Search Results (28,560)

Colorectal cancer (CRC) has shown significant heterogeneity regarding its response to immunotherapy. Long-lasting, beneficial effects have been observed in mismatch repair-deficient, microsatellite instability-high (dMMR/MSI-H) tumours, while mismatch repair-proficient, microsatellite stable (pMMR/MSS) tumours have remained resistant. Such differences in results have been studied in this review through the “hot” and “cold” tumour concept. It explains how various biological and microenvironmental factors play a role in immune resistance and T-cell priming and infiltration. Key factors include a low neoantigen load and defects in antigen presentation, which reduce the overall immune recognition of tumour cells. The review also studies certain processes such as Wnt/β-catenin and mitogen-activated protein kinase (MAPK) signalling and what input they have in the prevention of effective antitumour immune responses. Conventional treatments like chemotherapy and radiotherapy have been considered alongside more targeted treatments such as the inhibition of vascular endothelial growth factor (VEGF) signalling and the suppression of myeloid-mediated immune evasion, to convert “cold” MSS tumours into immune-responsive lesions. Methods which aim to modify the tumour microenvironment such as metabolic reprogramming and microbiome modulation have also been covered in this review. Artificial intelligence and nanomedicine are new technologies that could provide improved patient stratification and therapeutic precision, although their clinical application in pMMR/MSS CRC remains under investigation. A systematic literature search of PubMed and PubMed Central (PMC) was conducted from 10 June 2026 to 19 August 2026 using predefined eligibility criteria, with study selection reported according to PRISMA 2020. Because of substantial heterogeneity in study design, therapeutic approach and reported outcomes, the included evidence was synthesized narratively rather than by meta-analysis. A total of 158 studies were included. Full article

The single-row facility layout problem (SRFLP) orders unequal-length facilities on a line to minimize flow-weighted center distances. We present a time-adaptive multi-start simulated annealing (AMSA) framework that coordinates one spectral start, randomized restarts, incremental insertion and interchange moves, variable-neighborhood descent (VND), and exact fixed-exterior window optimization under a shared deadline. The primary experiment comprised 137 public instances with 8–1000 facilities and ten fixed seeds per instance, giving 1370 successful runs. Among 133 instances with traceable historical reference values, the best of ten runs reached or improved the study reference on 91 instances. The mean run-level relative gap was 0.00470%, and 57 instances produced the same objective for every seed. New controlled experiments compare six variants on 21 representative instances, nine parameter groups on six instances, serial and concurrent execution on nine instances, and four exclusive runtime stages on nine instances. Full AMSA had the best aggregate rank; only removal of adaptive time allocation differed significantly from the full method after Holm correction. All tested non-default parameter levels had paired Wilcoxon $p>0.05$. Profiling showed that annealing consumed 70.29%, 88.52%, and 96.69% of solver time in the small–medium, medium, and large groups, respectively. Two stored layouts below the archived reference snapshot were independently recomputed by two objective identities with zero discrepancy. These results support AMSA as a reproducible deadline-aware baseline; they do not establish superiority over recent methods evaluated on different platforms or budgets. Full article

