Designing water infrastructure and context-responsive housing: a case study in the Sabana de Bogotá.

The flood prone areas and agricultural soils along the Bogotá River in Colombia face a continuously increasing conflict between urban development, amongst others by low-cost housing projects, and environmental needs. This research investigates how current contested relations between water, settlement patterns, and productive landscapes can be turned into a constructive interplay. This paper presents a water urbanism research project, which uses interpretative mapping and research by design to critically understand the evolution of the relationship between water and settlements in the peri-urban areas of Funza and Mosquera. The project landscape typologies for adaptation and mitigation in view of climate change and to address demands of urbanization in the Bogotá River floodplain. The paper demonstrates how designing with water can re-qualify the peripheral areas of Bogotá, solving both qualitative and quantitative water issues, delivering a framework for new housing fabrics, and creating new sustainable relations between different water uses

A transcriptome-driven analysis of epithelial brushings and bronchial biopsies to define asthma phenotypes in U-BIOPRED

RATIONALE AND OBJECTIVES: Asthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms. We used transcriptomic profiling of airway tissues to help define asthma phenotypes. METHODS: The transcriptome from bronchial biopsies and epithelial brushings of 107 moderate-to-severe asthmatics were annotated by gene-set variation analysis (GSVA) using 42 gene-signatures relevant to asthma, inflammation and immune function. Topological data analysis (TDA) of clinical and histological data was used to derive clusters and the nearest shrunken centroid algorithm used for signature refinement. RESULTS: 9 GSVA signatures expressed in bronchial biopsies and airway epithelial brushings distinguished two distinct asthma subtypes associated with high expression of T-helper type 2 (Th-2) cytokines and lack of corticosteroid response (Group 1 and Group 3). Group 1 had the highest submucosal eosinophils, high exhaled nitric oxide (FeNO) levels, exacerbation rates and oral corticosteroid (OCS) use whilst Group 3 patients showed the highest levels of sputum eosinophils and had a high BMI. In contrast, Group 2 and Group 4 patients had an 86% and 64% probability of having non-eosinophilic inflammation. Using machine-learning tools, we describe an inference scheme using the currently-available inflammatory biomarkers sputum eosinophilia and exhaled nitric oxide levels along with OCS use that could predict the subtypes of gene expression within bronchial biopsies and epithelial cells with good sensitivity and specificity. CONCLUSION: This analysis demonstrates the usefulness of a transcriptomic-driven approach to phenotyping that segments patients who may benefit the most from specific agents that target Th2-mediated inflammation and/or corticosteroid insensitivity

The role of inflammation in anxiety and depression in the European U-BIOPRED asthma cohorts

BACKGROUND: Growing evidence indicates high comorbid anxiety and depression in patients with asthma. However, the mechanisms underlying this comorbid condition remain unclear. The aim of this study was to investigate the role of inflammation in comorbid anxiety and depression in three asthma patient cohorts of the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) project. METHODS: U-BIOPRED was conducted by a European Union consortium of 16 academic institutions in 11 European countries. A subset dataset from subjects with valid anxiety and depression measures and a large blood biomarker dataset were analysed, including 198 non-smoking patients with severe asthma (SAn), 65 smoking patients with severe asthma (SAs), 61 non-smoking patients with mild-to-moderate asthma (MMA), and 20 healthy non-smokers (HC). The Hospital Anxiety and Depression Scale was used to measure anxiety and depression and a series of inflammatory markers were analysed by the SomaScan v3 platform (SomaLogic, Boulder, Colo). ANOVA and the Kruskal-Wallis test were used for multiple-group comparisons as appropriate. RESULTS: There were significant group effects on anxiety and depression among the four cohort groups (p < 0.05). Anxiety and depression of SAn and SAs groups were significantly higher than that of MMA and HC groups (p < 0.05. There were significant differences in serum IL6, MCP1, CCL18, CCL17, IL8, and Eotaxin among the four groups (p < 0.05). Depression was significantly associated with IL6, MCP1, CCL18 level, and CCL17; whereas anxiety was associated with CCL17 only (p < 0.05). CONCLUSIONS: The current study suggests that severe asthma patients are associated with higher levels of anxiety and depression, and inflammatory responses may underlie this comorbid condition

