Towards a structural turn in consciousness science.

Recent activities in virtually all fields engaged in consciousness studies indicate early signs of a structural turn, where verbal descriptions or simple formalisations of conscious experiences are replaced by structural tools, most notably mathematical spaces. My goal here is to offer three comments that, in my opinion, are essential to avoid misunderstandings in these developments early on. These comments concern metaphysical premises of structural approaches, the viability of structure-preserving mappings, and the question of what a structure of conscious experience is in the first place. I will also explain what, in my opinion, are the great promises of structural methodologies and how they might impact consciousness science at large.

Impact of country governance mechanisms on carbon emissions performance of multinational entities.

This study investigates the impact of country governance mechanisms on carbon emissions performance of private sector organisations, using empirical evidence from 336 top multinational entities (MNEs) over a 15-year period. The results show that, at the aggregate level, Control of Corruption (b = -0.021, p < 0.01) and Voice & Accountability (b = -0.015, p < 0.05) are significantly and negatively associated with carbon emissions rate. While Political Stability (b = 0.007, p < 0.05) and Government Effectiveness (b = 0.018, p < 0.05) have significant positive impact on carbon emissions rate, the impact of Regulatory Quality and Rule of Law is negative but insignificant. Empirical evidence supports the conclusion that the existing institutional environment is not sufficient to deliver the net zero transition. There is a need for more coordination, strategic planning, and delivery monitoring in government institutions to achieve decarbonisation targets. The study contributes to knowledge within the context of the identified research gaps. First, the study adds to the limited literature on the impact of country governance on carbon emissions reduction, particularly with reference to scope 3 emissions. Second, with the sustainable development goals (SDGs) set to expire by 2030, the study provides empirical evidence on efforts governments of countries are making in achieving decarbonisation targets through improvement in country governance quality. Third, the study shows that the impact of the country governance on the carbon emissions performance of MNEs is contextual and varies across jurisdictions/geographical regions. Finally, the paper contributes to the debate on the actualisation of Agenda 2030, because presenting empirical evidence on the impact of country governance mechanisms on carbon emissions reduction-particularly scope 3 emissions-is an important discourse in the realisation of the SDGs.

A figurational approach to environmental sustainability in the context of sport.

Discourses around environmental sustainability and climate change are increasingly prominent in the sports sector, with a growing range of sports organisations developing policies to address these issues. This paper contends that figurational (or process) sociology can offer a useful framework for examining the development of policy as a process in the context of sport and, specifically, mega-events. The Olympic Games serve as an example for purposes of contextualisation, illustrating four interconnected dimensions of figurational sociology: lengthening chains of interdependence, established-outsider power relations, internalisation of social values, and unintended consequences. Further, the paper seeks to highlight the utility of a figurational perspective particularly when this is enhanced through the integration of complementary concepts, namely knowledge transfer, isomorphism, and diffusion of innovations. Thus, it is asserted that a blended figurational approach can help facilitate understanding of interdependencies and dynamic power relations across expanded stakeholder networks in relation to sports mega-events. Finally, the paper touches on the relevance of sport in relation to the United Nations (UN) Sustainable Development Goals to highlight the need for policy coherence that is arguably unachievable without the understanding of stakeholder interdependencies and power relationships a figurational lens enables. Such understanding is therefore considered to be important as a foundation for the enactment of meaningful policy in the fight against climate change.

Predicting treatment response in adolescents and young adults with major depressive episodes from fMRI using graph isomorphism network.

BACKGROUND: Major depressive episode (MDE) is the main clinical feature of mood disorders (major depressive disorder and bipolar disorder) in adolescents and young adults and accounts for most of the disease course. However, 30%-40% of MDE patients not responding to clinical first-line interventions. It is crucial to predict treatment response in the early stages and identify biomarkers associated with treatment response. Graph Isomorphism Network (GIN), a deep learning method, is promising for predicting treatment response for individual MDE patients with more powerful representation ability to capture the features of brain functional connectivity. METHODS: In this study, GIN was used to predict individual treatment response in 198 adolescents and young adults with MDE. The most discriminating regions were also identified for the treatment response prediction. RESULTS: Using GIN approach, the baseline functional connectivity could predict 79.8% responders and 67.4% non-responders to treatment (accuracy 74.24%). Furthermore, the most discriminating brain regions were mainly involved in paralimbic and subcortical areas. CONCLUSIONS: GIN has shown potential in predicting treatment response for individual patients, which may enable personalized treatment decisions. Furthermore, targeted interventions focused on modulating the activity and connectivity within paralimbic and subcortical regions could potentially improve treatment outcomes and enable personalized interventions for adolescents and young adults with MDE.

