Abstract: Hypertension is an important target for primordial prevention of complex, noncommunicable diseases and its prevalence remains high across populations. Urban population in India is at a high risk of hypertension but the genetic basis of hypertension in this population remains poorly understood. We conducted a pooled whole-blood genome-wide association study of 28 pools representing 1,402 participants of the Diabetes In Sindhi Families In Nagpur (DISFIN) study which enrolled families of probands with type 2 diabetes (T2D). Genotyping was done using Illumina’s Global Screening Array. From a total of 608,550 single nucleotide variants, 191 were found to be significantly associated with hypertension even after adjusting for metabolic comorbidities, batch effects, pooling error, kinship status and pooling variation. These variants mapped to 180 well-characterized genes that comprised 55 (31%) genes encoding long noncoding RNA (lncRNA). Many of the genes significantly associated with hypertension (including 35% of the lncRNAs) have also been reported by other studies. However, we identified novel genes (SBF2, ARHGAP12, EPAS1, CLEC16A and LRPPRC) to be associated with hypertension. The most significantly associated lncRNA gene was FLYWCH-AS1. Bioinformatic analyses indicated that these novel genes are likely to have functional importance in hypertension. Our study thus points to the potential candidate genes associated with hypertension in endogamous Sindhi families with T2D patients. The replicable and functional role of these candidate genes should be investigated in future studies.

Abstract: Urban shrinkage, driven by demographic and socioeconomic change, has become a pressing issue across Europe, particularly in small peripheral towns and semi-urban settlements that have historically relied on a single industry or company. This study investigates the demographic and socioeconomic factors contributing to the decline in Latvian mono-towns, thereby filling a void in empirical research on urban development in post-socialist contexts. Principal component analysis (PCA) was applied to a set of key demographic and socioeconomic indicators derived from census and administrative data to identify the principal dimensions driving urban shrinkage. The analysis reveals three principal components explaining 87% of the variance: socioeconomic vitality (57.1%), population change and peripherality (17.2%), and aging society dynamics (12.6%). The results contribute to a nuanced understanding of how mono-functional urban contexts shape the intensity and character of shrinkage. These results establish a basis for specific policy measures designed to promote resilience in small-settlement settings and contribute to the understanding of spatial planning and regional development approaches in the post-socialist urban transition context. The research underscores the need for context-specific approaches to address the multifaceted challenges of urban shrinkage.

Abstract: This study addresses the need for intelligent condition monitoring in high-complexity medical imaging systems by proposing a smart sensing architecture for the Revolution EVO Computed Tomography (CT) scanner. Ensuring operational reliability and minimizing unexpected downtime remain critical challenges in advanced CT platforms, motivating the integration of distributed sensing and data-driven analytics. The proposed framework combines Smart Sensor Networks with Machine Learning (ML)-based analysis to enable continuous acquisition and synchronization of heterogeneous operational data from key subsystems, including the X-ray tube assembly, detector array, rotational gantry mechanism, and data acquisition and processing unit. Multivariate feature extraction and sensor-level data fusion are employed to support anomaly detection and predictive assessment of system behavior. The methodology is informed by technical documentation and system specifications provided by GE HealthCare, together with established approaches in intelligent sensing and predictive analytics. The results demonstrate that structured integration of multi-sensor data and ML-based inference can enhance diagnostic sensitivity and enable early identification of abnormal operational patterns. It is concluded that a sensor-centric monitoring architecture provides a feasible pathway toward improved reliability, reduced unplanned interruptions, and more efficient lifecycle management of CT imaging systems.