Emission inventories at fine spatial and temporal scales were developed for light alkanes, volatile organic compounds (VOCs), and nitrogen oxides (NO_x) from upstream and midstream oil and gas operations in the Permian Basin oil and gas production region for 2022–2024. The inventories were spatially aggregated at basin, county, and 12 km by 12 km grid cell levels, and temporally resolved at hourly resolution, with underlying methods capable of generating inventories at other spatial and temporal scales. Spatial and temporal variability in emissions in the Permian were compared at various spatial scales with inventories for the Marcellus oil and gas production region, developed using the same methods. Emission sources that drive spatial and temporal variability differ by regional production characteristics, the level of spatial aggregation, and emitted species. Temporal variability in emissions decreases as the scale of spatial aggregation increases. Among counties with at least 10 active producing wells, maximum-to-annual-average hourly emission rate ratios reached 2.5 for methane, 2.8 for VOCs, and 2.3 for NO_x. At the 12 km by 12 km grid cell level, the corresponding maximum ratios were 33.7, 26.5, and 13.9. These ratios illustrate the magnitude of short-term emission variability and the extent to which peak hourly emissions can exceed annual average estimates, with potential implications for episodic air-quality impact assessment. Compared with the gas-dominated Marcellus Basin, the oil-dominated Permian Basin shows lower temporal variability in hydrocarbon emissions due to fewer episodic gas production related sources (e.g., liquid unloadings) and a greater contribution from near-continuous oil production related sources (e.g., associated gas venting and tank flash). In contrast, NOₓ emissions exhibit higher temporal variability in the Permian due to more frequent preproduction activities associated with new well development. The spatially and temporally resolved emission inventories by source category and chemical species can be further combined with chemical transport modeling and air quality modeling to support assessment of regional air quality events, such as localized and episodic ozone formation. Full article

The continued digitization of open museum collections provides new opportunities for the intelligent organization and visual discovery of cultural heritage. However, morphological similarity between ceramic forms, intra-class variation, and changing photographic conditions remain challenges for automated classification, model interpretation, and similar-object retrieval. This study uses 3305 Chinese ceramic objects from the open collection of The Metropolitan Museum of Art (The Met) to develop an explainable and traceable workflow for ceramic-form classification and visual retrieval. Museum metadata were standardized into 13 form categories, from which 2755 objects were used to establish a seven-class primary classification task. Explicit morphological features, handcrafted visual features, ResNet50 representations, DINOv2 representations, and morphology–deep feature fusion were evaluated under a unified data split and evaluation protocol. Explainable artificial intelligence (XAI) methods were further used to examine spatial model responses and feature attributions of explicit morphological variables, while different representations were evaluated for content-based visual retrieval. The results show that deep visual representations effectively support ceramic-form classification, with DINOv2 demonstrating comparatively stable performance across multiple random seeds. Morphology–deep feature fusion did not provide a consistent classification advantage over DINOv2-only, but the fused representation showed clearer complementary value in visual retrieval, achieving the highest Precision@5 (0.819) and mean average precision at 10 (mAP@10; 0.773). XAI analyses further indicated that structurally meaningful spatial responses and explicit geometric descriptors contributed to form discrimination. By linking classification, interpretation, and retrieval outputs to Object IDs and original collection records, the proposed workflow provides a practical computational approach for ceramic-form organization, similar-object discovery, and traceable visual retrieval in digital museum collections. Full article

Understanding the stress sensitivity of multiscale pore fracture structures (PFS) in coals with different ranks is critical for evaluating coalbed methane (CBM) reservoir behavior. In this study, low-rank and high-rank coals were subjected to effective confining pressure loading–unloading tests under constant pore pressure, and the dynamic evolution of PFS was investigated using low-field nuclear magnetic resonance (LF-NMR), nuclear magnetic resonance imaging (NMRI), and fractal analysis. For the tested specimens, the Fengjiata low-rank coals exhibited higher proportions of seepage pores (SPs) and generally greater stress sensitivity, whereas the Sijiazhuang high-rank coals were dominated by adsorption pores (APs) and showed comparatively stable PFS. SPs are more sensitive to effective stress than APs, and stress-induced pore deformation shows partial irreversibility after unloading. Furthermore, an NMR-based method was proposed to quantify stress-dependent pore compressibility, revealing that pore compressibility decreases logarithmically with increasing effective stress due to the progressive loss of compressible pore space. These findings provide new insights into the multiscale stress response of coal pore fracture systems and improve the evaluation of stress-sensitive permeability evolution in CBM reservoirs. Full article

