Predicting climate change impacts on poikilotherms using physiologically guided species abundance models.

Poikilothermic animals comprise most species on Earth and are especially sensitive to changes in environmental temperatures. Species conservation in a changing climate relies upon predictions of species responses to future conditions, yet predicting species responses to climate change when temperatures exceed the bounds of observed data is fraught with challenges. We present a physiologically guided abundance (PGA) model that combines observations of species abundance and environmental conditions with laboratory-derived data on the physiological response of poikilotherms to temperature to predict species geographical distributions and abundance in response to climate change. The model incorporates uncertainty in laboratory-derived thermal response curves and provides estimates of thermal habitat suitability and extinction probability based on site-specific conditions. We show that temperature-driven changes in distributions, local extinction, and abundance of cold, cool, and warm-adapted species vary substantially when physiological information is incorporated. Notably, cold-adapted species were predicted by the PGA model to be extirpated in 61% of locations that they currently inhabit, while extirpation was never predicted by a correlative niche model. Failure to account for species-specific physiological constraints could lead to unrealistic predictions under a warming climate, including underestimates of local extirpation for cold-adapted species near the edges of their climate niche space and overoptimistic predictions of warm-adapted species.

An electrocardiogram-based AI algorithm for early detection of pulmonary hypertension.

BACKGROUND: Early diagnosis of pulmonary hypertension (PH) is critical for effective treatment and management. We aimed to develop and externally validate an artificial intelligence algorithm that could serve as a PH screening tool, based on analysis of a standard 12-lead electrocardiogram (ECG). METHODS: The PH Early Detection Algorithm (PH-EDA) is a convolutional neural network developed using retrospective ECG voltage-time data, with patients classified as "PH-likely" or "PH-unlikely" (controls) based on right heart catheterisation or echocardiography. In total, 39 823 PH-likely patients and 219 404 control patients from Mayo Clinic were randomly split into training (48%), validation (12%), and test (40%) sets. ECGs taken within 1 month of PH diagnosis (diagnostic dataset) were used to train the PH-EDA at Mayo Clinic. Performance was tested on diagnostic ECGs within the test sets from Mayo Clinic (n=16 175/87 998 PH-likely/controls) and Vanderbilt University Medical Center (VUMC; n=6045/24 256 PH-likely/controls). Performance was also tested on ECGs taken 6-18 months (pre-emptive dataset), and up to 5 years prior to a PH diagnosis at both sites. RESULTS: Performance testing yielded an area under the receiver operating characteristic curve (AUC) of 0.92 and 0.88 in the diagnostic test set at Mayo Clinic and VUMC, respectively, and 0.86 and 0.81, respectively, in the pre-emptive test set. The AUC remained a minimum of 0.79 at Mayo Clinic and 0.73 at VUMC up to 5 years before diagnosis. CONCLUSION: The PH-EDA can detect PH at diagnosis and 6-18 months prior, demonstrating the potential to accelerate diagnosis and management of this debilitating disease.

Retrospective study of propionic acidemia using natural language processing in Mayo Clinic electronic health record data.

