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The integration of visual simulation and biorehabilitation devices promises great applications for sustainable electronics, on-demand integration and neuroscience. However, achieving a multifunctional synergistic biomimetic system with tunable optoelectronic properties at the individual device level remains a challenge. Here, an electro-optically configurable transistor employing conjugated-polymer as semiconductor layer and an insulating polymer (poly(1,8-octanediol-co-citrate) (POC)) with clusterization-triggered photoactive properties as dielectric layer is shown. These devices realize adeptly transition from electrical to optical synapses, featuring multiwavelength and multilevel optical synaptic memory properties exceeding 3 bits. Utilizing enhanced optical memory, the images learning and memory function for visual simulation are achieved. Benefiting from rapid electrical response akin to biological muscle activation, increased actuation occurs under increased stimulus frequency of gate voltage. Additionally, the transistor on POC substrate can be effectively degraded in NaOH solution due to degradation of POC. Pioneeringly, the electro-optically configurability stems from light absorption and photoluminescence of the aggregation cluster in POC layer after 200 °C annealing. The enhancement of optical synaptic plasticity and integration of motion-activation functions within a single device opens new avenues at the intersection of optoelectronics, synaptic computing, and bioengineering.

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Instance segmentation plays an important role in the automatic diagnosis of cervical cancer. Although deep learning-based instance segmentation methods can achieve outstanding performance, they need large amounts of labeled data. This results in a huge consumption of manpower and material resources. To solve this problem, we propose an unsupervised cervical cell instance segmentation method based on human visual simulation, named HVS-Unsup. Our method simulates the process of human cell recognition and incorporates prior knowledge of cervical cells. Specifically, firstly, we utilize prior knowledge to generate three types of pseudo labels for cervical cells. In this way, the unsupervised instance segmentation is transformed to a supervised task. Secondly, we design a Nucleus Enhanced Module (NEM) and a Mask-Assisted Segmentation module (MAS) to address problems of cell overlapping, adhesion, and even scenarios involving visually indistinguishable cases. NEM can accurately locate the nuclei by the nuclei attention feature maps generated by point-level pseudo labels, and MAS can reduce the interference from impurities by updating the weight of the shallow network through the dice loss. Next, we propose a Category-Wise droploss (CW-droploss) to reduce cell omissions in lower-contrast images. Finally, we employ an iterative self-training strategy to rectify mislabeled instances. Experimental results on our dataset MS-cellSeg, the public datasets Cx22 and ISBI2015 demonstrate that HVS-Unsup outperforms existing mainstream unsupervised cervical cell segmentation methods.

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Previous findings from the sentence-picture verification task demonstrated that comprehenders simulate visual information about intrinsic attributes of described objects. Of interest is whether comprehenders may also simulate the setting in which an event takes place, such as, for example, the light information. To address this question, four experiments were conducted in which participants (total N = 412) either listened to (Experiment 1) or read (Experiment 3) sentences like "The sun is shining onto a bench" followed by a picture with the matching object (bench) and either the matching lighting condition of the scene (sunlit bench against the sunlit background) or the mismatching one (moonlit bench against the moonlit background). In both experiments, response times (RTs) were shorter when the lighting condition of the pictured scene matched the one implied in the sentence. However, no difference in RTs was observed when the processing of spoken sentences was interfered with visual noise (Experiment 2). Specifically, the results showed that visual interference disrupted incongruent visual content activated by listening to the sentences, as evidenced by faster responses on mismatching trials. Similarly, no difference in RTs was observed when the lighting condition of the pictured scene matched sentence context, but the target object presented for verification mismatched sentence context (Experiment 4). Thus, the locus of simulation effect is on the lighting representation of the target object rather than the lighting representation of the background. These findings support embodied and situated accounts of cognition, suggesting that comprehenders do not simulate objects independently of background settings.

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BACKGROUND: In recent years, the development of digital imaging technology has had a significant influence in liver surgery. The ability to obtain a 3-dimensional (3D) visualization of the liver anatomy has provided surgery with virtual reality of simulation 3D computer models, 3D printing models and more recently holograms and augmented reality (when virtual reality knowledge is superimposed onto reality). In addition, the utilization of real-time fluorescent imaging techniques based on indocyanine green (ICG) uptake allows clinicians to precisely delineate the liver anatomy and/or tumors within the parenchyma, applying the knowledge obtained preoperatively through digital imaging. The combination of both has transformed the abstract thinking until now based on 2D imaging into a 3D preoperative conception (virtual reality), enhanced with real-time visualization of the fluorescent liver structures, effectively facilitating intraoperative navigated liver surgery (augmented reality). DATA SOURCES: A literature search was performed from inception until January 2021 in MEDLINE (PubMed), Embase, Cochrane library and database for systematic reviews (CDSR), Google Scholar, and National Institute for Health and Clinical Excellence (NICE) databases. RESULTS: Fifty-one pertinent articles were retrieved and included. The different types of digital imaging technologies and the real-time navigated liver surgery were estimated and compared. CONCLUSIONS: ICG fluorescent imaging techniques can contribute essentially to the real-time definition of liver segments; as a result, precise hepatic resection can be guided by the presence of fluorescence. Furthermore, 3D models can help essentially to further advancing of precision in hepatic surgery by permitting estimation of liver volume and functional liver remnant, delineation of resection lines along the liver segments and evaluation of tumor margins. In liver transplantation and especially in living donor liver transplantation (LDLT), 3D printed models of the donor's liver and models of the recipient's hilar anatomy can contribute further to improving the results. In particular, pediatric LDLT abdominal cavity models can help to manage the largest challenge of this procedure, namely large-for-size syndrome.

