The frequent occurrence of major public emergencies in China has caused significant human and economic losses. To carry out successful rescue operations in such emergencies, decisions need to be made as efficiently as possible. Using earthquakes as an example of a public emergency, this paper combines the Deep Belief Network (DBN) and Case-Based Reasoning (CBR) models to improve the case representation and case retrieval steps in the decision-making process, then designs and constructs a decision-making model. The validity of the model is then verified by an example. The results of this study can be applied to maximize the efficiency of emergency rescue decisions.

No abstract

The antler is the unique mammalian organ found to be able to regenerate completely and periodically after loss, and the continuous proliferation and differentiation of mesenchymal cells and chondrocytes together complete the regeneration of the antler. Circular non-coding RNAs (circRNAs) are considered to be important non-coding RNAs that regulate body development and growth. However, there are no reports on circRNAs regulating the antler regeneration process. In this study, full-transcriptome high-throughput sequencing was performed on sika deer antler interstitial and cartilage tissues, and the sequencing results were verified and analyzed. The competing endogenous RNA (ceRNA) network related to antler growth and regeneration was further constructed, and the differentially expressed circRNA2829 was screened out from the network to study its effect on chondrocyte proliferation and differentiation. The results indicated that circRNA2829 promoted cell proliferation and increased the level of intracellular ALP. The analysis of RT-qPCR and Western blot demonstrated that the mRNA and protein expression levels of genes involved in differentiation rose. These data revealed that circRNAs play a crucial regulatory role in deer antler regeneration and development. CircRNA2829 might regulate the antler regeneration process through miR-4286-R+1/FOXO4.

Accurate segmentation of skin lesions in dermoscopic images plays an important role in improving the survival rate of patients. However, due to the blurred boundaries of pigment regions, the diversity of lesion features, and the mutations and metastases of diseased cells, the effectiveness and robustness of skin image segmentation algorithms are still a challenging subject. For this reason, we proposed a bi-directional feedback dense connection network framework (called BiDFDC-Net), which can perform skin lesions accurately. Firstly, under the framework of U-Net, we integrated the edge modules into each layer of the encoder which can solve the problem of gradient vanishing and network information loss caused by network deepening. Then, each layer of our model takes input from the previous layer and passes its feature map to the densely connected network of subsequent layers to achieve information interaction and enhance feature propagation and reuse. Finally, in the decoder stage, a two-branch module was used to feed the dense feedback branch and the ordinary feedback branch back to the same layer of coding, to realize the fusion of multi-scale features and multi-level context information. By testing on the two datasets of ISIC-2018 and PH2, the accuracy on the two datasets was given by 93.51% and 94.58%, respectively.

Living materials that combine active cells and synthetic matrix materials have become a promising research field in recent years. While multicellular systems present exclusive benefits in developing living materials over single-cell systems, creating artificial multicellular systems can be challenging due to the difficulty in controlling the multicellular assemblies and the complexity of cell-to-cell interactions. Here, we propose a co-culture platform capable of isolating and controlling the spatial distribution of algal-bacterial consortia, which can be used to construct photosynthetic living fibers. Through coaxial extrusion-based 3D printing, hydrogel fibers containing bacteria or algae can be deposited into designated structures and further processed into materials with precise geometries. In addition, the photosynthetic living fibers demonstrate a significant synergistic catalytic effect resulting from the immobilization of both bacteria and algae, which effectively optimize sewage treatment for bioremediation purposes. The integration of microbial consortia and 3D printing gives functional living materials that have promising applications in biocatalysis, biosensing, and biomedicine. Our approach provides an optimized solution for constructing efficient multicellular systems and opens a new avenue for the development of advanced materials.