6,536 research outputs found
Interactions between Type III receptor tyrosine phosphatases and growth factor receptor tyrosine kinases regulate tracheal tube formation in Drosophila
The respiratory (tracheal) system of the Drosophila melanogaster larva is an intricate branched network of air-filled tubes. Its developmental logic is similar in some ways to that of the vertebrate vascular system. We previously described a unique embryonic tracheal tubulogenesis phenotype caused by loss of both of the Type III receptor tyrosine phosphatases (RPTPs), Ptp4E and Ptp10D. In Ptp4E Ptp10D double mutants, the linear tubes in unicellular and terminal tracheal branches are converted into bubble-like cysts that incorporate apical cell surface markers. This tube geometry phenotype is modulated by changes in the activity or expression of the epidermal growth factor receptor (Egfr) tyrosine kinase (TK). Ptp10D physically interacts with Egfr. Here we demonstrate that the Ptp4E Ptp10D phenotype is the consequence of the loss of negative regulation by the RPTPs of three growth factor receptor TKs: Egfr, Breathless and Pvr. Reducing the activity of any of the three kinases by tracheal expression of dominant-negative mutants suppresses cyst formation. By competing dominant-negative and constitutively active kinase mutants against each other, we show that the three RTKs have partially interchangeable activities, so that increasing the activity of one kinase can compensate for the effects of reducing the activity of another. This implies that SH2-domain downstream effectors that are required for the phenotype are likely to be able to interact with phosphotyrosine sites on all three receptor TKs. We also show that the phenotype involves increases in signaling through the MAP kinase and Rho GTPase pathways
Prediction-learning in Infants as a Mechanism for Gaze Control during Object Exploration
We are pursuing the hypothesis that visual exploration and learning in young infants is achieved by producing gaze-sample sequences that are sequentially predictable. Our recent analysis of infants\u27 gaze patterns during image free-viewing (Schlesinger & Amso, 2013) provides support for this idea. In particular, this work demonstrates that infants\u27 gaze samples are more easily learnable than those produced by adults, as well as those produced by three artificial-observer models. In the current study, we extend these findings to a well-studied object-perception task, by investigating 3-month-olds\u27 gaze patterns as they view a moving, partially-occluded object. We first use infants\u27 gaze data from this task to produce a set of corresponding center-of-gaze (COG) sequences. Next, we generate two simulated sets of COG samples, from image-saliency and random-gaze models, respectively. Finally, we generate learnability estimates for the three sets of COG samples by presenting each as a training set to an SRN. There are two key findings. First, as predicted, infants COG samples from the occluded-object task are learned by a pool of simple recurrent networks faster than the samples produced by the yoked, artificial-observer models. Second, we also find that resetting activity in the recurrent layer increases the network’s prediction errors, which further implicates the presence of temporal structure in infants’ COG sequences. We conclude by relating our findings to the role of image-saliency and prediction-learning during the development of object perception
A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding
Remote sensing (RS) of plant canopies permits non-intrusive, high-throughput monitoring of plant physiological characteristics. This study compared three RS approaches using a low flying UAV (unmanned aerial vehicle), with that of proximal sensing, and satellite-based imagery. Two physiological traits were considered, canopy temperature (CT) and a vegetation index (NDVI), to determine the most viable approaches for large scale crop genetic improvement. The UAV-based platform achieves plot-level resolution while measuring several hundred plots in one mission via high-resolution thermal and multispectral imagery measured at altitudes of 30-100 m. The satellite measures multispectral imagery from an altitude of 770 km. Information was compared with proximal measurements using IR thermometers and an NDVI sensor at a distance of 0.5-1m above plots. For robust comparisons, CT and NDVI were assessed on panels of elite cultivars under irrigated and drought conditions, in different thermal regimes, and on un-adapted genetic resources under water deficit. Correlations between airborne data and yield/biomass at maturity were generally higher than equivalent proximal correlations. NDVI was derived from high-resolution satellite imagery for only larger sized plots (8.5 x 2.4 m) due to restricted pixel density. Results support use of UAV-based RS techniques for high-throughput phenotyping for both precision and efficiency
A Moment-Based Polarimetric Radar Forward Operator for Rain Microphysics
There is growing interest in combining microphysical models and polarimetric radar observations to improve our understanding of storms and precipitation. Mapping model-predicted variables into the radar observational space necessitates a forward operator, which requires assumptions that introduce uncertainties into model-observation comparisons. These include uncertainties arising from the microphysics scheme a priori assumptions of a fixed drop size distribution (DSD) functional form, whereas natural DSDs display far greater variability. To address this concern, this study presents a moment-based polarimetric radar forward operator with no fundamental restrictions on the DSD form by linking radar observables to integrated DSD moments. The forward operator is built upon a dataset of > 200 million realistic DSDs from one-dimensional bin microphysical rain shaft simulations, and surface disdrometer measurements from around the world. This allows for a robust statistical assessment of forward operator uncertainty and quantification of the relationship between polarimetric radar observables and DSD moments. Comparison of "truth" and forward-simulated vertical profiles of the polarimetric radar variables are shown for bin simulations using a variety of moment combinations. Higher-order moments (especially those optimized for use with the polarimetric radar variables: the 6th and 9th) perform better than the lower-order moments (0th and 3rd) typically predicted by many bulk microphysics schemes
Optimal Survey Strategies and Predicted Planet Yields for the Korean Microlensing Telescope Network
The Korean Microlensing Telescope Network (KMTNet) will consist of three 1.6m
telescopes each with a 4 deg^{2} field of view (FoV) and will be dedicated to
