Showing papers by "Chinese Academy of Sciences published in 2021"

Characteristics of SARS-CoV-2 and COVID-19

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible and pathogenic coronavirus that emerged in late 2019 and has caused a pandemic of acute respiratory disease, named ‘coronavirus disease 2019’ (COVID-19), which threatens human health and public safety. In this Review, we describe the basic virology of SARS-CoV-2, including genomic characteristics and receptor use, highlighting its key difference from previously known coronaviruses. We summarize current knowledge of clinical, epidemiological and pathological features of COVID-19, as well as recent progress in animal models and antiviral treatment approaches for SARS-CoV-2 infection. We also discuss the potential wildlife hosts and zoonotic origin of this emerging virus in detail. In this Review, Shi and colleagues summarize the exceptional amount of research that has characterized acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease 2019 (COVID-19) since this virus has swept around the globe. They discuss what we know so far about the emergence and virology of SARS-CoV-2 and the pathogenesis and treatment of COVID-19.

A Comprehensive Survey on Transfer Learning

Abstract: Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target-domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning research studies, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey article reviews more than 40 representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over 20 representative transfer learning models are used for experiments. The models are performed on three different data sets, that is, Amazon Reviews, Reuters-21578, and Office-31, and the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.

Gene regulation by long non-coding RNAs and its biological functions.

Abstract: Evidence accumulated over the past decade shows that long non-coding RNAs (lncRNAs) are widely expressed and have key roles in gene regulation. Recent studies have begun to unravel how the biogenesis of lncRNAs is distinct from that of mRNAs and is linked with their specific subcellular localizations and functions. Depending on their localization and their specific interactions with DNA, RNA and proteins, lncRNAs can modulate chromatin function, regulate the assembly and function of membraneless nuclear bodies, alter the stability and translation of cytoplasmic mRNAs and interfere with signalling pathways. Many of these functions ultimately affect gene expression in diverse biological and physiopathological contexts, such as in neuronal disorders, immune responses and cancer. Tissue-specific and condition-specific expression patterns suggest that lncRNAs are potential biomarkers and provide a rationale to target them clinically. In this Review, we discuss the mechanisms of lncRNA biogenesis, localization and functions in transcriptional, post-transcriptional and other modes of gene regulation, and their potential therapeutic applications.

Pseudo-halide anion engineering for α-FAPbI3 perovskite solar cells.

Abstract: Metal halide perovskites of the general formula ABX3—where A is a monovalent cation such as caesium, methylammonium or formamidinium; B is divalent lead, tin or germanium; and X is a halide anion—have shown great potential as light harvesters for thin-film photovoltaics1–5. Among a large number of compositions investigated, the cubic α-phase of formamidinium lead triiodide (FAPbI3) has emerged as the most promising semiconductor for highly efficient and stable perovskite solar cells6–9, and maximizing the performance of this material in such devices is of vital importance for the perovskite research community. Here we introduce an anion engineering concept that uses the pseudo-halide anion formate (HCOO−) to suppress anion-vacancy defects that are present at grain boundaries and at the surface of the perovskite films and to augment the crystallinity of the films. The resulting solar cell devices attain a power conversion efficiency of 25.6 per cent (certified 25.2 per cent), have long-term operational stability (450 hours) and show intense electroluminescence with external quantum efficiencies of more than 10 per cent. Our findings provide a direct route to eliminate the most abundant and deleterious lattice defects present in metal halide perovskites, providing a facile access to solution-processable films with improved optoelectronic performance. Incorporation of the pseudo-halide anion formate during the fabrication of α-FAPbI3 perovskite films eliminates deleterious iodide vacancies, yielding solar cell devices with a certified power conversion efficiency of 25.21 per cent and long-term operational stability.

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Safety, tolerability, and immunogenicity of an inactivated SARS-CoV-2 vaccine in healthy adults aged 18-59 years: a randomised, double-blind, placebo-controlled, phase 1/2 clinical trial.

