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14 pages, 1089 KB

Open AccessArticle

Rapid and Accurate Quantification Detection of BHT in Edible Oils Using Raman Spectroscopy Combined with Chemometric Models

by Congli Mei, Shuai Lu, Xiaolin Zhou, Fanzhen Meng and Hui Jiang

Foods 2026, 15(4), 730; https://doi.org/10.3390/foods15040730 (registering DOI) - 15 Feb 2026

Abstract

The chemical composition of vegetable cooking oils is a key parameter in determining the quality of their products. Antioxidants are widely used in these products to extend their shelf life. In this study, the concentration of butylated hydroxytoluene (BHT) in edible oil was [...] Read more.

The chemical composition of vegetable cooking oils is a key parameter in determining the quality of their products. Antioxidants are widely used in these products to extend their shelf life. In this study, the concentration of butylated hydroxytoluene (BHT) in edible oil was quantitatively determined by Raman spectroscopy combined with chemometrics. Initially, Raman spectra of edible oil samples with varying concentrations of BHT were obtained. Subsequently, three variable selection methods were applied to the pre-processed spectra. Optimised characteristic wavelengths were then used to establish a Radial Basis Function (RBF) neural network and partial least squares (PLS) models. The impact of variable selection on feature wavelengths was evaluated for both models in both independent and combined cases. The results demonstrate that the features identified through multiple variable selection methods correlate highly with the BHT content and can be utilised to develop high-precision detection models. The findings indicate that the PLS model, optimised using competitive adaptive reweighting (CARS), achieved the best prediction performance, with an average

R_{P}^{2}

of 0.9687, and RMSEP of 3.1211. These results demonstrate the feasibility of using Raman spectroscopy combined with chemometrics for the rapid screening of BHT in edible oils. While the current study focuses on a broad concentration range to validate the method’s linearity, further optimisation is required for trace-level detection to meet strict regulatory limits. Full article

(This article belongs to the Special Issue Food Authentication: Techniques, Approaches and Application)

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19 pages, 6909 KB

Open AccessArticle

Glycolic Acid-Induced Surface Reconstruction and In Situ Carbon Coating for High-Electrochemical-Performance Lithium-Rich Manganese-Based Cathodes

by Xichen Yang, Jie Miao, Yongchao Chen, Yaoxun Fang, Hao Wang and Gongchang Peng

Batteries 2026, 12(2), 70; https://doi.org/10.3390/batteries12020070 (registering DOI) - 15 Feb 2026

Abstract

Lithium-rich manganese-based cathode materials (LRMs, Li_1.2Mn_0.54Ni_0.13Co_0.13O₂) are promising prospects for subsequent-generation lithium-ion batteries owing to their elevated operating voltage, large specific capacity, and affordability. Nonetheless, their actual implementation is significantly impeded by irreversible [...] Read more.

Lithium-rich manganese-based cathode materials (LRMs, Li_1.2Mn_0.54Ni_0.13Co_0.13O₂) are promising prospects for subsequent-generation lithium-ion batteries owing to their elevated operating voltage, large specific capacity, and affordability. Nonetheless, their actual implementation is significantly impeded by irreversible lattice-oxygen redox reactions, surface structural disorder, and interfacial phase collapse, leading to low initial Coulombic efficiency (ICE), inadequate rate capability, and sluggish Li⁺ transport. Herein, we report a simple and mild glycolic acid-assisted surface-engineering strategy to enhance the electrochemical performance of LRM. Glycolic acid treatment induces controlled H⁺/Li⁺ ion exchange at the particle surface and anchors surface transition metals through the formation of transition metals (TM)–OH and TM–O–C=O bonds. Subsequent calcination constructs an in situ carbon layer-spinel-layered heterostructure, accompanied by the generation of coupled anionic and cationic vacancies. This reconstructed surface provides fast Li⁺ diffusion pathways and stabilized ion-transport channels, while the dual-vacancy configuration enhances lattice-oxygen reversibility and suppresses structural disorder. Consequently, the modified LRM delivers a high initial discharge capacity of 285.3 mAh⋅g⁻¹ with an ICE of 89.9%, while maintaining 81% capacity retention after 100 cycles. Notably, it exhibits a significantly suppressed voltage decay of only 1.7 mV/cycle at 3C, markedly outperforming the pristine LRM. Density Functional Theory (DFT) calculations reveal that the surface-modified sample possesses enhanced electronic conductivity, as evidenced by the improved Density of States (DOS), and achieves superior structural stability through increased binding energies. This environmentally benign surface-engineering strategy offers a practical and efficient route toward the industrial application of LRM. Full article

(This article belongs to the Special Issue Toward Next-Generation Rechargeable Lithium-Ion Batteries: Current Status and Future Prospects—2nd Edition)

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26 pages, 1809 KB

Open AccessReview

Moyamoya Vasculopathy and Atypical Moyamoya-like Patterns: Insights into Diagnosis and Therapeutic Implications

by Rosalinda Calandrelli, Carlo Augusto Mallio, Caterina Bernetti, Luca Massimi and Fabio Pilato

NeuroSci 2026, 7(1), 27; https://doi.org/10.3390/neurosci7010027 (registering DOI) - 15 Feb 2026

Abstract

Purpose: The aim of this narrative review is to update current knowledge on Moyamoya vasculopathy (MMV) by addressing key diagnostic debates—including laterality; genetic subtypes; regional epidemiology; and features distinguishing Moyamoya Disease (MMD), Moyamoya Syndrome (MMS) and their mimics. Methods: Key and representative studies [...] Read more.

