Advanced Science Research Articles

Navigating the confluence of econometrics and data science: Implications for economic analysis and policy

This paper explores the transformative integration of econometrics and data science, a synergy poised to redefine empirical research within economics. By merging traditional econometric methods with advanced data science techniques, such as machine learning algorithms and big data analytics, this interdisciplinary approach enables a deeper, more nuanced understanding of complex economic phenomena. We delve into the theoretical foundations underlying this integration, highlighting how machine learning algorithms like random forests and neural networks complement conventional regression analysis, thereby enhancing model complexity and predictive accuracy. The paper further discusses methodological advancements, including handling high-dimensional data, incorporating unstructured data through natural language processing, and the evolution of model selection processes empowered by machine learning. Practical applications are thoroughly examined across three pivotal areas: economic forecasting and policy analysis, financial markets and risk management, and social economic analysis and public policy, showcasing the significant contributions of this convergence to economic forecasting, policy formulation, and the assessment of public interventions. This comprehensive exploration underscores the potential of combining econometrics and data science to offer more precise and actionable insights for policymakers, researchers, and practitioners in the field of economics.

Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Double anonymous • Conference submission management system: Morressier • Number of submissions received: 23 • Number of submissions sent for review: 18 • Number of submissions accepted: 8 • Acceptance Rate (Submissions Accepted / Submissions Received × 100): 34.7 % • Average number of reviews per paper: 2.33 • Total number of reviewers involved: 29 • Contact person for queries: Name: Fiona LEE Affiliation: Society of Advanced Science and Engineering Email: fionalee@ase-society.org

A journey into the innovations and expertise of Dubai police and the general department of forensic science and criminology

This manuscript offers an in-depth exploration of the Dubai Police and its General Department of Forensic Science and Criminology, emphasizing their critical role in law enforcement through advanced forensic science and innovative practices. Beginning with a historical overview, it traces the evolution of forensic disciplines from Edmond Locard's foundational principles to Sir Alec Jeffreys' groundbreaking DNA profiling techniques. The manuscript details the structure and functions of specialized sections within the department, including Biology and DNA, Forensic Chemistry, Forensic Toxicology, Firearms and Tool Marks, Questioned Documents, Forensic Explosive, Forensic Fire Investigation, Forensic and Mechanical Engineering, and Nuclear Physics. Key achievements, such as the Biology and DNA Section's processing of thousands of cases and the generation of highly discriminating DNA profiles, are highlighted. The manuscript also addresses the department's challenges, including the rapid evolution of technology and the increasing complexity of crimes. Future plans to enhance capabilities through investment in new technologies, international collaboration, and continuous professional development are outlined. By showcasing the department's commitment to innovation, excellence, and public safety, this manuscript underscores the pivotal contributions of the Dubai Police and its forensic department to crime resolution and community safety. The insights provided aim to foster a deeper understanding of the critical role of forensic science in the criminal justice system.

Bandgap engineering of novel Cu-vanadate/g-C3N4 hierarchical nanoflowers for enhanced photodegradation and excellent biosensing capability

Copper vanadate and graphitic carbon nitride (g-C3N4) are pivotal materials in the realm of advanced materials science, owing to their distinctive optical and electronic conductive properties. In the context of environmental photocatalysis and biosensing applications, their synergistic combination holds significant promise. Copper vanadate, renowned for its optical and electronic properties, complements the unique architecture and optical conduction characteristics of g-C3N4. Together, these materials exhibit immense potential for driving advancements in environmental sustainability and diagnostic techniques. In this study, we report the synthesis of copper vanadate (Cu0.261V2O5) and its composite with graphitic carbon nitride. XRD confirms the synthesis of the novel phase, FESEM and TEM analysis reveals hierarchical nanoflowers and UV-Visible spectroscopy divulges the visible light activation of the synthesized material and aids in the calculation of the optical bandgap. Interfacial charge transfer was studied with the help of EIS and Mott-Schottky analysis was employed to reveal the semiconductor type (n or p type) and determine the band edges. The as-prepared material was tested for the photocatalytic removal of methylene blue (MB), followed by the study of reaction kinetics, cyclic stability and active radicals participating in the photocatalysis process. The synthesized heterojunction showed 100 % removal of MB within a tenure of 90 minutes. The results of the experiment revealed that Cu0.261V2O5 decorated with g-C3N4 displays 1.5 and 1.4 times better photocatalytic degradation capability than pure Cu0.261V2O5 and g-C3N4, respectively. Furthermore, the synthesized material was evaluated for electrochemical biosensing of ascorbic acid in PBS (pH 7) analyte using a 3-electrode system. Compared to the pure material, the heterojunctions drew 1.9 times more current and provided near unity correlation (R2). It is evident that Cu0.261V2O5 decorated with g-C3N4 can be implemented for commercial eco-remediation and electrochemical ascorbic acid sensing.

