Nature Of Interactions Research Articles

Role of TGF-β1/SMADs signalling pathway in resveratrol-induced reduction of extracellular matrix deposition by dexamethasone-treated human trabecular meshwork cells.

Deposition of extracellular matrix (ECM) in the trabecular meshwork (TM) increases aqueous humour outflow resistance leading to elevation of intraocular pressure (IOP) in primary open-angle glaucoma, which remains the only modifiable risk factor. Resveratrol has been shown to counteract the steroid-induced increase in IOP and increase the TM expression of ECM proteolytic enzymes; however, its effects on the deposition of ECM components by TM and its associated pathways, such as TGF-β-SMAD signalling remain uncertain. This study, therefore, explored the effects of trans-resveratrol on the expression of ECM components, SMAD signalling molecules, plasminogen activator inhibitor-1 and tissue plasminogen activator in dexamethasone-treated human TM cells (HTMCs). We also studied the nature of molecular interaction of trans -resveratrol with SMAD4 domains using ensemble docking. Treatment of HTMCs with 12.5 µM trans-resveratrol downregulated the dexamethasone-induced increase in collagen, fibronectin and α-smooth muscle actin at gene and protein levels through downregulation of TGF-β1, SMAD4, and upregulation of SMAD7. Downregulation of TGF-β1 signalling by trans-resveratrol could be attributed to its effect on the transcriptional activity due to high affinity for the MH2 domain of SMAD4. These effects may contribute to resveratrol's IOP-lowering properties by reducing ECM deposition and enhancing aqueous humour outflow in the TM.

Let Pictures Explain the World: Text–Image Narration in Picturebooks

ABSTRACT Teachers have faced difficulties in selecting acceptable picturebooks for young students, particularly in terms of image selection, content, and the integration of both. The lack of meaning-making and interaction between text and images in picturebooks contributes to this trend. This study analyzes how images represent varieties of context and how they function to construct meaning in learning foreign language for young learners. Grammar of visual images by Kress and Van Leeuwen and picturebook typology framework by Gregersen are employed to analyze text-image relationship in the top 10 most rented picturebooks in China. This study indicated that the images provide the nature of the social interaction taking place and the setting familiar to young learners, which can help them to find new vocabulary to recognize their surrounding environment. The data on human depiction also indicated that the images representing Chinese/Asian appearance recognized young learners as global citizens in literature. We also found that the most popular text-image relationship is the one that shares equal representation. The study provided pedagogical implications to help teachers deal with different types of text-image interactions in picturebooks.

Design and synthesis of a new pyrazole-naphthalene Schiff base: Al3+recognition, DNA binding studies and theoretical calculations

Herin, a Schiff base (L) based on 2‑hydroxy-1-naphthaldehyde and 3-amino-4-pyrazolecarboxylic acid ethyl ester was successfully designed, synthesized and characterized. Spectroscopic experiments demonstrated that L can selectively recognize Al3+ with fluorescence, exhibiting a detection limit (DL) of 4.2 × 10−8 mol/L, significantly lower than that of previously reported Schiff base fluorescent sensors. The Job's plot analysis demonstrated the 1:1 stoichiometry between the Al3+ and L, with bright blue fluorescence observed under 365 nm ultraviolet light irradiation. The recognition mechanism involves the coordination of Al3+ ions with two oxygen atoms originating from the carboxylate and phenol groups, as well as the nitrogen atom of the imine moiety in Schiff bases. Additionally, the interaction nature between L and Zebra fish DNA (zf-DNA) was found to be intercalation and minor groove based on both spectroscopic method and theoretical docking study. Furthermore, the main interaction force between L and DNA was hydrophobic with a spontaneous process based on the fluorescence spectra obtained at different temperatures, which can also be attributed to static quenching. Above all, this Schiff base could be used to detect Al3+ ions in the environmental system with high accuracy. This study is also expected to contribute to the understanding of the binding mode of this type of complex to DNA, proposing a novel approach for development and creation of versatile sensors, and investigating new anticancer agents, modifying or detecting bio-molecules.

