Node Level Research Articles

Evolution characteristics and invulnerability simulation analysis of global zirconium ore trade network

The burgeoning demand for zirconium, driven by the rapid development of smart devices, low-carbon energy technologies, and other emerging industries, underscores the importance of understanding the dynamics of its global trade network. However, the evolutionary patterns of the international zircon ore trade network and its resilience to disruptions remain unclear. This study constructs the international zircon ore trade network from 2013 to 2022, analyzes its structural evolution at both the network and node levels, and evaluates its robustness in 2022 using five attack strategies: random node removal, random edge removal, edge degradation, targeted removal based on node degree, and targeted removal based on node betweenness centrality. Our findings reveal that: (1) the international zircon ore trade network exhibits a shift in the import market towards Asia, with the export market dominated by Spain, the United States, and Brazil. China plays a crucial role as a bridge connecting various countries, while Japan exerts significant influence within the network; (2) the network is particularly vulnerable to targeted attacks based on node degree and betweenness centrality, highlighting the potential for significant disruption following the removal of key nodes. This study provides valuable insights for ensuring the stable and sustainable supply and consumption of zirconium resources, informing the development of targeted policies for countries and relevant industries.

Altered Intracerebellar Functional Connectivity in Friedreich’s Ataxia: A Graph-Theory Functional MRI Study

Historically, Friedreich’s Ataxia (FRDA) has been linked to a relatively preserved cerebellar cortex. Recent advances in neuroimaging have revealed altered cerebello-cerebral functional connectivity (FC), but the extent of intra-cerebellar FC changes and their impact on cognition remains unclear. This study investigates intra-cerebellar FC alterations and their cognitive implications in FRDA. In this cross-sectional, single-center study, resting-state functional MRI data from 17 patients with FRDA (average age 27.7 ± 13.6 years; F/M = 6/11) and 20 healthy controls (HC) (average age 29.4 ± 9.7 years; F/M = 9/11), all of whom underwent neuropsychological testing, were analyzed. From functional connectivity matrices, graph measures were computed at both the network and node levels using two complementary parcellations. FRDA patients exhibited decreased global efficiency (p = 0.04), nodal degree (p = 0.001) and betweenness centrality (p = 0.04) in the vermal portion of lobule VIII, along with reduced global efficiency in cerebellar regions belonging to the Control-A network (p = 0.02), one of the three subdivisions of the Frontoparietal network. Verbal memory deficits correlated with global efficiency in both the vermal portion of lobule VIII (r = 0.53, p = 0.02) and the cerebellar regions of the Control-A network (r = 0.49, p = 0.05). Graph analysis revealed regional intra-cerebellar FC changes in FRDA, marked by reduced functional centrality in cerebellar regions of the vermis and responsible for executive functions. These changes correlated with cognitive alterations, highlighting the role of the cerebellar cortex in the cognitive impairment observed in FRDA.

Efficiency and scalability of fully-resolved fluid-particle simulations on heterogeneous CPU-GPU architectures

Current supercomputers often have a heterogeneous architecture using both conventional Central Processing Units (CPUs) and Graphics Processing Units (GPUs). At the same time, numerical simulation tasks frequently involve multiphysics scenarios whose components run on different hardware due to multiple reasons, e.g., architectural requirements, pragmatism, etc. This leads naturally to a software design where different simulation modules are mapped to different subsystems of the heterogeneous architecture. We present a detailed performance analysis for such a hybrid four-way coupled simulation of a fully resolved particle-laden flow. The Eulerian representation of the flow utilizes GPUs, while the Lagrangian model for the particles runs on conventional CPUs. Two characteristic model situations involving dense and dilute particle systems are used as benchmark scenarios. First, a roofline model is employed to predict the node level performance and to show that the lattice-Boltzmann-based Eulerian fluid simulation reaches very good performance on a single GPU. Furthermore, the GPU-GPU communication for a large-scale Eulerian flow simulation results in only moderate slowdowns. This is due to the efficiency of the CUDA-aware MPI communication, combined with the use of communication hiding techniques. On 1024 A100 GPUs, an overall parallel efficiency of up to 71% is achieved. While the flow simulation has good performance characteristics, the integration of the stiff Lagrangian particle system requires frequent CPU-CPU communications that can become a bottleneck, especially when simulating the dense particle system. Additionally, special attention is paid to the CPU-GPU communication overhead since this is essential for coupling the particles to the flow simulation. However, thanks to our problem-aware co-partitioning, the CPU-GPU communication overhead is found to be negligible. As a lesson learned from this development, four criteria are postulated that a hybrid implementation must meet for the efficient use of heterogeneous supercomputers.

