Topology Visualization Research Articles

From Monitoring to Observability: A Paradigm Shift in IT Operations

This article explores the paradigm shift from traditional monitoring to observability in IT operations, driven by the increasing complexity of modern distributed systems, data overload, evolving cybersecurity threats, and the need for proactive problem-solving. Observability platforms, built on the four pillars of events, metrics, traces, and logs, provide comprehensive insights into system behavior and performance. Adopting observability practices offers numerous benefits, including automated discovery of system components, dependency mapping, topology visualization, correlative intelligence, faster root cause diagnosis, and more accurate anomaly detection through auto-baselining. Organizations that embrace observability report significant improvements in incident response times, system reliability, and overall operational efficiency. The article guides successfully transitioning to an observability-focused approach by embracing automation, fostering a culture of observability, implementing the right tools, and leveraging AI and machine learning for advanced analytics and insights.

Protein Visualizer 2.0: Intuitive and Interactive Visualization of Protein Topology and Co/Post-Translational Modifications.

Topology and post-translational modifications (PTMs) are critical features in studying the structures and functions of proteins. Several popular and detailed protein visualization algorithms are available for such studies, such as PyMOL, NCBI's iCn3D, UniProt PTM viewing, and Protter. However, none of the studies depict the special relationships between key structural features such as N-glycosylation, disulfide bonds, potential N-glycosylation sites (free sequons), potential disulfide bonds (free cysteine sites), and protein topology in a way conducive to detailed analyses. We introduce Protein Visualizer 2.0 (https://sfu-sun-lab.github.io/protein-visualizer-2.0/), a web-based tool that visualizes these features and the topology of all human proteins. This tool allows users to readily assess potential conflicts among predicted protein topology and known co/post-translational modifications. This tool also helps to reveal hidden relationships among displayed structural features.

A benchmark quantum chemical probe into the structural stability of various anchoring groups to form graphene oxides

Graphene and graphene oxides (GO) are among the intriguing materials of this era, being widely used in many modern technologies. Many properties of GOs are owing to its more reactivity as compared with graphene. It is interesting to study the changes in chemical properties while changing graphene to GOs. There are commonly known three ways to change a graphene to GO by adding epoxy, hydroxyl and carboxyl groups. We used quantum chemical methods to systematically attach epoxy, hydroxyl and carboxyl groups on graphene surface. We designed GOs as hydroxyl (OH-GO), epoxy (O-GO), carboxyl (COOH-GO) derivatives of graphene and one derivative with all functional groups collectively (HEC-GO). In the current work, we have employed DFT method by using B3LYP functional along with 6-31G* basis set to calculate electronic properties computationally. The calculated band gap for GOs are less than pristine graphene (1.680 eV). Minimum band gap of 1.565 eV is shown by O-GO, while compound HEC-GO with collective functional groups shows 1.539 eV. Frontier molecular orbitals, molecular electrostatic potential maps and density of state analysis has been performed. Localized orbital locator (LOL) are drawn to check the charge transfer via topology visualization. Binding energy has been calculated using basis set superposition error (BSSE) correction. COOH-GO shows binding energy of −302.90 kcal/mol which is seen better than OH-GO and O-GO systems. NPA analysis showed that there is more ICT as functional groups are attached to the pristine graphene molecule because of the strong electron withdrawing nature of attached groups. TD-DFT with TD-B3LYP/6-31G* is employed to check the absorption properties of entitled compounds. Graphene shows maximum absorption at 341 nm, while GOs shows red shift, e.g., COOH-GO at 371 nm, OH-GO at 386 nm, HEC-GO at 409 nm and O-GO at 462 nm. The current investigation highlighted the molecular level insights about the stability and relative role of epoxy, hydroxyl and carboxyl groups on graphene surface during formation of GOs.

Modular pharmacology: research progress and development of analysis platform

Based on the systematic deconstruction of multi-dimensional and multi-target biological networks, modular pharmacology explains the complex mechanism of diseases and the interactions of multi-target drugs. It has made progress in the fields of pathogenesis of disease, biological basis of disease and traditional Chinese medicine(TCM) syndrome, pharmacological mechanism of multi-target herbs, compatibility of formulas, and discovery of new drug of TCM compound. However, the complexity of multi-omics data and biological networks brings challenges to the modular deconstruction and analysis of the drug networks. Here, we constructed the "Computing Platform for Modular Pharmacology" online analysis system, which can implement the function of network construction, module identification, module discriminant analysis, hub-module analysis, intra-module and inter-module relationship analysis, and topological visualization of network based on quantitative expression profiles and protein-protein interaction(PPI) data. This tool provides a powerful tool for the research on complex diseases and multi-target drug mechanisms by means of modular pharmacology. The platform may have broad range of application in disease modular identification and correlation mechanism, interpretation of scientific principles of TCM, analysis of complex mechanisms of TCM and formulas, and discovery of multi-target drugs.

