Yilong Chen

Optically driven active materials have received much attention because their deformation and motion can be controlled remotely, instantly, and precisely in a contactless way. In this study, we investigated an optically actuated elastomer with rapid response: polydopamine (PDA)-coated liquid crystal elastomer (LCE). Because of the photothermal effect of PDA coating and thermal responsiveness of LCE, the elastomer film contracted significantly with near-infrared (NIR) irradiation. With a fixed strain, light-induced actuating stress in the film could be as large as 1.5 MPa, significantly higher than the maximum stress generated by most mammalian skeletal muscle (0.35 MPa). The PDA-coated LCE films could also bend or roll up by surface scanning of an NIR laser. The response time of the film to light exposure could be as short as 1/10 of a second, comparable to or even faster than that of mammalian skeletal muscle. Using the PDA-coated LCE film, we designed and fabricated a prototype of robotic swimmer that was able to swim near the water-air interface by performing "swimming strokes" through reversible bending and unbending motions induced and controlled by an NIR laser. The results presented in this study clearly demonstrated that PDA-coated LCE is a promising optically driven artificial muscle, which may have great potential for applications of soft robotics and optomechanical coupling devices.

It is a challenge to automatically and accurately segment the liver and tumors in computed tomography (CT) images, as the problem of over-segmentation or under-segmentation often appears when the Hounsfield unit (Hu) of liver and tumors is close to the Hu of other tissues or background. In this paper, we propose the spatial channel-wise convolution, a convolutional operation along the direction of the channel of feature maps, to extract mapping relationship of spatial information between pixels, which facilitates learning the mapping relationship between pixels in the feature maps and distinguishing the tumors from the liver tissue. In addition, we put forward an iterative extending learning strategy, which optimizes the mapping relationship of spatial information between pixels at different scales and enables spatial channel-wise convolution to map the spatial information between pixels in high-level feature maps. Finally, we propose an end-to-end convolutional neural network called Channel-UNet, which takes UNet as the main structure of the network and adds spatial channel-wise convolution in each up-sampling and down-sampling module. The network can converge the optimized mapping relationship of spatial information between pixels extracted by spatial channel-wise convolution and information extracted by feature maps and realizes multi-scale information fusion. The proposed ChannelUNet is validated by the segmentation task on the 3Dircadb dataset. The Dice values of liver and tumors segmentation were 0.984 and 0.940, which is slightly superior to current best performance. Besides, compared with the current best method, the number of parameters of our method reduces by 25.7%, and the training time of our method reduces by 33.3%. The experimental results demonstrate the efficiency and high accuracy of Channel-UNet in liver and tumors segmentation in CT images.

The rapid acquisition of antibiotic resistance poses difficulties in the development of effective methods to eliminate pathogenic bacteria. New bactericides, especially those do not induce the emergence of resistance, are thus in great demand. In this work, we report an aggregation-induced emission fluorogen, TPE-Bac, for bacterial imaging and elimination. TPE-Bac can be readily dissolved in aqueous solution with weak emission. The presence of bacteria can turn on its emission, and thus no washing step is required in the imaging process. Meanwhile, TPE-Bac can be applied as a bactericide for elimination of bacteria. The amphiphilic TPE-Bac bearing two long alkyl chains and two positively charged amines can intercalate into the membrane of bacteria, increase membrane permeability and lead to dark toxicity. The efficiency of bacteria killing is greatly enhanced under light irradiation. TPE-Bac can serve as a photosensitizer to induce reactive oxygen species (ROS) generation, which ensures the efficient killing of bacteria. The TPE-Bac-containing agar plates can be continuously used for bacteria killing by applying light to induce ROS generation.

BACKGROUND: Psychiatric morbidities have been associated with a risk of severe infections through compromised immunity, health behaviours, or both. However, data are scarce on the association between multiple types of pre-pandemic psychiatric disorders and COVID-19. We aimed to assess the association between pre-pandemic psychiatric disorders and the subsequent risk of COVID-19 using UK Biobank. METHODS: For this cohort analysis, we included participants from UK Biobank who were registered in England and excluded individuals who died before Jan 31, 2020, (the start of the COVID-19 outbreak in the UK) or had withdrawn from UK Biobank. Participants diagnosed with a psychiatric disorder before Jan 31 were included in the group of individuals with pre-pandemic psychiatric disorders, whereas participants without a diagnosis before the outbreak were included in the group of individuals without pre-pandemic psychiatric disorders. We used the Public Health England dataset, UK Biobank hospital data, and death registers to collect data on COVID-19 cases. To examine the relationship between pre-pandemic psychiatric disorders and susceptibility to COVID-19, we used logistic regression models to estimate odds ratios (ORs), controlling for multiple confounders and somatic comorbidities. Key outcomes were all COVID-19, COVID-19 specifically diagnosed in inpatient care, and COVID-19-related deaths. ORs were also estimated separately for each psychiatric disorder and on the basis of the number of pre-pandemic psychiatric disorders. As a positive disease control, we repeated analyses for hospitalisation for other infections. FINDINGS: We included 421 014 UK Biobank participants in our study and assessed their COVID-19 status between Jan 31 and July 26, 2020. 50 809 participants were diagnosed with psychiatric disorders before the outbreak, while 370 205 participants had no psychiatric disorders. The mean age at outbreak was 67·80 years (SD 8·12). We observed an elevated risk of COVID-19 among individuals with pre-pandemic psychiatric disorders compared with that of individuals without such conditions. The fully adjusted ORs were 1·44 (95% CI 1·28-1·62) for All COVID-19 cases, 1·55 (1·34-1·78) for Inpatient COVID-19 cases, and 2·03 (1·59-2·59) for COVID-19-related deaths. We observed excess risk, defined as risk that increased with the number of pre-pandemic psychiatric disorders, across all diagnostic categories of pre-pandemic psychiatric disorders. We also observed an association between psychiatric disorders and elevated risk of hospitalisation due to other infections (OR 1·74, 95% CI 1·58-1·93). INTERPRETATION: Our findings suggest that pre-existing psychiatric disorders are associated with an increased risk of COVID-19. These findings underscore the need for surveillance of and care for populations with pre-existing psychiatric disorders during the COVID-19 pandemic. FUNDING: National Natural Science Foundation of China.