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Extensive literature has been proposed for the analysis of correlated survival data. Subjects within a cluster share some common characteristics, e.g., genetic and environmental factors, so their time-to-event outcomes are correlated. The frailty model under proportional hazards assumption has been widely applied for the analysis of clustered survival outcomes. However, the prediction performance of this method can be less satisfactory when the risk factors have complicated effects, e.g., nonlinear and interactive. To deal with these issues, we propose a neural network frailty Cox model that replaces the linear risk function with the output of a feed-forward neural network. The estimation is based on quasi-likelihood using Laplace approximation. A simulation study suggests that the proposed method has the best performance compared with existing methods. The method is applied to the clustered time-to-failure prediction within the kidney transplantation facility using the national kidney transplant registry data from the U.S. Organ Procurement and Transplantation Network. All computer programs are available at https://github.com/rivenzhou/deep_learning_clustered. 

Pb₂Ga₃F₆(SeO₃)₂X₃·2H₂O achieve a better balance between the large SHG effect and wide band gap in the current HTO family. 

There has been growing research interest in developing methodology to evaluate healthcare centers' performance with respect to patient outcomes. Conventional assessments can be conducted using fixed or random effects models, as seen in provider profiling. We propose a new method, using fusion penalty to cluster healthcare centers with respect to a survival outcome. Without any prior knowledge of the grouping information, the new method provides a desirable data‐driven approach for automatically clustering healthcare centers into distinct groups based on their performance. An efficient alternating direction method of multipliers algorithm is developed to implement the proposed method. The validity of our approach is demonstrated through simulation studies, and its practical application is illustrated by analyzing data from the national kidney transplant registry. 

Planar MO 3 (M = B, C, N) units have frequently been considered important structural components of novel birefringent crystal materials. An efficient approach for constructing new functional crystals is to simultaneously assemble multiple structural motifs together. Two compounds, Na 3 Rb 6 (CO 3 ) 3 (NO 3 ) 2 X·6H 2 O (X = Br and Cl), were synthesized by the integration of three kinds of anionic groups. More interestingly, the [CO 3 ] 2− and [NO 3 ] − groups in Na 3 Rb 6 (CO 3 ) 3 (NO 3 ) 2 X·6H 2 O are all coplanar with the aid of [NaO 7 ] 13− polyhedra, which can enhance the anisotropic polarizability. Na 3 Rb 6 (CO 3 ) 3 (NO 3 ) 2 X·6H 2 O have a large theoretical birefringence of ∼0.165 at 1064 nm and possess a short UV cut-off edge of ∼230 nm. Additionally, the two compounds exhibit good crystal growth habits. These properties illustrate that Na 3 Rb 6 (CO 3 ) 3 (NO 3 ) 2 X·6H 2 O are promising UV birefringent crystals. 

Abstract Birefringent materials are widely used in various advanced optical systems, owing to their vital role in creating and controlling polarized light. Currently, Sn²⁺‐based compounds containing stereochemically active lone‐pair (SCALP) cations are extensively investigated and considered as one class of promising birefringent materials. To solve the problem of relatively narrow bandgap of Sn²⁺‐based compounds, alkali metals and multiple halogens are introduced to widen the bandgap during the research. Based on this strategy, four new Sn²⁺‐based halides, A₂Sn₂F₅Cl and ASnFCl₂(A = Rb and Cs), with large birefringence, short ultraviolet (UV) cutoff edge, and wide transparent range are successfully found. The birefringences of A₂Sn₂F₅Cl (A = Rb and Cs) are 0.31 and 0.28 at 532 nm, respectively, which are among the largest in Sn‐based halide family. Remarkably, A₂Sn₂F₅Cl possess relatively shorter UV cutoff edge (<300 nm) and broad infrared (IR) transparent range (up to 16.6 µm), so they can become promising candidates as birefringent materials applied in both UV and IR regions. In addition, a comprehensive analysis on crystal structures and structure–property relationship of metal Sn²⁺‐based halides is performed to fully understand this family. Therefore, this work provides insights into designing birefringent materials with balanced optical properties. 

Abstract Optical absorption and scattering properties are often estimated from the diffusive reflection light intensity at only one distance from the material surface, which often encounters accuracy and convergence issues. In this work, a method was proposed to determine optical properties by using diffusive reflection light intensity profiles at multiple distances, which enhanced data richness as a result of the intensity profiles are linearly independent. In this method, five features of light intensity profiles (contrast, correlation, energy, homogeneity, and second moment) were used to reduce the data dimensions. To demonstrate the effectiveness of the proposed method, Monte Carlo (MC) simulations were used to generate diffusive reflection light intensity profiles with noise at different distances for various combinations of four optical properties (absorption coefficientμ_a, scattering coefficientμ_s, isotropic coefficientg, and refractive indexn). The five profile feature vectors were used as inputs and the four optical parameters were used as outputs to train and test a backpropagation (BP) neural network. The influences of noise levels and the number of diffusive light intensity profiles on parameter estimation accuracy were investigated. The four optical parameters estimated by the BP network were compared with the results estimated by the traditional least squares method, which shows that the proposed method can estimate the optical properties with higher accuracy and better convergence. 

Abstract DNA damage and epigenetic marks are well established to have profound influences on genome stability and cell phenotype, yet there are few technologies to obtain high-resolution genomic maps of the many types of chemical modifications of DNA. Here we present Nick-seq for quantitative, sensitive, and accurate mapping of DNA modifications at single-nucleotide resolution across genomes. Pre-existing breaks are first blocked and DNA modifications are then converted enzymatically or chemically to strand-breaks for both 3′-extension by nick-translation to produce nuclease-resistant oligonucleotides and 3′-terminal transferase tailing. Following library preparation and next generation sequencing, the complementary datasets are mined with a custom workflow to increase sensitivity, specificity and accuracy of the map. The utility of Nick-seq is demonstrated with genomic maps of site-specific endonuclease strand-breaks in purified DNA from Eschericia coli, phosphorothioate epigenetics in Salmonella enterica Cerro 87, and oxidation-induced abasic sites in DNA from E. coli treated with a sublethal dose of hydrogen peroxide. Nick-seq applicability is demonstrated with strategies for >25 types of DNA modification and damage.