Global Interaction Research Articles

A comprehensive two-hybrid analysis to explore the Legionella pneumophila effector-effector interactome.

Secreted bacterial effector proteins are typically viewed as modulators of host activity, entering the host cytosol to physically interact with and modify the activity of one or more host proteins in support of infection. A growing body of evidence suggests that a subset of effectors primarily function to modify the activities of other effectors inside the host. These "effectors of effectors" or metaeffectors are often identified through experimental serendipity during the study of canonical effector function against the host. We previously performed the first global effector-wide genetic interaction screen for metaeffectors within the arsenal of Legionella pneumophila, an intracellular bacterial pathogen with over 300 effectors. Here, using a high-throughput, scalable methodology, we present the first global interaction network of physical interactions between L. pneumophila effectors. This data set serves as a complementary resource to identify and understand both the scope and nature of non-canonical effector activity within this important human pathogen.

FHG-PR: a hybridized fuzzy-AHP and game theory model for assessing privacy risk on social media platforms

Social media platforms have become integral to our daily lives, enabling global connectivity and interaction. Current research provides general solutions for mitigating privacy risks, but it does not focus on particular platforms. However, the voluntary sharing of personal information on these platforms presents considerable privacy risks, highlighting the need for effective risk assessment mechanisms. This study presents a hybrid approach that uses the fuzzy Analytical Hierarchy Process (AHP) and game theory to estimate privacy risks on Meta and X. We used a fuzzy AHP to rank determinant variables based on surveys of social media users and professionals. These weighted criteria were then used in a Multi-Criteria Decision-Making (MCDM) framework using cooperative game theory to discover ways to lower privacy hazards. The model accurately analyzed the privacy threats on Meta and X, recommending alternate techniques for preventing breaches in data privacy. The cooperative game theory method allows stakeholders to collaborate on creating privacy-preserving measures. The findings emphasize the model’s ability to address platform-specific privacy threats, as well as its flexibility in other social media settings. The model’s potential for real-world application is demonstrated by its ability to provide practical risk reduction measures, which improve privacy protection for social media users.

Modulator-Directed Counterintuitive Catenation Control for Crafting Highly Porous and Robust Metal-Organic Frameworks with Record High SO2 Uptake Capacity.

Sulfur dioxide (SO2) is an important industrial feedstock that can be directly utilized or catalytically transformed to value-added chemicals such as sulfuric acid. The development of regenerable porous sorbents for the highly efficient storage and energy-minimal release of toxic SO2 operating under ambient conditions has attracted growing interest. Herein, we report the topology-guided construction of highly porous acs-type metal-organic frameworks (MOFs) through a counterintuitive modulator-directed catenation control approach. In contrast to the conventional modulator facilitated coordination competition that favors the thermodynamic catenated phase, we show that the elevation of modulator concentration can promote the formation of the noncatenated phase probably through a sublattice dissolution pathway. The assembly of a custom-designed trigonal prismatic triptycene-quinoxaline linker and trinuclear Fe3O cluster affords either the threefold catenated SJTU-219 or noncatenated SJTU-220 with desired acs net. Impressively, the synthetic approach is applicable to various metal ions, including Al3+, V3+, and even Ti4+. The noncatenated SJTU-220 exhibits an extraordinary SO2 sorption capacity of 29.6 mmol g-1 at 298 K and 1 bar, surpassing all reported solid sorbents. The uptake capacity can be further raised to 35.6 mmol g-1 via the replacement of Fe3+ with kinetically more inert Cr3+, resulting in a staggering ∼329-fold volume reduction compared with free ideal SO2 gas. Computational simulations suggest that unique Fe3+···S(SO2) interactions dominate the SO2 seeding process, facilitating the efficient packing of SO2 molecules in the large channels. Besides, the exceptionally low uptake at the low pressure region implies global weak framework-SO2 interactions, which offer great potential for practically implementing an "easy-on/easy-off" SO2 delivery system.