monitoring the Galactic Bulge to detect exoplanets via gravitational
microlensing. KMTNet's combination of aperture size, FoV, cadence, and
longitudinal coverage will provide a unique opportunity to probe exoplanet
demographics in an unbiased way. Here we present simulations that optimize the
observing strategy for, and predict the planetary yields of, KMTNet. We find
preferences for four target fields located in the central Bulge and an exposure
time of t_{exp} = 120s, leading to the detection of ~2,200 microlensing events
per year. We estimate the planet detection rates for planets with mass and
separation across the ranges 0.1 <= M_{p}/M_{Earth} <= 1000 and 0.4 <= a/AU <=
16, respectively. Normalizing these rates to the cool-planet mass function of
Cassan (2012), we predict KMTNet will be approximately uniformly sensitive to
planets with mass 5 <= M_{p}/M_{Earth} <= 1000 and will detect ~20 planets per
year per dex in mass across that range. For lower-mass planets with mass 0.1 <=
M_{p}/M_{Earth} < 5, we predict KMTNet will detect ~10 planets per year. We
also compute the yields KMTNet will obtain for free-floating planets (FFPs) and
predict KMTNet will detect ~1 Earth-mass FFP per year, assuming an underlying
population of one such planet per star in the Galaxy. Lastly, we investigate
the dependence of these detection rates on the number of observatories, the
photometric precision limit, and optimistic assumptions regarding seeing,
throughput, and flux measurement uncertainties.Comment: 29 pages, 31 figures, submitted to ApJ. For a brief video explaining
the key results of this paper, please visit:
https://www.youtube.com/watch?v=e5rWVjiO26
Rapidly Restoring Biological Soil Crusts and Ecosystem Functions in a Severely Disturbed Desert Ecosystem
Restoring biological soil crusts (biocrusts) in degraded drylands can contribute to recovery of ecosystem functions that have global implications, including erosion resistance and nutrient cycling. To examine techniques for restoring biocrusts, we conducted a replicated, factorial experiment on recently abandoned road surfaces by applying biocrust inoculation (salvaged and stored dry for two years), salvaged topsoil, an abiotic soil amendment (wood shavings), and planting of a dominant perennial shrub (Ambrosia dumosa). Eighteen months after treatments, we measured biocrust abundance and species composition, soil chlorophyll a content and fertility, and soil resistance to erosion. Biocrust addition significantly accelerated biocrust recovery on disturbed soils, including increasing lichen and moss cover and cyanobacteria colonization. Compared to undisturbed controls, inoculated plots had similar lichen and moss composition, recovered 43% of total cyanobacteria density, had similar soil chlorophyll content, and exhibited recovery of soil fertility and soil stability. Inoculation was the only treatment that generated lichen and moss cover. Topsoil application resulted in partial recovery of the cyanobacteria community and soil properties. Compared to untreated disturbed plots, topsoil application without inoculum increased cyanobacteria density by 186% and moderately improved soil chlorophyll and ammonium content and soil stability. Topsoil application produced 22% and 51% of the cyanobacteria density g−1 soil compared to undisturbed and inoculated plots, respectively. Plots not treated with either topsoil or inoculum had significantly lower cyanobacteria density, soil chlorophyll and ammonium concentrations, and significantly higher soil nitrate concentration. Wood shavings and Ambrosia had no influence on biocrust lichen and moss species recovery but did affect cyanobacteria composition and soil fertility. Inoculation of severely disturbed soil with native biocrusts rapidly restored biocrust communities and soil stability such that restored areas were similar to undisturbed desert within three years. Using salvaged biocrust as inoculum can be an effective tool in ecological restoration because of its efficacy and simple implementation. Although salvaging biocrust material can be technically difficult and potentially costly, utilizing opportunities to salvage material in planned future disturbance can provide additional land management tools
Semi-automated image analysis for the identification of bivalve larvae from a Cape Cod estuary
Author Posting. © Association for the Sciences of Limnology and Oceanography, 2012. This article is posted here by permission of Association for the Sciences of Limnology and Oceanography for personal use, not for redistribution. The definitive version was published in Limnology and Oceanography: Methods 10 (2012): 538-554, doi:10.4319/lom.2012.10.538.Machine-learning methods for identifying planktonic organisms are becoming well-established. Although similar morphologies among species make traditional image identification methods difficult for larval bivalves, species-specific shell birefringence patterns under polarized light permit identification by color and texture-based features. This approach uses cross-polarized images of bivalve larvae, extracts Gabor and color angle features from each image, and classifies images using a Support Vector Machine. We adapted this method, which was established on hatchery-reared larvae, to identify bivalve larvae from a series of field samples from a Cape Cod estuary in 2009. This method had 98% identification accuracy for four hatchery-reared species. We used a multiplex polymerase chain reaction (PCR) method to confirm field identifications and to compare accuracies to the software classifications. Image classification of larvae collected in the field had lower accuracies than both the classification of hatchery species and PCR-based identification due to error in visually classifying unknown larvae and variability in larval images from the field. A six-species field training set had the best correspondence to our visual classifications with 75% overall agreement and individual species agreements from 63% to 88%. Larval abundance estimates for a time-series of field samples showed good correspondence with visual methods after correction. Overall, this approach represents a cost- and time-saving alternative to molecular-based identifications and can produce sufficient results to address long-term abundance and transport-based questions on a species-specific level, a rarity in studies of bivalve larvae.This project was supported by an award to S. Gallager
and C. Mingione Thompson from the Estuarine Reserves Division, Office
of Ocean and Coastal Resource Management, National Ocean Service,
National Oceanic and Atmospheric Administration and a grant from
Woods Hole Oceanographic Institution’s Coastal Ocean Institute
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