Multiscale investigation of the mechanism and selectivity of CO2 hydrogenation over Rh(111)

CO2 hydrogenation over Rh catalysts comprises multiple reaction pathways, presenting a wide range of possible intermediates and end products, with selectivity toward either CO or methane being of particular interest. We investigate in detail the reaction mechanism of CO2 hydrogenation to the single-carbon (C1) products on the Rh(111) facet by performing periodic density functional theory (DFT) calculations and kinetic Monte Carlo (kMC) simulations, which account for the adsorbate interactions through a cluster expansion approach. We observe that Rh readily facilitates the dissociation of hydrogen, thus contributing to the subsequent hydrogenation processes. The reverse water–gas shift (RWGS) reaction occurs via three different reaction pathways, with CO hydrogenation to the COH intermediate being a key step for CO2 methanation. The effects of temperature, pressure, and the composition ratio of the gas reactant feed are considered. Temperature plays a pivotal role in determining the surface coverage and adsorbate composition, with competitive adsorption between CO and H species influencing the product distribution. The observed adlayer configurations indicate that the adsorbed CO species are separated by adsorbed H atoms, with a high ratio of H to CO coverage on the Rh(111) surface being essential to promote CO2 methanation

Exploiting Multiple Embeddings for Chinese Named Entity Recognition

Identifying the named entities mentioned in text would enrich many semantic applications at the downstream level. However, due to the predominant usage of colloquial language in microblogs, the named entity recognition (NER) in Chinese microblogs experience significant performance deterioration, compared with performing NER in formal Chinese corpus. In this paper, we propose a simple yet effective neural framework to derive the character-level embeddings for NER in Chinese text, named ME-CNER. A character embedding is derived with rich semantic information harnessed at multiple granularities, ranging from radical, character to word levels. The experimental results demonstrate that the proposed approach achieves a large performance improvement on Weibo dataset and comparable performance on MSRA news dataset with lower computational cost against the existing state-of-the-art alternatives.Comment: accepted at CIKM 201

A novel CpG ODN compound adjuvant enhances immune response to spike subunit vaccines of porcine epidemic diarrhea virus

CpG oligodeoxynucleotides (CpG ODNs) boost the humoral and cellular immune responses to antigens through interaction with Toll-like receptor 9 (TLR9). These CpG ODNs have been extensively utilized in human vaccines. In our study, we evaluated five B-type CpG ODNs that have stimulatory effects on pigs by measuring the proliferation of porcine peripheral blood mononuclear cells (PBMCs) and assessing interferon gamma (IFN-γ) secretion. Furthermore, this study examined the immunoenhancing effects of the MF59 and CpG ODNs compound adjuvant in mouse and piglet models of porcine epidemic diarrhea virus (PEDV) subunit vaccine administration. The in vitro screening revealed that the CpG ODN named CpG5 significantly stimulated the proliferation of porcine PBMCs and elevated IFN-γ secretion levels. In the mouse vaccination model, CpG5 compound adjuvant significantly bolstered the humoral and cellular immune responses to the PEDV subunit vaccines, leading to Th1 immune responses characterized by increased IFN-γ and IgG2a levels. In piglets, the neutralizing antibody titer was significantly enhanced with CpG5 compound adjuvant, alongside a considerable increase in CD8+ T lymphocytes proportion. The combination of MF59 adjuvant and CpG5 exhibits a synergistic effect, resulting in an earlier, more intense, and long-lasting immune response in subunit vaccines for PEDV. This combination holds significant promise as a robust candidate for the development of vaccine adjuvant

Analysis of lncRNA-Associated ceRNA Network Reveals Potential lncRNA Biomarkers in Human Colon Adenocarcinoma

