721 research outputs found
Forest inventory using sparsely sampled LIDAR and NFI: A case study using G-LiHT LiDAR and FIA across Tanana, Alaska
A two-stage hierarchical Bayesian model is proposed to estimate forest
biomass density and total given sparsely sampled LiDAR and georeferenced forest
inventory plot measurements. The model is motivated by the United States
Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis
(FIA) objective to provide biomass estimates for the remote Tanana Inventory
Unit (TIU) in interior Alaska. The proposed model yields stratum-level biomass
estimates for arbitrarily sized areas of interest. Model-based estimates are
compared with the TIU FIA design-based post-stratified estimates. Model-based
small area estimates (SAEs) for two experimental forests within the TIU are
compared with each forest's design-based estimates generated using a dense
network of independent inventory plots. Model parameter estimates and biomass
predictions are informed using FIA plot measurements, LiDAR data that is
spatially aligned with a subset of the FIA plots, and wall-to-wall remotely
sensed data used to define landuse/landcover stratum and percent forest canopy
cover. Results support a model-based approach to estimating forest variables
when inventory data are sparse or resources limit collection of enough data to
achieve desired accuracy and precision using design-based methods
Strings on conifolds from strong coupling dynamics, part I
A method to solve various aspects of the strong coupling expansion of the
superconformal field theory duals of AdS_5 x X geometries from first principles
is proposed. The main idea is that at strong coupling the configurations that
dominate the low energy dynamics of the field theory compactified on a three
sphere are given by certain non-trivial semi-classical configurations in the
moduli space of vacua.
We show that this approach is self-consistent and permits one to express most
of the dynamics in terms of an effective N=4 SYM dynamics. This has the
advantage that some degrees of freedom that move the configurations away from
moduli space can be treated perturbatively, unifying the essential low energy
dynamics of all of these theories. We show that with this formalism one can
compute the energies of strings in the BMN limit in the Klebanov-Witten theory
from field theory considerations, matching the functional form of results found
using AdS geometry. This paper also presents various other technical results
for the semiclassical treatment of superconformal field theories.Comment: 52 pages, JHEP3 styl
Lightcurves of Type Ia Supernovae from Near the Time of Explosion
We present a set of 11 type Ia supernova (SN Ia) lightcurves with dense,
pre-maximum sampling. These supernovae (SNe), in galaxies behind the Large
Magellanic Cloud (LMC), were discovered by the SuperMACHO survey. The SNe span
a redshift range of z = 0.11 - 0.35. Our lightcurves contain some of the
earliest pre-maximum observations of SNe Ia to date. We also give a functional
model that describes the SN Ia lightcurve shape (in our VR-band). Our function
uses the "expanding fireball" model of Goldhaber et al. (1998) to describe the
rising lightcurve immediately after explosion but constrains it to smoothly
join the remainder of the lightcurve. We fit this model to a composite observed
VR-band lightcurve of three SNe between redshifts of 0.135 to 0.165. These SNe
have not been K-corrected or adjusted to account for reddening. In this
redshift range, the observed VR-band most closely matches the rest frame
V-band. Using the best fit to our functional description of the lightcurve, we
find the time between explosion and observed VR-band maximum to be
17.6+-1.3(stat)+-0.07(sys) rest-frame days for a SN Ia with a VR-band Delta
m_{-10} of 0.52mag. For the redshifts sampled, the observed VR-band
time-of-maximum brightness should be the same as the rest-frame V-band maximum
to within 1.1 rest-frame days.Comment: 35 pages, 18 figures, 15 tables; Higher quality PDF available at
http://ctiokw.ctio.noao.edu/~sm/sm/SNrise/index.html; AJ accepte
Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping
Gathering information about forest variables is an expensive and arduous
activity. As such, directly collecting the data required to produce
high-resolution maps over large spatial domains is infeasible. Next generation
collection initiatives of remotely sensed Light Detection and Ranging (LiDAR)
