Resonance assignments for latherin, a natural surfactant protein from horse sweat

Latherin is an intrinsically surfactant protein of ~23 kDa found in the sweat and saliva of horses. Its function is probably to enhance the translocation of sweat water from the skin to the surface of the pelt for evaporative cooling. Its role in saliva may be to enhance the wetting, softening and maceration of the dry, fibrous food for which equines are adapted. Latherin is unusual in its relatively high content of aliphatic amino acids (~25 % leucines) that might contribute to its surfactant properties. Latherin is related to the palate, lung, and nasal epithelium carcinoma-associated proteins (PLUNCs) of mammals, at least one of which is now known to exhibit similar surfactant activity to latherin. No structures of any PLUNC protein are currently available. 15N,13C-labelled recombinant latherin was produced in Escherichia coli, and essentially all of the resonances were assigned despite the signal overlap due to the preponderance of leucines. The most notable exceptions include a number of residues located in an apparently dynamic loop region between residues 145 and 154. The assignments have been deposited with BMRB accession number 19067

The structure of latherin, a surfactant allergen protein from horse sweat and saliva

Latherin is a highly surface-active allergen protein found in the sweat and saliva of horses and other equids. Its surfactant activity is intrinsic to the protein in its native form, and is manifest without associated lipids or glycosylation. Latherin probably functions as a wetting agent in evaporative cooling in horses, but it may also assist in mastication of fibrous food as well as inhibition of microbial biofilms. It is a member of the PLUNC family of proteins abundant in the oral cavity and saliva of mammals, one of which has also been shown to be a surfactant and capable of disrupting microbial biofilms. How these proteins work as surfactants while remaining soluble and cell membrane-compatible is not known. Nor have their structures previously been reported. We have used protein nuclear magnetic resonance spectroscopy to determine the conformation and dynamics of latherin in aqueous solution. The protein is a monomer in solution with a slightly curved cylindrical structure exhibiting a ‘super-roll’ motif comprising a four-stranded anti-parallel β-sheet and two opposing α-helices which twist along the long axis of the cylinder. One end of the molecule has prominent, flexible loops that contain a number of apolar amino acid side chains. This, together with previous biophysical observations, leads us to a plausible mechanism for surfactant activity in which the molecule is first localized to the non-polar interface via these loops, and then unfolds and flattens to expose its hydrophobic interior to the air or non-polar surface. Intrinsically surface-active proteins are relatively rare in nature, and this is the first structure of such a protein from mammals to be reported. Both its conformation and proposed method of action are different from other, non-mammalian surfactant proteins investigated so far

An Algorithm for Detection of Ground and Canopy Cover in Micropulse Photon-Counting Lidar Altimeter Data in Preparation of the ICESat-2 Mission

The Ice, Cloud and Land Elevation Satellite-II (ICESat-2) mission has been selected by NASA as a Decadal Survey mission, to be launched in 2016. Mission objectives are to measure land ice elevation, sea ice freeboard/ thickness and changes in these variables and to collect measurements over vegetation that will facilitate determination of canopy height, with an accuracy that will allow prediction of future environmental changes and estimation of sea-level rise. The importance of the ICESat-2 project in estimation of biomass and carbon levels has increased substantially, following the recent cancellation of all other planned NASA missions with vegetation-surveying lidars. Two innovative components will characterize the ICESat-2 lidar: (1) Collection of elevation data by a multi-beam system and (2) application of micropulse lidar (photon counting) technology. A micropulse photon-counting altimeter yields clouds of discrete points, which result from returns of individual photons, and hence new data analysis techniques are required for elevation determination and association of returned points to reflectors of interest including canopy and ground in forested areas. The objective of this paper is to derive and validate an algorithm that allows detection of ground under dense canopy and identification of ground and canopy levels in simulated ICESat-2-type data. Data are based on airborne observations with a Sigma Space micropulse lidar and vary with respect to signal strength, noise levels, photon sampling options and other properties. A mathematical algorithm is developed, using spatial statistical and discrete mathematical concepts, including radial basis functions, density measures, geometrical anisotropy, eigenvectors and geostatistical classification parameters and hyperparameters. Validation shows that the algorithm works very well and that ground and canopy elevation, and hence canopy height, can be expected to be observable with a high accuracy during the ICESat-2 mission. A result relevant for instrument design is that even the two weaker beam classes considered can be expected to yield useful results for vegetation measurements (93.01-99.57% correctly selected points for a beam with expected return of 0.93 mean signals per shot (msp9) and 72.85% - 98.68% for 0.48 msp (msp4)). Resampling options affect results more than noise levels. The algorithm derived here is generally applicable for analysis of micropulse lidar altimeter data collected over forested areas as well as other surfaces, including land ice, sea ice and land surfaces

The trough-system algorithm and its application to spatial modeling of Greenland subglacial topography

