Showing papers by "Chinese Academy of Sciences published in 2018"

Squeeze-and-Excitation Networks

Abstract: The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within local receptive fields at each layer. A broad range of prior research has investigated the spatial component of this relationship, seeking to strengthen the representational power of a CNN by enhancing the quality of spatial encodings throughout its feature hierarchy. In this work, we focus instead on the channel relationship and propose a novel architectural unit, which we term the “Squeeze-and-Excitation” (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We show that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. We further demonstrate that SE blocks bring significant improvements in performance for existing state-of-the-art CNNs at slight additional computational cost. Squeeze-and-Excitation Networks formed the foundation of our ILSVRC 2017 classification submission which won first place and reduced the top-5 error to 2.251 percent, surpassing the winning entry of 2016 by a relative improvement of ${\sim }$ ∼ 25 percent. Models and code are available at https://github.com/hujie-frank/SENet .

fastp: an ultra-fast all-in-one FASTQ preprocessor.

Abstract: Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2-5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.

Minimal information for studies of extracellular vesicles 2018 (MISEV2018) : a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines

Abstract: The last decade has seen a sharp increase in the number of scientific publications describing physiological and pathological functions of extracellular vesicles (EVs), a collective term covering various subtypes of cell-released, membranous structures, called exosomes, microvesicles, microparticles, ectosomes, oncosomes, apoptotic bodies, and many other names. However, specific issues arise when working with these entities, whose size and amount often make them difficult to obtain as relatively pure preparations, and to characterize properly. The International Society for Extracellular Vesicles (ISEV) proposed Minimal Information for Studies of Extracellular Vesicles (“MISEV”) guidelines for the field in 2014. We now update these “MISEV2014” guidelines based on evolution of the collective knowledge in the last four years. An important point to consider is that ascribing a specific function to EVs in general, or to subtypes of EVs, requires reporting of specific information beyond mere description of function in a crude, potentially contaminated, and heterogeneous preparation. For example, claims that exosomes are endowed with exquisite and specific activities remain difficult to support experimentally, given our still limited knowledge of their specific molecular machineries of biogenesis and release, as compared with other biophysically similar EVs. The MISEV2018 guidelines include tables and outlines of suggested protocols and steps to follow to document specific EV-associated functional activities. Finally, a checklist is provided with summaries of key points.

fastp: an ultra-fast all-in-one FASTQ preprocessor

Abstract: Motivation: Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming, and quality filtering. These tools are often insufficiently fast as most are developed using high level programming languages (e.g., Python and Java) and provide limited multithreading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results: We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per read quality cutting, and many other operations with a single scan of the FASTQ data. It also supports unique molecular identifier preprocessing, poly tail trimming, output splitting, and base correction for paired-end data. It can automatically detect adapters for single-end and paired-end FASTQ data. This tool is developed in C++ and has multithreading support. Based on our evaluation, fastp is 2 to 5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and Implementation: The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp

Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018.

Abstract: Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field.

Heterogeneous single-atom catalysis

Abstract: Single-atom catalysis has arguably become the most active new frontier in heterogeneous catalysis. Aided by recent advances in practical synthetic methodologies, characterization techniques and computational modelling, we now have a large number of single-atom catalysts (SACs) that exhibit distinctive performances for a wide variety of chemical reactions. This Perspective summarizes recent experimental and computational efforts aimed at understanding the bonding in SACs and how this relates to catalytic performance. The examples described here illustrate the utility of SACs in a broad scope of industrially important reactions and highlight the advantages these catalysts have over those presently used. SACs have well-defined active centres, such that unique opportunities exist for the rational design of new catalysts with high activities, selectivities and stabilities. Indeed, given a certain practical application, we can often design a suitable SAC; thus, the field has developed very rapidly and afforded promising catalyst leads. Moreover, the control we have over certain SAC structures paves the way for designing base metal catalysts with the activities of noble metal catalysts. It appears that we are entering a new era of heterogeneous catalysis in which we have control over well-dispersed single-atom active sites whose properties we can readily tune. Single-atom catalysts are heterogeneous materials featuring active metals sites atomically dispersed on a surface. This Review describes methods by which we prepare and characterize these materials, as well as how we can tune their catalytic performance in a variety of important reactions.

