The 100 most-cited articles in Nature Communications, 2021

Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer ( http://www.cellchat.org/ ) will help discover novel intercellular communications…

The ice arches that usually develop at the northern and southern ends of Nares Strait play an important role in modulating the export of Arctic Ocean multi-year sea ice. The Arctic Ocean is evolving towards an ice pack that is younger, thinner, and more mobile and the fate of its multi-year ice is becoming of increasing interest. Here, we use sea ice motion retrievals from Sentinel-1 imagery to report on the recent behavior of these ice arches and the associated ice fluxes. We show that the duration of arch formation has decreased over the past 20 years, while the ice area and volume fluxes along Nares Strait have both increased. These results suggest that a transition is underway towards a state where the formation of these arches will become atypical with a concomitant increase in the export of multi-year ice accelerating the transition towards a younger and thinner Arctic ice pack.

Urbanization and climate change are together exacerbating water scarcity-where water demand exceeds availability-for the world's cities. We quantify global urban water scarcity in 2016 and 2050 under four socioeconomic and climate change scenarios, and explored potential solutions. Here we show the global urban population facing water scarcity is projected to increase from 933 million (one third of global urban population) in 2016 to 1.693-2.373 billion people (one third to nearly half of global urban population) in 2050, with India projected to be most severely affected in terms of growth in water-scarce urban population (increase of 153-422 million people). The number of large cities exposed to water scarcity is projected to increase from 193 to 193-284, including 10-20 megacities. More than two thirds of water-scarce cities can relieve water scarcity by infrastructure investment, but the potentially significant environmental trade-offs associated with large-scale water scarcity solutions must be guarded against.

Grid cells are part of a widespread network which supports navigation and spatial memory. Stable grid patterns appear late in development, in concert with extracellular matrix aggregates termed perineuronal nets (PNNs) that condense around inhibitory neurons. It has been suggested that PNNs stabilize synaptic connections and long-term memories, but their role in the grid cell network remains elusive. We show that removal of PNNs leads to lower inhibitory spiking activity, and reduces grid cells' ability to create stable representations of a novel environment. Furthermore, in animals with disrupted PNNs, exposure to a novel arena corrupted the spatiotemporal relationships within grid cell modules, and the stored representations of a familiar arena. Finally, we show that PNN removal in entorhinal cortex distorted spatial representations in downstream hippocampal neurons. Together this work suggests that PNNs provide a key stabilizing element for the grid cell network.

Abstract Electrochemically converting nitrate, a widespread water pollutant, back to valuable ammonia is a green and delocalized route for ammonia synthesis, and can be an appealing and supplementary alternative to the Haber-Bosch process. However, as there are other nitrate reduction pathways present, selectively guiding the reaction pathway towards ammonia is currently challenged by the lack of efficient catalysts. Here we report a selective and active nitrate reduction to ammonia on Fe single atom catalyst, with a maximal ammonia Faradaic efficiency of ~ 75% and a yield rate of up to ~ 20,000 μg h −1 mg cat. −1 (0.46 mmol h −1 cm −2 ). Our Fe single atom catalyst can effectively prevent the N-N coupling step required for N 2 due to the lack of neighboring metal sites, promoting ammonia product selectivity. Density functional theory calculations reveal the reaction mechanisms and the potential limiting steps for nitrate reduction on atomically dispersed Fe sites.

Quantifying the dynamics of land use change is critical in tackling global societal challenges such as food security, climate change, and biodiversity loss. Here we analyse the dynamics of global land use change at an unprecedented spatial resolution by combining multiple open data streams (remote sensing, reconstructions and statistics) to create the HIstoric Land Dynamics Assessment + (HILDA +). We estimate that land use change has affected almost a third (32%) of the global land area in just six decades (1960-2019) and, thus, is around four times greater in extent than previously estimated from long-term land change assessments. We also identify geographically diverging land use change processes, with afforestation and cropland abandonment in the Global North and deforestation and agricultural expansion in the South. Here, we show that observed phases of accelerating (~1960-2005) and decelerating (2006-2019) land use change can be explained by the effects of global trade on agricultural production.

Identifying and understanding COVID-19 vaccine hesitancy within distinct populations may aid future public health messaging. Using nationally representative data from the general adult populations of Ireland (N = 1041) and the United Kingdom (UK; N = 2025), we found that vaccine hesitancy/resistance was evident for 35% and 31% of these populations respectively. Vaccine hesitant/resistant respondents in Ireland and the UK differed on a number of sociodemographic and health-related variables but were similar across a broad array of psychological constructs. In both populations, those resistant to a COVID-19 vaccine were less likely to obtain information about the pandemic from traditional and authoritative sources and had similar levels of mistrust in these sources compared to vaccine accepting respondents. Given the geographical proximity and socio-economic similarity of the populations studied, it is not possible to generalize findings to other populations, however, the methodology employed here may be useful to those wishing to understand COVID-19 vaccine hesitancy elsewhere.

Abstract Construction of Z-scheme heterostructure is of momentous significance for realizing efficient photocatalytic water splitting. However, the consciously modulate of Z-scheme charge transfer is still a great challenge. Herein, interfacial Mo-S bond and internal electric field modulated Z-Scheme heterostructure composed by sulfur vacancies-rich ZnIn2S4 (Vs-ZIS) and MoSe2 was rationally fabricated for efficient photocatalytic hydrogen evolution. Systematic investigations reveal that Mo-S bond and internal electric field induce the Z-scheme charge transfer mechanism as confirmed by the SPS and DMPO spin-trapping EPR spectra. Under the intense synergy among the Mo-S bond, internal electric field and S-vacancies, the optimized photocatalyst exhibits ultrahigh hydrogen evolution rate of 63.21 mmol∙g-1·h-1 with an apparent quantum yield of 76.48% at 420 nm monochromatic light, which is about 18.8-fold of the pristine ZIS. This work affords a new inspiration on consciously modulating Z-scheme charge transfer by atomic-level interface control and internal electric field to signally…

Klebsiella pneumoniae is a leading cause of antimicrobial-resistant (AMR) healthcare-associated infections, neonatal sepsis and community-acquired liver abscess, and is associated with chronic intestinal diseases. Its diversity and complex population structure pose challenges for analysis and interpretation of K. pneumoniae genome data. Here we introduce Kleborate, a tool for analysing genomes of K. pneumoniae and its associated species complex, which consolidates interrogation of key features of proven clinical importance. Kleborate provides a framework to support genomic surveillance and epidemiology in research, clinical and public health settings. To demonstrate its utility we apply Kleborate to analyse publicly available Klebsiella genomes, including clinical isolates from a pan-European study of carbapenemase-producing Klebsiella, highlighting global trends in AMR and virulence as examples of what could be achieved by applying this genomic framework within more systematic genomic surveillance efforts. We also demonstrate the application of Kleborate to detect and type K.…

Variational quantum algorithms (VQAs) optimize the parameters θ of a parametrized quantum circuit V(θ) to minimize a cost function C. While VQAs may enable practical applications of noisy quantum computers, they are nevertheless heuristic methods with unproven scaling. Here, we rigorously prove two results, assuming V(θ) is an alternating layered ansatz composed of blocks forming local 2-designs. Our first result states that defining C in terms of global observables leads to exponentially vanishing gradients (i.e., barren plateaus) even when V(θ) is shallow. Hence, several VQAs in the literature must revise their proposed costs. On the other hand, our second result states that defining C with local observables leads to at worst a polynomially vanishing gradient, so long as the depth of V(θ) is [Formula: see text]. Our results establish a connection between locality and trainability. We illustrate these ideas with large-scale simulations, up to 100 qubits, of a quantum autoencoder implementation.

Soil salinization has become one of the major environmental and socioeconomic issues globally and this is expected to be exacerbated further with projected climatic change. Determining how climate change influences the dynamics of naturally-occurring soil salinization has scarcely been addressed due to highly complex processes influencing salinization. This paper sets out to address this long-standing challenge by developing data-driven models capable of predicting primary (naturally-occurring) soil salinity and its variations in the world's drylands up to the year 2100 under changing climate. Analysis of the future predictions made here identifies the dryland areas of South America, southern and western Australia, Mexico, southwest United States, and South Africa as the salinization hotspots. Conversely, we project a decrease in the soil salinity of the drylands in the northwest United States, the Horn of Africa, Eastern Europe, Turkmenistan, and west Kazakhstan in response to climate change over the same period.

Differential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulations. While many statistical methods are available to identify differentially expressed genes, the principles that distinguish these methods and their performance remain unclear. Here, we show that the relative performance of these methods is contingent on their ability to account for variation between biological replicates. Methods that ignore this inevitable variation are biased and prone to false discoveries. Indeed, the most widely used methods can discover hundreds of differentially expressed genes in the absence of biological differences. To exemplify these principles, we exposed true and false discoveries of differentially expressed genes in the injured mouse spinal cord.

Abstract As low-cost electrocatalysts for oxygen reduction reaction applied to fuel cells and metal-air batteries, atomic-dispersed transition metal-nitrogen-carbon materials are emerging, but the genuine mechanism thereof is still arguable. Herein, by rational design and synthesis of dual-metal atomically dispersed Fe,Mn/N-C catalyst as model object, we unravel that the O2 reduction preferentially takes place on FeIII in the FeN4 /C system with intermediate spin state which possesses one eg electron (t2g4eg1) readily penetrating the antibonding π-orbital of oxygen. Both magnetic measurements and theoretical calculation reveal that the adjacent atomically dispersed Mn-N moieties can effectively activate the FeIII sites by both spin-state transition and electronic modulation, rendering the excellent ORR performances of Fe,Mn/N-C in both alkaline and acidic media (halfwave positionals are 0.928 V in 0.1 M KOH, and 0.804 V in 0.1 M HClO4), and good durability, which outperforms and has almost the same activity of commercial Pt/C, respectively. In addition, it presents a superior power…

Abstract Antibiotic resistance genes (ARGs) are widespread among bacteria. However, not all ARGs pose serious threats to public health, highlighting the importance of identifying those that are high-risk. Here, we developed an ‘omics-based’ framework to evaluate ARG risk considering human-associated-enrichment, gene mobility, and host pathogenicity. Our framework classifies human-associated, mobile ARGs (3.6% of all ARGs) as the highest risk, which we further differentiate as ‘current threats’ (Rank I; 3%) - already present among pathogens - and ‘future threats’ (Rank II; 0.6%) - novel resistance emerging from non-pathogens. Our framework identified 73 ‘current threat’ ARG families. Of these, 35 were among the 37 high-risk ARGs proposed by the World Health Organization and other literature; the remaining 38 were significantly enriched in hospital plasmids. By evaluating all pathogen genomes released since framework construction, we confirmed that ARGs that recently transferred into pathogens were significantly enriched in Rank II (‘future threats’). Lastly, we applied the framework…