Multi-channel polarization manipulation based on graphene for encryption communication

Wave-based cryptography, at the vanguard of advancing technologies in advanced information science, is essential for establishing a diverse array of secure cryptographic platforms. The realization of these platforms hinges on the intelligent application of multiplexing techniques, seamlessly combined with appropriate metasurface technology. Nevertheless, existing multi-channel encryption technologies based on metasurfaces face challenges related to information leakage during partial channel decoding processes. In this paper, we present a reprogrammable metasurface for polarization modulation. This metasurface not only allows for the arbitrary customization of linearly polarized reflected waves but also enables real-time amplitude modulation. Here, relying on polarization amplitude control, a fully secure communication protocol is developed precisely in the terahertz (THz) spectrum to achieve real-time information encryption based on polarization modulation metasurfaces where access to information is highly restricted. The proposed metasurface employs the double random phase encryption (DRPE) algorithm for information encryption. It transmits the encrypted data through different polarization channels using two graphene nanoribbons, exclusively controlled by external biasing conditions. Various encryption scenarios have been outlined to fortify information protection against potential eavesdroppers. The simulated results show that this unique technology for hiding images by manipulating the polarization of the reflected wave provides new opportunities for various applications, including encryption, THz communications, THz secure data storage, and imaging.

An integrated computation framework for predicting mechanical performance of single-phase alloys manufactured using laser powder bed fusion: A case study of CoCrFeMnNi high-entropy alloy

Establishing a process-structure-property (PSP) relationship is crucial for understanding and optimizing the mechanical properties of components via laser powder bed fusion (L-PBF) additive manufacturing (AM). However, the parameters pertinent to the PSP relationship are encapsulated in a “black box” with a high dimension, resulting in relying on trial-and-error approaches to elucidate hidden associations becoming impracticable, while the acquisition of microscopic data proves financially burdensome. Thus this paper presents an integrated computation framework based on machine learning (ML) for predicting the tensile yield strengths and post-yield hardening rates of single-phase alloys fabricated through L-PBF, for efficient process optimization purposes. Research herein focuses on single-phase alloys to be built via L-PBF, taking CoCrFeMnNi high-entropy alloy as a study case, and provide an efficient integrated computation solution. The methodology proposed in this paper generates as-built microstructures associated with the varying L-PBF process parameters via cellular automata (CA). Subsequently, the mechanical responses of these microstructures are estimated by employing crystal plasticity finite element (CPFE) simulations. In addition, the approach raised in the paper leverages particle-level microstructure descriptors instead of average feature descriptors for the entire representative volume element (RVE) structure in an ML framework to predict the characteristics of the newly randomly growing grains. This research demonstrate the practicality and instructiveness of the methodology for modeling realistic grains. Then, trained ML model exhibits promising accuracy for the prediction of mechanical properties. This work showcases an integrated computation framework combining advanced materials science and ML techniques for enhancing understanding of the L-PBF process and offering valuable insights into process optimization for L-PBF-built diverse materials.

Atomic physics on a 50-nm scale: Realization of a bilayer system of dipolar atoms.

Controlling ultracold atoms with laser light has greatly advanced quantum science. The wavelength of light sets a typical length scale for most experiments to the order of 500 nanometers (nm) or greater. In this work, we implemented a super-resolution technique that localizes and arranges atoms on a sub-50-nm scale, without any fundamental limit in resolution. We demonstrate this technique by creating a bilayer of dysprosium atoms and observing dipolar interactions between two physically separated layers through interlayer sympathetic cooling and coupled collective excitations. At 50-nm distance, dipolar interactions are 1000 times stronger than at 500 nm. For two atoms in optical tweezers, this should enable purely magnetic dipolar gates with kilohertz speed.

