Dispersive Manipulation of Paired Superconducting Qubits

We combine the ideas of qubit encoding and dispersive dynamics to enable robust and easy quantum information processing (QIP) on paired superconducting charge boxes sharing a common bias lead. We establish a decoherence free subspace on these and introduce universal gates by dispersive interaction with a LC resonator and inductive couplings between the encoded qubits. These gates preserve the code space and only require the established local symmetry and the control of the voltage bias.Comment: 5 pages, incl. 1 figur

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

Hybrid Cross-Linking to Construct Functional Elastomers

ConspectusElastomers have been extensively used in diverse industrial sectors such as footwear, seals, tires, and cable jacketing and have attracted more and more attention in emerging fields such as regenerative medicine, soft robotics, and stretchable electronics. Global consumption of natural and synthetic elastomers amounted to nearly 27 million metric tons in 2020. In addition, to further enhance the common properties of elastomers, it is highly desired to endow elastomers with functionalities such as reprocessability, biomimetic mechanical properties, self-healing ability, bioactivity, and electrical conductivity, which will significantly broaden their applications. The covalent or noncovalent cross-linked structure is the essential factor for the elasticity of elastomers. Traditional elastomers usually comprise a single type of cross-linked molecular network, for which it is difficult to modulate the properties and introduce functionalities. Inspired by the simultaneous existence of multiple cross-linked structures in proteins, researchers have employed a hybrid cross-linking strategy to construct elastomers. Various noncovalent interactions (e.g., hydrogen bonds, metal–ligand coordination, ionic interactions, and chain folding) and dynamic covalent bonds (e.g., disulfide bonds, oxime–urethane bonds, and urea bonds) have been integrated in elastomers. Accordingly, the properties and functionalities of elastomers can be tuned by regulating the types, ratios, and distributions of cross-links. The hybrid cross-linking strategy provides a versatile and effective way to construct diverse functional elastomers for broad applications in various important fields.In this Account, we present our recent progress on functional elastomers constructed by a hybrid cross-linking strategy, including their design, preparation, properties, and diverse applications. First, we provide a brief introduction of the basic concept of functional elastomers and outline general strategies and mechanics for functional elastomers constructed by hybrid cross-linking. Then, we classify hybrid cross-linked elastomers by their design strategies, including multiple cross-linking, topological design, chemical coupling, and multiple networks. The relationships between the functionalities and hybrid cross-linked structures are summarized. At the same time, we also introduce diverse applications of these hybrid cross-linked elastomers in biomedicine, flexible electronics, soft robotics, 3D printing, and so on. Finally, we discuss our perspective on open challenges and future development trends of this rapidly evolving field. This Account highlighting the diverse hybrid cross-linked elastomers not only provides insights into strategies for elastomer functionalization but also provides new ideas for material design and inspires a variety of new applications

Using discriminative vector machine model with 2DPCA to predict interactions among proteins

Abstract Background The interactions among proteins act as crucial roles in most cellular processes. Despite enormous effort put for identifying protein-protein interactions (PPIs) from a large number of organisms, existing firsthand biological experimental methods are high cost, low efficiency, and high false-positive rate. The application of in silico methods opens new doors for predicting interactions among proteins, and has been attracted a great deal of attention in the last decades. Results Here we present a novelty computational model with the adoption of our proposed Discriminative Vector Machine (DVM) model and a 2-Dimensional Principal Component Analysis (2DPCA) descriptor to identify candidate PPIs only based on protein sequences. To be more specific, a 2DPCA descriptor is employed to capture discriminative feature information from Position-Specific Scoring Matrix (PSSM) of amino acid sequences by the tool of PSI-BLAST. Then, a robust and powerful DVM classifier is employed to infer PPIs. When applied on both gold benchmark datasets of Yeast and H. pylori, our model obtained mean prediction accuracies as high as of 97.06 and 92.89%, respectively, which demonstrates a noticeable improvement than some state-of-the-art methods. Moreover, we constructed Support Vector Machines (SVM) based predictive model and made comparison it with our model on Human benchmark dataset. In addition, to further demonstrate the predictive reliability of our proposed method, we also carried out extensive experiments for identifying cross-species PPIs on five other species datasets. Conclusions All the experimental results indicate that our method is very effective for identifying potential PPIs and could serve as a practical approach to aid bioexperiment in proteomics research

Electrospun Nanofibers for Tissue Engineering with Drug Loading and Release

Electrospinning technologies have been applied in the field of tissue engineering as materials, with nanoscale-structures and high porosity, can be easily prepared via this method to bio-mimic the natural extracellular matrix (ECM). Tissue engineering aims to fabricate functional biomaterials for the repairment and regeneration of defective tissue. In addition to the structural simulation for accelerating the repair process and achieving a high-quality regeneration, the combination of biomaterials and bioactive molecules is required for an ideal tissue-engineering scaffold. Due to the diversity in materials and method selection for electrospinning, a great flexibility in drug delivery systems can be achieved. Various drugs including antibiotic agents, vitamins, peptides, and proteins can be incorporated into electrospun scaffolds using different electrospinning techniques and drug-loading methods. This is a review of recent research on electrospun nanofibrous scaffolds for tissue-engineering applications, the development of preparation methods, and the delivery of various bioactive molecules. These studies are based on the fabrication of electrospun biomaterials for the repair of blood vessels, nerve tissues, cartilage, bone defects, and the treatment of aneurysms and skin wounds, as well as their applications related to oral mucosa and dental fields. In these studies, due to the optimal selection of drugs and loading methods based on electrospinning, in vitro and in vivo experiments demonstrated that these scaffolds exhibited desirable effects for the repair and treatment of damaged tissue and, thus, have excellent potential for clinical application