The vessels in the microcirculation keep adjusting their structure to meet the functional requirements of the different tissues. A previously developed theoretical model can reproduce the process of vascular structural adaptation to help the study of the microcirculatory physiology. However, until now, such model lacks the appropriate methods for its parameter settings with subsequent limitation of further applications. This study proposed an improved quantum-behaved particle swarm optimization (QPSO) algorithm for setting the parameter values in this model. The optimization was performed on a real mesenteric microvascular network of rat. The results showed that the improved QPSO was superior to the standard particle swarm optimization, the standard QPSO and the previously reported Downhill algorithm. We conclude that the improved QPSO leads to a better agreement between mathematical simulation and animal experiment, rendering the model more reliable in future physiological studies.
Due to the decline of motor ability and the impact of the diseases, abnormalities in gait is common in the elderly population, which will raise the risk of fall and cause serious injury. This study focuses on the analysis of the gait kinematics parameters of normal adults’ gait, aiming to investigate the characteristics of gait parameters in different age groups and to explore the role of gait parameters in motor function assessment and clinical diagnosis. Based on the gait data gained by electronic walkway, the relationship among the toe out angles and their correlation with age and gender etc. were quantitatively analyzed. The results show that most normal subjects walk with positive toe out angles, and the angles increase with age. Such changes are slow in the young and middle age groups. However, the elevations of the left out toe angle and the angles between the feet are statistically significant after entering elder age ( >60 years). The results also suggest that the angle between the feet is a kind of practical gait parameter for varying applications. This study concludes that feet angle analysis is potential to provide a convenient and quantitative tool for the assessment of lower limb motor ability and the diagnosis of knee joint diseases.
Aiming at the problem that the small samples of critical disease in clinic may lead to prognostic models with poor performance of overfitting, large prediction error and instability, the long short-term memory transferring algorithm (transLSTM) was proposed. Based on the idea of transfer learning, the algorithm leverages the correlation between diseases to transfer information of different disease prognostic models, constructs the effictive model of target disease of small samples with the aid of large data of related diseases, hence improves the prediction performance and reduces the requirement for target training sample quantity. The transLSTM algorithm firstly uses the related disease samples to pretrain partial model parameters, and then further adjusts the whole network with the target training samples. The testing results on MIMIC-Ⅲ database showed that compared with traditional LSTM classification algorithm, the transLSTM algorithm had 0.02-0.07 higher AUROC and 0.05-0.14 larger AUPRC, while its number of training iterations was only 39%-64% of the traditional algorithm. The results of application on sepsis revealed that the transLSTM model of only 100 training samples had comparable mortality prediction performance to the traditional model of 250 training samples. In small sample situations, the transLSTM algorithm has significant advantages with higher prediciton accuracy and faster training speed. It realizes the application of transfer learning in the prognostic model of critical disease with small samples.