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The three-dimensional (3D) geometry reconstruction method utilizing ISAR image sequence energy accumulation (ISEA) shows great performance on triaxial stabilized space targets but fails when there is unknown motion from the target itself. The orthogonal factorization method (OFM) can solve this problem well under certain assumptions. However, due to the sparsity and anisotropy of ISAR images, the extraction and association of feature points become very complicated, resulting in the reconstructed geometry usually being a relatively sparse point cloud. Therefore, combining the advantages of the above methods, an extended factorization framework (EFF) is proposed. First, the instance segmentation method based on deep learning is used for the extraction and association of a number of key points on multi-view ISAR images. Then, the projection vectors between the 3-D geometry of the space target and the multi-view ISAR images are obtained, using the improved factorization method. Finally, the 3D geometry reconstruction problem is transformed into an unconstrained optimization problem and solved via the quantum-behaved particle swarm optimization (QPSO) method. The proposed framework uses discretely observed multi-view range–Doppler ISAR images as an input, which can make full use of the long-term data of space targets from multiple perspectives and which is non-sensitive to movement. Therefore, the proposed framework shows high feasibility in practical applications. Experiments on simulated and measured data show the effectiveness and robustness of the proposed framework. [ABSTRACT FROM AUTHOR]

Geological reports are frequently used by geologists involved in geological surveys and scientific research to record the results and outcomes of geological surveys. With such a rich data source, a substantial amount of knowledge has yet to be mined and analyzed. This paper focuses on automatically information extraction from geological reports, namely, geological named entity recognition. Geological named entity recognition has an important role in data mining, knowledge discovery and Knowledge graph construction. Existing general named entity recognition models/tools are limited in the domain of geoscience due to the various language irregularities associated with geological text, such as informal sentence structures, several domain‐geoscience words, large character lengths and multiple combinations of independent words. We present Bidirectional encoder representations from transformers (BERT)‐(Bidirectional gated recurrent unit network) BiGRU‐ (Conditional random field) CRF, which is a deep learning‐based geological named entity recognition model that is designed specifically with these linguistic irregularities in mind. Based on the pretrained language model, an integrated deep learning model incorporating BERT, BiGRU and CRF is constructed to obtain character vectors rich in semantic information through the BERT pretrained language model to alleviate for the lack of specificity of static word vectors (e.g., word2vec) and to improve the extraction capability of complex geological entities. We demonstrate our proposed model by applying it to four test datasets, including a geoscience NER data set from regional geological reports, and by comparing its performance with those of five baseline models. Plain Language Summary: Geological named entity recognition has an important role in information extraction and knowledge discovery. This paper presents BERT‐BiGRU‐CRF, which is a deep learning‐based geological named entity recognition model that is designed specifically with these linguistic irregularities in mind. We hope that our approach will serve as an alternative method that deserves further study. Key Points: A named entity recognition model based on BERT‐BiGRU‐CRF is proposed and a set of detailed experimental tests on domain data setOur model is compared in detail with four other mainstream models, and experiments demonstrate that our model obtains better performanceWe share the source code of BERT‐BiGRU‐CRF and the annotated test data [ABSTRACT FROM AUTHOR]

The hypoxic microenvironment inside solid tumors, including hepatocellular carcinoma (HCC), is a major cause of tumor resistance to chemotherapy. The recently identified hypoxia-inducible factor (HIF)-2 executes the hypoxia response. Its expression feature and transcriptional targets indicate a possible dominance of HIF-2 in regulating genes in HCC. The aim of the present study was to determine whether transfection of siRNA targeting HIF-2α could enhance the efficacy of doxorubicin, the most commonly used drug in the treatment of HCC. Transfection of HIF-2 siRNA into human HCC cells downregulated the expression of HIF-2α, vascular endothelial growth factor (VEGF), transforming growth factor (TGF)-α, and cyclin D1, but had little effect on the expression of HIF-1α, fms-related tyrosine kinase-1 (Flt-1), the glucose transporter (GLUT)-1, and lactate dehydrogenase A (LDHA). Doxorubicin itself only downregulated VEGF expression. Furthermore, HIF-2 siRNA inhibited proliferation, induced cell cycle arrest at the G0/G1 phase, and acted synergistically with doxorubicin to inhibit the growth of human HCC cells in vitro. Transfection of HIF-2 siRNA also downregulated tumoral expression of HIF-2α, VEGF, TGF-α, and cyclin D1 in vivo, and acted synergistically with doxorubicin to suppress the growth of HepG2 tumors established in immunodeficient mice by inhibiting cell proliferation, tumor angiogenesis and microvessel perfusion. The results of the present study suggest that targeting HIF-2α with siRNA warrants investigation as a potential strategy to enhance the efficacy of doxorubicin in the treatment of HCC. ( Cancer Sci 2012; 103: 528-534) [ABSTRACT FROM AUTHOR]