Background/Aims: Long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) play significant roles in the development of tumors, but the functions of specific lncRNAs and lncRNA-related ceRNA networks have not been fully elucidated for colon adenocarcinoma (COAD). In this study, we aimed to clarify the lncRNA-microRNA (miRNA)-mRNA ceRNA network and potential lncRNA biomarkers in COAD. Methods: We extracted data from The Cancer Genome Atlas (TCGA) and identified COAD-specific mRNAs, miRNAs, and lncRNAs. The biological processes in Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed for COAD-specific mRNAs. We then constructed a ceRNA network of COAD-specific mRNAs, miRNAs and lncRNAs and analyzed the correlation between expression patterns and clinical features of the lncRNAs involved. After identifying potential mRNA targets of 4 lncRNAs related to overall survival (OS), we conducted stepwise analysis of these targets through GO and KEGG. Using tissue samples from our own patients, we also verified certain analytical results using quantitative real-time PCR (qRT-PCR). Results: Data from 521 samples (480 tumor tissue and 41 adjacent non-tumor tissue samples) were extracted from TCGA. A total of 258 specific lncRNAs, 206 specific miRNAs, and 1467 specific mRNAs were identified (absolute log2 [fold change] > 2, false discovery rate < 0.01). Analysis of KEGG revealed that specific mRNAs were enriched in cancer-related pathways. The ceRNA network was constructed with 64 lncRNAs, 18 miRNAs, and 42 mRNAs. Among these lncRNAs involved in the network, 3 lncRNAs (LINC00355, HULC, and IGF2-AS) were confirmed to be associated with certain clinical features and 4 lncRNAs (HOTAIR, LINC00355, KCNQ1OT1, and TSSC1-IT1) were found to be negatively linked to OS (log-rank p < 0.05). KEGG showed that the potential mRNA targets of these 4 lncRNAs may be concentrated in the MAPK pathway. Certain results were validated by qRT-PCR. Conclusion: This study providing novel insights into the lncRNA-miRNA-mRNA ceRNA network and reveals potential lncRNA biomarkers in COAD

Loss‐of‐Function Genetic Screening Identifies Aldolase A as an Essential Driver for Liver Cancer Cell Growth Under Hypoxia

Background and aims: Hypoxia is a common feature of the tumor microenvironment (TME), which promotes tumor progression, metastasis, and therapeutic drug resistance through a myriad of cell activities in tumor and stroma cells. While targeting hypoxic TME is emerging as a promising strategy for treating solid tumors, preclinical development of this approach is lacking in the study of HCC. Approach and results: From a genome-wide CRISPR/CRISPR-associated 9 gene knockout screening, we identified aldolase A (ALDOA), a key enzyme in glycolysis and gluconeogenesis, as an essential driver for HCC cell growth under hypoxia. Knockdown of ALDOA in HCC cells leads to lactate depletion and consequently inhibits tumor growth. Supplementation with lactate partly rescues the inhibitory effects mediated by ALDOA knockdown. Upon hypoxia, ALDOA is induced by hypoxia-inducible factor-1α and fat mass and obesity-associated protein-mediated N6 -methyladenosine modification through transcriptional and posttranscriptional regulation, respectively. Analysis of The Cancer Genome Atlas shows that elevated levels of ALDOA are significantly correlated with poor prognosis of patients with HCC. In a screen of Food and Drug Administration-approved drugs based on structured hierarchical virtual platforms, we identified the sulfamonomethoxine derivative compound 5 (cpd-5) as a potential inhibitor to target ALDOA, evidenced by the antitumor activity of cpd-5 in preclinical patient-derived xenograft models of HCC. Conclusions: Our work identifies ALDOA as an essential driver for HCC cell growth under hypoxia, and we demonstrate that inhibition of ALDOA in the hypoxic TME is a promising therapeutic strategy for treating HCC

RNA-binding protein RALY reprogrammes mitochondrial metabolism via mediating miRNA processing in colorectal cancer

