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DOE OSTI2020

Machine learning to predict biomass sorghum yields under future climate scenarios

Crop yield modeling is critical in the design of national strategies for agricultural production, particularly in the context of a changing climate. Forecasting yields of bioenergy crops at fine spatial resolutions can help to evaluate near-term and long-term pathways for scaling up bio-based fuel and chemical production, and for understanding the impacts of abiotic stressors such as severe droughts and temperature extremes on potential biomass supply. In this work we used a large dataset of 28,364 Sorghum bicolor yield samples (uniquely identified by county and year of observation), environmental variables, and multiple approaches to analyze historical trends in sorghum productivity across the USA. We selected the most accurate machine learning approach (a variation of the random forest approach) to predict future trends in sorghum yields under four greenhouse gas (GHG) emission scenarios and two irrigation regimes. We identified irrigation practices, vapor pressure deficit, and time (a proxy for technological improvement) as the most important predictors of sorghum productivity. Our results showed a decreasing trend of sorghum yields over future years (on average 2.7% from 2018 to 2099), with greater decline under a high GHG emissions scenario (3.8%) and in the absence of irrigation (4.6%). Geographically, we observed the steepest predicted declines in the Great Lakes (8.2%), Upper Midwest (7.5%), and Heartland (6.7%) regions. Our study demonstrates the use of machine learning to identify environmental controllers of sorghum biomass yield and predict yields with reasonable accuracy. These results can inform the development of more realistic biomass supply projections for bioenergy if sorghum production is scaled up. (c) 2020 Society of Chemical Industry and John Wiley & Sons, Ltd

09 BIOMASS FUELS↗

DOE OSTI2023

Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models

Abstract The temperature experienced by reactants during preparation in a reactor is a key component in determining the yield and homogeneity of usable chemical products such as biomass particles. Thermocouples with sensors can be used to monitor spatial temperature gradients within reactors but these sensors are often too expensive and/or invasive. The present work proposes a strategy to identify optimal machine learning models to infer the maximum effective temperature experienced by particles during oxidative biomass torrefaction using key thermochemical combustion parameters. The maximum rate of weight loss, the corresponding temperature, and fixed carbon content on a dry‐ash‐free basis are used as literature‐based predictor variables obtained from thermogravimetric analysis. The evaluation of 24 machine‐learning models using the standard tenfold cross‐validation method suggests that the exponential Gaussian process regression (GPR) model is the most effective, followed by other GPR models. These high‐performing GPR models were also utilized to predict the effective preparation temperature distribution of reactor‐produced biomass particles under eight conditions of varying residence time and air‐to‐biomass ratio. The effective preparation temperature and residence time of individual biomass particles were then encoded into the torrefaction severity factor and used to estimate the energy yield of the reactor output as a novel quality control method. © 2023 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

09 BIOMASS FUELS↗

DOE OSTI2020

A nomogram for prediction of stage III/IV gastric cancer outcome after surgery: A multicenter population‐based study

Abstract Most patients with gastric cancer (GC) are first diagnosed at stage III‐IV and surgery resection remains the primary therapeutic modality for these patients. However, clinical staging used for prediction of those patients provides limited information. We collected clinicopathological data and disease‐progression information from 508 patients with stage III‐IV GC at three Chinese hospitals and 1298 patients from the Surveillance, Epidemiology, and End Results database. Based on the stepwise multivariate regression model, we constructed a novel nomogram to predict overall survival (OS). The performance of discrimination for this model was measured using Harrell's concordance index (C‐index) and receiver‐operating characteristic curve (ROC), and was validated using calibration plots. Multivariate Cox regression analyses showed that tumor size, age at diagnosis, N stage, tumor grade, and distant metastases were outstanding independent prognostic factors of stage III‐IV GC. We developed a nomogram based on these five prognostic predictors. In the training set, the C‐index of the nomogram was 0.645 (95% CI: 0.611‐0.679), which was higher than that of the American Joint Committee on Cancer TNM system alone (sixth TNM: 0.544; seventh TNM: 0.575; eighth TNM: 0.568). Similar results were observed in validation cohort. Moreover, calibration blots demonstrated good consistency between the actual and predicted OS probabilities. According to the nomogram, GC individuals could be classified into three groups (low‐, middle‐, and high‐risk) ( P < .001). Our nomogram complements the current staging system for prediction of individual prognosis with stage III‐IV GC, and may be helpful for making individualized treatment decisions.

