Search Results (586)

Chronic liver diseases are frequently accompanied by metabolic dysfunction, making precision nutrition relevant for prevention, diagnosis, risk stratification, and management. This review summarizes nutritional omics evidence in hepatology, emphasizing metabolic dysfunction-associated steatotic liver disease as a prevalent model. Nutrigenetics provides information on inherited susceptibility by identifying genetic variation affecting hepatic lipid handling, triglyceride export, phospholipid remodeling, and glucose-driven lipogenesis. Nutrigenomics characterizes transcriptional programs involved in hepatic lipogenesis, inflammation, oxidative stress, and fibrogenesis. Nutriepigenetics captures exposure memory through DNA methylation, histone regulation, and small-RNA signaling, including miR-122, miR-21, miR-34a, and miR-192; diet may modulate this layer through one-carbon metabolism, methyl-donor availability, oxidative stress, acetyl-CoA and NAD+-dependent pathways, and lipid peroxidation. Nutrimetagenomics highlight taxa such as Ruminococcus, Faecalibacterium, Veillonella, Bacteroides, Escherichia, and Klebsiella, but translation requires functional characterization beyond stool taxa. Nutrimetabolomics and lipidomics, including OWL-liver platforms, track dietary adherence, lipid species, bile acids, amino acids, lipoproteins, and biological non-response. Future progress will require AI-supported, phenotype-first integrated models tested in multiethnic longitudinal studies with standardized dietary assessment, biospecimen collection, meaningful liver endpoints, realistic workflows, and adaptive lifestyle-care strategies. Full article

Climate change is intensifying drought, salinity, heat, chilling, flooding, and heavy-metal stresses across major fruit-producing regions, threatening yield stability and fruit quality in economically vital, perennial crops such as apple, grapevine, citrus, banana, strawberry, and peach. Because these species are long-lived, highly heterozygous, and polyploid, conventional breeding for climate resilience remains slow and often inadequate, necessitating molecular strategies informed by systems-level understanding. This review synthesizes recent advances in multi-omics research spanning genomics, transcriptomics, proteomics, metabolomics, epigenomics, ionomics, and phenomics that have collectively decoded the regulatory architecture underlying abiotic stress perception, signaling, and tolerance in fruit crops. Hormonal networks, particularly abscisic acid (ABA) crosstalk with jasmonate, salicylic acid, ethylene, and brassinosteroids, emerge as central integrators of stress responses, coordinating stomatal regulation, osmolyte accumulation, antioxidant defense, and secondary metabolite biosynthesis. Genomic and pangenomic approaches have identified stress-associated loci and cultivar-specific structural variants, while transcriptomic and proteomic studies reveal transcription factor networks (MdERF38–MdMYB1, MaMYB4–MaHDA2, VvDREB1, CsNAC29) and post-translational regulatory switches governing tolerance mechanisms across drought, cold, salinity, and flooding stress. Metabolomic and ionomic profiling link biochemical reprogramming to fruit quality traits, whereas epigenomic mechanisms including DNA methylation, histone modifications, and small RNA regulation provide a chromatin-level layer mediating stress memory across growing seasons. Integration of these omics layers through systems biology, machine learning, and high-throughput phenomics is enabling functional validation via CRISPR-Cas9 and marker-assisted selection, translating correlative associations into causally validated breeding targets. Despite this progress, challenges including batch effects, tissue heterogeneity, and methodological inconsistencies in data integration continue to constrain translational applications. This highlights convergent regulatory hubs across stress types and species, underscoring multi-omics-guided precision breeding as the most promising pathway toward developing climate-resilient, high-quality fruit crop cultivars for sustainable global production. Full article

