Spatial calibration and uncertainty reduction of the SWAT model using multiple remotely sensed data.

Remotely sensed products are often used in watershed modeling as additional constraints to improve model predictions and reduce model uncertainty. Remotely sensed products also enabled the spatial evaluation of model simulations due to their spatial and temporal coverage. However, their usability is not extensively explored in various regions. This study evaluates the effectiveness of incorporating remotely sensed evapotranspiration (RS-ET) and leaf area index (RS-LAI) products to enhance watershed modeling predictions. The objectives include reducing parameter uncertainty at the watershed scale and refining the model's capability to predict the spatial distribution of ET and LAI at sub-watershed scale. Using the Soil and Water Assessment Tool (SWAT) model, a systematic calibration procedure was applied. Initially, solely streamflow data was employed as a constraint, gradually incorporating RS-ET and RS-LAI thereafter. The results showed that while 14 parameter sets exhibit satisfactory performance for streamflow and RS-ET, this number diminishes to six with the inclusion of RS-LAI as an additional constraint. Furthermore, among these six sets, only three effectively captured the spatial patterns of ET and LAI at the sub-watershed level. Our findings showed that leveraging multiple remotely sensed products has the potential to diminish parameter uncertainty and increase the credibility of intra-watershed process simulations. These results contributed to broadening the applicability of remotely sensed products in watershed modeling, enhancing their usefulness in this field.

Brentuximab Vedotin-Driven Microtubule Disruption Results in Endoplasmic Reticulum Stress Leading to Immunogenic Cell Death and Antitumor Immunity.

Brentuximab vedotin, a CD30-directed antibody-drug conjugate (ADC), is approved for clinical use in multiple CD30-expressing lymphomas. The cytotoxic payload component of brentuximab vedotin is monomethyl auristatin E (MMAE), a highly potent microtubule-disrupting agent. Preclinical results provided here demonstrate that treatment of cancer cells with brentuximab vedotin or free MMAE leads to a catastrophic disruption of the microtubule network eliciting a robust endoplasmic reticulum (ER) stress response that culminates in the induction of the classic hallmarks of immunogenic cell death (ICD). In accordance with the induction of ICD, brentuximab vedotin-killed lymphoma cells drove innate immune cell activation in vitro and in vivo. In the "gold-standard" test of ICD, vaccination of mice with brentuximab vedotin or free MMAE-killed tumor cells protected animals from tumor rechallenge; in addition, T cells transferred from previously vaccinated animals slowed tumor growth in immunodeficient mice. Immunity acquired from killed tumor cell vaccination was further amplified by the addition of PD-1 blockade. In a humanized model of CD30+ B-cell tumors, treatment with brentuximab vedotin drove the expansion and recruitment of autologous Epstein-Barr virus-reactive CD8+ T cells potentiating the activity of anti-PD-1 therapy. Together, these data support the ability of brentuximab vedotin and MMAE to drive ICD in tumor cells resulting in the activation of antigen-presenting cells and augmented T-cell immunity. These data provide a strong rationale for the clinical combination of brentuximab vedotin and other MMAE-based ADCs with checkpoint inhibitors.

Global patterns of water storage in the rooting zones of vegetation.

The rooting-zone water-storage capacity-the amount of water accessible to plants-controls the sensitivity of land-atmosphere exchange of water and carbon during dry periods. How the rooting-zone water-storage capacity varies spatially is largely unknown and not directly observable. Here we estimate rooting-zone water-storage capacity globally from the relationship between remotely sensed vegetation activity, measured by combining evapotranspiration, sun-induced fluorescence and radiation estimates, and the cumulative water deficit calculated from daily time series of precipitation and evapotranspiration. Our findings indicate plant-available water stores that exceed the storage capacity of 2-m-deep soils across 37% of Earth's vegetated surface. We find that biome-level variations of rooting-zone water-storage capacities correlate with observed rooting-zone depth distributions and reflect the influence of hydroclimate, as measured by the magnitude of annual cumulative water-deficit extremes. Smaller-scale variations are linked to topography and land use. Our findings document large spatial variations in the effective root-zone water-storage capacity and illustrate a tight link among the climatology of water deficits, rooting depth of vegetation and its sensitivity to water stress.

