 25-, 50- and 100-year design storms was achieved by the floodplain restoration scenarios due to the steep setting, and flashy nature of the watershed. Yet, several scenarios of floodplain reconnection projects more than met the necessary annual phosphorus load reductions targeted under a Total Maximum Daily Load implementation plan. Our approach provided planners with a ranking of restoration scenarios that best met multiple stakeholder objectives and allowed effectiveness of alternate design scenarios to be quantified, justified, and visualized to promote consensus decision-making.

The shallow and deep hypothesis suggests that stream concentration-discharge (CQ) relationships are shaped by distinct source waters from different depths. Under this hypothesis, baseflows are typically dominated by groundwater and mostly reflect groundwater chemistry, whereas high flows are typically dominated by shallow soil water and mostly reflect soil water chemistry. Aspects of this hypothesis draw on applications like end member mixing analyses and hydrograph separation, yet direct data support for the hypothesis remains scarce. This work tests the shallow and deep hypothesis using co-located measurements of soil water, groundwater, and streamwater chemistry at two intensively monitored sites, the W-9 catchment at Sleepers River (Vermont, United States) and the Hafren catchment at Plynlimon (Wales). At both sites, depth profiles of subsurface water chemistry and stream CQ relationships for the 10 solutes analyzed are broadly consistent with the hypothesis. Solutes that are more abundant at depth (e.g., calcium) exhibit dilution patterns (concentration decreases with increasing discharge). Conversely, solutes enriched in shallow soils (e.g., nitrate) generally exhibit flushing patterns (concentration increases with increasing discharge). The hypothesis may hold broadly true for catchments that share such biogeochemical stratifications in the subsurface. Soil water and groundwater chemistries were estimated from high- and low-flow stream chemistries with average relative errors ranging from 24 to 82%. This indicates that streams mirror subsurface waters: stream chemistry can be used to infer scarcely measured subsurface water chemistry, especially where there are distinct shallow and deep end members.

The Acute Care Surgery model has been implemented by many hospitals in the United States. As complex adaptive systems, healthcare systems are composed of many interacting elements that respond to intrinsic and extrinsic inputs. Systems level analysis may reveal the underlying organizational structure of tactical block allocations like the Acute Care Surgery model. The purpose of this study is to demonstrate one method to identify a key characteristic of complex adaptive systems in the perioperative services. Start and end times for all surgeries performed at the University of Vermont Medical Center OR1 were extracted for two years prior to the transition to an Acute Care Surgery service and two years following the transition. Histograms were plotted for the inter-event times calculated from the difference between surgical cases. A power law distribution was fit to the post-transition histogram. The Kolmogorov–Smirnov test for goodness-of-fit at 95% level of significance shows the histogram plotted from post-transition inter-event times follows a power law distribution (K-S = 0.088, p = 0.068), indicating a Complex Adaptive System. Our analysis demonstrates that the strategic decision to create an Acute Care Surgery service has direct implications on tactical and operational processes in the perioperative services. Elements of complex adaptive systems can be represented by a power law distributions and similar methods may be applied to identify other processes that operate as complex adaptive systems in perioperative care. To make sustained improvements in the perioperative services, focus on manufacturing-based interventions such as Lean Six Sigma should instead be shifted towards the complex interventions that modify system-specific behaviors described by complex adaptive system principles when power law relationships are present.

Context Human connection can reduce suffering and facilitate meaningful decision-making amid the often terrifying experience of hospitalization for advanced cancer. Some conversational pauses indicate human connection, but we know little about their prevalence, distribution or association with outcomes. Purpose To describe the epidemiology of Connectional Silence during serious illness conversations in advanced cancer. Methods We audio-recorded 226 inpatient palliative care consultations at two academic centers. We identified pauses lasting 2+ seconds and distinguished Connectional Silences from other pauses, sub-categorized as either Invitational (ICS) or Emotional (ECS). We identified treatment decisional status pre-consultation from medical records and post-consultation via clinicians. Patients self-reported quality-of-life before and one day after consultation. Results Among all 6769 two-second silences, we observed 328 (4.8%) ECS and 240 (3.5%) ICS. ECS prevalence was associated with decisions favoring fewer disease-focused treatments (ORadj: 2.12; 95% CI: 1.12, 4.06). Earlier conversational ECS was associated with improved quality-of-life (p = 0.01). ICS prevalence was associated with clinicians' prognosis expectations. Conclusions Connectional Silences during specialist serious illness conversations are associated with decision-making and improved patient quality-of-life. Further work is necessary to evaluate potential causal relationships. Practice implications Pauses offer important opportunities to advance the science of human connection in serious illness decision-making.