Abstract: We deploy a spiking neural network (SNN)-equivalent intrusion detection system (IDS) on the STM32N6570-DK, a commodity ARM Cortex-M55 MCU with the Neural-ART NPU. Exploiting the approximate equivalence between single-timestep (T=1) SNN inference and INT8 quantized ANN inference, we compile a lightweight MLP classifier to the NPU without neuromorphic hardware. Evaluated on NSL-KDD (5-class) and UNSW-NB15 (10-class) with 10 random seeds, the ReLU model achieves 78.86±1.32% and 64.75±0.61% overall accuracy, respectively. INT8 accuracy stays within 1 percentage point of FP32 across all 24 tested calibration configurations, and layer-wise analysis shows 99.0% final prediction agreement between FP32 and INT8 models. On the NPU, the INT8 model infers in 0.46 ms on NSL-KDD and 0.29 ms on UNSW-NB15 (100% NPU execution), occupying 120.6–137.7 KB Flash and 0.5–1.25 KB RAM. A comparison with QCFS activation reveals that the Floor operator falls back to CPU on this NPU, adding 17.6% latency. Tree-based baselines (Random Forest, XGBoost) confirm that the MLP offers the best accuracy on NSL-KDD while being the only model eligible for NPU acceleration. To our knowledge, this is the first IDS deployment on an ARM Cortex-M NPU and the first empirical validation of T=1 SNN–ANN equivalence on commercial NPU silicon.

Abstract: Background: Advances in machine learning (ML) based survival modeling enable the analysis of high-dimensional biomedical data. However, many approaches rely on the proportional hazards (PH) assumption, which is frequently violated in oncology and can limit the interpretability of hazard ratio–based results. Using Estrogen Receptor (ER) status in the METABRIC breast cancer cohort as a case study, we propose a framework that integrates machine learning survival models with Restricted Mean Survival Time (RMST) to provide a more robust and clinically interpretable approach for survival analysis under non-proportional hazards. Methods: Overall survival was analyzed in 1104 patients. PH violations were confirmed using Schoenfeld residuals and Kaplan–Meier inspection. We compared four models: stratified Cox Elastic Net (Cox E-Net), Random Survival Forest (RSF), Gradient Boosting Survival Analysis (GBSA), and DeepHit. Performance was assessed using Harrell’s C-index, time-dependent IPCW C-index, and Integrated Brier Score (IBS). RMST at 180 months was utilized to quantify absolute survival differences between ER subgroups. To improve the stability of the estimates, 200 bootstrap resamples were performed, and 95% confidence intervals were derived from the bootstrap distribution. Results: ER status demonstrated significant PH violation (p < 0.005) with crossing survival curves. Discrimination (C-index 0.664–0.725) and calibration (IBS 0.149–0.169) were comparable across models, with RSF achieving the highest overall performance. Despite similar accuracy, survival curve structures differed substantially. Cox E-Net and RSF reproduced the observed crossing pattern, whereas GBSA generated smoother trajectories and DeepHit showed marked compression of subgroup separation. In the independent test cohort, the empirical RMST difference at 180 months was 16.6 months (ER-positive: 130.4; ER-negative: 113.8). Model-based RMST differences ranged from 1 month (DeepHit) to 27 months (Cox E-Net), with RSF and GBSA (12.8 and 13.8 months) most closely approximating the empirical benchmark. Conclusions: We propose a novel, model-agnostic ML + RMST framework that addresses non-proportional hazards while providing quantifiable, time-specific clinical benefit. Moreover, models with similar discrimination and calibration produced markedly different survival curve behavior and absolute RMST estimates, demonstrating that accuracy metrics alone are insufficient for clinical interpretation. By linking predictive modeling with absolute survival quantification, this framework advances survival evaluation beyond relative risk ranking toward clinically meaningful decision support.