Gao, Zhiying↗

DOE OSTI2022

Prognostic importance of the preoperative New‐Naples prognostic score for patients with gastric cancer

Abstract Background The wide applicability of the Naples prognostic score (NPS) is still worthy of further study in gastric cancer (GC). This study aimed to construct a New‐NPS based on the differences in immunity and nutrition in patients with upper and lower gastrointestinal tumors to help obtain an individualized prediction of prognosis. Methods This study retrospectively analyzed patients who underwent radical gastrectomy from April 2014 to September 2016. The cutoff values of the preoperative neutrophil‐to‐lymphocyte ratio (NLR), lymphocyte‐to‐monocyte ratio (LMR), serum albumin (Alb), and total cholesterol (TC) were calculated by ROC curve analysis. ROC and t‐ROC were used to evaluate the accuracy of the prognostic markers. The Kaplan–Meier method and log‐rank test were used to analyze the overall survival probability. Univariate and multivariate analyses based on Cox risk regression were used to show the independent predictors. The nomogram was made by R studio. The predictive accuracy of nomogram was assessed using a calibration plot, concordance index (C‐index), and decision curve. Results A total of 737 patients were included in training cohort, 411 patients were included in validation cohort. ROC showed that the New‐NPS was more suitable for predicting the prognosis of GC patients. NPS = 2 indicated a poor prognosis. Multivariate analysis showed that CEA ( P = 0.026), Borrmann type ( P = 0.001), pTNM ( P < 0.001), New‐NPS ( P < 0.001), and nerve infiltration ( P = 0.035) were independent risk factors for prognosis. Conclusion The New‐NPS based on the cutoff values of NLR, LMR, Alb, and TC is not only suitable for predicting prognosis but can also be combined with clinicopathological characteristics to construct a nomogram model for GC patients.

Wang, Hao↗

DOE OSTI2023

Peanut allergy burden survey: Factors associated with health‐related quality of life in adolescents

Abstract Background Patients with peanut allergy (PA) experience significant burden of illness, which impacts health‐related quality of life (HRQoL), particularly in adolescence. There is a paucity of research evaluating drivers of HRQoL scores. Methods A prospective, online survey of adolescents with self‐reported, provider‐diagnosed PA completed from November 2018 to January 2019 was used to explore drivers of the real‐world impact of PA on HRQoL using the Pediatric Quality of Life Inventory 4.0 (PedsQL) and other measures. Univariate and multivariate analyses were used to identify potential factors associated with PedsQL scores and to understand the level of association. Results A total of 102 adolescents were included. The final model included 10 variables: race, reported strict peanut avoidance, satisfaction with prophylaxis, moderate‐to‐severe reaction within the past 12 months, touching peanut as cause of most severe reaction, fear of reaction, age, gender, comorbidities, and daily life limitations. In total, three items were shown to be strong predictors of the PedsQL total score including cause of severe reaction was touching peanut (yes), level of agreement with avoiding peanut (completely agree), and satisfaction with prophylaxis (not very much/not at all). Conclusions There is substantial heterogeneity in the impact of the burden of PA on PedsQL scores across patients. This indicates the importance of shared and individualized decision making for PA management to optimize outcomes and improve HRQoL.

McCann, William A.↗

DOE OSTI2024

The impact of plant‐derived fire management prescriptions on fire‐responsive bird species

Abstract In fire‐prone regions, the occurrence of some faunal species is contingent on the presence of resources that arise through post‐fire plant succession. Through planned burning, managers can alter resource availability and aim to provide the conditions required to promote biodiversity. Understanding how species occurrence changes at different spatial and temporal scales after fire is essential to achieve this goal. However, many fire prescriptions are guided primarily by the responses of fire‐sensitive plants when setting tolerable fire intervals. This approach assumes that maintaining floristic diversity will satisfy the requirements of fauna. We surveyed bird species in two semi‐arid vegetation types across an environmental gradient in south‐eastern Australia. We conducted four surveys at each of 253 sites across a 75‐year chronosequence of time since fire and used generalized additive mixed models to examine changes in the occurrence of birds in response to time since fire. Model predictions were compared to plant‐derived fire prescriptions currently guiding fire management in the region. Time since fire was a significant predictor for 18 of 28 species modeled, in at least one vegetation type, over a gradient of 1.3° of latitude. We detected considerable variation in the responses of some species, both between vegetation types and geographically within a vegetation type. Our evaluation of plant‐derived fire prescriptions suggests that the intervals considered acceptable for maintaining floristic diversity may not be sustainable for populations of birds requiring longer unburnt vegetation, with 6 of the 12 species assessed attaining a mean occurrence probability of 20.3% by the minimum tolerable fire interval, and 57.3% by the maximum tolerable fire interval, in their respective vegetation types. Our findings highlight the potential vulnerability of fire‐responsive bird species if fire prescriptions are applied in a manner that fails to account for the slow development of habitat resources needed by some species, and the variation detected within the responses of species. This highlights the need for species‐specific data collected at an appropriate spatial scale to inform management plans.

