Three-dimensional quantum imaging of dynamic targets using quantum compressed sensing.

Quantum imaging based on entangled light sources exhibits enhanced background resistance compared to conventional imaging techniques in low-light conditions. However, direct imaging of dynamic targets remains challenging due to the limited count rate of entangled photons. In this paper, we propose a quantum imaging method based on quantum compressed sensing that leverages the strong correlation characteristics of entangled photons and the randomness inherent in photon pair generation and detection. This approach enables the construction of a compressed sensing system capable of directly imaging high-speed dynamic targets. The results demonstrate that our system successfully achieves imaging of a target rotating at a frequency of 10 kHz, while maintaining an impressive data compression rate of 10-6. This proposed method introduces a pioneering approach for the practical implementation of quantum imaging in real-world scenarios.

Latent class analysis to identify clinical profiles among indigenous infants with bronchiolitis.

OBJECTIVE: Better phenotyping of the heterogenous bronchiolitis syndrome may lead to targeted future interventions. This study aims to identify severe bronchiolitis profiles among hospitalized Australian Indigenous infants, a population at risk of bronchiectasis, using latent class analysis (LCA). METHODS: We included prospectively collected clinical, viral, and nasopharyngeal bacteria data from 164 Indigenous infants hospitalized with bronchiolitis from our previous studies. We undertook multiple correspondence analysis (MCA) followed by LCA. The best-fitting model for LCA was based on adjusted Bayesian information criteria and entropy R2 . RESULTS: We identified five clinical profiles. Profile-A's (23.8% of cohort) phenotype was previous preterm (90.7%), low birth-weight (89.2%) and weight-for-length z-score <-1 (82.7% from combining those with z-score between -1 and -2 and those in the z-score of <-2 group) previous respiratory hospitalization (39.6%) and bronchiectasis on chest high-resolution computed tomography scan (35.4%). Profile-B (25.3%) was characterized by the oxygen requirement (100%) and marked accessory muscle use (45.5%). Infants in profile-C (7.0%) had the most severe disease, with oxygen requirement and bronchiectasis in 100%, moderate accessory muscle use (85% vs 0%-51.4%) and bacteria detected (93.1% vs 56.7%-72.0%). Profile-D (11.6%) was dominated by rhinovirus (49.4%), mild accessory muscle use (73.8%), and weight-for-length z-score <-2 (36.0%). Profile-E (32.2%) included bronchiectasis (13.8%), RSV (44.0%), rhinovirus (26.3%) and any bacteria (72%). CONCLUSION: Using LCA in Indigenous infants with severe bronchiolitis, we identified five clinical profiles with one distinct profile for bronchiectasis. LCA can characterize distinct phenotypes for severe bronchiolitis and infants at risk for future bronchiectasis, which may inform future targeted interventions.