Skeletal muscle is the protein reservoir of our body and an important regulator of glucose and lipid homeostasis. Consequently, the growth or the loss of muscle mass can influence general metabolism, locomotion, eating and respiration. Therefore, it is not surprising that excessive muscle loss is a bad prognostic index of a variety of diseases ranging from cancer, organ failure, infections and unhealthy ageing. Muscle function is influenced by different quality systems that regulate the function of contractile proteins and organelles. These systems are controlled by transcriptional dependent programs that adapt muscle cells to environmental and nutritional clues. Mechanical, oxidative, nutritional and energy stresses, as well as growth factors or cytokines modulate signaling pathways that, ultimately, converge on protein and organelle turnover. Novel insights that control and orchestrate such complex network are continuously emerging and will be summarized in this review. Understanding the mechanisms that control muscle mass will provide therapeutic targets for the treatment of…

The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures. It outperforms current leading methods and sequence-based Convolutional Neural Networks and scales to the size of current sequence repositories. Augmenting the training set of experimental structures with homology models allows us to significantly expand the number of predictable functions. DeepFRI has significant de-noising capability, with only a minor drop in performance when experimental structures are replaced by protein models. Class activation mapping allows function predictions at an unprecedented resolution, allowing site-specific annotations at the residue-level in an automated manner. We show the utility and high performance of our method by annotating structures from the PDB and SWISS-MODEL, making several new…

ABSTRACT Despite their important and multiple roles in intercellular communications, the different populations of extracellular vesicles (EVs) and their secretion mechanisms are not fully characterized yet. In particular, how and to what extent EVs form either as intraluminal vesicles of endocytic compartments (exosomes), or at the plasma membrane (ectosomes) remains unclear. We followed in HeLa cells the intracellular trafficking of the EV markers CD9 and CD63 from the endoplasmic reticulum to their residency compartment and identified transient co-localization both at the plasma membrane (PM) and in endosomes, before they finally segregate. CD9 was more abundantly released in EVs than CD63. However, when forcing expression of CD63 at the PM, by mutating its lysosome-addressing motive, its secretion in EVs was increased. Thus, in HeLa cells, small ectosomes are more prominently released than exosomes. By comparative proteomic analysis, we identified a few surface proteins likely specific of either exosomes (e.g. LAMP1) or ectosomes (e.g. BSG, SLC3A2), based on their known…

Abstract It is unclear whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can directly infect human kidney, thus leading to acute kidney injury (AKI). Here, we perform a retrospective analysis of clinical parameters from 85 patients with laboratory-confirmed coronavirus disease 2019 (COVID-19); moreover, kidney histopathology from six additional COVID-19 patients with post-mortem examinations was performed. We find that 27% (23/85) of patients exhibited AKI. The elderly patients and cases with comorbidities (hypertension and heart failure) are more prone to develop AKI. Haematoxylin & eosin staining shows that the kidneys from COVID-19 autopsies have moderate to severe tubular damage. In situ hybridization assays illustrate that viral RNA accumulates in tubules. Immunohistochemistry shows nucleocapsid and spike protein deposits in the tubules, and immunofluorescence double staining shows that both antigens are restricted to the angiotensin converting enzyme-II-positive tubules. SARS-CoV-2 infection triggers the expression of hypoxic damage-associated molecules,…

Tuning metal-support interaction has been considered as an effective approach to modulate the electronic structure and catalytic activity of supported metal catalysts. At the atomic level, the understanding of the structure-activity relationship still remains obscure in heterogeneous catalysis, such as the conversion of water (alkaline) or hydronium ions (acid) to hydrogen (hydrogen evolution reaction, HER). Here, we reveal that the fine control over the oxidation states of single-atom Pt catalysts through electronic metal-support interaction significantly modulates the catalytic activities in either acidic or alkaline HER. Combined with detailed spectroscopic and electrochemical characterizations, the structure-activity relationship is established by correlating the acidic/alkaline HER activity with the average oxidation state of single-atom Pt and the Pt-H/Pt-OH interaction. This study sheds light on the atomic-level mechanistic understanding of acidic and alkaline HER, and further provides guidelines for the rational design of high-performance single-atom catalysts.

The use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies. However, machine learning tasks where data is provided can be considerably different than commonly studied computational tasks. In this work, we show that some problems that are classically hard to compute can be easily predicted by classical machines learning from data. Using rigorous prediction error bounds as a foundation, we develop a methodology for assessing potential quantum advantage in learning tasks. The bounds are tight asymptotically and empirically predictive for a wide range of learning models. These constructions explain numerical results showing that with the help of data, classical machine learning models can be competitive with quantum models even if they are tailored to quantum problems. We then propose a projected quantum model that provides a simple and rigorous quantum speed-up for a learning problem in the fault-tolerant regime. For near-term implementations, we demonstrate a significant prediction advantage over some classical models…

Urban heat stress poses a major risk to public health. Case studies of individual cities suggest that heat exposure, like other environmental stressors, may be unequally distributed across income groups. There is little evidence, however, as to whether such disparities are pervasive. We combine surface urban heat island (SUHI) data, a proxy for isolating the urban contribution to additional heat exposure in built environments, with census tract-level demographic data to answer these questions for summer days, when heat exposure is likely to be at a maximum. We find that the average person of color lives in a census tract with higher SUHI intensity than non-Hispanic whites in all but 6 of the 175 largest urbanized areas in the continental United States. A similar pattern emerges for people living in households below the poverty line relative to those at more than two times the poverty line.

Abstract All-day passive radiative cooling has recently attracted tremendous interest by reflecting sunlight and radiating heat to the ultracold outer space. While some progress has been made, it still remains big challenge in fabricating highly efficient and low-cost radiative coolers for all-day and all-climates. Herein, we report a hierarchically structured polymethyl methacrylate (PMMA) film with a micropore array combined with random nanopores for highly efficient day- and nighttime passive radiative cooling. This hierarchically porous array PMMA film exhibits sufficiently high solar reflectance (0.95) and superior longwave infrared thermal emittance (0.98) and realizes subambient cooling of ~8.2 °C during the night and ~6.0 °C to ~8.9 °C during midday with an average cooling power of ~85 W/m2 under solar intensity of ~900 W/m2, and promisingly ~5.5 °C even under solar intensity of ~930 W/m2 and relative humidity of ~64% in hot and moist climate. The micropores and nanopores in the polymer film play crucial roles in enhancing the solar reflectance and thermal emittance.

ABSTRACT Background Long-term shedding of viral RNA in COVID-19 prevents timely discharge from the hospital or de-escalation of infection prevention and control practices. Key questions are the duration and determinants of infectious virus shedding. We assessed these questions using virus cultures of respiratory tract samples from hospitalized COVID-19 patients as a proxy for infectious virus shedding. Methods Clinical and virological data were obtained from 129 hospitalized COVID-19 patients (89 intensive care, 40 medium care). Generalized estimating equations were used to identify if viral RNA load, detection of viral subgenomic RNA, serum neutralizing antibody response, duration of symptoms, or immunocompromised status were predictive for a positive virus culture. Findings Infectious virus shedding was detected in 23 of the 129 patients (17,8%). The median duration of shedding was 8 days post onset of symptoms (IQR 5 – 11) and the probability of detecting infectious virus dropped below 5% after 15,2 days post onset of symptoms (95% confidence interval (CI) 13,4 – 17,2).…

The largest terrestrial organic carbon pool, carbon in soils, is regulated by an intricate connection between plant carbon inputs, microbial activity, and the soil matrix. This is manifested by how microorganisms, the key players in transforming plant-derived carbon into soil organic carbon, are controlled by the physical arrangement of organic and inorganic soil particles. Here we conduct an incubation of isotopically labelled litter to study effects of soil structure on the fate of litter-derived organic matter. While microbial activity and fungal growth is enhanced in the coarser-textured soil, we show that occlusion of organic matter into aggregates and formation of organo-mineral associations occur concurrently on fresh litter surfaces regardless of soil structure. These two mechanisms-the two most prominent processes contributing to the persistence of organic matter-occur directly at plant-soil interfaces, where surfaces of litter constitute a nucleus in the build-up of soil carbon persistence. We extend the notion of plant litter, i.e., particulate organic matter, from solely…

Lung cancer is a highly heterogeneous disease. Cancer cells and cells within the tumor microenvironment together determine disease progression, as well as response to or escape from treatment. To map the cell type-specific transcriptome landscape of cancer cells and their tumor microenvironment in advanced non-small cell lung cancer (NSCLC), we analyze 42 tissue biopsy samples from stage III/IV NSCLC patients by single cell RNA sequencing and present the large scale, single cell resolution profiles of advanced NSCLCs. In addition to cell types described in previous single cell studies of early stage lung cancer, we are able to identify rare cell types in tumors such as follicular dendritic cells and T helper 17 cells. Tumors from different patients display large heterogeneity in cellular composition, chromosomal structure, developmental trajectory, intercellular signaling network and phenotype dominance. Our study also reveals a correlation of tumor heterogeneity with tumor associated neutrophils, which might help to shed light on their function in NSCLC.

