Search Results (1,026)

Background/Objectives: RELA encodes the p65 subunit of NF-κB and plays a critical role in immune regulation, epithelial protection, and anti-apoptotic signaling. Pathogenic RELA variants cause monogenic immune dysregulation with heterogeneous clinical manifestations. However, genotype–phenotype relationships and optimal treatment strategies remain incompletely defined. Methods: We conducted a comprehensive literature-based analysis of reported individuals with RELA variants and additionally described a family carrying a RELA c.706C>T (p.R236*) variant, including a clinically affected proband and his variant-positive father with isolated vitiligo. Clinical, immunological, genetic, endoscopic, and therapeutic data were extracted and summarized using a module-based phenotypic framework. Exploratory analyses were performed to examine potential genotype–phenotype patterns and reported treatment responses. Results: A total of 72 individuals with RELA variants, including the proband and his variant-positive father from the present family, were analyzed. RELA-associated disease exhibited marked clinical heterogeneity, encompassing mucocutaneous, systemic inflammatory, autoimmune, gastrointestinal, hematologic, allergic/eosinophilic, and infection-related manifestations. Exploratory analyses suggested that truncating or splice-site variants were more frequently observed among individuals with mucocutaneous lesions, whereas missense variants appeared to be more common among those with autoimmune manifestations. Tumor necrosis factor (TNF) inhibitors were among the therapies associated with favorable reported responses. In the present family, the proband presented with early-onset Behçet-like intestinal inflammation and achieved clinical and endoscopic remission after thalidomide and dose-escalated infliximab treatment. Conclusions: This study expands the clinical spectrum of RELA-associated disease and highlights preliminary variant-related clinical patterns that require confirmation in larger independent cohorts. The available treatment experience suggests that TNF blockade may be considered as a therapeutic option in selected patients, although comparative efficacy cannot be established from the available data. Full article

The dynamic distribution of braking forces between front and rear axles in electric commercial vehicles represents a critical multi-objective optimization challenge requiring simultaneous satisfaction of regulatory safety compliance, regenerative energy recovery, thermal stability, and actuator coordination under varying load and road conditions. This paper addresses this challenge through the development and experimental validation of an integrated adaptive brake force distribution strategy combining model predictive control (MPC) with Particle Swarm Optimization (PSO) within a unified framework that ensures compliance with ECE Regulation No. 13. A comprehensive experimental test bench was designed and instrumented, integrating three independent braking mechanisms: magnetic brakes with front and rear torque coefficients of 4.73 N·m/A and 3.65 N·m/A, respectively; an eddy current retarder with coefficient $k_0=$ 2.220 × 10⁻⁴ N·m·s/(A²·rad), producing braking torque that is quadratic in excitation current and linear in rotor speed; a regenerative braking system with 82–90% efficiency; and a switchable magnetic clutch for FWD/4WD operation. The MPC controller was formulated with a prediction horizon $N_p$ = 20, control horizon $N_c$ = 5, and sampling time $T_s$ = 20 ms. PSO was employed for systematic tuning of MPC weights using 30 particles over 50 iterations with cognitive and social coefficients $c_1$ = $c_2$ = 2.0 and linearly decreasing inertia from 0.8 to 0.4. A vehicle state estimation module using Kalman Filtering was developed for real-time estimation of vehicle mass (<3% error), road slope (<0.3% error), and road friction coefficient (<5% error). Experimental validation across eight comprehensive test scenarios demonstrates that the PSO-optimized MPC controller achieves 43% reduction in front RMSE (from 2.65 Nm to 1.52 Nm), 44% reduction in rear RMSE (from 0.78 Nm to 0.44 Nm), 100% ECE R13 compliance (improved from 67.5%), 57% settling time improvement (from 4.2 s to 1.8 s), 92% overshoot reduction (from 67% to 5%), and average recovered energy improvement from 3.51 kJ to 4.04 kJ. The proposed framework provides a comprehensive solution for next-generation electric commercial vehicle brake management systems. Full article