Evaluation of satellite Leaf Area Index in California vineyards for improving water use estimation.

Remote sensing estimation of evapotranspiration (ET) directly quantifies plant water consumption and provides essential information for irrigation scheduling, which is a pressing need for California vineyards as extreme droughts become more frequent. Many ET models take satellite-derived Leaf Area Index (LAI) as a major input, but how uncertainties of LAI estimations propagate to ET and the partitioning between evaporation and transpiration is poorly understood. Here we assessed six satellite-based LAI estimation approaches using Landsat and Sentinel-2 images against ground measurements from four vineyards in California and evaluated ET sensitivity to LAI in the thermal-based two-source energy balance (TSEB) model. We found that radiative transfer modeling-based approaches predicted low to medium LAI well, but they significantly underestimated high LAI in highly clumped vine canopies (RMSE ~ 0.97 to 1.27). Cubist regression models trained with ground LAI measurements from all vineyards achieved high accuracy (RMSE ~ 0.3 to 0.48), but these empirical models did not generalize well between sites. Red edge bands and the related vegetation index (VI) from the Sentinel-2 satellite contain complementary information of LAI to VIs based on near-infrared and red bands. TSEB ET was more sensitive to positive LAI biases than negative ones. Positive LAI errors of 50% resulted in up to 50% changes in ET, while negative biases of 50% in LAI caused less than 10% deviations in ET. However, even when ET changes were minimal, negative LAI errors of 50% led to up to a 40% reduction in modeled transpiration, as soil evaporation and plant transpiration responded to LAI change divergently. These findings call for careful consideration of satellite LAI uncertainties for ET modeling, especially for the partitioning of water loss between vine and soil or cover crop for effective vineyard irrigation management.

Improving the spatiotemporal resolution of remotely sensed ET information for water management through Landsat, Sentinel-2, ECOSTRESS and VIIRS data fusion.

Robust information on consumptive water use (evapotranspiration, ET) derived from remote sensing can significantly benefit water decision-making in agriculture, informing irrigation schedules and water management plans over extended regions. To be of optimal utility for operational usage, these remote sensing ET data should be generated at the sub-field spatial resolution and daily-to-weekly timesteps commensurate with the scales of water management activities. However, current methods for field-scale ET retrieval based on thermal infrared (TIR) imaging, a valuable diagnostic of canopy stress and surface moisture status, are limited by the temporal revisit of available medium-resolution (100 m or finer) thermal satellite sensors. This study investigates the efficacy of a data fusion method for combining information from multiple medium-resolution sensors toward generating high spatiotemporal resolution ET products for water management. TIR data from Landsat and ECOSTRESS (both at ~ 100-m native resolution), and VIIRS (375-m native) are sharpened to a common 30-m grid using surface reflectance data from the Harmonized Landsat-Sentinel dataset. Periodic 30-m ET retrievals from these combined thermal data sources are fused with daily retrievals from unsharpened VIIRS to generate daily, 30-m ET image timeseries. The accuracy of this mapping method is tested over several irrigated cropping systems in the Central Valley of California in comparison with flux tower observations, including measurements over irrigated vineyards collected in the GRAPEX campaign. Results demonstrate the operational value added by the augmented TIR sensor suite compared to Landsat alone, in terms of capturing daily ET variability and reduced latency for real-time applications. The method also provides means for incorporating new sources of imaging from future planned thermal missions, further improving our ability to map rapid changes in crop water use at field scales.

Application of a Machine Learning Algorithm in Generating an Evapotranspiration Data Product From Coupled Thermal Infrared and Microwave Satellite Observations.

Land surface evapotranspiration (ET) is one of the main energy sources for atmospheric dynamics and a critical component of the local, regional, and global water cycles. Consequently, accurate measurement or estimation of ET is one of the most active topics in hydro-climatology research. With massive and spatially distributed observational data sets of land surface properties and environmental conditions being collected from the ground, airborne or space-borne platforms daily over the past few decades, many research teams have started to use big data science to advance the ET estimation methods. The Geostationary satellite Evapotranspiration and Drought (GET-D) product system was developed at the National Oceanic and Atmospheric Administration (NOAA) in 2016 to generate daily ET and drought maps operationally. The primary inputs of the current GET-D system are the thermal infrared (TIR) observations from NOAA GOES satellite series. Because of the cloud contamination to the TIR observations, the spatial coverage of the daily GET-D ET product has been severely impacted. Based on the most recent advances, we have tested a machine learning algorithm to estimate all-weather land surface temperature (LST) from TIR and microwave (MW) combined satellite observations. With the regression tree machine learning approach, we can combine the high accuracy and high spatial resolution of GOES TIR data with the better spatial coverage of passive microwave observations and LST simulations from a land surface model (LSM). The regression tree model combines the three LST data sources for both clear and cloudy days, which enables the GET-D system to derive an all-weather ET product. This paper reports how the all-weather LST and ET are generated in the upgraded GET-D system and provides an evaluation of these LST and ET estimates with ground measurements. The results demonstrate that the regression tree machine learning method is feasible and effective for generating daily ET under all weather conditions with satisfactory accuracy from the big volume of satellite observations.

Dementia Caregiver Experiences and Recommendations for Using the Behavioral and Environmental Sensing and Intervention System at Home: Usability and Acceptability Study.

BACKGROUND: Caregiver burden associated with dementia-related agitation is one of the most common reasons for a community-dwelling person living with dementia to transition to a care facility. The Behavioral and Environmental Sensing and Intervention (BESI) for the Dementia Caregiver Empowerment system uses sensing technology, smartwatches, tablets, and data analytics to detect and predict agitation in persons living with dementia and to provide just-in-time notifications and dyad-specific intervention recommendations to caregivers. The BESI system has shown that there is a valid relationship between dementia-related agitation and environmental factors and that caregivers prefer a home-based monitoring system. OBJECTIVE: The aim of this study is to obtain input from caregivers of persons living with dementia on the value, usability, and acceptability of the BESI system in the home setting and obtain their insights and recommendations for the next stage of system development. METHODS: A descriptive qualitative design with thematic analysis was used to analyze 10 semistructured interviews with caregivers. The interviews comprised 16 questions, with an 80% (128/160) response rate. RESULTS: Postdeployment caregiver feedback about the BESI system and the overall experience were generally positive. Caregivers acknowledged the acceptability of the system by noting the ease of use and saw the system as a fit for them. Functionality issues such as timeliness in agitation notification and simplicity in the selection of agitation descriptors on the tablet interface were identified, and caregivers indicated a desire for more word options to describe agitation behaviors. Agitation intervention suggestions were well received by the caregivers, and the resulting decrease in the number and severity of agitation events helped confirm that the BESI system has good value and acceptability. Thematic analysis suggested several subjective experiences and yielded the themes of usefulness and helpfulness. CONCLUSIONS: This study determined preferences for assessing caregiver strain and burden, explored caregiver acceptance of the technology system (in-home sensors, actigraph or smart watch technology, and tablet devices), discerned caregiver insights on the burden and stress of caring for persons living with dementia experiencing agitation in dementia, and solicited caregiver input and recommendations for system changes. The themes of usefulness and helpfulness support the use of caregiver knowledge and experience to inform further development of the technology.

Improving Antibody-Tubulysin Conjugates through Linker Chemistry and Site-Specific Conjugation.