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

Abstract: The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts. To further enhance the visual quality, we thoroughly study three key components of SRGAN – network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block (RRDB) without batch normalization as the basic network building unit. Moreover, we borrow the idea from relativistic GAN to let the discriminator predict relative realness instead of the absolute value. Finally, we improve the perceptual loss by using the features before activation, which could provide stronger supervision for brightness consistency and texture recovery. Benefiting from these improvements, the proposed ESRGAN achieves consistently better visual quality with more realistic and natural textures than SRGAN and won the first place in the PIRM2018-SR Challenge (region 3) with the best perceptual index. The code is available at https://github.com/xinntao/ESRGAN.

Organic solar cells based on non-fullerene acceptors.

Abstract: Organic solar cells (OSCs) have been dominated by donor:acceptor blends based on fullerene acceptors for over two decades. This situation has changed recently, with non-fullerene (NF) OSCs developing very quickly. The power conversion efficiencies of NF OSCs have now reached a value of over 13%, which is higher than the best fullerene-based OSCs. NF acceptors show great tunability in absorption spectra and electron energy levels, providing a wide range of new opportunities. The coexistence of low voltage losses and high current generation indicates that new regimes of device physics and photophysics are reached in these systems. This Review highlights these opportunities made possible by NF acceptors, and also discuss the challenges facing the development of NF OSCs for practical applications.

Efficient generation of mouse models of human diseases via ABE- and BE-mediated base editing

Abstract: A recently developed adenine base editor (ABE) efficiently converts A to G and is potentially useful for clinical applications. However, its precision and efficiency in vivo remains to be addressed. Here we achieve A-to-G conversion in vivo at frequencies up to 100% by microinjection of ABE mRNA together with sgRNAs. We then generate mouse models harboring clinically relevant mutations at Ar and Hoxd13, which recapitulates respective clinical defects. Furthermore, we achieve both C-to-T and A-to-G base editing by using a combination of ABE and SaBE3, thus creating mouse model harboring multiple mutations. We also demonstrate the specificity of ABE by deep sequencing and whole-genome sequencing (WGS). Taken together, ABE is highly efficient and precise in vivo, making it feasible to model and potentially cure relevant genetic diseases. CRISPR-based base editors allow for single nucleotide genome editing in a range of organisms. Here the authors demonstrate the in vivo generation of mouse models carrying clinically relevant mutations using C→T and A→G editors.

High Performance Visual Tracking with Siamese Region Proposal Network

Abstract: Visual object tracking has been a fundamental topic in recent years and many deep learning based trackers have achieved state-of-the-art performance on multiple benchmarks. However, most of these trackers can hardly get top performance with real-time speed. In this paper, we propose the Siamese region proposal network (Siamese-RPN) which is end-to-end trained off-line with large-scale image pairs. Specifically, it consists of Siamese subnetwork for feature extraction and region proposal subnetwork including the classification branch and regression branch. In the inference phase, the proposed framework is formulated as a local one-shot detection task. We can pre-compute the template branch of the Siamese subnetwork and formulate the correlation layers as trivial convolution layers to perform online tracking. Benefit from the proposal refinement, traditional multi-scale test and online fine-tuning can be discarded. The Siamese-RPN runs at 160 FPS while achieving leading performance in VOT2015, VOT2016 and VOT2017 real-time challenges.

