Topological Distance Research Articles

Exploring quantitative structure-property relationship models for environmental fate assessment of petroleum hydrocarbons.

The rate and extent of biodegradation of petroleum hydrocarbons in the different aquatic environments is an important element to address. The major avenue for removing petroleum hydrocarbons from the environment is thought to be biodegradation. The present study involves the development of predictive quantitative structure-property relationship (QSPR) models for the primary biodegradation half-life of petroleum hydrocarbons that may be used to forecast the biodegradation half-life of untested petroleum hydrocarbons within the established models' applicability domain. These models use easily computable two-dimensional (2D) descriptors to investigate important structural characteristics needed for the biodegradation of petroleum hydrocarbons in freshwater (dataset 1), temperate seawater (dataset 2), and arctic seawater (dataset 3). All the developed models follow OECD guidelines. We have used double cross-validation, best subset selection, and partial least squares tools for model development. In addition, the small dataset modeler tool has been successfully used for the dataset with very few compounds (dataset 3 with 17 compounds), where dataset division was not possible. The resultant models are robust, predictive, and mechanisticallyinterpretable based on both internal and external validation metrics (R2 range of 0.605-0.959. Q2(Loo) range of 0.509-0.904, and Q2F1 range of 0.526-0.959). The intelligent consensus predictor tool has been used for theimprovement of the prediction quality for test set compounds which provided superior outcomes to those from individual partial least squares models based on several metrics (Q2F1 = 0.808 and Q2F2 = 0.805 for dataset 1 in freshwater). Molecular size and hydrophilic factor for freshwater, frequency of two carbon atoms at topological distance 4 for temperate seawater, and electronegative atom count relative to size for arctic seawater were found to be the most significant descriptors responsible for the regulation of biodegradation half-life of petroleum hydrocarbons.

Molecular engineering of interplanar spacing via π-conjugated phenothiazine linkages for high-power 2D covalent organic framework batteries

<h2>Summary</h2> Two-dimensional covalent organic frameworks (2D-COFs) represent an attractive platform for organic electrodes, yet they suffer from inferior power capability caused by poor Li⁺ intercalation in densely π-π stacked interlayers. Herein, featuring nonplanar π-conjugated heteroaromatic linkages, phenothiazine with "butterfly" conformation is integrated as a structural scaffold to instantly tune packing topology and interplanar distance. Corrugated 2D-COF maintaining aromaticity and crystallinity is formed with good electroactivity, enlarged d-spacing, and accessibility to interior Li⁺-interactive sites, which results in remarkable capacity of 220–773 mAh g⁻¹ at high rates ranging from 100 to 3,200 mA g⁻¹ with a good cycle life, bridging the performance gap between power and energy. Mechanistic studies reveal a dual storage mechanism with dominating capacitive storage promoted by π-Li⁺ interactions, as well as enhanced redox activity of carbonyls for better chemical accessibility. These findings elucidate inherent effects of molecular-level d-spacing regulation enabled by heteroaromatics, presenting a new design concept of interlayer engineering for organic porous energy storage materials.

Membrane Permeating Macrocycles: Design Guidelines from Machine Learning.

The ability to predict cell-permeable candidate molecules has great potential to assist drug discovery projects. Large molecules that lie beyond the Rule of Five (bRo5) are increasingly important as drug candidates and tool molecules for chemical biology. However, such large molecules usually do not cross cell membranes and cannot access intracellular targets or be developed as orally bioavailable drugs. Here, we describe a random forest (RF) machine learning model for the prediction of passive membrane permeation rates developed using a set of over 1000 bRo5 macrocyclic compounds. The model is based on easily calculated chemical features/descriptors as independent variables. Our random forest (RF) model substantially outperforms a multiple linear regression model based on the same features and achieves better performance metrics than previously reported models using the same underlying data. These features include: (1) polar surface area in water, (2) the octanol-water partitioning coefficient, (3) the number of hydrogen-bond donors, (4) the sum of the topological distances between nitrogen atoms, (5) the sum of the topological distances between nitrogen and oxygen atoms, and (6) the multiple molecular path count of order 2. The last three features represent molecular flexibility, the ability of the molecule to adopt different conformations in the aqueous and membrane interior phases, and the molecular "chameleonicity." Guided by the model, we propose design guidelines for membrane-permeating macrocycles. It is anticipated that this model will be useful in guiding the design of large, bioactive molecules for medicinal chemistry and chemical biology applications.

