Deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations.

Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realizing the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high-quality ground-truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope (NE) of HeLa cells imaged with serial blockface scanning electron microscopy. We present our approach for aggregating multiple volunteer annotations to generate a high-quality consensus segmentation and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the NE, which we share here, in addition to our archived benchmark data.

Direct observation of tunnelled intergrowth in SnO2/Ga2O3 complex nanowires.

ß-Ga2O3 intergrowths have been revealed in the SnO2 rutile structure when SnO2/Ga2O3 complex nanostructures are grown by thermal evaporation with a catalyst-free basis method. The structure is formed by a Ga2O3 nanowire trunk, around which a rutile SnO2 particle is formed with [001] aligned to the [010] Ga2O3 trunk axis. Inside the SnO2 particle, ß-Ga2O3 units occur separated periodically by hexagonal tunnels in the (210) rutile plane. Orange (620 nm) optical emission from tin oxide, with a narrow linewidth indicating localised electronic states, may be associated with this ß-Ga2O3 intergrowth.

SARS-CoV-2 detection by a clinical diagnostic RT-LAMP assay.

The ongoing pandemic of SARS-CoV-2 calls for rapid and cost-effective methods to accurately identify infected individuals. The vast majority of patient samples is assessed for viral RNA presence by RT-qPCR. Our biomedical research institute, in collaboration between partner hospitals and an accredited clinical diagnostic laboratory, established a diagnostic testing pipeline that has reported on more than 252,000 RT-qPCR results since its commencement at the beginning of April 2020. However, due to ongoing demand and competition for critical resources, alternative testing strategies were sought. In this work, we present a clinically-validated procedure for high-throughput SARS-CoV-2 detection by RT-LAMP in 25 minutes that is robust, reliable, repeatable, sensitive, specific, and inexpensive.

Author Correction: Scalable and robust SARS-CoV-2 testing in an academic center.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.