Abstract: Standard autoregressive language models typically generate text in an open-loop fashion, ignoring the accumulation of errors over time. Consequently, despite their local fluency, these systems frequently suffer from long-horizon pathologies such as repetitive loops, diminished lexical diversity, and distributional collapse when relying on truncation-based sampling. To address this, we present Narrative-Dynamical Systems (NDS), a closed-loop decoding architecture that couples a frozen generator with a frozen encoder through a modular pre-sampling logit processor. NDS actively monitors online statistics across three channels—representation drift, token-level redundancy, and distributional concentration—and intervenes only when these signals jointly indicate a transition into a degenerate regime (low-drift/high-redundancy). The control action is injected directly into the logit space as a combination of (i) an orthogonally projected ascent step derived from a quadratic KL trust-region surrogate, and (ii) a sparse dynamic barrier designed to suppress empirically identified attractor token sets. We provide explicit derivations for the KL approximation and projection steps, alongside a closed-form bound demonstrating the exponential attenuation of probability mass assigned to the attractor set.

Abstract: INTRODUCTION Major Depressive Disorder (MDD) is a leading cause of disability worldwide and contributes significantly to the global burden of disease. Recent data show an increasing prevalence of Treatment-Resistant Depression (TRD). Patients with Autism Spectrum Disorder (ASD) often exhibit MDD as a comorbidity and it is often resistant to conventional treatments. ASD determines emotional dysregulation and a reduced ability to understand mental states (mentalization). These features can lead to suicidal ideation and/or behaviour. Intranasal Esketamine, approved for TRD, may offer a novel therapeutic option for this population. METHODS Our study evaluates the clinical response to intranasal Esketamine in patients with autism and TRD. The sample was composed of three young patients (n=3, F/M 2:1, age range 20-25 y) with light to moderate autism (Level 1 or 2). Esketamine was administered in augmentation with Selective Serotonin Reuptake Inhibitors (SSRIs) or Serotonin-Norepinephrine Reuptake Inhibitors (SNRIs). A structured follow-up protocol was set to monitor depressive symptoms, social cognition and mentalization. Follow-up during treatment was maintained for six months and psychometric evaluations were performed at six time points: baseline (T0), 1 week (T1), 1 month (T2), 2 months (T3), 3 months (T4) and 6 months (T5). Also, subjective quality of life was investigated before and after the observation period. RESULTS Despite differences in clinical profile, all patients showed good efficacy of Esketamine in reducing depressive symptoms: two patients experienced clinical remission (MADRS decrease > 50% from T0 to T5), while one patient showed partial remission (dMADRS=43.24%). No major side effects were reported. Significant improvements were observed after the first week of treatment (P1: MADRS_T0=37, MADRS_T1=12. P2: MADRS_T0=32, MADRS_T1=21. P3: MADRS_T0=25, MADRS_T1=12). Depressive relapses occurred (e.g. P1, T3-T4) but they were not associated with hospitalizations and/or suicidal attempts. Suicidal ideation, when present, decreased by the end of the follow-up period. Lack in mentalization and in social cognition were noted, with just mild improvements during therapy. Subjective quality of life improved significantly for all patients (P1: 28% at T0, 73% at T5. P2: 25% at T0, 71% at T5. P3: 35% at T0, 80% at T5). CONCLUSIONS Intranasal Esketamine showed a favourable efficacy and safety profile in these three cases of TRD in comorbidity with ASD (at six months: total remission=66.66%, partial remission=33.33%, inefficacy=0%, drop-out=0, severe adverse events=0). Besides improvements in depressive symptoms, Esketamine was associated with a constant decrease in suicidal thoughts. The small sample size doesn’t allow us to formalize statistical conclusions; preliminary data warrant further investigation in larger and randomized control studies to validate the therapeutic potential of Esketamine in this population.

