Diffuse optical tomography (DOT) is a promising noninvasive imaging modality and is capable of providing functional characteristics of biological tissue by quantifying optical parameters. The DOT image reconstruction is ill-posed and ill-conditioned, due to the highly diffusive nature of light propagation in biological tissues and limited boundary measurements. The widely used regularization technique for DOT image reconstruction is Tikhonov regularization, which tends to yield oversmoothed and low-quality images containing severe artifacts. It is necessary to accurately choose a regularization parameter for Tikhonov regularization. To overcome these limitations, we develop a noniterative reconstruction method, whereby optical properties are recovered based on a back-propagation neural network (BPNN). We train the parameters of BPNN before DOT image reconstruction based on a set of training data. DOT image reconstruction is achieved by implementing a single evaluation of the trained network. To demonstrate the performance of the proposed algorithm, we compare with the conventional Tikhonov regularization-based reconstruction method. The experimental results demonstrate that image quality and quantitative accuracy of reconstructed optical properties are significantly improved with the proposed algorithm.

Background: The prediction of protein-protein binding site can provide structural annotation to the protein interaction data from proteomics studies. This is very important for the biological application of the protein interaction data that is increasing rapidly. Moreover, methods for predicting protein interaction sites can also provide crucial information for improving the speed and accuracy of protein docking methods.

A total power injection up to 0.3 GJ has been achieved in EAST long pulse H-mode operation of 101.2 s with an ITER-like water-cooled tungsten (W) mono-block divertor, which has steady-state power exhaust capability of 10 MWm−2. The peak temperature of W target saturated at 12 s to the value T ~ 500 °C with a heat flux ~3.3 MW m−2 being maintained during the discharge. By tailoring the 3D divertor plasma footprint through edge magnetic topology change, the heat load was broadly dispersed and thus peak heat flux and W sputtering were well controlled. Active feedback control of H-mode detachment with D2 fuelling or divertor impurity seeding has been achieved successfully, with excellent compatibility with the core plasma performance. Active feedback control of radiative power utilizing neon seeding was achieved with f  rad  =  18%–41% in H-mode operation, exhibiting potential for heat flux reduction with divertor and edge radiation. This has been further demonstrated in DIII-D high β P H-mode scenario within the joint DIII-D/EAST experiment using impurity seeding from the divertor volume. Steady-state particle control and impurity exhaust has been achieved for long pulse H-mode operation over 100 s with the W divertor by leveraging the effect of drifts and optimized divertor configuration, coupled with strong pumping and extensive wall conditioning. Approaches toward the reduction of divertor W sourcing, which is of crucial importance for a metal-wall tokamak, are also explored. These advances provide important experimental information on favourable core-edge integration for high power, long-pulse H-mode operation in EAST, ITER and CFETR.

As an alternative choice of solid plasma facing components (PFCs), flowing liquid lithium can serve as a limiter or divertor PFC and offers a self-healing surface with acceptable heat removal and good impurity control. Such a system could improve plasma performance, and therefore be attractive for future fusion devices. Recently, a continuously flowing liquid lithium (FLiLi) limiter has been successfully designed and tested in the EAST superconducting tokamak. A circulating lithium layer with a thickness of  <0.1 mm and a flow rate ~2 cm3 s−1 was achieved. A novel in-vessel electro-magnetic pump, working with the toroidal magnetic field of the EAST device, was reliable to control the lithium flow speed. The flowing liquid limiter was found to be fully compatible with various plasma scenarios, including high confinement mode plasmas heated by lower hybrid waves or by neutral beam injection. It was also found that the controllable lithium emission from the limiter was beneficial for the reduction of recycling and impurities, for the reduction of divertor heat flux, and in certain cases, for the improvement of plasma stored energy, which bodes well application for the use of flowing liquid lithium PFCs in future fusion devices.

