Westlake University

How SARS-CoV-2 binds to human cells Scientists are racing to learn the secrets of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2), which is the cause of the pandemic disease COVID-19. The first step in viral entry is the binding of the viral trimeric spike protein to the human receptor angiotensin-converting enzyme 2 (ACE2). Yan et al. present the structure of human ACE2 in complex with a membrane protein that it chaperones, B 0 AT1. In the context of this complex, ACE2 is a dimer. A further structure shows how the receptor binding domain of SARS-CoV-2 interacts with ACE2 and suggests that it is possible that two trimeric spike proteins bind to an ACE2 dimer. The structures provide a basis for the development of therapeutics targeting this crucial interaction. Science , this issue p. 1444

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate , where to evaluate , and how to evaluate . Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, education, natural and social sciences, agent applications, and other areas. Secondly, we answer the ‘where’ and ‘how’ questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing the performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs. Our key point is that evaluation should be treated as an essential discipline to better assist the development of LLMs. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/LLM-eval-survey

Object detection has been dominated by anchor-based detectors for several years. Recently, anchor-free detectors have become popular due to the proposal of FPN and Focal Loss. In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them. If they adopt the same definition of positive and negative samples during training, there is no obvious difference in the final performance, no matter regressing from a box or a point. This shows that how to select positive and negative training samples is important for current object detectors. Then, we propose an Adaptive Training Sample Selection (ATSS) to automatically select positive and negative samples according to statistical characteristics of object. It significantly improves the performance of anchor-based and anchor-free detectors and bridges the gap between them. Finally, we discuss the necessity of tiling multiple anchors per location on the image to detect objects. Extensive experiments conducted on MS COCO support our aforementioned analysis and conclusions. With the newly introduced ATSS, we improve state-of-the-art detectors by a large margin to 50.7% AP without introducing any overhead. The code is available at https://github.com/sfzhang15/ATSS.

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation is challenging. Here, we demonstrate a simple, yet robust, determinant of developmental potential-the number of expressed genes per cell-and leverage this measure of transcriptional diversity to develop a computational framework (CytoTRACE) for predicting differentiation states from scRNA-seq data. When applied to diverse tissue types and organisms, CytoTRACE outperformed previous methods and nearly 19,000 annotated gene sets for resolving 52 experimentally determined developmental trajectories. Additionally, it facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis. This study thus establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.

Developing therapeutics against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could be guided by the distribution of epitopes, not only on the receptor binding domain (RBD) of the Spike (S) protein but also across the full Spike (S) protein. We isolated and characterized monoclonal antibodies (mAbs) from 10 convalescent COVID-19 patients. Three mAbs showed neutralizing activities against authentic SARS-CoV-2. One mAb, named 4A8, exhibits high neutralization potency against both authentic and pseudotyped SARS-CoV-2 but does not bind the RBD. We defined the epitope of 4A8 as the N-terminal domain (NTD) of the S protein by determining with cryo-eletron microscopy its structure in complex with the S protein to an overall resolution of 3.1 angstroms and local resolution of 3.3 angstroms for the 4A8-NTD interface. This points to the NTD as a promising target for therapeutic mAbs against COVID-19.

Host-mediated lung inflammation is present 1 , and drives mortality 2 , in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development 3 . Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 10 -8 ) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 10 -8 ) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 10 -12 ) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 10 -8 ) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice.

Surface trap-mediated nonradiative charge recombination is a major limit to achieving high-efficiency metal-halide perovskite photovoltaics. The ionic character of perovskite lattice has enabled molecular defect passivation approaches through interaction between functional groups and defects. However, a lack of in-depth understanding of how the molecular configuration influences the passivation effectiveness is a challenge to rational molecule design. Here, the chemical environment of a functional group that is activated for defect passivation was systematically investigated with theophylline, caffeine, and theobromine. When N-H and C=O were in an optimal configuration in the molecule, hydrogen-bond formation between N-H and I (iodine) assisted the primary C=O binding with the antisite Pb (lead) defect to maximize surface-defect binding. A stabilized power conversion efficiency of 22.6% of photovoltaic device was demonstrated with theophylline treatment.

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Abstract The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-19 1,2 , host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases 3–7 . They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.