Gaia Data Release 2: The astrometric solution

Abstract: Context. Gaia Data Release 2 (Gaia DR2) contains results for 1693 million sources in the magnitude range 3 to 21 based on observations collected by the European Space Agency Gaia satellite during the first 22 months of its operational phase.Aims. We describe the input data, models, and processing used for the astrometric content of Gaia DR2, and the validation of these resultsperformed within the astrometry task.Methods. Some 320 billion centroid positions from the pre-processed astrometric CCD observations were used to estimate the five astrometric parameters (positions, parallaxes, and proper motions) for 1332 million sources, and approximate positions at the reference epoch J2015.5 for an additional 361 million mostly faint sources. These data were calculated in two steps. First, the satellite attitude and the astrometric calibration parameters of the CCDs were obtained in an astrometric global iterative solution for 16 million selected sources, using about 1% of the input data. This primary solution was tied to the extragalactic International Celestial Reference System (ICRS) by means of quasars. The resulting attitude and calibration were then used to calculate the astrometric parameters of all the sources. Special validation solutions were used to characterise the random and systematic errors in parallax and proper motion.Results. For the sources with five-parameter astrometric solutions, the median uncertainty in parallax and position at the reference epoch J2015.5 is about 0.04 mas for bright (G = 17 mag, and 0.7 masat G = 20 mag. In the proper motion components the corresponding uncertainties are 0.05, 0.2, and 1.2 mas yr−1 , respectively.The optical reference frame defined by Gaia DR2 is aligned with ICRS and is non-rotating with respect to the quasars to within 0.15 mas yr−1 . From the quasars and validation solutions we estimate that systematics in the parallaxes depending on position, magnitude, and colour are generally below 0.1 mas, but the parallaxes are on the whole too small by about 0.03 mas. Significant spatial correlations of up to 0.04 mas in parallax and 0.07 mas yr−1 in proper motion are seen on small ( DR2 astrometry are given in the appendices.

Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI

Abstract: A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological "atoms". Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal).

Metal–organic framework-based materials: superior adsorbents for the capture of toxic and radioactive metal ions

Abstract: Highly efficient removal of metal ion pollutants, such as toxic and nuclear waste-related metal ions, remains a serious task from the biological and environmental standpoint because of their harmful effects on human health and the environment. Recently, highly porous metal–organic frameworks (MOFs), with excellent chemical stability and abundant functional groups, have represented a new addition to the area of capturing various types of hazardous metal ion pollutants. This review focuses on recent progress in reported MOFs and MOF-based composites as superior adsorbents for the efficient removal of toxic and nuclear waste-related metal ions. Aspects related to the interaction mechanisms between metal ions and MOF-based materials are systematically summarized, including macroscopic batch experiments, microscopic spectroscopy analysis, and theoretical calculations. The adsorption properties of various MOF-based materials are assessed and compared with those of other widely used adsorbents. Finally, we propose our personal insights into future research opportunities and challenges in the hope of stimulating more researchers to engage in this new field of MOF-based materials for environmental pollution management.

Single-Shot Refinement Neural Network for Object Detection

Abstract: For object detection, the two-stage approach (e.g., Faster R-CNN) has been achieving the highest accuracy, whereas the one-stage approach (e.g., SSD) has the advantage of high efficiency. To inherit the merits of both while overcoming their disadvantages, in this paper, we propose a novel single-shot based detector, called RefineDet, that achieves better accuracy than two-stage methods and maintains comparable efficiency of one-stage methods. RefineDet consists of two inter-connected modules, namely, the anchor refinement module and the object detection module. Specifically, the former aims to (1) filter out negative anchors to reduce search space for the classifier, and (2) coarsely adjust the locations and sizes of anchors to provide better initialization for the subsequent regressor. The latter module takes the refined anchors as the input from the former to further improve the regression accuracy and predict multi-class label. Meanwhile, we design a transfer connection block to transfer the features in the anchor refinement module to predict locations, sizes and class labels of objects in the object detection module. The multitask loss function enables us to train the whole network in an end-to-end way. Extensive experiments on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO demonstrate that RefineDet achieves state-of-the-art detection accuracy with high efficiency. Code is available at https://github.com/sfzhang15/RefineDet.

General synthesis and definitive structural identification of MN4C4 single-atom catalysts with tunable electrocatalytic activities

Abstract: Single-atom catalysts (SACs) have recently attracted broad research interest as they combine the merits of both homogeneous and heterogeneous catalysts. Rational design and synthesis of SACs are of immense significance but have so far been plagued by the lack of a definitive correlation between structure and catalytic properties. Here, we report a general approach to a series of monodispersed atomic transition metals (for example, Fe, Co, Ni) embedded in nitrogen-doped graphene with a common MN4C4 moiety, identified by systematic X-ray absorption fine structure analyses and direct transmission electron microscopy imaging. The unambiguous structure determination allows density functional theoretical prediction of MN4C4 moieties as efficient oxygen evolution catalysts with activities following the trend Ni > Co > Fe, which is confirmed by electrochemical measurements. Determination of atomistic structure and its correlation with catalytic properties represents a critical step towards the rational design and synthesis of precious or nonprecious SACs with exceptional atom utilization efficiency and catalytic activities.

