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Cancer is a well-known killer of human beings, which has led to countless deaths and misery. Anticancer peptides open a promising perspective for cancer treatment, and they have various attractive advantages. Conventional wet experiments are expensive and inefficient for finding and identifying novel anticancer peptides. There is an urgent need to develop a novel computational method to predict novel anticancer peptides. In this study, we propose a deep learning long short-term memory (LSTM) neural network model, ACP-DL, to effectively predict novel anticancer peptides. More specifically, to fully exploit peptide sequence information, we developed an efficient feature representation approach by integrating binary profile feature and k-mer sparse matrix of the reduced amino acid alphabet. Then we implemented a deep LSTM model to automatically learn how to identify anticancer peptides and non-anticancer peptides. To our knowledge, this is the first time that the deep LSTM model has been applied to predict anticancer peptides. It was demonstrated by cross-validation experiments that the proposed ACP-DL remarkably outperformed other comparison methods with high accuracy and satisfied specificity on benchmark datasets. In addition, we also contributed two new anticancer peptides benchmark datasets, ACP740 and ACP240, in this work. The source code and datasets are available at https://github.com/haichengyi/ACP-DL.

A bstract The lifetimes of bottom and charmed hadrons are analyzed within the framework of the heavy quark expansion (HQE). Lifetime differences arise from the spectator effects such as weak W -annihilation and Pauli interference. Spectator effects originating from dimension-7 four-quark operators are derived. Hadronic matrix elements of four-quark operators are parameterized in a model-independent way. Using the dimension-6 bag parameters recently determined from HQET sum rules and the vacuum insertion approximation for meson matrix elements of dimension-7 operators, the calculated B meson lifetime ratios τ ( B + )/ τ ( B d 0 ) = 1.074 − 0.016 + 0.017 and τ ( B s 0 )/ τ ( B d 0 ) = 0.9962 ± 0.0024 are in excellent agreement with experiment. Likewise, based on the quark model evaluation of baryon matrix elements, the resulting baryon lifetime ratios τ (Ξ b − )/ τ (Λ b 0 ),  τ (Ξ b − )/ τ ( Ξ b 0 ),  τ (Ω b − )/ τ ( Ξ b − ) and the Λ b − B 0 lifetime ratio τ (Λ b 0 )/ τ ( B d 0 ) = 0.953 also agree well with the data. Contrary to the bottom hadron sector where the HQE in 1/ m b works well, the HQE to 1/ m c 3 fails to give a satisfactory description of the lifetimes of both charmed mesons and charmed baryons. This calls for the subleading 1/ m Q corrections to spectator effects. The relevant dimension-7 spectator effects are in the right direction for explaining the large lifetime ratio of τ (Ξ c + )/ τ (Λ c + ). However, the destructive 1/ m c corrections to Γ(Ω c 0 ) are too large to justify the validity of the HQE, namely, the predicted Pauli interference and semileptonic rates for Ω c 0 become negative. Demanding these rates to be positive for a sensible HQE, we find the lifetime pattern τ (Ξ c + ) >  τ ( Ω c 0 ) >  τ (Λ c + ) >  τ (Ξ c 0 ), contrary to the current hierarchy τ (Ξ c + ) >  τ (Λ c + ) >  τ (Ξ c 0 ) >  τ ( Ω c 0 ). We conclude that the Ω c 0 , which is naively expected to be shortest-lived in the charmed baryon system owing to the large constructive Pauli interference, could live longer than the Λ c + due to the suppression from 1/ m c corrections arising from dimension-7 four-quark operators. The new charmed baryon lifetime pattern needs to be tested in forthcoming experiments.

Ubiquitin ligases control the degradation of core clock proteins to govern the speed and resetting properties of the circadian pacemaker. However, few studies have addressed their potential to regulate other cellular events within clock neurons beyond clock protein turnover. Here, we report that the ubiquitin ligase, UBR4/POE, strengthens the central pacemaker by facilitating neuropeptide trafficking in clock neurons and promoting network synchrony. Ubr4 -deficient mice are resistant to jetlag, whereas poe knockdown flies are prone to arrhythmicity, behaviors reflective of the reduced axonal trafficking of circadian neuropeptides. At the cellular level, Ubr4 ablation impairs the export of secreted proteins from the Golgi apparatus by reducing the expression of Coronin 7, which is required for budding of Golgi-derived transport vesicles. In summary, UBR4/POE fulfills a conserved and unexpected role in the vesicular trafficking of neuropeptides, a function that has important implications for circadian clock synchrony and circuit-level signal processing. Although ubiquitin ligases are known to control clock protein degradation, their other roles in clock neurons are unclear. Here the authors report that UBR4 promotes export of neuropeptides from the Golgi for axonal trafficking, which is important for circadian clock synchrony in mice and flies.

Background The key to modern drug discovery is to find, identify and prepare drug molecular targets. However, due to the influence of throughput, precision and cost, traditional experimental methods are difficult to be widely used to infer these potential Drug-Target Interactions (DTIs). Therefore, it is urgent to develop effective computational methods to validate the interaction between drugs and target. Methods We developed a deep learning-based model for DTIs prediction. The proteins evolutionary features are extracted via Position Specific Scoring Matrix (PSSM) and Legendre Moment (LM) and associated with drugs molecular substructure fingerprints to form feature vectors of drug-target pairs. Then we utilized the Sparse Principal Component Analysis (SPCA) to compress the features of drugs and proteins into a uniform vector space. Lastly, the deep long short-term memory (DeepLSTM) was constructed for carrying out prediction. Results A significant improvement in DTIs prediction performance can be observed on experimental results, with AUC of 0.9951, 0.9705, 0.9951, 0.9206, respectively, on four classes important drug-target datasets. Further experiments preliminary proves that the proposed characterization scheme has great advantage on feature expression and recognition. We also have shown that the proposed method can work well with small dataset. Conclusion The results demonstration that the proposed approach has a great advantage over state-of-the-art drug-target predictor. To the best of our knowledge, this study first tests the potential of deep learning method with memory and Turing completeness in DTIs prediction.

This is essentially an update of Ref. [1] [H. Y. Cheng, Int. J. Mod. Phys. A 24 (Suppl. 1), 593 (2009)], a review of charmed baryon physics around 2007. Topics covered in this review include the spectroscopy, strong decays, lifetimes, nonleptonic and semileptonic weak decays, and electromagnetic decays of charmed baryons.

The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research.

Based on the predictions of the relevant form factors from the covariant light-front quark model, we show the branching fractions for the D ( D s ) → ( P , S , V , A ) ℓ ν ℓ ( ℓ = e or μ ) decays, where P denotes the pseudoscalar meson, S the scalar meson with a mass above 1 GeV, V the vector meson and A the axial-vector one. Comparison with the available experimental results are made, and we find an excellent agreement. The predictions for other decay modes can be tested in a charm factory, e.g., the BESIII detector. The future measurements will definitely further enrich our knowledge of the hadronic transition form factors as well as the inner structure of the even-parity mesons ( S and A ).

Tree-based and deep learning methods can automatically generate useful features. Not only can it enhance the original feature representation, but it can also learn to generate new features. This paper develops a strategy based on Light Gradient Boosting Machine (LightGBM or LGB) and Gated Recurrent Unit (GRU) to generate features to improve the expression ability of limited features. Moreover, a SARIMA-GRU prediction model considering the weekly periodicity is introduced. First, LightGBM is used to learn features and enhance the original features representation; secondly, GRU neural network is used to generate features; finally, the result ensemble is used as the input for prediction. Moreover, the SARIMA-GRU model is constructed for predicting. The GRU prediction consequences are revised by the SARIMA model that a better prediction can be obtained. The experiment was carried out with the data collected by Ride-hailing in Chengdu, and four predicted indicators and two performance indexes are utilized to evaluate the model. The results validate that the model proposed has significant improvements in the accuracy and performance of each component.

Background A non-invasive imaging technology that can monitor cell viability, retention, distribution, and interaction with host tissue after transplantation is needed for optimizing and translating stem cell-based therapies. Current cell imaging approaches are limited in sensitivity or specificity, or both, for in vivo cell tracking. The objective of this study was to apply a novel ferritin-based magnetic resonance imaging (MRI) platform to longitudinal tracking of human embryonic stem cells (hESCs) in vivo. Methods Human embryonic stem cells (hESCs) were genetically modified to stably overexpress ferritin using the CRISPR-Cas9 system. Cellular toxicity associated with ferritin overexpression and manganese (Mn) supplementation were assessed based on cell viability, proliferation, and metabolic activity. Ferritin-overexpressing hESCs were characterized based on stem cell pluripotency and cardiac-lineage differentiation capability. Cells were supplemented with Mn and imaged in vitro as cell pellets on a preclinical 3 T MR scanner. T1-weighted images and T1 relaxation times were analyzed to assess contrast. For in vivo study, three million cells were injected into the leg muscle of non-obese diabetic severe combined immunodeficiency (NOD SCID) mice. Mn was administrated subcutaneously. T1-weighted sequences and T1 mapping were used to image the animals for longitudinal in vivo cell tracking. Cell survival, proliferation, and teratoma formation were non-invasively monitored by MRI. Histological analysis was used to validate MRI results. Results Ferritin-overexpressing hESCs labeled with 0.1 mM MnCl 2 provided significant T1-induced bright contrast on in vitro MRI, with no adverse effect on cell viability, proliferation, pluripotency, and differentiation into cardiomyocytes. Transplanted hESCs displayed significant bright contrast on MRI 24 h after Mn administration, with contrast persisting for 5 days. Bright contrast was recalled at 4–6 weeks with early teratoma outgrowth. Conclusions The bright-ferritin platform provides the first demonstration of longitudinal cell tracking with signal recall, opening a window on the massive cell death that hESCs undergo in the weeks following transplantation before the surviving cell fraction proliferates to form teratomas.

A bstract Certain dark matter interactions with nuclei are mediated possibly by a scalar or pseudoscalar Higgs boson. The estimation of the corresponding cross sections requires a correct evaluation of the couplings between the scalar or pseudoscalar Higgs boson and the nucleons. Progress has been made in two aspects relevant to this study in the past few years. First, recent lattice calculations show that the strange-quark sigma term σ s and the strange-quark content in the nucleon are much smaller than what are expected previously. Second, lattice and model analyses imply sizable SU(3) breaking effects in the determination on the axial-vector coupling constant that in turn affect the extraction of the isosinglet coupling and the strange quark spin component Δ s from polarized deep inelastic scattering experiments. Based on these new developments, we re-evaluate the relevant nucleon matrix elements and compute the scalar and pseudoscalar couplings of the proton and neutron. We also find that the strange quark contribution in both types of couplings is smaller than previously thought.