Abstract Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes 1 . Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel 2 ) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. Here, we map and replicate protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, resulting in 451 pQTLs for 85 proteins. For each protein, we further perform pathway mapping to obtain trans-pQTL gene and regulatory designations. We substantiate these regulatory findings with orthogonal evidence for trans-pQTLs using mouse knockdown experiments (ABCA1 and TRIB1) and clinical trial results (chemokine receptors CCR2 and CCR5), with consistent regulation. Finally, we evaluate known drug targets, and suggest new target candidates or repositioning opportunities using Mendelian randomization. This identifies 11 proteins with causal evidence of involvement in human disease that have not previously been targeted, including EGF, IL-16, PAPPA, SPON1, F3, ADM, CASP-8, CHI3L1, CXCL16, GDF15 and MMP-12. Taken together, these findings demonstrate the utility of large-scale mapping of the genetics of the proteome and provide a resource for future precision studies of circulating proteins in human health. Folkersen et al. report the first results from the SCALLOP consortium, a collaborative framework for pQTL mapping and biomarker analysis of proteins on the Olink platform. A total of 315 primary and 136 secondary pQTLs for 85 circulating cardiovascular proteins from over 30,000 individuals were identified and replicated to yield new insights for translational studies and drug development.

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 promising water-alkali electrocatalysts.

Abstract Rational design of the catalysts is impressive for sustainable energy conversion. However, there is a grand challenge to engineer active sites at the interface. Herein, hierarchical transition bimetal oxides/sulfides heterostructure arrays interacting two-dimensional MoO x /MoS 2 nanosheets attached to one-dimensional NiO x /Ni 3 S 2 nanorods were fabricated by oxidation/hydrogenation-induced surface reconfiguration strategy. The NiMoO x /NiMoS heterostructure array exhibits the overpotentials of 38 mV for hydrogen evolution and 186 mV for oxygen evolution at 10 mA cm −2 , even surviving at a large current density of 500 mA cm −2 with long-term stability. Due to optimized adsorption energies and accelerated water splitting kinetics by theory calculations, the assembled two-electrode cell delivers the industrially relevant current densities of 500 and 1000 mA cm −2 at record low cell voltages of 1.60 and 1.66 V with excellent durability. This research provides a promising avenue to enhance the electrocatalytic performance of the catalysts by engineering interfacial active sites toward large-scale water splitting.

Abstract We present a microkinetic model for CO (2) reduction (CO (2) R) on Cu(211) towards C 2 products, based on energetics estimated from an explicit solvent model. We show that the differences in both Tafel slopes and pH dependence for C 1 vs C 2 activity arise from differences in their multi-step mechanisms. We find the depletion in C 2 products observed at high overpotential and high pH to arise from the 2 nd order dependence of C-C coupling on CO coverage, which decreases due to competition from the C 1 pathway. We further demonstrate that CO (2) reduction at a fixed pH yield similar activities, due to the facile kinetics for CO 2 reduction to CO on Cu, which suggests C 2 products to be favored for CO 2 R under alkaline conditions. The mechanistic insights of this work elucidate how reaction conditions can lead to significant enhancements in selectivity and activity towards higher value C 2 products.

China’s nationwide ozone monitoring network initiated in 2013 has observed severe surface ozone pollution. This network, combined with the recent Tropospheric Ozone Assessment Report (TOAR) data set, offers a more comprehensive view on global surface ozone distribution and trends. Here, we report quantitative estimates of the warm-season (April–September) surface ozone trends and resulting health impacts at Chinese cities in 2013–2019. Both the parametric and nonparametric linear trends for 12 ozone metrics relevant to human health and vegetation exposure are derived. We find that all ozone metrics averaged from Chinese urban sites have increased significantly since 2013. The warm-season daily maximum 8-h average (MDA8) ozone levels increased by 2.4 ppb (5.0%) year–1, with over 90% of the sites showing positive trends and 30% with trends larger than 3.0 ppb year–1. These rates are among the fastest trends, even faster in some Chinese cities, compared with the urban ozone trends in any other region worldwide reported in TOAR. Ozone metrics reflecting the cumulative exposure effect on human health and vegetation such as SOMO35 and AOT40 have increased at even faster rates (>10% year–1). We estimate that the total premature respiratory mortalities attributable to ambient MDA8 ozone exposure in 69 Chinese cities are 64,370 in 2019, which has increased by 60% compared to 2013 levels and requires urgent attention.

Interfacial layers (ILs) are prerequisites to form the selective charge transport for high-performance organic photovoltaics (OPVs) but mostly result in considerable parasitic absorption loss. Trimming the ILs down to a mono-molecular level via the self-assembled monolayer is an effective strategy to mitigate parasitic absorption loss. However, such a strategy suffers from inferior electrical contact with low surface coverage on rough surfaces and poor producibility. To address these issues, here, the self-assembled interlayer (SAI) strategy is developed, which involves a thin layer of 2-6 nm to form a full coverage on the substrate via both covalent and van der Waals bonds by using a self-assembled molecule of 2-(9H-carbazol-9-yl) (2PACz). Via the facile spin coating without further rinsing and annealing process, it not only optimizes the electrical and optical properties of OPVs, which enables a world-record efficiency of 20.17% (19.79% certified) but also simplifies the tedious processing procedure. Moreover, the SAI strategy is especially useful in improving the absorbing selectivity for semi-transparent OPVs, which enables a record light utilization efficiency of 5.34%. This work provides an effective strategy of SAI to optimize the optical and electrical properties of OPVs for high-performance and solar window applications.