Grammatical Features in Language Contact Setting: The Case of 3è and 1ère students of GBHS Koza and GHS Mozogo

The most common way that languages influence each other is in the exchange of words. The present study deals with grammatical features in essays written by students of GBHS Koza and BHS Mozogo and their natural occurring interactions which are the empirical foundations of primary data source. The research adopted the qualitative and quantitative approaches. The corpus comprises 250 essay writing scripts collected in February 2024. The data were processed and analysed using Kachru (1983a) theory on postcolonial and/or world Englishes. Results showed a new stream of CamFE in the grammatical features found in students’ essays and interactions which include haphazard relative pronouns, articles omission, object omission in nominal phrase, lack of subject-verb agreement, possessive adjectives, misplacement of adjectives, non-standard construction of interrogative sentences and adjectival reduplication.

Unveiling the Nature of lignin's Interaction with Molecules: A Mechanistic Understanding of Adsorption of Methylene Blue Dye.

The valorization of lignin into advanced materials for water and soil remediation is experiencing a surge in demand. However, it is imperative that material research and manufacturing be sustainable to prevent exacerbating environmental issues. Meeting these requirements necessitates a deeper understanding of the role of lignin's functional groups in attracting targeted species. This research delves into the interaction mechanisms between lignin and organic molecules, using the adsorption of the cationic dye Methylene Blue (MB+) as a case study. Herein, we aim to quantitatively estimate the contribution of different interaction types to the overall adsorption process. While carbonyl groups were found to have no significant role in attraction, carboxylic groups (-COOH) exhibited significantly lower adsorption compared with hydroxyl groups (-OH). Through alternately blocking aliphatic and phenolic -OH groups, we determined that 61% of the adsorption occurred through hydrogen bonding and 38% via electrostatic interactions. Performance studies of modified lignin along with spectroscopic methods (XPS, FTIR) confirmed the negligible role of π-π interactions in adsorption. This study offers fundamental insights into the mechanistic aspects of MB adsorption on lignin, laying the groundwork for potential modifications to enhance the performance of lignin-based adsorbents. The findings underscore the importance of hydroxyl groups and provide a roadmap for future studies examining the influence of steric factors and interactions with other organic molecules.

Lattice-guided assembly of optoelectronically active π-conjugated peptides on 1D van der Waals single crystals.

The deployment of organic molecules in high-performance devices strongly relies on the formation of well-ordered domains, which is often complicated by the dynamic and sensitive nature of supramolecular interactions. Here, we engineered the assembly of water-processable, optoelectronic π-conjugated peptides into well-defined organic-inorganic heterointerfaced assemblies by leveraging the long-range anisotropic ordering of 1D van der Waals (vdW) crystals composed of subnanometer-thick transition metal sulfide chains (MS3; M = Nb, Ta) as assembly templates. We found that the monomers can readily form 1D supramolecular assemblies onto the underlying crystal surface, owing to the structural correspondence between the π-π interactions of the quaterthiophene (4T)-based peptide units (DDD-4T) and sulfur atom ordering along the NbS3 (100) surface. The heterointerfaced assemblies exhibited substantially red-shifted photoluminescence and enhanced visible-range photocurrent generation compared to solution-assembled films. Our results underscore the role of lattice matching in forming ordered supramolecular assemblies, offering an emergent approach to assembling organic building blocks endowed with improved physical properties.

Unveiling the evolution of generative AI (GAI): a comprehensive and investigative analysis toward LLM models (2021–2024) and beyond

This comprehensive exploration of recent breakthroughs in artificial intelligence (AI) traversed the realms of language models, computer vision, and generative models, unraveling the intricacies of cutting-edge technologies such as GPT-3.5, GPT-4, Pix2Seq, and multimodal models in terms of generative AI. In this multifaceted journey, the focus extended beyond technological prowess to ethical considerations, emphasizing responsible AI practices guided by Google's AI Principles. The nuanced discussions encapsulated the transformative impact of AI on user experiences across various Google products and toolsets, paving the way for a future where natural language interaction, creative content generation, and multimodal understanding redefine human–computer interactions. The research investigation showcased not only the advancements themselves but also the critical lens through which these innovations are approached, underscoring the importance of ethical and responsible AI in shaping the technological landscape.