Anaplastic thyroid carcinoma (ATC) is an aggressive malignancy with poor response to standard therapies. While Dabrafenib plus Trametinib (DT) combination therapy has improved the survival of patients with BRAF-mutant ATC, resistance remains a challenge. Given the association of macrophages with poor prognosis, and that SPRY4 has emerged as a mediator in ATC–macrophage interactions, we investigated their role in DT resistance and evaluated the therapeutic potential of macrophage targeting. Transwell co-cultures of BRAF-mutant ATC cell lines (T235 and T238) with THP-1-derived macrophages were established, and viability, invasion, and macrophage phenotype were assessed. Macrophages were also characterised in tumour samples from eleven ATC patients treated with DT. In vitro, DT significantly reduced ATC cell viability and invasion. However, macrophage co-culture significantly restored these effects, also promoting cytoskeletal remodelling and increased vimentin expression, despite DT treatment. DT significantly downregulated SPRY4 and suppressed MAPK signalling and PD-L1. These effects were also significantly reversed by macrophages. Under DT, macrophages showed an increase in M2-like polarisation in co-culture. Patients’ tumour samples were highly infiltrated with macrophages. In vitro targeting of macrophages with Edicotinib, in combination with DT, significantly enhanced the anti-tumour effects of DT and decreased the M2-like polarisation, bypassing the macrophage-mediated DT resistance. Overall, the in vitro findings suggest that macrophages modulate ATC response to DT by enhancing viability, invasion, MAPK signalling and PD-L1, supporting a pro-tumoural phenotype. Targeting macrophages overcomes this resistance, highlighting the CSF1/CSF1R signalling pathway within the ATC–macrophage axis as a promising therapeutic target to improve DT efficacy. Full article

License plate recognition is a critical perception module in intelligent transportation systems, yet severe occlusion remains a fundamental challenge in unconstrained traffic scenes because the missing visual evidence is physically absent rather than merely degraded. Although multi-view observations provide complementary cues for recovering occluded characters, existing fusion strategies usually operate in the spatial domain and therefore suffer from feature misalignment, especially when Vision Transformers rely on local patch-wise positional encoding. To address this issue, we propose Wavelet-Enhanced Transformer, a domain-transformed multi-view recognition framework for occluded license plates. The central idea is to shift feature fusion from the spatial domain to the wavelet domain, where discrete wavelet transform decomposes encoded features into low-frequency structural components and high-frequency detail components. This transformation alleviates spatial semantic ambiguity caused by viewpoint variation and positional mismatch, enabling more robust cross-view feature aggregation. On top of this representation, we design an order-agnostic memory fusion mechanism to progressively accumulate complementary evidence from multiple partial observations, and introduce a discriminator-based stopping module to adaptively terminate unnecessary iterations for efficient deployment. Experiments on the CBLPRD-330k dataset and severe occlusion stress tests demonstrate the superiority of the proposed method. Under 20–50% character occlusion, our model maintains over 95% character accuracy and consistently outperforms mainstream baselines such as LPRNet, TrOCR, and PP-OCRv5 in character accuracy, length accuracy, and full-match rate (FMR). These results indicate that wavelet-domain feature fusion provides an effective and robust solution for highly occluded license plate recognition in complex traffic environments. Full article

Liver tumor segmentation is a significant task in clinical imaging that involves detecting liver tumors and distinguishing them from the surrounding liver tissue in CT scans. Precision segmentation performs important roles in the initial detection of liver cancer, treatment planning, and monitoring disease development, which also supports doctors, facilitating surgeries and radiation therapy more efficiently. Meanwhile, clinical imaging and segmentation algorithms have been enhanced over the years. The currently prevailing state-of-the-art methods still face multiple difficulties, though, in obtaining precision and reliability in their outcomes. Tumors with irregular shapes, variable sizes, and densities similar to those of surrounding tissues often lead to segmentation inaccuracies and potential misdiagnoses. In this work, we tackle these challenges by developing an advanced process for precise liver tumor segmentation by utilizing CT images from the LiTS dataset. The proposed DTARNU-Net was developed, trained, validated, and evaluated exclusively using the Liver Tumor Segmentation (LiTS) benchmark dataset. No experiments were conducted on the 3D-IRCADbI dataset in this study. All quantitative and qualitative results presented in the manuscript correspond to the LiTS dataset. The LiTS dataset contains contrast-enhanced abdominal CT scans with expert-annotated liver and tumor masks. The proposed model was evaluated using patient-level training, validation, and testing partitions (9:2:2 ratio), and all experiments were independently repeated five times. Statistical significance was assessed using paired Student’s t-test (p < 0.05), and the results confirmed that the performance improvements over competing methods are statistically significant. We introduce a novel three-level pre-processing approach that significantly enhances image quality through histogram equalization, noise removal, smoothing, and sharpening. Our approach is embodied in the Dense Tiered Attention Residual Nested U-Net (DTARNU-Net), a sophisticated model combining the strengths of a Siamese network and a nested U-Net architecture. This model incorporates the ACON-ReLU residual convolution block (A-R), which improves recognition accuracy in regions with subtle changes, reducing missed detection. The presented method enhances trait collaboration and spatial data by utilizing the Brownian Motion-based Butterfly Optimization Algorithm (BM-BOA). This algorithm efficiently integrates low-level trait details with high-level semantic data. The Dense Tiered Attention Residual Module (DTSRM) additionally improves these traits to obtain more precise segmentation. The model achieved segmentation robustness of 96.78% for liver segmentation and 97.00% for liver tumor segmentation on the LiTS dataset. These outcomes indicate that the presented method performs better than the prevailing state-of-the-art methods and has the capability to help computer-assisted detection and treatment by furnishing more precise and reliable liver tumor segmentation. Full article