Objective To investigate whether diets differing in fat content alter the gut microbiota and faecal metabolomic profiles, and to determine their relationship with cardiometabolic risk factors in healthy adults whose diet is in a transition from a traditional low-fat diet to a diet high in fat and reduced in carbohydrate. Methods In a 6-month randomised controlled-feeding trial, 217 healthy young adults (aged 18–35 years; body mass index <28 kg/m 2 ; 52% women) who completed the whole trial were included. All the foods were provided during the intervention period. The three isocaloric diets were: a lower-fat diet (fat 20% energy), a moderate-fat diet (fat 30% energy) and a higher-fat diet (fat 40% energy). The effects of the dietary interventions on the gut microbiota, faecal metabolomics and plasma inflammatory factors were investigated. Results The lower-fat diet was associated with increased α-diversity assessed by the Shannon index (p=0.03), increased abundance of Blautia (p=0.007) and Faecalibacterium (p=0.04), whereas the higher-fat diet was associated with increased Alistipes (p=0.04), Bacteroides (p<0.001) and decreased Faecalibacterium (p=0.04). The concentration of total short-chain fatty acids was significantly decreased in the higher-fat diet group in comparison with the other groups (p<0.001). The cometabolites p-cresol and indole, known to be associated with host metabolic disorders, were decreased in the lower-fat diet group. In addition, the higher-fat diet was associated with faecal enrichment in arachidonic acid and the lipopolysaccharide biosynthesis pathway as well as elevated plasma proinflammatory factors after the intervention. Conclusion Higher-fat consumption by healthy young adults whose diet is in a state of nutrition transition appeared to be associated with unfavourable changes in gut microbiota, faecal metabolomic profiles and plasma proinflammatory factors, which might confer adverse consequences for long-term health outcomes. Trial registration number NCT02355795 ; Results.

Abstract NO removal from exhausted gas is necessary owing to its damage to environment. Meanwhile, the electrochemical ammonia synthesis (EAS) from N 2 suffers from low reaction rate and Faradaic efficiency (FE). Now, an alternative route for ammonia synthesis is proposed from exhaust NO via electrocatalysis. DFT calculations indicate electrochemical NO reduction (NORR) is more active than N 2 reduction (NRR). Via a descriptor‐based approach, Cu was screened out to be the most active transition metal catalyst for NORR to NH 3 owing to its moderate reactivity. Kinetic barrier calculations reveal NH 3 is the most preferred product relative to H 2 , N 2 O, and N 2 on Cu. Experimentally, a record‐high EAS rate of 517.1 μmol cm −2 h −1 and FE of 93.5 % were achieved at −0.9 V vs. RHE using a Cu foam electrode, exhibiting stable electrocatalytic performances with a 100 h run. This work provides an alternative strategy to EAS from exhaust NO, coupled with NO removal.

Electrochemical water splitting is an appealing and promising approach for energy conversion and storage. As a key half-reaction of electricity-driven water splitting, the oxygen evolution reaction (OER) is a sluggish process due to the transfer of four protons and four electrons. Therefore, development of low-cost and robust OER electrocatalysts is of great importance for improving the efficiency of water splitting. Based on the merits of high surface area, rich pore structure, diverse composition and well-defined metal centers, metal-organic frameworks (MOFs) and their derivatives have been widely exploited as OER electrocatalysts. Herein, the current progress on MOFs and their derivatives for OER electrolysis is summarized, highlighting the design principle, synthetic methods and performance for MOF-based materials. In addition, the structure-performance relationships of MOFs and their derivatives toward the OER are discussed, providing valuable insights into rationally developing OER catalysts with high efficiency. The current scientific and technological challenges and future perspectives towards the purpose of sustainable industrial applications are addressed at the end.

Artificial intelligence (AI) has been developing rapidly in recent years in terms of software algorithms, hardware implementation, and applications in a vast number of areas. In this review, we summarize the latest developments of applications of AI in biomedicine, including disease diagnostics, living assistance, biomedical information processing, and biomedical research. The aim of this review is to keep track of new scientific accomplishments, to understand the availability of technologies, to appreciate the tremendous potential of AI in biomedicine, and to provide researchers in related fields with inspiration. It can be asserted that, just like AI itself, the application of AI in biomedicine is still in its early stage. New progress and breakthroughs will continue to push the frontier and widen the scope of AI application, and fast developments are envisioned in the near future. Two case studies are provided to illustrate the prediction of epileptic seizure occurrences and the filling of a dysfunctional urinary bladder.