Oxidation states of individual metal atoms within a metalloprotein can be assigned by examining X-ray absorption edges, which shift to higher energy for progressively more positive valence numbers. Indeed, X-ray crystallography is well suited for such a measurement, owing to its ability to spatially resolve the scattering contributions of individual metal atoms that have distinct electronic environments contributing to protein function. However, as the magnitude of the shift is quite small, about +2 eV per valence state for iron, it has only been possible to measure the effect when performed with monochromated X-ray sources at synchrotron facilities with energy resolutions in the range 2–3 × 10−4 (ΔE/E). This paper tests whether X-ray free-electron laser (XFEL) pulses, which have a broader bandpass (ΔE/E = 3 × 10−3) when used without a monochromator, might also be useful for such studies. The program nanoBragg is used to simulate serial femtosecond crystallography (SFX) diffraction images with sufficient granularity to model the XFEL spectrum, the crystal mosaicity and the wavelength-dependent anomalous scattering factors contributed by two differently charged iron centers in the 110-amino-acid protein, ferredoxin. Bayesian methods are then used to deduce, from the simulated data, the most likely X-ray absorption curves for each metal atom in the protein, which agree well with the curves chosen for the simulation. The data analysis relies critically on the ability to measure the incident spectrum for each pulse, and also on the nanoBragg simulator to predict the size, shape and intensity profile of Bragg spots based on an underlying physical model that includes the absorption curves, which are then modified to produce the best agreement with the simulated data. This inference methodology potentially enables the use of SFX diffraction for the study of metalloenzyme mechanisms and, in general, offers a more detailed approach to Bragg spot data reduction.

The optical design of a Hettrick–Underwood-style soft X-ray spectrometer with Wolter type 1 mirrors is presented. The spectrometer with a nominal length of 3.1 m can achieve a high resolving power (resolving power higher than 10000) in the soft X-ray regime when a small source beam (<3 µm in the grating dispersion direction) and small pixel detector (5 µm effective pixel size) are used. Adding Wolter mirrors to the spectrometer before its dispersive elements can realize the spatial imaging capability, which finds applications in the spectroscopic studies of spatially dependent electronic structures in tandem catalysts, heterostructures, etc. In the pump–probe experiments where the pump beam perturbs the materials followed by the time-delayed probe beam to reveal the transient evolution of electronic structures, the imaging capability of the Wolter mirrors can offer the pixel-equivalent femtosecond time delay between the pump and probe beams when their wavefronts are not collinear. In combination with some special sample handing systems, such as liquid jets and droplets, the imaging capability can also be used to study the time-dependent electronic structure of chemical transformation spanning multiple time domains from microseconds to nanoseconds. The proposed Wolter mirrors can also be adopted to the existing soft X-ray spectrometers that use the Hettrick–Underwood optical scheme, expanding their capabilities in materials research.

Quaternion methods for obtaining solutions to the problem of finding global rotations that optimally align pairs of corresponding lists of 3D spatial and/or orientation data are critically studied. The existence of multiple literatures and historical contexts is pointed out, and the algebraic solutions of the quaternion approach to the classic 3D spatial problem are emphasized. The treatment is extended to novel quaternion-based solutions to the alignment problems for 4D translation and orientation data.

Effective flood-risk management requires accurate risk-analysis models. Conventional analysis approaches, however, are based on the evaluation of spatially homogenous scenarios, which do not account for variation in flooding across a river reach/ region. Since flood events are often spatially heterogeneous (i.e. unevenly distributed), this paves the way for error. Now, scientists have developed a novel framework for risk analysis that accounts for their heterogeneity, and successfully demonstrated the accuracy of the approach by applying it in a proof-of-concept exercise in Vorarlberg, Austria. By facilitating improved prediction and quantification of flood events, this model is likely to inform future flood-risk management and related decision-making.