Purpose: The aim of this narrative review is to update current knowledge on Moyamoya vasculopathy (MMV) by addressing key diagnostic debates—including laterality; genetic subtypes; regional epidemiology; and features distinguishing Moyamoya Disease (MMD), Moyamoya Syndrome (MMS) and their mimics. Methods: Key and representative studies were identified through PubMed/MEDLINE and Scopus, focusing on publications from 2014–2025 while also considering earlier seminal works. Results: MMD typically presents with bilateral steno-occlusion of the terminal internal carotid arteries (ICAs) and proximal middle and anterior cerebral arteries (MCAs/ACAs) due to concentric vascular thickening, accompanied by characteristic ‘puff-of-smoke’ collaterals, whereas MMS shows a similar but more often unilateral pattern with fewer collaterals, influenced by the underlying condition. However, this distinction often fails to reflect the full clinical and radiological variability of the Moyamoya spectrum. Atypical moyamoya-like patterns, often confined to M1 or A1 segments, further complicate diagnosis. Clinical manifestations ranged from asymptomatic cases to ischemic or hemorrhagic strokes, and occasionally seizures. Diagnosis relied on multimodal imaging (DSA, MRA, CTA), but genetic mutations, contributing to radiological variability, often complicate differentiation between MMD, MMS, and mimics. Management is pattern-specific: MMS and atypical forms are generally managed conservatively, whereas MMD frequently requires surgical revascularization, particularly in children and symptomatic adults. Nevertheless, variability within diagnostic categories limits the applicability of rigid treatment protocols. Conclusions: Current diagnostic algorithms remain limited. Integrating advanced imaging findings with clinical, genetic, and epidemiological data is essential to define the full disease spectrum, improve diagnostic accuracy, and inform patient management and outcome assessment. Full article

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16 pages, 963 KB

Open AccessArticle

Clinical Predictors of Ultrasound-Guided Cervical Medial Branch Pulsed Radiofrequency Outcomes: A Cohort Study

by Ümit Akkemik, Sinan Oğuzhan Ulukaya, Mustafa Şen and Mehmet Sacit Güleç

Diagnostics 2026, 16(4), 590; https://doi.org/10.3390/diagnostics16040590 (registering DOI) - 15 Feb 2026

Abstract

Background/Objectives: Cervical facet joints are a common source of chronic neck pain, yet factors predicting treatment response to pulsed radiofrequency remain poorly defined. This study aimed to identify predictors of treatment success following ultrasound-guided cervical medial branch pulsed radiofrequency in patients with chronic [...] Read more.

Background/Objectives: Cervical facet joints are a common source of chronic neck pain, yet factors predicting treatment response to pulsed radiofrequency remain poorly defined. This study aimed to identify predictors of treatment success following ultrasound-guided cervical medial branch pulsed radiofrequency in patients with chronic cervical facet joint pain. Methods: This retrospective cohort study included 54 patients with chronic cervical facet joint pain who had positive response to diagnostic block. Pain intensity and functional disability were assessed at baseline and at 1-, 3-, and 6-months post-procedure, with treatment success defined as ≥50% pain reduction at 6 months. Results: The success rate was 35.2%, and multivariate logistic regression identified four independent predictors: presence of paraspinal tenderness on physical examination, shorter pain duration, lower baseline pain intensity, and lower baseline disability. Conclusions: These findings suggest that patients with localized facet joint pathology manifesting as paraspinal tenderness, shorter symptom duration, and lower baseline severity are most likely to benefit from this intervention, supporting early referral and careful clinical selection to optimize treatment outcomes. Full article

(This article belongs to the Special Issue Advances in Pain Medicine: Diagnosis and Management)

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19 pages, 260 KB

Open AccessArticle

Sexuality, Intimacy, and Loneliness in Later Life: How Older Single and Widowed Black Women Seek Support Beyond Family

by Margaret Salisu

Sexes 2026, 7(1), 11; https://doi.org/10.3390/sexes7010011 (registering DOI) - 15 Feb 2026

Abstract

Loneliness poses a significant risk to the physical and mental well-being of older individuals, making it a pressing public health concern. Particularly for minority groups like elderly single and widowed Black women, the consequences of loneliness can be even more pronounced. To gain [...] Read more.

Loneliness poses a significant risk to the physical and mental well-being of older individuals, making it a pressing public health concern. Particularly for minority groups like elderly single and widowed Black women, the consequences of loneliness can be even more pronounced. To gain deeper insights into the experiences of loneliness and coping strategies used by these women, a qualitative phenomenological research study was conducted, involving interviews with fourteen such individuals. The study revealed four main themes: lonelier with age; looking beyond the family for intimacy; family responses to loneliness; and coping with loneliness. Irrespective of living arrangements, all participants acknowledged experiencing varying degrees of loneliness. Despite having extensive social networks, many struggled with feelings of loneliness, alienation, and a lack of emotional closeness and connection. Consequently, these findings emphasize the importance of addressing loneliness in elderly single and widowed Black women, considering the intersectionality of race, gender, and mental health when assessing the risk of loneliness. Practical and policy implications suggest that professionals and physicians working with this group actively screen for loneliness and develop interventions and psychological support to help these women navigate their feelings of isolation. Full article

(This article belongs to the Section Gender Studies)

34 pages, 2315 KB

Open AccessArticle

RIME-Net: A Physics-Guided Unpaired Learning Framework for Automotive Radar Interference Mitigation and Weak Target Enhancement

by Jiajia Shi, Haojie Zhou, Liu Chu, Fengling Tan, Guocheng Sun and Yu Tao

Sensors 2026, 26(4), 1277; https://doi.org/10.3390/s26041277 (registering DOI) - 15 Feb 2026

Abstract

With the widespread deployment of automotive millimeter-wave radars, mutual interference and broadband noise severely degrade the signal-to-noise ratio (SNR) of range–Doppler (RD) maps, leading to the loss of weak targets. Existing deep learning methods rely on difficult-to-obtain paired training samples and often cause [...] Read more.

