3D Particle Research Articles

A large expert-curated cryo-EM image dataset for machine learning protein particle picking

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.

Long distance dispersal and oceanographic fronts shape the connectivity of the keystone sponge Phakellia ventilabrum in the deep northeast Atlantic

Little is known about dispersal in deep-sea ecosystems, especially for sponges, which are abundant ecosystem engineers. Understanding patterns of gene flow in deep-sea sponges is essential, especially in areas where rising pressure from anthropogenic activities makes difficult to combine management and conservation. Here, we combined population genomics and oceanographic modelling to understand how Northeast Atlantic populations (Cantabrian Sea to Norway) of the deep-sea sponge Phakellia ventilabrum are connected. The analysis comprised ddRADseq derived SNP datasets of 166 individuals collected from 57 sampling stations from 17 different areas, including two Marine Protected Areas, one Special Area of Conservation and other areas with different levels of protection. The 4,017 neutral SNPs used indicated high connectivity and panmixis amongst the majority of areas (Ireland to Norway), spanning ca. 2,500-km at depths of 99–900 m. This was likely due to the presence of strong ocean currents allowing long-distance larval transport, as supported by our migration analysis and by 3D particle tracking modelling. On the contrary, the Cantabrian Sea and Roscoff (France) samples, the southernmost areas in our study, appeared disconnected from the remaining areas, probably due to prevailing current circulation patterns and topographic features, which might be acting as barriers for gene flow. Despite this major genetic break, our results suggest that all protected areas studied are well-connected with each other. Interestingly, analysis of SNPs under selection replicated results obtained for neutral SNPs. The relatively low genetic diversity observed along the study area, though, highlights the potential fragility of this species to changing climates, which might compromise resilience to future threats.

Mechanical responses and acoustic emission behaviors of coal under compressive differential cyclic loading (DCL): a numerical study via 3D heterogeneous particle model

The stability of coal walls (pillars) can be seriously undermined by diverse in-situ dynamic disturbances. Based on a 3D particle model, this work strives to numerically replicate the major mechanical responses and acoustic emission (AE) behaviors of coal samples under multi-stage compressive cyclic loading with different loading and unloading rates, which is termed differential cyclic loading (DCL). A Weibull-distribution-based model with heterogeneous bond strengths is constructed by both considering the stress–strain relations and AE parameters. Six previously loaded samples were respectively grouped to indicate two DCL regimes, the damage mechanisms for the two groups are explicitly characterized via the time-stress-dependent variation of bond size multiplier, and it is found the two regimes correlate with distinct damage patterns, which involves the competition between stiffness hardening and softening. The numerical b-value is calculated based on the magnitudes of AE energy, the results show that both stress level and bond radius multiplier can impact the numerical b-value. The proposed numerical model succeeds in replicating the stress–strain relations of lab data as well as the elastic-after effect in DCL tests. The effect of damping on energy dissipation and phase shift in numerical model is summarized.

Understanding stability, oligomerization and deactivation during catalytic lignin hydrodeoxygenation by mechanistic reaction micro-kinetics linked with 3D catalyst particle nanotomography

Lignin is an aromatics’ natural source that can be converted into separate high value chemical compounds. Due to the highly-functionalised network structure of phenols, the mechanisms, activity and selectivity of the acid-supported transition metal rate catalyst (NiMo/Al2O3) for the hydrogenation, hydrodeoxygenation (HDO) and defunctionalisation of (l) phase model components (eugenol, guaiacol and 4-propylguaiacol) were investigated in this integrated experimental, characterisation and modelling study. Commercially available NiMo/Al2O3 intermediates exhibited strongly acidic properties that did little to promote the complete bond saturation of the allyl-benzenes to alkyl-benzenes, thermodynamically-favoured higher molecular weight compositions (e.g. dimers) formed instead, and deactivation followed. The condensation elementary steps for an independent system constituent were proposed, which is important for understanding, designing and optimizing an efficient valorisation industrial processes engineering. Interestingly, the stabilising steric hindrances of different defined representatives affected product functionality distribution, reactivity and stability. HDO promoted demethoxylation (reaction), while methoxy-phenols were more favourably de-methylated. In addition, NiMo/Al2O3 formulation was analysed to obtain the quantified surface, structural and morphological changes by NH3 activation temperature-programmed desorption (TPD), energy dispersive X-ray spectroscopy (EDS) mapping, Fourier transform infrared spectroscopy–attenuated total reflection (FTIR–ATR), nanometre computed tomography (nano-CT) and physi-sorption. Adsorbed carbonaceous species reduced its physicochemical interface, decreased by acidity.

