Characterization of 35 novel microsatellite DNA markers from the duck (Anas platyrhynchos) genome and cross-amplification in other birds

In order to study duck microsatellites, we constructed a library enriched for (CA)n, (CAG)n, (GCC)n and (TTTC)n. A total of 35 pairs of primers from these microsatellites were developed and used to detect polymorphisms in 31 unrelated Peking ducks. Twenty-eight loci were polymorphic and seven loci were monomorphic. A total of 117 alleles were observed from these polymorphic microsatellite markers, which ranged from 2 to 14 with an average of 4.18 per locus. The frequencies of the 117 alleles ranged from 0.02 to 0.98. The highest heterozygosity (0.97) was observed at the CAUD019 microsatellite locus and the lowest heterozygosity (0.04) at the CAUD008 locus, and 11 loci had heterozygosities greater than 0.50 (46.43%). The polymorphism information content (PIC) of 28 loci ranged from 0.04 to 0.88 with an average of 0.42. All the above markers were used to screen the polymorphism in other bird species. Two markers produced specific monomorphic products with the chicken DNA. Fourteen markers generated specific fragments with the goose DNA: 5 were polymorphic and 9 were monomorphic. But no specific product was detected with the peacock DNA. Based on sequence comparisons of the flanking sequence and repeat, we conclude that 2 chicken loci and 14 goose loci were true homologous loci of the duck loci. The microsatellite markers identified and characterized in the present study will contribute to the genetic map, quantitative traits mapping, and phylogenetic analysis in the duck and goose

LAL: linguistically aware learning for scene text recognition

Scene text recognition is the task of recognizing character sequences in images of natural scenes. The considerable diversity in the appearance of text in a scene image and potentially highly complex backgrounds make text recognition challenging. Previous approaches employ character sequence generators to analyze text regions and, subsequently, compare the candidate character sequences against a language model. In this work, we propose a bimodal framework that simultaneously utilizes visual and linguistic information to enhance recognition performance. Our linguistically aware learning (LAL) method effectively learns visual embeddings using a rectifier, encoder, and attention decoder approach, and linguistic embeddings, using a deep next-character prediction model. We present an innovative way of combining these two embeddings effectively. Our experiments on eight standard benchmarks show that our method outperforms previous methods by large margins, particularly on rotated, foreshortened, and curved text. We show that the bimodal approach has a statistically significant impact. We also contribute a new dataset, and show robust performance when LAL is combined with a text detector in a pipelined text spotting framework.Published versio

Power Pylon Reconstruction Based on Abstract Template Structures Using Airborne LiDAR Data

Paper describes the development and testing of a three-dimensional reconstruction method using airborne LiDAR (Light Detection And Ranging) point cloud

An Imaging Algorithm for Multireceiver Synthetic Aperture Sonar

For the multireceiver synthetic aperture sonar (SAS), the point target reference spectrum (PTRS) in the two-dimensional (2D) frequency domain and azimuth modulation in the range Doppler domain were first deduced based on a numerical evaluation method and accurate time delay. Then, the difference between the PTRS and azimuth modulation generated the coupling term in the 2D frequency domain. Compared with traditional methods, the PTRS, azimuth modulation and coupling term was better at avoiding approximations. Based on three functions, an imaging algorithm is presented in this paper. Considering the fact that the coupling term is characterized by range variance, the range-dependent sub-block processing method was exploited to perform the decoupling. Simulation results showed that the presented method improved the imaging performance across the whole swath in comparison with existing multireceiver SAS processor. Furthermore, real data was used to validate the presented method

Application and Validation of a Model for Terrain Slope Estimation Using Space-Borne LiDAR Waveform Data

The terrain slope is one of the most important surface characteristics for quantifying the Earth surface processes. Space-borne LiDAR sensors have produced high-accuracy and large-area terrain measurement within the footprint. However, rigorous procedures are required to accurately estimate the terrain slope especially within the large footprint since the estimated slope is likely affected by footprint size, shape, orientation, and terrain aspect. Therefore, based on multiple available datasets, we explored the performance of a proposed terrain slope estimation model over several study sites and various footprint shapes. The terrain slopes were derived from the ICESAT/GLAS waveform data by the proposed method and five other methods in this study. Compared with five other methods, the proposed method considered the influence of footprint shape, orientation, and terrain aspect on the terrain slope estimation. Validation against the airborne LiDAR measurements showed that the proposed method performed better than five other methods (R2 = 0.829, increased by ~0.07, RMSE = 3.596°, reduced by ~0.6°, n = 858). In addition, more statistics indicated that the proposed method significantly improved the terrain slope estimation accuracy in high-relief region (RMSE = 5.180°, reduced by ~1.8°, n = 218) or in the footprint with a great eccentricity (RMSE = 3.421°, reduced by ~1.1°, n = 313). Therefore, from these experiments, we concluded that this terrain slope estimation approach was beneficial for different terrains and various footprint shapes in practice and the improvement of estimated accuracy was distinctly related with the terrain slope and footprint eccentricity

