Diverse Science Research Articles

Salt Gradient-Induced Phoresis of Vesicles and Enhanced Membrane Fusion in a Crowded Milieu.

Phoresis of biocolloidal objects in response to chemical gradients is a matter of interest among diverse scientific disciplines owing to their importance in the spatiotemporal orchestration of biochemical processes. Although there are reports of soft matter transport/phoresis in the gradient of ions or salts in the aqueous system, their phoretic behavior in the presence of macromolecular crowder is largely unexplored. Notably, cellular cytoplasm is illustrated as a crowded milieu and thereby understanding biomolecular phoresis in the presence of polymeric macromolecules would endorse phoretic behavior in a biomimetic environment. Here, we report the phoresis-induced enhanced aggregation and fusion of vesicles in gradients of monovalent (NaCl) and divalent salt (MgCl2), in the presence of polymeric crowder, polyethylene glycol of molecular weight 400 (PEG 400). Apart from diffusiophoresis, depletion force plays a crucial factor in crowded environments to control localized vesicle aggregation in a salt gradient. This demonstration will potentially show the pathway to future research related to spatiotemporally correlated liposomal transport and membrane-dependent function (such as content mixing and signaling) in a physiologically relevant crowded environment.

An effective framework for measuring the novelty of scientific articles through integrated topic modeling and cloud model

Novelty is a critical characteristic of innovative scientific articles, and accurately identifying novelty can facilitate the early detection of scientific breakthroughs. However, existing methods for measuring novelty have two main limitations: (1) Metadata-based approaches, such as citation analysis, are retrospective and do not alleviate the pressures of the peer review process or enable timely tracking of scientific progress; (2) Content-based methods have not adequately addressed the inherent uncertainty between the qualitative concept of novelty and the textual representation of papers. To address these issues, we propose a practical and effective framework for measuring the novelty of scientific articles through integrated topic modeling and cloud model, referred to as MNSA-ITMCM. In this framework, papers are represented as topic combinations, and novelty is reflected in the organic reorganization of these topics. We use the BERTopic model to generate semantically informed topics, and then apply a topic selection algorithm based on maximum marginal relevance to obtain a topic combination that balances similarity and diversity. Furthermore, we leverage the cloud model from fuzzy mathematics to quantify novelty, overcoming the uncertainty inherent in natural language expression and topic modeling to improve the accuracy of novelty measurement. To validate the effectiveness of our framework, we conducted empirical evaluations on papers from the Cell 2021 journal (biomedical domain) and the ICLR 2023 conference (computer science domain). Through correlation analysis and prediction error analysis, our framework demonstrated the ability to identify different types of novel papers and accurately predict their novelty levels. The proposed framework is applicable across diverse scientific disciplines and publication venues, benefiting researchers, librarians, science evaluation agencies, policymakers, and funding organizations by improving the efficiency and comprehensiveness of identifying novelty research.

Natural and Synthetic Capsaicin Analogues: A Comprehensive Review of Biological Effects and Synthetic Pathways.

Capsaicin analogs, whether sourced from natural origins or synthesized de novo, have garnered significant attention across diverse scientific disciplines. This comprehensive investigation explores the expansive domain of medicinal chemistry and pharmacology, focusing on capsaicin and its analogs. Notably, these analogs exhibit a wideranging pharmacological spectrum, with a particular emphasis on their potent antitumor properties. Researchers frequently explore structural modifications, particularly in region C, consistently enhancing their pharmacological activities. A highlighted finding is that analogs with alterations in both regions A and C manifest a diverse array of effects, spanning from anti-obesity to protection against ischemia. They also demonstrate anti- Alzheimer's, anti-fibrotic, anti-inflammatory, anti-diabetic, antimalarial, and anti-epileptic properties. This underscores the potential of structural adaptations in these regions, expanding the therapeutic applications of capsaicin-like compounds. Additionally, manipulations in regions B and C result in compounds that possess antioxidant and anti-obesity properties, providing valuable insights for the development of novel compounds. The therapeutic potential of capsaicin analogs opens innovative avenues for drug design and development, promising to address a broad spectrum of diseases and enhance global quality of life. Moreover, this article meticulously examines various synthetic methodologies for synthesizing capsaicin analogs, complementing the main review. These methodologies distinguish themselves through their simplicity, mild reaction conditions, and reliance on readily available commercial reagents. The accessible synthesis pathways enable researchers from diverse backgrounds to explore these compounds, fostering investigations and potential therapeutic applications.

