A quick method for the simultaneous determination of ascorbic acid and sorbic acid in fruit juices by capillary zone electrophoresis

A method of quickly determining ascorbic acid and sorbic acid by capillary zone electrophoresis with ultraviolet detection was developed. The choice of background electrolyte, wavelength, injection time and applied voltage were discussed. Ascorbic acid and sorbic acid were well separated in 80 mmol L⁻¹ boric acid-5 mmol L⁻¹borax (pH = 8.0) in 5 min at the detecting wavelength of 270 nm. Under the optimum condition, the method has linear ranges of 2.54-352.00 mg L⁻¹ for ascorbic acid and 1.08-336.39 mg L⁻¹ for sorbic acid with the detection limit of 1.70 mg L⁻¹ for ascorbic acid and 0.54 mg L⁻¹ for sorbic acid, respectively. Other organic acids in fruit juices have no effect on the detection. This method is very feasible and simple and can be used to detect ascorbic acid and sorbic acid in fruit juices.     

Synthesis of fluorinated imines and carbodiimides from azides via aza-Wittig reaction

The Staudinger reaction of fluoroalkylazides were studied. A series of N-fluoroalkylimines were synthesized via aza-Wittig reaction of N-fluoroalkyliminophosphoranes. The N,N′-difluoroalkylated carbodiimide was also synthesized via the reaction of N-fluoroalkyliminophosphoranes with carbon dioxide or carbon disulfide.     

聚合物在碳纤维表面的电沉积

用电沉积方法将苯乙烯-马来酸酐、乙酸乙烯酯-马来酸酐、甲基丙烯酸甲酯-马来酸酐共聚物沉积于碳纤维表面。通过实验初步证实了此过程的负离子-自由基机理,以及沉积层在纤维表面的物理粘附。由于电沉积的聚合物中间层改善了碳纤维增强塑料中纤维-基体树脂间的界面粘接作用,使原来的剪切破坏基本上发生于界面的情况转变成发生于基体本身为主。并将单向碳纤维增强环氧树脂复合材料的层间剪切强度,由原来的600kg/cm~2左右提高到1000kg/cm~2以上,经沸水浸泡100小时后的强度损失也减少了。     

Semi-Supervised Cross-Subject Emotion Recognition Based on Stacked Denoising Autoencoder Architecture Using a Fusion of Multi-Modal Physiological Signals

In recent decades, emotion recognition has received considerable attention. As more enthusiasm has shifted to the physiological pattern, a wide range of elaborate physiological emotion data features come up and are combined with various classifying models to detect one’s emotional states. To circumvent the labor of artificially designing features, we propose to acquire affective and robust representations automatically through the Stacked Denoising Autoencoder (SDA) architecture with unsupervised pre-training, followed by supervised fine-tuning. In this paper, we compare the performances of different features and models through three binary classification tasks based on the Valence-Arousal-Dominance (VAD) affection model. Decision fusion and feature fusion of electroencephalogram (EEG) and peripheral signals are performed on hand-engineered features; data-level fusion is performed on deep-learning methods. It turns out that the fusion data perform better than the two modalities. To take advantage of deep-learning algorithms, we augment the original data and feed it directly into our training model. We use two deep architectures and another generative stacked semi-supervised architecture as references for comparison to test the method’s practical effects. The results reveal that our scheme slightly outperforms the other three deep feature extractors and surpasses the state-of-the-art of hand-engineered features.     

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.     

A Novel Strategy for Regulating mRNA’s Degradation via Interfering the AUF1’s Binding to mRNA

The study on the mechanism and kinetics of mRNA degradation provides a new vision for chemical intervention on protein expression. The AU enrichment element (ARE) in mRNA 3′-UTR can be recognized and bound by the ARE binding protein (AU-rich Element factor (AUF1) to recruit RNase for degradation. In the present study, we proposed a novel strategy for expression regulation that interferes with the AUF1-RNA binding. A small-molecule compound, JNJ-7706621, was found to bind AUF1 protein and inhibit mRNA degradation by screening the commercial compound library. We discovered that JNJ-7706621 could inhibit the expression of AUF1 targeted gene IL8, an essential pro-inflammatory factor, by interfering with the mRNA homeostatic state. These studies provide innovative drug design strategies to regulate mRNA homeostasis.