Rational design and efficient synthesis of cyclic polymers with visualized molecular topology and unique dielectric properties

Cyclic polymers without chain ends have attracted increasing attention because of the unique physical properties. However, the direct visualization of cyclic topology remains a formidable challenge, because the cyclic chains with flexible backbone or strong intramolecular interaction usually existed in a coiled state rather than cyclic topology. Herein, monocyclic and bicyclic polymers were prepared via blocking-cyclization technique, and the molecular topology of cyclic polymer containing phenyl pendant was directly observed depending on the reduced intrachain entanglement. The structure and characteristics of cyclic polymers were investigated, and the difference between cyclic polymer and linear counterpart was demonstrated. Compared to linear polymer, cyclic polymer exhibited improved dielectric properties, and bicyclic polymer displayed further increased dielectric constant and energy storage density than monocyclic polymer possessing the same repeating units. Therefore, this research presents a facile strategy in the design and construction of cyclic polymers with directly visualized topology and unique dielectric properties.

A comprehensive study on the intermolecular interactions and energy frameworks exist in crystal structures of Form-I, Form-II and Form-III polymorphs of Ethyl maltol

The present study serves as a comprehensive investigation into the molecular crystal structures of three polymorphic forms of ethyl maltol. Crystal structures of the polymorphs grown via slow evaporation and quench cooling crystallization processes were determined through single crystal X-ray diffraction (SCXRD) analysis. Through the utilization of SCXRD data CIF file as an input, we explored the nature and strength of intermolecular interactions, atom pair close contacts, interaction types, 2D fingerprint plots, surface characteristics and percentage of voids present within each polymorph of ethyl maltol. The comparative analysis involves the application of advanced methods, such as Hirshfeld surface analysis and 3D-energy frameworks using Crystal Explorer 17.5. In a supramolecular organization, intermolecular interaction between the carbonyl and hydroxyl groups is a collective structural motif employed among the three polymorphs. However the three polymorphs share common structural features, but the conformations within the crystal packing of each form are different from each other. Shape Index and Curvedness surfaces reveal π stacking interactions with varying properties on differing sides of the molecular moieties between the polymorph. Fingerprint analysis emphasizes that (H…H) is the predominant interaction whereas (O…H) and (H…O) interactions contribute more towards the generation of H-bond interactions in the structure of ethyl maltol polymorphs. The crystal structures of all the three ethyl maltol polymorphs stabilized by various intermolecular contacts within the Hirshfeld surface area was comparable. The interaction energy and the 3D-energy frameworks offer a visualization of the crystal packing topology, enhancing our understanding of the three-dimensional arrangement of molecules and their structural stability. The resulting overall average total energy values for Form-I, Form-II and Form-III polymorphs of ethyl maltol are -56.3 kJ /mol, -74 kJ /mol and -80.45 kJ /mol respectively which reveals that all the three polymorphs show a significant difference in their total energies and reveals further that Form-III polymorph of ethyl maltol is the most stable one while Form-I is the least stable and Form-II is metastable relatively. Moreover, the dispersion energy framework is predominant over the electrostatic energy framework. The void analysis reveals the percentage of volume occupied by the voids present in the unit cell of Form-I, Form-II, and Form-III polymorphs of ethyl maltol are found to be 7.20%, 6.55 %, and 9.43% respectively.

Analyzing Patterns of Service Utilization Using Graph Topology to Understand the Dynamic of the Engagement of Patients with Complex Problems with Health Services

Background: Providing care to persons with complex problems is inherently difficult due to several factors, including the impacts of proximal determinants of health, treatment response, the natural emergence of comorbidities, and service system capacity to provide timely required services. Providing visibility into the dynamics of patients’ engagement can help to optimize care for patients with complex problems. Method: In a previous work, graph machine learning and NLP methods were used to model the products of service system dynamics as atemporal entities, using a data model that collapsed patient encounter events across time. In this paper, the order of events is put back into the data model to provide topological depictions of the dynamics that are embodied in patients’ movement across a complex healthcare system. Result: The results show that directed graphs are well suited to the task of depicting the way that the diverse components of the system are functionally coupled—or remain disconnected—by patient journeys. Conclusion: By setting the resolution on the graph topology visualization, important characteristics can be highlighted, including highly prevalent repeating sequences of service events readily interpretable by clinical subject matter experts. Moreover, this methodology provides a first step in addressing the challenge of locating potential operational problems for patients with complex issues engaging with a complex healthcare service system.

