.
The ultrasonication-triggered interfacial assembly approach was developed to synthesize magnetic Janus amphiphilic nanoparticles (MJANPs) for cancer theranostic applications, where the biocompatible octadecylamine is used as a molecular linker to mediate the interactions between hydrophobic and hydrophilic nanoparticles across the oil-water interface. The obtained Co cluster-embedded Fe3O4 nanoparticles-graphene oxide (CCIO-GO) MJANPs exhibited superior magnetic heating efficiency and transverse relaxivity, 64 and 4 times higher than that of commercial superparamagnetic iron oxides, respectively. The methodology has been applicable to nanoparticles of various dimensions (5-100 nm), morphologies (sphere, ring, disk, and rod), and composition (metal oxides, noble metal and semiconductor compounds, etc.), thereby greatly enriching the array of MJANPs. In vivo theranostic applications using the tumor-bearing mice model further demonstrated the effectiveness of these MJANPs in high-resolution multimodality imaging and high-efficiency cancer therapeutics. The ubiquitous assembly approach developed in the current study pave the way for on-demand design of high-performance Janus amphiphilic nanoparticles for various clinical diagnoses and therapeutic applications.
Landslides have different topographic and morphological characteristics due to their different triggering mechanisms. However, the differences in the characteristics of earthquake- and rainstorm-induced landslides remain unclear. In this paper, we collect 12 cases of earthquake- and rainstorm-induced landslides around the world and reveal the differences in characteristics of the two types of landslides. By examining the geometric characteristics and location distribution of the landslides, the results show that earthquake-induced landslides tend to have larger areas, perimeter, lengths, widths, area to perimeter ratios (area/perimeter), major axis (SM), and minor axis (sm) than rainstorm-induced landslides. In addition, earthquake-induced landslides have more complex, rounded, and compact shapes than rainstorminduced landslides. Earthquake-induced landslides are predominantly clustered near ridges, whereas rainstorm-induced landslides are predominantly clustered near valleys. The results also indicate that earthquake- and rainstorm-induced landslides mostly occur on 30 degrees-50 degrees and 10 degrees-30 degrees slopes, respectively, and both types are more likely to occur on sunny slopes. Moreover, the compactness and major axis are negatively logarithmically correlated for earthquake-induced landslides, while they are negatively exponentially correlated for rainstorm-induced landslides. Additional earthquake- and rainstorm-induced landslide events have verified the reliability and extensibility of the research conclusions. This work is beneficial for the management of landslide hazards and the effective implementation of landslide prediction and risk assessment. (c) 2025 China University of Geosciences (Beijing) and Peking University. Published by Elsevier B.V. on behalf of China University of Geosciences (Beijing). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
It is crucial to accurately segment organs from abdominal CT images for clinical diagnosis, treatment planning, and surgical guidance, which remains an extremely challenging task due to low contrast between organs and surrounding tissues and the difference of organ size and shape. Previous works mainly focused on complex network architectures or task-specific modules but frequently failed to learn irregular boundaries and did not consider that different slices from the same case might contain targets of different numbers of categories. To tackle these issues, this paper proposes UAMSNet for abdominal multi-organ segmentation. In UAMSNet, a hybrid receptive field extraction (HRFE) module is introduced to adaptively learn the features of irregular targets, which has an adaptive dilation factor containing distance information to facilitate spatial and channel attention. The HRFE module can simultaneously learn multiple scales and deformations of different organs. Furthermore, a multi-organ boundary-enhanced attention (MBA) module in the encoder and decoder is designed to provide effective boundary information for feature extraction based on the large peak of the organ edge. Finally, the difference in the number of organ categories between different slices is first considered using a loss function, which can adjust the loss computation based on organ categories in the image. The loss function mitigates the effect of false positives during training to ensure the model can adapt to small organ segmentation. Experimental results on WORD and Synapse datasets demonstrate that our UAMSNet outperforms the existing state-of-the-art methods. Ablation experiments confirm the effectiveness of our designed modules and loss function. Our code is publicly available on https://github.com/HeyJGJu/UAMSNet.
Medical image segmentation faces two main challenges: first, the difficulty of distinguishing targets from backgrounds due to similar intensity and low contrast, and second, the displacement and deformation of targets and surrounding structures across slices. While existing methods incorporate multi-scale or multi-source data to enhance the network's target recognition, such methods still struggle with subtle boundary differences and interference from adjacent structures, limiting their generalization. To address these challenges, we propose a Collaborative Learning of Dynamic and Static Information Network (CLDSINet), designed to extract both static and dynamic features for precise target segmentation. CLDSINet consists of three key components: the Target-focused Perception (TP) branch, the Deformation-aware Learning (DL) branch, and the Static Dynamic Feature Fusion (SDFF) branch. The TP branch extracts static features via a two-stage strategy, while the DL branch captures inter-slice deformation. The SDFF branch integrates these features through a Cross Feature Aggregation (CFA) module in the encoder and adjusts the contributions of these features with the Dynamic Feature Weight Learning (DFWL) module in the decoder. Experiments on three public and two private datasets show that CLDSINet outperforms existing methods in segmentation accuracy and stability, particularly for low-contrast and highly-deformed images.
ECG monitoring during human activities is crucial since many heart attacks occur when people are exercising, driving a car, operating a machine, etc. Unfortunately, existing ECG monitoring devices fail to timely detect abnormal ECG signals during activities due to the need for many cables or a sustained press on devices (e.g., smartwatches). This paper intro-duces ZEROECG, a wireless, battery-free, lightweight, electronic-skin-like tag integrated with commodity RFIDs, which can continuously track a user’s ECG during activities. By exploring and leveraging the RFID MOSFET switch, which is traditionally used for backscatter modulation, we map the ECG signal to the RFID RSS and phase measurement. It opens a new RFID sensing approach for sensing any physical world variable that can be translated into voltage signals. We model and analyze the RFID MOSFET-based backscatter modulation principle, providing design guidance for other sensing tasks. Real-world results illustrate the effectiveness of ZEROECG on ECG sensing.
