Search Results (32,981)

Unbonded flexible pipes are key components of deepwater high-temperature oil and gas transportation systems, and their bending performance directly affects in-place response and fatigue assessment. Interlayer contact and sliding of tensile armor layers govern bending hysteresis; under high-temperature service, incompatible thermal expansion of metallic and polymer layers changes contact pressure and the associated slip conditions. This study develops a thermo-mechanical bending hysteresis model in which thermally induced interlayer contact pressure links the radial temperature field to the bending response. A steady-state multilayer-cylinder heat-transfer model and a thermoelastic compatibility formulation are used to determine temperature distributions and interlayer contact pressures. The contact-pressure variation is then introduced into the tensile-armor slip criterion and the incremental moment-curvature relationship, covering non-slip, partial-slip, and full-slip stages. A sequentially coupled finite element model of a 2.5-inch unbonded flexible pipe is established for validation. The numerical model predicts hysteresis loop area and unloading/reverse-loading stiffness with relative deviations of 6.02% and 5.09% from the finite element results, respectively. Increasing internal temperature increases contact pressure and critical slip curvature, prolongs partial slip, and substantially increases hysteretic energy dissipation. The model provides a basis for high-temperature bending stiffness determination and fatigue-oriented analysis of unbonded flexible pipes. Full article

Background: Analysis of digital images of histopathological sections is increasing due to widespread adoption of fully digitised workflows and the greater availability of whole-slide scanners. Currently, hormone receptor status in breast carcinoma is assessed by pathologists scoring separate immunohistochemically stained sections. Methods: In this study, we employed pathologist-verified pixel-level annotations to train nested pixel classifiers capable of making case-level predictions of oestrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status directly from H&E-stained sections using biopsy cases alone. The model was evaluated on both an internal test set and an external international evaluation set from an institution in a different continent using different scanner hardware without the need for image normalisation. Results: In the internal test set, the models achieved AUCs of 0.8030, 0.7956 and 0.7488 for ER, PR, and HER2, respectively, with AUCs of 0.7008 and 0.7488 for ER and PR using an external cohort from an institution from which no cases were used for training. Conclusions: Our data highlight a potential strategy by which a pixel-based classifier, typically developed to quantify histological features within individual cases, could be used to make case/slide-level predictions but illustrate the challenges associated with this approach. Full article

Bioactive peptides have health benefits, but the intense bitterness associated with their hydrolysis severely restricts their industrial applications. This paper systematically constructs a collaborative theoretical framework that integrates intelligent prediction, targeted debittering, and multi-dimensional evaluation. Firstly, it reviews the core applications of deep learning (such as quantitative structure–activity relationship (QSAR) and graph convolutional network (GCN)) combined with molecular docking technology in the high-throughput identification of bitter peptides and the analysis of target receptor interaction mechanisms. Secondly, it discusses how artificial intelligence and computational simulation can improve the efficiency of traditional debittering processes, emphasizing the advantages of multifunctional composite wall materials in the targeted encapsulation and delivery of bitter peptides, as well as the metabolic regulatory mechanisms behind controlling microbial fermentation for the debittering of specific peptide substrates. Finally, to provide a high-fidelity data closed loop for artificial intelligence (AI) models, a three-dimensional cross-validation system integrating standardized quantitative sensory evaluation and biomimetic electronic tongues was established. Future research should focus on developing large models for flavor generation to drive the green and targeted creation of low-bitterness and highly active peptides. Full article

