Multiple Modes Of Interaction Research Articles

Molecular Determinants for Guanine Binding in GTP-Binding Proteins: A Data Mining and Quantum Chemical Study.

GTP-binding proteins are essential molecular switches that regulate a wide range of cellular processes. Their function relies on the specific recognition and binding of guanine within their binding pockets. This study aims to elucidate the molecular determinants underlying this recognition. A large-scale data mining of the Protein Data Bank yielded 298 GTP-binding protein complexes, which provided a structural foundation for a systematic analysis of the intermolecular interactions that are responsible for the molecular recognition of guanine in proteins. It was found that multiple modes of non-bonded interactions including hydrogen bonding, cation-π interactions, and π-π stacking interactions are employed by GTP-binding proteins for binding. Subsequently, the strengths of non-bonded interaction energies between guanine and its surrounding protein residues were quantified by means of the double-hybrid DFT method B2PLYP-D3/cc-pVDZ. Hydrogen bonds, particularly those involving the N2 and O6 atoms of guanine, confer specificity to guanine recognition. Cation-π interactions between the guanine ring and basic residues (Lys and Arg) provide significant electrostatic stabilization. π-π stacking interactions with aromatic residues (Phe, Tyr, and Trp) further contribute to the overall binding affinity. This synergistic interplay of multiple interaction modes enables GTP-binding proteins to achieve high specificity and stability in guanine recognition, ultimately underpinning their crucial roles in cellular signaling and regulation. Notably, the NKXD motif, while historically considered crucial for guanine binding in GTP-binding proteins, is not universally required. Our study revealed significant variability in hydrogen bonding patterns, with many proteins lacking the NKXD motif but still effectively binding guanine through alternative arrangements of interacting residues.

Space Weather Forecasts of Ground Level Space Weather in the UK: Evaluating Performance and Limitations

AbstractGeomagnetically Induced Currents (GICs) are a severe space weather hazard, driven through coupling between the solar wind and magnetosphere. GICs are rarely measured directly, instead the ground magnetic field variability is often used as a proxy. Recently space weather models have been developed to forecast whether the magnetic field variability (R) will exceed specific, extreme thresholds. We test an example machine learning‐based model developed for the northern United Kingdom. We evaluate its performance (discriminative skill and calibration) as a function of magnetospheric state, solar wind input and magnetic local time. We find that the model's performance is highest during active conditions, for example, geomagnetic storms, and lowest during isolated substorms and “quiet” intervals, despite these conditions dominating the training data set. Correspondingly, the performance is high when the solar wind conditions are elevated (i.e., high velocity, large total magnetic field strength, and the interplanetary magnetic field oriented southward), and at a minimum when the north‐south component of the magnetic field is highly variable or around zero. Regarding magnetic local time, performance is highest within the dusk and night sectors, and lowest during the day. The model appears to capture multiple modes of magnetospheric activity, including substorms and viscous interactions, but poorly predicts impulsive phenomena (i.e., storm sudden commencements) and longer timescale coupling processes. Future models of mid‐latitude magnetic field variability will need to effectively use longer time intervals of unpropagated (i.e., observations from L1) solar wind to more completely describe the magnetospheric conditions and response.

Enhanced Corrosion Resistance of Ag Electrode Through Ionized 2‐Mercaptobenzothiazole in Inverted Perovskite Solar Cells

AbstractThe utilization of Ag electrodes in inverted perovskite solar cells (PSCs) is prevalent; however, their inherent reactivity and corrosive characteristics toward perovskite materials often lead to significant stability concerns. Here, 1‐ethyl‐3‐methyl‐1h‐imidazolium 2‐mercaptobenzothiazole (EM) ionic liquid is designed and introduce it into the bathocuproine (BCP) barrier layer to mitigate the potential chemical corrosion of the Ag electrode by the perovskite layer. EM forms multiple interaction modes through the coordination of two types of bonds, C─N, C═N, and C─S, and chelation with Ag, resulting in the formation of a dense chemical anti‐corrosion layer on the Ag surface. By raising the Ag electrode's corrosion potential and decreasing its corrosion current, the occurrence of chemical reactions and corrosion of the Ag electrode by the perovskite layer is effectively suppressed. Additionally, the ionized EM optimizes the band structure and conductivity of BCP and enhances the electron transfer capability at the electron transport layer/Ag interface. The BCP:EM‐based inverted PSCs exhibit an efficiency of 25.11% and excellent stability. Under continuous operation at 45 °C and one sun illumination, the encapsulated device maintains 85.6% of its initial efficiency after 1000 h at the maximum power point.