Abstract: Abstract Background: Acute appendicitis in girls presenting with lower abdominal pain repre-sents a frequent diagnostic dilemma, given the overlap in clinical presentation with gynecological and non-surgical causes. This study aimed to evaluate the diagnostic performance of IL-6 and CD64 and to compare them with classical inflammatory markers and the Alvarado score. Methods: We conducted an observational case–control study over a three-year period (December 2022–December 2025) at the First University Paediatric Surgery Clinic (General Hospital of Thessaloniki “Georgios Gen-nimatas”). Consecutive girls aged ≤16 years presenting with lower abdominal pain were included. The primary outcome was the presence of appendicitis (yes/no), defined by the final clinical diagnosis and, where applicable, intraoperative and/or histo-pathological confirmation. Diagnostic performance was assessed using ROC curves/AUC with 95% confidence intervals estimated by the DeLong method. The prespecified primary model was a logistic regression including the Alvarado score and log1p(IL-6). Results: Of 74 initially assessed cases, one was excluded (appendiceal neuroendocrine tumour, NET G1), yielding a final sample of 73 girls: 37 with appendi-citis and 36 without appendicitis. IL-6 was higher in the appendicitis group (median 19.41 vs 4.10 pg/mL) and showed moderate discrimination (AUC 0.696). CRP showed lower/borderline performance (AUC 0.595), whereas CD64 did not demonstrate useful discrimination (AUC 0.521). The Alvarado score had the highest discriminatory ability (AUC 0.885). Adding IL-6 to the Alvarado score did not materially improve the AUC in the common subset. Conclusions: IL-6 demonstrates moderate diagnostic perfor-mance as a standalone biomarker and may be useful as an adjunct, particularly when a clinical score is unavailable or unreliable. CD64 did not add diagnostic information in this setting. Larger, prespecified studies are required to identify clinically useful cut-offs.

Abstract: Warm rain events occur when moist air masses containing elevated precipitable water produce high rainfall rates capable of generating local flash floods. Catalonia, located on the northeastern Mediterranean coast of the Iberian Peninsula, is regularly affected by such episodes: approximately 70% of daily precipitation events exceeding 10 mm with fewer than ten cloud-to-ground lightning flashes can be classified as warm rain. The current research aimed to identify the meteorological conditions most conducive to heavy warm rain episodes in Catalonia. These cases are commonly associated with flash flood episodes in the study region. We have utilized rain gauges, lightning data, radar, and model fields, combined with radio sounding profiles. First, we have identified and characterized warm rain cases, and secondly, we have selected some relevant cases to characterize the phenomenon. These events occur predominantly along the Catalan coast during the warm season, typically following the passage of a cold front, and are associated with shallow convective clouds producing little or no lightning. However, the key determining factor is a characteristic vertical thermodynamic profile: a moist and saturated lower troposphere with high precipitable water beneath a low- to mid-level thermal inversion, weak instability concentrated near the surface. Furthermore, local wind convergence plays a principal role in the rainfall pattern.

Abstract: Accurate replication of road signal effects over the vehicles in laboratory environments is critical for vehicle durability testing and development. However, the traditional signal reconstruction methods often suffer from the inclusion of noise in the collected acceleration data. Thus, there is a limitation on the fidelity of hydraulic road simulations. This study proposes a comprehensive experimental-analytical framework for motorcycle testing in a laboratory environment. In the study, the integration of Fourier-based curve fitting with nonlinear adaptive control algorithms was done. Experimental signals were initially collected from a motorcycle on three different road surfaces. The displacement reference signals for the hydraulic actuators were generated using a harmonic curve-fitting approach from these signals. The performance analysis of the reconstruction signals was investigated in both the time and frequency domains. To ensure accurate trajectory tracking performance under parametric uncertainties, an adaptive backstepping control algorithm was designed. Experimental results revealed the superior performance of the proposed controller at all three road profiles, achieving Root Mean Square Errors (RMSE) as low as 1.3 mm. The controller exhibited robustness, maintaining consistent tracking precision with negligible performance variance across significantly different road characteristics, thereby validating the framework's utility for fatigue analysis.

Abstract: In this paper we introduce the CDF manifold algorithm that operates on data sets where a single target dimension is strictly increasing given a minimum of two or more number of input dimension which is very common in telco data. The manifold can then be used to compute the closest upper and lower limit to a given new point as well as its CDF. Training takes O(n.ln[n]) steps in the best case and O(n^3/2) in the worst case. Look up takes O(ln[n]) steps in the best case and O(n^1/2) in the worst case. The asymptotic computational cost is proven with a theorem. We compared our manifold method versus a standard dense neural network and show the asymptotic advantages both in terms of speed and accuracy. We also comment of potentials speed gains through the use of reference points. In summary, the manifold is a non-parametric and explanatory method to find the tightest data driven upper and lower limit of the output dimension given a new unseen input. This makes it ideal for planning new site deployments where we would need to find actual measurements as base-line performance.

