3DFlex: determining structure and motion of flexible proteins from cryo-EM.

Modeling flexible macromolecules is one of the foremost challenges in single-particle cryogenic-electron microscopy (cryo-EM), with the potential to illuminate fundamental questions in structural biology. We introduce Three-Dimensional Flexible Refinement (3DFlex), a motion-based neural network model for continuous molecular heterogeneity for cryo-EM data. 3DFlex exploits knowledge that conformational variability of a protein is often the result of physical processes that transport density over space and tend to preserve local geometry. From two-dimensional image data, 3DFlex enables the determination of high-resolution 3D density, and provides an explicit model of a flexible protein's motion over its conformational landscape. Experimentally, for large molecular machines (tri-snRNP spliceosome complex, translocating ribosome) and small flexible proteins (TRPV1 ion channel, αVß8 integrin, SARS-CoV-2 spike), 3DFlex learns nonrigid molecular motions while resolving details of moving secondary structure elements. 3DFlex can improve 3D density resolution beyond the limits of existing methods because particle images contribute coherent signal over the conformational landscape.

Deep generative priors for biomolecular 3D heterogeneous reconstruction from cryo-EM projections.

Cryo-electron microscopy has become a powerful tool to determine three-dimensional (3D) structures of rigid biological macromolecules from noisy micrographs with single-particle reconstruction. Recently, deep neural networks, e.g., CryoDRGN, have demonstrated conformational and compositional heterogeneity of complexes. However, the lack of ground-truth conformations poses a challenge to assess the performance of heterogeneity analysis methods. In this work, variational autoencoders (VAE) with three types of deep generative priors were learned for latent variable inference and heterogeneous 3D reconstruction via Bayesian inference. More specifically, VAEs with "Variational Mixture of Posteriors" priors (VampPrior-SPR), non-parametric exemplar-based priors (ExemplarPrior-SPR) and priors from latent score-based generative models (LSGM-SPR) were quantitatively compared with CryoDRGN. We built four simulated datasets composed of hypothetical continuous conformation or discrete states of the hERG K + channel. Empirical and quantitative comparisons of inferred latent representations were performed with affine-transformation-based metrics. These models with more informative priors gave better regularized, interpretable factorized latent representations with better conserved pairwise distances, less deformed latent distributions and lower within-cluster variances. They were also tested on experimental datasets to resolve compositional and conformational heterogeneity (50S ribosome assembly, cowpea chlorotic mottle virus, and pre-catalytic spliceosome) with comparable high resolution. Codes and data are available: https://github.com/benjamin3344/DGP-SPR.

Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction.

Cryogenic electron microscopy (cryo-EM) is widely used to study biological macromolecules that comprise regions with disorder, flexibility or partial occupancy. For example, membrane proteins are often kept in solution with detergent micelles and lipid nanodiscs that are locally disordered. Such spatial variability negatively impacts computational three-dimensional (3D) reconstruction with existing iterative refinement algorithms that assume rigidity. We introduce non-uniform refinement, an algorithm based on cross-validation optimization, which automatically regularizes 3D density maps during refinement to account for spatial variability. Unlike common shift-invariant regularizers, non-uniform refinement systematically removes noise from disordered regions, while retaining signal useful for aligning particle images, yielding dramatically improved resolution and 3D map quality in many cases. We obtain high-resolution reconstructions for multiple membrane proteins as small as 100 kDa, demonstrating increased effectiveness of cryo-EM for this class of targets critical in structural biology and drug discovery. Non-uniform refinement is implemented in the cryoSPARC software package.

3D variability analysis: Resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM.

Single particle cryo-EM excels in determining static structures of protein molecules, but existing 3D reconstruction methods have been ineffective in modelling flexible proteins. We introduce 3D variability analysis (3DVA), an algorithm that fits a linear subspace model of conformational change to cryo-EM data at high resolution. 3DVA enables the resolution and visualization of detailed molecular motions of both large and small proteins, revealing new biological insight from single particle cryo-EM data. Experimental results demonstrate the ability of 3DVA to resolve multiple flexible motions of α-helices in the sub-50 kDa transmembrane domain of a GPCR complex, bending modes of a sodium ion channel, five types of symmetric and symmetry-breaking flexibility in a proteasome, large motions in a spliceosome complex, and discrete conformational states of a ribosome assembly. 3DVA is implemented in the cryoSPARC software package.