Abstract Rational design of single atom catalyst is critical for efficient sustainable energy conversion. However, the atomic-level control of active sites is essential for electrocatalytic materials in alkaline electrolyte. Moreover, well-defined surface structures lead to in-depth understanding of catalytic mechanisms. Herein, we report a single-atomic-site ruthenium stabilized on defective nickel-iron layered double hydroxide nanosheets (Ru 1 /D-NiFe LDH). Under precise regulation of local coordination environments of catalytically active sites and the existence of the defects, Ru 1 /D-NiFe LDH delivers an ultralow overpotential of 18 mV at 10 mA cm −2 for hydrogen evolution reaction, surpassing the commercial Pt/C catalyst. Density functional theory calculations reveal that Ru 1 /D-NiFe LDH optimizes the adsorption energies of intermediates for hydrogen evolution reaction and promotes the O–O coupling at a Ru–O active site for oxygen evolution reaction. The Ru 1 /D-NiFe LDH as an ideal model reveals superior water splitting performance with potential for the development of…

Abstract Plastic wastes represent a largely untapped resource for manufacturing chemicals and fuels, particularly considering their environmental and biological threats. Here we report electrocatalytic upcycling of polyethylene terephthalate (PET) plastic to valuable commodity chemicals (potassium diformate and terephthalic acid) and H 2 fuel. Preliminary techno-economic analysis suggests the profitability of this process when the ethylene glycol (EG) component of PET is selectively electrooxidized to formate (>80% selectivity) at high current density (>100 mA cm −2 ). A nickel-modified cobalt phosphide (CoNi 0.25 P) electrocatalyst is developed to achieve a current density of 500 mA cm −2 at 1.8 V in a membrane-electrode assembly reactor with >80% of Faradaic efficiency and selectivity to formate. Detailed characterizations reveal the in-situ evolution of CoNi 0.25 P catalyst into a low-crystalline metal oxy(hydroxide) as an active state during EG oxidation, which might be responsible for its advantageous performances. This work demonstrates a sustainable way to implement waste PET…

Variational Quantum Algorithms (VQAs) may be a path to quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) computers. A natural question is whether noise on NISQ devices places fundamental limitations on VQA performance. We rigorously prove a serious limitation for noisy VQAs, in that the noise causes the training landscape to have a barren plateau (i.e., vanishing gradient). Specifically, for the local Pauli noise considered, we prove that the gradient vanishes exponentially in the number of qubits n if the depth of the ansatz grows linearly with n. These noise-induced barren plateaus (NIBPs) are conceptually different from noise-free barren plateaus, which are linked to random parameter initialization. Our result is formulated for a generic ansatz that includes as special cases the Quantum Alternating Operator Ansatz and the Unitary Coupled Cluster Ansatz, among others. For the former, our numerical heuristics demonstrate the NIBP phenomenon for a realistic hardware noise model.

Emerging data demonstrate that the activity of immune cells can be modulated by microbial molecules. Here, we show that the short-chain fatty acids (SCFAs) pentanoate and butyrate enhance the anti-tumor activity of cytotoxic T lymphocytes (CTLs) and chimeric antigen receptor (CAR) T cells through metabolic and epigenetic reprograming. We show that in vitro treatment of CTLs and CAR T cells with pentanoate and butyrate increases the function of mTOR as a central cellular metabolic sensor, and inhibits class I histone deacetylase activity. This reprogramming results in elevated production of effector molecules such as CD25, IFN-γ and TNF-α, and significantly enhances the anti-tumor activity of antigen-specific CTLs and ROR1-targeting CAR T cells in syngeneic murine melanoma and pancreatic cancer models. Our data shed light onto microbial molecules that may be used for enhancing cellular anti-tumor immunity. Collectively, we identify pentanoate and butyrate as two SCFAs with therapeutic utility in the context of cellular cancer immunotherapy.

Abstract High-activity catalysts in alkaline media are compelling for durable hydrogen evolution reaction (HER). Single-atom catalysts (SACs) provide an effective approach to reduce the amount of precious metals meanwhile maintain their catalytic activity. However, the sluggish activity of SACs for water dissociation in alkaline media has extremely hampered advances in highly efficient hydrogen production. Herein, we developed a platinum SAC immobilized NiO/Ni heterostructure (PtSA-NiO/Ni) as an alkaline HER catalyst. It was found that Pt SACs coupled with NiO/Ni heterostructure enable the tunable binding abilities of hydroxyl ions (OH*) and hydrogen (H*), which efficiently tailors the water dissociation energy for accelerating alkaline HER. In particular, the dual active sites consisting of metallic Ni sites and O vacancies modified NiO sites near the interfaces of NiO/Ni in PtSA-NiO/Ni have preferred adsorption affinity for H* and OH* groups, respectively, which efficiently lowers the energy barrier of water dissociation of Volmer step. Moreover, anchoring Pt single atoms at the…

Abstract Rechargeable aqueous zinc-ion batteries (RZIBs) provide a promising complementarity to the existing lithium-ion batteries due to their low cost, non-toxicity and intrinsic safety. However, Zn anodes suffer from zinc dendrite growth and electrolyte corrosion, resulting in poor reversibility. Here, we develop an ultrathin, fluorinated two-dimensional porous covalent organic framework (FCOF) film as a protective layer on the Zn surface. The strong interaction between fluorine (F) in FCOF and Zn reduces the surface energy of the Zn (002) crystal plane, enabling the preferred growth of (002) planes during the electrodeposition process. As a result, Zn deposits show horizontally arranged platelet morphology with (002) orientations preferred. Furthermore, F-containing nanochannels facilitate ion transport and prevent electrolyte penetration for improving corrosion resistance. The FCOF@Zn symmetric cells achieve stability for over 750 h at an ultrahigh current density of 40 mA cm −2 . The high-areal-capacity full cells demonstrate hundreds of cycles under high Zn utilization…

Abstract The oxygen evolution reaction (OER) is the bottleneck that limits the energy efficiency of water-splitting. The process involves four electrons’ transfer and the generation of triplet state O 2 from singlet state species (OH - or H 2 O). Recently, explicit spin selection was described as a possible way to promote OER in alkaline conditions, but the specific spin-polarized kinetics remains unclear. Here, we report that by using ferromagnetic ordered catalysts as the spin polarizer for spin selection under a constant magnetic field, the OER can be enhanced. However, it does not applicable to non-ferromagnetic catalysts. We found that the spin polarization occurs at the first electron transfer step in OER, where coherent spin exchange happens between the ferromagnetic catalyst and the adsorbed oxygen species with fast kinetics, under the principle of spin angular momentum conservation. In the next three electron transfer steps, as the adsorbed O species adopt fixed spin direction, the OER electrons need to follow the Hund rule and Pauling exclusion principle, thus to carry out…

Abstract Developing efficient and stable earth-abundant electrocatalysts for acidic oxygen evolution reaction is the bottleneck for water splitting using proton exchange membrane electrolyzers. Here, we show that nanocrystalline CeO 2 in a Co 3 O 4 /CeO 2 nanocomposite can modify the redox properties of Co 3 O 4 and enhances its intrinsic oxygen evolution reaction activity, and combine electrochemical and structural characterizations including kinetic isotope effect, pH- and temperature-dependence, in situ Raman and ex situ X-ray absorption spectroscopy analyses to understand the origin. The local bonding environment of Co 3 O 4 can be modified after the introduction of nanocrystalline CeO 2 , which allows the Co III species to be easily oxidized into catalytically active Co IV species, bypassing the potential-determining surface reconstruction process. Co 3 O 4 /CeO 2 displays a comparable stability to Co 3 O 4 thus breaks the activity/stability tradeoff. This work not only establishes an efficient earth-abundant catalysts for acidic oxygen evolution reaction, but also provides…

Glutathione peroxidase 4 (GPX4) utilizes glutathione (GSH) to detoxify lipid peroxidation and plays an essential role in inhibiting ferroptosis. As a selenoprotein, GPX4 protein synthesis is highly inefficient and energetically costly. How cells coordinate GPX4 synthesis with nutrient availability remains unclear. In this study, we perform integrated proteomic and functional analyses to reveal that SLC7A11-mediated cystine uptake promotes not only GSH synthesis, but also GPX4 protein synthesis. Mechanistically, we find that cyst(e)ine activates mechanistic/mammalian target of rapamycin complex 1 (mTORC1) and promotes GPX4 protein synthesis at least partly through the Rag-mTORC1-4EBP signaling axis. We show that pharmacologic inhibition of mTORC1 decreases GPX4 protein levels, sensitizes cancer cells to ferroptosis, and synergizes with ferroptosis inducers to suppress patient-derived xenograft tumor growth in vivo. Together, our results reveal a regulatory mechanism to coordinate GPX4 protein synthesis with cyst(e)ine availability and suggest using combinatorial therapy of mTORC1…

Abstract The electronic transport behaviour of materials determines their suitability for technological applications. We develop a computationally efficient method for calculating carrier scattering rates of solid-state semiconductors and insulators from first principles inputs. The present method extends existing polar and non-polar electron-phonon coupling, ionized impurity, and piezoelectric scattering mechanisms formulated for isotropic band structures to support highly anisotropic materials. We test the formalism by calculating the electronic transport properties of 23 semiconductors, including the large 48 atom CH 3 NH 3 PbI 3 hybrid perovskite, and comparing the results against experimental measurements and more detailed scattering simulations. The Spearman rank coefficient of mobility against experiment ( r s = 0.93) improves significantly on results obtained using a constant relaxation time approximation ( r s = 0.52). We find our approach offers similar accuracy to state-of-the art methods at approximately 1/500th the computational cost, thus enabling its use in…

Abstract Wildfires are becoming more frequent and destructive in a changing climate. Fine particulate matter, PM 2.5 , in wildfire smoke adversely impacts human health. Recent toxicological studies suggest that wildfire particulate matter may be more toxic than equal doses of ambient PM 2.5 . Air quality regulations however assume that the toxicity of PM 2.5 does not vary across different sources of emission. Assessing whether PM 2.5 from wildfires is more or less harmful than PM 2.5 from other sources is a pressing public health concern. Here, we isolate the wildfire-specific PM 2.5 using a series of statistical approaches and exposure definitions. We found increases in respiratory hospitalizations ranging from 1.3 to up to 10% with a 10 μg m −3 increase in wildfire-specific PM 2.5 , compared to 0.67 to 1.3% associated with non-wildfire PM 2.5 . Our conclusions point to the need for air quality policies to consider the variability in PM 2.5 impacts on human health according to the sources of emission.