BACKGROUND: Propionic acidemia (PA) is a rare autosomal recessive organic acidemia that classically presents within the first days of life with a metabolic crisis or via newborn screening and is confirmed with laboratory tests. Limited data exist on the natural history of patients with PA describing presentation, treatments, and clinical outcomes. OBJECTIVE: To retrospectively describe the natural history of patients with PA in a clinical setting from a real-world database using both structured and unstructured electronic health record (EHR) data using novel data extraction techniques in a unique care setting. DESIGN/METHODS: This retrospective study used EHR data to identify patients with PA seen at the Mayo Clinic. Unstructured clinical text (medical notes, pathology reports) were analyzed using augmented curation natural language processing models to enhance analysis of data extracted by structured data fields (International Classification of Diseases 9th or 10th revision [ICD-9/-10] codes, Current Procedural Terminology [CPT] codes, and medication orders). De-identified health records were also manually reviewed by clinical scientists to ensure data accuracy and completeness. The index date was defined as the patient's date of PA diagnosis at the Mayo Clinic. Results were reported as aggregate descriptive statistics relative to patients' index dates. Complications, therapeutic interventions, laboratory tests, procedures, and hospitalization encounters related to PA were described at and within 6 months of the patient's index date, and from medical history available before the index date. RESULTS: In total, 13 patients with PA were identified, with visits occurring from 1998 to 2022. Age at diagnosis ranged from birth to 3 years; age at initial evaluation at the Mayo Clinic ranged from 3 days to 28 years. The mean number of Mayo Clinic outpatient visits was 31 (median duration of care, 2 years). PA-related complications were documented in 85% of patients and included nutritional difficulties (46%), metabolic decompensation events (MDEs; 38%), neurologic abnormalities (38%), and cardiomyopathy (7%). One pair of affected siblings had mild symptoms and no complications or MDEs. All 5 patients with a history of MDEs presented with developmental delays. Among patients with MDEs, the mean frequency of outpatient clinical care visits was 10 per year, and 3 patients required inpatient hospitalization (mean duration, 16 days). The incidence of severe complications was higher among patients with MDEs than those without MDEs. Of the patients with MDEs, 2 experienced crises while receiving treatment at the Mayo Clinic, with 9 total MDEs occurring between the 2 patients. Symptoms at presentation included hyperammonemia (78%), fever and/or decreased nutritional intake (67%), hyperglycemia/hypoglycemia (56%), intercurrent upper respiratory infection and/or lethargy (44%), constipation (33%), altered mental status (33%), and cough (33%). CONCLUSIONS: This study highlights the range and frequency of clinical outcomes experienced by patients with PA and demonstrates the clinical burden of MDEs.

Estrogenic activity response to best management practice implementation in agricultural watersheds in the Chesapeake Bay watershed.

Best management practices (BMPs) have been predominantly used throughout the Chesapeake Bay watershed (CBW) to reduce nutrients and sediments entering streams, rivers, and the bay. These practices have been successful in reducing loads entering the estuary and have shown the potential to reduce other contaminants (pesticides, hormonally active compounds, pathogens) in localized studies and modeled load estimates. However, further understanding of relationships between BMPs and non-nutrient contaminant reductions at regional scales using sampled data would be beneficial. Total estrogenic activity was measured in surface water samples collected over a decade (2008-2018) in 211 undeveloped NHDPlus V2.1 watersheds within the CBW. Bayesian hierarchical modeling between total estrogenic activity and landscape predictors including landcover, runoff, BMP intensity, and a BMP*agriculture intensity interaction term indicates a 96% posterior probability that BMP intensity on agricultural land is reducing total estrogenic activity. Additionally, watersheds with high agriculture and low BMPs had a 49% posterior probability of exceeding an effects-based threshold in aquatic organisms of 1 ng/L but only a 1% posterior probability of exceeding this threshold in high-agriculture, high-BMP watersheds.

Point-of-care testing in pharmacies-An evaluation of the service from the lens of resource-based theory of competitive advantage.

BACKGROUND: Point-of-care tests (POCTs) are innovative services that are increasingly offered in community pharmacies. Assessments of these services should consider their financial sustainability in addition to their effectiveness if they are to be successful over time in a competitive environment. OBJECTIVES: The aim of this research was to review and evaluate the POCT practice innovations literature through the lens of the resource-based theory (RBT) of competitive advantage. DATA SOURCES: Articles describing POCT services were identified systematically through PubMed, exclusively. STUDY SELECTION: All POCT articles in the review met the following inclusion criteria: (1) articles were published after 1999; (2) interventions were pharmacist-led innovations within a community pharmacy; (3) articles described research studies with results; and (4) articles were published in English, Spanish, or Portuguese. DATA EXTRACTION: The RBT was operationalized using a strengths, weaknesses, opportunities, threats matrix and a business model canvas, which were employed to extract and analyze data. Articles were assessed according to the degree to which they articulated elements that the RBT needed to assess their financial sustainability in targeted markets. RESULTS: A total of 36 articles describing POCTs and associated services were included in this review. Most of the studies reported aspects pertaining to the contextual environment of the innovation, value proposition, key activities, partners, and channels of distribution. However, the competitive dimension of the environment, as well as the cost structure and revenue streams, were often neglected in the studies. CONCLUSIONS: The RBT is a widely tested framework that can be used for planning and reporting POCT practice innovations. On the basis of this framework, pharmacists seem to do a good job in describing how to provide POCT but fall short in explaining how these services are sustainable over time.

