GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network.

With the rapid development of 3D vision, point cloud has become an increasingly popular 3D visual media content. Due to the irregular structure, point cloud has posed novel challenges to the related research, such as compression, transmission, rendering and quality assessment. In these latest researches, point cloud quality assessment (PCQA) has attracted wide attention due to its significant role in guiding practical applications, especially in many cases where the reference point cloud is unavailable. However, current no-reference metrics which based on prevalent deep neural network have apparent disadvantages. For example, to adapt to the irregular structure of point cloud, they require preprocessing such as voxelization and projection that introduce extra distortions, and the applied grid-kernel networks, such as Convolutional Neural Networks, fail to extract effective distortion-related features. Besides, they rarely consider the various distortion patterns and the philosophy that PCQA should exhibit shift, scaling, and rotation invariance. In this paper, we propose a novel no-reference PCQA metric named the Graph convolutional PCQA network (GPA-Net). To extract effective features for PCQA, we propose a new graph convolution kernel, i.e., GPAConv, which attentively captures the perturbation of structure and texture. Then, we propose the multi-task framework consisting of one main task (quality regression) and two auxiliary tasks (distortion type and degree predictions). Finally, we propose a coordinate normalization module to stabilize the results of GPAConv under shift, scale and rotation transformations. Experimental results on two independent databases show that GPA-Net achieves the best performance compared to the state-of-the-art no-reference PCQA metrics, even better than some full-reference metrics in some cases. The code is available at: https://github.com/Slowhander/GPA-Net.git.

A random forest-based framework for genotyping and accuracy assessment of copy number variations.

Detection of copy number variations (CNVs) is essential for uncovering genetic factors underlying human diseases. However, CNV detection by current methods is prone to error, and precisely identifying CNVs from paired-end whole genome sequencing (WGS) data is still challenging. Here, we present a framework, CNV-JACG, for Judging the Accuracy of CNVs and Genotyping using paired-end WGS data. CNV-JACG is based on a random forest model trained on 21 distinctive features characterizing the CNV region and its breakpoints. Using the data from the 1000 Genomes Project, Genome in a Bottle Consortium, the Human Genome Structural Variation Consortium and in-house technical replicates, we show that CNV-JACG has superior sensitivity over the latest genotyping method, SV2, particularly for the small CNVs (≤1 kb). We also demonstrate that CNV-JACG outperforms SV2 in terms of Mendelian inconsistency in trios and concordance between technical replicates. Our study suggests that CNV-JACG would be a useful tool in assessing the accuracy of CNVs to meet the ever-growing needs for uncovering the missing heritability linked to CNVs.

Simulation of water environmental capacity and pollution load reduction using QUAL2K for water environmental management.

In recent years, water quality degradation associated with rapid socio-economic development in the Taihu Lake Basin, China, has attracted increasing attention from both the public and the Chinese government. The primary sources of pollution in Taihu Lake are its inflow rivers and their tributaries. Effective water environmental management strategies need to be implemented in these rivers to improve the water quality of Taihu Lake, and to ensure sustainable development in the region. The aim of this study was to provide a basis for water environmental management decision-making. In this study, the QUAL2K model for river and stream water quality was applied to predict the water quality and environmental capacity of the Hongqi River, which is a polluted tributary in the Taihu Lake Basin. The model parameters were calibrated by trial and error until the simulated results agreed well with the observed data. The calibrated QUAL2K model was used to calculate the water environmental capacity of the Hongqi River, and the water environmental capacities of COD(Cr) NH(3)-N, TN, and TP were 17.51 t, 1.52 t, 2.74 t and 0.37 t, respectively. The results showed that the NH(3)-N, TN, and TP pollution loads of the studied river need to be reduced by 50.96%, 44.11%, and 22.92%, respectively to satisfy the water quality objectives. Thus, additional water pollution control measures are needed to control and reduce the pollution loads in the Hongqi River watershed. The method applied in this study should provide a basis for water environmental management decision-making.

Selection of optimal river water quality improvement programs using QUAL2K: a case study of Taihu Lake Basin, China.

In recent years, water quality degradation associated with rapid socio-economic development in the Taihu Lake Basin, China, has attracted increasing attention from both the public and the Chinese government. The primary sources of pollution in Taihu Lake are its inflow rivers and their tributaries. Effective water quality improvement programs need to be implemented in these rivers to improve the water quality of Taihu Lake, and to ensure sustainable development in the region. To ensure effectiveness and efficiency, it is important that the optimal water quality improvement program for a specific situation be selected. The aim of this study was to facilitate the selection of this optimal program. The QUAL2K model for river and stream water quality was used to simulate the effects of a range of water quality improvement scenarios in the Hongqi River, which is a polluted tributary in the Taihu Lake Basin. These scenarios consisted of a series of three water treatment technologies in different configurations, from upstream to downstream. The results showed that the optimal scenario comprised a bio-contact oxidation system upstream, followed by an ecological floating bed, and a vertical moveable eco-bed downstream. The reduction rates achieved by this scenario for biochemical oxygen demand (BOD), ammonia nitrogen (NH(3)-N), total nitrogen (TN), and total phosphorus (TP) were 49.50%, 32.81%, 35.94%, and 45.27%, respectively. The QUAL2K model proved to be an effective tool in the comparative evaluation of potential water quality improvement programs. The method applied in this study can prevent the implementation of water quality improvement programs that would not achieve the desired goals.