Recent advances in murine cardiac studies with three-dimensional (3D) cone beam micro-CT used a retrospective gating technique. However, this sampling technique results in a limited number of projections with an irregular angular distribution due to the temporal resolution requirements and radiation dose restrictions. Both angular irregularity and undersampling complicate the reconstruction process, since they cause significant streaking artifacts. This work provides an iterative reconstruction solution to address this particular challenge. A sparseness prior regularized weighted l 2 norm optimization is proposed to mitigate streaking artifacts based on the fact that most medical images are compressible. Total variation is implemented in this work as the regularizer for its simplicity. Comparison studies are conducted on a 3D cardiac mouse phantom generated with experimental data. After optimization, the method is applied to in vivo cardiac micro-CT data.

Progressive fibrosis in chronic kidney disease (CKD) is the well-recognized cause leading to the progressive loss of renal function. Emerging evidence indicated a pathogenic role of the NACHT, LRR and PYD domains-containing protein 3 (NLRP3) inflammasome in mediating kidney injury. However, the role of NLRP3 in the remnant kidney disease model is still undefined. The present study was undertaken to evaluate the function of NLRP3 in modulating renal fibrosis in a CKD model of 5/6 nephrectomy (5/6 Nx) and the potential involvement of mitochondrial dysfunction in the pathogenesis. Employing NLRP3(+/+) and NLRP3(-/-) mice with or without 5/6 Nx, we examined renal fibrotic response and mitochondrial function. Strikingly, tubulointerstitial fibrosis was remarkably attenuated in NLRP3(-/-) mice as evidenced by the blockade of extracellular matrix deposition. Meanwhile, renal tubular cells in NLRP3(-/-) mice maintained better mitochondrial morphology and higher mitochondrial DNA copy number, indicating an amelioration of mitochondrial abnormality. Moreover, NLRP3 deletion also blunted the severity of proteinuria and CKD-related hypertension. To further evaluate the direct role of NLRP3 in triggering fibrogenesis, mouse proximal tubular cells (PTCs) were subjected to transforming growth factor β1 (TGF-β1), and the cellular phenotypic changes were detected. As expected, TGF-β1-induced alterations of PTC phenotype were abolished by NLRP3 small interfering RNA, in line with a protection of mitochondrial function. Taken together, NLRP3 deletion protected against renal fibrosis in the 5/6 Nx disease model, possibly via inhibiting mitochondrial dysfunction.

A recently proposed lattice model has demonstrated that words in character sequence can provide rich word boundary information for character-based Chinese NER model. In this model, word information is integrated into a shortcut path between the start and the end characters of the word. However, the existence of shortcut path may cause the model to degenerate into a partial word-based model, which will suffer from word segmentation errors. Furthermore, the lattice model can not be trained in batches due to its DAG structure. In this paper, we propose a novel wordcharacter LSTM(WC-LSTM) model to add word information into the start or the end character of the word, alleviating the influence of word segmentation errors while obtaining the word boundary information. Four different strategies are explored in our model to encode word information into a fixed-sized representation for efficient batch training. Experiments on benchmark datasets show that our proposed model outperforms other stateof-the-arts models.