RESUMO
Background: Washed microbiota transplantation (WMT) as the improved methods of fecal microbiota transplantation has been employed as a therapeutic approach for ameliorating symptoms associated with autism spectrum disorder (ASD). In this context, colonic transendoscopic enteral tubing (TET) has been utilized as a novel procedure for administering WMT. Methods: Data of children with ASD who received WMT by TET were retrospectively reviewed, including bowel preparation methods, TET operation time, success rate, tube retention time, the comfort of children, adverse events, and parent satisfaction. Results: A total of 38 participants underwent 124 colonic TET catheterization procedures. The average time of TET operation was 15 minutes, and the success rate was 100% (124/124). There was no significant difference in TET operation time between high-seniority physicians and low-seniority physicians. In 123 procedures (99%), the TET tube allowed the completion of WMT treatment for 6 consecutive days. In 118 procedures (95.2%), the tube was detached spontaneously after the end of the treatment course, and the average TET tube retention time was 8 days. There was no incidence of tube blockage during the treatment course. No severe adverse events occurred during follow-up. Parents of all participants reported a high level of satisfaction with TET. Conclusion: Colonic TET is a safe and feasible method for WMT in children with ASD.
RESUMO
Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33-100%, and ELM, with an accuracy rate of 98.01-100%. For level assessment, the R² related to the training set was above 0.97 and the R² related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016-0.3494, lower than the error of 0.5-1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level.
RESUMO
Multi-sensor data fusion can provide more comprehensive and more accurate analysis results. However, it also brings some redundant information, which is an important issue with respect to finding a feature-mining method for intuitive and efficient analysis. This paper demonstrates a feature-mining method based on variable accumulation to find the best expression form and variables' behavior affecting beer flavor. First, e-tongue and e-nose were used to gather the taste and olfactory information of beer, respectively. Second, principal component analysis (PCA), genetic algorithm-partial least squares (GA-PLS), and variable importance of projection (VIP) scores were applied to select feature variables of the original fusion set. Finally, the classification models based on support vector machine (SVM), random forests (RF), and extreme learning machine (ELM) were established to evaluate the efficiency of the feature-mining method. The result shows that the feature-mining method based on variable accumulation obtains the main feature affecting beer flavor information, and the best classification performance for the SVM, RF, and ELM models with 96.67%, 94.44%, and 98.33% prediction accuracy, respectively.