Abstract: Summary Background With the unprecedented morbidity and mortality associated with the COVID-19 pandemic, a vaccine against COVID-19 is urgently needed. We investigated CoronaVac (Sinovac Life Sciences, Beijing, China), an inactivated vaccine candidate against COVID-19, containing inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), for its safety, tolerability and immunogenicity. Methods In this randomised, double-blind, placebo-controlled, phase 1/2 clinical trial, healthy adults aged 18–59 years were recruited from the community in Suining County of Jiangsu province, China. Adults with SARS-CoV-2 exposure or infection history, with axillary temperature above 37·0°C, or an allergic reaction to any vaccine component were excluded. The experimental vaccine for the phase 1 trial was manufactured using a cell factory process (CellSTACK Cell Culture Chamber 10, Corning, Wujiang, China), whereas those for the phase 2 trial were produced through a bioreactor process (ReadyToProcess WAVE 25, GE, Umea, Sweden). The phase 1 trial was done in a dose-escalating manner. At screening, participants were initially separated (1:1), with no specific randomisation, into two vaccination schedule cohorts, the days 0 and 14 vaccination cohort and the days 0 and 28 vaccination cohort, and within each cohort the first 36 participants were assigned to block 1 (low dose CoronaVac [3 μg per 0·5 mL of aluminium hydroxide diluent per dose) then another 36 were assigned to block 2 (high-dose Coronavc [6 μg per 0·5 mL of aluminium hydroxide diluent per dse]). Within each block, participants were randomly assigned (2:1), using block randomisation with a block size of six, to either two doses of CoronaVac or two doses of placebo. In the phase 2 trial, at screening, participants were initially separated (1:1), with no specific randomisation, into the days 0 and 14 vaccination cohort and the days 0 and 28 vaccination cohort, and participants were randomly assigned (2:2:1), using block randomisation with a block size of five, to receive two doses of either low-dose CoronaVac, high-dose CoronaVac, or placebo. Participants, investigators, and laboratory staff were masked to treatment allocation. The primary safety endpoint was adverse reactions within 28 days after injection in all participants who were given at least one dose of study drug (safety population). The primary immunogenic outcome was seroconversion rates of neutralising antibodies to live SARS-CoV-2 at day 14 after the last dose in the days 0 and 14 cohort, and at day 28 after the last dose in the days 0 and 28 cohort in participants who completed their allocated two-dose vaccination schedule (per-protocol population). This trial is registered with ClinicalTrials.gov, NCT04352608, and is closed to accrual. Findings Between April 16 and April 25, 2020, 144 participants were enrolled in the phase 1 trial, and between May 3 and May 5, 2020, 600 participants were enrolled in the phase 2 trial. 743 participants received at least one dose of investigational product (n=143 for phase 1 and n=600 for phase 2; safety population). In the phase 1 trial, the incidence of adverse reactions for the days 0 and 14 cohort was seven (29%) of 24 participants in the 3 ug group, nine (38%) of 24 in the 6 μg group, and two (8%) of 24 in the placebo group, and for the days 0 and 28 cohort was three (13%) of 24 in the 3 μg group, four (17%) of 24 in the 6 μg group, and three (13%) of 23 in the placebo group. The seroconversion of neutralising antibodies on day 14 after the days 0 and 14 vaccination schedule was seen in 11 (46%) of 24 participants in the 3 μg group, 12 (50%) of 24 in the 6 μg group, and none (0%) of 24 in the placebo group; whereas at day 28 after the days 0 and 28 vaccination schedule, seroconversion was seen in 20 (83%) of 24 in the 3 μg group, 19 (79%) of 24 in the 6 μg group, and one (4%) of 24 in the placebo group. In the phase 2 trial, the incidence of adverse reactions for the days 0 and 14 cohort was 40 (33%) of 120 participants in the 3 μg group, 42 (35%) of 120 in the 6 μg group, and 13 (22%) of 60 in the placebo group, and for the days 0 and 28 cohort was 23 (19%) of 120 in the 3 μg group, 23 (19%) of 120 in the 6 μg group, and 11 (18%) of 60 for the placebo group. Seroconversion of neutralising antibodies was seen for 109 (92%) of 118 participants in the 3 μg group, 117 (98%) of 119 in the 6 μg group, and two (3%) of 60 in the placebo group at day 14 after the days 0 and 14 schedule; whereas at day 28 after the days 0 and 28 schedule, seroconversion was seen in 114 (97%) of 117 in the 3 μg group, 118 (100%) of 118 in the 6 μg group, and none (0%) of 59 in the placebo group. Interpretation Taking safety, immunogenicity, and production capacity into account, the 3 μg dose of CoronaVac is the suggested dose for efficacy assessment in future phase 3 trials. Funding Chinese National Key Research and Development Program and Beijing Science and Technology Program.