Collectively Working towards Plant-Pollinator Interactions Data Interoperability and Reuse: Lessons Learned from the WorldFAIR Project

The WorldFAIR project worked with eleven case studies to advance the implementation of the FAIR data principles (Findable, Accessible, Interoperable, and Reusable; Wilkinson et al. 2016). The Case Study on Agricultural Biodiversity (Drucker et al. 2022) addressed the challenges of advancing interoperability and mobilizing plant-pollinator interaction data for reuse. In the first phase of the project we performed a landscape analysis and found that a significant amount of data on plant-pollinator interaction was available as supplementary files of research articles, in a range of formats such as PDFs, Excel® spreadsheets, and text files. The diversity of approaches and the lack of appropriate data vocabularies led to confusion, information loss, and the need for complex data interpretation and transformation. In the second phase of the project, we performed six studies to adopt standards recommended in the first phase. Our approach for plant-pollinator data standardization was based on Darwin Core (Darwin Core Task Group 2009) and Ecological Data Language (EML, Jones et al. 2019), in conjunction with a data model and vocabulary proposed by the Brazilian Network of Plant-Pollinator Interactions (REBIPP). The studies underwent a process of “FAIRification” (i.e., transforming data into a format that adheres to the FAIR data principles) using the Global Biotic Interactions (GloBI, Poelen et al. 2014) platform. Two studies also explored the publishing model for Biotic Interactions as part of the GBIF New Data Model. The standardization strategy was successful and increased the interoperability of plant-pollinator interaction data, resulting in a process that allows tracking the provenance of the data, as well as facilitates the reuse of datasets (Drucker et al. 2024). This effort led to the development of "FAIR best practices" and guidelines for sharing plant-pollinator interaction data, as well as a FAIR assessment tool designed to help researchers and institutions evaluate adherence to the FAIR principles (Drucker et al. 2024). A tutorial to standardize plant-pollinator datasets (Gonzalez-Vaquero et al. 2024) and a cookbook with guidelines and recommendations for publishing agriculture-related plant-pollinator data (Salim et al. 2024) were developed to help different actors standardize plant-pollinator interaction data in accordance with the FAIR principles. Moving from diverse approaches and siloed initiatives to widely available FAIR plant-pollination interaction data for scientists and decision-makers will enable the development of integrative studies that enhance our understanding of species biology, behavior, ecology, phenology, and evolution.

Global Co-regulatory Cross Talk Between m6A and m5C RNA Methylation Systems Coordinate Cellular Responses and Brain Disease Pathways.

N6 adenosine and C5 cytosine modification of mRNAs, tRNAs and rRNAs are regulated by the behaviour of distinct sets of writer, reader and eraser effector proteins which are conventionally considered to function independently. Here, we provide evidence of global cross-regulatory and functional interaction between the m6A and m5C RNA methylation systems. We first show that m6A and m5C effector protein transcripts are subject to reciprocal base modification supporting the existence of co-regulatory post-transcriptional feedback loops. Using global mass spectrometry proteomic data generated after biological perturbation to identify proteins which change in abundance with effector proteins, we found novel co-regulatory cellular response relationships between m6A and m5C proteins such as between the m6A eraser, ALKBH5, and the m5C writer, NSUN4. Gene ontology analysis of co-regulated proteins indicated that m6A and m5C RNA cross-system control varies across cellular processes, e.g. proteasome and mitochondrial mechanisms, and post-translational modification processes such as SUMOylation and phosphorylation. We also uncovered novel relationships between effector protein networks including contributing to intellectual disability pathways. Finally, we provided in vitro confirmation of colocalisation between m6A-RNAs and the m5C reader protein, ALYREF, after synaptic NMDA activation. These findings have important implications for understanding control of RNA metabolism, cellular proteomic responses, and brain disease mechanisms.

Enabling blockchain-IoT convergence for industrial revolution 4.0 innovation: A critical review

The Fourth Industrial Revolution (4IR) has revolutionised global interactions through the Internet of Things (IoT), connecting over 40 billion devices by 2030, raising significant security, protection, and privacy concerns. Blockchain technology offers a decentralised, secure, and transparent solution for data sharing and storage, leveraging its core characteristics—immutability, data integrity, transparency, and consensus—to enable tamper-proof records, real-time tracking, and secure data sharing in supply chain management and healthcare. Despite high implementation costs, blockchain-IoT convergence promises transformative benefits, including enhanced efficiency, reduced costs, and improved security. This study provides an in-depth analysis of blockchain's applications in IoT, highlighting its potential to enhance data security and privacy, improve supply chain management, facilitate secure healthcare data exchange, and enable real-time tracking and monitoring. Future research directions include scalability solutions, interoperability protocols, regulatory frameworks, blockchain-based supply chain management, and quantum-resistant blockchain networks.

