Search Results (977)

We propose a Bayesian kernel machine regression (BKMR) framework for count outcomes with dynamic spatiotemporal dependence. The proposed model, termed Negative Binomial BKMR with spatiotemporal effects (NB-BKMR), integrates (i) a negative binomial likelihood to accommodate overdispersion, (ii) a kernel-based exposure–response surface for complex mixtures, (iii) hierarchical group-wise variable selection and (iv) a dynamic spatiotemporal random effect structure based on a Leroux conditional autoregressive (CAR) prior evolving over time. Posterior inference is conducted in a fully Bayesian framework using Polya-Gamma data augmentation. Through simulation studies, under varying nonlinear exposure–response functions, correlation structures, and spatiotemporal dependence patterns, we show that NB-BKMR yields well-calibrated uncertainty quantification and robust identification of dominant mixture drivers, even when exposures are highly correlated. An application to the U.S. state-level traffic fatality counts (1982–1988) illustrates how the model uncovers nonlinear effects and interactions among socioeconomic and behavioral predictors while improving predictive performance relative to generalized additive models with spatiotemporal smooths. This work extends existing BKMR methodology by unifying mixture modeling, count outcomes, and dynamic spatial dependence in a single coherent framework, with particular relevance for areal public health surveillance data. Full article

The expansion of the European Union Emissions Trading System to road transport and buildings (ETS2) raises significant concerns regarding the distributive social impacts of carbon pricing on vulnerable households, particularly in regions characterized by high car dependency, limited public transport accessibility, and pronounced territorial inequalities. This paper aims to develop an integrated conceptual framework for analyzing transport poverty in the context of ETS2 and the just climate transition. The study adopts a conceptual–analytical approach based on a structured literature review of peer-reviewed publications and EU policy documents, combined with a qualitative policy analysis focused on Bulgaria as a critical case. The paper identifies six interacting analytical dimensions of transport poverty—economic vulnerability, spatial vulnerability, mobility dependency, infrastructure vulnerability, climate-policy exposure, and social vulnerability—and maps the causal pathways through which carbon pricing mechanisms may intensify mobility deprivation, particularly among low-income, rural, and forced-car-ownership households. The analysis demonstrates that ETS2 may exacerbate existing socio-spatial inequalities unless accompanied by well-designed compensatory, accessibility-oriented, and territorially sensitive policy measures. The Bulgarian case illustrates the specific structural risk factors prevalent in Central and Eastern European countries. The paper contributes to the emerging academic literature on transport poverty by positioning it as a critical dimension of the just climate transition and by providing a conceptual foundation for future empirical research within the ACTETS2 project framework. Full article

Suburban areas increasingly shape contemporary urbanisation, yet public-space dynamics in these environments are weakly represented by conventional urban indicators. This study examines suburban public-space use as a behavioural dimension of urban metabolism, understood here as the observable patterns of human movement, activity, and co-presence occurring within suburban public spaces. It addresses the limited ability of density- or infrastructure-based measures to capture everyday spatial practices in dispersed, car-oriented settings. While urban metabolism research has expanded beyond material and energy flows, empirical evidence linking configurational accessibility with directly observed public-space behaviour in suburban contexts remains limited. To address this gap, we integrate district-scale space syntax analysis with site-scale UAV-based observation across five public spaces in and around Gdańsk, Poland. Based on a dataset comprising 30 standard observation sessions conducted in September and October 2024, spatial syntax indicators (integration and choice) were used to characterise configurational accessibility and support location selection, while UAV monitoring captured traffic intensity, stationary presence, diversity of activities, and temporal rhythms of use. The results reveal distinct behavioural metabolic profiles shaped by interactions between spatial configuration, functional programming, and temporal dynamics. These profiles vary depending on the function of public spaces and dominant modes of movement (pedestrian or vehicular). The study demonstrates that suburban urban metabolism cannot be interpreted through configurational accessibility or residential density alone. By linking space syntax measures with a repeatable UAV observation protocol, the proposed framework supports comparative assessment of suburban public-space performance and informs planning interventions aimed at suburban transformation and improved accessibility. Full article