Non-fullerene acceptors with branched side chains and improved molecular packing to exceed 18% efficiency in organic solar cells

Abstract: Molecular design of non-fullerene acceptors is of vital importance for high-efficiency organic solar cells. The branched alkyl chain modification is often regarded as a counter-intuitive approach, as it may introduce an undesirable steric hindrance that reduces charge transport in non-fullerene acceptors. Here we show the design and synthesis of a highly efficient non-fullerene acceptor family by substituting the beta position of the thiophene unit on a Y6-based dithienothiophen[3,2-b]-pyrrolobenzothiadiazole core with branched alkyl chains. It was found that such a modification to a different alkyl chain length could completely change the molecular packing behaviour of non-fullerene acceptors, leading to improved structural order and charge transport in thin films. An unprecedented efficiency of 18.32% (certified value of 17.9%) with a fill factor of 81.5% is achieved for single-junction organic solar cells. This work reveals the importance of the branched alkyl chain topology in tuning the molecular packing and blend morphology, which leads to improved organic photovoltaic performance. Molecular design of acceptor and donor molecules has enabled major progress in organic photovoltaics. Li et al. show that branched alkyl chains in non-fullerene acceptors allow favourable morphology in the active layer, enabling a certified device efficiency of 17.9%.

GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild

Abstract: We introduce here a large tracking database that offers an unprecedentedly wide coverage of common moving objects in the wild, called GOT-10k. Specifically, GOT-10k is built upon the backbone of WordNet structure [1] and it populates the majority of over 560 classes of moving objects and 87 motion patterns, magnitudes wider than the most recent similar-scale counterparts [19] , [20] , [23] , [26] . By releasing the large high-diversity database, we aim to provide a unified training and evaluation platform for the development of class-agnostic, generic purposed short-term trackers. The features of GOT-10k and the contributions of this article are summarized in the following. (1) GOT-10k offers over 10,000 video segments with more than 1.5 million manually labeled bounding boxes, enabling unified training and stable evaluation of deep trackers. (2) GOT-10k is by far the first video trajectory dataset that uses the semantic hierarchy of WordNet to guide class population, which ensures a comprehensive and relatively unbiased coverage of diverse moving objects. (3) For the first time, GOT-10k introduces the one-shot protocol for tracker evaluation, where the training and test classes are zero-overlapped . The protocol avoids biased evaluation results towards familiar objects and it promotes generalization in tracker development. (4) GOT-10k offers additional labels such as motion classes and object visible ratios, facilitating the development of motion-aware and occlusion-aware trackers. (5) We conduct extensive tracking experiments with 39 typical tracking algorithms and their variants on GOT-10k and analyze their results in this paper. (6) Finally, we develop a comprehensive platform for the tracking community that offers full-featured evaluation toolkits, an online evaluation server, and a responsive leaderboard. The annotations of GOT-10k’s test data are kept private to avoid tuning parameters on it.

Single-Junction Organic Photovoltaic Cell with 19% Efficiency

Abstract: Improving power conversion efficiency (PCE) is important for broadening the applications of organic photovoltaic (OPV) cells. Here, a maximum PCE of 19.0% (certified value of 18.7%) is achieved in single-junction OPV cells by combining material design with a ternary blending strategy. An active layer comprising a new wide-bandgap polymer donor named PBQx-TF and a new low-bandgap non-fullerene acceptor (NFA) named eC9-2Cl is rationally designed. With optimized light utilization, the resulting binary cell exhibits a good PCE of 17.7%. An NFA F-BTA3 is then added to the active layer as a third component to simultaneously improve the photovoltaic parameters. The improved light unitization, cascaded energy level alignment, and enhanced intermolecular packing result in open-circuit voltage of 0.879 V, short-circuit current density of 26.7 mA cm-2 , and fill factor of 0.809. This study demonstrates that further improvement of PCEs of high-performance OPV cells requires fine tuning of the electronic structures and morphologies of the active layers.

Viral targets for vaccines against COVID-19.

Abstract: Vaccines are urgently needed to control the coronavirus disease 2019 (COVID-19) pandemic and to help the return to pre-pandemic normalcy. A great many vaccine candidates are being developed, several of which have completed late-stage clinical trials and are reporting positive results. In this Progress article, we discuss which viral elements are used in COVID-19 vaccine candidates, why they might act as good targets for the immune system and the implications for protective immunity.