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Adaptive optics (AO) can measure and correct wavefront aberrations in real time, which enables the optical system to adapt to external changes and maintain excellent optical performance, and has been gradually paid attention in the field of ophthalmology.AO technology can carry out optometry according to wavefront aberrations to improve the efficiency and accuracy of subjective and objective refraction, eliminate the influence of ocular aberrations on retinal imaging, provide more accurate data for the evaluation of optic nerve function, improve the effectiveness of visual perception training and provide vision care and treatment for special people, as well as simulate and predict postoperative visual outcome and give personalized schemes for refractive surgery and intraocular lens implantation.Moreover, AO combined with optical coherence tomography, optical scanning laser ophthalmoscope, and confocal scanning laser ophthalmoscope, can realize fundus imaging and retinal vascular imaging in real time, provide better sensitivity and resolution of retinal detection, distinguish fine details of retinal vessels and cone cells, and characterize retinal pigment epithelium topology and deformation, the application of which in posterior segment laser surgery, glaucoma diagnosis and follow-up, color blindness and retinal physiological activity research has been attracting attention.In this article, the principle and application of AO in ophthalmology were briefly reviewed.

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As the human eye ages, the crystalline lens stiffens (presbyopia) and opacifies (cataract), requiring its replacement with an artificial lens [intraocular lens (IOL)]. Cataract surgery is the most frequently performed surgical procedure in the world. The increase in IOL designs has not been paralleled in practice by a sophistication in IOL selection methods, which rely on limited anatomical measurements of the eye and the surgeon's interpretation of the patient's needs and expectations. We propose that the future of IOL selection will be guided by 3D quantitative imaging of the crystalline lens to map lens opacities, anticipate IOL position, and develop fully customized eye models for ray-tracing-based IOL selection. Conversely, visual simulators (in which IOL designs are programmed in active elements) allow patients to experience prospective vision before surgery and to make more informed decisions about which IOL to choose. Quantitative imaging and optical and visual simulations of postsurgery outcomes will allow optimal treatments to be selected for a patient undergoing modern cataract surgery.

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BACKGROUND: This study aimed to simulate the visual field (VF) effects of patients with VF defects using deep learning and computer vision technology. METHODS: We collected 3,660 Humphrey visual fields (HVFs) as data samples, including 3,263 reliable 24-2 HVFs. The convolutional neural network (CNN) analyzed and converted the grayscale map of reliable samples into structured data. The artificial intelligence (AI) simulations were developed using computer vision technology. In statistical analyses, the pilot study determined 687 reliable samples to conduct clinical trials, and the two independent sample t-tests were used to calculate the difference of the cumulative gray values. Three volunteers evaluated the matching degree of shape and position between the grayscale map and the AI simulation, which was graded from 0 to100 scores. Based on the average ranking, the proportion of good and excellent grades was determined, and thus the reliability of the AI simulations was assessed. RESULTS: The reliable samples in the experimental data consisted of 1,334 normal samples and 1,929 abnormal samples. Based on the existing mature CNN model, the fully connected layer was integrated to analyze the VF damage parameters of the input images, and the prediction accuracy of the damage type of the VF defects was up to 89%. By mapping the area and damage information in the VF damage parameter quintuple data set into the real scene image and adjusting the darkening effect according to the damage parameter, the visual effects in patients were simulated in the real scene image. In the clinical validation, there was no statistically significant difference in the cumulative gray value (P>0.05). The good and excellent proportion of the average scores reached 96.0%, thus confirming the accuracy of the AI model. CONCLUSIONS: An AI model with high accuracy was established to simulate the visual effects in patients with VF defects.

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BACKGROUND: The aim of this study was to analyse the visual quality of the AcrySof IQ SN60WF(®) intraocular lens (IOL) when combined with different corneal profiles. METHODS: Ten eyes of 10 participants with no prior history of refractive or cataract surgery were evaluated. An adaptive optics visual simulator was used to simulate the wavefront aberration pattern of an aspheric aberration-correcting IOL (AcrySof IQ SN60WF(®)). Normal corneas (group A), low and high myopic corneal ablations (groups B and C, respectively) and low and high hyperopic corneal ablations (groups D and E, respectively) were also simulated. Monocular distance visual acuities at 100, 50 and 10 per cent of contrast were measured. RESULTS: At 100, 50 and 10 per cent contrast, no differences were found between groups A and B (p > 0.06 for all contrasts). Group A obtained better values than groups C, D and E for all contrasts (p = 0.031, p = 0.038, p = 0.032 at 100, 50 and 10 per cent of contrast, respectively). At the same time, group B obtained better values than groups C, D and E (p = 0.041, p = 0.042, p = 0.036 at 100, 50 and 10 per cent of contrast, respectively). Within the five groups, the worst results were always obtained for group E (p = 0.017, p = 0.021 and p = 0.025 at 100, 50 and 10 per cent of contrast, respectively). CONCLUSIONS: The results suggest that the aspheric aberration-correcting IOL studied provides comparable results, when it is combined with normal corneas and with corneas with simulated low myopic ablations. When negative amounts of residual spherical aberration after cataract surgery are expected to be achieved, IOLs with more positive spherical aberration should be considered.

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