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Josset D.,SSAI | Rogers R.,NASA | Pelon J.,University Pierre and Marie Curie | Hu Y.,NASA | And 3 more authors.
Optics Express

We are demonstrating on a few cases the capability of CALIPSO to retrieve the 532 nm lidar ratio over the ocean when CloudSat surface scattering cross section is used as a constraint. We are presenting the algorithm used and comparisons with the column lidar ratio retrieved by the NASA airborne high spectral resolution lidar. For the three cases presented here, the agreement is fairly good. The average CALIPSO 532 nm column lidar ratio bias is 13.7% relative to HSRL, and the relative standard deviation is 13.6%. Considering the natural variability of aerosol microphysical properties, this level of accuracy is significant since the lidar ratio is a good indicator of aerosol types. We are discussing dependencies of the accuracy of retrieved aerosol lidar ratio on atmospheric aerosol homogeneity, lidar signal to noise ratio, and errors in the optical depth retrievals. We are obtaining the best result (bias 7% and standard deviation around 6%) for a nighttime case with a relatively constant lidar ratio (in the vertical) indicative of homogeneous aerosol type. © 2011 Optical Society of America. Source

Lee K.O.,ETRI | Song H.Y.,ETRI | Chung H.,NIA
International Conference on Advanced Communication Technology, ICACT

The necessity, service categories, and merit points of smart-work were described for smart work implementation. Especially, the call set-up and call release procedure are shown for conference services, along with service scenarios and use case of A/V conferencing service in smart-work. © 2014 Global IT Research Institute (GIRI). Source

Suleman N.,NIA | Quinnell R.J.,University of Leeds | Compton S.G.,University of Leeds
Plant Systematics and Evolution

The host-specific relationship between fig trees (Ficus) and their pollinator wasps (Agaonidae) is a classic case of obligate mutualism. Pollinators reproduce within highly specialised inflorescences (figs) of fig trees that depend on the pollinator offspring for the dispersal of their pollen. About half of all fig trees are functionally dioecious, with separate male and female plants responsible for separate sexual functions. Pollen and the fig wasps that disperse it are produced within male figs, whereas female figs produce only seeds. Figs vary greatly in size between different species, with female flower numbers varying from tens to many thousands. Within species, the number of female flowers present in each fig is potentially a major determinant of the numbers of pollinator offspring and seeds produced. We recorded variation in female flower numbers within male and female figs of the dioecious Ficus montana growing under controlled conditions, and assessed the sources and consequences of inflorescence size variation for the reproductive success of the plants and their pollinator (Kradibia tentacularis). Female flower numbers varied greatly within and between plants, as did the reproductive success of the plants, and their pollinators. The numbers of pollinator offspring in male figs and seeds in female figs were positively correlated with female flower numbers, but the numbers of male flowers and a parasitoid of the pollinator were not. The significant variation in flower number among figs produced by different individuals growing under uniform conditions indicates that there is a genetic influence on inflorescence size and that this character may be subject to selection. © 2013 Springer-Verlag Wien. Source

Tolson R.H.,NIA | Prince J.L.H.,NASA | Konopliv A.A.,Group Solar
AIAA Modeling and Simulation Technologies (MST) Conference

Aerobraking has proven to be an enabling technology for planetary missions to Mars and has been proposed to enable low cost missions to Venus. Aerobraking saves a significant amount of propulsion fuel mass by exploiting atmospheric drag to reduce the eccentricity of the initial orbit. The solar arrays have been used as the primary drag surface and only minor modifications have been made in the vehicle design to accommodate the relatively modest aerothermal loads. However, if atmospheric density is highly variable from orbit to orbit, the mission must either accept higher aerothermal risk, a slower pace for aerobraking, or a tighter corridor likely with increased propulsive cost. Hence, knowledge of atmospheric variability is of great interest for the design of aerobraking missions. The first planetary aerobraking was at Venus during the Magellan mission. After the primary Magellan science mission was completed, aerobraking was used to provide a more circular orbit to enhance gravity field recovery. Magellan aerobraking took place between local solar times of 1100 and 1800 hrs, and it was found that the Venusian atmospheric density during the aerobraking phase had less than 10% 1σ orbit to orbit variability. On the other hand, at some latitudes and seasons, Martian variability can be as high as 40% 1σ. From both the MGN and PVO mission it was known that the atmosphere, above aerobraking altitudes, showed greater variability at night, but this variability was never quantified in a systematic manner. This paper proposes a model for atmospheric variability that can be used for aerobraking mission design until more complete data sets become available. Source

Crawled News Article
Site: http://phys.org/technology-news/

The algorithm and an initial round of testing are described in Nature's Scientific Reports. As neurons grow, they extend stringy appendages called neurites that form critical connections with neighboring cells. These networks of neurons and neurites are essential for healthy nervous system function, and scientists are interested in finding new ways to encourage neuron growth through drugs, electrical stimulation, or other means. To test the effects of those efforts, scientists grow neurons in the lab and apply different treatments to see if they stimulate growth. That usually involves taking hundreds of microscope pictures of neurons as they grow over the course of hours or days. "You're left with this gigantic stack of photos," said Tayhas Palmore, professor of engineering at Brown and the new paper's senior author. "You need to analyze the changes from one image to the next, and that can be really arduous." The details in those images are critical. Neurites are tiny structures that are hard to see under a microscope during live-cell imaging. But accurately measuring their length and thickness is important in assessing stimulated cell growth. There are a few algorithms available that automate the image analysis, but they don't do a terribly good job. They generally work by looking at individual pixels in an image and applying a uniform filter that picks out pixels with the highest intensity. Those high-intensity pixels are assumed to be neuron and neurite structures. The problem is microscope images often are not high quality, making it hard to discern cell structures from random artifacts that may be present in the image. As a result, the filters often include pixels that aren't relevant to neuron structures and edit out pixels that are important. This is especially a problem in measuring tiny neurite appendages. The filters often fail to measure the full extent of neurite growth. Kwang-Min Kim, a former graduate student in Palmore's lab and now a postdoctoral researcher at Stanford, wanted to find a better solution. Inspired by the previous work of Kilho Son, a graduate student in computer vision and co-first author on the paper, Kim developed a new method that dispenses with the uniform filters used in other approaches. Instead, the new approach, called Neuron Image Analyzer (NIA), takes into account how pixels are related to neighboring pixels. "We don't just look for high-intensity pixels," Kim said. "We look at the relational information between pixels. This way we can trace pixels that are connected to each other, which helps us trace the entire neuron structure." Another technique employed by the algorithm uses a particular statistical test that is good at picking out circular or elliptical structures. That test is used to accurately locate and measure the soma, the blob-shaped main body of a neuron. The researchers tested NIA against existing algorithms, using hand annotation of images as a benchmark. The results showed NIA to be 80 percent as accurate as hand coding, while the other algorithms were only 50 to 60 percent as accurate. The team hopes that other researchers will make use of the new approach. It could be especially useful in labs that lack the sophisticated and expensive equipment to take extremely high-quality neuron images. "We want to make this approach available to anyone who is interested in analyzing neuron images, regardless of the quality of their images," Kim said. Kim and Son plan to continue developing NIA in the hope of further improving its accuracy and speed. Explore further: Researchers restore image using version containing between 1 and 10 percent of information More information: Kwang-Min Kim et al. Neuron Image Analyzer: Automated and Accurate Extraction of Neuronal Data from Low Quality Images, Scientific Reports (2015). DOI: 10.1038/srep17062

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