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SARS-CoV-2, the causative agent of COVID-19 1 , features a receptor-binding domain (RBD) for binding to the host cell ACE2 protein 1 – 6 . Neutralizing antibodies that block RBD-ACE2 interaction are candidates for the development of targeted therapeutics 7 – 17 . Llama-derived single-domain antibodies (nanobodies, ~15 kDa) offer advantages in bioavailability, amenability, and production and storage owing to their small sizes and high stability. Here, we report the rapid selection of 99 synthetic nanobodies (sybodies) against RBD by in vitro selection using three libraries. The best sybody, MR3 binds to RBD with high affinity ( K D  = 1.0 nM) and displays high neutralization activity against SARS-CoV-2 pseudoviruses (IC 50  = 0.42 μg mL −1 ). Structural, biochemical, and biological characterization suggests a common neutralizing mechanism, in which the RBD-ACE2 interaction is competitively inhibited by sybodies. Various forms of sybodies with improved potency have been generated by structure-based design, biparatopic construction, and divalent engineering. Two divalent forms of MR3 protect hamsters from clinical signs after live virus challenge and a single dose of the Fc-fusion construct of MR3 reduces viral RNA load by 6 Log 10 . Our results pave the way for the development of therapeutic nanobodies against COVID-19 and present a strategy for rapid development of targeted medical interventions during an outbreak. Here, the authors report the engineering, structural and biological characterization of synthetic nanobodies (sybodies) that display potent therapeutic activity against SARS-CoV-2 infection in animal models via targeting the virus receptor-binding domain.

The stability of the landslide behind Yang'erpingfang, Zuopo Village, Luluo Town, Xingtai County is calculated and analyzed using the strength reduction method with MIDAS GTS finite element numerical simulation software, based on the treatment of Zhuwenping landslide in Bai'an Township, Xingtai County.

An essential step for SARS‐CoV‐2 infection is the attachment to the host cell receptor by its Spike receptor‐binding domain (RBD). Most of the existing RBD‐targeting neutralizing antibodies block the receptor‐binding motif (RBM), a mutable region with the potential to generate neutralization escape mutants. Here, we isolated and structurally characterized a non‐RBM‐targeting monoclonal antibody (FD20) from convalescent patients. FD20 engages the RBD at an epitope distal to the RBM with a K D of 5.6 nM, neutralizes SARS‐CoV‐2 including the current Variants of Concern such as B.1.1.7, B.1.351, P.1, and B.1.617.2 (Delta), displays modest cross‐reactivity against SARS‐CoV, and reduces viral replication in hamsters. The epitope coincides with a predicted “ideal” vulnerability site with high functional and structural constraints. Mutation of the residues of the conserved epitope variably affects FD20‐binding but confers little or no resistance to neutralization. Finally, in vitro mode‐of‐action characterization and negative‐stain electron microscopy suggest a neutralization mechanism by which FD20 destructs the Spike. Our results reveal a conserved vulnerability site in the SARS‐CoV‐2 Spike for the development of potential antiviral drugs. SYNOPSIS A monoclonal antibody (FD20) from convalescent COVID‐19 patients has been isolated and structurally and biologically characterized. Various SARS‐CoV‐2 strains, including the Alpha, Beta, Gamma, and Delta variants, and naturally occurring epitope mutants, can be neutralized by FD20 with similar potency. A broadly active mAb is identified with consistent neutralizing activity against 14 SARS‐CoV‐2 strains/mutants and weak activity against SARS‐CoV. The conservation of FD20's epitope residues is supported by their low mutation frequencies both in nature and in laboratory experiments. A neutralizing mechanism through which the surface glycoprotein is destructed by FD20 is proposed based on electron microscopy evidence. Graphical Abstract A monoclonal antibody (FD20) from convalescent COVID‐19 patients has been isolated and structurally and biologically characterized. Various SARS‐CoV‐2 strains, including the Alpha, Beta, Gamma, and Delta variants, and naturally occurring epitope mutants, can be neutralized by FD20 with similar potency.

On the basis of elaborating on the regional geological background, this paper analyzes the lithological and sedimentary characteristics and explorative prospects of new strata with oil and gas in the southern and surrounding areas of Dayangshu Basin. Based on the latest high-precision airborne gravity and magnetic comprehensive survey data, combined with the latest data from geological explorations, physical surveys, and drilling, and the use of basin structure layering combination methods, we clarified the characteristics of the bottom of the Jurassic–Cretaceous and the occurrence characteristics of the Upper Paleozoic in the study area and revealed the determinative effect of multi-period structures on the most important sedimentary layers. Then, we summarized the accumulation conditions and prediction methods of hydrocarbons and proposed the oil and gas prospects of these deep new strata. The results show that the Liuhe Sag in Dayangshu Basin, the depression in the northeast of Longjiang Basin, and the northern parts of the Taikang swell have good source–reservoir–cap combination conditions and favorable structural characteristics for oil and gas, where there is a high potential for exploration.

