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Recent Articles  

26 pages, 14479 KB  
Article
SpeQNet: Query-Enhanced Spectral Graph Filtering for Spatiotemporal Forecasting
by Zongyao Feng and Konstantin Markov
Appl. Sci. 2026, 16(3), 1176; https://doi.org/10.3390/app16031176 (registering DOI) - 23 Jan 2026
Abstract
Accurate spatiotemporal forecasting underpins high-stakes decision making in smart urban systems, from traffic control and energy scheduling to environment monitoring. Yet two persistent gaps limit current models: (i) spatial modules are often biased toward low-pass smoothing and struggle to reconcile slow global trends [...] Read more.
Accurate spatiotemporal forecasting underpins high-stakes decision making in smart urban systems, from traffic control and energy scheduling to environment monitoring. Yet two persistent gaps limit current models: (i) spatial modules are often biased toward low-pass smoothing and struggle to reconcile slow global trends with sharp local dynamics; and (ii) the graph structure required for forecasting is frequently latent, while learned graphs can be unstable when built from temporally derived node features alone. We propose SpeQNet, a query-enhanced spectral graph filtering framework that jointly strengthens node representations and graph construction while enabling frequency-selective spatial reasoning. SpeQNet injects global spatial context into temporal embeddings via lightweight learnable spatiotemporal queries, learns a task-oriented adaptive adjacency matrix, and refines node features with an enhanced ChebNetII-based spectral filtering block equipped with channel-wise recalibration and nonlinear refinement. Across twelve real-world benchmarks spanning traffic, electricity, solar power, and weather, SpeQNet achieves state-of-the-art performance and delivers consistent gains on large-scale graphs. Beyond accuracy, SpeQNet is interpretable and robust: the learned spectral operators exhibit a consistent band-stop-like frequency shaping behavior, and performance remains stable across a wide range of Chebyshev polynomial orders. These results suggest that query-enhanced spatiotemporal representation learning and adaptive spectral filtering form a complementary and effective foundation for effective spatiotemporal forecasting. Full article
(This article belongs to the Special Issue Research and Applications of Artificial Neural Network)
23 pages, 959 KB  
Review
Therapeutic Patient Education in Adults with Chronic Lower Limb Musculoskeletal Pain: A Scoping Review
by Carla Vanti, Michael Bianchini, Alessio Mantineo, Francesco Ballardin and Paolo Pillastrini
Healthcare 2026, 14(3), 290; https://doi.org/10.3390/healthcare14030290 (registering DOI) - 23 Jan 2026
Abstract
Background: Conservative treatment of chronic musculoskeletal pain includes exercise, manual therapy, medications, physical agents/modalities, and Therapeutic Patient Education (TPE). Research on TPE has predominantly focused on spinal pain, so we do not know the extent and scope of clinical research in other [...] Read more.
Background: Conservative treatment of chronic musculoskeletal pain includes exercise, manual therapy, medications, physical agents/modalities, and Therapeutic Patient Education (TPE). Research on TPE has predominantly focused on spinal pain, so we do not know the extent and scope of clinical research in other areas, particularly lower extremities. This review aimed to map current research on this topic. Methods: We searched PubMed, PEDro, CINAHL, PsycINFO, and Cochrane Library up to 1 April 2024. We included RCTs on adults with chronic lower limb musculoskeletal pain, written in English, French, Spanish, or Italian. Results: Fifty-two records concerning knee osteoarthritis (n.33), hip and knee osteoarthritis (n.8), hip osteoarthritis (n.3), chronic knee pain (n.3), patellofemoral pain (n.3), and gluteal tendinopathy (n.2) were included. TPE was delivered through self-management, disease-specific information, pain education, and the management of physical activity, load, diet, stress, and sleep. Interventions were both individual- and group-based; delivery methods included in-person intervention, telephone/video calls, and web tools/apps. TPE combined with exercise seemed to be more effective than exercise alone, information/little education, or usual care. The effects of TPE as a stand-alone intervention appeared uncertain. Conclusions: There is considerable variability in TPE in terms of teaching topics, providers, administration methods, and dosage of interventions. Future studies should investigate the effects of TPE in young adult populations and in ankle conditions. They should also investigate the effects of TPE on pain intensity versus pain interference with activities, by deepening TPE effects on disability and quality of life. Full article
(This article belongs to the Special Issue Dysfunctions or Approaches of the Musculoskeletal System)
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18 pages, 1307 KB  
Article
Industrial Hemp Finola Variety Microgreens: A Sustainable Source of Selenium Biofortified Functional Foods
by Boris Ravnjak, Ivana Varga, Manda Antunović, Josipa Jović, Monika Tkalec Kojić, Mariana Casari Parreira and Antonela Markulj Kulundžić
Agriculture 2026, 16(3), 292; https://doi.org/10.3390/agriculture16030292 (registering DOI) - 23 Jan 2026
Abstract
The aim of this study was to evaluate the effects of selenium (Se) biofortification on growth, biomass accumulation, and micronutrient composition of industrial hemp (Cannabis sativa L., cv. Finola) microgreens, with emphasis on Se uptake and its distribution among leaves, stems, and [...] Read more.
The aim of this study was to evaluate the effects of selenium (Se) biofortification on growth, biomass accumulation, and micronutrient composition of industrial hemp (Cannabis sativa L., cv. Finola) microgreens, with emphasis on Se uptake and its distribution among leaves, stems, and roots. Microgreens were subjected to four Se treatments (Se_0, Se_2, Se_4, and Se_6 µmol Se/L), and changes in morphological traits, micronutrient status (Mn, Fe, Cu, Zn), and Se accumulation were assessed. Selenium biofortification had a marked impact on plant morphology and biomass. Stem length decreased by 12–18% under Se treatments compared with the control, whereas root length increased slightly, particularly at Se_2 and Se_4 (up to +6%). Fresh industrial hemp microgreens biomass responded strongly to Se supply, with the highest stem, root, and total fresh mass recorded at Se_4—representing an increase of 15–22% relative to control plants. At the highest Se level (Se_6), biomass declined by approximately 10–14%, indicating potential growth inhibition at excessive Se concentrations. Micronutrient concentrations were significantly affected by Se. Leaf Mn increased from 152 mg kg−1 at Se_0 to 175 mg kg−1 at Se_6 (+15%), while leaf Zn decreased by 20–25% at higher Se exposure. Stems and roots showed similar antagonistic interactions, with Fe and Zn decreasing by up to 30% at elevated Se levels. Conversely, Mn in stems and roots increased with Se up to Se_4, reaching 400 mg kg−1 in roots. Selenium accumulation exhibited a strong linear response to biofortification, with high coefficients of determination (R2 = 0.9685–0.9943), confirming predictable and efficient Se uptake. Correlation analysis revealed strong positive associations among biomass-related traits and distinct interactions among micronutrients, especially the near-perfect correlation between Se and Cu in roots (r ≈ 0.99). Overall, industrial hemp microgreens demonstrate potential for selenium biofortification, provided that selenium application levels remain within safe dietary limits. Full article
(This article belongs to the Special Issue Greens—Biofortification for Improved Nutritional Quality)
12 pages, 935 KB  
Article
Should We Continue Liver Transplantation in Spain for Hepatic Metastases from Neuroendocrine Tumors?