A pilot Marine Spatial Planning (MSP) project in the UK has found MSP to be a useful approach in managing marine waters sustainably. Sharing the knowledge and experiences gained in developing the Shetland Islands’ Marine Spatial Plan (SMSP) can help other authorities in the process of developing similar plans, says the project team.

Eight key features for increasing the climate change resilience of water management and spatial planning projects are presented by new Dutch research. These include: focusing on the long term, integrating the projects with other sustainability measures and encouraging stakeholder participation.

A risk-based tool built using multi-criteria decision analysis has been developed to rank environmental contaminants, giving each a level of concern. It can be used by decision makers to prioritise areas for further assessments, based on expected human health impacts.

With Adobe Experience Manager as a Cloud Service, Esri's development time has improved by 20%-50%

Understanding the capacity to reduce wildfire risk and restore dry forests on Western national forests is a key part of prioritizing new accelerated restoration programs initiated by the Forest Service.

In frequent-fire forests of the interior Western United States, historical (prefire suppression) conditions are often used as a reference to set management objectives, guide prescriptions, and monitor treatment effectiveness. We quantified the historical size, density, composition, and spatial patterns of dry mixed-conifer forests in the Blue Mountains of Oregon to establish reference conditions that could be used for ongoing forest-restoration efforts.

A reaction-diffusion model was developed describing the spread of the COVID-19 virus considering the mean daily movement of susceptible, exposed and asymptomatic individuals. The model was calibrated using data on the confirmed infection and death from France as well as their initial spatial distribution. First, the system of partial differential equations is studied, then the basic reproduction number, R0 is derived. Second, numerical simulations, based on a combination of level-set and finite differences, shown the spatial spread of COVID-19 from March 16 to June 16. Finally, scenarios of unlockdown are compared according to variation of distancing, or partially spatial lockdown.

High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location. Semantic segmentation, which is an important task for element extraction, has been widely used in processing mass HRRSIs. However, HRRSIs often exhibit large intraclass variance and small interclass variance due to the diversity and complexity of ground objects, thereby bringing great challenges to a semantic segmentation task. In this paper, we propose a new end-to-end semantic segmentation network, which integrates lightweight spatial and channel attention modules that can refine features adaptively. We compare our method with several classic methods on the ISPRS Vaihingen and Potsdam datasets. Experimental results show that our method can achieve better semantic segmentation results. The source codes are available at https://github.com/lehaifeng/SCAttNet.

A persistent-mode High Temperature Superconductor (HTS) shim coil is provided having at least one rectangular shaped thin sheet of HTS, wherein the thin sheet of HTS contains a first long portion, a second long portion parallel to first long portion, a first end, and a second end parallel to the first end. The rectangular shaped thin sheet of high-temperature superconductor has a hollow center and forms a continuous loop. In addition, the first end and the second end are folded toward each other forming two rings, and the thin sheet of high-temperature superconductor has a radial build that is less than 5 millimeters (mm) and able to withstand very strong magnetic field ranges of greater than approximately 12 Tesla (T) within a center-portion of a superconducting magnet of a superconducting magnet assembly.

A method and system of providing a content guide that identifies a spatial relationship between the elements in the content guide is described. A controller receives electronic program guide data. The received electronic program guide data is parsed to identify a plurality of programs listed in the electronic program guide and data corresponding to at least one program description attribute for the identified programs. A relationship is determined between each identified program based on the at least one program description attribute. At least one cluster is generated and includes at least one of the plurality of identified programs based on the determined relationship. A user interface display processor generates a user interface display image representing a clustered content guide and including the at least one generated cluster enabling the user to view the relationship of the plurality of programs.

A machine-implemented display method that, with respect to a volume dataset being rendered, enables a user to navigate to any position in space and look in any direction. Preferably, the volume dataset is derived from a computer tomography (CT) or magnetic resonance imaging (MRI) scan. With the described approach, the user can see details within the dataset that are not available using conventional visualization approaches. The freedom-of-motion capability allows the user to go to places (positions) within the volume rendering that are not otherwise possible using conventional “orbit” and “zoom” display techniques. Thus, for example, using the described approach, the display image enables a user to travel inside physical structures (e.g., a patient's heart, brain, arteries, and the like).