Integration of Ecology, Larval Phenotypes, and Mate-Recognition Signals with Molecular and Morphological Data Indicate Taxonomic Inflation in Nyctibatrachus (Anura: Nyctibatrachidae)

Taxonomic studies over the past decade of the endemic Night Frog genus Nyctibatrachus (originally described in 1882) from Peninsular India have more than tripled, from 11 at the turn of this century to 36 by 2017. Despite these revisionary contributions, it is still challenging for field biologists to identify night frog species reliably, due to a near-complete absence of diagnostic, discrete character states or trait values. Worse, many questionably diagnosed night frog species' status has ostensibly been “supported” by phylogenies derived from sparsely sampled gene-trees that are based on a single locus or a handful of markers—with topology and arbitrary genetic distance thresholds of 3–6% used to support new species descriptions. We sought to re-evaluate and validate the species boundaries of six currently nominated species of Nyctibatrachus of the aliciae group (N. aliciae, N. periyar, N. deveni, N. pillaii), N. vasanthi, and N. poocha clade using a comprehensive integrative taxonomic approach that integrates classical taxonomy, molecular species delimitation analysis, statistical analysis of morphological characters of adults and larvae, analyses of bioacoustics, and natural history information. Our results indicate that recent descriptions of Nyctibatrachus deveni, N. periyar, and N. pillaii represent cases of taxonomic inflation (over-splitting), because the evidence cited in support of their recognition is irreproducible, subjective, and devoid of strong statistical support. We demonstrate the need for multidimensional species delimitation approaches in the celebrated Western Ghats biodiversity hotspot paleo-endemic genus Nyctibatrachus and suspect that this concerning trend of over-splitting amphibian species based on limited data and untenable support may be applicable to other amphibian groups.

On geometrically uniform codes and topological quantum MDS codes

In this paper, we present generalized edge-pairings for the family of hyperbolic tessellations {4λ, 4} with the purpose of obtaining the corresponding discrete group of isometries. These tessellations have greater density packing than the self-dual tessellations {4λ, 4λ}, implying that the associated codes achieve the least error probability or, equivalently, that these codes are optimum codes. We also consider the topological quantum error-correcting codes on surfaces with genus g ≥ 2, as proposed in other papers. From that, we focus on the class of topological quantum maximum distance separable codes, showing that the only such codes are precisely the ones with minimum distance d ≤ 2.

Topological–temporal properties of evolving networks

Abstract Many real-world complex systems including human interactions can be represented by temporal (or evolving) networks, where links activate or deactivate over time. Characterizing temporal networks is crucial to compare different real-world networks and to detect their common patterns or differences. A systematic method that can characterize simultaneously the temporal and topological relations of the time-specific interactions (also called contacts or events) of a temporal network, is still missing. In this article, we propose a method to characterize to what extent contacts that happen close in time occur also close in topology. Specifically, we study the interrelation between temporal and topological properties of the contacts from three perspectives: (1) the correlation (among the elements) of the activity time series which records the total number of contacts in a network that happen at each time step; (2) the interplay between the topological distance and time difference of two arbitrary contacts; (3) the temporal correlation of contacts within the local neighbourhood centred at each link (so-called ego-network) to explore whether such contacts that happen close in topology are also close in time. By applying our method to 13 real-world temporal networks, we found that temporal–topological correlation of contacts is more evident in virtual contact networks than in physical contact networks. This could be due to the lower cost and easier access of online communications than physical interactions, allowing and possibly facilitating social contagion, that is, interactions of one individual may influence the activity of its neighbours. We also identify different patterns between virtual and physical networks and among physical contact networks at, for example, school and workplace, in the formation of correlation in local neighbourhoods. Patterns and differences detected via our method may further inspire the development of more realistic temporal network models, that could reproduce jointly temporal and topological properties of contacts.

