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The SARS-CoV-2 B.1.1.529 (Omicron) variant contains 15 mutations of the receptor-binding domain (RBD). How Omicron evades RBD-targeted neutralizing antibodies requires immediate investigation. Here we use high-throughput yeast display screening 1 , 2 to determine the profiles of RBD escaping mutations for 247 human anti-RBD neutralizing antibodies and show that the neutralizing antibodies can be classified by unsupervised clustering into six epitope groups (A–F)—a grouping that is highly concordant with knowledge-based structural classifications 3 – 5 . Various single mutations of Omicron can impair neutralizing antibodies of different epitope groups. Specifically, neutralizing antibodies in groups A–D, the epitopes of which overlap with the ACE2-binding motif, are largely escaped by K417N, G446S, E484A and Q493R. Antibodies in group E (for example, S309) 6 and group F (for example, CR3022) 7 , which often exhibit broad sarbecovirus neutralizing activity, are less affected by Omicron, but a subset of neutralizing antibodies are still escaped by G339D, N440K and S371L. Furthermore, Omicron pseudovirus neutralization showed that neutralizing antibodies that sustained single mutations could also be escaped, owing to multiple synergetic mutations on their epitopes. In total, over 85% of the tested neutralizing antibodies were escaped by Omicron. With regard to neutralizing-antibody-based drugs, the neutralization potency of LY-CoV016, LY-CoV555, REGN10933, REGN10987, AZD1061, AZD8895 and BRII-196 was greatly undermined by Omicron, whereas VIR-7831 and DXP-604 still functioned at a reduced efficacy. Together, our data suggest that infection with Omicron would result in considerable humoral immune evasion, and that neutralizing antibodies targeting the sarbecovirus conserved region will remain most effective. Our results inform the development of antibody-based drugs and vaccines against Omicron and future variants. A high-throughput yeast display platform is used to analyse the profiles of mutations in the SARS-CoV-2 receptor-binding domain (RBD) that enable escape from antibodies, and suggests that most anti-RBD antibodies can be escaped by the Omicron variant.

Continuous evolution of Omicron has led to a rapid and simultaneous emergence of numerous variants that display growth advantages over BA.5 (ref. 1 ). Despite their divergent evolutionary courses, mutations on their receptor-binding domain (RBD) converge on several hotspots. The driving force and destination of such sudden convergent evolution and its effect on humoral immunity remain unclear. Here we demonstrate that these convergent mutations can cause evasion of neutralizing antibody drugs and convalescent plasma, including those from BA.5 breakthrough infection, while maintaining sufficient ACE2-binding capability. BQ.1.1.10 (BQ.1.1 + Y144del), BA.4.6.3, XBB and CH.1.1 are the most antibody-evasive strains tested. To delineate the origin of the convergent evolution, we determined the escape mutation profiles and neutralization activity of monoclonal antibodies isolated from individuals who had BA.2 and BA.5 breakthrough infections 2 , 3 . Owing to humoral immune imprinting, BA.2 and especially BA.5 breakthrough infection reduced the diversity of the neutralizing antibody binding sites and increased proportions of non-neutralizing antibody clones, which, in turn, focused humoral immune pressure and promoted convergent evolution in the RBD. Moreover, we show that the convergent RBD mutations could be accurately inferred by deep mutational scanning profiles 4 , 5 , and the evolution trends of BA.2.75 and BA.5 subvariants could be well foreseen through constructed convergent pseudovirus mutants. These results suggest that current herd immunity and BA.5 vaccine boosters may not efficiently prevent the infection of Omicron convergent variants. Convergent mutations in hotspots of the SARS-CoV-2 Omicron receptor-binding domain can cause immune evasion and maintain sufficient ACE2-binding capability.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron sublineages BA.2.12.1, BA.4 and BA.5 exhibit higher transmissibility than the BA.2 lineage 1 . The receptor binding and immune-evasion capability of these recently emerged variants require immediate investigation. Here, coupled with structural comparisons of the spike proteins, we show that BA.2.12.1, BA.4 and BA.5 (BA.4 and BA.5 are hereafter referred collectively to as BA.4/BA.5) exhibit similar binding affinities to BA.2 for the angiotensin-converting enzyme 2 (ACE2) receptor. Of note, BA.2.12.1 and BA.4/BA.5 display increased evasion of neutralizing antibodies compared with BA.2 against plasma from triple-vaccinated individuals or from individuals who developed a BA.1 infection after vaccination. To delineate the underlying antibody-evasion mechanism, we determined the escape mutation profiles 2 , epitope distribution 3 and Omicron-neutralization efficiency of 1,640 neutralizing antibodies directed against the receptor-binding domain of the viral spike protein, including 614 antibodies isolated from people who had recovered from BA.1 infection. BA.1 infection after vaccination predominantly recalls humoral immune memory directed against ancestral (hereafter referred to as wild-type (WT)) SARS-CoV-2 spike protein. The resulting elicited antibodies could neutralize both WT SARS-CoV-2 and BA.1 and are enriched on epitopes on spike that do not bind ACE2. However, most of these cross-reactive neutralizing antibodies are evaded by spike mutants L452Q, L452R and F486V. BA.1 infection can also induce new clones of BA.1-specific antibodies that potently neutralize BA.1. Nevertheless, these neutralizing antibodies are largely evaded by BA.2 and BA.4/BA.5 owing to D405N and F486V mutations, and react weakly to pre-Omicron variants, exhibiting narrow neutralization breadths. The therapeutic neutralizing antibodies bebtelovimab 4 and cilgavimab 5 can effectively neutralize BA.2.12.1 and BA.4/BA.5, whereas the S371F, D405N and R408S mutations undermine most broadly sarbecovirus-neutralizing antibodies. Together, our results indicate that Omicron may evolve mutations to evade the humoral immunity elicited by BA.1 infection, suggesting that BA.1-derived vaccine boosters may not achieve broad-spectrum protection against new Omicron variants. Biochemical and structural studies of the interactions between antibodies and spike proteins from SARS-CoV-2 Omicron subvariants indicate how these variants have evolved to escape antibody-mediated neutralization.