A spatially-aware radiation probe system/method allowing for detection and correction of radiation readings based on the position and/or movement of a radiation detector is disclosed. The system incorporates a radiation detector combined with a spatially-aware sensor to permit detection of spatial context parameters associated with the radiation detector and/or object being probed. This spatial context information is then used by analysis software to modify the detected radiation values and/or instruct the radiation probe operator as to appropriate measurement activity to ensure accurate radiation measurements. The spatially-aware sensor may include but is not limited to: distance sensors to determine the distance between the radiation detector and the object being monitored; accelerometers integrated within the radiation detector to detect movement of the radiation detector; and/or axial orientation sensors to determine the axial orientation of the radiation detector.

Methods, apparatuses, computer readable media for spatial reuse for uplink multi-user transmissions. An apparatus of a station comprising processing circuitry is disclosed. The processing circuitry may be configured to decode a first portion of a physical layer convergence procedure (PLCP) protocol data unit (PPDU), and configure the station to transmit a frame, if the PPDU is an overlapping basic service set (OBSS) PPDU, and a receive power of the PPDU is below an overlapping power detect level. An apparatus of an access point comprising processing circuitry is disclosed. The processing circuitry may be configured to encode a PPDU comprising a basic service set identifier of the access point, and encode the PPDU to indicate spatial reuse (SR) delay, SR restricted, or SR not permitted. The processing circuitry may be further configured to configure the access point to transmit the PPDU.

Obtain, run, and extend a Node.js starter application that uses the IBM Cloud Geospatial Analytics service. With the Geospatial Analytics service, you can monitor moving devices from the Internet of Things. The service analyzes a device message stream from MQTT and tracks device locations in real time with respect to one or more geographic regions.

Deploy and extend an Internet of Things (IoT) Connected Vehicle starter kit on IBM Cloud with the Internet of Things Platform and Geospatial services. The starter kit enables you to simulate, view, and manage vehicles driving through a city and set up geofences for notification.

TRB’s Airport Cooperative Research Program (ACRP) Report 39: Recommended Guidelines for the Collection and Use of Geospatially Referenced Data for Airfield Pavement Management offers recommended guidelines for the collection and use of geospatially referenced data for airfield pavement management. The guidelines provide a data schema, data collection methods, data quality requirements, and other relevant information required for developing specifications and standards for integrating geospatial data into...

S. A. Kashchenko
Trans. Moscow Math. Soc. 80 (2020), 53-71.
Abstract, references and article information

Israel Martínez-Hernández, Marc G. Genton.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 2, 204--229.

Abstract:
As high-dimensional and high-frequency data are being collected on a large scale, the development of new statistical models is being pushed forward. Functional data analysis provides the required statistical methods to deal with large-scale and complex data by assuming that data are continuous functions, for example, realizations of a continuous process (curves) or continuous random field (surfaces), and that each curve or surface is considered as a single observation. Here, we provide an overview of functional data analysis when data are complex and spatially correlated. We provide definitions and estimators of the first and second moments of the corresponding functional random variable. We present two main approaches: The first assumes that data are realizations of a functional random field, that is, each observation is a curve with a spatial component. We call them spatial functional data . The second approach assumes that data are continuous deterministic fields observed over time. In this case, one observation is a surface or manifold, and we call them surface time series . For these two approaches, we describe software available for the statistical analysis. We also present a data illustration, using a high-resolution wind speed simulated dataset, as an example of the two approaches. The functional data approach offers a new paradigm of data analysis, where the continuous processes or random fields are considered as a single entity. We consider this approach to be very valuable in the context of big data.

Achillefs Tzioufas.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 3, 674--684.

