Publications by authors named "M Perrin"

Background: This large-scale study analyzes factors affecting diagnostic accuracy of low-dose myocardial perfusion imaging and correlation with coronary angiography in a real-world practice.

Methods: We compared data extracted from routine reports of (i) low-dose [Tc]sestamibi stress-MPI performed with no attenuation correction and predominantly exercise stress testing and (ii) the corresponding coronary angiography.

Results: We considered 1070 pairs of coronary angiography/stress-MPI results reported by 11 physicians.

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Magic-angle twisted bilayer graphene (TBLG) has emerged as a versatile platform to explore correlated electron phases driven primarily by low-energy flat bands in moiré superlattices. While techniques for controlling the twist angle between graphene layers have spurred rapid experimental progress, understanding the effects of doping inhomogeneity on electronic transport in correlated electron systems remains challenging. In this work, we investigate the interplay of confinement and doping inhomogeneity on the electrical transport properties of TBLG by leveraging device dimensions and twist angles.

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Microbiology reference laboratories perform a crucial role within public health systems. This role was especially evident during the COVID-19 pandemic. In this Viewpoint, we emphasise the importance of microbiology reference laboratories and highlight the types of digital data and expertise they provide, which benefit national and international public health.

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The aim was to examine the associations between breastfeeding intensity and changes in concentrations of mammary gland involution markers (protein and lactose) among mothers participating in federal food assistance programs. Pregnant women in their third trimester who planned to breastfeed were recruited from local prenatal clinics ( = 25). After delivery, six weekly home visits were conducted to collect human milk samples and 24-hour infant feeding recalls.

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Neural Architecture Search (NAS) outperforms handcrafted Neural Network (NN) design. However, current NAS methods generally use hard-coded search spaces, and predefined hierarchical architectures. As a consequence, adapting them to a new problem can be cumbersome, and it is hard to know which of the NAS algorithm or the predefined hierarchical structure impacts performance the most.

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