Small-molecule-based regulation of RNA-delivered circuits in mammalian cells.

Synthetic mRNA is an attractive vehicle for gene therapies because of its transient nature and improved safety profile over DNA. However, unlike DNA, broadly applicable methods to control expression from mRNA are lacking. Here we describe a platform for small-molecule-based regulation of expression from modified RNA (modRNA) and self-replicating RNA (replicon) delivered to mammalian cells. Specifically, we engineer small-molecule-responsive RNA binding proteins to control expression of proteins from RNA-encoded genetic circuits. Coupled with specific modRNA dosages or engineered elements from a replicon, including a subgenomic promoter library, we demonstrate the capability to externally regulate the timing and level of protein expression. These control mechanisms facilitate the construction of ON, OFF, and two-output switches, with potential therapeutic applications such as inducible cancer immunotherapies. These circuits, along with other synthetic networks that can be developed using these tools, will expand the utility of synthetic mRNA as a therapeutic modality.

A novel quantitative framework for riverscape genetics.

Riverscape genetics, which applies concepts in landscape genetics to riverine ecosystems, lack appropriate quantitative methods that address the spatial autocorrelation structure of linear stream networks and account for bidirectional geneflow. To address these challenges, we present a general framework for the design and analysis of riverscape genetic studies. Our framework starts with the estimation of pairwise genetic distance at sample sites and the development of a spatially structured ecological network (SSEN) on which riverscape covariates are measured. We then introduce the novel bidirectional geneflow in riverscapes (BGR) model that uses principles of isolation-by-resistance to quantify the effects of environmental covariates on genetic connectivity, with spatial covariance defined using simultaneous autoregressive models on the SSEN and the generalized Wishart distribution to model pairwise distance matrices arising through a random walk model of geneflow. We highlight the utility of this framework in an analysis of riverscape genetics for brook trout (Salvelinus fontinalis) in north central Pennsylvania, USA. Using the fixation index (FST ) as the measure of genetic distance, we estimated the effects of 12 riverscape covariates on geneflow by evaluating the relative support of eight competing BGR models. We then compared the performance of the top-ranked BGR model to results obtained from comparable analyses using multiple regression on distance matrices (MRM) and the program STRUCTURE. We found that the BGR model had more power to detect covariate effects, particularly for variables that were only partial barriers to geneflow and/or uncommon in the riverscape, making it more informative for assessing patterns of population connectivity and identifying threats to species conservation. This case study highlights the utility of our modeling framework over other quantitative methods in riverscape genetics, particularly the ability to rigorously test hypotheses about factors that influence geneflow and probabilistically estimate the effect of riverscape covariates, including stream flow direction. This framework is flexible across taxa and riverine networks, is easily executable, and provides intuitive results that can be used to investigate the likely outcomes of current and future management scenarios.

Ecological prediction at macroscales using big data: Does sampling design matter?

Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially extensive data set to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8-million-km2 area. We estimated model predictive performance for different subsets of the data set to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non-random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in data sets from some forms of targeted sampling may limit predictive performance, compiling existing spatially extensive data sets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.

Spatial and temporal variation of ecosystem properties at macroscales.

