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Carbon market, which is capable of scientific quantifying and marked-based pricing of carbon emission, is an important way for countries to achieve the target of carbon emission reduction. The global carbon market, after more than ten years of development, has developed a mature mechanism. China started the trial of carbon market in 2011. After ten years of exploration, the national carbon trading market was officially launched in mid-July 2021. Against the backdrop of carbon neutrality, the national carbon market will shoulder a greater mission of carbon emission reduction and speed up its financialization and internationalization. However, it should take a dialectical attitude toward the opportunities and risks of carbon market financialization. In the future, China can promote the development of carbon market through efforts to develop market participants, clarify the attributes of carbon finance, prevent potential risks of carbon finance, improve the connection mechanism with the international carbon market, and innovate carbon finance services.

In the field of earth sciences and remote exploration, the classification and identification of surface materials on earth have been a significant research area that poses considerable challenges in recent times. Although deep learning technology has achieved certain results in remote sensing image classification, it still has certain challenges for multi-modality remote sensing data classification. In this paper, we propose a fusion network based on transformer and gist convolutional neural network (CNN), namely TGF-Net. To minimize the duplication of information in multimodal data, the TGF-Net network incorporates a feature reconstruction module (FRM) that employs matrix factorization and self-attention mechanism for decomposing and evaluating the similarity of multimodal features. This enables the extraction of distinct as well as common features. Meanwhile, the transformer-based spectral feature extraction module (TSFEM) was designed by combining the different characteristics of remote sensing images and considering the problem of orderliness of the sequence between hyperspectral image (HSI) channels. In order to address the issue of representing the relative positions of spatial targets in synthetic aperture radar (SAR) images, we proposed a spatial feature extraction module called gist-based spatial feature extraction module (GSFEM). To assess the efficacy and superiority of the proposed TGF-Net, we performed experiments on two datasets comprising HSI and SAR data.

In recent years, the classification and identification of surface materials on earth have emerged as fundamental yet challenging research topics in the fields of geoscience and remote sensing (RS). The classification of multi-modality RS data still poses certain challenges, despite the notable advancements achieved by deep learning technology in RS image classification. In this work, a deep learning architecture based on convolutional neural network (CNN) is proposed for the classification of multimodal RS image data. The network structure introduces a cross modality reconstruction (CMR) module in the multi-modality feature fusion stage, called CMR-Net. In other words, CMR-Net is based on CNN network structure. In the feature fusion stage, a plug-and-play module for cross-modal fusion reconstruction is designed to compactly integrate features extracted from multiple modalities of remote sensing data, enabling effective information exchange and feature integration. In addition, to validate the proposed scheme, extensive experiments were conducted on two multi-modality RS datasets, namely the Houston2013 dataset consisting of hyperspectral (HS) and light detection and ranging (LiDAR) data, as well as the Berlin dataset comprising HS and synthetic aperture radar (SAR) data. The results demonstrate the effectiveness and superiority of our proposed CMR-Net compared to several state-of-the-art methods for multi-modality RS data classification.

Background Anticancer peptides are defence substances with innate immune functions that can selectively act on cancer cells without harming normal cells and many studies have been conducted to identify anticancer peptides. In this paper, we introduce the anticancer peptide secondary structures as additional features and propose an effective computational model, CL-ACP, that uses a combined network and attention mechanism to predict anticancer peptides. Results The CL-ACP model uses secondary structures and original sequences of anticancer peptides to construct the feature space. The long short-term memory and convolutional neural network are used to extract the contextual dependence and local correlations of the feature space. Furthermore, a multi-head self-attention mechanism is used to strengthen the anticancer peptide sequences. Finally, three categories of feature information are classified by cascading. CL-ACP was validated using two types of datasets, anticancer peptide datasets and antimicrobial peptide datasets, on which it achieved good results compared to previous methods. CL-ACP achieved the highest AUC values of 0.935 and 0.972 on the anticancer peptide and antimicrobial peptide datasets, respectively. Conclusions CL-ACP can effectively recognize antimicrobial peptides, especially anticancer peptides, and the parallel combined neural network structure of CL-ACP does not require complex feature design and high time cost. It is suitable for application as a useful tool in antimicrobial peptide design.

Varicella-zoster virus (VZV) infection downregulates surface major histocompatibility complex class I (MHC-I) expression and retains MHC-I in the Golgi complex of infected cells. However, the underlying mechanism is not fully understood. The VZV IE4 protein is a multifunctional protein that is essential for VZV infection. In this study, the human leucocyte antigen C (HLA-C) protein was identified as a novel cellular factor associated with IE4. Ectopically expressed IE4 co-localizes with HLA-C, sequesters HLA-C to the Golgi complex and downregulates cellular surface MHC-I. VZV, with a mutated Golgi localization signal in IE4, denoted as mutated IE4 (mIE4) VZV, was constructed. In mIE4 VZV-infected cells, the cellular surface MHC-I was restored, and HLA-C was not retained in the Golgi complex. In summary, for the first time, we demonstrate a novel role of VZV IE4 in interfering with the MHC-I presentation pathway, suggesting that it may contribute to the evasion of host antiviral adaptive immunity.

