Essential Nodes Research Articles

Drone-based mobile full-duplex optical communication

Wireless optical communication plays a vital role in mobile communication networks, and both drones and vehicles are essential mobile communication nodes. Here, image identification technology, gimbal, and full-duplex optical communication systems are integrated to manage the challenging issues of light alignment and target tracking for mobile full-duplex optical communication under the transmission control protocol/internet protocol (TCP/IP) scheme. Both drones and vehicles are equipped with the proposed setup to realize full-duplex wireless optical data transmission at a communication rate of 2 Mbps in both air-to-ground and air-to-air scenarios. By further integrating wireless-fidelity (Wi-Fi) modules, drone-based mobile optical communication systems (MOCSs) can be interconnected with the network via the TCP/IP scheme, and real-time video transmission can be demonstrated. This work provides a feasible route toward a mobile optical communication network (MOCN) for diverse applications.

Essential cancer protein identification using graph-based random walk with restart

Protein-protein interaction (PPI) network analysis holds significant promise for cancer diagnosis and drug target identification. This paper introduces a novel random walk-based method called essential cancer protein identification using graph-based random walk with restart (EPI-GBRWR) to address this gap. This proposed method incorporates local and global topological features of proteins, enhancing the accuracy of essential protein identification in PPI networks. Starting with meticulous preprocessing of cancer gene datasets from NCBI, including breast, lung, colorectal, and ovarian cancers, and identifying a core set of common genes. The proposed method constructs PPI networks to capture complex protein interactions from these common cancer genes. Topological analysis, including a centrality measures matrix, is generated to perform the analysis to identify essential nodes. The study revealed that 40 essential proteins among breast, colorectal, lung and ovarian cancer showcase the potency of integrative methodologies in unravelling cancer complexity, signalling a transformative era in cancer research and treatment. The strength of the findings from the study has direct clinical relevance in cancer diseases. It contributes to the field of precision medicine to guide personalized treatment strategies.

A novel-approach for identifying sources of fluvial DOM using fluorescence spectroscopy and machine learning model

Rivers are well known as one of the most threatened aquatic environments, whose structure and water quality can be deeply impacted by intensive anthropogenic activities. Despite the fact that anthropogenic influences on river ecosystems could indeed be deduced from the composition and chemistry of fluvial dissolved organic matter (DOM), sources of anthropogenic loading to DOM are still poorly explored. Here, by uniting fluorescence excitation-emission matrices (EEM) and principal component absolute coefficient, four sources of DOM from seventeen rivers in major drainage basins of China could be identified, i.e., originating from municipal sewage, domestic wastewater, livestock wastewater, and natural origins, and thus being defined as MS-DOM, DW-DOM, LW-DOM, NO-DOM, respectively. Based on the random forest model, special nodes in EEM could be traced from four sources, respectively. According to parallel factor analysis, DOM mainly contained protein-like, microbial humic-like, and fulvic-like fluorescence substances, among which protein-like was dominant in MS-DOM and DW-DOM, microbial humic-like in LW-DOM, and fulvic-like in NO-DOM. Based on key peaks and essential nodes in EEM, the identifying source indices were first proposed, which could be introduced to simply distinguish the different anthropogenic-derived sources of fluvial DOM. It was associated with intensity ratios of the key peaks and the essential nodes of EEM spectra from four sources, i.e., municipal sewage (MS-SI: Ex/Em = 280/(335, 410) nm), domestic wastewater (DW-SI: Ex/Em = 280/(340, 410) nm), livestock wastewater (LW-SI: Ex/Em = 235/(345, 380) nm), and natural origins (NO-SI: Ex/Em = 260/(380, 430) nm). By statistical analysis, the high identifying source indices of municipal sewage (>0.5) and natural origins (>0.4) values could be related to MS-DOM and NO-DOM, respectively. The identifying source indices of domestic wastewater with 0.1–0.3 might be linked to DW-DOM and the identifying source indices of livestock wastewater with 0.3–0.4 to LW-DOM. Compared with conventional optical indices, the novel identifying source indices showed remarkable discrimination for the sources of fluvial DOM with different forms of anthropogenic disturbances. Hence the innovative approach could be relatively convenient and accurate to evaluate water quality or pollution risk in river ecosystems.