Abstract: Unmanned aerial vehicles require reliable autonomous positioning beyond the limitations of GNSS, motivating the development of a vision-based, end-to-end Finding Point in Map algorithm. This study introduces MuRDE-FPN, an enhanced Feature Pyramid Network (FPN) designed for precise UAV localization, building upon a lightweight one-stream OS-PCPVT transformer backbone. MuRDE-FPN integrates Efficient Channel Attention for adaptive channel recalibration and features two novel components: a MultiReceptive Deformable Enhancement block that utilizes DCNv2 with varying kernel sizes to refine the semantically rich final feature layer, and a Feature Alignment Module for robust layer merging. Evaluated on the UL14 dataset and a new, more diverse UAV-Sat dataset (derived from UAV-VisLoc), MuRDE-FPN consistently outperformed 5 state-of-the-art FPI methods (FPI, WAMF-FPI, OS-FPI, DCD-FPI). It achieved an RDS of 84.26 on UL14 and 63.74 on UAV-Sat, demonstrating superior localization precision. Ablation studies confirmed the cumulative benefits of ECA, MuRDE, and FAM. These findings highlight the effectiveness of custom FPN designs and targeted feature enhancements for UAV-Satellite precise positioning, with MuRDE-FPN providing a robust solution and the UAV-Sat dataset offering a new benchmark for evaluation. Future efforts will address computational efficiency and performance across varying data-quality environments.

Abstract: Breeding of small cattle is affordable for small farms and does not require signif-icant financial investments, however, forage is often characterized by an insufficient content of minerals and vitamins. The paper analyzes patent developments of mineral and vitamin complexes (MVCs) intended for dry sheep and lambs, as well as a review of scientific data justifying the use of their components. Based on a search in the World Intellectual Property Organization (WIPO) database for the keywords “vitamins for sheep” and “minerals for sheep”, 23 patents focused on dry sheep and lambs were se-lected from 120 patents on feed additives for sheep. Their component composition is analyzed, which is conditionally divided into four groups: minerals, vitamins, func-tional and feed additives. It is shown that modern MVCs are developed taking into account the requirements of environmental safety and the physiological needs of ani-mals. It is concluded that minerals and vitamins should be considered as elements of a scientifically based vitamin and mineral system, the effectiveness of which during pregnancy is determined by the initial level of provision, the physiological status of animals and the characteristics of mineral metabolism.

Abstract: This study develops a spatio-temporal forecasting artifact for road traffic accidents in Ecuador, addressing a critical limitation in existing predictive approaches that rely predominantly on point error metrics without validating the statistical assumptions underlying forecast uncertainty. Motivated by pronounced territorial heterogeneity in accident incidence and the need for reliable decision-support tools, the research proposes a multiregional modeling framework that integrates statistical residual validation to enhance the robustness of road safety planning. Using a dataset of 27{,}648 monthly observations covering all 24 provinces from 2014 to 2025, the study applies the Prophet model within a Design Science Research paradigm and a CRISP-DM implementation cycle. Separate provincial models are estimated with a 24-month forecasting horizon, and methodological rigor is ensured through systematic residual diagnostics using the Shapiro--Wilk test for normality and the Ljung--Box test for temporal independence. Empirical results indicate that the Prophet-based artifact outperforms a naïve seasonal benchmark in 70.8\% of the provinces, demonstrating excellent predictive accuracy in structurally stable regions such as Tungurahua (MAPE = 10.9\%). At the same time, the framework enables the identification of critical emerging risks in provinces such as Santo Domingo and Cotopaxi, where projected increases exceed 49\% despite acceptable point forecasts. The findings confirm that point accuracy alone does not guarantee the validity of confidence intervals and that residual validation is essential for trustworthy uncertainty quantification. Overall, the proposed approach provides a robust foundation for a predictive surveillance system capable of supporting differentiated, evidence-based road safety policies in territorially heterogeneous contexts.