Abstract Circular RNAs (circRNA) are a class of covalently closed single-stranded RNAs that have been implicated in cancer progression. Here we identify circNDUFB2 to be downregulated in non-small cell lung cancer (NSCLC) tissues, and to negatively correlate with NSCLC malignant features. Elevated circNDUFB2 inhibits growth and metastasis of NSCLC cells. Mechanistically, circNDUFB2 functions as a scaffold to enhance the interaction between TRIM25 and IGF2BPs, a positive regulator of tumor progression and metastasis. This TRIM25/circNDUFB2/IGF2BPs ternary complex facilitates ubiquitination and degradation of IGF2BPs, with this effect enhanced by N6-methyladenosine (m6A) modification of circNDUFB2. Moreover, circNDUFB2 is also recognized by RIG-I to activate RIG-I-MAVS signaling cascades and recruit immune cells into the tumor microenvironment (TME). Our data thus provide evidences that circNDUFB2 participates in the degradation of IGF2BPs and activation of anti-tumor immunity during NSCLC progression via the modulation of both protein ubiquitination and degradation, as well as…

Abstract Developing high-performance electrocatalysts toward hydrogen evolution reaction is important for clean and sustainable hydrogen energy, yet still challenging. Herein, we report a single-atom strategy to construct excellent metal-organic frameworks (MOFs) hydrogen evolution reaction electrocatalyst (NiRu 0.13 -BDC) by introducing atomically dispersed Ru. Significantly, the obtained NiRu 0.13 -BDC exhibits outstanding hydrogen evolution activity in all pH, especially with a low overpotential of 36 mV at a current density of 10 mA cm −2 in 1 M phosphate buffered saline solution, which is comparable to commercial Pt/C. X-ray absorption fine structures and the density functional theory calculations reveal that introducing Ru single-atom can modulate electronic structure of metal center in the MOF, leading to the optimization of binding strength for H 2 O and H*, and the enhancement of HER performance. This work establishes single-atom strategy as an efficient approach to modulate electronic structure of MOFs for catalyst design.

Most climate change detection and attribution studies have focused on mean or extreme temperature or precipitation, neglecting to explore long-term changes in drought characteristics. Here we provide evidence that anthropogenic forcing has impacted interrelated meteorological drought characteristics. Using SPI and SPEI indices generated from an ensemble of 9 CMIP6 models (using 3 realizations per model), we show that the presence of anthropogenic forcing has increased the drought frequency, maximum drought duration, and maximum drought intensity experienced in large parts of the Americas, Africa, and Asia. Using individual greenhouse gas and anthropogenic aerosol forcings, we also highlight that regional balances between the two major forcings have contributed to the drying patterns detected in our results. Overall, we provide a comprehensive characterization of the influence of anthropogenic forcing on drought characteristics, providing important perspectives on the role of forcings in driving changes in drought events.

Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical interference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast…

Abstract Electroreduction of carbon dioxide (CO 2 ) over copper-based catalysts provides an attractive approach for sustainable fuel production. While efforts are focused on developing catalytic materials, it is also critical to understand and control the microenvironment around catalytic sites, which can mediate the transport of reaction species and influence reaction pathways. Here, we show that a hydrophobic microenvironment can significantly enhance CO 2 gas-diffusion electrolysis. For proof-of-concept, we use commercial copper nanoparticles and disperse hydrophobic polytetrafluoroethylene (PTFE) nanoparticles inside the catalyst layer. Consequently, the PTFE-added electrode achieves a greatly improved activity and Faradaic efficiency for CO 2 reduction, with a partial current density >250 mA cm −2 and a single-pass conversion of 14% at moderate potentials, which are around twice that of a regular electrode without added PTFE. The improvement is attributed to a balanced gas/liquid microenvironment that reduces the diffusion layer thickness, accelerates CO 2 mass transport, and…

To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems.

Abstract Despite the growing interest in predicting global and regional trends in vegetation productivity in response to a changing climate, changes in water constraint on vegetation productivity (i.e., water limitations on vegetation growth) remain poorly understood. Here we conduct a comprehensive evaluation of changes in water constraint on vegetation growth in the extratropical Northern Hemisphere between 1982 and 2015. We document a significant increase in vegetation water constraint over this period. Remarkably divergent trends were found with vegetation water deficit areas significantly expanding, and water surplus areas significantly shrinking. The increase in water constraints associated with water deficit was also consistent with a decreasing response time to water scarcity, suggesting a stronger susceptibility of vegetation to drought. We also observed shortened water surplus period for water surplus areas, suggesting a shortened exposure to water surplus associated with humid conditions. These observed changes were found to be attributable to trends in temperature, solar…

The chemical structure of donors and acceptors limit the power conversion efficiencies achievable with active layers of binary donor-acceptor mixtures. Here, using quaternary blends, double cascading energy level alignment in bulk heterojunction organic photovoltaic active layers are realized, enabling efficient carrier splitting and transport. Numerous avenues to optimize light absorption, carrier transport, and charge-transfer state energy levels are opened by the chemical constitution of the components. Record-breaking PCEs of 18.07% are achieved where, by electronic structure and morphology optimization, simultaneous improvements of the open-circuit voltage, short-circuit current and fill factor occur. The donor and acceptor chemical structures afford control over electronic structure and charge-transfer state energy levels, enabling manipulation of hole-transfer rates, carrier transport, and non-radiative recombination losses.

Relationships between biodiversity and multiple ecosystem functions (that is, ecosystem multifunctionality) are context-dependent. Both plant and soil microbial diversity have been reported to regulate ecosystem multifunctionality, but how their relative importance varies along environmental gradients remains poorly understood. Here, we relate plant and microbial diversity to soil multifunctionality across 130 dryland sites along a 4,000 km aridity gradient in northern China. Our results show a strong positive association between plant species richness and soil multifunctionality in less arid regions, whereas microbial diversity, in particular of fungi, is positively associated with multifunctionality in more arid regions. This shift in the relationships between plant or microbial diversity and soil multifunctionality occur at an aridity level of ∼0.8, the boundary between semiarid and arid climates, which is predicted to advance geographically ∼28% by the end of the current century. Our study highlights that biodiversity loss of plants and soil microorganisms may have especially…

Abstract The two T cell inhibitory receptors PD-1 and TIM-3 are co-expressed during exhausted T cell differentiation, and recent evidence suggests that their crosstalk regulates T cell exhaustion and immunotherapy efficacy; however, the molecular mechanism is unclear. Here we show that PD-1 contributes to the persistence of PD-1 + TIM-3 + T cells by binding to the TIM-3 ligand galectin-9 (Gal-9) and attenuates Gal-9/TIM-3-induced cell death. Anti-Gal-9 therapy selectively expands intratumoral TIM-3 + cytotoxic CD8 T cells and immunosuppressive regulatory T cells (T reg cells). The combination of anti-Gal-9 and an agonistic antibody to the co-stimulatory receptor GITR (glucocorticoid-induced tumor necrosis factor receptor-related protein) that depletes T reg cells induces synergistic antitumor activity. Gal-9 expression and secretion are promoted by interferon β and γ, and high Gal-9 expression correlates with poor prognosis in multiple human cancers. Our work uncovers a function for PD-1 in exhausted T cell survival and suggests Gal-9 as a promising target for immunotherapy.

Abstract Macrophages perform diverse functions within tissues during immune responses to pathogens and injury, but molecular mechanisms by which physical properties of the tissue regulate macrophage behavior are less well understood. Here, we examine the role of the mechanically activated cation channel Piezo1 in macrophage polarization and sensing of microenvironmental stiffness. We show that macrophages lacking Piezo1 exhibit reduced inflammation and enhanced wound healing responses. Additionally, macrophages expressing the transgenic Ca 2+ reporter, Salsa6f, reveal that Ca 2+ influx is dependent on Piezo1, modulated by soluble signals, and enhanced on stiff substrates. Furthermore, stiffness-dependent changes in macrophage function, both in vitro and in response to subcutaneous implantation of biomaterials in vivo, require Piezo1. Finally, we show that positive feedback between Piezo1 and actin drives macrophage activation. Together, our studies reveal that Piezo1 is a mechanosensor of stiffness in macrophages, and that its activity modulates polarization responses.

Abstract Ambient fine particulate matter (PM 2.5 ) is the world’s leading environmental health risk factor. Reducing the PM 2.5 disease burden requires specific strategies that target dominant sources across multiple spatial scales. We provide a contemporary and comprehensive evaluation of sector- and fuel-specific contributions to this disease burden across 21 regions, 204 countries, and 200 sub-national areas by integrating 24 global atmospheric chemistry-transport model sensitivity simulations, high-resolution satellite-derived PM 2.5 exposure estimates, and disease-specific concentration response relationships. Globally, 1.05 (95% Confidence Interval: 0.74–1.36) million deaths were avoidable in 2017 by eliminating fossil-fuel combustion (27.3% of the total PM 2.5 burden), with coal contributing to over half. Other dominant global sources included residential (0.74 [0.52–0.95] million deaths; 19.2%), industrial (0.45 [0.32–0.58] million deaths; 11.7%), and energy (0.39 [0.28–0.51] million deaths; 10.2%) sectors. Our results show that regions with large anthropogenic contributions…

Hydrogen spillover phenomenon of metal-supported electrocatalysts can significantly impact their activity in hydrogen evolution reaction (HER). However, design of active electrocatalysts faces grand challenges due to the insufficient understandings on how to overcome this thermodynamically and kinetically adverse process. Here we theoretically profile that the interfacial charge accumulation induces by the large work function difference between metal and support (∆Φ) and sequentially strong interfacial proton adsorption construct a high energy barrier for hydrogen transfer. Theoretical simulations and control experiments rationalize that small ∆Φ induces interfacial charge dilution and relocation, thereby weakening interfacial proton adsorption and enabling efficient hydrogen spillover for HER. Experimentally, a series of Pt alloys-CoP catalysts with tailorable ∆Φ show a strong ∆Φ-dependent HER activity, in which PtIr/CoP with the smallest ∆Φ = 0.02 eV delivers the best HER performance. These findings have conclusively identified ∆Φ as the criterion in guiding the design of hydrogen…

Sleep dysregulation is a feature of dementia but it remains unclear whether sleep duration prior to old age is associated with dementia incidence. Using data from 7959 participants of the Whitehall II study, we examined the association between sleep duration and incidence of dementia (521 diagnosed cases) using a 25-year follow-up. Here we report higher dementia risk associated with a sleep duration of six hours or less at age 50 and 60, compared with a normal (7 h) sleep duration, although this was imprecisely estimated for sleep duration at age 70 (hazard ratios (HR) 1.22 (95% confidence interval 1.01-1.48), 1.37 (1.10-1.72), and 1.24 (0.98-1.57), respectively). Persistent short sleep duration at age 50, 60, and 70 compared to persistent normal sleep duration was also associated with a 30% increased dementia risk independently of sociodemographic, behavioural, cardiometabolic, and mental health factors. These findings suggest that short sleep duration in midlife is associated with an increased risk of late-onset dementia.

Recent years have witnessed incredible growth in RNA therapeutics, which has benefited significantly from decades of research on lipid nanoparticles, specifically its key component—the ionizable lipid. This comment discusses the major ionizable lipid types, and provides perspectives for future development. RNA therapeutics have benefited significantly from decades of research on lipid nanoparticles, specifically its key component—the ionizable lipid. This comment discusses the major ionizable lipid types, and provides perspectives for future development.