Cultural memory in classical poetry is carried not by isolated words but through textual relations, interpretive traditions, and paratextual frames. Taking “Nanshan” (南山) in the Shijing (詩經) as a mnemonic node, this study examines how its cultural memory is reconstructed in four English translations by James Legge, Arthur Waley, Bernhard Karlgren, and Xu Yuanchong. A small corpus of eleven Shijing poems containing these mountain expressions was compiled, yielding forty-four poem–translator records. The analysis combines manual coding, checked through independent second coding of a subset of records, with descriptive statistics and close case studies across five dimensions: naming strategy, spatial function, cultural memory strategy, poetic reconstruction, and paratextual mediation. The results show that geographical renderings such as “southern hill(s)” and “Southern Mountain” predominate, but they do not necessarily erase cultural memory; conversely, phonetic or proper-name retention does not automatically preserve it. More specifically, the visibility of Nanshan’s affective, political-ritual, auspicious, and agrarian associations depends on the interaction of translated names with poetic context, titles, notes, classification, selection, and verse form. Translating classical poetic imagery, the study argues, involves not merely retaining culture-specific names but rebuilding the interpretive conditions under which cultural memory can be recognized in the target language. Full article

Olfactory dysfunction is an early non-cognitive feature of Alzheimer’s disease (AD), but smell impairment has traditionally been used mainly as a behavioral marker. This review examines the emerging use of the olfactory cleft as an accessible source of living neuronal, epithelial, progenitor, and immune cells for studying AD pathobiology. Histopathological and patient-derived culture studies have reported amyloid-β, tau, oxidative-stress, mitochondrial, biometal, and transcriptional abnormalities in olfactory tissue. More recent endoscopically guided brush sampling with single-cell profiling has identified activated memory CD8 T-cell states, inflammatory myeloid programs, and neuronal metabolic changes, including in cognitively unimpaired individuals with abnormal cerebrospinal-fluid amyloid biomarkers. These findings support olfactory-cleft sampling as a research platform for investigating early neural–immune changes, but current evidence is based on small, largely cross-sectional cohorts and does not establish disease specificity, causality, or prognostic utility. Longitudinal multicenter studies integrating olfactory-tissue profiling with established fluid, imaging, genetic, cognitive, and olfactory biomarkers are required before clinical translation. Full article

Immersive Virtual Reality (VR) environments are increasingly adopted in clinical psychology and stress-management contexts; however, their therapeutic effectiveness depends on the system’s ability to understand and dynamically respond to users’ affective states. Emotional regulation plays a key role in psychological resilience, directly influencing stress coping mechanisms, cognitive performance, and overall mental well-being. Despite recent advances, automatic recognition of scenario-associated affective conditions in VR remains challenging because head-mounted displays occlude facial features. This study proposes a VR-based serious game for affective training and regulation, in which users interact with goal-oriented scenarios targeting fear, anger, and joy. We introduce a multimodal affective computing model to objectively assess users’ emotional responses by integrating electrocardiogram (ECG) signals and posture-based features extracted through computer vision. An early-fusion architecture combined with a Long Short-Term Memory (LSTM) network captures temporal dependencies in synchronized multimodal data. We established a controlled experimental framework using immersive VR scenarios, enabling the collection of synchronized physiological and behavioral data from a cohort of 20 healthy adult participants. The proposed model was evaluated under a strict Leave-One-Subject-Out (LOSO) cross-validation scheme across independent subjects, achieving a robust inter-subject accuracy of $78.94\%\pm 10.77\%$ and a global macro F1-score of 0.635, demonstrating strong generalization to entirely unseen users without data leakage. Furthermore, the system maintained an outstanding balance in detecting active emotional states (recall > 80.0% for fear, anger, and joy). Additionally, subjective evaluations using the PANAS and SGU questionnaires confirmed the coherence between detected and perceived emotional states, as well as the system’s high usability. The results suggest the potential viability of combining immersive environments and multimodal affective computing to explore the technical feasibility of adaptive frameworks that could eventually translate into healthcare contexts. This work may contribute to the development of intelligent digital health technologies by providing a foundation for responsive VR systems that can monitor emotional regulation and are fully aligned with sustainable well-being ecosystems (SDG 3: Good Health and Well-being and SDG 10: Reduced Inequalities). Full article

