Activation of Stimulation of Interferon Genes (STING) Signal and Cancer Immunotherapy

Stimulator of interferon gene (STING), an intracellular receptor in the endoplasmic reticulum, could induce the production of cytokines such as type I interferon (IFN) by activating the cGAS-STING signal pathway. In recent years, activation of STING has shown great potential to enhance anti-tumor immunity and reshape the tumor microenvironment, which is expected to be used in tumor immunotherapy. A number of STING agonists have demonstrated promising biological activity and showed excellent synergistic anti-tumor effects in combination with other cancer therapies in preclinical studies and some clinical trials. The combination of STING agonists and ICI also showed a potent effect in improving anti-tumor immunity. In this review, we introduce the cGAS-STING signaling pathway and its effect in tumor immunity and discuss the recent strategies of activation of the STING signaling pathway and its research progress in tumor immunotherapy.     

Molecular Simulation to Explore the Dissolution Behavior of Sulfur in Carbon Disulfide

Soluble sulfur (S₈) and insoluble sulfur (IS) have different application fields, and molecular dynamics simulation can reveal their differences in solubility in solvents. It is found that in the simulated carbon disulfide (CS₂) solvent, soluble sulfur in the form of clusters mainly promotes the dissolution of clusters through van der Waals interaction between solvent molecules (CS₂) and S₈, and the solubility gradually increases with the increase in temperature. However, the strong interaction between polymer chains of insoluble sulfur in the form of polymer hinders the diffusion of IS into CS₂ solvent, which is not conducive to high-temperature dissolution. The simulated solubility parameter shows that the solubility parameter of soluble sulfur is closer to that of the solvent, which is consistent with the above explanation that soluble sulfur is easy to dissolve.     

Conserved Control Path in Multilayer Networks

The determination of directed control paths in complex networks is important because control paths indicate the structure of the propagation of control signals through edges. A challenging problem is to identify them in complex networked systems characterized by different types of interactions that form multilayer networks. In this study, we describe a graph pattern called the conserved control path, which allows us to model a common control structure among different types of relations. We present a practical conserved control path detection method (CoPath), which is based on a maximum-weighted matching, to determine the paths that play the most consistent roles in controlling signal transmission in multilayer networks. As a pragmatic application, we demonstrate that the control paths detected in a multilayered pan-cancer network are statistically more consistent. Additionally, they lead to the effective identification of drug targets, thereby demonstrating their power in predicting key pathways that influence multiple cancers.     

Electrochemical Immunoassay for Tumor Marker CA19-9 Detection Based on Self-Assembled Monolayer

A CA19-9 electrochemical immunosensor was constructed using a hybrid self-assembled membrane modified with a gold electrode and applied to detect real samples. Hybrid self-assembled membranes were selected for electrode modification and used to detect antigens. First, the pretreated working electrodes were placed in a 3-mercaptopropionic acid (MPA)/β-mercaptoethanol (ME) mixture for 24 h for self-assembly. The electrodes were then placed in an EDC/NHS mixture for 1 h. Layer modification was performed by stepwise dropwise addition of CA19-9 antibody, BSA, and antigen. Differential pulse voltammetry was used to characterize this immunosensor preparation process. The assembled electrochemical immunosensor enables linear detection in the concentration range of 0.05–500 U/mL of CA19-9, and the detection limit was calculated as 0.01 U/mL. The results of the specificity measurement test showed that the signal change of the interfering substance was much lower than the response value of the detected antigen, indicating that the sensor has good specificity and strong anti-interference ability. The repeatability test results showed that the relative standard deviations were less than 5%, showing good accuracy and precision. The CA19-9 electrochemical immunosensor was used for the actual sample detection, and the experimental results of the standard serum addition method showed that the RSD values of the test concentrations were all less than 10%. The recoveries were 102.4–115.0%, indicating that the assay has high precision, good accuracy, and high potential application value.     

Deep Multilabel Multilingual Document Learning for Cross-Lingual Document Retrieval

Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the word level and then represent documents via word embeddings, which leads to insufficient structure information. In this work, the cross-lingual comparison at the document level is achieved through the cross-lingual semantic space. Our method, MDL (deep multilabel multilingual document learning), leverages a six-layer fully connected network to project cross-lingual documents into a shared semantic space. The semantic distances can be calculated when the cross-lingual documents are transformed into embeddings in semantic space. The supervision signals are automatically extracted from the data and then used to construct the semantic space via a linear classifier. The ambiguity of manual labels could be avoided and the multilabel supervision signals can be acquired instead of a single label. The representation of the semantic space is enriched by multilabel supervision signals, which improves the discriminative ability of the embeddings. The MDL is easy to extend to other fields since it does not depend on specific data. Furthermore, MDL is more efficient than the models training all languages jointly, since each language is trained individually. Experiments on Wikipedia data showed that the proposed method outperforms the state-of-the-art cross-lingual document retrieval methods.     