Although coconut water is recognized for its desirable sensory appeal and nutritional composition, its broader industrial use is constrained by the rapid deterioration that occurs after extraction. In this study, crossflow microfiltration of coconut water with a silicon carbide membrane was optimized by investigating pressure and temperature through a face-centered design (FCD) and artificial neural network modeling coupled with a genetic algorithm (ANN–GA). Permeate flux and fouling index were used as process responses, and the optimized condition was further examined in terms of hydraulic resistance, fouling behavior, and retention of minerals and primary metabolites. Pressure and temperature affected the process differently: permeate flux showed marked nonlinear behavior, whereas fouling index was governed mainly by pressure. At the sample level, ANN described permeate flux more accurately than FCD (R² = 0.99 vs. 0.96), whereas FCD showed better grouped cross-validated predictivity across unseen pressure–temperature conditions (Q² = 0.85 vs. 0.57). For the fouling index, FCD outperformed ANN in both sample-level fit and grouped validation (R² = 0.95 vs. 0.60; Q² = 0.70 vs. 0.61). Both approaches converged on the same favorable operating window, and experimental validation at 60 kPa and 35 °C yielded 1085.23 ± 23.12 L h⁻¹ m⁻² and 83.56 ± 1.56%. During concentration mode, flux decline was severe but predominantly reversible, with high clean-water permeance recovery after chemical cleaning. Resistance partition and fouling modeling indicated that the main hydraulic limitation was associated with concentration polarization and external cake-layer buildup rather than irreversible membrane damage. The clarified fraction also preserved high transmission of major minerals and relevant primary metabolites, indicating that the selected condition combined high productivity, manageable fouling, and satisfactory nutritional retention. Full article

Background/Objectives: Cervical intraepithelial neoplasia (CIN) exhibits heterogeneous clinical behavior, with some lesions regressing spontaneously, whereas others persist or progress to higher-grade disease. Identifying biomarkers that reflect lesion dynamics remains a major clinical challenge. This study aimed to evaluate the clinical significance of CD86 expression in cervical lesions by examining longitudinal changes and determining whether temporal alterations in CD86 expression are associated with lesion regression and epithelial-associated immune dynamics. Methods: Cervical samples were collected from patients with CIN, and gene expression was analyzed using reverse transcription–quantitative PCR. Longitudinal analyses were performed using paired samples to evaluate the temporal changes in CD86 expression. Regression status and time to regression were assessed, and associations with CD86 changes were evaluated using receiver operating characteristic analysis, logistic regression, and Cox proportional hazards models. Longitudinal patterns were further characterized using a spaghetti plot and slope analyses. Results: Baseline CD86 expression did not associate with regression status or CIN grade. However, longitudinal changes in CD86 expression differed significantly between the regression and non-regression group. CD86 change demonstrated moderate predictive performance for regression and was significantly associated with both regression and shorter time to regression. Longitudinal analyses revealed distinct temporal patterns between the regression and progression groups. Baseline CD86 expression was strongly correlated with FOXP3 expression, whereas CD86 dynamics were not independently associated with lymphocyte-related markers. Conclusions: Longitudinal changes in CD86 expression are significantly associated with lesion regression in CIN and may reflect lesion-associated immune dynamics during follow-up, particularly within epithelial-derived cervical cytology specimens. Full article

Extended target tracking requires both accurate shape representation and efficient recursive estimation. In non-ellipsoidal extended target tracking, ellipsoidal random-matrix models are computationally efficient and suitable for Bayesian recursion, but they mainly describe the overall spatial dispersion of measurements and cannot represent local contour variations such as protrusions and concavities. In contrast, non-ellipsoidal contour models provide stronger shape representation but usually introduce higher computational complexity and stronger prior assumptions. To address this trade-off, this paper proposes an extension-difference-mapping-based Poisson multi-Bernoulli mixture filter, termed EDM-PMBM, for non-ellipsoidal extended target tracking. First, each local Bernoulli component carries a Fourier-based contour estimate and an ellipsoidal baseline propagated from the previous posterior. At the current scan, the predicted EDM function is used to map each candidate measurement subset into the EDM domain, where the EDM-induced GGIW likelihood is evaluated for PMBM data association. After the association is determined, the assigned measurement subset is used to update the posterior contour, the EDM ratio, and the EDM-domain state. The updated EDM information is then propagated to subsequent scans. In this way, shape differences are introduced into likelihood evaluation and data association without changing the basic recursive structure of the PMBM filter. Simulation results in two scenarios show that the proposed EDM-PMBM filter achieves lower GOSPA error than the compared filters and maintains more stable tracks in dense crossing situations. These results indicate that the proposed method improves the discrimination ability for non-ellipsoidal extended targets. Full article

