Interaction Patterns Research Articles

Elucidating hippocampal proteome dynamics in moderate hepatic encephalopathy rats: insights from high-resolution mass spectrometry.

Hepatic encephalopathy (HE) is a debilitating neurological disorder associated with liver failure and characterized by impaired brain function. Decade-long studies have led to significant advances in our understanding of HE; however, effective therapeutic management of HE is lacking, and HE continues to be a significant cause of morbidity and mortality in patients, underscoring the need for continued research into its pathophysiology and treatment. Accordingly, the present study provides a comprehensive overview aimed at elucidating the molecular underpinnings of HE and identifying potential therapeutic targets. A moderate-grade HE model was induced in rats using thioacetamide, which simulates the liver damage observed in patients, and its impact on cognitive function, neuronal arborization, and cellular morphology was also evaluated. We employed label-free LC-MS/MS proteomics to quantitatively profile hippocampal proteins to explore the molecular mechanism of HE pathogenesis; 2175 proteins were identified, 47 of which exhibited significant alterations in moderate-grade HE. The expression of several significantly upregulated proteins, such as FAK1, CD9 and Tspan2, was further validated at the transcript and protein levels, confirming the mass spectrometry results. These proteins have not been previously reported in HE. Utilizing Metascape, a tool for gene annotation and analysis, we further studied the biological pathways integral to brain function, including gliogenesis, the role of erythrocytes in maintaining blood-brain barrier integrity, the modulation of chemical synaptic transmission, astrocyte differentiation, the regulation of organ growth, the response to cAMP, myelination, and synaptic function, which were disrupted during HE. The STRING database further elucidated the protein‒protein interaction patterns among the differentially expressed proteins. This study provides novel insights into the molecular mechanisms driving HE and paves the way for identifying novel therapeutic targets for improved disease management.

Hierarchical Alignment with Polar Contrastive Learning for Next-Basket Recommendation

Next-basket recommendation methods focus on the inference of the next basket by considering the corresponding bas- ket sequence. Although many methods have been developed for the task, they usually suffer from data sparsity. The number of inter- actions between entities is relatively small compared to their huge bases, so it is crucial to mine as much hidden information as possible from the limited historical interactions for prediction. However, the existing methods mainly just treat the next-basket recommendation task as a single-view sequential prediction problem, which leads to the inadequate mining of the information hidden in multiple views, and the mining of other patterns in the historical interactions is neglected, thus making it difficult to learn high-quality representa- tions and limiting the recommendation effect. To alleviate the above issues, we propose a novel method named Hap CL for next-basket recommendation, which mines information from multiple views and patterns with the help of polar contrastive learning. A hier- archical module is designed to mine multiple patterns of historical interactions from different views at two levels. In order to mine self- supervised signals, we design a polar contrastive learning module with a novel graph-based augmentation approach. Experiments on three real-world datasets validate the effectiveness of Hap CL.

Applying the human component model of parenting to other primates: Developmental patterns of mother-child interactions across primate species

The component model of human parenting has been extensively used to study parents’ interactions with their offspring and to examine variation across cultural contexts. The current study applies this model to nonhuman primates to investigate which forms of parenting humans share with other primates and how these interactions change over infants’ first year of life. We repeatedly observed 52 mother-infant pairs, including humans ( N = 11), chimpanzees and bonobos ( N = 21), and several species of small apes ( N = 20), during different daily activities when infants were 1, 6, and 12 months of age. Humans differed from apes in their higher probability of face-to-face contact and the use of object stimulation. Moreover, parenting seemed to be characterized by more variability within humans than within and possibly between ape species. Overall, the component model of parenting appears to be an effective tool to study the functional systems of parenting behavior in a comparative developmental perspective, by allowing direct comparisons between human and non-human primate species across development.