Predicting the maximum absorption wavelength of azo dyes using an interpretable machine learning strategy

The maximum absorption wavelength (λmax) is one of the most important properties of azo dyes. It is essential to obtain λmax of azo dyes for the development of new molecules in a short time. Herein, the machine learning algorithm “XGBoost” was used to establish a model for predicting λmax of azo dyes. It was found that the coefficient of determinations (R2) of leave-one-out cross-validation (LOOCV) and test set were 0.87, 0.73, respectively. According to SHapley Additive exPlanations (SHAP) analysis, the number of sulfur atoms of R2 group has a strong positive correlation with λmax. The more C–N pairs of topological distance 4 appear in R1 group, the more likely the molecular λmax is red-shifted. Further, the high-throughput screening strategy was adopted to screen out 26 azo molecules with larger λmax from nearly 20,000 virtual samples. These molecular λmax are expected to be red shifted from the 610 nm in the dataset. Our study provides a convenient way to search for azo dyes with larger λmax.

LEO Satellite Networks: When Do All Shortest Distance Paths Belong to Minimum Hop Path Set?

A low Earth orbit (LEO) satellite constellationnetwork (SCN) has become a promising solution for nonterrestrial networks. In LEO-SCNs, the shortest distance path (SDP) and the minimum hop path (MHP) are two types of important paths. This letter focuses on the proposition that all the SDPs belong to the MHP set and studies the conditions when the proposition holds or not. Based on the topological regularity and link distance variation patterns, this letter proves several simplified equivalent propositions and derives a discriminant function to judge if the proposition holds in an arbitrary constellation. Simulations verify the judging method and find that all the SDPs belong to the MHP set in constellations with small inclinations (less than 68$^\circ$) or large phasing offsets. The propositions can help to simplify the calculation of the SDP.

A new self-supervised task on graphs: Geodesic distance prediction

The heavy dependence on human-curated labels makes supervised algorithms hit a bottleneck in real applications. Fortunately, a more data-efficient learning paradigm—self-supervised learning (SSL) disrupts this dilemma. By creating pretext tasks, self-supervised algorithms learn from the data itself without external supervision. Until recently, SSL has yielded immense success in the image domain, but its research in graph mining has received relatively little scrutiny. In fact, rich topological links and affiliated attributes promise more potential for self-supervised graph learning. Thus, in this paper, we propose a new pretext task called geodesic distance prediction to guide node representation learning. Specifically, this task demands neural nets to learn to infer the geodesic distance of one node relative to the other. Our underlying hypothesis is that doing well on reasoning about the pairwise distance requires the model to extract relation rules between topological distances and attributes. In this way, the complex correlation between nodes can be measured with a simple distance scale. Experiments demonstrate that our S2GRL achieves competitive or better performance than many state-of-the-art self-supervised methods. A case study on distance inference shows that S2GRL can effectively evaluate the risk of business transactions or make recommendations from a topological view.

Topological structures and distance functions in complete co-residuated lattices

Most of the research in fuzzy rough sets and fuzzy topological structures have been studied on the basis of fuzzy partially ordered sets. Instead of fuzzy partially ordered sets, the concept of distance functions in complete co-residuated lattices is introduced. Using distance functions, we define Alexandrov pretopology, Alexandrov precotopology and fuzzy interior (fuzzy closure) operators in complete co-residuated lattices, and we investigate their properties. Moreover, we prove that there exist isomorphic categories and Galois correspondence between topological categories.

TP-DDI: A Two-Pathway Deep Neural Network for Drug-Drug Interaction Prediction.

Adverse drug-drug interactions (DDIs) can severely damage the body. Thus, it is essential to accurately predict DDIs. DDIs are complex processes in which many factors can cause interactions. Rather than merely considering one or two of the factors, we design a two-pathway drug-drug interaction framework named TP-DDI that uses multimodal data for DDI prediction. TP-DDI effectively explores the combined effect of a topological structure-based pathway and a biomedical object similarity-based pathway to obtain multimodal drug representations. For the topology-based pathway, we focus on drug chemistry structures through the self-attention mechanism, which can capture hidden critical relationships, especially between pairs of atoms at remote topological distances. For the similarity-based pathway, our model can emphasize useful biomedical objects according to the channel weights. Finally, the fusion of multimodal data provides a holistic view of DDIs by learning the complementary features. On a real-world dataset, experiments show that TP-DDI can achieve better performance than the state-of-the-art models. Moreover, we can find the most critical substructures with certain interpretability in the newly predicted DDIs.

