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Accurately calibrating camera–LiDAR systems is crucial for achieving effective data fusion, particularly in data collection vehicles. Data-driven calibration methods have gained prominence over target-based methods due to their superior adaptability to diverse environments. However, current data-driven calibration methods are susceptible to suboptimal initialization parameters, which can significantly impact the accuracy and efficiency of the calibration process. In response to these challenges, this paper proposes a novel general model for the camera–LiDAR calibration that abstracts away the technical details in existing methods, introduces an improved objective function that effectively mitigates the issue of suboptimal parameter initialization, and develops a multi-level parameter optimization algorithm that strikes a balance between accuracy and efficiency during iterative optimization. The experimental results demonstrate that the proposed method effectively mitigates the effects of suboptimal initial calibration parameters, achieving highly accurate and efficient calibration results. The suggested technique exhibits versatility and adaptability to accommodate various sensor configurations, making it a notable advancement in the field of camera–LiDAR calibration, with potential applications in diverse fields including autonomous driving, robotics, and computer vision.

Neoadjuvant immunochemotherapy (nICT) has dramatically changed the treatment landscape of operable esophageal squamous cell carcinoma (ESCC), but factors influencing tumor response to nICT are not well understood. Here, using single-cell RNA sequencing paired with T cell receptor sequencing, we profile tissues from ESCC patients accepting nICT treatment and characterize the tumor microenvironment context. CXCL13 + CD8 + Tex cells, a subset of exhausted CD8 + T cells, are revealed to highly infiltrate in pre-treatment tumors and show prominent progenitor exhaustion phenotype in post-treatment samples from responders. We validate CXCL13 + CD8 + Tex cells as a predictor of improved response to nICT and reveal CXCL13 to potentiate anti-PD-1 efficacy in vivo. Post-treatment tumors from non-responders are enriched for CXCL13 + CD8 + Tex cells with notably remarkable exhaustion phenotype and TNFRSF4 + CD4 + Tregs with activated immunosuppressive function and a significant clone expansion. Several critical markers for therapeutic resistance are also identified, including LRRC15 + fibroblasts and SPP1 + macrophages, which may recruit Tregs to form an immunosuppressive landscape. Overall, our findings unravel immune features of distinct therapeutic response to nICT treatment, providing a rationale for optimizing individualized neoadjuvant strategy in ESCC. The tumour microenvironment features influencing response to neoadjuvant immunochemotherapy (nICT) in esophageal squamous cell carcinoma (ESCC) remain to be explored. Here, single cell and TCR sequencing on pre- and post- nICT treatment ESCC tissues identifies the presence of CXCL13 + CD8 + T cells as a predictor of improved response and the enrichment of TNFRSF4 + CD4 + Tregs as a marker of treatment resistance.

Urban vitality, a multifaceted construct, is influenced by economic conditions and urban structural characteristics, and can significantly be impacted by public health emergencies. While extensive research has been conducted on urban vitality, prevailing studies often rely on singular data sources, limiting the scope for holistic assessment. Moreover, there is a conspicuous absence of longitudinal analyses on urban vitality’s evolution and a dearth of quantitative causal evaluations of the effects of public health emergencies. Addressing these gaps, this study devises a comprehensive framework for evaluating urban vitality, assessing Wuhan’s vitality from 2018 to 2020 across economic, social, spatial, and ecological dimensions. Utilizing a Difference-In-Difference (DID) model, the impact of public health emergencies is quantified. The findings indicate pronounced spatial variations in Wuhan’s urban vitality, with a gradational decline from the city center; public health emergencies exhibit differential impacts across vitality dimensions, detrimentally affecting economic, social, and spatial aspects, while bolstering ecological vitality. Moreover, high population and high public budget revenue are identified as factors enhancing urban vitality and bolstering the city’s resilience against sudden adversities. This study offers valuable insights for geographers and urban planners, contributing to the refinement of urban development strategies.

In biological systems, molecular network functionalities are usually switched in a flexible, facile, and programmable manner. Mimicking this, substantial studies are directed towards developing synthetic DNA networks that exhibit similar function-switching capabilities, though often hindered by extensive molecular architecture changes and stringent condition controls, which result in a time-consuming and labor-intensive process. Here, we develop a base stacking-mediated allostery strategy to manipulate the DNA computing function switching with minimal molecular architecture changes, usually as few as 1-2 nucleotide changes. We implement up to 20 distinct logic function switching within DNAzyme networks. We also validate our function switching platform to implement totally 84 kinds of gene regulation patterns in cancer cell lines, demonstrating its utility in RNA sensing and green fluorescent protein regulation. This strategy offers a simplified alternative approach to enrich DNA regulations, with potential applications in DNA computing and bioengineering. DNA computing systems face challenges in switching functions due to complex molecular redesigns. Here, the authors introduce a base Stacking-Mediated Allostery (SMALL) strategy enabling efficient function switching with minimal architecture changes (1-2 nucleotides), implemented across diverse logic operations and cellular gene regulation patterns.

Population spatialization reveals the distribution and quantity of the population in geographic space with gridded population maps. Fine-scale population spatialization is essential for urbanization and disaster prevention. Previous approaches have used remotely sensed imagery to disaggregate census data, but this approach has limitations. For example, large-scale population censuses cannot be conducted in underdeveloped countries or regions, and remote sensing data lack semantic information indicating the different human activities occurring in a precise geographic location. Geospatial big data and machine learning provide new fine-scale population distribution mapping methods. In this paper, 30 features are extracted using easily accessible multisource geographic data. Then, a building-scale population estimation model is trained by a random forest (RF) regression algorithm. The results show that 91% of the buildings in Lin’an District have absolute error values of less than six compared with the actual population data. In a comparison with a multiple linear (ML) regression model, the mean absolute errors of the RF and ML models are 2.52 and 3.21, respectively, the root mean squared errors are 8.2 and 9.8, and the R2 values are 0.44 and 0.18. The RF model performs better at building-scale population estimation using easily accessible multisource geographic data. Future work will improve the model accuracy in densely populated areas.

