Entire Node Research Articles

Contribution of Imagery in Larynx Cancer: About 61 Cases

Extensional imaging of laryngeal tumors relies on multi-bar helical CT scans. This examination requires compliance with a few simple rules in its preparation and performance. The protocol for performing cervico-mediastinal CT scans will be developed. Imaging must take into account not only the initial tumor pathology, but also the entire upper aero-digestive tract and lymph node areas, i.e. acquisition must extend from the base of the skull to the cervico-mediastinal orifice. Thoracic imaging should be performed as a matter of course for overall patient management. Certain deep extensions, only accessible by imaging, are decisive in the choice of treatment: surgery or radio-chemotherapy. In the case of surgery, imaging is a key factor in the choice of total or partial surgery. A complete lymph node survey is also essential in the management of these lymphophilic tumors.

Duo‐H: An Effectual Consensus Algorithm Using Two‐Tier Shard Consortium Blockchain Mechanism for Enhanced Privacy Protection

ABSTRACTBlockchain is an innovative technology for storing data in decentralized, distributed, and secure chain blocks. Consortium blockchains are commonly used in transactions where transactions between organizations are also achieved by the blockchain. In the classic consortium blockchain system, entire nodes are added to each other in the process of transaction consensus. This leads to lower confidentiality in protecting transaction data within the organizations in the consortium. The throughput of the existing consortium blockchain system is still low. To solve the above problems, the paper proposes a two‐tier consortium blockchain with transaction privacy based on sharding technology. First, a trust value assessment is carried out to select the nodes of the blockchain. The duo‐head observation strategy uses these trust values to identify the nonmalicious node. Finally, the consensus separation approach based on the guarantee mechanism strategy with the shard nodes is presented. This approach is used to select reliable nodes for the blocks to be stored. The proposed consortium blockchain approach evaluation is done in terms of latency, throughput, and transactions per second metrics. As a result of the evaluations, the proposed model with 32 shards possesses 143,891 of throughput and 1.11 s of latency. Moreover, by the proposed two‐tier consortium model, time consumption is also decreased when uploading data. For a data set of 50,000, the suggested model has a time consumption of 96 s. The proposed research results in higher throughput and less latency in transactions. Also, the research enhances the scalability and reliability by overcoming the poor node issues.

Neural discovery of balance-aware polarized communities

Signed graphs are a model to depict friendly (positive) or antagonistic (negative) interactions (edges) among users (nodes). 2-Polarized-Communities (2pc) is a well-established combinatorial-optimization problem whose goal is to find two polarized communities from a signed graph, i.e., two subsets of nodes (disjoint, but not necessarily covering the entire node set) which exhibit a high number of both intra-community positive edges and negative inter-community edges. The state of the art in 2pc suffers from the limitations that (i) existing methods rely on a single (optimal) solution to a continuous relaxation of the problem in order to produce the ultimate discrete solution via rounding, and (ii) 2pc objective function comes with no control on size balance among communities. In this paper, we provide advances to the 2pc problem by addressing both these limitations, with a twofold contribution. First, we devise a novel neural approach that allows for soundly and elegantly explore a variety of suboptimal solutions to the relaxed 2pc problem, so as to pick the one that leads to the best discrete solution after rounding. Second, we introduce a generalization of 2pc objective function – termed γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma $$\\end{document}-polarity – which fosters size balance among communities, and we incorporate it into the proposed machine-learning framework. Extensive experiments attest high accuracy of our approach, its superiority over the state of the art, and capability of function γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma $$\\end{document}-polarity to discover high-quality size-balanced communities.

The role of intraoperative central lymph node biopsy in the treatment of clinically low-risk PTMC.

To evaluate the role of intraoperative frozen biopsy of central lymph nodes in central neck dissection and thyroidectomy in patients of unilateral, clinically negative nodes (cN0) papillary thyroid microcarcinoma (PTMC) without extra-glandular invasion. The clinical data of 465 patients were collected retrospectively. Part of prelaryngeal, pretracheal and ipsilateral paratracheal lymph nodes were taken for frozen pathological examination during the operation. Then the thyroid lobe on the tumor side and isthmus were excised, and central neck dissection of the affected side was performed in all patients. The number of metastases in entire central lymph nodes of the affected side can be obtained by postoperative paraffin pathology. If the number of positive lymph nodes during surgery is ≥3, contralateral gland resection was performed. In this group of 465 patients, there were 186 cases with central lymph node metastasis. The Kappa coefficient of consistency between frozen pathology and paraffin pathology in central lymph nodes was 0.605. The ROC curve for the number of intraoperative frozen metastases-postoperative pathological metastases over 5 showed that the AUC of the curve was 0.793, while the maximum Youden index was 0.5259, whose corresponding number of positive lymph nodes was 3. Intraoperative central lymph nodes biopsy can be used as an important indicator for the status of central lymph node metastasis in unilateral cN0 PTMC patients without extra-glandular invasion and a determinant for central lymph node dissection. While the number of positive lymph nodes intraoperatively is ≥3, total thyroidectomy should be considered.

Stepwise Ultra-pH-Sensitive Micelles Overcome a pKa Barrier for Systemic Lymph Node Delivery.

While local nanoparticle delivery to lymph nodes is well studied, there are few design criteria for intravenous delivery to the entire lymph node repertoire. In this study, we investigated the effect of NP pH transition on lymph node targeting by employing a series of ultra-pH-sensitive (UPS) polymeric micelles. The UPS library responds to pH thresholds (pKa 6.9, 6.2, and 5.3) over a range of physiological pH. We observed a dependence of intravenous lymph node targeting on micelle pH transition. UPS6.9 (subscript indicates pKa) shows poor lymph node delivery, while UPS5.3 delivers efficiently to lymph node sets. We investigated targeting mechanisms of UPS5.3, observing an accumulation among lymph node lymphatics and a dependence on lymph node-resident macrophages. To overcome the pH-threshold barrier, which limits UPS6.9, we rationally designed a nanoparticle coassembly of UPS6.9 with UPS5.3, called HyUPS. The HyUPS micelle retains the constitutive pH transitions of each polymer, showing stepwise responses to discrete pH thresholds. We demonstrate that HyUPS improves UPS6.9 delivery to lymph nodes, extending this platform for disease detection of lymph node metastasis.

A multi-node attack scheme based on community partitioning in large scale infrastructure networks

Node attack plays a vital role in evaluating the reliability of large scale infrastructure networks, which has attracted intensive research interests. Unfortunately, most of them focus on single node attack. Multi-node attack based on MBA (Module based Adaptive) is a promising research vector with great potential in improving efficiency. However, MBA-based node attack typically needs to repetitively consider community structures of large networks during the entire node attack process, which brings significant loss in network reliability and causes computational overheads. In light of this, we propose a novel scheme based on MBA to implement multi-node attack, which adopts the Multilevel community partitioning algorithm to overcome the above limitations. We evaluate our scheme on two artificial synthetic networks and four real-world networks with comparison to four widely used node attack schemes, and demonstrate the non-negligible superiority of our scheme over all benchmarks. Especially in the US Powergrid, our scheme can always achieve robustness around 10 times that of other schemes when deleting the same percentage of nodes. To the best of our knowledge, we are the first to achieve an MBA-based node attack scheme that only needs to execute the community partitioning algorithm once during the entire node deletion process.

Learning to Approximate Adaptive Kernel Convolution on Graphs

Various Graph Neural Networks (GNN) have been successful in analyzing data in non-Euclidean spaces, however, they have limitations such as oversmoothing, i.e., information becomes excessively averaged as the number of hidden layers increases. The issue stems from the intrinsic formulation of conventional graph convolution where the nodal features are aggregated from a direct neighborhood per layer across the entire nodes in the graph. As setting different number of hidden layers per node is infeasible, recent works leverage a diffusion kernel to redefine the graph structure and incorporate information from farther nodes. Unfortunately, such approaches suffer from heavy diagonalization of a graph Laplacian or learning a large transform matrix. In this regards, we propose a diffusion learning framework where the range of feature aggregation is controlled by the scale of a diffusion kernel. For efficient computation, we derive closed-form derivatives of approximations of the graph convolution with respect to the scale, so that node-wise range can be adaptively learned.With a downstream classifier, the entire framework is made trainable in an end-to-end manner. Our model is tested on various standard datasets for node-wise classification for the state-of-the-art performance, and it is also validated on a real-world brain network data for graph classifications to demonstrate its practicality for Alzheimer classification.

Fixed-Time Pinning Common Synchronization and Adaptive Synchronization for Delayed Quaternion-Valued Neural Networks.