A Baseline Study of Oxygen Saturation in Parafoveal Vessels Using Visible Light Optical Coherence Tomography.

The retinal macula is at the center of our visual field, and thus pathological damage in the macula significantly impacts an individual's quality of life. The parafoveal vessels form the inner retina provide oxygen perfusion, and the measurement of parafoveal oxygen saturation (sO2) can evaluate macular metabolism and provide pathophysiological insight. In this paper, for the first time, we present a baseline study of microvascular oxygen saturation (sO2) in perifoveal macular region using visible light optical coherence tomography (VIS-OCT) on normal eyes. The arterial and venous sO2 from all eyes was 92.1 ± 7.1 (vol %) and 48.4 ± 5.0 (vol %) (mean ± SD), respectively. Arteriovenous sO2 difference was 43.8 ± 9.5 (vol %). Marginal correlation was found between venous sO2 and intraocular pressure (IOP) among eyes. No significant correlation was found between sO2 and vessel topological features, including length, diameter, and distance to fovea. This baseline study could serve as a benchmark for the future sO2 investigation of retinal macular pathologies.

TCDesc: Learning Topology Consistent Descriptors for Image Matching

The triplet loss is widely used in learning the local descriptors for image matching. However, existing triplet loss-based methods, like HardNet and DSM, employ the point-to-point distance metric, which neglects the neighborhood information of descriptors. Considering the fact that local neighborhood structures of matching descriptors would be similar under the ideal condition, this paper aims to learn the neighborhood topology-consistent descriptors (TCDesc). To this end, we first propose the linear combination weight as the topology weight to depict the neighborhood topology for each descriptor, where the difference between the center descriptor and the linear combination of its neighbors is minimized. For the global comparison, we then define a global topology vector by using the local topology weights. Next, beyond the Euclidean distance, we define a topology distance with the topology vectors to indicate the topological difference between the matching descriptors. Furthermore, we propose an adaptive weighting strategy to jointly minimize the topology distance and Euclidean distance in triplet loss. Experimental results on four widely-used datasets, i.e., UBC PhotoTourism, HPatches, W1BS and Oxford, demonstrate that our method can effectively improve the performance of both HardNet and DSM.

Visualization of Topological Pharmacophore Space with Graph Edit Distance.

A topological pharmacophore (TP) is a chemical graph-based pharmacophore representation, where nodes are pharmacophoric features (PF) and edges are topological distances between PFs. Previously proposed sparse pharmacophore graphs (SPhGs) for TPs were shown to be effective in identifying structurally different active compounds while maintaining the interpretability of the graphs. However, one limitation of using SPhGs as queries is that many structurally similar SPhGs can be identified from a set of active compounds, requiring the classification and visualization of SPhGs, followed by an understanding of the pharmacophore hypotheses. In this study, we propose a scheme for SPhG analysis based on dimensionality reduction techniques with the graph edit distance (GED) metric. This metric enables measuring similarities among SPhGs in a quantitative manner. The visualization of SPhGs, which themselves are the graphs shared by active compounds, can help us understand the pharmacophore hypotheses as well as the data set. As a proof-of-concept study, we generated two-dimensional SPhG-maps using three dimensionality reduction techniques for six biological targets. A comparison with other pharmacophore representations was also conducted. We demonstrated knowledge extraction (interpretation of the data set) from the generated maps. Our findings include a suitable mapping algorithm as well as a pharmacophore hypothesis analysis procedure using an SPhG-map.

Mathematical Models of Effective Topology of Computer Networks for Electric Power Supply Control on Railway Transport

The paper is devoted to analysis of modern directions of innovation-investment formation of intelligent computer networks that control the fast-moving technological processes of electricity supply. It is based on the conclusion that the problem of increasing the productivity of information exchange between information resources and consumers is dominant. A method for increasing the efficiency of information exchange is proposed as a search for the rational location of a new node and the organization of such a set of its connections among the whole set of nodes of the computer network, which provides a minimum average topological distance. Mathematical models of effective topological organization of connections in computer network of power consumption control at the level of traction substations, electric power distances and the railway in general are proposed.