Microsphere-assisted enhanced photothermal lens detection integrated with digital holographic microscopy for 3D particle sensing and thermal diffusivity measurement

In this paper, we show that by extending the concept of “microsphere-assisted imaging” into thermal lens (TL) detection, enhancement of photothermal detection is achieved. Furthermore, by integration of TL detection and digital holographic microscopy (DHM), we provide simultaneous measurement of photothermal properties as well as complementary imaging of the sample. Simultaneous acquisition of photothermal properties and imaging is essential for characterization of a variety of samples in biochemistry, material science, and process technologies. TL is a sensitive methodology to detect low concentrations of analytes in low-loss samples, and DHM in the transmission mode is an effective technique for label-free, non-contact, and real-time imaging and measurement of phase objects, such as biomaterials. We demonstrate the usefulness of the TL-DHM integrated system by applying it for acquisition of photothermal maps of samples as well as obtaining their morphometrical information. Additionally, we measure the thermal diffusivity of polydimethylsiloxane because of the focusing effect of polystyrene microspheres. The system has the potential to be applied for a variety of objects including bio-samples and may be proposed as a bench-top characterization device.

Numerical investigation of variable-mass seepage mechanism of broken rock mass in faults

The continuous migration and loss of small particles in fractured rock mass with water flow is the main cause of water inrush in fault zones. In this study, a fluid–solid coupling model for fault zone was established to study the complex coupling between the groundwater seepage and migration of broken rock mass, and reveal the mechanism of erosion-induced water inrush. The rock skeleton and broken rock mass in the fault zone were modelled by large skeleton particles and small filling particles, respectively, in 3D particle flow software while the fluid flow through the fault was modelled by the built-in computational fluid dynamics module. The numerical results show that: (1) with the increase of water pressure, the loss of filling particles increases continuously, and the whole seepage process can be divided into three stages: slow seepage, abrupt seepage and steady seepage, (2) the mass loss, porosity and permeability first increase slowly, then increase rapidly, and finally tend to be stable, (3) higher fine particle content are more likely to cause structural instability due to mass loss, thus increases the risk of water inrush and mud outburst.

Retrieving mean volumetric properties of multiphase flows from 2D images: A new approach combining deep learning algorithms and 3D modelling

Measuring the morphological properties of complex multiphase systems is a crucial problem in many areas of science and industry and is particularly difficult in dense environments with limited optical access. This paper presents a new approach capable of extracting three-dimensional (3D) information from spherical particle systems based solely on two-dimensional (2D) projections of the system. Synthetic images of the system are generated using a stochastic geometrical model from a simulated 3D particle system with the same geometrical features as the studied system, which is projected into 2D images labeled with the appropriate 3D information. These images are then fed to a convolutional neural network (CNN) for training before being tested on synthetic and experimental images. Validation results show that this technique successfully predicts the mean features of the studied systems, even for dense environments with overlapping particles, with high computational efficiency.

A quick and low-cost smartphone photogrammetry method for obtaining 3D particle size and shape

Reliable particle measurements are essential when investigating the size and shape of particles in various fields of study, including engineering geology. Common methods to determine the morphology of particles, such as 3D laser scanners, are typically expensive or require extensive preparation. Therefore, a setup-friendly, low-cost, and accurate approach is required. In this study, a practical Trick-Image-Capturing (TIC) method based on smartphone photogrammetry to obtain size and shape parameters of 3D coarse particles is proposed. The process of image acquisition and particle reconstruction of the TIC method is demonstrated. Then, the performance of the TIC method is comprehensively assessed under various conditions using a 3D laser scanner, including smartphone quality, particle shape, shooting method, and postprocessing software. An optimal setting of the TIC method is selected through the cloud-to-cloud distance. Finally, the size and shape parameters of 32 coarse particles using the TIC method are also evaluated. Results show that the optimum setup of the TIC method is using a smartphone camera with a resolution above 12 MP under the ICS mode with a 30° shooting angle with Metashape postprocessing software. The root mean square error (RMSE) of cloud-to-cloud distances and the average error ratios under the optimum setup are less than 0.075 mm and 3.5%, respectively. Due to its fast image capturing process, easy setup, and low cost, the TIC method is practical and we expect it to become one of popular methods of measuring 3D coarse particles.