Abstract:
The epidemic process on a graph is considered for which infectious contacts occur at rate which depends on whether a susceptible is infected for the first time or not. We show that the Vasershtein coupling extends if and only if secondary infections occur at rate which is greater than that of initial ones. Nonetheless we show that, with respect to the probability of occurrence of an infinite epidemic, the said proviso may be dropped regarding the totally asymmetric process in one dimension, thus settling in the affirmative this special case of the conjecture for arbitrary graphs due to [ Ann. Appl. Probab. 13 (2003) 669–690].

Salem M. Al-Gezeri, Robert G. Aykroyd.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 3, 498--519.

Abstract:
The use of Markov random field (MRF) models has proven to be a fruitful approach in a wide range of image processing applications. It allows local texture information to be incorporated in a systematic and unified way and allows statistical inference theory to be applied giving rise to novel output summaries and enhanced image interpretation. A great advantage of such low-level approaches is that they lead to flexible models, which can be applied to a wide range of imaging problems without the need for significant modification. This paper proposes and explores the use of conditional MRF models for situations where multiple images are to be processed simultaneously, or where only a single image is to be reconstructed and a sequential approach is taken. Although the coupling of image intensity values is a special case of our approach, the main extension over previous proposals is to allow the direct coupling of other properties, such as smoothness or texture. This is achieved using a local modulating function which adjusts the influence of global smoothing without the need for a fully inhomogeneous prior model. Several modulating functions are considered and a detailed simulation study, motivated by remote sensing applications in archaeological geophysics, of conditional reconstruction is presented. The results demonstrate that a substantial improvement in the quality of the image reconstruction, in terms of errors and residuals, can be achieved using this approach, especially at locations with rapid changes in the underlying intensity.

Jonathan R. Bradley, Noel Cressie, Tao Shi.

Source: Statistics Surveys, Volume 10, 100--131.

Abstract:
In this article, we review and compare a number of methods of spatial prediction, where each method is viewed as an algorithm that processes spatial data. To demonstrate the breadth of available choices, we consider both traditional and more-recently-introduced spatial predictors. Specifically, in our exposition we review: traditional stationary kriging, smoothing splines, negative-exponential distance-weighting, fixed rank kriging, modified predictive processes, a stochastic partial differential equation approach, and lattice kriging. This comparison is meant to provide a service to practitioners wishing to decide between spatial predictors. Hence, we provide technical material for the unfamiliar, which includes the definition and motivation for each (deterministic and stochastic) spatial predictor. We use a benchmark dataset of $mathrm{CO}_{2}$ data from NASA’s AIRS instrument to address computational efficiencies that include CPU time and memory usage. Furthermore, the predictive performance of each spatial predictor is assessed empirically using a hold-out subset of the AIRS data.

Disaggregation regression has become an important tool in spatial disease mapping for making fine-scale predictions of disease risk from aggregated response data. By including high resolution covariate information and modelling the data generating process on a fine scale, it is hoped that these models can accurately learn the relationships between covariates and response at a fine spatial scale. However, validating these high resolution predictions can be a challenge, as often there is no data observed at this spatial scale. In this study, disaggregation regression was performed on simulated data in various settings and the resulting fine-scale predictions are compared to the simulated ground truth. Performance was investigated with varying numbers of data points, sizes of aggregated areas and levels of model misspecification. The effectiveness of cross validation on the aggregate level as a measure of fine-scale predictive performance was also investigated. Predictive performance improved as the number of observations increased and as the size of the aggregated areas decreased. When the model was well-specified, fine-scale predictions were accurate even with small numbers of observations and large aggregated areas. Under model misspecification predictive performance was significantly worse for large aggregated areas but remained high when response data was aggregated over smaller regions. Cross-validation correlation on the aggregate level was a moderately good predictor of fine-scale predictive performance. While the simulations are unlikely to capture the nuances of real-life response data, this study gives insight into the effectiveness of disaggregation regression in different contexts.

