Cytokine-Based Generation of CD49a+Eomes−/+ Natural Killer Cell Subsets

Recent studies have identified CD49a+Eomes− and CD49a+Eomes+ subsets of tissue-resident NK (trNK) cells in different organs of the mouse. However, the characteristics of CD49a+Eomes−/+ NK cell development and the regulation of Eomes expression in NK cells remain unclear. Here, we established an in vitro cytokine-based feeder-free system in which bone marrow progenitor cells differentiate into CD49a+ NK cells. IL-15 was identified as being the key cytokine in this system that supported the development and maintenance of CD49a+ NK cells. The CD49a+ NK cells generated were Eomes−CD49b− and shared the same phenotype as hepatic trNK cells. IL-4 induced the expression of Eomes in generated NK cells and converted them into CD49a+Eomes+ cells, which were phenotypically and functionally similar to uterine trNK cells. Moreover, the IL-4/STAT6 axis was identified as being important in the generation of CD49a+Eomes+ induced NK cells. Collectively, these studies describe an approach to generate CD49a+Eomes−/+ subsets of NK cells and demonstrate important roles for IL-15 and IL-4 in the differentiation of these cells. These findings have potential for developmental research underlying the generation of different subsets of NK cells and the application of adoptive NK cell transfer therapies

RPRD1A and RPRD1B Are Human RNA Polymerase II C-Terminal Domain Scaffolds for Ser5 Dephosphorylation

The RNA polymerase II (RNAPII) carboxyl-terminal domain (CTD) heptapeptide repeats (Y1-S2-P3-T4-S5-P6-S7) undergo dynamic phosphorylation and dephosphorylation during the transcription cycle to recruit factors that regulate transcription, RNA processing and chromatin modification. We show here that RPRD1A and RPRD1B form homodimers and heterodimers through their coiled-coil domains and interact preferentially via CTD interaction domains (CIDs) with CTD repeats phosphorylated at S2 and S7. Our high resolution crystal structures of the RPRD1A, RPRD1B and RPRD2 CIDs, alone and in complex with CTD phosphoisoforms, elucidate the molecular basis of CTD recognition. In an interesting example of cross-talk between different CTD modifications, our data also indicate that RPRD1A and RPRD1B associate directly with RPAP2 phosphatase and, by interacting with CTD repeats where phospho-S2 and/or phospho-S7 bracket a phospho-S5 residue, serve as CTD scaffolds to coordinate the dephosphorylation of phospho-S5 by RPAP2

Development of Coal Mine Filling Paste with Certain Early Strength and Its Flow Characteristics

In coal mine paste filling technology, geomaterials like coal gangue and fly ash are used as the main component, and cement is applied as the cementing material. In the mining production, mining-and-filling is a cyclic work, where the filling immediately after mining and mining immediately after filling. Long solidification time after filling will affect mining; consequently, the paste should have early strength. In addition, the prepared paste will be conveyed to goaf through the pipeline. The paste flow characteristics will change to some extent in the conveying process, and there is uncertainty about whether the paste can meet the requirements of pumpability and strength. Therefore, the influence of pipeline conveying on flow characteristics of paste before filling the goaf should be taken into consideration. Based on the above two points, this paper studies the paste strength, backfill strength, and pumpability parameters in coal mine paste filling and determines the early and later strength of coal mine paste, as well as the pumpability parameters such as slump degree, segregation degree, setting time, and paste gradation. With the determined mass proportion of coal gangue, fly ash, and silicate cement, the orthogonal test was carried out with three factors including gypsum content, the content of early strength agent (Na2SO4), and the mass concentration, and at three levels. The factors affecting paste flow characteristics were determined by range analysis, and the factors affecting the paste’s early strength were determined by the XRD test and SEM test on its microstructure. With paste proportioning and pipeline conveying simulation system, taking slump, segregation degree, backfill strength, and other parameters as indicators, we obtain the influence law of pipeline conveying on the flow characteristics of paste. The research has great theoretical and practical significance for developing coal paste with early strength and its flow characteristics

Commensal Bacteria-Dependent CD8αβ+ T Cells in the Intestinal Epithelium Produce Antimicrobial Peptides

The epithelium of the intestine functions as the primary “frontline” physical barrier for protection from enteric microbiota. Intraepithelial lymphocytes (IELs) distributed along the intestinal epithelium are predominantly CD8+ T cells, among which CD8αβ+ IELs are a large population. In this investigation, the proportion and absolute number of CD8αβ+ IELs decreased significantly in antibiotic-treated and germ-free mice. Moreover, the number of CD8αβ+ IELs was correlated closely with the load of commensal microbes, and induced by specific members of commensal bacteria. Microarray analysis revealed that CD8αβ+ IELs expressed a series of genes encoding potent antimicrobial peptides (AMPs), whereas CD8αβ+ splenocytes did not. The antimicrobial activity of CD8αβ+ IELs was confirmed by an antimicrobial-activity assay. In conclusion, microbicidal CD8αβ+ IELs are regulated by commensal bacteria which, in turn, secrete AMPs that have a vital role in maintaining the homeostasis of the small intestine

Transfer Learning for Leaf Small Dataset Using Improved ResNet50 Network with Mixed Activation Functions