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

Abstract: It is widely known that neural networks (NNs) are universal approximators of continuous functions. However, a less known but powerful result is that a NN with a single hidden layer can accurately approximate any nonlinear continuous operator. This universal approximation theorem of operators is suggestive of the structure and potential of deep neural networks (DNNs) in learning continuous operators or complex systems from streams of scattered data. Here, we thus extend this theorem to DNNs. We design a new network with small generalization error, the deep operator network (DeepONet), which consists of a DNN for encoding the discrete input function space (branch net) and another DNN for encoding the domain of the output functions (trunk net). We demonstrate that DeepONet can learn various explicit operators, such as integrals and fractional Laplacians, as well as implicit operators that represent deterministic and stochastic differential equations. We study different formulations of the input function space and its effect on the generalization error for 16 different diverse applications. Neural networks are known as universal approximators of continuous functions, but they can also approximate any mathematical operator (mapping a function to another function), which is an important capability for complex systems such as robotics control. A new deep neural network called DeepONet can lean various mathematical operators with small generalization error.

Graph Convolutional Networks for Hyperspectral Image Classification

Abstract: Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classification due to their ability to capture spatial–spectral feature representations. Nevertheless, their ability in modeling relations between the samples remains limited. Beyond the limitations of grid sampling, graph convolutional networks (GCNs) have been recently proposed and successfully applied in irregular (or nongrid) data representation and analysis. In this article, we thoroughly investigate CNNs and GCNs (qualitatively and quantitatively) in terms of HS image classification. Due to the construction of the adjacency matrix on all the data, traditional GCNs usually suffer from a huge computational cost, particularly in large-scale remote sensing (RS) problems. To this end, we develop a new minibatch GCN (called miniGCN hereinafter), which allows to train large-scale GCNs in a minibatch fashion. More significantly, our miniGCN is capable of inferring out-of-sample data without retraining networks and improving classification performance. Furthermore, as CNNs and GCNs can extract different types of HS features, an intuitive solution to break the performance bottleneck of a single model is to fuse them. Since miniGCNs can perform batchwise network training (enabling the combination of CNNs and GCNs), we explore three fusion strategies: additive fusion, elementwise multiplicative fusion, and concatenation fusion to measure the obtained performance gain. Extensive experiments, conducted on three HS data sets, demonstrate the advantages of miniGCNs over GCNs and the superiority of the tested fusion strategies with regard to the single CNN or GCN models. The codes of this work will be available at https://github.com/danfenghong/IEEE_TGRS_GCN for the sake of reproducibility.

KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis.

Abstract: Gene set enrichment (GSE) analysis plays an essential role in extracting biological insight from genome-scale experiments. ORA (overrepresentation analysis), FCS (functional class scoring), and PT (pathway topology) approaches are three generations of GSE methods along the timeline of development. Previous versions of KOBAS provided services based on just the ORA method. Here we presented version 3.0 of KOBAS, which is named KOBAS-i (short for KOBAS intelligent version). It introduced a novel machine learning-based method we published earlier, CGPS, which incorporates seven FCS tools and two PT tools into a single ensemble score and intelligently prioritizes the relevant biological pathways. In addition, KOBAS has expanded the downstream exploratory visualization for selecting and understanding the enriched results. The tool constructs a novel view of cirFunMap, which presents different enriched terms and their correlations in a landscape. Finally, based on the previous version's framework, KOBAS increased the number of supported species from 1327 to 5944. For an easier local run, it also provides a prebuilt Docker image that requires no installation, as a supplementary to the source code version. KOBAS can be freely accessed at http://kobas.cbi.pku.edu.cn, and a mirror site is available at http://bioinfo.org/kobas.

MicroRNAs and complex diseases: from experimental results to computational models.

Abstract: Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules with a variety of biological functions. Studies have shown that circRNAs are involved in a variety of biological processes and play an important role in the development of various complex diseases, so the identification of circRNA-disease associations would contribute to the diagnosis and treatment of diseases. In this review, we summarize the discovery, classifications and functions of circRNAs and introduce four important diseases associated with circRNAs. Then, we list some significant and publicly accessible databases containing comprehensive annotation resources of circRNAs and experimentally validated circRNA-disease associations. Next, we introduce some state-of-the-art computational models for predicting novel circRNA-disease associations and divide them into two categories, namely network algorithm-based and machine learning-based models. Subsequently, several evaluation methods of prediction performance of these computational models are summarized. Finally, we analyze the advantages and disadvantages of different types of computational models and provide some suggestions to promote the development of circRNA-disease association identification from the perspective of the construction of new computational models and the accumulation of circRNA-related data.