Tubulysins have emerged in recent years as a compelling drug class for delivery to tumor cells via antibodies. The ability of this drug class to exert bystander activity while retaining potency against multidrug-resistant cell lines differentiates them from other microtubule-disrupting agents. Tubulysin M, a synthetic analogue, has proven to be active and well tolerated as an antibody-drug conjugate (ADC) payload, but has the liability of being susceptible to acetate hydrolysis at the C11 position, leading to attenuated potency. In this work, we examine the ability of the drug-linker and conjugation site to preserve acetate stability. Our findings show that, in contrast to a more conventional protease-cleavable dipeptide linker, the ß-glucuronidase-cleavable glucuronide linker protects against acetate hydrolysis and improves ADC activity in vivo. In addition, site-specific conjugation can positively impact both acetate stability and in vivo activity. Together, these findings provide the basis for a highly optimized delivery strategy for tubulysin M.

Aetiology and incidence of sudden cardiac arrest and death in young competitive athletes in the USA: a 4-year prospective study.

OBJECTIVE: To investigate the aetiology and incidence of sudden cardiac arrest and death (SCA/D) in US competitive athletes. METHODS: Prospective surveillance was conducted from 1 July 2014 to 30 June 2018 through the National Center for Catastrophic Sports Injury Research in collaboration with national sports organisations. Autopsy reports, death certificates, and medical records were reviewed by an expert panel to determine aetiology. Athlete participation statistics from the National Federation of State High School Associations and the National Collegiate Athletic Association (NCAA) were used to calculate incidence rates per athlete-years (AY). Comparisons of incidence rates were calculated using incidence rate ratios (IRR) with 95% CIs. RESULTS: 331 cases of confirmed SCA/D (158 survivors; 173 fatalities) were identified; 15.4% in middle school, 61.6% in high school and 16.6% in college and professional athletes. Average age was 16.7 (11-29) years, and the majority were in male (83.7%), basketball (28.7%) or American football (25.4%) athletes. Common causes included hypertrophic cardiomyopathy (20.6%), idiopathic left ventricular hypertrophy (13.4%), coronary artery anomalies (12.0%) and autopsy-negative sudden unexplained death (9.6%). Coronary anomalies were more common in middle school athletes (28%), while cardiomyopathies (hypertrophic, arrhythmogenic, dilated, non-compaction or restricted) accounted for 47% of cases in college and professional athletes. Incidence was higher in male versus female athletes at the high school (1:43 932 AY (95% CI 1:38 101 to 1:50 907) vs 1:203 786 AY (95% CI 1:145 251 to 1:293 794); IRR 4.6 (95% CI 3.1 to 7.2)) and NCAA (1:34 906 AY (95% CI 1:25 385 to 1:49 173) vs 1:123 278 AY (95% CI 1:66 078 to 1:249 853); IRR 3.5 (95% CI 1.5 to 9.5)) levels. African American male NCAA Division I basketball players had the highest annual incidence rate of SCA/D (1:2087 AY (95% CI 1:1073 to 1:4 450)). CONCLUSIONS: Cardiomyopathies account for nearly half of SCA/D cases in college and professional athletes, while coronary artery anomalies play a more prominent role than expected in middle school athletes. Over half of SCA cases in athletes result in sudden death, calling for improved prevention strategies.

Etiology of Sudden Cardiac Arrest and Death in US Competitive Athletes: A 2-Year Prospective Surveillance Study.

