2,239 research outputs found
Role of Disease & Insects in Aspen Ecology
Aspen is a keystone species in montane forests, and enhances a number of key resource values including wildlife habitat, water resources, and fire behavior. Recently, aspen forests have experienced episodes of dieback and decline across western North America (Worrall et al. 2010, Guyon and Hoffman 2011). A large proportion of the dieback and decline has been linked to drought stress or drought-prone locations (Hanna and Kulakowski 2012). However, insects and diseases are commonly found in declining aspen stands, leading to confusion about the role of insects and diseases in aspen ecology. Additionally, aspen has a reputation for being susceptible to many diseases and insects, but only a few cause significant damage in the western U.S. (Worrall et al. 2010, Guyon and Hoffman 2011)
Comparison of coronagraphs for high contrast imaging in the context of Extremely Large Telescopes
We compare coronagraph concepts and investigate their behavior and
suitability for planet finder projects with Extremely Large Telescopes (ELTs,
30-42 meters class telescopes). For this task, we analyze the impact of major
error sources that occur in a coronagraphic telescope (central obscuration,
secondary support, low-order segment aberrations, segment reflectivity
variations, pointing errors) for phase, amplitude and interferometric type
coronagraphs. This analysis is performed at two different levels of the
detection process: under residual phase left uncorrected by an eXtreme Adaptive
Optics system (XAO) for a large range of Strehl ratio and after a general and
simple model of speckle calibration, assuming common phase aberrations between
the XAO and the coronagraph (static phase aberrations of the instrument) and
non-common phase aberrations downstream of the coronagraph (differential
aberrations provided by the calibration unit). We derive critical parameters
that each concept will have to cope with by order of importance. We evidence
three coronagraph categories as function of the accessible angular separation
and proposed optimal one in each case. Most of the time amplitude concepts
appear more favorable and specifically, the Apodized Pupil Lyot Coronagraph
gathers the adequate characteristics to be a baseline design for ELTs.Comment: 12 pages, 6 figures, Accepted for publication in A&
Zenithal bistability in a nematic liquid crystal device with a monostable surface condition
The ground-state director configurations in a grating-aligned, zenithally bistable nematic device are calculated in two dimensions using a Q tensor approach. The director profiles generated are well described by a one-dimensional variation of the director across the width of the device, with the distorted region near the grating replaced by an effective surface anchoring energy. This work shows that device bistability can in fact be achieved by using a monostable surface term in the one-dimensional model. This implies that is should be possible to construct a device showing zenithal bistability without the need for a micropatterned surface
Three-dimensional foam flow resolved by fast X-ray tomographic microscopy
Thanks to ultra fast and high resolution X-ray tomography, we managed to
capture the evolution of the local structure of the bubble network of a 3D foam
flowing around a sphere. As for the 2D foam flow around a circular obstacle, we
observed an axisymmetric velocity field with a recirculation zone, and
indications of a negative wake downstream the obstacle. The bubble
deformations, quantified by a shape tensor, are smaller than in 2D, due to a
purely 3D feature: the azimuthal bubble shape variation. Moreover, we were able
to detect plastic rearrangements, characterized by the neighbor-swapping of
four bubbles. Their spatial structure suggest that rearrangements are triggered
when films faces get smaller than a characteristic area.Comment: 5 pages, 5 figure
Personalized Pancreatic Tumor Growth Prediction via Group Learning
Tumor growth prediction, a highly challenging task, has long been viewed as a
mathematical modeling problem, where the tumor growth pattern is personalized
based on imaging and clinical data of a target patient. Though mathematical
models yield promising results, their prediction accuracy may be limited by the
absence of population trend data and personalized clinical characteristics. In
this paper, we propose a statistical group learning approach to predict the
tumor growth pattern that incorporates both the population trend and
personalized data, in order to discover high-level features from multimodal
imaging data. A deep convolutional neural network approach is developed to
model the voxel-wise spatio-temporal tumor progression. The deep features are
combined with the time intervals and the clinical factors to feed a process of
feature selection. Our predictive model is pretrained on a group data set and
personalized on the target patient data to estimate the future spatio-temporal
progression of the patient's tumor. Multimodal imaging data at multiple time
points are used in the learning, personalization and inference stages. Our
method achieves a Dice coefficient of 86.8% +- 3.6% and RVD of 7.9% +- 5.4% on
a pancreatic tumor data set, outperforming the DSC of 84.4% +- 4.0% and RVD
13.9% +- 9.8% obtained by a previous state-of-the-art model-based method
