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The chemistry of the pore fluid within clayey sediments frequently changes in various processes. However, the impacts of pore fluid chemistry have not been well included in the hydraulic permeability model, and the physical bases behind the salinity sensitivity of the hydraulic permeability remains elusive. In this study, a theoretical model for the hydraulic permeability of clayey sediments is proposed, and impacts of the pore fluid chemistry are quantitatively considered by introducing electrokinetic flow theory. Available experimental data were used to verify the theoretical model, and the verified model was further applied as a sensitivity analysis tool to explore more deeply how hydraulic permeability depends on pore fluid chemistry under different conditions. Coupling effects of pore water desalination and the effective stress enhancement on the hydraulic permeability of marine sediments surrounding a depressurization wellbore during hydrate production are discussed. Results and discussion show that the hydraulic permeability reduction is significant only when the electric double layer thickness is comparable to the characteristic pore size, and the reduction becomes more obvious when the ion mobility of the saline solution is smaller and the surface dielectric potential of clay minerals is lower. During gas hydrate production in the ocean, the salinity sensitivity of the hydraulic permeability could become either stronger and weaker, depending on whether the original characteristic pore size of marine sediments is relatively large or small.

Many people suffer from hair loss and abnormal skin pigmentation, highlighting the need for simple assays to support drug discovery research. Current assays have various limitations, such as being in vitro only, not sensitive enough, or unquantifiable. We took advantage of the bilateral symmetry and large size of mouse whisker follicles to develop a novel in vivo assay called “whisker follicle microinjection assay”. In this assay, we plucked mouse whiskers and then injected molecules directly into one side of the whisker follicles using microneedles that were a similar size to the whiskers, and we injected solvent on the other side as a control. Once the whiskers grew out again, we quantitatively measured their length and color intensity to evaluate the effects of the molecules on hair growth and coloring. Several chemicals and proteins were used to test this assay. The chemicals minoxidil and ruxolitinib, as well as the protein RSPO1, promoted hair growth. The effect of the clinical drug minoxidil could be detected at a concentration as low as 0.001%. The chemical deoxyarbutin inhibited melanin production. The protein Nbl1 was identified as a novel hair-growth inhibitor. In conclusion, we successfully established a sensitive and quantitative in vivo assay to evaluate the effects of chemicals and proteins on hair growth and coloring and identified a novel regulator by using this assay. This whisker follicle microinjection assay will be useful when investigating protein functions and when developing drugs to treat hair loss and abnormal skin pigmentation.

Cancer is a heterogeneous disease that is driven by the accumulation of both genetic and nongenetic alterations, so integrating multiomics data and extracting effective information from them is expected to be an effective way to predict cancer driver genes. In this paper, we first generate comprehensive instructive features for each gene from genomic, epigenomic, transcriptomic levels together with protein–protein interaction (PPI)-networks-derived attributes and then propose a novel semisupervised deep graph learning framework GGraphSAGE to predict cancer driver genes according to the impact of the alterations on a biological system. When applied to eight tumor types, experimental results suggest that GGraphSAGE outperforms several state-of-the-art computational methods for driver genes identification. Moreover, it broadens our current understanding of cancer driver genes from multiomics level and identifies driver genes specific to the tumor type rather than pan-cancer. We expect GGraphSAGE to open new avenues in precision medicine and even further predict drivers for other complex diseases.