Patterns of exposure to SARS-CoV-2 carriers manifest multiscale association between urban landscape morphology and human activity

The outbreak of the Coronavirus disease 2019 (COVID-19), and the drastic measures taken to mitigate its spread through imposed social distancing, have brought forward the need to better understand the underlying factors controlling spatial distribution of human activities promoting disease transmission. Focusing on results from 17,250 epidemiological investigations performed during early stages of the pandemic outbreak in Israel, we show that the distribution of carriers of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes COVID-19, is spatially correlated with two satellite-derived surface metrics: night light intensity and landscape patchiness, the latter being a measure to the urban landscape’s scale-dependent spatial heterogeneity. We find that exposure to SARS-CoV-2 carriers was significantly more likely to occur in “patchy” parts of the city, where the urban landscape is characterized by high levels of spatial heterogeneity at relatively small, tens of meters scales. We suggest that this spatial association reflects a scale-dependent constraint imposed by the city’s morphology on the cumulative behavior of the people inhabiting it. The presented results shed light on the complex interrelationships between humans and the urban landscape in which they live and interact, and open new avenues for implementation of multi-satellite data in large scale modeling of phenomena centered in urban environments.

RADT-12. USE OF FDOPA PET FOR RADIATION THERAPY TARGETING IN GRADE 2 IDH MUTANT GLIOMA

BACKGROUND Low-grade, IDH mutant (IDHmt) gliomas typically do not enhance on MRI complicating radiotherapy (RT) target delineation. Amino acid PET using 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (FDOPA) has demonstrated avidity in IDHmt gliomas and may assist in RT planning for non-enhancing tumors. This study aims to compare conventional and FDOPA-defined target volumes in grade 2 IDHmt gliomas. METHODS In a prospective pilot study, patients underwent MRI and FDOPA PET using a 3T MRI/PET system followed by standard therapy. Gross tumor volumes (GTV) included the T2/FLAIR abnormality and surgical cavity; clinical target volumes (CTV) included a 1 cm expansion constrained anatomically. Metabolic target volumes (MTVs) were generated using the FDOPA SUV > 1.5-fold normal brain isocurve. Union of GTV and MTV generated a fusion GTV (fGTV); expanding fGTV by 1 cm yielded the fusion CTV (fCTV). Target volumes were compared volumetrically with overlap (Dice coefficient) and surface metrics (Hausdorff distance). Medians are reported with ranges. RESULTS Four patients with grade 2 IDHmt glioma (3 1p/19q codeleted oligodendrogliomas, 1 non-codeleted astrocytoma) received MRI/PET before treatment. All oligodendrogliomas exhibited FDOPA avidity; the astrocytoma showed no avidity. GTV and CTV measured 16.1 cc (4.9 - 82.2 cc) and 76.7 cc (29.5 - 256.1 cc), respectively. The MTV volume outside of GTV was 0.8 cc (0.2 – 6.1 cc), but was covered in each case by the CTV. Addition of FDOPA increased fGTV and fCTV volumes by 5.4% and 17.5%, respectively. Dice coefficient and Hausdorff distances for GTV vs fGTV were 0.96 (0.95 - 0.99) and 11.2 mm (10.0 – 11.9 mm), respectively, and for CTV vs fCTV were 0.87 (0.81 – 0.95) and 10.2 mm (10.0 - 11.0), respectively. CONCLUSIONS FDOPA PET identified tracer-avid regions outside of MRI-defined GTVs in a group of IDHmt gliomas. FDOPA PET provides useful metabolic information for RT planning and warrants further investigation.

Open-source Tools in R for Landscape Ecology

Purpose of Review Landscape ecology, the study of the complex interactions between landscapes and ecological processes, has hugely benefited from the increase in widely available open-source software in recent years. In particular, the R programming language provides a wealth of community developed tools for landscape ecology. Recent Findings In this paper, we examine existing packages for downloading, processing and visualisation of spatial data, as well as those specifically developed for spatial ecological analysis. Additionally, we outline the results of a survey of R users within the landscape ecology community. Summary We found that landscape ecologists are generally satisfied with the functionality available within R, and that as a community they are continually further developing the functionality available. Suggested future developments include improvement of computation performance; additional methods for landscape characterisation such as surface metrics; and advanced, accessible visualisation tools.

Mapping and statistical procedures applied to extract topographical features of fluvial terraces and erosive surfaces.