Although spatial and temporal variation in ecological properties has been well-studied, crucial knowledge gaps remain for studies conducted at macroscales and for ecosystem properties related to material and energy. We test four propositions of spatial and temporal variation in ecosystem properties within a macroscale (1000 km's) extent. We fit Bayesian hierarchical models to thousands of observations from over two decades to quantify four components of variation - spatial (local and regional) and temporal (local and coherent); and to model their drivers. We found strong support for three propositions: (1) spatial variation at local and regional scales are large and roughly equal, (2) annual temporal variation is mostly local rather than coherent, and, (3) spatial variation exceeds temporal variation. Our findings imply that predicting ecosystem responses to environmental changes at macroscales requires consideration of the dominant spatial signals at both local and regional scales that may overwhelm temporal signals.

A sustainable business model for comprehensive medication management in a patient-centered medical home.

OBJECTIVES: To develop a sustainable business model for pharmacist-provided comprehensive medication management services in a patient-centered medical home. Secondarily, to evaluate the impact that the pharmacist had on clinical (glycosylated hemoglobin [A1C], low-density lipoprotein [LDL], and blood pressure) and economic (physician productivity and cost avoidance) outcomes. PRACTICE DESCRIPTION: This pilot project took place at the Palmetto Primary Care Physicians Trident office in North Charleston, South Carolina, from October 2013 to September 2014. At the time, the practice employed 5 physicians and 2 nurse practitioners and served more than 20,000 patients. PRACTICE INNOVATION: The pharmacist targeted patients with diabetes, lipid disorders, hypertension, congestive heart failure, obesity, polypharmacy, and treatment regimen nonadherence for his comprehensive medication management services. The pharmacist was available for immediate consultation or referrals by appointment 5 days per week. Services provided by the pharmacist were billed as medication therapy management or "incident to" physician evaluation and management services codes. EVALUATION: Number of patients seen per day, revenue collected from services rendered by the pharmacist, physician productivity and payment, cost avoidance, and health quality metrics (A1C, LDL, and blood pressure) were measured to determine the financial sustainability and clinical impact of the project. RESULTS AND IMPLICATIONS: The pharmacist was able to see an average of 11 patients per day, which was 72% of his capacity. The practice collected about $7400 per month for services rendered by the pharmacist. The average daily payment for services rendered by the physicians in the practice increased by 20.6%. More than 70% of uncontrolled patients had an improvement in clinical outcomes, such as A1C, LDL, and blood pressure. CONCLUSION: This project demonstrates the sustainable business model for embedding a pharmacist into a patient-centered medical home.

Individual behaviour and resource use of thermally stressed brook trout Salvelinus fontinalis portend the conservation potential of thermal refugia.

Individual aggression and thermal refuge use were monitored in brook trout Salvelinus fontinalis in a controlled laboratory to determine how fish size and personality influence time spent in forage and thermal habitat patches during periods of thermal stress. On average, larger and more exploratory fish initiated more aggressive interactions and across all fish there was decreased aggression at warmer temperatures. Individual personality did not explain changes in aggression or habitat use with increased temperature; however, larger individuals initiated comparatively fewer aggressive interactions at warmer temperatures. Occupancy of forage patches generally declined as ambient stream temperatures approached critical maximum and fish increased thermal refuge use, with a steeper decline in forage patch occupancy observed in larger fish. These findings suggest that larger individuals may be more vulnerable to stream temperature rise. Importantly, even at thermally stressful temperatures, all fish periodically left the thermal refuge to forage. This indicates that the success of refugia at increasing population survival during periods of stream temperature rise may depend on the location of thermal refugia relative to forage locations within the larger habitat mosaic. These results provide insights into the potential for thermal refugia to improve population survival and can be used to inform predictions of population vulnerability to climate change.

Probabilistic measures of climate change vulnerability, adaptation action benefits, and related uncertainty from maximum temperature metric selection.

