DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting

Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to lack of mining road topology. To address the effect attenuation problem, we propose to take account of the traffic of surrounding locations(wider than adjacent range). We propose an end-to-end framework called DeepTransport, in which Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain spatial-temporal traffic information within a transport network topology. In addition, attention mechanism is introduced to align spatial and temporal information. Moreover, we constructed and released a real-world large traffic condition dataset with 5-minute resolution. Our experiments on this dataset demonstrate our method captures the complex relationship in temporal and spatial domain. It significantly outperforms traditional statistical methods and a state-of-the-art deep learning method

Phenotypic characteristics of the mycelium of Pleurotus geesteranus using image recognition technology

Phenotypic analysis has significant potential for aiding breeding efforts. However, there is a notable lack of studies utilizing phenotypic analysis in the field of edible fungi. Pleurotus geesteranus is a lucrative edible fungus with significant market demand and substantial industrial output, and early-stage phenotypic analysis of Pleurotus geesteranus is imperative during its breeding process. This study utilizes image recognition technology to investigate the phenotypic features of the mycelium of P. geesteranus. We aim to establish the relations between these phenotypic characteristics and mycelial quality. Four groups of mycelia, namely, the non-degraded and degraded mycelium and the 5th and 14th subcultures, are used as image sources. Two categories of phenotypic metrics, outline and texture, are quantitatively calculated and analyzed. In the outline features of the mycelium, five indexes, namely, mycelial perimeter, radius, area, growth rate, and change speed, are proposed to demonstrate mycelial growth. In the texture features of the mycelium, five indexes, namely, mycelial coverage, roundness, groove depth, density, and density change, are studied to analyze the phenotypic characteristics of the mycelium. Moreover, we also compared the cellulase and laccase activities of the mycelium and found that cellulase level was consistent with the phenotypic indices of the mycelium, which further verified the accuracy of digital image processing technology in analyzing the phenotypic characteristics of the mycelium. The results indicate that there are significant differences in these 10 phenotypic characteristic indices (P<0.001), elucidating a close relationship between phenotypic characteristics and mycelial quality. This conclusion facilitates rapid and accurate strain selection in the early breeding stage of P. geesteranus

Efficient display of active lipase LipB52 with a Pichia pastoris cell surface display system and comparison with the LipB52 displayed on Saccharomyces cerevisiae cell surface

<p>Abstract</p> <p>Background</p> <p>For industrial bioconversion processes, the utilization of surface-displayed lipase in the form of whole-cell biocatalysts is more advantageous, because the enzymes are displayed on the cell surface spontaneously, regarded as immobilized enzymes.</p> <p>Results</p> <p>Two <it>Pichia pastoris </it>cell surface display vectors based on the flocculation functional domain of FLO with its own secretion signal sequence or the α-factor secretion signal sequence were constructed respectively. The lipase gene <it>lipB52 </it>fused with the <it>FLO </it>gene was successfully transformed into <it>Pichia pastoris </it>KM71. The lipase LipB52 was expressed under the control of the <it>AOX1 </it>promoter and displayed on <it>Pichia pastoris </it>KM71 cell surface with the two <it>Pichia pastoris </it>cell surface display vectors. Localization of the displayed LipB52 on the cell surface was confirmed by the confocal laser scanning microscopy (CLSM). The LipB52 displayed on the <it>Pichia pastoris </it>cell surface exhibited activity toward <it>p</it>-nitrophenol ester with carbon chain length ranging from C₁₀to C₁₈, and the optimum substrate was <it>p</it>-nitrophenol-caprate (C₁₀), which was consistent with it displayed on the <it>Saccharomyces cerevisiae </it>EBY100 cell surface. The hydrolysis activity of lipase LipB52 displayed on <it>Pichia pastoris </it>KM71-pLHJ047 and KM71-pLHJ048 cell surface reached 94 and 91 U/g dry cell, respectively. The optimum temperature of the displayed lipases was 40°C at pH8.0, they retained over 90% activity after incubation at 60°C for 2 hours at pH 7.0, and still retained 85% activity after incubation for 3 hours.</p> <p>Conclusion</p> <p>The LipB52 displayed on the <it>Pichia pastoris </it>cell surface exhibited better stability than the lipase LipB52 displayed on <it>Saccharomyces cerevisiae </it>cell surface. The displayed lipases exhibited similar transesterification activity. But the <it>Pichia pastoris </it>dry cell weight per liter (DCW/L) ferment culture was about 5 times than <it>Saccharomyces cerevisiae</it>, the lipase displayed on <it>Pichia pastoris </it>are more suitable for whole-cell biocatalysts than that displayed on <it>Saccharomyces cerevisiae </it>cell surface.</p

Power-Line Extraction Method for UAV Point Cloud Based on Region Growing Algorithm

[Introduction] Since the power line has the characteristics of long transmission distance and a complex spatial environment, the UAV LiDAR point cloud technology can completely and efficiently obtain the geometric information of the power line and its surrounding spatial objects, and the existing supervised extraction and unsupervised extraction methods are deficient in point cloud data extraction in a large range of complex environments, according to the spatial environment characteristics of the main network and distribution network line point cloud data, a rapid extraction method of point cloud power line is proposed based on projection line characteristics and region growing algorithm. [Method] Firstly, in view of the characteristics that the overhead lines of the main network were usually higher than the surrounding spatial objects, the power lines were roughly extracted by the elevation histogram threshold method. Then, considering the characteristics that the vegetation canopy was higher than the distribution network line in the distribution network area, the KNN data points of the roughly extracted power line point cloud were obtained, and the point cloud was projected on the horizontal plane, and whether the point cloud was a power line point cloud was judged by the linear measurement of the point cloud. [Result] According to the existence of missing power line point clouds, all the power line point cloud clusters are obtained through a region growing mode, and on this basis, the catenary formula of each power line point cloud cluster is calculated through the catenary formula, and the point cloud with a fitting distance less than the threshold is merged as the same power line point cloud. [Conclusion] The proposed method aims at the problem of rapid power line extraction in inspection applications and overcomes the problem of power line point cloud missing and vegetation impact in the process of power line extraction, so this method can achieve power line point cloud extraction with high efficiency and accuracy