Retinal screening contributes to early detection of diabetic retinopathy and timely treatment. To facilitate the screening process, we develop a deep learning system, named DeepDR, that can detect early-to-late stages of diabetic retinopathy. DeepDR is trained for real-time image quality assessment, lesion detection and grading using 466,247 fundus images from 121,342 patients with diabetes. Evaluation is performed on a local dataset with 200,136 fundus images from 52,004 patients and three external datasets with a total of 209,322 images. The area under the receiver operating characteristic curves for detecting microaneurysms, cotton-wool spots, hard exudates and hemorrhages are 0.901, 0.941, 0.954 and 0.967, respectively. The grading of diabetic retinopathy as mild, moderate, severe and proliferative achieves area under the curves of 0.943, 0.955, 0.960 and 0.972, respectively. In external validations, the area under the curves for grading range from 0.916 to 0.970, which further supports the system is efficient for diabetic retinopathy grading.

Harnessing data to discover the underlying governing laws or equations that describe the behavior of complex physical systems can significantly advance our modeling, simulation and understanding of such systems in various science and engineering disciplines. This work introduces a novel approach called physics-informed neural network with sparse regression to discover governing partial differential equations from scarce and noisy data for nonlinear spatiotemporal systems. In particular, this discovery approach seamlessly integrates the strengths of deep neural networks for rich representation learning, physics embedding, automatic differentiation and sparse regression to approximate the solution of system variables, compute essential derivatives, as well as identify the key derivative terms and parameters that form the structure and explicit expression of the equations. The efficacy and robustness of this method are demonstrated, both numerically and experimentally, on discovering a variety of partial differential equation systems with different levels of data scarcity and noise…

Due to complex structure and surface functionalities, photoluminescence mechanisms of Carbon Dots are unknown, and it is challenging to synthesize Carbon Dots to achieve the desired optical properties. Herein, Carbon Dots simultaneously exhibiting high-color-purity (FWHM~24 nm) long wavelength one-photon fluorescence emission at 620 nm and NIR induced two-photon fluorescence emission at 630 and 680 nm are prepared by edge amino protonation treatment. Systematic analysis reveals that the protonation of 2,3-diaminophenazine changes the molecular state of Carbon Dots, decreases the photon transition band gap, and triggers red fluorescence emission with the dramatically narrowed peak width. As the oxidation products of reactant o-phenylendiamine, the emergence of 2,3-diaminophenazine as a photoluminescence determiner suggests that fluorophore products of precursor conversion are viable determinants of the desired luminescence properties of Carbon Dots. This work shows a new way for predicting and controlling photoluminescence properties of Carbon Dots, and may guide the development of…

Remdesivir is the only FDA-approved drug for the treatment of COVID-19 patients. The active form of remdesivir acts as a nucleoside analog and inhibits the RNA-dependent RNA polymerase (RdRp) of coronaviruses including SARS-CoV-2. Remdesivir is incorporated by the RdRp into the growing RNA product and allows for addition of three more nucleotides before RNA synthesis stalls. Here we use synthetic RNA chemistry, biochemistry and cryo-electron microscopy to establish the molecular mechanism of remdesivir-induced RdRp stalling. We show that addition of the fourth nucleotide following remdesivir incorporation into the RNA product is impaired by a barrier to further RNA translocation. This translocation barrier causes retention of the RNA 3'-nucleotide in the substrate-binding site of the RdRp and interferes with entry of the next nucleoside triphosphate, thereby stalling RdRp. In the structure of the remdesivir-stalled state, the 3'-nucleotide of the RNA product is matched and located with the template base in the active center, and this may impair proofreading by the viral…

Abstract Covalent organic frameworks have recently gained increasing attention in photocatalytic hydrogen generation from water. However, their structure-property-activity relationship, which should be beneficial for the structural design, is still far-away explored. Herein, we report the designed synthesis of four isostructural porphyrinic two-dimensional covalent organic frameworks (MPor-DETH-COF, M = H 2 , Co, Ni, Zn) and their photocatalytic activity in hydrogen generation. Our results clearly show that all four covalent organic frameworks adopt AA stacking structures, with high crystallinity and large surface area. Interestingly, the incorporation of different transition metals into the porphyrin rings can rationally tune the photocatalytic hydrogen evolution rate of corresponding covalent organic frameworks, with the order of CoPor-DETH-COF < H 2 Por-DETH-COF < NiPor-DETH-COF < ZnPor-DETH-COF. Based on the detailed experiments and calculations, this tunable performance can be mainly explained by their tailored charge-carrier dynamics via molecular engineering. This study not…

Abstract Reservoir computing is a highly efficient network for processing temporal signals due to its low training cost compared to standard recurrent neural networks, and generating rich reservoir states is critical in the hardware implementation. In this work, we report a parallel dynamic memristor-based reservoir computing system by applying a controllable mask process, in which the critical parameters, including state richness, feedback strength and input scaling, can be tuned by changing the mask length and the range of input signal. Our system achieves a low word error rate of 0.4% in the spoken-digit recognition and low normalized root mean square error of 0.046 in the time-series prediction of the Hénon map, which outperforms most existing hardware-based reservoir computing systems and also software-based one in the Hénon map prediction task. Our work could pave the road towards high-efficiency memristor-based reservoir computing systems to handle more complex temporal tasks in the future.

Reservoir computing is a best-in-class machine learning algorithm for processing information generated by dynamical systems using observed time-series data. Importantly, it requires very small training data sets, uses linear optimization, and thus requires minimal computing resources. However, the algorithm uses randomly sampled matrices to define the underlying recurrent neural network and has a multitude of metaparameters that must be optimized. Recent results demonstrate the equivalence of reservoir computing to nonlinear vector autoregression, which requires no random matrices, fewer metaparameters, and provides interpretable results. Here, we demonstrate that nonlinear vector autoregression excels at reservoir computing benchmark tasks and requires even shorter training data sets and training time, heralding the next generation of reservoir computing.

Abstract Here we benchmark device-to-device variation in field-effect transistors (FETs) based on monolayer MoS 2 and WS 2 films grown using metal-organic chemical vapor deposition process. Our study involves 230 MoS 2 FETs and 160 WS 2 FETs with channel lengths ranging from 5 μm down to 100 nm. We use statistical measures to evaluate key FET performance indicators for benchmarking these two-dimensional (2D) transition metal dichalcogenide (TMD) monolayers against existing literature as well as ultra-thin body Si FETs. Our results show consistent performance of 2D FETs across 1 × 1 cm 2 chips owing to high quality and uniform growth of these TMDs followed by clean transfer onto device substrates. We are able to demonstrate record high carrier mobility of 33 cm 2 V −1 s −1 in WS 2 FETs, which is a 1.5X improvement compared to the best reported in the literature. Our experimental demonstrations confirm the technological viability of 2D FETs in future integrated circuits.

In response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts. Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world. Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27-77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49-91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12-48%])…

Urban trees influence temperatures in cities. However, their effectiveness at mitigating urban heat in different climatic contexts and in comparison to treeless urban green spaces has not yet been sufficiently explored. Here, we use high-resolution satellite land surface temperatures (LSTs) and land-cover data from 293 European cities to infer the potential of urban trees to reduce LSTs. We show that urban trees exhibit lower temperatures than urban fabric across most European cities in summer and during hot extremes. Compared to continuous urban fabric, LSTs observed for urban trees are on average 0-4 K lower in Southern European regions and 8-12 K lower in Central Europe. Treeless urban green spaces are overall less effective in reducing LSTs, and their cooling effect is approximately 2-4 times lower than the cooling induced by urban trees. By revealing continental-scale patterns in the effect of trees and treeless green spaces on urban LST our results highlight the importance of considering and further investigating the climate-dependent effectiveness of heat mitigation measures…

Abstract Seawater electrolysis represents a potential solution to grid-scale production of carbon-neutral hydrogen energy without reliance on freshwater. However, it is challenged by high energy costs and detrimental chlorine chemistry in complex chemical environments. Here we demonstrate chlorine-free hydrogen production by hybrid seawater splitting coupling hydrazine degradation. It yields hydrogen at a rate of 9.2 mol h –1 g cat –1 on NiCo/MXene-based electrodes with a low electricity expense of 2.75 kWh per m 3 H 2 at 500 mA cm –2 and 48% lower energy equivalent input relative to commercial alkaline water electrolysis. Chlorine electrochemistry is avoided by low cell voltages without anode protection regardless Cl – crossover. This electrolyzer meanwhile enables fast hydrazine degradation to ~3 ppb residual. Self-powered hybrid seawater electrolysis is realized by integrating low-voltage direct hydrazine fuel cells or solar cells. These findings enable further opportunities for efficient conversion of ocean resources to hydrogen fuel while removing harmful pollutants.

The SARS-CoV-2 nucleocapsid (N) protein is an abundant RNA-binding protein critical for viral genome packaging, yet the molecular details that underlie this process are poorly understood. Here we combine single-molecule spectroscopy with all-atom simulations to uncover the molecular details that contribute to N protein function. N protein contains three dynamic disordered regions that house putative transiently-helical binding motifs. The two folded domains interact minimally such that full-length N protein is a flexible and multivalent RNA-binding protein. N protein also undergoes liquid-liquid phase separation when mixed with RNA, and polymer theory predicts that the same multivalent interactions that drive phase separation also engender RNA compaction. We offer a simple symmetry-breaking model that provides a plausible route through which single-genome condensation preferentially occurs over phase separation, suggesting that phase separation offers a convenient macroscopic readout of a key nanoscopic interaction.

Abstract In view of increasing drug resistance, ecofriendly photoelectrical materials are promising alternatives to antibiotics. Here we design an interfacial Schottky junction of Bi2S3/Ti3C2Tx resulting from the contact potential difference between Ti3C2Tx and Bi2S3. The different work functions induce the formation of a local electrophilic/nucleophilic region. The self-driven charge transfer across the interface increases the local electron density on Ti3C2Tx. The formed Schottky barrier inhibits the backflow of electrons and boosts the charge transfer and separation. The photocatalytic activity of Bi2S3/Ti3C2Tx intensively improved the amount of reactive oxygen species under 808 nm near-infrared radiation. They kill 99.86% of Staphylococcus aureus and 99.92% of Escherichia coli with the assistance of hyperthermia within 10 min. We propose the theory of interfacial engineering based on work function and accordingly design the ecofriendly photoresponsive Schottky junction using two kinds of components with different work functions to effectively eradicate bacterial infection.