With the widespread deployment of automotive millimeter-wave radars, mutual interference and broadband noise severely degrade the signal-to-noise ratio (SNR) of range–Doppler (RD) maps, leading to the loss of weak targets. Existing deep learning methods rely on difficult-to-obtain paired training samples and often cause excessive target smoothing due to a lack of physical constraints. To address these challenges, this paper proposes RIME-Net, a physics-guided unpaired learning framework designed to jointly achieve radar interference mitigation and weak target enhancement. First, based on a cycle-consistent adversarial architecture, we designed the Interference Mitigation Network (IM-Net). IM-Net integrates spectral consistency loss and identity mapping constraints, learning a robust mapping from the interference domain to the clean domain without paired supervision, effectively suppressing low-rank interference and preserving signal integrity. Second, to recover target details attenuated during denoising, we propose the saliency-aware Target Enhancement Network (TE-Net). TE-Net combines multi-scale residual blocks and channel-spatial attention mechanisms, selectively enhancing weak target features based on saliency priors. Extensive experiments on diverse datasets show that RIME-Net significantly outperforms existing supervised and model-driven methods in terms of SINR, recall, and structural similarity, providing a robust solution for reliable radar perception in complex electromagnetic environments. Full article

(This article belongs to the Special Issue Recent Advances of FMCW-Based Radar Sensors)

31 pages, 9256 KB

Open AccessArticle

Multi-Omics Integration Identifies Key Pathways and Regulatory Genes Driving Marbling Formation and Meat Quality in Yunling Cattle

by Lutao Gao, Lilian Zhang, Jian Chen, Lin Peng, Siqi Zhang and Linnan Yang

Animals 2026, 16(4), 623; https://doi.org/10.3390/ani16040623 (registering DOI) - 15 Feb 2026

Abstract

Marbling, or intramuscular fat (IMF), is a primary determinant of high-quality beef, defining key sensory attributes and nutritional value. Yunling (YL) cattle, an indigenous breed from Yunnan, China, are renowned for their superior marbling, yet the underlying molecular mechanisms remain unclear. This study [...] Read more.

Marbling, or intramuscular fat (IMF), is a primary determinant of high-quality beef, defining key sensory attributes and nutritional value. Yunling (YL) cattle, an indigenous breed from Yunnan, China, are renowned for their superior marbling, yet the underlying molecular mechanisms remain unclear. This study employed an integrated transcriptomic, lipidomic, and amino acid metabolomic approach to systematically compare the multi-omics profiles of the longissimus dorsi muscle among YL, Angus (AGS), and Simmental (XMTE) cattle. Transcriptome analysis identified 2053 and 2156 differentially expressed genes (DEGs) in XMTE vs. YL and AGS vs. YL, respectively. These DEGs were primarily enriched in the PI3K-Akt and MAPK signaling pathways, as well as oxidative phosphorylation. Lipidomic analysis revealed a distinct lipid profile in YL cattle, identifying 27 characteristic lipid molecules (e.g., SM(d20:0/24:1), DG(16:0/18:1(11Z)/0:0)) compared to XMTE and 17 differential lipids compared to AGS. The amino acid metabolome showed that Beta-Alanine and L-Aspartic acid levels in YL were 42.6% and 54.8% lower than in XMTE, respectively (p < 0.01), and levels of several functional amino acids were significantly reduced compared to AGS. Weighted Gene Co-expression Network Analysis (WGCNA) constructed a gene-metabolite network, identifying key modules strongly correlated with lipid and amino acid metabolism (|r| > 0.6). Within these modules, energy metabolism-related genes such as NDUFB1, COX7C, and IDH3B, along with signal transduction genes including ITGB3, PDGFRA, and FN1, were found to synergistically regulate marbling formation in YL cattle. This study systematically elucidates the molecular mechanisms underlying both marbling formation and the nutritional characteristics of meat in Yunling cattle. This provides a theoretical foundation for genetic improvement and offers potential molecular targets to enhance both marbling and overall meat quality in other indigenous cattle breeds worldwide. Full article

(This article belongs to the Section Cattle)

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11 pages, 537 KB

Open AccessArticle

Lower-Limb Neuromuscular Profiles from Force Plate Testing During Elite Women’s Basketball National Team Camps: A Retrospective Comparison by Final Roster Status

by Hiroki Ogata, Kazuya Yamazaki, Tomohiro Usui, Kotaro Shinchi, Katsuya Ikeda, Frederick James Henderson and Daichi Yamashita

Sports 2026, 14(2), 84; https://doi.org/10.3390/sports14020084 (registering DOI) - 15 Feb 2026

Abstract

This study compared force plate-derived lower-limb strength and power metrics between selected and non-selected female basketball players for major international competitions. Thirty-two female players attending the final national team camps for the 2022 World Cup and the 2024 Olympic Games completed isometric mid-thigh [...] Read more.

This study compared force plate-derived lower-limb strength and power metrics between selected and non-selected female basketball players for major international competitions. Thirty-two female players attending the final national team camps for the 2022 World Cup and the 2024 Olympic Games completed isometric mid-thigh pull (IMTP) and countermovement jump (CMJ) testing on dual force plates (1000 Hz). IMTP peak force, rate of force development (RFD) over 0–200 and 0–250 ms, CMJ height, and phase-specific kinetic variables were compared between roster (n = 14) and non-roster (n = 18) players. Eleven roster players had previous World Cup/Olympic experience (1.5 ± 1.2 selections across all 14 players), whereas non-roster players had none. The roster group was older than the non-roster group (26.8 ± 4.2 vs. 22.3 ± 3.1 years, p = 0.002); therefore, between-group comparisons were adjusted for age and playing position using analyses of covariance (ANCOVAs). After adjustment, no between-group differences were observed in IMTP- or CMJ-derived performance outcomes (all p ≥ 0.12; partial η² = 0.00–0.09). Therefore, in this elite cohort, roster status did not reflect force plate metrics but may reflect factors beyond these tests, including age and prior international experience. Full article

8 pages, 873 KB

Open AccessCommunication

Feasibility and Form Factor Validation of Reflective Shoulder-Mounted Pulse Oximeter in Patients with Suspected Sleep Apnea

by Katie N. Kanter, Aaron Wang, David Gordon, Adina Singer, Jacob S. Brenner, Indira Gurubhagavatula, Anush Lingamoorthy, Olumuyiwa Oni and Cameron M. Baston

Sensors 2026, 26(4), 1276; https://doi.org/10.3390/s26041276 (registering DOI) - 15 Feb 2026

Abstract

The shoulder may be an effective central site for continuous oxygen saturation (SpO2) monitoring but studies of shoulder-mounted pulse oximetry technology are limited. We hypothesized that an alternative location would be similar in function and user acceptance to a standard FDA-cleared finger-based pulse [...] Read more.