The vascular endothelial growth factor receptor 2 (VEGFR-2) and the mesenchymal-epithelial transition factor (c-Met) are critical in the pathogenesis and progression of various cancers by synergistically contributing to angiogenesis and tumor progression. The development of dual-target inhibitors for VEGFR-2 and c-Met holds promise for more effective cancer therapies that could overcome tumor cell resistance, a limitation often observed with inhibitors targeting a single receptor. In this study, a computational virtual screening approach involving drug likeness evaluation, pharmacophore modeling and molecular docking was employed to identify VEGFR-2/c-Met dual-target inhibitors from ChemDiv database. Subsequent molecular dynamics (MD) simulations and MM/PBSA calculations were conducted to assess the stability of the protein-ligand interactions. From the virtual screening process, 18 hit compounds were identified to exhibit potential inhibitory activity against VEGFR-2 and c-Met. Among them, compound17924 and compound4312 possessed the best inhibitory potential according to our screening criteria. The analysis of the MD simulation results indicated that compound17924 and compound4312 showed superior binding free energies to both VEGFR-2 and c-Met when compared to the positive ligands. These findings suggested that both compounds were promising candidates for further drug development and could potentially serve as improved alternatives of cancer therapeutics.

Snow cover on the Qinghai–Tibetan Plateau (QTP) plays a significant role in the global climate system and is an important water resource for rivers in the high-elevation region of Asia. At present, passive microwave (PMW) remote sensing data are the only efficient way to monitor temporal and spatial variations in snow depth at large scale. However, existing snow depth products show the largest uncertainties across the QTP. In this study, MODIS fractional snow cover product, point, line and intense sampling data are synthesized to evaluate the accuracy of snow cover and snow depth derived from PMW remote sensing data and to analyze the possible causes of uncertainties. The results show that the accuracy of snow cover extents varies spatially and depends on the fraction of snow cover. Based on the assumption that grids with MODIS snow cover fraction > 10 % are regarded as snow cover, the overall accuracy in snow cover is 66.7 %, overestimation error is 56.1 %, underestimation error is 21.1 %, commission error is 27.6 % and omission error is 47.4 %. The commission and overestimation errors of snow cover primarily occur in the northwest and southeast areas with low ground temperature. Omission error primarily occurs in cold desert areas with shallow snow, and underestimation error mainly occurs in glacier and lake areas. With the increase of snow cover fraction, the overestimation error decreases and the omission error increases. A comparison between snow depths measured in field experiments, measured at meteorological stations and estimated across the QTP shows that agreement between observation and retrieval improves with an increasing number of observation points in a PMW grid. The misclassification and errors between observed and retrieved snow depth are associated with the relatively coarse resolution of PMW remote sensing, ground temperature, snow characteristics and topography. To accurately understand the variation in snow depth across the QTP, new algorithms should be developed to retrieve snow depth with higher spatial resolution and should consider the variation in brightness temperatures at different frequencies emitted from ground with changing ground features.

