Bis[μ2-bis­(diphenyl­phosphan­yl)methane-κ2 P:P′]bis­(μ4-diphenyl­phosphinato-κ4 O:O:O′:O′)bis­[μ2-trifluoro­methane­sulfonato­(0.546/0.454)]-κ2 O:O′;κ2 O:O-tetra­silver(I) acetonitrile disolvate

In the centrosymmetric tetra­nuclear title compound, [Ag4(C12H10O2P)2(CF3O3S)2(C25H22P2)2]·2CH3CN, the AgI atom is coordinated by one P atom from a bis­(diphenyl­phosphan­yl)methane (dppm) ligand, two O atoms from two diphenyl­phosphinate (dpp) ligands and one O atom from a trifluoro­methane­sulfonate (OTf) anion in a highly distorted tetra­hedral geometry. Four AgI atoms are bridged by two dppm ligands, two dpp ligands and two OTf anions, forming a tetra­nuclear complex. An weak intra­molecular Ag⋯Ag [3.2692 (14) Å] inter­action is observed. The OTf anion and one of the phenyl groups in the dppm ligand are disordered over two sets of positions in a 0.546 (4):0.454 (4) ratio. The 0.546-occupied OTf is bonded to two Ag atoms in a μ-(κ2 O:O′) mode, while the 0.454-occupied OTf is bonded in a μ-(κ2 O:O) mode. The methyl group of the acetonitrile solvent mol­ecule is also disordered over two positions with equal occupancy factors

Modeling nitrogen loadings from agricultural soils in southwest China with modified DNDC

Degradation of water quality has been widely observed in China, and loadings of nitrogen (N) and other nutrients from agricultural systems play a key role in the water contamination. Process‐based biogeochemical models have been applied to quantify nutrient loading from nonpoint sources at the watershed scale. However, this effort is often hindered by the fact that few existing biogeochemical models of nutrient cycling are able to simulate the two‐dimensional soil hydrology. To overcome this challenge, we launched a new attempt to incorporate two fundamental hydrologic features, the Soil Conservation Service curve and the Modified Universal Soil Loss Equation functions, into a biogeochemistry model, Denitrification‐Decomposition (DNDC). These two features have been widely utilized to quantify surface runoff and soil erosion in a suite of hydrologic models. We incorporated these features in the DNDC model to allow the biogeochemical and hydrologic processes to exchange data at a daily time step. By including the new features, DNDC gained the additional ability to simulate both horizontal and vertical movements of water and nutrients. The revised DNDC was tested against data sets observed in a small watershed dominated by farmlands in a mountainous area of southwest China. The modeled surface runoff flow, subsurface drainage flow, sediment yield, and N loading were in agreement with observations. To further observe the behaviors of the new model, we conducted a sensitivity test with varied climate, soil, and management conditions. The results indicated that precipitation was the most sensitive factor determining the rate of N loading from the tested site. A Monte Carlo test was conducted to quantify the potential uncertainty derived by variations in four selected input parameters. This study demonstrates that it is feasible and effective to use enhanced biogeochemical models such as DNDC for quantifying N loadings by incorporating basic hydrological features into the model framework

microRNA-33a-5p increases radiosensitivity by inhibiting glycolysis in melanoma.

Glycolysis was reported to have a positive correlation with radioresistance. Our previous study found that the miR-33a functioned as a tumor suppressor in malignant melanoma by targeting hypoxia-inducible factor1-alpha (HIF-1α), a gene known to promote glycolysis. However, the role of miR-33a-5p in radiosensitivity remains to be elucidated. We found that miR-33a-5p was downregulated in melanoma tissues and cells. Cell proliferation was downregulated after overexpression of miR-33a-5p in WM451 cells, accompanied by a decreased level of glycolysis. In contrast, cell proliferation was upregulated after inhibition of miR-33a-5p in WM35 cells, accompanied by increased glycolysis. Overexpression of miR-33a-5p enhanced the sensitivity of melanoma cells to X-radiation by MTT assay, while downregulation of miR-33a-5p had the opposite effects. Finally, in vivo experiments with xenografts in nude mice confirmed that high expression of miR-33a-5p in tumor cells increased radiosensitivity via inhibiting glycolysis. In conclusions, miR-33a-5p promotes radiosensitivity by negatively regulating glycolysis in melanoma

Missing Data Imputation with Graph Laplacian Pyramid Network

Data imputation is a prevalent and important task due to the ubiquitousness of missing data. Many efforts try to first draft a completed data and second refine to derive the imputation results, or "draft-then-refine" for short. In this work, we analyze this widespread practice from the perspective of Dirichlet energy. We find that a rudimentary "draft" imputation will decrease the Dirichlet energy, thus an energy-maintenance "refine" step is in need to recover the overall energy. Since existing "refine" methods such as Graph Convolutional Network (GCN) tend to cause further energy decline, in this work, we propose a novel framework called Graph Laplacian Pyramid Network (GLPN) to preserve Dirichlet energy and improve imputation performance. GLPN consists of a U-shaped autoencoder and residual networks to capture global and local detailed information respectively. By extensive experiments on several real-world datasets, GLPN shows superior performance over state-of-the-art methods under three different missing mechanisms. Our source code is available at https://github.com/liguanlue/GLPN.Comment: 12 pages, 5 figure