Self-assembly of peptide-based nanostructures: Synthesis and biological activity
Peptide-based nanostructures have received much attention in the field of drug targeting. In fact, peptides have many advantages such as simplicity of the structure, biocompatibility, and chemical diversity. Moreover, some peptides, which are called cell-penetrating peptides, can cross cellular membranes and carry small molecules, small interfering RNA, or viruses inside live cells. These molecules are often covalently or noncovalently linked to cargoes, thus forming amphiphilic conjugates that can self-assemble. Supramolecular nanostructures formed from peptides are used in nanomedicine as a carrier to protect a drug and to target cancer cells. This review explores aliphatic-chain–conjugated peptides and drug-conjugated peptides that can self-assemble. Special emphasis is placed on the synthesis procedure, nanostructure formation, and biological activity
Adaptive optics in high-contrast imaging
The development of adaptive optics (AO) played a major role in modern
astronomy over the last three decades. By compensating for the atmospheric
turbulence, these systems enable to reach the diffraction limit on large
telescopes. In this review, we will focus on high contrast applications of
adaptive optics, namely, imaging the close vicinity of bright stellar objects
and revealing regions otherwise hidden within the turbulent halo of the
atmosphere to look for objects with a contrast ratio lower than 10^-4 with
respect to the central star. Such high-contrast AO-corrected observations have
led to fundamental results in our current understanding of planetary formation
and evolution as well as stellar evolution. AO systems equipped three
generations of instruments, from the first pioneering experiments in the
nineties, to the first wave of instruments on 8m-class telescopes in the years
2000, and finally to the extreme AO systems that have recently started
operations. Along with high-contrast techniques, AO enables to reveal the
circumstellar environment: massive protoplanetary disks featuring spiral arms,
gaps or other asymmetries hinting at on-going planet formation, young giant
planets shining in thermal emission, or tenuous debris disks and micron-sized
dust leftover from collisions in massive asteroid-belt analogs. After
introducing the science case and technical requirements, we will review the
architecture of standard and extreme AO systems, before presenting a few
selected science highlights obtained with recent AO instruments.Comment: 24 pages, 14 figure
Digging into acceptor splice site prediction : an iterative feature selection approach
Feature selection techniques are often used to reduce data dimensionality, increase classification performance, and gain insight into the processes that generated the data. In this paper, we describe an iterative procedure of feature selection and feature construction steps, improving the classification of acceptor splice sites, an important subtask of gene prediction.
We show that acceptor prediction can benefit from feature selection, and describe how feature selection techniques can be used to gain new insights in the classification of acceptor sites. This is illustrated by the identification of a new, biologically motivated feature: the AG-scanning feature.
The results described in this paper contribute both to the domain of gene prediction, and to research in feature selection techniques, describing a new wrapper based feature weighting method that aids in knowledge discovery when dealing with complex datasets
Reference-less detection, astrometry, and photometry of faint companions with adaptive optics
We propose a complete framework for the detection, astrometry, and photometry
of faint companions from a sequence of adaptive optics corrected short
exposures. The algorithms exploit the difference in statistics between the
on-axis and off-axis intensity. Using moderate-Strehl ratio data obtained with
the natural guide star adaptive optics system on the Lick Observatory's 3-m
Shane Telescope, we compare these methods to the standard approach of PSF
fitting. We give detection limits for the Lick system, as well as a first guide
to expected accuracy of differential photometry and astrometry with the new
techniques. The proposed approach to detection offers a new way of determining
dynamic range, while the new algorithms for differential photometry and
astrometry yield accurate results for very faint and close-in companions where
PSF fitting fails. All three proposed algorithms are self-calibrating, i.e.
they do not require observation of a calibration star thus improving the
observing efficiency.Comment: Astrophysical Journal 698 (2009) 28-4
Is This a Joke? Detecting Humor in Spanish Tweets
While humor has been historically studied from a psychological, cognitive and
linguistic standpoint, its study from a computational perspective is an area
yet to be explored in Computational Linguistics. There exist some previous
works, but a characterization of humor that allows its automatic recognition
and generation is far from being specified. In this work we build a
crowdsourced corpus of labeled tweets, annotated according to its humor value,
letting the annotators subjectively decide which are humorous. A humor
classifier for Spanish tweets is assembled based on supervised learning,
reaching a precision of 84% and a recall of 69%.Comment: Preprint version, without referra
- …