Capturing dynamic biological signals via bio-mimicking hydrogel for precise remodeling of soft tissue

Soft tissue remodeling is a sophisticated process that sequentially provides dynamic biological signals to guide cell behavior.However,capturing these signals within hydrogel and directing over time has still been unrealized owing to the poor comprehension of physiological processes.Here,a bio-mimicking hydrogel is designed via thiol-ene click reaction to capture the early physical signal triggered by inflammation,and the chemical signals provided with chemokine and natural adhesion sites,which guaranteed the precise soft tissue remodeling.This bio-mimicking hydrogel efficiently facilitated cell anchoring,migration,and invasion in the 3D matrix due to the permissive space and the interaction with integrin receptors.Besides,the covalently grafted chemokine-like peptide is optimal for colonization and functional differentiation of endothelial cells through a HIF-1αdependent signal pathway.Furthermore,the early polarization of macrophages,collagen deposition and angiogenesis in rat acute wound model,and the increased blood perfusion in mouse skin flap model have confirmed that the bio-mimicking hydrogel realized precise soft tissue remodeling and opens new avenues for the phased repair of different tissues such as nerve,myocardium,and even bone.

Action Mechanism of Components from Gardenia jasminoides on Eye Skin Based on Network Pharmacology

[Objectives]To explore the pharmacological effects of Gardenia jasminoides and its potential benefits on eye skin.[Methods]TCMSP and SymMap databases were used to screen the active components and corresponding targets of G.jasminoides.Human eye skin-related targets were screened,and the active component-target network and protein-protein interaction(PPI)network were established.Gene ontology(GO)analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis were performed.[Results]Twenty-six active compounds were screened out from G.jasminoides,and 277 targets were obtained.From the Gencards database,26652 disease targets were retrieved and 205 related gene targets were screened.The active component-action target network of G.jasminoides constructed by Cytoscape software revealed the potential of G.jasminoides to play a role in multiple biological pathways.In addition,PPI-network analysis,GO function analysis and KEGG pathway enrichment analysis revealed that the active components of G.jasminoides mainly regulate the biological processes such as inflammatory response,oxidative stress and apoptosis,involving MAPK,NF-κB and other important signaling pathways.[Conclusions]This study provides a theoretical basis for the eye skin protection of G.jasminoides and an important clue for future drug development.

The Experimental Exploration and Discovery of DNA Communication between the Plants

For the first time, through the invention of Compensating Bio-information Energy (CBE) technology and bioinformatics breeding machine, we have completed a number of experiments by using plant signals to transfer plant genetic traits in the same family or across families, and discovered the transfer phenomenon of life genetic information. The test results show that plants can change from random variation to controllable and directional variation, thus opening up plant asexual, no molecular transfer, fast and low-cost breeding. The new approach provides new evidence for the connection of information energy waves between plant DNA, which deserves the attention and in-depth study of the scientific community.

A Modified Statistically Optimal Null Filter Method for Recognizing Protein-coding Regions

Computer-aided protein-coding gene prediction in uncharacterized genomic DNA sequences is one of the most important issues of bio- logical signal processing. A modified filter method based on a statistically optimal null filter （SONF） theory is proposed for recognizing protein-coding regions. The square deviation gain （SDG） between the input and output of the model is used to identify the coding regions. The effective SDG amplification model with Class I and Class II enhancement is designed to suppress the non-coding regions. Also, an evaluation algorithm has been used to compare the modified model with most gene prediction methods currently available in terms of sensitivity, specificity and precision. The performance for identification of protein-coding regions has been evaluated at the nucleotide level using benchmark datasets and 91.4%, 96%, 93.7% were obtained for sensitivity, specificity and precision, respectively. These results suggest that the proposed model is potentially useful in gene finding field, which can help recognize protein-coding regions with higher precision and speed than present algorithms.