Altered microbial diversity, interaction pattern, and nitrogen transformation processes response to different iron addition amounts in constructed wetlands

Microbial interactions affect the biogeochemical cycles, pollutant transformation, and stable operation of constructed wetlands (CWs), and these interaction networks are vulnerable to environmental stress. Iron (Fe)-based CWs have been demonstrated as an intensified nitrogen (N) removal system, however, whether and how the addition of Fe, an exogenous environmental stressor, and its amount affect the microbial interactions and functions are poorly understood, which inevitably determine the stability and sustainability of CW system. Herein, we demonstrated that higher Fe addition could effectively improve the stability and sustainability of the CW system by regulating the diversity and interactions of the microbial communities rather than the relative abundance of functional genes. Specifically, low Fe-addition reduced the complexity and robustness of microbial network and the number of keystone taxa, while the high Fe-addition CWs showed the opposite pattern. In CWs with low Fe-addition, N-Fe cycles were accomplished by increasing competition represented by a high proportion of negative links between the same function, whereas increased cooperation was observed in CWs with high Fe-addition. Moreover, Fe addition shaped divergent N transformation processes, with denitrification-dissimilatory nitrate reduction to ammonium (DNRA) dominating in control group, and nitrification-partial denitrification/anammox (PD/A) dominating in Fe-based CWs, and the intensity of nitrification-PD/A increased with the addition amount of Fe. Our study provided insights into the potential ways to improve the stability and sustainability of Fe-CWs for intensified pollutant elimination, which would be valuable to advance the design, sustainable operation, and application of Fe-CWs.

A social network analysis approach to assess COVID19-related disruption to substance use treatment and informal social interactions among people who use drugs in Scotland.

To assess the extent of Coronavirus-related disruption to health and social care treatment and social interactions among people with lived or living experience of substance use in Scotland, and explore potential reasons for variations in disruption. Cross sectional mixed methods interview, incorporating a social network 'egonet interview' approach asking about whether participants had interactions with a range of substance use, health, social care or third sector organisations, or informal social interactions. Five Alcohol and Drug Partnership Areas in Scotland. 57 (42% women) participants were involved in the study, on average 42 years old. Five-point Likert scale reporting whether interactions with a range of services and people had gotten much better, better, no different (or no change), worse, or much worse since COVID19 and lockdown. Ratings were nested within participants (Individuals provided multiple ratings) and some ratings were also nested within treatment service (services received multiple ratings). The nested structure was accounted for using cross classified ordinal logistic multilevel models. While the overall average suggested only a slight negative change in interactions (mean rating 2.93), there were substantial variations according to type of interaction, and between individuals. Reported change was more often negative for mental health services (Adjusted OR = 0.93 95% CI 0.17,0.90), and positive for pharmacies (3.03 95% CI 1.36, 5.93). The models found between-participant variation of around 10%, and negligible between-service variation of around 1% in ratings. Ratings didn't vary by individual age or gender but there was variation between areas. Substance use treatment service adaptations due to COVID19 lockdown led to both positive and negative service user experiences. Social network methods provide an effective way to describe complex system-wide interaction patterns, and to measure variations at the individual, service, and area level.

A potent Henipavirus cross-neutralizing antibody reveals a dynamic fusion-triggering pattern of the G-tetramer

The Hendra and Nipah viruses (HNVs) are highly pathogenic pathogens without approved interventions for human use. In addition, the interaction pattern between the attachment (G) and fusion (F) glycoproteins required for virus entry remains unclear. Here, we isolate a panel of Macaca-derived G-specific antibodies that cross-neutralize HNVs via multiple mechanisms. The most potent antibody, 1E5, confers adequate protection against the Nipah virus challenge in female hamsters. Crystallography demonstrates that 1E5 has a highly similar binding pattern to the receptor. In cryo-electron microscopy studies, the tendency of 1E5 to bind to the upper or lower heads results in two distinct quaternary structures of G. Furthermore, we identify the extended outer loop β1S2-β1S3 of G and two pockets on the apical region of fusion (F) glycoprotein as the essential sites for G-F interactions. This work highlights promising drug candidates against HNVs and contributes deeper insights into the viruses.

Remapping the Document Object Model using Geometric and Hierarchical Data Structures for Efficient Eye Control

The Web Content Accessibility Guidelines (WCAG) are there to ensure that websites are perceivable, operable, understandable and robust across different user agents and assistive technologies. However, people who rely on eye trackers (ETs) may find that even WCAG-compliant websites are hard to access, and this is further accentuated by designs that offer little to no affordances for ET interaction. Areas with a high density of interactive elements, along with hierarchical navigation menus, such as megamenus or fly-out menus, are just two examples where ET interaction can be problematic. This paper introduces two novel interaction patterns as part of a purpose-built gaze-native web browser (Cactus), namely (a) Quadtree-based Target Selection with Secondary Confirmation and (b) Hierarchical Re-rendering of Navigation Menus. We present results from a between-subject single-blind study with 30 participants and report on metrics such as performance, perceived workload and usability, with demonstrable improvements over the state of the art.