Background: Two-stage coarse-to-fine architectures are commonly used in medical image synthesis and refinement. However, when the distributional gap between coarse inputs and high-resolution targets is large, the second-stage generator may produce unstable or weakly conditioned refinements rather than operating as a true residual corrector. Methods: We propose intentional degradation, a target-side distribution-alignment strategy that deliberately degrades high-resolution targets toward the coarse prediction space before residual learning. The degradation profile is guided by measured differences in contrast, saturation, and edge energy. We further evaluate refinement quality using two residual-space metrics: Var(Δ), the variance of the predicted residual (target minus input), and Δ-SSIM, the structural similarity between the predicted and true residual maps; both are designed to reveal refinement instability that conventional image-level metrics (e.g., SSIM computed on the full image) may not capture. We evaluated the approach in three medical imaging domains: wound-healing photography, retinal fundus imaging, and dermoscopy. Results: Across all three domains, intentional degradation consistently improved Δ-SSIM and normalized residual variance recovery relative to the baseline, and predicted residuals showed substantially stronger directional correspondence with the true residual (assessed via sign-agreement and cosine-similarity metrics) than the baseline, which converged toward directionally random predictions. These findings suggest that residual-space and direction-sensitive metrics can reveal refinement instability that may not be captured by conventional image-level metrics alone. Conclusions: Intentional degradation provides a simple, architecture-agnostic-by-design strategy for stabilizing residual learning in coarse-to-fine medical image refinement, validated within a single ConvLSTM-based refinement framework; extension to other architectures remains for future validation. Rather than introducing a new network architecture, the method modifies the target-side training distribution to reduce the mismatch between coarse inputs and high-resolution targets. Full article

Friction stir welding (FSW) is a solid-state manufacturing process widely used in joining aluminum and other metal workpieces. The FSW process can be modeled by a coupled system of non-Newtonian Navier–Stokes and heat-transfer equations. However, solving this non-linear system with high accuracy requires significant computational power. This work refines the system by introducing corrected coefficients and new treatments for boundary conditions near the tool. Then, model order reduction, including the Proper Orthogonal Decomposition (POD) and Discrete Empirical Interpolation Method (DEIM), is applied to efficiently solve the FSW system in a low-dimensional space. To enhance accuracy and effectiveness, two novel treatments have been adopted for the POD and DEIM models: the introduction of preconditioner matrices to avoid large condition numbers and the use of indicator matrices to generate the non-linear data. For different cases regarding operating parameters, the results show that the DEIM model dramatically speeds up the computation (e.g., over 250 times faster compared with the full model) while maintaining accuracy. This makes simulations of the FSW process more accessible and easily combined with machine learning techniques in future study. Full article

Vision-based unmanned aerial vehicle (UAV) detection has become increasingly important since it is a key technology in aerial collision avoidance systems. However, the detection of small UAVs is still unsatisfactory in practical applications. To address this problem, this paper proposes EWH-YOLO, a novel deep convolutional neural network-based method for small UAV detection. First, an efficient feature extraction network is designed to exact the multi-level features of small UAVs while reducing the network parameters and computational complexity. Second, a weighted bidirectional feature fusion network is proposed to enhance the low-level and high-level features in the output feature maps. Third, a hybrid bounding box regression loss is introduced to evaluate the difference between the predicted bounding box and the ground-truth bounding box during training and improve the detection accuracy. Finally, a new dataset is created on the basis of considering small UAVs to verify the detection performance. Compared with the state-of-the-art methods, the proposed method achieves higher detection accuracy with lower model complexity. The experimental results demonstrate that the proposed detector significantly improves the detection performance of small UAVs. Full article

