THE EFFECT OF DROPOUT IN TELEMETERED DATA ON SHOCK RESPONSE SPECTRA
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The Van Allen Probes mission operations materialized through a distributed model in which operational responsibility was divided between the Mission Operations Center (MOC) and separate instrument specific SOCs. The sole MOC handled all aspects of telemetering and receiving tasks as well as certain scientifically relevant ancillary tasks. Each instrument science team developed individual instrument specific SOCs proficient in unique capabilities in support of science data acquisition, data processing, instrument performance, and tools for the instrument team scientists. In parallel activities, project scientists took on the task of providing a significant modeling tool base usable by the instrument science teams and the larger scientific community. With a mission as complex as Van Allen Probes, scientific inquiry occurred due to constant and significant collaboration between the SOCs and in concert with the project science team. Planned cross-instrument coordinated observations resulted in critical discoveries during the seven-year mission. Instrument cross-calibration activities elucidated a more seamless set of data products. Specific topics include post-launch changes and enhancements to the SOCs, discussion of coordination activities between the SOCs, SOC specific analysis software, modeling software provided by the Van Allen Probes project, and a section on lessons learned. One of the most significant lessons learned was the importance of the original decision to implement individual team SOCs providing timely and well-documented instrument data for the NASA Van Allen Probes Mission scientists and the larger magnetospheric and radiation belt scientific community.
Mavacamten is a FDA-approved small-molecule therapeutic designed to regulate cardiac function at the sarcomere level by selectively but reversibly inhibiting the enzymatic activity of myosin. It shifts myosin toward ordered off states close to the thick filament backbone. It remains elusive whether these myosin heads in the off state(s) can be recruited in response to physiological stimuli when required to boost cardiac output. We show that cardiac myosins stabilized in these off state(s) by mavacamten are recruitable by 1) Ca 2+ , 2) increased chronotropy [heart rate (HR)], 3) stretch, and 4) β-adrenergic (β-AR) stimulation, all known physiological inotropic interventions. At the molecular level, we show that Ca 2+ increases myosin ATPase activity by shifting mavacamten-stabilized myosin heads from the inactive super-relaxed state to the active disordered relaxed state. At the myofilament level, both Ca 2+ and passive lengthening can shift mavacamten-ordered off myosin heads from positions close to the thick filament backbone to disordered on states closer to the thin filaments. In isolated rat cardiomyocytes, increased stimulation rates enhanced shortening fraction in mavacamten-treated cells. This observation was confirmed in vivo in telemetered rats, where left-ventricular dP/dt max , an index of inotropy, increased with HR in mavacamten-treated animals. Finally, we show that β-AR stimulation in vivo increases left-ventricular function and stroke volume in the setting of mavacamten. Our data demonstrate that the mavacamten-promoted off states of myosin in the thick filament are at least partially activable, thus preserving cardiac reserve mechanisms.
Inhaled irritant air pollutants may trigger stress-related metabolic dysfunction associated with altered circulating adrenal-derived hormones. We used implantable telemetry in rats to assess real-time changes in circulating glucose during and after exposure to ozone and mechanistically linked responses to neuroendocrine stress hormones. First, using a cross-over design, we monitored glucose during ozone exposures (0.0, 0.2, 0.4, and 0.8 ppm) and nonexposure periods in male Wistar Kyoto rats implanted with glucose telemeters. A second cohort of unimplanted rats was exposed to ozone (0.0, 0.4 or 0.8 ppm) for 30 min, 1 h, 2 h, or 4 h with hormones measured immediately post exposure. We assessed glucose metabolism in sham and adrenalectomized rats, with or without supplementation of adrenergic/glucocorticoid receptor agonists, and in a separate cohort, antagonists. Ozone (0.8 ppm) was associated with significantly higher blood glucose and lower core body temperature beginning 90 min into exposure, with reversal of effects 4–6 h post exposure. Glucose monitoring during four daily 4-h ozone exposures revealed duration of glucose increases, adaptation, and diurnal variations. Ozone-induced glucose changes were preceded by higher levels of adrenocorticotropic hormone, corticosterone, and epinephrine but lower levels of thyroid-stimulating hormone, prolactin, and luteinizing hormones. Higher glucose and glucose intolerance were inhibited in rats that were adrenalectomized or treated with adrenergic plus glucocorticoid receptor antagonists but exacerbated by agonists. We demonstrated the temporality of neuroendocrine-stress–mediated biological sequalae responsible for ozone-induced glucose metabolic dysfunction and mechanism in a rodent model.