Systems approach for congruence and selection of cancer models towards precision medicine.

Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine.

Visualizing Orientation and Topology of ER Membrane Proteins In Planta.

The orientation of membrane proteins within the lipid bilayer is key to understanding their molecular function. Similarly, the proper topology of multispanning membrane proteins is crucial for their function. Although bioinformatics tools can predict these parameters assessing the presence of hydrophobic protein domains sufficiently long to span the membrane and other structural features, the predictions from different algorithms are often inconsistent. Therefore, experimental analysis becomes mandatory. Redox-based topology analysis exploits the steep gradient in the glutathione redox potential (EGSH) across the ER membrane of about 80mV to visualize the orientation of ER membrane proteins by fusing the EGSH biosensor roGFP2 to either the N- or the C-termini of the investigated protein sequence. Transient expression of these fusion proteins in tobacco leaves allows direct visualization of orientation and topology of ER membrane proteins in planta. The protocol outlined here is based on either a simple merge of the two excitation channels of roGFP2 or a colocalization analysis of the two channels and thus avoids ratiometric analysis of roGFP2 fluorescence.

Graph-based learning of nonlinear physiological interactions for classification of emotions

Emotion recognition has been drawing the attention of researchers and practitioners in recent years. While various research studies show successful applications to recognize and distinguish emotions based on physiological responses using machine learning techniques, less research to date is focused on the network properties of physiological interactions under different emotional states. To this end, we propose a multi-modal graph learning framework to quantify the interactions among physiological systems and present representative networks associated with emotional states. More specifically, we introduce a novel information-theoretic-based time delay stability to quantify complex interactions between physiological modalities. We test our quantification approach on three publicly available benchmark databases for emotion recognition and demonstrate the comparative performances of measuring the interactions of physiological systems in response to emotional states. Finally, we present the visualization of multi-modal physiological network topology, which may be useful for emotional interpretations in practice.

Poincaré maps for visualization of large protein families.

In the era of constantly increasing amounts of the available protein data, a relevant and interpretable visualization becomes crucial, especially for tasks requiring human expertise. Poincaré disk projection has previously demonstrated its important efficiency for visualization of biological data such as single-cell RNAseq data. Here, we develop a new method PoincaréMSA for visual representation of complex relationships between protein sequences based on Poincaré maps embedding. We demonstrate its efficiency and potential for visualization of protein family topology as well as evolutionary and functional annotation of uncharacterized sequences. PoincaréMSA is implemented in open source Python code with available interactive Google Colab notebooks as described at https://www.dsimb.inserm.fr/POINCARE_MSA.

Topological visualization of the plasmonic resonance of a nano C-aperture.

The plasmonic response of a nano C-aperture is analyzed using the Vector Field Topology (VFT) visualization technique. The electrical currents that are induced on the metal surfaces when the C-aperture is excited by light is calculated for various wavelengths. The topology of this two-dimensional current density vector is analyzed using VFT. The plasmonic resonance condition is found to coincide with a distinct shift in the topology which leads to increased current circulation. A physical explanation of the phenomenon is discussed. Numerical results are presented to justify the claims. The analyses suggest that VFT can be a powerful tool for studying the physical mechanics of nano-photonic structures.

