Recently, nanobody-based therapeutics have emerged as a highly effective strategy for COVID-19 treatment. However, camelid-derived nanobodies often require humanization engineering to reduce immunogenicity in clinical applications while simultaneously preserving their target-binding affinities. Here, we employed a computational and engineering approach to optimize the binding affinities of complementarity-determining region (CDR)-grafted humanized variants of the camelid-derived nanobody Nb2-67, which exhibits potent SARS-CoV-2 neutralization. By grafting the three CDR loops of Nb2-67 onto the humanized scaffold of the approved therapeutic nanobody Caplacizumab and refining the target-binding interface, we generated five nanobody variants with improved computational humanness scores. Three of these variants (Nb491, Nb273, and Nb1052) retained neutralizing activity. To further enhance their potency, we fused these variants to a self-assembling scaffold, generating three multivalent constructs with higher humanness scores. Pseudovirus assays showed that all the trivalent nanobodies exhibited picomolar neutralizing potency comparable to the original trivalent Nb2-67. Our study presents a novel computational and multivalent engineering strategy that effectively restores the antiviral efficacy of humanized CDR-grafted nanobody variants, offering a valuable approach for developing nanobody-based therapeutics against COVID-19 and other diseases.

Millimeter-wave (mmW) automotive radar imaging technology shows significant promise in advanced driver assistance systems (ADAS). Super-resolution imaging methods can be employed the limited aperture length of automotive radar to improve azimuth (angular) resolution. However, automotive radar images typically exhibit large dynamic range (LDR) and large scene (LS), leading to pay extensive computational complexity and storage demands when striving for higher image quality. To tackle this challenge, a fast$l_{q}$sparsity-driven imaging method with adaptive-focusing framework (FLSD-AF) for mmWave automotive radar super-resolution imaging in this article. First, in AF framework, a detect-before-imaging (DBI) is proposed to make echo data to adaptive focused on potential target area (PTR), thereby reducing the dimension of the effective data to reduce computational complexity and storage demands. Second, a subspace-phase-compensation (SPC) is proposed to reduces storage demands of the measurement matrix by addressing the imaging model mismatch in near-field under LS. Finally, a fast$l_{q}$sparsity-driven (FLSD) imaging method is proposed. It employs$l_{q}$-norm nonconvex penalty function to address the biased problem to improve imaging quality under LDR, meanwhile the computational complexity of the matrix operation is greatly reduced by utilizing joint Kailath-Variant (K-V) formula and Gohberg-Semencul (G-S) factorization. In summary, the proposed FLSD-AF not only substantially enhances the imaging performance, but also significantly diminishes the storage demands and computational complexity under LDR and LS. The results of simulations and experimental data all verify the proposed method.

Millimeter-wave (mmW) automotive radar is widely used in advanced driving assistance systems. Coprime array can improve the imaging resolution of the small size automotive radar with limited number antennas. However, the covariance matrix of coprime array exhibits data missing compared to uniform linear array (ULA), which leads to the serious high-sidelobes interference. To solve this problem, a covariance matrix completion imaging method is proposed. Firstly, a Toeplitz matrix is got by using the covariance matrix of missing data in the coprime array. Secondly, based on the Toeplitz matrix, a nuclear norm optimization problem is established to complete the data missing of the covariance matrix. Finally, by vectorizing the covariance matrix of data completion and directly using fast Fourier transform (FFT) to obtain low sidelobes image and the imaging resolution is improved. The simulation results show that the proposed method can effectively suppress high-sidelobes interference meanwhile improve imaging resolution.

Millimeter-wave (mmW) automotive synthetic aperture radar (Auto-SAR) technology holds significant promise for advanced driver assistance systems (ADASs). Sparse imaging methods can improve the quality of Auto-SAR images, such as suppressing sidelobes and noise. However, the$l_{1}$convex regularization-based sparse imaging methods suffer from the bias estimation, which reduces the target amplitude and ignores the association between scatterers, weakening the target structure. To address these issues, we proposed joint generalized$l_{q}$and convolutional (Glq-Con) regularization to enhance mmW Auto-SAR sparse imaging in this article. First, to improve the target amplitude, we propose utilizing the nonconvexity of Glq to reduce the bias effect; meanwhile, the global convergence of Glq ensures the imaging accuracy. Then, considering the continuity of the imaging target in driving scenes, we propose to utilize convolution regularization to modify the previously reconstructed amplitude of Glq to improve the target structure. Besides, to reduce computational complexity, we establish an efficient sparse imaging model. In this model, the fast Fourier transform (FFT) operator is employed to approximate complex matrix operation in the iterative process. We also use an efficient optimizer to solve the imaging model. Finally, both simulations and measured typical driving scenario experiments demonstrate that the proposed method significantly enhanced the Auto-SAR image, especially for the targets of weak scatterers.

In this study, we address the challenges associated with Video Synthetic Aperture Radar (Video SAR) shadow tracking, a technique used for continuous monitoring of ground moving targets. Due to challenges such as changes in shadow appearance, low contrast between shadow and background, and scene occlusion in Video SAR, existing methods often encounter extensive matching errors in the data association process, resulting in unsatisfactory tracking performance. To overcome these issues, we propose a novel method, GNN-JFL, which is based on joint motion-appearance feature extraction and graph neural data association. This method uses the detector as a flexible plugin and introduces two key improvements in the tracker section to enhance tracking accuracy. Firstly, we introduce joint feature learning to extract the complementary appearance and motion features from shadow shapes and positions, obtaining more robust feature representations to improve tracking performance under intricate challenges. Secondly, by organically integrating Multi-object Tracking (MOT) problems and Graph Neural Networks (GNN), we propose a novel GNN-based shadow tracking architecture, which utilizes graph relationships to learn the associations between shadows for more accurate tracking predictions. Our method is validated using two measured datasets and demonstrate superior performance in terms of multi-object tracking accuracy (MOTA). It outperforms the suboptimal method by 4.2% and 3.6% in the two datasets, respectively. This research contributes to the advancement of continuous monitoring techniques employing Video SAR shadow tracking.

Millimeter-wave (mmW) automotive radar imaging technology has shown significant potential in autopilot assistance systems. The automotive radar with limited aperture can achieve high-resolution image by synthetic aperture technology. However, conventional imaging methods result in strong background clutter and high sidelobe interferences. To solve these problems, we propose a target-oriented Bayesian compressive sensing imaging method with region-adaptive extractor (TO-BCS-RAE) for mmW automotive radar imaging. (1) First, to extract the potential-target-regions (PTR) as well as subtracting the background clutters outside the PTR in a high-resolution initial image (by synthetic aperture), a region-adaptive extractor (RAE) is developed with utilizing 2D CFAR, isolated-point removing, and imaging clustering. Meanwhile, a more accurate prior distribution of target scattering points can be obtained in the PTR. (2) Then, to suppress the background clutters while enhancing the smooth structure of targets in the PTR, a target-oriented Bayesian compressive sensing (TO-BCS) imaging method is proposed by combining the prior probability distributions and inherent continuity of the target scattering points. It can also effectively reduce the high sidelobes. (3) Finally, to verify the effectiveness of TO-BCS-RAE, we conduct experiments on real data collected from an automotive radar with a vehicle platform in three typical driving scenarios. Both simulated and experimental results show the imaging quality of the proposed imaging method over conventional BP, OMP and ISTA methods.