Interval-valued bipolar fuzzy line graphs.

OBJECTIVES: The notion of Bipolarity based on positive and negative outcomes. It is well known that bipolar models give more precision, flexibility, and compatibility to the system as compared to the classical models and fuzzy models. A bipolar fuzzy graph(BFG) provides more flexibility while modeling human thinking as compared with a fuzzy graph, and an interval valued bipolar fuzzy graph(IVBFG) has numerous applications where the real-life problem are time dependent and there is a network structure complexity. The aim of this paper is to introduce an interval-valued bipolar line fuzzy graph(IVBFLG). RESULT: In this paper, we have proposed the notion of an IVBFLG and some of its characterizations. Also, some propositions and theorems related to an IVIFLGs are developed and proved. Furthermore, isomorphism between two IVIFLGs toward their IVIFGs was determined and verified. As a result, we derive a necessary and sufficient condition for an IVBFG to be isomorphic to its corresponding IVBFLG and some remarkable properties like degree, size, order, regularity, strength, and completeness of an IVBFLGs have been investigated, and the proposed concepts are illustrated with the examples.

Exploring QSAR models for activity-cliff prediction.

INTRODUCTION AND METHODOLOGY: Pairs of similar compounds that only differ by a small structural modification but exhibit a large difference in their binding affinity for a given target are known as activity cliffs (ACs). It has been hypothesised that QSAR models struggle to predict ACs and that ACs thus form a major source of prediction error. However, the AC-prediction power of modern QSAR methods and its quantitative relationship to general QSAR-prediction performance is still underexplored. We systematically construct nine distinct QSAR models by combining three molecular representation methods (extended-connectivity fingerprints, physicochemical-descriptor vectors and graph isomorphism networks) with three regression techniques (random forests, k-nearest neighbours and multilayer perceptrons); we then use each resulting model to classify pairs of similar compounds as ACs or non-ACs and to predict the activities of individual molecules in three case studies: dopamine receptor D2, factor Xa, and SARS-CoV-2 main protease. RESULTS AND CONCLUSIONS: Our results provide strong support for the hypothesis that indeed QSAR models frequently fail to predict ACs. We observe low AC-sensitivity amongst the evaluated models when the activities of both compounds are unknown, but a substantial increase in AC-sensitivity when the actual activity of one of the compounds is given. Graph isomorphism features are found to be competitive with or superior to classical molecular representations for AC-classification and can thus be employed as baseline AC-prediction models or simple compound-optimisation tools. For general QSAR-prediction, however, extended-connectivity fingerprints still consistently deliver the best performance amongs the tested input representations. A potential future pathway to improve QSAR-modelling performance might be the development of techniques to increase AC-sensitivity.

A Method Based on Timing Weight Priority and Distance Optimization for Quantum Circuit Transformation.

In order to implement a quantum circuit on an NISQ device, it must be transformed into a functionally equivalent circuit that satisfies the device's connectivity constraints. However, NISQ devices are inherently noisy, and minimizing the number of SWAP gates added to the circuit is crucial for reducing computation errors. To achieve this, we propose a subgraph isomorphism algorithm based on the timing weight priority of quantum gates, which provides a better initial mapping for a specific two-dimensional quantum architecture. Additionally, we introduce a heuristic swap sequence selection optimization algorithm that uses a distance optimization measurement function to select the ideal sequence and reduce the number of SWAP gates, thereby optimizing the circuit transformation. Our experiments demonstrate that our proposed algorithm is effective for most benchmark quantum circuits, with a maximum optimization rate of up to 43.51% and an average optimization rate of 13.51%, outperforming existing related methods.

DoubleSG-DTA: Deep Learning for Drug Discovery: Case Study on the Non-Small Cell Lung Cancer with EGFRT790M Mutation.

Drug-targeted therapies are promising approaches to treating tumors, and research on receptor-ligand interactions for discovering high-affinity targeted drugs has been accelerating drug development. This study presents a mechanism-driven deep learning-based computational model to learn double drug sequences, protein sequences, and drug graphs to project drug-target affinities (DTAs), which was termed the DoubleSG-DTA. We deployed lightweight graph isomorphism networks to aggregate drug graph representations and discriminate between molecular structures, and stacked multilayer squeeze-and-excitation networks to selectively enhance spatial features of drug and protein sequences. What is more, cross-multi-head attentions were constructed to further model the non-covalent molecular docking behavior. The multiple cross-validation experimental evaluations on various datasets indicated that DoubleSG-DTA consistently outperformed all previously reported works. To showcase the value of DoubleSG-DTA, we applied it to generate promising hit compounds of Non-Small Cell Lung Cancer harboring EGFRT790M mutation from natural products, which were consistent with reported laboratory studies. Afterward, we further investigated the interpretability of the graph-based "black box" model and highlighted the active structures that contributed the most. DoubleSG-DTA thus provides a powerful and interpretable framework that extrapolates for potential chemicals to modulate the systemic response to disease.