Disrupted Cross-Scale Network Associated With Cognitive-Emotional Disorders in Sudden Sensorineural Hearing Loss.

Sudden sensorineural hearing loss (SSNHL) is associated with abnormal changes in the brain's central nervous system. Previous studies on the brain networks of SSNHL have primarily focused on functional connectivity within the brain. However, in addition to functional connectivity, structural connectivity also plays a crucial role in brain networks. Moreover, traditional functional connectivity analyses often overlook the spatial and temporal characteristics of connectivity changes and fail to provide directional information and causal relationships. This study utilized Structural Covariance Network (SCN), multilayer network analysis, and Dynamic Causal Modeling (DCM) to investigate the cross-scale changes in neural network structure and function in SSNHL patients with accompanying cognitive and emotional disorders. We collected 3D-T1 structural magnetic resonance image data and functional magnetic resonance image data from 70 SSNHL patients and 81 healthy controls (HCs). SCN analysis was performed based on gray matter volume, and multilayer network analysis was used to calculate node switching rates. Based on the results of multilayer network analysis, six nodes exhibiting significant inter-group differences in node switching rates were selected as regions of interest (ROIs). DCM was then conducted to explore the causal relationships of functional connectivity between these nodes. Based on SCN, there were no significant inter-group differences in global network properties between SSNHL and HCs. At the node level, the left precentral gyrus in SSNHL showed a significant decrease in node efficiency. In the multilayer network analysis, SSNHL showed a significantly increased node switching rate at the level of the Left Superior Frontal Gyrus (L.SFG), Left Supplementary Motor Area (L.SMA), Left Superior Parietal Gyrus (L.SPG), Right Superior Parietal Gyrus (R.SPG), Right Inferior Parietal Lobe(R.IPL), and Left Thalamus (L.THA). Furthermore, the node switching rate of L.SFG showed a significant negative correlation with the Self-Rating Anxiety Scale (SAS) scores. DCM analysis of these six nodes revealed differences in the functional effective connectivity between the left superior parietal gyrus (L.SPG) and the left supplementary motor area (L.SMA), which were positively correlated with the AVLT-delay scores. These findings suggest that SSNHL patients experience structural and functional remodeling of the cerebral cortex, with hearing loss leading to the reallocation of cognitive resources. This provides new insights into understanding the potential mechanisms between cross-scale networks and cognitive-emotional disorders in SSNHL.

Structural changes in early-stage Parkinson's disease with resting tremor at node, edge and network level.

Resting tremor in Parkinson's disease (PD) is associated with the activity in the basal ganglia and cerebello-thalamo-cortical circuits/network. However, most insights stem from functional MRI research, and structural studies, which can provide basis for and constrain functional activity, remains limited. We investigated the structural change in PD patients with resting tremor (PD-WR) from a network perspective. 42 early-stage PD-WR, 27 PD patients without resting tremor (PD-NR), and 56 healthy controls (HC) were included. PD-WR showed lower cortical thickness in several motor-related lobules. Compared to HC, significant atrophy was found in right lobule VIIA (t = -3.076, p = 0.016, Cohen's d = 0.627), left lobule VI (t = -3.323, p = 0.007, Cohen's d = 0.678), and right lobule VI (t = -3.052, p = 0.017, Cohen's d = 0.623) in PD-WR. Compared to PD-NR, left lobule V also had a significant reduction (t = -2.958, p = 0.023, d = -0.657). PD-WR had higher fractional anisotropy in cerebello-cortical connection compared to HC (t = 3.209, p = 0.009, d = 0.926), with reduced radial (t = -2.561, p = 0.046, d = 0.739) and mean (t = 2.614, p = 0.046, d = 0.871) diffusivity compared to PD-NR. At the network level, better hierarchy (rho = 0.598, p = 0.004), small-worldness (rho = 0.621, p = 0.003), and increased nodal involvement of the thalamus (rho = 0.718, p = 0.031) and motor cortex (rho = 0.660, p = 0.055) were positively correlated with tremor amplitude. Our study supports the alternation of the cerebello-thalamo-cortical circuit in PD-WR. However, further research with other forms of PD, a wide range of disease stage and larger sample size is needed.