Methodological challenges and insights for researching embodied learning in museums

Embodied learning is a nascent term drawing on theories emphasising the role of the body, and body-based interaction in knowledge creation. Whilst embodied learning research has articulated pedagogical and design implications which resonate with museum practice and emphasis on hands-on approaches, translation between embodied learning research and everyday practice is limited. A key challenge is conducting research that is both methodologically rigorous whilst providing tractable implications for complex practice contexts. Whilst this tension is endemic in educational research, the field of embodied learning presents unique challenges. Here, we draw on experiences from a four-year, multisite, academic-practitioner research project investigating embodied learning with young children (3–6 years) in science centres/museums to synthesise, illustrate, and critically reflect on four key challenges: theoretical framing (how embodied learning is conceptualised), nature of the experience (what makes it embodied), evaluating embodied learning, and logistical challenges (capturing multiple modes of interaction, social context, communication). These challenges are illustrated through case studies, contributing a methodological lens for both academics and practitioners investigating the role and implications of embodied learning in museums.

The pseudogene GBP1P1 suppresses influenza A virus replication by acting as a protein decoy for DHX9.

Recently, substantial evidence has demonstrated that pseudogene-derived long noncoding RNAs (lncRNAs) as regulatory RNAs have been implicated in basic physiological processes and disease development through multiple modes of functional interaction with DNA, RNA, and proteins. Here, we report an important role for GBP1P1, the pseudogene of guanylate-binding protein 1, in regulating influenza A virus (IAV) replication in A549 cells. GBP1P1 was dramatically upregulated after IAV infection, which is controlled by JAK/STAT signaling. Functionally, ectopic expression of GBP1P1 in A549 cells resulted in significant suppression of IAV replication. Conversely, silencing GBP1P1 facilitated IAV replication and virus production, suggesting that GBP1P1 is one of the interferon-inducible antiviral effectors. Mechanistically, GBP1P1 is localized in the cytoplasm and functions as a sponge to trap DHX9 (DExH-box helicase 9), which subsequently restricts IAV replication. Together, these studies demonstrate that GBP1P1 plays an important role in antagonizing IAV replication.IMPORTANCELong noncoding RNAs (lncRNAs) are extensively expressed in mammalian cells and play a crucial role as regulators in various biological processes. A growing body of evidence suggests that host-encoded lncRNAs are important regulators involved in host-virus interactions. Here, we define a novel function of GBP1P1 as a decoy to compete with viral mRNAs for DHX9 binding. We demonstrate that GBP1P1 induction by IAV is mediated by JAK/STAT activation. In addition, GBP1P1 has the ability to inhibit IAV replication. Importantly, we reveal that GBP1P1 acts as a decoy to bind and titrate DHX9 away from viral mRNAs, thereby attenuating virus production. This study provides new insight into the role of a previously uncharacterized GBP1P1, a pseudogene-derived lncRNA, in the host antiviral process and a further understanding of the complex GBP network.

Single-cell transcriptional profiling of clear cell renal cell carcinoma reveals a tumor-associated endothelial tip cell phenotype

Clear cell renal cell carcinoma (ccRCC) is the most prevalent form of renal cancer, accounting for over 75% of cases. The asymptomatic nature of the disease contributes to late-stage diagnoses and poor survival. Highly vascularized and immune infiltrated microenvironment are prominent features of ccRCC, yet the interplay between vasculature and immune cells, disease progression and response to therapy remains poorly understood. Using droplet-based single-cell RNA sequencing we profile 50,236 transcriptomes from paired tumor and healthy adjacent kidney tissues. Our analysis reveals significant heterogeneity and inter-patient variability of the tumor microenvironment. Notably, we discover a previously uncharacterized vasculature subpopulation associated with epithelial-mesenchymal transition. The cell-cell communication analysis reveals multiple modes of immunosuppressive interactions within the tumor microenvironment, including clinically relevant interactions between tumor vasculature and stromal cells with immune cells. The upregulation of the genes involved in these interactions is associated with worse survival in the TCGA KIRC cohort. Our findings demonstrate the role of tumor vasculature and stromal cell populations in shaping the ccRCC microenvironment and uncover a subpopulation of cells within the tumor vasculature that is associated with an angiogenic phenotype.