The U-shaped Disassembly Line Balancing Problem (UDLBP) is a challenging combinatorial optimization problem for which efficient solution approaches remain limited. This study proposes an efficient branch-bound-and-remember (BBR) algorithm that integrates a memory-based mechanism and U-shaped dominance rules to effectively reduce the search space. Specifically, a new branching method, an additional lower bounding method, and new dominance rules are developed to suit the UDLBP, and different search strategies are developed and explored. Extensive computational experiments are conducted on a comprehensive set of benchmark instances to evaluate the performance of the proposed approach. The results demonstrate that the proposed BBR algorithm can consistently identify the best-known solutions for the evaluated benchmark instances. Compared with constraint programming, mixed-integer linear programming, and several state-of-the-art metaheuristic algorithms, the proposed approach achieves competitive solution quality and computational efficiency, consistently matching the best-known solutions with an average recorded CPU time of 0.0199 s under the 500 s computational setting. These findings indicate that the proposed algorithm provides an efficient optimization framework for solving UDLBP, achieving high-quality solutions with substantially low computational cost. Full article

With the advent of quantum computers, traditional key exchange mechanisms are under threat, necessitating the development of new methods. Recently, Module-Lattice-Based Key Encapsulation Mechanism (ML-KEM) has been standardized as a quantum-safe key exchange method. Another emerging technique is Quantum Key Distribution (QKD) and its most famous protocol, BB84, which relies on the principles of quantum physics to exchange key information. Both techniques are considered secure against attacks by quantum computers, but their security is based on different principles. However, both key exchange types include a statistical component which, if an attacker were lucky, could allow circumventing these underlying hard problems. In this paper, we suggest that as a key exchange mechanism, BB84 should be run with security parameters comparable to those of ML-KEM, and analyze performance implications if this choice is taken. We illustrate the impact by estimating the number of raw bits required to generate a 256-bit symmetric key using BB84 and assessing the resulting performance implications. We further show how the BB84 finite-size security parameter can be chosen such that the post-processing failure probability is of the same order as the cumulative decapsulation-failure probability of ML-KEM. According to our results, the security parameter in BB84 should be at most $2^{-75.8}$ if statistical failure probability comparable to the ML-KEM decapsulation-failure target is desired. These findings offer general guidelines for BB84 parameter selection, with hybrid protocol design representing one potential application context. Full article

Efron’s double bootstrap and hierarchical Bayesian nonparametric methods have largely developed along separate paths. This paper connects them and uses that connection to motivate a new resampling procedure. We show that a suitable two-level Dirichlet construction, with base measures matched to the data, can approach the classical double bootstrap when its concentration parameters become large, while the hierarchical Dirichlet process of Teh et al. does not share this limit. This distinction identifies the double bootstrap as the endpoint of a construction of hierarchical Dirichlet type—though not of the hierarchical Dirichlet process itself—and as the boundary of a broader family of two-level resampling methods. Moving away from that boundary leads to the Informative Bayesian Double Bootstrap (IBDB). The method introduces prior information at the first stage while keeping the second stage focused on calibration, as in the classical double bootstrap. We also establish finite-concentration bounds describing how the proposed construction differs from its classical counterpart and when it can move beyond the support of the observed data. In simulations against four competing methods, the IBDB performs best for tail-sensitive quantities and heavy-tailed settings, while it tends to over-cover simple location parameters. Similar patterns appear in the Danish fire-insurance and Siemens equity-loss examples. Its main advantage is improved calibration through interval repositioning rather than simply wider intervals. The gains are most relevant when sample information is limited. Full article

