An Automatic Evaluation Method for Parkinson's Dyskinesia Using Finger Tapping Video for Small Samples

PurposeThe assessment of dyskinesia in Parkinson's disease (PD) based on Artificial Intelligence technology is a significant and challenging task. At present, doctors usually use MDS-UPDRS scale to assess the severity of patients. This method is time-consuming and laborious, and there are subjective differences. The evaluation method based on sensor equipment is also widely used, but this method is expensive and needs professional guidance, which is not suitable for remote evaluation and patient self-examination. In addition, it is difficult to collect patient data in medical research, so it is of great significance to find an objective and automatic assessment method for Parkinson's dyskinesia based on small samples.MethodsIn this study, we design an automatic evaluation method combining manual features and convolutional neural network (CNN), which is suitable for small sample classification. Based on the finger tapping video of Parkinson's patients, we use the pose estimation model to obtain the action skeleton information and calculate the feature data. We then use the 5-folds cross validation training model to achieve optimum trade-of between bias and variance, and finally make multi-class prediction through fully connected network (FCN). ResultsOur proposed method achieves the current optimal accuracy of 79.7% in this research. We have compared with the latest methods of related research, and our method is superior to them in terms of accuracy, number of parameters and FLOPs. ConclusionThe method in this paper does not require patients to wear sensor devices, and has obvious advantages in remote clinical evaluation. At the same time, the method of using motion feature data to train CNN model obtains the optimal accuracy, effectively solves the problem of difficult data acquisition in medicine, and provides a new idea for small sample classification.

Application of Machine Learning for Cytometry Data

Modern cytometry technologies present opportunities to profile the immune system at a single-cell resolution with more than 50 protein markers, and have been widely used in both research and clinical settings. The number of publicly available cytometry datasets is growing. However, the analysis of cytometry data remains a bottleneck due to its high dimensionality, large cell numbers, and heterogeneity between datasets. Machine learning techniques are well suited to analyze complex cytometry data and have been used in multiple facets of cytometry data analysis, including dimensionality reduction, cell population identification, and sample classification. Here, we review the existing machine learning applications for analyzing cytometry data and highlight the importance of publicly available cytometry data that enable researchers to develop and validate machine learning methods.

Characterization, Classification, and Authentication of Polygonatum sibiricum Samples by Volatile Profiles and Flavor Properties

The importance of monitoring key aroma compounds as food characteristics to solve sample classification and authentication is increasing. The rhizome of Polygonatum sibiricum (PR, Huangjing in Chinese) has great potential to serve as an ingredient of functional foods owing to its tonic effect and flavor properties. In this study, we aimed to characterize and classify PR samples obtained from different processing levels through their volatile profiles and flavor properties by using electronic nose, electronic tongue, and headspace gas chromatography-mass spectrometry. Nine flavor indicators (four odor indicators and five taste indicators) had a strong influence on the classification ability, and a total of 54 volatile compounds were identified in all samples. The traditional Chinese processing method significantly decreased the contents of aldehydes and alkanes, while more ketones, nitrogen heterocycles, alcohols, terpenoids, sulfides, and furans/pyrans were generated in the processing cycle. The results confirmed the potential applicability of volatile profiles and flavor properties for classification of PR samples, and this study provided new insights for determining the processing level in food and pharmaceutical industries based on samples with specific flavor characteristics.

An Image Enhancement Method for Few-shot Classification

With the development of 5G/6G, IoT, and cloud systems, the amount of data generated, transmitted, and calculated is increasing, and fast and effective close-range image classification becomes more and more important. But many methods require a large number of samples to support in order to achieve sufficient functions. This allows the entire network to zoom in to meet a large number of effective feature extractions, which reduces the efficiency of small sample classification to a certain extent. In order to solve these problems, we propose an image enhancement method for the problems of few-shot classification. This method is an expanded convolutional network with data enhancement function. This network can not only meet the features required for image classification without increasing the number of samples, but also has the advantage of using a large number of effective features without sacrificing efficiency. structure. The cutout structure can enhance the matrix in the data image input process by adding a fixed area 0 mask. The structure of FAU uses dilated convolution and uses the characteristics of the sequence to improve the efficiency of the network. We conduct a comparative experiment on the miniImageNet and CUB datasets, and the proposed method is superior to the comparative method in terms of effectiveness and efficiency measurement in the 1-shot and 5-shot cases.