Susheela P. Singh, Ana-Maria Staicu, Robert R. Dunn, Noah Fierer, Brian J. Reich.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2341--2362.

Abstract:
The advent of high-throughput sequencing technologies has made data from DNA material readily available, leading to a surge of microbiome-related research establishing links between markers of microbiome health and specific outcomes. However, to harness the power of microbial communities we must understand not only how they affect us, but also how they can be influenced to improve outcomes. This area has been dominated by methods that reduce community composition to summary metrics, which can fail to fully exploit the complexity of community data. Recently, methods have been developed to model the abundance of taxa in a community, but they can be computationally intensive and do not account for spatial effects underlying microbial settlement. These spatial effects are particularly relevant in the microbiome setting because we expect communities that are close together to be more similar than those that are far apart. In this paper, we propose a flexible Bayesian spike-and-slab variable selection model for presence-absence indicators that accounts for spatial dependence and cross-dependence between taxa while reducing dimensionality in both directions. We show by simulation that in the presence of spatial dependence, popular distance-based hypothesis testing methods fail to preserve their advertised size, and the proposed method improves variable selection. Finally, we present an application of our method to an indoor fungal community found within homes across the contiguous United States.

Stephen Berg, Jun Zhu, Murray K. Clayton, Monika E. Shea, David J. Mladenoff.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2312--2340.

Abstract:
The Wisconsin Public Land Survey database describes historical forest composition at high spatial resolution and is of interest in ecological studies of forest composition in Wisconsin just prior to significant Euro-American settlement. For such studies it is useful to identify recurring subpopulations of tree species known as communities, but standard clustering approaches for subpopulation identification do not account for dependence between spatially nearby observations. Here, we develop and fit a latent discrete Markov random field model for the purpose of identifying and classifying historical forest communities based on spatially referenced multivariate tree species counts across Wisconsin. We show empirically for the actual dataset and through simulation that our latent Markov random field modeling approach improves prediction and parameter estimation performance. For model fitting we introduce a new stochastic approximation algorithm which enables computationally efficient estimation and classification of large amounts of spatial multivariate count data.

Cecilia Balocchi, Shane T. Jensen.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2235--2259.

Abstract:
Understanding the relationship between change in crime over time and the geography of urban areas is an important problem for urban planning. Accurate estimation of changing crime rates throughout a city would aid law enforcement as well as enable studies of the association between crime and the built environment. Bayesian modeling is a promising direction since areal data require principled sharing of information to address spatial autocorrelation between proximal neighborhoods. We develop several Bayesian approaches to spatial sharing of information between neighborhoods while modeling trends in crime counts over time. We apply our methodology to estimate changes in crime throughout Philadelphia over the 2006-15 period while also incorporating spatially-varying economic and demographic predictors. We find that the local shrinkage imposed by a conditional autoregressive model has substantial benefits in terms of out-of-sample predictive accuracy of crime. We also explore the possibility of spatial discontinuities between neighborhoods that could represent natural barriers or aspects of the built environment.

Christian Hirsch, Christian Mönch.

Source: Bernoulli, Volume 26, Number 2, 927--947.

Abstract:
This paper considers two asymptotic properties of a spatial preferential-attachment model introduced by E. Jacob and P. Mörters (In Algorithms and Models for the Web Graph (2013) 14–25 Springer). First, in a regime of strong linear reinforcement, we show that typical distances are at most of doubly-logarithmic order. Second, we derive a large deviation principle for the empirical neighbourhood structure and express the rate function as solution to an entropy minimisation problem in the space of stationary marked point processes.

Abhirup Datta, Sudipto Banerjee, James S. Hodges, Leiwen Gao.

Source: Bayesian Analysis, Volume 14, Number 4, 1221--1244.

