Cervical Spinal Cord Atrophy can be Accurately Quantified Using Head Images

Background Spinal cord atrophy provides a clinically relevant metric for monitoring MS. However, the spinal cord is imaged far less frequently than brain due to artefacts and acquisition time, whereas MRI of the brain is routinely performed. Objective To validate spinal cord cross-sectional area measurements from routine 3DT1 whole-brain MRI versus those from dedicated cord MRI in healthy controls and people with MS. Methods We calculated cross-sectional area at C1 and C2/3 using T2*-weighted spinal cord images and 3DT1 brain images, for 28 healthy controls and 73 people with MS. Correlations for both groups were assessed between: (1) C1 and C2/3 using cord images; (2) C1 from brain and C1 from cord; and (3) C1 from brain and C2/3 from cord. Results and Conclusion C1 and C2/3 from cord were strongly correlated in controls ( r = 0.94, p<0.0001) and MS ( r = 0.85, p<0.0001). There was strong agreement between C1 from brain and C2/3 from cord in controls ( r = 0.84, p<0.0001) and MS ( r = 0.81, p<0.0001). This supports the use of C1 cross-sectional area calculated from brain imaging as a surrogate for the traditional C2/3 cross-sectional area measure for spinal cord atrophy.

Results of Implementing Less-Favoured Area Subsidies in the 2014–2020 Time Frame: Are the Measures of Environmental Concern Complementary?

This article collated and interpreted data on land-based measures and capital investments support in Central Europe. Data collection is essential, due to more EU funds being distributed after 2020. Most evaluations focus on outcomes of agri-environmental measures within the most populous Member States. Unlike previous work, empirical data was hereby assessed on the public support of three measures of environmental concern to farmers (less favored area measure, capital investments, and agri-environmental measures). The study examined whether public goods are complementary. A prevailing focus was on spending for the farms in less favored areas, now renamed Areas of Natural Constraint (ANC). Cluster analysis was employed for seven countries out of 105 rural development programs (RDPs). The Countries include Austria, Czech Republic, Germany, Hungary, Poland, Slovakia, and Slovenia. The average share of the wider ANC measures was 0.31 of the total public funds. Within Central Europe, Hungary sets aside the smallest share (0.17), whereas the Czech Republic and Austria introduced a slightly larger share (0.51). All RDPs identified Capital Investments in physical assets as one of the significant measures. The study found that public goods for biodiversity and landscapes were entailed in the ANC measures, although the extent of measure complementarity across all regions will require further investigation. Finally, unresolved questions about expenditure are highlighted.

How Do COVID-19 Inpatients in the Denver Metropolitan Area Measure Up?

Background. Inpatient data for COVID-19 (SARS-CoV-2) afflicted inpatients remain sparse. Data are needed to create accurate projections for resource consumption as the pandemic continues. Published reports of inpatient data have come from China, Italy, Singapore, and both the East and West coasts of the United States. Objective. The objective is to present our inpatient experience with COVID-19. Design, Setting, and Participants. This is a retrospective study of 681 patients with laboratory-confirmed COVID-19 from six hospitals in the Denver metropolitan area admitted between February 18 and April 30, 2020. Clinical outcomes of patients discharged or expired by April 30, 2020, were analyzed. Main Outcomes. We compiled patient demographics, length of stay, number of patients transferred to or admitted to the ICU, ICU length of stay, mechanical ventilation requirements, and mortality rates. Results. Of the 890 patients with laboratory-confirmed COVID-19, 681 had discharged and were included in this analysis. We observed 100% survival of the 0–18 age group (n = 2), 97% survival of the 19–30 age group, 95% survival of the 31–64 age group, 79% survival of the 65–84 age group, and 75% survival of the 85 and older age group. Our total inpatient mortality was 13% (91 patients), rising to 29% (59 patients) for those requiring ICU care. Conclusions. Compared to similar reports from other metropolitan areas, our analysis of discharged or expired COVID-19 patients from six major hospitals in the Denver metropolitan area revealed a lower mortality. This includes the subset of patients admitted to the ICU regardless of the need for intubation. A lower ICU length of stay was also observed.