Water scarcity is dynamic and complex, emerging from the combined influences of climate change, basin-level water resources, and managed systems' adaptive capacities. Beyond geophysical stressors and responses, it is critical to also consider how multi-sector, multi-scale economic teleconnections mitigate or exacerbate water shortages. Here, we contribute a global-to-basin-scale exploratory analysis of potential water scarcity impacts by linking a global human-Earth system model, a global hydrologic model, and a metric for the loss of economic surplus due to resource shortages. We find that, dependent on scenario assumptions, major hydrologic basins can experience strongly positive or strongly negative economic impacts due to global trade dynamics and market adaptations to regional scarcity. In many cases, market adaptation profoundly magnifies economic uncertainty relative to hydrologic uncertainty. Our analysis finds that impactful scenarios are often combinations of standard scenarios, showcasing that planners cannot presume drivers of uncertainty in complex adaptive systems.

Abstract Interminable surveillance and reconnaissance through various sophisticated multispectral detectors present threats to military equipment and manpower. However, a combination of detectors operating in different wavelength bands (from hundreds of nanometers to centimeters) and based on different principles raises challenges to the conventional single-band camouflage devices. In this paper, multispectral camouflage is demonstrated for the visible, mid-infrared (MIR, 3-5 and 8-14 μm), lasers (1.55 and 10.6 μm) and microwave (8-12 GHz) bands with simultaneous efficient radiative cooling in the non-atmospheric window (5-8 μm). The device for multispectral camouflage consists of ZnS/Ge multilayer for wavelength selective emission and Cu-ITO-Cu metasurface for microwave absorption. In comparison with conventional broadband low emittance material (Cr), the IR camouflage performance of this device manifests 8.4/5.9 °C reduction of inner/surface temperature, and 53.4/13.0 % IR signal decrease in mid/long wavelength IR bands, at 2500 W∙m-2 input power density. Furthermore, we revealed…

Abstract Over 100 countries have set or are considering net-zero emissions or neutrality targets. However, most of the information on emissions neutrality (such as timing) is provided for the global level. Here, we look at national-level neutrality-years based on globally cost-effective 1.5 °C and 2 °C scenarios from integrated assessment models. These results indicate that domestic net zero greenhouse gas and CO 2 emissions in Brazil and the USA are reached a decade earlier than the global average, and in India and Indonesia later than global average. These results depend on choices like the accounting of land-use emissions. The results also show that carbon storage and afforestation capacity, income, share of non-CO 2 emissions, and transport sector emissions affect the variance in projected phase-out years across countries. We further compare these results to an alternative approach, using equity-based rules to establish target years. These results can inform policymakers on net-zero targets.

Genome-wide association studies (GWAS) have identified thousands of genomic regions affecting complex diseases. The next challenge is to elucidate the causal genes and mechanisms involved. One approach is to use statistical colocalization to assess shared genetic aetiology across multiple related traits (e.g. molecular traits, metabolic pathways and complex diseases) to identify causal pathways, prioritize causal variants and evaluate pleiotropy. We propose HyPrColoc (Hypothesis Prioritisation for multi-trait Colocalization), an efficient deterministic Bayesian algorithm using GWAS summary statistics that can detect colocalization across vast numbers of traits simultaneously (e.g. 100 traits can be jointly analysed in around 1 s). We perform a genome-wide multi-trait colocalization analysis of coronary heart disease (CHD) and fourteen related traits, identifying 43 regions in which CHD colocalized with ≥1 trait, including 5 previously unknown CHD loci. Across the 43 loci, we further integrate gene and protein expression quantitative trait loci to identify candidate causal genes.

The superconducting transmon qubit is a leading platform for quantum computing and quantum science. Building large, useful quantum systems based on transmon qubits will require significant improvements in qubit relaxation and coherence times, which are orders of magnitude shorter than limits imposed by bulk properties of the constituent materials. This indicates that relaxation likely originates from uncontrolled surfaces, interfaces, and contaminants. Previous efforts to improve qubit lifetimes have focused primarily on designs that minimize contributions from surfaces. However, significant improvements in the lifetime of two-dimensional transmon qubits have remained elusive for several years. Here, we fabricate two-dimensional transmon qubits that have both lifetimes and coherence times with dynamical decoupling exceeding 0.3 milliseconds by replacing niobium with tantalum in the device. We have observed increased lifetimes for seventeen devices, indicating that these material improvements are robust, paving the way for higher gate fidelities in multi-qubit processors.

Increased cerebrospinal fluid neurofilament light (NfL) is a recognized biomarker for neurodegeneration that can also be assessed in blood. Here, we investigate plasma NfL as a marker of neurodegeneration in 13 neurodegenerative disorders, Down syndrome, depression and cognitively unimpaired controls from two multicenter cohorts: King's College London (n = 805) and the Swedish BioFINDER study (n = 1,464). Plasma NfL was significantly increased in all cortical neurodegenerative disorders, amyotrophic lateral sclerosis and atypical parkinsonian disorders. We demonstrate that plasma NfL is clinically useful in identifying atypical parkinsonian disorders in patients with parkinsonism, dementia in individuals with Down syndrome, dementia among psychiatric disorders, and frontotemporal dementia in patients with cognitive impairment. Data-driven cut-offs highlighted the fundamental importance of age-related clinical cut-offs for disorders with a younger age of onset. Finally, plasma NfL performs best when applied to indicate no underlying neurodegeneration, with low false positives, in all…

Abstract Single-cell RNA sequencing (scRNA-seq) has revealed an unprecedented degree of immune cell diversity. However, consistent definition of cell subtypes and cell states across studies and diseases remains a major challenge. Here we generate reference T cell atlases for cancer and viral infection by multi-study integration, and develop ProjecTILs, an algorithm for reference atlas projection. In contrast to other methods, ProjecTILs allows not only accurate embedding of new scRNA-seq data into a reference without altering its structure, but also characterizing previously unknown cell states that “deviate” from the reference. ProjecTILs accurately predicts the effects of cell perturbations and identifies gene programs that are altered in different conditions and tissues. A meta-analysis of tumor-infiltrating T cells from several cohorts reveals a strong conservation of T cell subtypes between human and mouse, providing a consistent basis to describe T cell heterogeneity across studies, diseases, and species.

Stretchable ionic skins are intriguing in mimicking the versatile sensations of natural skins. However, for their applications in advanced electronics, good elastic recovery, self-healing, and more importantly, skin-like nonlinear mechanoresponse (strain-stiffening) are essential but can be rarely met in one material. Here we demonstrate a robust proton-conductive ionic skin design via introducing an entropy-driven supramolecular zwitterionic reorganizable network to the hydrogen-bonded polycarboxylic acid network. The design allows two dynamic networks with distinct interacting strength to sequentially debond with stretch, and the conflict among elasticity, self-healing, and strain-stiffening can be thus defeated. The representative polyacrylic acid/betaine elastomer exhibits high stretchability (1600% elongation), immense strain-stiffening (24-fold modulus enhancement), ~100% self-healing, excellent elasticity (97.9 ± 1.1% recovery ratio, <14% hysteresis), high transparency (99.7 ± 0.1%), moisture-preserving, anti-freezing (elastic at -40 °C), water reprocessibility, as well as…

The causative agent of the COVID-19 pandemic, SARS-CoV-2, is steadily mutating during continuous transmission among humans. Such mutations can occur in the spike (S) protein that binds to the ACE2 receptor and is cleaved by TMPRSS2. However, whether S mutations affect SARS-CoV-2 cell entry remains unknown. Here, we show that naturally occurring S mutations can reduce or enhance cell entry via ACE2 and TMPRSS2. A SARS-CoV-2 S-pseudotyped lentivirus exhibits substantially lower entry than that of SARS-CoV S. Among S variants, the D614G mutant shows the highest cell entry, as supported by structural and binding analyses. Nevertheless, the D614G mutation does not affect neutralization by antisera against prototypic viruses. Taken together, we conclude that the D614G mutation increases cell entry by acquiring higher affinity to ACE2 while maintaining neutralization susceptibility. Based on these findings, further worldwide surveillance is required to understand SARS-CoV-2 transmissibility among humans.

Abstract Congenital heart defects constitute the most common human birth defect, however understanding of how these disorders originate is limited by our ability to model the human heart accurately in vitro. Here we report a method to generate developmentally relevant human heart organoids by self-assembly using human pluripotent stem cells. Our procedure is fully defined, efficient, reproducible, and compatible with high-content approaches. Organoids are generated through a three-step Wnt signaling modulation strategy using chemical inhibitors and growth factors. Heart organoids are comparable to age-matched human fetal cardiac tissues at the transcriptomic, structural, and cellular level. They develop sophisticated internal chambers with well-organized multi-lineage cardiac cell types, recapitulate heart field formation and atrioventricular specification, develop a complex vasculature, and exhibit robust functional activity. We also show that our organoid platform can recreate complex metabolic disorders associated with congenital heart defects, as demonstrated by an in vitro…

Nationwide prospective surveillance of all-age patients with acute respiratory infections was conducted in China between 2009‒2019. Here we report the etiological and epidemiological features of the 231,107 eligible patients enrolled in this analysis. Children <5 years old and school-age children have the highest viral positivity rate (46.9%) and bacterial positivity rate (30.9%). Influenza virus, respiratory syncytial virus and human rhinovirus are the three leading viral pathogens with proportions of 28.5%, 16.8% and 16.7%, and Streptococcus pneumoniae, Mycoplasma pneumoniae and Klebsiella pneumoniae are the three leading bacterial pathogens (29.9%, 18.6% and 15.8%). Negative interactions between viruses and positive interactions between viral and bacterial pathogens are common. A Join-Point analysis reveals the age-specific positivity rate and how this varied for individual pathogens. These data indicate that differential priorities for diagnosis, prevention and control should be highlighted in terms of acute respiratory tract infection patients' demography, geographic locations…

Abstract While inheriting the exceptional merits of single atom catalysts, diatomic site catalysts (DASCs) utilize two adjacent atomic metal species for their complementary functionalities and synergistic actions. Herein, a DASC consisting of nickel-iron hetero-diatomic pairs anchored on nitrogen-doped graphene is synthesized. It exhibits extraordinary electrocatalytic activities and stability for both CO 2 reduction reaction (CO 2 RR) and oxygen evolution reaction (OER). Furthermore, the rechargeable Zn-CO 2 battery equipped with such bifunctional catalyst shows high Faradaic efficiency and outstanding rechargeability. The in-depth experimental and theoretical analyses reveal the orbital coupling between the catalytic iron center and the adjacent nickel atom, which leads to alteration in orbital energy level, unique electronic states, higher oxidation state of iron, and weakened binding strength to the reaction intermediates, thus boosted CO 2 RR and OER performance. This work provides critical insights to rational design, working mechanism, and application of hetero-DASCs.

