The role of earth observation in an integrated deprived area mapping “system” for low-to-middle income countries

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups

Adjuvant effects of a sequence-engineered mRNA vaccine: translational profiling demonstrates similar human and murine innate response

Additional file 1: Fig. S1. Components and assembly of the PTE module for innate responses. (A) Top panel—in vivo monocytes continuously emigrate from the blood into peripheral tissues with a half-life in the blood of ~1 day. Bottom panel: In vitro generation of cytokine derived DCs from monocytes involves the addition of IL-4 and GM-CSF and culture for 7–11 days. (B) Schematic of the components of a MIMIC-PTE module. In the 3D PTE, differentiation occurs in hours to about 2 days triggered by migration into and out of (reverse migration) through the endothelium: a process reminiscent of the movement of cells from tissues into lymphatic vessels

Novel Common Genetic Susceptibility Loci for Colorectal Cancer

BACKGROUND: Previous genome-wide association studies (GWAS) have identified 42 loci (P < 5 × 10-8) associated with risk of colorectal cancer (CRC). Expanded consortium efforts facilitating the discovery of additional susceptibility loci may capture unexplained familial risk. METHODS: We conducted a GWAS in European descent CRC cases and control subjects using a discovery-replication design, followed by examination of novel findings in a multiethnic sample (cumulative n = 163 315). In the discovery stage (36 948 case subjects/30 864 control subjects), we identified genetic variants with a minor allele frequency of 1% or greater associated with risk of CRC using logistic regression followed by a fixed-effects inverse variance weighted meta-analysis. All novel independent variants reaching genome-wide statistical significance (two-sided P < 5 × 10-8) were tested for replication in separate European ancestry samples (12 952 case subjects/48 383 control subjects). Next, we examined the generalizability of discovered variants in East Asians, African Americans, and Hispanics (12 085 case subjects/22 083 control subjects). Finally, we examined the contributions of novel risk variants to familial relative risk and examined the prediction capabilities of a polygenic risk score. All statistical tests were two-sided. RESULTS: The discovery GWAS identified 11 variants associated with CRC at P < 5 × 10-8, of which nine (at 4q22.2/5p15.33/5p13.1/6p21.31/6p12.1/10q11.23/12q24.21/16q24.1/20q13.13) independently replicated at a P value of less than .05. Multiethnic follow-up supported the generalizability of discovery findings. These results demonstrated a 14.7% increase in familial relative risk explained by common risk alleles from 10.3% (95% confidence interval [CI] = 7.9% to 13.7%; known variants) to 11.9% (95% CI = 9.2% to 15.5%; known and novel variants). A polygenic risk score identified 4.3% of the population at an odds ratio for developing CRC of at least 2.0. CONCLUSIONS: This study provides insight into the architecture of common genetic variation contributing to CRC etiology and improves risk prediction for individualized screenin

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

XVI International Congress of Control Electronics and Telecommunications: "Techno-scientific considerations for a post-pandemic world intensive in knowledge, innovation and sustainable local development"

Este título, sugestivo por los impactos durante la situación de la Covid 19 en el mundo, y que en Colombia lastimosamente han sido muy críticos, permiten asumir la obligada superación de tensiones sociales, políticas, y económicas; pero sobre todo científicas y tecnológicas. Inicialmente, esto supone la existencia de una capacidad de la sociedad colombiana por recuperar su estado inicial después de que haya cesado la perturbación a la que fue sometida por la catastrófica pandemia, y superar ese anterior estado de cosas ya que se encontraban -y aún se encuentran- muchos problemas locales mal resueltos, medianamente resueltos, y muchos sin resolver: es decir, habrá que rediseñar y fortalecer una probada resiliencia social existente - producto del prolongado conflicto social colombiano superado parcialmente por un proceso de paz exitoso - desde la tecnociencia local; como lo indicaba Markus Brunnermeier - economista alemán y catedrático de economía de la Universidad de Princeton- en su libro The Resilient Society…La cuestión no es preveerlo todo sino poder reaccionar…aprender a recuperarse rápido.This title, suggestive of the impacts during the Covid 19 situation in the world, and which have unfortunately been very critical in Colombia, allows us to assume the obligatory overcoming of social, political, and economic tensions; but above all scientific and technological. Initially, this supposes the existence of a capacity of Colombian society to recover its initial state after the disturbance to which it was subjected by the catastrophic pandemic has ceased, and to overcome that previous state of affairs since it was found -and still is find - many local problems poorly resolved, moderately resolved, and many unresolved: that is, an existing social resilience test will have to be redesigned and strengthened - product of the prolonged Colombian social conflict partially overcome by a successful peace process - from local technoscience; As Markus Brunnermeier - German economist and professor of economics at Princeton University - indicates in his book The Resilient Society...The question is not to foresee everything but to be able to react...learn to recover quickly.Bogot