gViz, a novel tool for the visualization of co-expression networks

<p>Abstract</p> <p>Background</p> <p>The quantity of microarray data available on the Internet has grown dramatically over the past years and now represents millions of Euros worth of underused information. One way to use this data is through co-expression analysis. To avoid a certain amount of bias, such data must often be analyzed at the genome scale, for example by network representation. The identification of co-expression networks is an important means to unravel gene to gene interactions and the underlying functional relationship between them. However, it is very difficult to explore and analyze a network of such dimensions. Several programs (Cytoscape, yEd) have already been developed for network analysis; however, to our knowledge, there are no available GraphML compatible programs.</p> <p>Findings</p> <p>We designed and developed gViz, a GraphML network visualization and exploration tool. gViz is built on clustering coefficient-based algorithms and is a novel tool to visualize and manipulate networks of co-expression interactions among a selection of probesets (each representing a single gene or transcript), based on a set of microarray co-expression data stored as an adjacency matrix.</p> <p>Conclusions</p> <p>We present here gViz, a software tool designed to visualize and explore large GraphML networks, combining network theory, biological annotation data, microarray data analysis and advanced graphical features.</p

PathEx: a novel multi factors based datasets selector web tool

<p>Abstract</p> <p>Background</p> <p>Microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science phenomena are reduced. The accumulation of publicly available data provides biomedical researchers with a valuable opportunity to either discover new phenomena or improve the interpretation and validation of other phenomena that partially understood or well known. This can only be achieved by intelligently exploiting this rich mine of information.</p> <p>Description</p> <p>Considering that technologies like microarrays remain prohibitively expensive for researchers with limited means to order their own experimental chips, it would be beneficial to re-use previously published microarray data. For certain researchers interested in finding gene groups (requiring many replicates), there is a great need for tools to help them to select appropriate datasets for analysis. These tools may be effective, if and only if, they are able to re-use previously deposited experiments or to create new experiments not initially envisioned by the depositors. However, the generation of new experiments requires that all published microarray data be completely annotated, which is not currently the case. Thus, we propose the PathEx approach.</p> <p>Conclusion</p> <p>This paper presents PathEx, a human-focused web solution built around a two-component system: one database component, enriched with relevant biological information (expression array, omics data, literature) from different sources, and another component comprising sophisticated web interfaces that allow users to perform complex dataset building queries on the contents integrated into the PathEx database.</p

Towards analytical typologies of plot systems: quantitative profile of five European cities

The importance of the plot (also referred to as ‘property’) as one of the fundamental elements of urban form is well recognized within the field of urban morphology. Despite the fact that it is often described as the basic element in the pattern of land divisions, which are essential as organizational frameworks for urban form, studies offering comprehensive descriptions and classifications of plot systems are quite scant. The aim of the paper is to introduce a classification of plot systems into typologies based on five European cities, in order to distinguish particular spatial differences and similarities in terms of their plot structure. The proposed typologies are developed using unsupervised k-means cluster analysis based on numeric attributes derived from central theories in urban morphology. The introduced typologies are essentially configurational, allowing collective systematic properties of plot systems to be captured. Numeric attributes include plot differentiation (or plot size), plot frontage and compactness ratio, corresponding to essential qualities of plot systems such as the capacity to carry differences in space, the ability to operate as interface between street and building and providing a framework for evolution of built form over time. All three attributes are translated into configurational measures in order to capture the context of the plot system, rather than the parameters of individual plots. The combination of these deductively defined variables with algorithmically defined classification methods results in seven plot types that can be used to scale up traditional urban morphological analysis to whole city regions and conduct substantial comparison of patterns within, but also between these regions. Further, it also makes it possible to describe commonly recognized plot patterns and discover new ones

SARS-CoV-2 is transmitted via contact and via the air between ferrets

SARS-CoV-2, a coronavirus that emerged in late 2019, has spread rapidly worldwide, and information about the modes of transmission of SARS-CoV-2 among humans is critical to apply appropriate infection control measures and to slow its spread. Here we show that SARS-CoV-2 is transmitted efficiently via direct contact and via the air (via respiratory droplets and/or aerosols) between ferrets, 1 to 3 days and 3 to 7 days after exposure respectively. The pattern of virus shedding in the direct contact and indirect recipient ferrets is similar to that of the inoculated ferrets and infectious virus is isolated from all positive animals, showing that ferrets are productively infected via either route. This study provides experimental evidence of robust transmission of SARS-CoV-2 via the air, supporting the implementation of community-level social distancing measures currently applied in many countries in the world and informing decisions on infection control measures in healthcare settings