Abstract: Extracellular vesicles (EVs) can disseminate replication-competent viral genomes complexed with selected host proteins, enabling stealth cell-to-cell transfer within lipid membrane-enclosed bubbles. In addition to complementing free-virion spread, EV-associated genomes can be protected from neutralizing antibodies and persist under conditions in which classical virion production decreases. Here, we propose a route-resolved framework in which interconnected cellular secretory pathways, including endoplasmic reticulum (ER) remodeling, multivesicular body (MVB) biogenesis, secretory autophagy, and plasma-membrane budding, jointly generate EV heterogeneity and create discrete opportunities for the capture, protection, and export of infectious cargo. We highlight reticulon-3 (RTN3), an ER-shaping protein, as an upstream regulator that can couple infection-induced ER microdomains to endosomal docking and autophagy-linked trafficking decisions that bias intermediates toward secretion rather than degradation. Supporting this view, transmission electron microscopy of dengue virus-infected cells reveals extensive vesicular remodeling, including irregular MVBs adjacent to the plasma membrane and autophagosome-like double-membrane structures, consistent with altered vesicular routing following RTN3 perturbation. Collectively, these route-resolved, spatially organized spatio-organelle changes support a pathomechanistic model in which RTN3-mediated ER remodeling reshapes ER-endosome-autophagy trafficking interfaces, creating regulated decision points that can be leveraged to stratify infectious EV subsets (with infectivity-linked single-vesicle and quantitative proteomics approaches) and to inform host-directed strategies that curb non-lytic viral dissemination.

Abstract: Developmental processes are usually described through dynamical systems and gradient-driven cellular rearrangements, yet their topological constraints are not well characterized. We introduce a mathematical approach linking morphogenesis with the Gömböc, a convex body whose equilibrium structure is minimal under topological constraints. We model developmental dynamics as gradient flows defined on a configuration space of tissue states where a morphogenetic potential integrates mechanical, chemical and adhesive cellular interactions. To explore how varying landscape parameters affect the stability of critical configurations and developmental trajectories, we simulated morphogenetic systems governed by gradient flows with Morse-type potentials. We found that systems approaching minimal critical-point structures display large basins of attraction and convergent trajectories despite diverse initial states. Developmental systems may operate near Gömböc-like dynamical regimes in which the topological properties of the configuration space constrain the number of accessible states, while attractors and gradient dynamics may induce a causal order. Our framework generates testable predictions. Developmental trajectories should concentrate into a small number of preferred channels, with transverse dispersion showing an exponential decay over time. In exponential morphogen gradients, migration time is expected to scale approximately linearly with the initial distance from the source. Saddle-like transitional configurations should appear as intermediate states in morphogenetic landscapes, detectable as brief phases of reduced migration speed and increased directional fluctuations. Overall, a quantitative framework is provided for analyzing developmental robustness, identifying transition bottlenecks in morphogenetic landscapes and predicting how physical or biochemical parameters could reshape developmental trajectories in synthetic and regenerative contexts.

Abstract: Inequalities persist throughout the globalized world. The overall economies of the Global South pales in comparison to the Global North. The difference between the haves and have nots are becoming more and more pronounced as the free economy continues to operate. In this study, we try to determine the factors that affect the differing degree of prosperity gained from globalization. Although we understand that there are numerous factors that contribute to a country’s prosperity that do not relate to the psychology of its constituents, we theorize that human behavior and psychology defined by cultural differences play a major role in how countries take advantage of globalization. Identifying the cause for inequalities in globalization will allow governments to tackle the root causes of their challenges, creating a more productive global community.