Dynamic patterns of COVID stress syndrome among university students during an outbreak: a time-series network analysis

Objective Assessing the progression of COVID stress syndrome (CSS) and understanding how cognitive and emotional factors play a role in the dynamic system is critical for prevention and intervention during the COVID-19 pandemic. We investigated the dynamic relationship between self-reported COVID stress syndrome, cognitive factors, and emotional factors through time-series network analysis. Methods and Measures Participants were 188 university students involved in an experience sampling method study that lasted 14 days, three times a day, during the COVID-19 pandemic period following a shift in pandemic prevention and control policy. Results CSS symptoms are usually present simultaneously, and xenophobia is the most central node of the network. There is a complex mutual predictive relationship between CSS symptoms, in which traumatic stress symptoms are crucial in developing and maintaining the CSS symptom network. Negative affect was associated with CSS symptoms at the same time, and subjective health cognition was a significant predictor of CSS symptoms the next time. Conclusions Traumatic stress symptoms are essential nodes in the CSS symptom network, and negative emotions and subjective health influence the occurrence and development of CSS symptoms.

Introduzione alla filosofia dello Stato e della politica di Luigi Sturzo

Introduction to the Luigi Sturzo’s philosophy of state and politics ABSTRACT: The paper offers an introductory overview of the thought of Luigi Sturzo, on the occasion of the 65th anniversary of his death, retracing the essential nodes of his philosophy of the State and politics. The issues addressed by sturzian reflection, in fact, not only stand as paradigmatic for the understanding of the historical phenomena that occurred in the 20th century, but due to their relevance and rationality they are also proposed as tools for understanding the current political and legal reality. SOMMARIO: 1. Introduzione - 2. Il contesto storico - 3. Filosofia dello Stato e della politica di Luigi Sturzo - 3.1 - Crisi dello Stato e democrazia - 3.2 - Stato e statalismo - 3.3 - Partiti e partitocrazia - 4. Conclusioni.

Compound climate change risk analysis for port infrastructures

Ports serve as essential nodes for coastal and maritime transportation and are key sources of income and economic activity in coastal zones. This significance, combined with their location in coastal areas, which are prone to climate-driven impacts, makes them highly susceptible to climate change effects. In this work, a climate change risk assessment methodology for port infrastructures that is focused on compound events analysis is presented. This approach is based on a spatial high-resolution probabilistic framework that enables the evaluation of port performance evolution under the effects of climate change. This assessment draws from a multimodel characterization of the evolution of several climate drivers for different emission scenarios and time horizons. It accounts for multiple port infrastructure risks and considers the compound effects of climate drivers and the interdependencies of infrastructures as complex systems. Performance indicators are developed for the physical assets and services at port locations on a highly granular scale, thus allowing port managers and planners to allocate reserves and develop adaptation plans that reduce climate change risks in the operations of maritime transportation nodes based on port performance forecasts. The methodology is implemented in two case studies set in the northern coast of Spain, demonstrating its applicability and replicability among several locations and scales.

Exploring the evolution trends of port integration policy in China by a text mining approach

Ports are essential and strategic nodes of international trade and economic activities. The integration of port resources helps enhance the optimal division of port functions to further promote economic development. This paper explores the evolutionary trends of port integration policy from 2011 to 2021 in China through a text-mining approach. We first used the Latent Dirichlet Allocation (LDA) topic analysis method to analyze the port integration policies and summarize the evolutionary trends of port policies. In addition, an association analysis was conducted to explore port integration policies' impacts by examining their relationships among the environmental protection, digitalization level, and port development scale. Our findings suggested that China's port integration policy has evolved from specific and simple to abstract and complicated at the managerial level. Port development has enriched from infrastructure construction to the ideology of economic development. Our findings show that China's port integration is gradually improving the port operation. The findings of the study contribute to the extant literature by analyzing the port integration policy evolution in China and can be referred to by other countries.

Analysis of the Network Efficiency of Chinese Ports in Global Shipping under the Impacts of Typhoons

