DNA protein binding recognition based on lifelong learning.

In recent years, research in the field of bioinformatics has focused on predicting the raw sequences of proteins, and some scholars consider DNA-binding protein prediction as a classification task. Many statistical and machine learning-based methods have been widely used in DNA-binding proteins research. The aforementioned methods are indeed more efficient than those based on manual classification, but there is still room for improvement in terms of prediction accuracy and speed.

Research on RNA secondary structure predicting via bidirectional recurrent neural network.

Background: RNA secondary structure prediction is an important research content in the field of biological information. Predicting RNA secondary structure with pseudoknots has been proved to be an NP-hard problem. Traditional machine learning methods can not effectively apply protein sequence information with different sequence lengths to the prediction process due to the constraint of the self model when predicting the RNA secondary structure.

Polydopamine coated manganese oxide nanoparticles with ultrahigh relaxivity as nanotheranostic agents for magnetic resonance imaging guided synergetic chemo-/photothermal therapy.

Mn-based nanoparticles have been regarded as a new class of probes for magnetic resonance imaging (MRI), but their low relaxivity is the major obstacle for applications . Herein, we designed and constructed a multifunctional nanotheranostic (FA-MnO@PDA@PEG) for MRI guided combinatorial chemo-/photothermal therapy (PTT) for cancer. The ultrahigh relaxivity of 14.

Nanoconfined nitrogen-doped carbon-coated MnO nanoparticles in graphene enabling high performance for lithium-ion batteries and oxygen reduction reaction.

To tackle the issues of inferior cycling stability and low conductivity for MnO as an anode material for lithium ion batteries (LIBs) and as a catalyst for oxygen reduction reaction (ORR), a facile and effective strategy is explored to confine N-doped carbon-coated MnO nanoparticles in a conductive graphene matrix. The synthesis of the GMNCs involves the two-step coating of MnO nanocrystals with polydopamine and graphene, followed by heat treatment to form the GNS@MnO@N-doped carbon composites (GMNCs). When evaluated as anode materials for LIBs, the as-prepared GMNCs exhibit an improved cycling stability (754.

Designed synthesis of multi-functional PEGylated Yb2O3:Gd@SiO2@CeO2 islands core@shell nanostructure.

Nanomaterials that can restrain or reduce the production of excessive reactive oxygen species such as H2O2 to defend and treat against Alzheimer's disease (AD) have attracted much attention. In this paper, we adopt the strategy of layer-by-layer deposition; namely, first synthesizing available gadolinium-doped ytterbia nanoparticles (Yb2O3:Gd NPs) as cores, and then coating them with silica via the classical Stöber method to prevent leakage and act as a carrier for subsequent ceria deposition and PEGylation, and finally obtain the expected core@shell-structured nanocomposite of PEGylated Yb2O3:Gd@SiO2@CeO2 islands. The nanomaterial has proved not only to be a high-performance dual-modal contrast agent for use in MRI and CT, but also to exhibit excellent catalase mimetic activity, which may help the prognosis, diagnosis and treatment of AD in the future.