Molecular recognition between volatile molecules and odorant binding proteins 7 by homology modeling, molecular docking and molecular dynamics simulation.

Odorant-binding proteins (OBPs) in insects are key to detection and recognition of external chemical signals associated with survival. OBP7 in Spodoptera frugiperda's larval stage (SfruOBP7) may search for host plants by sensing plant volatiles, which are important sources of pest attractants and repellents. However, the atomic-level basis of binding modes remains elusive. SfruOBP7 structure was constructed through homology modeling, and complex models of six plant volatiles ((E)-2-hexenol, α-pinene, (Z)-3-hexenyl acetate, lauric acid, O-cymene and 1-octanol) and SfruOBP7 were obtained through molecular docking. To study the detailed interactions between the six plant volatile molecules and SfruOBP7, we conducted three 300 ns molecular dynamics simulations for each study object. The correlation coefficients between binding free energy obtained by molecular mechanics/generalized Born surface area together with solvated interaction energy methods and experimental values are 0.90 and 0.88, respectively, showing a good correlation. By comparing binding free energy along with interaction patterns between SfruOBP7 and the six volatile molecules, hotspot residues of SfruOBP7 when binding with different volatile molecules were determined. Hydrophobic interactions stemming from van der Waals interactions play a significant role in SfruOBP7 and these plant volatile systems. The optimized three-dimensional structure of SfruOBP7 and its binding modes with six plant volatiles revealed their interactions, thus providing a means for estimating the binding energies of other plant volatiles. Our study will help to guide the rational design of effective and selective insect attractants. © 2024 Society of Chemical Industry.

Mechanism controlling groundwater chemistry in the hyper-arid basin with intermittent river flow: insights from long-term observations (2001–2023) in the lower Heihe River, Northwest China

The geochemical processes of groundwater in arid regions are generally influenced by both natural hydrological processes and human activities. However, impacts of water-rock interactions on groundwater recharge via hydrological processes, controlled by both intermittent river water flow and groundwater withdrawals, is still poorly understood. In this study, 327 groundwater chemistry datasets collected from the upper, middle (including Gobi and riparian zones), and lower regions of the Ejina Delta in Northwest China from 2001 to 2023 were analyzed. Our results revealed that the total dissolved solids (TDS) concentration of groundwater in Ejina Delta ranged from approximately 881.5 ± 331.6 mg/L in the upper regions to 1,953.6 ± 1,208.5 mg/L in the lower regions, with an increasing trend observed. Ecological water conveyance (EWC), recharging aquifer through intermittent river water flow, resulted in a decrease in TDS concentrations from 2001 to 2023 mainly in the upper region. While irrigation notably affected groundwater chemistry in the lower region, resulting in a substantial increase in groundwater salinity. Groundwater chemistry in the Middle Gobi region remained relatively stable over the study period. Generally, the hydrochemical composition shifted from the Na-Mg-SO4-HCO3 and Na-Mg-Ca-SO4-HCO3 types in the upper region to Na-Mg-SO4-HCO3 and Na-Mg-SO4-Cl types in the lower region, with Na-SO4-Cl predominant in the Middle Gobi. These shifts were likely be attributed to the interplay of water-rock interactions, coupled with evaporation-crystallization processes. Inverse modeling using PHREEQC revealed that in the upper-middle region, primary water-rock interactions involved calcite dissolution and the precipitation of dolomite, gypsum, halite, and sylvite salts, as well as cation exchange reactions (2NaX+Ca2+→CaX2+2Na+). In contrast, the hydrogeological system in the middle-lower region exhibited an opposite pattern of water-rock interactions. Overall, ecological water conveyance partially facilitated water-rock interactions during lateral groundwater flow, while irrigation disrupted the natural hydrogeochemical equilibrium, involving halite dissolution and opposite cation exchange reactions compared to other regions.