Objective: Dysregulated cellular metabolism is a distinct hallmark of human colorectal cancer (CRC). However, metabolic programme rewiring during tumour progression has yet to be fully understood. Design: We analysed altered gene signatures during colorectal tumour progression, and used a complex of molecular and metabolic assays to study the regulation of metabolism in CRC cell lines, human patient-derived xenograft mouse models and tumour organoid models. Results: We identified a novel RNA-binding protein, RALY (also known as hnRNPCL2), that is highly associated with colorectal tumour aggressiveness. RALY acts as a key regulatory component in the Drosha complex, and promotes the post-transcriptional processing of a specific subset of miRNAs (miR-483, miR-676 and miR-877). These miRNAs systematically downregulate the expression of the metabolism-associated genes (ATP5I, ATP5G1, ATP5G3 and CYC1) and thereby reprogramme mitochondrial metabolism in the cancer cell. Analysis of The Cancer Genome Atlas (TCGA) reveals that increased levels of RALY are associated with poor prognosis in the patients with CRC expressing low levels of mitochondrion-associated genes. Mechanistically, induced processing of these miRNAs is facilitated by their N6-methyladenosine switch under reactive oxygen species (ROS) stress. Inhibition of the m6A methylation abolishes the RALY recognition of the terminal loop of the pri-miRNAs. Knockdown of RALY inhibits colorectal tumour growth and progression in vivo and in organoid models. Conclusions: Collectively, our results reveal a critical metabolism-centric role of RALY in tumour progression, which may lead to cancer therapeutics targeting RALY for treating CRC

Environment-driven variability in absolute band edge positions and work functions of reduced ceria

The absolute band edge positions and work function (Φ) are the key electronic properties of metal oxides that determine their performance in electronic devices and photocatalysis. However, experimental measurements of these properties often show notable variations, and the mechanisms underlying these discrepancies remain inadequately understood. In this work, we focus on ceria (CeO2), a material renowned for its outstanding oxygen storage capacity, and combine theoretical and experimental techniques to demonstrate environmental modifications of its ionization potential (IP) and Φ. Under O-deficient conditions, reduced ceria exhibits a decreased IP and Φ with significant sensitivity to defect distributions. In contrast, the IP and Φ are elevated in O-rich conditions due to the formation of surface peroxide species. Surface adsorbates and impurities can further augment these variabilities under realistic conditions. We rationalize the shifts in energy levels by separating the individual contributions from bulk and surface factors, using hybrid quantum mechanical/molecular mechanical (QM/MM) embedded-cluster and periodic density functional theory (DFT) calculations supported by interatomic-potential-based electrostatic analyses. Our results highlight the critical role of on-site electrostatic potentials in determining the absolute energy levels in metal oxides, implying a dynamic evolution of band edges under catalytic conditions

An Improved YOLO Algorithm for Fast and Accurate Underwater Object Detection

Due to the abundant natural resources of the underwater world, autonomous exploration using underwater robots has become an effective technological tool in recent years. Real-time object detection is critical when employing robots for independent underwater exploration. However, when a robot detects underwater, its computing power is usually limited, which makes it challenging to detect objects effectively. To solve this problem, this study presents a novel algorithm for underwater object detection based on YOLOv4-tiny to achieve better performance with less computational cost. First, a symmetrical bottleneck-type structure is introduced into the YOLOv4-tiny’s backbone network based on dilated convolution and 1 × 1 convolution. It captures contextual information in feature maps with reasonable computational cost and improves the mAP score by 8.74% compared to YOLOv4-tiny. Second, inspired by the convolutional block attention module, a symmetric FPN-Attention module is constructed by integrating the channel-attention module and the spatial-attention module. Features extracted by the backbone network can be fused more efficiently by the symmetric FPN-Attention module, achieving a performance improvement of 8.75% as measured by mAP score compared to YOLOv4-tiny. Finally, this work proposed the YOLO-UOD for underwater object detection through the fusion of the YOLOv4-tiny structure, symmetric FPN-Attention module, symmetric bottleneck-type dilated convolutional layers, and label smoothing training strategy. It can efficiently detect underwater objects in an embedded system environment with limited computing power. Experiments show that the proposed YOLO-UOD outperforms the baseline model on the Brackish underwater dataset, with a detection mAP of 87.88%, 10.5% higher than that of YOLOv4-tiny’s 77.38%, and the detection result exceeds YOLOv5s’s 83.05% and YOLOv5m’s 84.34%. YOLO-UOD is deployed on the embedded system Jetson Nano 2 GB with a detection speed of 9.24 FPS, which shows that it can detect effectively in scenarios with limited computing power