Abstract: The robotic arm of the Wentian module can complete tasks such as supporting astronauts' extravehicular activities, installing and maintaining payloads, and inspecting the space station. The 7-joint SSRMS manipulator is critical for space missions. This study aims to build its kinematic model via screw theory. It simplifies SSRMS to right-angle rods, defines joint screw axes, twist coordinates, and initial pose matrix. Using PoE formula, the 7-DOF forward kinematics equation is derived. Besides, it derives fixed joint angle for inverse kinematics, including analytical solutions and numerical solutions. It elaborates analytical solutions for fixing joints 1/7 and 2/6 and numerical solutions for fixing joints 3/4/5,solves all joint angles via kinematic decoupling, and addresses special cases. Experiments with China’s space station small arm parameters show The probability of meeting the accuracy threshold of 10−4 is 99.79%,verifying model effectiveness, while noting singularity-related weak solving areas. This provides a reliable basis for subsequent inverse kinematics optimization.

Abstract: Genetically encoded biosensors represent a cutting-edge class of biosensors due to real-time monitoring and programmability in living cell. However, the development of eukaryotic genetically encoded biosensors for new analytes is constrained by the shortage of signal–receptor pairs. Bacterial biosensors have been transferred to eukaryote to expand the signal detection space, having achieved remarkable success. However, due to the significant differences between eukaryotic and prokaryotic gene expression systems, optimizing bacterial biosensors has proven challenging. Successful cases indicate that developing orthogonal signal–receptor pairs directly from eukaryotic systems may offer a viable solution. Indeed, the potential of filamentous fungi—a highly diverse group of organisms that share conserved as well as specific signaling and metabolic pathways with yeast or mammalian cells—has been largely overlooked in biosensor development. In this review, we systematically examine sensing systems in filamentous fungi and summarize their signal recognition receptors, signal transduction pathways，responsive transcription factors and describe potential mining strategies for sensing elements from filamentous fungi.

Abstract:

Photo-oxidative stress results from an imbalance between light absorption and photosynthetic carbon utilization, posing a major threat to plant productivity and resilience under climate change. This review synthesizes recent advances in the molecular mechanisms of photo-oxidation, focusing on the dual role of reactive oxygen species (ROS) as both toxic byproducts and key signaling molecules. We outline the specific sites of ROS generation in chloroplasts, particularly singlet oxygen (¹O₂) at Photosystem II (PSII) and hydrogen peroxide (H₂O₂) at Photosystem I (PSI), and describe their distinct retrograde signaling pathways that regulate nuclear gene expression for acclimation. A systems perspective reveals how photo-oxidative damage propagates through interconnected cycles of impaired photosystem repair, lipid peroxidation, and protein oxidation, ultimately risking cellular collapse. To cope, plants employ a multi-layered photoprotective arsenal, including non-photochemical quenching (NPQ), alternative electron sinks, and integrated antioxidant networks. These mechanisms are further examined within an ecological and evolutionary context, highlighting natural variation and trade-offs between growth and defense. Finally, we discuss future directions for translating this knowledge into strategies for engineering climate-resilient crops, emphasizing the role of synthetic biology, multi-omics integration, and genomics-assisted breeding in supporting global food security.

Abstract: Conductive thread is an integral aspect of smart textiles in the domain of electronic textiles (e-textiles). This study unveils the development of twelve distinct variants of conductive threads using twisting method, the fusion of copper filament with cotton and polyester threads. The threads are coated with a carbon paste solution enriched with dissolved sea salt. The carbon paste is obtained from non-functional dry cell batteries, conventionally categorized as hazardous electronic waste (e-waste), that underscores an economically viable and environmentally sustainable approach. Experiments proved that each variant demonstrates minimal electrical resistance. Comparative analysis against commercially available conductive threads on the market reveals a significant performance advantage. Notably, the ‘Carbon Coated Cotton Twisted Copper Thread-II’ showcases a record low resistance of 0.0164 Ω cm-1 which is approximately 19.39 times lower than the most efficient counterpart, ‘Bekinox VN type (12/1x275/100z)’. Further investigation also demonstrates the integration of these conductive threads into fabric-based flexible circuits marking a significant advancement in e-textiles. Future avenues of research may focus on optimization strategies for fabricating conductive threads and exploring their diverse applications in wearable technology and smart textiles, thus catalyzing further progress in the field.