Aspartame is a widely used artificial sweetener, but its possible relationship with rheumatoid arthritis (RA) remains insufficiently understood. This study aimed to explore, rather than prove, potential molecular links between aspartame-related targets and RA-associated gene networks. Three public RA transcriptomic datasets (GSE55235, GSE55457, and GSE77298) from the Gene Expression Omnibus (GEO) database were integrated as discovery/training data. Because these datasets included different tissue origins, batch correction was used to reduce dataset-level technical variation, whereas tissue-origin-related biological variation was not assumed to be fully removable. After differential expression analysis, RA-associated differentially expressed genes (DEGs) were identified. The single-cell dataset GSE200815 was used for cell annotation and cellular expression visualization; because its comparator group consists of psoriatic arthritis (PsA) samples rather than healthy controls, single-cell results were interpreted as RA-vs-PsA observations and were not treated as disease-versus-healthy-control evidence. Potential targets of aspartame were retrieved from ChEMBL, SwissTargetPrediction, and the Similarity Ensemble Approach (SEA), and were intersected with RA-related DEGs to construct an aspartame-gene-RA regulatory network. Diagnostic models were developed using 113 machine-learning algorithm combinations to determine an optimal multigene model and its core genes. HLA-DRB1 was selected for exploratory scTenifoldKnk-based virtual knockout mainly because it was included in the optimal model and has a well-established role in RA immunogenetics; the single-cell analysis was used only to describe cellular distribution in the RA/PsA dataset. Molecular docking was then used to evaluate the possible interaction between aspartame and HLA-DRB1. Forty-four intersected genes linked the predicted aspartame targets with RA DEGs. The random forest plus partial least-squares generalized linear model (RF + plsRglm) identified 16 core genes. Network-level interpretation indicated that these genes were distributed across immune/antigen-processing, inflammatory-signaling, protease/extracellular-matrix-remodeling, adhesion, metabolic, and proliferation-related modules; therefore, HLA-DRB1 was treated as a prioritized immune-module candidate rather than as the sole driver of the network. Following virtual knockout of HLA-DRB1, affected genes were enriched in extracellular matrix organization, extracellular structure organization, extracellular matrix, collagen trimer, extracellular matrix structural constituent, and collagen binding. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways included integrin signaling, focal adhesion, proteoglycans in cancer, cytoskeleton in muscle, and phosphoinositide 3-kinase/protein kinase B (PI3K/AKT) signaling. Molecular docking showed a minimum binding energy of −6.7 kcal/mol, which was more negative than the preset stability criterion of −5.0 kcal/mol, and the docking pose suggested contacts around ARG-146. This integrative analysis suggests a hypothesis-generating association between aspartame-related predicted targets and RA-relevant molecular networks involving HLA-DRB1 and other core genes. The findings do not establish causality and require experimental, epidemiological, biophysical, and tissue-stratified validation before any causal or clinical inference can be made. Full article

In this paper we implement robust switching model predictive control to solve the dual control problem of simultaneous regulation and system identification, for linear time varying systems subject to bounded external disturbances and confined instances of variation. We leverage the piece-wise linear control law resulting from a fictitious switching architecture to generate closed-loop data that ensures strong system identifiability, while guaranteeing stability and constraint satisfaction under unknown—but bounded—disturbances and parameter variation. We pair the switching controller with a standard recursive estimation algorithm with forgetting factor, which yields unbiased estimates with variance associated to the external disturbance, showcasing the success of the switching at producing information in the closed-loop trajectories. Full article