Abstract The discovery of superconducting H 3 S with a critical temperature T c ∼200 K opened a door to room temperature superconductivity and stimulated further extensive studies of hydrogen-rich compounds stabilized by high pressure. Here, we report a comprehensive study of the yttrium-hydrogen system with the highest predicted T c s among binary compounds and discuss the contradictions between different theoretical calculations and experimental data. We synthesized yttrium hydrides with the compositions of YH 3 , YH 4 , YH 6 and YH 9 in a diamond anvil cell and studied their crystal structures, electrical and magnetic transport properties, and isotopic effects. We found superconductivity in the Im-3m YH 6 and P6 3 /mmc YH 9 phases with maximal T c s of ∼220 K at 183 GPa and ∼243 K at 201 GPa, respectively. Fm-3m YH 10 with the highest predicted T c > 300 K was not observed in our experiments, and instead, YH 9 was found to be the hydrogen-richest yttrium hydride in the studied pressure and temperature range up to record 410 GPa and 2250 K.

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications.

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological…

Abstract Single-atom catalysts (SACs) are promising candidates to catalyze electrochemical CO 2 reduction (ECR) due to maximized atomic utilization. However, products are usually limited to CO instead of hydrocarbons or oxygenates due to unfavorable high energy barrier for further electron transfer on synthesized single atom catalytic sites. Here we report a novel partial-carbonization strategy to modify the electronic structures of center atoms on SACs for lowering the overall endothermic energy of key intermediates. A carbon-dots-based SAC margined with unique CuN 2 O 2 sites was synthesized for the first time. The introduction of oxygen ligands brings remarkably high Faradaic efficiency (78%) and selectivity (99% of ECR products) for electrochemical converting CO 2 to CH 4 with current density of 40 mA·cm -2 in aqueous electrolytes, surpassing most reported SACs which stop at two-electron reduction. Theoretical calculations further revealed that the high selectivity and activity on CuN 2 O 2 active sites are due to the proper elevated CH 4 and H 2 energy barrier and fine-tuned…

Abstract Tin perovskites have emerged as promising alternatives to toxic lead perovskites in next-generation photovoltaics, but their poor environmental stability remains an obstacle towards more competitive performances. Therefore, a full understanding of their decomposition processes is needed to address these stability issues. Herein, we elucidate the degradation mechanism of 2D/3D tin perovskite films based on (PEA)0.2(FA)0.8SnI3(where PEA is phenylethylammonium and FA is formamidinium). We show that SnI4, a product of the oxygen-induced degradation of tin perovskite, quickly evolves into iodine via the combined action of moisture and oxygen. We identify iodine as a highly aggressive species that can further oxidise the perovskite to more SnI4, establishing a cyclic degradation mechanism. Perovskite stability is then observed to strongly depend on the hole transport layer chosen as the substrate, which is exploited to tackle film degradation. These key insights will enable the future design and optimisation of stable tin-based perovskite optoelectronics.

Abstract Reactive oxygen species (ROS) are generated and consumed in living organism for normal metabolism. Paradoxically, the overproduction and/or mismanagement of ROS have been involved in pathogenesis and progression of various human diseases. Here, we reported a two-dimensional (2D) vanadium carbide (V 2 C) MXene nanoenzyme (MXenzyme) that can mimic up to six naturally-occurring enzymes, including superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), glutathione peroxidase (GPx), thiol peroxidase (TPx) and haloperoxidase (HPO). Based on these enzyme-mimicking properties, the constructed 2D V 2 C MXenzyme not only possesses high biocompatibility but also exhibits robust in vitro cytoprotection against oxidative stress. Importantly, 2D V 2 C MXenzyme rebuilds the redox homeostasis without perturbing the endogenous antioxidant status and relieves ROS-induced damage with benign in vivo therapeutic effects, as demonstrated in both inflammation and neurodegeneration animal models. These findings open an avenue to enable the use of MXenzyme as a remedial nanoplatform to treat…

Abstract Solar-driven hydrogen production from water using particulate photocatalysts is considered the most economical and effective approach to produce hydrogen fuel with little environmental concern. However, the efficiency of hydrogen production from water in particulate photocatalysis systems is still low. Here, we propose an efficient biphase photocatalytic system composed of integrated photothermal–photocatalytic materials that use charred wood substrates to convert liquid water to water steam, simultaneously splitting hydrogen under light illumination without additional energy. The photothermal–photocatalytic system exhibits biphase interfaces of photothermally-generated steam/photocatalyst/hydrogen, which significantly reduce the interface barrier and drastically lower the transport resistance of the hydrogen gas by nearly two orders of magnitude. In this work, an impressive hydrogen production rate up to 220.74 μmol h−1 cm−2 in the particulate photocatalytic systems has been achieved based on the wood/CoO system, demonstrating that the photothermal–photocatalytic biphase…

Abstract Au nanoparticles (NPs) have been found to be excellent glucose oxidase mimics, while the catalytic processes have rarely been studied. Here, we reveal that the process of glucose oxidation catalyzed by Au NPs is as the same as that of natural glucose oxidase, namely, a two-step reaction including the dehydrogenation of glucose and the subsequent reduction of O2 to H2O2 by two electrons. Pt, Pd, Ru, Rh, and Ir NPs can also catalyze the dehydrogenation of glucose, except that O2 is preferably reduced to H2O. By the electron transfer feature of noble metal NPs, we overcame the limitation that H2O2 must be produced in the traditional two-step glucose assay and realize the rapid colorimetric detections of glucose. Inspired by the electron transport pathway in the catalytic process of natural enzymes, noble metal NPs have also been found to mimic various enzymatic electron transfer reactions including cytochrome c, coenzymes as well as nitrobenzene reductions.

Liquid-liquid phase separation of proteins underpins the formation of membraneless compartments in living cells. Elucidating the molecular driving forces underlying protein phase transitions is therefore a key objective for understanding biological function and malfunction. Here we show that cellular proteins, which form condensates at low salt concentrations, including FUS, TDP-43, Brd4, Sox2, and Annexin A11, can reenter a phase-separated regime at high salt concentrations. By bringing together experiments and simulations, we demonstrate that this reentrant phase transition in the high-salt regime is driven by hydrophobic and non-ionic interactions, and is mechanistically distinct from the low-salt regime, where condensates are additionally stabilized by electrostatic forces. Our work thus sheds light on the cooperation of hydrophobic and non-ionic interactions as general driving forces in the condensation process, with important implications for aberrant function, druggability, and material properties of biomolecular condensates.

Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science. Most of the established methods, however, rely on local properties and are thus unable to take global changes in the electronic structure into account, which result from long-range charge transfer or different charge states. In this work we overcome this limitation by introducing a fourth-generation high-dimensional neural network potential that combines a charge equilibration scheme employing environment-dependent atomic electronegativities with accurate atomic energies. The method, which is able to correctly describe global charge distributions in arbitrary systems, yields much improved energies and substantially extends the applicability of modern machine learning potentials. This is demonstrated for a series of systems representing typical scenarios in chemistry and materials science that are incorrectly described by current methods, while the fourth-generation neural network potential is in excellent agreement with…

Abstract The COVID-19 pandemic continues to spread throughout the world with an urgent need for a safe and protective vaccine to effectuate herd immunity to control the spread of SARS-CoV-2. Here, we report the development of a SARS-CoV-2 subunit vaccine (NVX-CoV2373) produced from the full-length spike (S) protein, stabilized in the prefusion conformation. Purified NVX-CoV2373 S form 27.2nm nanoparticles that are thermostable and bind with high affinity to the human angiotensin-converting enzyme 2 (hACE2) receptor. In mice and baboons, low-dose NVX-CoV2373 with saponin-based Matrix-M adjuvant elicits high titer anti-S IgG that is associated with blockade of hACE2 receptor binding, virus neutralization, and protection against SARS-CoV-2 challenge in mice with no evidence of vaccine-associated enhanced respiratory disease (VAERD). NVX-CoV2373 vaccine also elicits multifunctional CD4 + and CD8 + T cells, CD4 + T follicular helper T cells (Tfh), and the generation of antigen-specific germinal center (GC) B cells in the spleen. These results support the ongoing phase 1/2 clinical…

Fire activity in Australia is strongly affected by high inter-annual climate variability and extremes. Through changes in the climate, anthropogenic climate change has the potential to alter fire dynamics. Here we compile satellite (19 and 32 years) and ground-based (90 years) burned area datasets, climate and weather observations, and simulated fuel loads for Australian forests. Burned area in Australia's forests shows a linear positive annual trend but an exponential increase during autumn and winter. The mean number of years since the last fire has decreased consecutively in each of the past four decades, while the frequency of forest megafire years (>1 Mha burned) has markedly increased since 2000. The increase in forest burned area is consistent with increasingly more dangerous fire weather conditions, increased risk factors associated with pyroconvection, including fire-generated thunderstorms, and increased ignitions from dry lightning, all associated to varying degrees with anthropogenic climate change.