Abstract: This study integrates a multi-target in silico screening campaign with in vitro experimental validation to assess a dual-oil phytochemical formulation (cold-pressed Prunus dulcis oil combined with Pinus sylvestris essential oil enriched in α-pinene; commercially referred to as “Naevus Support”) as a candidate adju-vant/alternative strategy against malignant melanoma. First, a comparative molecular docking workflow was applied across a melanoma-relevant target panel spanning the MAPK axis (BRAF, MEK1, ERK2), cell-cycle control (CDK4/6), DNA damage signaling (PARP1), inflammatory lipid signaling (COX-2), and melanogenesis-associated enzymes (tyrosinase), benchmarking major oil constituents and derived chemo-types against standard-of-care inhibitors. Docking energetics and pose-level interaction forensics supported a polypharmacology profile consistent with concurrent suppression of oncogenic signaling nodes and mi-croenvironmental permissive pathways. Second, the same formulation was tested in a Neutral Red Uptake (NRU) viability assay on B16F10 malignant melanoma cells and MRC-5 human fibroblasts, using cisplatin as a reference cytotoxic agent. Across a concentration range of 3–0.045% (v/v) for oils and 20–0.18 mM for cisplatin, the dual-oil formulation induced a dose-dependent reduction of melanoma viability while main-taining comparatively lower toxicity on fibroblasts, indicating a therapeutically relevant selectivity window. Individual-oil profiling suggested that the combined formulation’s anticancer activity cannot be explained by single-oil effects alone, supporting a true inter-oil synergistic enhancement that aligns with the mul-ti-node in silico predictions. Collectively, these data provide a coherent in silico-to-in vitro rationale for further mechanistic follow-up (target deconvolution, pathway readouts, and lipidomic/ROS endpoints) and in vivo translation.

Abstract: The rapid evolution of Large Language Models (LLMs) from static text generators to autonomous agents has revolutionized their ability to perceive, reason, and act within complex environments. However, this transition from single-model inference to System Engineering Security introduces unique structural vulnerabilities—specifically instruction-data conflation, persistent cognitive states, and untrusted coordination—that extend beyond traditional adversarial robustness. To address the fragmented nature of the existing literature, this article presents a comprehensive and systematic survey of the security landscape for LLM-based agents. We propose a novel, structure-aware taxonomy that categorizes threats into three distinct paradigms: (1) External Interaction Attacks, which exploit vulnerabilities in perception interfaces and tool usage; (2) Internal Cognitive Attacks, which compromise the integrity of reasoning chains and memory mechanisms; and (3) Multi-Agent Collaboration Attacks, which manipulate communication protocols and collective decision-making. Adapting to this threat landscape, we systematize existing mitigation strategies into a unified defense framework that includes input sanitization, cognitive fortification, and collaborative consensus. In addition, we provide the first in-depth comparative analysis of agent-specific security evaluation benchmarks. The survey concludes by outlining critical open problems and future research directions, aiming to foster the development of next-generation agents that are not only autonomous but also provably secure and trustworthy.

Abstract: Today, interest in natural remedies for biocontrol of crop pests is paramount. Punica granatum L. (pomegranate) is studied worldwide to obtain interesting bioactive compounds. Its anti-parasitic activity is associated with the presence of alkaloids in its roots. In this work, we explored the possibility of obtaining from P. granatum roots pelletierine-like alkaloids, which were extracted, characterized, isolated and used for the biocontrol of pests such as Spodoptera littoralis, Myzus persicae, Rhopalosiphum padi and Meloidogyne javanica. Two different extracts were obtained, characterised and quantified by GC-MS and LC-ESI-HRMS. In vitro assays of nematicidal activity were performed comparing the extracts with isopelletierine and pseudopelletierine as pure molecules. The results of these assays showed a difference in activity between iso- and pseudopelletierine, especially in terms of the nematocidal effect against M. javanica with isopelletierine being more active than pseudopelletierine. This leads us to conclude that only extracts from P. granatum roots with a high concentration of isopelletierine alkaloid can be used in effective pest control products.