Understanding and predicting catchment responses to a regional disturbance is difficult because catchments are spatially heterogeneous systems that exhibit unique moderating characteristics. Changes in precipitation composition in the Northeastern U.S. is one prominent example, where reduction in wet and dry deposition is hypothesized to have caused increased dissolved organic carbon (DOC) export from many northern hemisphere forested catchments; however, findings from different locations contradict each other. Using shifts in acid deposition as a test case, we illustrate an iterative “process and pattern” approach to investigate the role of catchment characteristics in modulating the steam DOC response. We use a novel dataset that integrates regional and catchment-scale atmospheric deposition data, catchment characteristics and co-located stream Q and stream chemistry data. We use these data to investigate opportunities and limitations of a pattern-to-process approach where we explore regional patterns of reduced acid deposition, catchment characteristics and stream DOC response and specific soil processes at select locations. For pattern investigation, we quantify long-term trends of flow-adjusted DOC concentrations in stream water, along with wet deposition trends in sulfate, for USGS headwater catchments using Seasonal Kendall tests and then compare trend results to catchment attributes. Our investigation of climatic, topographic, and hydrologic catchment attributes vs. directionality of DOC trends suggests soil depth and catchment connectivity as possible modulating factors for DOC concentrations. This informed our process-to-pattern investigation, in which we experimentally simulated increased and decreased acid deposition on soil cores from catchments of contrasting long-term DOC response [Sleepers River Research Watershed (SRRW) for long-term increases in DOC and the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO) for long-term decreases in DOC]. SRRW soils generally released more DOC than SSHCZO soils and losses into recovery solutions were higher. Scanning electron microscope imaging indicates a significant DOC contribution from destabilizing soil aggregates mostly from hydrologically disconnected landscape positions. Results from this work illustrate the value of an iterative process and pattern approach to understand catchment-scale response to regional disturbance and suggest opportunities for further investigations.

The Acute Care Surgery model has been implemented by many hospitals in the United States. As complex adaptive systems, healthcare systems are composed of many interacting elements that respond to intrinsic and extrinsic inputs. Systems level analysis may reveal the underlying organizational structure of tactical block allocations like the Acute Care Surgery model. The purpose of this study is to demonstrate one method to identify a key characteristic of complex adaptive systems in the perioperative services. Start and end times for all surgeries performed at the University of Vermont Medical Center OR1 were extracted for two years prior to the transition to an Acute Care Surgery service and two years following the transition. Histograms were plotted for the inter-event times calculated from the difference between surgical cases. A power law distribution was fit to the post-transition histogram. The Kolmogorov-Smirnov test for goodness-of-fit at 95% level of significance shows the histogram plotted from post-transition inter-event times follows a power law distribution (K-S = 0.088, p = 0.068), indicating a Complex Adaptive System. Our analysis demonstrates that the strategic decision to create an Acute Care Surgery service has direct implications on tactical and operational processes in the perioperative services. Elements of complex adaptive systems can be represented by a power law distributions and similar methods may be applied to identify other processes that operate as complex adaptive systems in perioperative care. To make sustained improvements in the perioperative services, focus on manufacturing-based interventions such as Lean Six Sigma should instead be shifted towards the complex interventions that modify system-specific behaviors described by complex adaptive system principles when power law relationships are present.

Excessive streambank erosion is a significant source of fine sediments and associated nutrients in many river systems as well as poses risk to infrastructure. Geomorphic change detection using high-resolution topographic data is a useful method for monitoring the extent of bank erosion along river corridors. Recent advances in an unmanned aircraft system (UAS) and structure from motion (SfM) photogrammetry techniques allow acquisition of high-resolution topographic data, which are the methods used in this study. To evaluate the effectiveness of UAS-based photogrammetry for monitoring bank erosion, a fixed-wing UAS was deployed to survey 20 km of river corridors in central Vermont, in the northeastern United States multiple times over a two-year period. Digital elevation models (DEMs) and DEMs of difference allowed quantification of volumetric changes along selected portions of the survey area where notable erosion occurred. Results showed that UAS was capable of collecting high-quality topographic data at fine resolutions even along vegetated river corridors provided that the surveys were conducted in early spring, after snowmelt but prior to summer vegetation growth. Longer term estimates of streambank movements using the UAS showed good comparison to previously collected airborne lidar surveys and allowed reliable quantification of significant geomorphic changes along rivers.