Accurate precipitation measurements are essential for understanding hydrological processes in high-altitude regions. Conventional gauge measurements often yield large underestimations of actual precipitation, prompting the development of statistical methods to correct the measurement bias. However, the complex conditions at high altitudes pose additional challenges to the statistical methods. To improve the correction of precipitation measurements in high-altitude areas, we selected the Yakou station, situated at an altitude of 4147 m on the Tibetan plateau, as the study site. In this study, we employed the machine learning method XGBoost regression to correct precipitation measurements using meteorological variables and remote sensing data, including Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Additionally, we examined the transferability of this method between different stations in our study site, Norway, and the United States. Our results show that the Yakou station experiences a large underestimation of precipitation, with a magnitude of 51.4%. This is significantly higher than similar measurements taken in the Arctic or lower altitudes. Furthermore, the remote sensing precipitation datasets underestimated precipitation when compared to the Double Fence Intercomparison Reference (DFIR) precipitation observation. Our findings suggest that the machine learning method outperformed the traditional statistical method in accuracy metrics and frequency distribution. Introducing remote sensing data, especially the GSMaP precipitation, could potentially replace the role of in situ wind speed in precipitation correction, highlighting the potential of remote sensing data for correcting precipitation rather than in situ meteorological observation. Moreover, our results indicate that the machine learning method with remote sensing data demonstrated better transferability than the traditional statistical method when we cross-validated the method with sites located in different countries. This study offers a promising strategy for obtaining more accurate precipitation measurements in high-altitude regions.

Excessive cadmium (Cd) accumulation in grains of rice ( L.) is a risk to food security. The transporters in the nodes of rice are involved in the distribution of mineral elements including toxic elements to different tissues such as grains. However, the mechanism of Cd accumulation in grains is largely unknown. Here, we report a node-expressed transporter gene, , a putative cation/calcium (Ca) exchanger, mediating Cd accumulation in the grains of rice. Knockout of caused a remarkable reduction of Cd content in the grains. Further study showed that disruption of this gene led to a reduced root-to-shoot translocation ratio of Cd. Moreover, Cd distribution was also disturbed in different levels of internode and leaf. OsCCX2 is localized to plasma membrane, and is mainly expressed in xylem region of vascular tissues at the nodes. OsCCX2 might function as an efflux transporter, responsible for Cd loading into xylem vessels. Therefore, our finding revealed a novel Cd transporter involved in grain Cd accumulation, possibly via a Ca transport pathway in the nodes of rice.

What are the main findings? * Optimized a multi-level decision tree cloud detection algorithm for cloud-snow discrimination. * Utilized the distinct bright temperature difference at 3.7 μm and 11 μm to enhance cloud detection performance. Optimized a multi-level decision tree cloud detection algorithm for cloud-snow discrimination. Utilized the distinct bright temperature difference at 3.7 μm and 11 μm to enhance cloud detection performance. What are the implications of the main findings? * Outperforms existing algorithms and significantly reduces the false cloud detection in snow-covered areas. * Provides accurate and efficient cloud detection in the Northern Hemisphere to support related cryospheric studies. Outperforms existing algorithms and significantly reduces the false cloud detection in snow-covered areas. Provides accurate and efficient cloud detection in the Northern Hemisphere to support related cryospheric studies. High-resolution optical remote sensing imagery plays a crucial role in monitoring the Earth’s surface. However, cloud obstruction and spectral confusion between clouds and snow significantly compromise data quality and application reliability, leading to persistent cloud overestimation in optical remote sensing products. To address this challenge, this study developed an enhanced multi-threshold cloud detection algorithm based on AVHRR surface reflectance data, which incorporates dynamic threshold optimization within a multi-level decision tree framework. Utilizing Landsat 5 SR as reference data, the algorithm demonstrated superior cloud-snow discrimination capability, achieving an overall accuracy (OA) of 82.08%, with the user’s accuracy (UA) and F-score reaching 79.41% and 82.55%. Comparative evaluation demonstrates that the proposed algorithm outperforms two existing algorithms, with OA improvements of 17.42% and 7.93%, respectively. A particularly notable enhancement is the significant reduction in cloud misidentification, as reflected by UA increases of 21.02% and 13.21%. These improvements are most pronounced in high-altitude mountainous regions with snow cover. The algorithm maintains computational efficiency while providing reliable cloud masking, thereby offering enhanced support for snow cover monitoring and broader environmental applications.

Leaf rust, caused by Puccinia triticina Eriksson ( Pt ), is one of the most severe foliar diseases of wheat. Breeding for leaf rust resistance is a practical and sustainable method to control this devastating disease. Here, we report the identification of Lr47 , a broadly effective leaf rust resistance gene introgressed into wheat from Aegilops speltoides . Lr47 encodes a coiled-coil nucleotide-binding leucine-rich repeat protein that is both necessary and sufficient to confer Pt resistance, as demonstrated by loss-of-function mutations and transgenic complementation. Lr47 introgression lines with no or reduced linkage drag are generated using the Pairing homoeologous1 mutation, and a diagnostic molecular marker for Lr47 is developed. The coiled-coil domain of the Lr47 protein is unable to induce cell death, nor does it have self-protein interaction. The cloning of Lr47 expands the number of leaf rust resistance genes that can be incorporated into multigene transgenic cassettes to control this devastating disease. Leaf rust is one of the most severe foliar diseases of wheat. Here, the authors report the cloning of Lr47 , a broadly effective leaf rust resistance gene introgressed into wheat from Aegilops speltoides , and show it encodes a coiled-coil nucleotide-binding leucine-rich repeat protein.