Cytosine Modifications and Distinct Functions of TET1 on Tumorigenesis

Vast emerging evidences are linking the base modifications and gene expression involved in essential metabolic pathways. Among the base modification markers extensively studied, 5-methylcytosine (5mC) and its oxidative derivatives (5-hydroxymethylcytosine (5-hmC), 5-formylcytosine (5-fC), and 5-carboxylcytosine (5-caC)) dynamically occur in DNA and RNA and have been acknowledged as the important epigenetic markers involved in regulation of cellular biological processes. The modification of C has been characterized biochemically, molecularly, and phenotypically, including elucidation of its methyltransferase complexes (writer), demethylases (eraser), 10-11 translocation proteins (TETs), and direct interaction proteins (readers). The levels and the landscapes of these epigenetic markers in the epitranscriptomes and epigenomes are precisely and dynamically regulated by the fine-tuned coordination of the writers and erasers in accordance with stages of the growth, development, and reproduction as naturally programmed during the life span. In mammalian genome, the TET family is consisted of three members, including TET1, TET2, and TET3. The link between aberrant modifications and diseases, such as cancers, neurodegenerative disorders, and heart diseases, has been appreciated. This review article will highlight the research advances in the writers and erasers for the modifications of cytosine in genome, as well as the dual function of TET1 in tumorigenesis as a tumor suppressor and a promoter. Additionally, the future research directions are addressed

Weakly supervised conditional random fields model for semantic segmentation with image patches.

Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model. As a result, it is often very time-consuming and labor-intensive, yet still subject to manual errors and subjective inconsistency. To tackle such difficulties, a weakly supervised ISS approach is proposed, in which the challenging problem of label inference from image-level to pixel-level will be particularly addressed, using image patches and conditional random fields (CRF). An improved simple linear iterative cluster (SLIC) algorithm is employed to extract superpixels. for image segmentation. Specifically, it generates various numbers of superpixels according to different images, which can be used to guide the process of image patch extraction based on the image-level labeled information. Based on the extracted image patches, the CRF model is constructed for inferring semantic class labels, which uses the potential energy function to map from the image-level to pixel-level image labels. Finally, patch based CRF (PBCRF) model is used to accomplish the weakly supervised ISS. Experiments conducted on two publicly available benchmark datasets, MSRC and PASCAL VOC 2012, have demonstrated that our proposed algorithm can yield very promising results compared to quite a few state-of-the-art ISS methods, including some deep learning-based models

Recent Intensified Winter Coldness in the Mid-High Latitudes of Eurasia and Its Relationship with Daily Extreme Low Temperature Variability

Observational records in recent decades show a large-scale decrease in the cold-season temperature variance in the Northern Hemisphere midlatitudes under continuous global warming. However, severe low temperature events in winter frequently occurred in midlatitude Eurasia (MEA) in the last decade. Here, we define a new coldness intensity (CI) index for the near-surface based on the amplitude of daily anomalously cold temperatures in winter to demonstrate the CI of the variability of low temperature extremes. The results show that a sign-consistent mode dominates the CI variation in MEA, with a marked intensification during the last decade via empirical orthogonal function (EOF) analysis. This leading mode is significantly related to the frequency of winter extreme events. The associated circulations are characterized by a remarkable anomalous anticyclone in Northwest Eurasia, which induced substantial cold advection in MEA. The widespread intensified CI in MEA is closely linked with strong surface anticyclones and synoptic blocking in the mid-high latitudes (25 ∘ E-85 ∘ E). Coincidently, positive phase shifts of the first two leading modes of the extratropical circulation, which feature similar blocking-like anomalies in the northwestern Eurasian subarctic, jointly play an important role in the recent frequency of severe winters

The complete chloroplast genome sequence of Hyssopus cuspidatus Boriss. and analysis of phylogenetic relationships

Hyssopus cuspidatus is a member of the Lamiaceae family, members of which are often used to treat cough and asthma by the Uigurs. However, the Hyssopus genus has a limited number of known chloroplast genomes, making it difficult to compare species within the genus and to classify species within and outside the genus accurately. The introduction of the chloroplast genome method would therefore help improve the classification of the Hyssopus genus. This report presents the complete chloroplast sequences of Hyssopus cuspidatus. The chloroplast genome of H. cuspidatus is 149,678 bp long and contains 129 genes, including 85 protein-coding genes, 36 tRNA genes, and 8 rRNA genes. We identified 46 single sequence repeats (SSRs), most of which were mononucleotide adenine–thymine. The analysis of the repeat sequences, codon usage, and comparison of chloroplast genomes showed a high degree of conservation. The plastid genomes exhibited a typical quartile structure. Four hypervariable regions were identified: accD–psal, psbZ–trnG–GCC, trnH–GUG–psbA, and atpH–atpI. Phylogenetic analysis revealed that the Hyssopus genus was closely related to the adjacent genus Dracocephalum. Our research conducted a comprehensive analysis of the characteristics of the Hyssopus genus and provided a detailed comparison of the differences between species within and outside of this genus. Through IR comparison, phylogenetic analysis, and variation region analysis, we discovered a close relationship between the genera Hyssopus and Dracocephalum and propose a new perspective on the phylogenetic classification of H. cuspidatus. These findings will support the continued identification, classification, and evolutionary analysis of this genus

Editorial: Rock landslide risk assessment, stability analysis and monitoring for the development of early warning systems and reinforcement measures

Peer Reviewe

Application and research progress of antibody drug conjugates in HER2 positive advanced gastric cancer

Gastric cancer is a malignant tumor with high heterogeneity and invasiveness. Its incidence rate ranks fifth in the world, and its mortality ranks third in the world. Most patients are in a state that cancer cannot be removed by surgery when symptoms appear. At present, systemic treatment is the main treatment for advanced gastric cancer, and human epidermal growth factor receptor 2 (HER2) is one of the important treatment targets for HER2 positive gastric cancer patients. With the continuous optimization of chemotherapy regimen and targeted drugs, the prognosis of some HER2 positive gastric cancer patients has improved significantly. However, the high incidence of drug resistance and high toxicity and side effects are still the bottlenecks limiting the application of HER2 targeted drugs. Therefore, the development of new anti-tumor drugs is of great significance to improve the long-term survival of HER2 positive gastric cancer patients. Antibody drug conjugate (ADC) is a new and efficient anti-tumor drug, which is composed of specific targeted monoclonal antibody, chemical connector and small molecular cytotoxic payload. Its main advantages are strong therapeutic effect and moderate tissue toxicity. In recent years, ADC has set off a huge upsurge in the targeted treatment of HER2 positive advanced gastric cancer. First, after years of development, a variety of ADC including DS-8201 and RC48 have been used in the second- and second-line treatment of gastric cancer. Secondly, with the progress of ADC bioengineering technology, including high proportion of drug antibodies, cleavable linkers, toxic loads that can trigger bystander effect, the new type of ADC can play a more significant therapeutic role in the treatment of specific target tumors, and some of them also have multiple targets and can have anti-tumor effect on multiple specific targets. At the same time, the research and development process of ADC has reached the third stage. The new generation of ADC, through site-specific coupling technology, has higher homogeneity and uniformity, more effective cytotoxic molecules, higher accuracy and lower non-targeted toxicity. In addition, the "targeted immunotherapy" composed of ADC and immune checkpoint inhibitor (ICI) may be a promising treatment strategy for advanced gastric cancer. This article briefly reviewed the application and the latest research progress of ADC in HER2 positive advanced gastric cancer patients in the era of targeted therapy, and discussed the treatment prospects and challenges of ADC combined with ICI in HER2 positive advanced gastric cancer

