Developing a Semantic-Driven Hybrid Segmentation Method for Point Clouds of 3D Shapes

With the rapid development of point cloud processing technologies and the availability of a wide range of 3D capturing devices, a geometric object from the real world can be directly represented digitally as a dense and fine point cloud. Decomposing a 3D shape represented in point cloud into meaningful parts has very important practical implications in the fields of computer graphics, virtual reality and mixed reality. In this paper, a semantic-driven automated hybrid segmentation method is proposed for 3D point cloud shapes. Our method consists of three stages: semantic clustering, variational merging, and region remerging. In the first stage, a new feature of point cloud, called Local Concave-Convex Histogram, is introduced to first extract saddle regions complying with the semantic boundary feature. All other types of regions are then aggregated according to this extracted feature. This stage often leads to multiple over-segmentation convex regions, which are then remerged by a variational method established based on the narrow-band theory. Finally, in order to recombine the regions with the approximate shapes, order relation is introduced to improve the weighting forms in calculating the conventional Shape Diameter Function. We have conducted extensive experiments with the Princeton Dataset. The results show that the proposed algorithm outperforms the state-of-the-art algorithms in this area. We have also applied the proposed algorithm to process the point cloud data acquired directly from the real 3D objects. It achieves excellent results too. These results demonstrate that the method proposed in this paper is effective and universal

Developing offloading-enabled application development frameworks for android mobile devices

Mobile devices, such as smartphones, offer people great convenience in accessing information and computation resources. However, mobile devices remain relatively limited in terms of computing, memory and energy capacity when compared with desktop machines. A promising solution to mitigate these limitations is to enhance the services mobile devices can provide by utilizing powerful cloud platforms through offloading mechanisms, i.e., offloading the heavy information processing tasks from mobile devices to the Cloud. This paper addresses this issue by developing two offloading-enabled application development frameworks by adapting certain Android OS interfaces. The applications developed using these frameworks will be equipped with offloading capability. In the first framework, each application is selfish and makes offloading decisions independently, whereas in the second, a central offloading manager resides in the mobile device and is responsible for making the offloading decisions for all applications. The two frameworks are designed in a way that application developers only need to make minimal changes to their programming behavior. Experiments have been conducted that verify the feasibility and effectiveness of the offloading mechanisms that are proposed

PSNet : fast data structuring for hierarchical deep learning on point cloud

In order to retain more feature information of local areas on a point cloud, local grouping and subsampling are the necessary data structuring steps in most hierarchical deep learning models. Due to the disorder nature of the points in a point cloud, the significant time cost may be consumed when grouping and subsampling the points, which consequently results in poor scalability. This paper proposes a fast data structuring method called PSNet (Point Structuring Net). PSNet transforms the spatial features of the points and matches them to the features of local areas in a point cloud. PSNet achieves grouping and sampling at the same time while the existing methods process sampling and grouping in two separate steps (such as using FPS plus kNN). PSNet performs feature transformation pointwise while the existing methods uses the spatial relationship among the points as the reference for grouping. Thanks to these features, PSNet has two important advantages: 1) the grouping and sampling results obtained by PSNet is stable and permutation invariant; and 2) PSNet can be easily parallelized. PSNet can replace the data structuring methods in the mainstream point cloud deep learning models in a plug-and-play manner. We have conducted extensive experiments. The results show that PSNet can improve the training and reasoning speed significantly while maintaining the model accuracy

MiR-455-3p regulates glioma cell proliferation by targeting PAX6

Purpose: To investigate the role of miR-455-3p in gliomas. Method: Quantitative real-time polymerase chain reaction was used to measure miR-455-3p and paired box 6 (PAX6) levels in glioma cell lines. Western blot analysis was used to determine the expression of cell cycle regulators. In addition to over-expression, silencing of miR-455-3p or PAX6 was performed to study the functions of miR-455-3p in gliomas. Results: The levels of miR-455-3p were significantly up-regulated in glioma cell lines (p < 0.05), while miR-455-3p over-expression increased glioma cell proliferation and interfered with the progress of the cell cycle (p < 0.01). Furthermore, endogenous miR-455-3p silencing prevented glioma cell proliferation by regulating cell cycle progression (p < 0.05).The results also showed that PAX6 controlled the cell cycle while PAX6 silencing selectively regulated p21 expression (p < 0.01). Furthermore, miR-455-3p and PAX6 influenced p53 expression. Re-introduction of PAX6 expressing vector into glioma cells rescued the pro-tumoral effect of miR-455-3p overexpression. Conclusion: These findings demonstrate the role of miR-455-3p as a tumour oncogene in gliomas via regulation of the cell cycle, indicating that miR-455-3p might act as a new treatment strategy for glioma cell tumours and a predictor of survival in glioma patients

