We reconstructed a phylogenetic tree of Chinese vascular plants (Tracheophyta) using sequences of the chloroplast genes atpB, matK, ndhF, and rbcL and mitochondrial matR. We produced a matrix comprising 6098 species and including 13 695 DNA sequences, of which 1803 were newly generated. Our taxonomic sampling spanned 3114 genera representing 323 families of Chinese vascular plants, covering more than 93% of all genera known from China. The comprehensive large phylogeny supports most relationships among and within families recognized by recent molecular phylogenetic studies for lycophytes, ferns (monilophytes), gymnosperms, and angiosperms. For angiosperms, most families in Angiosperm Phylogeny Group IV are supported as monophyletic, except for a paraphyletic Dipterocarpaceae and Santalaceae. The infrafamilial relationships of several large families and monophyly of some large genera are well supported by our dense taxonomic sampling. Our results showed that two species of Eberhardtia are sister to a clade formed by all other taxa of Sapotaceae, except Sarcosperma. We have made our phylogeny of Chinese vascular plants publically available for the creation of subtrees via SoTree (http://www.darwintree.cn/flora/index.shtml), an automated phylogeny assembly tool for ecologists.

Large-scale point clouds scanned by light detection and ranging (lidar) sensors provide detailed geometric characteristics of scenes due to the provision of 3D structural data. The semantic segmentation of large-scale point clouds is a crucial step for an in-depth understanding of complex scenes. Of late, although a large number of point cloud semantic segmentation algorithms have been proposed, semantic segmentation methods are still far from being satisfactory in terms of precision and accuracy of large-scale point clouds. For machine learning (ML) and deep learning (DL) methodologies, the semantic segmentation is largely influenced by the quality of training sets and methods themselves. Therefore, we construct a new point cloud dataset, namely CSPC-Dataset (Complex Scene Point Cloud Dataset) for large-scale scene semantic segmentation. CSPC-Dataset point clouds are acquired by a wearable laser mobile mapping robot. It covers five complex urban and rural scenes and mainly includes six types of objects, i.e., ground, car, building, vegetation, bridge, and pole. It provides large-scale outdoor scenes with color information, which has advantages such as the scene more complete, point density relatively uniform, diversity and complexity of objects and the high discrepancy between different scenes. Based on the CSPC-Dataset, we construct a new benchmark, which includes approximately 68 million points with explicit semantic labels. To extend the dataset into a wide range of applications, this paper provides the semantic segmentation results and comparative analysis of 7 baseline methods based on CSPC-Dataset. In the experiment part, three groups of experiments are conducted for benchmarking, which offers an effective way to make comparisons with different point-labeling algorithms. The labeling results have shown that the highest Intersection over Union (IoU) of pole, ground, building, car, vegetation, and bridge for all benchmarks is 36.0%, 97.8%,

Background Gastric cancer (GC) is the third leading cause of cancer-related death worldwide, partially due to the lack of effective screening strategies. Thus, there is a stringent need for non-invasive biomarkers to improve patient diagnostic efficiency in GC. Methods This study initially filtered messenger RNAs (mRNAs) as prospective biomarkers through bioinformatics analysis. Clinical validation was conducted using circulating mRNA in plasma from patients with GC. Relationships between expression levels of target genes and clinicopathological characteristics were calculated. Then, associations of these selected biomarkers with overall survival (OS) were analyzed using the Kaplan-Meier plotter online tool. Results Based on a comprehensive analysis of transcriptional expression profiles across 5 microarrays, top 3 over- and underexpressed mRNAs in GC were generated. Compared with normal controls, expression levels of collagen type VI alpha 3 chain (COL6A3), serpin family H member 1 (SERPINH1) and pleckstrin homology and RhoGEF domain containing G1 (PLEKHG1) were significantly upregulated in GC plasmas. Receiver-operating characteristic (ROC) curves on the diagnostic efficacy of plasma COL6A3, SERPINH1 and PLEKHG1 mRNAs in GC showed that the area under the ROC (AUC) was 0.720, 0.698 and 0.833, respectively. Combined, these three biomarkers showed an elevated AUC of 0.907. Interestingly, the higher COL6A3 level was significantly correlated with lymph node metastasis and poor prognosis in GC patients. High level of SERPINH1 mRNA expression was correlated with advanced age, poor differentiation, lower OS, and PLEKHG1 was also associated with poor OS in GC patients. Conclusion Our results suggested that circulating COL6A3, SERPINH1 and PLEKHG1 mRNAs could be putative noninvasive biomarkers for GC diagnosis and prognosis.