Conditional Screening for Ultra-high Dimensional Covariates with Survival Outcomes

Identifying important biomarkers that are predictive for cancer patients' prognosis is key in gaining better insights into the biological influences on the disease and has become a critical component of precision medicine. The emergence of large-scale biomedical survival studies, which typically involve excessive number of biomarkers, has brought high demand in designing efficient screening tools for selecting predictive biomarkers. The vast amount of biomarkers defies any existing variable selection methods via regularization. The recently developed variable screening methods, though powerful in many practical setting, fail to incorporate prior information on the importance of each biomarker and are less powerful in detecting marginally weak while jointly important signals. We propose a new conditional screening method for survival outcome data by computing the marginal contribution of each biomarker given priorly known biological information. This is based on the premise that some biomarkers are known to be associated with disease outcomes a priori. Our method possesses sure screening properties and a vanishing false selection rate. The utility of the proposal is further confirmed with extensive simulation studies and analysis of a Diffuse large B-cell lymphoma (DLBCL) dataset.Comment: 34 pages, 3 figure

Relationships between building attributes and COVID-19 infection in London

In the UK, all domestic COVID-19 restrictions have been removed since they were introduced in March 2020. After illustrating the spatial-temporal variations in COVID-19 infection rates across London, this study then particularly aimed to examine the relationships of COVID-19 infection rates with building attributes, including building density, type, age, and use, since previous studies have shown that the built environment plays an important role in public health. Multisource data from national health services and the London Geomni map were processed with GIS techniques and statistically analysed. From March 2020 to April 2022, the infection rate of COVID-19 in London was 3159.28 cases per 10,000 people. The spatial distribution across London was uneven, with a range from 1837.88 to 4391.79 per 10,000 people. During the whole COVID-19 control period, it was revealed that building attributes played a significant role in COVID-19 infection. It was noted that higher building density areas had lower COVID-19 infection rates in London. Moreover, a higher percentage of historic or flat buildings tended to lead to a decrease in infection rates. The percentage of residential buildings had a positive relationship with the infection rate. Variations in the infection rate were more sensitive to building type; in particular, the percentage of residents living in flats contributed the most to variations in COVID-19 infection rates, with a value of 2.5%. This study is expected to provide support for policy and practice towards pandemic-resilient architectural design

Sound Attenuation by Trunks in the Ground with thick Snow

A main way for sound reduction by tree belts is to scatter sound waves by trunks while previous studies of sound absorption by trunks always contain the ground effects. This study aims to examine the difference of reverberation time in different kinds of tree belts through eliminating the influence from ground effect. In order to estimate the scattering of sound waves by trunks, this study investigates sound (Impulse sound) attenuation by three woodlands with same tree species and one woodland with mixed species in the ground with thick snow. The effect of receiver distance from the sound source was also investigated (5 m,10 m and 20m). The results showed that reverberation time can be used to describe the sound propagation over snow cover through tree belts. The reverberation time in different kinds of forests is different, but the variation shows a similar trend with the increase of frequency. The distance between the receiver and sound source affects the reverberation time. The species with higher ability of sound scattering can be used in urban green space to reduce urban noise

Band renormalization and Fermi surface reconstruction in iron-based superconductors

Using the fluctuation exchange approximation and a three-orbital model, we study the band renormalization, Fermi surface reconstruction and the superconducting pairing symmetry in the newly-discovered iron-based superconductors. We find that the inter-orbital spin fluctuations lead to the strong anisotropic band renormalization and the renormalization is orbital dependent. As a result, the topology of Fermi surface displays distinct variation with doping from the electron type to the hole type, which is consistent with the recent experiments. This shows that the Coulomb interactions will have a strong effect on the band renormalization and the topology of the electron Fermi pocket. In addition, the pairing state mediated by the inter-orbital spin fluctuation is of an extended s-wave symmetry.Comment: 16 pages, 6 figure