Graph isomorphism-based algorithm for cross-checking chemical and crystallographic descriptions.

Published reports of chemical compounds often contain multiple machine-readable descriptions which may supplement each other in order to yield coherent and complete chemical representations. This publication presents a method to cross-check such descriptions using a canonical representation and isomorphism of molecular graphs. If immediate agreement between compound descriptions is not found, the algorithm derives the minimal set of simplifications required for both descriptions to arrive to a matching form (if any). The proposed algorithm is used to cross-check chemical descriptions from the Crystallography Open Database to identify coherently described entries as well as those requiring further curation.

Ergodic decompositions of Dirichlet forms under order isomorphisms.

We study ergodic decompositions of Dirichlet spaces under intertwining via unitary order isomorphisms. We show that the ergodic decomposition of a quasi-regular Dirichlet space is unique up to a unique isomorphism of the indexing space. Furthermore, every unitary order isomorphism intertwining two quasi-regular Dirichlet spaces is decomposable over their ergodic decompositions up to conjugation via an isomorphism of the corresponding indexing spaces.

Development of an open-source software for isomer enumeration.

This article documents enu, a freely-downloadable, open-source and stand-alone program written in C++ for the enumeration of the constitutional isomers and stereoisomers of a molecular formula. The program relies on graph theory to enumerate all the constitutional isomers of a given formula on the basis of their canonical adjacency matrix. The stereoisomers of a given constitutional isomer are enumerated as well, on the basis of the automorphism group of this matrix. The isomer list is then reported in the form of canonical SMILES strings within files in XML format. The specification of the molecule family of interest is very flexible and the code is optimized for computational efficiency. The algorithms and implementations underlying enu are described, and simple illustrative applications are presented. The enu code is freely available on GitHub at https://github.com/csms-ethz/CombiFF .

Structural properties and isomorphism theorems for Cayley digraphs of full transformation semigroups with respect to Green's equivalence classes.

Let T ( X ) be the full transformation semigroup on a nonempty set X. In this paper, the Cayley digraphs of T ( X ) with connection sets L and R, the Green's equivalence classes of T ( X ) according to the Green's relations L and R , are investigated. Furthermore, their connectedness properties are characterized. In addition, the isomorphism theorems for Cayley digraphs of T ( X ) are also presented.

Effect of MgO on crystallization behavior of MnO-SiO2-Al2O3 based inclusions in tire cord steel.

Increasingly MgO content in refining slag and molten steel is the major cause of corrosion of refractory materials at slag line during the refining process of Si-Mn deoxidized tire cord steel, the effects on the crystallization behavior of typical MnO-SiO2-Al2O3 based inclusions in tire cord steel were studied. The relation between the formation of high melting point and high hardness inclusions (Al2O3, 3Al2O3·2SiO2, MgO·Al2O3 and 2MgO·SiO2) with MnO-SiO2-Al2O3-(0wt.%, 4wt.%, 8wt.%, 12wt.%, 16wt.%) MgO system was verified by ICP, XRD, SEM-EDS and FactSage 8.0. The results indicated that the crystallization temperature of MnO-SiO2-Al2O3 system increases with the increase of MgO content. Mg x Mn(2-x)SiO4(1 ≤ x < 2) solid solution is the main crystalline phase due to isomorphism. Only a small amount of 2MgO·SiO2 which is not enough to cause wire breakage was formed in the trial with the highest MgO content (16wt.%) after the low melting point MnO-SiO2-Al2O3 composition added different MgO content and the other three kinds of inclusions were not produced in the five trials.

iADRGSE: A Graph-Embedding and Self-Attention Encoding for Identifying Adverse Drug Reaction in the Earlier Phase of Drug Development.

Adverse drug reactions (ADRs) are a major issue to be addressed by the pharmaceutical industry. Early and accurate detection of potential ADRs contributes to enhancing drug safety and reducing financial expenses. The majority of the approaches that have been employed to identify ADRs are limited to determining whether a drug exhibits an ADR, rather than identifying the exact type of ADR. By introducing the "multi-level feature-fusion deep-learning model", a new predictor, called iADRGSE, has been developed, which can be used to identify adverse drug reactions at the early stage of drug discovery. iADRGSE integrates a self-attentive module and a graph-network module that can extract one-dimensional sub-structure sequence information and two-dimensional chemical-structure graph information of drug molecules. As a demonstration, cross-validation and independent testing were performed with iADRGSE on a dataset of ADRs classified into 27 categories, based on SOC (system organ classification). In addition, experiments comparing iADRGSE with approaches such as NPF were conducted on the OMOP dataset, using the jackknife test method. Experiments show that iADRGSE was superior to existing state-of-the-art predictors.

