Taking advantages of the sparsity or compressibility inherent in real world signals, compressed sensing (CS) can collect compressed data at the sampling rate much lower than that needed in Shannon’s theorem. The combination of CS and low rank modeling is used to medical imaging techniques to increase the scanning speed of cardiac magnetic resonance (CMR), alleviate the patients’ suffering and improve the images quality. The alternating direction method of multipliers (ADMM) algorithm is proposed for multiscale low rank matrix decomposition of CMR images. The algorithm performance is evaluated quantitatively by the peak signal to noise ratio (PSNR) and relative l2 norm error (RLNE), with the human visual system and the local region magnification as the qualitative comparison. Compared to L + S, kt FOCUSS, k-t SPARSE SENSE algorithms, experimental results demonstrate that the proposed algorithm can achieve the best performance indices, and maintain the most detail features and edge contours. The proposed algorithm can encourage the development of fast imaging techniques, and improve the diagnoses values of CMR in clinical applications.
Multi-layer perceptron (MLP) neural network belongs to multi-layer feedforward neural network, and has the ability and characteristics of high intelligence. It can realize the complex nonlinear mapping by its own learning through the network. Bipolar disorder is a serious mental illness with high recurrence rate, high self-harm rate and high suicide rate. Most of the onset of the bipolar disorder starts with depressive episode, which can be easily misdiagnosed as unipolar depression and lead to a delayed treatment so as to influence the prognosis. The early identification of bipolar disorder is of great importance for patients with bipolar disorder. Due to the fact that the process of early identification of bipolar disorder is nonlinear, we in this paper discuss the MLP neural network application in early identification of bipolar disorder. This study covered 250 cases, including 143 cases with recurrent depression and 107 cases with bipolar disorder, and clinical features were statistically analyzed between the two groups. A total of 42 variables with significant differences were screened as the input variables of the neural network. Part of the samples were randomly selected as the learning sample, and the other as the test sample. By choosing different neural network structures, all results of the identification of bipolar disorder were relatively good, which showed that MLP neural network could be used in the early identification of bipolar disorder.
In deep learning-based image registration, the deformable region with complex anatomical structures is an important factor affecting the accuracy of network registration. However, it is difficult for existing methods to pay attention to complex anatomical regions of images. At the same time, the receptive field of the convolutional neural network is limited by the size of its convolution kernel, and it is difficult to learn the relationship between the voxels with far spatial location, making it difficult to deal with the large region deformation problem. Aiming at the above two problems, this paper proposes a cascaded multi-level registration network model based on transformer, and equipped it with a difficult deformable region perceptron based on mean square error. The difficult deformation perceptron uses sliding window and floating window techniques to retrieve the registered images, obtain the difficult deformation coefficient of each voxel, and identify the regions with the worst registration effect. In this study, the cascaded multi-level registration network model adopts the difficult deformation perceptron for hierarchical connection, and the self-attention mechanism is used to extract global features in the basic registration network to optimize the registration results of different scales. The experimental results show that the method proposed in this paper can perform progressive registration of complex deformation regions, thereby optimizing the registration results of brain medical images, which has a good auxiliary effect on the clinical diagnosis of doctors.
Objective To explore the impact of quarantine experiences on the public’s perceived infection risk and expectations following the shift in coronavirus disease 2019 (COVID-19) policy. Methods From December 7 to 10, 2022, an online questionnaire survey was conducted to collect data on respondents’ past quarantine experiences and their perceived infection risk and expectations after the relaxation of COVID-19 restrictions. Independent-samples t-tests and multiple linear regression analysis were used to examine the effect of quarantine experience on the public’s perceived infection risk and expectations. Results A total of 570 valid questionnaires were collected. Among the 570 respondents, 377 had quarantine experience. Those who had experienced quarantine reported a significantly higher perceived risk of COVID-19 infection than those who had not (3.07±1.28 vs. 2.77±1.23, P=0.007). Multiple linear regression analysis showed that quarantine experience [unstandardized partial regression coefficient (b)=0.278, 95% confidence interval (CI) (0.069, 0.487), P=0.009] and attitude change [b=0.319, 95%CI (0.251, 0.388), P<0.001] were significant influencing factors of perceived infection risk. Conclusions After the shift in COVID-19 policy, quarantine experience has a significant impact on the public’s perceived infection risk and expectations. Respondents with quarantine experiences have a higher perceived risk of contracting the virus and more pessimistic expectations.