Protein-phenolic interactions and reactions: Discrepancies, challenges, and opportunities.

Although noncovalent interactions and covalent reactions between phenolic compounds and proteins have been investigated across diverse scientific disciplines, a comprehensive understanding and identification of their products remain elusive. This review will initially outline the chemical framework and, subsequently, delve into unresolved or debated chemical and functional food-related implications, as well as forthcoming challenges in this topic. The primary objective is to elucidate the multiple aspects of protein-phenolic interactions and reactions, along with the underlying overwhelming dynamics and possibilities of follow-up reactions and potential crosslinking between proteins and phenolic compounds. The resulting products are challenging to identify and characterize analytically, as interactions and reactions occur concurrently, mutually influencing each other. Moreover, they are being modulated by various conditions such as the reaction parameters and, obviously, the chemical structure. Additionally, this review delineates the resulting discrepancies and challenges of properties and attributes such as color, taste, foaming, emulsion and gel formation, as well as effects on protein digestibility and allergenicity. Ultimately, this review is an opinion paper of a group of experts, dealing with these challenges for quite a while and aiming at equipping researchers with a critical and systematic approach to address current research gaps concerning protein-phenolic interactions and reactions.

A novel hybrid methodology integrating pixel- and object-based techniques for mapping land use and land cover from high-resolution satellite data

ABSTRACT The classification of very high-resolution satellite imagery remains a focal point in remote sensing, attracting increased attention across diverse scientific disciplines. Various classification methods, including pixel- and object-based techniques, have been proposed, and their performances and limitations have been discussed in the literature. This paper presents a hybrid method that combines the strengths of pixel- and object-based methods in image classification to minimize errors associated with the segmentation process, particularly under-segmentation errors in object-based image analysis. The core concept behind the method lies in categorizing segmented image objects as either homogeneous or heterogeneous based on their class probability. In this process, the estimated possibilities from the object-based classification model are considered, and segments are designated as homogeneous or heterogeneous using a user-defined threshold. The object-based classification model determines the class labels for homogeneous image objects, while the heterogeneous ones, containing pixels representing different land cover classes, are classified using the pixel-based model. The performance of hybrid classification models, created by varying thresholds, is analysed using high-resolution WorldView-3 and WorldView-2 imagery and compared with pixel- and object-based classification results. For the implementation of image classification methods, Canonical Correlation Forest (CCF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) were employed. The findings indicated that employing the suggested hybrid strategy with a threshold value selected within a specific range (e.g. between 60% and 80%) and employing a robust classification algorithm that provides class probabilities (e.g. CCF) results in a statistically significant improvement in overall accuracy compared to pixel and object-based methods, with gains of 5% and 4%, respectively. Visual analysis of the produced thematic maps revealed that the proposed method minimizes the salt-and-pepper effect associated with pixel-based classification while mitigating segmentation problems arising from object-based classification.

Tantalum oxide nanotube thin films: fabrication, optical properties, and porosity analysis

Tantalum oxide (Ta2O5) nanotubes exhibit remarkable properties and have garnered significant attention across diverse scientific disciplines and technological domains. This research article elucidates the successful preparation of amorphous, well-ordered Ta2O5 nanotubes through anodization in electrolytes incorporating hydrofluoric acid and concentrated sulfuric acid. The study comprehensively investigates the preparation methodology on the morphology of the nanotubes, elucidating their optical properties. Furthermore, the effective refractive index and porosity of the Ta2O5 nanotubes were quantitatively determined, offering valuable insights for the advanced utilization of Ta2O5 in several applications, encompassing battery electrode materials, memory resistors, and sensors.