Predictions of the projected changes in species distributions and potential adaptation action benefits can help guide conservation actions. There is substantial uncertainty in projecting species distributions into an unknown future, however, which can undermine confidence in predictions or misdirect conservation actions if not properly considered. Recent studies have shown that the selection of alternative climate metrics describing very different climatic aspects (e.g., mean air temperature vs. mean precipitation) can be a substantial source of projection uncertainty. It is unclear, however, how much projection uncertainty might stem from selecting among highly correlated, ecologically similar climate metrics (e.g., maximum temperature in July, maximum 30-day temperature) describing the same climatic aspect (e.g., maximum temperatures) known to limit a species' distribution. It is also unclear how projection uncertainty might propagate into predictions of the potential benefits of adaptation actions that might lessen climate change effects. We provide probabilistic measures of climate change vulnerability, adaptation action benefits, and related uncertainty stemming from the selection of four maximum temperature metrics for brook trout (Salvelinus fontinalis), a cold-water salmonid of conservation concern in the eastern United States. Projected losses in suitable stream length varied by as much as 20% among alternative maximum temperature metrics for mid-century climate projections, which was similar to variation among three climate models. Similarly, the regional average predicted increase in brook trout occurrence probability under an adaptation action scenario of full riparian forest restoration varied by as much as .2 among metrics. Our use of Bayesian inference provides probabilistic measures of vulnerability and adaptation action benefits for individual stream reaches that properly address statistical uncertainty and can help guide conservation actions. Our study demonstrates that even relatively small differences in the definitions of climate metrics can result in very different projections and reveal high uncertainty in predicted climate change effects.

Spatial and temporal variability of myxozoan parasite, Myxobolus inornatus, prevalence in young of the year smallmouth bass in the Susquehanna River Basin, Pennsylvania.

A myxozoan parasite, Myxobolus inornatus, is one disease agent identified in young of the year (YOY) smallmouth bass in the Susquehanna River Basin, Pennsylvania. We investigated spatial and temporal variability in M. Inornatus prevalence across the Susquehanna River Basin and at several out-of-basin sites. We examined potential land use drivers of M. Inornatus prevalence including agricultural and developed land use. In 1,267 YOY smallmouth bass collected from 32 sites during 2013-2016, M. Inornatus was documented in 43.6% of samples. Among-site variability in parasite prevalence was greater than among-year variability. The effect of agricultural land use on M. Inornatus prevalence had a high probability of being positively correlated at multiple spatial scales (probability of positive effect > 0.80). The effect of developed land use on M. Inornatus prevalence had a relatively high probability of being negatively correlated at multiple spatial scales (probability of negative effect > 0.70). Our results suggest that land use practices could be related to M. Inornatus infection of smallmouth bass. Further study will be necessary to determine whether disease dynamics are a consequence of effects on the host, alterations of instream habitat mediating invertebrate host dynamics and/or survival and dispersal of the parasite infective stage.

Unexpected stasis in a changing world: Lake nutrient and chlorophyll trends since 1990.

The United States (U.S.) has faced major environmental changes in recent decades, including agricultural intensification and urban expansion, as well as changes in atmospheric deposition and climate-all of which may influence eutrophication of freshwaters. However, it is unclear whether or how water quality in lakes across diverse ecological settings has responded to environmental change. We quantified water quality trends in 2913 lakes using nutrient and chlorophyll (Chl) observations from the Lake Multi-Scaled Geospatial and Temporal Database of the Northeast U.S. (LAGOS-NE), a collection of preexisting lake data mostly from state agencies. LAGOS-NE was used to quantify whether lake water quality has changed from 1990 to 2013, and whether lake-specific or regional geophysical factors were related to the observed changes. We modeled change through time using hierarchical linear models for total nitrogen (TN), total phosphorus (TP), stoichiometry (TN:TP), and Chl. Both the slopes (percent change per year) and intercepts (value in 1990) were allowed to vary by lake and region. Across all lakes, TN declined at a rate of 1.1% year-1 , while TP, TN:TP, and Chl did not change. A minority (7%-16%) of individual lakes had changing nutrients, stoichiometry, or Chl. Of those lakes that changed, we found differences in the geospatial variables that were most related to the observed change in the response variables. For example, TN and TN:TP trends were related to region-level drivers associated with atmospheric deposition of N; TP trends were related to both lake and region-level drivers associated with climate and land use; and Chl trends were found in regions with high air temperature at the beginning of the study period. We conclude that despite large environmental change and management efforts over recent decades, water quality of lakes in the Midwest and Northeast U.S. has not overwhelmingly degraded or improved.