by Andrea Boscà, Eva M. Montalvá, Marina Vila, Laura Lladó, Víctor López, Mikel Gastaca, Santiago Tomé, José M. Ramia, Javier Nuño, Fernando Rotellar, María Pérez, Óscar Caso, Ma Mar Achalandabaso, Isabel Jaén, Carmen García, Pablo Ramírez and Rafael López-Andújar
J. Clin. Med. 2026, 15(3), 938; https://doi.org/10.3390/jcm15030938 (registering DOI) - 23 Jan 2026
Abstract
Background/Objectives: Despite the long-standing history of liver transplantation (LT) in Spain, no multicenter study has reviewed national outcomes for LT in metastatic neuroendocrine tumors (NETs). In the current era of transplant oncology, auditing these results is essential to refine patient selection and [...] Read more.
Background/Objectives: Despite the long-standing history of liver transplantation (LT) in Spain, no multicenter study has reviewed national outcomes for LT in metastatic neuroendocrine tumors (NETs). In the current era of transplant oncology, auditing these results is essential to refine patient selection and improve long-term outcomes. Methods: This retrospective observational study analyzed data from 13 centers, including 91 patients who underwent LT for NET between 1995 and 2024. Patients were stratified into two groups: Milan IN (those meeting the Milan criteria) and Milan OUT (the remainder). Results: Recurrence occurred in 57.1% of cases, and overall mortality was 51.6%. Of the 91 patients, 71 (78.0%) were Milan IN and 20 (22.0%) were Milan OUT. Five-year overall survival was 71.0% in Milan IN and 58.0% in Milan OUT, with a statistically significant difference. The 5-year disease-free survival (DFS) rate was 58.8% in Milan IN and 36.3% in Milan OUT; this difference was not statistically significant. Conclusions: In conclusion, strict adherence to Milan criteria and incorporation of modern prognostic factors are critical to optimize long-term survival in LT for NET. While the overall outcomes in this historical cohort are modest, future improvements are expected through more rigorous selection and the potential use of bridging or downstaging therapies. Full article
(This article belongs to the Special Issue Current Challenges and New Perspectives in Liver Transplantation)
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17 pages, 392 KB  
Article
Beyond Morality and Technology: The Theory of “Fate” of Wang Chong
by Xiaofei Ma
Religions 2026, 17(2), 130; https://doi.org/10.3390/rel17020130 (registering DOI) - 23 Jan 2026
Abstract
Wang Chong established the most extensive and complex theoretical system of fate in the pre-Qin and Han dynasties, drawing upon Confucian and popular theories. The cores of Confucian and popular theories of fate lay respectively in moral cultivation and technical approaches, leaving room [...] Read more.
Wang Chong established the most extensive and complex theoretical system of fate in the pre-Qin and Han dynasties, drawing upon Confucian and popular theories. The cores of Confucian and popular theories of fate lay respectively in moral cultivation and technical approaches, leaving room for human autonomy in determining fate. However, based on his own experience of unfulfilled potential and his admiration for the Daoist concept of “nature”, Wang Chong denied the causal relationship between individual behaviors and the occurrences of good or bad fortune. In this way, Wang Chong’s theory of fate deprived individuals of their initiative over destiny as found in Confucian tradition and popular beliefs, thus moving towards the extreme of fatalism. The theory of fate served as a key instrument for Wang Chong in his “opposition to falsehood and fallacy” (jixuwang 疾虚妄), occupying a significant position within the philosophical system of Lunheng. Through this theory, Wang Chong criticized the prevailing theories of the interaction between Heaven and humanity centered on moral principles, as well as the numerology and worship of ghosts and deities rooted in technical principles. In doing so, he set apart himself from traditional Confucian scholars, and laid important intellectual groundwork for the subsequent development of metaphysical discourse in China. Full article
(This article belongs to the Special Issue The Multiple Images and Forms of Classical Confucian and Daoist Ethics for Living)
24 pages, 805 KB  
Review
Mathematics Teachers’ Pedagogical Content Knowledge in Strengthening Conceptual Understanding in Students with Learning Disabilities: A Practice-Based Conceptual Synthesis
by Friggita Johnson and Finita G. Roy
Educ. Sci. 2026, 16(2), 176; https://doi.org/10.3390/educsci16020176 (registering DOI) - 23 Jan 2026
Abstract
Students with learning disabilities (LD) often struggle to develop deep, transferable conceptual understanding in mathematics due to cognitive and processing challenges, underscoring the relevance of instruction grounded in strong teacher pedagogical content knowledge (PCK). This issue is critical given widening post-pandemic achievement gaps [...] Read more.
Students with learning disabilities (LD) often struggle to develop deep, transferable conceptual understanding in mathematics due to cognitive and processing challenges, underscoring the relevance of instruction grounded in strong teacher pedagogical content knowledge (PCK). This issue is critical given widening post-pandemic achievement gaps and increased expectations for conceptual understanding in inclusive classrooms. Although many studies document effective mathematics interventions for students with LD, relatively few examine how teachers’ PCK functions in these classrooms. In contrast, general education research highlights the importance of PCK for conceptual learning. This manuscript bridges these studies by examining how insights from broader PCK research may inform instruction for students with LD. This manuscript presents a practice-based conceptual synthesis of research on mathematics teachers’ PCK, integrating findings from special education and mathematics intervention literature with classroom vignettes and practitioner examples. The synthesis highlights how core PCK components—content knowledge, understanding of student thinking and misconceptions, and instructional strategies—may support early conceptual understanding in students with LD, emphasizing multiple representations, error analysis, and routines that promote generalization through distributed practice. Implications for practice, professional development, and future research are discussed, offering practice-informed pathways to support inclusive mathematics instruction for students with LD. Full article
(This article belongs to the Special Issue Conceptual Understanding in Mathematics: Focusing on Students with Learning Disabilities or Difficulties)
15 pages, 3380 KB  
Systematic Review
Re-Evaluating the Progesterone Challenge Test as a Physiologic Marker of Endometrial Cancer Risk: A Systematic Review and Meta-Analysis
by Rachel J. Woima, Derek S. Chiu, Elise Abi Khalil, Sabine El-Halabi, Andrea Neilson, Laurence Bernard, Jessica N. McAlpine and Aline Talhouk
Diagnostics 2026, 16(3), 378; https://doi.org/10.3390/diagnostics16030378 (registering DOI) - 23 Jan 2026
Abstract
Background/Objectives: With the rising incidence of obesity-related endometrial cancer, there is renewed interest in physiologic, low-cost approaches to identify women with hormonally active endometrium who may benefit from early preventive interventions. The progesterone challenge test (PCT), an established clinical tool for evaluating [...] Read more.