A Topology-Enhanced Multi-Viewed Contrastive Approach for Molecular Graph Representation Learning and Classification.

In recent times, graph representation learning has been becoming a hot research topic which has attracted a lot of attention from researchers. Graph embeddings have diverse applications across fields such as information and social network analysis, bioinformatics and cheminformatics, natural language processing (NLP), and recommendation systems. Among the advanced deep learning (DL) based architectures used in graph representation learning, graph neural networks (GNNs) have emerged as the dominant and highly effective framework. The recent GNN-based methods have demonstrated state-of-the-art performance on complex supervised and unsupervised tasks at both the node and graph levels. In recent years, to enhance multi-view and structured graph representations, contrastive learning-based techniques have been developed, introducing models known as graph contrastive learning (GCL) models. These GCL approaches leverage unsupervised contrastive methods to capture multi-view graph representations by comparing node and graph embeddings, yielding significant improvements in both graph-level representations and task-specific applications, such as molecular embedding and classification. However, as most GCL techniques are primarily designed to focus on the explicit graph structure through GNN-based encoders, they often overlook critical topological insights that could be provided through topological data analysis (TDA). Given the promising research indicating that topological features can greatly benefit various graph learning tasks, we propose a novel topology-enhanced, multi-view graph contrastive learning model called TMGCL. Our TMGCL model is designed to capture and utilize both comprehensive multi-scale topological and global structural information from graphs. This enhanced representation capability positions TMGCL to directly support a range of applications, such as molecular classification, with improved accuracy and robustness. Extensive experiments within two real-world datasets proved the effectiveness and outperformance of our proposed TMGCL in comparing with state-of-the-art GNN/GCL-based baselines.

Analyzing Hippocampal Neural Dynamics Under Acetylcholine Deficiency Related to Alzheimer’s Disease

Acetylcholine, as an important neurotransmitter, regulates neural excitability and discharge property distinctly, whose deficiency in the hippocampus can deteriorate cognitive and memory functions in Alzheimer’s Disease (AD). By establishing a hippocampal small-world network of pyramidal neurons and interneurons, this work aims to explore how acetylcholine deficiency influences the hippocampal neural dynamics from the perspective of neurocomputation. Based on some experimental results that acetylcholine can inhibit several [Formula: see text] channels in the pyramidal neuron via muscarinic receptors, we simulate acetylcholine deficiency by increasing the conductance [Formula: see text] of M-type (or [Formula: see text] of A-type) [Formula: see text] channel. The numerical results indicate that the enhancement of [Formula: see text] or [Formula: see text] can affect the pyramidal neuron dynamics in the individual node and network levels significantly. For the individual pyramidal neuron, its excitability and mean firing rate gradually decrease upon increasing [Formula: see text] or [Formula: see text], in particular, the neuron transitions from a firing state to a resting state when [Formula: see text] or [Formula: see text] increases over a threshold. Through dynamic bifurcation analysis, we find that the underlying dynamical mechanism behind these changes is attributed to sub-critical Andronov–Hopf or saddle-node bifurcations induced by acetylcholine deficiency. For pyramidal neurons in the network scale, acetylcholine deficiency can shift the network encoding mode from collective regular firing to bursting, and more importantly, the averaged mean firing rate and sample entropy of the network decrease significantly with severe acetylcholine deficiency. These results are consistent with previous studies reported in the electrophysiological experiments of AD. This work may help to deeply understand the AD pathogenesis of cholinergic hypothesis.

A Nash equilibria decision tree for binary classification

Decision trees rank among the most popular and efficient classification methods. They are used to represent rules for recursively partitioning the data space into regions from which reliable predictions regarding classes can be made. These regions are usually delimited by axis-parallel or oblique hyperplanes. Axis-parallel hyperplanes are intuitively appealing and have been widely studied. However, there is still room for exploring different approaches. In this paper, a splitting rule that constructs axis-parallel hyperplanes by computing the Nash equilibrium of a game played at the node level is used to induct a Nash Equilibrium Decision Tree for binary classification. Numerical experiments are used to illustrate the behavior of the proposed method.

Urban ecosystem services, ecological security patterns and ecological resilience in coastal cities: The impact of land reclamation in Macao SAR.

