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This work combines hydrophobic and heat-absorbing materials to solve the problem of water and frost resistance in industrial and domestic scenarios in harsh environments. Here, highly hydrophobic and fast endothermic (HHFE) surfaces were prepared by applying candle soot (nanocarbon) as the template, slides as the carrier and nano-TiO2 as a backbone and a connecting layer. The resulting HHFE surface exhibited a coral-like porous structure, which is beneficial to hydrophobic performance. The contact angle between the water droplet and the glass with the HHFE coating was about 120°, thus implying that the prepared HHFE surface with a TiO2 skeleton layer has excellent hydrophobicity. The hydrophobic mechanism of the HHFE surface can be explained by the Cassie–Baxter model. Infrared thermography and thermometry were used to record the thermal capacity and heat-absorbing rate of the HHFE surface. The temperature of the glass covered with the HHFE coating rose from 16°C to 38°C within 5 minutes, which is 46.2% higher in capacity and 2.2 times faster in rate than ordinary glass under the same solar irradiation. The resulting HHFE thin film consists of nanocarbon materials, and nano-TiO2 particles were hydrophobic and good heat absorbers. They have great potential for anti-freezing and water-proofing applications, especially in harsh environments. [ABSTRACT FROM AUTHOR]

This work combines hydrophobic and heat-absorbing materials to solve the problem of water and frost resistance in industrial and domestic scenarios in harsh environments. Here, highly hydrophobic and fast endothermic (HHFE) surfaces were prepared by applying candle soot (nanocarbon) as the template, slides as the carrier and nano-TiO2 as a backbone and a connecting layer. The resulting HHFE surface exhibited a coral-like porous structure, which is beneficial to hydrophobic performance. The contact angle between the water droplet and the glass with the HHFE coating was about 120°, thus implying that the prepared HHFE surface with a TiO2 skeleton layer has excellent hydrophobicity. The hydrophobic mechanism of the HHFE surface can be explained by the Cassie–Baxter model. Infrared thermography and thermometry were used to record the thermal capacity and heat-absorbing rate of the HHFE surface. The temperature of the glass covered with the HHFE coating rose from 16°C to 38°C within 5 minutes, which is 46.2% higher in capacity and 2.2 times faster in rate than ordinary glass under the same solar irradiation. The resulting HHFE thin film consists of nanocarbon materials, and nano-TiO2 particles were hydrophobic and good heat absorbers. They have great potential for anti-freezing and water-proofing applications, especially in harsh environments. [ABSTRACT FROM AUTHOR]

Purpose: The predictive ability of plasma ESR1 mutations for outcomes among patients with advanced breast cancer undergoing endocrine therapy (ET) remains disputable. We performed a comprehensive meta-analysis of published studies to clarify the impact of plasma ESR1 mutations on clinical outcomes for patients after subsequent ET. Materials and methods: An electronic search was performed to identify eligible studies. Studies analyzing progression-free survival (PFS) and/or overall survival (OS) according to plasma ESR1 mutation status after subsequent ET were included. HRs were calculated using a fixed- or random-effects model according to heterogeneity. Pooled HRs and 95% CIs were used to estimate the effects. Results: Six studies including 705 patients with advanced breast cancer met the inclusion criteria. The impact of plasma ESR1 mutations on PFS and OS after subsequent ET was reported in six studies (seven groups) and two studies, respectively. Meta-analysis results showed that the pooled HR for ESR1 mutations was 1.70 (95% CI, 1.05–2.74; P=0.03) for OS, which was statistically significant for predicting poor survival, and 1.56 (95% CI, 1.13–2.14; P=0.006) for PFS; however, Begg's and Egger's test results identified the presence of bias. The trim-and-fill method was used, and after incorporation of the imputed studies, the HR was 1.16 (95% CI, 0.88–1.53, P=0.30) for PFS, which indicates that plasma ESR1 mutation had no effect on PFS after subsequent ET. Subgroup analysis suggested that plasma ESR1 mutations were correlated with shorter PFS (HR, 1.98; 95% CI, 1.12–3.51; P=0.02) in patients subsequently treated with aromatase inhibitors (AIs), whereas no association with PFS was observed for patients subsequently treated with non-AI ET (HR, 1.08; 95% CI, 0.85–1.38; P=0.54) or fulvestrant (HR, 1.03; 95% CI, 0.79–1.34; P=0.83). Conclusion: The current meta-analysis demonstrates that plasma ESR1 mutation status is not a predictor of ET efficacy for all drugs without distinction in patients with hormone-receptor-positive advanced breast cancer. ESR1 mutation predicted a poor response to AIs, whereas it was not predictive of non-AI ET efficacy, especially for fulvestrant. [ABSTRACT FROM AUTHOR]

Recently, with the assumption that samples can be reconstructed by themselves, subspace clustering (SC) methods have achieved great success. Generally, SC methods contain some parameters to be tuned, and different affinity matrices can obtain with different parameter values. In this paper, for the first time, we study a method for fusing these different affinity matrices to promote clustering performance and provide the corresponding solution from a multi-view clustering (MVC) perspective. That is, we argue that the different affinity matrices are consistent and complementary, which is similar to the fundamental assumption of MVC methods. Based on this observation, in this paper, we use least squares regression (LSR), which is a typical SC method, as an example since it can be efficiently optimized and has shown good clustering performance and we propose a novel robust least squares regression method from an MVC perspective (RLSR/MVCP). Specifically, we first utilize LSR with different parameter values to obtain different affinity matrices. Then, to fully explore the information contained in these different affinity matrices and to remove noise, we further fuse these affinity matrices into a tensor, which is constrained by the tensor low-rank constraint, i.e., the tensor nuclear norm (TNN). The two steps are combined into a framework that is solved by the augmented Lagrange multiplier (ALM) method. The experimental results on several datasets indicate that RLSR/MVCP has very encouraging clustering performance and is superior to state-of-the-art SC methods.