Synthesis, Structural Analysis, and Peroxidase-Mimicking Activity of AuPt Branched Nanoparticles.

Bimetallic nanomaterials have generated significant interest across diverse scientific disciplines, due to their unique and tunable properties arising from the synergistic combination of two distinct metallic elements. This study presents a novel approach for synthesizing branched gold-platinum nanoparticles by utilizing poly(allylamine hydrochloride) (PAH)-stabilized branched gold nanoparticles, with a localized surface plasmon resonance (LSPR) response of around 1000 nm, as a template for platinum deposition. This approach allows precise control over nanoparticle size, the LSPR band, and the branching degree at an ambient temperature, without the need for high temperatures or organic solvents. The resulting AuPt branched nanoparticles not only demonstrate optical activity but also enhanced catalytic properties. To evaluate their catalytic potential, we compared the enzymatic capabilities of gold and gold-platinum nanoparticles by examining their peroxidase-like activity in the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB). Our findings revealed that the incorporation of platinum onto the gold surface substantially enhanced the catalytic efficiency, highlighting the potential of these bimetallic nanoparticles in catalytic applications.

Interaction as the foundation for eating practices in shared mealtimes

Mealtimes shared with other people define how, what, how much, and with whom we eat. On such occasions, whether in private or public spaces, and as formal or informal events, our eating practices are inseparable from our interactions with other people. In this Editorial for the Special Issue on Interactional approaches to eating together and shared mealtimes, we provide an overview of the interdisciplinary field of research on eating together and shared mealtimes to illustrate the breadth and depth of work that has been developed in this area to date. The overview is divided into three broad clusters of research that focus primarily on (1) cultural or societal aspects, (2) individual outcomes, or (3) interactional practices. Commonalities across these clusters are discussed, the need for more research across a greater global and cultural diversity of eating practices is highlighted, and the potential for interdisciplinary collaboration on research on eating together and shared mealtimes across diverse scientific disciplines is explored. The papers in this Special Issue showcase a sample of contemporary work from within the cluster of research on interactional practices, and a brief overview of these papers is discussed. Finally, it is argued that as a common area of interest, social interaction as the foundation of eating practices within shared mealtimes poses considerable potential for interdisciplinary collaboration across scientific disciplines, and between scientists, professionals, and participants from the study populations.

Precise measurement of Fe isotopes in marine and biological samples by pseudo-high-resolution multi-collector inductively coupled plasma mass spectrometry (MC-ICPMS).

This paper introduces an enhanced technique for analyzing iron isotopes in complex marine and biological samples. A dedicated iron purification method for biological marine matrices, utilizing three ion exchange columns, is validated. The MC-ICPMS in pseudo-high-resolution mode determines precise iron isotopic ratios, with sensitivity improved through the DSN-100 desolvating nebulizer system and Apex-IR. Only 2µg of iron on DSN versus 1µg on Apex is needed for six replicates (30-60 times improvement) while 10 to 20µg is required for a single measurement on awet system considering the resolution power (Rp) is maintained at 11,000-13,000. The Ni-doping method with a Fe/Ni ratio of 1 yields more accurate isotopic ratios than standard-sample bracketing alone. Measurement reproducibility of triplicate samples from marine biological experiments on MC-ICPMS is ± 0.03‰ (2SD) for δ56Fe and ± 0.07‰ for δ57Fe (2SD). This study introduces a novel iron purification process specifically designed for marine and biological samples, enhancing sensitivity and enabling more reliable measurements with smaller sample sizes and reduced uncertainties. It proposes iron isotopic compositions for biological reference materials, offering a valuable reference dataset in diverse scientific disciplines.

Unlocking the Future of Periodontal Regeneration: An Interdisciplinary Approach to Tissue Engineering and Advanced Therapeutics.