High-efficiency and economical uranium extraction from seawater with easily prepared supramolecular complexes

Developing effective adsorbents for uranium extraction from natural seawater is strategically significant for the sustainable fuel supply of nuclear energy. Herein, stable and low-cost supramolecular complexes (PA-bPEI complexes) were facilely constructed through the assembly of phytic acid and hyperbranched polyethyleneimine based on the multiple modes of electrostatic interaction and hydrogen bonding. The PA-bPEI complexes exhibited not only high uptake (841.7 mg g−1) and selectivity (uranium/vanadium selectivity = 84.1) toward uranium but also good antibacterial ability against biofouling. Mechanism analysis revealed that phosphate chelating groups and amine assistant groups coordinated the uranyl ions together with a high affinity. To be more suitable for practical applications, powdery PA-bPEI complexes were compounded with sodium alginate to fabricate various macroscopic adsorbents with engineered forms, which achieved an extraction capacity of 9.0 mg g−1 in natural seawater after 50 days of testing. Impressively, the estimated economic cost of the macroscopic adsorbent for uranium extraction from seawater ($96.5 ∼ 138.1 kg−1 uranium) was lower than that of all currently available uranium adsorbents. Due to their good uranium extraction performance and low economic cost, supramolecular complex-based adsorbents show great potential for industrial uranium extraction from seawater.

Research on a new cable-driven lower limb rehabilitation robot with bilateral coordination control

In this paper, a new type of cable-driven lower limb rehabilitation robot (CDLLRR) based on bilateral control is proposed. Compared with the traditional rehabilitation robot, it has multiple cooperative remote rehabilitation modes of patient-therapist interaction with better safety, mechanical compliance and adaptability. Firstly, according to the characteristics of gait movement of human lower limbs and the performance requirements of the robot in therapy and rehabilitation, the overall structure, main components and assembly arrangement of the robot are designed in detail. Secondly, in order to ensure that the rehabilitation robot has excellent force transparency and system stability while still maintaining a certain position tracking, according to the performance measurement index based on the introduced bilateral control strategy, this paper proposes an improved PD controller based on Anderson method (IPDAM). Finally, the feasibility of the proposed CDLLRR and the effectiveness of the IPDAM are verified by simulation experiments.

Exploring multi-relational spatial interaction imputation with distance-decay effects

ABSTRACT Spatial interaction imputation aims to compensate for missing stable connections in geographical space, bolstering interaction network integrity and accuracy. Graph neural networks excel in graph-structured interaction data. However, existing research often focuses on homogeneous networks, neglecting the impact of heterogeneous interaction relationships influenced by distance decay on interaction imputation. Neglecting edge heterogeneity constrains the ability to effectively model the network structure, consequently leading to suboptimal performance in interaction imputation. This study introduces an interaction imputation graph convolutional network model. It constructs a heterogeneous interaction network with multi-distance relationships, considering distance decay. The model performs graph embedding based on interaction relationships between nodes. It comprehensively incorporates multiple interaction modes, topological structures, and node attributes to enhance spatial interaction imputation accuracy. Empirically validated using Beijing taxi travel data, our model outperforms existing models, improving imputation accuracy by approximately 8.70%. Our model consistently maintains superior accuracy in interaction networks of various sizes, demonstrating the stable superiority of our model. We also demonstrated that the variation in the number of interaction relationships affects imputation accuracy. A reasonable number of relationships and a larger feature dimension of geographical units yield better interaction imputation results.

A novel multifunctional intelligent bed integrated with multimodal human–robot interaction approach and safe nursing methods

AbstractThe authors propose a multifunctional intelligent bed (MIB) that integrates multiple modes of interaction to improve the welfare of mobility‐impaired users and reduce the workload of medical personnel. The MIB features independent autonomous omnidirectional movement, position adjustment, multi‐degree‐of‐freedom (DOF) movement regulation and posture memory functions to facilitate comfortable and convenient interaction for mobility‐impaired users. In particular, an integrated “MIB‐state perception‐interaction interfaces” system is established, and a bed fall risk detection algorithm and assisted get‐up‐transfer algorithm is proposed. By recognising and sharing human body state characteristics, nursing collaboration can be achieved with caregivers or other nursing robots. Comprehensive experiments demonstrate that the MIB is a novel MIB that is highly adaptable to the environment, convenient to interact with and safe. By integrating the proposed algorithms, daily safety monitoring, assisted get‐up and defecation tasks can be effectively accomplished. This technology demonstrates excellent applicability and promising prospects for implementation in hospitals, nursing centres and homes catering to elderly and disabled individuals with mobility impairments.

