Recent publications
Visual Simultaneous Localization and Mapping (vSLAM) has become an important key factor in modern technology and is universally used due to the affordable and available camera infrastructure. However, vSLAM systems face challenges in dynamic environments where moving objects can reduce the accuracy of both localization and mapping, especially for mobile robots. To overcome this problem, a new vSLAM framework, Contour-Aware SLAM (CA-SLAM) is proposed. CA-SLAM integrates the YOLOv8-SEG deep learning model for the object detection, segmentation, and then contour-aware method to utilize the minimum Zero Normalized Cross-Correlation (ZNCC) across the object contour to accurately detect and create a mask of the dynamic objects. These masks are applied within the ORB-SLAM3 framework to remove dynamic features, thereby significantly improving localization accuracy. The dense mapping process utilize the RGB-D frames, camera trajectory, and dynamic masks. Additionally, depth inpainting is applied to fill missing values and refine depth information for dense map reconstruction. Extensive validation on public datasets demonstrates that CA-SLAM achieves better and comparable trajectory accuracy in both dynamic and static environments. Furthermore, CA-SLAM demonstrates exceptional performance in dense map reconstruction, delivering highly detailed and accurate 3D mapping even in complex environment.
Through millions of years of evolution, bones have developed a complex and elegant hierarchical structure, utilizing tropocollagen and hydroxyapatite to attain an intricate balance between modulus, strength, and toughness. In this study, continuous fiber silk composites (CFSCs) of large size are prepared to mimic the hierarchical structure of natural bones, through the inheritance of the hierarchical structure of fiber silk and the integration with a polyester matrix. Due to the robust interface between the matrix and fiber silk, CFSCs show maintained stable long-term mechanical performance under wet conditions. During in vivo degradation, this material primarily undergoes host cell-mediated surface degradation, rather than bulk hydrolysis. We demonstrate significant capabilities of CFSCs in promoting vascularization and macrophage differentiation toward repair. A bone defect model further indicates the potential of CFSC for bone graft applications. Our belief is that the material family of CFSCs may promise a novel biomaterial strategy for yet to be achieved excellent regen-erative implants.
Laminectomy represents an effective surgical procedure for the treatment of lumbar spinal stenosis. Due to the intricate anatomical structure of the lumbar spine, meticulous surgical path planning is essential to ensure the safety of the procedure and enhance the likelihood of successful outcomes. This study aims to implement multi-objective optimization techniques in the context of laminectomy, with a particular emphasis on identifying the optimal reference cutting path for the lamina. In clinical practice, the cutting path is typically characterized as a relatively straight line, akin to making an incision through the lamina with a sharp, rigid plane. Consequently, the optimal reference cutting path can be established by determining the ideal reference cutting plane. In our methodology, the cutting contour, defined as the intersection of the cutting plane with the lamina, is treated as a variable. Key features of the lamina are extracted and classified into three objective functions: the average thickness of the lamina, the derivative of the entry point of the cutting path, and the degree of overlap between adjacent vertebrae. We then apply multi-objective optimization algorithms and utilize the weighted sum method to solve the multi-objective problems in the laminectomy task. The experimental findings are validated by an enhanced laminectomy plane evaluation system, demonstrating that the automatically generated cutting planes achieve a high level of excellence (94%), thereby satisfying the surgical requirements validated by professional surgeons.
Recently, the concept of homotopic trajectory planning has emerged as a novel solution to navigation in large-scale obstacle environments for swarm robotics, offering a wide range of applications. However, it lacks an efficient homotopic path planning method in large-scale obstacle environments. This paper introduces Tube RRT*, an innovative homotopic path planning method that builds upon and improves the Rapidly-exploring Random Tree (RRT) algorithm. Tube RRT* is designed to efficiently generate homotopic paths belonging to the same homotopy class and simultaneously considers gap volume and path length to mitigate swarm congestion and enable agile navigation. Through comprehensive simulations and experiments, the effectiveness of Tube RRT* is validated.
