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.
Aiming at the disadvantages of traditional direct aperture optimization (DAO) method, such as slow convergence rate, prone to stagnation and weak global searching ability, a gradient-based direct aperture optimization (GDAO) is proposed. In this work, two different optimization methods are used to optimize the shapes and the weights of the apertures. Firstly, in order to improve the validity of the aperture shapes optimization of each search, the traditional simulated annealing (SA) algorithm is improved, the gradient is introduced to the algorithm. The shapes of the apertures are optimized by the gradient based SA method. At the same time, the constraints between the leaves of multileaf collimator (MLC) have been fully considered, the optimized aperture shapes are meeting the requirements of clinical radiation therapy. After that, the weights of the apertures are optimized by the limited-memory BFGS for bound-constrained (L-BFGS-B) algorithm, which is simple in calculation, fast in convergence rate, and suitable for solving large scale constrained optimization. Compared with the traditional SA algorithm, the time cost of this program decreased by 15.90%; the minimum dose for the planning target volume was improved by 0.29%, the highest dose for the planning target volume was reduced by 0.45%; the highest dose for the bladder and rectum, which are the organs at risk, decreased by 0.25% and 0.09%, respectively. The results of experiment show that the new algorithm can produce highly efficient treatment planning a short time and can be used in clinical practice.
In order to calibrate the hand-eye transformation of the surgical robot and laser range finder (LRF), a calibration algorithm based on a planar template was designed. A mathematical model of the planar template had been given and the approach to address the equations had been derived. Aiming at the problems of the measurement error in a practical system, we proposed a new algorithm for selecting coplanar data. This algorithm can effectively eliminate considerable measurement error data to improve the calibration accuracy. Furthermore, three orthogonal planes were used to improve the calibration accuracy, in which a nonlinear optimization for hand-eye calibration was used. With the purpose of verifying the calibration precision, we used the LRF to measure some fixed points in different directions and a cuboid’s surfaces. Experimental results indicated that the precision of a single planar template method was (1.37±0.24) mm, and that of the three orthogonal planes method was (0.37±0.05) mm. Moreover, the mean FRE of three-dimensional (3D) points was 0.24 mm and mean TRE was 0.26 mm. The maximum angle measurement error was 0.4 degree. Experimental results show that the method presented in this paper is effective with high accuracy and can meet the requirements of surgical robot precise location.
It is the main method for amplifying the specific gene to use the nucleic acid amplification system to accomplish polymerase chain reaction (PCR). The temperature retard between heat source and sample exists in the heating and cooling progresses of most nucleic acid amplification system. The retard would result in the problem that the sample would take a long time to reach the set temperature and the problem would reduce the speed of integrate reaction. Non-specific products would be created in the process of amplification when the sample cannot reach the set temperature within a certainly time and the amplified efficiency would be reduced. A miniaturization nucleic acid amplification system heated by air was designed in this study according to the principle of air-heated nucleic acid amplification system and the characteristics of the PCR instrument Smart-cycler. The heat transfer process was analyzed and the heat transfer time was calculated. The actual temperature was measured in real time, and the temperature curves were fitted. The heating time was chosen by analysis results and data fitting and the air temperature was changed, while the sample temperature was recorded. The retard between sample and air was optimized by choosing the best curve of sample temperature. The temperature retard between sample and air was reduced sharply and the required time of integrate progress is shortened to 50%. We confirmed from the amplification experiment of Listeria monocytogenes that the improved system could complete 3 cycles within 4 minutes, and the amplification effect was good. The amplification speed and effect could be improved effectively by optimizing the delay between sample and air.
Stress distribution of denture is an important criterion to evaluate the reasonableness of technological parameters, and the bite force derived from the antagonist is the critical load condition for the calculation of stress distribution. In order to improve the accuracy of stress distribution as much as possible, all-ceramic crown of the mandibular first molar with centric occlusion was taken as the research object, and a bite force loading method reflecting the actual occlusal situation was adopted. Firstly, raster scanning and three dimensional reconstruction of the occlusal surface of molars in the standard dental model were carried out. Meanwhile, the surface modeling of the bonding surface was carried out according to the preparation process. Secondly, the parametric occlusal analysis program was developed with the help of OFA function library, and the genetic algorithm was used to optimize the mandibular centric position. Finally, both the optimized case of the mesh model based on the results of occlusal optimization and the referenced case according to the cusp-fossa contact characteristics were designed. The stress distribution was analyzed and compared by using Abaqus software. The results showed that the genetic algorithm was suitable for solving the occlusal optimization problem. Compared with the reference case, the optimized case had smaller maximum stress and more uniform stress distribution characteristics. The proposed method further improves the stress accuracy of the prosthesis in the finite element model. Also, it provides a new idea for stress analysis of other joints in human body.