The shoulder may be an effective central site for continuous oxygen saturation (SpO2) monitoring but studies of shoulder-mounted pulse oximetry technology are limited. We hypothesized that an alternative location would be similar in function and user acceptance to a standard FDA-cleared finger-based pulse oximeter. We conducted a quantitative and descriptive pilot study of two prototype biosensor designs in patients with clinical suspicion of hypoxic episodes at an outpatient sleep center. Participants wore two prototype biosensors—the primary a shoulder-mounted adhesive and the secondary a combination ring–bracelet—in addition to a control FDA-approved finger-based pulse oximeter. We assessed the comfort of the devices based on a survey. We monitored 27 patients during an overnight polysomnography study. Participants rated the shoulder-mounted device more highly than the control device on a Likert scale survey of comfort (4.6 out of 5 versus 3.1 out of 5). Open-ended questionnaires showed that the two major criticisms of the control and ring devices were devices falling off and disruption to sleep, while only one participant commented on the shoulder device specifically. We also investigated SpO2 agreement between the primary shoulder-mounted prototype and the control finger-based pulse oximeter. This study confirms that alternative configurations for SpO2 monitoring offer potential as well-tolerated devices with preliminary findings of acceptable agreement. Problems with traditional pulse oximetry, such as false readings of hypoxia due to device removal or noisy data, were encountered less frequently in shoulder-mounted pulse oximetry than in the commercial finger-based device. Future directions include studies of additional populations that are at risk of respiratory collapse and surveys to elicit specific feedback on the configurations, whether positive or negative. Full article

(This article belongs to the Special Issue Advanced Sensing and Signal Processing Technologies for Medical Applications)

32 pages, 2234 KB

Open AccessArticle

Rethinking Cohesion: When and Where ESI Funds Drive Socio-Economic Change?

by Ana-Cristina Nicolescu, Oana-Ramona Lobonț, Sorana Vătavu, Andrei Pelin and Diana Balan

Systems 2026, 14(2), 209; https://doi.org/10.3390/systems14020209 (registering DOI) - 15 Feb 2026

Abstract

This study examines the non-linear relationship between European Structural and Investment (ESI) Funds and socio-economic development across EU member states from 2007 to 2020. To accomplish this, the study utilises a novel methodological approach, employing panel threshold regression to analyse the complex interactions [...] Read more.

This study examines the non-linear relationship between European Structural and Investment (ESI) Funds and socio-economic development across EU member states from 2007 to 2020. To accomplish this, the study utilises a novel methodological approach, employing panel threshold regression to analyse the complex interactions between these variables. Using the Human Development Index (HDI) as a comprehensive measure of socio-economic progress, this research goes beyond traditional metrics, such as GDP, to capture a multidimensional view of development. The threshold variable, represented by the ratio of ESI Funds paid to GDP, highlights critical inflexion points where the impact of funding shifts, revealing both positive and negative effects. The study finds that ESI Funds positively impact socio-economic development up to a threshold of 0.7% of GDP, beyond which their effectiveness diminishes, emphasising the need for strategic allocation and management. Additionally, the analysis of control variables identifies a critical threshold range between 2% and 2.3% of GDP, indicating the growing importance of ESI Funds in fostering development within complex socio-economic contexts. This paper contributes to the foundational model of socio-economic development informed by ESI Funds, offering valuable insights for policymakers by emphasising the importance of balancing funding levels with strategic allocation to avoid diminishing returns. Full article

(This article belongs to the Section Systems Practice in Social Science)

13 pages, 1054 KB

Open AccessArticle

A New Method Facilitates Bermudagrass Growth During Spring Transition

by Xiang Yao, Dongli Hao, Dandan Li, Jingjing Wang, Sheng Zhu and Haoran Wang

Horticulturae 2026, 12(2), 238; https://doi.org/10.3390/horticulturae12020238 (registering DOI) - 15 Feb 2026

Abstract

The spring transition in bermudagrass (Cynodon dactylon) overseeded with perennial ryegrass (Lolium perenne) remains a major challenge in turf management due to persistent competition from the cool-season species. Conventional practices such as core cultivation can damage bermudagrass stands and [...] Read more.

The spring transition in bermudagrass (Cynodon dactylon) overseeded with perennial ryegrass (Lolium perenne) remains a major challenge in turf management due to persistent competition from the cool-season species. Conventional practices such as core cultivation can damage bermudagrass stands and delay recovery. This study evaluated a novel, non-damaging approach using a yeast-based fertilizer to enhance bermudagrass regrowth during the transition period. The fertilizer consisted of Saccharomyces cerevisiae and glucose applied as a soil drench. A greenhouse experiment was conducted over two years (2023–2024) using “Yangjiang” bermudagrass overseeded with “Wintergame” perennial ryegrass. Five treatments were compared: control (0 g·m⁻² yeast + 0 g·m⁻² glucose), yeast alone (200 g·m⁻²), and yeast combined with glucose at 100, 200, or 400 g·m⁻². Growth parameters were assessed at 7, 14, and 28 days after treatment. The application of 200 g·m⁻² yeast + 200 g·m⁻² glucose yielded the most significant improvements. At 14 days, bermudagrass shoot density and turf cover significantly (p < 0.05) increased by 45.81% and 129.51%, respectively, compared to the control. By 28 days, aboveground and belowground biomass significantly (p < 0.05) increased by 308.14% and 51.35%, respectively. Root system architecture was also significantly (p < 0.05) enhanced, with total root length, surface area, and volume rising by 62.05%, 40.59%, and 63.51%. These results demonstrate that yeast fertilizer strongly promotes bermudagrass shoot and root growth during spring transition, likely by generating CO₂ to improve soil porosity without physical turf injury. This method provides a practical and complementary strategy for managing overseeded turfgrass systems. Full article

(This article belongs to the Section Floriculture, Nursery and Landscape, and Turf)

23 pages, 385 KB

Open AccessArticle

Etiologic Patterns and Evolution of Healthcare-Associated Infections in the Pandemic and Post-Pandemic Periods: A County-Level Multicenter Study from Southeastern Romania

by Corina Voinea, Elena Mocanu, Elena Dantes, Sanda Jurja, Ana-Maria Neculai, Aurora Craciun, Lucian Serbanescu, Ana-Maria Dascalu, Mihaela Cezarina Mehedinti and Sorin Rugina

Antibiotics 2026, 15(2), 214; https://doi.org/10.3390/antibiotics15020214 (registering DOI) - 15 Feb 2026

Abstract

Background/Objectives: Healthcare-associated infections (HAIs) remain a major source of morbidity, mortality, and healthcare burden, and were profoundly affected by the COVID-19 pandemic through changes in case mix, care organization, and antimicrobial use. This study aimed to compare the epidemiology, etiology, ward distribution, [...] Read more.

