Brick‐and‐mortar microstructures of artificial nacre‐like chitosan–montmorillonite (MTM) bionanocomposite films can be readily fabricated by using chitosan–MTM hybrid nanosheets as building blocks ...(see picture). The fire‐retardant nacre‐like films have a higher mechanical performance (Young's modulus: 10 GPa, tensile strength: 100 MPa) than a film made by conventional methods.
Reverse engineering of gene regulatory networks (GRNs) is a central task in systems biology. Most of the existing methods for GRN inference rely on gene co-expression analysis or TF-target binding ...information, where the determination of co-expression is often unreliable merely based on gene expression levels, and the TF-target binding data from high-throughput experiments may be noisy, leading to a high ratio of false links and missed links, especially for large-scale networks. In recent years, the microscopy images recording spatial gene expression have become a new resource in GRN reconstruction, as the spatial and temporal expression patterns contain much abundant gene interaction information. Till now, the spatial expression resources have been largely underexploited, and only a few traditional image processing methods have been employed in the image-based GRN reconstruction. Moreover, co-expression analysis using conventional measurements based on image similarity may be inaccurate, because it is the local-pattern consistency rather than global-image-similarity that determines gene-gene interactions. Here we present GripDL (Gene regulatory interaction prediction via Deep Learning), which incorporates high-confidence TF-gene regulation knowledge from previous studies, and constructs GRNs for Drosophila eye development based on Drosophila embryonic gene expression images. Benefitting from the powerful representation ability of deep neural networks and the supervision information of known interactions, the new method outperforms traditional methods with a large margin and reveals new intriguing knowledge about Drosophila eye development.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
RNA-binding proteins (RBPs) play crucial roles in various biological processes. Deep learning-based methods have been demonstrated powerful on predicting RBP sites on RNAs. However, the training of ...deep learning models is very time-intensive and computationally intensive.
Here we present a deep learning-based RBPsuite, an easy-to-use webserver for predicting RBP binding sites on linear and circular RNAs. For linear RNAs, RBPsuite predicts the RBP binding scores with them using our updated iDeepS. For circular RNAs (circRNAs), RBPsuite predicts the RBP binding scores with them using our developed CRIP. RBPsuite first breaks the input RNA sequence into segments of 101 nucleotides and scores the interaction between the segments and the RBPs. RBPsuite further detects the verified motifs on the binding segments gives the binding scores distribution along the full-length sequence.
RBPsuite is an easy-to-use online webserver for predicting RBP binding sites and freely available at http://www.csbio.sjtu.edu.cn/bioinf/RBPsuite/ .
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Carbon nanotubes are promising materials for various applications. In recent years, progress in manufacturing and functionalizing carbon nanotubes has been made to achieve the control of bulk and ...surface properties including the wettability, acid-base properties, adsorption, electric conductivity and capacitance. In order to gain the optimal benefit of carbon nanotubes, comprehensive understanding on manufacturing and functionalizing carbon nanotubes ought to be systematically developed. This review summarizes methodologies of manufacturing carbon nanotubes
via
arc discharge, laser ablation and chemical vapor deposition and functionalizing carbon nanotubes through surface oxidation and activation, doping of heteroatoms, halogenation, sulfonation, grafting, polymer coating, noncovalent functionalization and nanoparticle attachment. The characterization techniques detecting the bulk nature and surface properties as well as the effects of various functionalization approaches on modifying the surface properties for specific applications in catalysis including heterogeneous catalysis, photocatalysis, photoelectrocatalysis and electrocatalysis are highlighted.
Carbon nanotubes are promising materials for various applications.
Plasmonic nanostructures are capable of driving photocatalysis through absorbing photons in the visible region of the solar spectrum. Unfortunately, the short lifetime of plasmon‐induced hot carriers ...and sluggish surface chemical reactions significantly limit their photocatalytic efficiencies. Moreover, the thermodynamically favored excitation mechanism of plasmonic photocatalytic reactions is unclear. The mechanism of how the plasmonic catalyst could enhance the performance of chemical reaction and the limitation of localized surface plasmon resonance devices is proposed. In addition, a design is demonstrated through co‐catalyst decorated plasmonic nanoparticles Au/IrOX upon a semiconductor nanowire‐array TiO2 electrode that are able to considerably improve the lifetime of plasmon‐induced charge‐carriers and further facilitate the kinetics of chemical reaction. A thermodynamically favored excitation with improved kinetics of hot carriers is revealed through electrochemical studies and characterization of X‐ray absorption spectrum. This discovery provides an opportunity to efficiently manage hot carriers that are generated from metal nanostructures through surface plasmon effects for photocatalysis applications.
Surface plasmon resonance (SPR) of metal is a promising avenue for application to solar energy. Thermodynamics and kinetics of hot carriers generated by the SPR effect limit the photocatalytic performance. Using the synergistic effect of co‐catalyst IrOX, the photoelectrochemical performance is enhanced by 100% due to the accelerated charge transfer of hot holes and massive hot‐electron injection into TiO2.