The method of directly using speed information and angle information to drive attractors model of grid cells to encode environment has poor anti-interference ability and is not bionic. In response to the problem, this paper proposes a grid field calculation model based on perceived speed and perceived angle. The model has the following characteristics. Firstly, visual stream is decoded to obtain visual speed, and speed cell is modeled and decoded to obtain body speed. Visual speed and body speed are integrated to obtain perceived speed information. Secondly, a one-dimensional circularly connected cell model with excitatory connection is used to simulate the firing mechanism of head direction cells, so that the robot obtains current perception angle information in a biomimetic manner. Finally, the two kinds of perceptual information of speed and angle are combined to realize the driving of grid cell attractors model. The proposed model was experimentally verified. The results showed that this model could realize periodic hexagonal firing field mode of grid cells and precise path integration function. The proposed algorithm may provide a foundation for the research on construction method of robot cognitive map based on hippocampal cognition mechanism.
As the most efficient perception system in nature, the perception mechanism of the insect (such as honeybee) antennae is the key to imitating the high-performance sensor technology. An automated experimental device suitable for collecting electrical signals (including antenna reaction time information) of antennae was developed, in response to the problems of the non-standardized experimental process, interference of manual operation, and low efficiency in the study of antenna perception mechanism. Firstly, aiming at the automatic identification and location of insect heads in experiments, the image templates of insect head contour features were established. Insect heads were template-matched based on the Hausdorff method. Then, for the angle deviation of the insect heads relative to the standard detection position, a method that calculates the angle of the insect head mid-axis based on the minimum external rectangle of the long axis was proposed. Eventually, the electrical signals generated by the antennae in contact with the reagents were collected by the electrical signal acquisition device. Honeybees were used as the research object in this study. The experimental results showed that the accuracy of template matching could reach 95.3% to locate the bee head quickly, and the deviation angle of the bee head was less than 1°. The distance between antennae and experimental reagents could meet the requirements of antennae perception experiments. The parameters, such as the contact reaction time of honeybee antennae to sucrose solution, were consistent with the results of the manual experiment. The system collects effectively antenna contact signals in an undisturbed state and realizes the standardization of experiments on antenna perception mechanisms, which provides an experimental method and device for studying and analyzing the reaction time of the antenna involved in biological antenna perception mechanisms.
目的 了解成都市社區老年慢性病患者對關愛的感知和需求，為更好地關愛老年慢性病患者提供依據。 方法 于2011年8月－10月采用隨機抽樣和問卷調查的方法，對成都市玉林社區、二仙橋社區、草堂街社區和駟馬橋社區的180名老年慢性病患者的關愛感知和需求進行調查，并根據調查結果提出相應對策。 結果 180例老年慢性病患者中有98.89%能感受到關愛，1.11%自覺缺乏關愛；感知到的關愛主要來源于家庭成員,占91.01%，其次來源于親戚朋友和鄰居，占7.87%，最少來源于單位同事，占1.12%。關愛需求主要為家人團聚、關心體貼、尊重理解、日常照顧和心理情感支持、幫助解決困難、給予經濟資助、提供情感支持等；護理關愛需求以尊重理解排在首位，其次是慢性病日常護理、慢性病的防治、老年保健和慢性病基本知識等。 結論 加強對社區衛生服務人員的能力培訓，強化尊老愛老家庭氛圍和社會風氣，提高老年慢性病患者的關愛感知，有效地為老年慢性病患者提供關愛，更好地促進他們的健康。
Cross-modal unsupervised domain adaptation (UDA) aims to transfer segmentation models trained on a labeled source modality to an unlabeled target modality. However, existing methods often fail to fully exploit shape priors and intermediate feature representations, resulting in limited generalization ability of the model in cross-modal transfer tasks. To address this challenge, we propose a segmentation model based on shape-aware adaptive weighting (SAWS) that enhance the model's ability to perceive the target area and capture global and local information. Specifically, we design a multi-angle strip-shaped shape perception (MSSP) module that captures shape features from multiple orientations through an angular pooling strategy, improving structural modeling under cross-modal settings. In addition, an adaptive weighted hierarchical contrastive (AWHC) loss is introduced to fully leverage intermediate features and enhance segmentation accuracy for small target structures. The proposed method is evaluated on the multi-modality whole heart segmentation (MMWHS) dataset. Experimental results demonstrate that SAWS achieves superior performance in cross-modal cardiac segmentation tasks, with a Dice score (Dice) of 70.1% and an average symmetric surface distance (ASSD) of 4.0 for the computed tomography (CT)→magnetic resonance imaging (MRI) task, and a Dice of 83.8% and ASSD of 3.7 for the MRI→CT task, outperforming existing state-of-the-art methods. Overall, this study proposes a cross-modal medical image segmentation method with shape-aware, which effectively improves the structure-aware ability and generalization performance of the UDA model.