Background/Objectives: Healthcare-associated infections (HAIs) remain a major source of morbidity, mortality, and healthcare burden, and were profoundly affected by the COVID-19 pandemic through changes in case mix, care organization, and antimicrobial use. This study aimed to compare the epidemiology, etiology, ward distribution, risk factors, and outcomes of HAIs during the pandemic and post-pandemic periods in southeastern Romania, with particular emphasis on Clostridioides difficile infection (CDI), multidrug-resistant (MDR) pathogens, and in-hospital mortality. Methods: This retrospective observational study included 3929 patients with confirmed HAIs reported by 10 hospitals in one Romanian county between March 2020 and December 2024, divided into a pandemic period (March 2020–March 2022) and a post-pandemic period (April 2022–December 2024). Sociodemographic, clinical, ward-related, therapeutic, and microbiological variables, together with discharge status and cause of death, were analyzed using Fisher’s exact test, Z-tests with Bonferroni correction, the Mann–Whitney U test, and multivariable models, applying national and ECDC-aligned surveillance definitions for HAIs. Results: Patients were predominantly older adults (median age 67 years), with a slight male and urban predominance. Hospital stays were longer during the pandemic. Immunosuppression, previous surgery, antisecretory therapy, and chemotherapy were more frequent post-pandemic. HAIs were mainly reported from medical wards, with a relative shift towards intensive care units during the pandemic; pediatric wards carried a smaller burden. CDI was the leading HAI (about half of all cases) with higher post-pandemic prevalence, whereas SARS-CoV-2 infections predominated in medical and surgical wards; Acinetobacter baumannii and Klebsiella pneumoniae clustered in intensive care units during the pandemic, and were more often associated with mortality. Overall, 59.7% of patients improved and 17.5% died, with higher mortality during the pandemic, while post-pandemic deaths were more frequently unrelated to HAIs. Conclusions: This study demonstrates a substantial and ongoing burden of healthcare-associated infections in southeastern Romania, with elderly patients with prolonged hospital stays and complex medical conditions being most affected and experiencing considerable mortality, particularly in medical and intensive care units. After the pandemic, Clostridioides difficile infections became more prevalent in the context of repeated antibiotic use and immunosuppression. Mortality among patients with HAIs was higher during the pandemic, whereas in the post-pandemic period deaths were more often unrelated to HAIs, underscoring the need to strengthen antimicrobial stewardship programs and infection prevention strategies. Full article

30 pages, 13874 KB

Open AccessArticle

MBACA-YOLO: A High-Precision Underwater Target Detection Algorithm for Unmanned Underwater Vehicles

by Chuang Han, Shanshan Chen, Tao Shen and Chengli Guo

Machines 2026, 14(2), 231; https://doi.org/10.3390/machines14020231 (registering DOI) - 15 Feb 2026

Abstract

This paper addresses the issue of low detection accuracy in underwater optical images for unmanned underwater vehicles (UUVs) during practical operations, caused by factors such as uneven lighting, blur, complex backgrounds, and target occlusion. To enhance the autonomous perception and control capabilities of [...] Read more.

This paper addresses the issue of low detection accuracy in underwater optical images for unmanned underwater vehicles (UUVs) during practical operations, caused by factors such as uneven lighting, blur, complex backgrounds, and target occlusion. To enhance the autonomous perception and control capabilities of UUVs, a high-precision algorithm named MBACA-YOLO is proposed based on the YOLOv13n model. Firstly, the convolutional layers in the backbone network of YOLOv13n are optimized by replacing stride-2 convolutions with stride-1 and embedding SPD layers to enable richer feature extraction. Secondly, the newly proposed MBACA attention mechanism is integrated into the final layer of the backbone network, enhancing effective features and suppressing background noise interference. Thirdly, traditional upsampling in the neck network is replaced with CARAFE upsampling to mitigate noise pollution. Finally, an Alpha-Focal-CIoU loss function is designed to improve the accuracy of bounding box regression for underwater targets. To validate the algorithm’s effectiveness, experiments were conducted on the URPC dataset with the following evaluation protocol: 640 × 640 input resolution, batch size 1, FP32 precision, and standard NMS. All results are from a single random seed with 300 epochs of training. The proposed MBACA-YOLO algorithm outperforms the baseline YOLOv13n model, improving mAP@0.5 and mAP@0.5:0.95 by 3.1% and 2.8% respectively, while adding only 0.49M parameters and 1.0 GFLOPs, with an FPS drop of just 2 frames. This makes it an efficient, deployable perception solution for automated Unmanned Underwater Vehicles (UUVs), significantly advancing intelligent underwater systems. Full article

(This article belongs to the Section Vehicle Engineering)

17 pages, 2374 KB

Open AccessArticle

Study on the Reduction Mechanisms and Synergistic Effects of PM_2.5 and PM₁₀ by the Spatial Pattern of Green Spaces in Urban Community Parks: A Case Study of Zhengzhou, China

by Junfeng Zhang and Haoyang Li

Appl. Sci. 2026, 16(4), 1957; https://doi.org/10.3390/app16041957 (registering DOI) - 15 Feb 2026

Abstract

The accelerated pace of urbanization has intensified the urban heat island effect and deteriorated air quality, adversely affecting residents’ living environments and physical health. Community parks serve as the most accessible “terminal units” within the urban green space system, making research on their [...] Read more.