data are specifically aimed at producing complete-coverage maps over large
spatial domains. Given that LiDAR data and forest characteristics are often
strongly correlated, it is possible to make use of the former to model,
predict, and map forest variables over regions of interest. This entails
dealing with the high-dimensional () spatially dependent LiDAR
outcomes over a large number of locations (~10^5-10^6). With this in mind, we
develop the Spatial Factor Nearest Neighbor Gaussian Process (SF-NNGP) model,
and embed it in a two-stage approach that connects the spatial structure found
in LiDAR signals with forest variables. We provide a simulation experiment that
demonstrates inferential and predictive performance of the SF-NNGP, and use the
two-stage modeling strategy to generate complete-coverage maps of forest
variables with associated uncertainty over a large region of boreal forests in
interior Alaska
Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping
Gathering information about forest variables is an expensive and arduous
activity. As such, directly collecting the data required to produce
high-resolution maps over large spatial domains is infeasible. Next generation
collection initiatives of remotely sensed Light Detection and Ranging (LiDAR)
data are specifically aimed at producing complete-coverage maps over large
spatial domains. Given that LiDAR data and forest characteristics are often
strongly correlated, it is possible to make use of the former to model,
predict, and map forest variables over regions of interest. This entails
dealing with the high-dimensional () spatially dependent LiDAR
outcomes over a large number of locations (~10^5-10^6). With this in mind, we
develop the Spatial Factor Nearest Neighbor Gaussian Process (SF-NNGP) model,
and embed it in a two-stage approach that connects the spatial structure found
in LiDAR signals with forest variables. We provide a simulation experiment that
demonstrates inferential and predictive performance of the SF-NNGP, and use the
two-stage modeling strategy to generate complete-coverage maps of forest
variables with associated uncertainty over a large region of boreal forests in
interior Alaska
Human Skeletal Stem Cell Response to Multiscale Topography Induced by Large Area Electron Beam Irradiation Surface Treatment
The healthcare socio-economic environment is irreversibly changing as a consequence of an increasing aging population, consequent functional impairment, and patient quality of life expectations. The increasing complexity of ensuing clinical scenarios compels a critical search for novel musculoskeletal regenerative and replacement strategies. While joint arthroplasty is a highly effective treatment for arthritis and osteoporosis, further innovation and refinement of uncemented implants are essential in order to improve implant integration and reduce implant revision rate. This is critical given financial restraints and the drive to improve cost-effectiveness and quality of life outcomes. Multi-scale modulation of implant surfaces, offers an innovative approach to enhancement in implant performance. In the current study, we have examined the potential of large area electron beam melting to alter the surface nanotopography in titanium alloy (Ti6Al4V). We evaluated the in vitro osteogenic response of human skeletal stem cells to the resultant nanotopography, providing evidence of the relationship between the biological response, particularly Collagen type I and Osteocalcin gene activation, and surface nanoroughness. The current studies demonstrate osteogenic gene induction and morphological cell changes to be significantly enhanced on a topography Ra of ~40 nm with clinical implications therein for implant surface treatment and generation
Evaluation of a candidate breast cancer associated SNP in ERCC4 as a risk modifier in BRCA1 and BRCA2 mutation carriers. Results from the Consortium of Investigators of Modifiers of BRCA1/BRCA2 (CIMBA)