Enhanced Health Management: Tailored Solutions for Elderly Care

Abstract: In an era characterized by a globally aging pop- ulation, the imperative to address the comprehensive health needs of older adults has intensified. Among these needs, dietary wellness emerges as a fundamental aspect crucial for sustaining vitality and managing age-related conditions. Recognizing this imperative, our work introduces a pioneering personalized health ecosystem tailored to the unique dietary requirements of seniors. Through the utilization of advanced data science and machine learning methodologies, our platform presents a holistic solution aimed at enhancing dietary habits and overall well-being among seniors. Therefore, our health management system endeavors to foster healthy eating practices and augment the quality of lifefor older adults, thereby cultivating a proactive ethos of wellness and self-care.

Nanotechnology: “Advancing Material Science and Medicine”

Nanotechnology has been a rapidly growing field of advanced science at the inception of this century. Nanotechnology of advanced materials, polymers, principally revolves around endeavours to plan materials at a sub-atomic level to accomplish alluring properties and applications at a naturally visible level. Nanotechnology can be used for the advancement of technologies, ranging from communication and information, health and medicine, future energy, environment and climate change to transport and cultural heritage, personal protective equipment (PPE), fuels, fuel cells, biosensors, disease sensors etc. Nanomaterials will lead to a new approach to manufacturing materials and devices. Faster computers, advanced pharmaceuticals, controlled drug delivery, biocompatible materials, nerve and tissue repair, crackproof surface coatings, better skin care and protection, more efficient catalysts, better and smaller sensors, even more efficient telecommunication. For example, a low risk solution using antibody modified bismuth nanoparticle, in combination with an X-ray dose equivalent to a chest X-ray specifically, has been shown to kill the common bacterium Pseudomonas aeruginosa in a set up designed to resemble a deep wound in human tissue. Nanosized gold particle could catalyse the oxidation of carbon monoxide better than anything previously known. Heparin functionalized nanoparticles have been use for targeted delivery of anti-malarial drugs. Heparin is abundant and cheap, compared to treatments that involve antibodies, an important consideration, since malaria is most common in developing countries. A bone repairing nano-particle paste has been developed that promises faster repair of fractures and breakages. DNA containing two growth genes is encapsulated inside synthetic calcium phosphate nanoparticles. In a remarkable demonstration of the extreme limits of nanoscale engineering, researchers have used the tip of a scanning tunnelling microscope to cleave and form selected chemical bonds in a complex molecule. Many medicinal and industrial endeavours have seen the use of Nanotechnology. Nanoparticles can attach to SARS COV-2 viruses, disrupting their structure and so kill the virus. These and other more recent advances in nanotechnology will be presented at this conference.

ANALYSIS OF NOVASAR-1 S-BAND DATA IN DEVELOPING AN ALTERNATIVE LAND COVER MAPPING

Abstract. The Advanced Science and Technology Institute of the Department of Science and Technology (DOST-ASTI), through its Synthetic Aperture Radar and Automatic Identification System (SARwAIS) Project, has gained access to S-Band SAR images acquired by the NovaSAR-1 satellite of UK’s Surrey Satellite Technology Ltd. (SSTL) To help maximize the utility of these images especially in the aspect of terrain-related applications, their viability as potential alternatives to datasets like optical satellite images and other SAR images in characterizing land cover types was evaluated. Statistical analyses on the backscatter values from the tri-polarization ScanSAR datasets using Multivariate Analysis of Variance (MANOVA) and its corresponding post-hoc tests showed that there is a significant difference on the mean backscatter values at 0.05 level of significance. Moreover, Tukey’s honestly significant difference (Tukey’s HSD) test determined which pairs contribute to the significant difference. Using the Random Forest algorithm resulted in an accuracy of 66.93% without further optimization and/or reduction in the number of land covers being classified. Despite the relatively unremarkable accuracy score, it still showed potential for data augmentation with optical satellites for land cover mapping and other terrain-related applications.

AUGMENTING THE PHILIPPINES’ DOST-ASTI’S POTENTIAL FLOOD EXTENTS MAPPING SERVICE WITH S-BAND NOVASAR-1 IMAGES

Abstract. The Philippines’ Advanced Science and Technology Institute under the Department of Science and Technology (DOST-ASTI) has developed an AI-based and near real-time flood extent mapping service that utilizes C-Band Sentinel-1 SAR images. However, this method is limited by the availability of the Sentinel-1 images during flooding events. To address this issue, the institute, through its SARwAIS Project, utilized the S-Band NovaSAR-1 satellite, which was designed and launched by Surrey Satellite Technology, Ltd. With a 10% share to NovaSAR-1’s imaging capacity, the country can task image acquisitions that could help augment the Sentinel-1 datasets. Successfully captured images are prepared using the institute’s developed pre-processing workflow. Afterwards, a thresholding method, adopted from UN-SPIDER’s recommended practices for flood mapping, is employed to identify potentially flooded areas from these images. Generated products are then assessed to determine their relative accuracy in detecting potential floods. Satisfactory products are then distributed to relevant disaster management agencies and are also published in the agency’s social media page for further information dissemination. Python scripts were then developed to automate the established workflows, which were initially done manually. These scripts also help expedite the generation of flood maps especially when processing multiple SAR images. The acquisition and utilization of NovaSAR-1 images substantially help the country address the gaps on the availability of workable data for a more informative disaster response especially during flooding events.