This study aimed to characterize the modulation in immune cell subpopulations in murine lungs following influenza A virus (IAV) infection, assessing the effects of infectious dose, viral adaptation, and NS1 expression. Immune cell subsets were profiled by surface receptor expression using multiparametric flow cytometry with a 10-antibody immunophenotyping panel. Neutrophils expressing Ly-6G were significantly increased in the lungs following lethal-dose infection with IAV, independently of NS1 expression; in contrast, lethal-dose infection with all viruses reduced CD163⁺ and F4/80⁺ neutrophil subpopulations. Lethal-dose infection increased pulmonary CD68⁺ macrophages while decreasing CD163⁺, CD193⁺, and F4/80⁺ macrophage subsets, as well as F4/80⁺ myeloid cells, by day 3 post-infection; these reductions were independent of NS1 expression and infectious dose. Following lethal-dose IAV infection, NK cells exhibited upregulation of IL-23R⁺ and IL-12Rβ2⁺ subsets, while the CD193⁺ NK subpopulation was decreased on day 3 post-infection. Profiling of NKT cells revealed an expansion of the IL-12Rβ2⁺ NKT subset on day 3 post-infection. Adaptive immune profiling of lung CD4⁺ T cells revealed a selective increase in Th1-like cells (IL-12Rβ2⁺ CD4⁺) after WSN infection, a marked reduction in Th2-like cells (CD193⁺ CD4⁺) following infection with IAV regardless of NS1 status or dose, and an expansion of CD4⁺NK1.1⁺ cells only after lethal-dose infection. Immune cell subset frequencies were comparable between infections with NS1-expressing and wild-type viruses; NS1 expression did not alter subset composition, whereas the infection dose modulated their abundance. These findings expand our understanding of the subpopulation of immune cells and their possible role in influenza virus pathogenesis. Full article

Seasonal variation in SARS-CoV-2 transmissibility is difficult to estimate directly from year-round epidemic data because interventions, behavior, reporting, immunity, and viral evolution change concurrently. We therefore asked whether cross-country differences observed during the initial exponential-growth phase could be used to infer country-specific seasonal modulation. Early-pandemic basic reproduction numbers (R₀) from 118 countries were linked to 96 harmonized environmental and population-level predictors. Among nine candidate algorithms evaluated across 100 repeated train–test splits, ridge regression using the combined predictor set provided the best balance of predictive accuracy, generalization, and temporal stability. The selected model was then driven by daily climatological covariates to reconstruct annual baseline R₀(t) profiles. Predicted transmissibility generally peaked during winter in the Northern Hemisphere and approximately six months later in the Southern Hemisphere, whereas equatorial countries showed weaker or multimodal patterns. Seasonal forcing amplitude increased strongly with absolute latitude (r = 0.85; mean 0.064 across 77 temperate countries), and predicted R₀ peaks aligned more closely with minimum ultraviolet radiation than with minimum temperature. These ecological associations do not establish causality, but they provide country-specific seasonal-forcing parameters for epidemic models and a baseline environmental context for comparing early-pandemic trajectories. Full article