Land reclamation from the sea is a common practice to create territorial space and accommodate urbanisation in coastal cities. However, previous studies did not adequately examine the changes in ecosystem service values, the spatial transformation of the ecological network and the ecological resilience at an urban scale in the context of land reclamation. This study uses Macao SAR, a fully urbanised city with two-thirds of its land reclaimed from the sea, as a case study. We quantify urban ecosystem services (UES) values and model the spatial transformation of the ecological security patterns (ESPs) throughout the city's land reclamation history from 2000 to 2020. Subsequently, we evaluate the resilience of the ESPs' components and their relationships with urbanisation by using spatial element elasticity indices and regression analysis. Results show that urban land expanded by 27% through land reclamation, increasing the value of UES by 13%. The growth rates of different categories of UES vary significantly. ESPs extended over the newly reclaimed and urbanised lands, but the resilience levels of ecological sources, corridors and nodes decreased. There is a strong positive correlation between UES values and urbanisation, and a strong negative correlation between ecological resilience and urbanisation. This study recommends five specific environmental spatial planning and management strategies to address the expansion and integration of ESPs, aiming not only to provide ecological services but also to enhance ecological resilience in coastal cities.

Differential Control of T-Cell Subsets by Recombinant Human PLD2 in a Mouse Model of Allergic Asthma

ABSTRACT Background Phospholipase D2 (PLD2) enzymes are expressed on the cytoplasmic membrane of bacteria, fungi, plants, and animals. Recently, extensive research has linked PLD2 to the chronic inflammatory activity of cells. Allergic asthma is a chronic airway inflammation disease. In this context, a recombinant human phospholipase D2 (rhPLD2) was designed and modified from the wild-type PLD2 to study its effects in an ovalbumin (OVA) induced murine model of asthma. Methods Hematoxylin and eosin staining was used for lung histopathology. Cytokine concentrations in bronchoalveolar lavage fluid (BALF) were measured using ELISA kits. The ratio of T-bet and GATA-3 expression level in spleen and lymph nodes following rhPLD2 administration was assessed through RT-PCR. Phenotyping analysis of Treg cells from peripheral blood was performed by flow cytometry. Results It indicated that OVA-induced mice exhibited elevated pulmonary eosinophilia and allergic inflammation in the airways, along with increased expression of IFN-γ, IL-4 in the lung BALF. Administration of rhPLD2 alleviated lung inflammation and significantly reduce the number of eosinophils in peripheral blood and BALF. RhPLD2 also reversed the IFN-γ/IL-4 ratio at the molecular level in BALF and the T-bet/GATA-3 ratio in lymphocytes of the lung, spleen, lymph nodes at the genetic level. Furthermore, FACS analysis demonstrated that rhPLD2 increased the frequency of both IL-10+Treg cells and CD25+ Treg cells. Conclusion From a therapeutic perspective, rhPLD2 alleviates allergic airway inflammation by balancing Th1/Th2 homeostasis and increasing Treg cells. It has been shown to function in immunoregulatory activities in OVA-induced asthma mice.

The clinical target volume delineation based on the correlation analysis of neck node levels in oral cavity squamous cell carcinoma (OCSCC)

BackgroundThe delineation of clinical target volume (CTV) base on the correlation analysis between neck node levels of OCSCC has not been reported in detail. This study analyzes the correlations between the neck node levels in 208 cases of OCSCC, and aims to provide preliminary reference for the CTV delineation in OCSCC patients.Materials and methodsThe records of 208 OCSCC patients were retrospectively analyzed. The neck node levels were evaluated according to the 2013 updated guidelines. The Chi-square test and logistic regression model were used to analyze the correlation of each level.ResultsThe most common involved level in OCSCC is Ib (45.7%) and IIa (40.4%). Correlation analysis of OCSCC showed that nodal spread in level Ia is related to levels Ib and VIa. Level Ib is related to levels Ia, IIa, IX. Level IIa is related to levels Ib, IIb, III. Level IIb is related to levels IIa, III, Va. Level III is related to levels IIa, IIb, Va. Level Va is related to levels IIb and III. Level VIa is related to level Ia and T stage. Level IX is only related to level Ib. T stage is only related to level VIa. All the above P values are < 0.05.ConclusionsThe related levels can be considered as high-risk region (HRR) and the unrelated levels as low-risk region (LRR) in the radiotherapy of OCSCC patients. This study can make the CTV delineation more individualized and accurate.

Establishment of multiple machine learning prognostic model for gene differences between primary tumors and lymph nodes in luminal breast cancer.

