Data-Driven Insights Into Urban Intersections: Visual Analytics of High-Value Scene.

In this article, we propose TraVis, an interactive system that allows users to explore and analyze complex traffic trajectory data at urban intersections. Trajectory data contain a large amount of spatio-temporal information, and while previous studies have mainly focused on the macroscopic aspects of traffic flow, TraVis employs visualization methods to investigate and analyze microscopic traffic events (i.e., high-value scenes in trajectory data). TraVis contains a novel view design and provides multiple interaction modalities to offer users the most intuitive insights into high-value scenes. With this system, users can gain a better understanding of urban intersection traffic information, identify different types of high-value scenes, explore the reasons behind their occurrence, and gain deeper insights into urban intersection traffic. Through two case studies, we illustrate how to use the system and validate its effectiveness.

Using Multi-Level Consistency Learning for Partial-to-Partial Point Cloud Registration.

Point cloud registration is a basic task in computer vision and computer graphics. Recently, deep learning-based end-to-end methods have made great progress in this field. One of the challenges of these methods is to deal with partial-to-partial registration tasks. In this work, we propose a novel end-to-end framework called MCLNet that makes full use of multi-level consistency for point cloud registration. First, the point-level consistency is exploited to prune points located outside overlapping regions. Second, we propose a multi-scale attention module to perform consistency learning at the correspondence-level for obtaining reliable correspondences. To further improve the accuracy of our method, we propose a novel scheme to estimate the transformation based on geometric consistency between correspondences. Compared to baseline methods, experimental results show that our method performs well on smaller-scale data, especially with exact matches. The reference time and memory footprint of our method are relatively balanced, which is more beneficial for practical applications.

Mass-Driven Topology-Aware Curve Skeleton Extraction from Incomplete Point Clouds.

We introduce a mass-driven curve skeleton as a curve skeleton representation for 3D point cloud data. The mass-driven curve skeleton presents geometric properties and mass distribution of a curve skeleton simultaneously. The computation of the mass-driven curve skeleton is formulated as a minimization of Wasserstein distance, with an entropic regularization term, between mass distributions of point clouds and curve skeletons. Assuming that the mass of one sampling point should be transported to a line-like structure, a topology-aware rough curve skeleton is extracted via the optimal transport plan. A Dirichlet energy regularization term is then used to obtain a smooth curve skeleton via geometric optimization. Given that rough curve skeleton extraction does not depend on complete point clouds, our algorithm can be directly applied to curve skeleton extraction from incomplete point clouds. We demonstrate that a mass-driven curve skeleton can be directly applied to an unoriented raw point scan with significant noise, outliers and large areas of missing data. In comparison with state-of-the-art methods on curve skeleton extraction, the performance of the proposed mass-driven curve skeleton is more robust in terms of extracting a correct topology.

The voxel visibility model: an efficient framework for transfer function design.

Volume visualization is a very important work in medical imaging and surgery plan. However, determining an ideal transfer function is still a challenging task because of the lack of measurable metrics for quality of volume visualization. In the paper, we presented the voxel vibility model as a quality metric to design the desired visibility for voxels instead of designing transfer functions directly. Transfer functions are obtained by minimizing the distance between the desired visibility distribution and the actual visibility distribution. The voxel model is a mapping function from the feature attributes of voxels to the visibility of voxels. To consider between-class information and with-class information simultaneously, the voxel visibility model is described as a Gaussian mixture model. To highlight the important features, the matched result can be obtained by changing the parameters in the voxel visibility model through a simple and effective interface. Simultaneously, we also proposed an algorithm for transfer functions optimization. The effectiveness of this method is demonstrated through experimental results on several volumetric data sets.

Atlas-based liver segmentation and hepatic fat-fraction assessment for clinical trials.