Abstract:
Hierarchical models for regionally aggregated disease incidence data commonly involve region specific latent random effects that are modeled jointly as having a multivariate Gaussian distribution. The covariance or precision matrix incorporates the spatial dependence between the regions. Common choices for the precision matrix include the widely used ICAR model, which is singular, and its nonsingular extension which lacks interpretability. We propose a new parametric model for the precision matrix based on a directed acyclic graph (DAG) representation of the spatial dependence. Our model guarantees positive definiteness and, hence, in addition to being a valid prior for regional spatially correlated random effects, can also directly model the outcome from dependent data like images and networks. Theoretical results establish a link between the parameters in our model and the variance and covariances of the random effects. Simulation studies demonstrate that the improved interpretability of our model reaps benefits in terms of accurately recovering the latent spatial random effects as well as for inference on the spatial covariance parameters. Under modest spatial correlation, our model far outperforms the CAR models, while the performances are similar when the spatial correlation is strong. We also assess sensitivity to the choice of the ordering in the DAG construction using theoretical and empirical results which testify to the robustness of our model. We also present a large-scale public health application demonstrating the competitive performance of the model.

Marcos Oliveira Prates, Renato Martins Assunção, Erica Castilho Rodrigues.

Source: Bayesian Analysis, Volume 14, Number 2, 623--647.

Abstract:
Spatial confounding between the spatial random effects and fixed effects covariates has been recently discovered and showed that it may bring misleading interpretation to the model results. Techniques to alleviate this problem are based on decomposing the spatial random effect and fitting a restricted spatial regression. In this paper, we propose a different approach: a transformation of the geographic space to ensure that the unobserved spatial random effect added to the regression is orthogonal to the fixed effects covariates. Our approach, named SPOCK, has the additional benefit of providing a fast and simple computational method to estimate the parameters. Also, it does not constrain the distribution class assumed for the spatial error term. A simulation study and real data analyses are presented to better understand the advantages of the new method in comparison with the existing ones.

RU Muller
Jul 1, 1987; 7:1951-1968
Articles

Gregor Thut
Sep 13, 2006; 26:9494-9502
BehavioralSystemsCognitive

The detection and segmentation of meaningful figures from their background is one of the primary functions of vision. While work in nonhuman primates has implicated early visual mechanisms in this figure–ground modulation, neuroimaging in humans has instead largely ascribed the processing of figures and objects to higher stages of the visual hierarchy. Here, we used high-field fMRI at 7 Tesla to measure BOLD responses to task-irrelevant orientation-defined figures in human early visual cortex (N = 6, four females). We used a novel population receptive field mapping-based approach to resolve the spatial profiles of two constituent mechanisms of figure–ground modulation: a local boundary response, and a further enhancement spanning the full extent of the figure region that is driven by global differences in features. Reconstructing the distinct spatial profiles of these effects reveals that figure enhancement modulates responses in human early visual cortex in a manner consistent with a mechanism of automatic, contextually driven feedback from higher visual areas.

SIGNIFICANCE STATEMENT A core function of the visual system is to parse complex 2D input into meaningful figures. We do so constantly and seamlessly, both by processing information about visible edges and by analyzing large-scale differences between figure and background. While influential neurophysiology work has characterized an intriguing mechanism that enhances V1 responses to perceptual figures, we have a poor understanding of how the early visual system contributes to figure–ground processing in humans. Here, we use advanced computational analysis methods and high-field human fMRI data to resolve the distinct spatial profiles of local edge and global figure enhancement in the early visual system (V1 and LGN); the latter is distinct and consistent with a mechanism of automatic, stimulus-driven feedback from higher-level visual areas.

An increasing number of children are unvaccinated at entry into public schools, potentially endangering children who cannot be vaccinated for medical reasons and threatening herd immunity. Voluntary exemptions from immunizations vary geographically and by parental characteristics.