Machine Learning in Enhancing Protein Binding Sites Predictions - What Has Changed Since Then?

Accurate identification of protein binding sites is pivotal for understanding molecular interactions and facilitating drug discovery efforts. However, the dynamic nature of proteinligand interactions presents a formidable challenge, necessitating innovative approaches to bridge the gap between theoretical predictions and experimental realities. This review explores the challenges and recent advancements in protein binding site prediction. Specifically, we highlight the integration of molecular dynamics simulations, machine learning, and deep learning techniques to capture the dynamic and complex nature of protein-ligand interactions. Additionally, we discuss the importance of integrating experimental data, such as structural information and biochemical assays, to enhance prediction accuracy and reliability. By navigating the intersection of classical and the onset of machine learning and deep learning approaches, we aim to provide insights into current state-of-the-art techniques and chart a course for future protein binding site prediction advancements. Ultimately, these efforts could unravel the mysteries of protein-ligand interactions and accelerate drug discovery endeavors.

Microbial diversity, genomics, and phage–host interactions of cyanobacterial harmful algal blooms

ABSTRACT The occurrence of cyanobacterial harmful algal blooms (cyanoHABs) is related to their physical and chemical environment. However, less is known about their associated microbial interactions and processes. In this study, cyanoHABs were analyzed as a microbial ecosystem, using 1 year of 16S rRNA sequencing and 70 metagenomes collected during the bloom season from Lake Okeechobee (Florida, USA). Biogeographical patterns observed in microbial community composition and function reflected ecological zones distinct in their physical and chemical parameters that resulted in bloom “hotspots” near major lake inflows. Changes in relative abundances of taxa within multiple phyla followed increasing bloom severity. Functional pathways that correlated with increasing bloom severity encoded organic nitrogen and phosphorus utilization, storage of nutrients, exchange of genetic material, phage defense, and protection against oxidative stress, suggesting that microbial interactions may promote cyanoHAB resilience. Cyanobacterial communities were highly diverse, with picocyanobacteria ubiquitous and oftentimes most abundant, especially in the absence of blooms. The identification of novel bloom-forming cyanobacteria and genomic comparisons indicated a functionally diverse cyanobacterial community with differences in its capability to store nitrogen using cyanophycin and to defend against phage using CRISPR and restriction-modification systems. Considering blooms in the context of a microbial ecosystem and their interactions in nature, physiologies and interactions supporting the proliferation and stability of cyanoHABs are proposed, including a role for phage infection of picocyanobacteria. This study displayed the power of “-omics” to reveal important biological processes that could support the effective management and prediction of cyanoHABs. IMPORTANCE Cyanobacterial harmful algal blooms pose a significant threat to aquatic ecosystems and human health. Although physical and chemical conditions in aquatic systems that facilitate bloom development are well studied, there are fundamental gaps in the biological understanding of the microbial ecosystem that makes a cyanobacterial bloom. High-throughput sequencing was used to determine the drivers of cyanobacteria blooms in nature. Multiple functions and interactions important to consider in cyanobacterial bloom ecology were identified. The microbial biodiversity of blooms revealed microbial functions, genomic characteristics, and interactions between cyanobacterial populations that could be involved in bloom stability and more coherently define cyanobacteria blooms. Our results highlight the importance of considering cyanobacterial blooms as a microbial ecosystem to predict, prevent, and mitigate them.

