Proposal of dental demineralization diagnosis with OCT echo based on multiscale entropy analysis

Optical coherence tomography (OCT) has been widely used for the diagnosis of dental demineralization. Most methods rely on extracting optical features from OCT echoes for evaluation or diagnosis. However, due to the diversity of biological samples and the complexity of tissues, the separability and robustness of extracted optical features are inadequate, resulting in a low diagnostic efficiency. Given the widespread utilization of entropy analysis in examining signals from biological tissues, we introduce a dental demineralization diagnosis method using OCT echoes, employing multiscale entropy analysis. Three multiscale entropy analysis methods were used to extract features from the OCT one-dimensional echo signal of normal and demineralized teeth, and a probabilistic neural network (PNN) was used for dental demineralization diagnosis. By comparing diagnostic efficiency, diagnostic speed, and parameter optimization dependency, the multiscale dispersion entropy-PNN (MDE-PNN) method was found to have comprehensive advantages in dental demineralization diagnosis with a diagnostic efficiency of 0.9397. Compared with optical feature-based dental demineralization diagnosis methods, the entropy features-based analysis had better feature separability and higher diagnostic efficiency, and showed its potential in dental demineralization diagnosis with OCT

Interpreting Deep Learning-Based Networking Systems

While many deep learning (DL)-based networking systems have demonstrated superior performance, the underlying Deep Neural Networks (DNNs) remain blackboxes and stay uninterpretable for network operators. The lack of interpretability makes DL-based networking systems prohibitive to deploy in practice. In this paper, we propose Metis, a framework that provides interpretability for two general categories of networking problems spanning local and global control. Accordingly, Metis introduces two different interpretation methods based on decision tree and hypergraph, where it converts DNN policies to interpretable rule-based controllers and highlight critical components based on analysis over hypergraph. We evaluate Metis over several state-of-the-art DL-based networking systems and show that Metis provides human-readable interpretations while preserving nearly no degradation in performance. We further present four concrete use cases of Metis, showcasing how Metis helps network operators to design, debug, deploy, and ad-hoc adjust DL-based networking systems.Comment: To appear at ACM SIGCOMM 202

CCR: Capacity-Constrained Replication for Data Delivery in Vehicular Networks

Given the unique characteristics of vehicular networks, specifically, frequent communication unavailability and short encounter time, packet replication has been commonly used to facilitate data delivery. Replication enables multiple copies of the same packet to be forwarded towards the destination, which increases the chance of delivery to a target destination. However, this is achieved at the expense of consuming extra already scarce bandwidth resource in vehicular networks. Therefore, it is crucial to investigate the fundamental problem of exploiting constrained network capacity with packet replication. We make the first attempt in this work to address this challenging problem. We first conduct extensive empirical analysis using three large datasets of real vehicle GPS traces. We show that a replication scheme that either underestimates or overestimates the network capacity results in poor delivery performance. Based on the observation, we propose a Capacity-Constrained Replication scheme or CCR for data delivery in vehicular networks. The key idea is to explore the residual capacity for packet replication. We introduce an analytical model for characterizing the relationship among the number of replicated copies of a packet, replication limit and queue length. Based on this insight, we derive the rule for adaptive adjustment towards the optimal replication strategy. We then design a distributed algorithm to dictate how each vehicle can adaptively determine its replication strategy subject to the current network capacity. Extensive simulations based on real vehicle GPS traces show that our proposed CCR can significantly improve delivery ratio comparing with the state-of-the-art algorithms

MSC-derived exosomes deliver ZBTB4 to mediate transcriptional repression of ITIH3 in astrocytes in spinal cord injury

Background: BMSC-secreted exosomes (BMSC-Exos) have shown potential for promoting behavioral recovery following spinal cord injury (SCI). However, its role in blocking astrocyte activation remains unclear. Thus, this study aimed to determine whether BMSC-Exos impair the function of astrocytes following SCI in mice and to seek the mechanism. Methods: BMSC-Exos were collected by ultracentrifugation and identified. The SCI mice were developed by laminectomy combined with spinal cord shock, followed by BMSC-Exos or nerve growth factor (positive control) treatment. HE staining, Nissl staining, and TUNEL were conducted to analyze the pathological structural damage and neuronal damage in the mouse spinal cord. Bioinformatics was used to screen altered molecules under the BMSC-Exos treatment. Effects of BMSC-Exos and changes in ZBTB4 and ITIH3 expression on neuronal damage induced by activated astrocytes in the co-culture system were analyzed by CCK-8 and flow cytometry. Results: Nerve growth factor and BMSC-Exos promoted motor function recovery, alleviated nerve injury, and reduced apoptosis in mice with SCI. ZBTB4 was enriched in BMSC-Exos and lowly expressed in SCI. Downregulation of ZBTB4 diminished the therapeutic effects of BMSC-Exos against SCI. ITIH3 was a downstream target of ZBTB4. Neurotoxic activation of astrocytes induced neuronal injury, which was alleviated by BMSC-Exos. However, ZBTB4 knockdown overturned the effects of BMSC-Exos in vitro and combined ITIH3 knockdown alleviated the accentuating effects of ZBTB4 knockdown on neuronal injury. Conclusion: BMSC-Exos protected against astrocyte-induced neuronal injury by delivering ZBTB4 to repress ITIH3, ultimately improving motor function in mice with SCI