Artificial Visual Synaptic Architecture with High-Linearity Light-Modulated Weight for Optoelectronic Neuromorphic Computing.

A brain-like neuromorphic computing system, as compared with traditional Von Neumann architecture, has broad application prospects in the fields of emerging artificial intelligence (AI) due to its high fault tolerance, excellent plasticity, and parallel computing capability. A neuromorphic visuosensory and memory system, an important branch of neuromorphic computing, is the basis for AI to perceive, process, and memorize optical information, now still suffering from nonlinearity of synaptic weight, crosstalk issues, and integration incompatibility, hindering the high-level training and inference accuracy. In this work, we propose a new optoelectronic neuromorphic architecture by integrating an electrochromic device and a perovskite photodetector. Ascribing to the superior memory characteristics of the electrochromic device and sensitive light response of the perovskite photodetector, the neuromorphic device shows typical visual synaptic functionalities such as light triggering, neural facilitation, long-term potentiation, and depression (LTP and LTD). Furthermore, by adjusting the intensity and wavelength of external light signals, the visual synaptic function of the device can be modulated, enabling the device to exhibit high weight linearity in all current output ranges and improve information processing capability and image recognition accuracy. Moreover, both the electrochromic and perovskite layers possess the advantage of large area fabrication and integration, which enables the fabrication of large device arrays with high integration compatibility and scalability. In this study, 10 × 10 device arrays are demonstrated and each device shows uniform light responses, memory behaviors, and synaptic performances. MNIST and CIFAR-10 algorithms are used to simulate the image recognition properties of the synaptic architecture, and the calculated recognition accuracy is 97.94 and 91.04%, respectively, with an error less than 2.5%. The proposed artificial visual neuromorphic architecture will provide a potential device prototype for efficient visual neuromorphic systems.

UFL1 promotes antiviral immune response by maintaining STING stability independent of UFMylation.

The precise regulation of STING homeostasis is essential for its antiviral function. Post-translational modification, especially ubiquitination, is important for the regulation of STING homeostasis. Previous studies have focused on how STING is degraded, but little is known about its maintenance. Here, we show that UFM1 specific ligase UFL1 promotes innate immune response by maintaining STING expression independent of UFMylation. Mechanistically, UFL1 inhibits TRIM29 to interact with STING, thereby reducing its ubiquitination at K338/K347/K370 and subsequent proteasomal degradation. DNA virus infection reduces the UFL1 expression, which may promote STING degradation and facilitate viral expansion. Our study identifies UFL1 as a crucial regulator for the maintenance of STING stability and antiviral function, and provides novel insights into the mechanistic explanation for the immunological escape of DNA virus.

Integrating single-cell and bulk transcriptomic analyses to develop a cancer-associated fibroblast-derived biomarker for predicting prognosis and therapeutic response in breast cancer.

Background: Cancer-associated fibroblasts (CAFs) contribute to the progression and treatment of breast cancer (BRCA); however, risk signatures and molecular targets based on CAFs are limited. This study aims to identify novel CAF-related biomarkers to develop a risk signature for predicting the prognosis and therapeutic response of patients with BRCA. Methods: CAF-related genes (CAFRGs) and a risk signature based on these genes were comprehensively analyzed using publicly available bulk and single-cell transcriptomic datasets. Modular genes identified from bulk sequencing data were intersected with CAF marker genes identified from single-cell analysis to obtain reliable CAFRGs. Signature CAFRGs were screened via Cox regression and least absolute shrinkage and selection operator (LASSO) analyses. Multiple patient cohorts were used to validate the prognosis and therapeutic responsiveness of high-risk patients stratified based on the CAFRG-based signature. In addition, the relationship between the CAFRG-based signature and clinicopathological factors, tumor immune landscape, functional pathways, chemotherapy sensitivity and immunotherapy sensitivity was examined. External datasets were used and sample experiments were performed to examine the expression pattern of MFAP4, a key CAFRG, in BRCA. Results: Integrated analyses of single-cell and bulk transcriptomic data as well as prognostic screening revealed a total of 43 prognostic CAFRGs; of which, 14 genes (TLN2, SGCE, SDC1, SAV1, RUNX1, PDLIM4, OSMR, NT5E, MFAP4, IGFBP6, CTSO, COL12A1, CCDC8 and C1S) were identified as signature CAFRGs. The CAFRG-based risk signature exhibited favorable efficiency and accuracy in predicting survival outcomes and clinicopathological progression in multiple BRCA cohorts. Functional enrichment analysis suggested the involvement of the immune system, and the immune infiltration landscape significantly differed between the risk groups. Patients with high CAF-related risk scores (CAFRSs) exhibited tumor immunosuppression, enhanced cancer hallmarks and hyposensitivity to chemotherapy and immunotherapy. Five compounds were identified as promising therapeutic agents for high-CAFRS BRCA. External datasets and sample experiments validated the downregulation of MFAP4 and its strong correlation with CAFs in BRCA. Conclusions: A novel CAF-derived gene signature with favorable predictive performance was developed in this study. This signature may be used to assess prognosis and guide individualized treatment for patients with BRCA.

Development of a metabolism-related signature for predicting prognosis, immune infiltration and immunotherapy response in breast cancer.

