DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity.

MOTIVATION: Exploring potential associations between diseases can help in understanding pathological mechanisms of diseases and facilitating the discovery of candidate biomarkers and drug targets, thereby promoting disease diagnosis and treatment. Some computational methods have been proposed for measuring disease similarity. However, these methods describe diseases without considering their latent multi-molecule regulation and valuable supervision signal, resulting in limited biological interpretability and efficiency to capture association patterns. RESULTS: In this study, we propose a new computational method named DiSMVC. Different from existing predictors, DiSMVC designs a supervised graph collaborative framework to measure disease similarity. Multiple bio-entity associations related to genes and miRNAs are integrated via cross-view graph contrastive learning to extract informative disease representation, and then association pattern joint learning is implemented to compute disease similarity by incorporating phenotype-annotated disease associations. The experimental results show that DiSMVC can draw discriminative characteristics for disease pairs, and outperform other state-of-the-art methods. As a result, DiSMVC is a promising method for predicting disease associations with molecular interpretability. AVAILABILITY AND IMPLEMENTATION: Datasets and source codes are available at https://github.com/Biohang/DiSMVC.

DNMT3a-mediated methylation of PPARγ promote intervertebral disc degeneration by regulating the NF-κB pathway.

Intervertebral disc degeneration (IVDD) is a common chronic musculoskeletal disease that causes chronic low back pain and imposes an immense financial strain on patients. The pathological mechanisms underlying IVDD have not been fully elucidated. The development of IVDD is closely associated with abnormal epigenetic changes, suggesting that IVDD progression may be controlled by epigenetic mechanisms. Consequently, this study aimed to investigate the role of epigenetic regulation, including DNA methyltransferase 3a (DNMT3a)-mediated methylation and peroxisome proliferator-activated receptor γ (PPARγ) inhibition, in IVDD development. The expression of DNMT3a and PPARγ in early and late IVDD of nucleus pulposus (NP) tissues was detected using immunohistochemistry and western blotting analyses. Cellularly, DNMT3a inhibition significantly inhibited IL-1ß-induced apoptosis and extracellular matrix (ECM) degradation in rat NP cells. Pretreatment with T0070907, a specific inhibitor of PPARγ, significantly reversed the anti-apoptotic and ECM degradation effects of DNMT3a inhibition. Mechanistically, DNMT3a modified PPARγ promoter hypermethylation to activate the nuclear factor-κB (NF-κB) pathway. DNMT3a inhibition alleviated IVDD progression. Conclusively, the results of this study show that DNMT3a activates the NF-κB pathway by modifying PPARγ promoter hypermethylation to promote apoptosis and ECM degradation. Therefore, we believe that the ability of DNMT3a to mediate the PPARγ/NF-κB axis may provide new ideas for the potential pathogenesis of IVDD and may become an attractive target for the treatment of IVDD.

Temperature-Tunable Operando Nondestructive Detection of Electronic and Geometrical Structures in Battery Electrodes.

Real-time monitoring of the structural evolution of battery materials is crucial for understanding their underlying reaction mechanisms, which cannot be satisfied by the typically used post-mortem analysis. While more and more operando techniques were constructed and employed, they are all based on ambient working conditions that are not generally the case for real-world applications. Indeed, batteries work in an environment where self-heat dissipation increases the surrounding temperature, and extreme temperature applications (<-20 °C or >60 °C) are also frequently proposed. Operando characterization techniques under variable temperatures are therefore highly desired for tracking battery reactions under real-working conditions. Here, we develop a methodology to operando monitor the electronic and geometrical structures of battery materials over a wide range of temperatures based on X-ray spectroscopies. It is substantiated with data collected on a model LiNi0.90Co0.05Mn0.05O2/Si@C pouch cell under operando quick X-ray absorption fine structure spectroscopy, by which we found a temperature-dependent structure evolution behavior that is highly correlated with the electrochemical performance. Our work establishes an exemplary protocol for analyzing battery materials under temperature-variable environments that can be widely used in other related fields.

idenMD-NRF: a ranking framework for miRNA-disease association identification.

Identifying miRNA-disease associations is an important task for revealing pathogenic mechanism of complicated diseases. Different computational methods have been proposed. Although these methods obtained encouraging performance for detecting missing associations between known miRNAs and diseases, how to accurately predict associated diseases for new miRNAs is still a difficult task. In this regard, a ranking framework named idenMD-NRF is proposed for miRNA-disease association identification. idenMD-NRF treats the miRNA-disease association identification as an information retrieval task. Given a novel query miRNA, idenMD-NRF employs Learning to Rank algorithm to rank associated diseases based on high-level association features and various predictors. The experimental results on two independent test datasets indicate that idenMD-NRF is superior to other compared predictors. A user-friendly web server of idenMD-NRF predictor is freely available at http://bliulab.net/idenMD-NRF/.

iPiDA-SWGCN: Identification of piRNA-disease associations based on Supplementarily Weighted Graph Convolutional Network.

Accurately identifying potential piRNA-disease associations is of great importance in uncovering the pathogenesis of diseases. Recently, several machine-learning-based methods have been proposed for piRNA-disease association detection. However, they are suffering from the high sparsity of piRNA-disease association network and the Boolean representation of piRNA-disease associations ignoring the confidence coefficients. In this study, we propose a supplementarily weighted strategy to solve these disadvantages. Combined with Graph Convolutional Networks (GCNs), a novel predictor called iPiDA-SWGCN is proposed for piRNA-disease association prediction. There are three main contributions of iPiDA-SWGCN: (i) Potential piRNA-disease associations are preliminarily supplemented in the sparse piRNA-disease network by integrating various basic predictors to enrich network structure information. (ii) The original Boolean piRNA-disease associations are assigned with different relevance confidence to learn node representations from neighbour nodes in varying degrees. (iii) The experimental results show that iPiDA-SWGCN achieves the best performance compared with the other state-of-the-art methods, and can predict new piRNA-disease associations.

Uptake, subcellular distribution, and fate of tetracycline in two wetland plants supplemented with microbial agents: Effect and mechanism.

The use of wetland plants in the context of phytoremediation is effective in the removal of antibiotics from contaminated water. However, the effectiveness and efficiency of many of these plants in the removal of antibiotics remain undetermined. In this study, the effectiveness of two plants-Phragmites australis and Iris pseudacorus-in the removal of tetracycline (TC) in hydroponic systems was investigated. The uptake of TC at the roots of I. pseudacorus and P. australis occurred at concentrations of 588.78 and 106.70 µg/g, respectively, after 7-day exposure. The higher uptake of TC in the root of I. pseudacorus may be attributed to its higher secretion of root exudates, which facilitate conditions conducive to the reproduction of microorganisms. These rhizosphere-linked microorganisms then drove the TC uptake, which was higher than that in the roots of P. australis. By elucidating the mechanisms underlying these uptake-linked outcomes, we found that the uptake of TC for both plants was significantly suppressed by metabolic and aquaporin inhibition, suggesting uptake and transport of TC were active (energy-dependent) and passive (aquaporin-dominated) processes, respectively. The subcellular distribution patterns of I. pseudacorus and P. australis in the roots were different, as expressed by differences in organelles, cell wall concentration levels, and transport-related dynamics. Additionally, the microbe-driven enhancement of the remediation capacities of the plants was studied comprehensively via a combined microbial-phytoremediation hydroponic system. We confirmed that the microbial agents increased the secretion of root exudates, promoting the variation of TC chemical speciation and thus enhancing the active transport of TC. These results contribute toward the improved application of wetland plants in the context of antibiotic phytoremediation.

iPiDi-PUL: identifying Piwi-interacting RNA-disease associations based on positive unlabeled learning.

Accumulated researches have revealed that Piwi-interacting RNAs (piRNAs) are regulating the development of germ and stem cells, and they are closely associated with the progression of many diseases. As the number of the detected piRNAs is increasing rapidly, it is important to computationally identify new piRNA-disease associations with low cost and provide candidate piRNA targets for disease treatment. However, it is a challenging problem to learn effective association patterns from the positive piRNA-disease associations and the large amount of unknown piRNA-disease pairs. In this study, we proposed a computational predictor called iPiDi-PUL to identify the piRNA-disease associations. iPiDi-PUL extracted the features of piRNA-disease associations from three biological data sources, including piRNA sequence information, disease semantic terms and the available piRNA-disease association network. Principal component analysis (PCA) was then performed on these features to extract the key features. The training datasets were constructed based on known positive associations and the negative associations selected from the unknown pairs. Various random forest classifiers trained with these different training sets were merged to give the predictive results via an ensemble learning approach. Finally, the web server of iPiDi-PUL was established at http://bliulab.net/iPiDi-PUL to help the researchers to explore the associated diseases for newly discovered piRNAs.

Telecom-wavelength conversion in a high optical depth cold atomic system.

We experimentally investigate the frequency down-conversion through the four-wave mixing (FWM) process in a cold 85Rb atomic ensemble, with a diamond-level configuration. An atomic cloud with a high optical depth (OD) of 190 is prepared to achieve a high efficiency frequency conversion. Here, we convert a signal pulse field (795 nm) attenuated to a single-photon level, into a telecom light at 1529.3 nm within near C-band range and the frequency-conversion efficiency can reach up to 32%. We find that the OD is an essential factor affecting conversion efficiency and the efficiency may exceed 32% with an improvement in the OD. Moreover, we note the signal-to-noise ratio of the detected telecom field is higher than 10 while the mean signal count is larger than 0.2. Our work may be combined with quantum memories based on cold 85Rb ensemble at 795 nm and serve for long-distance quantum networks.

Optical memory for arbitrary perfect Poincaré states in an atomic ensemble.

Inherent spin angular momentum (SAM) and orbital angular momentum (OAM), which manifest as polarization and spatial degrees of freedom (DOFs) of photons, hold a promise of large capability for applications in classical and quantum information processing. To enable these photonic spin and orbital dynamic properties strongly coupled with each other, Poincaré states have been proposed and offer advantages in data multiplexing, information encryption, precision metrology, and quantum memory. However, since the transverse size of Laguerre-Gaussian beams strongly depends on their topological charge numbers | l |, it is difficult to store asymmetric Poincaré states due to the significantly different light-matter interaction for distinct spatial modes. Here, we experimentally realize the storage of perfect Poincaré states with arbitrary OAM quanta using the perfect optical vortex, in which 121 arbitrarily selected perfect Poincaré states have been stored with high fidelity. The reported work has great prospects in optical communication and quantum networks for dramatically increased encoding flexibility of information.

Highly Efficient Storage of 25-Dimensional Photonic Qudit in a Cold-Atom-Based Quantum Memory.

Building an efficient quantum memory in high-dimensional Hilbert spaces is one of the fundamental requirements for establishing high-dimensional quantum repeaters, where it offers many advantages over two-dimensional quantum systems, such as a larger information capacity and enhanced noise resilience. To date, it remains a challenge to develop an efficient high-dimensional quantum memory. Here, we experimentally realize a quantum memory that is operational in Hilbert spaces of up to 25 dimensions with a storage efficiency of close to 60% and a fidelity of 84.2±0.6%. The proposed approach exploits the spatial-mode-independent interaction between atoms and photons which are encoded in transverse-size-invariant vortex modes. In particular, our memory features uniform storage efficiency and low crosstalk disturbance for 25 individual spatial modes of photons, thus allowing the storing of qudit states programmed from 25 eigenstates within the high-dimensional Hilbert spaces. These results have great prospects for the implementation of long-distance high-dimensional quantum networks and quantum information processing.

iPiDA-GCN: Identification of piRNA-disease associations based on Graph Convolutional Network.

MOTIVATION: Piwi-interacting RNAs (piRNAs) play a critical role in the progression of various diseases. Accurately identifying the associations between piRNAs and diseases is important for diagnosing and prognosticating diseases. Although some computational methods have been proposed to detect piRNA-disease associations, it is challenging for these methods to effectively capture nonlinear and complex relationships between piRNAs and diseases because of the limited training data and insufficient association representation. RESULTS: With the growth of piRNA-disease association data, it is possible to design a more complex machine learning method to solve this problem. In this study, we propose a computational method called iPiDA-GCN for piRNA-disease association identification based on graph convolutional networks (GCNs). The iPiDA-GCN predictor constructs the graphs based on piRNA sequence information, disease semantic information and known piRNA-disease associations. Two GCNs (Asso-GCN and Sim-GCN) are used to extract the features of both piRNAs and diseases by capturing the association patterns from piRNA-disease interaction network and two similarity networks. GCNs can capture complex network structure information from these networks, and learn discriminative features. Finally, the full connection networks and inner production are utilized as the output module to predict piRNA-disease association scores. Experimental results demonstrate that iPiDA-GCN achieves better performance than the other state-of-the-art methods, benefitted from the discriminative features extracted by Asso-GCN and Sim-GCN. The iPiDA-GCN predictor is able to detect new piRNA-disease associations to reveal the potential pathogenesis at the RNA level. The data and source code are available at http://bliulab.net/iPiDA-GCN/.

Stable and Active Au Catalyst Supported on CeMnO3 Perovskite for Selective Oxidation of Glycerol.

The selective oxidation of glycerol holds promise to transform glycerol into value-added chemicals. However, it remains a big challenge to achieve satisfactory selectivity toward the specific product at high conversion due to the multiple reaction pathways. Here, we prepare a hybrid catalyst via supporting Au nanoparticles on CeMnO3 perovskite with a modest surface area, achieving promoted conversion of glycerol (90.1%) and selectivity of glyceric acid (78.5%), which are much higher than those of CeMnOx solid-solution-supported Au catalysts with larger surface area and other Ce-based or Mn-based Au catalysts. The strong interaction between Au and CeMnO3 perovskite facilitates the electron transfer from the B-site metal (Mn) in the CeMnO3 perovskite to Au and stabilizes Au nanoparticles, which results in the enhanced catalytic activity and stability for glycerol oxidation. Valence band photoemission spectral analysis reveals that the uplifted d-band center of Au/CeMnO3 promotes the adsorption of the glyceraldehyde intermediate on the catalyst surface, which benefits further oxidation of glyceraldehyde into glyceric acid. The flexibility of the perovskite support provides a promising strategy for the rational design of high-performance glycerol oxidation catalysts.

CD45- erythroid progenitor cells promote lymph node metastasis in gastric cancer by inducing a hybrid epithelial/mesenchymal state in lymphatic endothelial cells.

BACKGROUND AND AIMS: Specific mechanisms of lymph node (LN) metastasis in early-stage gastric cancer (GC) have not been elucidated. The role of anemia, a vital clinical feature of GC, in LN metastasis is also unclear. Since the number of erythroid progenitor cells (EPCs) is increased in chronic anemia, we investigated its association with LN metastasis in GC. METHODS: Flow cytometry and immunofluorescence analyses were performed to sort and study EPCs from the circulation and tumors of patients with stage I-III GC. The effect of these EPCs on the activation of T and B cells and on the functions of lymphatic endothelial cells (LECs) was determined, and their ability to promote LN metastasis was evaluated using a footpad-popliteal LN metastasis model based on two human adenocarcinoma GC cell lines in nude mice. The prognostic value of EPCs was also analyzed. RESULTS: The proportion of CD45- EPCs was higher in the mononuclear cells in the circulation, tumors, and LNs of GC patients with LN metastasis (N+) than in those of GC patients without LN metastasis (N0). In N+ patients, CD45- EPCs were more abundant in metastatic LNs than in non-metastatic LNs. Lymphatic vessel endothelial hyaluronan receptor 1 immunoreactivity in tumors revealed that CD45- EPCs were positively associated with nodal stages and lymph vessel density. Furthermore, CD45- EPCs increased LEC proliferation and migration through their S100A8/A9 heterodimer-induced hybrid epithelial/mesenchymal (E/M) state; however, they did not influence the invasion and tubulogenesis of LECs or T and B cell proliferation. CD45- EPCs promoted LN metastasis in vivo; the S100A8/A9 heterodimer mimicked this phenomenon. Finally, CD45- EPCs predicted the overall and disease-free survival of stage I-III GC patients after radical resection. CONCLUSIONS: The CD45- EPCs accumulated in GC tissues and metastatic LNs and promoted LN metastasis via the S100A8/9-induced hybrid E/M state of LECs, which was the specific mechanism of LN metastasis in the early stages of GC.

