Saved Queries

by Charalampia Pylarinou, Francesk Mulita, Efstratios Koletsis, Vasileios Leivaditis, Elias Liolis, Lefteris Gortzis and Dimosthenis Mavrilas

Clin. Pract. 2026, 16(5), 93; https://doi.org/10.3390/clinpract16050093 (registering DOI) - 15 May 2026

Abstract

Background: Post-surgical cardiovascular monitoring places a heavy information burden on clinical teams, requiring the rapid synthesis of patient history, intraoperative data, monitoring streams, and surgical outcome evidence. Existing clinical decision support systems handle this integration poorly, and most offer little visibility into their reasoning. We present a Retrieval-Augmented Generation (RAG) architecture designed specifically for this domain, with a focus on evidence traceability and practical workflow integration. Methods: We describe a three-layer RAG architecture comprising a retrieval layer that creates 768-dimensional representations of clinical scenarios; an augmentation layer using a stacking ensemble (Random Forest and XGBoost base learners with a logistic-regression meta-learner) to integrate patient-specific data with retrieved evidence and produce calibrated probability estimates; and a generative layer using a fine-tuned BERT classifier together with Gemini 2.5 Pro to synthesise actionable clinical recommendations. Components were prototyped on publicly available, de-identified data from MIMIC-III and the MIMIC-III-Ext-PPG benchmark to verify pipeline integrity. Proposed Evaluation Framework: This paper presents a system architecture rather than a clinically validated implementation. We outline a structured evaluation framework to assess the technical performance and clinical applicability of the RAG architecture, encompassing the technical validation of system components, expert assessment of clinical workflow integration potential, and analysis of interpretability features essential for healthcare deployment. Specific technical targets include retrieval precision >90% for relevant evidence, query response time <3 s, and a clinical appropriateness rating of >85% from expert review. Conclusions: We describe a RAG architecture for post-surgical cardiovascular monitoring in which every recommendation is linked to retrievable source documents, making the reasoning visible and challengeable. A structured evaluation framework is proposed to guide the system towards clinical validation. Full article

13 pages, 651 KB

Open AccessArticle

Associated Factors for Non-Diagnostic Cytopathology in the Endobronchial Ultrasound-Transbronchial Needle Aspiration: A Retrospective Cohort Study

by Umran Ozden Sertcelik, Ebru Sengul Parlak, Habibe Hezer, Eren Goktug Ceylan, Ahmet Sertcelik and Ayşegul Karalezli

Diagnostics 2026, 16(10), 1509; https://doi.org/10.3390/diagnostics16101509 (registering DOI) - 15 May 2026

Abstract

Introduction: Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is widely used for diagnosing pulmonary diseases causing mediastinal lymphadenopathy. However, non-diagnostic results may occur. This study investigated factors associated with non-diagnostic cytological results in EBUS-TBNA. Methods: This retrospective study included patients who underwent EBUS-TBNA at a tertiary hospital between March 2019 and December 2023. Data on demographics, biopsy techniques, cyto-/histopathological results, sonographic lymph node measurements, and pre-procedural PET-CT SUVmax values were recorded. Cytological results were classified as diagnostic or non-diagnostic. We analyzed the characteristics and associated factors of patients who were non-diagnostically identified. Results: Among 776 patients undergoing EBUS-TBNA, 502 (64.7%) were male, with a mean age of 61.5 ± 12.6 years. A total of 1110 lymph nodes were sampled. Of the patients, 14.1% had a non-diagnostic cytology. Among the diagnosed patients, cytological findings showed 58.9% non-malignant, 41.1% malignant. The most sampled station was station 7 (72.9%), with an average of 5.9 ± 1.4 aspirations. Diagnostic cases had significantly more aspirations (p = 0.022) and sampled larger lymph node sizes (p < 0.001). Each 1 mm increase in lymph node size raised the likelihood of diagnostic results by 1.04 times (adjOR = 1.04, 95% CI = 1.02–1.08, p = 0.002). The largest lymph node size significantly predicted diagnostic results (AUROC = 0.611, p < 0.001). A cut-off of 19.55 mm had 67.0% sensitivity and 52.2% specificity. Conclusion: Sampled larger lymph nodes increase diagnostic yield in EBUS-TBNA, reducing the need for repeat procedures and enabling earlier treatment, thereby decreasing morbidity and mortality. Full article