Replicate analyses of OSPAR beach litter data.

Replicate surveys of beach litter have seldom been performed in the past. In this study, replicate surveys of beach litter were conducted on the beach north of Hörnum (Sylt, Germany), from 2015 to 2019, applying a slightly modified OSPAR protocol of beach litter monitoring. Descriptive statistics and power analyses were calculated on data resulting from these replicate surveys, to find out whether the scatter of replicate beach litter data decreases and the statistical power increases with increasing numbers of replicate surveys. From 2015 to 2019, mean total abundances, given as numbers of litter items, ranged from 19 to 185 litter items on a 50 m section of beach. With increasing numbers of replicate surveys, the scatter given by the coefficient of variation (CV) significantly increased up to 113%. Statistical power considerably increased with increasing numbers of replicate beach sections, e.g. from 82% (two beach sections) to nearly 100% (five beach sections) for a given reduction of beach litter of 50%. Based on these results from a morphologically straight coastline, the use of replicate surveys would be sensible for the future monitoring of beach litter. However, there is high need for studies, which consider coastlines with varying morphology.

cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination.

Single-particle electron cryomicroscopy (cryo-EM) is a powerful method for determining the structures of biological macromolecules. With automated microscopes, cryo-EM data can often be obtained in a few days. However, processing cryo-EM image data to reveal heterogeneity in the protein structure and to refine 3D maps to high resolution frequently becomes a severe bottleneck, requiring expert intervention, prior structural knowledge, and weeks of calculations on expensive computer clusters. Here we show that stochastic gradient descent (SGD) and branch-and-bound maximum likelihood optimization algorithms permit the major steps in cryo-EM structure determination to be performed in hours or minutes on an inexpensive desktop computer. Furthermore, SGD with Bayesian marginalization allows ab initio 3D classification, enabling automated analysis and discovery of unexpected structures without bias from a reference map. These algorithms are combined in a user-friendly computer program named cryoSPARC (http://www.cryosparc.com).

Improving healthcare practice behaviors: an exploratory study identifying effective and ineffective behaviors in healthcare.

PURPOSE: The purpose of this paper is to present the results of exploratory research designed to develop an awareness of healthcare behaviors, with a view toward improving the customer satisfaction with healthcare services. It examines the relationship between healthcare providers and their consumers/patients/clients. DESIGN/METHODOLOGY/APPROACH: The study uses a critical incident methodology, with both effective and ineffective behavioral specimens examined across different provider groups. FINDINGS: The effects of these different behaviors on what Berry (1999) identified as the common core values of service organizations are examined, as those values are required to build a lasting service relationship. Also examined are categories of healthcare practice based on the National Quality Strategy priorities. RESEARCH LIMITATIONS/IMPLICATIONS: The most obvious is the retrospective nature of the method used. How accurate are patient or consumer memories? Are they capable of making valid judgments of healthcare experiences (Berry and Bendapudi, 2003)? While an obvious limitation, such recollections are clearly important as they may be paramount in following the healthcare practitioners' instructions, loyalty for repeat business, making recommendations to others and the like. Further, studies have shown retrospective reports to be accurate and useful (Miller et al., 1997). PRACTICAL IMPLICATIONS: With this information, healthcare educators should be in a better position to improve the training offered in their programs and practitioners to better serve their customers. SOCIAL IMPLICATIONS: The findings would indicate that the human values of excellence, innovation, joy, respect and integrity play a significant role in building a strong service relationship between consumer and healthcare provider. ORIGINALITY/VALUE: Berry (1999) has argued that the overriding importance in building a lasting service business is human values. This exploratory study has shown how critical incident analysis can be used to determine both effective and ineffective practices of different medical providers. It also provides guidelines as to what are effective and ineffective behaviors in healthcare.

Hand Grasp Classification in Egocentric Video After Cervical Spinal Cord Injury.

