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Abstract As signalling organelles, cilia regulate their G protein-coupled receptor content by ectocytosis, a process requiring localised actin dynamics to alter membrane shape. Photoreceptor outer segments comprise an expanse of folded membranes (discs) at the tip of highly-specialised connecting cilia, into which photosensitive GPCRs are concentrated. Discs are shed and remade daily. Defects in this process, due to mutations, cause retinitis pigmentosa (RP). Whilst fundamental for vision, the mechanism of photoreceptor disc generation is poorly understood. Here, we show membrane deformation required for disc genesis is driven by dynamic actin changes in a process akin to ectocytosis. We show RPGR, a leading RP gene, regulates actin-binding protein activity central to this process. Actin dynamics, required for disc formation, are perturbed in Rpgr mouse models, leading to aborted membrane shedding as ectosome-like vesicles, photoreceptor death and visual loss. Actin manipulation partially rescues this, suggesting the pathway could be targeted therapeutically. These findings help define how actin-mediated dynamics control outer segment turnover.

Abstract Sulphur hexafluoride (SF6) decomposed products analysis is highly critical in the early‐stage fault diagnosis of gas‐insulated switchgear (GIS). Spectrum technology outperforms traditional methods on non‐invasiveness, no sample preparation, and no consumption. Here, the authors present an improved fibre‐enhanced Raman spectroscopy (FERS) as a comprehensive analytical tool to detect a suite of SF6 decomposed products (SO2F2, SOF2, SO2, H2S, CF4, OCS, CO2, and CO). The FERS approach is combined with two iris diaphragms for spatial filtering and a rear‐end reflector for additional Raman signal enhancement. Limits of detection down to 1 × 10−6–8 × 10−6 are achieved for different SF6 decompositions, and quantification of an undefined multigas, sampled from an 800 kV GIS in service, is realised utilising SF6 as the internal standard gas and with a maximum error of 5.5 %. The GIS is diagnosed according to the results and confirmed by an on‐site check. The authors foresee that this technique will provide a route for trace gas analysis in the power industry.

Deep learning is proliferating within the field of computer vision. Road damage detection technology, as its offshoot, already plays vital role in road maintenance and traffic safety. With road damage such as potholes and cracks, accurate and efficient detection results are essential for timely road safety repair and maintenance. Therefore, road damage detection algorithms based on deep learning have attracted wide attention. YOLOv5 is an advanced target detection algorithm known for its efficient detection speed and good accuracy. However, there is still room for further improvement in its performance for road damage detection. The ability of multi-scale damage detection and spatial structure capture is not perfect. Therefore, this paper proposes three improvement points to improve the accuracy of road damage detection based on YOLOv5. The first introduced module is the Non-linear Spatial Pyramidal Pooling-Fast (NSPPF) module. This module allows for better capture of detailed features of road damage areas. Non-linear transformation and fast pyramid operation improve the sensing ability and multi-scale damage detection ability. Secondly, a combination of the CoordConv and SK attention modules is constructed. The CoordConv module fuses coordinate information with features to provide a more spatially informed representation. The SK attention module also learns correlations between global and local features, enhancing the model's ability to detect damages at different scales. This paper can better capture road injuries' spatial structure and context information by combining these two modules. Finally, experimental results on the RDD2020 dataset demonstrate the effectiveness of our model. Compared to the baseline model, the proposed improvement algorithm increases the accuracy by 2.2%, resulting in a mAP of 58.2%. This demonstrates its effectiveness and feasibility. [ABSTRACT FROM AUTHOR]