Abstract: This study investigates the utilization of Abies Alba exudate resin for the development of hybrid resins intended as matrices for composite materials. Two formulation routes were explored: (i) dilution of spruce resin in turpentine derived from pine buds, and (ii) dilution in food-grade ethanol (96%). The diluted resins were subsequently blended with an epoxy resin, whose addition initiated polymerization and enabled the formation of a solid hybrid matrix. The resulting hybrid resins were characterized by multiple testing methods and further applied in the fabrication of cotton fiber–reinforced composites.

Abstract:

Bacterial panicle blight (BPB) of rice, a disease caused by Burkholderia glumae and B. gladioli, threatens global rice yields and has recently emerged in Bangladesh. We analyzed 300 BPB-infected samples from 20 Bangladesh districts using S-PG medium and gyrB PCR amplification, identifying 46 B. gladioli and 5 B. glumae isolates. Twenty of these isolates were chosen for in-depth characterization. Pathogenicity tests identified B. glumae BD_21g as the most virulent strain, followed by B. gladioli BDBgla132A. Disease severity on rice strongly correlated with onion bulb assays, validating the assay as a rapid virulence-screening tool. Phenotypic characterization of the 20 isolates revealed substantial variation in toxoflavin production, lipase activity, polygalacturonase activity, motility, and type III secretion system. Comparative genomic analysis of virulence-associated genes between BDBgla132A and BD_21g showed high protein sequence identity, particularly in toxoflavin biosynthesis and transport genes, while genes encoding lipase (lipA/lipB), polygalacturonase (pehA/pehB), and those involved in motility, displayed moderate to high identity. Both strains retained virulence-related genes that are homologous to those of B. cepacia but displayed distinct pathogenic mechanisms. Real time RT-qPCR revealed significantly higher expression of toxoflavin and lipase-encoding genes in BD_21g compared with BDBgla132A, consistent with its elevated enzymatic activities. Conversely, BD_21g showed reduced expression of pectinolytic and flagellar genes over BDBgla132A, consistent with the enhanced pectinolytic activity and motility observed in BDBgla132A. These findings reveal that B. glumae BD_21g and B. gladioli BDBgla132A employ distinct virulence strategies to infect rice, providing critical insights for developing targeted BPB management approaches in Bangladesh.

Abstract: The Normalized Difference Vegetation Index (NDVI) derived from polar-orbiting satellites is widely used for vegetation monitoring; however, its temporal continuity is often limited by cloud contamination and fixed revisit cycles. This study investigates the feasibility of using geostationary satellite observations to support NDVI gap filling applications and continuous regional monitoring. Geostationary Ocean Color Imager II (GOCI-II) data were used as input, while Sentinel-2 Multispectral Instrument (MSI) NDVI served as the primary reference dataset. Landsat Operational Land Imager NDVI was additionally employed for independent cross-sensor comparison. A data-driven transformation framework was developed and applied to convert GOCI-II NDVI into MSI-equivalent NDVI while maintaining physically interpretable NDVI values. The transformed NDVI was evaluated through spatial comparisons and pixel-level statistical metrics, including correlation coefficient, mean absolute error, root mean square error, and structural similarity index measure. The results indicate that NDVI transformed from geostationary observations can capture broad spatial patterns and relative variability observed in MSI NDVI, particularly at the field scale. At the same time, reduced contrast and NDVI underestimation are observed, mainly due to spatial resolution differences and sub-pixel heterogeneity. This study emphasizes the potential role of geostationary satellite data as a complementary source for polar-orbiting NDVI products. The findings suggest that integrating geostationary and polar-orbiting satellite observations may contribute to improving NDVI continuity and supporting sustained vegetation monitoring over fixed regions where high temporal resolution is required.