We find that exemption behavior is highest in peripheral areas of cities and that specific types of student populations are associated with high exemption rates. Additionally, there is spatial overlap between clusters of high personal exemption and medical exemption populations. (Read the full article)

Advanced analytics programs can incorporate geospatial data. Learn how such data can be used to augment local marketing plans

Manchester is the City Region that has most enthusiastically embraced the devolution agenda – unsurprisingly, perhaps, given its long standing commitment to the cause of devolved powers and city region autonomy. In addition to embracing the Me...

ABSTRACT

Cell division requires proper spatial coordination with the chromosome, which undergoes dynamic changes during chromosome replication and segregation. FtsZ is a bacterial cytoskeletal protein that assembles into the Z-ring, providing a platform to build the cell division apparatus. In the model bacterium Caulobacter crescentus, the cellular localization of the Z-ring is controlled during the cell cycle in a chromosome replication-coupled manner. Although dynamic localization of the Z-ring at midcell is driven primarily by the replication origin-associated FtsZ inhibitor MipZ, the mechanism ensuring accurate positioning of the Z-ring remains unclear. In this study, we showed that the Z-ring colocalizes with the replication terminus region, located opposite the origin, throughout most of the C. crescentus cell cycle. Spatial organization of the two is mediated by ZapT, a previously uncharacterized protein that interacts with the terminus region and associates with ZapA and ZauP, both of which are part of the incipient division apparatus. While the Z-ring and the terminus region coincided with the presence of ZapT, colocalization of the two was perturbed in cells lacking zapT, which is accompanied by delayed midcellular positioning of the Z-ring. Moreover, cells overexpressing ZapT showed compromised positioning of the Z-ring and MipZ. These findings underscore the important role of ZapT in controlling cell division processes. We propose that ZapT acts as a molecular bridge that physically links the terminus region to the Z-ring, thereby ensuring accurate site selection for the Z-ring. Because ZapT is conserved in proteobacteria, these findings may define a general mechanism coordinating cell division with chromosome organization.

IMPORTANCE Growing bacteria require careful tuning of cell division processes with dynamic organization of replicating chromosomes. In enteric bacteria, ZapA associates with the cytoskeletal Z-ring and establishes a physical linkage to the chromosomal replication terminus through its interaction with ZapB-MatP-DNA complexes. However, because ZapB and MatP are found only in enteric bacteria, it remains unclear how the Z-ring and the terminus are coordinated in the vast majority of bacteria. Here, we provide evidence that a novel conserved protein, termed ZapT, mediates colocalization of the Z-ring with the terminus in Caulobacter crescentus, a model organism that is phylogenetically distant from enteric bacteria. Given that ZapT facilitates cell division processes in C. crescentus, this study highlights the universal importance of the physical linkage between the Z-ring and the terminus in maintaining cell integrity.

Real geography is continuous, but standard models in population genetics are based on discrete, well-mixed populations. As a result, many methods of analyzing genetic data assume that samples are a random draw from a well-mixed population, but are applied to clustered samples from populations that are structured clinally over space. Here, we use simulations of populations living in continuous geography to study the impacts of dispersal and sampling strategy on population genetic summary statistics, demographic inference, and genome-wide association studies (GWAS). We find that most common summary statistics have distributions that differ substantially from those seen in well-mixed populations, especially when Wright’s neighborhood size is < 100 and sampling is spatially clustered. "Stepping-stone" models reproduce some of these effects, but discretizing the landscape introduces artifacts that in some cases are exacerbated at higher resolutions. The combination of low dispersal and clustered sampling causes demographic inference from the site frequency spectrum to infer more turbulent demographic histories, but averaged results across multiple simulations revealed surprisingly little systematic bias. We also show that the combination of spatially autocorrelated environments and limited dispersal causes GWAS to identify spurious signals of genetic association with purely environmentally determined phenotypes, and that this bias is only partially corrected by regressing out principal components of ancestry. Last, we discuss the relevance of our simulation results for inference from genetic variation in real organisms.