Microencapsulation of Origanum heracleoticum L. and Thymus vulgaris L. essential oils – Novel strategy to combat multi-resistant Acinetobacter baumannii

Within the global rise of antimicrobial resistance enhanced by the COVID-19 pandemic, where Acinetobacter baumannii has been distinguished as an emerging multi-resistant pathogen, essential oils, become the focus of novel therapeutic approaches. Hypothesizing that encapsulated Origanum heracleoticum L. and Thymus vulgaris L. essential oils could express multi-target approach against A. baumannii, this study aimed to develop microencapsulated systems with optimal technological qualities using 2-hydroxypropyl-β-cyclodextrin as a carrier, and to evaluate their pharmacological potential against A. baumannii, and their morphological and physicochemical characteristics, safety and stability profiles. The highest yield and encapsulation efficiency were obtained with 1:10 essential oil to carrier, and 1.5:10 carrier to water w/w ratios. The formation of inclusion complexes was confirmed by Fourier Transform Infrared Spectroscopy. Both microencapsulates achieved improved homogeneity, particle surface, and thermal stability compared with the pure carrier. Dominant bioactive compounds (carvacrol and p-cymene in O. heracleoticum essential oil, and thymol and p-cymene in T. vulgaris essential oil) remained the most abundant after encapsulation. While non-encapsulated essential oils revealed similar antimicrobial activity towards clinical A. baumannii isolates obtained from COVID-19 patients, encapsulated O. heracleoticum essential oil inhibited the bacterial growth at lower concentrations than T. vulgaris essential oil microencapsulate. All samples significantly reduced the formation of A. baumannii biofilm, for at least 53.90% towards the most infective isolate according to the Caenorhabditis elegans assay. Further, in silico molecular docking study revealed strong interaction pattern of carvacrol and thymol with the outer membrane protein A, which is the main factor for the A. baumannii biofilm formation. Cytotoxicity investigation on human lung A549 cells showed high survival rate in the presence of all tested concentrations, and the stability study revealed notable preservation of the bioactives’ content and pharmacological potential. Altogether, microencapsulated essential oils exhibited a multi-target approach towards A. baumannii, with satisfactory preserving capability during storage.

Mathematics teachers' interaction patterns and role changes in online research-practice partnerships: A social network analysis

Research–Practice Partnerships (RPPs), in which researchers and teachers come together with the shared aim of driving educational improvement, have lately gained recognition in the education community. Whilst studies have demonstrated the positive effects of RPPs, potential challenges in establishing long-term and mutually beneficial RPPs have been reported. To understand these challenges, the present study explores dynamic changes in team interaction patterns and roles using Social Network Analysis (SNA), then employs qualitative analysis to elaborate the mechanisms underlying these changes and resulting outcomes. The visual and quantitative results of the SNA reveal that interaction patterns shifted from a researcher-centered (hierarchical) pattern to a decentralized pattern, with increasing numbers of participants exhibiting a higher level of activity and exchange over time across the four discussions in a RPP. As for role changes, researchers played more of a leading role in the first two discussions, while teachers played a more equal role in subsequent discussions. The qualitative findings identify some key factors that lead to the changes in interaction patterns and roles, including: the process of negotiating meaning through lesson study; the establishment of trust between participants; and a concern for how role changes may positively impact teachers' and researchers’ professional development. These findings thus offer insights into how to create more effective and long-lasting RPPs.

Link prediction method for social networks based on a hierarchical and progressive user interaction matrix

Link prediction within social networks represents a notable research area. Leveraging semantic text and multidimensional user interaction features, this paper introduces a hierarchical and progressive user interaction matrix-based link prediction method in social networks. First, considering the dynamic nature of a user’s semantic text features, the word2vec algorithm was employed to extract text features. Given the long short-term memory algorithm’s proficiency in learning long-term sequence dependencies, we proposed a method to represent time-series text called “TT2vec” in order to capture the evolution pattern of a user’s semantic text features. Second, to address the multidimensionality of user interactions, we constructed a hierarchical and progressive interaction network adjacency matrix, thereby deriving advanced user interaction features using an enhanced graph convolutional network. Then, using TT2vec to further capture the interaction network’s evolution, we proposed HPIN2vec, which is a method for representing hierarchical and progressive user interaction networks. Finally, for the users’ semantic text features and multidimensional interaction features, we proposed a link prediction method based on graph attention networks (GATs) and users’ dynamic features, considering the advantage of GAT aggregating neighbor node features. Experimental findings indicated that the proposed method not only identifies immediate dynamics in user features but also enhances link prediction accuracy through complex hierarchical and progressive interaction patterns.