Background/Objectives: With the rising incidence of obesity-related endometrial cancer, there is renewed interest in physiologic, low-cost approaches to identify women with hormonally active endometrium who may benefit from early preventive interventions. The progesterone challenge test (PCT), an established clinical tool for evaluating amenorrhea, has been previously proposed as a method to detect endometrial pathology. This study systematically evaluated the diagnostic accuracy of the PCT for detecting endometrial hyperplasia, intraepithelial neoplasia, and carcinoma in asymptomatic postmenopausal women to determine its potential role as a physiologic marker of endometrial cancer risk. Methods: A systematic review and meta-analysis were conducted following PRISMA-DTA guidelines. MEDLINE, EMBASE, EBM Reviews, and CINAHL were searched from inception to 20 January 2025, along with ClinicalTrials.gov and grey literature. Eligible studies prospectively evaluated the PCT with endometrial biopsy as the reference standard. Data extraction and risk-of-bias assessment were performed in duplicate. Risk of bias was assessed using QUADAS-2. Pooled sensitivity, specificity, and predictive values were estimated using hierarchical summary receiver operating characteristic models. Results: Nineteen studies (n = 3902) met the inclusion criteria. The pooled sensitivity and specificity of the PCT for detecting endometrial pathology were 95% (95% CI 86–100%) and 87% (76–96%), respectively. The positive predictive value was 32% (95% CI, 16–50%) and the negative predictive value was 100% (100–100%). When endometrial proliferation was included in the target condition, sensitivity decreased to 82%, but positive predictive value increased to 70%. Conclusions: The PCT shows high diagnostic accuracy for identifying estrogen-driven endometrial pathology in asymptomatic postmenopausal women. Re-evaluating this simple, physiologic test as a functional risk-stratification tool could inform precision prevention strategies for endometrial cancer. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Management of Endometrial Diseases)
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17 pages, 4145 KB  
Article
Acoustic Metadata Design on Object-Based Audio Using Estimated 3D-Position from Visual Image Toward Depth-Directional Sound Image Localization
by Subaru Kato, Masato Nakayama, Takanobu Nishiura and Yoshiharu Soeta
Acoustics 2026, 8(1), 3; https://doi.org/10.3390/acoustics8010003 (registering DOI) - 23 Jan 2026
Abstract
Multichannel audio is a sound field reproduction technology that uses multiple loudspeakers. Object-based audio is a playback method for multichannel audio that enables the construction of sound images at specified positions using coordinates within the playback space. However, the sound image positions must [...] Read more.
Multichannel audio is a sound field reproduction technology that uses multiple loudspeakers. Object-based audio is a playback method for multichannel audio that enables the construction of sound images at specified positions using coordinates within the playback space. However, the sound image positions must be manually specified by audio content creators, which increases the production workload, especially for works containing many sound images or feature films. We have previously proposed a method to reduce the workload of content creators by constructing sound images based on object positions in visual images. However, a significant challenge remains since depth localization of the sound image is not accurate enough. This paper aims to improve localization accuracy by changing the range of sound image movement along the depth direction. To confirm the localization accuracy of sound images constructed using the proposed method, we conducted a subjective evaluation experiment. The experiment identified the optimal movement range by presenting participants with visual images synchronized with sound images moving across varying spatial scales. Consequently, we were able to identify the range of sound image movement in the depth direction necessary for presenting sound images with high consistency with the visual images. Full article
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40 pages, 4616 KB  
Article
Model Predictive Control for Dynamic Positioning of a Fireboat Considering Non-Linear Environmental Disturbances and Water Cannon Reaction Forces Based on Numerical Modeling
by Dabin Lee and Sewon Kim
Mathematics 2026, 14(3), 401; https://doi.org/10.3390/math14030401 (registering DOI) - 23 Jan 2026
Abstract
Dynamic positioning (DP) systems play a critical role in maintaining vessel position and heading under environmental disturbances such as wind, waves, and currents. This study presents a model predictive control (MPC)-based DP system for a fireboat equipped with a rudder–propeller configuration, explicitly accounting [...] Read more.
Dynamic positioning (DP) systems play a critical role in maintaining vessel position and heading under environmental disturbances such as wind, waves, and currents. This study presents a model predictive control (MPC)-based DP system for a fireboat equipped with a rudder–propeller configuration, explicitly accounting for both environmental loads and the reaction force generated during water cannon operation. Unlike conventional DP architectures in which DP control and thrust allocation are treated as separate modules, the proposed framework integrates both functions within a unified MPC formulation, enabling real-time optimization under actuator constraints. Environmental loads are modeled by incorporating nonlinear second-order wave drift effects, while nonlinear rudder–propeller interaction forces are derived through computational fluid dynamics (CFD) analysis and embedded in a control-oriented dynamic model. This modeling approach allows operational constraints, including rudder angle limits and propeller thrust saturation, to be explicitly considered in the control formulation. Simulation results demonstrate that the proposed MPC-based DP system achieves improved station-keeping accuracy, enhanced stability, and increased robustness against combined environmental disturbances and water cannon reaction forces, compared to a conventional PID controller. Full article
(This article belongs to the Special Issue High-Order Numerical Methods and Computational Fluid Dynamics)
19 pages, 1026 KB  
Article
Impact of Climate Change Awareness and Perception on Pro-Environmental Behaviour in Türkiye: A Structural Equation Modelling Approach
by Cengiz Gazeloğlu
Sustainability 2026, 18(3), 1175; https://doi.org/10.3390/su18031175 (registering DOI) - 23 Jan 2026
Abstract
This study investigated the influence of awareness, knowledge, and risk perceptions on environmental attitudes and behaviours in Türkiye, specifically in the context of climate change, using structural equation modelling (SEM). Data were collected from all 81 provinces covering the seven geographical regions of [...] Read more.
This study investigated the influence of awareness, knowledge, and risk perceptions on environmental attitudes and behaviours in Türkiye, specifically in the context of climate change, using structural equation modelling (SEM). Data were collected from all 81 provinces covering the seven geographical regions of the country. The results revealed that awareness and risk perception have the strongest direct impact on pro-environmental behaviour. Environmental attitudes also demonstrated a significant positive effect, though the findings suggest that high awareness and risk perception can directly drive action even independently of attitude. Uniquely, this study fills a critical gap in the developing country literature by demonstrating that in Türkiye, perceiving the risk translates directly into action, contrasting with the ‘value-action gap’ often observed in Western contexts. Practically, the findings suggest that policymakers should prioritize risk-communication strategies and disaster-preparedness drills over passive information campaigns to effectively stimulate pro-environmental behaviours. Full article
(This article belongs to the Section Air, Climate Change and Sustainability)
27 pages, 1996 KB  
Article
Salient Object Detection for Optical Remote Sensing Images Based on Gated Differential Unit
by Mingsi Sun, Ting Lan, Wei Wang and Pingping Liu
Remote Sens. 2026, 18(3), 389; https://doi.org/10.3390/rs18030389 (registering DOI) - 23 Jan 2026
Abstract
Salient object detection in optical remote sensing images has attracted extensive research interest in recent years. However, CNN-based methods are generally limited by local receptive fields, while ViT-based methods suffer from common defects in noise suppression, channel selection, foreground-background distinction, and detail enhancement. [...] Read more.