With the increasing volume of international trade and maritime demand, the requirements for the stability and reliability of the global shipping system are also increasing. The research on the network efficiency of Chinese ports for global shipping can not only examine the importance of Chinese ports in the shipping network but also find out the aspects that need to be improved in the construction of the port’s climate adaptability in the resilience assessment to strengthen port construction and further improve the efficiency of the network. The current study builds a shipping network based on RCEP and systematically examines the key ports in China within the networks. The research paper aims to improve the resilience of the ports and the whole shipping network in response to typhoon disasters. As such, this paper focuses on shipping research based on complex networks and network multi-centricity analysis, followed by a ranking of ports. Firstly, this paper uses UCINET 6 software to build a global shipping network. Such a network evaluates the centrality of ports, calculates the degree of centrality, proximity to centrality, and centrality, and scores them according to the ranking. Then, it selects the top 20 ports in China according to the ranking and researches network efficiency for the listed ports considering the typhoon risks. The analysis of network robustness, average shortest path length, and network efficiency are carried out for the shipping network and China’s essential port nodes in the network. According to the experimental results, no matter the robustness, average shortest path length, or network efficiency, when the important ports of China in the shipping network are affected, they will cause different degrees of impact, and the performance loss caused by multiple ports is higher than that of a single port. They emphasise the significant impact of typhoons on multiple ports and remind people to minimise losses as much as possible based on experimental results, ensuring the stable operation of ports and improving resilience in typhoon prevention under the changing climate. Additionally, they provide a solid foundation to further sustain global shipping network resilience.

Navigating Conflict

Situated strategically in the Atlantic Ocean, the Azores Islands emerged as pivotal theaters during World War I and World War II and profoundly shaped these global conflicts through its geographic significance and technological advancements. In this paper, the evolution of navigation techniques, routes, and the strategic importance of the Azores according to the changing nature of warfare was analyzed. Through an examination of historical narratives and technological progress, insights were made into how the islands developed into critical hubs for naval and air operations. Advancements in airport infrastructure, telecommunications, and harbor facilities transformed the Azores into essential nodes for transatlantic shipping, communication networks, and surveillance in the Atlantic theater. Navigational strategies, informed by technological innovations, optimized air routes and access to strategic bases which bolstered military capabilities in both conflicts. Through the exploration of the relationship between technological progress, evolving warfare dynamics, and geopolitical significance, the enduring impact of the Azores Islands on shaping global conflicts throughout history was revealed.

The Identification of Influential Nodes Based on Neighborhood Information in Asymmetric Networks

Identifying influential nodes, with pivotal roles in practical domains like epidemic management, social information dissemination optimization, and transportation network security enhancement, is a critical research focus in complex network analysis. Researchers have long strived for rapid and precise identification approaches for these influential nodes that are significantly shaping network structures and functions. The recently developed SPON (sum of proportion of neighbors) method integrates information from the three-hop neighborhood of each node, proving more efficient and accurate in identifying influential nodes than traditional methods. However, SPON overlooks the heterogeneity of neighbor information, derived from the asymmetry properties of natural networks, leading to its lower accuracy in identifying essential nodes. To sustain the efficiency of the SPON method pertaining to the local method, as opposed to global approaches, we propose an improved local approach, called the SSPN (sum of the structural proportion of neighbors), adapted from the SPON method. The SSPN method classifies neighbors based on the h-index values of nodes, emphasizing the diversity of asymmetric neighbor structure information by considering the local clustering coefficient and addressing the accuracy limitations of the SPON method. To test the performance of the SSPN, we conducted simulation experiments on six real networks using the Susceptible–Infected–Removed (SIR) model. Our method demonstrates superior monotonicity, ranking accuracy, and robustness compared to seven benchmarks. These findings are valuable for developing effective methods to discover and safeguard influential nodes within complex networked systems.

NCOG-04. WHOLE-BRAIN STRUCTURAL CONNECTIVITY PREDICTS COGNITIVE DEFICITS IN PRETREATED PATIENTS WITH CNS WHO GRADE 3 OR 4 GLIOMAS

Abstract BACKGROUND Glioma patients frequently suffer from cognitive dysfunction potentially caused by tumor invasion or treatment effects. We hypothesized that cognitive functioning in pretreated glioma patients critically depends on the maintained structural connectivity of multiple brain networks. PATIENTS AND METHODS The study included 121 pretreated glioma patients (median age, 52 years; median ECOG score, 1; CNS WHO grade, 3 or 4) who had biopsy or resection plus chemoradiation as first-line therapy. Cognitive performance was assessed by ten tests in five main cognitive domains after a median time of 14 months (range, 1-214 months) after treatment initiation. Hybrid amino acid PET/MRI using the tracer O-(2-[18F]fluoroethyl)-L-tyrosine, a network-based cortical parcellation, and advanced tractography tools were used to generate whole-brain fiber count-weighted connectivity matrices. These matrices were applied to a machine learning-based model to identify predictive fiber connections, essential cortical nodes, and the networks underlying cognitive performance in the evaluated domains. RESULTS Compared to healthy controls (n=121), the cognitive scores were significantly lower in nine cognitive tests. For each test, a subset of connections between nodes (median number, 254; range, 32-542) was identified whose fiber count sum was related to the actual scores in a linear model (median R2, 0.37; range, 0.16-0.44). Leave-one-out cross-validation confirmed the model's generalizability in 7 of 10 tests (median correlation coefficient for predicted vs. observed scores, 0.47; range, 0.39-0.57). Critically involved cortical regions (≥ 10 adjacent predictive edges) included predominantly left-sided cortical nodes of the visual, somatomotor, dorsal/ventral attention, and default mode networks. Highly critical nodes (≥ 15-20 edges) included the default-mode network’s left temporal and bilateral posterior cingulate cortex. CONCLUSION These results suggest that the cognitive performance of pretreated glioma patients is strongly related to structural connectivity between multiple brain networks and depends on the integrity of known network hubs also involved in other neurological disorders.