Makdissi, Rhys↗

DOE OSTI2024

Identifying microbial drivers in biological phenotypes with a Bayesian network regression model

Abstract In Bayesian Network Regression models, networks are considered the predictors of continuous responses. These models have been successfully used in brain research to identify regions in the brain that are associated with specific human traits, yet their potential to elucidate microbial drivers in biological phenotypes for microbiome research remains unknown. In particular, microbial networks are challenging due to their high dimension and high sparsity compared to brain networks. Furthermore, unlike in brain connectome research, in microbiome research, it is usually expected that the presence of microbes has an effect on the response (main effects), not just the interactions. Here, we develop the first thorough investigation of whether Bayesian Network Regression models are suitable for microbial datasets on a variety of synthetic and real data under diverse biological scenarios. We test whether the Bayesian Network Regression model that accounts only for interaction effects (edges in the network) is able to identify key drivers (microbes) in phenotypic variability. We show that this model is indeed able to identify influential nodes and edges in the microbial networks that drive changes in the phenotype for most biological settings, but we also identify scenarios where this method performs poorly which allows us to provide practical advice for domain scientists aiming to apply these tools to their datasets. BNR models provide a framework for microbiome researchers to identify connections between microbes and measured phenotypes. We allow the use of this statistical model by providing an easy‐to‐use implementation which is publicly available Julia package at https://github.com/solislemuslab/BayesianNetworkRegression.jl .

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Evaluating the influence of life‐history characteristics on genetic structure: a comparison of small mammals inhabiting complex agricultural landscapes

Abstract Conversion of formerly continuous native habitats into highly fragmented landscapes can lead to numerous negative demographic and genetic impacts on native taxa that ultimately reduce population viability. In response to concerns over biodiversity loss, numerous investigators have proposed that traits such as body size and ecological specialization influence the sensitivity of species to habitat fragmentation. In this study, we examined how differences in body size and ecological specialization of two rodents (eastern chipmunk; Tamias striatus and white‐footed mouse; Peromyscus leucopus ) impact their genetic connectivity within the highly fragmented landscape of the Upper Wabash River Basin ( UWB ), Indiana, and evaluated whether landscape configuration and complexity influenced patterns of genetic structure similarly between these two species. The more specialized chipmunk exhibited dramatically more genetic structure across the UWB than white‐footed mice, with genetic differentiation being correlated with geographic distance, configuration of intervening habitats, and complexity of forested habitats within sampling sites. In contrast, the generalist white‐footed mouse resembled a panmictic population across the UWB , and no landscape factors were found to influence gene flow. Despite the extensive previous work in abundance and occupancy within the UWB , no landscape factor that influenced occupancy or abundance was correlated with genetic differentiation in either species. The difference in predictors of occupancy, abundance, and gene flow suggests that species‐specific responses to fragmentation are scale dependent.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Potential and limitations of inferring ecosystem photosynthetic capacity from leaf functional traits