An Ensemble Classification Method for High-Dimensional Data Using Neighborhood Rough Set

Mining useful knowledge from high-dimensional data is a hot research topic. Efficient and effective sample classification and feature selection are challenging tasks due to high dimensionality and small sample size of microarray data. Feature selection is necessary in the process of constructing the model to reduce time and space consumption. Therefore, a feature selection model based on prior knowledge and rough set is proposed. Pathway knowledge is used to select feature subsets, and rough set based on intersection neighborhood is then used to select important feature in each subset, since it can select features without redundancy and deals with numerical features directly. In order to improve the diversity among base classifiers and the efficiency of classification, it is necessary to select part of base classifiers. Classifiers are grouped into several clusters by k-means clustering using the proposed combination distance of Kappa-based diversity and accuracy. The base classifier with the best classification performance in each cluster will be selected to generate the final ensemble model. Experimental results on three Arabidopsis thaliana stress response datasets showed that the proposed method achieved better classification performance than existing ensemble models.

Few-Shot Abnormal Network Traffic Detection Based on Multi-scale Deep-CapsNet and Adversarial Reconstruction

Detecting various attacks and abnormal traffic in the network is extremely important to network security. Existing detection models used massive amounts of data to complete abnormal traffic detection. However, few-shot attack samples can only be intercepted in certain special scenarios. In addition, the discrimination of traffic attributes will also be affected by the change of feature attitude. But the traditional neural network model cannot detect this kind of attitude change. Therefore, the accuracy and efficiency of few-shot sample abnormal traffic detection are very low. In this paper, we proposed a few-shot abnormal network traffic detection method. It was composed of the multi-scale Deep-CapsNet and adversarial reconstruction. First, we designed an improved EM vector clustering of the Deep-CapsNet. The attitude transformation matrix was used to complete the prediction from low-level to high-level features. Second, a multi-scale convolutional capsule was designed to optimize the Deep-CapsNet. Third, an adversarial reconstruction classification network (ARCN) was proposed. The supervised source data classification and the unsupervised target data reconstruction were achieved. Moreover, we proposed an adversarial training strategy, which alleviated the noise interference during reconstruction. Fourth, the few-shot sample classification were obtained by combining multi-scale Deep-CapsNet and adversarial reconstruction. The ICSX2012 and CICIDS2017 datasets were used to verify the performance. The experimental results show that our method has better training performance. Moreover, it has the highest accuracy in two-classification and multi-classification. Especially it has good anti-noise performance and short running time, which can be used for real-time few-shot abnormal network traffic detection.

Diagnostic Accuracy of Liquid Biopsy in Endometrial Cancer

Background: Liquid biopsy is a minimally invasive collection of a patient body fluid sample. In oncology, they offer several advantages compared to traditional tissue biopsies. However, the potential of this method in endometrial cancer (EC) remains poorly explored. We studied the utility of tumor educated platelets (TEPs) and circulating tumor DNA (ctDNA) for preoperative EC diagnosis, including histology determination. Methods: TEPs from 295 subjects (53 EC patients, 38 patients with benign gynecologic conditions, and 204 healthy women) were RNA-sequenced. DNA sequencing data were obtained for 519 primary tumor tissues and 16 plasma samples. Artificial intelligence was applied to sample classification. Results: Platelet-dedicated classifier yielded AUC of 97.5% in the test set when discriminating between healthy subjects and cancer patients. However, the discrimination between endometrial cancer and benign gynecologic conditions was more challenging, with AUC of 84.1%. ctDNA-dedicated classifier discriminated primary tumor tissue samples with AUC of 96% and ctDNA blood samples with AUC of 69.8%. Conclusions: Liquid biopsies show potential in EC diagnosis. Both TEPs and ctDNA profiles coupled with artificial intelligence constitute a source of useful information. Further work involving more cases is warranted.

A Method for Quality Control of Bauxites: Case Study of Brazilian Bauxites Using PLSR on Transmission XRD Data

Available Alumina (AvAl2O3) and Reactive Silica (RxSiO2), the main parameters of bauxite controlled in the beneficiation process are traditionally measured by laborious, expensive, and time-consuming wet chemistry methods. Alternative methods based on XRD analysis, capable to provide a reliable estimation of these parameters and valuable mineralogical information of the ore, are being studied. In this work, X-ray diffraction data in transmission mode was used to estimate AvAl2O3 and RxSiO2 from Brazilian bauxites using the Partial Least Square Regression (PLSR) statistical tool. The proposed method comprises a routine of sample classification according to their similarities by Principal Component Analysis (PCA) and K-means, calibration of the PLSR model for each group of samples, grouping new bauxite samples according to the generated clustering model, and subsequent estimation of the parameters AvAl2O3 and RxSiO2 using the PLSR models for these samples. The results showed good accuracy and precision of the models generated for samples of the main ore lithology. The quality and pre-processing of the XRD data required for this method are discussed. The results demonstrated that this method has the potential to be industrially applied to quality control of bauxites as a rapid and automated procedure.