Using Local Toponyms to Reconstruct the Historical River Networks in Hubei Province, China

As an important data source for historical geography research, toponyms reflect the human activities and natural landscapes within a certain area and time period. In this paper, a novel quantitative method of reconstructing historical river networks using toponyms with the characteristics of water and direction is proposed. It is suitable for the study area which possesses rich water resources. To reconstruct the historical shape of the river network, (1) water-related toponyms and direction-related toponyms are extracted as two datasets based on the key words in each village toponym; (2) the feasibility of the river network reconstruction based on these toponyms is validated via a quantitative analysis, according to the spatial distributions of toponyms and rivers; (3) the reconstructed historical shape of the river network can be obtained via qualitative knowledge and geometrical analysis; and (4) the reconstructed rivers are visualized to display their general historical trends and shapes. The results of this paper demonstrate the global correlation and local differences between the toponyms and the river network. The historical river dynamics are revealed and can be proven by ancient maps and local chronicles. The proposed method provides a novel way to reconstruct historical river network shapes using toponym datasets

Predicting Intermetallic Surface Energies with High-Throughput DFT and Convolutional Neural Networks

Surface energy of inorganic crystals is crucial in understanding experimentally-relevant surface properties and thus important in designing materials for many applications including catalysis. Predictive methods and datasets exist for surface energies of monometallic crystals but predicting these properties for bimetallic or more complicated surfaces is an open challenge. Here we present a workflow for predicting surface energies \textit{ab initio} using high-throughput DFT and a machine learning framework. We calculate the surface energy of 3,285 intermetallic alloys with combinations of 36 elements and 47 space groups. We used this high-throughput workflow to seed a database of surface energies, which we used to train a crystal graph convolutional neural network (CGCNN). The CGCNN model was able to predict surface energies with a mean absolute test error of 0.0082 eV/angstrom^2 and can qualitatively reproduce nanoparticle surface distributions (Wulff constructions). Our workflow provides quantitative insights into which surfaces are more stable and therefore more realistic. It allows us to down-select interesting candidates that we can study with robust theoretical and experimental methods for applications such as catalysts screening and nanomaterials synthesis

Associations of Polymorphism of rs9944155, rs1051052, and rs1243166 Locus Allele in Alpha-1-antitrypsin with Chronic Obstructive Pulmonary Disease in Uygur Population of Kashgar Region

Background: Previous studies conducted in various geographical and ethnical populations have shown that Alpha-1-antitrypsin (Alpha-1-AT) expression affects the occurrence and progression of chronic obstructive pulmonary disease (COPD). We aimed to explore the associations of rs9944155AG, rs1051052AG, and rs1243166AG polymorphisms in the Alpha-1-AT gene with the risk of COPD in Uygur population in the Kashgar region. Methods: From March 2013 to December 2015, a total of 225 Uygur COPD patients and 198 healthy people were recruited as cases and controls, respectively, in Kashgar region. DNA was extracted according to the protocol of the DNA genome kit, and Sequenom MassARRAY single-nucleotide polymorphism technology was used for genotype determination. Serum concentration of Alpha-1-AT was detected by enzyme-linked immunosorbent assay. A logistic regression model was used to estimate the associations of polymorphisms with COPD. Results: The rs1243166-G allele was associated with a higher risk of COPD (odds ratio [OR] = 2.039, 95% confidence interval [CI]: 1.116–3.725, P = 0.019). In cases, Alpha-1-AT levels were the highest among participants carrying rs1243166 AG genotype, followed by AA and GG genotype (χ2 = 11.89, P = 0.003). Similarly, the rs1051052-G allele was associated with a higher risk of COPD (OR = 19.433, 95% CI: 8.783–43.00, P < 0.001). The highest Alpha-1-AT levels were observed in cases carrying rs1051052 AA genotype, followed by cases with AG and GG genotypes (χ2 = 122.45, P < 0.001). However, individuals with rs9944155-G allele exhibited a lower risk of COPD than those carrying the rs9944155-A allele (OR = 0.121, 95% CI: 0.070–0.209, P < 0.001). In both cases and controls, no significant difference in Alpha-1-AT levels was observed among various rs9944115 genotypes. Conclusions: rs1243166, rs9944155, and rs1051052 sites of Alpha-1-AT may be associated with the COPD morbidity in Uygur population. While rs1243166-G allele and rs1051052-G allele are associated with an increased risk of developing COPD, rs9944155-G allele is a protect locus in Uygur population. Alpha-1-AT levels in Uygur COPD patients were lower than those in healthy people and differed among patients with different rs1051052 AG and rs1243166 AG genotypes