聚合物气体辅助注射成型制品的形态结构

作为一种由常规注射成型发展起来的聚合物加工技术,气体辅助注射成型具有节约原材料、缩短成型周期以及提高制品性能等优点,已得到广泛的应用.由于气辅成型过程是一个在刚、柔双重约束界面条件下进行的多相复杂体系的多次流动过程,因而其形态结构的形成、发展和演化要远比常规注塑成型复杂.然而在气辅成型的形态结构方面,国内外的研究一直以来开展得较少.近年来,作者在聚合物及其共混物、复合材料气辅成型制品的形态结构方面已开展了广泛的研究工作,本文对这些工作和一些重要结果作了总结,并简要分析了成型过程中剪切场对形态演化的影响,最后对该研究方向的发展趋势作了展望.     

Tailoring Pore Structure and Morphologies in Covalent Organic Frameworks for Xe/Kr Capture and Separation

As a rising star among porous solid materials, covalent organic frameworks(COFs) with excellent properties including but not limit to facilely controllable structure, high porosity, and multi-chemical functionality represent significant potential for efficient ¹²⁷Xe/⁸⁵Kr capture and separation. In this study, through tuning the length of the organic ligands, two-dimensional(2D) COF mate-rials with identical connection group but different pore properties, denoted as ATFG-COF and TpPa-COF with AA-stacking model and TpBD-COF with AB-stacking model were synthesized and tested for Kr and Xe adsorption for the first time. Adsorption measurements indicate that the narrower pore apertures and higher porosity are conducive for COF materials to capture Xe and Kr. Furthermore, the Henry's constant, isosteric heat of adsorption(Q_st), and ideal adsorbed solution theory(IAST) selectivity of ATFG-COF, the pore size of which is closest to the kinetic diameter of the Xe atom(0.41 nm) among 2D COF materials, were carried out based on the single component sorption isotherms. The results illustrate that the high isosteric heat values of Xe/Kr adsorption on ATFG-COF are 25 and 16 kJ/mol at room temperature, respectively. Henry's law predicts that the selectivity factor of Xe to Kr is 6.07, consistent with the adsorption selectivity(ca. 6) calculated based on the IAST.     

Genetically engineered materials: Proteins and beyond

Information-rich molecules provide opportunities for evolution. Genetically engineered materials are superior in that their properties are coded within genetic sequences and could be fine-tuned. In this review, we elaborate the concept of genetically engineered materials(GEMs) using examples ranging from engineered protein materials to engineered living materials. Proteinbased materials are the materials of choice by nature. Recent progress in protein engineering has led to opportunities to tune...     

Quantum Circuit Optimization for Solving Discrete Logarithm of Binary Elliptic Curves Obeying the Nearest-Neighbor Constrained

In this paper, we consider the optimization of the quantum circuit for discrete logarithm of binary elliptic curves under a constrained connectivity, focusing on the resource expenditure and the optimal design for quantum operations such as the addition, binary shift, multiplication, squaring, inversion, and division included in the point addition on binary elliptic curves. Based on the space-efficient quantum Karatsuba multiplication, the number of CNOTs in the circuits of inversion and division has been reduced with the help of the Steiner tree problem reduction. The optimized size of the CNOTs is related to the minimum degree of the connected graph.     

An Improvement to Conformer-Based Model for High-Accuracy Speech Feature Extraction and Learning

Owing to the loss of effective information and incomplete feature extraction caused by the convolution and pooling operations in a convolution subsampling network, the accuracy and speed of current speech processing architectures based on the conformer model are influenced because the shallow features of speech signals are not completely extracted. To solve these problems, in this study, we researched a method that used a capsule network to improve the accuracy of feature extraction in a conformer-based model, and then, we proposed a new end-to-end model architecture for speech recognition. First, to improve the accuracy of speech feature extraction, a capsule network with a dynamic routing mechanism was introduced into the conformer model; thus, the structural information in speech was preserved, and it was input to the conformer blocks via sequestered vectors; the learning ability of the conformed-based model was significantly enhanced using dynamic weight updating. Second, a residual network was added to the capsule blocks, thus, the mapping ability of our model was improved and the training difficulty was reduced. Furthermore, the bi-transformer model was adopted in the decoding network to promote the consistency of the hypotheses in different directions through bidirectional modeling. Finally, the effectiveness and robustness of the proposed model were verified against different types of recognition models by performing multiple sets of experiments. The experimental results demonstrated that our speech recognition model achieved a lower word error rate without a language model because of the higher accuracy of speech feature extraction and learning using our model architecture with a capsule network. Furthermore, our model architecture benefited from the advantage of the capsule network and the conformer encoder, and also has potential for other speech-related applications.