Suppression of MHC class I surface expression by calreticulin's P-domain in a calreticulin deficient cell line

AbstractCalreticulin (CRT) is an important chaperone protein, comprising an N-domain, P-domain and C-domain. It is involved in the folding and assembly of multi-component protein complexes in the endoplasmic reticulum, and plays a critical role in MHC class I antigen processing and presentation. To dissect the functional role and molecular basis of individual domains of the protein, we have utilized individual domains to rescue impaired protein assembly in a CRT deficient cell line. Unexpectedly, both P-domain fragment and NP domain of CRT not only failed to rescue defective cell surface expression of MHC class I molecules but further inhibited their appearance on the surface of cells. Formation of the TAP-associated peptide-loading complex and trafficking of the few detectable MHC class I molecules were not significantly impaired. Instead, this further suppression of MHC class I molecules on the cell surface appears due to the complex missing antigenic peptides, the third member of fully assembled MHC class I molecules. Therefore the P-domain of calreticulin appears to play a significant role in antigen presentation by MHC class I molecules

Multiple-Crop Human Mesh Recovery with Contrastive Learning and Camera Consistency in A Single Image

We tackle the problem of single-image Human Mesh Recovery (HMR). Previous approaches are mostly based on a single crop. In this paper, we shift the single-crop HMR to a novel multiple-crop HMR paradigm. Cropping a human from image multiple times by shifting and scaling the original bounding box is feasible in practice, easy to implement, and incurs neglectable cost, but immediately enriches available visual details. With multiple crops as input, we manage to leverage the relation among these crops to extract discriminative features and reduce camera ambiguity. Specifically, (1) we incorporate a contrastive learning scheme to enhance the similarity between features extracted from crops of the same human. (2) We also propose a crop-aware fusion scheme to fuse the features of multiple crops for regressing the target mesh. (3) We compute local cameras for all the input crops and build a camera-consistency loss between the local cameras, which reward us with less ambiguous cameras. Based on the above innovations, our proposed method outperforms previous approaches as demonstrated by the extensive experiments

Characterization of a multidrug-resistant porcine Klebsiella pneumoniae sequence type 11 strain coharboring blaKPC-2 and fosA3 on two novel hybrid plasmids

The occurrence of carbapenemase-producing Enterobacteriaceae (CPE) poses a considerable risk for public health. The gene for Klebsiella pneumoniae carbapenemase-2 (KPC-2) has been reported in many countries worldwide, and KPC-2-producing strains are mainly of human origin. In this study, we identified two novel hybrid plasmids that carry either blaKPC-2 or the fosfomycin resistance gene fosA3 in the multiresistant K. pneumoniae isolate K15 of swine origin in China. The blaKPC-2-bearing plasmid pK15-KPC was a fusion derivative of an IncF33:A−:B− incompatibility group (Inc) plasmid and chromosomal sequences of K. pneumoniae (CSKP). A 5-bp direct target sequence duplication (GACTA) was identified at the boundaries of the CSKP, suggesting that the integration might have been due to a transposition event. The blaKPC-2 gene on pK15-KPC was in a derivative of ΔTn6296-1. The multireplicon fosA3-carrying IncN-IncR plasmid pK15-FOS also showed a mosaic structure, possibly originating from a recombination between an epidemic fosA3-carrying pHN7A8-like plasmid and a pKPC-LK30-like IncR plasmid. Stability tests demonstrated that both novel hybrid plasmids were stably maintained in the original host without antibiotic selection but were lost from the transformants after approximately 200 generations. This is apparently the first description of a porcine sequence type 11 (ST11) K. pneumoniae isolate coproducing KPC-2 and FosA3 via pK15-KPC and pK15-FOS, respectively. The multidrug resistance (MDR) phenotype of this high-risk K. pneumoniae isolate may contribute to its spread and its persistence

Single domain antibody multimers confer protection against rabies infection

Post-exposure prophylactic (PEP) neutralizing antibodies against Rabies are the most effective way to prevent infection-related fatality. The outer envelope glycoprotein of the Rabies virus (RABV) is the most significant surface antigen for generating virus-neutralizing antibodies. The small size and uncompromised functional specificity of single domain antibodies (sdAbs) can be exploited in the fields of experimental therapeutic applications for infectious diseases through formatting flexibilities to increase their avidity towards target antigens. In this study, we used phage display technique to select and identify sdAbs that were specific for the RABV glycoprotein from a naïve llama-derived antibody library. To increase their neutralizing potencies, the sdAbs were fused with a coiled-coil peptide derived from the human cartilage oligomeric matrix protein (COMP48) to form homogenous pentavalent multimers, known as combodies. Compared to monovalent sdAbs, the combodies, namely 26424 and 26434, exhibited high avidity and were able to neutralize 85-fold higher input of RABV (CVS-11 strain) pseudotypes in vitro, as a result of multimerization, while retaining their specificities for target antigen. 26424 and 26434 were capable of neutralizing CVS-11 pseudotypes in vitro by 90–95% as compared to human rabies immunoglobulin (HRIG), currently used for PEP in Rabies. The multimeric sdAbs were also demonstrated to be partially protective for mice that were infected with lethal doses of rabies virus in vivo. The results demonstrate that the combodies could be valuable tools in understanding viral mechanisms, diagnosis and possible anti-viral candidate for RABV infection