Periodontal defects present a significant challenge in dentistry, necessitating innovative solutions for comprehensive regeneration. Traditional restoration methods have inherent limitations in achieving complete and functional periodontal tissue reconstruction. Tissue engineering, a multidisciplinary approach integrating cells, biomaterials, and bioactive factors, holds tremendous promise in addressing this challenge. Central to tissue engineering strategies are scaffolds, pivotal in supporting cell behavior and orchestrating tissue regeneration. Natural and synthetic materials have been extensively explored, each offering unique advantages in terms of biocompatibility and tunable properties. The integration of growth factors and stem cells further amplifies the regenerative potential, contributing to enhanced tissue healing and functional restoration. Despite significant progress, challenges persist. Achieving the seamless integration of regenerated tissues, establishing proper vascularization, and developing biomimetic scaffolds that faithfully replicate the natural periodontal environment are ongoing research endeavors. Collaborative efforts across diverse scientific disciplines are essential to overcoming these hurdles. This comprehensive review underscores the critical need for continued research and development in tissue engineering strategies for periodontal regeneration. By addressing current challenges and fostering interdisciplinary collaborations, we can unlock the full regenerative potential, paving the way for transformative advancements in periodontal care. This research not only enhances our understanding of periodontal tissues but also offers innovative approaches that can revolutionize dental therapies, improving patient outcomes and reshaping the future of periodontal treatments.

Wildfire risk management in the era of climate change.

The August 8, 2023R Lahaina fire refocused attention on wildfires, public alerts, and emergency management. Wildfire risk is on the rise, precipitated through a combination of climate change, increased development in the wildland-urban interface (WUI), decades of unmitigated biomass accumulation in forests, and a long history of emphasis on fire suppression over hazard mitigation. Stemming the tide of wildfire death and destruction will involve bringing together diverse scientific disciplines into policy. Renewed emphasis is needed on emergency alerts and community evacuations. Land management strategies need to account for the impact of climate change and hazard mitigation on forest ecosystems. Here, we propose a long-term strategy consisting of integrating wildfire risk management in wider-scope forest land management policies and strategies, and we discuss new technologies and possible scientific breakthroughs.

Self‐Powered Broadband Photodetectors Based on Si/SnS2 and Si/SnSe2 p–n Heterostructures

AbstractThe escalating interest in 2D nanoflakes‐based group‐IVA metal chalcogenides is attributed to their noteworthy properties, such as high electron mobility, exceptional chemical stability, and applicability across diverse scientific disciplines such as sensing, energy storage applications, supercapacitors, and lithium‐ion batteries. This study introduces an innovative self‐powered photodetector based on 2D metal chalcogenide nanoflakes (SnS2 and SnSe2) formed into nanoflowers on a silicon substrate through the hydrothermal method. The amalgamation of the light‐induced pyroelectric effect into the SnS2 photodetector achieves impressive enhancement ratios of 353%, 425%, 351%, 662%, and 153% in photoresponsivity and detectivity compared to the photovoltaic effect at 365, 456, 532, 632, and 850 nm, respectively, at zero bias. The SnSe2 photodetector achieved responsivity values of 14, 82, 36, 4, and 28 mA W−1 at the corresponding wavelengths in self‐powered mode. The SnSe2 device exhibits superior photosensitivity of 30792%, 55692%, 28803%, 9678%, and 68587% at the corresponding wavelengths, under zero bias. In addition, the photocurrent response caused by the photovoltaic‐pyroelectric effect is thoroughly defined, and the impacts of light wavelength, power intensity, and bias voltage are studied. This study presents a cutting‐edge self‐powered broadband photodetector utilizing pyroelectric materials and opens possibilities for designing high‐performance, fast photodetectors based on the pyro‐phototronic effect.

AptaShield: A Universal Signal-Transduction System for Fast and High-Throughput Optical Molecular Biosensing.

