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Aftereffect of Cellulose as well as Cellulose Nanocrystal Material around the Biodegradation, beneath Decomposing Problems, of Ordered PLA Biocomposites.
The purpose of this study is to analyze the biomechanics of ankle cartilage and ligaments during a typical Tai Chi movement-Brush Knee and Twist Step (BKTS). The kinematic and kinetic data were acquired in one experienced male Tai Chi practitioner while performing BKTS and in normal walking. The measured parameters were used as loading and boundary conditions for further finite element analysis. This study showed that the contact stress of the ankle joint during BKTS was generally less than that during walking. However, the maximum tensile force of the anterior talofibular ligament, the calcaneofibular ligament and the posterior talofibular ligament during BKTS was 130 N, 169 N and 89 N, respectively, while it was only 57 N, 119 N and 48 N during walking. Therefore, patients with arthritis of the ankle can properly practice Tai Chi. Practitioners with sprained lateral ligaments of the ankle joint were suggested to properly reduce the ankle movement range during BKTS.In order to study the effect of middle ear malformations on energy absorbance, we constructed a mechanical model that can simulate the energy absorbance of the human ear based on our previous human ear finite element model. The validation of this model was confirmed by two sets of experimental data. Based on this model, three common types of middle ear malformations, i. e. incudostapedial joint defect, incus fixation and malleus fixation, and stapes fixation, were simulated by changing the structure and material properties of the corresponding tissue. Then, the effect of these three common types of middle ear malformations on energy absorbance was investigated by comparing the corresponding energy absorbance. The results showed that the incudostapedial joint defect significantly increased the energy absorbance near 1 000 Hz. The incus fixation and malleus fixation dramatically reduced the energy absorbance in the low frequency, which made the energy absorbance less than 10% at frequencies lower than 1 000 Hz. At the same time, the peak of energy absorbance shifted to the higher frequency. These two kinds of middle ear malformations had obvious characteristics in the wideband acoustic immittance test. In contrast, the stapes fixation only reduced the energy absorbance in the low frequency and increased energy absorbance in the middle frequency slightly, which had no obvious characteristic in the wideband acoustic immittance test. These results provide a theoretical reference for the wideband acoustic immittance diagnosis of middle ear malformations in clinic.The three-dimensional (3D) liver and tumor segmentation of liver computed tomography (CT) has very important clinical value for assisting doctors in diagnosis and prognosis. This paper proposes a tumor 3D conditional generation confrontation segmentation network (T3scGAN) based on conditional generation confrontation network (cGAN), and at the same time, a coarse-to-fine 3D automatic segmentation framework is used to accurately segment liver and tumor area. This paper uses 130 cases in the 2017 Liver and Tumor Segmentation Challenge (LiTS) public data set to train, verify and test the T3scGAN model. Finally, the average Dice coefficients of the validation set and test set segmented in the 3D liver regions were 0.963 and 0.961, respectively, while the average Dice coefficients of the validation set and test set segmented in the 3D tumor regions were 0.819 and 0.796, respectively. Experimental results show that the proposed T3scGAN model can effectively segment the 3D liver and its tumor regions, so it can better assist doctors in the accurate diagnosis and treatment of liver cancer.Right ventricular (RV) failure has become a deadly complication of left ventricular assist device (LVAD) implantation, for which desynchrony in bi-ventricular pulse resulting from a LVAD is among the important factor. This paper investigated how different control modes affect the synchronization of pulse between LV (left ventricular) and RV by numerical method. The numerical results showed that the systolic duration between LV and RV did not significantly differ at baseline (LVAD off and cannula clamped) (48.52% vs. 51.77%, respectively). The systolic period was significantly shorter than the RV systolic period in the continuous-flow mode (LV vs. RV 24.38% vs. 49.16%) and the LV systolic period at baseline. The LV systolic duration was significantly shorter than the RV systolic duration in the pulse mode (LV vs. RV 28.38% vs. 50.41%), but longer than the LV systolic duration in the continuous-flow mode. There was no significant difference between the LV and RV systolic periods in the counter-pulse mode (LV vs. RV 43.13% vs. 49.23%). However, the LV systolic periods was shorter than the no-pump mode and much longer than the continuous-flow mode. Compared with continuous-flow and pulse mode, the reduction in rotational speed (RS) brought out by counter-pulse mode significantly corrected the duration of LV systolic phase. check details The shortened duration of systolic phase in the continuous-flow mode was corrected as re-synchronization in the counter-pulse mode between LV and RV. Hence, we postulated that the beneficial effects on RV function were due to re-synchronizing of RV and LV contraction. In conclusion, decreased RS delivered during the systolic phase using the counter-pulse mode holds promise for the clinical correction of desynchrony in bi-ventricular pulse resulting from a LVAD and confers a benefit on RV function.Early accurate detection of inferior myocardial infarction is an important way to reduce the mortality from inferior myocardial