Estimating normals with globally consistent orientations for a raw point cloud has many downstream geometry processing applications. Despite tremendous efforts in the past decades, it remains challenging to deal with an unoriented point cloud with various imperfections, particularly in the presence of data sparsity coupled with nearby gaps or thin-walled structures. In this paper, we propose a smooth objective function to characterize the requirements of an acceptable winding-number field, which allows one to find the globally consistent normal orientations starting from a set of completely random normals. By taking the vertices of the Voronoi diagram of the point cloud as examination points, we consider the following three requirements: (1) the winding number is either 0 or 1, (2) the occurrences of 1 and the occurrences of 0 are balanced around the point cloud, and (3) the normals align with the outside Voronoi poles as much as possible. Extensive experimental results show that our method outperforms the existing approaches, especially in handling sparse and noisy point clouds, as well as shapes with complex geometry/topology.

To investigate the effect of machine learning methods on predicting the Overall Survival (OS) for non-small cell lung cancer based on radiomics features analysis. A total of 339 radiomic features were extracted from the segmented tumor volumes of pretreatment computed tomography (CT) images. These radiomic features quantify the tumor phenotypic characteristics on the medical images using tumor shape and size, the intensity statistics and the textures. The performance of 5 feature selection methods and 8 machine learning methods were investigated for OS prediction. The predicted performance was evaluated with concordance index between predicted and true OS for the non-small cell lung cancer patients. The survival curves were evaluated by the Kaplan-Meier algorithm and compared by the log-rank tests. The gradient boosting linear models based on Cox’s partial likelihood method using the concordance index feature selection method obtained the best performance (Concordance Index: 0.68, 95% Confidence Interval: 0.62~ 0.74). The preliminary results demonstrated that certain machine learning and radiomics analysis method could predict OS of non-small cell lung cancer accuracy.

Diabetes mellitus is a major threat to human health. Both its incidence and prevalence have been rising steadily over the past few decades. Biomacromolecular agents such as insulin and glucagon-like peptide 1 receptor agonists are commonly used hypoglycemic drugs that play important roles in the treatment of diabetes. However, their traditional frequent administration may cause numerous side effects, such as pain, infection or local tissue necrosis. To address these issues, many novel subcutaneous delivery systems have been developed in recent years. In this review, we survey recent developments in subcutaneous delivery systems of biomacromolecular hypoglycemic drugs, including sustained-release delivery systems and stimuli-responsive delivery systems, and summarize the advantages and limitations of these systems. Future opportunities and challenges are discussed as well.