Background/Objectives: Live attenuated mumps virus (MuV) vaccine strains have significantly reduced disease incidence since their introduction in the 1960s; however, a recent resurgence of outbreaks has led to calls for the development of new vaccines to overcome what appears to be reduced effectiveness linked to waning immunity and the emergence of strains antigenically mismatched to vaccine strains. A major obstacle to the development of newer live, attenuated MuV vaccines is ensuring their safety, particularly given the virus’s neurotropic properties. Indeed, several vaccine strains licensed for use outside the US have proven to be insufficiently attenuated (such as the Urabe AM9 vaccine strain) and have caused aseptic meningitis in recipients, despite efforts to test these strains for neurotoxicity pre-licensure. Historically, attenuation was achieved empirically, and pre-clinical testing for neurovirulence safety has proven unreliable. Despite efforts in recent years, the genetic basis of attenuation of MuV vaccines remains inadequately understood. The objective of this study was to elucidate the genetic basis of neurovirulence of the Urabe AM9 vaccine strain. Methods: To this end, we generated a series of chimeric viruses in which genes of the highly attenuated Jeryl Lynn (JL) vaccine strain were exchanged with corresponding genes from the Urabe AM9 vaccine strain. The resulting chimeric viruses were tested for neurovirulence in a rat model and characterized for replication in vitro and in vivo. Using a multi-tiered approach consisting of independent rescue and analysis of two to three viruses per cDNA construct, possible off-target effects of identified single-nucleotide heterogeneities were mitigated. Results: All viruses were shown to be replication-competent in Vero cells, but differences in replication efficiencies and virus-induced neurotoxicity were observed in the in vivo model. In a rat neuronal cell line, the Urabe AM9 M and HN genes had opposite effects on the growth of chimeric JL- and Urabe AM9-based viruses. Conclusions: The results presented herein indicate that MuV neuroattenuation is mediated by the concerted action of multiple genes, with the matrix (M) gene exerting a dominant effect. These findings contribute to a growing body of evidence that informs the rational design of next-generation live-attenuated mumps virus vaccines. Full article

Backpack and handheld LiDAR simultaneous localization and mapping (SLAM) systems have become an important solution for large-scale 3D data acquisition. Since Global Navigation Satellite System (GNSS) positioning is not always available in many LiDAR SLAM systems, point clouds acquired from different surveying projects or devices are represented in independent local coordinate systems and require coarse registration to fuse all data into a unified coordinate system. Existing coarse registration approaches based on local feature descriptors often depend on locally estimated surface normals or reference directions, whose repeatability can be affected by measurement noise and non-uniform sampling. To address this issue, this paper proposes a Global-Prior Augmented Fast Point Feature Histogram (GA-FPFH) descriptor. The proposed method constructs a Z-axis-augmented local reference frame (Z-LRF) using the gravity-aligned vertical direction provided by the SLAM system. Three new geometric components are proposed based on the Z-LRF and combined with conventional FPFH features to form a six-component and 66-dimensional descriptor. Experiments on 12 real-world point-cloud pairs show that GA-FPFH increases the inlier ratio by 26.9–148.9% and, across five registration algorithms, reduces the rotation error, translation error, and RMSE by 31.5–87.9%, 29.6–97.8%, and 42.4–97.6%, respectively, while increasing the overall registration success rate from 71.7% to 88.3%. The significant error reductions are partly attributable to the higher registration success rate and fewer failure cases. Full article

Background: Public payers must regularly decide which prescribers to look at more closely, with records covering thousands of doctors per quarter and review capacity for only a fraction. How that choice is made bears on equitable access to reimbursed medicines as well as on public funds. Methods: We propose a multi-indicator concentration index scoring each prescriber 0–100 as the sum of nine weighted sub-scores, each measuring the doctor’s deviation from their primary-care centre median on one indicator from the payer’s routine quarterly report; eight are computable from a single quarter. Every score decomposes into named components. The index measures the concentration of indicators above the peer profile, not an estimated probability of misconduct. It is a screening tool for prioritising documentary review. We demonstrate it on one quarter of anonymised data from 335 doctors in 30 Albanian primary-care centres. Results: The eight active sub-scores are mostly weakly correlated (mean $\left|r\right|=0.28$), the two exceptions each pairing a case count with its financial effect. Rankings are robust to alternative weights (Spearman $\rho$ between 0.93 and 0.98), to collapsing those pairs ($\rho \ge 0.98$), and to pooling the smallest centres against a shared comparator ($\rho =0.95$). Against three machine-learning anomaly detectors and two simpler screens, the index selects a different top cohort: doctors whose spending is only moderately raised, but whose new-case and therapy-change counts exceed three times the centre median. In an internal concordance check, two co-author reviewers blinded to the score rated 60 cases sampled partly by index rank; they agreed with each other (Cohen’s $\kappa =0.87$, 95% CI 0.70 to 1.00), and the index’s ranking against their consensus gives an AUC of 0.88 (95% CI 0.62 to 0.97). Conclusions: The index gives clinical-administrative reviewers a reproducible, explainable priority order consistent with senior expert judgement on the same indicators. Because no review outcomes are linked to the records and the window is a single quarter, it is supported as a prioritisation tool, not a validated predictor of inappropriate reimbursement; multi-quarter and outcome-linked evaluation are the next steps. Full article