infarction. Regrading the existing problems in the detection of inferior myocardial infarction, complex model structures and redundant features, this paper proposed a novel inferior myocardial infarction detection algorithm. Firstly, based on the clinic pathological information, the peak and area features of QRS and ST-T wavebands as well as the slope feature of ST waveband were extracted from electrocardiogram (ECG) signals leads Ⅱ, Ⅲ and aVF. In addition, according to individual features and the dispersion between them, we applied genetic algorithm to make judgement and then input the feature with larger degree into support vector machine (SVM) to realize the accurate detection of inferior myocardial infarction. The proposed method in this paper was verified by Physikalisch-Technische Bundesanstalt (PTB) diagnostic electrocardio signal database and the accuracy rate was up to 98.33%. Conforming to the clinical diagnosis and the characteristics of specific changes in inferior myocardial infarction ECG signal, the proposed method can effectively make precise detection of inferior myocardial infarction by morphological features, and therefore is suitable to be applied in portable devices development for clinical promotion.Medical magnetic nanoparticles are nano-medical materials with superparamagnetism, which can be collected in the tumor tissue through blood circulation, and magnetic particle imaging technology can be used to visualize the concentration of magnetic nanoparticles in the living body to achieve the purpose of tumor imaging. Based on the nonlinear magnetization characteristics of magnetic particles and the frequency characteristics of their magnetization, a differential detection method for the third harmonic of magnetic particle detection signals is proposed. It was modeled and analyzed, to study the nonlinear magnetization response characteristics of magnetic particles under alternating field, and the spectral characteristics of magnetic particle signals. At the same time, the relationship between each harmonic and the amount of medical magnetic nanoparticle samples was studied. On this basis, a signal detection experimental system was built to analyze the spectral characteristics and power spectral density of the detection signal was reached at the excitation frequency of 1 kHz. It provides theoretical and technical support for the detection of medical magnetic nanoparticle imaging signals in magnetic particle imaging research.The pathogenesis of Alzheimer's disease (AD), a common neurodegenerative disease, is still unknown. It is difficult to determine the atrophy areas, especially for patients with mild cognitive impairment (MCI) at different stages of AD, which results in a low diagnostic rate. Therefore, an early diagnosis model of AD based on 3-dimensional convolutional neural network (3DCNN) and genetic algorithm (GA) was proposed. Firstly, the 3DCNN was used to train a base classifier for each region of interest (ROI). And then, the optimal combination of the base classifiers was determined with the GA. Finally, the ensemble consisting of the chosen base classifiers was employed to make a diagnosis for a patient and the brain regions with significant classification capability were decided. The experimental results showed that the classification accuracy was 88.6% for AD vs. normal control (NC), 88.1% for MCI patients who will convert to AD (MCIc) vs. NC, and 71.3% for MCI patients who will not convert to AD (MCInc) vs. MCIc. In addition, with the statistical analysis of the behavioral domains corresponding to ROIs (i.e. brain regions), besides left hippocampus, medial and lateral amygdala, and left para-hippocampal gyrus, anterior superior temporal sulcus of middle temporal gyrus and dorsal area 23 of cingulate gyrus were also found with GA. It is concluded that the functions of the selected brain regions mainly are relevant to emotions, memory, cognition and the like, which is basically consistent with the symptoms of indifference, memory losses, mobility decreases and cognitive declines in AD patients. All of these show that the proposed method is effective.At present the prediction method of epilepsy patients is very time-consuming and vulnerable to subjective factors, so this paper presented an automatic recognition method of epilepsy electroencephalogram (EEG) based on common spatial model (CSP) and support vector machine (SVM). In this method, the CSP algorithm for extracting spatial characteristics was applied to the detection of epileptic EEG signals. However, the algorithm did not consider the nonlinear dynamic characteristics of the signals and ignored the time-frequency information, so the complementary characteristics of standard deviation, entropy and wavelet packet energy were selected for the combination in the feature extraction stage. The classification process adopted a new double classification model based on SVM. First, the normal, interictal and ictal periods were divided into normal and paroxysmal periods (including interictal and ictal periods), and then the samples belonging to the paroxysmal periods were classified into interictal and ictal periods. Finally, three categories of recognition were realized. The experimental data came from the epilepsy study at the University of Bonn in Germany. The average recognition rate was 98.73% in the first category and 99.90% in the second category. The experimental results show that the introduction of spatial characteristics and double classification model can effectively solve the problem of low recognition rate between interictal and ictal periods in many literatures, and improve the identification efficiency of each period, so it provides an effective detecting means for the prediction of epilepsy.
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