A new query dependent feature fusion approach for medical image retrieval based on one-class SVM

With the development of the internet, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the medical images in the content-based ways through automatically extracting visual information of the medical images. Since a single feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Furthermore, a special feature is not equally important for different image queries since a special feature has different importance in reflecting the content of different images. However, most existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, based on multiply query samples provided by the user, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. The proposed query dependent feature fusion method for medical image retrieval can learn different feature fusion models for different image queries, and the learned feature fusion models can reflect the different importance of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.<br /

Experimental investigation on fracture behavior of lignite and its fracturing significance:taking Shengli Coalfield as an example

The anisotropy of fracture properties at different bedding orientations of lignite plays an important role in controlling the formation of fracture network of reservoir racturing. The static fracture behavior of the lignite semi-circular bending specimens of the 6 coal group in the Shengli coalfield, Inner Mongolia, is studied. The results show that: ① Bedding When the angle θ is 90°, 60°, 45°, 30° and divider, the mode I fracture toughness KIC of lignite of Shengli coalfield is 0.045, 0.058, 0.073, 0.084 and 0.096 MPa·m0.5, respectively. on the whole, the fracture toughness of the sample with the notch-cut layered surface is much higher than that of the notch-parallel bedding-surface sample, and for the notch-parallel bedding-surface sample, the There is an increasing trend with decreasing fracture toughness, and the fracture properties of lignite in the study area have strong anisotropic characteristics; ② The displacement-load curves shows that the fracture process of the five lignite semi-circular bending samples under three-point bending loading includes: the compaction stage of pores and cracks, the linear elastic compression deformation energy storage stage, the critical fracture failure stage, and the load unloading stage after fracture. The large stage, and the rapid load unloading of the above samples in the later stage of fracture indicates that the material fracture forms are brittle fractures, the coal has a large fracture rate, and the reservoir is suitable for large-scale volume fracturing; ③ The fracture energy Γ of five kinds of lignite semi-circular bending samples with the bedding angle θ is 90°, 60°, 45°, 30° and dividing during three-point bending loading process is 64.38, 80.49, 112.50, 146.66, and 355.00 J/m2, respectively. The fracture energy of the sample with the notch-cut layered surface in the whole process is much larger than that of the lignite sample of the incision-parallel bedding plane. Under the same conditions, the energy consumption of fracturing along the bedding direction of the split lignite is high, and the expansion scale of the fracturing fracture is limited; ④ Under three-point bending loading, the fractures of the lignite semi-circular bending specimens in the study area mainly include tensile fractures and shear fractures. The fracture path of the lignite specimens is controlled by the spatial relations between the external loading direction and the orientation of the coal bedding plane When the bedding angle θ is between 45° and 60°, the degree of tortuosity of the fracture of the sample is the highest, and the fracture network is most likely to be formed in the coal reservoir fracturing; ⑤ Based on the fracture mechanics anisotropy of lignite, this paper believes that horizontal wells + staged fracturing in lignite reservoirs can greatly improve the complexity of fracturing fractures. In addition, considering the inertia of particles, the horizontal wells with the borehole direction parallel to the maximum horizontal principal stress can carry proppant smoothly and avoid plugging, and the fracture filling effect is better. The above understanding is expected to provide a certain scientific reference for the reservoir volume fracturing stimulation and fracturing network construction in the study area

Exo84c-regulated degradation is involved in the normal self-incompatible response in Brassicaceae

The self-incompatibility system evolves in angiosperms to promote cross-pollination by rejecting self-pollination. Here, we show the involvement of Exo84c in the SI response of both Brassica napus and Arabidopsis. The expression of Exo84c is specifically elevated in stigma during the SI response. Knocking out Exo84c in B.napus and SI Arabidopsis partially breaks down the SI response. The SI response inhibits both the protein secretion in papillae and the recruitment of the exocyst complex to the pollen-pistil contact sites. Interestingly, these processes can be partially restored in exo84c SI Arabidopsis. After incompatible pollination, the turnover of the exocyst-labeled compartment is enhanced in papillae. However, this process is perturbed in exo84c SI Arabidopsis. Taken together, our results suggest that Exo84c regulates the exocyst complex vacuolar degradation during the SI response. This process is likely independent of the known SI pathway in Brassicaceae to secure the SI response. [Abstract copyright: Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.