(This article belongs to the Section Pathology and Molecular Diagnostics)

26 pages, 4852 KB

Open AccessArticle

Virtual Reality for Large-Scale Laboratories Based on Colorized Point Clouds

by Lei Fan and Yuxin Li

Buildings 2026, 16(10), 1968; https://doi.org/10.3390/buildings16101968 (registering DOI) - 15 May 2026

Abstract

Effective laboratory training is essential in engineering education, yet conventional on-site instruction is often constrained by time, accessibility, and safety considerations. To address these challenges, this study presents the design, implementation, and evaluation of a web-based virtual reality (WebVR) representation of a large-scale engineering laboratory constructed from massive colorized point cloud data. This study proposes a novel WebVR approach that integrates Unity and Potree for high-fidelity point-cloud visualization combined with advanced interactive capabilities in a browser-based virtual laboratory. It supports immersive first-person exploration, guided navigation, interactive hotspots conveying equipment and safety information, and emergency evacuation simulations. The usability, usefulness, and acceptance of the virtual laboratory were evaluated through an anonymous questionnaire administered to students and laboratory staff. User evaluation results indicated consistently positive feedback, with 100% of respondents rating the interface/navigation and visual/interactive content as good or excellent, 88.6% identifying scene realism as the biggest system strength (the most frequently selected), 74.3% reporting significantly higher engagement compared with traditional online laboratory training, and 82.9% indicating they would definitely recommend the system as a learning resource. In addition, a thematic analysis of qualitative feedback was performed to inform future enhancements of the WebVR environment. Overall, the findings demonstrate that the WebVR-based virtual laboratory can effectively complement conventional on-site laboratory instruction, offering a scalable, accessible, and low-risk platform that enhances learning experiences in engineering education. Full article

(This article belongs to the Special Issue Big Data and Machine/Deep Learning in Construction—2nd Edition)

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29 pages, 12190 KB

Open AccessArticle

Identification, Screening and Mechanism Analysis of Anti-Parkinson’s Disease Peptides from Rapana venosa Protein Hydrolysates

by Qingzhong Wang, Shuqin Shao, Yizhuo Wang, Wenshuai Fan, Zilong Wang, Xuchang Liu, Kechun Liu and Shanshan Zhang

Mar. Drugs 2026, 24(5), 180; https://doi.org/10.3390/md24050180 (registering DOI) - 15 May 2026

Abstract

At present, there is still a lack of effective treatments to slow the progression of Parkinson’s disease. Naturally derived active substances, valued for their safety and multi-target potential, have become an important direction in anti-PD drug development, with marine organisms representing a valuable source of bioactive peptides. This study aimed to isolate and identify anti-PD peptides from Rapana venosa protein hydrolysates. Through bioactivity-guided screening combined with an MPTP-induced zebrafish PD model, three novel active peptides—KSTELLI, FLVKLPMFM, and SDSLSEILIS—were successfully identified. The study showed that these peptides significantly alleviated dopaminergic neuron loss, improved the cerebral vascular system, restored motor and sensory function, and alleviated oxidative stress. Molecular docking confirmed their stable binding to key PD targets (DDC, α-synuclein, and MAO-B). Further transcriptomic and gene expression analyses revealed that their neuroprotective effects involve the regulation of pathways related to metabolism, oxidative stress, inflammation, and apoptosis, with the three peptides exhibiting distinct mechanistic emphases. The research demonstrates that these marine-derived peptides exert neuroprotective effects through a synergistic multi-target mechanism, laying a foundation for the development of novel lead compounds against Parkinson’s disease. Full article

(This article belongs to the Special Issue Marine Proteins: Biological Activities and Applications)

