Research on Green Express Packaging in the Era of Online Shopping

With the rapid development of electronic commerce in China, the rapid increase in the number of express packages has brought about great environmental problems while bringing great convenience to us. Based on the concept of green logistics, this paper discusses the problems of unreasonable express packaging materials, excessive packaging and the absence of packaging waste recovery system from both theoretical and practical aspects, and puts forward the countermeasures to promote the green express packaging in the era of online shopping. This paper aims to find the ways, measures and methods to solve the problems of express packaging, so as to realize the load reduction and greening of express packaging in China

A Comparative Study on Deep Learning Models for Text Classification of Unstructured Medical Notes with Various Levels of Class Imbalance

Background Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical notes data that can entail subsequent actionable results in the medical domain. This study aims to explore the model performance of various deep learning algorithms in text classification tasks on medical notes with respect to different disease class imbalance scenarios. Methods In this study, we employed seven artificial intelligence models, a CNN (Convolutional Neural Network), a Transformer encoder, a pretrained BERT (Bidirectional Encoder Representations from Transformers), and four typical sequence neural networks models, namely, RNN (Recurrent Neural Network), GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), and Bi-LSTM (Bi-directional Long Short-Term Memory) to classify the presence or absence of 16 disease conditions from patients’ discharge summary notes. We analyzed this question as a composition of 16 binary separate classification problems. The model performance of the seven models on each of the 16 datasets with various levels of imbalance between classes were compared in terms of AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic), AUC-PR (Area Under the Curve of Precision and Recall), F1 Score, and Balanced Accuracy as well as the training time. The model performances were also compared in combination with different word embedding approaches (GloVe, BioWordVec, and no pre-trained word embeddings). Results The analyses of these 16 binary classification problems showed that the Transformer encoder model performs the best in nearly all scenarios. In addition, when the disease prevalence is close to or greater than 50%, the Convolutional Neural Network model achieved a comparable performance to the Transformer encoder, and its training time was 17.6% shorter than the second fastest model, 91.3% shorter than the Transformer encoder, and 94.7% shorter than the pre-trained BERT-Base model. The BioWordVec embeddings slightly improved the performance of the Bi-LSTM model in most disease prevalence scenarios, while the CNN model performed better without pre-trained word embeddings. In addition, the training time was significantly reduced with the GloVe embeddings for all models. Conclusions For classification tasks on medical notes, Transformer encoders are the best choice if the computation resource is not an issue. Otherwise, when the classes are relatively balanced, CNNs are a leading candidate because of their competitive performance and computational efficiency

Study on the isolation of active constituents in Lonicera japonica and the mechanism of their anti-upper respiratory tract infection action in children

Background: Lonicera japonica has been studied extensively by scholars at home and abroad, a number of compounds have been isolated from it, which mainly include organic acids and flavonoids. Pharmacological studies have shown that Lonicera japonica has antibacterial and gall bladder-protective effects.Objective: To study the active constituents in Lonicera japonica and the mechanism of their anti-upper respiratory tract infection action in children.Methods: Compounds were identified by chromatographic methods, and the mechanism of anti-pediatric upper respiratory tract infection action of Lonicera japonica decoction was studied using experimental animals.Results: A total of four compounds were isolated, after injection of egg white, toe edema in rats in the control group was very obvious, different test concentrations of Lonicera japonica decoction all inhibited toe edema in rats to some extents, the edema was the mildest in the Lonicera japonica decoction high-dose group, which had the strongest inhibitory effect on the development of inflammation, the Lonicera japonica decoction showed certain dose-effect relationship with toe edema in rats. In the rat body temperature control experiment, while body temperature of rats in the blank group had already risen, other groups were still able to lower the body temperature of rats under the action of test drugs. The severity of ear edema in mice in the blank control group was obvious, with increased thickness which showed significant difference between left and right ears. Under test doses, three Lonicera japonica decoction groups all inhibited xylene-induced ear edema in mice.Conclusion: Lonicera japonica has an anti-upper respiratory tract infection action in children.Keywords: Lonicera japonica, chlorogenic acid butyl ester, oleanolic acid, mouse ear edem

A novel activator-type ERF of Thinopyrum intermedium, TiERF1, positively regulates defence responses

Thinopyrum intermedium is resistant to many different pathogens. To understand the roles of ethylene response factors (ERFs) in defence responses, the first member of the ERF family in T. intermedium, TiERF1, was characterized and functionally analysed in this study. The TiERF1 gene encodes a putative protein of 292 amino acids, belonging to the B3 subgroup of the ERF transcription factor family. Biochemical assays demonstrated that the TiERF1 protein is capable of binding to the GCC box, a cis-element present in the promoters of pathogenesis-related (PR) genes, and possessing transactivation activity, as well as localizing to the nucleus. The transcript of TiERF1 in T. intermedium is rapidly induced by infection with Rhizoctonia cerealis, Fusarium graminearum, or Blumeria graminis, and ethylene, jasmonic acid, and salicylic acid treatments. More importantly, the ectopic expression of TiERF1 in tobacco activated the transcript of the PR genes of tobacco with a GCC box cis-element, and ACO and ACS genes key to ethylene synthesis, and in turn improved the resistance level to Alternaria alternata and tobacco mosaic virus, as well as causing some phenotypic changes associated with ethylene response in the transgenic tobacco plants. Taken together, TiERF1 protein as an ERF transcription activator positively regulates defence responses via the activation of some defence-related genes

Quasiparticle band alignment and stacking-independent exciton in MA2_2Z4_4 (M = Mo, W, Ti; A= Si, Ge; Z = N, P, As)

Motivated by the recently synthesized two-dimensional semiconducting MoSi$_2$N$_4$, we systematically investigate the quasiparticle band alignment and exciton in monolayer MA$_2$Z$_4$ (M = Mo, W, Ti; A= Si, Ge; Z = N, P, As) using ab initio GW and Bethe-Salpeter equation calculations. Compared with the results from density functional theory (DFT), our GW calculations reveal substantially more significant band gaps and different absolute quasiparticle energy but predict the same types of band alignments