ERK mediates interferon gamma-induced melanoma cell death

BackgroundInterferon-gamma (IFNγ) exerts potent growth inhibitory effects on a wide range of cancer cells through unknown signaling pathways. We pursued complementary screening approaches to characterize the growth inhibition pathway.MethodsWe performed chemical genomics and whole genome targeting CRISPR/Cas9 screens using patient-derived melanoma lines to uncover essential nodes in the IFNγ-mediated growth inhibition pathway. We used transcriptomic profiling to identify cell death pathways activated upon IFNγ exposure. Live imaging experiments coupled with apoptosis assays confirmed the involvement of these pathways in IFNγ-mediated cell death.ResultsWe show that IFNγ signaling activated ERK. Blocking ERK activation rescued IFNγ-mediated apoptosis in 17 of 23 (~ 74%) cell lines representing BRAF, NRAS, NF1 mutant, and triple wild type subtypes of cutaneous melanoma. ERK signaling induced a stress response, ultimately leading to apoptosis through the activity of DR5 and NOXA proteins.ConclusionsOur results provide a new understanding of the IFNγ growth inhibition pathway, which will be crucial in defining mechanisms of immunotherapy response and resistance.

SALA-IoT: Self-Reduced Internet of Things With Learning Automaton Sleep Scheduling Algorithm

The extensive development of wireless communications has led to the popularity of the Self-Powered Internet of Things (SPIoT) networks. Even though significant advances made in keeping energy consumption at a low level, making such networks live longer are still one of the biggest challenges. In particular, a significant question is how to cover the maximum range of the environment by the sensor nodes with the lowest amount of energy. In this research, we have proposed a sleep Scheduling Algorithm based on Learning Automaton for IoT (SALA-IoT), utilizing machine learning approaches to find the optimal set of sensor nodes that can cover a wide range of the environment. This algorithm consists of learning and covering phases, in which the essential sensor nodes are activated, and the rest are turned off. Although the implementation of learning algorithms requires a high energy level, after the learning phase’s performance, the network gathers complete information about the status of the nodes. It can meet the needs of the network with a limited amount of energy. We have evaluated the proposed algorithm with several simulation scenarios and considered the coverage area and the number of active sensor nodes as the main evaluation metrics. The simulation results show that the sensors sensing and covering ranges are directly related to their transmitted power, and the transmitted power of the sensors can be adjusted to match the expected network requirements in terms of primary coverage factors; the proposed method improved by 6.743 for 50 nodes and 5.204 for 100 nodes, respectively, for distance and intervals in a different number of nodes.

Precise Interference of RNA-Protein Interaction by CRISPR-Cas13-Mediated Peptide Competition.

RNA-protein interactions are essential nodes of cellular regulatory circuits and play critical roles in normal physiology and disease. However, the precise roles of individual RNA-protein interactions remain elusive. Here we report a method for precise interference of endogenous RNA interacting with the RNA binding protein (RBP). TTP is an RBP that recognizes the AU-rich element (ARE) of mRNA via the binding domain TZF and represses gene expression. We engineer Cas13b, a class 2 type VI CRISPR-Cas endonuclease that exclusively targets RNA, to direct the peptide of TZF to the binding site and compete with endogenous TTP. We show that this tool specifically interferes with TTP interacting with the PIM1 and IL-2 3' UTR under the guidance of the gRNA specific for the AREs. Further, precise interference with the TTP-PIM1 interaction exerts a distinct effect on cell proliferation compared to transcriptome-wide interference. Thus, our work establishes a tool for deep understanding of RNA-RBP interactions.