This study aimed to explore the correlation between primary tumors (PT) and paired metastatic lymph nodes (LN) and to develop a predictive model to provide evidence for forecasting patient prognoses. We obtained single-cell and bulk transcriptome data from the Gene Expression Omnibus database. Furthermore, mRNA transcriptomic data, encompassing 112 normal tissues and 1066 breast cancer samples, along with survival, clinical, and mutation information for breast cancer patients, were acquired from The Cancer Genome Atlas (TCGA). Employing a machine learning integration framework incorporating ten distinct algorithms, we developed and validated a prognostic model. We constructed a prognostic model named Lymph Node Metastasis-Related Scores (LMRS) using 26 differentially expressed genes trained on eight TCGA datasets. Across validation sets, the model demonstrated a high C-index, signifying its stability and effectiveness, outperforming 64 models from other studies. Notably, cytolytic activity and T cell co-stimulation were downregulated in the high LMRS group, alongside a downregulation of immune cells, including B cells, CD8 + T cells, iDCs, and TILs. Similarly, most immune checkpoints exhibited a decreasing trend with high LMRS expression. Finally, we selected the hub biomarkers PGK1 and HSP90 for pathological verification. Results indicated higher expression levels in PT and LN compared to normal and benign tumors, with higher expression levels in LN than in PT. This comprehensive analysis sheds light on gene expression differences between PT and LN in breast cancer, culminating in the development of a multiple-gene prognostic model with high clinical accuracy for prognosis prediction.

Node-Alive Index Driven Redundancy Elimination for Energy-Efficient Wireless Sensor Networks

Wireless Sensor Networks (WSNs) generate correlated and redundant data. This redundancy increases energy consumption during transmission and aggregation, which reduces the network lifespan. Eliminating data redundancy using appropriate data aggregation mechanisms in the dynamic environment is challenging. To address these issues, we designed the Data Aggregation with Redundancy Removal (DARR) protocol and implemented it in two phases. In Phase I, the DARR protocol identifies redundant nodes by calculating the spatial distance between the adjacent nodes. Over time, nodes may run out of energy and stop working after continuously sensing, aggregating, and transmitting the data. The dead nodes can obstruct data forwarding to intermediate nodes, so it is important to check periodically whether the nodes are alive or dead. The periodic time check identifies the status of each node, allowing the protocol to focus only on active nodes. It sets redundant nodes to sleep, which conserves network energy. In Phase II, the protocol reduces data redundancy at the source nodes using temporal correlation between data measurements. We enhanced the DARR protocol by incorporating a High Compression Temporal (HCT) mechanism, which further reduces data redundancy. Simulations show that the DARR protocol reduces data transmissions by 24% and lowers network energy consumption by up to 31% by eliminating redundant data at both the network and node levels.

A novel analytical tool for complex propagation processes in networks: High-order dynamic equation.

Controlling the spread of epidemics in complex networks has always been an important research problem in the field of network science and has been widely studied by many scholars so far. One of the key problems in the transmission process of epidemics in complex networks is the transmission mechanism. At present, the transmission mechanism in complex networks can be divided into simple transmission and complex transmission. Simple transmission has been widely studied and the theory is relatively mature, while complex transmission still has many questions to answer. In fact, in the complex transmission process, the higher-order structure of the network plays a very important role, which can affect the transmission speed, final scale, and transmission path of the epidemic by strengthening the mechanism. However, due to the lack of complex dynamic analysis tools, the measurement of influence on propagation is still at the low-dimensional node level. Therefore, in this paper, we propose a set of closed dynamic higher-order structure equations to gain insight into the complex propagation process in the network, which breaks the inherent thinking and enables us to reexamine the complex dynamic behavior more clearly from the higher-order level rather than just from the node level, opening up a new way to analyze the higher-order interaction on the dynamic network. We apply the proposed high-order dynamic equations to a complex susceptible-infection-recovery epidemiological model on two real and synthetic networks, and extensive numerical simulation results demonstrate the effectiveness of the proposed approach. Our research results help to deepen the understanding of the relationship between complex propagation mechanisms and higher-order structures and develop a complete set of complex dynamic analysis tools that can be extended to higher-order forms to help in-depth understanding of the propagation rules and mechanisms in complex propagation processes, providing an important theoretical basis for predicting, analyzing, and controlling complex propagation processes.