The accelerated pace of urbanization has intensified the urban heat island effect and deteriorated air quality, adversely affecting residents’ living environments and physical health. Community parks serve as the most accessible “terminal units” within the urban green space system, making research on their pollutant concentration reduction capabilities highly relevant. Existing studies predominantly focus on the impact of city-scale green spaces or localized plant arrangements on air pollution, lacking a systematic exploration of synergistic reduction effects across multiple pollutants. To address this gap, six community parks with distinct spatial patterns of green spaces in Zhengzhou were selected as study sites. Six representative indicators of the spatial pattern of green spaces were extracted. Field measurements of PM_2.5 and PM₁₀ concentrations were conducted using a combination of control points and transect sampling methods. Correlation and linear regression analyses were employed to investigate the mechanisms by which the spatial pattern of green spaces in community parks influences PM_2.5 and PM₁₀ reduction. We aimed to investigate the pollutant concentration reduction boundaries of community parks of varying scales, as well as their synergistic effects and differences in reducing PM_2.5 and PM₁₀ concentrations. Results indicate the following: (1) The area, perimeter, and area-weighted shape index of community park green patches showed significant positive correlations with PM_2.5 and PM₁₀ reduction capacity, while fractal dimension, shape index, and proximity index did not exhibit correlations; (2) larger green space patches expand the reduction boundaries for both PM_2.5 and PM₁₀; (3) community parks exhibit a positive synergistic trend in reduction rates for both pollutants. When park areas range between 2 × 10⁴ and 4 × 10⁴ m², their reduction effects show a significant synergistic increase; and (4) community parks with similar spatial configurations but differing canopy closure exhibit varying PM_2.5 and PM₁₀ reduction capacities. These findings provide theoretical foundations and empirical references for optimizing the design of community park green spaces and enhancing ecological benefits. Full article

18 pages, 608 KB

Open AccessSystematic Review

Mentoring in Hospital Settings: A Systematic Review of Guidance, Care, and Professional Development

by Giuliana Ventimiglia, Ilaria Setti and Marina Maffoni

Healthcare 2026, 14(4), 505; https://doi.org/10.3390/healthcare14040505 (registering DOI) - 15 Feb 2026

Abstract

Background/Objectives: Mentoring is defined as a supportive relationship between an experienced professional (mentor) and a less experienced individual (mentee), influencing skill development, professional confidence, and psychological well-being. This systematic review addresses the question: “Can support from a senior colleague positively impact junior healthcare [...] Read more.

Background/Objectives: Mentoring is defined as a supportive relationship between an experienced professional (mentor) and a less experienced individual (mentee), influencing skill development, professional confidence, and psychological well-being. This systematic review addresses the question: “Can support from a senior colleague positively impact junior healthcare workers?” Methods: Following PRISMA 2020 guidelines, a systematic literature search was performed (January 2004–December 2024) in Web of Science, PubMed, and Scopus databases, yielding 399 studies. Results: After rigorous screening and quality assessment using the QuADS checklist, 74 studies were included in the final analysis. The reviewed articles span various healthcare fields, including nursing, medicine, and midwifery, utilizing qualitative, quantitative, observational, and mixed-methods approaches. Key findings highlight the mentor’s role in academic and emotional support; fostering clinical and transversal skills such as communication, collaboration, and problem-solving; and enhancing self-efficacy, resilience, and autonomy, particularly during transitional or emotionally demanding periods. Challenges identified include the need for inclusive environments and standardized mentoring models. Conclusions: Overall, mentoring supports the professional and personal growth of junior healthcare professionals and contributes positively to training quality and clinical work. However, issues regarding equitable access, program standardization, and the need for further research to establish consolidated guidelines remain. Full article

(This article belongs to the Special Issue Organizational Approaches That Support Nurses and Healthcare Workers to Thrive: Moving Beyond Burnout)

31 pages, 1987 KB

Open AccessArticle

Pre-Sale Strategies Considering Consumer Anticipated Regret

by Wei Yao, Yudong Li and Yan Chen

Mathematics 2026, 14(4), 692; https://doi.org/10.3390/math14040692 (registering DOI) - 15 Feb 2026

Abstract

Pre-sale mechanisms are widely used by e-tailers to manage demand uncertainty and stimulate early purchases, yet existing research has largely emphasized economic incentives while giving limited attention to consumers’ psychological responses to early commitment. This study examines how anticipated regret shapes the relative [...] Read more.

Pre-sale mechanisms are widely used by e-tailers to manage demand uncertainty and stimulate early purchases, yet existing research has largely emphasized economic incentives while giving limited attention to consumers’ psychological responses to early commitment. This study examines how anticipated regret shapes the relative performance of two prevalent pre-sale strategies—advance discounts and deposit expansion—across different market structures. We develop game-theoretic models of monopolistic and duopolistic markets in which consumers anticipate post-purchase regret and incorporate this behavioral concern into their pre-sale decisions. Our analysis shows that deposit expansion consistently attracts higher demand than advance discounts by offering post-decision flexibility, and this demand advantage increases with consumers’ regret sensitivity. However, the profitability implications are non-monotonic. While deposit expansion dominates advance discounts when anticipated regret is low to moderate, advance discounts become more profitable once regret is sufficiently strong. Competition further moderates these effects by amplifying demand differences while compressing profit margins, without altering the regret threshold at which profit dominance reverses. Full article

(This article belongs to the Special Issue Advances in Operations Research for Logistic and Operations Management of Supply Chain Management)

16 pages, 2073 KB

Open AccessArticle

A Cyclic Pentapeptide Inhibits AgrC as a Quorum-Sensing Quenching Agent in Staphylococcus aureus

by Duiyuan Ai, Huanhuan Duan and Jiahao Yao

Antibiotics 2026, 15(2), 213; https://doi.org/10.3390/antibiotics15020213 (registering DOI) - 15 Feb 2026

Abstract

Background/Objectives:Staphylococcus aureus virulence is tightly regulated by the agr (accessory gene regulator) quorum-sensing system. Targeting AgrC, the histidine kinase receptor that serves as a core regulator of agr signaling, represents a promising antivirulence strategy that circumvents conventional bactericidal pressure. Methods: In [...] Read more.