Topological Insights into Nanostar Dendrimers by Computing the Augmented Zagreb Index.

The field of nanobiotechnology uses precise nanofabrication techniques to advance our understanding and control of biological systems. Due to their remarkable properties, dendrimers, which are hyperbranched macromolecular structures with distinct and well-defined architectures, have emerged as pivotal entities within this field. They are gaining increasing attention for their potential to catalyze a paradigm shift in medical therapeutics, biotechnological applications, and advanced material sciences. This paper focuses on a novel analytical expression and determines the precise value of the augmented Zagreb index, a topological descriptor, for eight classes of nanostar dendrimers. The Zagreb index is a topological invariant to predict molecular behaviour and reactivity. In this paper, we have explored its application in characterizing the branching of nanostar dendrimers through computational modelling and mathematical rigor. Our research has measured the augmented Zagreb index for nanostar dendrimers, which fall into eight distinct classes. The results better explain the relationship between the dendrimers' topology and chemical properties. This correlation has implications for their structural stability and reactivity, potentially leading to new applications. Developing the augmented Zagreb index for nanostar dendrimers is a significant breakthrough in nanobiotechnology. Based on the correlation between the calculated topological index and the corresponding molecular attributes, our analytical approach has opened up new possibilities for designing and synthesizing dendrimers tailored to specific functions in medical and material science applications. This precise topological quantification could significantly enhance the utility and functionalization of dendrimers in cutting-edge nanotechnological applications.

SWARN BHASM IS A TRADITIONAL AYURVEDIC MEDICINE, SYNTHESIS, CHARACTERIZATION AND USES OF SWARN BHASM AS A NANOPARTICLES

Nanotechnology is an emerging technology which involve advance study of particles having size between 1 to 100 nm. In Indian continents an advanced medical science exists from a very long-time 7th century AD. During ayurvedic treatment mostly bhasm was used, these were prepared reducing by particle size by calcination process. In calcination process pure metal that are zero valent changed into multi- valent. in this study gold bhasm was prepared and particle size was studied by using XRD, FTIR, UV-VIS and SEM spectra and confirmed by laser particle size distribution, and it has found it was 10 nm. elemental analysis of bhasm (ash) was done by atomic absorption spectrophotometer for the determination of gold purity. During study the size of gold particles was 45 nm and purity of gold was 99 %.

How different are offline and online diplomacy? A comparative analysis of public statements and SNS posts by delegates to the United Nations.

This article investigates the evolving landscape of diplomacy in the digital age, focusing on diplomats at the United Nations (UN) Headquarters in New York. The central inquiry revolves around how diplomatic actors use digital tools to complement or augment traditional face-to-face diplomacy. We systematically compare a substantial corpus of X posts (tweets) from UN diplomats with their public statements at the United Nations Security Council (UNSC), employing advanced computational social science techniques. This study applies a range of large-scale text analysis methods, including word embedding, topic modeling, and sentiment analysis, to investigate systematic differences between offline and online communication. Our analysis reveals that, while the essence of diplomacy remains consistent across both domains, there is strategic selectivity in the use of online platforms by diplomats. Online communication emphasizes non-security topics, ceremonial matters, and prominent policy stances, in contrast to the operational issues common in UNSC deliberations. Additionally, online discourse adopts a less confrontational, more public diplomacy-oriented tone, with variations among countries. This study offers one of the first systematic comparisons between offline and online diplomatic messages. It illuminates how diplomats navigate the digital realm to complement traditional roles. The findings indicate that some elements of public diplomacy and nation branding, directed toward a wider audience far beyond the council chamber, have become an integral part of multilateral diplomacy unfolding at the UNSC.