Salient object detection in optical remote sensing images has attracted extensive research interest in recent years. However, CNN-based methods are generally limited by local receptive fields, while ViT-based methods suffer from common defects in noise suppression, channel selection, foreground-background distinction, and detail enhancement. To address these issues and integrate long-distance contextual dependencies, we introduce GDUFormer, an ORSI-SOD detection method based on the ViT backbone and Gated Differential Units (GDU). Specifically, the GDU consists of two key components—Full-Dimensional Gated Attention (FGA) and Hierarchical Differential Dynamic Convolution (HDDC). FGA consists of two branches aimed at filtering effective features from the information flow. The first branch focuses on aggregating spatial local information under multiple receptive fields and filters the local feature maps via a grouping mechanism. The second branch imitates the Vision Mamba to acquire high-level reasoning and abstraction capabilities, enabling weak channel filtering. HDDC primarily utilizes distance decay and hierarchical intensity difference capture mechanisms to generate dynamic kernel spatial weights, thereby facilitating the convolution kernel to fully mix long-range contextual dependencies. Among these, the intensity difference capture mechanism can adaptively divide hierarchies and allocate parameters according to kernel size, thus realizing varying levels of difference capture in the kernel space. Extensive quantitative and qualitative experiments demonstrate the effectiveness and rationality of GDUFormer and its internal components. Full article
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16 pages, 3865 KB  
Article
Data-Augmented Deep Learning for Downhole Depth Sensing and Validation
by Si-Yu Xiao, Xin-Di Zhao, Tian-Hao Mao, Yi-Wei Wang, Yu-Qiao Chen, Hong-Yun Zhang, Jian Wang, Jun-Jie Wang, Shuang Liu, Tu-Pei Chen and Yang Liu
Sensors 2026, 26(3), 775; https://doi.org/10.3390/s26030775 (registering DOI) - 23 Jan 2026
Abstract
Accurate downhole depth measurement is essential for oil and gas well operations, directly influencing reservoir contact, production efficiency, and operational safety. Collar correlation using a casing collar locator (CCL) is fundamental for precise depth calibration. While neural network has achieved significant progress in [...] Read more.
Accurate downhole depth measurement is essential for oil and gas well operations, directly influencing reservoir contact, production efficiency, and operational safety. Collar correlation using a casing collar locator (CCL) is fundamental for precise depth calibration. While neural network has achieved significant progress in collar recognition, preprocessing methods for such applications remain underdeveloped. Moreover, the limited availability of real well data poses substantial challenges for training neural network models that require extensive datasets. This paper presents a system integrated into a downhole toolstring for CCL log acquisition to facilitate dataset construction. Comprehensive preprocessing methods for data augmentation are proposed, and their effectiveness is evaluated using baseline neural network models. Through systematic experimentation across diverse configurations, the contribution of each augmentation method is analyzed. Results demonstrate that standardization, label distribution smoothing (LDS), and random cropping are fundamental prerequisites for model training, while label smoothing regularization (LSR), time scaling, and multiple sampling significantly enhance model generalization capabilities. Incorporating the proposed augmentation methods into the two baseline models results in maximum F1 score improvements of 0.027 and 0.024 for the TAN and MAN models, respectively. Furthermore, applying these techniques yields F1 score gains of up to 0.045 for the TAN model and 0.057 for the MAN model compared to prior studies. Performance evaluation on real CCL waveforms confirms the effectiveness and practical applicability of our approach. This work addresses the existing gaps in data augmentation methodologies for training casing collar recognition models under CCL data-limited conditions, and provides a technical foundation for the future automation of downhole operations. Full article
(This article belongs to the Special Issue Intelligent Sensors and Signal Processing in Industry)
24 pages, 3559 KB  
Article
Design of a Dynamic Key Generation Mechanism and Secure Image Transmission Based on Synchronization of Fractional-Order Chaotic Systems
by Chih-Yung Chen, Teh-Lu Liao, Jun-Juh Yan and Yu-Han Chang
Mathematics 2026, 14(3), 402; https://doi.org/10.3390/math14030402 (registering DOI) - 23 Jan 2026
Abstract
With the rapid development of Internet of Things (IoT) and Artificial Intelligence (AI) technologies, information security has become a critical issue. To develop a highly secure image encryption transmission system, this study proposes a novel key generation mechanism based on the combination of [...] Read more.
With the rapid development of Internet of Things (IoT) and Artificial Intelligence (AI) technologies, information security has become a critical issue. To develop a highly secure image encryption transmission system, this study proposes a novel key generation mechanism based on the combination of fractional-order chaotic system synchronization control and the SHA-256 algorithm. This proposed method dynamically generates high-quality synchronous random number sequences and is combined with the Advanced Encryption Standard (AES) algorithm. To quantitatively evaluate the mechanism, the generated sequences are tested using NIST SP 800-22, ENT, and DIEHARD suites. The comparative results show that the key generation mechanism produces sequences with higher randomness and unpredictability. In the evaluation of image encryption, histogram distribution, information entropy, adjacent pixel correlation, NPCR, and UACI are used as performance metrics. Experimental results show that the histogram distributions are uniform, the values of information entropy, NPCR, and UACI are close to their ideal levels, and the pixel correlation is significantly reduced. Compared to recent studies, the proposed method demonstrates higher encryption performance and stronger resistance to statistical attacks. Furthermore, the system effectively addresses key distribution and management problems inherent in traditional symmetric encryption schemes. These results validate the reliability and practical feasibility of the proposed approach. Full article
25 pages, 742 KB  
Article
Hybrid Poly Commitments for Scalable Binius Zero-Knowledge Proofs in Federated Learning
by Hasina Andriambelo, Hery Zo Andriamanohisoa and Naghmeh Moradpoor
Electronics 2026, 15(3), 500; https://doi.org/10.3390/electronics15030500 (registering DOI) - 23 Jan 2026
Abstract
Federated learning enables collaborative model training without sharing raw data, but practical deployments increasingly require verifiable guarantees that clients compute updates correctly. Zero-knowledge proofs can provide such guarantees, yet existing approaches face scalability limits due to the combined cost of polynomial commitments and [...] Read more.