OBJECTIVE: To determine the etiology of sudden cardiac arrest and death (SCA/D) in competitive athletes through a prospective national surveillance program. DESIGN: Sudden cardiac arrest and death cases in middle school, high school, college, and professional athletes were identified from July 2014 to June 2016 through traditional and social media searches, reporting to the National Center for Catastrophic Sports Injury Research, communication with state and national high school associations, review of the Parent Heart Watch database, and search of student-athlete deaths on the NCAA Resolutions List. Autopsy reports and medical records were reviewed by a multidisciplinary panel to determine the underlying cause. SETTING AND PARTICIPANTS: US competitive athletes with SCA/D. MAIN OUTCOME MEASURES: Etiology of SCA/D. RESULTS: A total of 179 cases of SCA/D were identified (74 arrests with survival, 105 deaths): average age 16.6 years (range 11-29), 149 (83.2%) men, 94 (52.5%) whites, and 54 (30.2%) African American. One hundred seventeen (65.4%) had an adjudicated diagnosis, including 83 deaths and 34 survivors. The most common etiologies included hypertrophic cardiomyopathy (19, 16.2%), coronary artery anomalies (16, 13.7%), idiopathic left ventricular hypertrophy/possible cardiomyopathy (13, 11.1%), autopsy-negative sudden unexplained death (8, 6.8%), Wolff-Parkinson-White (8, 6.8%), and long QT syndrome (7, 6.0%). Hypertrophic cardiomyopathy was more common in male basketball (23.3%), football (25%), and African American athletes (30.3%). An estimated 56.4% of cases would likely demonstrate abnormalities on an electrocardiogram. CONCLUSIONS: The etiology of SCA/D in competitive athletes involves a wide range of clinical disorders. More robust reporting mechanisms, standardized autopsy protocols, and accurate etiology data are needed to better inform prevention strategies.

Neonatal Organ and Tissue Donation for Research: Options Following Death by Natural Causes.

The donation of organs and tissues from neonates (birth to 28 days) for transplantation has been a relatively infrequent occurrence. Less common has been the use of neonatal organs and tissues for research. Specific ethical and legal questions beg for rational and transparent guidelines with which to evaluate referrals of potential donors. Donation of organs and tissues from a neonate can play a key role in the care and support provided to families by health care professionals around the time of a neonate's death. We report on the recovery of neonatal organs and tissues for research. A working group made up of bioethicists, neonatologists, lawyers, obstetric practioners as well as organ procurement and tissue banking professionals evaluated legal, ethical and medical issues. Neonatal donor family members were also consulted. Our primary goals were (a) to ensure that referrals were made in compliance with all applicable federal and state laws, regulations and institutional protocols, and (b) to follow acceptable ethical standards. Algorithms and policies designed to assist in the evaluation of potential neonatal donors were developed. Neonatal donation is proving increasingly valuable for research into areas including diabetes, pulmonary, gastrointestinal, genitourinary and neurological development, rheumatoid arthritis, autism, childhood psychiatric and neurologic disorders, treatment of MRSA infection and pediatric emergency resuscitation. The development of policies and procedures will assist medical professionals who wish to offer the option of donation to family members anticipating the death of a neonate.

BESI: Behavioral and Environmental Sensing and Intervention for Dementia Caregiver Empowerment-Phases 1 and 2.

BACKGROUND AND OBJECTIVES: Caregiver burden associated with dementia-related agitation is one of the commonest reasons a community-dwelling person with dementia (PWD) transitions to a care facility. Behavioral and Environmental Sensing and Intervention for Dementia Caregiver Empowerment (BESI) is a system of body-worn and in-home sensors developed to provide continuous, noninvasive agitation assessment and environmental context monitoring to detect early signs of agitation and its environmental triggers. RESEARCH DESIGN AND METHODS: This mixed methods, remote ethnographic study is explored in a 3-phase, multiyear plan. In Phase 1, we developed and refined the BESI system and completed usability studies. Validation of the system and the development of dyad-specific models of the relationship between agitation and the environment occurred in Phase 2. RESULTS: Phases 1 and 2 results facilitated targeted changes in BESI, thus improving its overall usability for the final phase of the study, when real-time notifications and interventions will be implemented. CONCLUSION: Our results show a valid relationship between the presence of dementia related agitation and environmental factors and that persons with dementia and their caregivers prefer a home-based monitoring system like BESI.

Data Assimilation of High-Resolution Thermal and Radar Remote Sensing Retrievals for Soil Moisture Monitoring in a Drip-Irrigated Vineyard.