Mobility hubs promise to reduce car dependence and make multimodal travel work in practice, yet behavioural evidence remains limited when hub improvements coexist with easier car access. This article examines the tension at Rome Trastevere, an urban rail node that gradually acquires mobility-hub functions while facing improved parking access near Piazza della Radio. The empirical analysis combines a pilot survey of 83 users with an on-site stated preference survey of 204 valid respondents. The stated preference instrument uses a route-based feasible-choice design with nine choice sets per experiment: respondents evaluate alternatives among bikes, walking, e-scooters, e-mopeds, public transport, private cars, and shared cars under variations in travel time, travel cost, and search time. The paper estimates a multinomial logit model in Apollo and uses sample enumeration, supported by Monte Carlo simulation, to assess four parking and shared-mobility scenarios and produce confidence intervals around predicted probabilities. Results show that users respond to time, monetary cost, and search friction in coherent and policy-relevant ways. Setting the car parking search time to zero increases predicted car probability only marginally, by about 0.9% relative to the baseline. By contrast, a EUR 1/h increase in parking cost reduces predicted car probability by about 14.7%, while a EUR 1.5/h increase reduces it by about 22.4%. A coordinated scenario combining higher parking cost and lower shared-mode search time produces the lowest predicted car probability and strengthens e-scooter and e-moped alternatives, while public transport remains the dominant option. Findings indicate that parking pricing steers behaviour more clearly than parking convenience destabilizes it in the tested range. The paper shows that mobility-hub performance depends on coordinated access management, including parking regulation, shared-service reliability, and legible multimodal transfer. Full article

Lane-changing and overtaking constitute a typical complex driving manoeuvre for intelligent vehicles operating on structured roads; this task demands that the vehicle not only plan a safe and smooth lane-change trajectory but also requires the control system to maintain high tracking accuracy and lateral stability. Addressing the challenges of real-time path planning and stable tracking control inherent in lane-changing and overtaking scenarios, this paper proposes a trajectory planning and control method that integrates an improved artificial potential field (APF) approach with a lateral–longitudinal cooperative controller. Regarding path planning, the proposed method constructs attractive and repulsive fields based on the APF framework, while introducing virtual target points, elliptical obstacle models, and velocity-dependent repulsive fields to mitigate the risk of local minima and enhance dynamic obstacle avoidance capabilities. To ensure trajectory continuity and trackability, a fifth-order polynomial is employed to smooth the planned path. Regarding control, the method utilises a Linear Quadratic Regulator (LQR)—optimised via a genetic algorithm—for lateral control; this is coupled with a dual-PID longitudinal controller that generates throttle and braking commands based on vehicle speed errors, thereby establishing a cooperative lateral–longitudinal tracking control strategy. The proposed method is validated using a CarSim–MATLAB/Simulink co-simulation platform. Simulation results demonstrate that the proposed method significantly improves trajectory-tracking accuracy and vehicle stability during lane-changing and overtaking manoeuvres. In a single lane-change scenario, the maximum lateral error is reduced from approximately 0.62 m to 0.22 m, and the heading angle error decreases from about 0.058 rad to 0.01 rad; in a continuous lane-changing scenario, the maximum lateral error drops from approximately 0.30 m to 0.04 m, while the heading angle error falls from about 0.016 rad to 0.005 rad. Furthermore, the yaw rate, sideslip angle, and lateral acceleration are reduced by 39.1%, 22.2%, and 28.9%, respectively. These results confirm that, under the specified simulation conditions, the proposed method exhibits superior tracking performance and stability. Future research could further explore more complex driving scenarios, such as curved roads, multi-vehicle interactions, sensor uncertainties, actuator delays, and real-vehicle field experiments. Full article

Background/Objectives: Immune checkpoints are critical regulatory pathways that maintain peripheral tolerance and prevent autoimmunity. Among these, the programmed death-1/programmed death-ligand 1 (PD-1/PD-L1) axis serves as a major inhibitory pathway that terminates T cell responses. While protein-based checkpoint blockade (ICB) targeting this axis has revolutionized clinical cancer therapy, its clinical efficacy is frequently limited by low response rates, immune-related adverse events (irAEs), and the emergence of adaptive resistance. To break through these bottlenecks, genetic interruption has emerged as a high-precision alternative to modulate the PD-1/PD-L1 pathway at the nucleotide level. Methods: A comprehensive systematic review of literature was performed across major databases (PubMed, Web of Science), with a focus on high quality studies published up to 2026. Results: Direct genomic disruption via CRISPR/Cas9 and post-transcriptional silencing through RNA interference can effectively neutralize inhibitory signaling at its source. Recent advances demonstrate that targeting upstream regulatory nodes—including metabolic checkpoints (e.g., lactate metabolism) and biophysical mechanisms (e.g., liquid–liquid phase separation)—provides superior transcriptional control over PD-L1. Furthermore, engineering CAR-T cells with multiplex gene editing (e.g., TCR/B2M/PD-1 knockout) or localized scFv secretion significantly enhances antitumor potency while reducing systemic toxicity. Innovations in organ-targeted lipid nanoparticles and stimuli-responsive biomimetic carriers further address the delivery barriers in solid tumors. Conclusions: Gene therapy provides a high-precision platform for PD-1/PD-L1 modulation, offering a viable strategy to overcome adaptive resistance. Future clinical application depends on the refinement of safer editing tools, such as base editing, and the standardization of intelligent delivery systems to ensure controllable and scalable cancer immunotherapy. Full article