Background: In this study we aimed to evaluate the role of a SNP in intron 1 of the ERCC4 gene (rs744154), previously reported to be associated with a reduced risk of breast cancer in the general population, as a breast cancer risk modifier in BRCA1 and BRCA2 mutation carriers. Methods: We have genotyped rs744154 in 9408 BRCA1 and 5632 BRCA2 mutation carriers from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and assessed its association with breast cancer risk using a retrospective weighted cohort approach. Results: We found no evidence of association with breast cancer risk for BRCA1 (per-allele HR: 0.98, 95% CI: 0.93–1.04, P=0.5) or BRCA2 (per-allele HR: 0.97, 95% CI: 0.89–1.06, P=0.5) mutation carriers. Conclusion: This SNP is not a significant modifier of breast cancer risk for mutation carriers, though weak associations cannot be ruled out. A Osorio1, R L Milne2, G Pita3, P Peterlongo4,5, T Heikkinen6, J Simard7, G Chenevix-Trench8, A B Spurdle8, J Beesley8, X Chen8, S Healey8, KConFab9, S L Neuhausen10, Y C Ding10, F J Couch11,12, X Wang11, N Lindor13, S Manoukian4, M Barile14, A Viel15, L Tizzoni5,16, C I Szabo17, L Foretova18, M Zikan19, K Claes20, M H Greene21, P Mai21, G Rennert22, F Lejbkowicz22, O Barnett-Griness22, I L Andrulis23,24, H Ozcelik24, N Weerasooriya23, OCGN23, A-M Gerdes25, M Thomassen25, D G Cruger26, M A Caligo27, E Friedman28,29, B Kaufman28,29, Y Laitman28, S Cohen28, T Kontorovich28, R Gershoni-Baruch30, E Dagan31,32, H Jernström33, M S Askmalm34, B Arver35, B Malmer36, SWE-BRCA37, S M Domchek38, K L Nathanson38, J Brunet39, T Ramón y Cajal40, D Yannoukakos41, U Hamann42, HEBON37, F B L Hogervorst43, S Verhoef43, EB Gómez García44,45, J T Wijnen46,47, A van den Ouweland48, EMBRACE37, D F Easton49, S Peock49, M Cook49, C T Oliver49, D Frost49, C Luccarini50, D G Evans51, F Lalloo51, R Eeles52, G Pichert53, J Cook54, S Hodgson55, P J Morrison56, F Douglas57, A K Godwin58, GEMO59,60,61, O M Sinilnikova59,60, L Barjhoux59,60, D Stoppa-Lyonnet61, V Moncoutier61, S Giraud59, C Cassini62,63, L Olivier-Faivre62,63, F Révillion64, J-P Peyrat64, D Muller65, J-P Fricker65, H T Lynch66, E M John67, S Buys68, M Daly69, J L Hopper70, M B Terry71, A Miron72, Y Yassin72, D Goldgar73, Breast Cancer Family Registry37, C F Singer74, D Gschwantler-Kaulich74, G Pfeiler74, A-C Spiess74, Thomas v O Hansen75, O T Johannsson76, T Kirchhoff77, K Offit77, K Kosarin77, M Piedmonte78, G C Rodriguez79, K Wakeley80, J F Boggess81, J Basil82, P E Schwartz83, S V Blank84, A E Toland85, M Montagna86, C Casella87, E N Imyanitov88, A Allavena89, R K Schmutzler90, B Versmold90, C Engel91, A Meindl92, N Ditsch93, N Arnold94, D Niederacher95, H Deißler96, B Fiebig97, R Varon-Mateeva98, D Schaefer99, U G Froster100, T Caldes101, M de la Hoya101, L McGuffog49, A C Antoniou49, H Nevanlinna6, P Radice4,5 and J Benítez1,3 on behalf of CIMB
20,000 years of societal vulnerability and adaptation to climate change in southwest Asia.
The Fertile Crescent, its hilly flanks and surrounding drylands has been a critical region for studying how climate has influenced societal change, and this review focuses on the region over the last 20,000 years. The complex social, economic, and environmental landscapes in the region today are not new phenomena and understanding their interactions requires a nuanced, multidisciplinary understanding of the past. This review builds on a history of collaboration between the social and natural palaeoscience disciplines. We provide a multidisciplinary, multiscalar perspective on the relevance of past climate, environmental, and archaeological research in assessing present day vulnerabilities and risks for the populations of southwest Asia. We discuss the complexity of palaeoclimatic data interpretation, particularly in relation to hydrology, and provide an overview of key time periods of palaeoclimatic interest. We discuss the critical role that vegetation plays in the human-climate-environment nexus and discuss the implications of the available palaeoclimate and archaeological data, and their interpretation, for palaeonarratives of the region, both climatically and socially. We also provide an overview of how modelling can improve our understanding of past climate impacts and associated change in risk to societies. We conclude by looking to future work, and identify themes of "scale" and "seasonality" as still requiring further focus. We suggest that by appreciating a given locale's place in the regional hydroscape, be it an archaeological site or palaeoenvironmental archive, more robust links to climate can be made where appropriate and interpretations drawn will demand the resolution of factors acting across multiple scales. This article is categorized under:Human Water > Water as Imagined and RepresentedScience of Water > Water and Environmental ChangeWater and Life > Nature of Freshwater Ecosystems
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