Efficient water use assessment and irrigation management is critical for the sustainability of irrigated agriculture, especially under changing climate conditions. Due to the impracticality of maintaining ground instrumentation over wide geographic areas, remote sensing and numerical model-based fine-scale mapping of soil water conditions have been applied for water resource applications at a range of spatial scales. Here, we present a prototype framework for integrating high-resolution thermal infrared (TIR) and synthetic aperture radar (SAR) remote sensing data into a soil-vegetation-atmosphere-transfer (SVAT) model with the aim of providing improved estimates of surface- and root-zone soil moisture that can support optimized irrigation management strategies. Specifically, remotely-sensed estimates of water stress (from TIR) and surface soil moisture retrievals (from SAR) are assimilated into a 30-m resolution SVAT model over a vineyard site in the Central Valley of California, U.S. The efficacy of our data assimilation algorithm is investigated via both the synthetic and real data experiments. Results demonstrate that a particle filtering approach is superior to an ensemble Kalman filter for handling the nonlinear relationship between model states and observations. In addition, biophysical conditions such as leaf area index are shown to impact the relationship between observations and states and must therefore be represented accurately in the assimilation model. Overall, both surface and root-zone soil moisture predicted via the SVAT model are enhanced through the assimilation of thermal and radar-based retrievals, suggesting the potential for improving irrigation management at the agricultural sub-field scale using a data assimilation strategy.

An in Vitro Assay Using Cultured Kupffer Cells Can Predict the Impact of Drug Conjugation on in Vivo Antibody Pharmacokinetics.

While antibody-drug conjugates (ADCs) are advancing through clinical testing and receiving new marketing approvals, improvements to the technology continue to be developed in both academic and industrial laboratories. Among the key ADC attributes that can be improved upon with new technology are their biodistribution and pharmacokinetic properties. During the course of ADC development, it has become apparent that conjugation of drugs to the surface of a monoclonal antibody can alter its physicochemical characteristics in a manner that results in increased nonspecific interactions and more rapid elimination from plasma. Researchers in the field have typically relied upon in vivo studies in preclinical models to understand how a particular ADC chemistry will impact these biological characteristics. In previous work, we described how animal studies have revealed a relationship between ADC hydrophobicity, pharmacokinetics, and nonspecific hepatic clearance, particularly by sinusoidal endothelium and Kupffer cells. Here, we describe a fluorescence-based assay using cultured Kupffer cells to recapitulate the nonspecific interactions that lead to ADC clearance in an in vitro setting with the aim of developing a tool for predicting the pharmacokinetics of novel ADC designs. Output from this assay has demonstrated an excellent correlation with plasma clearance for a series of closely related ADCs bearing discrete PEG chains of varying length and has proven useful in interrogating the mechanism of the interactions between ADCs and Kupffer cells.

Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized Landsat-Sentinel surface reflectances.

Land surface temperature (LST) is a key diagnostic indicator of agricultural water use and crop stress. LST data retrieved from thermal infrared (TIR) band imagery, however, tend to have a coarser spatial resolution (e.g., 100 m for Landsat 8) than surface reflectance (SR) data collected from shortwave bands on the same instrument (e.g., 30 m for Landsat). Spatial sharpening of LST data using the higher resolution multi-band SR data provides an important path for improved agricultural monitoring at sub-field scales. A previously developed Data Mining Sharpener (DMS) approach has shown great potential in the sharpening of Landsat LST using Landsat SR data co-collected over various landscapes. This work evaluates DMS performance for sharpening ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST (~70 m native resolution) and Visible Infrared Imaging Radiometer Suite (VIIRS) LST (375 m) data using Harmonized Landsat and Sentinel-2 (HLS) SR data, providing the basis for generating 30-m LST data at a higher temporal frequency than afforded by Landsat alone. To account for the misalignment between ECOSTRESS/VIIRS and Landsat/HLS caused by errors in registration and orthorectification, we propose a modified version of the DMS approach that employs a relaxed box size for energy conservation (EC). Sharpening experiments were conducted over three study sites in California, and results were evaluated visually and quantitatively against LST data from unmanned aerial vehicles (UAV) flights and from Landsat 8. Over the three sites, the modified DMS technique showed improved sharpening accuracy over the standard DMS for both ECOSTRESS and VIIRS, suggesting the effectiveness of relaxing EC box in relieving misalignment-induced errors. To achieve reasonable accuracy while minimizing loss of spatial detail due to the EC box size increase, an optimal EC box size of 180-270 m was identified for ECOSTRESS and about 780 m for VIIRS data based on experiments from the three sites. Results from this work will facilitate the development of a prototype system that generates high spatiotemporal resolution LST products for improved agricultural water use monitoring by synthesizing multi-source remote sensing data.