Docking Study, Synthesis, Characterization and Preliminary Cytotoxic Evaluation of New 1,3,4- Thiadiazole Derivatives

Objectives: This study has determined that these newly synthesized analogs hold promise as potential sources of novel anticancer agents for combating breast cancer.
 Methods: We initiated our research by obtaining the crystal structure of Histone deacetylases (HDACs-8) bound with Vorinostat (SAHA) from the Protein Data Bank (PDB code 4QA0). Subsequently, we conducted docking experiments, which revealed that compounds (V c,s, V d,s, V c,t, and V d,t) exhibited favorable docking scores when compared to the standard. These compounds, synthesized through multi-step procedures involving the reaction of intermediate derivatives (IV c,d) with thiosemicarbazide or semicarbazide, were subjected to confirmation of their chemical structures using FT-IR and 1H NMR analysis.
 Results: The results of our in-vitro cytotoxicity assay (MTT assay) highlighted that compounds V c,t and V d,t exhibited notable inhibition ratios in the breast cancer cell line (MCF-7), while V c,t displayed similar efficacy in human colon adenocarcinoma (HRT-18) compared to the control drug, Vorinostat (SAHA).
 Conclusion: Our docking analysis led us to conclude that the C=S moiety demonstrated exceptional binding affinity to the zinc binding group of the HDAC enzyme, establishing multiple interaction modes. This finding suggests the potential of these compounds as valuable candidates in the development of anticancer treatments.

A unified inferring framework of multiplex epidemic networks under multiple interlayer interaction modes

Different epidemic transmissions on multiplex networks may interact and display intertwined effects, leading to a challenge and urgently needed problem of the inference of multiplex epidemic network structures under multiple interlayer interaction modes (IIMs). However, in reality, few existing studies have focused on multiplex epidemic network inference responding to the intertwined IIMs, much less the corresponding consistency analysis. To fill these two gaps, we first propose an inference framework of multiplex epidemic networks under a unified form of IIMs. In particular, we develop a Hamming distance-based two-step estimator for an intricate IIM called infection-probability-dependent interaction (IPDI). The proposed two-step estimator overcomes the problems of unobservable and time-varying variable inference in the IPDI mode. Second, we solve the theoretical difficulty of consistency analysis for the network inference under multiple IIMs and obtain sufficient conditions for exponentially decaying inferring error. Under the guarantee of consistently sufficient conditions, the sample size scale enables our framework's high-dimensional inferring ability. The synthetic network simulations show the validity of the proposed algorithm and consistency analysis. In addition, the analysis of real data of Twitter and Foursquare indicates the effectiveness and practicality of the proposed inferring framework.

Protein interactions: anything new?

How do proteins interact in the cellular environment? Which interactions stabilize liquid-liquid phase separated condensates? Are the concepts, which have been developed for specific protein complexes also applicable to higher-order assemblies? Recent discoveries prompt for a universal framework for protein interactions, which can be applied across the scales of protein communities. Here, we discuss how our views on protein interactions have evolved from rigid structures to conformational ensembles of proteins and discuss the open problems, in particular related to biomolecular condensates. Protein interactions have evolved to follow changes in the cellular environment, which manifests in multiple modes of interactions between the same partners. Such cellular context-dependence requires multiplicity of binding modes (MBM) by sampling multiple minima of the interaction energy landscape. We demonstrate that the energy landscape framework of protein folding can be applied to explain this phenomenon, opening a perspective toward a physics-based, universal model for cellular protein behaviors.

Sequence-Dependent Backbone Dynamics of Intrinsically Disordered Proteins.