Improved stormwater management for the protection of water resources requires bottom-up stewardship from landowners, including adoption of Green Stormwater Infrastructure (GSI). We use a statewide survey of Vermont paired with a cross-scale and spatial analysis to evaluate the influence of interacting spatial, social, and physical factors on residential intention to adopt GSI across a complex social-ecological landscape. Specifically, we focus on how three GSI practices, (“rain garden (bio retention),” “infiltration trenches,” and “actively divert roof runoff to a rain barrel/lawn/garden instead of the street/sewer”) vary with barriers to adoption, and household attributes across stormwater contexts from the household to watershed scale. Private landowners, who may be motivated more by on-site and neighborhood stormwater problems, may gravitate toward practices like infiltration trenches compared with practices (e.g., rain gardens) perceived to serve stormwater function over larger areas. Diversion of roof runoff was found to be more likely to be a part of a larger assembly of green behaviors. Improved stormwater management outcomes at the watershed, town, neighborhood, and household levels depend on adaptive approaches and adjusting strategies along the rural-urban gradient, across the bio-physical landscape, and according to varying norms and institutional arrangements.

About 20–25% of dengue virus (DENV) infections become symptomatic ranging from self-limiting fever to shock. Immune gene expression changes during progression to severe dengue have been documented in hospitalized patients; however, baseline or kinetic information is difficult to standardize in natural infection. Here we profile the host immunotranscriptome response in humans before, during, and after infection with a partially attenuated rDEN2Δ30 challenge virus (ClinicalTrials.gov NCT02021968). Inflammatory genes including type I interferon and viral restriction pathways are induced during DENV2 viremia and return to baseline after viral clearance, while others including myeloid, migratory, humoral, and growth factor immune regulation factors pathways are found at non-baseline levels post-viremia. Furthermore, pre-infection baseline gene expression is useful to predict rDEN2Δ30-induced immune responses and the development of rash. Our results suggest a distinct immunological profile for mild rDEN2Δ30 infection and offer new potential biomarkers for characterizing primary DENV infection., Dengue virus causes a range of inflammatory pathology but understanding critical phases of the infection during human infection has been challenging. Here the author’s present immunotranscriptomic changes during the acute and clearance phases of a dengue virus serotype 2 human challenge model.

Harmful Algal Blooms (HABs) are a major environmental problem worldwide. Apart from their adverse effects on aquatic habitat, and possible economic losses, they also pose a serious threat to public health. Future climatic uncertainties that include possible shifts in patterns of climatic variables are points of concern in terms of how such changes would affect the development, growth, and duration of HABs. Weather whiplash, abrupt dry-to-wet or wet-to-dry condition transitions, is one of these shifts in climatic patterns and despite its potential environmental impacts, few studies have examined the implications of such changes on lake water quality. Lake Champlain, located on the US-Canada border, has repeatedly faced cyanobacterial HABs predominately in its shallow bays. The aim of the current work is to (i) investigate potential changes in the persistence of hydroclimatic variables (precipitation and temperature) and (ii) examine their effects on cyanobacterial HABs in the lake’s shallow Missisquoi Bay. Our approach focuses on short-term persistence (STP) shifts over different timescales (daily, monthly, seasonal, and annual). STP scenarios that capture these plausible shifts are constructed using projected climate scenarios for the period 2000-2040. An Integrated Assessment Model that simulates the Missisquoi Basin’s physical processes, including watershed hydrology, management, and the Missisquoi Bay’s water quality dynamics, is utilized to run the modeled STP scenarios for each timescale. The determination of changes in STP through a scenario-based approach offers a framework to rigorously investigate the effects of persistence at different timescales on lake cyanobacterial HABs.

