Related Articles

Intelligent shipping: integrating autonomous maneuvering and maritime knowledge in the Singapore-Rotterdam Corridor

Designing safe and reliable routes is the core of intelligent shipping. However, existing methods for industrial use are inadequate, primarily due to the lack of considering company preferences and ship maneuvering characteristics. To address these challenges, here we introduce a methodological framework that integrates maritime knowledge and autonomous maneuvering model. Based on historical maritime big data, the framework offers customized routes for companies with specific routing preferences. The autonomous maneuvering model then evaluates the safety and reliability of the routes by considering ship motion characteristics and ocean hydrodynamics. We validate its effectiveness on the world’s longest Green and Digital Shipping Corridor between Singapore and Rotterdam. Results demonstrate that our model can provide customized route design for companies and enhance safety for shipping. The framework could serve as a fundamental structure to build a fully digitalized platform for route customization and evaluation for global shipping, optimizing operational decision-making and safety assurance.

Compact and reciprocal probe-signal-integrated rotational Doppler velocimetry with fiber-sculpted light

In recent years, with the clarification of the mechanism of the rotational Doppler effect (RDE), there has attracted extensive attention to its development of applications, especially in the detection of the angular velocity of rotating objects. On the other hand, optical fiber technology is widely applied in laser velocimetry from beam delivery to scattered light collection, aiding the miniaturization of instruments. Here we report the first all-fiber rotational Doppler velocimetry (AF-RDV) with a single probe based on a fabricated mode-sculpted fiber-optic element. The constructed AF-RDV can be operated in two reciprocal schemes wherein exchanging the illuminating mode and detected mode. Using this, we experimentally demonstrate the mode-changing dependent nature of the RDE. Particularly, the results suggest that the rotational Doppler shift can be observed by mode-filtering the scattered signal even with a non-twisted probe light. We also show the achromatic property of the RDE by scanning the incident wavelength, enabling the AF-RDV within an ultra-broadband operation range. The AF-RDV exhibits favorable performance for detecting spinning rough surfaces. It may provide an exciting new practical sensing instrument with significant prospects for monitoring angular motion in both research and industry.

Integrated proteogenomic characterization of ampullary adenocarcinoma

Ampullary adenocarcinoma (AMPAC) is a rare and heterogeneous malignancy. Here we performed a comprehensive proteogenomic analysis of 198 samples from Chinese AMPAC patients and duodenum patients. Genomic data illustrate that 4q loss causes fatty acid accumulation and cell proliferation. Proteomic analysis has revealed three distinct clusters (C-FAM, C-AD, C-CC), among which the most aggressive cluster, C-AD, is associated with the poorest prognosis and is characterized by focal adhesion. Immune clustering identifies three immune clusters and reveals that immune cluster M1 (macrophage infiltration cluster) and M3 (DC cell infiltration cluster), which exhibit a higher immune score compared to cluster M2 (CD4+ T-cell infiltration cluster), are associated with a poor prognosis due to the potential secretion of IL-6 by tumor cells and its consequential influence. This study provides a comprehensive proteogenomic analysis for seeking for better understanding and potential treatment of AMPAC.

A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy

Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that generalize across experimental protocols and map neuronal activity at the laminar and subpopulation-specific levels, beyond atlas-defined regions. Here, we present artficial intelligence-based cartography of ensembles (ACE), an end-to-end pipeline that employs three-dimensional deep learning segmentation models and advanced cluster-wise statistical algorithms, to enable unbiased mapping of local neuronal activity and connectivity. Validation against state-of-the-art segmentation and detection methods on unseen datasets demonstrated ACE’s high generalizability and performance. Applying ACE in two distinct neurobiological contexts, we discovered subregional effects missed by existing atlas-based analyses and showcase ACE’s ability to reveal localized or laminar neuronal activity brain-wide. Our open-source pipeline enables whole-brain mapping of neuronal ensembles at a high level of precision across a wide range of neuroscientific applications.

Responses

Your email address will not be published. Required fields are marked *