For intrinsically disordered proteins (IDPs), a pressing question is how sequence codes for function. Dynamics serves as a crucial link, reminiscent of the role of structure in sequence-function relations of structured proteins. To define general rules governing sequence-dependent backbone dynamics, we carried out long molecular dynamics simulations of eight IDPs. Blocks of residues exhibiting large amplitudes in slow dynamics are rigidified by local inter-residue interactions or secondary structures. A long region or an entire IDP can be slowed down by long-range contacts or secondary-structure packing. On the other hand, glycines promote fast dynamics and either demarcate rigid blocks or facilitate multiple modes of local and long-range inter-residue interactions. The sequence-dependent backbone dynamics endows IDPs with versatile response to binding partners, with some blocks recalcitrant while others readily adapting to intermolecular interactions.

SEM/EDX analysis of stomach contents of a sea slug snacking on a polluted seafloor reveal microplastics as a component of its diet

Understanding the impacts of microplastics on living organisms in aquatic habitats is one of the hottest research topics worldwide. Despite increased attention, investigating microplastics in underwater environments remains a problematic task, due to the ubiquitous occurrence of microplastic, its multiple modes of interactions with the biota, and to the diversity of the synthetic organic polymers composing microplastics in the field. Several studies on microplastics focused on marine invertebrates, but to date, the benthic sea slugs (Mollusca, Gastropoda, Heterobranchia) were not yet investigated. Sea slugs are known to live on the organisms on which they feed on or to snack while gliding over the sea floor, but also as users of exogenous molecules or materials not only for nutrition. Therefore, they may represent a potential biological model to explore new modes of transformation and/or management of plastic, so far considered to be a non-biodegradable polymer. In this study we analysed the stomachal content of Bursatella leachii, an aplysiid heterobranch living in the Mar Piccolo, a highly polluted coastal basin near Taranto, in the northern part of the Ionian Sea. Microplastics were found in the stomachs of all the six sampled specimens, and SEM/EDX analyses were carried out to characterize the plastic debris. The SEM images and EDX spectra gathered here should be regarded as a baseline reference database for future investigations on marine Heterobranchia and their interactions with microplastics.

Utilising the triboelectricity of the human body for human-computer interactions

Human-computer interaction (HCI) strategies communicate the human mind and machine intelligence based on different devices and technologies. The majority of HCI strategies assume normal physical conditions that limit accessibility for users with disabilities. Certain products, such as Braille keyboards, work fine for people with specific disabilities. However, a more general HCI strategy that can neglect users’ physical conditions would enhance the accessibility of these tools for disabled persons. Here, we report an HCI strategy that utilises triboelectricity of the human body (TEHB) for HCI. The TEHB can be generated by many parts of the human body, eliminating the obstacles imposed by physical function disabilities. Such an HCI approach has been used for text inputs, graphical inputs, and mimicked mouse functions. With the assistance of deep learning, an accuracy of approximately 98.4 % is achieved for text inputs obtained directly from handwriting. Our findings provide a new approach for HCI and demonstrate the feasibility of multiple interaction modes.

Finite-Time Interactive Control of Robots with Multiple Interaction Modes.

This paper proposes a finite-time multi-modal robotic control strategy for physical human–robot interaction. The proposed multi-modal controller consists of a modified super-twisting-based finite-time control term that is designed in each interaction mode and a continuity-guaranteed control term. The finite-time control term guarantees finite-time achievement of the desired impedance dynamics in active interaction mode (AIM), makes the tracking error of the reference trajectory converge to zero in finite time in passive interaction mode (PIM), and also guarantees robotic motion stop in finite time in safety-stop mode (SSM). Meanwhile, the continuity-guaranteed control term guarantees control input continuity and steady interaction modes transition. The finite-time closed-loop control stability and the control effectiveness is validated by Lyapunov-based theoretical analysis and simulations on a robot manipulator.

A conformational switch in the SCF-D3/MAX2 ubiquitin ligase facilitates strigolactone signalling.