Dissection of the regulatory mechanism of a heat-shock responsive promoter in Haloarchaea: a new paradigm for general transcription factor directed archaeal gene regulation

Multiple general transcription factors (GTFs), TBP and TFB, are present in many haloarchaea, and are deemed to accomplish global gene regulation. However, details and the role of GTF-directed transcriptional regulation in stress response are still not clear. Here, we report a comprehensive investigation of the regulatory mechanism of a heat-induced gene (hsp5) from Halobacterium salinarum. We demonstrated by mutation analysis that the sequences 5′ and 3′ to the core elements (TATA box and BRE) of the hsp5 promoter (Phsp5) did not significantly affect the basal and heat-induced gene expression, as long as the transcription initiation site was not altered. Moreover, the BRE and TATA box of Phsp5 were sufficient to render a nonheat-responsive promoter heat-inducible, in both Haloferax volcanii and Halobacterium sp. NRC-1. DNA–protein interactions revealed that two heat-inducible GTFs, TFB2 from H. volcanii and TFBb from Halobacterium sp. NRC-1, could specifically bind to Phsp5 likely in a temperature-dependent manner. Taken together, the heat-responsiveness of Phsp5 was mainly ascribed to the core promoter elements that were efficiently recognized by specific heat-induced GTFs at elevated temperature, thus providing a new paradigm for GTF-directed gene regulation in the domain of Archaea

Significant mitral regurgitation as a predictor of long-term prognosis in patients receiving cardiac resynchronisation therapy

Background: Cardiac resynchronisation therapy (CRT) has been shown to reduce functional mitral regurgitation, although the relationship between significant mitral regurgitation (SMR) and the clinical prognosis of CRT remains uncertain. Aim: We sought to investigate the association of baseline SMR with long-term outcomes in patients undergoing CRT. Methods: A total of 296 consecutive patients undergoing CRT were enrolled. SMR was quantified by colour Doppler in all patients at baseline and defined as level ≥ 3 on the severity scale. The primary endpoints included all-cause death, heart failure hospitalisation (HFH), and heart transplantation, and the secondary endpoints were response to CRT and New York Heart Association (NYHA) class III or IV six months after CRT implantation. Results: The mean age was 59 ± 11 years, and 202 (68.2%) patients were male. Among all patients, 124 (41.9%) presented with baseline SMR. Over a mean follow-up of 4.17 ± 3.16 years, there were 53 (17.9%) cases of all-cause death, 41 (13.8%) cases of HFH, and four (1.4%) cases of heart transplantation. SMR was positively associated with primary endpoint events (hazard ratio [HR] 1.602, 95% confidence interval [CI] 1.083–2.371, p = 0.019), HFH (HR 3.567, 95% CI 1.763–7.219, p < 0.001) and NYHA class III or IV (HR 2.101, 95% CI 1.313–3.363, p = 0.002). After adjusting for multiple factors, we found that SMR (HR 1.785, 95% CI 1.091–2.920, p = 0.021), ischaemic heart disease (HR 1.628, 95% CI 1.062–2.494, p = 0.025), and the lack of use of spironolactone (HR 2.044, 95% CI 1.040–4.017, p = 0.038) were independent predictors of primary endpoints, and SMR remained an independent predictor of HFH (HR 4.622, 95% CI 1.955–10.923, p < 0.001). Conclusions: Significant mitral regurgitation before CRT implantation was strongly associated with long-term poor progno­sis. SMR was positively associated with HFH rather than all-cause death and CRT response