Dissolved oxygen (DO) is critical for aquatic ecosystems. Our focus is on the long-term DO dynamics in hyporheic zone of rivers, which are a function of both transport (hydrologic exchange between river and hyporheic zone) and uptake by biogeochemical reactions or respiration. The study site is the alpine East River watershed in Colorado, USA, meander A downstream from the pump house. Opti O2 probes were deployed in the water column and directly within the river-bed at 10, 20, and 35 cm depth (38°55'23.38"N, 106°57'4.21"W, 9046.88m) to monitor DO and temperature. A continuous data stream from July 24, 2017 to Oct 24, 2018 was autonomously telemetered to the cloud. This 14-month DO and temperature time series were obtained without any servicing for maintenance or data downloads; additionally the ability to remotely verify probe performance during field deployment was essential to confirm data validity during winter freeze-in and hydrological/weather events, such as spring melt and summer monsoons. We investigate the variations in dissolved oxygen dynamics of this snow-pack dominated watershed during a comparatively low flow water year (2018) and a relatively normal water year (2017), enabled by distinctive, in-situ, high frequency (∆t = 5min) sensors that provided a continuous time-series from the undisturbed study site over multiple seasons.
Hyperspectral imaging is a very promising technology for nuclear proliferation detection. However, due to size and weight restrictions, small hyperspectral platforms such as satellites and small drones lack the on-board computing resources for accurate, real-time analysis of the enormous flow of data that a continuously operating hyperspectral sensor generates. This severely limits satellite systems, which can collect far more data than what they can telemeter, and hinders the ability of all platforms to adapt their missions on the fly in response to observations. This program addresses the hyperspectral data processing challenge through development of new, fast and accurate algorithms that produce data products in real time. The algorithms circumvent the major computational bottlenecks in existing processing streams, and would be incorporated in lightweight, power-efficient single-board computer systems. The toolkit of fast algorithms will be immediately useful in current and future hyperspectral systems being built by the Government and by private industry, including drone-based systems and satellite constellations that acquire timely global imagery.
In an effort to inform the Senior Seismic Hazard Analysis Committee for the Idaho National Laboratory, we provide an improved aftershock catalog related to the March 31, 2020, Mw6.5 Stanley, Idaho earthquake from picks related to a temporary network of two real-time and 15 non-telemetered seismometers within the epicentral area. From the permanent and temporary (XP) real-time network, the USGS cataloged 1,946 aftershocks between April 1, 2020 and October 31, 2020. To improve aftershock location and magnitudes, we manually picked arrival times of P and S waves from off-line stations in the XP temporary network, generated a new crustal velocity model, and independently relocated each event using the HypoDD double-difference earthquake algorithm. We created our new velocity model from existing broadband and active source seismic campaign data that were acquired near the epicentral region prior to the 2020 earthquake. We compare arrival time differences, epicentral locations and depths between aftershocks recorded with the two catalogs. We find the addition of local stations provides tighter aftershock clustering that suggests an improved aftershock locations. To detect lower magnitude events, we employed deep learning. Our method solves common problems associated with detecting many events that have a low signal-to-noise ratio. From the machine learning database, we detected more than 74,000 aftershocks. Based on the number of identified earthquakes and Gutenberg-Richter relationships derived from the USGS catalog, we estimate that we have reduced the completion magnitude for the Stanley earthquake sequence to below M1 using this machine learning approach. We located each aftershock with our new velocity model. Our new velocity model and picks suggests aftershocks occurred mostly at shallower depths than assessed in the USGS catalog. These aftershocks align along two linear trends that suggest the activation of two unnamed primary faults.
This dataset contains raw data collected by sentinel buoy 3 during the WFIP3 project period at the WFIP3 location. It was deployed from 3/14/2024 19:10Z to the present day, but stopped telemetering data on 4/17/2024 13:00Z.
Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood of agricultural communities. In the agriculture-dominated San Joaquin River Basin of California, real-time salinity management (RTSM) is a state-sanctioned program that helps to maximize allowable salt export while protecting existing basin beneficial uses of water supply. The RTSM strategy supplants the federal total maximum daily load (TMDL) approach that could impose fines associated with exceedances of monthly and annual salt load allocations of up to $1 million per year based on average year hydrology and salt load export limits. The essential components of the current program include the establishment of telemetered sensor networks, a web-based information system for sharing data, a basin-scale salt load assimilative capacity forecasting model and institutional entities tasked with performing weekly forecasts of river salt assimilative capacity and scheduling west-side drainage export of salt loads. Web-based information portals have been developed to share model input data and salt assimilative capacity forecasts together with increasing stakeholder awareness and involvement in water quality resource management activities in the river basin. Two modeling approaches have been developed simultaneously. The first relies on a statistical analysis of the relationship between flow and salt concentration at three compliance monitoring sites and the use of these regression relationships for forecasting. The second salt load forecasting approach is a customized application of the Watershed Analysis Risk Management Framework (WARMF), a watershed water quality simulation model that has been configured to estimate daily river salt assimilative capacity and to provide decision support for real-time salinity management at the watershed level. Analysis of the results from both model-based forecasting approaches over a period of five years shows that the regression-based forecasting model, run daily Monday to Friday each week, provided marginally better performance. However, the regression-based forecasting model assumes the same general relationship between flow and salinity which breaks down during extreme weather events such as droughts when water allocation cutbacks among stakeholders are not evenly distributed across the basin. A recent test case shows the utility of both models in dealing with an exceedance event at one compliance monitoring site recently introduced in 2020.