Stable 3D particle manipulation using rotating elliptical Gauss–Schell model vortex partially coherent light sources

The Partially coherent light source can be applied to the noninvasive capture of particles, and increasing the freedom of capturing different types of particles simultaneously is of great significance. This paper studies the optical field distribution of a rotating elliptical Gaussian-Schell model vortex beam focused by a lens and the radiation force acting on Rayleigh particles. The beam’s rotation parameter, the vortex’s topological charge, and the spatial coherence length have different effects on the light intensity, which will determine the optical capture ability of Rayleigh particles with other refractive indices and generate equilibrium points at different positions. The longitudinal equilibrium points of high-refractive-index particles and the transverse equilibrium points of low-refractive-index particles are increased by adjusting the value of the rotation parameter and the topological charge. Finally, the appropriate size range for trapping Rayleigh particles is analyzed according to the comparison of Brownian force and radiant force. The results show that this partially coherent light source has a high degree of freedom in capturing particles with different refractive indices, which provides a new way for optical capture technology and three-dimensional (3D) particle manipulation.

Numerical approximation of 3D particle beams by multi-scale paraxial Vlasov-Maxwell equations

Even now, solving numerically the 3D time-dependent Vlasov-Maxwell equations is a challenging problem. Hence, it is always interesting to develop simpler but accurate approximate models. By introducing a small parameter, we derived paraxial asymptotic models that approximate these equations, allowing us to treat relativistic cases, much slower beams or even non-relativistic cases. These models are static or quasi-static and with a nth order accuracy that may be chosen as required. Here, we propose a 3D numerical approximation of this multi-scale model. It is based, for the paraxial Maxwell model, on a finite element method coupled with a Particle-In-Cell approximation of the paraxial Vlasov model. Numerical results illustrated the efficiency of the method.

On the characterization of bias errors in defocusing-based 3D particle tracking velocimetry for microfluidics

In this work, we provide a systematic theoretical and experimental characterization of bias errors in defocusing particle tracking (DPT) methods based on a single calibration function, with respect to microfluidic applications, in which it is not possible to use calibration targets inside the measurement volume. This approach is widely used in microfluidic experiments, but bias errors are often neglected and to date only few works reported empirical procedures to compensate for that. A systematic characterization of the impact of such error in DPT measurements is still lacking. We show that the field curvature aberration and the refractive index mismatch are the main sources of bias error in these applications. We present a correction methodology for the bias error based on the determination of a reference surface, and in addition we propose a procedure based on a reference measurement of a Poiseuille flow to determine the reference surface on microfluidic channels with constant cross section. We discuss the impact of the refractive index mismatch and how to correctly compensate for it. We validated our methodology and quantified the bias errors on 10 different experimental setups, using different working fluids, materials, geometries, and microscope objective lenses ranging from 5times to 40times magnification. Our results indicate that the impact of this type of bias errors is in general not predictable and must be evaluated case by case. The proposed methodology allows to estimate and minimize the bias error in most microfluidic setups and is suitable for any single-camera DPT approach.

Evolution of a five-hole probe calibration near a wall

Five-hole probes (5HP) are a common tool for measurements of the velocity in turbomachinery studies. In those applications, the spaces are confined, which drives the probes to get close to walls. The proximity of the wall affects the behavior of the 5HP. The wall-related evolution of the calibration of two hemispheric L-shaped 3D printed five-hole probes is investigated in a low speed wind tunnel. 2D Particle Image Velocimetry (PIV) and pressure measurements were carried out. The wall proximity causes the probe to measure that the flow goes away from the wall whereas the boundary layer causes the probe to feel that the flow direction is towards the wall.

Two-Pulse 3D particle tracking with Shake-The-Box

This study presents an approach to two-pulse 3D particle tracking using methods developed within the Shake-The-Box (STB, Schanz et al. in Exp Fluids 57:70, 2016) Lagrangian particle tracking (LPT) framework. The original STB algorithm requires time-resolved data and reconstructs 3D trajectories using a particle position prediction–correction scheme. However, dual-frame 3D acquisition systems, consisting of a dual-cavity laser and double-frame cameras, remain commonly used for many particle-image-based investigations in a wide range of flow velocities and applications. While such systems can be used to capture short Multi-Pulse particle trajectories (Multi-Pulse STB, MP-STB—Novara et al. in Exp Fluids 57:128, 2016a; Novara et al. in Exp Fluids 60:44, 2019), the most widespread application is still a single-pulse illumination of each of the two available frames. As a consequence, 3D LPT approaches capable of dealing with two-pulse recordings are of high interest for both the scientific community and industry. Several methods based on various evaluation schemes have been developed in the past. In the present study, a Two-Pulse Shake-The-Box approach (TP-STB) is proposed, based on the advanced IPR algorithm presented by Jahn et al. (Exp Fluids 62:179, 2021), in combination with an iterative scheme of reconstruction and tracking, ideally with the help of a predictor gained by Particle Space Correlation. It basically constitutes a lean version of the MP-STB technique, with lower demands on experimental setup and processing time. The performances of TP-STB are assessed by means of comparison with the results from the time-resolved STB algorithm (TR-STB) both concerning synthetic and experimental data. The suitability of the algorithm for the analysis of dual-frame 3D particle imaging datasets is assessed based on the processing of existing images from a tomographic PIV experiment from 2012. The comparison with the results published by Henningsson et al. (J R Soc Interface 12:20150119, 2015) confirms the capability of TP-STB to accurately reconstruct individual particle tracks despite the limited time-resolution information offered by two-frame recordings.