Background: Idiopathic epilepsy (IE) is among the most frequent chronic neurological disorders of cats, but by definition it is associated with an unremarkable conventional brain MRI, leaving its temporal lobe pathophysiology poorly characterised. Objectives: To quantify interictal temporal lobe neurometabolite ratios in cats with IE using bilateral single-voxel ¹H-MRS and to compare them with healthy controls, and to explore relationships with seizure timing and signalment. Methods: In this prospective case–control study, 20 client-owned cats with idiopathic epilepsy and 20 healthy control cats underwent bilateral single-voxel ¹H-MRS (PRESS; TE 135 ms; 10 × 10 × 10 mm voxel centred on the mid-hippocampus and amygdala). N-acetylaspartate (NAA), total choline (tCho), and total creatine (tCr) were quantified and the NAA/tCr, NAA/tCho, tCho/tCr, and tCho/NAA ratios were compared (independent-samples t-tests; one-way ANOVA; Spearman correlation; α = 0.05). Results: Overall, no metabolite ratio differed significantly between groups across both hemispheres, and no ratio correlated with age, sex, breed, coat length, or neutering status. However, the right temporal lobe NAA/tCr ratio was significantly lower in epileptic cats (1.32 ± 0.29) than in controls (1.50 ± 0.20; p < 0.05). Seizure timing modulated the left-hemisphere spectrum: left tCho/tCr correlated negatively with the interval since the last seizure (Spearman r = −0.48; p = 0.035), and cats with ≤3 days between first and last seizures showed lower left NAA/tCho (0.89 ± 0.23 vs. 1.14 ± 0.24; p = 0.032) and higher left tCho/NAA (1.21 ± 0.40 vs. 0.91 ± 0.17; p = 0.039) than cats with a longer interval. Conclusions: Interictal ¹H-MRS detects temporal lobe metabolic disturbances, including a reduced right temporal lobe NAA/tCr ratio suggesting neuronal or metabolic dysfunction and seizure timing-dependent membrane turnover shifts, in cats with normal-appearing MRI. ¹H-MRS is therefore a promising non-invasive adjunct for characterising, monitoring, and potentially assisting in the localisation of metabolic abnormalities in feline IE and warrants validation in larger, longitudinally sampled cohorts. Full article

Background: Streptococcus suis serotype 2 is a major zoonotic pathogen responsible for severe systemic infections in pigs and humans, including septicemia, meningitis, and high mortality outcomes. Cefquinome, a fourth-generation β-lactam antibiotic widely used in veterinary medicine, is commonly applied for the treatment of S. suis infections. However, optimized dosing strategies remain insufficiently defined, particularly under conditions of varying bacterial burden, inoculum size, and reduced susceptibility or resistance phenotypes. These factors may significantly alter pharmacodynamic responses and compromise the predictive value of conventional MIC-based approaches. Objectives: This study aimed to characterize the pharmacokinetics (PK) and pharmacodynamics (PD) of cefquinome against S. suis serotype 2 using an integrated ex vivo serum time-kill experiments and semi-mechanistic PK/PD modeling. A secondary objective was to evaluate optimized dosing regimens across different inoculum levels and susceptibility phenotypes, including a cefquinome-resistant mutant. Methods: Cefquinome pharmacokinetics following intramuscular administration at 2 and 4 mg/kg in piglets were described using a two-compartment model. Dose proportionality, exposure linearity, and clearance parameters were assessed. Ex vivo serum time-kill experiments were conducted using a parental strain and a cefquinome-resistant mutant (M1) under normal-inoculum (NI), high-inoculum (HI), and mutant/resistant (MS) conditions. A semi-mechanistic PK/PD model incorporating logistic bacterial growth, sigmoidal Emax killing, nutrient limitation, and a time-delay function was developed to describe dynamic bacterial responses. Model parameters (k₀, k_max, EC₅₀) were estimated using nonlinear least-squares regression (Scientist v2.0), and simulations were performed by integrating time-varying PK input functions. Results: Cefquinome demonstrated linear pharmacokinetics with dose-proportional increases in Cmax and AUC between 2 and 4 mg/kg, with comparable clearance across doses. Ex vivo studies revealed time-dependent antibacterial activity with a pronounced inoculum effect. Higher bacterial burdens significantly reduced bactericidal efficiency and promoted regrowth during declining drug exposure. No tested concentrations achieved ≥3-log₁₀ killing in HI or MS conditions, whereas the NI group achieved a maximal reduction of 3.5-log₁₀ CFU/mL. MIC values in serum and medium were consistent (0.03, 0.06, and 0.24 µg/mL for NI, HI, and MS, respectively), indicating minimal protein binding influence. The semi-mechanistic model accurately described observed bacterial dynamics (R² > 0.99; MSC > 1.5), capturing delayed drug effects, inoculum-dependent growth suppression, and regrowth phenomena. Growth rates were reduced under serum conditions, reflecting nutrient limitation. Importantly, inoculum size exerted a stronger impact on pharmacodynamic outcomes than resistance phenotype, as reflected by reductions in kmax and increases in EC₅₀ under HI conditions. Although %T>MIC exceeded conventional β-lactam targets (>40%) in most regimens, MIC-based indices poorly correlated with observed dynamic killing responses. Conclusions: Cefquinome exhibited time-dependent antibacterial activity against S. suis serotype 2, strongly modulated by inoculum size and reduced susceptibility. The developed semi-mechanistic PK/PD model provided robust prediction of bacterial time-kill behavior and outperformed MIC-based metrics in guiding dose optimization. Simulation results support 2 mg/kg every 24 h for normal infections and 2 mg/kg every 12 h for high-inoculum or less susceptible infections, emphasizing the value of model-informed dosing strategies for optimizing β-lactam therapy. Full article

