Entanglement of Imaging and Imagining of Nanotechnology

Images, ranging from visualizations of the nanoscale to future visions, abound within and beyond the world of nanotechnology. Rather than the contrast between imaging, i.e. creating images that are understood as offering a view on what is out there, and imagining, i.e. creating images offering impressions of how the nanoscale could look like and images presenting visions of worlds that might be realized, it is the entanglement between imaging and imagining which is the key to understanding what images do. Three main arenas of entanglement of imag(in)ing and the tensions involved are discussed: production practices and use of visualizations of the nanoscale; imag(in)ing the future and the present; and entanglements of nanoscience and art. In these three arenas one sees struggles about which images might stand for nanotechnology, but also some stabilization of the entanglement of imag(in)ing, for example in established rules in the practices of visualizing the nanoscale. Three images have become iconic, through the combination of their wide reception and further circulation. All three, the IBM logo, the Foresight Institute’s Nanogear image, and the so-called Nanolouse, depict actual or imagined technoscientific objects and are thus seen as representing technoscientific achievements – while marking out territory

Promise and ontological ambiguity in the In Vitro Meat imagescape: From laboratory myotubes to the cultured burger

In vitro meat, also known as cultured meat, involves growing cells into muscle tissue to be eaten as food. The technology had its most high profile moment in 2013 when a cultured burger was cooked and tasted in a press conference. Images of the burger featured in the international media and were circulated across the internet. These images – literally marks on a two-dimension surface - do important work in establishing what in vitro meat is and what it can do. A combination of visual semiotics and narrative analysis shows that images of in vitro meat afford readings of their story that are co-created by the viewer. Before the cultured burger, during 2011, images of in vitro meat fell into four distinct categories: cell images, tissue images, flowcharts, and meat in a dish images. The narrative infrastructure of each image type affords different interpretations of what in vitro meat can accomplish and what it is. The 2013 cultured burger images both draw upon and depart from these images types in an attempt to present in vitro meat as a normal food stuff, and as ‘matter in place’ when placed on the plate. The analysis of individual images and the collection of images about a certain object or subject – known as the imagescape – is a productive approach to understanding the ontology and promise of in vitro meat and is applicable to other areas of social life

Efficacy of Baricitinib in the Treatment of Chilblains Associated With Aicardi-Goutieres Syndrome, a Type I Interferonopathy

Genetics of disease, diagnosis and treatmen

Heterozygous Loss-of-Function Mutations in DLL4 Cause Adams-Oliver Syndrome.

Adams-Oliver syndrome (AOS) is a rare developmental disorder characterized by the presence of aplasia cutis congenita (ACC) of the scalp vertex and terminal limb-reduction defects. Cardiovascular anomalies are also frequently observed. Mutations in five genes have been identified as a cause for AOS prior to this report. Mutations in EOGT and DOCK6 cause autosomal-recessive AOS, whereas mutations in ARHGAP31, RBPJ, and NOTCH1 lead to autosomal-dominant AOS. Because RBPJ, NOTCH1, and EOGT are involved in NOTCH signaling, we hypothesized that mutations in other genes involved in this pathway might also be implicated in AOS pathogenesis. Using a candidate-gene-based approach, we prioritized DLL4, a critical NOTCH ligand, due to its essential role in vascular development in the context of cardiovascular features in AOS-affected individuals. Targeted resequencing of the DLL4 gene with a custom enrichment panel in 89 independent families resulted in the identification of seven mutations. A defect in DLL4 was also detected in two families via whole-exome or genome sequencing. In total, nine heterozygous mutations in DLL4 were identified, including two nonsense and seven missense variants, the latter encompassing four mutations that replace or create cysteine residues, which are most likely critical for maintaining structural integrity of the protein. Affected individuals with DLL4 mutations present with variable clinical expression with no emerging genotype-phenotype correlations. Our findings demonstrate that DLL4 mutations are an additional cause of autosomal-dominant AOS or isolated ACC and provide further evidence for a key role of NOTCH signaling in the etiology of this disorder

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing

Purpose Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the “ClinVar low-hanging fruit” reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. Results We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). Conclusion The “ClinVar low-hanging fruit” analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock