Free-space dissemination of time and frequency with 10-19 instability over 113 km.

Networks of optical clocks find applications in precise navigation1,2, in efforts to redefine the fundamental unit of the 'second'3-6 and in gravitational tests7. As the frequency instability for state-of-the-art optical clocks has reached the 10-19 level8,9, the vision of a global-scale optical network that achieves comparable performances requires the dissemination of time and frequency over a long-distance free-space link with a similar instability of 10-19. However, previous attempts at free-space dissemination of time and frequency at high precision did not extend beyond dozens of kilometres10,11. Here we report time-frequency dissemination with an offset of 6.3 × 10-20 ± 3.4 × 10-19 and an instability of less than 4 × 10-19 at 10,000 s through a free-space link of 113 km. Key technologies essential to this achievement include the deployment of high-power frequency combs, high-stability and high-efficiency optical transceiver systems and efficient linear optical sampling. We observe that the stability we have reached is retained for channel losses up to 89 dB. The technique we report can not only be directly used in ground-based applications, but could also lay the groundwork for future satellite time-frequency dissemination.

An integrated space-to-ground quantum communication network over 4,600 kilometres.

Quantum key distribution (QKD)1,2 has the potential to enable secure communication and information transfer3. In the laboratory, the feasibility of point-to-point QKD is evident from the early proof-of-concept demonstration in the laboratory over 32 centimetres4; this distance was later extended to the 100-kilometre scale5,6 with decoy-state QKD and more recently to the 500-kilometre scale7-10 with measurement-device-independent QKD. Several small-scale QKD networks have also been tested outside the laboratory11-14. However, a global QKD network requires a practically (not just theoretically) secure and reliable QKD network that can be used by a large number of users distributed over a wide area15. Quantum repeaters16,17 could in principle provide a viable option for such a global network, but they cannot be deployed using current technology18. Here we demonstrate an integrated space-to-ground quantum communication network that combines a large-scale fibre network of more than 700 fibre QKD links and two high-speed satellite-to-ground free-space QKD links. Using a trusted relay structure, the fibre network on the ground covers more than 2,000 kilometres, provides practical security against the imperfections of realistic devices, and maintains long-term reliability and stability. The satellite-to-ground QKD achieves an average secret-key rate of 47.8 kilobits per second for a typical satellite pass-more than 40 times higher than achieved previously. Moreover, its channel loss is comparable to that between a geostationary satellite and the ground, making the construction of more versatile and ultralong quantum links via geosynchronous satellites feasible. Finally, by integrating the fibre and free-space QKD links, the QKD network is extended to a remote node more than 2,600 kilometres away, enabling any user in the network to communicate with any other, up to a total distance of 4,600 kilometres.

Recognition of cyanobacteria promoters via Siamese network-based contrastive learning under novel non-promoter generation.

It is a vital step to recognize cyanobacteria promoters on a genome-wide scale. Computational methods are promising to assist in difficult biological identification. When building recognition models, these methods rely on non-promoter generation to cope with the lack of real non-promoters. Nevertheless, the factitious significant difference between promoters and non-promoters causes over-optimistic prediction. Moreover, designed for E. coli or B. subtilis, existing methods cannot uncover novel, distinct motifs among cyanobacterial promoters. To address these issues, this work first proposes a novel non-promoter generation strategy called phantom sampling, which can eliminate the factitious difference between promoters and generated non-promoters. Furthermore, it elaborates a novel promoter prediction model based on the Siamese network (SiamProm), which can amplify the hidden difference between promoters and non-promoters through a joint characterization of global associations, upstream and downstream contexts, and neighboring associations w.r.t. k-mer tokens. The comparison with state-of-the-art methods demonstrates the superiority of our phantom sampling and SiamProm. Both comprehensive ablation studies and feature space illustrations also validate the effectiveness of the Siamese network and its components. More importantly, SiamProm, upon our phantom sampling, finds a novel cyanobacterial promoter motif ('GCGATCGC'), which is palindrome-patterned, content-conserved, but position-shifted.

Entanglement-based secure quantum cryptography over 1,120 kilometres.

Quantum key distribution (QKD)1-3 is a theoretically secure way of sharing secret keys between remote users. It has been demonstrated in a laboratory over a coiled optical fibre up to 404 kilometres long4-7. In the field, point-to-point QKD has been achieved from a satellite to a ground station up to 1,200 kilometres away8-10. However, real-world QKD-based cryptography targets physically separated users on the Earth, for which the maximum distance has been about 100 kilometres11,12. The use of trusted relays can extend these distances from across a typical metropolitan area13-16 to intercity17 and even intercontinental distances18. However, relays pose security risks, which can be avoided by using entanglement-based QKD, which has inherent source-independent security19,20. Long-distance entanglement distribution can be realized using quantum repeaters21, but the related technology is still immature for practical implementations22. The obvious alternative for extending the range of quantum communication without compromising its security is satellite-based QKD, but so far satellite-based entanglement distribution has not been efficient23 enough to support QKD. Here we demonstrate entanglement-based QKD between two ground stations separated by 1,120 kilometres at a finite secret-key rate of 0.12 bits per second, without the need for trusted relays. Entangled photon pairs were distributed via two bidirectional downlinks from the Micius satellite to two ground observatories in Delingha and Nanshan in China. The development of a high-efficiency telescope and follow-up optics crucially improved the link efficiency. The generated keys are secure for realistic devices, because our ground receivers were carefully designed to guarantee fair sampling and immunity to all known side channels24,25. Our method not only increases the secure distance on the ground tenfold but also increases the practical security of QKD to an unprecedented level.

Angiotensin II receptor inhibition ameliorates liver fibrosis and enhances hepatocellular carcinoma infiltration by effector T cells.

Although viral hepatocellular carcinoma (HCC) is declining, nonviral HCC, which often is the end stage of nonalcoholic or alcoholic steatohepatitis (NASH, ASH), is on an upward trajectory. Immune checkpoint inhibitors (ICIs) that block the T cell inhibitory receptor PD-1 were approved for treatment of all HCC types. However, only a minority of HCC patients show a robust and sustained response to PD-1 blockade, calling for improved understanding of factors that negatively impact response rate and duration and the discovery of new adjuvant treatments that enhance ICI responsiveness. Using a mouse model of NASH-driven HCC, we identified peritumoral fibrosis as a potential obstacle to T cell-mediated tumor regression and postulated that antifibrotic medications may increase ICI responsiveness. We now show that the angiotensin II receptor inhibitor losartan, a commonly prescribed and safe antihypertensive drug, reduced liver and peritumoral fibrosis and substantially enhanced anti-PD-1-induced tumor regression. Although losartan did not potentiate T cell reinvigoration, it substantially enhanced HCC infiltration by effector CD8+ T cells compared to PD-1 blockade alone. The beneficial effects of losartan correlated with blunted TGF-ß receptor signaling, reduced collagen deposition, and depletion of immunosuppressive fibroblasts.

Exploring the Origins and Evolution of Oxygenic and Anoxygenic Photosynthesis in Deeply Branched Cyanobacteriota.

Cyanobacteriota, the sole prokaryotes capable of oxygenic photosynthesis (OxyP), occupy a unique and pivotal role in Earth's history. While the notion that OxyP may have originated from Cyanobacteriota is widely accepted, its early evolution remains elusive. Here, by using both metagenomics and metatranscriptomics, we explore 36 metagenome-assembled genomes from hot spring ecosystems, belonging to two deep-branching cyanobacterial orders: Thermostichales and Gloeomargaritales. Functional investigation reveals that Thermostichales encode the crucial thylakoid membrane biogenesis protein, vesicle-inducing protein in plastids 1 (Vipp1). Based on the phylogenetic results, we infer that the evolution of the thylakoid membrane predates the divergence of Thermostichales from other cyanobacterial groups and that Thermostichales may be the most ancient lineage known to date to have inherited this feature from their common ancestor. Apart from OxyP, both lineages are potentially capable of sulfide-driven AnoxyP by linking sulfide oxidation to the photosynthetic electron transport chain. Unexpectedly, this AnoxyP capacity appears to be an acquired feature, as the key gene sqr was horizontally transferred from later-evolved cyanobacterial lineages. The presence of two D1 protein variants in Thermostichales suggests the functional flexibility of photosystems, ensuring their survival in fluctuating redox environments. Furthermore, all MAGs feature streamlined phycobilisomes with a preference for capturing longer-wavelength light, implying a unique evolutionary trajectory. Collectively, these results reveal the photosynthetic flexibility in these early-diverging cyanobacterial lineages, shedding new light on the early evolution of Cyanobacteriota and their photosynthetic processes.

A social theory-enhanced graph representation learning framework for multitask prediction of drug-drug interactions.

Current machine learning-based methods have achieved inspiring predictions in the scenarios of mono-type and multi-type drug-drug interactions (DDIs), but they all ignore enhancive and depressive pharmacological changes triggered by DDIs. In addition, these pharmacological changes are asymmetric since the roles of two drugs in an interaction are different. More importantly, these pharmacological changes imply significant topological patterns among DDIs. To address the above issues, we first leverage Balance theory and Status theory in social networks to reveal the topological patterns among directed pharmacological DDIs, which are modeled as a signed and directed network. Then, we design a novel graph representation learning model named SGRL-DDI (social theory-enhanced graph representation learning for DDI) to realize the multitask prediction of DDIs. SGRL-DDI model can capture the task-joint information by integrating relation graph convolutional networks with Balance and Status patterns. Moreover, we utilize task-specific deep neural networks to perform two tasks, including the prediction of enhancive/depressive DDIs and the prediction of directed DDIs. Based on DDI entries collected from DrugBank, the superiority of our model is demonstrated by the comparison with other state-of-the-art methods. Furthermore, the ablation study verifies that Balance and Status patterns help characterize directed pharmacological DDIs, and that the joint of two tasks provides better DDI representations than individual tasks. Last, we demonstrate the practical effectiveness of our model by a version-dependent test, where 88.47 and 81.38% DDI out of newly added entries provided by the latest release of DrugBank are validated in two predicting tasks respectively.

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction.

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, natural language processing based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.

DMGL-MDA: A dual-modal graph learning method for microbe-drug association prediction.

The interaction between human microbes and drugs can significantly impact human physiological functions. It is crucial to identify potential microbe-drug associations (MDAs) before drug administration. However, conventional biological experiments to predict MDAs are plagued by drawbacks such as time-consuming, high costs, and potential risks. On the contrary, computational approaches can speed up the screening of MDAs at a low cost. Most computational models usually use a drug similarity matrix as the initial feature representation of drugs and stack the graph neural network layers to extract the features of network nodes. However, different calculation methods result in distinct similarity matrices, and message passing in graph neural networks (GNNs) induces phenomena of over-smoothing and over-squashing, thereby impacting the performance of the model. To address these issues, we proposed a novel graph representation learning model, dual-modal graph learning for microbe-drug association prediction (DMGL-MDA). It comprises a dual-modal embedding module, a bipartite graph network embedding module, and a predictor module. To assess the performance of DMGL-MDA, we compared it against state-of-the-art methods using two benchmark datasets. Through cross-validation, we illustrated the superiority of DMGL-MDA. Furthermore, we conducted ablation experiments and case studies to validate the effective performance of the model.

Oncoprotein-induced transcript 3 protein-enriched extracellular vesicles promotes NLRP3 ubiquitination to alleviate acute lung injury after cardiac surgery.

Acute lung injury (ALI) including acute respiratory distress syndrome (ARDS) is a major complication and increase the mortality of patients with cardiac surgery. We previously found that the protein cargoes enriched in circulating extracellular vesicles (EVs) are closely associated with cardiopulmonary disease. We aimed to evaluate the implication of EVs on cardiac surgery-associated ALI/ARDS. The correlations between "oncoprotein-induced transcript 3 protein (OIT3) positive" circulating EVs and postoperative ARDS were assessed. The effects of OIT3-overexpressed EVs on the cardiopulmonary bypass (CPB) -induced ALI in vivo and inflammation of human bronchial epithelial cells (BEAS-2B) were detected. OIT3 enriched in circulating EVs is reduced after cardiac surgery with CPB, especially with postoperative ARDS. The "OIT3 positive" EVs negatively correlate with lung edema, hypoxemia and CPB time. The OIT3-overexpressed EVs can be absorbed by pulmonary epithelial cells and OIT3 transferred by EVs triggered K48- and K63-linked polyubiquitination to inactivate NOD-like receptor protein 3 (NLRP3) inflammasome, and restrains pro-inflammatory cytokines releasing and immune cells infiltration in lung tissues, contributing to the alleviation of CPB-induced ALI. Overexpression of OIT3 in human bronchial epithelial cells have similar results. OIT3 promotes the E3 ligase Cbl proto-oncogene B associated with NLRP3 to induce the ubiquitination of NLRP3. Immunofluorescence tests reveal that OIT3 is reduced in the generation from the liver sinusoids endothelial cells (LSECs) and secretion in liver-derived EVs after CPB. In conclusion, OIT3 enriched in EVs is a promising biomarker of postoperative ARDS and a therapeutic target for ALI after cardiac surgery.

AC092100.1 promotes angiogenesis in pre-eclampsia through YTHDC2/VEGFA signaling.

Aberrant long non-coding RNA (lncRNA) expression has been shown to be involved in the pathological process of pre-eclampsia (PE), yet only a small portion of lncRNAs has been characterized concerning the function and molecular mechanisms involved in PE. This study aimed to investigate the regulatory mechanism of the lncRNA AC092100.1 (AC092100.1) in angiogenesis in PE. In our study, bioinformatics analysis was performed to screen for differentially expressed lncRNAs between normal subjects and PE patients. The levels of AC092100.1 in placental tissues of patients with or without PE were validated using qRT-PCR. The effect of AC092100.1 overexpression on the proliferation, migration, and tube formation of human umbilical vein endothelial cells (HUVECs) was investigated. The binding of AC092100.1 and YT521-B homology domain-containing 2 (YTHDC2) was predicted and verified. The effect of AC092100.1/YTHDC2 on the expression of vascular endothelial growth factor-A (VEGFA) in HUVECs was determined. Finally, a PE mice model was conducted. Fetal mouse growth, the abundance of mesenchymal morphology markers, including hypoxia-inducible factor 1-alpha (HIF-1α), soluble fms-like tyrosine kinase-1 (sFlt-1), soluble endoglin (sEng), Slug, and Vimentin, and endothelial markers, including placental growth factor (PLGF), CD31, and vascular endothelial (VE)-cadherin, in placental tissues were assessed. Here, we found that AC092100.1 was abnormally downregulated in placental tissues from PE patients. We established that AC092100.1 overexpression promoted HUVEC proliferation, migration, and tube formation in vitro. Mechanistically, AC092100.1 induced the accumulation of YTHDC2 and VEGFA through binding to YTHDC2 in HUVECs. Inhibition of YTHDC2 or VEGFA reversed AC092100.1-promoted tube formation. AC092100.1 overexpression contributed to alleviating fetal growth disorder, decreased levels of sEng, HIF-1α, sFlt-1, Slug, and Vimentin, and increased levels of VEGFA, PLGF, CD31, and VE-cadherin in PE mice. Our findings provided evidence supporting the role of the AC092100.1/YTHDC2/VEGFA axis in regulating angiogenesis, which demonstrated a therapeutic pathway for PE targeting angiogenesis.

Directed graph attention networks for predicting asymmetric drug-drug interactions.

It is tough to detect unexpected drug-drug interactions (DDIs) in poly-drug treatments because of high costs and clinical limitations. Computational approaches, such as deep learning-based approaches, are promising to screen potential DDIs among numerous drug pairs. Nevertheless, existing approaches neglect the asymmetric roles of two drugs in interaction. Such an asymmetry is crucial to poly-drug treatments since it determines drug priority in co-prescription. This paper designs a directed graph attention network (DGAT-DDI) to predict asymmetric DDIs. First, its encoder learns the embeddings of the source role, the target role and the self-roles of a drug. The source role embedding represents how a drug influences other drugs in DDIs. In contrast, the target role embedding represents how it is influenced by others. The self-role embedding encodes its chemical structure in a role-specific manner. Besides, two role-specific items, aggressiveness and impressionability, capture how the number of interaction partners of a drug affects its interaction tendency. Furthermore, the predictor of DGAT-DDI discriminates direction-specific interactions by the combination between two proximities and the above two role-specific items. The proximities measure the similarity between source/target embeddings and self-role embeddings. In the designated experiments, the comparison with state-of-the-art deep learning models demonstrates the superiority of DGAT-DDI across a direction-specific predicting task and a direction-blinded predicting task. An ablation study reveals how well each component of DGAT-DDI contributes to its ability. Moreover, a case study of finding novel DDIs confirms its practical ability, where 7 out of the top 10 candidates are validated in DrugBank.

Drug-drug interaction prediction with learnable size-adaptive molecular substructures.

Drug-drug interactions (DDIs) are interactions with adverse effects on the body, manifested when two or more incompatible drugs are taken together. They can be caused by the chemical compositions of the drugs involved. We introduce gated message passing neural network (GMPNN), a message passing neural network which learns chemical substructures with different sizes and shapes from the molecular graph representations of drugs for DDI prediction between a pair of drugs. In GMPNN, edges are considered as gates which control the flow of message passing, and therefore delimiting the substructures in a learnable way. The final DDI prediction between a drug pair is based on the interactions between pairs of their (learned) substructures, each pair weighted by a relevance score to the final DDI prediction output. Our proposed method GMPNN-CS (i.e. GMPNN + prediction module) is evaluated on two real-world datasets, with competitive results on one, and improved performance on the other compared with previous methods. Source code is freely available at https://github.com/kanz76/GMPNN-CS.

CProMG: controllable protein-oriented molecule generation with desired binding affinity and drug-like properties.

MOTIVATION: Deep learning-based molecule generation becomes a new paradigm of de novo molecule design since it enables fast and directional exploration in the vast chemical space. However, it is still an open issue to generate molecules, which bind to specific proteins with high-binding affinities while owning desired drug-like physicochemical properties. RESULTS: To address these issues, we elaborate a novel framework for controllable protein-oriented molecule generation, named CProMG, which contains a 3D protein embedding module, a dual-view protein encoder, a molecule embedding module, and a novel drug-like molecule decoder. Based on fusing the hierarchical views of proteins, it enhances the representation of protein binding pockets significantly by associating amino acid residues with their comprising atoms. Through jointly embedding molecule sequences, their drug-like properties, and binding affinities w.r.t. proteins, it autoregressively generates novel molecules having specific properties in a controllable manner by measuring the proximity of molecule tokens to protein residues and atoms. The comparison with state-of-the-art deep generative methods demonstrates the superiority of our CProMG. Furthermore, the progressive control of properties demonstrates the effectiveness of CProMG when controlling binding affinity and drug-like properties. After that, the ablation studies reveal how its crucial components contribute to the model respectively, including hierarchical protein views, Laplacian position encoding as well as property control. Last, a case study w.r.t. protein illustrates the novelty of CProMG and the ability to capture crucial interactions between protein pockets and molecules. It's anticipated that this work can boost de novo molecule design. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this article are freely available at https://github.com/lijianing0902/CProMG.

CMMS-GCL: cross-modality metabolic stability prediction with graph contrastive learning.

MOTIVATION: Metabolic stability plays a crucial role in the early stages of drug discovery and development. Accurately modeling and predicting molecular metabolic stability has great potential for the efficient screening of drug candidates as well as the optimization of lead compounds. Considering wet-lab experiment is time-consuming, laborious, and expensive, in silico prediction of metabolic stability is an alternative choice. However, few computational methods have been developed to address this task. In addition, it remains a significant challenge to explain key functional groups determining metabolic stability. RESULTS: To address these issues, we develop a novel cross-modality graph contrastive learning model named CMMS-GCL for predicting the metabolic stability of drug candidates. In our framework, we design deep learning methods to extract features for molecules from two modality data, i.e. SMILES sequence and molecule graph. In particular, for the sequence data, we design a multihead attention BiGRU-based encoder to preserve the context of symbols to learn sequence representations of molecules. For the graph data, we propose a graph contrastive learning-based encoder to learn structure representations by effectively capturing the consistencies between local and global structures. We further exploit fully connected neural networks to combine the sequence and structure representations for model training. Extensive experimental results on two datasets demonstrate that our CMMS-GCL consistently outperforms seven state-of-the-art methods. Furthermore, a collection of case studies on sequence data and statistical analyses of the graph structure module strengthens the validation of the interpretability of crucial functional groups recognized by CMMS-GCL. Overall, CMMS-GCL can serve as an effective and interpretable tool for predicting metabolic stability, identifying critical functional groups, and thus facilitating the drug discovery process and lead compound optimization. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this article are freely available at https://github.com/dubingxue/CMMS-GCL.

Identification, expression analysis, and functional verification of three opsin genes related to the phototactic behaviour of Ostrinia furnacalis.

Ostrinia furnacalis is a disreputable herbivorous pest that poses a serious threat to corn crops. Phototaxis in nocturnal moths plays a crucial role in pest prediction and control. Insect opsins are the main component of insect visual system. However, the inherent molecular relationship between phototactic behaviour and vision of insects remains a mystery. Herein, three opsin genes were identified and cloned from O. furnacalis (OfLW, OfBL, and OfUV). Bioinformatics analysis revealed that all opsin genes had visual pigment (opsin) retinal binding sites and seven transmembrane domains. Opsin genes were distributed across different developmental stages and tissues, with the highest expression in adults and compound eyes. The photoperiod-induced assay elucidated that the expression of three opsin genes in females were higher during daytime, while their expression in males tended to increase at night. Under the sustained darkness, the expression of opsin genes increased circularly, although the increasing amplitude in males was lower when compared with females. Furthermore, the expression of OfLW, OfBL, and OfUV was upregulated under green, blue, and ultraviolet light, respectively. The results of RNA interference showed that the knockout of opsin genes decreased the phototaxis efficiency of female and male moths to green, blue, and ultraviolet light. Our results reveal that opsin genes are involved in the phototactic behaviour of moths, providing a potential target gene for pest control and a basis for further investigation on the phototactic behaviour of Lepidoptera insects.

Time transfer over 113†km free space laser communication channel.

The space time frequency transfer plays a crucial role in applications such as space optical clock networks, navigation, satellite ranging, and space quantum communication. Here, we propose a high-precision space time frequency transfer and time synchronization scheme based on a simple intensity modulation/direct detection (IM/DD) laser communication system, which occupies a communication bandwidth of approximately 0.2%. Furthermore, utilizing an optical-frequency comb time frequency transfer system as an out-of-loop reference, experimental verification was conducted on a 113 km horizontal atmospheric link, with a long-term stability approximately 8.3 × 10-16 over a duration of 7800 seconds. Over an 11-hour period, the peak-to-peak wander is approximately 100 ps. Our work establishes the foundation of the time frequency transfer, based on the space laser communication channel, for future ground-to-space and inter-satellite links.

The lncRNA LINC01605 promotes the progression of pancreatic ductal adenocarcinoma by activating the mTOR signaling pathway.

BACKGROUND: This study investigated the molecular mechanism of long intergenic non-protein coding RNA 1605 (LINC01605) in the process of tumor growth and liver metastasis of pancreatic ductal adenocarcinoma (PDAC). METHODS: LINC01605 was filtered out with specificity through TCGA datasets (related to DFS) and our RNA-sequencing data of PDAC tissue samples from Renji Hospital. The expression level and clinical relevance of LINC01605 were then verified in clinical cohorts and samples by immunohistochemical staining assay and survival analysis. Loss- and gain-of-function experiments were performed to estimate the regulatory effects of LINC01605 in vitro. RNA-seq of LINC01605-knockdown PDAC cells and subsequent inhibitor-based cellular function, western blotting, immunofluorescence and rescue experiments were conducted to explore the mechanisms by which LINC01605 regulates the behaviors of PDAC tumor cells. Subcutaneous xenograft models and intrasplenic liver metastasis models were employed to study its role in PDAC tumor growth and liver metastasis in vivo. RESULTS: LINC01605 expression is upregulated in both PDAC primary tumor and liver metastasis tissues and correlates with poor clinical prognosis. Loss and gain of function experiments in cells demonstrated that LINC01605 promotes the proliferation and migration of PDAC cells in vitro. In subsequent verification experiments, we found that LINC01605 contributes to PDAC progression through cholesterol metabolism regulation in a LIN28B-interacting manner by activating the mTOR signaling pathway. Furthermore, the animal models showed that LINC01605 facilitates the proliferation and metastatic invasion of PDAC cells in vivo. CONCLUSIONS: Our results indicate that the upregulated lncRNA LINC01605 promotes PDAC tumor cell proliferation and migration by regulating cholesterol metabolism via activation of the mTOR signaling pathway in a LIN28B-interacting manner. These findings provide new insight into the role of LINC01605 in PDAC tumor growth and liver metastasis as well as its value for clinical approaches as a metabolic therapeutic target in PDAC.

Feasibility study on epithelial cells regeneration after excising the demucosalized ileum for ureteral reconstructive surgery.

This study aimed to investigate the regeneration of epithelial cells in the long-term observation of ureter reconstruction by excising the demucosalized ileum. First, 8 Beagle dogs were anesthetized and the abdominal cavity was inspected for abnormalities via an abdominal incision. The right kidney and ureter were subsequently separated, and the ureter was severed from its connection to the renal pelvis and bladder and ligated distally. The 10-15 cm of ileum was used to reconstruct the ureter. The biopsies of the proximal, middle, and distal reconstructed ureter (neo-ureter) were collected at the first, third, fifth, and sixth month postoperatively. The regeneration of ileal mucosa at the first, third, fifth, and sixth month was observed by hematoxylin-eosin (HE) staining and immunofluorescence staining cytokeratin 18 (CK18). HE staining results showed irregular cytoarchitecture, severe nuclear consolidation, and inflammatory infiltration in the proximal, middle, and distal neo-ureters of dogs at the first month after ureteral reconstruction. With longer follow-up, the injuries of the proximal, middle, and distal neo-ureters were alleviated at the third, fifth, and sixth month after surgery. The expression of CK18 was higher in the middle neo-ureters than that in the proximal and distal neo-ureters at different time points after ureteral reconstruction and decreased with time. In summary, the present study demonstrated that demucosalized ileum was feasible for ureteral reconstructive surgery with satisfying prognostic effects.

Chelatococcus albus sp. nov., a bacterium isolated from hot spring microbial mat.

A Gram-stain-negative, non-endospore-forming, motile, short rod-shaped strain, designated SYSU G07232T, was isolated from a hot spring microbial mat, sampled from Rehai National Park, Tengchong, Yunnan Province, south-western China. Strain SYSU G07232T grew at 25-50 °C (optimum, 37 °C), at pH 5.5-9.0 (optimum, pH 6.0) and tolerated NaCl concentrations up to 1.0 % (w/v). Phylogenetic analysis based on the 16S rRNA gene sequences revealed that strain SYSU G07232T showed closest genetic affinity with Chelatococcus daeguensis K106T. The genomic features and taxonomic status of this strain were determined through whole-genome sequencing and a polyphasic approach. The predominant quinone of this strain was Q-10. Major cellular fatty acids comprised C19 : 0 cyclo ω8c and summed feature 8. The whole-genome length of strain SYSU G07232T was 4.02 Mbp, and the DNA G+C content was 69.26 mol%. The average nucleotide identity (ANIm ≤84.85 % and ANIb ≤76.08  %) and digital DNA-DNA hybridization (≤ 21.9 %) values between strain SYSU G07232T and the reference species were lower than the threshold values recommended for distinguishing novel prokaryotic species. Thus, based on the provided phenotypic, phylogenetic, and genetic data, it is proposed that strain SYSU G07232T (=KCTC 8141T=GDMCC 1.4178T) be designated as representing a novel species within the genus Chelatococcus, named Chelatococcus albus sp. nov.