Comparing the efficacy of linovera spray plus hydrocortisone cream for breast cancer patients undergoing conformal radiotherapy

BackgroundBreast cancer is one of the most prevalent malignancies globally, with a significant proportion of patients undergoing radiotherapy, a crucial treatment modality that substantially reduces recurrence rates. However, radiotherapy is often complicated by dermatological adverse effects ranging from mild erythema to severe dermal burns. Despite various interventions aimed at mitigating skin toxicities, the efficacy of hyperoxygenated fatty acids (HOFA) in this context remains underexplored. ObjectiveTo evaluate the effectiveness of topical HOFA (Linovera spray) in preventing radiotherapy-induced skin reactions in breast cancer patients undergoing conformal radiotherapy. MethodsThis prospective, randomized, controlled trial enrolled post-partial mastectomy breast cancer patients indicated for conformal radiotherapy targeting lymph node levels and the supraclavicular region, with dosages of 45–50 Gy and a 60 Gy tumor bed boost. Patients with active skin diseases or sensitivity to the study spray were excluded. The intervention group applied hydrocortisone ointment and Linovera spray twice daily to the treatment site, while the control group used only hydrocortisone ointment. Skin reactions were graded using the validated Global Skin Reaction Classification and assessed during radiotherapy weeks 3 and 5. ResultsAll 60 randomized patients (intervention n = 24, control n = 28) developed radiation dermatitis. By week 5, the intervention group showed a statistically significant reduction in dermatitis severity versus controls (p = 0.04). Dermatitis grades also significantly worsened within both groups from week 3–5 (all p < 0.001). ConclusionWhile HOFA-based moisturizers did not statistically impact overall incidence or severity, marginal benefits were observed with prolonged Linovera spray plus hydrocortisone use versus hydrocortisone alone by week 5. Further research is warranted to explore HOFA's potential skin-protective effects.

Incidence of Skip Metastasis in Squamous Cell Carcinoma of Tongue.

Objective: This study aimed to determine the incidence of skip metastasis in patients with tongue squamous cell carcinoma, focusing on those who presented to tertiary care settings in Lahore. Methodology: This was a cross-sectional study with a prospective time frame. The sample size was determined using the formula for estimating proportions, which was 59. Sixty patients with TSCC were selected based on rigorous inclusion criteria, and informed consent was taken before participants were included in the study. Paper-based forms were used to collect data following the examination of the patient by the researcher after obtaining informed consent. After the collection of results, an analysis was done to determine the correlation between the prevalence of SM and factors such as age, gender, level of lymph node involvement, smoking status, betel chewing, and alcohol usage. Results: Of the 60 participants, 71% showed evidence of metastasis involving lymph node levels I-IV. Isolated skip metastasis (SM), involving only levels IV and V, was only seen in 5% of the total group. The prevalence of skip metastasis could be correlated with gender, indicating a male predilection. Advanced age did not impact the prevalence of skip metastasis, as the mean age of participants was 54, while that for showing skip metastasis was 51.3 years. Conclusion: The study provides clinicians with a deeper understanding of the prevalence of skip metastasis. However, further research is necessary to improve the diagnosis and management of TSCC. This study provides avenues for further research into skip metastasis, its existence in the Pakistani population, and its thorough management.

Long-Term Outcome in Cervical Lymph Node-Positive Nasopharyngeal Carcinoma Following Intensity Modulated Radiation Therapy With One-Step Cervical Nodal Clinical Target Volume Delineation by Geometric-Anatomic Expansion From Nodal Gross Target Volume: A Double-Center Experience.

To report long-term results of patients with cervical node-positive (CLN+) nasopharyngeal carcinoma (NPC) treated with intensity modulated radiation therapy with one-step nodal clinical target volume (CTVn) delineation by geometric-anatomic expansion from the nodal gross target volume (GTVn). Patients with CLN+ NPC treated with the same one-step-CTVn delineation in two Chinese academic centers were pooled for this study. GTVn was prescribed to 70 Gy equivalent, CTVn1 was omitted, CTVn2 was prescribed to 45 to 55 Gy equivalent, and defined as GTVn + 3 mm geometric expansion (5 mm if radiological extranodal extension-positive [rENE+]) + elective nodal regions defined by anatomic boundary of cervical nodal levels. Regional control (RC) and overall survival (OS) were analyzed. Fifteen randomly selected cases were recontoured for CTVn according to the 2018 International Guidelines (2018-IG). Dose/volume was compared between the two clinical target volume delineation methods. A total of 807 patients were included (center 1, n = 459; center 2, n = 348). Five-year RC and OS were 95.8% and 86.2%, respectively. Thirty-four patients developed regional failure, and 13/34 (38%) were outside CTVn2: level VIII (parotid node) (9/13), Ib (4/13), and IV (2/13). Seven out of these 9 level VIII failures had pre-existing "equivocal" nodes. All 4 level 1b failures had "equivocal" nodes with very advanced rENE or large (>5 cm) nodal mass in level II. Compared with the 2018-IG, our strategy resulted in significant reduction in nodal volumes received therapeutic (V70) (mean, 100.7 vs. 27.5 cc; P < .001) and prophylactic (V45) (mean, 343.5 vs. 261.2 cc; P < .001) doses and further dose reduction in surrounding organs at risks. Our one-step-CTVn delineation by geometric-anatomic expansion from GTVn appears to be a safe and efficient approach in CLN+ NPC, with excellent RC and potential dosimetric benefits in selected patients. Caution is needed for parotid sparing in patients with pre-existing "equivocal" nodes or level Ib sparing in cases with advanced rENE or large (>5 cm) nodal mass in level II.