Semiconductor‐based photocatalysis and photoelectrocatalysis have received considerable attention as alternative approaches for solar energy harvesting and storage. The photocatalytic or ...photoelectrocatalytic performance of a semiconductor is closely related to the design of the semiconductor at the nanoscale. Among various nanostructures, one‐dimensional (1D) nanostructured photocatalysts and photoelectrodes have attracted increasing interest owing to their unique optical, structural, and electronic advantages. In this article, a comprehensive review of the current research efforts towards the development of 1D semiconductor nanomaterials for heterogeneous photocatalysis and photoelectrocatalysis is provided and, in particular, a discussion of how to overcome the challenges for achieving full potential of 1D nanostructures is presented. It is anticipated that this review will afford enriched information on the rational exploration of the structural and electronic properties of 1D semiconductor nanostructures for achieving more efficient 1D nanostructure‐based photocatalysts and photoelectrodes for high‐efficiency solar energy conversion.
One‐dimensional nanostructured photocatalysts and photoelectrodes are reviewed in a comprehensive manner. The basic principles of photocatalysis and photoelectrocatalysis, strategies for improving the photocatalytic and photoelectrochemical water splitting performances of 1D semiconductors, and applications of 1D semiconductors in these two fields are elucidated. Moreover, the challenges and perspectives for achieving full potential of 1D nanostructures are discussed.
The huge diversity of hierarchical micro-/nano-rigid structures existing in biological systems is increasingly becoming a source of inspiration of materials scientists and engineers to create ...next-generation advanced functional materials. In the past decades, these multiscale hierarchical structures have been intensively investigated to show their contributions to high performance in mechanical properties. Recently, accompanied with the development of nanotechnology, some biologically hierarchical rigid structures have been duplicated and mimicked in artificial materials through hierarchical organization of micro-/nano-building blocks. In this
critical review
, we will present biological rigid structural models, functional micro-/nano-building blocks, and hierarchical assembly techniques for the manufacture of bio-inspired rigid structural functional materials (177 references).
In this critical review, the concept of hierarchically assembled construction of bio-inspired structural functional materials is overviewed. The hierarchical structures of natural models, bio-inspired rigid structural materials, functional building blocks, and hierarchical assembly techniques have been reviewed.
Combinations of drugs are now ubiquitous in treating complex diseases such as cancer and HIV due to their potential for enhanced efficacy and reduced side effects. The traditional combination ...experiments of drugs focus primarily on the dose effects of the constituent drugs. However, with the doses of drugs remaining unchanged, different sequences of drug administration may also affect the efficacy endpoint. Such drug effects shall be called as order effects. The common order‐effect linear models are usually inadequate for analyzing combination experiments due to the nonlinear relationships and complex interactions among drugs. In this article, we propose a random field model for order‐effect modeling. This model is flexible, allowing nonlinearities, and interaction effects to be incorporated with a small number of model parameters. Moreover, we propose a subtle experimental design that will collect good quality data for modeling the order effects of drugs with a reasonable run size. A real‐data analysis and simulation studies are given to demonstrate that the proposed design and model are effective in predicting the optimal drug sequences in administration.
A number of important reactions such as the oxygen evolution reaction (OER) are catalyzed by transition metal oxides (TMOs), the surface reactivity of which is rather elusive. Therefore, rationally ...tailoring adsorption energy of intermediates on TMOs to achieve desirable catalytic performance still remains a great challenge. Here we show the identification of a general and tunable surface structure, coordinatively unsaturated metal cation (MCUS), as a good surface reactivity descriptor for TMOs in OER. Surface reactivity of a given TMO increases monotonically with the density of MCUS, and thus the increase in MCUS improves the catalytic activity for weak-binding TMOs but impairs that for strong-binding ones. The electronic origin of the surface reactivity can be well explained by a new model proposed in this work, wherein the energy of the highest-occupied d-states relative to the Fermi level determines the intermediates’ bonding strength by affecting the filling of the antibonding states. Our model for the first time well describes the reactivity trends among TMOs, and would initiate viable design principles for, but not limited to, OER catalysts.
Abstract
Motivation
Generating molecules of high quality and drug-likeness in the vast chemical space is a big challenge in the drug discovery. Most existing molecule generative methods focus on ...diversity and novelty of molecules, but ignoring drug potentials of the generated molecules during the generation process.
Results
In this study, we present a novel de novo multiobjective quality assessment-based drug design approach (QADD), which integrates an iterative refinement framework with a novel graph-based molecular quality assessment model on drug potentials. QADD designs a multiobjective deep reinforcement learning pipeline to generate molecules with multiple desired properties iteratively, where a graph neural network-based model for accurate molecular quality assessment on drug potentials is introduced to guide molecule generation. Experimental results show that QADD can jointly optimize multiple molecular properties with a promising performance and the quality assessment module is capable of guiding the generated molecules with high drug potentials. Furthermore, applying QADD to generate novel molecules binding to a biological target protein DRD2 also demonstrates the algorithm’s efficacy.
Availability and implementation
QADD is freely available online for academic use at https://github.com/yifang000/QADD or http://www.csbio.sjtu.edu.cn/bioinf/QADD.
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