Biosensing technologies are often described to provide facile, sensitive, and minimally to noninvasive detection of molecular analytes across diverse scientific, environmental, and clinical diagnostic disciplines. However, commercialization has been very limited mostly due to the difficulty of biosensor reconfiguration for different analyte(s) and limited high-throughput capabilities. The immobilization of different biomolecular probes (e.g., antibodies, peptides, and aptamers) requires the sensor surface chemistry to be tailored to provide optimal probe coupling, orientation, and passivation and prevent nonspecific interactions. To overcome these challenges, here we report the development of a solution-phase biosensor consisting of an engineered aptamer, the AptaShield, capable of universally binding to any antigen recognition site (Fab') of fluorescently labeled immunoglobulins (IgG) produced in rabbits. The resulting AptaShield biosensor relies on a low affinity dynamic equilibrium between the fluorescently tagged aptamer and IgG to generate a specific Förster resonance energy transfer (FRET) signal. As the analyte binds to the IgG, the AptaShield DNA aptamer-IgG complex dissociates, leading to an analyte concentration-dependent decrease of the FRET signal. The biosensor demonstrates high selectivity, specificity, and reproducibility for analyte quantification in different biological fluids (e.g., urine and blood serum) in a one-step and low sample volume (0.5-6.25 μL) format. The AptaShield provides a universal signal transduction mechanism as it can be coupled to different rabbit antibodies without the need for aptamer modification, therefore representing a robust high-throughput solution-phase technology suitable for point-of-care applications, overcoming the current limitations of gold standard enzyme-linked immunosorbent assays (ELISA) for molecular profiling.

La educación un proceso articulador de diversas ciencias y ramas del conocimiento

Abstract The logical historical process followed by education up to our days, shows not to have the level of science; pedagogues, theoreticians, philosophers, epistemologists imprecise boundaries with the other branches of knowledge. The problem manifests itself when we try to clarify: how to base its scientificity or its evident articulating process that favors formative qualities of people, its main objective is to base articulations and relational integration of diverse disciplines favoring permanent humanization. Hypothetically, it configures a Dialectical, Holistic, Systemic, Complex epistemic model (DIAHOLSIC), based on the theories: dialectic, holistic configurational, systemic, complex; it assumes intradisciplinary, pluridisciplinary, interdisciplinary, interdisciplinary, transdisciplinary, multidisciplinary, multidimensional approaches, allowing to explain, demonstrate, delineate, clarify, sustain; establishing epistemological principles to consider it a bridge between diverse scientific disciplines, fields of knowledge, influencing human formation, creation, cultural transmission, humanization, communication, social coexistence; it accepts theoretical – practical contributions always using them to last until imperishable confines. It is fundamental, as an integral formation of individuals, making them capable of reasoning, developing independent thinking, enriching and improving; it perfects existing educational experiences placed at the service of students. Keywords: Education, articulation process, sciences and branches of knowledge.

Droplets for Gene Editing Using CRISPR-Cas9 and Clonal Selection Improvement Using Hydrogels.

Gene editing tools have triggered a revolutionary transformation in the realms of cellular and molecular physiology, serving as a fundamental cornerstone for the evolution of disease models and assays in cell culture reactions, marked by various enhancements. Concurrently, microfluidics has emerged over recent decades as a versatile technology capable of elevating performance and reducing costs in daily experiments across diverse scientific disciplines, with a pronounced impact on cell biology. The amalgamation of these groundbreaking techniques holds the potential to amplify the generation of stable cell lines and the production of extracellular matrix hydrogels. These hydrogels, assuming a pivotal role in isolating cells at the single-cell level, facilitate a myriad of analyses. This study presents a novel method that seamlessly integrates CRISPR-Cas9 gene editing techniques with single-cell isolation methods in induced pluripotent stem cell (hiPSC) lines, utilizing the combined power of droplets and hydrogels. This innovative approach is designed to optimize clonal selection, thereby concurrently reducing costs and the time required for generating a stable genetically modified cell line. By bridging the advancements in gene editing and microfluidic technologies, our approach not only holds significant promise for the development of disease models and assays but also addresses the crucial need for efficient single-cell isolation. This integration contributes to streamlining processes, making it a transformative method with implications for enhancing the efficiency and cost-effectiveness of stable cell line generation. As we navigate the intersection of gene editing and microfluidics, our study marks a significant stride toward innovative methodologies in the dynamic landscape of cellular and molecular physiology research.

