The construction of brain functional network based on resting-state functional magnetic resonance imaging (fMRI) is an effective method to reveal the mechanism of human brain operation, but the common brain functional network generally contains a lot of noise, which leads to wrong analysis results. In this paper, the least absolute shrinkage and selection operator (LASSO) model in compressed sensing is used to reconstruct the brain functional network. This model uses the sparsity of L1-norm penalty term to avoid over fitting problem. Then, it is solved by the fast iterative shrinkage-thresholding algorithm (FISTA), which updates the variables through a shrinkage threshold operation in each iteration to converge to the global optimal solution. The experimental results show that compared with other methods, this method can improve the accuracy of noise reduction and reconstruction of brain functional network to more than 98%, effectively suppress the noise, and help to better explore the function of human brain in noisy environment.
Organoids are an in vitro model that can simulate the complex structure and function of tissues in vivo. Functions such as classification, screening and trajectory recognition have been realized through organoid image analysis, but there are still problems such as low accuracy in recognition classification and cell tracking. Deep learning algorithm and organoid image fusion analysis are the most advanced organoid image analysis methods. In this paper, the organoid image depth perception technology is investigated and sorted out, the organoid culture mechanism and its application concept in depth perception are introduced, and the key progress of four depth perception algorithms such as organoid image and classification recognition, pattern detection, image segmentation and dynamic tracking are reviewed respectively, and the performance advantages of different depth models are compared and analyzed. In addition, this paper also summarizes the depth perception technology of various organ images from the aspects of depth perception feature learning, model generalization and multiple evaluation parameters, and prospects the development trend of organoids based on deep learning methods in the future, so as to promote the application of depth perception technology in organoid images. It provides an important reference for the academic research and practical application in this field.
Evolutionary psychology holds such an opinion that negative situation may threaten survival, trigger avoidance motive and have poor effects on the human body function and the psychological quality. Both disgusted and sad situations can induce negative emotions. However, differences between the two situations on attention capture and emotion cognition during the emotion induction are still not well known. Typical disgusted and sad situation images were used in the present study to induce two negative emotions, and 15 young students (7 males and 8 females, aged 27±3) were recruited in the experiments. Electroencephalogram of 32 leads was recorded when the subjects were viewing situation images, and event-related potentials (ERP) of all leads were obtained for future analysis. Paired sample t tests were carried out on two ERP signals separately induced by disgusted and sad situation images to get time quantum with significant statistical differences between the two ERP signals. Root-mean-square deviations of two ERP signals during each time quantum were calculated and the brain topographic map based on root-mean-square deviations was drawn to display differences of two ERP signals in spatial. Results showed that differences of ERP signals induced by disgusted and sad situation images were mainly manifested in T1 (120-450 ms) early and T2 (800-1 000 ms) later. During the period of T1, the occipital lobe reflecting attention capture was activated by both disgusted and sad situation images, but the prefrontal cortex reflecting emotion sense was activated only by disgusted situation images. During the period of T2, the prefrontal cortex was activated by both disgusted and sad situation images. However, the parietal lobe was activated only by disgusted situation images, which showed stronger emotional perception. The research results would have enlightenment to deepen understanding of negative emotions and to explore deep cognitive neuroscience mechanisms of negative emotion induction.
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.
Electric and electronic products are required to pass through the certification on electrical safety performance before entering into the market in order to reduce electrical shock and electrical fire so as to protect the safety of people and property. The leakage current is the most important factor in testing the electrical safety performance and the test theory is based on the perception current effect and threshold. The traditional method testing the current threshold for perception only depends on the sensing of the human body and is affected by psychological factors. Some authors filter the effect of subjective sensation by using physiological and psychological statistical algorithm in recent years and the reliability and consistency of the experiment data are improved. We established an experiment system of testing the human body's current threshold for perception based on EEG feature analysis, and obtained 967 groups of data. We used wavelet packet analysis to detect α wave from EEG, and used FFT to do spectral analysis on α wave before and after the current flew through the human body. The study has shown that about 97.72% α wave energy changes significantly when electrical stimulation occurs. It is well proved that when the EEG feature identification is applied to test the human body current threshold for perception, and meanwhile α wave energy change and human body sensing are used together to confirm if the current flowing through the human body reaches the perception threshold, the measurement of the human body current threshold for perception could be carried out objectively and accurately.
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.
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 investigate the level and influencing factors of perceived HIV stigma and discrimination among people living with HIV/AIDS (PLWHA).
Methods
By using convenience sampling method, 123 patients were recruited from the department of infectious diseases in a tertiary hospital in Chengdu from April to May in 2017. Berger HIV stigma scale was used to measure the level of perceived HIV stigma.
Results
The mean score of Berger HIV stigma scale was 113.72±17.890, which revealed a middle to upper level. Among the four subscales, the score of disclosure concerns (3.07±0.462) was the highest, while the score of negative self-image (2.70±0.494) was the lowest. Multiple regression analysis showed that gender and self-perceived health status were the influencing factors of perceived HIV stigma.
Conclusions
The level of perceived HIV stigma among PLWHA is from middle to upper level. Female gender and poor self-perceived health status are associated with a higher level of perceived HIV stigma. Individualized interventions are required in order to reduce the level of HIV stigma.