Sequence-to-Sequence Multi-Agent Reinforcement Learning for Multi-UAV Task Planning in 3D Dynamic Environment

Task planning involving multiple unmanned aerial vehicles (UAVs) is one of the main research topics in the field of cooperative unmanned aerial vehicle control systems. This is a complex optimization problem where task allocation and path planning are dealt with separately. However, the recalculation of optimal results is too slow for real-time operations in dynamic environments due to a large amount of computation required, and traditional algorithms are difficult to handle scenarios of varying scales. Meanwhile, the traditional approach confines task planning to a 2D environment, which deviates from the real world. In this paper, we design a 3D dynamic environment and propose a method for task planning based on sequence-to-sequence multi-agent deep deterministic policy gradient (SMADDPG) algorithm. First, we construct the task-planning problem as a multi-agent system based on the Markov decision process. Then, the DDPG is combined sequence-to-sequence to learn the system to solve task assignment and path planning simultaneously according to the corresponding reward function. We compare our approach with the traditional reinforcement learning algorithm in this system. The simulation results show that our approach satisfies the task-planning requirements and can accomplish tasks more efficiently in competitive as well as cooperative scenarios with dynamic or constant scales

Sequence-to-Sequence Multi-Agent Reinforcement Learning for Multi-UAV Task Planning in 3D Dynamic Environment

Task planning involving multiple unmanned aerial vehicles (UAVs) is one of the main research topics in the field of cooperative unmanned aerial vehicle control systems. This is a complex optimization problem where task allocation and path planning are dealt with separately. However, the recalculation of optimal results is too slow for real-time operations in dynamic environments due to a large amount of computation required, and traditional algorithms are difficult to handle scenarios of varying scales. Meanwhile, the traditional approach confines task planning to a 2D environment, which deviates from the real world. In this paper, we design a 3D dynamic environment and propose a method for task planning based on sequence-to-sequence multi-agent deep deterministic policy gradient (SMADDPG) algorithm. First, we construct the task-planning problem as a multi-agent system based on the Markov decision process. Then, the DDPG is combined sequence-to-sequence to learn the system to solve task assignment and path planning simultaneously according to the corresponding reward function. We compare our approach with the traditional reinforcement learning algorithm in this system. The simulation results show that our approach satisfies the task-planning requirements and can accomplish tasks more efficiently in competitive as well as cooperative scenarios with dynamic or constant scales

A Particle Swarm Optimization Method for AI Stream Scheduling in Edge Environments

With the development of IoT and 5G technologies, edge computing has become a key driver for providing compute, network and storage services. The dramatic increase in data size and the complexity of AI computation models have put higher demands on the performance of edge computing. Rational and optimal scheduling of AI data-intensive computation tasks can greatly improve the overall performance of edge computing. To this end, a particle swarm algorithm based on objective ranking is proposed to optimize task execution time and scheduling cost by designing a task scheduling model to achieve task scheduling in an edge computing environment. It is necessary to fully understand the concept of symmetry of resource utilization and task execution cost indicators. The method utilizes nonlinear inertia weights and shrinkage factor update mechanisms to improve the optimization-seeking ability and convergence speed of the particle-to-task scheduling solution space. The task execution time and scheduling cost are greatly reduced. Simulation experiments are conducted using the Cloudsim toolkit to experimentally compare the proposed algorithm TS-MOPSO with three other particle swarm improvement algorithms, and the experimental results show that the task execution time, maximum completion time and total task scheduling cost are reduced by 31.6%, 23.1% and 16.6%, respectively. The method is suitable for handling large and complex AI data-intensive task scheduling optimization efforts

Sclerostin induced tumor growth, bone metastasis and osteolysis in breast cancer

Abstract Breast cancer is the second leading cause of cancer-related deaths among women worldwide. Many patients suffer from bone metastasis. Sclerostin, a key regulator of normal bone remodeling, is critically involved in osteolytic bone diseases. However, its role in breast cancer bone metastasis remains unknown. Here, we found that sclerostin was overexpressed in breast cancer tumor tissues and cell lines. Inhibition of sclerostin by antibody (Scl-Ab) significantly reduced migration and invasion of MDA-MB-231 and MCF-7 cells in a time- and dose-dependent manner. In xenograft model, sclerostin inhibition improved survival of nude mice and prevented osteolytic lesions resulting from tumor metastasis. Taken together, sclerostin promotes breast cancer cell migration, invasion and bone osteolysis. Inhibition of sclerostin may serve as an efficient strategy for interventions against breast cancer bone metastasis or osteolytic bone diseases