Thermographic 3D particle tracking velocimetry for turbulent gas flows

Turbulent flows are characterized by diverse and unsteady three-dimensional (3D) features that require 3D measurements to study. In case of non-isothermal flows, combined 3D measurements of temperature and velocity are necessary. In this paper, a thermographic 3D particle tracking velocimetry (thermographic 3D-PTV) concept is introduced for simultaneous 3D temperature and velocity measurements in turbulent gas flows. It is based on sub-micron thermographic phosphor particles seeded into the flow as flow tracers with low response times of a few microseconds. To obtain each tracer’s position and velocity, the measurement region is illuminated volumetrically using a double-pulse green laser and Mie-scattered light is imaged by four double-frame cameras. Following the pinhole model-based calibration of all cameras, 3D particle positions are computed for both laser pulse-times using a fast minimum line of sight reconstruction code. Three-component velocities are derived from tracking individual particles between these time steps. For simultaneous 3D thermometry, temperature dependent luminescence emissions from the same phosphor particles are exploited. These emissions are excited using a UV laser synchronized with the first green laser pulse and imaged using two cameras equipped with spectral filters for ratiometric phosphor thermometry. As a result, instantaneous 3D fields of discrete temperature and velocity measurements are obtained throughout the volume. The concept is demonstrated in a turbulent heated gas jet emerging from a circular nozzle at a particle image concentration of 0.005 particle per pixel, where the symmetry of the velocity and temperature distributions about the jet axis is successfully reconstructed.

Fe4 cluster as the smallest 3D Fe cluster with unique quantum magnetic levitation effect on graphene

The unique quantum magnetic levitation effect of small metallic clusters adsorbed on graphene has been studied for their minimal energy configurations in this work. Using first-principles calculations, we specially focus on the tetrahedral Fe4 clusters in a corn-like configuration for the induced magnetic moments in the Fe4 cluster and graphene. Firstly, the 3D magnetic Fe4 cluster spontaneously induced a diamagnetic moment in graphene in the absence of external energy injection. Additionally, as the smallest 3D particle (among Fen clusters), the Fe4 cluster can be levitated by the repulsive magnetic interaction between the Fe cluster and graphene. The maximum energy barrier between different adsorption configurations of the Fe4 cluster on graphene is as low as 0.8 eV, making the Fe4 cluster diffusible between sites with a minimal external force. The conversion energy barrier is relatively low, resulting in low energy consumption. Importantly, the repulsion between Fe clusters may lead to the formation of an ultrahigh-density (1.18 × 1014/cm2) isolated quantum dot array and avoids the formation of larger clusters. These quantum states might be usable for nanoscale data storage devices for next-generation computing applications, and the small diffusion barrier might offer potential applications in quantum mechanical sensing and nanoscale magnetic levitation.

Reconstructing the pressure field around swimming fish using a physics-informed neural network.

Fish detect predators, flow conditions, environments, and each other through pressure signals. Lateral line ablation is often performed to understand the role of pressure sensing. In this study, the authors propose a non-invasive method for reconstructing the instantaneous pressure field sensed by a fish's lateral line system from 2D particle image velocimetry (PIV) measurements. The method uses a physics-informed neural network (PINN) to predict an optimized solution for the pressure field near and on the fish's body that satisfy both the Navier-Stokes equations and the constraints put forward by the PIV measurements. The method was validated using a direct numerical simulation of a swimming mackerel, Scomber scombrus, and was applied to experimental data of a turning zebrafish, Danio rerio. The results demonstrate that this method is relatively insensitive to the spatio-temporal resolution of the PIV measurements and accurately reconstructs the pressure on the fish's body.

Determining 3D Distributions of Pulsatile Blood Flow Using Orthogonal Simultaneous Biplane High-Speed Angiography (SB-HSA) with 1000 fps CdTe Photon Counting Detectors for 3D X-ray Particle Image Velocimetry (3D-XPIV) compared to Results Using Computational Fluid Dynamics (CFD).