Background/Objectives:Staphylococcus aureus virulence is tightly regulated by the agr (accessory gene regulator) quorum-sensing system. Targeting AgrC, the histidine kinase receptor that serves as a core regulator of agr signaling, represents a promising antivirulence strategy that circumvents conventional bactericidal pressure. Methods: In this study, structure-based virtual screening using AutoDock Vina was performed, followed by molecular dynamics simulations, to identify potent analogs of known AgrC inhibitors. Results: A cyclo[Ala-Phe-OLeu-Phe-D-Leu] exhibiting high binding affinity and stable receptor interaction was selected for further evaluation. Antimicrobial susceptibility testing confirmed that the compound did not inhibit bacterial growth. However, at a concentration of 16 µg/mL, it significantly inhibited hemolytic activity with high reproducibility, and the inhibition rate reached 77.60%. Quantitative reverse transcription PCR (RT-qPCR) demonstrated that the compound decreased some key AgrC-mediated genes, including agrC, agrA, saeS, hla, spa, fnbA, and lukS. Conclusions: These findings identify a promising cyclic pentapeptide inhibitor of AgrC that effectively attenuates S. aureus virulence without exerting bactericidal pressure. This work provides a valuable lead compound and offers novel insights for the development of advanced, safe, and effective antivirulence therapeutics. Full article

(This article belongs to the Section Novel Antimicrobial Agents)

60 pages, 3203 KB

Open AccessReview

Advances in Porous Silicon Materials for Sensing, Energy Storage, and Microelectronics

by Yujie Wang and Donghua Wang

Nanomaterials 2026, 16(4), 257; https://doi.org/10.3390/nano16040257 (registering DOI) - 15 Feb 2026

Abstract

Porous silicon (PSi), characterized by its high specific surface area and highly tunable morphology, presents significant potential across optoelectronics, energy storage, and biomedical applications. This review provides a systematic analysis of the synthesis methodologies, interfacial chemical engineering, and diverse applications of PSi. Initially, [...] Read more.

Porous silicon (PSi), characterized by its high specific surface area and highly tunable morphology, presents significant potential across optoelectronics, energy storage, and biomedical applications. This review provides a systematic analysis of the synthesis methodologies, interfacial chemical engineering, and diverse applications of PSi. Initially, fabrication techniques are examined, contrasting the pore formation mechanisms of electrochemical anodization, metal-assisted chemical etching (MACE), and emerging vapor-phase etching methods, while elucidating the control of geometric parameters from microporous to macroporous scales. To address the thermodynamic instability of the hydride-terminated surface, this review systematically evaluates modification strategies such as thermal oxidation, hydrosilylation, carbonization, and atomic layer deposition (ALD). We critically analyze their efficacy in mitigating oxidative drift and enabling specific functionalization. Subsequently, the review summarizes current applications in sensing (refractive index and photoluminescence modulation), energy storage (lithium-ion battery anodes and supercapacitors), and microsystem technologies (radio frequency (RF) isolation, gettering, and micro-electro-mechanical systems (MEMS) sacrificial layers), emphasizing the critical role of structure–property relationships. Finally, an objective assessment is provided regarding the challenges in translating PSi technology to industrial scales, specifically addressing the trade-offs between biodegradability and stability, wafer-scale process uniformity, and the compatibility of wet-chemical processing with standard complementary metal–oxide–semiconductor (CMOS) integration flows. Full article

(This article belongs to the Section Nanoelectronics, Nanosensors and Devices)

19 pages, 1431 KB

Open AccessArticle

Robust Trajectory Prediction for Mobile Robots via Minimum Error Entropy Criterion and Adaptive LSTM Networks

by Da Xie, Zengxun Li, Chun Zhang, Chunyang Wang and Xuyang Wei

Entropy 2026, 28(2), 227; https://doi.org/10.3390/e28020227 (registering DOI) - 15 Feb 2026

Abstract

Trajectory prediction is critical for safe robot navigation, yet standard deep learning models predominantly rely on the Mean Squared Error (MSE) criterion. While effective under ideal conditions, MSE-based optimization is inherently fragile to non-Gaussian impulsive noise—such as sensor glitches and occlusions—common in real-world [...] Read more.

Trajectory prediction is critical for safe robot navigation, yet standard deep learning models predominantly rely on the Mean Squared Error (MSE) criterion. While effective under ideal conditions, MSE-based optimization is inherently fragile to non-Gaussian impulsive noise—such as sensor glitches and occlusions—common in real-world deployment. To address this limitation, this paper proposes MEE-LSTM, a robust forecasting framework that integrates Long Short-Term Memory networks with the Minimum Error Entropy (MEE) criterion. By minimizing Renyi’s quadratic entropy of the prediction error, our loss function introduces an intrinsic “gradient clipping” mechanism that effectively suppresses the influence of outliers. Furthermore, to overcome the convergence challenges of fixed-kernel information theoretic learning, we introduce a Silverman-based Adaptive Annealing (SAA) strategy that dynamically regulates the kernel bandwidth. Extensive evaluations on the ETH and UCY datasets demonstrate that MEE-LSTM maintains competitive accuracy on clean benchmarks while exhibiting superior resilience in degraded sensing environments. Notably, we identify a “Scissor Plot” phenomenon under stress testing: in the presence of 20% impulsive noise, the proposed model maintains a stable Average Displacement Error (ADE “≈” 0.51 m), whereas MSE baselines suffer catastrophic degradation (ADE > 2.1 m), representing a 75.7% improvement in robustness. This work provides a statistically grounded paradigm for reliable causal inference in hostile robotic perception. Full article

(This article belongs to the Special Issue Bayesian Networks and Causal Discovery)

53 pages, 2892 KB

Open AccessReview

Federated Learning in Edge Computing: Vulnerabilities, Attacks, and Defenses—A Survey

by Sahar Saleh Alhawas and Murad A. Rassam

Sensors 2026, 26(4), 1275; https://doi.org/10.3390/s26041275 (registering DOI) - 15 Feb 2026

Abstract

Federated Learning (FL), a distributed machine learning framework, enables collaborative model training across multiple devices without sharing raw data, thereby preserving privacy and reducing communication costs. When combined with Edge Computing (EC), FL brings computations closer to data sources, enabling low-latency, real-time decision-making [...] Read more.