PARP in colorectal cancer: Molecular mechanisms, immunity, clinical trials, and drug combinations.

The changes in cell homeostasis in the tumor microenvironment may affect the development of colorectal cancer (CRC). Genomic instability is an important factor. Persistent genomic instability leads to epigenetic changes, and mutations are a major factor in the progression of CRC. Based on these mechanisms, it is reasonable to link poly (ADP-ribose) polymerase (PARP) with the treatment of CRC. PARP is mainly involved in DNA repair, which has an essential role in the DNA damage response and prevention of DNA damage, and maintains oxidation and superoxide redox homeostasis in the intracellular environment of the tumor. This article reviews the latest research progress on PARP and PARP inhibitors (PARPi) in CRC. It mainly includes molecular mechanisms, immunity, clinical trials, and combination strategies of CRC. The research of PARPi in CRC has broad prospects, and the combinations with other drugs are the main research direction in the future.

A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability.

BACKGROUND: Intelligent cardiotocography (CTG) classification can assist obstetricians in evaluating fetal health. However, high classification performance is often achieved by complex machine learning (ML)-based models, which causes interpretability concerns. The trade-off between accuracy and interpretability makes it challenging for most existing ML-based CTG classification models to popularize in prenatal clinical applications. METHODS: Aiming to improve CTG classification performance and prediction interpretability, a hybrid model was proposed using a stacked ensemble strategy with mixed features and Kernel SHapley Additive exPlanations (SHAP) framework. Firstly, the stacked ensemble classifier was established by employing support vector machines (SVM), extreme gradient boosting (XGB), and random forests (RF) as base learners, and backpropagation (BP) as a meta learner whose input was mixed with the CTG features and the probability value of each category output by base learners. Then, the public and private CTG datasets were used to verify the discriminative performance. Furthermore, Kernel SHAP was applied to estimate the contribution values of features and their relationships to the fetal states. RESULTS: For intelligent CTG classification using 10-fold cross-validation, the accuracy and average F1 score were 0.9539 and 0.9249 in the public dataset, respectively; and those were 0.9201 and 0.8926 in the private dataset, respectively. For interpretability, the explanation results indicated that accelerations (AC) and the percentage of time with abnormal short-term variability (ASTV) were the key determinants. Specifically, the probability of abnormality increased and that of the normal state decreased as the value of ASTV grew. In addition, the likelihood of the normal status rose with the increase of AC. CONCLUSIONS: The proposed model has high classification performance and reasonable interpretability for intelligent fetal monitoring.

iCircDA-MF: identification of circRNA-disease associations based on matrix factorization.

Circular RNAs (circRNAs) are a group of novel discovered non-coding RNAs with closed-loop structure, which play critical roles in various biological processes. Identifying associations between circRNAs and diseases is critical for exploring the complex disease mechanism and facilitating disease-targeted therapy. Although several computational predictors have been proposed, their performance is still limited. In this study, a novel computational method called iCircDA-MF is proposed. Because the circRNA-disease associations with experimental validation are very limited, the potential circRNA-disease associations are calculated based on the circRNA similarity and disease similarity extracted from the disease semantic information and the known associations of circRNA-gene, gene-disease and circRNA-disease. The circRNA-disease interaction profiles are then updated by the neighbour interaction profiles so as to correct the false negative associations. Finally, the matrix factorization is performed on the updated circRNA-disease interaction profiles to predict the circRNA-disease associations. The experimental results on a widely used benchmark dataset showed that iCircDA-MF outperforms other state-of-the-art predictors and can identify new circRNA-disease associations effectively.

iCircDA-LTR: identification of circRNA-disease associations based on Learning to Rank.