Interaction of Statherin-Derived Peptide with the Surface of Hydroxyapatite: Perspectives Based on Molecular Dynamics Simulations

Introduction: Statherin-derived peptide (StatpSpS) has shown promise against erosive tooth wear. To elucidate its interaction with the hydroxyapatite (HAP) surface, the mechanism related to adsorption of this peptide with HAP was investigated through nanosecond-long all-atom molecular dynamics simulations. Methods: StatpSpS was positioned parallel to the HAP surface in 2 orientations: 1 – neutral and negative residues facing the surface and 2 – positive residues facing the surface. A system containing StatpSpS without HAP was also simulated as control. In the case of systems with HAP, both partially restrained surface and unrestrained surface were constructed. Structural analysis, interaction pattern, and binding-free energy were calculated. Results: In the peptide system without the HAP, there were some conformational changes during the simulation. In the presence of the surface, only moderate changes were observed. Many residues exhibited short and stable distances to the surface, indicating strong interaction. Specially, the residues ASP1 and SER2 have an important role to anchor the peptide to the surface, with positively charged residues, mainly arginine, playing a major role in the further stabilization of the peptide in an extended conformation, with close contacts to the HAP surface. Conclusion: The interaction between StatpSpS and HAP is strong, and the negative charged residues are important to the anchoring of the peptide in the surface, but after the initial placement the peptide rearranges itself to maximize the interactions between positive charged residues.

A Coordinated Data Analysis of Four Studies Exploring Age Differences in Social Interactions and Loneliness During a Global Pandemic.

Examining loneliness and social isolation during population-wide historical events may shed light on important theoretical questions about age differences, including whether these differences hold across different regions and the timecourse of the unfolding event. We used a systematic, preregistered approach of coordinated data analysis (CDA) of four studies (total N = 1,307; total observations = 18,492) that varied in design (intensive repeated-measures and cross-sectional), region, timing, and timescale during the first year of the COVID-19 pandemic. We harmonized our datasets to a common period within 2020-2021 and created a common set of variables. We used a combination of ordinary least squares regression and multilevel modeling to address the extent to which there was within- and between-person variation in the associations between social isolation and loneliness, and whether these associations varied as a function of age. Within- and between-person effects of social interactions were negatively associated with loneliness in one study; in follow-up sensitivity analyses, these patterns held across early and later pandemic periods. Across all datasets, there was no evidence of age differences in the within-person or between-person associations of social interactions and loneliness. Applying the CDA methodological framework allowed us to detect common and divergent patterns of social interactions and loneliness across samples, ages, regions, periods, and study designs.

Integrative analysis discovers Imidurea as dual multitargeted inhibitor of CD69, CD40, SHP2, lysozyme, GATA3, cCBL, and S-cysteinase from SARS-CoV-2 and M. tuberculosis

Two of the deadliest infectious diseases, COVID-19 and tuberculosis (TB), have combined to establish a worldwide pandemic, wreaking havoc on economies and claiming countless lives. The optimised, multitargeted medications may diminish resistance and counter them together. Based on computational expression studies, 183 genes were co-expressed in COVID-19 and TB blood samples. We used the multisampling screening algorithms on the top ten co-expressed genes (CD40, SHP2, Lysozyme, GATA3, cCBL, SIVmac239 Nef, CD69, S-adenosylhomocysteinase, Chemokine Receptor-7, and Membrane Protein). Imidurea is a multitargeted inhibitor for COVID-19 and TB, as confirmed by extensive screening and post-filtering utilising MM\\GBSA algorithms. Imidurea has shown docking and MM\\GBSA scores of −8.21 to −4.75 Kcal/mol and −64.16 to −29.38 Kcal/mol, respectively. The DFT, pharmacokinetics, and interaction patterns suggest that Imidurea may be a drug candidate, and all ten complexes were tested for stability and bond strength using 100 ns for all MD atoms. The modelling findings showed the complex's repurposing potential, with a cumulative deviation and fluctuation of <2 Å and significant intermolecular interaction, which validated the possibilities. Finally, an inhibition test was performed to confirm our in-silico findings on SARS-CoV-2 Delta variant infection, which was suppressed by adding imidurea to Vero E6 cells after infection.