Phosphorylation of disordered proteins tunes local and global intramolecular interactions.

Protein post-translational modifications, such as phosphorylation, are important regulatory signals for diverse cellular functions. In particular, intrinsically disordered protein regions (IDRs) are subject to phosphorylation as a means to modulate their interactions and functions. Toward understanding the relationship between phosphorylation in IDRs and specific functional outcomes, we must consider how phosphorylation affects the IDR conformational ensemble. Various experimental techniques are suited to interrogate the features of IDR ensembles; molecular simulations can provide complementary insights and even illuminate ensemble features that may be experimentally inaccessible. Therefore, we sought to expand the tools available to study phosphorylated IDRs by all-atom Monte Carlo simulations. To this end, we implemented parameters for phosphoserine (pSer) and phosphothreonine (pThr) into the OPLS version of the continuum solvent model, ABSINTH, and assessed their performance in all-atom simulations compared to published findings. We simulated short (< 20 residues) and long (> 80 residues) phospho-IDRs that, collectively, survey both local and global phosphorylation-induced changes to the ensemble. Our simulations of four well-studied phospho-IDRs show near-quantitative agreement with published findings for these systems via metrics including changes to radius of gyration, transient helicity, and persistence length. We also leveraged the inherent advantage of sequence control in molecular simulations to explore the conformational effects of diverse combinations of phospho-sites in two multi-phosphorylated IDRs. Our results support and expand on prior observations that connect phosphorylation to changes in the IDR conformational ensemble. Herein, we describe phosphorylation as a means to alter sequence chemistry, net charge and charge patterning, and intramolecular interactions, which can collectively modulate the local and global IDR ensemble features.

The Mediating Role of Employee Relations Climate in the Relationship Between Organizational Learning and Social Responsibility

Today, organizations face a rapidly changing environment, leading to numerous challenges that compel them to optimize their resources and assets. A crucial response to these changes is the heightened focus on human resources, which have become recognized as the most vital capital in gaining a competitive edge. By leveraging their greatest asset—human talent—organizations can significantly enhance productivity, with one effective strategy being the promotion of employee engagement. This research aims to explore the mediating role of the employee relations climate in the relationship between organizational learning and social responsibility. Utilizing a descriptive-correlational method, the study analyzed data through structural equation modeling. The findings suggest that increasing global interactions and deeper engagement with surrounding environments motivate organizations to undertake activities that foster competitive advantages, helping them adapt to complex and dynamic conditions. Many companies indicate that competitive pressures from globalization are intensifying, and an organization's capacity to manage these pressures hinges on its effective utilization of human resources. The ethical climate refers to the collective understanding among employees about appropriate behaviors and the management of ethical dilemmas within the organization. Concurrently, the employee relations climate emerges from shared beliefs about expected ethical conduct, influenced by the organization’s policies and leadership. This climate plays a pivotal role in shaping employees' ethical decisions and behaviors, creating a significant challenge for human resources as industries and knowledge demands evolve.

Attention Score Enhancement Model Through Pairwise Image Comparison

This study proposes the Pairwise Attention Enhancement (PAE) model to address the limitations of the Vision Transformer (ViT). While the ViT effectively models global relationships between image patches, it encounters challenges in medical image analysis where fine-grained local features are crucial. Although the ViT excels at capturing global interactions within the entire image, it may potentially underperform due to its inadequate representation of local features such as color, texture, and edges. The proposed PAE model enhances local features by calculating cosine similarity between the attention maps of training and reference images and integrating attention maps in regions with high similarity. This approach complements the ViT’s global capture capability, allowing for a more accurate reflection of subtle visual differences. Experiments using Clock Drawing Test data demonstrated that the PAE model achieved a precision of 0.9383, recall of 0.8916, F1-Score of 0.9133, and accuracy of 92.69%, showing a 12% improvement over API-Net and a 1% improvement over the ViT. This study suggests that the PAE model can enhance performance in computer vision fields where local features are crucial by overcoming the limitations of the ViT.

