Mechanical Cues Regulate Histone Modifications and Cell Behavior.

Change of biophysical factors in tissue microenvironment is an important step in a chronic disease development process. A mechanical and biochemical factor from cell living microniche can regulate cell epigenetic decoration and, therefore, further induce change of gene expression. In this review, we will emphasize the mechanism that biophysical microenvironment manipulates cell behavior including gene expression and protein decoration, through modifying histone amino acid residue modification. The influence given by different mechanical forces, including mechanical stretch, substrate surface stiffness, and shear stress, on cell fate and behavior during chronic disease development including tumorigenesis will also be teased out. Overall, the recent work summarized in this review culminates on the hypothesis that a mechanical factor stimulates the modification on histone which could facilitate disease detection and potential therapeutic target.

Swin-HSTPS: Research on Target Detection Algorithms for Multi-Source High-Resolution Remote Sensing Images.

Traffic port stations are composed of buildings, infrastructure, and transportation vehicles. The target detection of traffic port stations in high-resolution remote sensing images needs to collect feature information of nearby small targets, comprehensively analyze and classify, and finally complete the traffic port station positioning. At present, deep learning methods based on convolutional neural networks have made great progress in single-target detection of high-resolution remote sensing images. How to show good adaptability to the recognition of multi-target complexes of high-resolution remote sensing images is a difficult point in the current remote sensing field. This paper constructs a novel high-resolution remote sensing image traffic port station detection model (Swin-HSTPS) to achieve high-resolution remote sensing image traffic port station detection (such as airports, ports) and improve the multi-target complex in high-resolution remote sensing images The recognition accuracy of high-resolution remote sensing images solves the problem of high-precision positioning by comprehensive analysis of the feature combination information of multiple small targets in high-resolution remote sensing images. The model combines the characteristics of the MixUp hybrid enhancement algorithm, and enhances the image feature information in the preprocessing stage. The PReLU activation function is added to the forward network of the Swin Transformer model network to construct a ResNet-like residual network and perform convolutional feature maps. Non-linear transformation strengthens the information interaction of each pixel block. This experiment evaluates the superiority of the model training by comparing the two indicators of average precision and average recall in the training phase. At the same time, in the prediction stage, the accuracy of the prediction target is measured by confidence. Experimental results show that the optimal average precision of the Swin-HSTPS reaches 85.3%, which is about 8% higher than the average precision of the Swin Transformer detection model. At the same time, the target prediction accuracy is also higher than the Swin Transformer detection model, which can accurately locate traffic port stations such as airports and ports in high-resolution remote sensing images. This model inherits the advantages of the Swin Transformer detection model, and is superior to mainstream models such as R-CNN and YOLOv5 in terms of the target prediction ability of high-resolution remote sensing image traffic port stations.

Stretchable, dynamic covalent polymers for soft, long-lived bioresorbable electronic stimulators designed to facilitate neuromuscular regeneration.

Bioresorbable electronic stimulators are of rapidly growing interest as unusual therapeutic platforms, i.e., bioelectronic medicines, for treating disease states, accelerating wound healing processes and eliminating infections. Here, we present advanced materials that support operation in these systems over clinically relevant timeframes, ultimately bioresorbing harmlessly to benign products without residues, to eliminate the need for surgical extraction. Our findings overcome key challenges of bioresorbable electronic devices by realizing lifetimes that match clinical needs. The devices exploit a bioresorbable dynamic covalent polymer that facilitates tight bonding to itself and other surfaces, as a soft, elastic substrate and encapsulation coating for wireless electronic components. We describe the underlying features and chemical design considerations for this polymer, and the biocompatibility of its constituent materials. In devices with optimized, wireless designs, these polymers enable stable, long-lived operation as distal stimulators in a rat model of peripheral nerve injuries, thereby demonstrating the potential of programmable long-term electrical stimulation for maintaining muscle receptivity and enhancing functional recovery.