Where Maps Lie: Visualization of Perceptual Fallacy in Choropleth Maps at Different Levels of Aggregation

This paper proposes a method for quantitative evaluation of perception deviations due to generalization in choropleth maps. The method proposed is based on comparison of class values assigned to different aggregation units chosen for representing the same dataset. It is illustrated by the results of application of the method to population density maps of Lithuania. Three spatial aggregation levels were chosen for comparison: the 1 × 1 km statistical grid, elderships (NUTS3), and municipalities (NUTS2). Differences in density class values between the reference grid map and the other two maps were calculated. It is demonstrated that a perceptual fallacy on the municipality level population map of Lithuania leads to a misinterpretation of data that makes such maps frankly useless. The eldership level map is, moreover, also largely misleading, especially in sparsely populated areas. The method proposed is easy to use and transferable to any other field where spatially aggregated data are mapped. It can be used for visual analysis of the degree to which a generalized choropleth map is liable to mislead the user in particular areas.

The effect of livestock density on Trypanosoma brucei gambiense and T. b. rhodesiense : a causal inference-based approach

Domestic and wild animals are important reservoirs of the rhodesiense form of human African trypanosomiasis (rHAT), however quantification of this effect offers utility for deploying non-medical control activities, and anticipating their success when wildlife are excluded. Further, the uncertain role of animal reservoirs—particularly pigs—threatens elimination of transmission (EOT) targets set for the gambiense form (gHAT). Using a new time series of high-resolution cattle and pig density maps, HAT surveillance data collated by the WHO Atlas of HAT, and methods drawn from causal inference and spatial epidemiology, we conducted a retrospective ecological cohort study in Uganda, Malawi, Democratic Republic of Congo (DRC) and South Sudan to estimate the effect of cattle and pig density on HAT risk.

Improving AlphaFold modeling using implicit information from experimental density maps

Machine learning prediction algorithms such as AlphaFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy. We hypothesized that by implicitly including experimental information, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt based on experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We find that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for crystallographic and electron cryo-microscopy map interpretation.

Impingement Density Analysis on Heat Transfer and the Appearance of Edge Cracks in Conventional Slab Using Hydraulic Nozzles

In this work, the fluid dynamics and heat transfer of two hydraulic nozzles used in the secondary cooling of the conventional slab continuous casting machine were analyzed. Impingement density maps, the jet opening angle and heat flux associated with different operating conditions (impingement distance, pressure) were experimentally determined. The opening angle and impingement density footprint were found to vary considerably in shape and magnitude with varying operating pressure and distances. Finally, it was found that when short operating distances are used, a greater heat extraction gradient occurs in the major axis of the impingement footprint, which promotes edge-cracks in the slab in plant.

Sex-Related Differences in Atrial Remodeling in Patients With Atrial Fibrillation: Relationship to Ablation Outcomes

Background: Population studies have demonstrated a range of sex differences including a higher prevalence of atrial fibrillation (AF) in men and a higher risk of AF recurrence in women. However, the underlying reasons for this higher recurrence are unknown. This study evaluated whether sex-based electrophysiological substrate differences exist to account for worse AF ablation outcomes in women. Methods: High-density electroanatomic mapping of the left atrium was performed in 116 consecutive patients with AF. Regional analysis was performed across 6 left atrium segments. High-density maps were created using a multipolar catheter (Biosense Webster) during distal coronary sinus pacing at 600 and 300 ms. Mean voltage and conduction velocity was determined. Complex fractionated signals and double potentials were manually annotated. Results: Overall, 42 (36%) were female, mean age was 61±8 years and AF was persistent in 52%. Global mean voltage was significantly lower in females compared with males at 600 ms (1.46±0.17 versus 1.84±0.15 mV, P <0.001) and 300 ms (1.27±0.18 versus 1.57±0.18 mV, P =0.013) pacing. These differences were seen uniformly across the left atrium. Females demonstrated significant conduction velocity slowing (34.9±6.1 versus 44.1±6.9 cm/s, P =0.002) and greater proportion of complex fractionated signals (9.9±1.7% versus 6.0±1.7%, P =0.014). After a median follow-up of 22 months (Q1–Q3: 15–29), females had significantly lower single-procedure (22 [54%] versus 54 [75%], P =0.029) and multiprocedure (24 [59%] versus 60 [83%], P =0.005) arrhythmia-free survival. Female sex and persistent AF were independent predictors of single and multiprocedure arrhythmia recurrence. Conclusions: Female patients demonstrated more advanced atrial remodeling on high-density electroanatomic mapping and greater post-AF ablation arrhythmia recurrence compared with males. These changes may contribute to sex-based differences in the clinical course of females with AF and in part explain the higher risk of recurrence.