Abstract Cancer represents a major worldwide disease burden marked by escalating incidence and mortality. While therapeutic advances persist, developing safer and precisely targeted modalities remains imperative. Nanomedicines emerges as a transformative paradigm leveraging distinctive physicochemical properties to achieve tumor-specific drug delivery, controlled release, and tumor microenvironment modulation. By synergizing passive enhanced permeation and retention effect-driven accumulation and active ligand-mediated targeting, nanoplatforms enhance pharmacokinetics, promote tumor microenvironment enrichment, and improve cellular internalization while mitigating systemic toxicity. Despite revolutionizing cancer therapy through enhanced treatment efficacy and reduced adverse effects, translational challenges persist in manufacturing scalability, longterm biosafety, and cost-efficiency. This review systematically analyzes cutting-edge nanoplatforms, including polymeric, lipidic, biomimetic, albumin-based, peptide engineered, DNA origami, and inorganic nanocarriers, while evaluating their strategic advantages and technical limitations across three therapeutic domains: immunotherapy, chemotherapy, and radiotherapy. By assessing structure-function correlations and clinical translation barriers, this work establishes mechanistic and translational references to advance oncological nanomedicine development.

Deep learning has become a popular topic among scholars and has attracted widespread attention. However, deep learning methods typically require large datasets to determine model parameters and can only process data in batches. To address the challenges of deep learning models, which rely on batch data and struggle to adapt to industrial streaming data scenarios in gearbox fault diagnosis, this study proposes an online gearbox fault diagnosis method based on a DeepWalk graph embedding-enhanced extreme learning machine (ELM) approach. The method constructs a graph structure in real time for each newly collected vibration signal, uses DeepWalk for unsupervised embedding learning, and extracts low-dimensional features with strong discriminative power. These features are then input into the ELM classifier to achieve adaptive fault type recognition and online incremental model updates. This method does not require historical data to be retrained, thus effectively overcoming the bottleneck of batch retraining and significantly improving diagnostic efficiency and resource utilization. The experimental results show that, under various operating conditions, the proposed method achieves fast and accurate diagnosis of multiple gearbox fault types, with an average accuracy consistently above 95%, thereby demonstrating excellent engineering applicability and real-time performance.

Drugs that induce thrombosis in the tumour vasculature have not resulted in long-term tumour eradication owing to tumour regrowth from tissue in the surviving rim of the tumour, where tumour cells can derive nutrients from adjacent non-tumoral blood vessels and tissues. Here, we report the performance of a combination of tumour-infarction therapy and chemotherapy, delivered via chitosan-based nanoparticles decorated with a tumour-homing peptide targeting fibrin–fibronectin complexes overexpressed on tumour-vessel walls and in tumour stroma, and encapsulating the coagulation-inducing protease thrombin and the chemotherapeutic doxorubicin. Systemic administration of the nanoparticles into mice and rabbits bearing subcutaneous or orthotopic tumours resulted in higher tumour growth suppression and decreased tumour recurrence than nanoparticles delivering only thrombin or doxorubicin, with histological and haematological analyses indicating an absence of detectable toxicity. The co-administration of a cytotoxic payload and a protease to elicit vascular infarction in tumours with biodegradable tumour-targeted nanoparticles represents a promising strategy for improving the therapeutic index of coagulation-based tumour therapy. The combination of tumour-infarction therapy and chemotherapy, delivered via nanoparticles decorated with a tumour-homing peptide and encapsulating thrombin and doxorubicin, outperforms the corresponding monotherapies in tumour-bearing mice and rabbits.

Urban villages (UVs) are the most typical urban informal settlements in China, and the study of an effective identification method for UVs can help to provide a reference for the development of locally adapted UV transformation policies. In order to reduce the cost of labeling and enhance transferability, this study integrates remote sensing and social sensing data and applies sample migration from a labeled area to a less labeled area based on the theory of transfer learning. There are two main results of this study: (1) This study constructed a feature system for UV identification based on multi-feature extraction using a block as a unit, and experiments based on Tianhe District achieved an overall accuracy of 90% and a kappa coefficient of 0.76. (2) Using Tianhe District as the source domain and Jiangan District as the target domain, samples from the source domain were reused based on the KMM, TCA, and CORAL algorithms. The CORAL+RF algorithm showed the best performance, where its overall accuracy reached 97.06% and its kappa coefficient reached 0.89, and its overall accuracy reached 91.17% and its kappa coefficient reached 0.67 in the case of no target domain labeling. To sum up, the identification method for UVs proposed in the present study provides theoretical references for identification methods for UVs in different geographical areas.

Geographic information collection platforms are widely used for acquiring geographic information. However, existing geographic information collection platforms have limited adaptability and configurability, negatively affecting their usability. They do not support complete field collection workflows or capture data with complex nested structures. To address these limitations, this paper proposes a generic geographic information collection platform based on a comprehensive XML schema definition and a corresponding XML toolkit. This platform includes professional and non-professional versions of collection software, as well as a management system. Users can configure controls and define nested tables within this platform to collect heterogeneous and complex nested data. Moreover, the platform supports functions such as task assignment, local deployment servers, multitasking parallelism, and summary statistics of heterogeneous data, ensuring complete workflow support for field data collection. The platform has been applied in agriculture, forestry, and related fields. This paper uses the agricultural industry structure survey as a case study. Practical applications and our case study show that this platform can reduce software development costs, lower user knowledge prerequisites, and fulfill 95% of geographic information collection scenarios.