DNA-binding proteins (DBPs) have a significant impact on many life activities, so identification of DBPs is a crucial issue. And it is greatly helpful to understand the mechanism of protein-DNA interactions. In traditional experimental methods, it is significant time-consuming and labor-consuming to identify DBPs. In recent years, many researchers have proposed lots of different DBP identification methods based on machine learning algorithm to overcome shortcomings mentioned above. However, most existing methods cannot get satisfactory results. In this paper, we focus on developing a new predictor of DBPs, called Multi-View Hypergraph Restricted Kernel Machines (MV-H-RKM). In this method, we extract five features from the three views of the proteins. To fuse these features, we couple them by means of the shared hidden vector. Besides, we employ the hypergraph regularization to enforce the structure consistency between original features and the hidden vector. Experimental results show that the accuracy of MV-H-RKM is 84.09% and 85.48% on PDB1075 and PDB186 data set respectively, and demonstrate that our proposed method performs better than other state-of-the-art approaches. The code is publicly available at https://github.com/ShixuanGG/MV-H-RKM.

Lane change for automated vehicles (AVs) is an important but challenging task in complex dynamic traffic environments. Due to difficulties in guaranteeing safety as well as a high efficiency, AVs are inclined to choose relatively conservative strategies for lane change. To avoid the conservatism, this paper presents a cooperation-aware lane change method utilizing interactions between vehicles. We first propose an interactive trajectory prediction method to explore possible cooperations between an AV and the others. Further, an evaluation on safety, efficiency and comfort is designed to make a decision on lane change. Thereafter, we propose a motion planning algorithm based on model predictive control (MPC), which incorporates AV’s decision and surrounding vehicles’ interactive behaviors into constraints so as to avoid collisions during lane change. Quantitative testing results show that compared with the methods without an interactive prediction, our method enhances driving efficiencies of the AV and other vehicles by 14.8% and 2.6%, respectively, which indicates that a proper utilization of vehicle interactions can effectively reduce the conservatism of the AV and promote the cooperation between the AV and others.

BACKGROUND: Acute myeloid leukemia (AML) is a group of highly heterogenous and aggressive blood cancer. Despite recent progress in its diagnosis and treatment, patient outcome is variable and drug resistance results in increased mortality. The siglec family plays an important role in tumorigenesis and aging. Increasing age is a risk factor for AML and cellular aging contributes to leukemogenesis via various pathways. METHODS: The differential expression of the siglec family was compared between 151 AML patients and 70 healthy controls, with their information downloaded from TCGA and GTEx databases, respectively. How siglec expression correlated to AML patient clinical features, immune cell infiltration, drug resistance and survival outcome was analyzed. Differentially expressed genes in AML patients with low- and high-expressed siglec9 and siglec14 were analyzed and functionally enriched. The aging-related gene set was merged with the differentially expressed genes in AML patients with low and high expression of siglec9, and merged genes were subjected to lasso regression analysis to construct a novel siglec-based and aging-related prognostic model. The prediction model was validated using a validation cohort from GEO database (GSE106291). RESULTS: The expression levels of all siglec members were significantly altered in AML. The expression of siglecs was significantly correlated with AML patient clinical features, immune cell infiltration, drug resistance, and survival outcome. Based on the differentially expressed genes and aging-related gene set, we developed a 9-gene prognostic model and decision curve analysis revealed the net benefit generated by our prediction model. The siglec-based and aging-related 9-gene prognostic model was tested using a validation data set, in which AML patients with higher risk scores had significantly reduced survival probability. Time-dependent receiver operating characteristic curve and nomogram were plotted and showed the diagnostic accuracy and predictive value of our 9-gene prognostic model, respectively. CONCLUSIONS: Overall, our study indicates the important role of siglec family in AML and the good performance of our novel siglec-based and aging-related 9-gene signature in predicting AML patient outcome.

In this paper, we focus on detection of quantum colored noise and present a novel detection method which employs a quantum harmonic oscillator as a noise probe. With respect to an unknown spectrum of the quantum colored noise, quantum spectral decomposition theorem is applied to obtain a linear system model for the internal modes of the quantum colored noise, where the spectrum is parameterized. Then we establish a linear augmented model for the whole system which consists of a probe of a quantum oscillator and the quantum linear system model of the quantum colored noise. Due to the equivalence between the augmented model and the actual system, we estimate unknown parameters of the linear system model for the noise by solving an optimization problem. The effectiveness of our method is verified by an example of identification of quantum Lorentzian noise.

