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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. 

Heterozygous mutations in two genes encoding key regulators of development improve kernel row number in inbred and hybrid maize, revealing their potential for yield improvement. 

Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classi cation approaches only assess disease presence at the time of acquisition, neglecting the common clinical setting of longitudinal imaging. For slow, progressive eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo repeated imaging over time to track disease progression and forecasting the future risk of developing a disease is critical to properly plan treatment. Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging, modeling the time to disease from sequences of fundus photography images captured over long, irregular time periods. Using longitudinal imaging data from the Age-Related Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS), LTSA signi cantly outperformed a single-image baseline in 19/20 head-to- head comparisons on late AMD prognosis and 18/20 comparisons on POAG prognosis. A temporal attention analysis also suggested that, while the most recent image is typically the most in uential, prior imaging still provides additional prognostic value. 

Abstract We report a novel approach for dynamically tuning and reconfiguring microwave bandpass filters (BPFs) based on optically controlled switching elements using photoconductivity modulation in semiconductors. For a prototype demonstration, a BPF circuit featuring a second‐order design using two closely coupled split‐ring resonators embedded with multiple silicon chips (as switching elements) was designed, fabricated, and characterized. The silicon chips were optically linked to fiber‐coupled laser diodes (808 nm light) for switching/modulation, enabling dynamic tuning and reconfiguring of the BPF without any complex biasing circuits. By turning on and off the two laser diodes simultaneously, the BPF response can be dynamically reconfigured between bandpass and broadband suppression. Moreover, the attenuation level of the passband can be continuously adjusted (from 0.7 to 22 dB at the center frequency of 3.03 GHz) by varying the light intensity from 0 to 40 W/cm². The tuning/reconfiguring 3‐dB bandwidth is estimated to be ~200 kHz. In addition, the potential and limitations of the proposed approach using photoconductivity modulation are discussed. With the strong tuning/reconfiguring capability demonstrated and the great potential for high‐frequency operation, this approach holds promise for the development of more advanced tunable filters and other adaptive circuits for next‐generation sensing, imaging, and communication systems.