Solving problems associated with the efficient distribution and organization of resources has generated increasing interest in the scientific community. One of the most commonly used approaches consists of approximate solution techniques, which have been able to solve complex covering problems within acceptable computational time and cost. One of the benchmarks used to evaluate these approaches is the Set Covering Problem, which is an NP-hard combinatorial optimization problem. Among the techniques that have been investigated, metaheuristics play an important role. These methods are commonly developed for continuous search spaces and, in order to be applied to covering problems, must be modified to operate in discrete domains. This modification presents an important challenge: finding an appropriate transformation method that translates continuous solutions into binary solutions. This issue has been addressed through two main strategies: binarization using two-step schemes, and, in our proposal, the use of repair operators orchestrated according to their performance through an Adaptive Repair Selection Mechanism based on the multi-armed bandit framework. To evaluate our proposal, we selected the Binary Hunger Games Search metaheuristic because the relative quality of each individual determines its hunger level, which in turn regulates the movement of the population and the influence of the best solution found. Infeasible solutions are handled through a set of Tabu Search-based repair operators. Instead of applying a single repair rule throughout the entire execution, the proposed approach dynamically selects among these operators according to their observed contribution during the search. Each repair operator also incorporates Tabu memory to discourage repetitive decisions during feasibility restoration. The experiments were conducted using the classical Beasley benchmark instances for the Set Covering Problem. Full article

Memory-safety vulnerabilities remain the dominant class of security defects in C/C++ software underpinning Internet infrastructure. Rust offers a structural solution through its ownership system, yet migrating existing codebases remains costly. This paper defines a structured methodology for LLM-assisted porting of security-critical C libraries to idiomatic Rust and applies it to cJSON (∼3200 LOC, 14 CVEs). A manual expert porting serves as the baseline; five LLMs (Claude Opus 4.6, Gemini 3 Pro, GPT-5.4, Kimi K2.7-Code, and Qwen3.5-27B) produce independent portings in agentic mode. Verification uses an end-to-end pipeline: CVE-specific tests, coverage-guided and differential fuzzing (>1.7 billion executions), Miri analysis, and comparative benchmarking. All six portings eliminate all in-scope CVE classes by construction, with zero unsafe blocks and zero memory-safety crashes. In this case study, structural safety holds consistently across all five evaluated models and across all five Kimi repetitions, whereas code quality varies widely (0–9 residual bugs). Because only Kimi was repeated ($N=5$), its variance bounds run-to-run noise at the 95% confidence level, against which some but not all between-model differences are distinguishable from chance; a single porting attempt costs approximately $3 in API usage. Differential fuzzing reveals complementary bugs in the manual and LLM portings, supporting a hybrid workflow, and we translate these findings into concrete practical guidance for teams planning a similar migration. These results are scoped to one compact, single-threaded C library. The entire codebase and evaluation pipeline are publicly released. Full article

RNA vaccines—comprising linear mRNA, self-amplifying RNA, and circular RNA (circRNA)—constitute a core next-generation platform for the prevention and control of viral diseases; among these, circRNA vaccines possess notable structural stability, yet their technical bottlenecks and application prospects in veterinary medicine have not been systematically reviewed. This review synthesizes current research on circRNA vaccine design, circularization strategies, translation mechanisms, delivery systems, and immunological outcomes, and compares their antiviral performance with that of linear mRNA vaccines. Owing to their covalently closed circular conformation, circRNA vaccines exhibit enhanced resistance to nucleases and superior thermal stability, enabling sustained transfection activity at ambient temperatures without reliance on strict cold chains; through cap-independent translation driven by internal ribosome entry sites or N⁶-methyladenosine modifications, and in conjunction with optimized circularization protocols and lipid nanoparticle carriers, circRNA vaccines elicit substantially higher antiviral IgG titers and durable antigen-specific T-cell memory relative to linear mRNA vaccines. These vaccines have been deployed against COVID-19, monkeypox, influenza, and livestock viral diseases, demonstrating strong adaptability to viral variants and compatibility with mucosal or needle-free administration routes. CircRNA vaccines are well suited for both emergency outbreak response and routine immunization programs; nevertheless, challenges persist, including low circularization efficiency for long sequences, elevated manufacturing costs, and inadequate quality control standards. Addressing these issues through improved production workflows and delivery technologies adapted to resource-limited settings will be critical to establishing circRNA vaccines as a pillar of livestock disease management and as a strategic reserve for emerging zoonotic threats. Full article