Abstract:

The air quality index (AQI) depends on the concentrations of six pollutants (PM_2.5, PM₁₀, SO₂, NO₂, O₃, and CO). In this paper, a Prophet-LSTM model with improved particle swarm optimization (PSO) is proposed to analyze the time series of six pollutant concentrations in Wuhan city. First, the time series are decomposed by Prophet, and Prophet is used to predict the trend term and periodic term. Then, LSTM is used to predict the error term. Finally, the improved PSO algorithm is used for optimization. These experimental results indicated that (1) Prophet’s decomposition method has good applicability to time series with the multiplication form. The Prophet-LSTM model can overcome the influence of PM series irregularity, large fluctuations and multiple noise on the prediction effect, which improves the prediction ability of the model. (2) The improved PSO algorithm can greatly improve the accuracy of the weight solution space and has the attribute of parallel computing, which makes the solution forms more diversified. (3) The hybrid model has better prediction ability than the comparison model (LSTM, Prophet, Prophet-LSTM). The hybrid model combines the advantages of Prophet and LSTM, which has strong adaptability to the randomness of sample selection and has strong accuracy in predicting pollutant concentrations.

Abstract: Macrophages differentiated with macrophage colony-stimulating factor (M-CSF) (M-Mac) are widely used as an experimental model. Interleukin 27 (IL-27)-polarized M-Mac (27M-Mac) suppress HIV replication; however, the effects of IL-27 polarization on granulocyte-macrophage colony-stimulating factor (GM-CSF)-induced macrophages (GM-Mac) remain less investigation. Here, we compare multiple functional properties and gene expression profiles of 27M-Mac and IL-27-polarized GM-Mac (27GM-Mac). M-Mac and GM-Mac were generated from monocytes of healthy donors and subsequently treated with IL-27 for three-day. HIV replication in 27M-Mac, GM-Mac, and 27GM-Mac was suppressed to nearly 10 % of that in M-Mac; however, single-cell RNA sequencing showed that M-Mac clustered with GM-Mac, and 27M-Mac clustered with 27GM-Mac. Expression of CD38 and secretion of CXCL9 and C1q were significantly increased in 27M-Mac and 27GM-Mac compared with M-Mac and GM-Mac. Although CD16 and CD64 expression increased in 27M-Mac and 27GM-Mac relative to their respective controls, phagocytic activity in 27M-Mac and 27GM-Mac was 30% of that in M-Mac. Autophagy was induced 3.7-fold more strongly in 27M-Mac than in M-Mac, reaching levels comparable to those in GM-Mac and 27GM-Mac. Collectively, these findings indicate that IL-27 polarizes M-Mac and GM-Mac toward transcriptionally and functionally similar subtypes, providing insight into the role of IL-27 in macrophage polarization and plasticity.

Abstract: We study a variety of stochastic contact processes --directly related to models of rumor and disease spreading-- from the viewpoint of their constants of motion, either exact or approximated. Much as in deterministic systems, constants of motion in stochastic dynamics make it possible to reduce the number of relevant variables, confining the set of accessible states, and thus facilitating their analytical treatment. For processes of rumor propagation based on the Maki-Thompson model, we show how to construct exact constants of motion as linear combinations of conserved quantities in each elementary contact event, and how they relate to the constants of motion of the corresponding mean-field equations, which are obtained as the continuous-time, large-size limit of the stochastic process. For SIR epidemic models, both in homogeneous systems and on heterogeneous networks, we find that a similar procedure produces approximate constants of motion, whose average value is preserved along the evolution. We also give examples of exact and approximate constants of motion built as nonlinear combinations of the relevant variables, whose expressions are suggested by their mean-field counterparts.