The Acute Care Surgery model has been widely adopted by hospitals across the United States, with Acute Care Surgery services managing Emergency General Surgery patients that were previously being treated by General Surgery. In this analysis, we evaluate the impact of an Acute Care Surgery service model on General Surgery at the University of Vermont Medical Center using three metrics: under-utilized time, spillover time, and a financial ratio of work Relative Value Units over clinical Full Time Equivalents. These metrics are evaluated and used to identify three-dimensional Pareto optimality of General Surgery prior to and after the October 2015 tactical allocation to the Acute Care Surgery model. Our analysis was further substantiated using a Markov Chain Monte Carlo model for Bayesian Inference. We applied multi-objective Pareto and Bayesian breakpoint analysis to three operating room metrics to assess the impact of new operating room management decisions. In the two-dimensional space of Fig. 2, panel a), the post-tactical allocation front lies closer to the origin representing more optimal solutions for productivity and under-utilized time. The post-tactical allocation front is also closer to the origin for productivity and spillover time as shown in the two-dimensional space of Fig. 2, panel b). The results of the three-dimensional multi-objective analysis of Fig. 3 illustrate that the GS post-tactical allocation Pareto-surface is contained within a much smaller volume of space than the GS pre-tactical allocation Pareto-surface. The post-tactical allocation Pareto-surface is slightly lower along the z-axis, representing lower productivity than the pre-tactical allocation surface. This methodology might contribute to the external benchmarking and monitoring of perioperative services by visualizing the operational implications following tactical decisions in operating room management.

The authors would like to make the following correction for the published paper [...]

Many recent studies have attributed the observed variability of cyanobacteria blooms to meteorological drivers and have projected blooms with worsening societal and ecological impacts under future climate scenarios. Nonetheless, few studies have jointly examined their sensitivity to projected changes in both precipitation and temperature variability. Using an Integrated Assessment Model (IAM) of Lake Champlain's eutrophic Missisquoi Bay, we demonstrate a factorial design approach for evaluating the sensitivity of concentrations of chlorophyll a (chl-a), a cyanobacteria surrogate, to global climate model-informed changes in the central tendency and variability of daily precipitation and air temperature. An Analysis of Variance (ANOVA) and multivariate contour plots highlight synergistic effects of these climatic changes on exceedances of the World Health Organization's moderate 50 μg/L concentration threshold for recreational contact. Although increased precipitation produces greater riverine total phosphorus loads, warmer and drier scenarios produce the most severe blooms due to the greater mobilization and cyanobacteria uptake of legacy phosphorus under these conditions. Increases in daily precipitation variability aggravate blooms most under warmer and wetter scenarios. Greater temperature variability raises exceedances under current air temperatures but reduces them under more severe warming when water temperatures exceed optimal values for cyanobacteria growth more often. Our experiments, controlled for wind-induced changes to lake water quality, signal the importance of larger summer runoff events for curtailing bloom growth through reductions of water temperature, sunlight penetration and stratification. Finally, the importance of sequences of wet and dry periods in generating cyanobacteria blooms motivates future research on bloom responses to changes in interannual climate persistence.

The Acute Care Surgery model has been widely adopted by hospitals across the United States, with Acute Care Surgery services managing Emergency General Surgery patients that were previously being treated by General Surgery. In this analysis, we evaluate the impact of an Acute Care Surgery service model on General Surgery at the University of Vermont Medical Center using three metrics: under-utilized time, spillover time, and a financial ratio of work Relative Value Units over clinical Full Time Equivalents. These metrics are evaluated and used to identify three-dimensional Pareto optimality of General Surgery prior to and after the October 2015 tactical allocation to the Acute Care Surgery model. Our analysis was further substantiated using a Markov Chain Monte Carlo model for Bayesian Inference. We applied multi-objective Pareto and Bayesian breakpoint analysis to three operating room metrics to assess the impact of new operating room management decisions. In the two-dimensional space of Fig. 2, panel a), the post-tactical allocation front lies closer to the origin representing more optimal solutions for productivity and under-utilized time. The post-tactical allocation front is also closer to the origin for productivity and spillover time as shown in the two-dimensional space of Fig. 2, panel b). The results of the three-dimensional multi-objective analysis of Fig. 3 illustrate that the GS post-tactical allocation Pareto-surface is contained within a much smaller volume of space than the GS pre-tactical allocation Pareto-surface. The post-tactical allocation Pareto-surface is slightly lower along the z-axis, representing lower productivity than the pre-tactical allocation surface. This methodology might contribute to the external benchmarking and monitoring of perioperative services by visualizing the operational implications following tactical decisions in operating room management.

The motivation for this study is the damage caused to over 300 long-span bridges in Vermont in 2011 by Tropical Storm Irene, which is part of the Hurricane Irene system. This study of the ...

