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The deposition of 0.3-1 μm particles were influenced greatly by convention and varied with the change of respiratory mode. The research methods and results in this paper can provide theoretical basis and data support for the further exploration of the mechanism, prevention and treatment of lung diseases.As drug carriers, magnetic nanoparticles can specifically bind to tumors and have the potential for targeted therapy. It is of great significance to explore non-invasive imaging methods that can detect the distribution of magnetic nanoparticles. Based on the mechanism that magnetic nanoparticles can generate ultrasonic waves through the pulsed magnetic field excitation, the sound pressure wave equation containing the concentration information of magnetic nanoparticles was derived. Kynurenic acid order Using the finite element method and the analytical solution, the consistent transient pulsed magnetic field was obtained. A three-dimensional simulation model was constructed for the coupling calculation of electromagnetic field and sound field. The simulation results verified that the sound pressure waveform at the detection point reflected the position of magnetic nanoparticles in biological tissue. Using the sound pressure data detected by the ultrasonic transducer, the B-scan imaging of the magnetic nanoparticles was achieved. The maximum error of the target area position was 1.56%, and the magnetic nanoparticles regions with different concentrations were distinguished by comparing the amplitude of the boundary signals in the image. Studies in this paper indicate that B-scan imaging can quickly and accurately obtain the dimensional and positional information of the target region and is expected to be used for the detection of magnetic nanoparticles in targeted therapy.Denoising methods based on wavelet analysis and empirical mode decomposition cannot essentially track and eliminate noise, which usually cause distortion of heart sounds. Based on this problem, a heart sound denoising method based on improved minimum control recursive average and optimally modified log-spectral amplitude is proposed in this paper. The proposed method uses a short-time window to smoothly and dynamically track and estimate the minimum noise value. The noise estimation results are used to obtain the optimal spectrum gain function, and to minimize the noise by minimizing the difference between the clean heart sound and the estimated clean heart sound. In addition, combined with the subjective analysis of spectrum and the objective analysis of contribution to normal and abnormal heart sound classification system, we propose a more rigorous evaluation mechanism. The experimental results show that the proposed method effectively improves the time-frequency features, and obtains higher scores in the normal and abnormal heart sound classification systems. The proposed method can help medical workers to improve the accuracy of their diagnosis, and also has great reference value for the construction and application of computer-aided diagnosis system.Heart sound segmentation is a key step before heart sound classification. It refers to the processing of the acquired heart sound signal that separates the cardiac cycle into systolic and diastolic, etc. To solve the accuracy limitation of heart sound segmentation without relying on electrocardiogram, an algorithm based on the duration hidden Markov model (DHMM) was proposed. Firstly, the heart sound samples were positionally labeled. Then autocorrelation estimation method was used to estimate cardiac cycle duration, and Gaussian mixture distribution was used to model the duration of sample-state. Next, the hidden Markov model (HMM) was optimized in the training set and the DHMM was established. Finally, the Viterbi algorithm was used to track back the state of heart sounds to obtain S1, systole, S2 and diastole. 500 heart sound samples were used to test the performance of our algorithm. The average evaluation accuracy score (F1) was 0.933, the average sensitivity was 0.930, and the average accuracy rate was 0.936. Compared with other algorithms, the performance of our algorithm was more superior. It is proved that the algorithm has high robustness and anti-noise performance, which might provide a novel method for the feature extraction and analysis of heart sound signals collected in clinical environments.Repetitive transcranial magnetic stimulation(rTMS) is a painless and non-invasive method for stimulation and modulation in the field of cognitive neuroscience research and clinical neurological regulation. In this paper, adult Wistar rats were divided into the rTMS group and control group randomly. Rats in the rTMS group were stimulated with 5 Hz rTMS for 14 days, while the rats in the control group did not accept any stimulation. Then, the behavior and local field potentials (LFPs) were recorded synchronously when the rats perform a working memory (WM) task with T-maze. Finally, the time-frequency distribution and coherence characteristics of the LFPs signal in the prefrontal cortex (PFC) during working memory task were analyzed. The results showed that the rats in the rTMS group needed less training days to reach the task correction criterion than the control group (P less then 0.05). Compared with the control group, the rTMS group has higher energy (P less then 0.01) in θ band (4~12 Hz) and γ band (30~80 Hz). The coherence between the channel pairs decreases as the spatial distance of the channel pairs increases, and the rTMS group exhibits a higher coherence than the control group (P less then 0.01). It is concluded that 5 Hz rTMS can improve the excitability of rat prefrontal cortical neurons to a certain extent, and has a positive effect on the working memory ability of normal rats. The results of this paper may provide important theoretical support for further research on the mechanism of action of rTMS on WM.Aiming at the difference between the brain networks of children with attention deficit hyperactivity disorder (ADHD) and normal children in the task-executing state, this paper conducted a comparative study using the network features of the visual function area. Functional magnetic resonance imaging (fMRI) data of 23 children with ADHD ［age (8.27 ± 2.77) years］ and 23 normal children ［age (8.70 ± 2.58) years］ were obtained by the visual capture paradigm when the subjects were performing the guessing task. First, fMRI data were used to build a visual area brain function network. Then, the visual area brain function network characteristic indicators including degree distribution, average shortest path, network density, aggregation coefficient, intermediary, etc. were obtained and compared with the traditional whole brain network. Finally, support vector machines (SVM) and other classifiers in the machine learning algorithm were used to classify the feature indicators to distinguish ADHD children from normal children.