Field hyperspectral sensing can estimate crop biomass, but model ranking may depend strongly on validation design. We evaluated winter wheat aboveground dry biomass (AGDB) estimation using 84 canopy spectra collected across two growing seasons and seven nitrogen-management treatments in Shanxi, China. Six spectral inputs were compared with CARS-UVE band selection, partial least squares regression (PLSR), and support vector regression (SVR). Under a conventional 70/30 pooled split, SG + CARS-UVE + SVR gave the highest apparent accuracy (R² = 0.8864, RMSE = 0.1174 kg m⁻², RPD = 2.9665). This advantage was not stable. Across 20 SG-based repeated splits, CARS-UVE-SVR reached a mean R² of 0.7413 with a 95% confidence interval of 0.6941–0.7885, similar to full-band PLSR (0.7448, 0.7058–0.7837), and pairwise tests showed no significant R² advantage. Cross-year transfer further favored simpler latent-variable models: SG + CARS-UVE + PLSR reached R² = 0.7577 in the 2021 → 2022 direction, whereas the pooled best SVR model dropped to R² = 0.3402. A stricter same-window cross-year analysis produced weak or negative R² values, showing that broad phenological biomass gradients supported much of the pooled accuracy. Recurrent selected regions occurred near 436–441 nm, 506–516 nm, and 711–713 nm. These findings suggest that repeated and transfer-oriented validation should be used routinely before hyperspectral biomass models are interpreted for cross-season crop monitoring. Full article

Glioblastoma (GB) remains one of the most therapy-resistant solid tumors, characterized by profound metabolic plasticity, intratumoral heterogeneity, and a highly immunosuppressive microenvironment. While immunotherapies such as chimeric antigen receptor T (CAR-T) cells have shown promise in hematological malignancies, their efficacy in GB has been limited. Emerging evidence suggests that tumor-specific metabolic dependencies and post-translational modifications (PTMs) may represent exploitable vulnerabilities. This review discusses disulfidptosis, a recently described form of regulated cell death driven by disulfide stress under conditions of limited reducing capacity, as a context-dependent metabolic–redox vulnerability in GB. We further discuss how altered protein glycosylation and glycocalyx architecture in glioblastoma regulate cell survival, death signaling, and immune recognition. Particular emphasis is placed on the glycosylation of surface antigens targeted by CAR-T cells, including EGFR/EGFRvIII, IL-13Rα2, mesothelin, B7-H3, HER2, and GD2, and on how glycan-dependent epitope accessibility may limit therapeutic efficacy. Finally, we distinguish disulfidptosis, whose direct relevance to CAR-T-cell responses remains to be established, from glycosylation and glycocalyx remodeling as more direct determinants of target–antigen accessibility and immune recognition. Therapeutic strategies addressing these vulnerabilities may provide rational opportunities to improve CAR-T-based and combinatorial therapies for GB. Full article

Medium-sized cities in the Global South are at the center of future urban growth, yet their transportation systems remain dominated by car-dependent trajectories. This paper examines how urban infrastructure shapes inclusive access to active commuting using a latent class model across three Mexican cities. We identify two distinct commuter environments defined by infrastructure quality. In low-infrastructure settings, active commuting is concentrated among younger men, consistent with existing literature. In contrast, in high-infrastructure environments, the baseline probability of active commuting is nearly three times higher, so that women and older individuals commute actively at substantially higher absolute rates even though demographic penalties remain present in both environments. Attitudinal variables, often emphasized in policy discourse, are not significant predictors of mode choice. These findings suggest that infrastructure investment is not only a tool for increasing active commuting rates but also a mechanism for expanding mobility access across demographic groups. For rapidly growing medium-sized cities, prioritizing non-motorized infrastructure can play a central role in building inclusive, low-carbon transportation systems. Full article