Breast cancer (BRCA) is the most commonly diagnosed cancer and among the top causes of cancer deaths globally. The abnormality of the metabolic process is an important characteristic that distinguishes cancer cells from normal cells. Currently, there are few metabolic molecular models to evaluate the prognosis and treatment response of BRCA patients. By analyzing RNA-seq data of BRCA samples from public databases via bioinformatic approaches, we developed a prognostic signature based on seven metabolic genes (PLA2G2D, GNPNAT1, QPRT, SHMT2, PAICS, NT5E and PLPP2). Low-risk patients showed better overall survival in all five cohorts (TCGA cohort, two external validation cohorts and two internal validation cohorts). There was a higher proportion of tumor-infiltrating CD8+ T cells, CD4+ memory resting T cells, gamma delta T cells and resting dendritic cells and a lower proportion of M0 and M2 macrophages in the low-risk group. Low-risk patients also showed higher ESTIMATE scores, higher immune function scores, higher Immunophenoscores (IPS) and checkpoint expression, lower stemness scores, lower TIDE (Tumor Immune Dysfunction and Exclusion) scores and IC50 values for several chemotherapeutic agents, suggesting that low-risk patients could respond more favorably to immunotherapy and chemotherapy. Two real-world patient cohorts receiving anti-PD-1 therapy were applied for validating the predictive results. Molecular subtypes identified based on these seven genes also showed different immune characteristics. Immunohistochemical data obtained from the human protein atlas database demonstrated the protein expression of signature genes. This research may contribute to the identification of metabolic targets for BRCA and the optimization of risk stratification and personalized treatment for BRCA patients.

MHC Class I Molecules Exacerbate Viral Infection by Disrupting Type I Interferon Signaling.

MHC class I molecules are key in the presentation of antigen and initiation of adaptive CD8+ T cell responses. In addition to its classical activity, MHC I may possess nonclassical functions. We have previously identified a regulatory role of MHC I in TLR signaling and antibacterial immunity. However, its role in innate antiviral immunity remains unknown. In this study, we found a reduced viral load in MHC I-deficient macrophages that was independent of type I IFN production. Mechanically, MHC I mediated viral suppression by inhibiting the type I IFN signaling pathway, which depends on SHP2. Cross-linking MHC I at the membrane increased SHP2 activation and further suppressed STAT1 phosphorylation. Therefore, our data revealed an inhibitory role of MHC I in type I IFN response to viral infection and expanded our understanding of MHC I and antigen presentation.

The transcription factor Foxp1 regulates the differentiation and function of dendritic cells.

Dendritic cells (DCs) are the sentinels of the immune system and play a critical role in initiating adaptive immune responses against pathogens. As the most powerful antigen presenting cells, DCs are also important in maintaining immune homeostasis and participating in the development of autoimmune diseases. How the maturation and function of DCs is regulated in these conditions and what is the function of various transcription factors is still unclear. In this study, we found that the expression of the transcription factor Foxp1 gradually increased during the maturation of DCs. Then, we constructed a recombinant adenovirus carrying Foxp1-interfering RNA (Ad-simFoxp1) and transfected murine bone marrow-derived DCs in vitro. DCs transfected with Ad-simFoxp1 exhibited markedly lower costimulatory molecules, and decreased cytokines. And Ad-simFoxp1 greatly inhibited mature DC-induced T cell responses. Moreover, in vivo infusion with Ad-simFoxp1-modified DCs significantly delayed the onset of experimental autoimmune encephalomyelitis (EAE). Therefore, adoptive transfection of Ad-simFoxp1 in DCs may be a potential treatment strategy against autoimmune diseases.

mKLRL1 regulates the maturation of dendritic cells and plays important roles in immune tolerance.

KLRL1 is a member of C-type lectin-like receptors (CLEC) and preferentially expressed on the surface of immune cells. We have previously illustrated its inhibitory role in Natural killer (NK) cells. Though cloned from dendritic cells (DCs), its role in DCs has not been fully identified. Here, we found that mKLRL1 markedly decreased during DC maturation; mKLRL1-modifed DCs showed enhanced phagocytic capability and reduced ability to induce T cell proliferation, which mimics immature DCs. Further investigation revealed that IL-10 was indispensable for mKLRL1 to suppress DC maturation. And p38 activation was responsible for preferential IL-10 production. Pretreatment with mKLRL1-modified DCs protected mice from subsequently EAE induction, indicating a role in immune tolerance. Taken together, our results have revealed an inhibitory role of KLRL1 in mouse DCs.

Facile and economical synthesis of an electrochromic copolymer for black based on electropolymerization of thiophene and 3,4-ethylenedioxythiophene.

We report the facile and economical synthesis of an electrochromic copolymer for black based on electrochemical copolymerization of thiophene and 3, 4-ethylenedioxythiophene in boron trifluoride diethyl etherate. The resultant copolymer presents multicolor electrochromism with reversible color change between drab color and blue black. Furthermore, in the polar state the resultant copolymer shows strong and broad absorption in the whole visible region and then exhibits black color. The copolymer presents a transmittance variation of 25% at 522 nm, and corresponding response times for bleaching and coloration are 4.2 and 3.3 s, respectively. Good electrochemical stability can be achieved by the copolymer film, which retains 87% of its original electroactivity after 2000 cycles.