THP Snow Program: Future Planning and Activities
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snow remote sensing↗
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Induction of ceruloplasmin synthesis by IFN-gamma in human monocytic cells
Ceruloplasmin is a 132-kDa glycoprotein abundant in human plasma. It has multiple in vitro activities, including copper transport, lipid pro- and antioxidant activity, and oxidation of ferrous ion and aromatic amines; however, its physiologic role is uncertain. Although ceruloplasmin is synthesized primarily by the liver in adult humans, production by cells of monocytic origin has been reported. We here show that IFN-gamma is a potent inducer of ceruloplasmin synthesis by monocytic cells. Activation of human monoblastic leukemia U937 cells with IFN-gamma increased the production of ceruloplasmin by at least 20-fold. The identity of the protein was confirmed by plasmin fingerprinting. IFN-gamma also increased ceruloplasmin mRNA. Induction followed a 2- to 4-h lag and was partially blocked by cycloheximide, indicating a requirement for newly synthesized factors. Ceruloplasmin induction in monocytic cells was agonist specific, as IL-1, IL-4, IL-6, IFN-alpha, IFN-beta, TNF-alpha, and LPS were completely ineffective. The induction was also cell type specific, as IFN-gamma did not induce ceruloplasmin synthesis in endothelial or smooth muscle cells. In contrast, IFN-gamma was stimulatory in other monocytic cells, including THP-1 cells and human peripheral blood monocytes, and also in HepG2 cells. Ceruloplasmin secreted by IFN-gamma-stimulated U937 cells had ferroxidase activity and was, in fact, the only secreted protein with this activity. Monocytic cell-derived ceruloplasmin may contribute to defense responses via its ferroxidase activity, which may drive iron homeostasis in a direction unfavorable to invasive organisms.
Non-NASA Center↗
Tool for Automated Retrieval of Generic Event Tracks (TARGET)
Methods have been developed to identify and track tornado-producing mesoscale convective systems (MCSs) automatically over the continental United States, in order to facilitate systematic studies of these powerful and often destructive events. Several data sources were combined to ensure event identification accuracy. Records of watches and warnings issued by National Weather Service (NWS), and tornado locations and tracks from the Tornado History Project (THP) were used to locate MCSs in high-resolution precipitation observations and GOES infrared (11-micron) Rapid Scan Operation (RSO) imagery. Thresholds are then applied to the latter two data sets to define MCS events and track their developments. MCSs produce a broad range of severe convective weather events that are significantly affecting the living conditions of the populations exposed to them. Understanding how MCSs grow and develop could help scientists improve their weather prediction models, and also provide tools to decision-makers whose goals are to protect populations and their property. Associating storm cells across frames of remotely sensed images poses a difficult problem because storms evolve, split, and merge. Any storm-tracking method should include the following processes: storm identification, storm tracking, and quantification of storm intensity and activity. The spatiotemporal coordinates of the tracks will enable researchers to obtain other coincident observations to conduct more thorough studies of these events. In addition to their tracked locations, their areal extents, precipitation intensities, and accumulations all as functions of their evolutions in time were also obtained and recorded for these events. All parameters so derived can be catalogued into a moving object database (MODB) for custom queries. The purpose of this software is to provide a generalized, cross-platform, pluggable tool for identifying events within a set of scientific data based upon specified criteria with the possibility of storing identified events into a searchable database. The core of the application uses an implementation of the connected component labeling (CCL) algorithm to identify areas of interest, then uses a set of criteria to establish spatial and temporal relationships between identified components. The CCL algorithm is used for identifying objects within images for computer vision. This application applies it to scientific data sets using arbitrary criteria. The most novel concept was applying a generalized CCL implementation to scientific data sets for establishing events both spatially and temporally. The combination of several existing concepts (pluggable components, generalized CCL algorithm, etc.) into one application is also novel. In addition, how the system is designed, i.e., its extensibility with pluggable components, and its configurability with a simple configuration file, is innovative. This allows the system to be applied to new scenarios with ease.
Clune, Thomas↗
Development of a Higher Fidelity Model for the Cascade Distillation Subsystem (CDS)
Significant improvements have been made to the ACM model of the CDS, enabling accurate predictions of dynamic operations with fewer assumptions. The model has been utilized to predict how CDS performance would be impacted by changing operating parameters, revealing performance trade-offs and possibilities for improvement. CDS efficiency is driven by the THP coefficient of performance, which in turn is dependent on heat transfer within the system. Based on the remaining limitations of the simulation, priorities for further model development include: center dot Relaxing the assumption of total condensation center dot Incorporating dynamic simulation capability for the buildup of dissolved inert gasses in condensers center dot Examining CDS operation with more complex feeds center dot Extending heat transfer analysis to all surfaces
Perry, Bruce↗