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Doping engineering is considered as a facile and effective method for improving the physical and chemical properties of CeO2. Herein, rare earth (RE) ions (La and Nd) doped Pd/CeO2 with the coaxial double-nanotube structure were manufactured by the electrospinning and high-temperature calcination. Through the doping engineering, the original morphologies of Pd/CeO2 were not destroyed and all the elements were uniformly distributed on the surface of nanotubes. XRD, Raman and UV–vis characterizations revealed that La3+ and Nd3+ ions were incorporated into the CeO2 lattice, while the cubic fluorite structure of CeO2 was maintained. Moreover, it has been proved that there were oxygen vacancies existed on the surface of CeO2 and the concentration of oxygen vacancies increased significantly as the radius of RE ions increases. In addition, an electron transfer pathway between CeO2 and Pd nanoparticles was proposed based on the experimental data and Density Functional Theory (DFT) calculations. The Pd/CeO2-La catalyst with abundant oxygen vacancies showed a high catalytic performance for Suzuki coupling reaction under mild conditions without any phase-transfer reagent, toxic solvent and inert protective atmosphere, which realizing the green catalysis.

To develop a grading prediction model of traumatic hemorrhage volume based on deep learning and assist in predicting traumatic hemorrhage volume.A retrospective observational study was conducted based on the experimental data of pig gunshot wounds in the time-effect assessment database for experiments on war-traumatized animals constructed by the General Hospital of the Chinese People's Liberation Army. The hemorrhage volume data of the study population were extracted, and the animals were divided into 0-300 mL, 301-600 mL, and600 mL groups according to the hemorrhage volume. Using vital signs indexes as the predictive variables and hemorrhage volume grading as the outcome variable, trauma hemorrhage volume grading prediction models were developed based on four traditional machine learning and ten deep learning methods. Using laboratory test indexes as predictive variables and hemorrhage volume grading as outcome variables, trauma hemorrhage volume grading prediction models were developed based on the above fourteen methods. The effect of the two groups of models was evaluated by accuracy and area under the receiver operator characteristic curve (AUC), and the optimal models in the two groups were mixed to obtain hybrid model 1. Feature selection was conducted according to the genetic algorithm, and hybrid model 2 was constructed according to the best feature combination. Finally, hybrid model 2 was deployed in the animal experiment database system.Ninety-six traumatic animals in the database were enrolled, including 27 pigs in the 0-300 mL group, 40 in the 301-600 mL group, and 29 in the600 mL group. Among the fourteen models based on vital signs indexes, fully convolutional network (FCN) model was the best [accuracy: 60.0%, AUC and 95% confidence interval (95%CI) was 0.699 (0.671-0.727)]. Among the fourteen models based on laboratory test indexes, recurrent neural network (RNN) model was the best [accuracy: 68.9%, AUC (95%CI) was 0.845 (0.829-0.860)]. After mixing the FCN and RNN models, the hybrid model 1, namely RNN-FCN model was obtained, and the performance of the model was improved [accuracy: 74.2%, AUC (95%CI) was 0.847 (0.833-0.862)]. Feature selection was carried out by genetic algorithm, and the hybrid model 2, namely RNN-FCNBased on deep learning, a grading prediction model of traumatic hemorrhage volume was developed and deployed in the information system to realize the intelligent grading prediction of traumatic animal hemorrhage volume.

Improving resistance to Verticillium wilt is of great significance for achieving high and stable yields of Gossypium hirsutum. To deeply understand the genetic basis of cotton resistance to Verticillium wilt, the G. hirsutum Verticillium wilt-resistant material Lumianyan 28 and four Verticillium wilt-susceptible Acala cotton materials were used to create an NAM population of 469 families through nested hybridization. Phenotypic data collected in five stressful environments were used to screen resistant and sensitive materials and create a mixed pool of extreme phenotypes for BSA-seq. A total of 8 QTLs associated with Verticillium wilt resistance were identified on 4 chromosomes, of which qVW-A12-5 was detected simultaneously in the NAM population and in 1 RIL population that composed the NAM population and was identified for the first time. According to the sequence comparison and transcriptome analysis of candidate genes in the QTL interval between parents and pools, 4 genes were screened in the qVW-A12-5 interval. qRT-PCR of parental and phenotypically extreme materials revealed that Gh_CPR30 was induced by and may be a candidate gene for resistance to Verticillium wilt in G. hirsutum. Furthermore, VIGS technology revealed that the disease severity index (DSI) of a Gh_CPR30-silenced strain was significantly higher than that of the control. These results indicate that the Gh_CPR30 gene plays an important role in the resistance of G. hirsutum to Verticillium wilt, and the study provides a molecular basis for analyzing the molecular mechanism underlying G. hirsutum resistance to Verticillium wilt.

Image caption is an important application field of artificial intelligence technique. When a machine can describe a picture reasonably like a human, it represents that the machine has higher intelligence to understand the picture. However, for complex machine learning tasks such as image caption, data annotation is time-consuming and laborious. Usually in a new application scenario, data annotation rarely results in poor model performance. A large number of easily available unlabeled image data make it possible to semi-supervised learning of image caption methods. Based on the existing end-to-end deep learning paradigm, a semi-supervised deep learning method is proposed in this paper, called N-gram + Pseudo Label NIC method. The method combines the current mainstream deep neural network method, e.g. the NIC (Neural Image Caption) model, and the semi-supervised deep learning idea of pseudo labels, and N-gram. This method generates pseudo labels by N-gram Search algorithm, and improves the effect of the model by utilizing the prior knowledge of the N-gram table and people's descriptive habits. This method has achieved better results than the original NIC model on different sub-data sets of Flickr 8K data set and MSCOCO data set of 0.5k, 1k, 2k and 3k under BLEU-1 evaluation criteria.

Airway obstruction is one of the crucial causes of death in trauma patients during the first aid. It is extremely challenging to accurately treat a great deal of casualties with airway obstruction in hospitals. The diagnosis of airway obstruction in an emergency mostly relies on the medical experience of physicians. In this paper, we propose the feature selection approach genetic algorithm-mean decrease impurity (GA-MDI) to effectively minimize the number of features as well as ensure the accuracy of prediction. Furthermore, we design a multi-modal neural network, called fully convolutional network with squeeze-and-excitation and multilayer perceptron (FCN-SE + MLP), to help physicians to predict the severity of airway obstruction. We validate the effectiveness of the proposed feature selection approach and multi-modal model on the emergency medical database from the Chinese General Hospital of the PLA. The experimental results show that GA-MDI outperforms the existing feature selection algorithms, while it is also validated that the model FCN-SE + MLP can effectively and accurately achieve the prediction of the severity of airway obstruction, which can assist clinicians in making treatment decisions for airway obstruction casualties.

The stability of metal nanoparticles is one of the key issues for their catalytic applications. In this study, we fabricated a sandwich structure to protect the metal nanoparticles. A carbon layer was used to wrap the PdNi nanoparticles on the carbon fiber, and the whole preparation process was simple and green. Electron transfer occurs between the carbon layer and the metal nanoparticles, making the two more closely combined. As a catalyst for the Suzuki reaction, it exhibits highly efficient catalysis and excellent stability. The calculated TOF reaches 18 662 h