Tuberculosis (TB) remains a global health problem, and attention is focused on host immunometabolic pathways influencing antimicrobial immunity, tissue injury, and treatment response. This comprehensive review integrates evidence on the kynurenine pathway (KP) of tryptophan (TRP) metabolism in TB and evaluates its potential as a host-directed therapeutic target. Evidence from human cohorts, in vitro systems, animal models, and non-human primates indicates that Mycobacterium tuberculosis activates the KP across myeloid, antigen-presenting, non-hematopoietic lung, granulomatous, pleural, and central nervous system compartments. KP activation is shaped by interferon-γ signaling, mycobacterial burden, infection duration, strain virulence, and host regulatory mechanisms. Increased indoleamine 2,3-dioxygenase-related (IDO) activity, TRP depletion, kynurenine accumulation, and kynurenine–aryl hydrocarbon receptor (AhR) signaling are associated with impaired T-cell proliferation and recruitment, suppressive myeloid phenotypes, and spatially restricted immunoregulatory niches within granulomas. Accumulated data indicate context-dependent protective effects of the KP manipulations, through restriction of excessive inflammation. Furthermore, the KP-related changes may have biomarker value. Integrated assessment of enzyme expression, kynurenine-to-tryptophan ratio (K/T ratio), downstream metabolites, and signaling pathways will be essential for translational studies. Future studies should therefore evaluate KP activity using integrated readouts, including IDO expression, metabolites and their ratios, and KYN–AhR signaling, and should determine whether context-specific pathway modulation can safely improve outcomes when combined with conventional anti-TB therapy. Full article

The production of hot mix asphalt (HMA) is energy-intensive, resulting in carbon dioxide (CO₂) and greenhouse gas (GHG) emissions. The primary energy source (accounting for over 97%) and emissions source is the rotary drum employed for the drying and heating of the aggregates. Quantifying the CO₂ emissions associated with combustion is of crucial importance in order to facilitate a more profound comprehension of the environmental impacts of HMA production. The objectives of this study are to develop a methodological framework for the quantification of combustion-related carbon dioxide emissions in the context of asphalt production. The proposed framework investigates three complementary approaches: (i) an energy-balance-based thermal energy (TE) model, (ii) recordings of fuel consumption and (iii) direct measurement of exhaust-gas composition. By applying these methods in parallel and cross-comparing their results batch by batch, the framework enables reliable verification of actual CO₂ emissions from the module A3—production stage of asphalt manufacturing. In this stage, the predominant source of greenhouse gases is fuel combustion during aggregate drying and heating. A comprehensive set of data was collected from two HMA batch plants, each operating under distinct conditions. The parameters considered included fuel type, asphalt mixture type, asphalt production time, aggregate moisture content, mixing temperature, and production rate. The TE model demonstrated a robust linear correlation with measured energy consumption (R² = 0.97), and fuel-based CO₂ estimates exhibited minimal discrepancy compared to direct exhaust-gas measurements on average (mean difference 1.0%; t-test p = 0.674). However, systematic discrepancies were observed between the two plants (with overestimation of up to 20% at one plant (AP1) and underestimation of up to 12% at the other (AP2)). This demonstrates that energy-based CO₂ estimation methods require plant-specific calibration against direct measurement before they can be reliably applied in life cycle assessment (LCA) and environmental product declaration (EPD) practice. Measured CO₂ emission intensities ranged from 17.39 to 21.76 kg/t at AP1 and from 16.05 to 18.44 kg/t at AP2; the casing-losses factor of the TE model was calibrated to CL = 23% for the studied diesel-fired plants (mean deviation +0.4% from measured energy); and aggregate moisture content explained 74% of the variance in measured energy consumption (R² = 0.743). Full article

