BACKGROUND: Parkinson's disease (PD) is a leading cause of death and disability worldwide, and is associated with a significant Global Burden of Disease (GBD). We analyzed the trends in PD incidence, mortality, and disability-adjusted life year (DALY) burden in China, and compared them with global data. METHODS: Estimates and 95% uncertainty intervals (UIs) for incidence, mortality, DALYs, years lived with disability (YLDs), and years of life lost (YLLs) for PD were extracted from the GBD, Injuries, and Risk Factors Study 2021. We describe the epidemiology of PD at global and Chinese levels, analyze trends in incidence and mortality from 1990 to 2021 by joinpoint regression models, and decompose PD burden according to population size, age structure, and epidemiological changes. RESULTS: GBD 2021 estimated 508,378 (95% UI: 430,499-592,748) incident cases of PD, 92,035 (95% UI: 75,908-108,133) deaths, and 2,159,514 (95% UI: 1,826,196-2,521,344) DALYs in China, with the higher age-standardized rate (ASR) in incidence, mortality and DALYs than the global levels. The DALY burden of PD in China increased slightly from 1990 to 2021, consistent with the global upward trend. Joinpoint regression analysis indicated that the ASR of incidence in China increased faster than the global average, while the ASR of mortality decreased, with the fastest decline in 2004-2014. Decomposition analysis revealed that men and the middle sociodemographic index (SDI) quintile (32.82%) were responsible for the most significant DALYs, whose changes were primarily driven by population growth and aging. CONCLUSIONS The burden of PD showed an overall increasing trend from 1990 to 2021, which was primarily driven by population growth and aging. This study highlights the significant challenges in controlling and managing PD, including the increase in cases and gender differences, which may provide guidance for comprehensive strategies to address the changing profiles of PD in China.
Humans ; Parkinson Disease/mortality* ; China/epidemiology* ; Global Burden of Disease ; Male ; Incidence ; Female ; Disability-Adjusted Life Years ; Middle Aged ; Aged ; Adult ; Quality-Adjusted Life Years ; Aged, 80 and over ; Cost of Illness ; Adolescent ; Pattern Analysis, Machine

This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4 ^th International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.
Brain-Computer Interfaces ; Humans ; Support Vector Machine ; Algorithms ; Neural Networks, Computer ; Imagination/physiology* ; Pattern Recognition, Automated/methods* ; Electroencephalography ; Wavelet Analysis

This study evaluated the feasibility of using texture analysis and machine learning to distinguish radiographic lung patterns. A total of 1200 regions of interest (ROIs) including four specific lung patterns (normal, alveolar, bronchial, and unstructured interstitial) were obtained from 512 thoracic radiographs of 252 dogs and 65 cats. Forty-four texture parameters based on eight methods of texture analysis (first-order statistics, spatial gray-level-dependence matrices, gray-level-difference statistics, gray-level run length image statistics, neighborhood gray-tone difference matrices, fractal dimension texture analysis, Fourier power spectrum, and Law's texture energy measures) were used to extract textural features from the ROIs. The texture parameters of each lung pattern were compared and used for training and testing of artificial neural networks. Classification performance was evaluated by calculating accuracy and the area under the receiver operating characteristic curve (AUC). Forty texture parameters showed significant differences between the lung patterns. The accuracy of lung pattern classification was 99.1% in the training dataset and 91.9% in the testing dataset. The AUCs were above 0.98 in the training set and above 0.92 in the testing dataset. Texture analysis and machine learning algorithms may potentially facilitate the evaluation of medical images.
Animals ; Area Under Curve ; Cats ; Classification ; Dataset ; Dogs ; Fourier Analysis ; Fractals ; Lung ; Machine Learning ; Neural Networks (Computer) ; Pattern Recognition, Visual ; Radiography, Thoracic ; Residence Characteristics ; ROC Curve

In the field of computational molecule biology, there is still a challenging question of how to detect non-coding RNA gene in lots of unlabeled sequences. Generally, the methods of machine learning and classification are employed to answer this question. However, only a limited number of positive training samples and unlabeled samples are available. The negative samples are difficult to define appropriately, yet they are necessary for usual learning-then-classification method. The common way for most of the existing non-coding RNA gene finding methods is to produce a number of random sequences as negative samples, which may hold some characteristic of positive sample sequences. Consequently, the contrived uncertain factor was introduced and the performance of methods was not good enough. In this paper, Support Vector Data Description (SVDD) is in use for to learning and classification as well as for detecting non-coding RNA gene in lots of unlabeled sequences, and the k-means clustering algorithm is employed before SVDD training to deal with the high flase positive fault in the result of SVDD. The training samples (target samples) are non-coding RNA genes validated by experiment. Moreover, appropriate features were constructed by Principal Component Analysis (PCA). The effectiveness and performance of the method are demonstrated by testing the cases in NONCODE databases and E. coli genome.
Algorithms ; Cluster Analysis ; Escherichia coli ; genetics ; Humans ; Pattern Recognition, Automated ; methods ; RNA, Untranslated ; genetics ; Support Vector Machine