Accurate 3D reconstruction of the small bowel skeleton is vital for understanding intestinal morphology, de-tecting structural abnormalities, and supporting diagnosis, yet limited resolution, organ adhesion, complex anatomy, and scarce annotations make continuous skeleton extraction from masks challenging. Voxel-based methods often struggle with the sparse topology and geometric directionality inherent in the small bowel skeleton, leading to inefficiency and high memory cost. To address these limitations, we propose a novel octree-based conditional diffusion model (i.e., Tree-Diffusion) that generates anatomically consistent small bowel skeletons guided by 3D segmentation masks. Specifically, we introduce two modules that captures structural priors from masks and topology characteristics from skeletons, ensuring cross-domain alignment and high-quality skeleton generation. Besides, we design a synthesis strategy to generate anatomically plausible skeleton-mask pairs, serving as topological priors to guide the diffusion model toward realis-tic structure predictions. To efficiently represent the elongated skeleton, we adopt an octree- based spatial encoding of hierarchical geometric features. Compared with baselines, our model achieves superior performance in anatomical fidelity, directional consistency, and inference efficiency. The code is available at: https://github.com/Small-Bowel-Skeleton-GenerationlCode

Electrical neuromodulation as a palliative treatment has been increasingly used in the control of epilepsy. However, current neuromodulations commonly implement predetermined actuation strategies and lack the capability of self-adaptively adjusting stimulation inputs. In this work, rooted in optimal control theory, we propose a Koopman-MPC framework for real-time closed-loop electrical neuromodulation in epilepsy, which integrates i) a deep Koopman operator based dynamical model to predict the temporal evolution of epileptic electroencephalogram (EEG) with an approximate finite-dimensional linear dynamics and ii) a model predictive control (MPC) module to design optimal seizure suppression strategies. The Koopman operator based linear dynamical model is embedded in the latent state space of the autoencoder neural network, in which we can approximate and update the Koopman operator online. The linear dynamical property of the Koopman operator ensures the convexity of the optimization problem for subsequent MPC control. The proposed deep Koopman operator model shows greater predictive capability than the baseline models (e.g., vector autoregressive model, kernel based method and recurrent neural network (RNN)) in both synthetic and real epileptic EEG data. Moreover, compared with the RNN-MPC framework, our Koopman-MPC framework can suppress seizure dynamics with better computational efficiency in both the Jansen-Rit model and the Epileptor model. Koopman-MPC framework opens a new window for model-based closed-loop neuromodulation and sheds light on nonlinear neurodynamics and feedback control policies.

Due to its efficiency, low overhead, and high scalability, consortium blockchain has been deeply applied in various fields of society. Order financing is one of the scenarios of applying consortium blockchain. Since data on the consortium blockchain is available to the blockchain members, information of a financing order written directly to the blockchain will leak the commercial privacy of the purchaser and supplier. Therefore, the financing order data should be encrypted when published as a transaction on the consortium blockchain. However, the investor needs to verify the financing order data on a consortium blockchain before loaning money to the supplier. It is tricky to efficiently satisfy the verifiability of encrypted financing order data on the consortium blockchain. This work proposes VmppOrder, a verifiable model for privacy-preserving financing orders on a consortium blockchain based on zero-knowledge Succinct Non-interactive ARguments of Knowledge (zk-SNARKs). By the supplier publishing zero-knowledge proofs generated from the financing order, the investor can verify the encrypted financing order published on the consortium blockchain without decrypting it. We elaborate on the specific construction of VmppOrder and analyze the security of the constructed circuit with zero-knowledge proof. We implement a prototype of the model on Hyperledger Fabric based on Libsnark and conduct comprehensive experiments to evaluate its performance. Our experimental results validate the efficiency of the proposed model. Its order proof generation takes about 6.31 seconds, the order verification takes only 2.58 milliseconds, and the transaction processing speed is about 660 transactions per second on a moderately equipped machine.

The oxytocin effects on large-scale brain networks such as Default Mode Network (DMN) and Frontoparietal Network (FPN) have been largely studied using fMRI data. However, these studies are mainly based on the statistical correlation or Bayesian causality inference, lacking interpretability at the physical and neuroscience level. Here, we propose a physics-based framework of the Kuramoto model to investigate oxytocin effects on the phase dynamic neural coupling in DMN and FPN. Testing on fMRI data of 59 participants administrated with either oxytocin or placebo, we demonstrate that oxytocin changes the topology of brain communities in DMN and FPN, leading to higher synchronization in the FPN and lower synchronization in the DMN, as well as a higher variance of the coupling strength within the DMN and more flexible coupling patterns at group level. These results together indicate that oxytocin may increase the ability to overcome the corresponding internal oscillation dispersion and support the flexibility in neural synchrony in various social contexts, providing new evidence for explaining the oxytocin modulated social behaviors. Our proposed Kuramoto model-based framework can be a potential tool in network neuroscience and offers physical and neural insights into phase dynamics of the brain.

As an emerging molecular imaging modality, Photoacoustic Tomography (PAT) is capable of mapping tissue physiological metabolism and exogenous contrast agent information with high specificity. Due to its ultrasonic detection mechanism, the precise localization of targeted lesions has long been a challenge for PAT imaging. The poor soft-tissue contrast of the PAT image makes this process difficult and inaccurate. To meet this challenge, in this study, we first make use of the rich and clear structural information brought about by another advanced imaging modality, Magnetic Resonance Imaging (MRI), to assist organ segmentation and correct for the light fluence attenuation of PAT. We demonstrate improved feature visibility and enhanced localization of endogenous and exogenous agents in the fluence corrected PAT images. Compared with PAT-based methods, the contrast-to-noise ratio (CNR) of our MRI-assisted method increases by 29.1% in live animal experiments. Furthermore, we show that the co-registered MRI image can also be incorporated into PAT image restoration, and achieves improved anatomical landscape and soft-tissue contrast (CNR increased by 25.36%) while preserving similar spatial resolution. This PAT-MRI combination provides excellent structural, functional and molecular images of the subject, and may enable more comprehensive analysis of various preclinical research applications.

Blockchain is well known for its storage consistency, decentralization and tamper-proof, but the privacy disclosure and difficulty in auditing discourage the innovative application of blockchain technology. As compared to public blockchain and private blockchain, consortium blockchain is widely used across different industries and use cases due to its privacy-preserving ability, auditability and high transaction rate. However, the present co-audit of privacy-preserving data on consortium blockchain is inefficient. Private data is usually encrypted by a session key before being published on a consortium blockchain for privacy preservation. The session key is shared with transaction parties and auditors for their access. For decentralizing auditorial power, multiple auditors on the consortium blockchain jointly undertake the responsibility of auditing. The distribution of the session key to an auditor requires individually encrypting the session key with the public key of the auditor. The transaction initiator needs to be online when each auditor asks for the session key, and one encryption of the session key for each auditor consumes resources. This work proposes GAChain and applies group key agreement technology to efficiently co-audit privacy-preserving data on consortium blockchain. Multiple auditors on the consortium blockchain form a group and utilize the blockchain to generate a shared group encryption key and their respective group decryption keys. The session key is encrypted only once by the group encryption key and stored on the consortium blockchain together with the encrypted private data. Auditors then obtain the encrypted session key from the chain and decrypt it with their respective group decryption key for co-auditing. The group key generation is involved only when the group forms or group membership changes, which happens very infrequently on the consortium blockchain. We implement the prototype of GAChain based on Hyperledger Fabric framework. Our experimental studies demonstrate that GAChain improves the co-audit efficiency of transactions containing private data on Fabric, and its incurred overhead is moderate.