MOTIVATION: Due to the inherent stability and close relationship with the progression of diseases, circRNAs are serving as important biomarkers and drug targets. Efficient predictors for identifying circRNA-disease associations are highly required. The existing predictors consider circRNA-disease association prediction as a classification task or a recommendation problem, failing to capture the ranking information among the associations and detect the diseases associated with new circRNAs. However, more and more circRNAs are discovered. Identification of the diseases associated with these new circRNAs remains a challenging task. RESULTS: In this study, we proposed a new predictor called iCricDA-LTR for circRNA-disease association prediction. Different from any existing predictor, iCricDA-LTR employed a ranking framework to model the global ranking associations among the query circRNAs and the diseases. The Learning to Rank (LTR) algorithm was employed to rank the associations based on various predictors and features in a supervised manner. The experimental results on two independent test datasets showed that iCircDA-LTR outperformed the other competing methods, especially for predicting the diseases associated with new circRNAs. As a result, iCircDA-LTR is more suitable for the real-world applications. AVAILABILITY AND IMPLEMENTATION: For the convenience of researchers to detect new circRNA-disease associations. The web server of iCircDA-LTR was established and freely available at http://bliulab.net/iCircDA-LTR/.

SMI-BLAST: a novel supervised search framework based on PSI-BLAST for protein remote homology detection.

MOTIVATION: As one of the most important and widely used mainstream iterative search tool for protein sequence search, an accurate Position-Specific Scoring Matrix (PSSM) is the key of PSI-BLAST. However, PSSMs containing non-homologous information obviously reduce the performance of PSI-BLAST for protein remote homology. RESULTS: To further study this problem, we summarize three types of Incorrectly Selected Homology (ISH) errors in PSSMs. A new search tool Supervised-Manner-based Iterative BLAST (SMI-BLAST) is proposed based on PSI-BLAST for solving these errors. SMI-BLAST obviously outperforms PSI-BLAST on the Structural Classification of Proteins-extended (SCOPe) dataset. Compared with PSI-BLAST on the ISH error subsets of SCOPe dataset, SMI-BLAST detects 1.6-2.87 folds more remote homologous sequences, and outperforms PSI-BLAST by 35.66% in terms of ROC1 scores. Furthermore, this framework is applied to JackHMMER, DELTA-BLAST and PSI-BLASTexB, and their performance is further improved. AVAILABILITY AND IMPLEMENTATION: User-friendly webservers for SMI-BLAST, JackHMMER, DELTA-BLAST and PSI-BLASTexB are established at http://bliulab.net/SMI-BLAST/, by which the users can easily get the results without the need to go through the mathematical details. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Long-Lived Memory for Orbital Angular Momentum Quantum States.

Quantum memories that are capable of storing multiple spatial modes offer advantages in speed and robustness when incorporated into quantum networks. When it comes to spatial degrees of freedom, orbital angular momentum (OAM) modes have received widespread attention since they enable encoding with inherent infinite number of dimensions. Although the faithful storage of OAM qubits or qutrits has been realized in previous works, the achieved lifetimes are still on the order of a few microseconds as limited by the spatially dependent decoherence. We here demonstrate a long-lived quantum memory for OAM qutrits by suppressing the decoherence in the transverse and longitude direction simultaneously; the achieved fidelity beats the quantum-classical criteria after a storage time of 400 µs, which is 2 orders of magnitude longer than earlier works. The present work is promising for establishing high-dimensional quantum networks.

Quinoline alkaloids with anti-inflammatory activity from Zanthoxylum avicennae.

Zanthoxylum avicennae fruits were traditionally used to treat many inflammatory-related diseases, such as icterohepatitis, nephritis and colitis, which inspired us to explore the active chemicals and pharmacological activity. As a result, ten quinoline alkaloids, including six new ones, avicenines A-F (1-6), were isolated and structurally characterized by solid data. Compounds 1, 7 and 8 were identified as three pairs of enantiomers by chiral HPLC separation, of which 1 was an unusual 6/6/5/5-fused quinoline alkaloid bearing a unique cis-hexahydrofuro[3,2-b]furan moiety. The putative biosynthetic pathway for enantiomeric compounds was also proposed. In addition, compound 6 significantly suppressed the gene expression and secretion of pro-inflammatory cytokines IL-1ß and IL-6 in macrophages.