Raman spectroscopy of graphene-based materials and its applications in related devices.

Abstract: Graphene-based materials exhibit remarkable electronic, optical, and mechanical properties, which has resulted in both high scientific interest and huge potential for a variety of applications. Furthermore, the family of graphene-based materials is growing because of developments in preparation methods. Raman spectroscopy is a versatile tool to identify and characterize the chemical and physical properties of these materials, both at the laboratory and mass-production scale. This technique is so important that most of the papers published concerning these materials contain at least one Raman spectrum. Thus, here, we systematically review the developments in Raman spectroscopy of graphene-based materials from both fundamental research and practical (i.e., device applications) perspectives. We describe the essential Raman scattering processes of the entire first- and second-order modes in intrinsic graphene. Furthermore, the shear, layer-breathing, G and 2D modes of multilayer graphene with different stacking orders are discussed. Techniques to determine the number of graphene layers, to probe resonance Raman spectra of monolayer and multilayer graphenes and to obtain Raman images of graphene-based materials are also presented. The extensive capabilities of Raman spectroscopy for the investigation of the fundamental properties of graphene under external perturbations are described, which have also been extended to other graphene-based materials, such as graphene quantum dots, carbon dots, graphene oxide, nanoribbons, chemical vapor deposition-grown and SiC epitaxially grown graphene flakes, composites, and graphene-based van der Waals heterostructures. These fundamental properties have been used to probe the states, effects, and mechanisms of graphene materials present in the related heterostructures and devices. We hope that this review will be beneficial in all the aspects of graphene investigations, from basic research to material synthesis and device applications.

A library of atomically thin metal chalcogenides

Abstract: Investigations of two-dimensional transition-metal chalcogenides (TMCs) have recently revealed interesting physical phenomena, including the quantum spin Hall effect1,2, valley polarization3,4 and two-dimensional superconductivity 5 , suggesting potential applications for functional devices6–10. However, of the numerous compounds available, only a handful, such as Mo- and W-based TMCs, have been synthesized, typically via sulfurization11–15, selenization16,17 and tellurization 18 of metals and metal compounds. Many TMCs are difficult to produce because of the high melting points of their metal and metal oxide precursors. Molten-salt-assisted methods have been used to produce ceramic powders at relatively low temperature 19 and this approach 20 was recently employed to facilitate the growth of monolayer WS2 and WSe2. Here we demonstrate that molten-salt-assisted chemical vapour deposition can be broadly applied for the synthesis of a wide variety of two-dimensional (atomically thin) TMCs. We synthesized 47 compounds, including 32 binary compounds (based on the transition metals Ti, Zr, Hf, V, Nb, Ta, Mo, W, Re, Pt, Pd and Fe), 13 alloys (including 11 ternary, one quaternary and one quinary), and two heterostructured compounds. We elaborate how the salt decreases the melting point of the reactants and facilitates the formation of intermediate products, increasing the overall reaction rate. Most of the synthesized materials in our library are useful, as supported by evidence of superconductivity in our monolayer NbSe2 and MoTe2 samples21,22 and of high mobilities in MoS2 and ReS2. Although the quality of some of the materials still requires development, our work opens up opportunities for studying the properties and potential application of a wide variety of two-dimensional TMCs.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

PySCF: the Python-based simulations of chemistry framework

Abstract: Python-based simulations of chemistry framework (PySCF) is a general-purpose electronic structure platform designed from the ground up to emphasize code simplicity, so as to facilitate new method development and enable flexible computational workflows. The package provides a wide range of tools to support simulations of finite-size systems, extended systems with periodic boundary conditions, low-dimensional periodic systems, and custom Hamiltonians, using mean-field and post-mean-field methods with standard Gaussian basis functions. To ensure ease of extensibility, PySCF uses the Python language to implement almost all of its features, while computationally critical paths are implemented with heavily optimized C routines. Using this combined Python/C implementation, the package is as efficient as the best existing C or Fortran-based quantum chemistry programs. In this paper, we document the capabilities and design philosophy of the current version of the PySCF package. WIREs Comput Mol Sci 2018, 8:e1340. doi: 10.1002/wcms.1340 This article is categorized under: Structure and Mechanism > Computational Materials Science Electronic Structure Theory > Ab Initio Electronic Structure Methods Software > Quantum Chemistry