<p>In recent decades, a huge advance in data collection has favoured the study of many questions related to geomorphic processes and associated landforms (Viles, 2016). This increment in data collection let face new questions and develop new methodologies in Geomorphology (Sofia et al., 2020). In this way, geomorphometry is a challenging discipline with the objective of quantify land-surface analysis and extract as well as detect geomorphological elements (Guyon and Elisseeff, 2008). As result, this discipline complement classis geomorphological maps with others extracted from digital elevation models providing land-surface metrics to investigate the full spectrum of geomorphology (Seijmonsbergen et al., 2011).</p><p>In this study, we investigate the extraction of topographical features from fluvial terraces and erosive surfaces by means of mapping procedures applied to a digital elevation model (spatial resolution of 5x5 m) using ArcGIS 10.7. This procedure was focussed on the quantification of elements (altitude, slope angle, length, and curvature, exposure) to characterize morphological elements that may define the presence of fluvial terraces as well as erosive surfaces. A Principal Component Analysis was performed to validate that procedure.</p><p>The procedure was conducted in two study areas: detection of fluvial terraces from the watershed of Guadalmedina river as well as of erosive surfaces in Sierras Subbéticas Geopark, both areas located in southern Spain.</p>

Roughness Parameters for Classification of As-Built AM Surfaces

One of the challenges facing the industrial adoption of additively manufactured parts is the surface roughness on the as-built part. The surface roughness of parts is frequently characterized by metrics specified by international standards organizations. However, these standards list many surface metrics that can make it unclear which to use to best describe the surface. In this work, the ability of the various surface metrics to successfully classify the as-built and post-processed surfaces is studied using linear classification models. Laser polishing via remelting and manual grinding are the post-processing techniques used to smooth the as-built surface. The ability of the linear classifier to successfully categorize the various surfaces is demonstrated, and the various surface metrics are ranked according to the strength of their individual ability to classify the surfaces. This work promotes the method as a potential way to autonomously classify as-built and laser polished surfaces.

The Heritability of Cortical Folding: Evidence from the Human Connectome Project

The mechanisms underlying cortical folding are incompletely understood. Prior studies have suggested that individual differences in sulcal depth are genetically mediated, with deeper and ontologically older sulci more heritable than others. In this study, we examine FreeSurfer-derived estimates of average convexity and mean curvature as proxy measures of cortical folding patterns using a large (N = 1096) genetically informative young adult subsample of the Human Connectome Project. Both measures were significantly heritable near major sulci and primary fissures, where approximately half of individual differences could be attributed to genetic factors. Genetic influences near higher order gyri and sulci were substantially lower and largely nonsignificant. Spatial permutation analysis found that heritability patterns were significantly anticorrelated to maps of evolutionary and neurodevelopmental expansion. We also found strong phenotypic correlations between average convexity, curvature, and several common surface metrics (cortical thickness, surface area, and cortical myelination). However, quantitative genetic models suggest that correlations between these metrics are largely driven by nongenetic factors. These findings not only further our understanding of the neurobiology of gyrification, but have pragmatic implications for the interpretation of heritability maps based on automated surface-based measurements.

Patterns of exposure to SARS-CoV-2 carriers manifest multiscale association between urban landscape morphology and human activity

The outbreak of the Coronavirus disease 2019 (COVID-19), and the drastic measures taken to mitigate its spread through imposed social distancing, have brought forward the need to better understand the underlying factors controlling spatial distribution of human activities promoting disease transmission. Focusing on results from 17,250 epidemiological investigations performed during early stages of the pandemic outbreak in Israel, we show that the distribution of carriers of the respiratory severe acute syndrome coronavirus-2 (SARS-CoV-2), which causes COVID-19, is spatially correlated with two satellite-derived surface metrics: night light intensity and landscape patchiness, the latter being a measure to the urban landscape’s scale-dependent spatial heterogeneity. We find that exposure to SARS-CoV-2 carriers was significantly more likely to occur in “patchy” parts of the city, where the urban landscape is characterized by high levels of spatial heterogeneity at relatively small scales (~10-100m). We suggest that this spatial association reflects a scale-dependent constraint imposed by the city’s morphology on the cumulative behavior of the people inhabiting it. The presented results shed light on the complex interrelationships between humans and the urban landscape in which they live and interact, and open new avenues for implementation of multi-satellite data in large scale modeling of phenomena centered in urban environments.