GEOSPATIAL VALUATION OF URBAN FARMING IN IMPROVING CITIES RESILIENCE: A CASE OF MALANG CITY, INDONESIA

Urban farming is recently acknowledged as a strategy with various services in improving cities resilience but facing cons such as land competition and rapid urbanization. The study attempts to inventory available areas for urban farming implementation and estimate the total values with case study in Malang city, Indonesia. The study divided urban farming into five forms i.e. nursery, allotment, residential, institutional and rooftop farming based on its characteristics. Land inventory has been done by estimating existing and potential areas. Existing area was manually delineated by Field Area Measure App through field visit and visualized by ArcGIS. Potential area was identified through geospatial assessment considering land use and land cover map provided by the Government of Indonesia and parcel zoning based on Guideline of Urban Farming development and literatures. The study employed Contingent Valuation Method (CVM) and Market Price Method to estimate total values of urban farming. Currently there is 1.38 ha of urban farming which is equal to 0.01 % of city’s area distributed in 21 plots and 211.46 ha potential area or equal to 1.92 % of city’s area. Urban farming has services for amount of US$ 28.68 m−2 yr−1, specifically 22.86, 3.60, 0.80, 1.10 and 0.34 US$ m−2 yr−1 in terms of provisioning food; income generation; recreation and community building; education and learning; and maintenance urban comfort, respectively. If existing and potential area used for urban farming, then it could contribute to US$ 395,095.68 annually for existing and potentially up to US$ 60,646,800.35 annually for entire city.

Development of Food Agriculture Based on Superiority Cultivation Area in Tanjung Api-Api, Banyuasin District

Leading commodity development area is a developed area for one or a combination of superior commodities that comply with the land use characteristics that provide the highest farm household's income, communities, and the region. This research was aimed to determine and describe the factors which have a significant effect on the superiority of Tanjung Api-Api (TAA) area, measure and analyze of the superiority of TAA cultivation area for food crops,and formulate a grand strategy matrix for food crops development based on its regional. The descriptive methods, Policy Analysis Matrix (PAM), and Grand Strategy Analysis. The factors which affect the superiority of TAA cultivation area include ecological, socio-cultural, and economic factors. The result shows, rice and corn plants included in the SI category or suitable for cultivation in the TAA region with Soil Mapping Unit (SPT) value of 20.70%. Socio-cultural aspects that affect the development of food agriculture such as the labors, Village Unit Cooperative (KUD), and Farmers Group that support agricultural development. Rice and corn crops have 0.69 and 0.97 on the competitive advantage (PCR) value respectively, therefore the rice and corn plants are concluded to have a competitive advantage. The comparative advantages (DRCR) value shows 0.68 and 0.65 for rice and corn plants respectively, which have comparative advantages and have the potential to develop in TAA area. Food crops in the TAA area are in a good competitive position. The strategy that must be applied under these conditions is to support an aggressive growth policy (Growth-oriented strategy).

Methods of crop yield measurement on multi-cropped plots: Examples from Tanzania

Precise agricultural statistics are necessary to track productivity and design sound agricultural policies. Yet, in settings where multi-cropping is prevalent, even crop yield—perhaps the most common productivity metric—can be challenging to measure. In a survey of the literature on crop yield in low-income settings, we find that scholars specify how they estimate the area denominator used to measure yield in under 10% of cases. Using household survey data from Tanzania, we consider four alternative methods of allocating land area on multi-cropped plots, ranging from treatment of the entire plot as the yield denominator to increasingly precise approaches that account for the space taken up by other crops. We then explore the implications of this measurement decision for analyses of yield, focusing on one staple crop that is often grown on its own (rice) and one that is frequently found on mixed plots and in intercropped arrangements (maize). A majority (64%) of cultivated plots contain more than one crop, and average yield estimates vary with different methods of calculating area planted—particularly for maize. Importantly, the choice among area methods influences which of these two crops is found to be more calorie-productive per hectare. This choice also influences the statistically significant correlates of crop yield, such that the benefits of intercropping and including legumes on a maize plot are only evident when using an area measure that accounts for mixed cropping arrangements. We conclude that the literature would benefit from greater clarity regarding how yield is measured across studies.