Context Advancing the science of serious illness communication requires methods for measuring characteristics of conversations in large studies. Understanding which characteristics predict clinically important outcomes can help prioritize attention to scalable measure development. Objectives To understand whether audibly recognizable expressions of distressing emotion during palliative care serious illness conversations are associated with ratings of patient experience or six-month enrollment in hospice. Methods We audiorecorded initial palliative care consultations involving 231 hospitalized people with advanced cancer at two large academic medical centers. We coded conversations for expressions of fear, anger, and sadness. We examined the distribution of these expressions and their association with pre/post ratings of feeling heard and understood and six-month hospice enrollment after the consultation. Results Nearly six in 10 conversations included at least one audible expression of distressing emotion (59%; 137 of 231). Among conversations with such an expression, fear was the most prevalent (72%; 98 of 137) followed by sadness (50%; 69 of 137) and anger (45%; 62 of 137). Anger expression was associated with more disease-focused end-of-life treatment preferences, pre/post consultation improvement in feeling heard and understood and lower six-month hospice enrollment. Fear was strongly associated with preconsultation patient ratings of shorter survival expectations. Sadness did not exhibit strong association with patient descriptors or outcomes. Conclusion Fear, anger, and sadness are commonly expressed in hospital-based palliative care consultations with people who have advanced cancer. Anger is an epidemiologically useful predictor of important clinical outcomes.

Successful adaptation to global climate change and enhancement of agricultural watersheds' resilience requires widespread use of Nutrient Best Management Practices (NBMPs) by farms of all sizes. In the US, adoption of many NBMP practices is voluntary and insufficient to achieve local and downstream conservation objectives. Despite evidence that both social-psychological factors and socio-economic factors influence farmer decision-making, very few studies of farmers' decision-making related to NBMP adoption combine these two factor groups in a theoretically rigorous way. To better understand farmers' management decisions, we test the social-psychological Theory of Planned Behavior (TPB) to determine the relative influence of attitudes, perceived social norms, and perceived behavioral control on adoption of nine NBMPs. A survey was designed by the research team and implemented by the U.S. Department of Agriculture-National Agricultural Statistics Service (USDA-NASS) in 2013, and replicated in 2016, on a stratified sample of 129 farmers (including panel data on 56 farmers). Farmers were located in the Missisquoi, and Lamoille River watersheds of the Lake Champlain Basin in the Northeast region of the United States. Survey responses revealed variation in past adoption of NBMPs was sensitive to practice type and farm size. We developed nine weighted structural equation models to test endogenous (social-psychological) and exogenous (policy, economic and demographic) predictors of farmer intention to adopt NBMPs. We found that perceived behavioral control had the largest effect size and strongest statistical significance on the farmers' expressed intentions to adopt NBMPs in the future. For a subset of NBMPs, perceived social norms and farmer attitudes toward these NBMPs were each also significant drivers of intention to adopt individual practices. Among the exogenous variables, we found that large farm size, college education, and having a conservation easement all had a positive influence on farmers' intention to adopt NBMPs. This study suggests that for widespread adoption of NBMPs, environmental managers, policy makers, and program developers should be attentive to farmers' perceived behavioral control, and support the design and execution of outreach and technical assistance programs that build on drivers of farmers' decision making.

This paper presents the first time series clustering benchmark utilizing all time series datasets currently available in the University of California Riverside (UCR) archive -- the state of the art repository of time series data. Specifically, the benchmark examines eight popular clustering methods representing three categories of clustering algorithms (partitional, hierarchical and density-based) and three types of distance measures (Euclidean, dynamic time warping, and shape-based). We lay out six restrictions with special attention to making the benchmark as unbiased as possible. A phased evaluation approach was then designed for summarizing dataset-level assessment metrics and discussing the results. The benchmark study presented can be a useful reference for the research community on its own; and the dataset-level assessment metrics reported may be used for designing evaluation frameworks to answer different research questions., Typos corrected, figures resolution changed

Dissolved air flotation (DAF) separates phosphorus (P)-rich fine solids from anaerobically digested dairy manure, creating opportunities to export surplus P to the marketplace as a bagged plant food product. Seedlings of tomato and marigold were amended at various volume per volume (v/v) ratios with plant foods consisting of fine solids upcycled (i.e., transformed into a higher quality product) by drying and blending with other organic residuals. A plate competition assay was conducted to assess the fine solids&rsquo, potential to suppress the plant pathogen Rhizoctonia solani. Plant foods were comprised of 2.0&ndash, 2.1% N, 0.8&ndash, 0.9% P and 0.6&ndash, 0.8% K. Extractions indicated that plant foods contained a mixture of plant-available and slow-release P. At 6% v/v plant food, dry biomass of marigold and tomato were six-times greater than the unamended control and not significantly different from a market alternative treatment. Fine solids exhibited negligible potential to suppress R. solani. This study indicates that DAF-separated fine solids could be used to support horticulture, providing information for design of a circular economy approach to dairy manure nutrient management. Life cycle assessment and business model development for this nutrient recovery strategy are necessary next steps to further guide sustainability efforts.