Histone Deacetylases (HDAC) Inhibitor-Valproic Acid Sensitizes Human Melanoma Cells to Dacarbazine and PARP Inhibitor.

The inhibition of histone deacetylases (HDACs) holds promise as a potential anti-cancer therapy as histone and non-histone protein acetylation is frequently disrupted in cancer, leading to cancer initiation and progression. Additionally, the use of a histone deacetylase inhibitor (HDACi) such as the class I HDAC inhibitor-valproic acid (VPA) has been shown to enhance the effectiveness of DNA-damaging factors, such as cisplatin or radiation. In this study, we found that the use of VPA in combination with talazoparib (BMN-673-PARP1 inhibitor-PARPi) and/or Dacarbazine (DTIC-alkylating agent) resulted in an increased rate of DNA double strand breaks (DSBs) and reduced survival (while not affecting primary melanocytes) and the proliferation of melanoma cells. Furthermore, the pharmacological inhibition of class I HDACs sensitizes melanoma cells to apoptosis following exposure to DTIC and BMN-673. In addition, the inhibition of HDACs causes the sensitization of melanoma cells to DTIV and BMN-673 in melanoma xenografts in vivo. At the mRNA and protein level, the histone deacetylase inhibitor downregulated RAD51 and FANCD2. This study aims to demonstrate that combining an HDACi, alkylating agent and PARPi could potentially enhance the treatment of melanoma, which is commonly recognized as being among the most aggressive malignant tumors. The findings presented here point to a scenario in which HDACs, via enhancing the HR-dependent repair of DSBs created during the processing of DNA lesions, are essential nodes in the resistance of malignant melanoma cells to methylating agent-based therapies.

A network analysis of disordered eating symptoms, big-five personality traits, and psychological distress in Chinese adults.

Previous studies have revealed associations between disordered eating symptoms, big-five personality traits, and psychological distress. However, limited research has explored these relationships as a network, including their interconnections, and even less has done so in non-Western populations. We employed network analysis to investigate the co-occurrence of disordered eating symptoms, big-five personality traits, and psychological distress in Chinese adults. A sample of 500 Chinese adults (256 men) completed measures assessing big-five personality traits, psychological distress, and disordered eating symptoms. The network of personality traits, psychological distress, and disordered eating symptoms was estimated, including its central and bridge nodes. The central nodes in the network were the facets of openness (like adventure), extraversion (like going to social and recreational parties), and disordered eating symptoms (dissatisfaction with body weight or shape). Moreover, certain facets of neuroticism (always worrying something bad will happen), psychological distress (feeling worthless), and an inverse facet of extraversion (bored by parties with lots of people) were identified as essential bridge nodes in maintaining the structure of the network. Our findings suggest that personality traits (e.g., openness and extraversion) and body dissatisfaction are important in maintaining the network in a community sample of Chinese adults. While future replication is needed, findings from this study suggest that individuals with negative self-thinking, predisposed neuroticism, and extraversion may be at risk of developing disordered eating symptoms. The present study contributes to existing knowledge by employing a network perspectiveto examine the associations between disordered eating symptoms, big-five personality traits, and psychological distress in a Chinese adult community sample. The identified facets of neuroticism and extraversion and symptoms of psychological distress may be worthy of targeting in the prevention and treatment of disordered eating in the Chinese context.

Supply risk propagation of global copper industry chain based on multi-layer complex network

Copper is an essential strategic mineral resource. The copper industry chain starts with the extraction of raw materials, after processing, and finally sells the final product. The uniqueness of copper and the distribution characteristics of geographical space make the relationship between copper trade and supply more complicated. The imbalance between supply and demand can easily lead to resource supply security problems. The supply shortage will affect the stable development of the industrial chain. Based on the industrial chain perspective, this study calculates the national competitiveness index at different stages and analyzes the degree of competition among different countries. The study constructs a copper multi-layer network risk propagation model (CMNRP) to simulate the impact and propagation path of supply shocks in Peru, Chile, China, and the USA on the entire industry chain. The study found that: (1) The competitiveness of countries trading along the industrial chain has gradually increased. (2) As the supply shock intensifies, the impact of the crisis gradually shifts downstream, and the number of affected countries gradually increases. (3) Peru, China, and the USA are essential nodes for risk propagation in the copper industry chain. The critical factor in ensuring the stability of the industrial chain is the stable supply of raw materials. (4) Indirect infection occupies a large proportion in different stages of the industrial chain. Influenced countries and indirect influences in the middle and lower reaches of the industrial chain are higher than those upstream. Research can provide multi-dimensional policy recommendations for China to respond to extreme disturbances, build a more resilient trade system, and improve the resilience of trade networks.