Multiple-Instance Learning for Sparse Behavior Modeling from Wearables: Toward Dementia-Related Agitation Prediction.

Agitation in persons with dementia (PWD) poses major health risks both for themselves and for their caregivers. Passive sensing based continuous behavior tracking can prevent the escalation of such episodes. But, predicting such behavior from sensor streams, especially in real-world residential settings, is still an active area of research. Major challenges include the sparsity, unpredictability, and variations in such behavior, as well as the "weak" annotations from real-world participants. This paper proposes a novel approach to overcome these issues in predicting agitation episodes from the PWD's wrist motion data. In a transdisciplinary study on dementia dyads residing in their homes, the PWD motion is continuously sensed from their smart watch inertial sensors, while agitation episodes are actively marked by the caregivers. The data from 10 residential deployments, each with 30 days duration, are analyzed in this paper, and multiple-instance learning (MIL) based models are implemented to learn from such sparse and weakly annotated data. These models are compared with single-instance models in predicting the agitated behavior. The results show the potential of MIL models in sparsely labeled behavior inference from wearables in-the-wild.

Earth observations and integrative models in support of food and water security.

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries.

Evaluation of TSEB turbulent fluxes using different methods for the retrieval of soil and canopy component temperatures from UAV thermal and multispectral imagery.

The thermal-based Two-Source Energy Balance (TSEB) model partitions the evapotranspiration (ET) and energy fluxes from vegetation and soil components providing the capability for estimating soil evaporation (E) and canopy transpiration (T). However, it is crucial for ET partitioning to retrieve reliable estimates of canopy and soil temperatures and net radiation, as the latter determines the available energy for water and heat exchange from soil and canopy sources. These two factors become especially relevant in row crops with wide spacing and strongly clumped vegetation such as vineyards and orchards. To better understand these effects, very high spatial resolution remote-sensing data from an unmanned aerial vehicle were collected over vineyards in California, as part of the Grape Remote sensing and Atmospheric Profile and Evapotranspiration eXperiment and used in four different TSEB approaches to estimate the component soil and canopy temperatures, and ET partitioning between soil and canopy. Two approaches rely on the use of composite T rad, and assume initially that the canopy transpires at the Priestley-Taylor potential rate. The other two algorithms are based on the contextual relationship between optical and thermal imagery partition T rad into soil and canopy component temperatures, which are then used to drive the TSEB without requiring a priori assumptions regarding initial canopy transpiration rate. The results showed that a simple contextual algorithm based on the inverse relationship of a vegetation index and T rad to derive soil and canopy temperatures yielded the closest agreement with flux tower measurements. The utility in very high-resolution remote-sensing data for estimating ET and E and T partitioning at the canopy level is also discussed.

Survival After Exercise-Related Sudden Cardiac Arrest in Young Athletes: Can We Do Better?