Isoflavones and lysozyme interplay: Molecular insights into binding mechanisms and inhibitory efficacies of isoflavones against protein modification

In this study, we investigated the complexation of bioactive isoflavones, specifically biochanin A (BCA) and genistein (GEN), with hen egg white lysozyme (HEWL) and explored their inhibitory effects on HEWL modification using a combination of multi-spectroscopic and computational methods. The observed binding affinity was of a moderate nature (on the order of 104 M−1), and a static quenching mechanism was identified in the fluorescence quenching process. Notably, the binding constant (Kb) for GEN (4.449 ± 0.262 × 104 M−1) was found to be higher than that for BCA (3.707 ± 0.108 × 104 M−1) towards HEWL. Our spectroscopic measurements, complemented by molecular docking calculations, suggested the involvement of Trp62 in the binding site of the isoflavones within the geometry of HEWL. The micro-environment surrounding the Trp-residues exhibited an increase in hydrophilicity, as indicated by Synchronous fluorescence (SFS) and three-dimensional fluorescence (3D) studies. Interestingly, circular dichroism (CD) studies revealed no marked alteration in the secondary structure of HEWL upon binding with the isoflavones. Furthermore, our investigation into the interaction patterns, employing FTIR and molecular docking studies, revealed a predominance of hydrogen bonding and hydrophobic interactions. Beyond the binding study, the isoflavones demonstrated a promising inhibitory effect on the d-ribose-mediated glycation of HEWL, as well as on HEWL fibrillation, as evidenced by fluorescence emission studies. Our findings not only exhibited an excellent correlation with experimental observations but also provided precise insights into the location and dynamics of isoflavones within the binding site through detailed analyses of molecular docking and molecular dynamics simulation data.

Understanding the interactions between repurposed drugs sertindole and temoporfin with receptor for advanced glycation endproducts: Therapeutic implications in cancer and metabolic diseases.

In the pursuit of novel therapeutic possibilities, repurposing existing drugs has gained prominence as an efficient strategy. The findings from our study highlight the potential of repurposed drugs as promising candidates against receptor for advanced glycation endproducts (RAGE) that offer therapeutic implications in cancer, neurodegenerative conditions and metabolic syndromes. Through careful analyses of binding affinities and interaction patterns, we identified a few promising candidates, ultimately focusing on sertindole and temoporfin. These candidates exhibited exceptional binding affinities, efficacy, and specificity within the RAGE binding pocket. Notably, they displayed a pronounced propensity to interact with the active site of RAGE. Our investigation further revealed that sertindole and temoporfin possess desirable pharmacological properties that highlighted them as attractive candidates for targeted drug development. Overall, our integrated computational approach provides a comprehensive understanding of the interactions between repurposed drugs, sertindole and temoporfin and RAGE that pave the way for future experimental validation and drug development endeavors. We present an integrated approach utilizing molecular docking and extensive molecular dynamics (MD) simulations to evaluate the potential of FDA-approved drugs, sourced from DrugBank, against RAGE. To gain deeper insights into the binding mechanisms of the elucidated candidate repurposed drugs, sertindole and temoporfin with RAGE, we conducted extensive all-atom MD simulations, spanning 500nanoseconds (ns). These simulations elucidated the conformational dynamics and stability of the RAGE-sertindole and RAGE-temoporfin complexes.

Comparison of additive main effect–multiplicative interaction model and factor analytic model for genotypes ordination from multi‐environment trials

