Long short‐distance topology modelling of 3D point cloud segmentation with a graph convolution neural network

3D point cloud segmentation is a non-trivial problem due to its irregular, sparse, and unordered data structure. Existing methods only consider structural relationships of a 3D point and its spatial neighbours. However, the inner-point interactions and long-distance context of a 3D point cloud have been less investigated. In this study, we propose an effective plug-and-play module called the Long Short-Distance Topologically Modelled (LSDTM) Graph Convolutional Neural Network (GCNN) to learn the underlying structure of 3D point clouds. Specifically, we introduce the concept of subgraph to model the contextual-point relationships within a short distance. Then the proposed topology can be reconstructed by recursive aggregation of subgraphs, and importantly, to propagate the contextual scope to a long range. The proposed LSDTM can parse the point cloud data with maximisation of preserving the geometric structure and contextual structure, and the topological graph can be trained end-to-end through a seamlessly integrated GCNN. We provide a case study of triple-layer ternary topology and experimental results on ShapeNetPart, Stanford 3D Indoor Semantics and ScanNet datasets, indicating a significant improvement on the task of 3D point cloud segmentation and validating the effectiveness of our research

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

A dual-mode fluorescence "turn-on" biosensor based on an aggregation-induced emission luminogen

10.1039/c3tb21576hJournal of Materials Chemistry B2121717-1723JMCB

Polymerizations of terminal and internal diynes

Hyperbranched polyphenylenes were synthesized using a new methodology. Polymers of terminal diynes underwent postpolymerization in the solid state. Since the as-prepared polymer at low temperature was soluble, the crosslinking reaction in the film state made the polymer a promising candidate for coating materials. The nature of the catalysts played a key role in determining the molecular structures of the polymers

Powered Air Purifying Respirator (PAPR) restores the N95 face mask induced cerebral hemodynamic alterations among Healthcare Workers during COVID-19 Outbreak

Background and aim: COVID-19 pandemic has resulted in an unprecedented increased usage of Personal protective equipment (PPE) by healthcare-workers. PPE usage causes headache in majority of users. We evaluated changes in cerebral hemodynamics among healthcare-workers using PPE. Methods: Frontline healthcare-workers donning PPE at our tertiary center were included. Demographics, co-morbidities and blood-pressure were recorded. Transcranial Doppler (TCD) monitoring of middle cerebral artery was performed with 2-MHz probe. Mean flow velocity (MFV) and pulsatility index (PI) were recorded at baseline, after donning N95 respirator-mask, and after donning powered air-purifying respirator (PAPR), when indicated. End-tidal carbon-dioxide (ET-CO2) pressure was recorded for participants donning PAPR in addition to the N95 respirator-mask. Results: A total of 154 healthcare-workers (mean age 29 ± 12 years, 67% women) were included. Migraine was the commonest co-morbidity in 38 (25%) individuals while 123 (80%) developed de-novo headache due to N95 mask. Donning of N95 respirator-mask resulted in significant increase in MFV (4.4 ± 10.4 cm/s, p < 0.001) and decrease in PI (0.13 ± 0.12; p < 0.001) while ET-CO2 increased by 3.1 ± 1.2 mmHg (p < 0.001). TCD monitoring in 24 (16%) participants donning PAPR and N95 respirator mask together showed normalization of PI, accompanied by normalization of ET-CO2 values within 5-min. Combined use of N95 respirator-mask and PAPR was more comfortable as compared to N95 respirator-mask alone. Conclusion: Use of N95 respirator-mask results in significant alterations in cerebral hemodynamics. However, these effects are mitigated by the use of additional PAPR. We recommend the use of PAPR together with the N95 mask for healthcare-workers doing longer duties in the hospital wards. © 2020 Elsevier B.V