Abstract:

The critical role of lithium in powering the new energy economy necessitates prioritizing efficient extraction methods. This study investigates a novel zeolitic imidazolate framework (ZIF-8)-coated manganese-based lithium ion sieve (LIS) for enhanced lithium recovery. The precursor of LIS, Li1.6Mn1.6O4, was synthesized via the hydrothermal method, followed by acid pickling to obtain the spinel lithium ion sieve H_1.6Mn_1.6O₄. The material was then immersed in a 2-methylimidazole/Zn(NO₃)₂ solution, undergoing ultrasonic-assisted hydrothermal growth to form ZIF@H_1.6Mn_1.6O₄ composites. Under optimized conditions (30 °C, pH=11, 24 h), the composite demonstrated superior lithium extraction performance compared to single-phase adsorbents, reaching 26.44 mg/g at the solution with 250 mg/L Li⁺. The adsorption capacity of the composite increased with Li⁺ concentration and reaction time. The adsorption kinetics followed a pseudo-second-order kinetic model and is dominated by chemisorption.

Abstract:

Music conveys emotion through a complex interplay of structural and acoustic cues, yet how these features map onto specific affective interpretations remains a key question in music cognition. This study explored how listeners, unaware of contextual information, categorized 110 emotionally diverse excerpts—varying in key, tempo, note density, acoustic energy, and expressive gestures—from works by Bach, Beethoven, and Chopin. Twenty classically trained participants labeled each excerpt using six predefined emotional categories. Emotion judgments were analyzed within a supervised multi-class classification framework, allowing systematic quantification of recognition accuracy, misclassification patterns, and category reliability. Behavioral responses were consistently above chance, indicating shared decoding strategies. Quantitative analyses of live performance recordings revealed systematic links between expressive features and emotional tone: high-arousal emotions showed increased acoustic intensity, faster gestures, and dominant right-hand activity, while low-arousal states involved softer dynamics and more left-hand involvement. Major-key excerpts were commonly associated with positive emotions—“Peacefulness” with slow tempos and low intensity, “Joy” with fast, energetic playing. Minor-key excerpts were linked to negative/ambivalent emotions, aligning with prior research on the emotional complexity of minor modality. Within the minor mode, a gradient of arousal emerged, from “Melancholy” to “Power,” the latter marked by heightened motor activity and sonic force. Results support an embodied view of musical emotion, where expressive meaning emerges through dynamic motor-acoustic patterns that transcend stylistic and cultural boundaries.

Abstract:

The paradigm shift toward cloud-based big data analytics has empowered organizations to derive actionable insights from massive datasets through scalable, on-demand computational resources. However, the migration of sensitive data to third-party cloud environments introduces profound privacy concerns, ranging from unauthorized data access to the risk of re-identification in multi-tenant architectures. This paper provides a comprehensive evaluation of current Privacy-Preserving Mechanisms (PPMs), systematically analyzing their efficacy in safeguarding data throughout its lifecycle—at rest, in transit, and during computation. The evaluation covers a broad spectrum of Privacy-Enhancing Technologies (PETs), including Differential Privacy (DP), Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC), and Trusted Execution Environments (TEEs). We examine the inherent trade-offs between data utility and privacy protection, specifically addressing the “utility-privacy” bottleneck where high levels of noise injection or encryption complexity often degrade the accuracy and performance of analytical models. Furthermore, the study explores the integration of Federated Learning as a decentralized approach to privacy, allowing for collaborative model training without the need for raw data movement. Critical challenges are identified, such as the scalability of cryptographic protocols in high-volume data streams and the regulatory pressures imposed by global standards like the GDPR and the EU AI Act. By synthesizing current industry practices with academic research, this paper highlights the gap between theoretical privacy models and their practical implementation in production-grade cloud infrastructures. The discourse concludes with a strategic roadmap for future research, emphasizing the need for Post-Quantum Cryptography (PQC) and automated privacy-orchestration frameworks. This comprehensive review serves as a foundational reference for researchers and system architects aiming to design resilient, privacy-centric cloud analytical systems that maintain compliance without sacrificing computational efficiency.