Approximately 40-50 percent of all drugs targets are G protein-coupled receptors (GPCRs). Three-dimensional structure of GPCRs is important to probe their biophysical and biochemical functions and their pharmaceutical applications. Lacking reliable and high quality free function is one of the ugent problems of computational predicting the three-dimensional structure in this community. We proposed a GPCR-specified energy function composed of four novel empirical potential energy terms: a two-dimensional contact energy force field, knowledge-based helix pair connection distance energy term, knowledge-based helix pair angle restraint energy term and a disulfide bond energy term. To validate the energy function, we employed an ab initio GPCR three-dimensional structure predictor to test if the energy function improved the accuracy of prediction. We evaluated 28 solved GPCRs and found that 21(75 percent) targets were correctly folded (TM-score>0.5). Also, the average TM-score using the energy function was 0.54, which was improved 134 percent than the TM-score 0.23 for MODELLER energy function and 170 percent than the TM-score 0.20 for Rosetta membrane energy function. The results confirmed that our empirical potential energy function toward ab initio folding is competitive to state-of-the-art solutions for structural prediction of GPCRs.

the purpose of this paper is to promote a butterfly quantum network coding scheme for remote state preparation of multi-qudit states. Firstly, we perform channel preparation and amplitude modulation; secondly, each sender simultaneously performs projection measurement; thirdly, both senders send measurement results as precoding information and auxiliary information to the intermediate node and the neighboring receivers respectively; fourthly, the first intermediate node performs encoding operation and sends the encoded information to both receivers via next intermediate node. Finally, by decoding the coded information with auxiliary information, two receivers perform corresponding unitary operations to recover the target multi-qudit quantum states. Compared with other schemes, our scheme can enhance the throughput and transmission efficiency of communication network for remote state preparation of multi-qudit states.

Consider in an asymmetric scenario that Alice and Bob wish to communicate unequal amount of classical information, we propose an asymmetric quantum dialogue (QD) protocol based on the entanglement swapping between two-qubit bell state and four-qubit cluster state. In this scheme, assume that there are two legitimate participants Alice and Bob. In a dialogue, the information that is delivered by Alice to Bob is twice the amount of her information obtained from Bob. The analysis demonstrates that our protocol is efficient and can meet the requirements of unequal amount of information transmission in a real communication scenario. Moreover, we discuss the security of this protocol and it demonstrates that our protocol could resist an external attack.

In this paper, we present a gradient algorithm to identify a damping rate function for a non-Markovian single qubit system. The dynamics of the single qubit system in a non-Markovian environment are assumed to obey a time convolutionless master equation, where all the non-Markovian effects of the environment are combined in the unknown damping rate function. To identify the damping rate function, we measure time trace observables of the qubit such that we can formulate the identification procedure as an optimization problem. Thus, we design a gradient algorithm to optimally reveal the damping rate function.

Transmembrane proteins play important roles in cellular energy production, signal transmission, and metabolism. Many shallow machine learning methods have been applied to transmembrane topology prediction, but the performance was limited by the large size of membrane proteins and the complex biological evolution information behind the sequence. In this paper, we proposed a novel deep approach based on conditional random fields named as dCRF-TM for predicting the topology of transmembrane proteins. Conditional random fields take into account more complicated interrelation between residue labels in full-length sequence than HMM and SVM-based methods. Three widely-used datasets were employed in the benchmark. DCRF-TM had the accuracy 95 percent over helix location prediction and the accuracy 78 percent over helix number prediction. DCRF-TM demonstrated a more robust performance on large size proteins (>350 residues) against 11 state-of-the-art predictors. Further dCRF-TM was applied to ab initio modeling three-dimensional structures of seven-transmembrane receptors, also known as G protein-coupled receptors. The predictions on 24 solved G protein-coupled receptors and unsolved vasopressin V2 receptor illustrated that dCRF-TM helped abGPCR-I-TASSER to improve TM-score 34.3 percent rather than using the random transmembrane definition. Two out of five predicted models caught the experimental verified disulfide bonds in vasopressin V2 receptor.

Incorporating constraints into the kernel-based regression is an effective means to improve regression performance. Nevertheless, in many applications, the constraints are continuous with respect to some parameters so that computational difficulties arise. Discretizing the constraints is a reasonable solution for these difficulties. However, in the context of kernel-based regression, most of existing works utilize the prior discretization strategy; this strategy suffers from a few inherent deficiencies: it cannot ensure that the regression result totally fulfills the original constraints and can hardly tackle high-dimensional problems. This paper proposes a cutting plane method (CPM) for constrained kernel-based regression problems and a relaxed CPM (R-CPM) for high-dimensional problems. The CPM discretizes the continuous constraints iteratively and ensures that the regression result strictly fulfills the original constraints. For high-dimensional problems, the R-CPM accepts a slight and controlled violation to attain a dimensional-independent computational complexity. The validity of the proposed methods is verified by numerical experiments.