Dynamic interactions among new energy metals and price adjustment strategies: A cross-industry chain perspective

Lithium, Cobalt, Nickel are the most important new energy metals for energy transition. Their rapid increasing demand drives significant prices fluctuations, often causing simultaneous extensive price spikes in several products among their industry chains, reflecting their interaction nature. This paper first reconstructs products price interaction network among the Lithium–Cobalt–Nickel cross-industry chains to analyze their dynamic interactions. By applying network structural control theory, we then simulate three price reduction scenarios under high price volatility. Product price adjustment difficulties are quantified and distinct adjustment patterns are identified. Our results demonstrate substantial intra-chain and cross-chain dynamic interactions among products, particularly within the nickel chain. Products like the midstream product electrolytic nickel, downstream products Nickel Plate and Nickel exhibit intense interactions with others. Adjusting the prices of 17 key products proves effective in stabilizing the entire system. Electrolytic cobalt and lithium cobaltate always require significant adjustment efforts for most products during high-levels, expect for the nickel ore and some products in lithium chain where they are not critical. Lithium cobaltate always follows a U-shape adjustment pattern, while electrolytic cobalt follows a ∩-shape pattern. When the entire nickel industry chain experiences sharp price increases, the largest adjustment efforts are required to turn down prices.

The role of mitochondria in sex- and age-specific gene expression in a species without sex chromosomes

Mitochondria perform an array of functions, many of which involve interactions with gene products encoded by the nucleus. These mitochondrial functions, particularly those involving energy production, can be expected to differ between sexes and across ages. Here, we measured mitochondrial effects on sex- and age-specific gene expression in parental and reciprocal F1 hybrids between allopatric populations of Tigriopus californicus with over 20% mitochondrial DNA divergence. Because the species lacks sex chromosomes, sex-biased mitochondrial effects are not confounded by the effects of sex chromosomes. Results revealed pervasive sex differences in mitochondrial effects, including effects on energetics and aging involving nuclear interactions throughout the genome. Using single-individual RNA sequencing, sex differences were found to explain more than 80% of the variance in gene expression. Males had higher expression of mitochondrial genes and mitochondrially targeted proteins (MTPs) involved in oxidative phosphorylation (OXPHOS), while females had elevated expression of non-OXPHOS MTPs, indicating strongly sex-dimorphic energy metabolism at the whole organism level. Comparison of reciprocal F1 hybrids allowed insights into the nature of mito-nuclear interactions, showing both mitochondrial effects on nuclear expression, and nuclear effects on mitochondrial expression. While based on a small set of crosses, sex-specific increases in mitochondrial expression with age were associated with longer life. Network analyses identified nuclear components of strong mito-nuclear interactions and found them to be sexually dimorphic. These results highlight the profound impact of mitochondria and mito-nuclear interactions on sex- and age-specific gene expression.

Pathway Selection in Temporal Evolution of Supramolecular Polymers of Ionic π-Systems: Amphiphilic Organic Solvent Dictates the Fate of Water.

Understanding solvent-solute interactions is essential to designing and synthesising soft materials with tailor-made functions. Although the interaction of the solute with the solvent mixture is more complex than the single solvent medium, solvent mixtures are exciting to unfold several unforeseen phenomena in supramolecular chemistry. Here, we report two unforeseen pathways observed during the hierarchical assembly of cationic perylene diimides (cPDIs) in water and amphiphilic organic solvent (AOS) mixtures. When the aqueous supramolecular polymers (SPs) of cPDIs are injected into AOS, initially kinetically trapped short SPs are formed, which gradually transform into thermodynamically stable high aspect ratio SP networks. Using various experimental and theoretical investigations, we found that this temporal evolution follows two distinct pathways depending on the nature of the water-AOS interactions. If the AOS is isopropanol (IPA), water is released from cPDIs into bulk IPA due to strong hydrogen bonding interactions, which further decreases the monomer concentration of cPDIs (Pathway-1). In the case of dioxane AOS, cPDI monomer concentration further increases as water is retained among cPDIs (Pathway-2) due to relatively weak interactions between dioxane and water. Interestingly, these two pathways are accelerated by external stimuli such as heat and mechanical agitation.

