1,169 research outputs found
Morphological studies and molecular data on a new marine ciliate, Apokeronopsis sinica n. sp. (Ciliophora: Urostylida), from the South China Sea
No full textLiu, Weiwei, Li, Jiqiu, Gao, Shan, Shao, Chen, Gong, Jun, Lin, Xiaofeng, Liu, Hongbin, Song, Weibo (2009): Morphological studies and molecular data on a new marine ciliate, Apokeronopsis sinica n. sp. (Ciliophora: Urostylida), from the South China Sea. Zootaxa 2005 (1): 57-66, DOI: 10.11646/zootaxa.2005.1.5, URL: http://dx.doi.org/10.11646/zootaxa.2005.1.
FIGURE 4 in Morphological studies and molecular data on a new marine ciliate, Apokeronopsis sinica n. sp. (Ciliophora: Urostylida), from the South China Sea
No full textFIGURE 4. Unmatched sites from SSrRNA gene sequences alignment. Numbers represent the positions of the nucleotides. Missing sites are compensated by gaps (-). Matched sites are marked with dots (.).Published as part of <i>Liu, Weiwei, Li, Jiqiu, Gao, Shan, Shao, Chen, Gong, Jun, Lin, Xiaofeng, Liu, Hongbin & Song, Weibo, 2009, Morphological studies and molecular data on a new marine ciliate, Apokeronopsis sinica n. sp. (Ciliophora: Urostylida), from the South China Sea, pp. 57-66 in Zootaxa 2005 (1)</i> on page 63, DOI: 10.11646/zootaxa.2005.1.5, <a href="http://zenodo.org/record/10093145">http://zenodo.org/record/10093145</a>
sj-docx-1-tan-10.1177_17562864221104508 – Supplemental material for Prediction of the generalization of myasthenia gravis with purely ocular symptoms at onset: a multivariable model development and validation
No full textSupplemental material, sj-docx-1-tan-10.1177_17562864221104508 for Prediction of the generalization of myasthenia gravis with purely ocular symptoms at onset: a multivariable model development and validation by Feng Li, Hongbin Zhang, Ya Tao, Frauke Stascheit, Jiaojiao Han, Feng Gao, Hongbo Liu, Alberto Carmona-Bayonas, Zhongmin Li, Jens-C. Rueckert, Andreas Meisel and Song Zhao in Therapeutic Advances in Neurological Disorders</p
FIGURE 3 in Morphological studies and molecular data on a new marine ciliate, Apokeronopsis sinica n. sp. (Ciliophora: Urostylida), from the South China Sea
No full textFIGURE 3. Comparison of Apokeronopsis species. A. crassa (A, B, from Song et al. 2004), A. wrighti (C, D, from Long et al. 2008), A. antarctica (E, F, from Petz 1995), A. ovalis (G, H, from Shao et al. 2009), A. bergeri (I, J, from Li et al. 2008), and A. sinica (K, L, original), to show the morphology of live cells (A, C, E, G, I, K), infraciliature (B, D, F, H, J, L). Scale bars in (A, B, I, J) =100 µm, in (C–H, K, L) = 60 µm.Published as part of <i>Liu, Weiwei, Li, Jiqiu, Gao, Shan, Shao, Chen, Gong, Jun, Lin, Xiaofeng, Liu, Hongbin & Song, Weibo, 2009, Morphological studies and molecular data on a new marine ciliate, Apokeronopsis sinica n. sp. (Ciliophora: Urostylida), from the South China Sea, pp. 57-66 in Zootaxa 2005 (1)</i> on page 62, DOI: 10.11646/zootaxa.2005.1.5, <a href="http://zenodo.org/record/10093145">http://zenodo.org/record/10093145</a>
Vision-Based Multiple Interacting Targets Tracking via On-Line Supervised Learning
No full textSuccessful multi-target tracking requires locating the targets and labeling their identities. This mission becomes significantly more challenging when many targets frequently interact with each other (present partial or complete occlusions). This paper presents an on-line supervised learning based method for tracking multiple interacting targets. When the targets do not interact with each other, multiple independent trackers are employed for training a classifier for each target. When the targets are in close proximity or present occlusions, the learned classifiers are used to assist in tracking. The tracking and learning supplement each other in the proposed method, which not only deals with tough problems encountered in multi-target tracking, but also ensures the entire process to be completely on-line. Various evaluations have demonstrated that this method performs better than previous methods when the interactions occur, and can maintain the correct tracking under various complex tracking situations, including crossovers, collisions and occlusions.Computer Science, Artificial IntelligenceComputer Science, Software EngineeringComputer Science, Theory & MethodsImaging Science & Photographic TechnologyCPCI-S(ISTP)
Bayesian Fusion of Laser and Vision for Multiple People Detection and Tracking
No full textWe present a promising system to simultaneously detect and track multiple humans in the outside scene using laser and vision. The useful information of laser and vision is automatically extracted and combined in a Bayesian formulation. In order to compute MAP estimation, an effective Probabilistic Detection-based Particle Filter (PD-PF) has been proposed. Experiments and evaluations demonstrate that not only can our system perform robustly in real environments, but also obtain better approximation of MAP than previous methods in most complex situations.Automation & Control SystemsInstruments & InstrumentationCPCI-S(ISTP)
The Promise of Beijing: Evaluating the Impact of the 2008 Olympic Games on Air Quality
No full textTo prepare for the 2008 Olympic Games, China adopted a number of radical measures to improve air quality. Using officially reported air pollution index (API) from 2000 to 2009, we show that these measures improved the API of Beijing during and after the Games, but 60% of the effect faded away by the end of October 2009. Since the credibility of API data has been questioned, an objective and indirect measure of air quality at a high spatial resolution – aerosol optimal depth (AOD), derived using the data from the NASA satellites – was analyzed and compared with the API trend. The analysis confirms that the improvement was real but temporary and most improvement was attributable to plant closure and traffic control. Our results suggest that it is possible to achieve real environmental improvement in an authoritarian regime but the magnitude of the effect and how long it lasts depend on the political motivation behind the policy interventions.