BACKGROUND:: Sudden cardiac arrest (SCA) is the leading cause of death in young athletes during sports. HYPOTHESIS:: Survival after SCA in young athletes is variable. STUDY DESIGN:: Prospective, active surveillance study. LEVEL OF EVIDENCE:: Level 3. METHODS:: From July 1, 2014, to June 30, 2016, exercise-related SCA in competitive young athletes was identified through a systematic search of traditional and social media sources, direct reporting to the National Center for Catastrophic Sports Injury Research, searching of the National Collegiate Athletic Association Resolutions List, regular communication with national and state high school athletic associations, and review of cases in the Parent Heart Watch database. RESULTS:: A total of 132 cases were identified during the 2-year study period (mean patient age, 16 years; age range, 11-27 years; 84% male; 51% white non-Hispanic/Latino, 30% black/African American, and 11% white Hispanic/Latino). High school athletes accounted for 78 (59%) cases, with 28 (21%) in middle school and 15 (11%) in college athletes. Overall survival was 48% (95% CI, 40%-57%; 64 survivors, 68 deaths). Survival was similar in male versus female athletes but higher in white non-Hispanic/Latino (40/67; 60%) versus black/African American (13/39; 33%) athletes (difference, 27%; 95% CI, 7%-45%; P = 0.008) and white non-Hispanic/Latino versus all minority (18/59; 31%) athletes (difference, 29%; 95% CI, 13%-46%; P = 0.001). Basketball accounted for 30% of cases, followed by football (25%), track/cross-country (12%), and soccer (11%). The majority (93%) of cases were witnessed. If a certified athletic trainer was on-site and involved in the resuscitation, 83% of athletes survived. If an on-site automated external defibrillator was used in the resuscitation, 89% of athletes survived. CONCLUSION:: Exercise-related SCA in young, competitive athletes is typically witnessed, providing an opportunity for rapid resuscitation. Additional research is needed to identify factors that affect survival in different athlete populations. CLINICAL RELEVANCE:: Public access defibrillator programs should be universal in schools and youth sporting venues and have the potential to increase survival after SCA in young athletes.

Microwave implementation of two-source energy balance approach for estimating evapotranspiration.

A newly developed microwave (MW) land surface temperature (LST) product is used to substitute thermal infrared (TIR) based LST in the Atmosphere Land Exchange Inverse (ALEXI) modelling framework for estimating ET from space. ALEXI implements a two-source energy balance (TSEB) land surface scheme in a time-differential approach, designed to minimize sensitivity to absolute biases in input records of LST through the analysis of the rate of temperature change in the morning. Thermal infrared (TIR) retrievals of the diurnal LST curve, traditionally from geostationary platforms, are hindered by cloud cover, reducing model coverage on any given day. This study tests the utility of diurnal temperature information retrieved from a constellation of satellites with microwave radiometers that together provide 6-8 observations of Ka-band brightness temperature per location per day. This represents the first ever attempt at a global implementation of ALEXI with MW-based LST and is intended as the first step towards providing all-weather capability to the ALEXI framework. The analysis is based on 9-year long, global records of ALEXI ET generated using both MW and TIR based diurnal LST information as input. In this study, the MW-LST sampling is restricted to the same clear sky days as in the IR-based implementation to be able to analyse the impact of changing the LST dataset separately from the impact of sampling all-sky conditions. The results show that long-term bulk ET estimates from both LST sources agree well, with a spatial correlation of 92% for total ET in the Europe/Africa domain and agreement in seasonal (3-month) totals of 83-97 % depending on the time of year. Most importantly, the ALEXI-MW also matches ALEXI-IR very closely in terms of 3-month inter-annual anomalies, demonstrating its ability to capture the development and extent of drought conditions. Weekly ET output from the two parallel ALEXI implementations is further compared to a common ground measured reference provided by the FLUXNET consortium. Overall, the two model implementations generate similar performance metrics (correlation and RMSE) for all but the most challenging sites in terms of spatial heterogeneity and level of aridity. It is concluded that a constellation of MW satellites can effectively be used to provide LST for estimating ET through ALEXI, which is an important step towards all-sky satellite-based retrieval of ET using an energy balance framework.