Federated learning enables collaborative model training without sharing raw data, but practical deployments increasingly require verifiable guarantees that clients compute updates correctly. Zero-knowledge proofs can provide such guarantees, yet existing approaches face scalability limits due to the combined cost of polynomial commitments and fast Fourier transform (FFT) intensive verification. Pairing-based schemes offer compact proofs but incur high prover and verifier overhead, while hash-based constructions reduce algebraic cost at the expense of rapidly growing proof sizes. This paper proposes Hybrid-Commit, a polynomial commitment architecture for Binius zero-knowledge proofs that aligns cryptographic primitives with the algebraic structure of federated learning workloads. The scheme separates verification into additive and multiplicative phases: linear aggregation is handled using batched additive commitments optimized for binary fields, while non-linear constraints are verified via hash-based commitments over sparsely selected FFT domains. Proofs from multiple clients are combined through recursive aggregation while preserving non-interactivity. Experiments demonstrate scalability in prover time and proof size (near-constant prover time across 4–11 clients; 160 bytes per client representing 341× and 813× reductions vs. FRI-PCS and Orion), although verification time (762 ms per client) does not scale favorably, making the scheme suitable for bandwidth-constrained scenarios. The scheme achieves under 2% end-to-end training overhead with no impact on model accuracy, indicating that workload-aware commitment design can improve specific scalability dimensions of zero-knowledge verification in federated learning systems. Full article
(This article belongs to the Special Issue Next-Generation Machine Learning and Deep Learning Models for Complex Data, Vision, and Intelligent Applications)
11 pages, 857 KB  
Article
Factors Associated with the Anamnestic Immune Response Following Hepatitis B Booster Vaccination in the Elderly
by Chen Wang, Yan Zou, Xiaofei Wang and Na Liu
Vaccines 2026, 14(2), 111; https://doi.org/10.3390/vaccines14020111 (registering DOI) - 23 Jan 2026
Abstract
Objective: To investigate factors influencing the anamnestic immune response 9 years after hepatitis B vaccination in elderly people (aged > 60 years). Methods: We quantitatively tested 630 elderly people who participated in the free hepatitis B vaccination program for adults in Zhangjiagang City [...] Read more.
Objective: To investigate factors influencing the anamnestic immune response 9 years after hepatitis B vaccination in elderly people (aged > 60 years). Methods: We quantitatively tested 630 elderly people who participated in the free hepatitis B vaccination program for adults in Zhangjiagang City during 2015 for hepatitis B surface antibody (anti-HBs) titers. Three booster doses of hepatitis B vaccine were given to subjects with anti-HBs titers below 10 mIU/mL, while a single booster dose was administered to those with titers between 10 and 100 mIU/mL, in accordance with their antibody titer measurements. The post-booster anti-HBs titers were evaluated at 2–3 months. A logistic regression model was used to identify factors influencing the anamnestic immune response, and a receiver operating characteristic curve analysis was conducted. Results: Among the 90 participants who received three doses and the 101 participants who received one dose, baseline characteristics did not differ significantly between the two cohorts. Both groups exhibited robust anamnestic immune responses. Significant differences were observed before and after booster vaccination within each group (Z = −8.24, p < 0.001; Z = −8.73, p < 0.001). Multivariate logistic regression indicated that individuals with higher pre-booster anti-HBs titers were less likely to show weak anamnestic responses compared to those with lower pre-booster titers (OR = 0.30, 95% CI: 0.16–0.58). Furthermore, a high anamnestic immune response (>1000 mIU/mL) was significantly more frequent among subjects with pre-booster titers ≥ 4.58 mIU/mL. Conclusions: Booster immunization administered nine years after hepatitis B vaccination induces robust anamnestic immunity, with its magnitude significantly correlated with pre-booster anti-HBs titers. Particular attention should be given to individuals with extremely low pre-booster anti-HBs levels. Full article
(This article belongs to the Special Issue Preventing Outbreak Through Vaccination)
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22 pages, 3855 KB  
Article
The Biostatistical Landscape of Scientific Output in the Field of Open Bite: Trends, Themes, and Publication Dynamics
by Ali Vasfi Ağlarcı and Cahide Ağlarcı
Appl. Sci. 2026, 16(3), 1175; https://doi.org/10.3390/app16031175 (registering DOI) - 23 Jan 2026
Abstract
Background and Objectives: There is a lack of comprehensive, focused reviews on the topic of open bite in the literature. This study aims to quantitatively reveal publication productivity, annual trends, publication sources, key themes, and citation patterns in the field of open [...] Read more.
Background and Objectives: There is a lack of comprehensive, focused reviews on the topic of open bite in the literature. This study aims to quantitatively reveal publication productivity, annual trends, publication sources, key themes, and citation patterns in the field of open bite. Materials and Methods: A total of 1208 articles and reviews published between 1973 and 2025, obtained from the Web of Science database, were analyzed using bibliometric and network analysis methods. Results: A significant increase in the number of publications after 2010, acceleration particularly after 2015, and high productivity observed in the 2018–2024 period. A clear increasing trend was observed over time. 71.5% of publications are included in SCI-Expanded. Journal distribution is centralized, with the American Journal of Orthodontics and Dentofacial Orthopedics and Angle Orthodontist being the dominant publications. Keyword and cluster analyses showed that the literature is concentrated on four main thematic axes: (1) etiology and biomechanical processes, (2) surgical approaches and orthognathic interventions, (3) early intervention and habit control, (4) post-treatment stability and relapse. Furthermore, treatment-oriented concepts such as “miniscrew/temporary anchorage device,” “molar intrusion,” and “cephalometric analysis” are central. Conclusions: The study reveals that open bite has become an increasingly prevalent and thematically diverse area of research in the orthodontic literature. The current distribution indicates that research focuses on both clinical application and treatment outcomes; however, it also highlights the importance of long-term comparative data and studies on treatment stability. In the future, methodological standardization and comparable long-term data will contribute to the maturation of the literature. Full article
(This article belongs to the Section Applied Dentistry and Oral Sciences)
10 pages, 252 KB  
Article
Quantum-like Cognition and Decision-Making: Interpretation of Phases in Quantum-like Superposition
by Andrei Khrennikov
Entropy 2026, 28(2), 134; https://doi.org/10.3390/e28020134 (registering DOI) - 23 Jan 2026
Abstract
This paper addresses a central conceptual challenge in Quantum-like Cognition and Decision-Making (QCDM) and the broader research program of Quantum-like Modeling (QLM): the interpretation of phases in quantum-like state superpositions. In QLM, system states are represented by normalized vectors in a complex [...] Read more.