TUCAN: A molecular identifier and descriptor applicable to the whole periodic table from hydrogen to oganesson.

TUCAN is a canonical serialization format that is independent of domain-specific concepts of structure and bonding. The atomic number is the only chemical feature that is used to derive the TUCAN format. Other than that, the format is solely based on the molecular topology. Validation is reported on a manually curated test set of molecules as well as a library of non-chemical graphs. The serialization procedure generates a canonical "tuple-style" output which is bidirectional, allowing the TUCAN string to serve as both identifier and descriptor. Use of the Python NetworkX graph library facilitated a compact and easily extensible implementation.

The number of boundedly rational choices on four elements.

We use a combinatorial approach to identify and compute the number of non-isomorphic choices on four elements that can be explained by several models of bounded rationality. •These estimates offer a tool to analyze choice experiments designed on four-element sets.•The presented methodology allows the application of an algorithm to estimate the fraction of choices justifiable by these models on finite sets.•Our approach can be extended to evaluate other - existing or future - models of bounded rationality.

NASMDR: a framework for miRNA-drug resistance prediction using efficient neural architecture search and graph isomorphism networks.

As a frontier field of individualized therapy, microRNA (miRNA) pharmacogenomics facilitates the understanding of different individual responses to certain drugs and provides a reasonable reference for clinical treatment. However, the known drug resistance-associated miRNAs are not yet sufficient to support precision medicine. Although existing methods are effective, they all focus on modelling miRNA-drug resistance interaction graphs, making their performance bounded by the interaction density. In this study, we propose a framework for miRNA-drug resistance prediction through efficient neural architecture search and graph isomorphism networks (NASMDR). NASMDR uses attribute information instead of the commonly used interactive graph information. In the cross-validation experiment, the proposed framework can achieve an AUC of 0.9468 on the ncDR dataset, which is 2.29% higher than the state-of-the-art method. In addition, we propose a novel sequence characterization approach, k-mer Sparse Nonnegative Matrix Factorization (KSNMF). The results show that NASMDR provides novel insights for integrating efficient neural architecture search and graph isomorphic networks into a unified framework to predict drug resistance-related miRNAs. The codes for NASMDR are available at https://github.com/kaizheng-academic/NASMDR.

Identification of Young High-Functioning Autism Individuals Based on Functional Connectome Using Graph Isomorphism Network: A Pilot Study.

Accumulated studies have determined the changes in functional connectivity in autism spectrum disorder (ASD) and spurred the application of machine learning for classifying ASD. Graph Neural Network provides a new method for network analysis in brain disorders to identify the underlying network features associated with functional deficits. Here, we proposed an improved model of Graph Isomorphism Network (GIN) that implements the Weisfeiler-Lehman (WL) graph isomorphism test to learn the graph features while taking into account the importance of each node in the classification to improve the interpretability of the algorithm. We applied the proposed method on multisite datasets of resting-state functional connectome from Autism Brain Imaging Data Exchange (ABIDE) after stringent quality control. The proposed method outperformed other commonly used classification methods on five different evaluation metrics. We also identified salient ROIs in visual and frontoparietal control networks, which could provide potential neuroimaging biomarkers for ASD identification.

Interval Valued Intuitionistic Fuzzy Line Graphs.

OBJECTIVES: In the field of graph theory, an intuitionistic fuzzy set becomes a useful tool to handle problems related to uncertainty and impreciseness. We introduced the interval-valued intuitionistic fuzzy line graphs (IVIFLG) and explored the results related to IVIFLG. RESULT: Some propositions and theorems related to IVIFLG are proposed and proved, which are originated from intuitionistic fuzzy graphs (IVIG). Furthermore, Isomorphism between two IVIFLGs toward their IVIFGs was determined and verified.

Entropy Treatment of Evolution Algebras.

In this paper, by introducing an entropy of Markov evolution algebras, we treat the isomorphism of S-evolution algebras. A family of Markov evolution algebras is defined through the Hadamard product of structural matrices of non-negative real S-evolution algebras, and their isomorphism is studied by means of their entropy. Furthermore, the isomorphism of S-evolution algebras is treated using the concept of relative entropy.