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Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials and private citizens alike. In this work, we focus on a machine-learned anonymized mobility map (hereon referred to as AMM) aggregated over hundreds of millions of smartphones and evaluate its utility in forecasting epidemics. We factor AMM into a metapopulation model to retrospectively forecast influenza in the USA and Australia. We show that the AMM model performs on-par with those based on commuter surveys, which are sparsely available and expensive. We also compare it with gravity and radiation based models of mobility, and find that the radiation model’s performance is quite similar to AMM and commuter flows. Additionally, we demonstrate our model’s ability to predict disease spread even across state boundaries. Our work contributes towards developing timely infectious disease forecasting at a global scale using human mobility datasets expanding their applications in the area of infectious disease epidemiology., Human mobility plays a central role in the spread of infectious diseases and can help in forecasting incidence. Here the authors show a comparison of multiple mobility benchmarks in forecasting influenza, and demonstrate the value of a machine-learned mobility map with global coverage at multiple spatial scales.

Differential privacy (DP) provides formal guarantees that the output of a database query does not reveal too much information about any individual present in the database. While many differentially private algorithms have been proposed in the scientific literature, there are only a few end-to-end implementations of differentially private query engines. Crucially, existing systems assume that each individual is associated with at most one database record, which is unrealistic in practice. We propose a generic and scalable method to perform differentially private aggregations on databases, even when individuals can each be associated with arbitrarily many rows. We express this method as an operator in relational algebra, and implement it in an SQL engine. To validate this system, we test the utility of typical queries on industry benchmarks, and verify its correctness with a stochastic test framework we developed. We highlight the promises and pitfalls learned when deploying such a system in practice, and we publish its core components as open-source software., Proceedings on Privacy Enhancing Technologies, 2020 (2), ISSN:2299-0984

The recent trend of rapid urbanization makes it imperative to understand urban characteristics such as infrastructure, population distribution, jobs, and services that play a key role in urban livability and sustainability. A healthy debate exists on what constitutes optimal structure regarding livability in cities, interpolating, for instance, between mono- and poly-centric organization. Here anonymous and aggregated flows generated from three hundred million users, opted-in to Location History, are used to extract global Intra-urban trips. We develop a metric that allows us to classify cities and to establish a connection between mobility organization and key urban indicators. We demonstrate that cities with strong hierarchical mobility structure display an extensive use of public transport, higher levels of walkability, lower pollutant emissions per capita and better health indicators. Our framework outperforms previous metrics, is highly scalable and can be deployed with little cost, even in areas without resources for traditional data collection., The growing availability of human mobility data can help assess the structure and dynamics of urban environments and their relation to the performance of cities. Here the authors introduce a metric of hierarchy in urban travel and find correlations between levels of hierarchy and other urban indicators.

Social distancing remains an important strategy to combat the COVID-19 pandemic in the United States. However, the impacts of specific state-level policies on mobility and subsequent COVID-19 case trajectories have not been completely quantified. Using anonymized and aggregated mobility data from opted-in Google users, we found that state-level emergency declarations resulted in a 9.9% reduction in time spent away from places of residence. Implementation of one or more social distancing policies resulted in an additional 24.5% reduction in mobility the following week, and subsequent shelter-in-place mandates yielded an additional 29.0% reduction. Decreases in mobility were associated with substantial reductions in case growth two to four weeks later. For example, a 10% reduction in mobility was associated with a 17.5% reduction in case growth two weeks later. Given the continued reliance on social distancing policies to limit the spread of COVID-19, these results may be helpful to public health officials trying to balance infection control with the economic and social consequences of these policies., Nature Communications, 12 (1), ISSN:2041-1723

Physics-based simulation represents a powerful method for investigating the time-varying behavior of dynamic protein systems at high spatial and temporal resolution. Such simulations, however, can be prohibitively difficult or lengthy for large proteins or when probing the lower-resolution, long-timescale behaviors of proteins generally. Importantly, not all questions about a protein system require full space and time resolution to produce an informative answer. For instance, by avoiding the simulation of uncorrelated, high-frequency atomic movements, a larger, domain-level picture of protein dynamics can be revealed. The purpose of this review is to highlight the growing body of complementary work that goes beyond simulation. In particular, this review focuses on methods that address kinematics and dynamics, as well as those that address larger organizational questions and can quickly yield useful information about the long-timescale behavior of a protein.