Feature fusion and context interaction for RGB-D indoor semantic segmentation

Depth information in indoor scenes is crucial to understanding the spatial and occluding relationships of objects. Depth information contains rich geometric details and is unaffected by lighting, texture, and color, significantly enhancing traditional color images. RGB images and depth images have been widely applied in various image analysis. However, effectively leveraging the complementary information from both modalities remains a challenge. To address this issue, we propose a unified and effective feature fusion and context interaction network (FCINet). It includes three modules. Firstly, we construct a multi-modal feature fusion module (MFFM), which can achieve the aggregation of two modalities along spatial and channel dimensions. Secondly, we construct a global and local information interaction context module (GLCM) to encode rich semantic information. Finally, we construct a feature alignment and fusion module (FAFM), which integrates upsampled low-level features with high-level features to mitigate spatial information loss. Experimental results indicate that the model achieved favorable outcomes on both the NYU Depth v2 dataset and the more complex SUN RGB-D dataset.

Educator capacity for online global collaborative learning: developing a framework

ABSTRACT Despite recent moves to online learning, online global collaborative learning does not seem to have been taken up as a daily practice by most educators in the K–12 context. Yet it is evident that early adopters are seeing the benefits of such practice, not only for students but for their own professional learning. The research described here uses single case-study methodology to investigate a small group of educators, located in a range of countries and with varied teaching experiences, who have identified the potential of global interactions. Data analysis, using manual and software-supported coding, identified three key influences on the educators’ personal pedagogies: their disposition to online learning, their approach to professional learning, and conceptual change. The findings were used to construct an online global collaborative learning framework, which has the potential to inform considerations about building educator capacity in digital pedagogical knowledge.

Graph Neural Network-Based Speech Emotion Recognition: A Fusion of Skip Graph Convolutional Networks and Graph Attention Networks

In speech emotion recognition (SER), our research addresses the critical challenges of capturing and evaluating node information and their complex interrelationships within speech data. We introduce Skip Graph Convolutional and Graph Attention Network (SkipGCNGAT), an innovative model that combines the strengths of skip graph convolutional networks (SkipGCNs) and graph attention networks (GATs) to address these challenges. SkipGCN incorporates skip connections, enhancing the flow of information across the network and mitigating issues such as vanishing gradients, while also facilitating deeper representation learning. Meanwhile, the GAT in the model assigns dynamic attention weights to neighboring nodes, allowing SkipGCNGAT to focus on both the most relevant local and global interactions within the speech data. This enables the model to capture subtle and complex dependencies between speech segments, thus facilitating a more accurate interpretation of emotional content. It overcomes the limitations of previous single-layer graph models, which were unable to effectively represent these intricate relationships across time and in different speech contexts. Additionally, by introducing a pre-pooling SkipGCN combination technique, we further enhance the ability of the model to integrate multi-layer information before pooling, improving its capacity to capture both spatial and temporal features in speech. Furthermore, we rigorously evaluated SkipGCNGAT on the IEMOCAP and MSP-IMPROV datasets, two benchmark datasets in SER. The results demonstrated that SkipGCNGAT consistently achieved state-of-the-art performance. These findings highlight the effectiveness of the proposed model in accurately recognizing emotions in speech, offering valuable insights and a solid foundation for future research on capturing complex relationships within speech signals for emotion recognition.

Global induced local network for infrared: dim small target detection

Abstract It is challenging to detect infrared dim targets submerged in complicated backgrounds due to their small size and faint intensity. The previous attention-based detection networks frequently require global long-range dependence. Significant calculations are required to determine the target's sparse but meaningful position. To prevent wasting calculations on the background, this paper offers a detection network guided by global context for local feature learning, named GILNet (Global Induced Local Network). It designs a global location module (GLM) and a local feature interaction module (LFIM) to capture the global position and features of targets, respectively. More specifically, using global context interaction, the GLM finds the region that might contain dim small targets, that is, the coarse location. In the coarsely located regions, the LFIM further acquires feature information about targets. Next, we also design an eight-directional attention operation to obtain the contour information of targets in the low feature map. It is fused with the high feature map in the multi-directional feature fusion module (MFFM), which retains more semantic and spatial information about targets. Finally, quantitative and qualitative analysis show that the GILNet performs better than eight comparison methods on two public datasets.