Automated Ultrasound Object Segmentation Using Combinatorial Active Contour Method

Active Contour (AC) is an algorithm widely used in segmentation for developing Computer-Aided Diagnosis (CAD) systems in ultrasound imaging. Existing AC models still retain an interactive nature. This is due to the large number of parameters and coefficients that require manual tuning to achieve stability. Which can result in human error and various issues caused by the inhomogeneity of ultrasound images, such as leakage, false areas, and local minima. In this study, an automatic object segmentation method was developed to assist radiologists in an efficient diagnosis process. The proposed method is called Automatic Combinatorial Active Contour (ACAC), which combines the simplification of the global region-based CV (Chan-Vese) model and improved-GAC (Geodesic Active Contour) for local segmentation. The results of testing with 50 datasets showed an accuracy value of 98.83%, precision of 95.26%, sensitivity of 86.58%, specificity of 99.63%, similarity of 90.58%, and IoU (Intersection over Union) of 82.87%. These quantitative performance metrics demonstrate that the ACAC method is suitable for implementation in a more efficient and accurate CAD system.

Tailoring interactions for cisPro peptide bond stabilization

Elucidation of the nature of non-covalent interactions that govern the rate-limiting cis-trans isomerism at Xaa-Pro peptide bonds is fundamental to unravelling the protein folding mechanism, the stereoelectronic control elements of the structure and dynamics of the peptide bond, and the design of novel peptide isosteres. CisPro rotamers are stabilized by very few interactions compared to the transPro rotamer and are hence relatively scarcely populated. Design of novel interactions that can bias cisPro stability, with least mutations to the prolyl peptide bond, is crucial for accessing the cisPro motif in a variety of peptides. This mini review discusses the various interactions tailored for improving cisPro stability.

Co2iAR: Co-located audio-visual enabled mobile collaborative industrial AR wiring harness assembly

Existing augmented reality (AR) assembly mainly provides visual instructions for operators from a first-person perspective, and it is hard to share individual working intents for co-located workers on the shop floor, especially for large-scale product assembly task that requires multiple operators working together. To bridge this gap for practical deployments, this paper proposes Co2iAR, a co-located audio-visual enabled mobile collaborative AR assembly. Firstly, according to the stereo visual-inertial fusion strategy, robust and accurate self-contained motion tracking is achieved for the resource-constrained mobile AR platform, followed by a co-located alignment from multiple mobile AR clients on the shop floor. Then, a lightweight text-aware network for online wiring harness character recognition is proposed, as well as the audio-based confirming strategy, enabling natural audio-visual interaction among co-located workers within a shared immersive workplace, which can also monitor the current wiring assembly status and activate the step-by-step tutorials automatically. The novelty of this work is focused on the deployment of audio-visual aware interaction using the same device that is being used to deploy the co-located collaborative AR work instructions, establishing shared operating intents among multiple co-located workers. Finally, comprehensive experiments are carried out on the collaborative performance among multiple AR clients, and results illustrate that the proposed Co2iAR can alleviate the cognitive load and achieve superior performance for the co-located AR assembly tasks, providing a more human-centric collaborative assembly performance.

Atmosphere and ocean interactions

The nature and extent of the main interactions between the Earth’s atmosphere and the ocean are briefly reviewed, introducing the main properties of these two planetary systems, the hierarchy of timescales and spatial scales characterizing their processes, and the main mechanisms involved. To clarify these mechanisms, a few examples of exchanges of momentum, energy, and mass exchanges are briefly outlined. The paper aims at stimulating students to discover the fascinating interplay among two of the most relevant components of our planet, such as the atmosphere and the ocean, and suggests resources for further exploration.Graphical abstract

Influence of a static electric field on a one-dimensional Bose-Fermi mixture confined in a double-well potential