OBJECTIVE: The hand function of individuals with spinal cord injury (SCI) plays a crucial role in their independence and quality of life. Wearable cameras provide an opportunity to analyze hand function in non-clinical environments. Summarizing the video data and documenting dominant hand grasps and their usage frequency would allow clinicians to quickly and precisely analyze hand function. METHOD: We introduce a new hierarchical model to summarize the grasping strategies of individuals with SCI at home. The first level classifies hand-object interaction using hand-object contact estimation. We developed a new deep model in the second level by incorporating hand postures and hand-object contact points using contextual information. RESULTS: In the first hierarchical level, a mean of 86% ±1.0% was achieved among 17 participants. At the grasp classification level, the mean average accuracy was 66.2 ±12.9%. The grasp classifier's performance was highly dependent on the participants, with accuracy varying from 41% to 78%. The highest grasp classification accuracy was obtained for the model with smoothed grasp classification, using a ResNet50 backbone architecture for the contextual head and a temporal pose head. DISCUSSION: We introduce a novel algorithm that, for the first time, enables clinicians to analyze the quantity and type of hand movements in individuals with spinal cord injury at home. The algorithm can find applications in other research fields, including robotics, and most neurological diseases that affect hand function, notably, stroke and Parkinson's.

Grasp Analysis in the Home Environment as a Measure of Hand Function After Cervical Spinal Cord Injury.

BACKGROUND: Following a spinal cord injury, regaining hand function is a top priority. Current hand assessments are conducted in clinics, which may not fully represent real-world hand function. Grasp strategies used in the home environment are an important consideration when examining the impact of rehabilitation interventions. OBJECTIVE: The main objective of this study is to investigate the relationship between grasp use at home and clinical scores. METHOD: We used a previously collected dataset in which 21 individuals with spinal cord injuries (SCI) recorded egocentric video while performing activities of daily living in their homes. We manually annotated 4432 hand-object interactions into power, precision, intermediate, and non-prehensile grasps. We examined the distributions of grasp types used and their relationships with clinical assessments. RESULTS: Moderate to strong correlations were obtained between reliance on power grasp and the Spinal Cord Independence Measure III (SCIM; P < .05), the upper extremity motor score (UEMS; P < .01), and the Graded Redefined Assessment of Strength Sensibility and Prehension (GRASSP) Prehension (P < .01) and Strength (P < .01). Negative correlations were observed between the proportion of non-prehensile grasping and SCIM (P < .05), UEMS (P < .05), and GRASSP Prehension (P < .01) and Strength (P < .01). CONCLUSION: The types of grasp types used in naturalistic activities at home are related to upper limb impairment after cervical SCI. This study provides the first direct demonstration of the importance of hand grasp analysis in the home environment.

Image Super-Resolution via Iterative Refinement.

We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models (Ho et al. 2020), (Sohl-Dickstein et al. 2015) to image-to-image translation, and performs super-resolution through a stochastic iterative denoising process. Output images are initialized with pure Gaussian noise and iteratively refined using a U-Net architecture that is trained on denoising at various noise levels, conditioned on a low-resolution input image. SR3 exhibits strong performance on super-resolution tasks at different magnification factors, on faces and natural images. We conduct human evaluation on a standard 8× face super-resolution task on CelebA-HQ for which SR3 achieves a fool rate close to 50%, suggesting photo-realistic outputs, while GAN baselines do not exceed a fool rate of 34%. We evaluate SR3 on a 4× super-resolution task on ImageNet, where SR3 outperforms baselines in human evaluation and classification accuracy of a ResNet-50 classifier trained on high-resolution images. We further show the effectiveness of SR3 in cascaded image generation, where a generative model is chained with super-resolution models to synthesize high-resolution images with competitive FID scores on the class-conditional 256×256 ImageNet generation challenge.

Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging.