FIGURE 1. Apokeronopsis sinica n in Morphological studies and molecular data on a new marine ciliate, Apokeronopsis sinica n. sp. (Ciliophora: Urostylida), from the South China Sea
No full textFIGURE 1. Apokeronopsis sinica n. sp. from live cells (A–D) and after protargol impregnation (E–F). (A, B) Ventral views of two typical specimens. (C) Dorsal view; to show the sparsely distributed large granules. (D) Details of the cortex, double-arrowheads indicate the smaller cortical granules, arrows mark the vermeil larger ones and arrowheads mark the colorless larger ones. (E, F) Ventral and dorsal views of the infraciliature and nuclear apparatus. AZM = adoral zone of membranelles; BC = buccal cirri; BiC = bicoronal cirri; DK = dorsal kineties; EM = endoral membrane; FTC = frontoterminal cirri; LMR = left marginal row; Ma = macronuclei; MC = midventral complex; PM = paroral membrane; RMR = right marginal row; TC = transverse cirri. Scale bars = 80 µm.Published as part of <i>Liu, Weiwei, Li, Jiqiu, Gao, Shan, Shao, Chen, Gong, Jun, Lin, Xiaofeng, Liu, Hongbin & Song, Weibo, 2009, Morphological studies and molecular data on a new marine ciliate, Apokeronopsis sinica n. sp. (Ciliophora: Urostylida), from the South China Sea, pp. 57-66 in Zootaxa 2005 (1)</i> on page 59, DOI: 10.11646/zootaxa.2005.1.5, <a href="http://zenodo.org/record/10093145">http://zenodo.org/record/10093145</a>
Tracking interacting targets with laser scanner via on-line supervised learning
No full textSuccessful multi-target tracking requires locating the targets and labeling their identities. For the laser based tracking system, the latter becomes significantly more challenging when the targets frequently interact with each other. This paper presents a novel online supervised learning based method for tracking interacting targets with laser scanner. When the targets do not interact with each other, we collect samples and train a classifier for each target. When the targets are in close proximity, we use these classifiers to assist in tracking. Different evaluations demonstrate that this method has a better tracking performance than previous methods when interactions occur, and can maintain correct tracking under various complex tracking situations.Automation & Control SystemsRoboticsEICPCI-S(ISTP)1
Modeling phytoplankton dynamics in the River Darling (Australia) using the radial basis function neural network
No full textA radial basis function neural network was employed to model the abundance of cyanobacteria. The trained network could predict the populations of two bloom forming algal taxa with high accuracy, Nostocales spp. and Anabaena spp., in the River Darling, Australia. To elucidate the population dynamics for both Nostocales spp. and Anabaena spp., sensitivity analysis was performed with the following results. Total Kjeldahl nitrogen had a very strong influence on the abundance of the two algal taxa, electrical conductivity had a very strong negative relationship with the population of the two algal species, and flow was identified as one dominant factor influencing algal blooms after a scatter plot revealed that high flow could significantly reduce the algal biomass for both Nostocales spp. and Anabaena spp. Other variables such as turbidity, color, and pH were less important in determining the abundance and succession of the algal blooms. © 2006, Copyright Taylor & Francis Group, LLC.Guoxiang Hou, Hongbin Li, Friedrich Recknagel and Lirong Songhttp://trove.nla.gov.au/work/2552784
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