Neural Correlates of Psychedelic, Sleep, and Sedated States Support Global Theories of Consciousness.

Understanding neural mechanisms of consciousness remains a challenging question in neuroscience. A central debate in the field concerns whether consciousness arises from global interactions that involve multiple brain regions or focal neural activity, such as in sensory cortex. Additionally, global theories diverge between the Global Neuronal Workspace (GNW) hypothesis, which emphasizes frontal and parietal areas, and the Integrated Information Theory (IIT), which focuses on information integration within posterior cortical regions. To disentangle the global vs. local and frontoparietal vs. posterior dilemmas, we measured global functional connectivity and local neural synchrony with functional magnetic resonance imaging (fMRI) data across a spectrum of conscious states in humans induced by psychedelics, sleep, and deep sedation. We found that psychedelic states are associated with increased global functional connectivity and decreased local neural synchrony. In contrast, non-REM sleep and deep sedation displayed the opposite pattern, suggesting that consciousness arises from global brain network interactions rather than localized activity. This mirror-image pattern between enhanced and diminished states was observed in both anterior-posterior (A-P) and posterior-posterior (P-P) brain regions but not within the anterior part of the brain alone. Moreover, anterior transmodal regions played a key role in A-P connectivity, while both posterior transmodal and posterior unimodal regions were critical for P-P connectivity. Overall, these findings provide empirical evidence supporting global theories of consciousness in relation to varying states of consciousness. They also bridge the gap between two prominent theories, GNW and IIT, by demonstrating how different theories can converge on shared neuronal mechanisms.

Tunable enhanced chiroptical response of a twisted L-shaped plasmon nanoparticle system

Chiroptical responses in plasmon systems have aroused widespread interest, manifesting potential application in fields including physics, biology, and pharmacy, as well as other disciplines. However, the enhancement and tunability of chiroptical responses by strong plasmon coupling, which have been seldom discussed, remain wanting. In this paper, we propose a stacked and twisted L-shaped nanoparticle system, which exhibits an enhanced chiroptical response and the dynamic modulation of chiroptical response. By adjusting the twist angle and the gap between L-shaped nanoparticles, the anisotropy factor g, which quantifies the relative strength of the chiroptical response, can reach up to −1.5, and the peak position and linewidth of the g spectrum can be modified. Furthermore, in instances where the chiroptical response is weak, we construct a finite-size 1D chain by using the proposed system as the unit cell. By harnessing the global interaction among the unit cell of the 1D chain, the maximum value of g can be effectively improved and adjusted. Such an L-shaped nanoparticle system as a fundamental structure has potential applications in tunable chiroptical devices and also extends methods for device design.

ConvAtt Network: A Low Parameter Approach For Sign Language Recognition

Despite recent advances in Large Language Models in text processing, Sign Language Recognition (SLR) remains an unresolved task. This is, in part, due to limitations in the available data. In this paper, we investigate combining 1D convolutions with transformer layers to capture local features and global interactions in a low-parameter SLR model. We experimented using multiple data augmentation and regularization techniques to categorize signs of the French Belgian Sign Language. We achieved a top-1 accuracy of 42.7% and a top-10 accuracy of 81.9% in 600 different signs. This model is competitive with the current state of the art while using a significantly lower number of parameters.

PhoXplex: Combining Phospho-enrichable Cross-Linking with Isobaric Labeling for Quantitative Proteome-Wide Mapping of Protein Interfaces.

