Hemant D. Tagare

Parkinson's Disease (PD) Motor Scores (MDS-UPDRS Part III) are quite noisy. This paper proposes a new methodology for processing these scores by first denoising the scores to enhance the underlying progression signal, and then conducting a high-dimensional analysis which does not sum the scores into a total movement score. The analysis gives novel insights into PD progression heterogeneity: it reveals that the heterogeneity is continuously variable rather than clustered into "subtypes" and that the variability is along two easily understood axes. This analysis also resolves some of the discrepancies in previously reported progression subtypes. Finally, the analysis reveals that patient-specific progression cannot be predicted from baseline using only MDS-UPDRS Part III scores.

Purpose: To standardize T‐weighted images from clinical Turbo Spin Echo (TSE) scans by generating corresponding T maps with the goal of removing scanner‐ and/or protocol‐specific heterogeneity.Methods: The T map is estimated by minimizing an objective function containing a data fidelity term in a Virtual Conjugate Coils (VCC) framework, where the signal evolution model is expressed as a linear constraint. The objective function is minimized by Projected Gradient Descent (PGD).Results: The algorithm achieves accuracy comparable to methods with customized sampling schemes for accelerated T mapping. The results are insensitive to the tunable parameters, and the relaxed background phase prior produces better T maps compared to the strict real‐value enforcement. It is worth noting that the algorithm works well with challenging Tw‐TSE data using typical clinical parameters. The observed normalized root mean square error ranges from 6.8% to 12.3% over grey and white matter, a clinically common level of quantitative map error.Conclusion: The novel methodological development creates an efficient algorithm that allows for T map generated from TSE data with typical clinical parameters, such as high resolution, long echo train length, and low echo spacing. Reconstruction of T maps from TSE data with typical clinical parameters has not been previously reported.

We propose two new measures of resolution anisotropy for cryogenic electron microscopy maps: Fourier shell occupancy (FSO), and the Bingham test (BT). FSO varies from 1 to 0, with 1 representing perfect isotropy, and lower values indicating increasing anisotropy. The threshold FSO = 0.5 occurs at Fourier shell correlation resolution. BT is a hypothesis test that complements the FSO to ensure the existence of anisotropy. FSO and BT allow visualization of resolution anisotropy. We illustrate their use with different experimental cryogenic electron microscopy maps.

Cryo-EM (cryogenic electron microscopy) particle identification from micrographs (i.e., picking) is challenging due to the low signal-to-noise ratio and lack of ground truth for particle locations. Moreover, current computational methods (“pickers”) identify different particle sets, complicating the selection of the best-suited picker for a protein of interest. Here, we present REPIC, an ensemble learning methodology that uses multiple pickers to find consensus particles. REPIC identifies consensus particles by framing its task as a graph problem and using integer linear programming to select particles. REPIC picks high-quality particles when the best picker is not known a priori and for known difficult-to-pick particles (e.g., TRPV1). Reconstructions using consensus particles achieve resolutions comparable to those from particles picked by experts, without the need for downstream particle filtering. Overall, our results show REPIC requires minimal (often no) manual picking and significantly reduces the burden on cryo-EM users for picker selection and particle picking. Availability https://github.com/ccameron/REPIC

Human immunodeficiency virus 1 (HIV-1) infection is initiated by binding of the viral envelope glycoprotein (Env) to the cell-surface receptor CD4. Although high resolution structures of Env complexed with soluble domains of CD4 have been determined, the binding process is less understood on native membranes. Here, we apply cryo-electron tomography (cryo-ET) to monitor Env-CD4 interactions at membrane-membrane interfaces formed between HIV-1 and CD4-presenting virus-like particles. Env-CD4 complexes organized into clusters and rings, bringing opposing membranes closer together. Additionally, Env-CD4 clustering was dependent on capsid maturation. Subtomogram averaging and classification revealed that Env bound one, two, and finally three CD4 molecules, upon which Env adopted a partially open state. Our data indicate that asymmetric HIV-1 Env trimers bound to one and two CD4 molecules are detectable intermediates during virus binding to host cell membranes, which likely has consequences for antibody-mediated immune responses and vaccine immunogen design.

Classifying SPECT images requires a preprocessing step which normalizes the images using a normalization region. The choice of the normalization region is not standard, and using different normalization regions introduces normalization region-dependent variability. This paper mathematically analyzes the effect of the normalization region to show that normalized-classification is exactly equivalent to a subspace separation of the half rays of the images under multiplicative equivalence. Using this geometry, a new self-normalized classification strategy is proposed. This strategy eliminates the normalizing region altogether. The theory is used to classify DaTscan images of 365 Parkinson’s disease (PD) subjects and 208 healthy control (HC) subjects from the Parkinson’s Progression Marker Initiative (PPMI). The theory is also used to understand PD progression from baseline to year 4.

This paper proposes a mixture of linear dynamical systems model for quantifying the heterogeneous progress of Parkinson's disease from DaTscan Images. The model is fitted to longitudinal DaTscans from the Parkinson's Progression Marker Initiative. Fitting is accomplished using robust Bayesian inference with collapsed Gibbs sampling. Bayesian inference reveals three image-based progression subtypes which differ in progression speeds as well as progression trajectories. The model reveals characteristic spatial progression patterns in the brain, each pattern associated with a time constant. These patterns can serve as disease progression markers. The subtypes also have different progression rates of clinical symptoms measured by MDS-UPDRS Part III scores.

Using a new consensus-based image-processing approach together with principal component analysis, the flexibility and conformational dynamics of the SARS-CoV-2 spike in the prefusion state have been analysed. These studies revealed concerted motions involving the receptor-binding domain (RBD), N-terminal domain, and subdomains 1 and 2 around the previously characterized 1-RBD-up state, which have been modeled as elastic deformations. It is shown that in this data set there are not well defined, stable spike conformations, but virtually a continuum of states. An ensemble map was obtained with minimum bias, from which the extremes of the change along the direction of maximal variance were modeled by flexible fitting. The results provide a warning of the potential image-processing classification instability of these complicated data sets, which has a direct impact on the interpretability of the results.

Particle tracking velocimetry (PTV) gives quantitative estimates of fluid flow velocities from images. But particle tracking is a complicated problem, and it often produces results that need substantial post-processing. We propose a novel Gaussian process regression-based post-processing step for PTV: The method smooths (“denoises”) and densely interpolates velocity estimates while rejecting track irregularities. The method works under a large range of particle densities and fluid velocities. In addition, the method calculates standard deviances (error bars) for the velocity estimates, opening the possibility of propagating the standard deviances through subsequent processing and analysis. The accuracy of the method is experimentally evaluated using Optical Coherence Tomography images of particles in laminar flow in a pipe phantom. Following this, the method is used to quantify cilia-driven fluid flow and vorticity patterns in a developing Xenopus embryo.