Machine-learning models for medical tasks can match or surpass the performance of clinical experts. However, in settings differing from those of the training dataset, the performance of a model can deteriorate substantially. Here we report a representation-learning strategy for machine-learning models applied to medical-imaging tasks that mitigates such 'out of distribution' performance problem and that improves model robustness and training efficiency. The strategy, which we named REMEDIS (for 'Robust and Efficient Medical Imaging with Self-supervision'), combines large-scale supervised transfer learning on natural images and intermediate contrastive self-supervised learning on medical images and requires minimal task-specific customization. We show the utility of REMEDIS in a range of diagnostic-imaging tasks covering six imaging domains and 15 test datasets, and by simulating three realistic out-of-distribution scenarios. REMEDIS improved in-distribution diagnostic accuracies up to 11.5% with respect to strong supervised baseline models, and in out-of-distribution settings required only 1-33% of the data for retraining to match the performance of supervised models retrained using all available data. REMEDIS may accelerate the development lifecycle of machine-learning models for medical imaging.

Absolute oral bioavailability of almorexant, a dual orexin receptor antagonist, in healthy human subjects.

BACKGROUND/AIMS: The aim of this single-center, open-label study was to assess the absolute bioavailability of an oral (tablet) versus intravenous formulation of almorexant in healthy subjects. METHODS: A pilot phase in 3 healthy male subjects, which preceded the main study, consisted of a single 30-min intravenous infusion of 10 mg almorexant. Its objectives were to ensure the tolerability of the intravenous formulation and to select the intravenous dose for the main study. The main study was a randomized, two-way crossover study in 20 healthy subjects (10 males and 10 females). Subjects received a single oral dose of 200 mg almorexant and a single 30-min intravenous infusion of 20 mg almorexant. RESULTS: All 23 subjects completed the study as planned. Almorexant was well tolerated; the main observed adverse events were somnolence and fatigue. A geometric mean total body clearance of 43 l/h (95% CI 39-47) and a volume of distribution of 683 liters (95% CI 552-845) were determined. The absolute oral bioavailability of almorexant was 11.2% (90% CI 9.6-13.1). CONCLUSION: Almorexant seems to possess a pronounced first-pass effect and metabolism.

Synaptic vesicle mobility and presynaptic F-actin are disrupted in a N-ethylmaleimide-sensitive factor allele of Drosophila.

N-ethylmaleimide sensitive factor (NSF) can dissociate the soluble NSF attachment receptor (SNARE) complex, but NSF also participates in other intracellular trafficking functions by virtue of SNARE-independent activity. Drosophila that express a neural transgene encoding a dominant-negative form of NSF2 show an 80% reduction in the size of releasable synaptic vesicle pool, but no change in the number of vesicles in nerve terminal boutons. Here we tested the hypothesis that vesicles in the NSF2 mutant terminal are less mobile. Using a combination of genetics, pharmacology, and imaging we find a substantial reduction in vesicle mobility within the nerve terminal boutons of Drosophila NSF2 mutant larvae. Subsequent analysis revealed a decrease of filamentous actin in both NSF2 dominant-negative and loss-of-function mutants. Lastly, actin-filament disrupting drugs also decrease vesicle movement. We conclude that a factor contributing to the NSF mutant phenotype is a reduction in vesicle mobility, which is associated with decreased presynaptic F-actin. Our data are consistent with a model in which actin filaments promote vesicle mobility and suggest that NSF participates in establishing or maintaining this population of actin.

Baseline and power analyses for the assessment of beach litter reductions in the European OSPAR region.

Marine litter pollution is a global environmental problem. Beach litter is a part of this problem, and is widely monitored in Europe. The European Marine Strategy Framework Directive (MSFD) requires a reduction of beach litter. A reduction of 30% has been proposed in the European Plastics Strategy. The aims of this study are to develop (a) a method to calculate sufficiently stable and precise baseline values for beach litter, and (b) to derive a method of power analysis to estimate the number of beach litter surveys, necessary to detect a given reduction, using these baseline values. Beach litter data from the OSPAR (Oslo Paris Convention) region were used, and tailor-made statistical methods were implemented in open source software, litteR. Descriptive statistics and Theil-Sen and Mann-Kendall trend analyses were calculated for the most abundant beach litter types, for 14 survey sites. The length of a baseline period necessary to obtain a specified precision of the mean baseline value, expressed as Coefficient of Variation (CV), was calculated. Power analyses were performed using Monte Carlo simulations combined with Wilcoxon tests to determine significant deviations of the simulated datasets from the baseline mean values. For most survey sites, the mean length of monitoring periods necessary to achieve a CV < 10% amounts to four to five years with four surveys a year. The mean number of surveys necessary to detect a statistically significant reduction of 30% with 80% power ranges from 14 to 20. Power analyses show that a reduction of 10% is difficult to detect, because more than 24 surveys are needed. In contrary, a reduction of 40-50% can be detected easily with a small (<12) number of surveys. The new methods could also be applied to other areas where similar beach litter surveys are performed.

