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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. 

Coenzyme Q (CoQ) is a redox-active lipid molecule that acts as an electron carrier in the mitochondrial electron transport chain. In Saccharomyces cerevisiae, CoQ is synthesized in the mitochondrial matrix by a multi-subunit protein-lipid complex termed the CoQ synthome, the spatial positioning of which is coordinated by the Endoplasmic Reticulum-Mitochondria Encounter Structure (ERMES). The MDM12 gene encoding the cytosolic subunit of ERMES, is co-expressed with COQ-10, which encodes the putative CoQ chaperone Coq10, via a shared bidirectional promoter. Deletion of COQ10 results in respiratory deficiency, impaired CoQ biosynthesis, and reduced spatial coordination between ERMES and the CoQ Synthome. While Coq10 protein content is maintained upon deletion of MDM12, we show that deletion of COQ10 by replacement with a HIS3 marker results in diminished Mdm12 protein content. Since deletion of individual ERMES subunits prevents ERMES formation, we asked whether some or all of the phenotypes associated with COQ10 deletion result from ERMES dysfunction. To identify the phenotypes resulting solely due to the loss of Coq10, we constructed strains expressing a functionally impaired (coq10-L96S) or truncated (coq10-R147*) Coq10 isoform using CRISPR-Cas9. We show that both coq10 mutants preserve Mdm12 protein content and exhibit impaired respiratory capacity like the coq10Δ mutant, indicating that Coq10’s function is vital for respiration regardless of ERMES integrity. Moreover, the maintenance of CoQ synthome stability and efficient CoQ biosynthesis observed for the coq10-R147* mutant suggests these deleterious phenotypes in the coq10Δ mutant result from ERMES disruption. Overall, this study clarifies the role of Coq10 in modulating CoQ biosynthesis. 

Abstract Advancements in quantum system lifetimes and control have enabled the creation of increasingly complex quantum states, such as those on multiple bosonic cavity modes. When characterizing these states, traditional tomography scales exponentially with the number of modes in both computational and experimental measurement requirement, which becomes prohibitive as the system size increases. Here, we implement a state reconstruction method whose sampling requirement instead scales polynomially with system size, and thus mode number, for states that can be represented within such a polynomial subspace. We demonstrate this improved scaling with Wigner tomography of multimode entangled W states of up to 4 modes on a 3D circuit quantum electrodynamics (cQED) system. This approach performs similarly in efficiency to existing matrix inversion methods for 2 modes, and demonstrates a noticeable improvement for 3 and 4 modes, with even greater theoretical gains at higher mode numbers. 

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.