The radius-luminosity ( ) relationship of active galactic nuclei (AGNs) established by the reverberation mapping (RM) observations has been widely used as a single-epoch black hole mass estimator in ...the research of large AGN samples. However, the recent RM campaigns discovered that the AGNs with high-accretion rates show shorter time lags by factors of a few comparing with the predictions from the relationship. The explanation of the shortened time lags has not been finalized yet. We collect eight different single-epoch spectral properties to investigate how the shortening of the time lags correlates with those properties and to determine the origin of the shortened lags. We find that the flux ratio between Fe ii and Hβ emission lines shows the most prominent correlation, thus confirming that accretion rate is the main driver for the shortened lags. In addition, we establish a new scaling relation including the relative strength of Fe ii emission. This new scaling relation can provide less biased estimates of the black hole mass and accretion rate from the single-epoch spectra of AGNs.
Abstract
Cell-penetrating peptides (CPPs) have been shown to be a transport vehicle for delivering cargoes into live cells, offering great potential as future therapeutics. It is essential to ...identify CPPs for better understanding of their functional mechanisms. Machine learning-based methods have recently emerged as a main approach for computational identification of CPPs. However, one of the main challenges and difficulties is to propose an effective feature representation model that sufficiently exploits the inner difference and relevance between CPPs and non-CPPs, in order to improve the predictive performance. In this paper, we have developed CPPred-FL, a powerful bioinformatics tool for fast, accurate and large-scale identification of CPPs. In our predictor, we introduce a new feature representation learning scheme that enables one to learn feature representations from totally 45 well-trained random forest models with multiple feature descriptors from different perspectives, such as compositional information, position-specific information and physicochemical properties, etc. We integrate class and probabilistic information into our feature representations. To improve the feature representation ability, we further remove redundant and irrelevant features by feature space optimization. Benchmarking experiments showed that CPPred-FL, using 19 informative features only, is able to achieve better performance than the state-of-the-art predictors. We anticipate that CPPred-FL will be a powerful tool for large-scale identification of CPPs, facilitating the characterization of their functional mechanisms and accelerating their applications in clinical therapy.
ncRNAs play important roles in a variety of biological processes by interacting with RNA-binding proteins. Therefore, identifying ncRNA-protein interactions is important to understanding the ...biological functions of ncRNAs. Since experimental methods to determine ncRNA-protein interactions are always costly and time-consuming, computational methods have been proposed as alternative approaches. We developed a novel method NPI-RGCNAE (predicting ncRNA-Protein Interactions by the Relational Graph Convolutional Network Auto-Encoder). With a reliable negative sample selection strategy, we applied the Relational Graph Convolutional Network encoder and the DistMult decoder to predict ncRNA-protein interactions in an accurate and efficient way. By using the 5-fold cross-validation, we found that our method achieved a comparable performance to all state-of-the-art methods. Our method requires less than 10% training time of all state-of-the-art methods. It is a more efficient choice with large datasets in practice.
Noncoding RNAs (ncRNAs) play crucial roles in many biological processes. Experimental methods for identifying ncRNA-protein interactions (NPIs) are always costly and time-consuming. Many ...computational approaches have been developed as alternative ways. In this work, we collected five benchmarking datasets for predicting NPIs. Based on these datasets, we evaluated and compared the prediction performances of existing machine-learning based methods. Graph neural network (GNN) is a recently developed deep learning algorithm for link predictions on complex networks, which has never been applied in predicting NPIs. We constructed a GNN-based method, which is called Noncoding RNA-Protein Interaction prediction using Graph Neural Networks (NPI-GNN), to predict NPIs. The NPI-GNN method achieved comparable performance with state-of-the-art methods in a 5-fold cross-validation. In addition, it is capable of predicting novel interactions based on network information and sequence information. We also found that insufficient sequence information does not affect the NPI-GNN prediction performance much, which makes NPI-GNN more robust than other methods. As far as we can tell, NPI-GNN is the first end-to-end GNN predictor for predicting NPIs. All benchmarking datasets in this work and all source codes of the NPI-GNN method have been deposited with documents in a GitHub repo (https://github.com/AshuiRUA/NPI-GNN).
Proteins must be sorted to specific subcellular compartments to perform their functions. Abnormal protein subcellular localizations are related to many diseases. Although many efforts have been made ...in predicting protein subcellular localization from various static information, including sequences, structures and interactions, such static information cannot predict protein mis-localization events in diseases. On the contrary, the IHC (immunohistochemistry) images, which have been widely applied in clinical diagnosis, contains information that can be used to find protein mis-localization events in disease states. In this study, we create the Vislocas method, which is capable of finding mis-localized proteins from IHC images as markers of cancer subtypes. By combining CNNs and vision transformer encoders, Vislocas can automatically extract image features at both global and local level. Vislocas can be trained with full-sized IHC images from scratch. It is the first attempt to create an end-to-end IHC image-based protein subcellular location predictor. Vislocas achieved comparable or better performances than state-of-the-art methods. We applied Vislocas to find significant protein mis-localization events in different subtypes of glioma, melanoma and skin cancer. The mis-localized proteins, which were found purely from IHC images by Vislocas, are in consistency with clinical or experimental results in literatures. All codes of Vislocas have been deposited in a Github repository (https://github.com/JingwenWen99/Vislocas). All datasets of Vislocas have been deposited in Zenodo (https://zenodo.org/records/10632698).
Vislocas combines vision transformers and CNNs together to extract features from IHC images at both global and local level. The images are automatically augmented before entering Vislocas. Vislocas can be trained with full-sized IHC images in an end-to-end fashion. It can be used to find significant protein translocation events in different cancer subtypes. Display omitted
•Vislocas combines vision transformers and CNNs to extract local and global features from IHC images.•Vislocas can be trained with full-sized IHC images from scratch in an end-to-end fashion.•We reported a list of significant protein translocation evens in different subtypes of glioma, melanoma, and skin cancer.
Super-Eddington accreting massive black holes (SEAMBHs) reach saturated luminosities above a certain accretion rate due to photon trapping and advection in slim accretion disks. We show that these ...SEAMBHs could provide a new tool for estimating cosmological distances if they are properly identified by hard x-ray observations, in particular by the slope of their 2-10 keV continuum. To verify this idea we obtained black hole mass estimates and x-ray data for a sample of 60 narrow line Seyfert 1 galaxies that we consider to be the most promising SEAMBH candidates. We demonstrate that the distances derived by the new method for the objects in the sample get closer to the standard luminosity distances as the hard x-ray continuum gets steeper. The results allow us to analyze the requirements for using the method in future samples of active black holes and to demonstrate that the expected uncertainty, given large enough samples, can make them into a useful, new cosmological ruler.
Silencers are repressive cis-regulatory elements that play crucial roles in transcriptional regulation. Experimental methods for identifying silencers are always costly and time-consuming. ...Computational methods, which relies on genomic sequence features, have been introduced as alternative approaches. However, silencers do not have significant epigenomic signature. Therefore, we explore a new way to computationally identify silencers, by incorporating chromatin structural information. We propose the SilenceREIN method, which focuses on finding silencers on anchors of chromatin loops. By using graph neural networks, we extracted chromatin structural information from a regulatory element interaction network. SilenceREIN integrated the chromatin structural information with linear genomic signatures to find silencers. The predictive performance of SilenceREIN is comparable or better than other states-of-the-art methods. We performed a genome-wide scanning to systematically find silencers in human genome. Results suggest that silencers are widespread on anchors of chromatin loops. In addition, enrichment analysis of transcription factor binding motif support our prediction results. As far as we can tell, this is the first attempt to incorporate chromatin structural information in finding silencers. All datasets and source codes of SilenceREIN have been deposited in a GitHub repository (https://github.com/JianHPan/SilenceREIN).
ABSTRACT The origin of the broad line region (BLR) in active galaxies remains unknown. It seems to be related to the underlying accretion disk, but an efficient mechanism is required to raise the ...material from the disk surface without giving signatures of the outflow that are too strong in the case of the low ionization lines. We discuss in detail two proposed mechanisms: (1) radiation pressure acting on dust in the disk atmosphere creating a failed wind and (2) the gravitational instability of the underlying disk. We compare the predicted location of the inner radius of the BLR in those two scenarios with the observed position obtained from the reverberation studies of several active galaxies. The failed dusty outflow model well represents the observational data while the predictions of the self-gravitational instability are not consistent with observations. The issue that remains is why do we not see any imprints of the underlying disk instability in the BLR properties.
Abstract
Reverberation mapping (RM) is a widely used method for probing the physics of broad-line regions (BLRs) in active galactic nuclei (AGNs). There is increasing preliminary evidence that the RM ...behaviors of broad emission lines are influenced by BLR densities; however, the influences have not been investigated systematically from a theoretical perspective. In this paper, we adopt a locally optimally emitting cloud model and use CLOUDY to obtain the one-dimensional transfer functions of the prominent UV and optical emission lines for different BLR densities. We find that the influences of BLR densities to RM behaviors mainly have three aspects. First, rarefied BLRs (with low gas densities) may show anomalous responses in RM observations. Their emission-line light curves inversely respond to the variations in continuum light curves, which may have been observed in some UV RM campaigns. Second, the different BLR densities in AGNs may result in correlations between the time lags and equivalent widths of emission lines, and may contribute to the scatters of the radius–luminosity relationships. Third, the variations in BLR densities may explain the changes in time lags in individual objects for different years. Some weak emission-line quasars are probably extreme cases of rarefied BLRs. We predict that their RM observations may show anomalous responses.
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