Text units with purpose semantics in a discursive perspective

INTRODUCTION. The correlation of the terms “text units” and “discourse units” is currently opaque due to numerous approaches to defining text units and ambiguous decisions regarding discursive ones. The aim is to identify the features of the correlation of structural units of text and discourse and to establish the specifics of the rhetorical relations of Purpose at the element node level of discourse.MATERIALS AND METHODS. The research is conducted on the basis of contemporary mass media material through an unselected sampling from interviews with Russian political leaders using the methodology of the theory of rhetorical structures, general philological argumentation, as well as conversation and discourse analysis.RESULTS AND DISCUSSION. A structural-semantic dependence is established when identifying the correlation of text units with the purpose semantics and discursive units organized according to the principle of rhetorical relations of the purpose; the specificity of the rhetorical connections of Purpose as relations of the subject type, an asymmetric structure, characterized by a complete paradigm of the signs of polarity and direction is revealed.CONCLUSION. The study has prospects for further study of global structures of natural discourse, as well as practical significance in the context of computational linguistics in the development of algorithms for creating texts using artificial intelligence.

Superselective Versus Selective Neck Dissection in the Treatment of Papillary Thyroid Carcinoma.

Superselective lateral neck dissection (SSND), defined as dissection of 2 or less contiguous lateral cervical nodal levels, has emerged as a treatment option for metastatic papillary thyroid carcinoma (PTC), in place of a selective neck dissection (SND). There are few studies exploring outcomes of SSND compared to SND in this context. This was a single institution retrospective study. All patients who were diagnosed with PTC and had a lateral neck dissection from 2019 to 2022 were reviewed. Patient demographics and outcomes were compared. A total of 136 patients met inclusion criteria. About 106 (78%) had a SND and 30 (22%) had a SSND. There was no significant difference between patients who underwent SND versus SSND regarding frequency of post-operative complications, length of stay (2.49 vs 2.31 days), post-operative thyroglobulin >10 ng/mL (21 vs 3 patients), regional recurrence (14% vs 17%), reoperation rates (16.7% vs 17.4%), or adjuvant radioactive iodine therapy (75% vs 63%; P > .05). SSND for PTC had similar short-term clinical outcomes when compared to SND.

The role of the social network in the study of adherence to diabetic retinopathy screening programs

Diabetic retinopathy screenings are a vital strategy to avoid the severe consequences of this disease. However, their success depends on the adherence of the target population. The present work aims to review the adherence to diabetic retinopathy screening, more specifically the influence of the persons with diabetes’ social network (contacts between persons with diabetes) on their screening behaviour. The used data set comprises information of 75,921 persons with diabetes, distributed by 20 Primary Health Centre Groups of the Portuguese North Region. Persons with diabetes of the same Group were organized in an N-by-N matrix, resulting in 20 social networks. Network metrics were calculated and its relationship with the adherence to screening was analysed using two perspectives: correlation between global network metrics and adherence rate; cluster analyses based on node level metrics. The results obtained show that: (1) Less connected networks, strongly divided into communities and with a great number of connected components, present the highest adherence rates. (2) The node level metrics allow the identification of groups where the problem of non-adherence is especially high. (3) The non-adherence phenomenon is especially evident in a small group of highly connected individuals. We believe that these results are of utmost relevance as a starting point for future research and as support to the planning of interventions related to diabetic retinopathy screening adherence.