AbstractAn additive main effects and multiplicative interaction (AMMI) model is used to explore the genotype × environment interaction (GEI) in complete multi‐environmental trials. This model orders genotypes (G) according to their performance across environments (E) on a vectorial plane generated by the first two axes of a principal component analysis (AMMI‐biplot). Alternatively, interaction terms can be regarded as random effects, which can be predicted from linear mixed models using a factor analytic (FA) covariance structure for the GEI terms. Here, an FA‐biplot was obtained by plotting the G and E scores derived from the FA mixed model with complete and incomplete data. The aim of this work was to compare AMMI‐biplot with FA‐biplot for balanced data and then show the impact of the imbalance on the FA‐biplot. The G ordinations were assessed in four scenarios generated using datasets of 3 consecutive years obtained from comparative wheat trials conducted under a complete random block design in different environments across the Argentine network of cultivar assessment. For each scenario, G with the lowest performance in the third year were deleted, one by one, from all sites to generate a scenario with missing G. Although we used different statistical procedures to obtain AMMI‐biplot and FA‐biplot, they showed the same interaction pattern in the case of up to 50% of G dropped from all E in the last year of the multiyear trials. We conclude that the FA‐biplot yields a robust G ordination even when with incomplete datasets.

Evolutionary Digital Twin-Oriented Complex Networked Systems driven by node features and the mutation of feature preferences.

Accurate modelling of complex social systems, where people interact with each other and those interactions change over time, has been a research challenge for many years. This study proposes an evolutionary Digital Twin-Oriented Complex Networked System (DT-CNS) framework that considers heterogeneous node features and changeable connection preferences. We create heterogeneous preference mutation mechanisms to characterise nodes' adaptive decisions on preference mutation in response to interaction patterns and epidemic risks. In this space, we use nodes' interaction utilities to characterise the positive feedback from interactions and negative impact of epidemic risks. We also introduce social capital constraint to harness the density of social connections better. The nodes' heterogeneous preference mutation styles include the (i)inactive style that keeps initial social preferences, (ii) ignorant style that randomly mutates preferences, (iii) egocentric style that optimises individual interaction utility, (iv) cooperative style that optimises the total interaction utilities by group decisions and (v) collaborative style that further allows the cooperative nodes to transfer social capital. Our simulation experiments on evolutionary DT-CNSs reveal that heterogeneous preference mutation styles lead to various interaction and infection patterns. The results also show that (i) increasing social capital enables higher interactions but higher infection risks and uncertainty in decision-making; (ii) group decisions outperform individual decisions by eliminating the unawareness of the decisions of other nodes; (iii) the collaborative nodes under a strict social capital limit can promote interactions, reduce infection risks and achieve higher overall interaction utilities.

Interpretable online network dictionary learning for inferring long-range chromatin interactions.

Dictionary learning (DL), implemented via matrix factorization (MF), is commonly used in computational biology to tackle ubiquitous clustering problems. The method is favored due to its conceptual simplicity and relatively low computational complexity. However, DL algorithms produce results that lack interpretability in terms of real biological data. Additionally, they are not optimized for graph-structured data and hence often fail to handle them in a scalable manner. In order to address these limitations, we propose a novel DL algorithm called online convex network dictionary learning (online cvxNDL). Unlike classical DL algorithms, online cvxNDL is implemented via MF and designed to handle extremely large datasets by virtue of its online nature. Importantly, it enables the interpretation of dictionary elements, which serve as cluster representatives, through convex combinations of real measurements. Moreover, the algorithm can be applied to data with a network structure by incorporating specialized subnetwork sampling techniques. To demonstrate the utility of our approach, we apply cvxNDL on 3D-genome RNAPII ChIA-Drop data with the goal of identifying important long-range interaction patterns (long-range dictionary elements). ChIA-Drop probes higher-order interactions, and produces data in the form of hypergraphs whose nodes represent genomic fragments. The hyperedges represent observed physical contacts. Our hypergraph model analysis has the objective of creating an interpretable dictionary of long-range interaction patterns that accurately represent global chromatin physical contact maps. Through the use of dictionary information, one can also associate the contact maps with RNA transcripts and infer cellular functions. To accomplish the task at hand, we focus on RNAPII-enriched ChIA-Drop data from Drosophila Melanogaster S2 cell lines. Our results offer two key insights. First, we demonstrate that online cvxNDL retains the accuracy of classical DL (MF) methods while simultaneously ensuring unique interpretability and scalability. Second, we identify distinct collections of proximal and distal interaction patterns involving chromatin elements shared by related processes across different chromosomes, as well as patterns unique to specific chromosomes. To associate the dictionary elements with biological properties of the corresponding chromatin regions, we employ Gene Ontology (GO) enrichment analysis and perform multiple RNA coexpression studies.