Understanding the Essential Metabolic Nodes in the Synthesis of 4-Acetylantroquinol B (4-AAQB) by Antrodia cinnamomea Using Transcriptomic Analysis

4-Acetylantroquinol B (4-AAQB) is a ubiquinone that has been shown to have multiple anticancer activities and is only found in the rare medicinal fungus A. cinnamomea in Taiwan. The large-scale production and application of 4-AAQB is thus limited due to the high host specificity, long production cycle, and low 4-AAQB content of A. cinnamomea. Additionally, the lack of molecular genetic studies on A. cinnamomea has hindered the study of the synthetic pathway of 4-AAQB. In this work, transcriptomic analysis was conducted to understand the essential metabolic nodes in the synthesis of 4-AAQB by A. cinnamomea based on the differences using glucose and fructose as carbon sources, respectively. The results showed that the glyoxylate and TCA cycle, terpenoid synthesis pathway, and the quinone ring modification pathway were clarified as the most significant factors associated with 4-AAQB synthesis. The enzymes ACS, ACU7, ACUE, GPS, PPT, P450, GEDA, YAT1, CAT2, and METXA in these pathways were the essential metabolic nodes in the synthesis of 4-AAQB. When fructose was used as the substrate, the expressions of these enzymes were upregulated, and the synthesis of some important intermediate metabolites was enhanced, thus promoting the accumulation of 4-AAQB. Our work understood the mechanism of fructose promoting the synthesis of 4-AAQB and identified the essential metabolic nodes which could provide the theoretical basis for the development of fermentation strategies to produce 4-AAQB by A. cinnamomea.

Modeling Passenger Car Unit and Delay at Signalized Intersections in Mixed Traffic Conditions

The signalized intersections are essential nodes that directly affect the performance of the whole roadway network. The heterogeneous traffic at approaches signalized intersection makes traffic flow more chaotic. Evaluating the performance of signalized intersections is essential by taking queue length, PCU, delay, and capacity for improving the overall service quality. The guidelines followed in developed countries are not directly applicable in a country like India for practical evaluation of traffic operations. Therefore, it is necessary to analyze performance of intersections by making use of queued vehicles to develop PCU and delay models at signalized intersections in mixed traffic conditions. The present study analyzed queue length and developed a model to estimate PCU values, capacity, and delay by collecting field data from the approaches of signalized intersections in Warangal City using video graphic technic. The statistical distribution was carried out to show the significance of observed queue length, acceleration, and deceleration obtained from the field. Furthermore, new models proposed in the present study were also validated using field data, which confirms the successful validation of the models under mixed traffic conditions. The study recommends evaluating the level of service based on queue length and the proposed delay model at approaches of signalized intersections.

Link as a complete invariant of Morse-Smale 3-diffeomorphisms

In this paper we consider gradient-like Morse-Smale diffeomorphisms defined on the three-dimensional sphere S3 . For such diffeomorphisms, a complete invariant of topological conjugacy was obtained in the works of C. Bonatti, V. Grines, V. Medvedev, E. Pecu. It is an equivalence class of a set of homotopically non-trivially embedded tori and Klein bottles embedded in some closed 3-manifold whose fundamental group admits an epimorphism to the group Z . Such an invariant is called the scheme of the gradient-like diffeomorphism f:S3→S3 . We single out a class G of diffeomorphisms whose complete invariant is a topologically simpler object, namely, the link of essential knots in the manifold S2×S1 . The diffeomorphisms under consideration are determined by the fact that their non-wandering set contains a unique source, and the closures of stable saddle point manifolds bound three-dimensional balls with pairwise disjoint interiors. We prove that, in addition to the closure of these balls, a diffeomorphism of the class G contains exactly one nonwandering point, which is a fixed sink. It is established that the total invariant of topological conjugacy of class G diffeomorphisms is the space of orbits of unstable saddle separatrices in the basin of this sink. It is shown that the space of orbits is a link of non-contractible knots in the manifold S2×S1 and that the equivalence of links is tantamount to the equivalence of schemes. We also provide a realization of diffeomorphisms of the considered class along an arbitrary link consisting of essential nodes in the manifold S2×S1 .