Stretchable human-machine interface based on skin-conformal sEMG electrodes with self-similar geometry

Current stretchable surface electrodes have attracted increasing attention owing to their potential applications in biological signal monitoring, wearable human-machine interfaces（HMIs） and the Internet of Things. The paper proposed a stretchable HMI based on a surface electromyography（sEMG） electrode with a self-similar serpentine configuration. The sEMG electrode was transfer-printed onto the skin surface conformally to monitor biological signals, followed by signal classification and controlling of a mobile robot. Such electrodes can bear rather large deformation（such as 〉30%） under an appropriate areal coverage. The sEMG electrodes have been used to record electrophysiological signals from different parts of the body with sharp curvature, such as the index finger,back of the neck and face, and they exhibit great potential for HMI in the fields of robotics and healthcare. The electrodes placed onto the two wrists would generate two different signals with the fist clenched and loosened. It is classified to four kinds of signals with a combination of the gestures from the two wrists, that is, four control modes. Experiments demonstrated that the electrodes were successfully used as an HMI to control the motion of a mobile robot remotely.

A high-throughput study on endothelial cell adhesion and growth mediated by adsorbed serum protein via signaling pathway PCR array

The purpose of this paper is to utilize the signaling pathway polymerase chain reaction(PCR)arrays to investigate the activation of two important biological signaling pathways in endothelial cell adhesion and growth mediated by adsorbed serum protein on the surface of bare and titanium nitride(TiN)-coated nickel titanium(NiTi)alloys.First,the endothelial cells were cultured on the bare and TiN-coated NiTi alloys and chitosan films as control for 4 h and 24 h,respectively.Then,the total RNA of the cells was collected and the PCR arrays were performed.After that,the differentially expressed genes in the transforming growth factor beta(TGF-b)signaling pathway and the regulation of actin cytoskeleton pathway were screened out;and the further bioinformatics analyses were performed.The results showed that both TGF-b signaling pathway and regulation of actin cytoskeleton pathway were activated in the cells after 4 h and 24 h culturing on the surface of bare and TiN-coated NiTi alloys compared to the chitosan group.The activated TGF-b signaling pathway promoted cell adhesion;the activated regulation of actin cytoskeleton pathway promoted cell adhesion,spreading,growth and motility.In addition,the activation of both pathways was much stronger in the cells cultured for 24 h versus 4 h,which indicated that cell adhesion and growth became more favorable with longer time on the surface of two NiTi alloy materials.

DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning

The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field.However,a persistent issue remains unsolved during experiments:the interferential technical noises caused by systematic errors(e.g.,temperature,reagent concentration,and well location)are always mixed up with the real biological signals,leading to misinterpretation of any conclusion drawn.Here,we reported a mean teacher-based deep learning model(Deep Noise)that can disentangle biological signals from the experimental noises.Specifically,we aimed to classify the phenotypic impact of 1108 different genetic perturbations screened from 125,510 fluorescent microscopy images,which were totally unrecognizable by the human eye.We validated our model by participating in the Recursion Cellular Image Classification Challenge,and Deep Noise achieved an extremely high classification score(accuracy:99.596%),ranking the 2nd place among 866 participating groups.This promising result indicates the successful separation of biological and technical factors,which might help decrease the cost of treatment development and expedite the drug discovery process.The source code of Deep Noise is available at https://github.com/Scu-sen/Recursion-Cellular-Image-Classification-Challenge.

Synergy and Redundancy in a Signaling Cascade with Different Feedback Mechanisms

Feedback plays an important role in various biological signal transmission systems. In this paper, a signaling cascade system(including three layers: input(S), intermediate(V), output(X) components) is employed to study the fluctuations and net synergy in information transmission, in which the V component is regulated by itself or the X component, and each feedback on V is either positive or negative. The Fano factor, the net synergy, and the signalto-noise ratio(SNR) of signaling cascade with the four possible feedback types are theoretically derived by using linear noise approximation of the master equation, and the ability of information transmission through the signaling cascade is characterized by using the partial information decomposition of information theory. It is found that the signaling cascade exhibits different responses to the four feedback mechanisms, which depend on the relationships between degradation rates of components. Our results not only clarify the dependence of the Fano factor, net synergy, and SNR on the feedback regulations with the varying of degradation rates of components, but also imply that living cells could utilize different feedback mechanisms to adapt to the external fluctuating environments.