Gravel soil used as backfill behind rockfall barriers in mountainous roads can extend structural service life and support sustainable resource utilization. However, rainfall-induced erosion may cause soil loss and reduce its buffering capacity. The fibers are short discrete fibers with a length of approximately 12 mm and an average diameter of 32.7 μm, corresponding to an aspect ratio of approximately 367. Reinforcement is achieved through fiber–soil interaction mechanisms, including particle bridging, interfacial friction, and pull-out resistance. The effects of polyurethane and fiber contents on compressive strength, shear strength, and impact resistance were evaluated using response surface methodology. Scanning electron microscopy was used to examine the microstructural features associated with the reinforcement mechanisms, and engineering-scale model tests were conducted to assess erosion and impact resistance under representative service conditions. The results show that polyurethane and fibers produce significant nonlinear enhancement effects on the mechanical properties of gravel soil, mainly through their individual contributions, whereas their interaction is limited. Multi-objective optimization indicates that the optimal mixture contains 6.8% polyurethane and 0.19% fiber, with prediction errors below 5%. The unconfined compressive strength of the gravelly soil increased from 107.6 kPa to 931.5 kPa, representing a 765.7% increase. Cohesion increased from 23.4 kPa to 83.44 kPa, representing a 256.4% increase. The internal friction angle increased from 43.4° to 61.23°, corresponding to a 41.08% increase. Under 1 h of intense rainfall erosion, the stabilized soil exhibited only slight surface particle detachment and maintained overall integrity. In impact tests, the velocity attenuation rate reached 65.6–71.4%. The proposed material provides a sustainable solution for improving buffer layers in rockfall barriers. Full article

Background: Metastasis and poor chemotherapy response have stagnated therapeutic progress in osteosarcoma (OS) for the past three decades. Defining the transition from localized to metastatic OS before overt dissemination is fundamental for improving survival. However, effective early diagnostic tools remain scarce, largely due to limited exploitation of the metastasis-associated tumor microenvironment’s own record of prior environmental and stress exposures encoded in cell-intrinsic transcriptional states. Here, we employed a supervised machine learning framework with iterative resampling and multi-stage model selection to identify molecular markers associated with metastasis in osteosarcoma and to develop a computational signature, Auto-RS. Methods: Transcriptomic and clinical data from 139 OS patients with ≥5 years of follow-up were analyzed. A LASSO–Cox framework was applied to derive a gene expression-based risk score, Auto-RS, from which a nomogram integrating age and sex was generated for individualized prognosis. Model interpretability was assessed across six independent single-cell OS patient datasets, and drug sensitivity predictions were inferred by integrating Auto-RS with the Precily algorithm to uncover actionable therapeutic vulnerabilities. Results: Auto-RS, constructed from the expression of four autophagy genes (BNIP3, MYC, PEA15, and SAR1A), served as an independent prognostic factor for overall survival (HR = 1.091; 95% CI, 1.047–1.136; p < 0.001). Time-dependent ROC analysis showed that Auto-RS was the most accurate single predictor (AUC = 0.88), exceeding metastasis (0.83), sex (0.45), and age (0.39). A basic prognostic model (BpM) incorporating metastasis status yielded a C-index of 0.741 (95% CI, 0.679–0.803). The addition of Auto-RS (CpM) improved discrimination (C-index = 0.788; 95% CI, 0.731–0.845), whereas a model without metastasis information (ApM) retained predictive ability (C-index = 0.709; 95% CI, 0.640–0.778). Single-cell analysis confirmed that Auto-RS features aligned with known metastatic trajectories, reflecting the transition from proliferative to invasive tumor states and highlighting coordinated programs among cancer-associated fibroblasts and immune cells. Drug sensitivity integration through Precily identified gemcitabine and cytarabine as FDA-approved agents predicted in silico to show greater sensitivity in the high-risk subgroup. Conclusions: We identified autophagy-mediated transcriptional ‘stress fingerprints’ that are tightly associated with OS metastasis. The Auto-RS signature, composed of BNIP3, MYC, PEA15, and SAR1A, enables early therapeutic stratification of patients independent of overt metastatic status. Moreover, Auto-RS delineates key molecular underpinnings of OS metastasis at single-cell resolution. As a practical laboratory tool, Auto-RS may represent a step toward improved risk stratification, where advances in metastasis prediction and therapeutic guidance converge to improve outcomes in OS. Full article

