End-to-End Delay Minimization based on Joint Optimization of DNN Partitioning and Resource Allocation for Cooperative Edge Inference

Cooperative inference in Mobile Edge Computing (MEC), achieved by deploying partitioned Deep Neural Network (DNN) models between resource-constrained user equipments (UEs) and edge servers (ESs), has emerged as a promising paradigm. Firstly, we consider scenarios of continuous Artificial Intelligence (AI) task arrivals, like the object detection for video streams, and utilize a serial queuing model for the accurate evaluation of End-to-End (E2E) delay in cooperative edge inference. Secondly, to enhance the long-term performance of inference systems, we formulate a multi-slot stochastic E2E delay optimization problem that jointly considers model partitioning and multi-dimensional resource allocation. Finally, to solve this problem, we introduce a Lyapunov-guided Multi-Dimensional Optimization algorithm (LyMDO) that decouples the original problem into per-slot deterministic problems, where Deep Reinforcement Learning (DRL) and convex optimization are used for joint optimization of partitioning decisions and complementary resource allocation. Simulation results show that our approach effectively improves E2E delay while balancing long-term resource constraints.Comment: 7 pages, 9 figures, 1 table, 1 algorithm, to be published in IEEE 98th Vehicular Technology Conference (VTC2023-Fall

NLRP6 Serves as a Negative Regulator of Neutrophil Recruitment and Function During Streptococcus pneumoniae Infection

Streptococcus pneumoniae is an invasive pathogen with high morbidity and mortality in the immunocompromised children and elderly. NOD-like receptor family pyrin domain containing 6 (NLRP6) plays an important role in the host innate immune response against pathogen infections. Our previous studies have shown that NLRP6 plays a negative regulatory role in host defense against S. pneumoniae, but the underlying mechanism is still unclear. The further negative regulatory role of NLRP6 in the host was investigated in this study. Our results showed that NLRP6(-/-) mice in the lung had lower bacterial burdens after S. pneumoniae infection and expressed higher level of tight junction (TJ) protein occludin compared to WT mice, indicating the detrimental role of NLRP6 in the host defense against S. pneumoniae infection. Transcriptome analysis showed that genes related to leukocytes migration and recruitment were differentially expressed between wild-type (WT) and NLRP6 knockout (NLRP6(-/-)) mice during S. pneumoniae infection. Also, NLRP6(-/-) mice showed higher expression of chemokines including C-X-C motif chemokine ligand 1 (CXCL1) and 2 (CXCL2) and lower gene expression of complement C3a receptor 1 (C3aR1) and P-selectin glycoprotein ligand-1 (PSGL-1) which are the factors that inhibit the recruitment of neutrophils. Furthermore, NLRP6(-/-) neutrophils showed increased intracellular bactericidal ability and the formation of neutrophil extracellular traps (NETs) during S. pneumoniae infection. Taken together, our study suggests that NLRP6 is a negative regulator of neutrophil recruitment and function during S. pneumoniae infection. Our study provides a new insight to develop novel strategies to treat invasive pneumococcal infection

E-Cadherin/β-Catenin Complex and the Epithelial Barrier

E-Cadherin/β-catenin complex plays an important role in maintaining epithelial integrity and disrupting this complex affect not only the adhesive repertoire of a cell, but also the Wnt-signaling pathway. Aberrant expression of the complex is associated with a wide variety of human malignancies and disorders of fibrosis resulting from epithelial-mesenchymal transition. These associations provide insights into the complexity that is likely responsible for the fibrosis/tumor suppressive action of E-cadherin/β-catenin

CE–RAA–CRISPR Assay: A Rapid and Sensitive Method for Detecting <i>Vibrio parahaemolyticus</i> in Seafood

Vibrio parahaemolyticus is one of the major pathogenic Vibrio species that contaminate seafood. Rapid and accurate detection is crucial for avoiding foodborne diseases caused by pathogens and is important for food safety management and mariculture. In this study, we established a system that combines chemically enhanced clustered regularly interspaced short palindromic repeats (CRISPR) and recombinase-aided amplification (RAA) (CE–RAA–CRISPR) for detecting V. parahaemolyticus in seafood. The method combines RAA with CRISPR-associated protein 12a (Cas12a) for rapid detection in a one-pot reaction, effectively reducing the risk of aerosol contamination during DNA amplifier transfer. We optimized the primers for V. parahaemolyticus, determined the optimal crRNA/Cas12a ratio, and demonstrated that chemical additives (bovine serum albumin and L-proline) could enhance the detection capacity of Cas12a. The limit of detection (at optimal conditions) was as low as 6.7 × 101 CFU/mL in pure cultures and 7.3 × 101 CFU/g in shrimp. Moreover, this method exhibited no cross-reactivity with other microbial pathogens. The CE–RAA–CRISPR assay was compared with the quantitative polymerase chain reaction assay using actual food samples, and it showed 100% diagnostic agreement

NDGGNET-A Node Independent Gate based Graph Neural Networks

Graph Neural Networks (GNNs) is an architecture for structural data, and has been adopted in a mass of tasks and achieved fabulous results, such as link prediction, node classification, graph classification and so on. Generally, for a certain node in a given graph, a traditional GNN layer can be regarded as an aggregation from one-hop neighbors, thus a set of stacked layers are able to fetch and update node status within multi-hops. For nodes with sparse connectivity, it is difficult to obtain enough information through a single GNN layer as not only there are only few nodes directly connected to them but also can not propagate the high-order neighbor information. However, as the number of layer increases, the GNN model is prone to over-smooth for nodes with the dense connectivity, which resulting in the decrease of accuracy. To tackle this issue, in this thesis, we define a novel framework that allows the normal GNN model to accommodate more layers. Specifically, a node-degree based gate is employed to adjust weight of layers dynamically, that try to enhance the information aggregation ability and reduce the probability of over-smoothing. Experimental results show that our proposed model can effectively increase the model depth and perform well on several datasets