The paragraph “mIE4 VZV fails to downregulate MHC-I on the cell surface To characterize the function of IE4 during VZV infection, a mIE4 VZV was constructed as described in our previous study (Fig. 4A) [30] and showed a similar growth curve with WT VZV (data not shown). [...]flow cytometry analysis revealed that WT or ORF66-flag VZV significantly downregulated cellular surface MHC-I, whereas mIE4 VZV had a weaker effect (Fig. 4B). Confocal microscopy analysis revealed that WT VZV infection resulted in the retention of HLA-C-Red in the Golgi complex, similar to the ectopic expression of IE4 (Fig. 3A). [...]flow cytometry analysis revealed that WT or ORF66-flag VZV significantly downregulated cellular surface MHC-I, whereas mIE4 VZV had less effect (Fig. 4C). Confocal microscopy analysis revealed that WT VZV infection resulted in the retention of HLA-C-Red in the Golgi complex, similar to the ectopic expression of IE4 (Fig. 3A). The sentence Nonetheless, PAA was used to inhibit viral DNA replication in their study, suggesting that VZV IE or early gene product(s) is involved in the regulation of cellular surface MHC-I, and ORF66 delay the MHC-I transport from the ER to the cis/ medial-Golgi [20]. [...]they also demonstrated that VZV lacking ORF66 expression maintains the ability to downregulate cellular surface MHC-I [20], implying that one or more ORF66-independent genes might affect cellular surface MHC-I. in this article should have read as Nonetheless, PAA was used to inhibit viral DNA replication in their study, suggesting that VZV IE or early gene product(s) are involved in the regulation of cellular surface MHC-I, and ORF66 delay the MHC-I transport from the ER to the cis/medial-Golgi [20].

SAMHD1 is an intrinsic limiting factor that effectively prevents HIV-1 infection in macrophages, dendritic cells, and resting CD4+ T cells. Extensive studies have underscored the indispensable role of the dNTPase activity of SAMHD1 in its antiviral function by primarily depleting dNTPs in quiescent cells, thereby impeding HIV-1 cDNA synthesis. However, recent advancements in understanding posttranslational modifications of SAMHD1 have revealed specific modification site mutants that maintain their ability to reduce dNTP levels while impairing the inhibition of HIV-1 replication. Thus, the precise anti-HIV-1 mechanism of SAMHD1 remains enigmatic, necessitating a comprehensive understanding of the underlying mechanisms to develop novel therapeutic strategies targeting its antiviral activity. Recent findings by Guo et al. shed light on the role of SAMHD1 as an HIV-1 core sensor in suppressing HIV-1 infection after viral cDNA synthesis through its interaction with MX2 (H. Guo, W. Yang, H. Li, J. Yang, et al., mBio 15:e01363-24, 2024, https://doi.org/10.1128/mbio.01363-24).

A recent study published in by Cao et al. demonstrated that the helminth (Ts) alleviates COVID-19-related cytokine storms in an IL-9-dependent way (Z. Cao, J. Wang, X. Liu, Y. Liu, et al., mBio 15:e00905-24, 2024, https://doi.org/10.1128/mbio.00905-24). A cytokine storm is a severe immune response characterized by the overproduction of proinflammatory cytokines, such as TNF-α and IFN-γ, leading to tissue damage and mortality in COVID-19 patients. This study indicated that IL-9 is crucial in protecting against cytokine storm syndromes associated with SARS-CoV-2 infection and proposed that anti-inflammatory molecules from Ts excretory/secretory (TsES) products could be a novel source for treating such illnesses.

Numerous coreceptors have been shown to facilitate hACE2-dependent or hACE2-independent infection by SARS-CoV-2. A recent study published in by Yu et al. showed that the histamine receptor H1 (HRH1) functions as an alternative receptor for SARS-CoV-2 via direct binding to viral spike proteins (F. Yu, X. Liu, H. Ou, X. Li, et al., mBio e01088-24, 2024, https://doi.org/10.1128/mbio.01088-24). Furthermore, they present compelling evidence that antihistamine drugs targeting HRH1 potently inhibit SARS-CoV-2 entry. This study highlights the therapeutic potential of repurposable antihistamines against COVID-19.

Ovarian cancer is a malignant tumor with different clinicopathological and molecular characteristics. Due to its nonspecific early symptoms, the majority of patients are diagnosed with local or extensive metastasis, severely affecting treatment and prognosis. The occurrence of ovarian cancer is influenced by multiple complex mechanisms including genomics, transcriptomics, and proteomics. Integrating multiple types of omics data aids in predicting the survival rate of ovarian cancer patients. However, existing methods only fuse multi-omics data at the feature level, neglecting the shared and complementary neighborhood information among samples of multi-omics data, and failing to consider the potential interactions between different omics data at the molecular level. In this paper, we propose a prognostic model for ovarian cancer prediction named Dual Fusion Channels and Stacked Graph Convolutional Neural Network (DFASGCNS). The DFASGCNS utilizes dual fusion channels to learn feature representations of different omics data and the associations between samples. Stacked graph convolutional network is used to comprehensively learn the deep and intricate correlation networks present in multi-omics data, enhancing the model’s ability to represent multi-omics data. An attention mechanism is introduced to allocate different weights to important features of different omics data, optimizing the feature representation of multi-omics data. Experimental results demonstrate that compared to existing methods, the DFASGCNS model exhibits significant advantages in ovarian cancer prognosis prediction and survival analysis. Kaplan-Meier curve analysis results indicate significant differences in the survival subgroups predicted by the DFASGCNS model, contributing to a deeper understanding of the pathogenesis of ovarian cancer and providing more reliable auxiliary diagnostic information for the prognosis assessment of ovarian cancer patients.