PurposeSoil Phosphorous (P) availability is critical for many important ecological processes and oil-contaminated soil remediation. Despites a few studies confirmed directly effects of crude oil exposure on soil Phosphorus-cycling (P-cycling), how soil microbes and functional genes affiliated with P-cycling respond to crude oil remains poorly understood.MethodsHere, metagenomics was implemented to analyze variations in the microbial community structure and potential functions associated with P transformation in the coastal soil contaminated by crude oil.ResultsResults showed a dramatic scarcity of P in the contaminated soil. Microbial inorganic P solubilization was governed by genes gcd and ppx in CK soil. In contrast, genes encoding C-P lyase (phnGHIJKLN) and alkaline phosphatase (phoA) displayed significantly greater abundances in the contaminated soils. Taxa annotation revealed that oil contamination altered the structure of the P-cycling microbial community with a bias towards those with oligotrophic characteristics. Specifically, the oil-contaminated soils were characterized by a stronger contribution of Proteobacteria, Ascomycota and Firmicutes. Overall, the strategy for acquiring P in the CK is inorganic P solubilization, while it converted to organic P mineralization under petroleum contamination. Soil N/P ratio played a key role in affecting P-cycling functional genes.ConclusionOur results highlighted that oil contamination with unbalanced N/P ratio greatly altered the microbial strategy for obtaining available P (AP) in soil. A better understanding of P-cycling mechanism exposed to oil contamination and further scientifically regulating it may set the stage for in-depth improvement for current bioremediation practices.

Nowadays, Internet has become an indispensable part of daily life and is used in many fields. Due to the large amount of Internet traffic, computers are subject to various security threats, which may cause serious economic losses and even endanger national security. It is hoped that an effective security method can systematically classify intrusion data in order to avoid leakage of important data or misuse of data. As machine learning technology matures, deep learning is widely used in various industries. Combining deep learning with network security and intrusion detection is the current trend. In this paper, the problem of data classification in intrusion detection system is studied. We propose an intrusion detection model based on stack bidirectional long shortterm memory (LSTM), introduce stack bidirectional LSTM into the field of intrusion detection and apply it to the intrusion detection. In order to determine the appropriate parameters and structure of stack bidirectional LSTM network, we have carried out experiments on various network structures and parameters and analyzed the experimental results. The classic KDD Cup’1999 dataset was selected for experiments so that we can obtain convincing and comparable results. Experimental results derived from the KDD Cup’1999 dataset show that the network with three hidden layers containing 80 LSTM cells is superior to other algorithms in computational cost and detection performance due to stack bidirectional LSTM model’s ability to review time and correlate with connected records continuously. The experiment shows the effectiveness of stack bidirectional LSTM network in intrusion detection.

In the plant innate immune system, pattern recognition receptor (PRR) and nucleotide-binding domain leucine-rich repeat (NLR) proteins recognize pathogens and activate defenses. To prevent excessive immune responses that could affect growth, plants regulate PRRs and NLRs at the transcriptional and post-transcriptional levels. Poised or bivalent chromatin states, marked by the simultaneous presence of active and repressive epigenetic modifications, maintain genes in a transcriptionally primed state, keeping their expression low while enabling their rapid activation in response to stress. Here, we investigated how poised chromatin states regulate PRR and NLR genes in soybean ( Glycine max ). Our integrative epigenomic and transcriptomic analysis revealed that although NLR and PRR genes both harbor abundant active and repressive histone modifications and exhibit high chromatin accessibility, their basal expression levels remain relatively low. Moreover, clustered NLR and PRR genes residing within the same topologically associating domains shared similar chromatin states and expression dynamics, suggesting coordinated control. These gene families had distinct epigenetic features: NLR genes displayed narrow H3K27me3 peaks together with strong pausing of RNA Polymerase II at their 5′ ends, whereas PRR genes were characterized by broader H3K27me3 peaks. Together, our results shed light on the role of poised chromatin states in coordinating growth and defense responses in soybean.

There are two difficult in the existing image restoration methods. One is that the method is difficult to repair the image with a large damaged, the other is the result of image completion is not good and the speed is slow. With the development and application of deep learning, the image repair algorithm based on generative adversarial networks can repair images by simulating the distribution of data. In the process of image completion, the first step is trained the generator to simulate data distribution and generate samples. Then a large number of falsified images are quickly generated using the generative adversarial network and search for the code of the closest damaged image. Finally, the generator generates missing content by using this code. On this basis, this paper combines the semantic loss function and the perceptual loss function. Experimental result show that the method successfully predicts the information of large areas missing in the image, and realizes the photorealism, producing clearer and more consistent results than previous methods.

Image conversion refers to converting an image from one style to another and ensuring that the content of the image remains unchanged. Using Generative Adversarial Networks (GAN) for image conversion can achieve good results. However, if there are enough samples, any image in the target domain can be mapped to the same set of inputs. On this basis, the Cycle Consistency Generative Adversarial Network (CycleGAN) was developed. This article verifies and discusses the advantages and disadvantages of the CycleGAN model in image style conversion. CycleGAN uses two generator networks and two discriminator networks. The purpose is to learn the mapping relationship and inverse mapping relationship between the source domain and the target domain. It can reduce the mapping and improve the quality of the generated image. Through the idea of loop, the loss of information in image style conversion is reduced. When evaluating the results of the experiment, the degree of retention of the input image content will be judged. Through the experimental results, CycleGAN can understand the artist’s overall artistic style and successfully convert real landscape paintings. The advantage is that most of the content of the original picture can be retained, and only the texture line of the picture is changed to a level similar to the artist’s style.