MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal-organic frameworks utilizing graph convolutional networks.

Metal-organic frameworks (MOFs), are porous crystalline structures comprising of metal ions or clusters intricately linked with organic entities, displaying topological diversity and effortless chemical flexibility. These characteristics render them apt for multifarious applications such as adsorption, separation, sensing, and catalysis. Predominantly, the distinctive properties and prospective utility of MOFs are discerned post-manufacture or extrapolation from theoretically conceived models. For empirical researchers unfamiliar with hypothetical structure development, the meticulous crystal engineering of a high-performance MOF for a targeted application via a bottom-up approach resembles a gamble. For example, the precise pore limiting diameter (PLD), which determines the guest accessibility of any MOF cannot be easily inferred with mere knowledge of the metal ion and organic ligand. This limitation in bottom-up conceptual understanding of specific properties of the resultant MOF may contribute to the cautious industrial-scale adoption of MOFs.Consequently, in this study, we take a step towards circumventing this limitation by designing a new tool that predicts the guest accessibility-a MOF key performance indicator-of any given MOF from information on only the organic linkers and the metal ions. This new tool relies on clustering different MOFs in a galaxy-like social network, MOFGalaxyNet, combined with a Graphical Convolutional Network (GCN) to predict the guest accessibility of any new entry in the social network. The proposed network and GCN results provide a robust approach for screening MOFs for various host-guest interaction studies.

MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis.

The number of metal-organic frameworks (MOF) as well as the number of applications of this material are growing rapidly. With the number of characterized compounds exceeding 100,000, manual sorting becomes impossible. At the same time, the increasing computer power and established use of automated machine learning approaches makes data science tools available, that provide an overview of the MOF chemical space and support the selection of suitable MOFs for a desired application. Among the different data science tools, graph theory approaches, where data generated from numerous real-world applications is represented as a graph (network) of interconnected objects, has been widely used in a variety of scientific fields such as social sciences, health informatics, biological sciences, agricultural sciences and economics. We describe the application of a particular graph theory approach known as social network analysis to MOF materials and highlight the importance of community (group) detection and graph node centrality. In this first application of the social network analysis approach to MOF chemical space, we created MOFSocialNet. This social network is based on the geometrical descriptors of MOFs available in the CoRE-MOFs database. MOFSocialNet can discover communities with similar MOFs structures and identify the most representative MOFs within a given community. In addition, analysis of MOFSocialNet using social network analysis methods can predict MOF properties more accurately than conventional ML tools. The latter advantage is demonstrated for the prediction of gas storage properties, the most important property of these porous reticular networks.

RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm.

Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender systems do not evolve optimal clustering with sufficient accuracy over time. Moreover, the behavior history of the users is determined by their neighbours. The purpose of the time parameter for this system is to extend the time-based priority. This paper has been carried out a time-aware recommender systems based on memetic evolutionary clustering algorithm called RecMem for recommendations. In this system, clusters that evolve over time using the memetic evolutionary algorithm and extract the best clusters at every timestamp, and improve the memetic algorithm using the chaos criterion. The system provides appropriate suggestions to the user based on optimum clustering. The system uses optimal evolutionary clustering using item attributes for the cold-start item problem and demographic information for the cold start user problem. The results show that the proposed method has an accuracy of approximately 0.95, which is more effective than existing systems.

Machine Learning Approach to Community Detection in a High-Entropy Alloy Interaction Network.

There is a growing trend toward the use of interaction network methods and algorithms, including community-based detection methods, in various fields of science. The approach is already used in many applications, for example, in social sciences and health informatics to analyze behavioral patterns during the COVID-19 pandemic, protein-protein networks in biological sciences, agricultural science, economy, and so forth. This paper attempts to build interaction networks based on high-entropy alloy (HEA) descriptors in order to discover HEA communities with similar functionality. In addition, these communities could be leveraged to discover new alloys not yet included in the data set without any experimental laboratory effort. This research has been carried out using two community detection algorithms, the Louvain algorithm and the enhanced particle swarm optimization (PSO) algorithm. The data set, which is used in this paper, includes 90 HEAs and 6 descriptors. The results reveal 13 alloy communities, and the accuracy of the results is validated by the modularity. The experimental results show that the method with the PSO-based community detection algorithm can achieve alloy communities with an average accuracy improvement of 0.26 compared to the Louvain algorithm. Furthermore, some characteristics of HEAs, for example, their phase composition, could be predicted by the extracted communities. Also, the HEA phase composition has been predicted by the proposed method and achieved about 93% precision.

The contribution of beta-2 adrenergic, muscarinic and histamine (H1) receptors, calcium and potassium channels and cyclooxygenase pathway in the relaxant effect of Allium cepa L. on the tracheal smooth muscle.

ETHNOPHARMACOLOGICAL RELEVANCE: There are report regarding therapeutic effects for Allium cepa L. (A. cepa) in Iranian traditional medicine and the plant has showed anti-inflammatory, anti-allergic, anti-hyperglycemic, antioxidant, anti-cancer, anti-hypertension, anti-hypercholesterolemia and anti-asthmatic activities in previous studies. AIM OF THE STUDY: In this study, the contribution of ß2 adrenergic, muscarinic and histamine (H1) receptors, calcium and potassium channels, and cyclooxygenase pathway in the relaxant effect of A. cepa extract on tracheal smooth muscle (TSM) was assessed. MATERIALS AND METHODS: TSM was contracted by KCl (60 mM) or methacholine (10 µM) for 5 min and cumulative concentrations of A. cepa extract (2, 4, 8, 16, 32 and 64 mg/ml) were added to organ bath every 5 min. Theophylline (0.2, 0.4, 0.6 and 0.8 mM) as positive control, and saline (1 ml) as negative control were also examined in non-incubated tissues. The relaxant effect of A. cepa extract was examined on non-incubated and incubated TSM with propranolol, chlorpheniramine, diltiazem, atropine, glibenclamide and indomethacin. RESULTS: A. cepa showed concentration-dependent relaxant effects on non-incubated TSM contracted by KCl (60 mM) or methacholine (10 µM), (P < 0.01 to p < 0.001). There was no significant difference in the relaxant effects of A. cepa between non-incubated and incubated tissues with glibenclamide, atropine, chlorpheniramine and indomethacin. The plant extract showed significant lower relaxant effects in incubated TSM with propranolol and diltiazem compared to non-incubated tissues. EC50 values of A. cepa extract in incubated TSM with propranolol and diltiazem were significantly lower than non-incubated tissues (p < 0.001 and p < 0.05, respectively). The relaxant effects of different concentrations of the extract of A. cepa were not significantly different with those of theophylline. The concentrations of A. cepa extract and theophylline were significant correlated with their relaxant effects (p < 0.05 to p < 0.001). In incubated TSM with propranolol and diltiazem, concentration ratio minus one (CR-1) values was positive (2.65 ±â€¯0.63 and 1.28 ±â€¯0.43 respectively). CONCLUSION: The A. cepa extract showed relatively potent relaxant effect on TSM which was comparable to the effect of theophylline. The results showed that ß2-adrenergic stimulatory and/or calcium channel blockade are the possible mechanisms for the relaxant effects of the plant.

Adolescents and drug use: toward a more comprehensive approach.

The pattern and prevalence of drug use among adolescents is explored in a survey of 2131 high school students. Findings suggest that the use of drugs and alcohol by adolescents should be viewed as a complex phenomenon greatly influenced by psychosocial contexts. The importance of preventive measures and of social psychiatric approaches is emphasized, with direct psychiatric care reserved for compulsive users in whom psychopathology is clearly manifested.