Background/Objectives: Alzheimer’s disease (AD) is a multifactorial neurodegenerative condition characterised by cognitive impairment, cholinergic deficiency and neuroinflammation. Carvacrol and thymol are two related natural monoterpenes with reported antioxidant, anti-inflammatory, anti-apoptotic and cholinesterase-inhibiting properties. This scoping review aims to map the current evidence regarding the effects of carvacrol and thymol on AD-related pathologies and identify their mechanisms of action. Methods: A systematic literature search was conducted in February 2026 across PubMed, Scopus and Web of Science. This review included original, English-language primary research articles investigating the effects of carvacrol and thymol on AD using in vitro, in vivo, or in silico models. Results: From an initial pool of 87 unique records, 30 primary studies met the inclusion criteria, encompassing direct AD pathology models (e.g., amyloid-beta), AD-associated risk models (e.g., metabolic or hypertensive impairment), and general cognitive impairment models. Both carvacrol and thymol reduced escape latency during spatial learning acquisition training and increased time spent in the target quadrant during probe trials, indicating improvements in both learning and memory retention, although several studies reported non-linear relationships. At the cellular level, carvacrol and thymol modulated distinct redox and inflammatory pathways, including nuclear factor erythroid 2-related factor 2 upregulation and tumour necrosis factor-alpha suppression. Synthetic derivatives and nanocarrier formulations (e.g., liposomes, nanoemulsions) demonstrated enhanced acetylcholinesterase inhibition and biological stability relative to parent monoterpenes. No clinical trials were identified, and formal critical appraisal was not performed. Conclusions: Preclinical evidence indicates that carvacrol and thymol show potential multi-targeted neuroprotective activity across diverse models of cognitive dysfunction. However, clinical translation remains unproven due to heterogeneous experimental designs, pharmacokinetic limitations, a lack of human trials, and unassessed study quality. Full article

The rapid growth of artificial-intelligence (AI) and high-performance computing workloads has reshaped the power-delivery requirements of high-bandwidth memory (HBM), neural processing units (NPUs), and advanced systems-on-chip (SoCs). These platforms draw large currents that vary rapidly at aggressively scaled supply voltages, so their on-chip regulators must combine high current density, nanosecond-scale settling with minimal droop, wideband power-supply rejection (PSR), and stable capacitor-less operation, while also mitigating issues such as power supply-induced jitter (PSIJ) in high-speed clock and data paths. On-chip low-dropout (LDO) regulators have become the key building block at the point of load, and a wide range of architectures have emerged to meet these demands. This paper reviews on-chip LDOs for HBM and SoC power integrity. We translate application-level power-integrity requirements, including PSIJ, into circuit specifications; organize the design space into fast-transient, wideband high-PSR, high-current and distributed, and capacitor-less and digital/hybrid architectures; benchmark representative state-of-the-art designs using both conventional and application-relevant metrics such as data rate, jitter, and eye margin; and distill the resulting technology trends. Full article

