The combined application of morphology, immunohistochemistry, and special staining may facilitate the diagnosis of this malignancy. Surgery plus chemotherapy remains the most common treatment for MS. The prognosis of MS was bad.

Summary Objective Forkhead box Q1 (FOXQ1), a member of the forkhead transcription factor family, plays important parts in cell cycle, apoptosis, metabolism, immunology and tumour genesis. Its expression has been associated with poor clinical prognosis in various tumours. However, the clinical significance of FOXQ1 in papillary thyroid carcinoma (PTC) has not been fully studied. The purpose of this study was to investigate whether FOXQ1 is correlated with poor prognosis in PTC. Design/Methods We performed a retrospective study of 136 PTCs. Immunohistochemistry (IHC) was used to examine the expression of FOXQ1 in 136 PTCs and 47 nodular goitre specimens. Rank‐sum test, chi‐square test, Kaplan‐Meier survival analysis, univariate and multivariate Cox analyses were used to investigate the clinical and prognostic significance of FOXQ1 expression in PTC. Results The comparison of PTC specimens with nodular goitre with papillary hyperplasia specimens revealed an upregulation of FOXQ1 in PTC. Overexpression of FOXQ1 was observed in 63.24% of PTC and correlated with classic variant, tall variant, distant metastasis, AJCC stage and recurrence. FOXQ1‐positive expression was associated with shorter disease‐free survival: median disease‐free survival of FOXQ1‐positive patients was 23 months compared with 128 months for FOXQ1‐negative patients (Log‐rank χ2 = 12.31, P = 0.00045). Additional independent risk factors in this study were multifocality (recurrence‐free survival [RFS]: hazard ratio [HR] = 2.391, P < 0.05), extrathyroidal extension (RFS: HR = 3.906, P < 0.05) and positive expression of FOXQ1 (RFS: HR = 6.385, P < 0.01). Conclusions Our results indicated that FOXQ1 may be a useful additional biomarker to evaluate the progression of PTC and to predict likely relapse of disease.

When a monocular vision-based unmanned aerial vehicle (UAV) is flown to the final approach to intercept the glide slope, the position and orientation of the airport runway in the image must be detected accurately for a host of suitable procedures to be followed. The approaching marking on the runway is showed as some white spots of high intensity as well as the complicated backgrounds. In our paper, we use pin-hole perspective principle, the constraint condition of the rectangle in inertial space, the front shot constraint condition of the target, as well as the clustering algorithm to identify the runway and output its position and orientation in image space. The results of the experiments show that by this algorithm, even from a place far away from the runway with marks being unclear, effective detection is possible. After all, single-frame detection errors exist, so we extend the basic runway-detection algorithm to the runway tracking. A full filtering strategy using particle filter can guard against potentially catastrophic results and improve the detection rate. Apparently, the whole algorithm of our paper can be treated as a special vision sensor for landing equipment of UAV.

When an monocular vision-based unmanned aerial vehicle (UAV) based on vision is flown to the final approach fix to intercept the glide slope without the navigation of Global Positioning System (GPS), the position and orientation of the airport runway in image must be detected accurately so as to a host of suitable procedures have to be followed. The optimum length of the final approach is about five miles from the runway threshold. The front view of the runway, which is achieved at the moment, is very illegible. The approaching marking (cross bar) of the runway are showed as some white spots of high intensity and the complicated backgrounds of the airport are included in the images. In this case, spots with high intensity should be extracted and classified, some of these spots are just the images of the background noises and the pseudo-targets, which can't be separated with the spots of the runway as in the view there is no significant characteristic difference among them ostensibly. Fortunately, in the terrestrial coordinate space, most of the runway marks are located at the apexes of a rectangle, having some geometric relationships. The relationship among the projection coordinates of the runway spots in the images can be determined according to the perspective principle, the constraint condition of the rectangle as well as the front shot constraint condition of the target, by using this relationship, the runway approaching marks can be separated, the position and the direction of the runway in the images can be identified. In this paper, the clustering management is adopted so as to greatly reduce the computing time. The consequence of the experiments shows that by this algorithm, even from a place far away from the runway whose marks are unclear, we also can effectively detect the runway.