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Identifying sources of light plays an important role in the development of many photonic technologies, such as lidar, remote sensing, and microscopy. Traditionally, identifying light sources as diverse as sunlight, laser radiation, or molecule fluorescence has required millions of measurements, particularly in low-light environments, which limits the realistic implementation of quantum photonic technologies. Researchers demonstrated a smart quantum technology that enables a dramatic reduction in the number of measurements required to identify light sources.
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Deformable image registration of the treatment planning CT with proton radiographies in perspective of adaptive proton therapy.
Phys Med Biol. 2020 May 04;:
Authors: Palaniappan P, Meyer S, Kamp F, Belka C, Riboldi M, Parodi K, Gianoli C
Abstract
The purpose of the work is to investigate the potentiality of using a limited number of in-room proton radiographies to compensate anatomical changes in adaptive proton therapy. The treatment planning CT is adapted to the treatment delivery scenario relying on 2D-3D deformable image registration (DIR). The proton radiographies, expressed in water equivalent thickness (WET) are simulated for both list-mode and integration-mode detector configurations in pencil beam scanning. Geometrical and analytical simulations of an anthropomorphic phantom in presence of anatomical changes due to breathing are adopted. A Monte Carlo simulation of proton radiographies based on a clinical CT image in presence of artificial anatomical changes is also considered. The accuracy of the 2D-3D DIR, calculated as root mean square error, strongly depends on the considered anatomical changes and is considered adequate for promising adaptive proton therapy when comparable to the accuracy of conventional 3D-3D DIR. In geometrical simulation, this is achieved with a minimum of eight/nine radiographies (more than 90% accuracy). Negligible improvement (~1%) is obtained with the use of 180 radiographies. Comparing different detector configurations, superior accuracy is obtained with list-mode than integration-mode max (WET with maximum occurrence) and mean (average WET weighted by occurrences). Moreover, integration-mode max performs better than integration-mode mean. Results are minimally affected by proton statistics. In analytical simulation, the anatomical changes are approximately compensated (about 60-70% accuracy) with two proton radiographies and minor improvement is observed with nine proton radiographies. In clinical data, two proton radiographies from list-mode demonstrate to perform better than nine from integration-mode (more than 100% and about 50-70% accuracy, respectively), even avoiding the finer grid spacing of the last numerical optimization stage. In conclusion, the choice of detector configuration as well as the amount and the complexity of the considered anatomical changes determine the minimum number of radiographies to be used.
PMID: 32365335 [PubMed – as supplied by publisher]
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Bayesian group selection in logistic regression with application to MRI data analysis.
Biometrics. 2020 May 04;:
Authors: Lee K, Cao X
Abstract
We consider Bayesian logistic regression models with group-structured covariates. In high-dimensional settings, it is often assumed that only a small portion of groups are significant, thus consistent group selection is of significant importance. While consistent frequentist group selection methods have been proposed, theoretical properties of Bayesian group selection methods for logistic regression models have not been investigated yet. In this paper, we consider a hierarchical group spike and slab prior for logistic regression models in high-dimensional settings. Under mild conditions, we establish strong group selection consistency of the induced posterior, which is the first theoretical result in the Bayesian literature. Through simulation studies, we demonstrate that the proposed method outperforms existing state-of-the-art methods in various settings. We further apply our method to an MRI data set for predicting Parkinson’s disease and show its benefits over other contenders. This article is protected by copyright. All rights reserved.
PMID: 32365231 [PubMed – as supplied by publisher]
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Dynamic statistical model for predicting the risk of death among older Chinese people, using longitudinal repeated measures of the frailty index: a prospective cohort study.
Age Ageing. 2020 May 04;:
Authors: Chen Q, Tang B, Zhai Y, Chen Y, Jin Z, Han H, Gao Y, Wu C, Chen T, He J
Abstract
BACKGROUND: Frailty is a common characteristic of older people with the ageing process. We aimed to develop and validate a dynamic statistical prediction model to calculate the risk of death in people aged ≥65 years, using a longitudinal frailty index (FI).
METHODS: One training dataset and three validation datasets from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) were used in our study. The training dataset and validation datasets 1 to 3 included data from 9,748, 7,459, 9,093 and 6,368 individuals, respectively. We used 35 health deficits to construct the FI and a longitudinal FI based on repeated measurement of FI at every wave of the CLHLS. A joint model was used to build a dynamic prediction model considering both baseline covariates and the longitudinal FI. Areas under time-dependent receiver operating characteristic curves (AUCs) and calibration curves were employed to assess the predictive performance of the model.
RESULTS: A linear mixed-effects model used time, sex, residence (city, town, or rural), living alone, smoking and alcohol consumption to calculate a subject-specific longitudinal FI. The dynamic prediction model was built using the longitudinal FI, age, residence, sex and an FI-age interaction term. The AUCs ranged from 0.64 to 0.84, and both the AUCs and the calibration curves showed good predictive ability.
CONCLUSIONS: We developed a dynamic prediction model that was able to update predictions of the risk of death as updated measurements of FI became available. This model could be used to estimate the risk of death in individuals aged >65 years.
PMID: 32365173 [PubMed – as supplied by publisher]