Abstract: TBEV is a major cause of viral central nervous system infections in Europe, with heterogeneous geographical distribution and substantial underdiagnosis in low-incidence regions. This study aimed to evaluate the validity of regional TBE risk classification in Poland by combining surveillance-based incidence data with serological markers of TBEV exposure. Plasma samples from 5,541 blood donors residing in nine regions were tested by anti-TBEV IgG ELISA, followed by confirmatory VNT, IFA and anti-NS1 IgG ELISA to differentiate infection-induced from vaccine-induced antibodies. Regions were classified based on average TBE incidence from 2015–2019. Overall, anti-TBEV IgG screening reactivity was detected in 4.9% of donors, with significant regional variation (p &lt; 0.001). The highest seroprevalence was observed in highly affected regions; however, unexpectedly elevated seroprevalence was also detected in regions classified as low affected. Markers consistent with TBEV infection (anti-NS1 IgG) were identified in only 2.6% of donors, whereas vaccine-induced immunity accounted for the majority of seropositive results. Male sex was independently associated with higher odds of seropositivity. Our findings suggest that passive surveillance data alone may insufficiently capture population-level exposure to TBEV, particularly in regions considered non-endemic. Integrating sero-epidemiological data with surveillance systems may improve risk assessment and inform targeted prevention strategies.

Abstract: Adults with physical disabilities living in rural areas often experience limited access to structured physical activity and prolonged waiting times for rehabilitation services. Traditional one-to-one therapy models may not adequately support sustained physical activity participation while maintaining service capacity. This study evaluated the functional and service-level impact of a community-based group exercise programme implemented within a rural adult disability service. A prospective service evaluation using a repeated-measures pre–post design was conducted in County Longford, Ireland. Adults with neurological and neuromuscular conditions were signposted through HSE Adult Disability Services to an 8-week supervised group exercise programme delivered in partnership with community infrastructure. Outcome measures included the 10-Metre Walk Test, 30-Second Sit-to-Stand Test, Berg Balance Scale, and EQ-5D health status scale. Changes in functional outcomes were interpreted using established minimal clinically important difference thresholds, while service-level impact was assessed through comparison of physiotherapy waiting list volume and maximum waiting times before and after implementation. Participants demonstrated improvements in mobility, lower limb strength, balance, and self-reported health status, with several achieving clinically meaningful gains in gait speed and functional strength. At a service level, physiotherapy waiting list volume reduced by 93.9% (66 to 4 clients) and maximum waiting time decreased by 86.7% (30 to 4 weeks). Embedding structured community-based exercise pathways within rural disability services may improve functional outcomes while alleviating pressure on rehabilitation services and supporting more sustainable models of care.

Abstract: Background: Early and accurate assessment of traumatic injuries in pediatric patients is critical for timely diagnosis and prevention of missed associated injuries. The Pediatric Trauma Support System (PTSS) is a digital clinical decision support tool designed to integrate physiological parameters, trauma mechanism, and demographic characteristics to provide automated trauma risk assessment in children. Aim: To evaluate the clinical applicability of the Pediatric Trauma Support System (PTSS) in the primary assessment of pediatric patients with blunt trauma and to analyze the sensitivity of the Pediatric Trauma Score (PTS), particularly in children under one year of age. Materials and Methods: A prospective observational study was conducted in the Emergency Department and the Department of Pediatric Surgery at the University Hospital “St. George”, Plovdiv, Bulgaria. The study included 100 pediatric patients aged 0-18 years presenting with blunt traumatic injuries. Standard clinical evaluation was performed for all patients, including assessment of vital signs and neurological status using the Glasgow Coma Scale (GCS). PTSS was used to automatically calculate the Pediatric Trauma Score (PTS) and generate recommendations for imaging diagnostics. Twenty-five patients were under two years of age, including nine infants younger than one year (6 boys and 3 girls). Demographic and clinical variables were analyzed descriptively. Results: Among the nine patients under one year of age, all had PTS values below the normal age-adjusted threshold. In cases of mild trauma the PTS was 9/12, while in moderate trauma it was 8/12. The PTSS algorithm generated recommendations for head computed tomography (CT) and abdominal ultrasound in all infants. Head imaging was performed in all nine patients, revealing a parietal bone fracture in one 27-day-old infant. Abdominal ultrasound was not performed in clinical practice despite the algorithmic recommendation. In patients older than one year with similar trauma severity, PTS values were higher (11/12), and PTSS recommended only head CT without additional abdominal imaging. Conclusion: PTSS provides a structured and automated approach to pediatric trauma assessment by integrating PTS with clinical and demographic parameters. The system demonstrates increased sensitivity in infants under one year of age, where physiological characteristics may mask significant associated injuries. Automated recommendations for additional imaging may contribute to earlier detection of occult injuries and improved patient safety in pediatric trauma care.