Strigolactones (SLs) are a class of plant hormones that regulate numerous processes of growth and development. SL perception and signal activation involves interaction between F-box E3 ubiquitin ligase D3/MAX2 and DWARF14 (D14) α/β-hydrolase in a SL-dependent manner and targeting of D53/SMXL6/7/8 transcriptional repressors (SMXLs) for proteasome-mediated degradation. D3/MAX2 has been shown to exist in multiple conformational states in which the C-terminal helix (CTH) undergoes a closed-to-open dynamics and regulates D14 binding and SL perception. Despite the multiple modes of D3-D14 interactions found in vitro, the residues that regulate the conformational switch of D3/MAX2 CTH in targeting D53/SMXLs and the subsequent effect on SL signalling remain unclear. Here we elucidate the functional dynamics of ASK1-D3/MAX2 in SL signalling by leveraging conformational switch mutants in vitro and in plants. We report the crystal structure of a dislodged CTH of the ASK1-D3 mutant and demonstrate that disruptions in CTH plasticity via either CRISPR-Cas9 genome editing or expression of point mutation mutants result in impairment of SL signalling. We show that the conformational switch in ASK1-D3/MAX2 CTH directly regulates ubiquitin-mediated protein degradation. A dislodged conformation involved in D53/SMXLs SL-dependent recruitment and ubiquitination and an engaged conformation are required for the release of polyubiquitinated D53/SMXLs and subsequently D14 for proteasomal degradation. Finally, we uncovered an organic acid metabolite that can directly trigger the D3/MAX2 CTH conformational switch. Our findings unravel a new regulatory function of a SKP1-CUL1-F-box ubiquitin ligase in plant signalling.

How human-robot collaboration impacts construction productivity: An agent-based multi-fidelity modeling approach

Though construction robots have drawn attention in research and practice for decades, human-robot collaboration (HRC) remains important to conduct complex construction tasks. Considering its complexity and uniqueness, it is still unclear how HRC process will impact construction productivity, which is difficult to handle with conventional methods such as field tests, mathematical modeling and physical simulation approaches. To this end, an agent-based (AB) multi-fidelity modeling approach is introduced to simulate and evaluate how HRC influences construction productivity. A high-fidelity model is first proposed for a scenario with one robot. Then, a low-fidelity model is established to extract key parameters that capture the inner relationship among scenarios. The multi-fidelity models work together to simulate complex scenarios. Based on the simulation model, the twofold influence of HRC on productivity, namely the supplement strategy on the worker side, and the design for proactive interaction on the robot side, are fully investigated. Experimental results show that: 1) the proposed approach is feasible and flexible for simulation of complex HRC processes, and can cover multiple collaboration and interaction modes; 2) the influence of the supplement strategy is simple when there is only one robot, where lower Check Interval (CI) and higher Supplement Limit (SL) will improve productivity. But the influence becomes much more complicated when there are more robots due to the internal competition among robots for the limited time of workers; 3) HRC has a scale effect on productivity per robot, which means the productivity improves if there are more robots and workers, even if the human-robot ratio remains the same; 4) introducing proactive interaction between robots and workers could improve productivity significantly, up to 22% in our experiments, which further depends on the supplement strategy and the human-robot ratio. Overall, this research contributes an integrated approach to simulate and evaluate HRC’s impacts on productivity as well as valuable insights on how to optimize HRC for better performance and occupational health. The proposed approach is also useful for the evaluation and development of new robots.

Identification of a Long Noncoding RNA (lncPRDM16) Inhibiting Preadipocyte Proliferation in the Chicken.

Long noncoding RNAs are vital to a variety of biological and physiological processes through multiple modes of functional interaction with DNA, RNA, and proteins. In chickens, numerous lncRNAs were discovered to be important to growth or disease progression. However, the detailed molecular function and role of lncRNAs remain less explored. Here, we performed lncRNA sequencing on abdominal adipose tissues from broiler lines divergently selected for abdominal fat content, and significantly differentially expressed lncRNAs were found, including lncPRDM16, a divergently transcribed and conserved lncRNA near PRDM16. Full lengths of two transcripts of lncPRDM16 were obtained, and their genomic structures were compared. Expression dynamics of lncPRDM16 in different tissues and during preadipocyte proliferation and differentiation were profiled. Moreover, a 250-nucleotide sequence at 5'-end was found to be inevitable to the function of lncPRDM16 in inhibiting preadipocyte proliferation and regulating the promoter activities of both lncPRDM16 and PRDM16. Taken together, we identified the 5'-end functional elements of lncPRDM16 and their potential importance in inhibiting preadipocyte proliferation. Our findings provide the foundation for further exploration of lncPRDM16 function and potential improvement of chicken muscle quality.