This article focuses on the fixed-time pinning common synchronization and adaptive synchronization for quaternion-valued neural networks with time-varying delays. First, to reduce transmission burdens and limit convergence time, a pinning controller which only controls partial nodes directly rather than the entire nodes is proposed based on fixed-time control theory. Then, by Lyapunov function approach and some inequalities techniques, fixed-time common synchronization criterion is established. Second, further to realize the self-regulation function of pinning controller, an adaptive pinning controller which can adjust automatically the control gains is developed, the desired fixed-time adaptive synchronization is achieved for the considered system, and the corresponding criterion is also derived. Finally, the availability of these results is tested by simulation example.

Adjuvant medial versus entire supraclavicular lymph node irradiation in high-risk early breast cancer (SUCLANODE): a protocol for a multicenter, randomized, open-label, phase 3 trial

BackgroundSupraclavicular nodal (SCL) irradiation is commonly used for patients with high-risk breast cancer after breast surgery. The Radiation Therapy Oncology Group (RTOG) and European Society for Radiotherapy and Oncology (ESTRO) breast contouring atlases delineate the medial part of the SCL region, while excluding the posterolateral part. However, recent studies have found that a substantial proportion of SCL failures are located in the posterolateral SCL region, outside of the RTOG/ESTRO-defined SCL target volumes. Consequently, many radiation oncologists advocate for enlarging the SCL irradiation target volume to include both the medial and posterolateral SCL regions. Nevertheless, it remains uncertain whether adding the posterolateral SCL irradiation improves survival outcomes for high-risk breast cancer patients.MethodsThe SUCLANODE trial is an open-label, multicenter, randomized, phase 3 trial comparing the efficacy and adverse events of medial SCL irradiation (M-SCLI group) and medial plus posterolateral SCL irradiation (entire SCL irradiation, E-SCLI group) in high-risk breast cancer patients who underwent breast conserving-surgery or mastectomy. Patients with pathological N2-3b disease following initial surgery, or clinical stage III or pathological N1-3b if receiving neoadjuvant systemic therapy, are eligible and randomly assigned (1:1) to M-SCLI group and E-SCLI group. Stratification is by chemotherapy sequence (neoadjuvant vs. adjuvant), T stage (T3-4 vs. T1-2), N stage (N1-2 vs. N3), and ER status (positive vs. negative). Other radiation volumes are identical in the two arms, including breast/chest wall, undissected axillary lymph node, and internal mammary node. Advanced intensity modulated radiation therapy (IMRT), volumetric modulated arc therapy (VMAT), or tomotherapy techniques are recommended. Both hypofractionated and conventional fractionation schedules are permitted. The primary end point is invasive disease-free survival, and secondary end points included overall survival, SCL recurrence, local-regional recurrence, distance recurrence, safety outcome, and patient-reported outcomes. The target sample size is 1650 participants.DiscussionThe results of the SUCLANODE trial will provide high-level evidence regarding whether adding posterolateral SCL irradiation to medial SCL target volume provides survival benefit in patients with high-risk breast cancer.Trial registrationClinicalTrials.gov Identifier: NCT05059379. Registered 28 September 2021, https://www.clinicaltrials.gov/ct2/show/NCT05059379.

Predicting Global Average Temperature Time Series Using an Entire Graph Node Training Approach

Predicting Global Average Temperature Time Series Using an Entire Graph Node Training Approach

Advantages of contrast-enhanced ultrasound in the localization and diagnostics of sentinel lymph nodes in breast cancer.

Sentinel lymph nodes (SLNs) are the first station of lymph nodes that extend from the breast tumor to the axillary lymphatic drainage. The pathological status of these LNs can predict that of the entire axillary lymph node. Therefore, the accurate identification of SLNs is necessary for sentinel lymph node biopsy (SLNB) to replace axillary lymph node dissection (ALND). The quality of life and prognosis of breast cancer patients are related to proper surgical treatment after the precise identification of SLNs. Some of the SLN tracers that have been identified include radioisotope, nano-carbon, indocyanine green (ICG), and methylene blue (MB). However, these tracers have certain limitations, such as pigmentation, radiation dangers, and the requirement for costly detection equipment. Ultrasound contrast agents (UCAs) have good specificity and sensitivity, and thus can compensate for some shortcomings of the mentioned tracers. This technique is also being applied to SLNB in patients with breast cancer, and can even provide an initial judgment on SLN status. Contrast-enhanced ultrasound (CEUS) has the advantages of high distinguishability, simple operation, no radiation harm, low cost, and accurate localization; therefore, it is expected to replace the traditional biopsy methods. In addition, it can significantly enhance the accuracy of SLN localization and shorten the operation time.