Digital registration versus cone-beam computed tomography for evaluating implant position: a prospective cohort study

Abstract Background Postoperative cone-beam computed tomography (CBCT) examination is considered a reliable method for clinicians to assess the positions of implants. Nevertheless, CBCT has drawbacks involving radiation exposure and high costs. Moreover, the image quality can be affected by artifacts. Recently, some literature has mentioned a digital registration method (DRM) as an alternative to CBCT for evaluating implant positions. The aim of this clinical study was to verify the accuracy of the DRM compared to CBCT scans in postoperative implant positioning. Materials and methods A total of 36 patients who received anterior maxillary implants were included in this clinical study, involving a total of 48 implants. The study included 24 patients in the single implant group and 12 patients in the dual implant group. The postoperative three-dimensional (3D) positions of implants were obtained using both CBCT and DRM. The DRM included three main steps. Firstly, the postoperative 3D data of the dentition and intraoral scan body (ISB) was obtained through the intraoral scan (IOS). Secondly, a virtual model named registration unit which comprised an implant replica and a matching ISB was created with the help of a lab scanner and reverse engineering software. Thirdly, by superimposing the registration unit and IOS data, the postoperative position of the implant was determined. The accuracy of DRM was evaluated by calculating the Root Mean Square (RMS) values after superimposing the implant positions obtained from DRM with those from postoperative CBCT. The accuracy of DRM was compared between the single implant group and the dual implant group using independent sample t-tests. The superimposition deviations of CBCT and IOS were also evaluated. Results The overall mean RMS was 0.29 ± 0.05 mm. The mean RMS was 0.30 ± 0.03 mm in the single implant group and 0.29 ± 0.06 mm in the dual implant group, with no significant difference (p = 0.27). The overall registration accuracy of the IOS and CBCT data ranged from 0.14 ± 0.05 mm to 0.21 ± 0.08 mm. Conclusion In comparison with the 3D implant positions obtained by CBCT, the implant positions located by the DRM showed clinically acceptable deviation ranges. This method can be used in single and dual implant treatments to assess the implant positions

Self-Supervised Mixture-of-Experts by Uncertainty Estimation

Learning related tasks in various domains and transferring exploited knowledge to new situations is a significant challenge in Reinforcement Learning (RL). However, most RL algorithms are data inefficient and fail to generalize in complex environments, limiting their adaptability and applicability in multi-task scenarios. In this paper, we propose SelfSupervised Mixture-of-Experts (SUM), an effective algorithm driven by predictive uncertainty estimation for multitask RL. SUM utilizes a multi-head agent with shared parameters as experts to learn a series of related tasks simultaneously by Deep Deterministic Policy Gradient (DDPG). Each expert is extended by predictive uncertainty estimation on known and unknown states to enhance the Q-value evaluation capacity against overfitting and the overall generalization ability. These enable the agent to capture and diffuse the common knowledge across different tasks improving sample efficiency in each task and the effectiveness of expert scheduling across multiple tasks. Instead of task-specific design as common MoEs, a self-supervised gating network is adopted to determine a potential expert to handle each interaction from unseen environments and calibrated completely by the uncertainty feedback from the experts without explicit supervision. To alleviate the imbalanced expert utilization as the crux of MoE, optimization is accomplished via decayedmasked experience replay, which encourages both diversification and specialization of experts during different periods. We demonstrate that our approach learns faster and achieves better performance by efficient transfer and robust generalization, outperforming several related methods on extended OpenAI Gym’s MuJoCo multi-task environments

CRISPR/Cas12a-based assay for the rapid and high-sensitivity detection of Streptococcus agalactiae colonization in pregnant women with premature rupture of membrane

Abstract Background Streptococcus agalactiae or group B Streptococcus (GBS) is a leading infectious cause of neonatal morbidity and mortality. It is essential to establish a robust method for the rapid and ultra-sensitive detection of GBS in pregnant women with premature rupture of membrane (PROM). Methods This study developed a CRISPR-GBS assay that combined the advantages of the recombinase polymerase amplification (RPA) and CRISPR/Cas12a system for GBS detection. The clinical performance of the CRISPR-GBS assay was assessed using vaginal or cervical swabs that were collected from 179 pregnant women with PROM, compared in parallel to culture-based matrix-assisted laser desorption ionization time-of-flight mass spectrometry (culture-MS) method and real-time quantitative polymerase chain reaction (qPCR) assay. Results The CRISPR-GBS assay can be completed within 35 min and the limit of detection was as low as 5 copies μL−1. Compared with the culture-MS, the CRISPR-GBS assay demonstrated a sensitivity of 96.64% (144/149, 95% confidence interval [CI] 92.39–98.56%) and a specificity of 100% (30/30, 95% CI  88.65–100%). It also had a high concordance rate of 98.88% with the qPCR assay. Conclusions The established CRISPR-GBS platform can detect GBS in a rapid, accurate, easy-to-operate, and cost-efficient manner. It offered a promising tool for the intrapartum screening of GBS colonization