Correcting model misspecification in physics-informed neural networks (PINNs)

Data-driven discovery of governing equations in computational science has emerged as a new paradigm for obtaining accurate physical models and as a possible alternative to theoretical derivations. The recently developed physics-informed neural networks (PINNs) have also been employed to learn governing equations given data across diverse scientific disciplines, e.g., in biology and fluid dynamics. Despite the effectiveness of PINNs for discovering governing equations, the physical models encoded in PINNs may be misspecified in complex systems as some of the physical processes may not be fully understood, leading to the poor accuracy of PINN predictions. In this work, we present a general approach to correct the misspecified physical models in PINNs for discovering governing equations, given some sparse and/or noisy data. Specifically, we first encode the assumed physical models, which may be misspecified in PINNs, and then employ other deep neural networks (DNNs) to model the discrepancy between the imperfect models and the observational data. Due to the expressivity of DNNs, the proposed method is capable of reducing the computational errors caused by the model misspecification and thus enables the applications of PINNs in complex systems where the physical processes are not exactly known. Furthermore, we utilize the Bayesian physics-informed neural networks (B-PINNs) and/or ensemble PINNs to quantify uncertainties arising from noisy and/or gappy data in the discovered governing equations. A series of numerical examples including reaction-diffusion systems and non-Newtonian channel and cavity flows demonstrate that the added DNNs are capable of correcting the model misspecification in PINNs and thus reduce the discrepancy between the physical models encoded in PINNs and the observational data. In addition, the B-PINNs and ensemble PINNs can provide reasonable uncertainty bounds in the discovered physical models, which makes the predictions more reliable. We also demonstrate that we can seamlessly combine the present approach with the symbolic regression to obtain the explicit governing equations upon the training of PINNs. We envision that the proposed approach will extend the applications of PINNs for discovering governing equations in problems where the physico-chemical or biological processes are not well understood.

Adapting an Organizational Culture to Stay Abreast of Technological Changes in Indonesia’s Government Scientific Sector

This research navigates the disruptions the government science sector faces in an era of rapid technological change, examining the interplay of government science funding, information technology integration, and research collaboration networks. The study employs a cross-sectional design, exploring the impact of government scientific communication policies and access to advanced research technologies as independent variables, mediated by a researcher's digital literacy and skills, on the dependent variable of innovation resilience in government science. Additionally, the moderating role of government scientific infrastructure and capabilities is investigated. A comprehensive survey was administered to participants within the government science sector in Indonesia, encompassing diverse demographics and scientific disciplines. Results reveal a significant positive association between transparent government communication policies and innovation resilience, highlighting the pivotal role of effective communication in fostering adaptability. Furthermore, the study underscores the importance of government initiatives in building digital literacy and skills among researchers. The positive impact of communication policies on digital capabilities emphasizes the need for proactive strategies to enhance workforce proficiency in a technologically dynamic environment. The conceptual framework, validated through empirical findings, introduces the central construct of innovation resilience in government science, emphasizing adaptability to disruptive changes and consistent innovation.

Unveiling the Dual-Enhancing Mechanisms of Kinetically Controlled Silver Nanoparticles on Piezoelectric PVDF Nanofibers for Optimized SERS Performance.