This is the published version. Copyright 2014 Herzfeld et al.Dynamic ice-sheet models are used to assess the contribution of mass loss from the Greenland ice sheet to sea-level rise. Mass transfer from ice sheet to ocean is in a large part through outlet glaciers. Bed topography plays an important role in ice dynamics, since the acceleration from the slow-moving inland ice to an ice stream is in many cases caused by the existence of a subglacial trough or trough system. Problems are that most subglacial troughs are features of a scale not resolved in most ice-sheet models and that radar measurements of subglacial topography do not always reach the bottoms of narrow troughs. The trough-system algorithm introduced here employs mathematical morphology and algebraic topology to correctly represent subscale features in a topographic generalization, so the effects of troughs on ice flow are retained in ice-dynamic models. The algorithm is applied to derive a spatial elevation model of Greenland subglacial topography, integrating recently collected radar measurements (CReSIS data) of the Jakobshavn Isbræ, Helheim, Kangerdlussuaq and Petermann glacier regions. The resultant JakHelKanPet digital elevation model has been applied in dynamic ice-sheet modeling and sea-level-rise assessment

Advancing the Application of Design of Experiments (DOE) to Synthetic Theater Operations Research Model (STORM) Data

NPS NRP Project PosterThe Navy uses simulation-based campaign analysis to help measure risk for investment options for how best to equip, organize, supply, maintain, train, and employ our naval forces. The Synthetic Theater Operations Research Model (STORM) is a stochastic simulation model used to support campaign analysis by the U.S. Navy, Marine Corps, and Air Force. Building, testing, running, and analyzing campaign scenarios in STORM can be a complex, time-consuming process. The goal of this research is to apply Design Of Experiment (DOE) methods in the selection and creation of Design Points (DPs) to minimize the number of modeling runs required for meaningful comparisons. Another objective is to understand how best DOE methods can complement traditional baseline and excursion modeling. In addition to regular reviews, the research deliverables will include: (1) a final brief and/or technical report, in addition to student theses (if applicable); (2) all findings, methods, and data used in the study; and (3) appropriate conference or journal papers related to this research.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Advancing the Application of Design of Experiments (DOE) to Synthetic Theater Operations Research Model (STORM) Data

NPS NRP Technical ReportThe Navy uses simulation-based campaign analysis to help measure risk for investment options for how best to equip, organize, supply, maintain, train, and employ our naval forces. The Synthetic Theater Operations Research Model (STORM) is a stochastic simulation model used to support campaign analysis by the U.S. Navy, Marine Corps, and Air Force. Building, testing, running, and analyzing campaign scenarios in STORM can be a complex, time-consuming process. The goal of this research is to apply Design Of Experiment (DOE) methods in the selection and creation of Design Points (DPs) to minimize the number of modeling runs required for meaningful comparisons. Another objective is to understand how best DOE methods can complement traditional baseline and excursion modeling. In addition to regular reviews, the research deliverables will include: (1) a final brief and/or technical report, in addition to student theses (if applicable); (2) all findings, methods, and data used in the study; and (3) appropriate conference or journal papers related to this research.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Velocity Segregation and Systematic Biases In Velocity Dispersion Estimates With the SPT-GMOS Spectroscopic Survey

The velocity distribution of galaxies in clusters is not universal; rather, galaxies are segregated according to their spectral type and relative luminosity. We examine the velocity distributions of different populations of galaxies within 89 Sunyaev Zel'dovich (SZ) selected galaxy clusters spanning $0.28 < z < 1.08$. Our sample is primarily draw from the SPT-GMOS spectroscopic survey, supplemented by additional published spectroscopy, resulting in a final spectroscopic sample of 4148 galaxy spectra---2868 cluster members. The velocity dispersion of star-forming cluster galaxies is $17\pm4$% greater than that of passive cluster galaxies, and the velocity dispersion of bright ($m < m^{*}-0.5$) cluster galaxies is $11\pm4$% lower than the velocity dispersion of our total member population. We find good agreement with simulations regarding the shape of the relationship between the measured velocity dispersion and the fraction of passive vs. star-forming galaxies used to measure it, but we find a small offset between this relationship as measured in data and simulations in which suggests that our dispersions are systematically low by as much as 3\% relative to simulations. We argue that this offset could be interpreted as a measurement of the effective velocity bias that describes the ratio of our observed velocity dispersions and the intrinsic velocity dispersion of dark matter particles in a published simulation result. Measuring velocity bias in this way suggests that large spectroscopic surveys can improve dispersion-based mass-observable scaling relations for cosmology even in the face of velocity biases, by quantifying and ultimately calibrating them out.Comment: Accepted to ApJ; 21 pages, 11 figures, 5 table

Advancing the Application of Design of Experiments (DOE) to Synthetic Theater Operations Research Model (STORM) Data

NPS NRP Executive SummaryThe Navy uses simulation-based campaign analysis to help measure risk for investment options for how best to equip, organize, supply, maintain, train, and employ our naval forces. The Synthetic Theater Operations Research Model (STORM) is a stochastic simulation model used to support campaign analysis by the U.S. Navy, Marine Corps, and Air Force. Building, testing, running, and analyzing campaign scenarios in STORM can be a complex, time-consuming process. The goal of this research is to apply Design Of Experiment (DOE) methods in the selection and creation of Design Points (DPs) to minimize the number of modeling runs required for meaningful comparisons. Another objective is to understand how best DOE methods can complement traditional baseline and excursion modeling. In addition to regular reviews, the research deliverables will include: (1) a final brief and/or technical report, in addition to student theses (if applicable); (2) all findings, methods, and data used in the study; and (3) appropriate conference or journal papers related to this research.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.