Macrophages are highly plastic innate immune cells that adopt context-dependent phenotypes along a continuum, integrating developmental origin with local microenvironmental cues rather than conforming to discrete M1/M2 states. This review delineates the molecular circuits shaping macrophage identity—TLR/cytokine signaling, microRNA networks, metabolic rewiring, and epigenetic mechanisms including histone lactylation—and traces how circulating monocyte subsets contribute to tissue macrophage diversity. We examine macrophage plasticity across a broad disease spectrum—oncology, autoimmune and rheumatic diseases, inflammatory bowel disease, infectious diseases, metabolic disorders, and neurological conditions—showing that the pathogenic phenotype is strikingly context-dependent: for instance, M2-like tumor-associated macrophages promote immune evasion in solid tumors, whereas M1-skewed programs drive tissue damage in autoimmunity. Soluble markers (sCD163, sCD14, soluble mannose receptor) are emerging biomarkers of disease activity and prognosis. High-dimensional flow cytometry and mass cytometry (CyTOF) bridge molecular biology and clinical phenotyping, enabling integrated readouts of surface phenotype, intracellular signaling, and metabolic state. Therapeutic strategies discussed include selective tumor-associated macrophage (TAM) reprogramming, chimeric antigen receptor (CAR)-M cell therapies, and biomaterial-based platforms. Future priorities encompass spatially resolved multi-omics, epigenetic and metabolic targeting, and macrophage-centered vaccine approaches. Standardized cytometry panels will be essential for biomarker-guided stratification and context-specific interventions. Full article

Leaf nitrogen concentration (LNC) is an important indicator of Ginkgo nutritional status, but its hyperspectral estimation remains challenging because leaf spectra are high dimensional, strongly collinear, and affected by overlapping structural and biochemical signals. This study examined how spectral preprocessing, wavelength selection sequence, and regression model choice influence leaf scale Ginkgo LNC estimation, while separating simulation-assisted model development from measured sample-based prediction assessment. We assembled 717 field measured Ginkgo leaf spectra with corresponding laboratory measured LNC values and used PROSPECT-PRO simulated spectra only for wavelength screening or calibration augmentation, not as independent validation data. Three evaluation schemes were compared: measured-only analysis, simulated spectra-assisted wavelength selection followed by measured data calibration and testing, and simulated spectra-assisted wavelength selection and calibration followed by measured-only testing. The third scheme was used as the main inference framework because it retained an independent measured sample test boundary. Within this framework, multiple preprocessing methods, two wavelength selection sequences, and four regression models (PLSR, GPR, 1D-CNN, and DGP) were evaluated. MSC showed comparatively low error in the preprocessing comparison, and CARS-SPA identified a compact set of informative wavelengths concentrated mainly in the shortwave infrared region. Under the simulation-assisted calibration framework, the combination of MSC preprocessing, CARS-SPA wavelength selection, and DGP regression produced the lowest test error on the measured sample set (R² = 0.82; RMSE = 2.07 mg g⁻¹). These results indicate that Ginkgo LNC estimation depends on the combined choice of preprocessing method, wavelength selection strategy, and regression model, and provide a methodological reference for simulation-assisted hyperspectral modeling. Full article

(1) Background: Cognitive dysfunction represents an emerging concern in chimeric antigen receptor T-cell (CAR-T) therapy recipients, yet longitudinal data using simple, clinically applicable tools are lacking. (2) Methods: We conducted a single-center prospective cohort study of consecutive adults with hematologic malignancies treated with commercially available CAR-T cell products between May 2023 and November 2025 at our center. Cognitive function was evaluated with the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) at baseline (before the administration of lymphodepleting chemotherapy) (T1), 6 h after infusion (T2), at 3 months (T3), and at 6 months (T4). MoCA scores ≤ 25 and/or MMSE scores ≤ 23 were considered indicative of impaired cognitive function. (3) Results: Thirty-six patients were enrolled in the present study, while cytokine release syndrome occurred in 33/36 patients (91.7%), and immune effector cell-associated neurotoxicity syndrome of any grade occurred in 23/36 (63.9%). At baseline (T1), cognitive impairment was identified in 12/36 patients (33.3%) by MoCA. Following infusion (T2), 11/35 (31.4%) exhibited cognitive impairment, while baseline cognitive impairment and older age were associated with early post-infusion cognitive dysfunction. Across follow-up (T3 and T4), no significant overall changes were observed in MoCA- or MMSE-defined cognitive status or in total test scores. However, abstraction in MoCA and attention/calculation in MMSE showed time-dependent variation. (4) Conclusions: These findings support the use of simple longitudinal cognitive assessment in CAR-T recipients. Full article