Low-resource written-query keyword spotting detects a text-specified target in speech without spoken enrollment or full automatic speech recognition. We present Tibetan Phonology-Aware Speech Evidence Matching (Tibetan-PASEM), a method that encodes graphemic, approximate phonological, and dialect-related query information, matches it with local acoustic windows, aggregates window-level evidence, and applies a validation-selected operating point. Querybank198 contains 198 Tibetan written queries derived from four public speech resources. A complete-file and decoded pulse-code modulation (PCM) audit produced a corrected 213,128-pair evaluation with no identified recording-content, source-qualified speaker, or session-proxy overlap across the development-to-held-out boundary. In a three-seed corrected-index re-evaluation, the acoustically strengthened PASEM (PASEM-AS) achieved F1 scores of 66.16 ± 2.14% on seen_test, 26.51 ± 3.42% on unseen_test, and 72.10 ± 1.39% on the confusable-negative diagnostic condition, which comprises 20 training-exposed query forms and is used for negative-set analysis rather than difficulty ranking. Under the original pre-correction pair index, PASEM with transfer regularization (PASEM-TR) increased the unseen_test F1 from 25.80 ± 2.71% to 35.25 ± 4.17% and the Recall from 18.48 ± 2.98% to 33.85 ± 8.22%; the corresponding seed-level 95% confidence intervals were 24.89–45.61% and 13.43–54.27%. These results establish strong familiar-query discrimination, an audited fixed-threshold evaluation protocol, and partial, seed-sensitive transfer to held-out written queries. Full article

In this study, the Boscovich curve of 1763 is reinterpreted as a mean-field potential for interacting particles in condensed matter. In a dense many-body system, each particle experiences an effective potential arising from the average distribution of all the others. This mean-field potential, which exhibits alternating maxima (energy barriers) and minima (coordination shells), thereby reducing the complexity of the N-body problem to an effective two-body radial problem, with the correlation distance r as the key variable. The relationship between the PMF and the radial distribution function g(r) is given by the Kirkwood equation U_B(r) = −kT ln g(r), which provides a multi-well potential in condensed matter. Furthermore, the system is described by the Fisher density functional equation for the correlation amplitudes, −2kT ∇²ψ(r) + U_B(r)ψ(r) = μψ(r) whose eigenvalues μ_i correspond to potential levels and whose eigenfunctions ψ_i are the correlation amplitudes of the coordination shell structure. Based on the multi-well potential picture, the oscillatory behavior of U_B(r) is modeled analytically by a weighted sum of Lennard-Jones potentials, modulated by sigmoid functions. The parameters—well depths, widths, and coordination distances—are assigned on the basis of known structural properties of the system, derived either from experimental data or from geometric models such as FCC or HCP lattices. The radial distribution function is then reconstructed as a linear combination of the squared eigenfunctions obtained from the Fisher equation. The resulting discrete eigenvalue spectrum provides a spectral interpretation of the shell structure of condensed matter, wherein the complexity of many-body interactions is encoded in a hierarchy of correlation modes, each associated with a specific coordination shell. Unlike classical DFT—which relies on approximate excess free-energy functionals—and Ornstein–Zernike theory—which requires closure approximations—our approach provides a direct spectral interpretation of the coordination shell structure through the eigenvalue spectrum of the Fisher equation, where the PMF acts as the effective potential and the radial distribution function is reconstructed as a combination of squared eigenfunctions. The method is validated for liquid argon and FCC lattices and establishes a historical connection with Boscovich’s curve as a statistical potential. Full article