Influence of hydroxyl group in stabilizing the Schiff base crystal structure: Crystal structure, computational and molecular docking studies

To investigate the synergetic effect in the formation of crystal structure having two independent molecules in the asymmetric unit and the structure-property relationship of the molecule, we have synthesized the Schiff base compound (E)-4-(((4-chlorobenzyl)imino)methyl)benzene-1,2,3-triol. The formation of the desired compound was confirmed by the FTIR and single-crystal X-ray studies. The enriched hydroxyl group exhibits more intra and inter-strong hydrogen bonds. The chlorine-hydrogen and C6-H6‧‧‧π interaction along with other interactions form a supramolecular architecture. The Hirshfeld surface analysis and 2D fingerprint analysis provide valuable information about the intermolecular interaction present in the crystal structure. The likelihood of occurrence of intermolecular contacts present in the crystal structure was analyzed using enrichment ratio. The visualization of the interaction topology along with interaction energy between the molecules are carried out using an energy framework. QTAIM, NCI, ELF and LOL studies provide information about electron charge density distribution and the nature of intramolecular and dimeric interaction. The DFT studies provide ground state energy of the optimized structure and the HOMO-LUMO energy gap. The molecular electrostatic potential provides the reactive site and anisotropy of charge distribution of chlorine atom. The NBO studies provide stabilization energy associated with the delocalization of orbitals. The docking studies were also carried out for SARS COVID-19 and the effect of hydroxyl group substitution on binding interaction was investigated.

A 3D mixed reality visualization of network topology and activity results in better dyadic cyber team communication and cyber situational awareness.

Cyber defense decision-making during cyber threat situations is based on human-to-human communication aiming to establish a shared cyber situational awareness. Previous studies suggested that communication inefficiencies were among the biggest problems facing security operation center teams. There is a need for tools that allow for more efficient communication of cyber threat information between individuals both in education and during cyber threat situations. In the present study, we compared how the visual representation of network topology and traffic in 3D mixed reality vs. 2D affected team performance in a sample of cyber cadets (N = 22) cooperating in dyads. Performance outcomes included network topology recognition, cyber situational awareness, confidence in judgements, experienced communication demands, observed verbal communication, and forced choice decision-making. The study utilized network data from the NATO CCDCOE 2022 Locked Shields cyber defense exercise. We found that participants using the 3D mixed reality visualization had better cyber situational awareness than participants in the 2D group. The 3D mixed reality group was generally more confident in their judgments except when performing worse than the 2D group on the topology recognition task (which favored the 2D condition). Participants in the 3D mixed reality group experienced less communication demands, and performed more verbal communication aimed at establishing a shared mental model and less communications discussing task resolution. Better communication was associated with better cyber situational awareness. There were no differences in decision-making between the groups. This could be due to cohort effects such as formal training or the modest sample size. This is the first study comparing the effect of 3D mixed reality and 2D visualizations of network topology on dyadic cyber team communication and cyber situational awareness. Using 3D mixed reality visualizations resulted in better cyber situational awareness and team communication. The experiment should be repeated in a larger and more diverse sample to determine its potential effect on decision-making.

Topological visualization and graph analysis of rough sets via neighborhoods: A medical application using human heart data

<abstract><p>In the field of medical applications, graph theory offers diverse topological models for representing the human heart. The key challenge is identifying the optimal structure as an effective diagnostic model. This paper explains the rationale behind using topological visualization, graph analysis, and rough sets via neighborhood systems. We introduce the novel $ 1 $-neighborhood system ($ 1 $-$ NS $) tools, enabling rough set generalization and a heart topological graph model. Exploring minimal and core minimal neighborhoods, vital for classifying subsets and accuracy computation, these approaches outperform existing methods while preserving Pawlak's properties. Multiple topologies are constructed and examined using these systems. The paper presents a real-world example showcasing innovative topological spaces through a human heart's vertex network. These spaces enhance understanding of the heart's structural organization. Two algorithms are introduced for decision-making and generating graph topologies, defining unique spaces. Beyond graph theory, these techniques apply to medical contexts like blood circulation and geographical scenarios such as community street mapping. Implemented using MATLAB, they are valuable tools.</p></abstract>

Visualization of Discontinuous Vector Field Topology.

This paper extends the concept and the visualization of vector field topology to vector fields with discontinuities. We address the non-uniqueness of flow in such fields by introduction of a time-reversible concept of equivalence. This concept generalizes streamlines to streamsets and thus vector field topology to discontinuous vector fields in terms of invariant streamsets. We identify respective novel critical structures as well as their manifolds, investigate their interplay with traditional vector field topology, and detail the application and interpretation of our approach using specifically designed synthetic cases and a simulated case from physics.

Cyclic–hanging-multicyclic polymer synthesized by ladderphane-mediated blocking-cyclization technique for the direct visualization of cyclic macromolecular topology

Cyclic–hanging-multicyclic polymer topology was developed by ROMP-based grafting through approach and blocking-cyclization technique, and directly visualized without tedious post-modification process.

