Rotatable LYSO-GAPD DEXA detector for providing improved spatial resolution

A rotatable lutetium-yttrium-oxyorthosilicate-Geiger-mode-avalanche photodiode (LYSO-GAPD) DEXA detector that can be configured into either a normal-resolution or a high-resolution mode, was proposed and examined. A 3 × 3 × 2 mm3 LYSO was coupled to a 3 × 3 mm2 GAPD. The versatile transformation of the high-resolution mode was possible by employing the rotating controller for the DEXA detector on its own axis, and the intrinsic resolution in this mode was improved by ∼ 33% compared to the normal-resolution mode. Dual-energy X-ray spectra and imaging capabilities were evaluated in both acquisition modes. The respective peak positions of low- and high-energy-beam of normal-resolution mode (high-resolution mode) were 1330 mV (1262 mV) and 2347 mV (2267 mV). The respective peak-to-valley ratios of low- and high-energy-beam of normal-resolution mode (high-resolution mode) were ∼ 2.8 (∼ 2.9) and ∼ 1.2 (∼ 1.1). Considerable improvements in phantom images such as overall contrast and fine-spot detectability were observed in the high-resolution mode. It should be noted that spatial resolution was improved by reducing the detection-area from 3 × 3 mm2 to 2 × 3 mm2 in the high-resolution mode, but count rate was also decreased. These results demonstrated that a rotatable LYSO-GAPD DEXA detector allows to provide high versatility for both high-resolution mode and normal-resolution mode with a single detector.

A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields

<div><div><div><p>Molecular dynamics simulations of all-atom and coarse-grained lipid bilayer models are increasingly used to obtain insights useful for understanding the structural dynamics of these assemblies. In this context, one crucial point concerns the comparison of the performance and accuracy of classical force fields (FFs), which sometimes remains elusive. To date, the assessments performed on different classical potentials are mostly based on the comparison with experimental observables, which typically regard average properties. However, local differences of structure and dynamics, which are poorly captured by average measurements, can make a difference, but these are non-trivial to catch. Here we propose an agnostic way to compare different FFs at different resolutions (atomistic, united-atom, and coarse-grained), by means of a high-dimensional similarity metrics built on the framework of Smooth Overlap of Atomic Positions (SOAP). We compare and classify a set of 13 force fields, modeling 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayers. Our SOAP kernels-based metrics allows us to compare, discriminate and correlate different force fields at different model resolutions in an unbiased, high-dimensional way. This also captures differences between FFs in modeling non-average events (originating from local transitions), such as for example the liquid-to-gel phase transition in dipalmitoylphosphatidylcholine (DPPC) bilayers, for which our metrics allows to identify nucleation centers for the phase transition, highlighting some intrinsic resolution limitations in implicit vs. explicit solvent force fields.</p></div></div></div>

A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields

<div><div><div><p>Molecular dynamics simulations of all-atom and coarse-grained lipid bilayer models are increasingly used to obtain insights useful for understanding the structural dynamics of these assemblies. In this context, one crucial point concerns the comparison of the performance and accuracy of classical force fields (FFs), which sometimes remains elusive. To date, the assessments performed on different classical potentials are mostly based on the comparison with experimental observables, which typically regard average properties. However, local differences of structure and dynamics, which are poorly captured by average measurements, can make a difference, but these are non-trivial to catch. Here we propose an agnostic way to compare different FFs at different resolutions (atomistic, united-atom, and coarse-grained), by means of a high-dimensional similarity metrics built on the framework of Smooth Overlap of Atomic Positions (SOAP). We compare and classify a set of 13 force fields, modeling 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayers. Our SOAP kernels-based metrics allows us to compare, discriminate and correlate different force fields at different model resolutions in an unbiased, high-dimensional way. This also captures differences between FFs in modeling non-average events (originating from local transitions), such as for example the liquid-to-gel phase transition in dipalmitoylphosphatidylcholine (DPPC) bilayers, for which our metrics allows to identify nucleation centers for the phase transition, highlighting some intrinsic resolution limitations in implicit vs. explicit solvent force fields.</p></div></div></div>

A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields

<div><div><div><p>Molecular dynamics simulations of all-atom and coarse-grained lipid bilayer models are increasingly used to obtain insights useful for understanding the structural dynamics of these assemblies. In this context, one crucial point concerns the comparison of the performance and accuracy of classical force fields (FFs), which sometimes remains elusive. To date, the assessments performed on different classical potentials are mostly based on the comparison with experimental observables, which typically regard average properties. However, local differences of structure and dynamics, which are poorly captured by average measurements, can make a difference, but these are non-trivial to catch. Here we propose an agnostic way to compare different FFs at different resolutions (atomistic, united-atom, and coarse-grained), by means of a high-dimensional similarity metrics built on the framework of Smooth Overlap of Atomic Positions (SOAP). We compare and classify a set of 13 force fields, modeling 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayers. Our SOAP kernels-based metrics allows us to compare, discriminate and correlate different force fields at different model resolutions in an unbiased, high-dimensional way. This also captures differences between FFs in modeling non-average events (originating from local transitions), such as for example the liquid-to-gel phase transition in dipalmitoylphosphatidylcholine (DPPC) bilayers, for which our metrics allows to identify nucleation centers for the phase transition, highlighting some intrinsic resolution limitations in implicit vs. explicit solvent force fields.</p></div></div></div>

A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields

<div><div><div><p>Molecular dynamics simulations of all-atom and coarse-grained lipid bilayer models are increasingly used to obtain insights useful for understanding the structural dynamics of these assemblies. In this context, one crucial point concerns the comparison of the performance and accuracy of classical force fields (FFs), which sometimes remains elusive. To date, the assessments performed on different classical potentials are mostly based on the comparison with experimental observables, which typically regard average properties. However, local differences of structure and dynamics, which are poorly captured by average measurements, can make a difference, but these are non-trivial to catch. Here we propose an agnostic way to compare different FFs at different resolutions (atomistic, united-atom, and coarse-grained), by means of a high-dimensional similarity metrics built on the framework of Smooth Overlap of Atomic Positions (SOAP). We compare and classify a set of 13 force fields, modeling 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayers. Our SOAP kernels-based metrics allows us to compare, discriminate and correlate different force fields at different model resolutions in an unbiased, high-dimensional way. This also captures differences between FFs in modeling non-average events (originating from local transitions), such as for example the liquid-to-gel phase transition in dipalmitoylphosphatidylcholine (DPPC) bilayers, for which our metrics allows to identify nucleation centers for the phase transition, highlighting some intrinsic resolution limitations in implicit vs. explicit solvent force fields.</p></div></div></div>