Hierarchical Threshold Adaptive for Point Cloud Filter Algorithm of Moving Surface Fitting

In order to improve the accuracy,efficiency and adaptability of point cloud filtering algorithm,a hierarchical threshold adaptive for point cloud filter algorithm of moving surface fitting was proposed.Firstly,the noisy points are removed by using a statistic histogram method.Secondly,the grid index is established by grid segmentation,and the surface equation is set up through the lowest point among the neighborhood grids.The real height and fit are calculated.The difference between the elevation and the threshold can be determined.Finally,in order to improve the filtering accuracy,hierarchical filtering is used to change the grid size and automatically set the neighborhood size and threshold until the filtering result reaches the accuracy requirement.The test data provided by the International Photogrammetry and Remote Sensing Society (ISPRS) is used to verify the algorithm.The first and second error and the total error are 7.33%,10.64% and 6.34% respectively.The algorithm is compared with the eight classical filtering algorithms published by ISPRS.The experiment results show that the method has well-adapted and it has high accurate filtering result

Ten-a Affects Fusion of Central Complex Primordia in Drosophila

The central complex of Drosophila melanogaster plays important functions in various behaviors, such as visual and olfactory memory, visual orientation, sleep, and movement control. However little is known about the genes regulating the development of the central complex. Here we report that a mutant gene affecting central complex morphology, cbd (central brain defect), was mapped to ten-a, a type II trans-membrane protein coding gene. Down-regulation of ten-a in pan-neural cells contributed to abnormal morphology of central complex. Over-expression of ten-a by C767-Gal4 was able to partially restore the abnormal central complex morphology in the cbd mutant. Tracking the development of FB primordia revealed that C767-Gal4 labeled interhemispheric junction that separated fan-shaped body precursors at larval stage withdrew to allow the fusion of the precursors. While the C767-Gal4 labeled structure did not withdraw properly and detached from FB primordia, the two fan-shaped body precursors failed to fuse in the cbd mutant. We propose that the withdrawal of C767-Gal4 labeled structure is related to the formation of the fan-shaped body. Our result revealed the function of ten-a in central brain development, and possible cellular mechanism underlying Drosophila fan-shaped body formation

Effects of Heating on the Binding of Rare Earth Elements to Humic Acids

In deep underground environments, temperature is one of the key factors affecting the geochemistry behaviors of rare earth elements (REE) in organic-rich fluid. However, the influence of temperature on the interaction between humic acids (HA) and REE is not well known. In the present study, the influence of temperature on the HA–REE-binding behavior was evaluated based on heating experiments of REE-doped HA solution. Lignite-extracted HA and REE-binding experiments were conducted over a temperature range of 20 to 200 °C to quantify HA–REE complexation and the influence of temperature on HA binding sites. Results showed that increasing temperature and decreasing [REE]/[HA] ratio cause an increase of Kd value (the partition coefficient of REE between HA and aqueous solution). During heating KdREE KdREE patterns gradually change from middle REE-enriched-type (M-type) at 20 °C to light and middle REE-enriched-type (L-M-type) at 50 and 100 °C, and to light REE-enriched-type (L-type) at 150 °C and 200 °C. The increase of REE bonded with HA and modifications of KdREE patterns during the thermal treatment may be attributed to the increase of REE-binding sites, especially carboxylic sites, as a consequent of HA decomposition. This study provides a glimpse into the HA–REE-binding behaviors in the deep underground environment, which may shed light on the geochemical characteristics of REE in some organic-bearing rocks, and their changes during the coalification process

Effects of Heating on the Binding of Rare Earth Elements to Humic Acids

In deep underground environments, temperature is one of the key factors affecting the geochemistry behaviors of rare earth elements (REE) in organic-rich fluid. However, the influence of temperature on the interaction between humic acids (HA) and REE is not well known. In the present study, the influence of temperature on the HA–REE-binding behavior was evaluated based on heating experiments of REE-doped HA solution. Lignite-extracted HA and REE-binding experiments were conducted over a temperature range of 20 to 200 °C to quantify HA–REE complexation and the influence of temperature on HA binding sites. Results showed that increasing temperature and decreasing [REE]/[HA] ratio cause an increase of Kd value (the partition coefficient of REE between HA and aqueous solution). During heating KdREE KdREE patterns gradually change from middle REE-enriched-type (M-type) at 20 °C to light and middle REE-enriched-type (L-M-type) at 50 and 100 °C, and to light REE-enriched-type (L-type) at 150 °C and 200 °C. The increase of REE bonded with HA and modifications of KdREE patterns during the thermal treatment may be attributed to the increase of REE-binding sites, especially carboxylic sites, as a consequent of HA decomposition. This study provides a glimpse into the HA–REE-binding behaviors in the deep underground environment, which may shed light on the geochemical characteristics of REE in some organic-bearing rocks, and their changes during the coalification process