Topological approach of liver segmentation based on 3D visualization technology in surgical planning for split liver transplantation.

Split liver transplantation (SLT) is a complex procedure. The left-lateral and right tri-segment splits are the most common surgical approaches and are based on the Couinaud liver segmentation theory. Notably, the liver surface following right tri-segment splits may exhibit different degrees of ischemic changes related to the destruction of the local portal vein blood flow topology. There is currently no consensus on preoperative evaluation and predictive strategy for hepatic segmental necrosis after SLT. To investigate the application of the topological approach in liver segmentation based on 3D visualization technology in the surgical planning of SLT. Clinical data of 10 recipients and 5 donors who underwent SLT at Shenzhen Third People's Hospital from January 2020 to January 2021 were retrospectively analyzed. Before surgery, all the donors were subjected to 3D modeling and evaluation. Based on the 3D-reconstructed models, the liver splitting procedure was simulated using the liver segmentation system described by Couinaud and a blood flow topology liver segmentation (BFTLS) method. In addition, the volume of the liver was also quantified. Statistical indexes mainly included the hepatic vasculature and expected volume of split grafts evaluated by 3D models, the actual liver volume, and the ischemia state of the hepatic segments during the actual surgery. Among the 5 cases of split liver surgery, the liver was split into a left-lateral segment and right tri-segment in 4 cases, while 1 case was split using the left and right half liver splitting. All operations were successfully implemented according to the preoperative plan. According to Couinaud liver segmentation system and BFTLS methods, the volume of the left lateral segment was 359.00 ± 101.57 mL and 367.75 ± 99.73 mL, respectively, while that measured during the actual surgery was 397.50 ± 37.97 mL. The volume of segment IV (the portion of ischemic liver lobes) allocated to the right tri-segment was 136.31 ± 86.10 mL, as determined using the topological approach to liver segmentation. However, during the actual surgical intervention, ischemia of the right tri-segment section was observed in 4 cases, including 1 case of necrosis and bile leakage, with an ischemic liver volume of 238.7 mL. 3D visualization technology can guide the preoperative planning of SLT and improve accuracy during the intervention. The simulated operation based on 3D visualization of blood flow topology may be useful to predict the degree of ischemia in the liver segment and provide a reference for determining whether the ischemic liver tissue should be removed during the surgery.

A new hydrazide functionalized Schiff’s base derivative: Insights into crystallography, Hirshfeld surface, and energy framework analysis

A new hydrazide functionalized Schiff’s base derivative, N'-(3,4-dichlorobenzylidene)-4-hydroxybenzohydrazide (I), has been synthesized using a solvent-assisted mechano-chemical grinding strategy and structurally characterized using elemental analysis, 1H NMR and crystallographic studies. The single crystal X-ray diffraction study depicts that molecule is puckered with two aromatic rings lying out-of-plane in near anti-configuration across the C=N bond. The weak interactions involved in supramolecular framework formation are Cl···O, Cl···Cl, Cl···H, Cl···N, C···H, and O···H contacts. The intermolecular O···H interaction being stronger than other dispersive interactions such as halogen bonding, interlocks the molecules in a 2D sheet-type packing. All the structure directing interactions involved in developing crystal architecture are addressed with Hirshfeld surface analysis and fingerprint plots. The energy framework analysis shows visualization of 3D topology of short contacts related to molecular packing of compound I which further clarifies the predominance of both Coulombic and dispersive energies in developing supramolecular architecture.

Elucidating dynamics and mechanism of cyclic bioreaction networks using topologically-equivalent electrical circuits

A novel methodology is established to convert a set of elementary chemical reactions into topologically-equivalent electrical circuits. The circuit analog is then simulated to elucidate the dynamics of complex cyclic bionetworks. The pyramidal reaction network catalyzed by the universal enzyme dihydrofolate reductase – which has important pharmacological and medical relevance in connection with cancer therapy – is used to illustrate the application of our model. The finite number of mechanisms, sub-cycles, or graphs constituting the cyclic network are analyzed, their contribution to the overall steady-state reaction rate determined, and the most probable mechanism is identified. The developed methodology is elegant, modular, and permits representation and visualization of network structure and topology at a glance. It can handle linear and nonlinear kinetics/rate laws, does not require simplifying assumptions such as use of the quasi-steady-state/Bodenstein approximation or the absence of nonlinear kinetic steps in the intermediates, and is suitable for coupling and integration with other flux-based reaction models.