46 pages, 4599 KB

Open AccessArticle

Multi-Strategy Enhanced Beaver Behavior Optimizer for Global Optimization and Enterprise Bankruptcy Prediction

by Haoyuan He and Mingyang Yu

Symmetry 2026, 18(5), 848; https://doi.org/10.3390/sym18050848 (registering DOI) - 15 May 2026

Abstract

Enterprise bankruptcy prediction is a critical research issue in financial risk early warning, credit evaluation, and investment decision-making. To address the limitations of traditional methods in handling high-dimensional, nonlinear, and complex financial data, including parameter sensitivity, susceptibility to local optima, and insufficient prediction stability, this study proposes a multi-strategy enhanced Beaver Behavior Optimizer and applies it to optimize kernel extreme learning machines, constructing the MEBBO KELM prediction model. Three improvement mechanisms are introduced, including an elite pool enhanced exploration strategy, a stochastic centroid reverse learning strategy, and a leader guided boundary control strategy, which improve population diversity, global search capability, boundary handling capacity, and convergence accuracy. The proposed algorithm is evaluated on CEC2017 and CEC2022 benchmark datasets and compared with EWOA, HPHHO, MELGWO, TACPSO, CFOA, ALA, AOO, RIME, and BBO. Statistical analyses are conducted using the Wilcoxon rank sum test and the Friedman test. The results demonstrate that MEBBO achieves superior solution accuracy and stability, indicating strong global optimization capability and robustness. Further experiments on the Wieslaw Corporate Bankruptcy Dataset show that MEBBO-KELM achieves strong and robust performance across multiple evaluation metrics, including ACC, MCC, Sensitivity, Specificity, Precision, Recall, and F1 score. Specifically, ACC reaches 79.7578, MCC reaches 0.6050, and F1 score reaches 78.8504, confirming its effectiveness. Full article

(This article belongs to the Special Issue Symmetry and Metaheuristic Algorithms)

32 pages, 13955 KB

Open AccessArticle

A Finite Element Simulation-Informed Machine Learning Framework for Screening Average Thermal Stress Responses in SLM-Fabricated 316L Stainless Steel

by Yuan Zheng and Shaoding Sheng

Materials 2026, 19(10), 2088; https://doi.org/10.3390/ma19102088 (registering DOI) - 15 May 2026

Abstract

To improve the efficiency of comparative process-window screening in selective laser melting (SLM), this study developed a finite element simulation-driven machine learning framework for 316L stainless steel. A simulation dataset covering laser power (LP), scanning speed (SS), heat-source diameter (HSD), and substrate preheating temperature (SPH) was generated using ANSYS and used to train nine regression models. In the present work, the primary machine learning target was defined as the simulated average thermal stress, σ_avg, which is used as a simulation-derived comparative thermal stress indicator for ranking process conditions within the investigated parameter window rather than as a direct prediction of the final residual-stress field. Among the evaluated models, the Backpropagation Neural Network (BPNN) showed the best predictive performance and was selected as the representative surrogate model because of its strong predictive accuracy, stable behavior, and direct applicability to the present structured tabular dataset. Shapley additive explanations (SHAP) and partial dependence plots (PDPs) indicated that LP is the dominant variable governing the σ_avg-based response, followed by SPH, whereas SS and HSD mainly affect the response through secondary or coupled effects. Within the investigated parameter window, conditions near 180–200 W corresponded to a relatively lower predicted σ_avg level. Experimental observations provided limited but meaningful trend-level support for the simulation-guided screening results: metallographic examination showed improved forming quality near 200 W, while XRD-derived macroscopic stress estimates exhibited a similar variation trend to the simulated σ_avg values under the tested LP–SS conditions. These results suggest that the proposed framework can serve as an efficient surrogate-based tool for comparative parameter screening in SLM-fabricated 316L stainless steel within the assumptions and parameter range of the present model. Full article

(This article belongs to the Section Materials Simulation and Design)

57 pages, 5985 KB

Open AccessReview

Mathematical Framework for Explainable Vehicle Systems Integrating Graph-Theoretic Road Geometry and Constrained Optimization

by Asif Mehmood and Faisal Mehmood

Mathematics 2026, 14(10), 1710; https://doi.org/10.3390/math14101710 (registering DOI) - 15 May 2026