Abstract: Background/Objectives: Nuclear receptor corepressors NCOR1 and NCOR2 are key regulators of transcriptional repression, chromatin remodelling, and immunometabolic signalling. While NCOR1 has already been linked to vascular biology, its relevance in abdominal aortic aneurysm (AAA) remains unclear, particularly for NCOR2. This study aimed to investigate the expression, cellular localisation, and molecular interactions of NCOR1/2 in human AAA tissue. Methods: Human AAA samples (elective and ruptured) (n=45) and non-aneurysmal control aortas (n=18) were obtained from our Swiss Vascular Biobank. Transcriptomic profiling was performed using ribosomal RNA-depleted RNA sequencing. Differential expression and correlation analyses were performed using DESeq2/EdgeR and Spearman rank correlation with Benjamini–Hochberg correction. Cellular localisation was assessed through immunohistochemistry (IHC). Results: Bulk transcriptomic analyses showed no significant differences in NCOR1 or NCOR2 expression between AAA and controls. IHC revealed that NCOR1 was found in endothelial cells (ECs), smooth muscle cells (SMCs), and inflammatory infiltrates, while NCOR2 was primarily associated with macrophages. Correlation analyses suggest NCOR1 linking with various cellular markers, proteolytic enzymes, inflammatory mediators, and epigenetic regulators, including lncRNA MALAT1. NCOR2 showed distinct associations with remodelling enzymes, TGFB1 signalling, selective epigenetic modifiers, and lncRNA H19. Conclusions: The lack of transcriptional differences in NCOR1 and NCOR2 between AAA and controls does not exclude cell-type-specific regulation or functional relevance. The specific cellular distributions and molecular associations in human AAA imply that NCOR1 and NCOR2 play non-redundant roles in vascular remodelling, inflammation, and epigenetic regulation. Our findings highlight NCOR pathways as potential modulators of AAA pathophysiology and promising targets for future therapies.

Abstract:

Unregulated Housing (UrH) is a widespread urban phenomenon in Morocco, largely driven by rapid population growth and accelerated urbanization. It has expanded mainly on the outskirts of cities and within housing developments that already benefit from basic infrastructure and superstructure services. In response to this challenge, public authorities have adopted several urban planning instruments, particularly the Land Management Plan (LMP). According to Law No. 12-90 on urban planning, the LMP seeks to regulate urban expansion, improve the architectural and aesthetic quality of the built environment, and preserve the overall coherence of developed areas. As a legally binding planning document, the LMP establishes strict land-use regulations, and any breach of these rules constitutes an offence. Traditionally, detecting such violations requires on-site inspections by control officers, followed by the preparation of official reports submitted to the competent legal authorities. However, recent advances in aerial image acquisition and processing technologies provide powerful tools to improve and facilitate the monitoring of urban planning compliance. This paper proposes a conceptual framework that integrates artificial intelligence with urban planning regulations to enable the automatic detection of urban planning offences using RGB orthophotos covering areas subject to a Land Management Plan, relying on deep learning techniques.