3D hemodynamic distributions are useful for the diagnosis and treatment of aneurysms. Detailed blood-flow patterns and derived velocity maps can be obtained using 1000 fps High Speed Angiography (HSA). The novel orthogonal Simultaneous Biplane High-Speed Angiography (SB-HSA) system enables flow information to be quantified in multiple planes, and with additional components of flow at depth, accurate 3D flow distributions are available. Computational Fluid Dynamics (CFD) is the current standard for derivation of volumetric flow distributions, but obtaining solution convergence is computationally expensive and time intensive. More importantly, matching in-vivo boundary conditions is non-trivial. Therefore, an experimentally derived 3D flow distribution method could offer realistic results with less computation time. Using SB-HSA image sequences, 3D X-Ray Particle Image Velocimetry (3D-XPIV) was explored as a new method for assessing 3D flow. 3D-XPIV was demonstrated using an in-vitro setup, where a patient-specific internal carotid artery aneurysm model was attached to a flow loop, and an automated injection of iodinated microspheres was used as a flow tracer. Two 1000 fps photon-counting detectors were placed orthogonally with the aneurysm model in the FOV of both planes. Frame-synchronization of the two detectors made correlation of single-particle velocity components at a given timepoint possible. With frame-rates of 1000 fps, small particle displacements between frames resolved realistic time varying flow, where accurate velocity distributions depended on near-instantaneous velocities. 3D-XPIV velocity distributions were compared to CFD velocity distributions, where the simulation boundary conditions matched the in-vitro setup. Results showed similar velocity distributions between CFD and 3D-XPIV.

Preface to Special Topic: Plasma Physics from the Magnetospheric Multiscale Mission

NASA's Magnetospheric Multiscale (MMS) mission is a four-spacecraft formation of Earth orbiting satellites that have been providing unparalleled measurements of the local kinetic-scale plasma dynamics in near-Earth space for the past 8 years. The spacecraft carry a full complement of space plasma instrumentation capable of measuring the 3D electromagnetic fields and particle distribution functions at cadences up to 100 times faster than previous missions and with interspacecraft separations as small as ∼ 5 km, approaching the characteristic electron scales in many of the plasmas that MMS samples. In this Special Topic, we bring together 26 papers covering a broad range of topics—from magnetic reconnection, shocks, and turbulence to some of the basic nuances of collisionless dynamics—highlighting the many ways in which MMS is helping us to better understand both the dynamics of Earth's magnetosphere and the fundamental physics of plasmas.

Imaging-based 3D particle tracking system for field characterization of particle dynamics in atmospheric flows

A particle tracking velocimetry apparatus is presented that is capable of measuring three-dimensional particle trajectories across large volumes, of the order of several meters, during natural snowfall events. Field experiments, aimed at understanding snow settling kinematics in atmospheric flows, were conducted during the 2021/2022 winter season using this apparatus, from which we show preliminary results. An overview of the methodology, wherein we use a UAV-based calibration method, is provided, and analysis is conducted of a select dataset to demonstrate the capabilities of the system for studying inertial particle dynamics in atmospheric flows. A modular camera array is used, designed specifically for handling the challenges of field deployment during snowfall. This imaging system is calibrated using synchronized imaging of a UAV-carried target to enable measurements centered 10 m above the ground within approximately a 4 m $$\times$$ 4 m $$\times$$ 6 m volume. Using the measured Lagrangian particle tracks, we present data concerning 3D trajectory curvature and acceleration statistics, as well as clustering behavior using Voronoï analysis. The limitations, as well as potential future developments, of such a system are discussed in the context of applications with other inertial particles.

Double helix point spread function with variable spacing for precise 3D particle localization.

To extend the axial depth of nanoscale 3D-localization microscopy, we propose here a splicing-type vortex singularities (SVS) phase mask, which has been meticulously optimized with a Fresnel approximation imaging inverse operation. The optimized SVS DH-PSF has proven to have high transfer function efficiency with adjustable performance in its axial range. The axial position of the particle was computed by using both the main lobes' spacing and the rotation angle, an improvement of the localization precision of the particle. Concretely, the proposed optimized SVS DH-PSF, with a smaller spatial extent, can effectively reduce the overlap of nanoparticle images and realize the 3D localization of multiple nanoparticles with small spacing, with respect to PSFs for large axial 3D localization. Finally, we successfully conducted extensive experiments on 3D localization for tracking dense nanoparticles at 8µm depth with a numerical aperture of 1.4, demonstrating its great potential.