Abstract

Deep learning models are widely used in autonomous vehicle systems for perception, localization, and decision-making. However, their lack of transparency poses significant challenges in safety-critical environments. This systematic review presents a unified mathematical framework for explainable deep learning which integrates multimodal inputs, graph-theoretic road geometry, uncertainty modeling, and intrinsically interpretable representations. Road-structured priors that include lane topology and spatial constraints are incorporated into learning and optimization processes for ensuring model predictions and explanations to remain physically and semantically grounded. The review synthesizes methods across saliency-based, concept-based, causal, and intrinsic explainability, and extends them to vision-language models. This enables language-grounded, human-interpretable reasoning in autonomous vehicle systems. While vision-language models offer a new paradigm for semantic explainability, their limitations such as hallucinations, misgrounding, and reduced reliability under distribution shifts are also critically examined. Along with the role of road priors in improving alignment and robustness, another key contribution of this work is its quantitative evaluation metrics for road-aware explainability. These evaluation metrics link the explanations to spatial consistency, uncertainty alignment, and graph-structured reasoning. The overall framework connects latent representations, predictions, and explanations within a single formulation, enabling systematic comparison and analysis across models. Based on a PRISMA-guided review of 164 studies, this research identifies gaps in real-world reliability, temporal reasoning, and standardized evaluation, and outlines future directions including human-in-the-loop systems, regulatory readiness, and language-based auditing. Overall, this study advances a mathematically grounded and road-aware perspective on explainable vehicle AI which significantly bridges the gap between high-performance models and transparent, trustworthy autonomous systems. Full article

(This article belongs to the Special Issue Applications of Deep Learning and Convolutional Neural Network)

26 pages, 1400 KB

Open AccessArticle

Rural–Urban Transition and Control of Agricultural Land Change in Greater Bandung Area, Indonesia

by Setyardi Pratika Mulya, Dilla Fathiyatur Rohmah, Ernan Rustiadi and Andrea Emma Pravitasari

Sustainability 2026, 18(10), 5016; https://doi.org/10.3390/su18105016 (registering DOI) - 15 May 2026

Abstract

Rapid urbanisation is threatening agriculture in major cities worldwide. In the Greater Bandung Area (GBA), large-scale conversion of agricultural land into built-up areas has occurred over recent decades. Therefore, this study aimed to understand the rural–urban transition and its control in the agricultural context over the last 20 years. The methods adopted were multitemporal analysis of land cover change (2003–2023), calculation of the sub-district development index (SDI) (2005–2014–2021), spatial clustering analysis, and assessment of the level of agricultural land control. The results showed a transformation of GBA’s spatial structure from a monocentric growth pattern to a polycentric configuration, with the peri-urban zone within a 10–20 km radius evolving as a high-performance area. This shift has diminished the dominance of the traditional city centre and produced a pronounced “donut effect”. An integrated analysis of SDI and spatial clustering identified three interrelated functional zones, namely urban, peri-urban, and rural, forming a continuous spatial gradient. The peri-urban area functioned as a dynamic interface where agricultural activities coexisted and competed with urban expansion pressures. These results outlined the need for context-specific and differentiated planning methods, supported by selective spatial control to guide metropolitan transition toward balanced and sustainable development. Full article

(This article belongs to the Special Issue Sustaining Growth: Balancing Economic, Social, and Environmental Concerns in Rural and Agricultural Development)

28 pages, 982 KB

Open AccessReview

From Pareto Front to Preferred Design: Human-in-the-Loop Preference-Guided Decision Making in Multi-Objective Energy Systems Optimization—A Scoping Review

by Marwa Mekky and Raphael Lechner

Appl. Sci. 2026, 16(10), 4966; https://doi.org/10.3390/app16104966 (registering DOI) - 15 May 2026