As the “blood” of Li−O2 batteries (LOBs), electrolytes with various solvation structures of Li⁺ can greatly influence the composition of solid electrolyte interphase (SEI) on anode and the growth kinetics of Li2O2 on cathode, and further the battery performance. However, achieving delicate modulation of the multi‐composition electrolytes remains significantly challenging to simultaneously give consideration to both the anode and cathode reactions. In this work, we employed Bayesian optimization to develop advanced electrolytes for LOBs, enabling the formation of a stable inorganic‐rich SEI, and modulation of Li2O2 morphologies. Thus obtained LOBs using the optimized dual‐solvent electrolyte could deliver a discharge capacity of 14,063 mAh g⁻¹ at a current density of 500 mA g⁻¹, which is far higher than those using the single‐solvent electrolytes. This study not only highlights the critical role of the solvation structure for improving the battery performance, but also provides new insights and important theoretical guidance for delicate modulation of electrolyte compositions.
- Jiaqian Li
- Lishuang Guo
- Yuan Feng
- [...]
- Yu An
Lymphodepletion before tumor-infiltrating lymphocytes (TIL) infusion can activate the immune system, enhance the release of homeostatic cytokines, and decrease the number of immunosuppressive cells. This process is crucial for improving the therapeutic efficacy of TIL therapy. However, the challenge of in vivo assessing TILs targeting tumors limits the optimization of lymphodepleting conditioning regimen (LDC).
This study aims to employ magnetic particle imaging (MPI) and fluorescence molecular imaging (FMI) to monitor TIL biodistribution in vivo and optimize LDC in triple-negative breast cancer TIL therapy. MPI provides quantitative imaging capabilities without depth limitations, effectively complementing the high sensitivity of FMI. The efficacy of different LDCs in enhancing TIL therapy was assessed using FMI, and MPI quantified the number of TILs accumulated in the 4T1 tumor.
TILs preserved viability, phenotypes, and anti-tumor efficacy after being labeled with superparamagnetic iron oxide and fluorescence dye DiR. The dual-modality imaging system effectively discerned variations in LDC treatments that enhanced TIL therapy. Compared to TIL monotherapy, lymphodepletion with TIL therapy improves tumor dual-modality imaging signal intensity, increases the expression of monocyte chemotactic protein-1 in serum and tumor tissue, and enhances the therapeutic effect of TILs.
Our results confirm the utility of optical-magnetic dual-modality imaging for tracking the biodistribution of TILs in vivo. With the help of optical-magnetic dual-modality imaging, we successfully optimize TIL combination therapy. Optical-magnetic dual-modality imaging provides a new approach to develop personalized immunotherapy strategies and mine potential therapeutic mechanisms for TIL.
Simulated microgravity (SMG) poses substantial challenges to astronaut health, particularly impacting osteoblast function and leading to disuse osteoporosis. This study investigates the adverse effects of SMG on osteoblasts, focusing on changes in mitochondrial dynamics and their consequent effects on cellular energy metabolism and mechanotransduction pathways. We discovered that SMG markedly reduced the expression of osteoblast differentiation markers and promoted mitochondrial fission, as indicated by an increase in punctate mitochondria, a decrease in mitochondrial length, and a reduction in cristae density. These mitochondrial alterations are linked to elevated reactive oxygen species levels, a decrease in ΔΨm, and a metabolic shift from oxidative phosphorylation to glycolysis, resulting in decreased adenosine triphosphate production, which are all indicative of mitochondrial dysfunction. Our results showed that treatment with mitochondrial division inhibitor-1 (mdivi-1), a mitochondrial fission inhibitor, effectively inhibited these SMG-induced effects, thereby maintaining mitochondrial structure and function and promoting osteoblast differentiation. Furthermore, SMG disrupted critical mechanotransduction processes, by affecting paxillin expression, the RhoA–ROCK–Myosin II pathway, and actin dynamics, which subsequently altered nuclear morphology and disrupted Yes-associated protein signaling. Notably, treatment with mdivi-1 prevented these disruptions in mechanotransduction pathways. Moreover, our study showed that SMG-induced chromatin remodeling and histone methylation, which are epigenetic barriers to osteogenic differentiation, can be reversed by targeting mitochondrial fission, further highlighting the significance of mitochondrial dynamics in osteoblast function in an SMG environment. Therefore, targeting mitochondrial fission emerges as a promising therapeutic strategy to alleviate osteoblast dysfunction under SMG conditions, providing novel approaches to maintain bone health during prolonged space missions and safeguard the astronaut well-being.