Abstract:

Astragalus membranaceus (AM) is a traditional medicinal and edible herb with well-documented immunomodulatory activities; however, its application in functional beverages is limited by the low bioavailability of its bioactive constituents. Probiotic fermentation has emerged as an effective strategy to enhance the nutritional and functional properties of herbal materials, yet the underlying metabolic mechanisms remain insufficiently understood. In this study, untargeted metabolomics based on ultra-high-performance liquid chromatography coupled with Orbitrap mass spectrometry (UHPLC-Orbitrap MS) was employed to comprehensively characterize metabolic alterations in AM aqueous extracts before and after fermentation with Pediococcus acidilactici (P. acidilactici) for 48 h. Multivariate statistical analyses combined with pathway enrichment analysis were used to identify differential metabolites and key metabolic pathways affected by fermentation. A total of 659 significantly altered metabolites were identified, including 350 upregulated and 309 downregulated metabolites after fermentation. These metabolites were mainly associated with organic acids, flavonoids, amino acid derivatives, nucleotides, and phenylpropanoids. Notably, fermentation markedly enhanced metabolites related to arginine biosynthesis, carbon metabolism, and nicotinate and nicotinamide metabolism, accompanied by a substantial accumulation of functional compounds such as lactate, phenyllactic acid, indolelactic acid, and nicotinamide adenine dinucleotide (NAD⁺). Overall, P. acidilactici fermentation induced extensive metabolic reprogramming of AM aqueous extracts, leading to the enrichment of multiple bioactive metabolites and the activation of key functional processes. These findings provide mechanistic insights into probiotic fermentation of medicinal and edible herbs and offer a scientific basis for the development of value-added fermented AM beverages with improved nutritional and functional properties.

Abstract:

Background: The global rise of Multidrug-Resistant (MDR) Escherichia coli (E. coli) represents a critical public health threat, severely compromising the treatment of infections. While Sequence Type 224 (ST224) is recognized as an emergent, high-risk lineage associated with extra-intestinal pathogenic E. coli (ExPEC) and MDR phenotypes globally, its specific genomic features and epidemiological footprint in Southeast Asia, particularly Myanmar, remain largely underexplored. Given Myanmar's vulnerability as an AMR hotspot, comprehensive genomic surveillance is critically leveraged. Method: A laboratory based cross sectional descriptive study conducted at Defense Services Medical Research Centre (DSMRC) during 20th January to 11th November 2025 and aimed to develop Oxford Nanopore Technologies (ONT) long-read sequencing, an early manifestation of this approach for bacterial genomic characterization in Myanmar. Five clinical MDR E. coli isolates (NMA_MM001) from (No.1) Defense Service General Hospital (DSGH) which were identified by Vitek2 analyzer were collected. Extracted DNA was sequenced on the MinION device at DSMRC. Bioinformatic analysis utilized the ONT EPI2ME platform for de novo assembly, followed by MLST, ResFinder, and PlasmidFinder analyses to characterize the isolate's resistome, mobilome, and virulence. Results: Out of five isolates, MDR E. coli (NMA_MM001) of ONT sequencing successfully generated a high-quality, near-closed assembly (N50: 4,911,841 bp, 5 contigs). MLST classified the isolate as ST224. This study confirmed a severe MDR phenotype, identifying bla_DHA-1 (AmpC beta-lactamase), bla_TEM-1, and two plasmid-mediated quinolone resistance genes (qepA4 and qnrB4). Crucially, the carbapenemase gene bla_NDM-5 was identified, located on a highly mobile IncFII plasmid (pAMA1167-NDM-5). This constitutes the first report detailing the emergence of this NDM-5-producing ST224 lineage and its high genomic complexity in Myanmar. Conclusion: This study validates ONT long-read sequencing as an indispensable tool for resolving complex MDR genomes in resource-limited settings. The findings confirm the establishment of an MDR E. coli ST224 isolate in Myanmar carrying the critical bla_NDM-5 carbapenemase gene on a highly mobile IncFII plasmid. This genomic information, identification of E. coli ST224, provides an urgent early warning of a highly resistant pathogen, mandating the immediate implementation of targeted infection control measures and regional One Health surveillance programs.