Abstract Excessive inflammatory responses induced upon SARS-CoV-2 infection are associated with severe symptoms of COVID-19. Inflammasomes activated in response to SARS-CoV-2 infection are also associated with COVID-19 severity. Here, we show a distinct mechanism by which SARS-CoV-2 N protein promotes NLRP3 inflammasome activation to induce hyperinflammation. N protein facilitates maturation of proinflammatory cytokines and induces proinflammatory responses in cultured cells and mice. Mechanistically, N protein interacts directly with NLRP3 protein, promotes the binding of NLRP3 with ASC, and facilitates NLRP3 inflammasome assembly. More importantly, N protein aggravates lung injury, accelerates death in sepsis and acute inflammation mouse models, and promotes IL-1β and IL-6 activation in mice. Notably, N-induced lung injury and cytokine production are blocked by MCC950 (a specific inhibitor of NLRP3) and Ac-YVAD-cmk (an inhibitor of caspase-1). Therefore, this study reveals a distinct mechanism by which SARS-CoV-2 N protein promotes NLRP3 inflammasome activation and induces…

Abstract Oxygen reduction reaction towards hydrogen peroxide (H 2 O 2 ) provides a green alternative route for H 2 O 2 production, but it lacks efficient catalysts to achieve high selectivity and activity simultaneously under industrial-relevant production rates. Here we report a boron-doped carbon (B-C) catalyst which can overcome this activity-selectivity dilemma. Compared to the state-of-the-art oxidized carbon catalyst, B-C catalyst presents enhanced activity (saving more than 210 mV overpotential) under industrial-relevant currents (up to 300 mA cm −2 ) while maintaining high H 2 O 2 selectivity (85–90%). Density-functional theory calculations reveal that the boron dopant site is responsible for high H 2 O 2 activity and selectivity due to low thermodynamic and kinetic barriers. Employed in our porous solid electrolyte reactor, the B-C catalyst demonstrates a direct and continuous generation of pure H 2 O 2 solutions with high selectivity (up to 95%) and high H 2 O 2 partial currents (up to ~400 mA cm −2 ), illustrating the catalyst’s great potential for practical applications…

Abstract We propose a new type of spin-valley locking (SVL), named C-paired SVL, in antiferromagnetic systems, which directly connects the spin/valley space with the real space, and hence enables both static and dynamical controls of spin and valley to realize a multifunctional antiferromagnetic material. The new emergent quantum degree of freedom in the C-paired SVL is comprised of spin-polarized valleys related by a crystal symmetry instead of the time-reversal symmetry. Thus, both spin and valley can be accessed by simply breaking the corresponding crystal symmetry. Typically, one can use a strain field to induce a large net valley polarization/magnetization and use a charge current to generate a large noncollinear spin current. We predict the realization of the C-paired SVL in monolayer V2Se2O, which indeed exhibits giant piezomagnetism and can generate a large transverse spin current. Our findings provide unprecedented opportunities to integrate various controls of spin and valley with nonvolatile information storage in a single material, which is highly desirable for versatile…

Bright and efficient blue emission is key to further development of metal halide perovskite light-emitting diodes. Although modifying bromide/chloride composition is straightforward to achieve blue emission, practical implementation of this strategy has been challenging due to poor colour stability and severe photoluminescence quenching. Both detrimental effects become increasingly prominent in perovskites with the high chloride content needed to produce blue emission. Here, we solve these critical challenges in mixed halide perovskites and demonstrate spectrally stable blue perovskite light-emitting diodes over a wide range of emission wavelengths from 490 to 451 nanometres. The emission colour is directly tuned by modifying the halide composition. Particularly, our blue and deep-blue light-emitting diodes based on three-dimensional perovskites show high EQE values of 11.0% and 5.5% with emission peaks at 477 and 467 nm, respectively. These achievements are enabled by a vapour-assisted crystallization technique, which largely mitigates local compositional heterogeneity and ion…

Sign language recognition, especially the sentence recognition, is of great significance for lowering the communication barrier between the hearing/speech impaired and the non-signers. The general glove solutions, which are employed to detect motions of our dexterous hands, only achieve recognizing discrete single gestures (i.e., numbers, letters, or words) instead of sentences, far from satisfying the meet of the signers' daily communication. Here, we propose an artificial intelligence enabled sign language recognition and communication system comprising sensing gloves, deep learning block, and virtual reality interface. Non-segmentation and segmentation assisted deep learning model achieves the recognition of 50 words and 20 sentences. Significantly, the segmentation approach splits entire sentence signals into word units. Then the deep learning model recognizes all word elements and reversely reconstructs and recognizes sentences. Furthermore, new/never-seen sentences created by new-order word elements recombination can be recognized with an average correct rate of 86.67%.…

Abstract Layered transition-metal oxides have attracted intensive interest for cathode materials of sodium-ion batteries. However, they are hindered by the limited capacity and inferior phase transition due to the gliding of transition-metal layers upon Na+ extraction and insertion in the cathode materials. Here, we report that the large-sized K+ is riveted in the prismatic Na+ sites of P2-Na0.612K0.056MnO2 to enable more thermodynamically favorable Na+ vacancies. The Mn-O bonds are reinforced to reduce phase transition during charge and discharge. 0.901 Na+ per formula are reversibly extracted and inserted, in which only the two-phase transition of P2 ↔ P’2 occurs at low voltages. It exhibits the highest specific capacity of 240.5 mAh g−1 and energy density of 654 Wh kg−1 based on the redox of Mn3+/Mn4+, and a capacity retention of 98.2% after 100 cycles. This investigation will shed lights on the tuneable chemical environments of transition-metal oxides for advanced cathode materials and promote the development of sodium-ion batteries.

Rechargeable Li-based battery technologies utilising silicon, silicon-based, and Si-derivative anodes coupled with high-capacity/high-voltage insertion-type cathodes have reaped significant interest from both academic and industrial sectors. This stems from their practically achievable energy density, offering a new avenue towards the mass-market adoption of electric vehicles and renewable energy sources. Nevertheless, such high-energy systems are limited by their complex chemistry and intrinsic drawbacks. From this perspective, we present the progress, current status, prevailing challenges and mitigating strategies of Li-based battery systems comprising silicon-containing anodes and insertion-type cathodes. This is accompanied by an assessment of their potential to meet the targets for evolving volume- and weight-sensitive applications such as electro-mobility.

Abstract COVID-19 is associated with a wide range of clinical manifestations, including autoimmune features and autoantibody production. Here we develop three protein arrays to measure IgG autoantibodies associated with connective tissue diseases, anti-cytokine antibodies, and anti-viral antibody responses in serum from 147 hospitalized COVID-19 patients. Autoantibodies are identified in approximately 50% of patients but in less than 15% of healthy controls. When present, autoantibodies largely target autoantigens associated with rare disorders such as myositis, systemic sclerosis and overlap syndromes. A subset of autoantibodies targeting traditional autoantigens or cytokines develop de novo following SARS-CoV-2 infection. Autoantibodies track with longitudinal development of IgG antibodies recognizing SARS-CoV-2 structural proteins and a subset of non-structural proteins, but not proteins from influenza, seasonal coronaviruses or other pathogenic viruses. We conclude that SARS-CoV-2 causes development of new-onset IgG autoantibodies in a significant proportion of hospitalized…

Abstract High nickel content in LiNi x Co y Mn z O 2 (NCM, x ≥ 0.8, x + y + z = 1) layered cathode material allows high specific energy density in lithium-ion batteries (LIBs). However, Ni-rich NCM cathodes suffer from performance degradation, mechanical and structural instability upon prolonged cell cycling. Although the use of single-crystal Ni-rich NCM can mitigate these drawbacks, the ion-diffusion in large single-crystal particles hamper its rate capability. Herein, we report a strategy to construct an in situ Li 1.4 Y 0.4 Ti 1.6 (PO 4 ) 3 (LYTP) ion/electron conductive network which interconnects single-crystal LiNi 0.88 Co 0.09 Mn 0.03 O 2 (SC-NCM88) particles. The LYTP network facilitates the lithium-ion transport between SC-NCM88 particles, mitigates mechanical instability and prevents detrimental crystalline phase transformation. When used in combination with a Li metal anode, the LYTP-containing SC-NCM88-based cathode enables a coin cell capacity of 130 mAh g −1 after 500 cycles at 5 C rate in the 2.75-4.4 V range at 25 °C. Tests in Li-ion pouch cell configuration (i.e.,…

Fibrotic skin disease represents a major global healthcare burden, characterized by fibroblast hyperproliferation and excessive accumulation of extracellular matrix. Fibroblasts are found to be heterogeneous in multiple fibrotic diseases, but fibroblast heterogeneity in fibrotic skin diseases is not well characterized. In this study, we explore fibroblast heterogeneity in keloid, a paradigm of fibrotic skin diseases, by using single-cell RNA-seq. Our results indicate that keloid fibroblasts can be divided into 4 subpopulations: secretory-papillary, secretory-reticular, mesenchymal and pro-inflammatory. Interestingly, the percentage of mesenchymal fibroblast subpopulation is significantly increased in keloid compared to normal scar. Functional studies indicate that mesenchymal fibroblasts are crucial for collagen overexpression in keloid. Increased mesenchymal fibroblast subpopulation is also found in another fibrotic skin disease, scleroderma, suggesting this is a broad mechanism for skin fibrosis. These findings will help us better understand skin fibrotic pathogenesis, and provide…

Abstract Li-ion-conducting chloride solid electrolytes receive considerable attention due to their physicochemical characteristics such as high ionic conductivity, deformability and oxidative stability. However, the raw materials are expensive, and large-scale use of this class of inorganic superionic conductors seems unlikely. Here, a cost-effective chloride solid electrolyte, Li 2 ZrCl 6 , is reported. Its raw materials are several orders of magnitude cheaper than those for the state-of-the-art chloride solid electrolytes, but high ionic conductivity (0.81 mS cm –1 at room temperature), deformability, and compatibility with 4V-class cathodes are still simultaneously achieved in Li 2 ZrCl 6 . Moreover, Li 2 ZrCl 6 demonstrates a humidity tolerance with no sign of moisture uptake or conductivity degradation after exposure to an atmosphere with 5% relative humidity. By combining Li 2 ZrCl 6 with the Li-In anode and the single-crystal LiNi 0.8 Mn 0.1 Co 0.1 O 2 cathode, we report a room-temperature all-solid-state cell with a stable specific capacity of about 150 mAh g –1 for 200…

Abstract High-entropy alloys (HEAs) with unique physicochemical properties have attracted tremendous attention in many fields, yet the precise control on dimension and morphology at atomic level remains formidable challenges. Herein, we synthesize unique PtRuNiCoFeMo HEA subnanometer nanowires (SNWs) for alkaline hydrogen oxidation reaction (HOR). The mass and specific activities of HEA SNWs/C reach 6.75 A mgPt+Ru−1 and 8.96 mA cm−2, respectively, which are 2.8/2.6, 4.1/2.4, and 19.8/18.7 times higher than those of HEA NPs/C, commercial PtRu/C and Pt/C, respectively. It can even display enhanced resistance to CO poisoning during HOR in the presence of 1000 ppm CO. Density functional theory calculations reveal that the strong interactions between different metal sites in HEA SNWs can greatly regulate the binding strength of proton and hydroxyl, and therefore enhances the HOR activity. This work not only provides a viable synthetic route for the fabrication of Pt-based HEA subnano/nano materials, but also promotes the fundamental researches on catalysis and beyond.