Ultraviolet (UV) detectors have been widely applied to extensive fields, such as photoelectric microsensors, UV imaging, optical communications, and biological detection. The demand for high-performance UV detectors with low cost and easy fabrication drives the development of novel materials and structures such as wide-bandgap semiconductors, polymers, photodiodes, and phototransistors. Here, we demonstrate a new UV photodiode implemented using solution-processing-based zinc oxide (ZnO) nanoparticles (NPs) and a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) heterojunction. Moreover, the fabrication process is simple and suitable for large-scale production. First, solution-based films of ZnO NPs and PEDOT:PSS were prepared, and their optical and electrical properties were individually characterized. Strong optical absorption by ZnO NPs for wavelengths below 400 nm was experimentally observed, while absorption by PEDOT:PSS in the same wavelength range is trivial. The ZnO-NPs-PEDOT:PSS heterojunction was then characterized for its electrical rectification behavior and photoresponse, from which a potential barrier height of 0.64 eV and UV current gain of 91.2 were determined. The resistor–capacitor time constant was also calculated to be 946.46 ms at zero bias, and its dependence on reverse bias voltage was determined. Finally, negative capacitance was first observed in this kind of heterojunctions under forwarding bias, which was further investigated regarding a threshold effect and transition frequency. This new type of UV photodiodes based on ZnO-NPs-PEDOT:PSS heterojunctions is highly promising for high-resolution applications, such as ZnO NPs-based spatial light modulators (SLMs), where the ascendant properties of the heterojunction can be fully utilized to achieve the high-resolution capability of optically addressed SLMs.
With the rapid growth of data volumes in real-world applications, anomaly detection has become a crucial task across various scenarios. Anomalies are generally defined as data points that constitute a small proportion yet exhibit significantly different patterns. Numerous detection methods have been proposed and applied, ranging from statistical analysis to the recently extensively studied graph representation learning and large language models (LLMs). Existing surveys on the best-performing detection methods based on graph neural networks (GNNs) or graph structures often attempt to classify and summarize these methods based on anomalous structures or categories. However, these studies frequently conflate the data distribution characteristics with detection approaches, failing to clarify the adaptability of detection methods to different data contexts. To address this issue, we propose a more practical taxonomy for GNN-based anomaly detection methods from the perspective of data characteristics and assumptions regarding anomaly distribution. Specifically, we summarize common characteristics and assumptions, discussing the corresponding detection approaches and methods with a focus on their key aspects. We also analyze the attention given to key subspaces of data, clarify the embedding and classification methods that are often conflated in existing surveys, and summarize decision-based detection methods that are frequently overlooked. Additionally, we discuss the application of LLMs in this field, providing insights for future research.
- Yao He
- Lijing Wang
- Sheng Yang
- [...]
- Liangxu Lin
In lithium-sulfur batteries (LSBs), the dissolution of lithium polysulfides (LiPSs) triggers the shuttle effect to lose active materials irreversibly, leading to the fast deterioration of electrochemical performance. Rational designs on the separator membrane could mitigate the shuttle effect. However, the development of efficient separators economically remains a challenging task, aggressively limiting the commercial use of LSBs. This work reports the engineering of commercial molybdenum diselenides (MoSe2) flakes to mitigate the shuttle effect of LSBs, by forming rich Se vacancies via a potassium (K) intercalation and de-intercalation reaction. The Se vacancy in MoSex flakes significantly enhances the adsorption capacity of LiPSs and accelerates the Li+ diffusion kinetics, thereby alleviating the shuttle effect and enhancing the energy storage performance. This directly improves the energy storage performance of the LSBs by incorporating the MoSex flakes into the separator membrane, giving a high capacity retention rate of 94.6% at 2C after 500 cycles, with a reversible specific capacity as high as 452 mAh g-1. This work offers a new strategy for the design and synthesis of vacancy rich transition metal chalcogenides for high-performance LSBs and beyond.
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