During the past two decades instrumentation in scanning transmission electron microscopy (STEM) has pushed toward higher intensity electron probes to increase the signal-to-noise ratio of recorded images. While this is suitable for robust specimens, biological specimens require a much reduced electron dose for high-resolution imaging. We describe here protocols for low-dose STEM image recording with a conventional field-emission gun STEM, while maintaining the high-resolution capability of the instrument. Our findings show that a combination of reduced pixel dwell time and reduced gun current can achieve radiation doses comparable to low-dose TEM.

Proteins are at the root of many biological functions, often performing complex tasks as the result of large changes in their structure. Describing the exact details of these conformational changes, however, remains a central challenge for computational biology due the enormous computational requirements of the problem. This has engendered the development of a rich variety of useful methods designed to answer specific questions at different levels of spatial, temporal, and energetic resolution. These methods fall largely into two classes: physically accurate, but computationally demanding methods and fast, approximate methods. We introduce here a new hybrid modeling tool, the Structured Intuitive Move Selector (sims), designed to bridge the divide between these two classes, while allowing the benefits of both to be seamlessly integrated into a single framework. This is achieved by applying a modern motion planning algorithm, borrowed from the field of robotics, in tandem with a well-established protein modeling library. sims can combine precise energy calculations with approximate or specialized conformational sampling routines to produce rapid, yet accurate, analysis of the large-scale conformational variability of protein systems. Several key advancements are shown, including the abstract use of generically defined moves (conformational sampling methods) and an expansive probabilistic conformational exploration. We present three example problems that sims is applied to and demonstrate a rapid solution for each. These include the automatic determination of “active” residues for the hinge-based system Cyanovirin-N, exploring conformational changes involving long-range coordinated motion between non-sequential residues in Ribose-Binding Protein, and the rapid discovery of a transient conformational state of Maltose-Binding Protein, previously only determined by Molecular Dynamics. For all cases we provide energetic validations using well-established energy fields, demonstrating this framework as a fast and accurate tool for the analysis of a wide range of protein flexibility problems.

Electron crystallography of membrane proteins uses cryo-transmission electron microscopy to record images and diffraction patterns of frozen-hydrated 2D crystals. Each two-dimensional (2D) crystal is only imaged once, at one specific tilt angle, and the recorded images can be automatically processed with the 2dx/MRC software package. Processed image data from non-tilted and tilted 2D crystals then need to be merged into a 3D reconstruction of the membrane protein structure. We here describe the process of the 3D merging, using the 2dx software system.

Electron crystallography of membrane proteins uses cryo-transmission electron microscopy to image frozen-hydrated 2D crystals. The processing of recorded images exploits the periodic arrangement of the structures in the images to extract the amplitudes and phases of diffraction spots in Fourier space. However, image imperfections require a crystal unbending procedure to be applied to the image before evaluation in Fourier space. We here describe the process of 2D crystal image unbending, using the 2dx software system.

In this chapter, the basic principles of atomic resolution scanning transmission electron microscopy (STEM) will be described. Particular attention will be paid to the benefits of the incoherent Z-contrast imaging technique for structural determination and the benefits of aberration correction for improved spatial resolution and sensitivity in the acquired images. In addition, the effect that the increased beam current in aberration corrected systems has on electron beam-induced structural modifications of inorganic systems will be discussed. Procedures for controlling the electron dose will be described along with image processing methods that enable quantified information to be extracted from STEM images. Several examples of the use of aberration-corrected STEM for the study of nanoscale systems will be presented; a quantification of vacancies in clathrate systems, a quantification of N doping in GaAs, a quantification of the size distribution in nanoparticle catalysts, and an observation of variability in dislocation core composition along a low-angle grain boundary in SrTiO3. The potential for future standardized methods to reproducibly quantify structures determined by STEM and/or high-resolution TEM will also be discussed.

Electron crystallography of 2D protein crystals provides a powerful tool for the determination of membrane protein structure. In this method, data is acquired in the Fourier domain as randomly sampled, uncoupled, amplitudes and phases. Due to physical constraints on specimen tilting, those Fourier data show a vast un-sampled "missing cone" of information, producing resolution loss in the direction perpendicular to the membrane plane. Based on the flexible language of projection onto sets, we provide a full solution for these problems with a projective constraint optimization algorithm that, for sufficiently oversampled data, produces complete recovery of unmeasured data in the missing cone. We apply this method to an experimental data set of Bacteriorhodopsin and show that, in addition to producing superior results compared to traditional reconstruction methods, full, reproducible, recovery of the missing cone from noisy data is possible. Finally, we present an automatic implementation of the refinement routine as open source, freely distributed, software that will be included in our 2dx software package.