Frontier financial markets face a diagnostic gap in forecasting volatility: linear and single-regime GARCH fails to capture breaks, spillovers, and regime transitions. Despite the importance of these markets, there is a gap in the literature: lack of a Kenya-specific, regime-sensitive Early Warning System (EWS) that can integrate long-memory filtering of volatility with nonlinear classification models. Therefore, policymakers lack signals to anticipate systemic stress. This study constructed a multi-stage pipeline using 6703 daily observations of the NSE 20 Share Index, USD/KES exchange rate, and Brent spot prices (1997–2024). K-Means clustering, FIGARCH filtering, LSTM–XGBoost ensemble classification, a logistic threshold model, and VaR/ES back-testing were used to generate alarm signals and validate risk-management performance. Across model estimation and evaluation phases, time horizons were assessed, showing that one-day signals were reactive and ten-day forecasts diluted precision, while five-day predictions achieved the strongest balance. Across model iterations, accuracy improved from 0.86 in the initial FIGARCH–LSTM pipeline, to 0.92 in the unbalanced classification model, and 0.94 in the weighted baseline, culminating in 0.98 with the final hybrid LSTM–XGBoost ensemble. The ensemble model delivered robust detection across Calm (F1 = 0.99), Moderate (F1 = 0.82), and Stress (F1 = 0.69) regimes. Stress thresholds were developed to translate regime signals into actionable alarms. This study provides an empirical application of a hybrid framework, combining long memory modelling of volatility with the adaptability of deep learning models to deliver Basel-compliant tail-risk alarms and a state-dependent policy matrix for regulators. Full article

This study presents a healthy-only bearing fault detection framework in which a stacked long short-term memory (LSTM) predictor learns normal vibration dynamics through multi-step forecasting, and deviations between predicted and observed vibration sequences are used for residual-based anomaly detection. The methodological contribution lies in the controlled stage-wise development of the complete prediction-to-decision pipeline, including temporal configuration, predictor selection, residual scoring, and decision-threshold calibration, together with strict bearing-level separation between framework development and final evaluation. The framework was evaluated on the Paderborn bearing dataset using independent healthy bearings for training, validation, calibration, and testing, artificially damaged bearings for detector development, and previously unseen real-damage bearings for final fault evaluation. The finalized detector achieved an ROC–AUC of 0.8385 and a PR–AUC of 0.9200, with a healthy false-positive rate of 0.13% and precision of 0.9971. However, recall at the selected operating threshold was limited to 22.57%, showing that strong anomaly-score discrimination did not translate into equally high fault sensitivity under the conservative global threshold. Performance remained consistent across independent model initializations, while evaluation on a fully reserved healthy bearing produced a 17.95% false-positive rate, highlighting threshold calibration and healthy-domain generalization as the principal limitations of the current framework. Full article

Floods degrade road networks at the same time as demand for emergency medical services (EMSs) rises, yet national EMS command systems rarely receive any information on flood-induced road barriers. This paper presents Ambulance STARS (SaTellite-assisted Ambulance Routing System), a service-oriented framework that links satellite observation with ambulance dispatch. A cloud-based flood detection service derives flood extent from Sentinel-1 SAR amplitude change detection executed in a cloud-based Earth observation data and compute backend and translates it into road passability layers. A routing engine then maintains an in-memory road graph whose travel times are calibrated with empirical ambulance speed models built from four years (2020–2023) of GPS records of an EMS fleet in southern Poland, with separate speeds for driving with and without emergency signals (61.8 and 37.2 km/h, respectively). An API gateway with single-file tile delivery, a replicated relational data tier, and an observability stack complete the architecture, and a web client offers dispatchers live routing and multi-unit incident simulation. The framework was tested on the September 2024 flood in the Municipality of Nysa, Poland. The SAR module delineated 665 ha of inundation and marked 8.5 km of the 656.7 km routing network as impassable (508 barrier points), and the same procedure applied to a reference optical mask of 18 September yielded 17.3 km and 1006 points. Because the SAR and optical acquisitions captured different phases of the flood wave, agreement on the rare impassable-road class was low, and the two products were, therefore, used to bracket operational uncertainty rather than to define a single ground truth. Applied without local retuning to Lewin Brzeski, the same flood detection workflow showed consistent performance against the CEMS reference product. The routing module produced statutory 8/15/20 min accessibility maps in 12–34 s under warm-cache benchmark conditions. With SAR-derived barriers, the share of the network reachable within 15 min fell from 88% to 80%, and 2 villages with 938 inhabitants lost road access to EMS entirely. With barriers derived from the optical mask, the 15 min share fell to 39.8% and seventeen settlements lost road access entirely, underlining how strongly the barrier source shapes the operational picture. Post-acquisition processing completes in under one minute under warm-cache conditions with road data preloaded, and satellite-derived road passability is fast enough to support near-real-time decision-making, subject to the constellation revisit time and to integration with EMS command systems. Full article