Greedy Weight Matrix Topological Adaptive Ad Hoc On-demand Multipath Distance Vector Protocol for QOS Improvement in MANET

Greedy Weight Matrix Topological Adaptive Ad Hoc On-demand Multipath Distance Vector Protocol for QOS Improvement in MANET

New mathematical measures for apprehending complexity of chiral molecules using information entropy.

In this paper, we present several new theoretical measures based on information entropy that can be used to analyze the information content of a chiral molecule. Starting from a differentiation between "chiral" and "achiral" portions in a chiral molecule, we define a new concept that allows us to quantify the complexity of chiral constitutional 2D-isomers of C10 to C20 alkanes. Various new chiral and achiral information measures founded on joint entropy, mutual information, and conditional entropy are presented providing an access to a set of regression equations. Then, introducing a case-based measure of entropy, we demonstrate that the distribution of the chiral complexity in these molecules is mostly skewed-right: 60% of the chiral isomers follow a 60/40 distribution rule, which indicates a concentration of chiral complexity in a small number of topological features. Furthermore, by replacing 2D topological distances by 3D distances, the application of these new information measures goes from conformational to racemization and deracemization studies. Interestingly, when the geometrical distances between atoms and the chiral center(s) are taken into account when determining the chiral information entropy, one can observe a significative Pearson correlation coefficient (R= 0.70) between the chiral entropy of 3D molecules and the continuous chirality measure. Finally, we show that our approach is applicable to almost any type of chiral organic chemical structures if in the entropy equation, atoms are represented by their electrotopological state (E-state) index instead of connectivity.

A multi-modal algorithm based on an NSGA-II scheme for phylogenetic tree inference

The analysis of evolutionary data allows uncovering information about the organisms and how they have adapted and evolved. This information could provide us with new insights about the specialisation of organisms (or part of them), how they adapt, how similar they are with other species, among others. Unfortunately, this evolutionary history can only be estimated, and for that, several computational methods exist. Among the methods, optimisation methods are one of the main approaches to deal with this problem, with multiobjective optimisation producing promising results. In this paper, we deal with multiobjective phylogenetic inference, using a multi-modal metaheuristic approach that exploits the decision space in the multiobjective formulation of the problem. In particular, we incorporate a new metric based on a topological tree distance. We compare the method with state of the art algorithms in terms of performance. Additionally, we perform a thorough analysis of a study case on a yeast Saccharomyces cerevisiae dataset. Results show that our proposal is able to improve the diversity of solutions while improving or keeping the quality of solutions in terms of hypervolume.

Map Matching Algorithm: Empirical Review Based on Indian Open Street Map Road Network Data

Locating devices on the road network is crucial for any location-based system. Accuracy of map matching algorithms may highly affect the accuracy of any location-based service. This paper includes an empirical review of five major map matching algorithms for locating a device on a digital road network. A standard dataset was used to simulate the working of map matching algorithms. After ascertaining the accuracy of map matching algorithms, it was tested on a real road network. Six different routes varying from 0.6 kilometers to 32 kilometers, covering a total distance of 82.2 kilometers were included in the experiment. Performance of map matching algorithms was evaluated on a total of 2094 road nodes with 1271070 Global Positioning System (GPS) points on the basis of matched, unmatched nodes with root mean square error. It was concluded that Hidden-Markov Model based map matching algorithms has reasonably good accuracy (96% using global data and 89% using Indian dataset) and execution time in comparison to geometric, topological, Kalman filter and Frechet distance based algorithms.

AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing.

We present an approach utilizing Topological Data Analysis to study the structure of face poses used in affective computing, i.e., the process of recognizing human emotion. The approach uses a conditional comparison of different emotions, both respective and irrespective of time, with multiple topological distance metrics, dimension reduction techniques, and face subsections (e.g., eyes, nose, mouth, etc.). The results confirm that our topology-based approach captures known patterns, distinctions between emotions, and distinctions between individuals, which is an important step towards more robust and explainable emotion recognition by machines.