Lake nutrient stoichiometry is less predictable than nutrient concentrations at regional and sub-continental scales.

Production in many ecosystems is co-limited by multiple elements. While a known suite of drivers associated with nutrient sources, nutrient transport, and internal processing controls concentrations of phosphorus (P) and nitrogen (N) in lakes, much less is known about whether the drivers of single nutrient concentrations can also explain spatial or temporal variation in lake N:P stoichiometry. Predicting stoichiometry might be more complex than predicting concentrations of individual elements because some drivers have similar relationships with N and P, leading to a weak relationship with their ratio. Further, the dominant controls on elemental concentrations likely vary across regions, resulting in context dependent relationships between drivers, lake nutrients and their ratios. Here, we examine whether known drivers of N and P concentrations can explain variation in N:P stoichiometry, and whether explaining variation in stoichiometry differs across regions. We examined drivers of N:P in ~2,700 lakes at a sub-continental scale and two large regions nested within the sub-continental study area that have contrasting ecological context, including differences in the dominant type of land cover (agriculture vs. forest). At the sub-continental scale, lake nutrient concentrations were correlated with nutrient loading and lake internal processing, but stoichiometry was only weakly correlated to drivers of lake nutrients. At the regional scale, drivers that explained variation in nutrients and stoichiometry differed between regions. In the Midwestern U.S. region, dominated by agricultural land use, lake depth and the percentage of row crop agriculture were strong predictors of stoichiometry because only phosphorus was related to lake depth and only nitrogen was related to the percentage of row crop agriculture. In contrast, all drivers were related to N and P in similar ways in the Northeastern U.S. region, leading to weak relationships between drivers and stoichiometry. Our results suggest ecological context mediates controls on lake nutrients and stoichiometry. Predicting stoichiometry was generally more difficult than predicting nutrient concentrations, but human activity may decouple N and P, leading to better prediction of N:P stoichiometry in regions with high anthropogenic activity.

Evaluation of a regional monitoring program's statistical power to detect temporal trends in forest health indicators.

Forests are socioeconomically and ecologically important ecosystems that are exposed to a variety of natural and anthropogenic stressors. As such, monitoring forest condition and detecting temporal changes therein remain critical to sound public and private forestland management. The National Parks Service's Vital Signs monitoring program collects information on many forest health indicators, including species richness, cover by exotics, browse pressure, and forest regeneration. We applied a mixed-model approach to partition variability in data for 30 forest health indicators collected from several national parks in the eastern United States. We then used the estimated variance components in a simulation model to evaluate trend detection capabilities for each indicator. We investigated the extent to which the following factors affected ability to detect trends: (a) sample design: using simple panel versus connected panel design, (b) effect size: increasing trend magnitude, (c) sample size: varying the number of plots sampled each year, and (d) stratified sampling: post-stratifying plots into vegetation domains. Statistical power varied among indicators; however, indicators that measured the proportion of a total yielded higher power when compared to indicators that measured absolute or average values. In addition, the total variability for an indicator appeared to influence power to detect temporal trends more than how total variance was partitioned among spatial and temporal sources. Based on these analyses and the monitoring objectives of the Vital Signs program, the current sampling design is likely overly intensive for detecting a 5 % trend·year(-1) for all indicators and is appropriate for detecting a 1 % trend·year(-1) in most indicators.

The Effects of Wastewater Reuse on Smallmouth Bass (Micropterus dolomieu) Relative Abundance in the Shenandoah River Watershed, USA.