The Tutte Embedding of the Poisson–Voronoi Tessellation of the Brownian Disk Converges to $$\sqrt{8/3}$$-Liouville Quantum Gravity

Recent works have shown that an instance of a Brownian surface (such as the Brownian map or Brownian disk) a.s. has a canonical conformal structure under which it is equivalent to a $$\sqrt{8/3}$$8/3-Liouville quantum gravity (LQG) surface. In particular, Brownian motion on a Brownian surface is well-defined. The construction in these works is indirect, however, and leaves open a basic question: is Brownian motion on a Brownian surface the limit of simple random walk on increasingly fine discretizations of that surface, the way Brownian motion on $$\mathbb {R}^2$$R2 is the $$\epsilon \rightarrow 0$$ϵ→0 limit of simple random walk on $$\epsilon \mathbb {Z}^2$$ϵZ2? We answer this question affirmatively by showing that Brownian motion on a Brownian surface is (up to time change) the $$\lambda \rightarrow \infty $$λ→∞ limit of simple random walk on the Voronoi tessellation induced by a Poisson point process whose intensity is $$\lambda $$λ times the associated area measure. Among other things, this implies that as $$\lambda \rightarrow \infty $$λ→∞ the Tutte embedding (a.k.a. harmonic embedding) of the discretized Brownian disk converges to the canonical conformal embedding of the continuum Brownian disk, which in turn corresponds to $$\sqrt{8/3}$$8/3-LQG. Along the way, we obtain other independently interesting facts about conformal embeddings of Brownian surfaces, including information about the Euclidean shapes of embedded metric balls and Voronoi cells. For example, we derive moment estimates that imply, in a certain precise sense, that these shapes are unlikely to be very long and thin.

Given the problem of projection, are heat maps an oxymoron?

<p><strong>Abstract.</strong> With the proliferation of data analysis and visualization tools, we see more and more heat maps. But should we? Are such displays meaningful? At large scales, heat maps need not be controversial (though common tools can blight even simple cases). But what about small-scale maps? Is anyone thinking about the effects of projection on heat maps? How do map projections change the semantics of heat maps? What projections permit meaningful heat maps? How should heat maps be calculated in the presence of a map projection? We explore these problems and questions in this presentation to offer critique and advice.</p><p>For the purposes of this discussion, a heat map is a representation of the density or magnitude of a spatial phenomenon on two dimensions, treating the density or magnitude as a continuous measure whether or not the underlying phenomenon is continuous. If the data are too sparse in the presentation space, then the fiction of continuity ought to be avoided; a heat map would not be an appropriate visualization. While real world examples of heat maps that violate this principle are easy to find, we take the principle for granted and do not elaborate further here.</p><p>Unfortunately, there are several other ways to construct ineffective heat maps. One of the primary offenses is to ignore the effect of map projection on the presentation of density. It should be clear that a projection whose area measure varies widely across the presentation space necessarily distorts density. If the heat map is a presentation of density &amp;ndash; which most are &amp;ndash; then poor choice of projection would contradict the purpose of a heat map. The result would be a blatant fiction. Surprisingly, the Mercator projection often can be found in small scale heat maps, for the reason that the projection is common, is the default in many sets of tooling, and is sometimes the only projection available with the set of map construction tools. And yet, as far as density variation goes, a worse case than Mercator cannot be found among common projections.</p><p>Even if density remains constant across the map, a poor heat map could be generated if the analysis for the heat map mixes phenomenon space, which is geographic, with projected space, which is not. Common tools commit this fallacy. The result is that a phenomenon whose density diminishes radially (for example) from a hot point might show as concentric circles of decreasing intensity on the projected map, whereas we would expect elongations of the heat field in accordance with the projection’s distortion metric.</p><p>We conclude that, while it is possible to construct responsible heat maps of geographic data, there are several pitfalls. Among these pitfalls, we find that common tools conspire to assist in the presentation of fiction instead of fact.</p>