How Bayesian modelling could use the Big Tech competition in producing built-up maps: predicting population in data-scarce contexts

Satellite-imagery derived products represent an exciting opportunity to map and estimate current population with high spatial precision in context where traditional demographic data is not available. Bayesian methods have been developped to harness the potential of built-up maps as a proxy for human settlements, thereby predicting population sizes of hard-to-reach areas across sub-saharan Africa. The increased availibility of high-resolution satellite imagery has fostered the competition between high-profile institutions to produce global-scale built-up maps. Given the key role of built-up maps to estimate population sizes we need to understand (1) how do the different sources impact population predictions, (2) how do they compare with human-made maps and finally (3) how can they be articulated together

Modelling urban deprivation in Kinshasa, DRC

We modelled and predicted an urban deprivation score for the 23 communes of Kinshasa province and stored in urban_deprivation.tif. It is the outcome of a confirmatory factor analysis stored in code_simplified.R that combined four geospatial covariates stored in the covariates folder: distance to river and residential road density in a 1km window derived from OpenStreetMap and building landscape shape and building area coefficient of variation derived from a building footprint layer. The model structure was validated through several metrics that indicate a good reproduction of the correlation matrix between the covariates at study sites. The predicted gridded score was then compared with qualitative information collected from the litterature. We provide for comparison this mapped information in qualitative_info.shp where the source is stored in the attribute table and matches the bibliography stored in qualitative_info_source.txt.</span

Statistical population modelling for census support

This github repo contains the raw teaching materials for the Statistical Population Modelling for Census Support workshop, funded by the United Nations Population Fund. It has been developed by the WorldPop Research Group, University of Southampton.The repo consists in a series of tutorials in Bayesian statistics for population modelling with hands-on experience. It includes example code and other resources designed to expedite the learning curve.The key concepts that are covered in the tutorial series include: Introduction to software for Bayesian statistical modelling: R and Stan, Simple linear regression in a Bayesian context, Random effects to account for settlement type (e.g. urban/rural) and other types of stratification in survey data, Quantifying and mapping uncertainties in population estimates and Diagnostics to evaluate model performance (e.g. cross-validation).It has been first taught to the Brazilian Stats Office, Instituto Brasileiro de Geografia e Estatística (IBGE), in October 2021

A bottom-up population modelling approach to complement the population and housing census

Population and housing censuses provide essential demographic information for local, national and international decision-making and response. However, census data in the most vulnerable countries are often outdated or partial because political instability, conflict and natural disasters prevent a nationwide enumeration. The bottom-up modelling approach complements outdated or incomplete census data by estimating population counts and age/sex structures in grid cells of about 100 m using required population data on a set of fully enumerated locations and auxiliary geospatial covariates. We present the modelling effort in the Democratic Republic of Congo - the last census was conducted in 1984 - and in Burkina Faso - the last census was conducted in 2020 but covered only 70% of the country. Both models showed good predictive performance, denoted by R2 values of 0.73 and 0.63 for the respective out-of-sample predictions of population counts. The resulting bottom-up and gridded population estimates are currently used for census support and humanitarian response in both countries. This work has highlighted the flexibility of the bottom-up modelling approach, in terms of input population data, model specification and aggregation of population estimates to support specific use cases

High-resolution population mapping and estimation in the western part of the Democratic Republic of Congo

According to the United Nations, the Democratic Republic of Congo (DRC) is the fourth most populous country in Africa. Yet, accurate demographic figures are currently unavailable because the last national census was conducted over three decades ago. This situation has shown to be critical to policymaking and intervention planning in the domain of public health. In the western part of the country, for instance, human African trypanosomiasis is highly endemic but accurate sub-national population figures are required for effective disease mapping. This work aims at producing gridded population estimates with a 100 meters spatial resolution for the five most western provinces of DRC. In doing so, we adopt a bottom-up modeling approach incorporating demographic data from recent micro-census surveys and various geospatial datasets in a Bayesian hierarchical framework. The resulting high-resolution population estimates and associated uncertainty measures are meant to inform spatial epidemiological applications in the region and foster the use of similar modeling approaches in other parts of the country