Municipal and industrial wastewater effluent is an important source of water for lotic systems, especially during periods of low flow. The accumulated wastewater effluent flows-expressed as a percentage of total streamflow (ACCWW%)-contain chemical mixtures that pose a risk to aquatic life; fish may be particularly vulnerable when chronically exposed. Although there has been considerable focus on individual-level effects of exposure to chemical mixtures found in wastewater effluent, scaling up to population-level effects remains a challenging component needed to better understand the potential consequences of exposure in wild populations. This may be particularly important under a changing climate in which wastewater reuse could be essential to maintain river flows. We evaluated the effects of chronic exposure to wastewater effluent, as measured by ACCWW%, on the relative abundance of young-of-year (YOY), juvenile, and adult smallmouth bass (Micropterus dolomieu) populations in the Shenandoah River Watershed (USA). We found that increases in ACCWW% in the previous year and during the prespawn period were negatively correlated with the relative abundance of YOY, resulting in an average 41% predicted decrease in abundance (range = 0.5%-94% predicted decrease in abundance). This lagged effect suggests that adult fish reproductive performance may be compromised by chemical exposure during periods of high ACCWW%. No relationships between ACCWW% and juvenile or adult relative abundance were found, suggesting that negative effects of ACCWW% on YOY abundance may be offset due to compensatory mechanisms following higher ACCWW% exposure. Understanding the effects of wastewater effluent exposure at multiple levels of biological organization will help in the development of management strategies aimed at protecting aquatic life. Environ Toxicol Chem 2024;43:1138-1148. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Predicting responses to climate change using a joint species, spatially dependent physiologically guided abundance model.

Predicting the effects of warming temperatures on the abundance and distribution of organisms under future climate scenarios often requires extrapolating species-environment correlations to climatic conditions not currently experienced by a species, which can result in unrealistic predictions. For poikilotherms, incorporating species' thermal physiology to inform extrapolations under novel thermal conditions can result in more realistic predictions. Furthermore, models that incorporate species and spatial dependencies may improve predictions by capturing correlations present in ecological data that are not accounted for by predictor variables. Here, we present a joint species, spatially dependent physiologically guided abundance (jsPGA) model for predicting multispecies responses to climate warming. The jsPGA model uses a basis function approach to capture both species and spatial dependencies. We apply the jsPGA model to predict the response of eight fish species to projected climate warming in thousands of lakes in Minnesota, USA. By the end of the century, the cold-adapted species was predicted to have high probabilities of extirpation across its current range-with 10% of lakes currently inhabited by this species having an extirpation probability >0.90. The remaining species had varying levels of predicted changes in abundance, reflecting differences in their thermal physiology. Though the model did not identify many strong species dependencies, the variation in estimated spatial dependence across species suggested that accounting for both dependencies was important for predicting the abundance of these fishes. The jsPGA model provides a new tool for predicting changes in the abundance, distribution, and extirpation probability of poikilotherms under novel thermal conditions.

Identification and Characterization of Immune Checkpoint Inhibitor-Induced Toxicities From Electronic Health Records Using Natural Language Processing.

PURPOSE: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet their use is associated with immune-related adverse events (irAEs). Estimating the prevalence and patient impact of these irAEs in the real-world data setting is critical for characterizing the benefit/risk profile of ICI therapies beyond the clinical trial population. Diagnosis codes, such as International Classification of Diseases codes, do not comprehensively illustrate a patient's care journey and offer no insight into drug-irAE causality. This study aims to capture the relationship between ICIs and irAEs more accurately by using augmented curation (AC), a natural language processing-based innovation, on unstructured data in electronic health records. METHODS: In a cohort of 9,290 patients treated with ICIs at Mayo Clinic from 2005 to 2021, we compared the prevalence of irAEs using diagnosis codes and AC models, which classify drug-irAE pairs in clinical notes with implied textual causality. Four illustrative irAEs with high patient impact-myocarditis, encephalitis, pneumonitis, and severe cutaneous adverse reactions, abbreviated as MEPS-were analyzed using corticosteroid administration and ICI discontinuation as proxies of severity. RESULTS: For MEPS, only 70% (n = 118) of patients found by AC were also identified by diagnosis codes. Using AC models, patients with MEPS received corticosteroids for their respective irAE 82% of the time and permanently discontinued the ICI because of the irAE 35.9% (n = 115) of the time. CONCLUSION: Overall, AC models enabled more accurate identification and assessment of patient impact of ICI-induced irAEs not found using diagnosis codes, demonstrating a novel and more efficient strategy to assess real-world clinical outcomes in patients treated with ICIs.