Objective To analyze the risk factors of type 2 diabetes mellitus and establish BP neural network model for screening of type 2 diabetes mellitus based on particle swarm optimization (PSO) algorithm. Methods Inpatients with type 2 diabetes mellitus in the Department of Endocrinology of the Affiliated Hospital of Guangdong Medical University and the Second Affiliated Hospital of Guangdong Medical University between July 2021 and August 2022 were selected as the case group and healthy people in the Health Management Center of the Affiliated Hospital of Guangdong Medical University as the control group. Basic information and physical and laboratory examination indicators were collected for comparative analysis. PSO-BP neural network model, BP neural network model and logistic regression models were established using MATLAB R2021b software and the optimal screening model of type 2 diabetes mellitus was selected. Based on the optimal model, the mean impact value algorithm was used to screen the risk factors of type 2 diabetes mellitus. Results A total of 1 053 patients were included in the case group and 914 healthy peoples in the control group. Except for type of salt, family history of comorbidities, body mass index, total cholesterol, low density lipoprotein cholesterol and staple food intake (P>0.05), the other indexes showed significant differences between the two groups. The performance of the PSO-BP neural network model outperformed the BP neural network model and the logistic regression model. Based on PSO-BP neural network model, the mean impact value algorithm showed that the risk factors for type 2 diabetes mellitus were fasting blood glucose , heart rate, age , waist-arm ratio and marital status , and the protective factors for type 2 diabetes mellitus were high density lipoprotein cholestero, vegetable intake, residence, education level, fruit intake and meat intake. Conclusions There are many influencing factors of type 2 diabetes mellitus. Focus should be placed on high-risk groups and regular disease screening should be carried out to reduce the risk of type 2 diabetes. The screening model of PSO-BP neural network performs the best, and it can be extended to the early screening and diagnosis of other diseases in the future.
To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32 female adolescents (16 depressed and 16 healthy, age: 16.3 ± 1.3) with eyes colsed for 4 min in a resting state. First, based on the phase synchronization between the signals, the phase-locked value (PLV) method was used to calculate brain functional connectivity in the θ and α frequency bands, respectively. Then based on the graph theory method, the network parameters, such as strength of the weighted network, average characteristic path length, and average clustering coefficient, were calculated separately (P < 0.05). Next, using the relationship between multiple thresholds and network parameters, the area under the curve (AUC) of each network parameter was extracted as new features (P < 0.05). Finally, support vector machine (SVM) was used to classify the two groups with the network parameters and their AUC as features. The study results show that with strength, average characteristic path length, and average clustering coefficient as features, the classification accuracy in the θ band is increased from 69% to 71%, 66% to 77%, and 50% to 68%, respectively. In the α band, the accuracy is increased from 72% to 79%, 69% to 82%, and 65% to 75%, respectively. And from overall view, when AUC of network parameters was used as a feature in the α band, the classification accuracy is improved compared to the network parameter feature. In the θ band, only the AUC of average clustering coefficient was applied to classification, and the accuracy is improved by 17.6%. The study proved that based on graph theory, the method of feature optimization of brain function network could provide some theoretical support for the computer-aided diagnosis of adolescent depression.
Day surgery is flourishing in public hospitals in China with the advantages of strong planning, short stay and high efficiency. Under the background of “diversification of surgeons, diversification of disease structure and precision of scheduling needs”, higher requirements are put forward for refined scheduling strategies of day surgery. The research of scientific and precise surgical scheduling strategy is of great significance to realize efficient coordination and optimal allocation of day surgical resources. This article reviews the necessity of precise scheduling of day surgery, the current situation of scheduling of day surgery, the key dimensions affecting the scheduling of day surgery, and the evaluation system for precise scheduling of day surgery.
To obtain recombinant human β2-microglobulin (rhβ2M) with properties of good solubility and high purity from E.coli, prokaryotic expression conditions were optimized and protein purification was performed in this study. After testing the effect of different IPTG concentrations, temperatures and induction times on the production of rhβ2M, the optimum expression conditions were determined, i.e. joining IPTG to final concentration being 0.8 mmol/L and inducing time 6 h and at temperature of 25℃. Under the optimum induction conditions, the ratio of soluble rhβ2M to soluble bacterial protein was 63.7%. After purified by Ni Sepharose 6 Fast Flow, the purity of rhβ2M achieved a greater value of 95%. Western blot analysis revealed that rhβ2M possessed the antigen property that specifically interacted with anti-β2M antibody.
Online hashing methods are receiving increasing attention in cross modal medical image retrieval research. However, existing online methods often lack the learning ability to maintain semantic correlation between new and existing data. To this end, we proposed online semantic similarity cross-modal hashing (OSCMH) learning framework to incrementally learn compact binary hash codes of medical stream data. Within it, a sparse representation of existing data based on online anchor datasets was designed to avoid semantic forgetting of the data and adaptively update hash codes, which effectively maintained semantic correlation between existing and arriving data and reduced information loss as well as improved training efficiency. Besides, an online discrete optimization method was proposed to solve the binary optimization problem of hash code by incrementally updating hash function and optimizing hash code on medical stream data. Compared with existing online or offline hashing methods, the proposed algorithm achieved average retrieval accuracy improvements of 12.5% and 14.3% on two datasets, respectively, effectively enhancing the retrieval efficiency in the field of medical images.