People's Liberation Army No. 150 Hospital

Coastal zones are densely populated and subject to frequent human activities, making accurate surveying essential for ecological monitoring and navigation safety. Compared with full-waveform LiDAR, photon-counting LiDAR enables more efficient data acquisition and denser point clouds, but also introduces substantial noise that complicates point cloud classification. Using coastal data collected by a four-channel UAV-borne photon-counting LiDAR system, this study proposes a supervised 3D point cloud classification algorithm. The method employs multi-dimensional grid-based neighborhoods and integrates multi-channel features, including polarization characteristics, to directly classify noise, land, sea surface, and seabed without prior denoising. In addition, a category-adaptive optimization algorithm is introduced to correct misclassified points. Experimental results show that the proposed method achieves an overall accuracy of 96% and a Kappa coefficient of 0.92. The classified point clouds further support integrated marine-terrestrial surveying applications.

Immune reconstitution (IR) is a critical factor influencing the quality of life and long-term prognosis in hematopoietic stem cell transplant (HSCT) recipients. But delayed IR post-transplant may lead to infections and disease recurrence, while impaired reconstitution is closely associated with graft-versus-host disease (GVHD). To formulate strategies that promote immune recovery, it is crucial to understand the fundamental principles and influencing factors of IR. The emergence of technologies such as single-cell sequencing enables researchers to reveal the specific patterns and mechanisms of immune reconstitution under different conditions, which has given us a better understanding of immune reconstitution than before. Current evidence indicates that delayed IR is related to several aspects, mainly the strength of the conditioning regimen, the composition of the graft, and the occurrence of GVHD. This review summarizes the main characteristics of immune reconstitution after allogeneic hematopoietic stem cell transplantation, combined with the latest research progress Carefully explore the influencing factors of immune reconstitution and the existing monitoring methods.

To investigate the significance of DCUN1D5 in colorectal cancer (CRC) progression and determine the underlying mechanism. We analyzed DCUN1D5 expression levels in CRC tissues using the TCGA and GEO databases and conducted immunohistochemistry and qPCR in retrieved CRC tissues. shRNA-mediated knockdown of DCUN1D5 in CRC cell lines was performed to investigate its effects on cell growth and motility. In addition, DCUN1D5-mediated promotion of CUL1 neddylation was examined using immunoblotting, the antitumor effects of inhibiting the neddylation pathway was evaluated using the inhibitor MLN4924, and the effects of silencing DCUN1D5 and MLN4924 treatment were validated in vivo using xenograft models. DCUN1D5 was found to be significantly overexpressed in CRC tissues, and its knockdown impaired CRC cell proliferation and migration, which was associated with reduced CUL1 neddylation. Inhibition of the neddylation pathway using the NEDD8 inhibitor MLN4924 supported these observations. In vivo, DCUN1D5 silencing and MLN4924 treatment led to reduced tumor growth and metastasis. DCUN1D5 contributes to the growth and motility of CRC in vitro and in vivo.

Elderly patients are at increased risk of infection with methicillin-resistant Staphylococcus aureus (MRSA). Therefore, this article comprehensively reviews the progress in therapeutic drug monitoring (TDM) and pharmacokinetic (PK) studies of linezolid, contezolid and tedizolid in the elderly population, to inform individualized dosing strategies. This review synthesized evidence from published and ongoing studies examining the TDM and PK studies of linezolid, contezolid and tedizolid in elderly patients, encompassing investigations into drug concentrations monitoring, PK parameters, and population PK studies. No date restrictions were applied to the study selection. The distinct pathophysiology of elderly patients leads to significant alterations in the PK characteristics of various antibacterial agents, necessitating individualized dosage adjustments. In this population, linezolid exhibits a prolonged half-life, markedly decreased clearance, and elevated risk of severe overexposure. Therefore, its dosage requires adjustment based on renal function and age. Contezolid exhibitscomparable pharmacokineticsin patients aged 65-74 yearsto younger adults; however, in patients aged 80 and above,its time to peak concentration is delayed, elimination is accelerated, while Cmaxand AUC0-t increase dose-dependently. Tedizolid plasma exposure was similar in elderly and younger control subjects, indicating that no dose adjustment was warranted for elderly subjects. Moreover, contezolid and tedizolid exhibit a more favorable safety profile compared with linezolid. The PK characteristics of oxazolidinone antibiotics may be altered in the elderly population. TDM and model-informed precision dosing (MIPD) are crucial approaches for achieving individualized dosing strategies.

With the rapid development of computer technology and Artificial Intelligence (AI), unmanned systems have been widely applied and studied in various fields. For unmanned systems based on Reinforcement Learning (RL) principles, user satisfaction evaluation of their performance can provide accurate reward functions, which deeply influence the optimization of system control strategies. This paper takes the towing and following system of autonomous vehicles as the research object, and proposes a user satisfaction evaluation method for autonomous vehicle behavior based on statistical characteristics and data-driven approach, realizing the mapping from raw sequence data of towing and following to user satisfaction. The core innovation of this paper lies in the first attempt to use the dynamic state of the tow rope (length, angular velocity, etc.) as a key feature source, which is fused with vehicle kinematic data to evaluate user subjective experience; and a self-supervised feedback mechanism is designed to optimize model parameters, enabling the model to have certain self-learning ability. Experimental results show that this method can effectively reflect user satisfaction with autonomous vehicle driving strategies, and achieve progressive optimization of satisfaction evaluation through self-supervised feedback mechanism. This study lays a theoretical foundation for building more intelligent and human-oriented human-machine collaborative following systems, and can be embedded as a reward function in reinforcement learning frameworks for training and optimizing towing and following strategies.

Uncontrolled satellites and other space targets usually exhibit slow-spinning motion. Accurately estimating their rotational motion parameters (RMP) is of great significance for uncontrolled state analysis. To address this challenge, a method is proposed herein to estimate the RMP of slow-spinning space targets, based on similarity analysis of sequential inverse synthetic aperture radar (ISAR) images. Firstly, an ISAR imaging model for slow-spinning space targets is constructed. It is then derived that two adjacent ISAR images exhibit an approximate planar rotational geometric relationship, and the planar rotation matrix in this relationship is directly associated with the RMP. Then, RMP, namely the rotation axis and angular velocity, are taken as optimization variables, while the similarity between successive ISAR images serves as the objective function. In this manner, the estimation of RMP is constructed as an unconstrained optimization problem. Ultimately, the particle swarm optimization (PSO) algorithm is utilized to estimate the RMP. Without depending on any prior knowledge about the target's structure, the proposed method can realize RMP estimation. Experimental results obtained from electromagnetic simulation data demonstrate its effectiveness and robustness.