Electron crystallography determines the structure of two-dimensional (2D) membrane protein crystals and other 2D crystal systems. Cryo-transmission electron microscopy records high-resolution electron micrographs, which require computer processing for three-dimensional structure reconstruction. We present a new software system 2dx, which is designed as a user-friendly, platform-independent software package for electron crystallography. 2dx assists in the management of an image-processing project, guides the user through the processing of 2D crystal images, and provides transparence for processing tasks and results. Algorithms are implemented in the form of script templates reminiscent of c-shell scripts. These templates can be easily modified or replaced by the user and can also execute modular stand-alone programs from the MRC software or from other image processing software packages. 2dx is available under the GNU General Public License at 2dx.org.

Extended abstract of a paper presented at Microscopy and Microanalysis 2008 in Albuquerque, New Mexico, USA, August 3 – August 7, 2008

SummaryThe gating ring of cyclic nucleotide-modulated channels is proposed to be either a two-fold symmetric dimer of dimers or a four-fold symmetric tetramer based on high-resolution structure data of soluble cyclic nucleotide-binding domains and functional data on intact channels. We addressed this controversy by obtaining structural data on an intact, full-length, cyclic nucleotide-modulated potassium channel, MloK1, from Mesorhizobium loti, which also features a putative voltage-sensor. We present here the 3D single-particle structure by transmission electron microscopy and the projection map of membrane-reconstituted 2D crystals of MloK1 in the presence of cAMP. Our data show a four-fold symmetric arrangement of the CNBDs, separated by discrete gaps. A homology model for full-length MloK1 suggests a vertical orientation for the CNBDs. The 2D crystal packing in the membrane-embedded state is compatible with the S1-S4 domains in the vertical “up” state.

Electron crystallography determines the structure of membrane proteins and other periodic samples by recording either images or diffraction patterns. Computer processing of recorded images requires the determination of the reciprocal lattice parameters in the Fourier transform of the image. We have developed a set of three programs 2dx_peaksearch, 2dx_findlat and 2dx_getlat, which can determine the reciprocal lattice from a Fourier transformation of a 2D crystal image automatically. 2dx_peaksearch determines a list of Fourier peak coordinates from a processed calculated diffraction pattern. These coordinates are evaluated by 2dx_findlat to determine one or more lattices, using a-priori knowledge of the real-space crystal unit cell dimensions, and the sample tilt geometry. If these are unknown, then the program 2dx_getlat can be used to obtain a guess for the unit cell dimensions. These programs are available as part of the 2dx software package for the image processing of 2D crystal images at http://2dx.org. (c) 2007 Elsevier Inc. All rights reserved.

Electron crystallography of 2D protein crystals can determine the structure of membrane embedded proteins at high resolution. Images or electron diffraction patterns are recorded with the electron microscope of the frozen hydrated samples, and the 3D structure of the proteins is then determined by computer data processing. Here we introduce the image-processing algorithms for crystallographic Fourier space based methods using the Medical Research Council (MRC) programs, and illustrate the usage of the software packages 2dx, XDP, and IPLT.

Electron crystallography of membrane proteins records images and diffraction patterns of frozen-hydrated two-dimensional (2D) crystals. To reconstruct the high-resolution three-dimensional (3D) structure of a membrane protein, a multitude of images of 2D crystals have to be processed. Certain processing steps are thereby similar for batches of images that were recorded under similar conditions. Here we describe how the 2dx software package can be used to automate the processing of 2D crystal images, and how the 2D and 3D merging results can be used to iteratively reprocess the images. While the processing of 2D crystal images has been fully automated, the merging process is still semi-manual.

Extended abstract of a paper presented at MC 2007, 33rd DGE Conference in Saarbrücken, Germany, September 2 – September 7, 2007

Electron crystallography of membrane proteins determines the structure of membrane-reconstituted and two-dimensionally (2D) crystallized membrane proteins by low-dose imaging with the transmission electron microscope, and computer image processing. We have previously presented the software system 2dx, for user-friendly image processing of 2D crystal images. Its central component 2dx_image is based on the MRC program suite, and allows the optionally fully automatic processing of one 2D crystal image. We present here the program 2dx_merge, which assists the user in the management of a 2D crystal image processing project, and facilitates the merging of the data from multiple images. The merged dataset can be used as a reference to re-process all images, which usually improves the resolution of the final reconstruction. Image processing and merging can be applied iteratively, until convergence is reached. 2dx is available under the GNU General Public License at http://2dx.org. (c) 2007 Elsevier Inc. All rights reserved.

Extended abstract of a paper presented at Microscopy and Microanalysis 2008 in Albuquerque, New Mexico, USA, August 3 – August 7, 2008