Background: Cancer-associated thrombosis is a major cause of morbidity and mortality in oncology patients. Routinely available hematological parameters, including platelet count (PLT), mean platelet volume (MPV), platelet distribution width (PDW), and neutrophil-to-lymphocyte ratio (NLR), may reflect thrombo-inflammatory alterations accompanying thrombotic events in malignancy. Methods: This retrospective exploratory study included 93 patients with solid malignancies who developed radiologically confirmed thrombotic events between 2006 and 2018. Clinical and laboratory data were retrospectively reviewed. Hematological parameters obtained within seven days before and after thrombotic events were compared using appropriate parametric and non-parametric statistical methods. Results: Thrombotic events were most frequently observed in patients with lung, colorectal, breast, and gastric cancers. Gastrointestinal malignancies accounted for 47.3% of cases. Venous thrombotic events represented the majority of cases (63.4%), whereas arterial thrombosis was observed in a smaller subset of patients (10.7%). Pulmonary embolism was identified in 23.7% of patients. Central venous catheter use was significantly associated with subclavian/femoral vein thrombosis (p < 0.001). PLT significantly decreased following thrombotic events (329.3 × 10³/µL vs. 260.8 × 10³/µL, p < 0.001), whereas MPV increased modestly (9.23 ± 1.57 fL vs. 9.40 ± 1.45 fL, p < 0.001). PDW significantly decreased (14.37 ± 2.78 vs. 13.63 ± 3.24, p = 0.011). NLR increased numerically (3.33 ± 2.32 vs. 4.26 ± 4.74) but did not reach statistical significance (p = 0.089). An inverse correlation was observed between PLT and MPV (r = −0.268, p = 0.009). Conclusions: Routinely available hematological parameters, including PLT, MPV, PDW, and NLR, demonstrated measurable alterations in cancer patients with thrombotic events and may reflect thrombo-inflammatory processes associated with malignancy. However, because of the retrospective design, heterogeneous study population, and absence of a non-thrombotic control group, these findings should be considered exploratory and hypothesis-generating rather than evidence of predictive biomarkers. Larger prospective controlled studies are required to clarify their clinical significance. Full article

Background/Objectives: Cranioplasty is a common reconstructive procedure following decompressive craniectomy, yet postoperative complications requiring reoperation remain frequent. Sarcopenia has been associated with adverse surgical outcomes. Temporalis muscle thickness (TMT), readily assessed on routine cranial CT, has been proposed as a surrogate marker of sarcopenia; however, its role in predicting cranioplasty outcomes remains to be established. This study aimed to evaluate the association between TMT and postoperative complications requiring reoperation after cranioplasty. Methods: In this retrospective single-center cohort study, 71 patients undergoing cranioplasty after decompressive craniectomy were included. Patients were stratified according to the occurrence of postoperative complications requiring reoperation into a complication group (n = 28) and an uneventful postoperative course group (n = 43). TMT was measured on preoperative CT scans obtained prior to craniectomy and prior to cranioplasty. Reduced TMT was defined as ≤5 mm. Results: Postoperative complications requiring surgical revision occurred in 39.4% of patients. Reduced TMT (≤5 mm) was significantly associated with greater reoperation risk in univariate analysis (p = 0.003). Patients undergoing surgical revision had significantly lower TMT prior to craniectomy (4.6 mm vs. 5.3 mm; p = 0.03) and TMT remained an independent predictor in multivariate analysis. Conclusions: Reduced TMT is independently associated with an increased risk of postoperative complications after cranioplasty requiring surgical revision and may serve as a simple imaging-based marker for preoperative risk stratification. Full article