In this study, we conducted a detailed investigation into the time evolution of the probability density within a 1D double-well potential hosting a Bose-Fermi mixture. This system comprised spinless bosons and spin one-half fermions with weak repulsive contact interactions. Notably, even at very low effective coupling constants, periodic probabilities were observed, indicating correlated tunneling of both bosons and fermions, leading to complete miscibility, which disappears when an external electric field is turned on. The electric field accentuated fermion-fermion interactions due to the Pauli exclusion principle, altering both boson density and interactions and leading to spatial redistribution of particles. These findings underscore the complex interplay between interactions, external fields, and spatial distributions within confined quantum systems. Our exploration of higher interaction strengths revealed conditions under which probability density functions are decoupled. Furthermore, we observed that increased fermion interaction, driven by the electric field, led to higher tunneling frequencies for both species because of the repulsive nature of the boson-fermion interaction. Conversely, increased boson-boson interaction resulted in complete tunneling of both species, especially when boson density was high, leading to effective fermion repulsion. Expanding our analysis to scenarios involving four bosons demonstrated that higher interaction values corresponded to increased oscillation frequencies in tunneling probabilities. Finally, by manipulating interaction parameters and activating the electric field, we achieved complete tunneling of both species, further increasing oscillation frequencies and resulting in intervals characterized by overlapping probability functions.

Handwritten Digit Recognition

Abstract: This paper presents a powerful Handwritten Digit Recognition System that combines a Graphical User Interface (GUI) based on Tkinter with Convolutional Neural Networks (CNNs). Our approach, which makes use of the MNIST dataset, includes careful data pretreatment to facilitate efficient CNN model training. Convolutional and pooling layers are included in the design of the model, and they are optimized with the Adam optimizer for higher learning rates. The evaluation's findings demonstrate excellent memory, accuracy, and precision. Furthermore, real-time digit drawing is made possible via an intuitive Tkinter GUI, which confirms the model's applicability. By showing the effectiveness of CNNs and offering an interactive platform for natural user interaction, the research provides a holistic solution to handwritten digit recognition. This method is promising for various uses in digit recognition scenarios, highlighting its flexibility and usefulness in real-world situations.

Desktop Voice Assistant

Abstract: Desktop voice assistants have become a popular interface for human-computer interaction because they allow users to speak with their computers in natural language. However, modern desktop voice assistants sometimes struggle to understand and respond to complex queries and context. Privacy concerns concerning the collection and use of user data also remain significant barriers. In order to enhance desktop voice assistants, we present a novel personalization and multimodal interaction technique in this research. By combining text, voice, and visual inputs, our approach improves the accuracy and relevance of the responses. By combining several modalities, our system is able to better understand context and user intent, leading to more meaningful interactions. Furthermore, we propose a user-centric personalization strategy whereby the voice assistant progressively learns each user's preferences and usage patterns. Because it depends less on centralized data collection and processing, this customized approach improves user experience while simultaneously addressing privacy concerns. Through a series of experiments and user surveys, we show how effective our approach is in improving the overall performance and user experience of desktop voice assistants. Our results show that the combination of multimodal interaction with personalization produces more accurate and contextually relevant responses, improving the user experience. All things considered, our research advances desktop voice assistant technology by addressing significant interface and personalization issues. Our approach has the potential to significantly improve desktop voice assistant usability and user acceptance, which could result in considerably more intuitive and natural human-computer interaction.

ChatGPT based recommendation system for retail shops

The rapid growth of e-commerce platforms has emphasized the significance of personalized recommendation systems in enhancing user engagement and satisfaction. This research paper presents the development and evaluation of an innovative Product Recommendation System that leverages advanced Artificial Intelligence (AI) techniques to provide tailored product suggestions. The primary objective is to create a user-centric experience by integrating an AI assistant, enabling natural and interactive interactions. Through a comprehensive survey conducted to understand customer behaviour while purchasing product using AI, the study aims to assess the system's effectiveness in delivering accurate recommendations and providing a seamless purchasing experience. The paper contributes to the field by showcasing the practical implementation of AI-driven recommendation systems, highlighting their potential to transform e-commerce interactions.