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The double revolving fiber positioning unit (FPU) is one of the key technologies of The Large Sky Area Multi-Object Fiber Spectroscope Telescope (LAMOST). The positioning accuracy of the computer controlled FPU depends on robot accuracy as well as the initial parameters of FPU. These initial parameters may deteriorate with time when FPU is running in non-supervision mode, which would lead to bad fiber position accuracy and further efficiency degradation in the subsequent surveys. In this paper, we present an algorithm based on deep learning to detect the FPU’s initial angle using the front illuminated image of LAMOST focal plane. Preliminary test results show that the detection accuracy of the FPU initial angle is better than 2.°5, which is good enough to distinguish those obvious bad FPUs. Our results are further well verified by direct measurement of fiber position from the back illuminated image and the correlation analysis of the spectral flux in LAMOST survey data.

To date, the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) has been in operation for 12 years. To improve the telescope’s astronomical observation accuracy, the original open-loop fibre positioning system of LAMOST is in urgent need of upgrading. The upgrade plan is to locate several fibre view cameras (FVCs) around primary mirror B to build a closed-loop feedback control system. The FVCs are ~20 m from the focal surface. To reduce a series of errors when the cameras detect the positions of the optical fibres, we designed fiducial fibres on the focal surface to be fiducial points for the cameras. Increasing the number of fiducial fibres can improve the detection accuracy of the FVC system, but it will also certainly reduce the number of fibre positioners that can be used for observation. Therefore, the focus of this paper is how to achieve the quantity and distribution that meet the requirements of system detection. In this paper, we introduce the necessity of using fiducial fibres, propose a method for selecting their number, and present several methods for assessing the uniformity of their distribution. Finally, we use particle swarm optimization to find the best distribution of fiducial fibres.

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Correlation of osteopontin (OPN) gene expression with proliferation and apoptosis of ovarian cancer cells and prognosis of patients was investigated. The expression levels of OPN in 81 pairs of ovarian cancer tissues and para-carcinoma tissues obtained via surgical resection were detected using immunohistochemistry (IHC). The correlation of OPN protein expression with clinicopathological features of patients was analyzed. All patients were followed up for 3 years. The disease-free survival (DFS) and overall survival (OS) curves of patients in high/low OPN expression groups were drawn using the Kaplan-Meier method. The expression levels of OPN in normal ovarian epithelial IOSE80 cells and 5 ovarian cancer cell lines were detected via western blotting. Moreover, two cell lines with high OPN expression were interfered with lentiviral transfection technique. The effects of OPN on ovarian cancer cell proliferation and apoptosis were detected and analyzed via Cell Counting Kit-8 (CCK8) assay and flow cytometry. The positive expression rate of OPN protein in tumor tissues was higher than that in para-carcinoma tissues (P<0.05). Survival curves suggested that both DFS and OS in OPN negative group were superior to those in OPN positive group (P<0.05). Results of western blotting showed that OPN was weakly expressed in IOSE80 cells, whereas it was highly expressed in SKOV-3, COC1, A2780, HO-8910 and OVCAR-3 cells, among which the OPN protein expression levels were relatively higher in SKOV-3 and OVCAR-3 cell lines. After knockdown of OPN gene with sh-OPN, the cell proliferation rates of OVCAR-3 and SKOV-3 were significantly decreased from 48 h (P<0.05), but the apoptosis level was increased remarkably (28.2 vs. 1.3% and 25.3 vs. 3.2%), and differences were statistically significant (P<0.05). In conclusion, overexpression of OPN enhances the proliferation of ovarian cancer cells, which is an adverse factor for patient survival and prognosis.