State of the Art and Prospects for Halide Perovskite Nanocrystals.

Abstract: Metal-halide perovskites have rapidly emerged as one of the most promising materials of the 21st century, with many exciting properties and great potential for a broad range of applications, from photovoltaics to optoelectronics and photocatalysis. The ease with which metal-halide perovskites can be synthesized in the form of brightly luminescent colloidal nanocrystals, as well as their tunable and intriguing optical and electronic properties, has attracted researchers from different disciplines of science and technology. In the last few years, there has been a significant progress in the shape-controlled synthesis of perovskite nanocrystals and understanding of their properties and applications. In this comprehensive review, researchers having expertise in different fields (chemistry, physics, and device engineering) of metal-halide perovskite nanocrystals have joined together to provide a state of the art overview and future prospects of metal-halide perovskite nanocrystal research.

Agriculture Development, Pesticide Application and Its Impact on the Environment.

Abstract: Pesticides are indispensable in agricultural production. They have been used by farmers to control weeds and insects, and their remarkable increases in agricultural products have been reported. The increase in the world's population in the 20th century could not have been possible without a parallel increase in food production. About one-third of agricultural products are produced depending on the application of pesticides. Without the use of pesticides, there would be a 78% loss of fruit production, a 54% loss of vegetable production, and a 32% loss of cereal production. Therefore, pesticides play a critical role in reducing diseases and increasing crop yields worldwide. Thus, it is essential to discuss the agricultural development process; the historical perspective, types and specific uses of pesticides; and pesticide behavior, its contamination, and adverse effects on the natural environment. The review study indicates that agricultural development has a long history in many places around the world. The history of pesticide use can be divided into three periods of time. Pesticides are classified by different classification terms such as chemical classes, functional groups, modes of action, and toxicity. Pesticides are used to kill pests and control weeds using chemical ingredients; hence, they can also be toxic to other organisms, including birds, fish, beneficial insects, and non-target plants, as well as air, water, soil, and crops. Moreover, pesticide contamination moves away from the target plants, resulting in environmental pollution. Such chemical residues impact human health through environmental and food contamination. In addition, climate change-related factors also impact on pesticide application and result in increased pesticide usage and pesticide pollution. Therefore, this review will provide the scientific information necessary for pesticide application and management in the future.

The role of m6A modification in the biological functions and diseases.

Abstract: N6-methyladenosine (m6A) is the most prevalent, abundant and conserved internal cotranscriptional modification in eukaryotic RNAs, especially within higher eukaryotic cells. m6A modification is modified by the m6A methyltransferases, or writers, such as METTL3/14/16, RBM15/15B, ZC3H3, VIRMA, CBLL1, WTAP, and KIAA1429, and, removed by the demethylases, or erasers, including FTO and ALKBH5. It is recognized by m6A-binding proteins YTHDF1/2/3, YTHDC1/2 IGF2BP1/2/3 and HNRNPA2B1, also known as “readers”. Recent studies have shown that m6A RNA modification plays essential role in both physiological and pathological conditions, especially in the initiation and progression of different types of human cancers. In this review, we discuss how m6A RNA methylation influences both the physiological and pathological progressions of hematopoietic, central nervous and reproductive systems. We will mainly focus on recent progress in identifying the biological functions and the underlying molecular mechanisms of m6A RNA methylation, its regulators and downstream target genes, during cancer progression in above systems. We propose that m6A RNA methylation process offer potential targets for cancer therapy in the future.