Arbitrary-Oriented Scene Text Detection via Rotation Proposals

Abstract: This paper introduces a novel rotation-based framework for arbitrary-oriented text detection in natural scene images. We present the Rotation Region Proposal Networks , which are designed to generate inclined proposals with text orientation angle information. The angle information is then adapted for bounding box regression to make the proposals more accurately fit into the text region in terms of the orientation. The Rotation Region-of-Interest pooling layer is proposed to project arbitrary-oriented proposals to a feature map for a text region classifier. The whole framework is built upon a region-proposal-based architecture, which ensures the computational efficiency of the arbitrary-oriented text detection compared with previous text detection systems. We conduct experiments using the rotation-based framework on three real-world scene text detection datasets and demonstrate its superiority in terms of effectiveness and efficiency over previous approaches.

Over 14% Efficiency in Polymer Solar Cells Enabled by a Chlorinated Polymer Donor.

Abstract: Fluorine-contained polymers, which have been widely used in highly efficient polymer solar cells (PSCs), are rather costly due to their complicated synthesis and low yields in the preparation of components. Here, the feasibility of replacing the critical fluorine substituents in high-performance photovoltaic polymer donors with chlorine is demonstrated, and two polymeric donors, PBDB-T-2F and PBDB-T-2Cl, are synthesized and compared in parallel. The synthesis of PBDB-T-2Cl is much simpler than that of PBDB-T-2F. The two polymers have very similar optoelectronic and morphological properties, except the chlorinated polymer possess lower molecular energy levels than the fluorinated one. As a result, the PBDB-T-2Cl-based PSCs exhibit higher open circuit voltage (Voc ) than the PBDB-T-2F-based devices, leading to an outstanding power conversion efficiency of over 14%. This work establishes a more economical design paradigm of replacing fluorine with chlorine for preparing highly efficient polymer donors.

Skin-inspired highly stretchable and conformable matrix networks for multifunctional sensing

Abstract: Mechanosensation electronics (or Electronic skin, e-skin) consists of mechanically flexible and stretchable sensor networks that can detect and quantify various stimuli to mimic the human somatosensory system, with the sensations of touch, heat/cold, and pain in skin through various sensory receptors and neural pathways. Here we present a skin-inspired highly stretchable and conformable matrix network (SCMN) that successfully expands the e-skin sensing functionality including but not limited to temperature, in-plane strain, humidity, light, magnetic field, pressure, and proximity. The actualized specific expandable sensor units integrated on a structured polyimide network, potentially in three-dimensional (3D) integration scheme, can also fulfill simultaneous multi-stimulus sensing and achieve an adjustable sensing range and large-area expandability. We further construct a personalized intelligent prosthesis and demonstrate its use in real-time spatial pressure mapping and temperature estimation. Looking forward, this SCMN has broader applications in humanoid robotics, new prosthetics, human-machine interfaces, and health-monitoring technologies.

A DNA nanorobot functions as a cancer therapeutic in response to a molecular trigger in vivo.

Abstract: Nanoscale robots have potential as intelligent drug delivery systems that respond to molecular triggers. Using DNA origami we constructed an autonomous DNA robot programmed to transport payloads and present them specifically in tumors. Our nanorobot is functionalized on the outside with a DNA aptamer that binds nucleolin, a protein specifically expressed on tumor-associated endothelial cells, and the blood coagulation protease thrombin within its inner cavity. The nucleolin-targeting aptamer serves both as a targeting domain and as a molecular trigger for the mechanical opening of the DNA nanorobot. The thrombin inside is thus exposed and activates coagulation at the tumor site. Using tumor-bearing mouse models, we demonstrate that intravenously injected DNA nanorobots deliver thrombin specifically to tumor-associated blood vessels and induce intravascular thrombosis, resulting in tumor necrosis and inhibition of tumor growth. The nanorobot proved safe and immunologically inert in mice and Bama miniature pigs. Our data show that DNA nanorobots represent a promising strategy for precise drug delivery in cancer therapy.

