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