Communication and Content Trust Aware Routing For Clustered IoT Network

Security has become a major concern in practical applications related to Internet of Things, a Trust Aware Routing is found as second line of defence. To ensure a secure and hassle-free communication in IoT, this paper proposes a new routing strategy called as Communication and Content Trust Aware Routing (CCTAR) for Clustered IoT network. CCTAR is applied on a clustered IoT network in which the entire nodes are clustered into different clusters. Distance, initial energy, transmission range, angle of overlap and the sensing range are the fur major metrics used to cluster the network into hierarchical clusters followed by Cluster Head Selection. Next, the Trust Aware routing computes three different trust metrics namely Nobility rust, bilateral trust and Data oriented trust to determine the trustworthiness of Cluster Heads. The experimental evaluation of the proposed mechanism shows its superiority in terms of malicious nodes identification, Storage overhead reduction and Network lifetime improvisation.

GraphHop: An Enhanced Label Propagation Method for Node Classification.

A scalable semisupervised node classification method on graph-structured data, called GraphHop, is proposed in this work. The graph contains all nodes' attributes and link connections but labels of only a subset of nodes. Graph convolutional networks (GCNs) have provided superior performance in node label classification over the traditional label propagation (LP) methods for this problem. Nevertheless, current GCN algorithms suffer from a considerable amount of labels for training because of high model complexity or cannot be easily generalized to large-scale graphs due to the expensive cost of loading the entire graph and node embeddings. Besides, nonlinearity makes the optimization process a mystery. To this end, an enhanced LP method, called GraphHop, is proposed to tackle these problems. GraphHop can be viewed as a smoothening LP algorithm, in which each propagation alternates between two steps: label aggregation and label update. In the label aggregation step, multihop neighbor embeddings are aggregated to the center node. In the label update step, new embeddings are learned and predicted for each node based on aggregated results from the previous step. The two-step iteration improves the graph signal smoothening capacity. Furthermore, to encode attributes, links, and labels on graphs effectively under one framework, we adopt a two-stage training process, i.e., the initialization stage and the iteration stage. Thus, the smooth attribute information extracted from the initialization stage is consistently imposed in the propagation process in the iteration stage. Experimental results show that GraphHop outperforms state-of-the-art graph learning methods on a wide range of tasks in graphs of various sizes (e.g., multilabel and multiclass classification on citation networks, social graphs, and commodity consumption graphs).

Differential Evolution Algorithm for Coordination of SVC Modules in MV Distribution Systems

This paper proposes a new strategy based on the differential evolution algorithm to optimize the performance of distribution networks through the optimal coordination of Static VAR Compensator modules (SVCs). Installation costs minimization and savings maximization due to reducing power losses are merged in one multi-objective function. In order to investigate the influences of varying loading conditions, various regular loadings are further combined. This framework implemented on a 37-bus real feeder connected to the Egyptian Unified Network (EUN). The findings of the simulation reveal evident technical and economical characteristics of the proposed algorithm. The reactive power compensation using SVCs based on the pro-posed scheme leads to major quality improvements of the entire nodes’ voltage with variations of loads. Especially, in light loading condition, the SVCs control their performance characteristics according to the reactive power demands in the adjacent nodes.

Subsampling spectral clustering for stochastic block models in large-scale networks

The rapid development of science and technology has generated large amounts of network data, leading to significant computational challenges for network community detection. A novel subsampling spectral clustering algorithm is proposed to address this issue, which aims to identify community structures in large-scale networks with limited computing resources. The algorithm constructs a subnetwork by simple random subsampling from the entire network, and then extends the existing spectral clustering to the subnetwork to estimate the community labels for entire network nodes. As a result, for large-scale datasets, the method can be realized even using a personal computer. Moreover, the proposed method can be generalized in a parallel way. Theoretically, under the stochastic block model and its extension, the degree-corrected stochastic block model, the theoretical properties of the subsampling spectral clustering method are correspondingly established. Finally, to illustrate and evaluate the proposed method, a number of simulation studies and two real data analyses are conducted.