Taxonomic studies of leaves are one of the most effective means of correctly identifying plant species. In this paper, mixed activation function is used to improve the ResNet50 network in order to further improve the accuracy of leaf recognition. Firstly, leaf images of 15 common tree species in northern China were collected from the Urban Forestry Demonstration Base of Northeast Forestry University (45°43′–45°44′ N, 126°37′–126°38′ E, forest type was artificial forest), and a small leaf dataset was established. After that, seven commonly used activation functions were selected to improve the ResNet50 network structure, and the improved network was applied to the transfer learning research of the leaf small dataset. On this basis, five activation functions with better performance were selected for the study of mixed activation functions in deep learning. Two of these five activation functions are arbitrarily selected for combination, and a total of twenty combinations are obtained. Further, the first activation function was used in each combination to replace the first ReLU function after all addition operations in the ResNet50 network residual block structure, and another activation function was used to replace the other position ReLU functions. The experimental results show that in the transfer learning of the leaf small dataset using the ResNet50 deep residual network, the appropriate combination of mixed activation functions can increase the performance of the improved network to a certain extent. Among them, the ELU-Swish1 combination has the most significant improvement effect on the network performance, whose final effective validation accuracy reaches 98.17%. Furthermore, the comparison with GoogLeNet and VGG-16 also demonstrates the excellent performance of the improved ELU-Swish1 ResNet50 (ES-ResNet50) network architecture. Finally, tests on the other two small leaf datasets, Flavia and Swedish, also demonstrate the performance improvement of ES-ResNet50. The validation accuracy of the improved ES-Resnet 50 algorithm on these two datasets reaches 99.30% and 99.39%, respectively. All these experiments prove that the recognition performance of leaf transfer learning using the ES-ResNet50 network is indeed improved, which may be caused by the complementarity of the e-exponential gradient of ELU and Swish1 activation functions in the negative region

Transfer Learning for Leaf Small Dataset Using Improved ResNet50 Network with Mixed Activation Functions

Taxonomic studies of leaves are one of the most effective means of correctly identifying plant species. In this paper, mixed activation function is used to improve the ResNet50 network in order to further improve the accuracy of leaf recognition. Firstly, leaf images of 15 common tree species in northern China were collected from the Urban Forestry Demonstration Base of Northeast Forestry University (45°43′–45°44′ N, 126°37′–126°38′ E, forest type was artificial forest), and a small leaf dataset was established. After that, seven commonly used activation functions were selected to improve the ResNet50 network structure, and the improved network was applied to the transfer learning research of the leaf small dataset. On this basis, five activation functions with better performance were selected for the study of mixed activation functions in deep learning. Two of these five activation functions are arbitrarily selected for combination, and a total of twenty combinations are obtained. Further, the first activation function was used in each combination to replace the first ReLU function after all addition operations in the ResNet50 network residual block structure, and another activation function was used to replace the other position ReLU functions. The experimental results show that in the transfer learning of the leaf small dataset using the ResNet50 deep residual network, the appropriate combination of mixed activation functions can increase the performance of the improved network to a certain extent. Among them, the ELU-Swish1 combination has the most significant improvement effect on the network performance, whose final effective validation accuracy reaches 98.17%. Furthermore, the comparison with GoogLeNet and VGG-16 also demonstrates the excellent performance of the improved ELU-Swish1 ResNet50 (ES-ResNet50) network architecture. Finally, tests on the other two small leaf datasets, Flavia and Swedish, also demonstrate the performance improvement of ES-ResNet50. The validation accuracy of the improved ES-Resnet 50 algorithm on these two datasets reaches 99.30% and 99.39%, respectively. All these experiments prove that the recognition performance of leaf transfer learning using the ES-ResNet50 network is indeed improved, which may be caused by the complementarity of the e-exponential gradient of ELU and Swish1 activation functions in the negative region

REVIEW: EFFECTS OF WOOD QUALITY AND REFINING PROCESS ON TMP PULP AND PAPER QUALITY

For the thermomechanical pulping (TMP) process both wood chip quality and the refining process have important effects on the resulting pulp and paper quality. Properties of wood raw material give a framework for final pulp properties. During TMP refining the specific energy consumption and refining intensity strongly impact fibre and pulp qualities. Increasing specific energy consumption benefits the development of fibres and improves their properties. However, high intensity refining tends to shorten the fibres and produces more fines content when compared with low intensity refining. This review focuses on the influence of key variables of chip qualities and the refining process on TMP pulp and paper qualities

A Novel Multi-Scale Attention PFE-UNet for Forest Image Segmentation

The precise segmentation of forest areas is essential for monitoring tasks related to forest exploration, extraction, and statistics. However, the effective and accurate segmentation of forest images will be affected by factors such as blurring and discontinuity of forest boundaries. Therefore, a Pyramid Feature Extraction-UNet network (PFE-UNet) based on traditional UNet is proposed to be applied to end-to-end forest image segmentation. Among them, the Pyramid Feature Extraction module (PFE) is introduced in the network transition layer, which obtains multi-scale forest image information through different receptive fields. The spatial attention module (SA) and the channel-wise attention module (CA) are applied to low-level feature maps and PFE feature maps, respectively, to highlight specific segmentation task features while fusing context information and suppressing irrelevant regions. The standard convolution block is replaced by a novel depthwise separable convolutional unit (DSC Unit), which not only reduces the computational cost but also prevents overfitting. This paper presents an extensive evaluation with the DeepGlobe dataset and a comparative analysis with several state-of-the-art networks. The experimental results show that the PFE-UNet network obtains an accuracy of 94.23% in handling the real-time forest image segmentation, which is significantly higher than other advanced networks. This means that the proposed PFE-UNet also provides a valuable reference for the precise segmentation of forest images