Integrating cross-linking mass spectrometry (XL-MS) into structural biology workflows provides valuable information about the spatial arrangement of amino acid stretches, which can guide elucidation of protein assembly architecture. Additionally, the combination of XL-MS with peptide quantitation techniques is a powerful approach to delineate protein interface dynamics across diverse conditions. While XL-MS is increasingly effective with isolated proteins or small complexes, its application to whole-cell samples poses technical challenges related to analysis depth and throughput. The use of enrichable cross-linkers has greatly improved the detectability of protein interfaces in a proteome-wide scale, facilitating global protein-protein interaction mapping. Therefore, bringing together enrichable cross-linking and multiplexed peptide quantification is an appealing approach to enable comparative characterization of structural attributes of proteins and protein interactions. Here, we combined phospho-enrichable cross-linking with TMT labeling to develop a streamline workflow (PhoXplex) for the detection of differential structural features across a panel of cell lines in a global scale. We achieved deep coverage with quantification of over 9000 cross-links and long loop-links in total including potentially novel interactions. Overlaying AlphaFold predictions and disorder protein annotations enables exploration of the quantitative cross-linking data set, to reveal possible associations between mutations and protein structures. Lastly, we discuss current shortcomings and perspectives for deep whole-cell profiling of protein interfaces at large-scale.

Deciphering the evolution of metaverse - A techno-functional perspective in digital marketing

The metaverse has disrupted the traditional marketing practices and it has potential to transform entire world of marketing activities with thrilling immersive experiences. This study provides an analysis of evolving field of metaverse marketing in the context of information systems using state of the art bibliometric and scientometric tools coupled with machine learning algorithms. Utilizing 257 documents from Scopus database that published between 1996 and 2024, this research maps and unveils the development of metaverse marketing from its inception and the role of information systems in its evolution. The analysis of literature resulted in five main emerging themes of the role of information systems in metaverse marketing research as User Experience, Customer engagement, Convergence of metaverse Technology, Design of virtual goods & experience and Global Social Interaction. The major sub-themes of the study are User Behaviors and Preferences, Branding on virtual environment, Virtual reality, Virtual wearables and Virtual Socialization. This study also reveals the emerging trends and gaps in literature that pave the ways for future research expansion in the information systems and metaverse marketing. Few of the important future research areas identified are understanding user experience, design of immersive customer engagement strategies, customer virtual presence and Security & privacy concerns of the users on metaverse platform.

External and internal driving forces of community and functional group stability in the forest mammal community

Conserving mammal community stability in forest ecosystems is an urgent challenge in the face of global biodiversity threats and complex trophic interaction. However, understanding of the stability of mammal communities and targeted functional groups is still limited, hindering adaptive strategy development. We collected 210 120 camera trap photos across 5897 km 2 within the Amur tiger ( Panthera tigris altaica ) range in China’s natural forests. We decomposed stability into compensatory effects, statistical-averaging effects and population stability. Using structural equation modelling, we explored the impact of external factors, species diversity and vertical effects on the stability of the community and of four functional groups. As external factors, primary productivity increased the stability of top predators, while precipitation had inconsistent effects. Horizontal diversity (richness and evenness) positively impacted community stability. However, the vertical structure of the community, including trophic-level richness and functional groups’ compensatory dynamics, were the largest drivers for maintaining community stability. Horizontal diversity within functional groups and the diversity or biomass of their food resources/consumers both influenced the stability of functional groups. Our results recognize the importance of conserving and even re-establishing vertical trophic structures in forest communities. Acknowledging multiple levels of stability, from functional groups to communities, is an indispensable step for effective conservation strategies.

Data-Driven Method for Nonlinear Modal Interaction Identification in Fighter Aircraft

The purpose of this paper is to report a new data-driven analysis methodology for identifying nonlinear modal interactions and its application to aeroelastic wind-tunnel data from a generic fighter configuration, which reveals global modal interactions in this system caused by multiple resonance capture events initiated by local nonlinearities. It is shown that during supersonic testing at the Transonic Dynamics Tunnel, vibro-impacts at the embedded external fuel tank shakers on the inboard underwing stations of the generic fighter introduce strong nonlinearity that gives rise to nonlinear modal interactions. Specifically, time–frequency analysis of the acceleration data demonstrates that 6∶5 and 7∶6 transient resonance captures occur across the aircraft exclusively during this vibro-impact forcing window. Further analysis suggests that these resonance captures are dependent on Mach number. These results show experimentally that local vibro-impacts can cause global nonlinear effects in an aeroelastic system and that these effects can be identified using the new methodology herein. The data-driven analysis scheme employed shows applicability to full-aircraft flight test data for identifying nonlinear modal interactions without knowledge of the precise nonlinear contributors.