COVID-19 and the case for global development.

COVID-19 accentuates the case for a global, rather than an international, development paradigm. The novel disease is a prime example of a development challenge for all countries, through the failure of public health as a global public good. The COVID-19 pandemic has highlighted the falsity of any assumption that the global North has all the expertise and solutions to tackle global challenges, and has further highlighted the need for multi-directional learning and transformation in all countries towards a more sustainable and equitable world. We illustrate our argument for a global development paradigm by examining the implications of the COVID-19 pandemic across four themes or 'vignettes': global value chains, digitalisation, debt, and climate change. We conclude that development studies must adapt to a very different context from when the field emerged in the mid-20th century.

Brain Data Standards - A method for building data-driven cell-type ontologies.

Large-scale single-cell 'omics profiling is being used to define a complete catalogue of brain cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem: How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a partial solution to these problems, but no existing ontology schemas support the definition of cell types by direct reference to supporting data, classification of cell types using classifications derived directly from data, or links from cell types to marker sets along with confidence scores. Here we describe a generally applicable schema that solves these problems and its application in a semi-automated pipeline to build a data-linked extension to the Cell Ontology representing cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole-brain atlases currently in preparation.

Representing Normal and Abnormal Physiology as Routes of Flow in ApiNATOMY.

We present (i) the ApiNATOMY workflow to build knowledge models of biological connectivity, as well as (ii) the ApiNATOMY TOO map, a topological scaffold to organize and visually inspect these connectivity models in the context of a canonical architecture of body compartments. In this work, we outline the implementation of ApiNATOMY's knowledge representation in the context of a large-scale effort, SPARC, to map the autonomic nervous system. Within SPARC, the ApiNATOMY modeling effort has generated the SCKAN knowledge graph that combines connectivity models and TOO map. This knowledge graph models flow routes for a number of normal and disease scenarios in physiology. Calculations over SCKAN to infer routes are being leveraged to classify, navigate and search for semantically-linked metadata of multimodal experimental datasets for a number of cross-scale, cross-disciplinary projects.

The SPARC DRC: Building a Resource for the Autonomic Nervous System Community.

The Data and Resource Center (DRC) of the NIH-funded SPARC program is developing databases, connectivity maps, and simulation tools for the mammalian autonomic nervous system. The experimental data and mathematical models supplied to the DRC by the SPARC consortium are curated, annotated and semantically linked via a single knowledgebase. A data portal has been developed that allows discovery of data and models both via semantic search and via an interface that includes Google Map-like 2D flatmaps for displaying connectivity, and 3D anatomical organ scaffolds that provide a common coordinate framework for cross-species comparisons. We discuss examples that illustrate the data pipeline, which includes data upload, curation, segmentation (for image data), registration against the flatmaps and scaffolds, and finally display via the web portal, including the link to freely available online computational facilities that will enable neuromodulation hypotheses to be investigated by the autonomic neuroscience community and device manufacturers.

Genotyping of Porcine teschovirus from nervous tissue of pigs with and without polioencephalomyelitis in Indiana.

Porcine teschovirus (PTV) was isolated in cell culture and/or demonstrated by polymerase chain reaction in samples of brain and/or spinal cord in pigs in Indiana during the 2002-2007 period. Testing was initiated on pigs originating from populations exhibiting nervous clinical disease and/or pigs with microscopic lesions in central nervous tissues, indicating viral encephalitis and/or myelitis. Virus was demonstrated in pigs with and without lesions as well as with and without nervous clinical disease. Nucleotide sequence analysis of the 5'-nontranslated region of the viral genome revealed that these isolates had low-level genetic heterogeneity but were homologous to porcine PTV serotype 1 (PTV-1). These findings indicate that low-to-moderate virulence strains of PTV with some homology to PTV-1 are endemic in many swineherds of Indiana and are associated with subclinical and clinical nervous disease in weaned pigs.

Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging.

There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the "last mile" implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain.

Coastal blue space and depression in older adults.

This paper tests whether higher exposure to coastal blue space is associated with lower risk of depression using data from The Irish Longitudinal Study on Ageing (TILDA), a nationally representative longitudinal study of people aged fifty and over in Ireland. We contribute to the literature on blue space and health by (i) using scores from the Center for Epidemiologic Studies Depression Scale (CES-D) to measure depression outcomes (ii) using new measures of coastal blue space visibility (iii) studying the association in an older population (iv) using data from Ireland. Our results indicate that exposure to coastal blue space is associated with beneficial mental health outcomes: TILDA respondents with the highest share of sea view visibility have lower depression (CES-D) scores, while distance from coastline is not statistically significant when views and proximity are both included in the model. This finding supports the idea that the primary channel through which coastal blue space operates to reduce depression scores is visual rather than related to physical proximity.