Myriam Franzke, Christian Kraus, David Dreyer, Keram Pfeiffer, M. Jerome Beetz, Anna L. Stöckl, James J. Foster, Eric J. Warrant, and Basil el Jundi

Monarch butterflies (Danaus plexippus) are prominent for their annual long-distance migration from North America to their overwintering area in Central Mexico. To find their way on this long journey, they use a sun compass as their main orientation reference but will also adjust their migratory direction with respect to mountain ranges. This indicates that the migratory butterflies also attend to the panorama to guide their travels. While the compass has been studied in detail in migrating butterflies, little is known about the orientation abilities of non-migrating butterflies. Here we studied if non-migrating butterflies - that stay in a more restricted area to feed and breed - also use a similar compass system to guide their flights. Performing behavioral experiments on tethered flying butterflies in an indoor LED flight simulator, we found that the monarchs fly along straight tracks with respect to a simulated sun. When a panoramic skyline was presented as the only orientation cue, the butterflies maintained their flight direction only during short sequences suggesting that they potentially use it for flight stabilization. We further found that when we presented the two cues together, the butterflies incorporate both cues in their compass. Taken together, we here show that non-migrating monarch butterflies can combine multiple visual cues for robust orientation, an ability that may also aid them during their migration.

RiVax is a promising recombinant ricin toxin A subunit (RTA) vaccine antigen that has been shown to be safe and immunogenic in humans and effective at protecting rhesus macaques against lethal-dose aerosolized toxin exposure. We previously used a panel of RTA-specific monoclonal antibodies (MAbs) to demonstrate, by competition enzyme-linked immunosorbent assay (ELISA), that RiVax elicits similar serum antibody profiles in humans and macaques. However, the MAb binding sites on RiVax have yet to be defined. In this study, we employed hydrogen exchange-mass spectrometry (HX-MS) to localize the epitopes on RiVax recognized by nine toxin-neutralizing MAbs and one nonneutralizing MAb. Based on strong protection from hydrogen exchange, the nine MAbs grouped into four spatially distinct epitope clusters (namely, clusters I to IV). Cluster I MAbs protected RiVax's α-helix B (residues 94 to 107), a protruding immunodominant secondary structure element known to be a target of potent toxin-neutralizing antibodies. Cluster II consisted of two subclusters located on the "back side" (relative to the active site pocket) of RiVax. One subcluster involved α-helix A (residues 14 to 24) and α-helices F-G (residues 184 to 207); the other encompassed β-strand d (residues 62 to 69) and parts of α-helices D-E (154 to 164) and the intervening loop. Cluster III involved α-helices C and G on the front side of RiVax, while cluster IV formed a sash from the front to back of RiVax, spanning strands b, c, and d (residues 35 to 59). Having a high-resolution B cell epitope map of RiVax will enable the development and optimization of competitive serum profiling assays to examine vaccine-induced antibody responses across species.

Most British railway embankments were constructed between 120 and 180 years ago without the benefit of modern design and construction methods. This can result in undesirable load-deformation characteristics and consequent disruption to present-day railway operations, for which there is unprecedented demand. Annual rail passenger kilometres have approximately doubled in the last 20 years and freight has increased by 60% over the same period. Whereas elements such as rails or bridges can be refurbished or replaced to meet increasing demand, the same is not usually feasible for embankments. Development of techniques to assess embankment performance risks posed by operational capacity enhancements is therefore of increasing significance to railway geotechnical asset management. The two case studies presented in this paper demonstrate how geospatial analysis and data management techniques may be applied to this challenge at both strategic (regional or national) and tactical (site-specific) scales for embankments incorporating plastic clay fill. The case studies also demonstrate, in a world of ever more abundant data, the growing need for engineering geologists and geotechnical engineers to augment their traditional knowledge with comprehensive data management and geospatial analysis skills, these being essential for modern infrastructure asset management.

Thematic collection: This article is part of the ‘Ground-related risk to transportation infrastructure’ collection available at: https://www.lyellcollection.org/cc/Ground-related-risk-to-transportation-infrastructure