Abstract

Background: Multi-objective optimization (MOO) is widely used in engineering design and energy systems to represent trade-offs through Pareto fronts. Yet practical deployment requires moving from a non-dominated set to an implementable preferred design, and this decision step is often treated implicitly. Many studies equate decision support with improved Pareto front generation or visualization, while decision-maker preferences are assumed, weakly specified, or not elicited from stakeholders. Methods: A two-phase scoping evidence synthesis with PRISMA-informed reporting was adopted to map the literature and synthesize explicit Pareto-front decision-support mechanisms. Phase 1 produced a broad evidence map of how Pareto-front decision support is framed and clustered studies by primary contribution, while Phase 2 conducted a focused synthesis of explicit Pareto-front decision-support methods using refined searches in Scopus and SpringerLink. Results: Phase 1 mapped 46 studies; only 10 reported an explicit reproducible Pareto front solution-selection mechanism. Phase 2 included 17 studies and identified four method families: post hoc scoring and ranking, compromise aggregation, interactive preference-guided exploration, and preference elicitation and learning. Conclusions: The literature remains dominated by Pareto front generation and exploration rather than reproducible final solution selection; future work should strengthen preference elicitation, transparency, sensitivity analysis, and uncertainty-aware recommendation stability. Full article

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45 pages, 18550 KB

Open AccessReview

Cyberworthiness for Corporate Organisations: A Structured Review of Standards, Frameworks, and Future Directions

by Saad Almarri, Wael Issa, Marwa Keshk, Benjamin Turnbull and Nour Moustafa

Electronics 2026, 15(10), 2133; https://doi.org/10.3390/electronics15102133 (registering DOI) - 15 May 2026

Abstract

Cyberworthiness extends the concept of cybersecurity by evaluating whether systems and networks can perform their intended functions securely while maintaining protection against cyber threats. In corporate environments, cyberworthiness aims to ensure security, operational resilience, and trustworthiness across interconnected business processes and digital infrastructures. Modern organisations increasingly rely on complex cyber–physical and information systems, where vulnerabilities in software, networks, and devices can introduce significant operational and security risks. Cyberworthiness, therefore, encompasses security controls, risk management practices, and compliance with recognised cybersecurity standards and governance frameworks. It supports the assessment of information technology components and their exposure to both known and emerging cyber attacks, enabling organisations to evaluate system robustness and operational continuity. While cyberworthiness has historical foundations in system assurance and dependability, it also provides a conceptual basis for contemporary cyber resilience strategies. This paper discusses the concept of cyberworthiness in corporate organisations and identifies potential pathways for its practical implementation. It analyses existing cybersecurity standards and governance frameworks to support structured cyberworthiness assessment. This study presents a structured comparative review of fifteen cyberworthiness-relevant standards, supported by a Source Quality Appraisal Framework, a Framework Selection Guide specifying when each standard should be preferred and where conflicts arise, and a five-dimensional Cyberworthiness Assessment Readiness Model (CARM), a directional self-assessment instrument. The Efficient Automatic Safety and Security Assurance (EASSA) concept is proposed as a direction for future research, not a validated deployed system. Ensuring cyberworthiness remains challenging due to automation limitations in all reviewed standards, evolving threat landscapes, and governance complexity, requiring organisations to adopt integrated and measurable approaches to safeguard their digital assets and operational systems. Full article

(This article belongs to the Special Issue Recent Advances in Hardware-Oriented Security and Trust in IoT Systems)

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51 pages, 2921 KB

Open AccessSystematic Review

Uncovering the Mechanisms of Organisational Resilience: A Critical Realist Systematic Review

by Moataz Mahmoud, Ka Ching Chan and Mustafa Ally

Sustainability 2026, 18(10), 5003; https://doi.org/10.3390/su18105003 (registering DOI) - 15 May 2026