The resurgence of trade policy interventions in the global economy has renewed interest in the long-run macroeconomic implications of commercial barriers. While previous research has largely focused on the short-term effects of tariff reforms and trade liberalization, relatively less attention has been paid to the persistence of trade policy regimes over time. This study addresses this gap by analysing the relationship between trade policy persistence—proxied by the trajectory of the Nominal Average Tariff (NAT)—and Peru’s real GDP during the period 1980–2025. Using a Johansen cointegration framework combined with a Vector Error Correction Model (VECM), the study evaluates both the existence of a long-run equilibrium relationship and the dynamics of adjustment following deviations from that equilibrium. The econometric evidence confirms the existence of a stable long-run relationship between the NAT and aggregate GDP. The normalized cointegrating vector suggests that higher and persistent levels of tariff protection are associated with lower levels of real GDP in the long run. The estimated error-correction mechanism further indicates that deviations from equilibrium are gradually corrected through adjustments in the trajectory of real GDP, whereas the tariff equation does not exhibit a statistically significant adjustment process at conventional levels. This asymmetric structure suggests that trade policy persistence operates as a relatively stable macroeconomic condition, while aggregate GDP gradually adjusts to long-run disequilibria. By framing tariffs not only as policy instruments but also as indicators of persistent policy orientations, the study contributes to the trade and growth literature from a persistence-based perspective. The findings additionally highlight the potential relevance of policy consistency and predictability in small open economies characterized by high external dependence and prolonged processes of trade liberalization. Full article

Background: Routine full blood count (FBC) testing is part of the haematological workup in diabetes management. There is limited information regarding the contributions of individual haematological parameters to regression models for glycated haemoglobin (HbA1c), estimated whole-blood viscosity (eWBV) and the resulting blood viscosity complications. Importantly, because association and prediction represent distinct concepts, this study extends previous work with a focus on comparative and exploratory relationships. The objective was to compare FBC parameters between higher and lower HbA1c and eWBV groups and identify variables contributing to the Akaike Information Criterion (AIC)-based regression model among diabetics. Methods: This laboratory-based mixed quantitative study involved cross-sectional and regression analyses. Fifteen parameters were evaluated, including the following: red blood cell count (RBC) and indices (MCV, MCH, MCHC); platelet count and derived ratios (PRR, PWR, RPR); and white blood cell count (WBC) with lymphocyte ratios (MLR, NLR, PLR). HbA1c and eWBV data were used to create dichotomous subgroups for univariate comparison, followed by exploratory AIC-based model identification of variables. Results: HbA1c, RDW, MCV, and RPR, differed significantly between HbA1c groups (p < 0.1). Regression analysis identified RDW, MCV, RPR, MCH and RBC as contributors to the HbA1c model. For eWBV, five out of seven parameters (HCT, HB, RBC, WBC, and MLR) showed a significant association. Conclusions: These findings highlight haematological parameters with potential values for future predictive model development. Overall, the study supports the usefulness of selected FBC variables as adjuncts in diabetes monitoring with potential utility in understanding glycaemia control and blood viscosity-related complications. Full article

Cadmium contamination of soils poses a global threat to food security and ecosystem stability. Soil bacteria play a key role in mitigating Cd-induced stress, and their adaptive capabilities can be modulated by the application of organic amendments such as compost, fermented bark, or preparations containing humic acid. This article presents the results of studies on soil bacterial communities using culture-dependent and next-generation sequencing approaches. Based on the obtained data, colony development indices and ecophysiological diversity indices were determined for organotrophic bacteria and actinobacteria. Alpha and beta diversity of bacteria were also assessed, common and unique genera occurring in the studied soils were identified, and the predicted metabolic functions of microorganisms were determined. It was found that cadmium reduced the abundance of organotrophic bacteria and actinobacteria by 54.5% and 12.9%, respectively, compared to the control, resulting in a shift in the bacterial community structure from r-strategists toward K-strategists. Humic acid increased the abundance of organotrophic bacteria and actinobacteria by 42.8% and 57.3%. Compost most effectively mitigated cadmium effects by stabilizing the colony development index and bacterial ecophysiological diversity. Cadmium strongly altered the soil bacterial microbiome, reducing the abundance of Actinomycetota while increasing that of Pseudomonadota and Bacteroidota. The application of organic amendments influenced the bacterial response to Cd²⁺-induced stress. Fermented bark was associated with an increased abundance of Sphingomonas, whereas compost was associated with an increased abundance of Cellulosimicrobium. Although none of the organic amendments affected the overall diversity index under these conditions, compost improved the evenness and ecological stability of the bacterial community. The dominance of aerobic chemoheterotrophs involved in the carbon cycle and the degradation of organic compounds was demonstrated. Compost most effectively supported biogeochemical processes. Full article