El discurso sobre la biología sintética y la innovación responsable : Observaciones desde una perspectiva histórica = Discourse about synthetic biology and responsible innovation : Notes from a historical perspective

El discurso sobre la biología sintética, altamente visionario y marcado por el enfoque de ‘investigación e innovación responsable’ (RRI) puede interpretarse como un terreno para confrontar perspectivas sobre el futuro de nuestras sociedades en su conjunto. En un momento en el que, con el final de la confrontación de sistemas entre capitalismo y socialismo, los debates sociales amplios sobre cuestiones políticas y socioeconómicas fundamentales se han vuelto infrecuentes, los discursos sobre las ciencias naturales y la tecnología parecen poder ayudar a tematizar estas cuestiones de manera indirecta. Esta función del discurso sobre la biología sintética puede clarificarse mediante una contextualización histórica del mismo, en la que se analizan aproximaciones anteriores sobre la relevancia social de la biología y sus aplicaciones tecnológicas

Quantum Technologies and Society: Towards a Different Spin

Due primarily to technological advances over the last decade, quantum research has become a key priority area for science and technology policy all over the world. With this manifesto, we wish to prevent quantum technology from running into fiascos of implementation at the interface of science and society. To this end, we identify key stumbling blocks and propose recommendations

Semi-supervised segmentation of concrete aggregate using consensus regularisation and prior guidance

In order to leverage and profit from unlabelled data, semi-supervised frameworks for semantic segmentation based on consistency training have been proven to be powerful tools to significantly improve the performance of purely supervised segmentation learning. However, the consensus principle behind consistency training has at least one drawback, which we identify in this paper: imbalanced label distributions within the data. To overcome the limitations of standard consistency training, we propose a novel semi-supervised framework for semantic segmentation, introducing additional losses based on prior knowledge. Specifically, we propose a lightweight architecture consisting of a shared encoder and a main decoder, which is trained in a supervised manner. An auxiliary decoder is added as additional branch in order to make use of unlabelled data based on consensus training, and we add additional constraints derived from prior information on the class distribution and on auto-encoder regularisation. Experiments performed on our concrete aggregate dataset presented in this paper demonstrate the effectiveness of the proposed approach, outperforming the segmentation results achieved by purely supervised segmentation and standard consistency training

Mutant mitochondrial elongation factor G1 and combined oxidative phosphorylation deficiency

Although most components of the mitochondrial translation apparatus are encoded by nuclear genes, all known molecular defects associated with impaired mitochondrial translation are due to mutations in mitochondrial DNA. We investigated two siblings with a severe defect in mitochondrial translation, reduced levels of oxidative phosphorylation complexes containing mitochondrial DNA (mtDNA)–encoded subunits, and progressive hepatoencephalopathy. We mapped the defective gene to a region on chromosome 3q containing elongation factor G1 (EFG1), which encodes a mitochondrial translation factor. Sequencing of EFG1 revealed a mutation affecting a conserved residue of the guanosine triphosphate (GTP)–binding domain. These results define a new class of gene defects underlying disorders of oxidative phosphorylation