Background: Female sex is widely regarded as an independent risk factor for adverse outcomes after conventional coronary artery bypass grafting (CABG) and is incorporated as a risk variable in EuroSCORE II. Whether this disadvantage persists in the setting of minimally invasive direct coronary artery bypass (MIDCAB), in which sternotomy and cardiopulmonary bypass are avoided, remains insufficiently characterised. We assessed sex-specific short- and long-term outcomes after MIDCAB in a single-centre cohort with extended follow-up. Methods: We retrospectively analysed 350 consecutive patients who underwent MIDCAB at Hannover Medical School between July 1999 and April 2025 (follow-up to April 2025). Eligibility criteria and heart team-applied exclusion criteria (prior left thoracotomy, unfavourable LAD anatomy, prohibitive respiratory reserve, hostile chest wall, active endocarditis, or haemodynamic instability requiring on-pump revascularization) are detailed in the Methods. Females (n = 102) and males (n = 248) were compared before and after 1:1 propensity score matching using greedy nearest-neighbour matching with a caliper of 0.2 × SD of the logit propensity score. The primary endpoint was all-cause long-term mortality; secondary endpoints included perioperative complications and in-hospital outcomes. Long-term survival was assessed by Kaplan–Meier analysis and multivariable Cox proportional hazards regression performed in the full unmatched cohort. A pre-specified subgroup analysis of long-term survival by coronary disease pattern (single-vessel vs. multivessel disease) was also performed. Results: Matching produced 100 female–male pairs with excellent covariate balance (all standardized mean differences < 0.20). MIDCAB was completed without intraoperative conversion in all patients. Thirty-day mortality was 0% in both sexes; no postoperative stroke or new requirement for dialysis occurred. New-onset atrial fibrillation (3.0% vs. 1.0%, p = 0.621), length of intensive care unit stay (median 1 day in both groups), and hospital length of stay (median 8 days in both groups) were comparable between females and males. Re-exploration for bleeding was numerically more frequent in women (5.0% vs. 0.0%; Newcombe 95% CI for the risk difference +0.3 to +11.2%; Fisher’s exact p = 0.059). At a median follow-up of 19.0 years (IQR 11.8–23.9), all-cause mortality was identical (12.0% vs. 12.0%, p = 1.000; log-rank p = 0.703). In multivariable Cox regression in the full unmatched cohort, female sex was not associated with long-term mortality (adjusted HR 0.80, 95% CI 0.38–1.70, p = 0.560); only advancing age emerged as a strong independent predictor (HR 1.10 per year, 95% CI 1.05–1.15, p < 0.001), with EuroSCORE II approaching significance (HR 1.67 per unit, 95% CI 1.00–2.82, p = 0.052). Long-term survival in patients with multivessel disease (20-year Kaplan–Meier 90.9%) was equivalent to that in single-vessel disease (92.3%; log-rank p = 0.94). Conclusions: In this propensity-matched analysis with two decades of follow-up, MIDCAB conferred equivalent perioperative safety and long-term survival in women and men. Female sex was not an independent predictor of adverse outcome. These findings support MIDCAB as a sex-neutral revascularization strategy for single-vessel and LAD-predominant coronary artery disease in the very low-risk, appropriately selected population studied, and are consistent with the most recent published MIDCAB literature. Full article