Abstract The aim of this study was to systematically analyze the potential and limitations of using plant functional trait observations from global databases versus in situ data to improve our understanding of vegetation impacts on ecosystem functional properties ( EFP s). Using ecosystem photosynthetic capacity as an example, we first provide an objective approach to derive robust EFP estimates from gross primary productivity ( GPP ) obtained from eddy covariance flux measurements. Second, we investigate the impact of synchronizing EFP s and plant functional traits in time and space to evaluate their relationships, and the extent to which we can benefit from global plant trait databases to explain the variability of ecosystem photosynthetic capacity. Finally, we identify a set of plant functional traits controlling ecosystem photosynthetic capacity at selected sites. Suitable estimates of the ecosystem photosynthetic capacity can be derived from light response curve of GPP responding to radiation (photosynthetically active radiation or absorbed photosynthetically active radiation). Although the effect of climate is minimized in these calculations, the estimates indicate substantial interannual variation of the photosynthetic capacity, even after removing site‐years with confounding factors like disturbance such as fire events. The relationships between foliar nitrogen concentration and ecosystem photosynthetic capacity are tighter when both of the measurements are synchronized in space and time. When using multiple plant traits simultaneously as predictors for ecosystem photosynthetic capacity variation, the combination of leaf carbon to nitrogen ratio with leaf phosphorus content explains the variance of ecosystem photosynthetic capacity best (adjusted R 2 = 0.55). Overall, this study provides an objective approach to identify links between leaf level traits and canopy level processes and highlights the relevance of the dynamic nature of ecosystems. Synchronizing measurements of eddy covariance fluxes and plant traits in time and space is shown to be highly relevant to better understand the importance of intra‐ and interspecific trait variation on ecosystem functioning.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Prokaryotic taxa play keystone roles in the soil microbiome associated with woody perennial plants in the genus Buxus

The microbiome associated with ornamental plants has largely been neglected, despite its potential for impacting plant health. This work characterized the composition, diversity, and microbial co-associations in the soil microbiome associated with species and cultivars of plant in the genus Buxus (common name boxwood), a group of woody perennial shrubs commonly used in residential landscapes and found in native ecosystems. Soil was collected from 82 individual curated boxwood accessions at the U.S. National Arboretum National Boxwood Collection. Amplicon libraries targeting archaea, bacteria, and fungi were generated and sequenced using the Illumina MiSeq platform. Identification of individual sequence variants resulted in 275 archaeal, 15,580 bacterial, and 7,525 fungal taxa. Neither spatial distance among samples nor association with different types of boxwood were significant predictors of soil microbiome composition. However, archaeal and bacterial diversity was significantly different in soil from distinct types of boxwood. Co-association networks indicated that archaea and bacteria show greater evidence of being keystone taxa than fungi. Overall, this work demonstrates the potential for targeting specific keystone taxa to shift the soil microbiome associated with these boxwood accessions and that planting different species or cultivars in the landscape may shift the diversity of prokaryotic microorganisms.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Ecological niche models display nonlinear relationships with abundance and demographic performance across the latitudinal distribution of Astragalus utahensis (Fabaceae)

Abstract The potential for ecological niche models (ENMs) to accurately predict species' abundance and demographic performance throughout their geographic distributions remains a topic of substantial debate in ecology and biogeography. Few studies simultaneously examine the relationship between ENM predictions of environmental suitability and both a species' abundance and its demographic performance, particularly across its entire geographic distribution. Yet, studies of this type are essential for understanding the extent to which ENMs are a viable tool for identifying areas that may promote high abundance or performance of a species or how species might respond to future climate conditions. In this study, we used an ensemble ecological niche model to predict climatic suitability for the perennial forb Astragalus utahensis across its geographic distribution. We then examined relationships between projected climatic suitability and field‐based measures of abundance, demographic performance, and forecasted stochastic population growth (λ s ). Predicted climatic suitability showed a J‐shaped relationship with A. utahensis abundance, where low‐abundance populations were associated with low‐to‐intermediate suitability scores and abundance increased sharply in areas of high predicted climatic suitability. A similar relationship existed between climatic suitability and λ s from the center to the northern edge of the latitudinal distribution. Patterns such as these, where density or demographic performance only increases appreciably beyond some threshold of climatic suitability, support the contention that ENM‐predicted climatic suitability does not necessarily represent a reliable predictor of abundance or performance across large geographic regions.

Baer, Kathryn C.↗

DOE OSTI2017

Seasonally contrasting responses of evapotranspiration to warming and elevated CO 2 in a semiarid grassland

Abstract Global climate change is expected to alter seasonal patterns and rates of evapotranspiration in dry regions. Although climate change will involve elevated CO 2 and increased temperatures, independently, these factors may have different impacts on actual evapotranspiration (AET) due to their opposing effects on transpiration. We used canopy gas exchange chambers to quantify AET in a semiarid grassland experimentally altered by elevated CO 2 and warming over 3 years with contrasting ambient precipitation. Seasonal and interannual variations in AET due to background climate variability were larger than the effects of climate manipulation treatments. However, in a year with average precipitation, cumulative growing season AET was suppressed by warming by 23%. Across years, warming increased AET early in the growing season and suppressed it later in the growing season. By contrast, elevated CO 2 suppressed AET early in the growing season and enhanced it later, but only in years with average or above‐average precipitation. Vegetation greenness (a proxy for photosynthetically active leaf area) was consistently the strongest predictor of AET, whereas soil moisture and vapor pressure deficit were secondary drivers. Our research demonstrates that effects of increased atmospheric CO 2 and temperature on AET will be mediated by plant phenological development and seasonal climatic conditions.

Sorokin, Y.↗

DOE OSTI2017

Weather, hydroregime, and breeding effort influence juvenile recruitment of anurans: implications for climate change

Amphibians that primarily breed in ephemeral wetlands are especially vulnerable to climate change because they rely on rainfall or temperature to initiate breeding and create suitable hydroregimes (water duration, timing, frequency, depth) for reproductive success. Hydroregime effects on reproductive success are likely to differ among species because of differences in reproductive strategies: the length and timing of breeding period, rate of larval development, and timing of metamorphosis. We applied an information-theoretic approach to 22 consecutive years of continuous amphibian trapping data at eight ephemeral wetlands to test hypotheses regarding environmental (hydroregime, weather) and biological (adult breeding effort) factors affecting juvenile recruitment (JR) by six focal species representing four reproductive strategies. We hypothesized that (1) JR by species with similar reproductive strategies would be influenced by similar variables; (2) JR would be higher for all species when models encompassed the maximum time span of potential tadpole occurrence and development; and (3) JR rates within individual wetlands and breeding cycles would correlate most closely between species with similar breeding strategies. The best model for all focal species (except Scaphiopus holbrookii) encompassed the maximum time span and indicated that ≥1 hydroregime variable, total precipitation, or both were important drivers of reproductive success; average air temperature was not. Continuous hydroperiod through peak juvenile emigration was an important predictor of JR for species with prolonged breeding periods, slow larval development, and a “fixed” late spring start date for juvenile emigration (regardless of when oviposition occurred, or cohort age; Lithobates capito , Lithobates sphenocephalus ), but not for species with rapid larval development and continual emigration as cohorts complete metamorphosis ( Anaxyrus terrestris, Anaxyrus quercicus, Gastrophryne carolinensis, S. holbrookii ). Total rainfall was positively associated with recruitment for most species; depth characteristics affected species differently. Annual JR was positively correlated among species with similar reproductive strategies. Our results indicate that weather and hydroregime characteristics interact with reproductive strategies that differ among amphibian species and influence reproductive plasticity, opportunity, and success. In conclusion, effects of altered weather patterns associated with climate change on amphibian reproductive success may correspond more closely among species having similar reproductive strategies, with critical implications for population trends and assemblages.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Scavenger removal of bird carcasses at simulated wind turbines: Does carcass type matter?

Abstract Wind energy development can negatively impact bird populations due to bird–turbine collisions. To accurately estimate bird mortality at wind farms, the number of dead birds found under turbines is commonly corrected for carcass removal by scavengers, which is quantified by measuring persistence of experimental carcasses through time. These studies often use domestic birds as surrogates because carcasses of wild birds (e.g., raptors) are difficult to obtain. We assessed scavenger removal of carcasses from five bird species at simulated turbines to determine whether domestic surrogates are scavenged at a different rate than raptors, species of interest for wind turbine mortality. The percentage of carcasses scavenged during 14‐d rounds ranged from 34.6% for American kestrels ( Falco sparverius ) to 65.4% for chickens ( Gallus gallus ), and the percentage of carcasses completely removed ranged from 13.5% for red‐tailed hawks ( Buteo jamaicensis ) to 67.3% for northern bobwhites ( Colinus virginianus ). Carcass type (i.e., species) was the only predictor included in the best‐fit logistic regression model of complete carcass removal, and a survival analysis indicated carcass type influenced elapsed time to scavenging events. Our results suggest the use of surrogate species to quantify carcass removal at wind turbines could lead to inaccurate mortality estimates.