The creation of innovative learning environments in courses to provide sustained talent support has long remained a central research concern for high-quality social development. Neglected foundational skills in professional course clusters are often a hidden barrier to higher education for sustainable development (HESD). To close five persistent foundational skill gaps across improper citation (J1), ineffective figure use (J2), poor analysis (J3), irresponsible AI use (J4), and comprehensive application (J5) within the hydraulic engineering course cluster, a four-stage deliberate practice module (5Di-40Pr-5Tr-3Cm) has been embedded into a two-week hydraulic model experiment course, and its learning outcomes are systematically evaluated. A systematic analysis of its achievement levels across neglected foundational skill indicators of J1~J5 at each stage was conducted, stratified by the overall cohort and subgroups (P: objective demand, T: behavior type, G: optimization methods). The key findings include: ① deliberate practice demonstrates better teaching outcomes than lecture-based instruction, which can be evidenced in 2026, when J5’s achievement levels at the 3Cm stage yielded a moderate effect size relative to the 2025 lecture-based condition (d = 0.42); compared with the 2024 no-intervention baseline, the cumulative effect is a obvious increasing trend (d = 1.43); ② In far-transfer subgroup diagnosis, P2 (medium objective demand) shows a rank-order reversal, low at 40Pr but higher at 3Cm, and is identified as the “partial understanding” group and providing a diagnostic anchor for tiered intervention; ③ In near-transfer pathway diagnosis, J5’s low performance in 5Tr (65.35%, below overall mean of 83.09%; CV = 7%) stems from two distinct pathways: a “knowledge-deficit pathway” (max-decay subgroups) and a “processing-load pathway” (subgroups where J1, J2 do not exhibit max decay). In addition, stage-specific thresholds (40Pr: 90%, range 60~99%; 5Tr and 3Cm: 83% ± 3%, range of for 40Pr, mean = 90%, recommended range = 60~99%; for 5Tr, mean = 83% ± 3%, range = 65~96% and 75~90%) provide quantitative benchmarks for targeted intervention. These cumulative findings are intended to advance the evaluation paradigm of engineering practice courses from “total score attainment” toward “structural diagnosis” and align with the competency-oriented philosophy of higher education for sustainable development (HESD). Full article

Circulating extracellular vesicle-associated microRNAs (EV-miRNAs) are emerging as promising peripheral biomarkers in multiple sclerosis (MS). This prospective, observational pilot study was conceived as a hypothesis-generating investigation to characterize the expression profile of four candidate EV-miRNAs (miR-223-5p, miR-155-5p, miR-30a-5p, and miR-146a-5p) within an EV-enriched plasma fraction. The cohort comprised 16 patients with secondary progressive MS (SPMS) undergoing siponimod therapy, 13 age- and sex-matched healthy controls (HCs), and 7 patients with relapsing–remitting MS (RRMS) included as an exploratory comparator. Quantification was performed by quantitative real-time PCR employing the ΔΔC_t methodology, with miR-16-5p as the endogenous normalizer. Analyses were conducted cross-sectionally and longitudinally, the latter within a paired subgroup of 11 SPMS patients evaluated at baseline and after twelve months of uninterrupted treatment. Cross-sectional comparisons demonstrated a significant downregulation of EV-miR-223-5p in SPMS patients relative to HCs (fold-change [FC] = 0.26; FDR q = 0.026), whereas EV-miR-155-5p was significantly reduced in both the SPMS (FC = 0.35; FDR q = 0.033) and RRMS (FC = 0.28; FDR q = 0.046) cohorts compared with HCs. No significant intergroup differences were observed for EV-miR-30a-5p or EV-miR-146a-5p. Longitudinal assessment revealed no significant modulation of any target EV-miRNA following one year of siponimod therapy. These preliminary observations should be interpreted with caution, given the exploratory nature and modest cohort size. Importantly, the isolation of total plasma EVs does not permit resolution of the specific cellular provenance of the observed signals, nor does it capture their downstream functional consequences. Nevertheless, the selective downregulation of EV-miR-223-5p and EV-miR-155-5p may tentatively suggest candidate molecular signatures warranting further interrogation. Adequately powered studies incorporating cell-specific EV sorting and paired cerebrospinal fluid sampling will be required to substantiate these signals and clarify their potential utility in monitoring disease progression and therapeutic response in progressive MS. Full article