Long non-coding RNA (lnc) HCG18 has been reported to contribute progression of a variety of tumors. However, its roles in hepatocellular carcinoma (HCC) remains unknown. In the current study, we intended to uncover the biological functions of HCG18 in HCC. Quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to detect the expression of HCG18, microRNA-214-3p (miR-214-3p) and centromere protein M (CENPM) messenger RNA (mRNA). The role of HCG18 in the growth and migration were assessed by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, colony formation assay, wound healing assay and flow cytometry in vitro and animal experiments in vivo. The results showed that HCG18 was highly expressed in HCC tissues. HCG18 silencing inhibited the proliferation and migration while induced the apoptosis of HCC cells. Besides, miR-214-3p was downregulated in HCC cells. Further experiments revealed that miR-214-3p could directly bind to HCG18 and exerted an anti-tumor role to counteracted siHCG18-1-mediated influence in HCC cells. Moreover, miR-214-3p could directly interact with CENPM mRNA and down-regulating the expression of CENPM. While HCG18 could up-regulated the expression of CENPM through acting as a sponge of miR-214-3p. Therefore, those results suggested HCG18 functioned as an oncogene to promote the proliferation and migration of HCC cells via miR-214-3p/CENPM axis.

Background Adenomyosis (AM) is a common benign chronic gynaecological disorder; however, the precise pathogenesis of adenomyosis is still poorly understood. Single-cell RNA sequencing (scRNA-seq) can uncover rare subpopulations, explore genetic and functional heterogeneity, and reveal the uniqueness of each cell. It provides us a new approach to reveal biological issues from a more detailed and microscopic perspective. Here, we utilize this revolutionary technology to identify the changes of gene expression patterns between ectopic lesions and the eutopic endometrium at the single-cell level and explore a potential novel pathogenesis of AM. Methods A control endometrium (sample with leiomyoma excluding endometrial disorders, n = 1), eutopic endometrium and ectopic lesion (from a patient with adenomyosis, n = 1) samples were analysed by scRNA-seq, and additional leiomyoma (n = 3) and adenomyosis (n = 3) samples were used to confirm colocalization and vasculogenic mimicry (VM) formation. Protein colocalization was visualized by immunofluorescence, and CD34-periodic acid-Schiff (PAS) double staining was used to assess the formation of VM. Results The scRNA-seq results suggest that cancer-, cell motility- and inflammation- (CMI) associated terms, cell proliferation and angiogenesis play important roles in the progression of AM. Moreover, the colocalization of EPCAM and PECAM1 increased significantly in the ectopic endometrium group (P < 0.05), cell subpopulation with high copy number variation (CNV) levels possessing tumour-like features existed in the ectopic lesion sample, and VNN1- and EPCAM-positive cell subcluster displayed active cell motility in endometrial epithelial cells. Furthermore, during the transformation of epithelial cells to endothelial cells, we observed the significant accumulation of VM formation (positively stained with PAS but not CD34, P < 0.05) in ectopic lesions. Conclusions In the present study, our results support the theory of adenomyosis derived from the invasion and migration of the endometrium. Moreover, cell subcluster with high CNV level and tumour-associated characteristics is identified. Furthermore, epithelial-endothelial transition (EET) and the formation of VM in tumours, the latter of which facilitates the blood supply and plays an important role in maintaining cell growth, were also confirmed to occur in AM. These results indicated that the inhibition of EET and VM formation may be a potential strategy for AM management.

ANN and RSM based modelling for optimization of cell dry mass of Bacillus sp. strain B67 and its antifungal activity against Botrytis cinerea

Recently, deep neural networks have attracted great attention in photoacoustic imaging (PAI). In PAI, reconstructing the initial pressure distribution from acquired photoacoustic (PA) signals is a typically inverse problem. In this paper, an end-to-end Unet with residual blocks (Res-Unet) is designed and trained to solve the inverse problem in PAI. The performance of the proposed algorithm is explored and analyzed by comparing a recent model-resolution-based regularization algorithm (MRR) with numerical and physical phantom experiments. The improvement obtained in the reconstructed images was more than 95% in pearson correlation and 39% in peak signal-to-noise ratio in comparison to the MRR. The Res-Unet also achieved superior performance over the state-of-the-art Unet++ architecture by more than 18% in PSNR in simulation experiments.