Single Cobalt Atoms Anchored on Porous N-Doped Graphene with Dual Reaction Sites for Efficient Fenton-like Catalysis

Abstract: The Fenton-like process presents one of the most promising strategies to generate reactive oxygen-containing radicals to deal with the ever-growing environmental pollution. However, developing improved catalysts with adequate activity and stability is still a long-term goal for practical application. Herein, we demonstrate single cobalt atoms anchored on porous N-doped graphene with dual reaction sites as highly reactive and stable Fenton-like catalysts for efficient catalytic oxidation of recalcitrant organics via activation of peroxymonosulfate (PMS). Our experiments and density functional theory (DFT) calculations show that the CoN4 site with a single Co atom serves as the active site with optimal binding energy for PMS activation, while the adjacent pyrrolic N site adsorbs organic molecules. The dual reaction sites greatly reduce the migration distance of the active singlet oxygen produced from PMS activation and thus improve the Fenton-like catalytic performance.

Dynamics and function of DNA methylation in plants.

Abstract: DNA methylation is a conserved epigenetic modification that is important for gene regulation and genome stability. Aberrant patterns of DNA methylation can lead to plant developmental abnormalities. A specific DNA methylation state is an outcome of dynamic regulation by de novo methylation, maintenance of methylation and active demethylation, which are catalysed by various enzymes that are targeted by distinct regulatory pathways. In this Review, we discuss DNA methylation in plants, including methylating and demethylating enzymes and regulatory factors, and the coordination of methylation and demethylation activities by a so-called methylstat mechanism; the functions of DNA methylation in regulating transposon silencing, gene expression and chromosome interactions; the roles of DNA methylation in plant development; and the involvement of DNA methylation in plant responses to biotic and abiotic stress conditions.

Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries

Abstract: A passivation layer called the solid electrolyte interphase (SEI) is formed on electrode surfaces from decomposition products of electrolytes. The SEI allows Li+ transport and blocks electrons in order to prevent further electrolyte decomposition and ensure continued electrochemical reactions. The formation and growth mechanism of the nanometer thick SEI films are yet to be completely understood owing to their complex structure and lack of reliable in situ experimental techniques. Significant advances in computational methods have made it possible to predictively model the fundamentals of SEI. This review aims to give an overview of state-of-the-art modeling progress in the investigation of SEI films on the anodes, ranging from electronic structure calculations to mesoscale modeling, covering the thermodynamics and kinetics of electrolyte reduction reactions, SEI formation, modification through electrolyte design, correlation of SEI properties with battery performance, and the artificial SEI design. Multi-scale simulations have been summarized and compared with each other as well as with experiments. Computational details of the fundamental properties of SEI, such as electron tunneling, Li-ion transport, chemical/mechanical stability of the bulk SEI and electrode/(SEI/) electrolyte interfaces have been discussed. This review shows the potential of computational approaches in the deconvolution of SEI properties and design of artificial SEI. We believe that computational modeling can be integrated with experiments to complement each other and lead to a better understanding of the complex SEI for the development of a highly efficient battery in the future.

Chemical Vapor Deposition Growth and Applications of Two-Dimensional Materials and Their Heterostructures

Abstract: Two-dimensional (2D) materials have attracted increasing research interest because of the abundant choice of materials with diverse and tunable electronic, optical, and chemical properties. Moreover, 2D material based heterostructures combining several individual 2D materials provide unique platforms to create an almost infinite number of materials and show exotic physical phenomena as well as new properties and applications. To achieve these high expectations, methods for the scalable preparation of 2D materials and 2D heterostructures of high quality and low cost must be developed. Chemical vapor deposition (CVD) is a powerful method which may meet the above requirements, and has been extensively used to grow 2D materials and their heterostructures in recent years, despite several challenges remaining. In this review of the challenges in the CVD growth of 2D materials, we highlight recent advances in the controlled growth of single crystal 2D materials, with an emphasis on semiconducting transition meta...