When Taekwondo Meets Artificial Intelligence: The Development of Taekwondo

This study explores the comprehensive understanding of taekwondo, the application of fourth industrial revolution technologies in various kinds of sports, the development of taekwondo through artificial intelligence (AI), and essential technology in the fourth industrial revolution while suggesting advanced science directions through a literature review. Literature was sourced from six internet search electronic databases, consisting of three English databases and three Korean databases, from January 2016 to August 2023. The literature indicated cases of sports convergence with the application of fourth industrial revolution technologies, such as the game of go, golf, table tennis, soccer, American football, skiing, archery, and fencing. These sports not only use big data but also virtual reality and augmented reality. Taekwondo is a traditional martial art that originated in Republic of Korea and gradually became a globally recognized sport. Since taekwondo’s competition analysis is an analysis in which researchers manually write events, it takes a very long time to analyze, and the scale of the analysis varies depending on the researcher’s tendencies. This study presented the development of an AI Taekwondo performance improvement analysis and evaluation system and a metaverse-based virtual Taekwondo pumsae/fighting coaching platform through an AI-based motion tracking analysis method.

Advanced intelligent monitoring technologies for animals: A survey

Effective animal intelligent monitoring is of great value in terms of ecological protection and endangered specie conservation. At present, computer vision technologies have shed light on animal intelligent monitoring. Especially, numerous deep learning-based models and methods have been developed to address various challenges, and have made substantial strides in this field. However, there are still several problems to be solved and related areas to be mined, such as exploring new strategies to enhance the robustness and generalization ability of models, designing novel models for complex environments, and establishing large-scale publicly available animal datasets for performance verification of models. Therefore, we comprehensively elaborated and analyzed existing works on animal intelligent monitoring based on advanced information science technologies, so as to provide useful information assistance for relevant researchers. In this paper, we focus on three primary task fields: precise animal localization, tracking and individual identification. Specifically, we elucidate the definition and significance of each monitoring task, and summarize the baseline models for addressing different problems. We provide a specific analysis of strategies and prototypes of the models and methods employed in each tasks following by the technical progression from traditional machine learning to deep learning. In addition, we make a comparison and analysis of the relevant methods, summarize their similarities and differences between them, and point out the advantages and disadvantages of these methods. Finally, we present several unresolved challenges and problems in animal intelligent monitoring and provide potential research directions in the future. We expect that our review can serve as reference and guidance for related research fields.

Socioeconomic determinants of modern climate change adaptation of small-scale vegetable farmers in Bohlabela District, Mpumalanga Province

Climate change significantly impacts small-scale agriculture, with limited adaptation capabilities due to lack of access to advanced science and technology. Traditional methods are ideal, but modern adaptations require significant financial investment, affecting the smallholder under-resourced agricultural sector's economic activities. The study analyzed climate change adaptation drivers among small-scale vegetable farmers, using a representative sample of 244 farmers from four villages through face-to-face interviews and semi-structured questionnaires. Using the Logistic regression model, the results showed resources (extension services), institutional (association membership) and societal influence (farm produce theft, and animal trespassing in the farming plots) to be associated with the use of modern climate change adaptation measures in the study area. The study recommends expanding extension services, strengthening community policing, creating community grazing guidelines, and training farmers on climate change causes, social cohesion, and mitigation strategies to address farm produce theft and animal trespassing. The study contributes new knowledge to the discourse of climate change adaptation by providing empirical evidence pointing out the need to consider critical non-climate factors for farmers when making climate change adaptations interventions in the smallholder farming sector.

Cybercrime Risk Found in Employee Behavior Big Data Using Semi-Supervised Machine Learning with Personality Theories

A critical worldwide problem is that ransomware cyberattacks can be costly to organizations. Moreover, accidental employee cybercrime risk can be challenging to prevent, even by leveraging advanced computer science techniques. This exploratory project used a novel cognitive computing design with detailed explanations of the action-research case-study methodology and customized machine learning (ML) techniques, supplemented by a workflow diagram. The ML techniques included language preprocessing, normalization, tokenization, keyword association analytics, learning tree analysis, credibility/reliability/validity checks, heatmaps, and scatter plots. The author analyzed over 8 GB of employee behavior big data from a multinational Fintech company global intranet. The five-factor personality theory (FFPT) from the psychology discipline was integrated into semi-supervised ML to classify retrospective employee behavior and then identify cybercrime risk. Higher levels of employee neuroticism were associated with a greater organizational cybercrime risk, corroborating the findings in empirical publications. In stark contrast to the literature, an openness to new experiences was inversely related to cybercrime risk. The other FFPT factors, conscientiousness, agreeableness, and extroversion, had no informative association with cybercrime risk. This study introduced an interdisciplinary paradigm shift for big data cognitive computing by illustrating how to integrate a proven scientific construct into ML—personality theory from the psychology discipline—to analyze human behavior using a retrospective big data collection approach that was asserted to be more efficient, reliable, and valid as compared to traditional methods like surveys or interviews.