Towards Efficient Resource Allocation for Federated Learning in Virtualized Managed Environments

Federated learning (FL) is a transformative approach to Machine Learning that enables the training of a shared model without transferring private data to a central location. This decentralized training paradigm has found particular applicability in edge computing, where IoT devices and edge nodes often possess limited computational power, network bandwidth, and energy resources. While various techniques have been developed to optimize the FL training process, an important question remains unanswered: how should resources be allocated in the training workflow? To address this question, it is crucial to understand the nature of these resources. In physical environments, the allocation is typically performed at the node level, with the entire node dedicated to executing a single workload. In contrast, virtualized environments allow for the dynamic partitioning of a node into containerized units that can adapt to changing workloads. Consequently, the new question that arises is: how can a physical node be partitioned into virtual resources to maximize the efficiency of the FL process? To answer this, we investigate various resource allocation methods that consider factors such as computational and network capabilities, the complexity of datasets, as well as the specific characteristics of the FL workflow and ML backend. We explore two scenarios: (i) running FL over a finite number of testbed nodes and (ii) hosting multiple parallel FL workflows on the same set of testbed nodes. Our findings reveal that the default configurations of state-of-the-art cloud orchestrators are sub-optimal when orchestrating FL workflows. Additionally, we demonstrate that different libraries and ML models exhibit diverse computational footprints. Building upon these insights, we discuss methods to mitigate computational interferences and enhance the overall performance of the FL pipeline execution.

The Inference of Complicated Networks by Mutual Information

Unsupervised machine learning affords a general idea about complicated data using a graphical representation of networks by nodes and edges to provide a better and easier understanding of the data. The existence of an edge between two entire nodes is determined by their relationship in terms of any kind of dependence i.e., conditional dependence, linear and non-linear, directed or undirected. This study tries to show the accuracy of a non-parametric approach i.e., mutual information (MI) on a real data set named by the Rochdale data that is composed of eight factors that affected women’s economic activity by comparing with some methods such as reversible jump MCMC and birth-death MCMC those tried to detect the conditional dependence between the variables. As a result, MI is not only a very simple but also a very accurate method in the inference of data with complexities.

D2OP: A Fair Dual-Objective Weighted Scheduling Scheme in Internet of Everything

In times of the Internet of Everything (IoE), the power of the Internet is growing exponentially, followed by a surge in the number of network requests. The conflict between people’s high requirements for quality of experience (QoE) and limited computing resources are becoming increasingly prominent. Therefore, an appropriate offloading method is required to better ease this conflict. In this paper, a highly efficient scheduling architecture of information processing under the big data flow of the IoE is proposed to enhance the scheduling performance. First, we construct a dual-channel processing model to describe the entire data flow and node devices. Second, we carefully consider the choice of weighting method to better find a balance between dual objectives. Third, a dual-objective deep Q-network (DQN)-based offloading algorithm with principal component analysis weighting method (D2OP) is proposed to collaboratively minimize task response time and machine load in a more reasonable allocation. To verify the performance of the D2OP, a series of experiments are conducted from multiple angles. The experimental results demonstrate its better performance than the three comparison algorithms in reducing response time, load balance, and increasing task success ratio.

Dendritic cells regulate innate cell trafficking and lymph node organization during inflammation

Abstract Organization of immune cells within lymph nodes (LNs) is critical for effective host defense. During vaccination or infection, layers of innate cell activity in the outer periphery of the LN provide immediate protection, while antigen-presenting dendritic cells (DCs) migrate into the T cell zone (TZ), located deeper in the LN, to initiate later-stage adaptive responses. Using mouse models of vaccination and infection, we now find that DC activation and repositioning into the TZ is tightly associated with localized recruitment of multiple innate effector cells, including neutrophils, monocytes, and NK cells, which also rapidly infiltrate the TZ. As activated DCs migrated towards the TZ, they passed by the high endothelial venules (HEVs), where they expressed multiple inflammatory chemokines and high levels of the integrin ligand, ICAM-1, generating a chemokine- and adhesion molecule-rich matrix ripe for innate cell migration. Depletion of DCs, or conditional ablation of ICAM-1, significantly impaired innate cell trafficking into LNs, and cells which did enter remained largely trapped near the HEVs and were less able to infiltrate the TZ. While essential for innate defense, neutrophil influx also led to large-scale disruption of the TZ and impaired early T cell activation. However, over time this was controlled by DCs and DC-recruited monocytes, which efferocytosed the infiltrating neutrophils and cleared the TZ to allow productive adaptive responses to occur. Thus, in addition to their classical roles in antigen presentation, DCs orchestrate innate cell recruitment and activation across the entire LN, to promote both effective innate defense as well as rapid restoration of LN architecture for the generation of adaptive immunity. Supported by grant from NIH (1F31AI161316-01).

Evaluation of the one-step nucleic acid amplification method for rapid detection of lymph node metastases in endometrial cancer: prospective, multicenter, comparative study

ObjectiveTo evaluate the diagnostic performance of the one-step nucleic acid amplification (OSNA) method for the detection of sentinel lymph node (SLN) metastases in women with apparent early-stage endometrial cancer compared...