Noble metal nanoparticle (NMP)-based composite substrates have garnered significant attention as a highly promising technique for surface-enhanced Raman scattering (SERS) in diverse scientific disciplines because their remarkable ability to amplify and functionalize Raman signals has positioned them as valuable tools for molecular detection. However, optimizing the size and distribution of NMPs has not received sufficient emphasis because of challenges associated with the precise control of deposition and the modulation of reducing rates during growth. In this research, we achieved the optimized size and spatial patterns of AgNWs on electrospun poly(vinylidene fluoride) (PVDF) nanofibers by utilizing a polydopamine (PDA) layer as a mild and controllable reduction mediator, by which the size and density of the AgNWs could be relatively precisely manipulated, achieving a dense distribution of effective "hot spots". On the other hand, harnessing the inherent piezoelectric properties of the electrospun PVDF nanofibers further boosted the LSPR effect during the SERS test, forming a flexible dual-enhancing composite SERS substrate with excellent sensitivity. In addition to addressing structural aspects, exploiting synergistic systems capable of transferring external energy or forces to enhance the SERS performances presents a compelling avenue to broaden the practical applications of SERS. The dual-enhanced substrate achieved an exceptional enhancement factor (EF) of 1.05 × 108 and a low detection limit (LOD) of 10-10 M during the SERS test. This study focuses on integrating NMPs with electrospun piezoelectric polymer nanofibers to develop a dual-enhancing SERS substrate with excellent sensitivity and practicality. The findings provide valuable insights into controllably depositing NMPs on electrospun polymer fibers and hold significant implications for the development of highly sensitive and practical SERS substrates across various applications.

Artificial intelligence (AI)-it's the end of the tox as we know it (and I feel fine).

The rapid progress of AI impacts diverse scientific disciplines, including toxicology, and has the potential to transform chemical safety evaluation. Toxicology has evolved from an empirical science focused on observing apical outcomes of chemical exposure, to a data-rich field ripe for AI integration. The volume, variety and velocity of toxicological data from legacy studies, literature, high-throughput assays, sensor technologies and omics approaches create opportunities but also complexities that AI can help address. In particular, machine learning is well suited to handle and integrate large, heterogeneous datasets that are both structured and unstructured-a key challenge in modern toxicology. AI methods like deep neural networks, large language models, and natural language processing have successfully predicted toxicity endpoints, analyzed high-throughput data, extracted facts from literature, and generated synthetic data. Beyond automating data capture, analysis, and prediction, AI techniques show promise for accelerating quantitative risk assessment by providing probabilistic outputs to capture uncertainties. AI also enables explanation methods to unravel mechanisms and increase trust in modeled predictions. However, issues like model interpretability, data biases, and transparency currently limit regulatory endorsement of AI. Multidisciplinary collaboration is needed to ensure development of interpretable, robust, and human-centered AI systems. Rather than just automating human tasks at scale, transformative AI can catalyze innovation in how evidence is gathered, data are generated, hypotheses are formed and tested, and tasks are performed to usher new paradigms in chemical safety assessment. Used judiciously, AI has immense potential to advance toxicology into a more predictive, mechanism-based, and evidence-integrated scientific discipline to better safeguard human and environmental wellbeing across diverse populations.

Exact soliton solutions and the significance of time-dependent coefficients in the Boussinesq equation: theory and application in mathematical physics

This article effectively establishes the exact soliton solutions for the Boussinesq model, characterized by time-dependent coefficients, employing the advanced modified simple equation, generalized Kudryashov and modified sine–Gordon expansion methods. The adaptive applicability of the Boussinesq system to coastal dynamics, fluid behavior, and wave propagation enriches interdisciplinary research across hydrodynamics and oceanography. The solutions of the system obtained through these significant techniques make a path to understanding nonlinear phenomena in various fields, surpassing traditional barriers and further motivating research and application. Significant impacts of the coefficients of the equation, wave velocity, and related parameters are evident in the profiles of soliton-shaped waves in both 3D and 2D configurations when all these factors are treated as variables, which are not seen in the case for constant coefficients. This study enhances the understanding of the significant role played by nonlinear evolution equations with time-dependent coefficients through careful dynamic explanations and detailed analyses. This revelation opens up an interesting and challenging field of study, with promising insights that resonate across diverse scientific disciplines.