Topaz-Denoise: general deep denoising models for cryoEM and cryoET

Cryo-electron microscopy (cryoEM) is becoming the preferred method for resolving protein structures. Low signal-to-noise ratio (SNR) in cryoEM images reduces the confidence and throughput of structure determination during several steps of data processing, resulting in impediments such as missing particle orientations. Denoising cryoEM images can not only improve downstream analysis but also accelerate the time-consuming data collection process by allowing lower electron dose micrographs to be used for analysis. Here, we present Topaz-Denoise, a deep learning method for reliably and rapidly increasing the SNR of cryoEM images and cryoET tomograms. By training on a dataset composed of thousands of micrographs collected across a wide range of imaging conditions, we are able to learn models capturing the complexity of the cryoEM image formation process. The general model we present is able to denoise new datasets without additional training. Denoising with this model improves micrograph interpretability and allows us to solve 3D single particle structures of clustered protocadherin, an elongated particle with previously elusive views. We then show that low dose collection, enabled by Topaz-Denoise, improves downstream analysis in addition to reducing data collection time. We also present a general 3D denoising model for cryoET. Topaz-Denoise and pre-trained general models are now included in Topaz. We expect that Topaz-Denoise will be of broad utility to the cryoEM community for improving micrograph and tomogram interpretability and accelerating analysis.

The His-tag as a decoy modulating preferred orientation in cryoEM

The His-tag is a widely used affinity tag that facilitates purification by means of affinity chromatography of recombinant proteins for functional and structural studies. We show here that His-tag presence affects how coproheme decarboxylase interacts with the water-air interface during grid preparation for cryoEM. Depending on His-tag presence or absence, we observe significant changes in patterns of preferred orientation. The analysis of particle orientations suggests that His-tag presence can mask the hydrophobic patches on a protein’s surface that mediate the interactions with the water-air interface, while the hydrophobic linker between a His-tag and the coding sequence of the protein may enhance other interactions with water-air interface. Our observations suggest that tagging, including rational design of the linkers between an affinity tag and a protein of interest, offer a promising approach to modulating interactions with the water-air interface.SynopsisA His-tag affects the interactions of particles with the water-air interface in cryo-electron microscopy (cryoEM) single particle reconstruction (SPR), and thus may be used to modulate these interactions, including inducing changes in patterns of preferred orientation.

Rheology of a Dilute Suspension of Aggregates in Shear-Thinning Fluids

The prediction of the viscosity of suspensions is of fundamental importance in several fields. Most of the available studies have been focused on particles with simple shapes, for example, spheres or spheroids. In this work, we study the viscosity of a dilute suspension of fractal-shape aggregates suspended in a shear-thinning fluid by direct numerical simulations. The suspending fluid is modeled by the power-law constitutive equation. For each morphology, a map of particle angular velocities is obtained by solving the governing equations for several particle orientations. The map is used to integrate the kinematic equation for the orientation vectors and reconstruct the aggregate orientational dynamics. The intrinsic viscosity is computed by a homogenization procedure along the particle orbits. In agreement with previous results on Newtonian suspensions, the intrinsic viscosity, averaged over different initial orientations and aggregate morphologies characterized by the same fractal parameters, decreases by increasing the fractal dimension, that is, from rod-like to spherical-like aggregates. Shear-thinning further reduces the intrinsic viscosity showing a linear dependence with the flow index in the investigated range. The intrinsic viscosity can be properly scaled with respect to the number of primary particles and the flow index to obtain a single curve as a function of the fractal dimension.

Calculations of Light Scattering Matrix of Dust Aerosols for Problems of Lidar Remote Sensing

The light scattering matrix is calculated for large dust particles with irregular shape and refractive index of 1.3116+i0.0. The scattering matrix in the backward direction needed for lidar studies is separately discussed. In this case, the obtained results for the lidar and depolarization ratios are in good agreement with experimental data. It is shown that for randomly oriented particles the number of particle orientations needed for numerical calculations by exact methods like DDA becomes a crucial parameter. In particular, for particles with size parameters larger than 40 the number of orientations should be more than 1000.

Topaz-Denoise: general deep denoising models for cryoEM and cryoET

Cryo-electron microscopy (cryoEM) is becoming the preferred method for resolving protein structures. Low signal-to-noise (SNR) in cryoEM images reduces the confidence and throughput of structure determination during several steps of data processing, resulting in impediments such as missing particle orientations. Denoising cryoEM images can not only improve downstream analysis but also accelerate the time-consuming data collection process by allowing lower electron dose micrographs to be used for analysis. Here, we present Topaz-Denoise, a deep learning method for reliably and rapidly increasing the SNR of cryoEM images and cryoET tomograms. By training on a dataset composed of thousands of micrographs collected across a wide range of imaging conditions, we are able to learn models capturing the complexity of the cryoEM image formation process. The general model we present is able to denoise new datasets without additional training. Denoising with this model improves micrograph interpretability, enabling us to solve the first 3D single particle closed and partially open structures of clustered protocadherin, an elongated particle with previously-elusive views. We then show for the first time that low dose collection, enabled by Topaz-Denoise, improves downstream analysis in addition to reducing data collection time. We also present the first general 3D denoising model for cryoET. Topaz-Denoise and pre-trained general models are now included in Topaz. We expect that Topaz-Denoise will be of broad utility to the cryoEM community for improving micrograph and tomogram interpretability and accelerating analysis.

Study of Metal Hydride Orientations by a Modified Hough Transform Algorithm

A modified Hough transform algorithm for the study of metal hydride orientations was developed, and optimum parameter for the transform was obtained based on numerical analysis result. By assigning an orientation to each hydride pixel, the algorithm removes the ambiguities in handling interconnected hydrides. It is expected that the algorithm will find applications in particle orientations measurement in composites and precipitation reinforced materials in general.

Variations in Snow Crystal Riming and ZDR: A Case Analysis

A case study in terms of variations in differential reflectivity ZDR observed at X band and snow crystal riming is presented for a light-snow event that occurred near Greeley, Colorado, on 26–27 November 2015. In the early portion of the event, ZDR values at near-surface levels were low (0–0.25 dB). During a second time period approximately 8 h later, ZDR values became distinctly positive (+2–3 dB). Digital photographs of the snow particles were obtained by a Multi-Angle Snowflake Camera (MASC) installed at a range of 13 km from the radar. Image-processing and machine-learning techniques applied to the MASC data showed that the snow particles were more heavily rimed during the low-ZDR time period. The aerodynamic effects of these rime deposits promoted a wider distribution of hydrometeor canting angles. The shift toward more random particle orientations underlies the observed reduction in ZDR during the period when more heavily rimed particles were observed in the MASC data.

Microstructural study of deformation zones during cone penetration in silt at variable penetration rates

During conventional cone penetration testing in silt, the soil will normally be partially drained. If the penetration rate varies, time for drainage is altered and therefore the measured cone resistance and pore pressure will change. This paper studies the change in soil microstructure around the probe during cone penetration carried out at different penetration rates to investigate the failure mechanism and the processes controlling drainage in silt. Backscattered electron images of polished thin sections prepared from frozen samples at the end of penetration were used. Making use of advanced image-processing techniques, the statistical distribution of particle orientations and the local porosity were investigated for zones around the cone tip and shaft. The spatial distribution of the measured microscale parameters in the region near the probe indicates that the soil deformation during a piezometric cone penetration test (CPTU) in silt leads to the formation of both contractive and dilative zones. The macro response of the material, presented by the pore pressure and cone penetration resistance measured during the test, results from the competition between these zones during penetration, which is shown to be dependent on the penetration rate.