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In personalised decision making, evidence is required to determine whether an action (treatment) is suitable for an individual. Such evidence can be obtained by modelling treatment effect heterogeneity in subgroups. The existing interpretable modelling methods take a top-down approach to search for subgroups with heterogeneous treatment effects and they may miss the most specific and relevant context for an individual. In this paper, we design a \emph{Treatment effect pattern (TEP)} to represent treatment effect heterogeneity in data. To achieve an interpretable presentation of TEPs, we use a local causal structure around the outcome to explicitly show how those important variables are used in modelling. We also derive a formula for unbiasedly estimating the \emph{Conditional Average Causal Effect (CATE)} using the local structure in our problem setting. In the discovery process, we aim at minimising heterogeneity within each subgroup represented by a pattern. We propose a bottom-up search algorithm to discover the most specific patterns fitting individual circumstances the best for personalised decision making. Experiments show that the proposed method models treatment effect heterogeneity better than three other existing tree based methods in synthetic and real world data sets., 17

Purpose To investigate the biologic effect of an 810 nm diode laser on the induction of heat shock protein 70 (Hsp70) in choroid-retinal endothelial cells in vitro. Methods Cultured rhesus macaque choroid-retinal endothelial cells (RF/6A) were irradiated using an 810 nm diode laser (spot size, 10 mm; duration, 60 s; power, 400–1,500 mW). Cell viability was assessed by annexin V- fluorescein isothiocyanate (FITC) and propidium iodide flow cytometric assay. Hsp70 expression was determined by western blot at 6, 12, 18, 24, and 48 h following laser exposure. Intracellular distribution of Hsp70 was examined by immunofluorescence staining. Results The laser-induced cell injury threshold was found to be at a power of 1,100 mW power (fluence, 84.08 J/cm2), above which there was significant cell death. Under this power, Hsp70 expression elevated obviously and was stronger at 600–1,000 mW power settings (fluences, 45.86–76.43 J/cm2). The expression of Hsp70 peaked at 12–18 h postirradiation, and returned to baseline by 48 h. Immunofluorescence staining indicated the induced Hsp70 expression in both the cytoplasm and the nucleus. Conclusions Subthreshold 810 nm diode laser exposure can induce Hsp70 hyperexpression from 12 to 18 h postirradiation in cultured choroid-retinal endothelial cells without obvious cell death. The results could be useful for investigating and designing more effective laser therapies.

In this article, we introduce a hybrid iterative scheme for finding a common element of the set of solutions for a generalized equilibrium problems, the set of common fixed point for a family of infinite k -strict pseudo-contractive mappings, and the set of solutions of the variational inclusion problem with multi-valued maximal monotone mappings and inverse-strongly monotone mappings in Hilbert space. Under suitable conditions, some strong convergence theorems are proved. Our results extends the recent results in G.L.Acedo and H.K.Xu [2], Zhang, Lee and Chan [8], Takahashi and Toyoda [9], Takahashi and Takahashi [10] and S. S. Chang, H. W. Joseph Lee and C. K. Chan [11], S.Takahashi and W.Takahashi [12]. Moreover, the method of proof adopted in this article is different from those of [4] and [12].

In personalised decision making, evidence is required to determine whether an action (treatment) is suitable for an individual. Such evidence can be obtained by modelling treatment effect heterogeneity in subgroups. The existing interpretable modelling methods take a top-down approach to search for subgroups with heterogeneous treatment effects and they may miss the most specific and relevant context for an individual. In this paper, we design a Treatment effect pattern (TEP) to represent treatment effect heterogeneity in data. To achieve an interpretable presentation of TEPs, we use a local causal structure around the outcome to explicitly show how those important variables are used in modelling. We also derive a formula for unbiasedly estimating the Conditional Average Causal Effect (CATE) using the local structure in our problem setting. In the discovery process, we aim at minimising heterogeneity within each subgroup represented by a pattern. We propose a bottom-up search algorithm to discover the most specific patterns fitting individual circumstances the best for personalised decision making. Experiments show that the proposed method models treatment effect heterogeneity better than three other existing tree based methods in synthetic and real world data sets.