The comparison of the real weights and the weights estimated from different methods.

<p>The comparison of the real weights and the weights estimated from different methods.</p

SBW25 density and richness

SBW25 density and richnes

Boxplots of RMSE of inferred concentrations.

<p>The 6 subplots depict the results of applying inference method to 6 benchmark networks. For each network, its inference results under different numbers of perturbations, varying from 2 to 7, are shown individually. The median values of RMSE approximate to 0 and the quartile values range from 0.0031 to 0.011.</p

community structure

Species richness, Mean phylogenetic distance (MPD), and Net relatedness index (NRI) for all experimental plots

phylogeny

Phylogenetic tree for the 58 species that were observed in the experimental area

Data_Sheet_1_Rho-Associated Protein Kinase Inhibitor Treatment Promotes Proliferation and Phagocytosis in Trabecular Meshwork Cells.pdf

PurposeContinuous reductions in trabecular meshwork (TM) cellularity inhibit aqueous humor (AH) outflow, which is the main cause of primary open-angle glaucoma. Rho-associated protein kinase inhibitor (ROCKi) targets the TM to reduce intraocular pressure (IOP) and increase AH outflow facility. However, the underlying mechanisms are not entirely clear. Here, we aimed to investigate the effect of a ROCKi (Y-27632) on TM cell proliferation and phagocytosis.MethodsImmortalized human TM (iHTM) cells, glaucomatous TM (GTM3) cells, and primary human TM (pTM) cells were cultured and identified. The effects of various concentrations of Y-27632 on F-actin cytoskeleton were assessed using immunofluorescence. Cell proliferation effects were evaluated using a cell counting kit-8 (CCK8), cell counting, and Ki67 immunostaining. Cell phagocytosis was evaluated using immunofluorescence and flow cytometry in immortalized TM cells. C57BL/6J and Tg-MYOCY437H mice were used to investigate the proliferative effects of Y-27632 on TM cells in vivo. The effect of Y-27632 on IOP was monitored for 2 weeks, and the outflow facility was detected 2 weeks after IOP measurement. TM cells in mice were counted using immunohistochemistry.ResultsY-27632 (100 μM) significantly promoted the proliferation of both immortal TM cells and pTM cells. In GTM3 cells, phagocytosis was significantly greater in the Y-27632 group than in the control group, nearly reaching the level of phagocytosis in iHTM, as determined using immunofluorescence and flow cytometry. In Tg-MYOCY437H mice, treatment with Y-27632 significantly decreased IOP and increased outflow facility, which greatly influenced the long-term IOP-lowering effect. The number of TM cells in Tg-MYOCY437H mice was significantly improved after Y-27632 administration.ConclusionY-27632 promoted cell proliferation and phagocytosis of TM cells, and its proliferative effect was demonstrated in a transgenic mouse model. These results revealed a new IOP-lowering mechanism of Y-27632 through effects on TM cells, suggesting the potential for a correlation between TM cellularity and long-term recovery of IOP.</p

Bar charts of RMSE for inferred transition matrices.

<p>These charts show the results of both Step 2 and Step 3 for 6 different benchmark networks with different numbers of perturbations varying from 2 to 7. In Step 2, the RMSE values range from to with the mean value of ; in Step 3, the RMSE values range from to with the mean value of . The RMSE ratios (Step 3/Step 2) vary from 0.14% to 51% with the mean value of 17%.</p

The workflow of CCELL.

<p>The CCELL method consists of 3 steps: concentration inference, network inference and inference refinement (including refined concentration inference and refined network inference). The core algorithm of the first two steps is Bayesian compressive sensing. The two substeps in Step 3 are based on Bayesian linear regression and extended Bayesian compressive sensing respectively. The input of CCELL is a measurement matrix and its corresponding observation vectors that are time-series generated by immunoprecipitation assays. The output of CCELL is a refined transition matrix representing the cell-scale signalling network.</p

Relationship between noise levels and RMSE.

<p>This chart shows the RMSE values for inferred transition matrices of n-39 network under 6 perturbations at different noise levels. The standard deviations of noises vary from 10 to 1. In Step 2, the RMSE values range from to ; in Step 3, the RMSE values range from to .</p

Precision-recall curves of network structure inference.

<p>The performance of structure inference, under 6 different numbers of perturbations (from 2 to 7), is evaluated by Precision-recall curves. Each subplot contains the inference results for 6 benchmark networks. The average AUPR is 0.95. More specifically, the maximum AUPR value 1.0 is achieved by the n-4 network (3–7 perturbations) and the n-11 network (6–7 perturbations), while the minimum AUPR value 0.75 is obtained by the n-58 network (2 perturbations).</p