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Nanofiber encapsulation of Pseudomonas aeruginosa for the sustained release of mosquito larvicides
Arterial pulsations and transmantle pressure synergetically drive glymphatic flow
Abstract Clearance of waste material from the brain by the glymphatic system results from net flow of cerebrospinal fluid (CSF) through perivascular spaces surrounding veins and arteries. In periarterial spaces, this bulk flow is directed from the cranial subarachnoid space towards the brain’s interior. The precise pumping mechanism explaining this net inflow remains unclear. While in vivo experiments have shown that the pulsatile motion in periarterial spaces is synchronized with arterial pulsations, peristalsis alone has been deemed insufficient to explain bulk flow. In this study we examine an alternative mechanism based on the interaction between arterial pulsations and fluctuations in transmantle pressure. Previously studied using pressure data from a hydrocephalus patient, this mechanism is analyzed here in healthy subjects using in vivo flow measurements obtained via phase-contrast magnetic resonance imaging. Arterial pulsations are derived from flow-rate measurements of arterial blood entering the cranial cavity, while transmantle-pressure fluctuations are computed using measurements of CSF flow in the cerebral aqueduct. The two synchronized waveforms are integrated into a canonical multi-branch model of the periarterial spaces, yielding a closed-form expression for the bulk flow. The results confirm that the dynamic interactions between arterial pulsations and transmantle pressure are sufficient to generate a positive inflow along periarterial spaces.
Design of C24 fullerene-based sensors for gamma-butyrolactone detection as advanced tools for biomedical and pharmaceutical applications
Deep learning-based recognition model of football player’s technical action behavior using PCA–LBP algorithm
Mitigating side channel attacks on FPGA through deep learning and dynamic partial reconfiguration
Abstract This paper introduces a framework that combines Deep Learning (DL) models and Dynamic Partial Reconfiguration (DPR) in Field Programmable Gate Arrays (FPGA) to mitigate Side Channel Attacks (SCA). Traditional static defense mechanisms often fail to fully mitigate SCA because they lack the ability to adapt dynamically to attacks. The proposed approach overcomes this limitation by adaptively reconfiguring the FPGA resources in real-time, disrupting the SCA patterns, and reducing the effectiveness of potential attacks. One of the notable advantages of this approach is its ability to defend against side-channel attacks while the FPGA design is operational. The framework accomplishes this by reconfiguring the FPGA resources to optimize response times, achieving latency levels beyond the reach of traditional static defense mechanisms. In particular, this study concentrates on mitigating power side-channel attacks, highlighting the resilience of the DL-DPR integration. Beyond its demonstrated efficacy against power SCA, the proposed framework can be extended to be adaptable to other types of side-channel attacks, making it a potential solution for hardware security. The integration of DL models allows for sophisticated threat analysis, while DPR provides the flexibility to implement countermeasures dynamically. Experimental results show that the latency from detection to mitigation is within 20 clock cycles. This combination represents a paradigm shift in securing hardware systems, moving from reactive to proactive defense mechanisms. The framework’s real-time adaptability ensures it stays ahead of attackers, continuously evolving to neutralize new threats. The findings presented in this paper underscore the potential of combining Artificial Intelligence (AI) and FPGA technologies to redefine hardware security. By addressing detection and mitigation in a unified framework, the proposed methodology significantly enhances the resilience of FPGA designs and lays the groundwork for future research in adaptive security mechanisms.
A modified surgical approach to induce circle Willis perforation in mice using the common carotid artery
Abstract The Circle of Willis perforation (cWp) mouse model is widely used in subarachnoid hemorrhage (SAH) research but involves sacrificing the external carotid artery (ECA), which may have a potential affect on the hemodynamic in the carotid arteries and cortical perfusion. We propose a modified approach using needle puncture via the common carotid artery (CCA) to preserve carotid vascular integrity. Twenty-seven C57BL/6 mice were randomly assigned into three groups and underwent cWp surgery in three procedures: sham (n = 3), ECA (n = 12), and CCA (n = 12). Surgical duration, success rate, intraoperative intracranial pressure (ICP) fluctuations, 24-hour mortality, and neurological deficits were assessed. The CCA approach achieved a 100% success rate and shorter surgical duration than the ECA approach (ECA 73 ± 18 vs. CCA 36 ± 10 min, P < 0.05). ICP fluctuations and mortality rates were comparable between the ECA and CCA groups (P > 0.05), indicating that the CCA approach shared a similar pattern with the ECA approach. Neurological outcomes were similar across SAH groups (CCA SAH induction and ECA SAH induction) but worse than sham (P < 0.05) in terms of body weight loss, open-filed test and Rotarod test performances. This modified cWp CCA approach, which preserves the carotid structures, helps eliminate hemodynamic bias and offers a potentially more efficient alternative with a shorter surgical duration compared to the classical ECA approach. It may prove to be a valuable option for broader application in SAH preclinical research.
Prognostic factors and clinical outcomes of stenting on malignant central airway obstruction
Mechanisms and optimization of system bolts in shallow four-track HSR tunnels based on deformation pressure theory
A streamlined POCT solution for rapid infectious disease detection
Design, development and performance evaluation of henna harvester
Restoration of multi-channel signal loss using autoencoder with recursive input strategy
Abstract Multi-channel sensor data often suffer from missing or corrupted values due to sensor failures, communication disruptions, or environmental interference. These issues severely limit the accuracy of intelligent systems relying on sensor data integration. Existing data restoration techniques often fail to capture complex correlations among sensor channels, especially when data losses occur randomly and continuously. To overcome these limitations, we propose an autoencoder-based data recovery algorithm that recursively feeds reconstructed outputs back into the model to progressively refine estimates. A dynamic termination criterion monitors reconstruction improvements, automatically stopping iterations when further refinements become negligible. This recursive input strategy significantly enhances restoration accuracy and computational efficiency compared to conventional single-step methods. Experiments on multivariate sensor datasets show that the proposed method significantly outperforms the one-time autoencoder restoration method and maintains robust performance across diverse datasets and missing data scenarios. This approach provides a scalable and adaptable solution to ensure data integrity in complex sensor networks, enabling improved reliability and operational efficiency in industrial and technological applications.