17 WIND ENERGY↗

DOE OSTI2018

Functional divergence in nitrogen uptake rates explains diversity–productivity relationship in microalgal communities

Abstract The relationship between biodiversity and productivity has emerged as a central theme in ecology. Mechanistic explanations for this relationship suggest that the role organisms play in the ecosystem (i.e., niches or functional traits) is a better predictor of ecosystem stability and productivity than taxonomic richness. Here, we tested the capacity of functional diversity in nitrogen uptake in experimental microalgal communities to predict the complementarity effect (CE) and selection effect (SE) of biodiversity on productivity. We grew five algal species as monocultures and as polycultures in pairwise combinations in homogeneous (ammonium, nitrate, or urea alone) and heterogeneous nitrogen (mixed nitrogen) environments to determine whether complementarity between species may be enhanced in heterogeneous environments. We show that the positive diversity effects on productivity in heterogeneous environments resulted from complementarity effects with no positive contribution by species‐specific SEs. Positive biodiversity effects in homogeneous environments, when present (nitrate and urea treatments but not ammonium), were driven both by CE and SE. Our results suggest that functional diversity increases species complementarity and productivity mainly in heterogeneous resource environments. These results provide evidence that the positive effect of functional diversity on community productivity depends on the diversity of resources present in the environment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Differences in soil organic matter between EcM- and AM-dominated forests depend on tree and fungal identity

As global change shifts the species composition of forests, we need to understand which species characteristics affect soil organic matter (SOM) cycling to predict future soil carbon (C) storage. Recently, whether a tree species forms a symbiosis with arbuscular (AM) versus ectomycorrhizal (EcM) fungi has been suggested as a strong predictor of soil C storage, but there is wide variability within EcM systems. In this study, we investigated how mycorrhizal associations and the species composition of canopy trees and mycorrhizal fungi related to the proportion of soil C and nitrogen (N) in mineral associations and soil C:N across four sites representing distinct climates and tree communities in the eastern US broadleaf forest biome. In two of our sites, we found the expected relationship of declining mineral-associated C and N and increasing soil C:N ratios as the basal area of EcM-associating trees increased. However, across all sites these soil properties strongly correlated with canopy tree and fungal species composition. Sites where the expected pattern with EcM basal area was observed were (1) dominated by trees with lower quality litter in the Pinaceae and Fagaceae families and (2) dominated by EcM fungi with medium-distance exploration type hyphae, melanized tissues, and the potential to produce peroxidases. Here, this observational study demonstrates that differences in SOM between AM and EcM systems are dependent on the taxa of trees and EcM fungi involved. Important information is lost when the rich mycorrhizal symbiosis is reduced to two categories.

mineral-associated organic matter↗

DOE OSTI2024

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

Abstract 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.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Night‐time electromechanical activation time, pulsatile hemodynamics, and discharge outcomes in patients with acute heart failure

Abstract Aims Both electromechanical activation time (EMAT) and pulsatile hemodynamics measured during the hospitalization course are useful in the prediction of cardiovascular outcomes in patients with acute heart failure syndrome (AHFS). We investigated whether night‐time monitoring of EMAT with the ambulatory acoustic cardiography is superior to the measures of pulsatile hemodynamics for prediction of AHFS post‐discharge outcomes. Methods and results A total of 97 patients (71.1 ± 15.4 years old, 81% male, and 73.8% systolic heart failure) hospitalized for AHFS were included. Before discharge, 24 h ambulatory acoustic cardiography and a comprehensive echocardiographic and pulsatile hemodynamic study were performed to assess the mean 24 h, daytime, and night‐time EMAT, carotid systolic blood pressure (SBP) and pulse pressure (PP), amplitude of the reflected pressure wave from a decomposed carotid pressure wave (Pb), and carotid–femoral pulse wave velocity (cfPWV), in addition to measurement of N‐terminal pro‐brain natriuretic peptide (NT‐proBNP) levels. During a mean follow‐up of 389 ± 281 days, 49 patients (50.5%) experienced events including re‐hospitalization for heart failure, myocardial infarction, stroke, or death. Pulsatile hemodynamics, including carotid SBP and PP and Pb, but not cfPWV, and night‐time EMAT, but not daytime EMAT, significantly predicted post‐discharge events when age and NT‐proBNP were accounted for (all P < 0.05). In a final model with adjustment for age and NT‐proBNP, night‐time EMAT, but not Pb, significantly predicted post‐discharge events [hazard ratio per 1 SD and 95% confidence intervals: 1.33 (1.05–1.69), P < 0.05]. Conclusion Pre‐discharge night‐time EMAT may be a better predictor for post‐discharge adverse events than the measures of the pulsatile hemodynamics in patients with AHFS.

Chang, Chun‐Chin↗