This paper addresses a central conceptual challenge in Quantum-like Cognition and Decision-Making (QCDM) and the broader research program of Quantum-like Modeling (QLM): the interpretation of phases in quantum-like state superpositions. In QLM, system states are represented by normalized vectors in a complex Hilbert space, |ψ⟩=∑kXk|k⟩, where the squared amplitudes Pk=|Xk|2 are outcome probabilities. However, the meaning of the phase factors eiϕk in the coefficients Xk=Pkeiϕk has remained elusive, often treating them as purely phenomenological parameters. This practice, while successful in describing cognitive interference effects (the "interference of the mind”), has drawn criticism for expanding the model’s parameter space without a clear physical or cognitive underpinning. Building on a recent framework that connects QCDM to neuronal network activity, we propose a concrete interpretation. We argue that the phases in quantum-like superpositions correspond directly to the phases of random oscillations generated by neuronal circuits in the brain. This interpretation not only provides a natural, non-phenomenological basis for phase parameters within QCDM but also helps to bridge the gap between quantum-like models and classical neurocognitive frameworks, offering a consistent physical analogy for the descriptive power of QLM. Full article
(This article belongs to the Special Issue Quantum Information and Probability: From Foundations to Engineering IV)
18 pages, 6355 KB  
Article
From Human Teams to Autonomous Swarms: A Reinforcement Learning-Based Benchmarking Framework for Unmanned Aerial Vehicle Search and Rescue Missions
by Julian Bialas, Mohammad Reza Mohebbi, Michiel J. van Veelen, Abraham Mejia-Aguilar, Robert Kathrein and Mario Döller
Drones 2026, 10(2), 79; https://doi.org/10.3390/drones10020079 (registering DOI) - 23 Jan 2026
Abstract
The adoption of novel technologies such as Unmanned Aerial Vehicles (UAVs) in Search and Rescue (SAR) operations remains limited. As a result, their full potential is not yet realized. Although UAVs have been deployed on an ad hoc basis, typically under manual control [...] Read more.
The adoption of novel technologies such as Unmanned Aerial Vehicles (UAVs) in Search and Rescue (SAR) operations remains limited. As a result, their full potential is not yet realized. Although UAVs have been deployed on an ad hoc basis, typically under manual control by dedicated operators, assisted and fully autonomous configurations remain largely unexplored. In this study, three SAR frameworks are systematically evaluated within a unified benchmarking framework: conventional ground missions, UAV-assisted missions, and fully autonomous UAV operations. As the key performance indicator, the target localization time was quantified and used as the means of comparison amongst frameworks. The conventional and assisted frameworks were experimentally tested through physical hardware in a controlled outdoor setting, wherein simulated callouts occurred via rescue teams. The autonomous swarm framework was simulated in the form of a multi-agent Reinforcement Learning (RL) method via the use of the Proximal Policy Optimization (PPO) algorithm. This enabled the optimization of the decentralized cooperative actions that could occur for efficient exploration of a partially observed three-dimensional environment. Our results demonstrated that the autonomous swarm significantly outperformed the conventional and assisted approaches in terms of speed and coverage. Finally, a detailed depiction of the framework’s integration into an operational system is provided. Full article
(This article belongs to the Special Issue Drone Communication, Networking, and Trajectory Control in Urban Environments)
19 pages, 566 KB  
Article
Modeling a Reliable Intermodal Routing Problem for Emergency Materials in the Early Stage of Post-Disaster Recovery Under Uncertainty of Demand and Capacity
by Yu Huang, Haochu Cui, Yue Lu and Yan Sun
Appl. Syst. Innov. 2026, 9(2), 27; https://doi.org/10.3390/asi9020027 (registering DOI) - 23 Jan 2026
Abstract
This study investigates an intermodal routing problem for emergency materials in the early stage of post-disaster recovery, in which the rapid transportation of emergency materials is formulated as the objective. To achieve reliable transportation that can avoid transportation interruption, this study formulates the [...] Read more.
This study investigates an intermodal routing problem for emergency materials in the early stage of post-disaster recovery, in which the rapid transportation of emergency materials is formulated as the objective. To achieve reliable transportation that can avoid transportation interruption, this study formulates the uncertainty of both emergency materials’ demand and the network capacity by LR triangular fuzzy numbers, and thus explores a reliable routing problem for transporting emergency materials that is further formulated by a fuzzy linear programming model. Considering the decision makers’ cautious attitude on the transportation of emergency materials to avoid transportation interruption, this study adopts chance-constrained programming based on necessity measure to build a solvable reformulation of the proposed model. A numerical case study is carried out to reveal the conflicting relationship between improving the reliability and reducing the time of transporting emergency materials. The decision-makers of the emergency materials transportation organization should select a reasonable confidence level based on the actual decision-making scenario to plan the reliable intermodal route for emergency materials. By comparing with deterministic modeling, this study verifies the feasibility of the modeling the uncertainty of both demand and capacity in avoiding unreliable transportation and enhancing the flexibility of the intermodal routing for emergency materials. By comparing with chance-constrained programming using possibility measure, this study demonstrates the feasibility of the necessity measure in planning the reliable intermodal route. This study further analyzes how the capacity level of the intermodal network, demand level of the emergency materials and stability of the LR triangular fuzzy parameters influence the optimization results. Accordingly, this study emphasizes the importance of objectively evaluating the uncertain demand for emergency materials, and reveals that the enhancement of the capacity level of the intermodal network and stability of LR triangular fuzzy parameters is able to reduce the transportation time of emergency materials and meanwhile maintain a high reliability. Full article
12 pages, 1320 KB  
Article
Anti-Inflammatory Potential of Beesioside O: Target Prediction, Docking Studies, and Molecular Dynamics
by Qian Qiang, Qiong-Yu Zou, Lei Jin, Zheng Hu, Zi-Xuan Zhao, Hai-Feng Wu and Ji Zhang
Curr. Issues Mol. Biol. 2026, 48(2), 129; https://doi.org/10.3390/cimb48020129 (registering DOI) - 23 Jan 2026
Abstract
Triterpenoids with diverse structural features have shown considerable potential as pharmaceutical precursors for anti-inflammatory therapies. Beesioside O (BO), a representative triterpenoid (cycloartane triterpene saponin), has previously been reported to exhibit notable anti-HIV and anticancer activities. However, its anti-inflammatory mechanisms have not been fully [...] Read more.
Triterpenoids with diverse structural features have shown considerable potential as pharmaceutical precursors for anti-inflammatory therapies. Beesioside O (BO), a representative triterpenoid (cycloartane triterpene saponin), has previously been reported to exhibit notable anti-HIV and anticancer activities. However, its anti-inflammatory mechanisms have not been fully elucidated. In this study, we investigated the anti-inflammatory activity and underlying molecular mechanisms of BO in LPS-induced RAW264.7 macrophages. In addition, NP AI Engine predictions, molecular docking, density functional theory (DFT) calculations, and molecular dynamics simulations were conducted to characterize the anti-inflammatory properties of BO further. The experimental results indicated that BO inhibited the mRNA expression levels of iNOS and COX-2. Moreover, it can regulate the phosphorylation of ERK at 3 h. Potential signaling pathways and targets were subsequently analyzed. The structural and electronic properties of BO were calculated using the B3LYP/6-311+G (d,p) basis set. The BO–ERK2 kinase complex was also constructed for simulation. Furthermore, a BO derivative was prepared through hydrolysis followed by acylation, and its anti-inflammatory activity was evaluated. Overall, this study provides deeper insight into the anti-inflammatory effects of BO and supports its potential for further development as an anti-inflammatory agent. Full article
(This article belongs to the Section Molecular Pharmacology)
11 pages, 513 KB  
Article
Development of a TaqMan qPCR Method for Detecting Angiostrongylus cantonensis (Rhabditida: Angiostrongylidae) Infection in Snails from Hainan Province, China
by Kun Wang, Tian Tian, Yunhai Guo, Muxin Chen, Xiaonen Wu, Zhiying Hou, Binbin Xie, Fanna Wei, Zhiheng Qi, Zhisheng Dang, Dingwei Sun, Yang Hong, Jun-Hu Chen and Yue Wang
Trop. Med. Infect. Dis. 2026, 11(2), 34; https://doi.org/10.3390/tropicalmed11020034 (registering DOI) - 23 Jan 2026
Abstract
Angiostrongylus cantonensis (A. cantonensis) is the primary causative agent of human angiostrongyliasis and is widely distributed in Southeast Asia and China, with increasing reports from the Americas. Achatina fulica (A. fulica), Pomacea canaliculata (P. canaliculata), and slugs [...] Read more.