Abstract

This systematic review examines how organisational resilience is conceptualised, enacted, and enabled in the Digital Age, characterised by Artificial Intelligence (AI), Generative AI, the Internet of Things (IoT), Big Data, and Robotics. Despite their transformative potential, these technologies are often treated as peripheral tools rather than core mechanisms in resilience architectures. Adopting a critical realist paradigm, we conducted a Systematic Literature Review (SLR) following the PRISMA 2020 protocol to review thirty (30) peer-reviewed empirical studies (2017–present). A pre-SLR conceptual framework, linking Business Intelligence and Responsiveness constructs, guided data extraction and synthesis. Building on this, we propose a conceptual framework and explanatory model grounded in the Context–Mechanism–Outcome logic. The model distinguishes generative mechanisms (real domain), organisational responses (actual domain), and observable indicators (empirical domain). The review identifies Collective Capability (CC), Adaptive Capability (AC) and Dynamic Capability (DC) mechanisms as key generative powers, with Digital Age enablers embedded within Adaptive Capability (AC) and Dynamic Capability (DC). Together, these mechanisms contribute to Systemic Preparedness (SP), Rapid Recovery (RR) and Generative Stability (GS), thereby supporting the emergence of Organisational Resilience (OR). This reconceptualises resilience as an emergent, non-linear outcome of mechanism interactions, offering a unified direction. Future research should prioritise longitudinal multi-case studies and quantitative testing of Context–Mechanism–Outcome configurations, supported by mixed-method designs to validate and refine the proposed framework. Full article

(This article belongs to the Special Issue Building Resilience and Sustainable Societies: The Intersection of Digital Technologies, Cyber Resilience, and Sustainable Development)

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15 pages, 339 KB

Open AccessArticle

Indexed Subset Construction: A Structured Algorithmic Framework

by Bakhtgerey Sinchev, Askar Sinchev, Aksulu Mukhanova, Tolkynai Sadykova, Anel Auyezova and Kuanysh Baimirov

Algorithms 2026, 19(5), 397; https://doi.org/10.3390/a19050397 (registering DOI) - 15 May 2026

Abstract

This paper studies subset construction in NP-complete problems from the perspective of structured exploration of combinatorial search spaces. Classical approaches rely on exhaustive enumeration of subsets, which leads to exponential growth in time and memory requirements. To address this limitation, we introduce an indexed framework based on the correspondence between a finite set and its associated index set. Within this framework, subsets are represented as ordered index sequences, allowing subset construction to be reformulated as a constraint-guided search process over index space. Candidate subsets are characterized by numerical descriptors derived from their indices (referred to as index certificates), which guide and filter the construction process. Subset generation is further organized through admissible index intervals that restrict feasible transitions and reduce the effective search space. The framework is based on an index-based representation and structured traversal of pairwise index combinations. Computational experiments on representative instances illustrate the behavior of the indexed construction procedure and indicate its efficiency relative to classical enumeration-based methods for small and medium-sized instances. The proposed approach provides a structured perspective on combinatorial search and offers a basis for further development of algorithms based on constrained exploration of subset structures. Full article

(This article belongs to the Collection Feature Papers in Combinatorial Optimization, Graph, and Network Algorithms)

26 pages, 94235 KB

Open AccessArticle

CLIP-HBD: Hierarchical Boundary-Constrained Decoding for Open-Vocabulary Semantic Segmentation

by Jing Wang, Quan Zhou, Anyi Yang and Junyu Lin

Computers 2026, 15(5), 318; https://doi.org/10.3390/computers15050318 (registering DOI) - 15 May 2026

Abstract

Open-vocabulary semantic segmentation (OVSS) aims to achieve pixel-level object segmentation guided by arbitrary natural language descriptions. Although pre-trained vision–language models (VLMs) have significantly advanced the development of OVSS, their reliance on the Vision Transformer (ViT) architecture imposes a fundamental constraint on dense prediction. Specifically, the absence of hierarchical downsampling in ViT-based VLM results in single-scale representations that trade spatial localization for global semantics. To address these issues, this paper proposes a hierarchical boundary-constrained decoding network for OVSS, called CLIP-HBD. Our approach leverages VLM semantic priors to reconstruct multi-scale features and introduces a boundary-constrained decoding strategy to refine edge details. Specifically, CLIP-HBD leverages a ConvNeXt-based backbone alongside a hierarchical adaptation mechanism to fuse multi-layer VLM features, generating a comprehensive multi-scale representation. To address the issue of boundary inaccuracy, we perform explicit boundary prediction based on multi-scale representations, where the resulting boundary maps are subsequently transformed into structural constraints to steer the decoder’s focus toward boundary regions. By integrating structural constraints with hierarchical features, the decoding process effectively maintains semantic consistency and restores precise object boundaries. Extensive experiments demonstrate that CLIP-HBD achieves superior performance in both segmentation precision and boundary quality across multiple benchmarks. Full article