Immunometabolism changes in fibrosis: from mechanisms to therapeutic strategies

Immune cells are essential for initiating and developing the fibrotic process by releasing cytokines and growth factors that activate fibroblasts and promote extracellular matrix deposition. Immunometabolism describes how metabolic alterations affect the function of immune cells and how inflammation and immune responses regulate systemic metabolism. The disturbed immune cell function and their interactions with other cells in the tissue microenvironment lead to the origin and advancement of fibrosis. Understanding the dysregulated metabolic alterations and interactions between fibroblasts and the immune cells is critical for providing new therapeutic targets for fibrosis. This review provides an overview of recent advances in the pathophysiology of fibrosis from the immunometabolism aspect, highlighting the altered metabolic pathways in critical immune cell populations and the impact of inflammation on fibroblast metabolism during the development of fibrosis. We also discuss how this knowledge could be leveraged to develop novel therapeutic strategies for treating fibrotic diseases

Hyperforin Promotes Post-stroke Neuroangiogenesis via Astrocytic IL-6-Mediated Negative Immune Regulation in the Ischemic Brain

Hyperforin has been shown to be capable of promoting angiogenesis and functional recovery after ischemic stroke in our previous study. However, the exact mechanisms involved are not fully elucidated. In this study, adult male mice were subjected to 60-min transient middle cerebral artery occlusion followed by reperfusion for 28 days. Hyperforin was administrated to MCAO mice every 24 h for 2 weeks starting at 14 days post-ischemia (dpi). Then flow cytometry, quantitative Real-time PCR (RT-qPCR), western blotting, immunohistochemistry, and functional assays were performed to explore the molecular mechanisms in vivo and in vitro. Our data showed that hyperforin increased astrocytic interleukin (IL)-6 in the ischemic hemisphere via TLR4 at 28 dpi. The astrocytic IL-6 was essential to the promoting effects of hyperforin on the neural precursor cells proliferation, neuronal differentiation, angiogenesis, and functional recovery after stroke. Furthermore, hyperforin promoted the infiltration of regulatory T cells (Tregs) to the ischemic hemisphere and increased Tregs-derived cytokine IL-10 and transforming growth factor-β (TGF-β) in a manner that was dependent on astrocytic IL-6. Astrocytic IL-6 was critical to the role of hyperforin in promoting the infiltration of T-helper (Th) type 2 cells to the ischemic hemisphere and Th2-derived cytokine IL-4, relative to Th1 and Th1-derived cytokine interferon-γ (IFN-γ), which decreased during stroke recovery. After depletion of CD25+ Tregs, the promoting effects of hyperforin on post-stroke neurogenesis was attenuated. Moreover, blockade of IL-4 and TGF-β abrogated the promoting role of hyperforin in post-stroke neurogenesis, angiogenesis and functional recovery. Our results reveal a previously uncharacterized role of astrocytic IL-6-mediated negative immune regulation in the promoting effects of hyperforin on post-stroke neurovascular regeneration and functional recovery

IRGen: Generative Modeling for Image Retrieval

While generative modeling has been ubiquitous in natural language processing and computer vision, its application to image retrieval remains unexplored. In this paper, we recast image retrieval as a form of generative modeling by employing a sequence-to-sequence model, contributing to the current unified theme. Our framework, IRGen, is a unified model that enables end-to-end differentiable search, thus achieving superior performance thanks to direct optimization. While developing IRGen we tackle the key technical challenge of converting an image into quite a short sequence of semantic units in order to enable efficient and effective retrieval. Empirical experiments demonstrate that our model yields significant improvement over three commonly used benchmarks, for example, 22.9\% higher than the best baseline method in precision@10 on In-shop dataset with comparable recall@10 score

Model-enhanced Vector Index

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and decent recall performance. Recent research indicates that deep retrieval solutions offer better model quality, but are hindered by unacceptable serving latency and the inability to support document updates. In this paper, we aim to enhance the vector index with end-to-end deep generative models, leveraging the differentiable advantages of deep retrieval models while maintaining desirable serving efficiency. We propose Model-enhanced Vector Index (MEVI), a differentiable model-enhanced index empowered by a twin-tower representation model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the sequence-to-sequence deep retrieval and embedding-based models. To substantially reduce the inference time, instead of decoding the unique document ids in long sequential steps, we first generate some semantic virtual cluster ids of candidate documents in a small number of steps, and then leverage the well-adapted embedding vectors to further perform a fine-grained search for the relevant documents in the candidate virtual clusters. We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions