The WinBUGS software can be called from either R (provided R2WinBUGS as an R package) or Stata software for network meta-analysis. Unlike R, Stata software needs to create relevant ADO scripts at first which simplify operation process greatly. Similar with R, Stata software also needs to load another package when drawing network plots. This article briefly introduces how to implement network meta-analysis using Stata software by calling WinBUGS software.
The phase-locking relationship between the firings of neuronal action potentials (i.e., spikes) and the oscillations of local field potentials (LFP) reflects important neural coding information. However, the present analysis methods can only determine whether there has phase-locking, but not the different strengths among various types of phase-locking. In the present paper, we used spike-triggered average (STA) signals and the percentage ratio (named φ) of the STA power to the power of original LFP as an index to evaluate the strengths of phase-locking. Experimental recordings obtained from rat hippocampal CA1 region as well as simulation data were used to evaluate the method. The results showed that the index φ changed monotonically as a function of the strength of phase-locking, and it could provide an effective critical value to divide phase-locking from non-phase-locking. Because the calculation of the index does not need pre-filtering, it can avoid the unwanted influences caused by intentionally limiting the frequencies of LFP oscillations such as in the traditional bin statistical method. Therefore, the index φ provides a novel method to investigate the mechanisms underlying neuronal coding in brain.
The key for performing meta-analysis using WinBUGS software is to construct a model of Bayesian statistics. The hand-written code model and Doodle model are two major methods for constructing it. The approach of hand-written code is flexible and convenient, but the language programming is fallibility. The Doodle is complicated, but it is benefit to understand the structure of hand-written code model and prevent error. This article briefly describes how to construct the Doodle model for binary and continuous data of head to head meta-analysis, indirect comparison and network meta-analysis, and ordinal variables meta-analysis.
Objective To assess the completion of the under 5 mortality rate (U5MR) of Millennium Development Goals in 194 member countries of WHO, and to analyze the present situation of the global U5MR. Methods Based on the U5MR and the proportion of main causes of death in the "World Health Statistics 2015", the Millennium Development Goals of the decline of U5MR from 1990 to 2013 was assessed, the U5MR was analyzed by comparison between 2000 and 2013. Bivariate Pearson correlation analysis was used to determine the correlation between mortality and the ratio of infection to non infectious diseases and GDP per person in U5MR. Results By 2013, in 194 WHO member states, the U5MR in 46 (23.71%) countries achieved the millennium development goals. Comparison between 2000 and 2013, there was significant difference between low and high mortality groups in six continents (P<0.05), there was no significant difference between the moderate death groups (P>0.05), there was no significant difference in the ratio of infection to non infectious diseases between the middle and low mortality groups (P>0.05), however there was significant difference between the high mortality groups (P<0.05). There was significant difference in the average decline of U5MR and the ratio of non infectious diseases between low and medium, middle and high mortality groups (P<0.05). The Global U5MR had significant regional differences, the highest U5MR was in Africa, the lowest U5MR was in Europe, the medium U5MR was in North America, Oceania, South America, Asia was becoming the middle level. The U5MR was highly correlated with the ratio of infection to non-infectious diseases in every country (r2000y=0.934,r2013y=0.911,P<0.05), and it was low negatively correlated with GDP per capita (r2000y=–0.443,r2013y=–0.433,P<0.05). Conclusions There is a long way to reduce global child mortality. Prevention and control should focus on Africa and Asia. Prevention and control of infectious diseases is an effective measure for middle and high mortality countries. Prevention and control of non-infectious diseases is an important measure for low mortality countries. Increasing health investment is an important means to further reduce global U5MR.
This study aims to determine the salient brain regions with abnormal changes in white matter structures from diffusion tensor imaging (DTI) images of the patients with temporal lobe epilepsy (TLE), and to discriminate the patients with TLE from normal controls (NCs). Firstly, the DTI images from 50 subjects (28 NCs and 22 TLE) were acquired. Secondly, the four measures including the fractional anisotropy (FA), the mean diffusivity (MD), the axial diffusivity (AD) and the radial diffusivity (RD) were calculated. Thirdly, the tract-based spatial statistics (TBSS) was adopted to extract the measures in brain regions with significant differences between the two compared groups. Fourthly, the obtained measures were used as input features of the support vector machine (SVM) for classification, and the support vector machine-recursive feature elimination (SVM-RFE) was compared with the support vector machine-tract-based spatial statistics (SVM-TBSS) method. Finally, the essential brain regions and their spatial distribution were analyzed and discussed. The experimental results showed that the FA measures of the TLE group decreased significantly in the corpus callosum, superior longitudinal fasciculus, corona radiata, external capsule, internal capsule, inferior fronto-occipital fasciculus, fasciculus uncinatus and sagittal stratum, which were nearly bilaterally distributed, while the MD and RD increased significantly in most of these brain regions of the TLE group. Although the AD also increased, the differences were not statistically significant. The SVM-TBSS classifier obtained accuracies of 82%, 76% and 76% using the FA, MD and RD for classification, respectively, and 80% using combined measures. The SVM-RFE classifier obtained accuracies of 90%, 90% and 92% using the FA, MD and RD respectively, while the highest accuracy was 100% using combined measures. These results demonstrated that the SVM-RFE outperformed the SVM-TBSS, and the dominant characteristic influencing classification in brain regions were in associative and commissural fibers. These results illustrated that the measures of DTI images could reveal the abnormal changes in white matter structure of patients with TLE, providing effective information to clarify its pathological mechanism, localize the focus and diagnose automatically.
Objective To analyze the data of external fixation instruments (including Ilizarov instruments) used by QIN Sihe orthopaedic surgical team in the treatment of limb deformities in the past 30 years, and to explore the indications for the application of modern external fixation techniques in the correction of limb deformities and individual device configuration selection strategy. Methods According to QIN Sihe orthopaedic surgical team, the use of external fixator between January 1988 and December 2017 was analyzed retrospectively. The total use of external fixation and the proportion of different external fixators were analyzed in gender, different operation time, different age, different parts, and different diseases. Results External fixators were used in 8 113 patients, 69 of them were used simultaneously in both lower extremity surgery, so 8 182 external fixators were used. Among them, there were 4 725 (57.74%) combined external fixators, 3 388 (41.41%) Ilizarov circle fixators, 64 (0.78%) single arm external fixators (including Orthofix), 5 (0.06%) Taylor space external fixators. There were 4 487 males (55.31%) and 3 626 females (44.69%). According to the analysis of different time periods, the number of external fixators increased year by year, and the number of applications increased after 2000. The main age of the patients was 11-30 years old, of which 1 819 sets (22.23%) were used at the age of 21-25 years. The use of the external fixator covered almost all parts of the limbs, with the ankle and toe areas being the most common, reaching 4 664 sets (57.00%), and the upper extremities the least, with 152 sets (1.86%). The 8 113 cases covered more than a dozen disciplines and more than 150 kinds of diseases. The top 5 diseases were poliomyelitis sequelae, cerebral palsy, deformity of lower extremity after spina bifida, traumatic sequelae, and congenital equinovarus foot. Conclusion Ilizarov technique has been widely used in extremity deformity, disability, and complicated orthopedic diseases caused by vascular, lymphoid, nerve, skin, endocrine, and other diseases. The indication of operation is far beyond the scope of orthopedics. The domestic external fixator and its mounting tools can basically meet the requirements of various treatments. The technique of external fixation has entered a new era of tension tissue regeneration under stress control, natural repair of tissue trauma and deformity, and reconstruction of limb function.
There are so many biomechanical risk factors related with glaucoma and their relationship is much complex. This paper reviewed the state-of-the-art research works on glaucoma related mechanical effects. With regards to the development perspectives of studies on glaucoma biomechanics, a completely novel biomechanical evaluation factor -- Fractional Flow Reserve (FPR) for glaucoma was proposed, and developing clinical application oriented glaucoma risk assessment algorithm and application system by using the new techniques such as artificial intelligence and machine learning were suggested.