Silver Lining Study: Missileer Fatigue Mitigation During 2020 Coronavirus Pandemic.

INTRODUCTION: The U.S. Air Force's Intercontinental Ballistic Missile (ICBM) force stands ready to launch weapons 365 days per year. Since its inception, missileers vigilantly operate launch consoles on a 3-day cycle: minimum 24-hour alert-shift/24-hour travel-admin/24-hour off, leading to concerns that health, morale, and alertness are chronically impacted. In 2020, a Missileer Occupational Health Assessment (OHA) revealed 76% of respondents struggle with being rested for duty and 29% of respondents never feel adequately rested for duty. Later that year, 20th Air Force initiated long-duration operations to safeguard from the SARS CoV-2 (COVID-19) Pandemic, resulting in increased operations tempo, and exacerbating crew fatigue.341st Operations Group and 341st Medical Group at Malmstrom Air Force Base enacted interventions to mitigate crew fatigue and support continued readiness during pandemic operations. They recorded, analyzed, and compiled findings in this report, including recommendations for long-term ICBM operations at Missile Wings. MATERIALS AND METHODS: All participants were Nuclear and Missile Operations Officers, or missileers, were continuously evaluated with qualitative and quantitative measures to ensure safety of the force during a period of unprecedented change. Interventions implemented and evaluated during the 9-month period included: environmental modifications, scheduling changes, and crew education on fatigue management, nutrition, anticipatory sleep preparation, and fitness. Most notably, the 341st Operations Group examined various 3-person and 4-person shift-length and alert duration schedules. Psychomotor vigilance testing results validated safety of operators and delta between pre- and post-shift measurements. Crew force readiness trends were analyzed for force-health awareness. Pre- and post-OHA results were compared for subjective changes. Fatigue and health-related outcomes were collected from a safety monitoring effort during standard and COVID-19 operations at 341st Missile Wing. RESULTS: Findings from qualitative and quantitative data indicate the optimal schedule is a 3-week cycle:7-day alert/7-day recovery/7-day training-administrative utilizing 4-member or 3-member crews for low tempo operations. Crews experimented with shift-lengths of 24hrs-on/24hrs-off, 16hrs-on/8hrs-off, and 12hrs-on/12hrs-off. Maximum safe alert duration is 7 days due to task fatigue onset between 8 and 10 days. Short and long duration Duties Not to Include Flight (DNIF) (also known as Duties Not to Include Alert (DNIA) among missileers) rates decreased from the first to last month of the period by 74.6% and 79.2%, respectively. The number of alerts missed per month decreased 86% from baseline. The 2021 OHA found a 7% decline in members seeking separation, and absence of sleep, fatigue, and physical or mental health as missileer concerns. CONCLUSIONS: This analysis has identified a sustainable alert rotation of 7/7/7 with emphasis on protected recovery and training time and has been continued after concluding pandemic operations, creating consistent schedule stability where there once was none. If executed properly, this alert rotation, regardless of shift-length selected, has potential to improve trust between crews and leadership, provides adequate recovery time between alerts to maintain health, and improves wellness, family stability, morale, unit cohesion, and crew force retention. Notably, all Air Force Global Strike Missile Operations Groups adjusted scheduling practices to align with these findings.