Smart city development depends on the readiness of Connected, Autonomous, Shared, and Electric (C.A.S.E.) mobility systems to deliver sustainable, data-driven urban transportation. This paper assesses C.A.S.E. mobility readiness in Thailand—Southeast Asia’s largest automotive manufacturing economy and an active smart city developer—situating each dimension within Thailand’s national seven-pillar smart city framework. A dual-axis supply–demand positioning framework synthesises peer-reviewed evidence, Thailand-specific infrastructure assessments, consumer surveys, and Monte Carlo simulation outputs across all four dimensions. Electric mobility is the most advanced dimension, with Thailand positioned as a regional production hub; Monte Carlo Total Cost of Ownership (TCO) analysis confirms 23–38% savings per route for electric bus adoption and fleet-wide net savings of approximately 236 million THB over ten years. Shared mobility is constrained by absent Mobility-as-a-Service (MaaS) governance, though mode choice evidence confirms a 24–36% car trip reduction potential through congestion pricing and shared taxi deployment. Connected mobility occupies a demand-led position; Autonomous mobility remains nascent on road, with trust identified as the dominant adoption barrier in a Technology Acceptance Model (TAM) survey of 797 Bangkok residents. Thailand’s seven-pillar smart city framework—particularly the Smart Mobility and Smart Governance pillars—provides the institutional architecture for an integrated C.A.S.E. National Mobility Strategy that could resolve governance fragmentation and accelerate sustainable urban mobility transition. Full article

Understanding the gap between individuals’ intention to reduce car use and their actual willingness to adopt public transport is critical for advancing sustainable urban mobility. This case study in Rome examines how perceived public transport service quality and travel burden influence car-dependent employees’ willingness to shift to public transport. The analysis draws on survey data collected from 392 respondents, including 190 car-dependent employees, between May and July 2024. The results reveal that perceived public transport service quality has a significant positive direct effect on willingness to use public transport. In contrast, its indirect effect through intention to reduce car use is not significant. In contrast, travel burden does not show a significant total effect on willingness; however, in the combined model, it exhibits a positive direct effect on willingness, while its indirect pathway through intention is weak. Furthermore, travel burden has a marginal negative effect on intention, reflecting structural constraints associated with car dependency. Intention is a strong predictor of willingness but does not significantly mediate the effect of service quality. It also shows a significant interaction effect with travel burden in the combined model. Overall, the findings suggest that improving public transport service quality is more effective in encouraging modal shift than increasing the burden of car use. This highlights the importance of service-oriented and user-centered interventions, as well as the need to address structural barriers that limit behavioral change. Full article

The electrification of public transport, particularly Bus Rapid Transits (BRT), is a significant step toward achieving sustainable urban mobility and reducing dependency on fossil fuels. However, rapid adoption of Electric Vehicles Smart Charging (EVSC) infrastructure presents grid stability, economic and environmental concerns. The rising demand for electric cars, particularly in developing nations such as Nigeria, highlights the urgent need for a sustainable hybrid renewable energy charging infrastructure for BRT systems. This study presents a techno-economic assessment of an off-grid hybrid systems that use photovoltaic (PV), wind turbines (WTs), hydrogen (H₂), fuel cell (FC) and battery technologies to power Electric Vehicles Smart Charging within Bus Rapid Transits networks. The Lagos BRT charging system at City Mall Station (CMS) serves as a case study, with hourly renewable resources obtained from National Aeronautics and Space Administration database (NASA). Using the HOMER pro-optimization tool, a multi-criteria analysis is performed to evaluate system viability, with special focus on key metrics such as levelized cost of energy (LCOE), net present cost (NPC), renewable energy fraction (REF), and greenhouse gas (GHG) emissions. The simulation results demonstrate that the hybrid PV/wind/FC/battery configuration is exceptionally economical, with an LCOE as low as $0.222/kWh, $2.03M NPC, 51.3% REF, and 159,209 kg of carbon dioxide emissions per year compared to grid-dependent charging. The study shows that integrated renewable-hydrogen systems are not only financially feasible, but also provide significant insights for policymakers, transportation authorities, and energy planners seeking to accelerate the transition to green public transportation infrastructure through innovative hybrid energy schemes. Full article