The deoxyribonucleic acid (DNA) molecule damage simulations with an atom level geometric model use the traversal algorithm that has the disadvantages of quite time-consuming, slow convergence and high-performance computer requirement. Therefore, this work presents a density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm based on the spatial distributions of energy depositions and hydroxyl radicals (·OH). The algorithm with probability and statistics can quickly get the DNA strand break yields and help to study the variation pattern of the clustered DNA damage. Firstly, we simulated the transportation of protons and secondary particles through the nucleus, as well as the ionization and excitation of water molecules by using Geant4-DNA that is the Monte Carlo simulation toolkit for radiobiology, and got the distributions of energy depositions and hydroxyl radicals. Then we used the damage probability functions to get the spatial distribution dataset of DNA damage points in a simplified geometric model. The DBSCAN clustering algorithm based on damage points density was used to determine the single-strand break (SSB) yield and double-strand break (DSB) yield. Finally, we analyzed the DNA strand break yield variation trend with particle linear energy transfer (LET) and summarized the variation pattern of damage clusters. The simulation results show that the new algorithm has a faster simulation speed than the traversal algorithm and a good precision result. The simulation results have consistency when compared to other experiments and simulations. This work achieves more precise information on clustered DNA damage induced by proton radiation at the molecular level with high speed, so that it provides an essential and powerful research method for the study of radiation biological damage mechanism.
The study aims to investigate whether there is difference in pre-treatment white matter parameters in treatment-resistant and treatment-responsive schizophrenia. Diffusion tensor imaging (DTI) was acquired from 60 first-episode drug-naïve schizophrenia (39 treatment-responsive and 21 treatment-resistant schizophrenia patients) and 69 age- and gender-matched healthy controls. Imaging data was preprocessed via FSL software, then diffusion parameters including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were extracted. Besides, structural network matrix was constructed based on deterministic fiber tracking. The differences of diffusion parameters and topology attributes between three groups were analyzed using analysis of variance (ANOVA). Compared with healthy controls, treatment-responsive schizophrenia showed altered white matter mainly in anterior thalamus radiation, splenium of corpus callosum, cingulum bundle as well as superior longitudinal fasciculus. While treatment-resistant schizophrenia patients showed white matter abnormalities in anterior thalamus radiation, cingulum bundle, fornix and pontine crossing tract relative to healthy controls. Treatment-resistant schizophrenia showed more severe white matter abnormalities in anterior thalamus radiation compared with treatment-responsive patients. There was no significant difference in white matter network topological attributes among the three groups. The performance of support vector machine (SVM) showed accuracy of 63.37% in separating the two patient subgroups (P = 0.04). In this study, we showed different patterns of white matter alterations in treatment-responsive and treatment-resistant schizophrenia compared with healthy controls before treatment, which may help guiding patient identification, targeted treatment and prognosis improvement at baseline drug-naïve state.
For speech detection in Parkinson’s patients, we proposed a method based on time-frequency domain gradient statistics to analyze speech disorders of Parkinson’s patients. In this method, speech signal was first converted to time-frequency domain (time-frequency representation). In the process, the speech signal was divided into frames. Through calculation, each frame was Fourier transformed to obtain the energy spectrum, which was mapped to the image space for visualization. Secondly, deviations values of each energy data on time axis and frequency axis was counted. According to deviations values, the gradient statistical features were used to show the abrupt changes of energy value in different time-domains and frequency-domains. Finally, KNN classifier was applied to classify the extracted gradient statistical features. In this paper, experiments on different speech datasets of Parkinson’s patients showed that the gradient statistical features extracted in this paper had stronger clustering in classification. Compared with the classification results based on traditional features and deep learning features, the gradient statistical features extracted in this paper were better in classification accuracy, specificity and sensitivity. The experimental results show that the gradient statistical features proposed in this paper are feasible in speech classification diagnosis of Parkinson’s patients.