Pattern-recognition receptors are required for NLR-mediated plant immunity

Abstract: The plant immune system is fundamental for plant survival in natural ecosystems and for productivity in crop fields. Substantial evidence supports the prevailing notion that plants possess a two-tiered innate immune system, called pattern-triggered immunity (PTI) and effector-triggered immunity (ETI). PTI is triggered by microbial patterns via cell surface-localized pattern-recognition receptors (PRRs), whereas ETI is activated by pathogen effector proteins via predominantly intracellularly localized receptors called nucleotide-binding, leucine-rich repeat receptors (NLRs)1–4. PTI and ETI are initiated by distinct activation mechanisms and involve different early signalling cascades5,6. Here we show that Arabidopsis PRR and PRR co-receptor mutants—fls2 efr cerk1 and bak1 bkk1 cerk1 triple mutants—are markedly impaired in ETI responses when challenged with incompatible Pseudomonas syrinage bacteria. We further show that the production of reactive oxygen species by the NADPH oxidase RBOHD is a critical early signalling event connecting PRR- and NLR-mediated immunity, and that the receptor-like cytoplasmic kinase BIK1 is necessary for full activation of RBOHD, gene expression and bacterial resistance during ETI. Moreover, NLR signalling rapidly augments the transcript and/or protein levels of key PTI components. Our study supports a revised model in which potentiation of PTI is an indispensable component of ETI during bacterial infection. This revised model conceptually unites two major immune signalling cascades in plants and mechanistically explains some of the long-observed similarities in downstream defence outputs between PTI and ETI. Bacteria elicit two distinct immune responses in Arabidopsis thaliana, mediated by diverse signalling receptors but working in a synergistic manner.

Borates: A Rich Source for Optical Materials.

Abstract: The primary goal of this review is to present a clear chemical perspective of borates in order to stimulate and facilitate the discovery of new borate-based optical materials. These materials, whic...

Abiotic stress responses in plants.

Abstract: Plants cannot move, so they must endure abiotic stresses such as drought, salinity and extreme temperatures. These stressors greatly limit the distribution of plants, alter their growth and development, and reduce crop productivity. Recent progress in our understanding of the molecular mechanisms underlying the responses of plants to abiotic stresses emphasizes their multilevel nature; multiple processes are involved, including sensing, signalling, transcription, transcript processing, translation and post-translational protein modifications. This improved knowledge can be used to boost crop productivity and agricultural sustainability through genetic, chemical and microbial approaches.

Electroceramics for High-Energy Density Capacitors: Current Status and Future Perspectives

Abstract: Materials exhibiting high energy/power density are currently needed to meet the growing demand of portable electronics, electric vehicles and large-scale energy storage devices. The highest energy densities are achieved for fuel cells, batteries, and supercapacitors, but conventional dielectric capacitors are receiving increased attention for pulsed power applications due to their high power density and their fast charge-discharge speed. The key to high energy density in dielectric capacitors is a large maximum but small remanent (zero in the case of linear dielectrics) polarization and a high electric breakdown strength. Polymer dielectric capacitors offer high power/energy density for applications at room temperature, but above 100 °C they are unreliable and suffer from dielectric breakdown. For high-temperature applications, therefore, dielectric ceramics are the only feasible alternative. Lead-based ceramics such as La-doped lead zirconate titanate exhibit good energy storage properties, but their toxicity raises concern over their use in consumer applications, where capacitors are exclusively lead free. Lead-free compositions with superior power density are thus required. In this paper, we introduce the fundamental principles of energy storage in dielectrics. We discuss key factors to improve energy storage properties such as the control of local structure, phase assemblage, dielectric layer thickness, microstructure, conductivity, and electrical homogeneity through the choice of base systems, dopants, and alloying additions, followed by a comprehensive review of the state-of-the-art. Finally, we comment on the future requirements for new materials in high power/energy density capacitor applications.

Heterostructured materials: superior properties from hetero-zone interaction

Abstract: Heterostructured materials are an emerging class of materials with superior performances that are unattainable by their conventional homogeneous counterparts. They consist of heterogeneous zones wi...

Extra storage capacity in transition metal oxide lithium-ion batteries revealed by in situ magnetometry.

Abstract: In lithium-ion batteries (LIBs), many promising electrodes that are based on transition metal oxides exhibit anomalously high storage capacities beyond their theoretical values. Although this phenomenon has been widely reported, the underlying physicochemical mechanism in such materials remains elusive and is still a matter of debate. In this work, we use in situ magnetometry to demonstrate the existence of strong surface capacitance on metal nanoparticles, and to show that a large number of spin-polarized electrons can be stored in the already-reduced metallic nanoparticles (that are formed during discharge at low potentials in transition metal oxide LIBs), which is consistent with a space charge mechanism. Through quantification of the surface capacitance by the variation in magnetism, we further show that this charge capacity of the surface is the dominant source of the extra capacity in the Fe3O4/Li model system, and that it also exists in CoO, NiO, FeF2 and Fe2N systems. The space charge mechanism revealed by in situ magnetometry can therefore be generalized to a broad range of transition metal compounds for which a large electron density of states is accessible, and provides pivotal guidance for creating advanced energy storage systems.