Gaussian process dynamical models for human motion.

We introduce Gaussian process dynamical models (GPDM) for nonlinear time series analysis, with applications to learning models of human pose and motion from high-dimensionalmotion capture data. A GPDM is a latent variable model. It comprises a low-dimensional latent space with associated dynamics, and a map from the latent space to an observation space. We marginalize out the model parameters in closed-form, using Gaussian process priors for both the dynamics and the observation mappings. This results in a non-parametric model for dynamical systems that accounts for uncertainty in the model. We demonstrate the approach, and compare four learning algorithms on human motion capture data in which each pose is 50-dimensional. Despite the use of small data sets, the GPDM learns an effective representation of the nonlinear dynamics in these spaces.

Building Proteins in a Day: Efficient 3D Molecular Structure Estimation with Electron Cryomicroscopy.

Discovering the 3D atomic-resolution structure of molecules such as proteins and viruses is one of the foremost research problems in biology and medicine. Electron Cryomicroscopy (cryo-EM) is a promising vision-based technique for structure estimation which attempts to reconstruct 3D atomic structures from a large set of 2D transmission electron microscope images. This paper presents a new Bayesian framework for cryo-EM structure estimation that builds on modern stochastic optimization techniques to allow one to scale to very large datasets. We also introduce a novel Monte-Carlo technique that reduces the cost of evaluating the objective function during optimization by over five orders of magnitude. The net result is an approach capable of estimating 3D molecular structure from large-scale datasets in about a day on a single CPU workstation.

OSPAR standard method and software for statistical analysis of beach litter data.

The aim of this study is to develop standard statistical methods and software for the analysis of beach litter data. The optimal ensemble of statistical methods comprises the Mann-Kendall trend test, the Theil-Sen slope estimation, the Wilcoxon step trend test and basic descriptive statistics. The application of Litter Analyst, a tailor-made software for analysing the results of beach litter surveys, to OSPAR beach litter data from seven beaches bordering on the south-eastern North Sea, revealed 23 significant trends in the abundances of beach litter types for the period 2009-2014. Litter Analyst revealed a large variation in the abundance of litter types between beaches. To reduce the effects of spatial variation, trend analysis of beach litter data can most effectively be performed at the beach or national level. Spatial aggregation of beach litter data within a region is possible, but resulted in a considerable reduction in the number of significant trends.

The need for thorough phase II studies in medicines development for Alzheimer's disease.

An important factor in the universal failure in phase III trials in mild to moderate Alzheimer's disease in the past decade is the lack of phase II clinical data prior to entering phase III, with common reliance on biomarker results alone. Conduction of two learn-confirm cycles according to the Sheiner model would allow go/no-go decision making to include reliable clinical efficacy data prior to conducting phase III and would likely bring the rate of late stage failure more into line with that of other neurological indications. In studies in earlier disease stages, combined phase IIB/III adaptive approaches merit consideration in view of the long timelines of each study, though advantages and disadvantages of this approach versus the classical development pathway still need careful assessment.

Efficient Optimization for Sparse Gaussian Process Regression.

We propose an efficient optimization algorithm to select a subset of training data as the inducing set for sparse Gaussian process regression. Previous methods either use different objective functions for inducing set and hyperparameter selection, or else optimize the inducing set by gradient-based continuous optimization. The former approaches are harder to interpret and suboptimal, whereas the latter cannot be applied to discrete input domains or to kernel functions that are not differentiable with respect to the input. The algorithm proposed in this work estimates an inducing set and the hyperparameters using a single objective. It can be used to optimize either the marginal likelihood or a variational free energy. Space and time complexity are linear in training set size, and the algorithm can be applied to large regression problems on discrete or continuous domains. Empirical evaluation shows state-of-art performance in discrete cases, competitive prediction results as well as a favorable trade-off between training and test time in continuous cases.