FP‐JSC: Job failure prediction on supercomputers through job application sequence correlation

SummarySupercomputers are advanced computing systems interconnected through high‐speed communication networks, consisting of independent computational nodes. During the unfolding of the big data era, the potent computational capabilities of these supercomputers play a pivotal role in scientific computing. Despite executing numerous advanced computational science and engineering tasks on supercomputers, many submitted jobs fail due to various factors, resulting in user inefficiencies. These failures not only consume system resources but also reduce the overall efficiency of the system. Previous research often couples job performance features with a single machine learning method for predicting job failure. However, a primary hurdle emerges from the high cost of gathering these features, complicating their real‐world applicability. To address this challenge, our study establishes correlations among job applications through extensive job log analysis. Leveraging correlations, we propose a predictive framework based on job application sequence correlation (called FP‐JSC). This innovative framework employs multiple machine learning models to offer holistic predictions, selecting the most suitable model based on its learning effectiveness. Moreover, the framework optimizes feature collection expenses without adversely affecting job execution. We determine job applications using both job paths and job names, with the former emerging as a novel feature derived from supplementary monitoring data. Empirical results underscore FP‐JSC's effectiveness, accurately identifying over 89% of jobs with 95% specificity and 89% sensitivity—outperforming single prediction methods employed in related works.

Abstract 1258: A cholesterol metabolite can modulate neutrophil-derived extracellular vesicles to promote breast cancer cell epithelial to mesenchymal transition (EMT) and stemness

Abstract Breast cancer is the second leading cause of cancer-related death in women, with mortality most often due to metastatic recurrence. Elevated cholesterol is associated with poor prognosis, with several potential mechanisms proposed. Work in murine models found that many of the pro-metastatic actions of cholesterol required the enzymatic conversion of cholesterol to 27-hydroxycholesterol (27HC), which functions as a ligand for both the estrogen receptors (ERs) and liver X receptors (LXRs). Importantly, we have found that 27HC promotes metastasis, in part through its actions on neutrophils. When examining mechanisms, we have made the observation that 27HC resulted in increased secretion of small extracellular vesicles (sEVs) from neutrophils. These 27HC-neutrophil-sEVs independently promote metastatic colonization. Since we have found that breast cancer cell lines can take up neutrophil-derived sEVs, we hypothesized that 27HC results in altered cargo within sEVs which leads to functional changes within cancer cells, the ultimate consequence of which being their increased metastatic potential. Using a non-biased small RNA-seq approach we have found that sEVs from 27HC-treated neutrophils had an altered miRNA signature. Bioinformatic analysis revealed several downregulated miRNAs from the let-7 subgroup. Let-7 miRs are known to target several genes associated with the WNT/β-catenin pathway, a pathway strongly implicated in epithelial to mesenchymal transition (EMT) and cancer cell stemness. Strikingly, breast cancer cells treated with sEVs from neutrophils treated with 27HC lost their adherent properties and adopted a mesenchymal and stem-like phenotype. In order to elucidate the mechanism by which these sEVs promote EMT and stemness, we performed comprehensive transcriptomics through time. Weighted gene co-expression network analysis (WGCNA) confirmed these phenotypic shifts, and implicated the WNT/β-catenin pathway, providing support to our hypothesis that the loss of let-7 miRs is the upstream mediator. Collectively, our data suggests that 27HC increases metastasis in part through its effects on neutrophils, by shifting sEVs cargo towards a loss of protective microRNA, resulting in increased EMT and stemness, and thus the ability of cancer cells to migrate, invade and metastasize. Funding: National Cancer Institute (ERN: R01CA234025) Department of Defense (ERN: BCRP Era of Hope Award), and Beckman Institute for Advanced Science and Technology (NK) Citation Format: Natalia J. Krawczynska, Yu Wang, Anasuya Das Gupta, Jenny Drnevich, Julie Ostrander, Erik R. Nelson. A cholesterol metabolite can modulate neutrophil-derived extracellular vesicles to promote breast cancer cell epithelial to mesenchymal transition (EMT) and stemness [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1258.