Electrocatalysis for CO2 conversion: from fundamentals to value-added products

Abstract: The continuously increasing CO2 released from human activities poses a great threat to human survival by fluctuating global climate and disturbing carbon balance among the four reservoirs of the biosphere, earth, air, and water. Converting CO2 to value-added feedstocks via electrocatalysis of the CO2 reduction reaction (CO2RR) has been regarded as one of the most attractive routes to re-balance the carbon cycle, thanks to its multiple advantages of mild operating conditions, easy handling, tunable products and the potential of synergy with the rapidly increasing renewable energy (i.e., solar, wind). Instead of focusing on a special topic of electrocatalysts for the CO2RR that have been extensively reviewed elsewhere, we herein present a rather comprehensive review of the recent research progress, in the view of associated value-added products upon selective electrocatalytic CO2 conversion. We initially provide an overview of the history and the fundamental science regarding the electrocatalytic CO2RR, with a special introduction to the design, preparation, and performance evaluation of electrocatalysts, the factors influencing the CO2RR, and the associated theoretical calculations. Emphasis will then be given to the emerging trends of selective electrocatalytic conversion of CO2 into a variety of value-added products. The structure-performance relationship and mechanism will also be discussed and investigated. The outlooks for CO2 electrocatalysis, including the challenges and opportunities in the development of new electrocatalysts, electrolyzers, the recently rising operando fundamental studies, and the feasibility of industrial applications are finally summarized.

Stabilizing black-phase formamidinium perovskite formation at room temperature and high humidity.

Abstract: The stabilization of black-phase formamidinium lead iodide (α-FAPbI3) perovskite under various environmental conditions is considered necessary for solar cells. However, challenges remain regarding the temperature sensitivity of α-FAPbI3 and the requirements for strict humidity control in its processing. Here we report the synthesis of stable α-FAPbI3, regardless of humidity and temperature, based on a vertically aligned lead iodide thin film grown from an ionic liquid, methylamine formate. The vertically grown structure has numerous nanometer-scale ion channels that facilitate the permeation of formamidinium iodide into the lead iodide thin films for fast and robust transformation to α-FAPbI3 A solar cell with a power-conversion efficiency of 24.1% was achieved. The unencapsulated cells retain 80 and 90% of their initial efficiencies for 500 hours at 85°C and continuous light stress, respectively.

High-entropy-stabilized chalcogenides with high thermoelectric performance.

Abstract: Thermoelectric technology generates electricity from waste heat, but one bottleneck for wider use is the performance of thermoelectric materials. Manipulating the configurational entropy of a material by introducing different atomic species can tune phase composition and extend the performance optimization space. We enhanced the figure of merit (zT) value to 1.8 at 900 kelvin in an n-type PbSe-based high-entropy material formed by entropy-driven structural stabilization. The largely distorted lattices in this high-entropy system caused unusual shear strains, which provided strong phonon scattering to largely lower lattice thermal conductivity. The thermoelectric conversion efficiency was 12.3% at temperature difference ΔT = 507 kelvin, for the fabricated segmented module based on this n-type high-entropy material. Our demonstration provides a paradigm to improve thermoelectric performance for high-entropy thermoelectric materials through entropy engineering.

11 TOPS photonic convolutional accelerator for optical neural networks

Abstract: Convolutional neural networks, inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to provide greatly reduced parametric complexity and to enhance the accuracy of prediction. They are of great interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis1–7. Optical neural networks offer the promise of dramatically accelerating computing speed using the broad optical bandwidths available. Here we demonstrate a universal optical vector convolutional accelerator operating at more than ten TOPS (trillions (1012) of operations per second, or tera-ops per second), generating convolutions of images with 250,000 pixels—sufficiently large for facial image recognition. We use the same hardware to sequentially form an optical convolutional neural network with ten output neurons, achieving successful recognition of handwritten digit images at 88 per cent accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as autonomous vehicles and real-time video recognition. An optical vector convolutional accelerator operating at more than ten trillion operations per second is used to create an optical convolutional neural network that can successfully recognize handwritten digit images with 88 per cent accuracy.