(This article belongs to the Special Issue Advanced Image Processing and Computer Vision (3rd Edition))

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14 pages, 6747 KB

Open AccessArticle

Structure-Guided Glycosylation of Hemagglutinin Enhances Stability and Modulates Immunogenicity of Influenza Vaccines

by Zheng Zhang, Zhiying Xiao, Xu Zhang, Qian Ye, Xin Zhang and Wen-Song Tan

Vaccines 2026, 14(5), 443; https://doi.org/10.3390/vaccines14050443 (registering DOI) - 15 May 2026

Abstract

Background: Antigenic drift limits the protective efficacy of influenza vaccine. Glycosylation of hemagglutinin (HA) represents a promising immunofocusing strategy that enhances neutralizing antibody responses by masking immunodominant non-neutralizing epitopes. Methods: B-cell epitopes of influenza viruses were retrieved from the Immune Epitope Database and were mapped onto the HA structure of A/Puerto Rico/8/1934 (H1N1). Structure-guided analysis identified residues 136 and 137 as candidate sites for N-linked glycosylation (NLG). Single-site mutants (136NLG and 137NLG) were generated using reverse genetics and evaluated for stability, receptor binding, viral replication, and immunogenicity in a murine model with inactivated whole-virus vaccines. Results: Both mutants exhibited increased thermostability at 42 °C. Glycosylation reduced the HA–sialic acid affinity, resulting in decreased viral adsorption and internalization efficiency in MDCK cells, and delayed viral replication at low multiplicity of infection (MOI). In vivo, all vaccine groups provided complete protection against lethal challenge; notably, the 136NLG group exhibited reduced weight loss, indicating improved protective efficacy compared with wild-type (WT). Conclusions: Targeted glycosylation at residue 136 in the HA head domain effectively enhances the viral stability and elicits a 1.78-fold increase in hemagglutination inhibition titer (GMT) relative to the WT, thereby improving vaccine performance. These findings establish a rational and structure-based design strategy for developing more stable and effective influenza vaccines. Full article

(This article belongs to the Section Influenza Virus Vaccines)

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18 pages, 1024 KB

Open AccessArticle

CALM: Curriculum Anatomy-Guided Learning Method with Population Template Priors for Source-Free Cross-Modality Prostate MRI Segmentation

by Xiyu Zhang, Xu Chen, Yang Wang, Yifeng Hong and Yuntian Bai

Information 2026, 17(5), 487; https://doi.org/10.3390/info17050487 (registering DOI) - 15 May 2026

Abstract

Source-free domain adaptation (SFDA) for cross-modality prostate MRI segmentation is challenging because source data are unavailable and pseudo-labels on target ADC images are often noisy. To address this problem, we propose Curriculum Anatomy-guided Learning Method with Population Template Priors (CALM), a source-free adaptation framework for this task. CALM constructs a population template prior from target predictions using top-k consensus aggregation and cross-round exponential moving average, then combines this prior with instance-level predictions through Soft-AND fusion. A high-confidence background constraint is further introduced to provide reliable negative supervision, and a coverage-driven curriculum is used to expand training from easy to hard cases based on pseudo-label/template agreement. This design forms an iterative process in which prior refinement and sample-reliability refinement reinforce each other during adaptation. Experiments on the PI-CAI dataset under the T2W-to-ADC setting show that CALM achieves an average Dice score of 73.63% and outperforms representative SFDA baselines in both segmentation accuracy and boundary quality. Ablation and model analyses support the contribution of each component. These results suggest that population-level anatomical priors can provide practical structural guidance for source-free cross-modality adaptation. Full article

(This article belongs to the Section Biomedical Information and Health)

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