Lateral carbon flux through river networks is an important and poorly understood component of the global carbon budget. This work investigates how temperature and hydrology control the production and export of dissolved organic carbon (DOC) in the Susquehanna Shale Hills Critical Zone Observatory in Pennsylvania, USA. Using field measurements of daily stream discharge, evapotranspiration, and stream DOC concentration, we calibrated the catchment-scale biogeochemical reactive transport model BioRT-Flux-PIHM (Biogeochemical Reactive Transport–Flux–Penn State Integrated Hydrologic Model, BFP), which met the satisfactory standard of a Nash–Sutcliffe efficiency (NSE) value greater than 0.5. We used the calibrated model to estimate and compare the daily DOC production rates (Rp; the sum of the local DOC production rates in individual grid cells) and export rate (Re; the product of the concentration and discharge at the stream outlet, or load). Results showed that daily Rp varied by less than an order of magnitude, primarily depending on seasonal temperature. In contrast, daily Re varied by more than 3 orders of magnitude and was strongly associated with variation in discharge and hydrological connectivity. In summer, high temperature and evapotranspiration dried and disconnected hillslopes from the stream, driving Rp to its maximum but Re to its minimum. During this period, the stream only exported DOC from the organic-poor groundwater and from organic-rich soil water in the swales bordering the stream. The DOC produced accumulated in hillslopes and was later flushed out during the wet and cold period (winter and spring) when Re peaked as the stream reconnected with uphill and Rp reached its minimum. The model reproduced the observed concentration–discharge (C–Q) relationship characterized by an unusual flushing–dilution pattern with maximum concentrations at intermediate discharge, indicating three end-members of source waters. A sensitivity analysis indicated that this nonlinearity was caused by shifts in the relative contribution of different source waters to the stream under different flow conditions. At low discharge, stream water reflected the chemistry of organic-poor groundwater; at intermediate discharge, stream water was dominated by the organic-rich soil water from swales; at high discharge, the stream reflected uphill soil water with an intermediate DOC concentration. This pattern persisted regardless of the DOC production rate as long as the contribution of deeper groundwater flow remained low ( % of the streamflow). When groundwater flow increased above 18 %, comparable amounts of groundwater and swale soil water mixed in the stream and masked the high DOC concentration from swales. In that case, the C–Q patterns switched to a flushing-only pattern with increasing DOC concentration at high discharge. These results depict a conceptual model that the catchment serves as a producer and storage reservoir for DOC under hot and dry conditions and transitions into a DOC exporter under wet and cold conditions. This study also illustrates how different controls on DOC production and export – temperature and hydrological flow paths, respectively – can create temporal asynchrony at the catchment scale. Future warming and increasing hydrological extremes could accentuate this asynchrony, with DOC production occurring primarily during dry periods and lateral export of DOC dominating in major storm events.

Given the variable biogeochemical, physical, and hydrological processes driving fluvial sediment and nutrient export, the water science and management communities need data-driven methods to identify regions prone to production and transport under variable hydrometeorological conditions. We use Bayesian analysis to segment concentration-discharge linear regression models for total suspended solids (TSS) and particulate and dissolved phosphorus (PP, DP) using 22 years of monitoring data from 18 Lake Champlain watersheds. Bayesian inference was leveraged to estimate segmented regression model parameters and identify threshold position. The identified threshold positions demonstrated a considerable range below and above the median discharge—which has been used previously as the default breakpoint in segmented regression models to discern differences between pre and post-threshold export regimes. We then applied a Self-Organizing Map (SOM), which partitioned the watersheds into clusters of TSS, PP, and DP export regimes using watershed characteristics, as well as Bayesian regression intercepts and slopes. A SOM defined two clusters of high-flux basins, one where PP flux was predominantly episodic and hydrologically driven; and another in which the sediment and nutrient sourcing and mobilization were more bimodal, resulting from both hydrologic processes at post-threshold discharges and reactive processes (e.g., nutrient cycling or lateral/vertical exchanges of fine sediment) at prethreshold discharges. A separate DP SOM defined two high-flux clusters exhibiting a bimodal concentration-discharge response, but driven by differing land use. Our novel framework shows promise as a tool with broad management application that provides insights into landscape drivers of riverine solute and sediment export.