3D scene graph prediction from point clouds

Background In this study,we propose a novel 3D scene graph prediction approach for scene understanding from point clouds.Methods It can automatically organize the entities of a scene in a graph,where objects are nodes and their relationships are modeled as edges.More specifically,we employ the DGCNN to capture the features of objects and their relationships in the scene.A Graph Attention Network(GAT)is introduced to exploit latent features obtained from the initial estimation to further refine the object arrangement in the graph structure.A one loss function modified from cross entropy with a variable weight is proposed to solve the multi-category problem in the prediction of object and predicate.Results Experiments reveal that the proposed approach performs favorably against the state-of-the-art methods in terms of predicate classification and relationship prediction and achieves comparable performance on object classification prediction.Conclusions The 3D scene graph prediction approach can form an abstract description of the scene space from point clouds.

Viral vectored vaccines:design,development,preventive and therapeutic applications in human diseases

Human diseases,particularly infectious diseases and cancers,pose unprecedented challenges to public health security and the global economy.The development and distribution of novel prophylactic and therapeutic vaccines are the prioritized countermeasures of human disease.Among all vaccine platforms,viral vector vaccines offer distinguished advantages and represent prominent choices for pathogens that have hampered control efforts based on conventional vaccine approaches.Currently,viral vector vaccines remain one of the best strategies for induction of robust humoral and cellular immunity against human diseases.Numerous viruses of different families and origins,including vesicular stomatitis virus,rabies virus,parainfluenza virus,measles virus,Newcastle disease virus,influenza virus,adenovirus and poxvirus,are deemed to be prominent viral vectors that differ in structural characteristics,design strategy,antigen presentation capability,immunogenicity and protective efficacy.This review summarized the overall profile of the design strategies,progress in advance and steps taken to address barriers to the deployment of these viral vector vaccines,simultaneously highlighting their potential for mucosal delivery,therapeutic application in cancer as well as other key aspects concerning the rational application of these viral vector vaccines.Appropriate and accurate technological advances in viral vector vaccines would consolidate their position as a leading approach to accelerate breakthroughs in novel vaccines and facilitate a rapid response to public health emergencies.

Nanobodies with cross-neutralizing activity provide prominent therapeutic efficacy in mild and severe COVID-19 rodent models

The weakened protective efficacy of COVID-19 vaccines and antibodies caused by SARS-CoV-2 variants presents a global health emergency,which underscores the urgent need for universal therapeutic antibody intervention for clinical patients.Here,we screened three alpacas-derived nanobodies(Nbs)with neutralizing activity from twenty RBD-specific Nbs.The three Nbs were fused with the Fc domain of human IgG,namely aVHH-11-Fc,aVHH-13-Fc and aVHH-14-Fc,which could specifically bind RBD protein and competitively inhibit the binding of ACE2 receptor to RBD.They effectively neutralized SARS-CoV-2 pseudoviruses D614G,Alpha,Beta,Gamma,Delta,and Omicron sub-lineages BA.1,BA.2,BA.4,and BA.5 and authentic SARS-CoV-2 prototype,Delta,and Omicron BA.1,BA.2 strains.In mice-adapted COVID-19 severe model,intranasal administration of aVHH-11-Fc,aVHH-13-Fc and aVHH-14-Fc effectively protected mice from lethal challenges and reduced viral loads in both the upper and lower respiratory tracts.In the COVID-19 mild model,aVHH-13-Fc,which represents the optimal neutralizing activity among the above three Nbs,effectively protected hamsters from the challenge of SARS-CoV-2 prototype,Delta,Omicron BA.1 and BA.2 by significantly reducing viral replication and pathological alterations in the lungs.In structural modeling of aVHH-13 and RBD,aVHH-13 binds to the receptor-binding motif region of RBD and interacts with some highly conserved epitopes.Taken together,our study illustrated that alpaca-derived Nbs offered a therapeutic countermeasure against SARS-CoV-2,including those Delta and Omicron variants which have evolved into global pandemic strains.

An inactivated recombinant rabies virus chimerically expressed RBD induces humoral and cellular immunity against SARS-CoV-2 and RABV

Many studies suggest that severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)can infect various animals and transmit among animals,and even to humans,posing a threat to humans and animals.There is an urgent need to develop inexpensive and efficient animal vaccines to prevent and control coronavirus disease 2019(COVID-19)in animals.Rabies virus(RABV)is another important zoonotic pathogen that infects almost all warmblooded animals and poses a great public health threat.The present study constructed two recombinant chimeric viruses expressing the S1 and RBD proteins of the SARS-CoV-2 Wuhan01 strain based on a reverse genetic system of the RABV SRV9 strain and evaluated their immunogenicity in mice,cats and dogs.The results showed that both inactivated recombinant viruses induced durable neutralizing antibodies against SARS-CoV-2 and RABV and a strong cellular immune response in mice.Notably,inactivated SRV-nCoV-RBD induced earlier antibody production than SRV-nCoV-S1,which was maintained at high levels for longer periods.Inactivated SRV-nCoV-RBD induced neutralizing antibodies against both SARS-CoV-2 and RABV in cats and dogs,with a relatively broadspectrum cross-neutralization capability against the SARS-CoV-2 pseudoviruses including Alpha,Beta,Gamma,Delta,and Omicron,showing potential to be used as a safe bivalent vaccine candidate against COVID-19 and rabies in animals.

Robust and efficient edge-based visual odometry

Visual odometry,which aims to estimate relative camera motion between sequential video frames,has been widely used in the fields of augmented reality,virtual reality,and autonomous driving.However,it is still quite challenging for stateof-the-art approaches to handle low-texture scenes.In this paper,we propose a robust and efficient visual odometry algorithm that directly utilizes edge pixels to track camera pose.In contrast to direct methods,we choose reprojection error to construct the optimization energy,which can effectively cope with illumination changes.The distance transform map built upon edge detection for each frame is used to improve tracking efficiency.A novel weighted edge alignment method together with sliding window optimization is proposed to further improve the accuracy.Experiments on public datasets show that the method is comparable to stateof-the-art methods in terms of tracking accuracy,while being faster and more robust.