Angiostrongylus cantonensis (A. cantonensis) is the primary causative agent of human angiostrongyliasis and is widely distributed in Southeast Asia and China, with increasing reports from the Americas. Achatina fulica (A. fulica), Pomacea canaliculata (P. canaliculata), and slugs constitute established intermediate hosts of A. cantonensis, whereas Camaena hainanensis (C. hainanensis) has been newly reported as a host species in Hainan. A TaqMan quantitative PCR (qPCR) method assay targeting a novel genomic region of A. cantonensis was developed to detect infection in 150 snails collected from Hainan Province, China. The assay was employed to detect the parasite larvae across various snail tissues (lung sac, mucus, and foot), and its performance was compared with conventional lung sac microscopy. Out of the 120 A. fulica examined, 75 tested positive using the qPCR assay, yielding a significantly higher detection rate than lung-sac examination (p < 0.05). Significant differences were also observed in the positivity rates across the three snail tissues (lung sac, mucus, and foot) (p < 0.05), with the lung sac showing the highest rate of infection. Importantly, the detection of A. cantonensis DNA in snail mucus highlights its potential for development as a non-invasive diagnostic sample. Additionally, C. hainanensis was identified as a new host of A. cantonensis in Hainan, suggesting its possible contribution to parasite transmission. The newly developed qPCR assay demonstrated superior sensitivity (reflected by lower Ct values) compared with previously published TaqMan qPCR methods. The established qPCR method provides a sensitive and non-invasive tool for detecting A. cantonensis in snails, and can be applied for monitoring and early warning of parasite prevalence and transmission. Full article
(This article belongs to the Special Issue Emerging Vector-Borne Diseases and Public Health Challenges)
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26 pages, 4940 KB  
Article
Monitoring and Control System Based on Mixed Reality and the S7.Net Library
by Tudor Covrig, Adrian Duka and Liviu Miclea
IoT 2026, 7(1), 10; https://doi.org/10.3390/iot7010010 (registering DOI) - 23 Jan 2026
Abstract
The predominant approach in the realm of industrial process monitoring and control involves the utilization of HMI (Human–Machine Interface) interfaces and conventional SCADA (Supervisory Control and Data Acquisition) systems. This limitation restricts user mobility, interaction with industrial equipment, and process status assessment. In [...] Read more.
The predominant approach in the realm of industrial process monitoring and control involves the utilization of HMI (Human–Machine Interface) interfaces and conventional SCADA (Supervisory Control and Data Acquisition) systems. This limitation restricts user mobility, interaction with industrial equipment, and process status assessment. In the context of Industry 4.0, the ability to monitor and control industrial processes in real time is paramount. The present paper designs and implements a system for monitoring and controlling an industrial assembly line based on mixed reality. The technology employed to facilitate communication between the system and the industrial line is S7.Net. These elements facilitate direct communication with the industrial process equipment. The system facilitates the visualization of operating parameters and the status of the equipment utilized in the industrial process and its control. All data is superimposed on the physical environment through virtual operational panels. The system functions independently, negating the necessity for intermediate servers or other complex structures. The system’s operation is predicted on a series of algorithms. These instruments facilitate the automated analysis of industrial process parameters. These devices are utilized to ascertain the operational dynamics of the industrial line. The experimental results were obtained using a real industrial line. These models are employed to demonstrate the performance of data transmission, the identification of the system’s operating states, and the system’s ability to shut down in the event of operating errors. The proposed system is designed to function in a variety of industrial environments within the paradigm of Industry 4.0, facilitating the utilization of multiple virtual interfaces that enable user interaction with various elements through which the assembly process is monitored and controlled. Full article
19 pages, 1666 KB  
Article
Impacts of Single and Sequential Enzymatic Extraction on the Functional Properties of Khao Dawk Mali 105 Rice Bran Proteins at Two Maturity Stages
by Tarathep Siripan, Apichaya Bunyatratchata, Wanida Chuenta, Jiranan Ratseewo, Hua Li and Sirithon Siriamornpun
Foods 2026, 15(3), 419; https://doi.org/10.3390/foods15030419 (registering DOI) - 23 Jan 2026
Abstract
Proteins from the bran of Khao Dawk Mali 105 rice at two maturity stages, green (GB) and fully ripe (RB), were extracted using single and sequential enzyme-assisted processes. Non-enzymatic extraction (control), α-amylase (AA), protease (PT), and two sequential treatments (AA-PT and PT-AA) were [...] Read more.
Proteins from the bran of Khao Dawk Mali 105 rice at two maturity stages, green (GB) and fully ripe (RB), were extracted using single and sequential enzyme-assisted processes. Non-enzymatic extraction (control), α-amylase (AA), protease (PT), and two sequential treatments (AA-PT and PT-AA) were applied to defatted bran to evaluate their effects on protein yield, structural attributes, and functional properties. Protease-based extractions, particularly PT, produced the highest protein contents (28% in GB and 23% in RB) and significantly improved solubility, water- and oil-holding capacities, and foaming performance. GB extracts consistently outperformed RB across all functional and antioxidant measurements, indicating greater extractability and bioactive potential in green rice bran. Enzymatic hydrolysis also enhanced phenolic and flavonoid release, leading to markedly higher DPPH and FRAP activities. SDS-PAGE profiles demonstrated reduced band complexity and lower-molecular-weight protein in enzymatically treated samples, while FTIR spectra confirmed secondary structural modifications associated with hydrolysis. Overall, protease and sequential assisted extractions provide an efficient and sustainable approach to improving rice bran protein recovery and functionality. These findings highlight green rice bran as a promising source of high-value plant proteins for food and nutraceutical applications. Full article
(This article belongs to the Special Issue Cereal Bran: Novel Processing Techniques, Nutritional Composition and Health Benefits)
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11 pages, 432 KB  
Article
Making Symptoms Visible: The Impact of Real-Time PROM Integration in Pediatric Oncology
by Natalie Bradford, Ethan Whalan, Paula Condon, Remziye Semerci, Alison Bowers and Xiomara Skrabal Ross
Children 2026, 13(2), 164; https://doi.org/10.3390/children13020164 (registering DOI) - 23 Jan 2026
Abstract
Background/Objectives: Children undergoing cancer treatment experience multiple distressing symptoms that often go undetected in routine care. This study evaluated the potential impact of integrating the Symptom Screening in Pediatrics Tool (SSPedi) into clinical workflows, focusing on symptom detection and implications for service delivery. [...] Read more.