Automated assessment of hepatic fat-fraction is clinically important. A robust and precise segmentation would enable accurate, objective and consistent measurement of hepatic fat-fraction for disease quantification, therapy monitoring and drug development. However, segmenting the liver in clinical trials is a challenging task due to the variability of liver anatomy as well as the diverse sources the images were acquired from. In this paper, we propose an automated and robust framework for liver segmentation and assessment. It uses single statistical atlas registration to initialize a robust deformable model to obtain fine segmentation. Fat-fraction map is computed by using chemical shift based method in the delineated region of liver. This proposed method is validated on 14 abdominal magnetic resonance (MR) volumetric scans. The qualitative and quantitative comparisons show that our proposed method can achieve better segmentation accuracy with less variance comparing with two other atlas-based methods. Experimental results demonstrate the promises of our assessment framework.

[Application of damage control surgery for severe abdominal trauma].

OBJECTIVE: To evaluate the value of damage control surgery(DCS) for severe abdominal trauma. METHODS: Forty-six patients with severe abdominal trauma were treated according to DCS principles and 58 patients were not. Clinical data of the two groups were analyzed retrospectively. DCS included simple surgery, resuscitation, and definitive operation. RESULTS: Perioperative morality was 15.2%(7/46) in the DCS group and 34.5%(20/58) in the non-DCS group, and the difference was statistically significant(χ²=4.954, P=0.026). The perioperative complication rate was 37.0%(17/46) in the DCS group and 67.2%(39/58) in the non-DCS group, and the difference was statistically significant(χ²=9.468, P=0.002). CONCLUSION: Damage control surgery can reduce perioperative morbidity and mortality in patients with severe abdominal trauma, and its efficacy is satisfactory.

Identification of molecular targets for immunotherapy of patients with head and neck squamous cell carcinoma.

To identify molecular targets for immunotherapy of head and neck squamous cell carcinoma (HNSCC) patients, we analyzed gene expression profile in matched tumor (HN) and normal fibroblast (FB) cell lines established from a HNSCC patient using microarray technique followed by real-time RT-PCR. Screening against a series of established normal and malignant cell lines followed by screening against a panel of normal human tissues led to the identification of 7 genes (AREG, CDH3, KLK10, NmU, SLPI, ANAX3 and MAL2), which were over-expressed at least 10-fold in tumors over any of the normal tissues. We determined the expression of mRNA encoding these genes against a panel of 15 HNSCC primary tumor samples. Relative expression of these genes was at least 20-fold. Expression of AREG, CDH3, KLK10, NmU and SLPI at the protein level was determined by immunohistochemistry in seven supraglottic laryngeal cancer specimens. All five proteins were expressed in these tumor samples with high intensity. We conclude that these molecules are potential targets for immunotherapy of HNSCC patients.

HCV quasispecies evolution: association with progression to end-stage liver disease in hemophiliacs infected with HCV or HCV/HIV.

Patients with inherited bleeding disorders who received clotting factor concentrates before 1987 have high rates of hepatitis C virus (HCV) or HCV/HIV infection. We evaluated HCV quasispecies evolution in longitudinally collected specimens comparing those from patients with progression to end-stage liver disease (ESLD; cases) to those with compensated chronic hepatitis (controls). Plasma samples were obtained from the National Cancer Institute Multicenter Hemophilia Cohort Study. Controls were matched for age, sex, infection duration, and presence/absence of HIV. Samples from early infection were compared to those obtained after onset of ESLD in the cases. The first hypervariable (HVR1) and core proteincoding regions were amplified, subcloned, and sequenced. Complexity and diversity were determined. More than 700 sub-clones from 10 pairs of patients (8 with HIV) followed over approximately 9.3 years were evaluated. HVR1 complexity narrowed over time in the cases, whereas it increased in controls (P = .01). Similar trends were observed for diversity within HVR1 and the core region (P = .04). HCV-infected patients with inherited bleeding disorders undergo quasispecies evolution over time. Evolution patterns differ for progressors and nonprogressors. Further understanding of these mechanisms may help identify factors related to progression rate and treatment response.