In SRAM-based compute-in-memory (CIM), read-bitline (RBL) charging and discharging depend on the physical bit pattern stored in the memory array, so the energy-relevant code statistic should be defined with respect to the actual read-port polarity. This paper presents a read-polarity-aware row-wise offset-encoding method for W4A8 INT4 weights. In the evaluated Q-sensed 8T topology, the stored logical one is the discharge-active state; hence, the topology-specific read-active density equals the stored-one fraction. Under the exact whole-row INT4-feasibility protocol, a nonzero row offset is accepted only when every translated valid signed-INT4 code remains within [−8, 7]; no clipping, saturation, wraparound, or remapping is permitted, and zero offset remains the fallback. The complete software evaluation covers 286 quantized modules, 579,464 physical 16 × 16 tile positions, and 147,156,296 quantized weights across ResNet-18, MobileNetV3-Small, and SmolLM2-135M. Circuit re-validation uses 300 independent tile-policy samples, 1200 matched baseline-selected bitplane pairs, and 2400 successfully completed transistor-level Spectre simulations. The balanced circuit population yields an aggregate local SRAM readout-energy reduction of 5.03%, with a sample-cluster bootstrap 95% confidence interval of 4.12–6.02%. After four-bitplane aggregation, relative read-active-density reduction and local SRAM readout-energy reduction exhibit Pearson r = 0.81 and Spearman $\rho$ = 0.75, indicating a substantial but imperfect relationship. The directly validated no-offset, positive-offset, and signed-offset policies preserve the corresponding model-level Top-1 accuracy or perplexity. Proposal-specific digital overheads and metadata-storage capacity are quantified separately, whereas representative physical SRAM/ROM metadata-access energy remains uncharacterized. Accordingly, the measured energy benefit is limited to local SRAM readout; a net energy reduction at the complete CIM-macro or system level, robustness across all evaluated PVT conditions, and robustness to process mismatch are not established by the present evidence. Full article

Chinese ethnic minority cultures are integral to the Chinese cultural system and play a significant role in shaping the nation’s image through international dissemination. Amid the profound integration of internationalization and digitalization, ethnic minority cultures draw on Guochao (national tide)—a distinctive contemporary Chinese dissemination paradigm—to merge traditional ethnic heritage with modern artistic forms, thereby enabling a structural shift from passive heritage preservation to dynamic cultural reproduction. This process helps consolidate China’s national memory and enhance the international dissemination of its cultural soft power. Taking Kam Big Song (in Chinese, Dong Zu Da Ge) as a typical case, this paper examines how ethnic minority cultures can adopt Guochao as a contemporary paradigm for cultural dissemination to transcend mere protection of intangible cultural heritage (ICH). The paper proposes a fourfold strategic framework—encompassing talent cultivation, symbolic translation, dissemination improvement, and technological empowerment—to facilitate the international outreach of ethnic minority cultures while preserving their cultural subjectivity and authenticity. By integrating locally rooted core values, modern cultural translation, and international recognition, ethnic minority cultures can fully showcase the cultural diversity and interconnectedness of Chinese civilization. Through this systematic approach to strengthening the Chinese national community, these efforts help enhance China’s international cultural influence. Full article