A mosaic bulk-solvent model improves density maps and the fit between model and data

Bulk solvent is a major component of bio-macromolecular crystals and therefore contributes significantly to diffraction intensities. Accurate modeling of the bulk-solvent region has been recognized as important for many crystallographic calculations, from computing of R-factors and density maps to model building and refinement. Owing to its simplicity and computational and modeling power, the flat (mask-based) bulk-solvent model introduced by Jiang & Brunger (1994) is used by most modern crystallographic software packages to account for disordered solvent. In this manuscript we describe further developments of the mask-based model that improves the fit between the model and the data and aids in map interpretation. The new algorithm, here referred to as mosaic bulk-solvent model, considers solvent variation across the unit cell. The mosaic model is implemented in the computational crystallography toolbox and can be used in Phenix in most contexts where accounting for bulk-solvent is required. It has been optimized and validated using a sufficiently large subset of the Protein Data Bank entries that have crystallographic data available.

Scalable Adaptive Protein Ensemble Refinement Integrating Flexible Fitting

Recent advances in cryo-electron microscopy (cryo-EM) has enabled modeling macromolecular complexes that are essential components of life. The density maps obtained from cryo-EM experiments is often integrated with ab-initio, knowledge-driven or first principles-based computational methods to build, fit and refine protein structures inside the electron density maps. Going beyond a single stationary-structure determination scheme, it is becoming more common to interpret the experimental data with a set of multiple physical models all of which contributes to the average observation seen by the experiment. Hence, there is a need to decide on the quality of an ensemble of protein structures on-the-fly, while refining them against the density maps. In this work, we demonstrate such adaptive decision making capabilities during flexible fitting of biomolecules. Our solution uses RADICAL tools (RCT) and we test this new implementation in exascale high performance computing environment for two proteins, Adenylate Kinase (ADK) and Carbon Monoxide Dehydrogenase (CODH). Our results indicate that using multiple replicas in flexible fitting with adaptive decision making improves the overall quality of fit and model by 40 % improvement when compared against the traditional flexible fitting approach. These advances are agnostic to system-size and computing environments.

Monte Carlo Physarum Machine: Characteristics of Pattern Formation in Continuous Stochastic Transport Networks

We present Monte Carlo Physarum Machine (MCPM): a computational model suitable for reconstructing continuous transport networks from sparse 2D and 3D data. MCPM is a probabilistic generalization of Jones's (2010) agent-based model for simulating the growth of Physarum polycephalum (slime mold). We compare MCPM to Jones's work on theoretical grounds, and describe a task-specific variant designed for reconstructing the large-scale distribution of gas and dark matter in the Universe known as the cosmic web. To analyze the new model, we first explore MCPM's self-patterning behavior, showing a wide range of continuous network-like morphologies—called polyphorms—that the model produces from geometrically intuitive parameters. Applying MCPM to both simulated and observational cosmological data sets, we then evaluate its ability to produce consistent 3D density maps of the cosmic web. Finally, we examine other possible tasks where MCPM could be useful, along with several examples of fitting to domain-specific data as proofs of concept.

Thermal History of Matrix Forsterite Grains from Murchison Based on High-resolution Tomography

Protoplanetary disks are dust- and gas-rich structures surrounding protostars. Depending on the distance from the protostar, this dust is thermally processed to different degrees and accreted to form bodies of varying chemical compositions. The primordial accretion processes occurring in the early protoplanetary disk such as chondrule formation and metal segregation are not well understood. One way to constrain them is to study the morphology and composition of forsteritic grains from the matrix of carbonaceous chondrites. Here, we present high-resolution ptychographic X-ray nanotomography and multimodal chemical microtomography (X-ray diffraction and X-ray fluorescence) to reveal the early history of forsteritic grains extracted from the matrix of the Murchison CM2.5 chondrite. The 3D electron density maps revealed, at unprecedented resolution (64 nm), spherical inclusions containing Fe–Ni, very little silica-rich glass and void caps (i.e., volumes where the electron density is consistent with conditions close to vacuum) trapped in forsterite. The presence of the voids along with the overall composition, petrological textures, and shrinkage calculations is consistent with the grains experiencing one or more heating events with peak temperatures close to the melting point of forsterite (∼2100 K), and subsequently cooled and contracted, in agreement with chondrule-forming conditions.