Radar-based Hail-producing Storm Detection Using Positive Unlabeled Classification

Machine learning methods have been widely used in many fields of weather forecasting. However, some severe weather, such as hailstorm, is difficult to be completely and accurately recorded. These inaccurate data sets will affect the performance of machine-learning-based forecasting models. In this paper, a weather-radar-based hail-producing storm detection method is proposed. This method utilizes the bagging class-weighted support vector machine to learn from partly labeled hail case data and the other unlabeled data, with features extracted from radar and sounding data. The real case data from three radars of North China are used for evaluation. Results suggest that the proposed method could improve both the forecast accuracy and the forecast lead time comparing with the commonly used radar parameter methods. Besides, the proposed method works better than the method with the supervised learning model in any situation, especially when the number of positive samples contaminated in the unlabeled set is large

Expression profiling and integrative analysis of the CESA/CSL superfamily in rice

<p>Abstract</p> <p>Background</p> <p>The cellulose synthase and cellulose synthase-like gene superfamily (<it>CESA</it>/<it>CSL</it>) is proposed to encode enzymes for cellulose and non-cellulosic matrix polysaccharide synthesis in plants. Although the rice (<it>Oryza sativa </it>L.) genome has been sequenced for a few years, the global expression profiling patterns and functions of the <it>OsCESA</it>/<it>CSL </it>superfamily remain largely unknown.</p> <p>Results</p> <p>A total of 45 identified members of <it>OsCESA</it>/<it>CSL </it>were classified into two clusters based on phylogeny and motif constitution. Duplication events contributed largely to the expansion of this superfamily, with Cluster I and II mainly attributed to tandem and segmental duplication, respectively. With microarray data of 33 tissue samples covering the entire life cycle of rice, fairly high <it>OsCESA </it>gene expression and rather variable <it>OsCSL </it>expression were observed. While some members from each <it>CSL </it>family (<it>A1</it>, <it>C9</it>, <it>D2</it>, <it>E1</it>, <it>F6 </it>and <it>H1</it>) were expressed in all tissues examined, many of <it>OsCSL </it>genes were expressed in specific tissues (stamen and radicles). The expression pattern of <it>OsCESA</it>/<it>CSL </it>and <it>OsBC1L </it>which extensively co-expressed with <it>OsCESA</it>/<it>CSL </it>can be divided into three major groups with ten subgroups, each showing a distinct co-expression in tissues representing typically distinct cell wall constitutions. In particular, <it>OsCESA1, -3 & -8 </it>and <it>OsCESA4, -7 & -9 </it>were strongly co-expressed in tissues typical of primary and secondary cell walls, suggesting that they form as a cellulose synthase complex; these results are similar to the findings in <it>Arabidopsis</it>. <it>OsCESA5</it>/<it>OsCESA6 </it>is likely partially redundant with <it>OsCESA3 </it>for OsCESA complex organization in the specific tissues (plumule and radicle). Moreover, the phylogenetic comparison in rice, <it>Arabidopsis </it>and other species can provide clues for the prediction of orthologous gene expression patterns.</p> <p>Conclusions</p> <p>The study characterized the <it>CESA</it>/<it>CSL </it>of rice using an integrated approach comprised of phylogeny, transcriptional profiling and co-expression analyses. These investigations revealed very useful clues on the major roles of <it>CESA</it>/<it>CSL</it>, their potentially functional complement and their associations for appropriate cell wall synthesis in higher plants.</p

Advances in Clinical Application of Bone Mineral Density and Bone Turnover Markers

Bone mineral density is the main basis for the diagnosis of osteoporosis. The measurement methods of bone mineral density include dual X-ray absorptiometry (DXA), quantitative computer tomography (QCT), quantitative ultrasound (QUS), magnetic resonance imaging (MRI) and so on. Currently, bone mineral density measured by dual-energy X-ray absorptiometry (DXA) is the gold standard for the diagnosis of osteoporosis. Bone turnover markers (BTMs) are biochemical products that reflect the activity of bone cells and the metabolic level of bone matrix, and they reflect the dynamic changes of bone tissue in the whole body earlier than bone mineral-density, procollagen type 1 N-terminal propeptide (PINP) and carboxy-terminal cross-linked telopeptide of type 1 collagen (CTX) is sensitive BTMs, widely used in clinical practice, and can predict the occurrence of fractures. Some new markers such as Periostin, AGEs/RAGE, Gelsolin, and Annexin A2 provide new clues for exploring the mechanism of osteoporosis. The combination of the two can better carry out the diagnosis and differential diagnosis of multiple metabolic bone diseases, evaluate the therapeutic response of anti-osteoporotic medicines, and predict fracture risk

Robust Geometric Formation Control of Multiple Autonomous

©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the American Control Conference (ACC 2013), 17-19 June 2013, Washington, D.C.This paper develops a robust controller for autonomous underwater vehicles with bounded time delays, so that the AUVs form and keep a desired formation shape and track a desired trajectory. We use a six-degree-of-freedom dynamic model for each AUV to describe its motions in the three-dimensional space. We design an orientation controller based on feedback linearization, so that the orientation of each AUV converges to its desired value. We derive formation dynamics of AUVs and decouple the dynamics into a formation shape and a formation center, using the Jacobi transform. We treat couplings in the formation dynamics as perturbations and design a robust formation-keeping controller to tolerate both the perturbations and the time delays. We demonstrate the effectiveness of our controller in simulations

Identification of Topping Responsive Proteins in Tobacco Roots

Tobacco plant has many responses to topping, such as the increase in ability of nicotine synthesis and secondary growth of roots. Some topping responsive miRNAs and genes had been identified in our previous work, but it is not enough to elaborate mechanism of tobacco response to topping. Here, topping responsive proteins were screened from tobacco roots with two-dimensional electrophoresis. Of these proteins, calretulin (CRT) and Auxin-responsive protein IAA9 were related to the secondary growth of roots, LRR disease resistance, heat shock protein 70 and farnesyl pyrophosphate synthase 1（FPPS）were involved in wounding stress response, and F-box protein played an important role in promoting the ability of nicotine synthesis after topping. In addition, there were five tobacco bHLH proteins (NtbHLH, NtMYC1a, NtMYC1b, NtMYC2a and NtMYC2b) related to nicotine synthesis. It was suggested that NtMYC2 might be the main positive transcription factor and NtbHLH protein is a negative regulator in the JA-mediating activation of nicotine synthesis after topping. Tobacco topping activates some comprehensive biology processes involving IAA and JA signaling pathway, and the identification of these proteins will be helpful to understand the process of topping response