Background/Objectives: Children undergoing cancer treatment experience multiple distressing symptoms that often go undetected in routine care. This study evaluated the potential impact of integrating the Symptom Screening in Pediatrics Tool (SSPedi) into clinical workflows, focusing on symptom detection and implications for service delivery. Methods: Seventy children (aged 4–18 years) receiving active treatment, and/or their caregivers completed SSPedi weekly for eight weeks (n = 479 completions). Medical records were audited for documentation of symptom assessments and symptom prevalence. SSPedi completions were categorized using a clinical algorithm (low, moderate, immediate concerns) and compared with score-only threshold. Results: The most bothersome symptoms were appetite changes (12%), fatigue (11%), nausea/vomiting (9%) and pain (9%). Severe bother detected by SSPedi was more frequent while hospitalized than at home (e.g., appetite changes 17% versus 9%). Documentation rates of severe symptoms in medical records were substantially lower than SSPedi reports—12% when SSPedi was completed at home and 49% when completed in hospital. Applying the clinical algorithm flagged 58% of SSPedi completions as an immediate concern in home and 63% in hospital, compared with score-only thresholds (31% at home and 17% in hospital). Algorithm-based alerts for immediate concerns would have triggered almost twice as many phone calls as score-based thresholds (168 vs. 91). Conclusions: Routine PROM integration could improve symptom detection and timely intervention. Clinical algorithms enhance sensitivity but increase alert burden, highlighting the need to review thresholds and redesign workflows. Full article
(This article belongs to the Section Global Pediatric Health)
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18 pages, 1423 KB  
Article
NaOH-Only Pretreated Wood Densification: A Simplified Sulfite-Free Route Across Wood Species
by Laura Andze, Vadims Nefjodovs, Juris Zoldners, Ulla Milbreta, Marite Skute, Linda Vecbiskena, Inese Filipova and Martins Andzs
Polymers 2026, 18(3), 312; https://doi.org/10.3390/polym18030312 (registering DOI) - 23 Jan 2026
Abstract
The development of high-performance wood-based materials has attracted increasing interest as a means of enhancing the mechanical properties of wood for structural applications. Mechanical densification combined with chemical pretreatment is an effective approach; however, many reported methods rely on complex multi-component chemical systems [...] Read more.
The development of high-performance wood-based materials has attracted increasing interest as a means of enhancing the mechanical properties of wood for structural applications. Mechanical densification combined with chemical pretreatment is an effective approach; however, many reported methods rely on complex multi-component chemical systems or severe chemical conditions designed to dissolve lignin or hemicelluloses. In this study, a simplified NaOH-only pretreatment followed by hot-press densification was investigated, targeting selective cell-wall plasticization rather than extensive polymer dissolution. Juniper (Juniperus communis), hawthorn (Crataegus monogyna), and birch (Betula pendula) were used as samples of softwood and hardwood species. Wood specimens were pretreated in 1 M NaOH at 145 °C for 10–30 min and subsequently densified by radial compression. Changes in chemical composition were evaluated by HPLC after acid hydrolysis and FTIR spectroscopy, while microstructural changes were examined using SEM. Physical and mechanical properties were assessed through density measurements and three-point bending tests. The results show that NaOH-only pretreatment induces hemicellulose deacetylation and modification of interpolymer linkages without substantial changes in the main wood polymer contents. Densification resulted in effective lumen collapse and a compact microstructure, leading to a significant increase in density and mechanical properties. Overall, the results demonstrate that efficient wood densification and mechanical enhancement can be achieved by promoting polymer mobility through selective cleavage of interpolymer bonds, using a simplified, single-alkali pretreatment that reduces chemical complexity and material loss while avoiding extensive lignin or hemicellulose dissolution. Full article
(This article belongs to the Special Issue Recent Progress on Lignocellulosic-Based Polymeric Materials)
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18 pages, 8749 KB  
Article
Reduced LOXL3 Expression Disrupts Microtubule Acetylation and Drives TP53-Dependent Cell Fate in Glioblastoma
by Talita de Sousa Laurentino, Roseli da Silva Soares, Antônio Marcondes Lerario, Ricardo Cesar Cintra, Suely Kazue Nagahashi Marie and Sueli Mieko Oba-Shinjo
Cells 2026, 15(3), 219; https://doi.org/10.3390/cells15030219 (registering DOI) - 23 Jan 2026
Abstract
Glioblastoma (GBM) is the most aggressive primary brain tumor, marked by molecular heterogeneity and poor clinical prognosis. Lysyl oxidase-like 3 (LOXL3) is frequently upregulated in GBM, but its mechanistic contribution remains insufficiently defined. Here, we investigated the functional role of LOXL3 in GBM [...] Read more.
Glioblastoma (GBM) is the most aggressive primary brain tumor, marked by molecular heterogeneity and poor clinical prognosis. Lysyl oxidase-like 3 (LOXL3) is frequently upregulated in GBM, but its mechanistic contribution remains insufficiently defined. Here, we investigated the functional role of LOXL3 in GBM using CRISPR-Cas9-mediated LOXL3 knockdown in two genetically distinct GBM cell lines: U87MG (wild-type TP53) and U251 (mutant TP53). Reduced LOXL3 expression markedly reduced α-tubulin acetylation, particularly in U87MG cells, and downregulated genes involved in cell cycle progression and proliferation. Both cell lines exhibited mitotic defects, including delayed cell cycle progression and spindle abnormalities; however, cell fate diverged according to TP53 status. U87MG cells, sustained spindle checkpoint activation triggered a p53-dependent spindle checkpoint response culminating in apoptosis, while U251 cells underwent mitotic slippage and senescence. Transcriptomic analyses confirmed differential regulation of apoptosis versus senescence pathways in accordance with TP53 functionality. Additionally, reduced LOXL3 expression markedly impaired adhesion and migration in U87MG cells, whereas U251 cells were minimally affected, consistent with more pronounced microtubule destabilization. Collectively, these findings identify that LOXL3 is a key regulator of microtubule homeostasis, mitotic fidelity, adhesion, and invasive behavior in GBM. Targeting LOXL3 may therefore provide a therapeutic opportunity for genotype-informed intervention in GBM. Full article
(This article belongs to the Special Issue Mechanisms and Dynamics in Cellular Adhesion in Development and Disease)
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