Federated Learning (FL), a distributed machine learning framework, enables collaborative model training across multiple devices without sharing raw data, thereby preserving privacy and reducing communication costs. When combined with Edge Computing (EC), FL brings computations closer to data sources, enabling low-latency, real-time decision-making in resource-constrained environments. However, this decentralization introduces several vulnerabilities, including data poisoning, backdoor attacks, inference leaks, and Byzantine behaviors, which are worsened by the heterogeneity of edge devices and their intermittent connectivity. This survey presents a comprehensive review of the intersection of FL and EC, focusing on vulnerabilities, attack vectors, and defense mechanisms. We analyze existing methods for robust aggregation, anomaly detection, differential privacy, and secure aggregation, with a focus on their feasibility within edge environments. Additionally, we identify open research challenges, such as scalability, resilience to heterogeneity, and energy-efficient defenses, and provide insights into the evolving landscape of FL in edge computing. This review aims to inform future research on enhancing the security, privacy, and efficiency of FL systems deployed in real-world edge environments. Full article

(This article belongs to the Section Internet of Things)

13 pages, 777 KB

Open AccessArticle

Origin Identification of Scodelario radix Based on Multidimensional Quality Indicators and Machine Learning Algorithms

by Xiao-Lu Liu, Tong Zhu, Ming-Yue Zhang, Jun-Xuan Yang, Hua Li and Bin Yang

Molecules 2026, 31(4), 680; https://doi.org/10.3390/molecules31040680 (registering DOI) - 15 Feb 2026

Abstract

This study aims to establish an origin identification method for Scutellariae radix that integrates multidimensional quality indicators and machine learning algorithms, enabling accurate and rapid traceability of Scutellariae radix medicinal materials from four production areas: Hebei (HB), Shanxi (SX), Shaanxi (SAX), and Chengde [...] Read more.

This study aims to establish an origin identification method for Scutellariae radix that integrates multidimensional quality indicators and machine learning algorithms, enabling accurate and rapid traceability of Scutellariae radix medicinal materials from four production areas: Hebei (HB), Shanxi (SX), Shaanxi (SAX), and Chengde (CD). The study collected a total of 43 batches of Scutellariae radix samples from the aforementioned origins. It systematically measured 12 key quality indicators covering flavonoids, physicochemical parameters, chromaticity values, and biological activity. These specifically include four flavonoid components: baicalin, wogonoside, baicalein, and wogonin; three physicochemical parameters: moisture content, ash content, and alcohol-soluble extract; four chromaticity values: L*, a*, b*, and ΔE; and in vitro anti-inflammatory activity (IC₅₀ value for NO clearance). On the basis of these parameters, in this study there were five machine learning models constructed based on the following algorithms and methods: Random Forest (RF), Extreme Learning Machine (ELM), Backpropagation Neural Network (BP), and Radial Basis Function Neural Network (RBF). A comparative analysis was conducted to evaluate the origin identification performance of each model. The results indicate significant differences (p < 0.05) in the contents of baicalin, wogonoside, L*, a*, b*, ΔE, and alcohol-soluble extract among Scutellariae radix from different origins. The comparative analysis of four machine learning models reveals that RF outperforms ELM, BP, and RBF in multiclass classification, achieving a test accuracy of 75% and consistent precision, recall, and F1-score of 79.17%. In contrast, the three neural networks attain only 66.67% test accuracy, with RBF showing high precision but low recall, ELM delivering moderate performance, and BP performing poorly. These results underscore the strength of ensemble methods like RF in small-sample settings, where they mitigate overfitting and enhance generalization, whereas neural networks struggle with limited data. We therefore recommend RF for deployment under current data constraints and suggest future work should focus on data expansion, especially for under-performing classes, along with hyperparameter tuning to further improve classification. Full article

(This article belongs to the Special Issue 30th Anniversary of Molecules—Recent Advances in Food Chemistry)

29 pages, 4269 KB

Open AccessArticle

Genomic Characterisation of Pyometra-Associated Escherichia coli in a Lombardy Veterinary Clinic: A Nanopore-Based Case Series

by Gabriele Meroni, Alessio Soggiu, Davide Sciannimanico, Raul Alexandru Pop, Luigi Bonizzi and Piera Anna Martino

Antibiotics 2026, 15(2), 212; https://doi.org/10.3390/antibiotics15020212 (registering DOI) - 15 Feb 2026

Abstract

Background/Objectives: Pyometra is a life-threatening uterine infection of intact bitches and queens. Despite growing reports of multidrug-resistant (MDR) Escherichia coli in canine reproductive and urinary infections, no whole-genome data were previously available for pyometra isolates from Italy. This study aimed to characterise, [...] Read more.

Background/Objectives: Pyometra is a life-threatening uterine infection of intact bitches and queens. Despite growing reports of multidrug-resistant (MDR) Escherichia coli in canine reproductive and urinary infections, no whole-genome data were previously available for pyometra isolates from Italy. This study aimed to characterise, by whole-genome sequencing and comparative genomics, the population structure, resistome and virulome of E. coli causing pyometra in companion animals from northern Italy in the context of European datasets. Methods: Four E. coli isolates (two canine, two feline) from pyometra cases underwent nanopore long-read sequencing. Genomes were compared with Brazilian and Finnish pyometra isolates using core- and accessory-genome analyses, pan-genome partitioning, phylogeny, and gene-based profiling of antimicrobial resistance and virulence determinants. Results: All Italian isolates belonged to phylogroup B2 and to recognised ExPEC sequence types (ST706/O51:H1, ST141/O2:H6, ST372/O75:H31, ST646/O22:H5). Phenotypically, they were uniformly resistant to several penicillins and early/third-generation cephalosporins but remained susceptible to fluoroquinolones, aminoglycosides and trimethoprim–sulphonamide. The combined 57-genome pan-genome was open yet strongly core-dominated; Italian strains shared an efflux- and regulator-centred intrinsic resistome and a rich ExPEC virulence repertoire (P, S, F1C and type 1 fimbriae, multiple siderophores, colibactin, Vat, haemolysin, CNF1) with Brazilian and Finnish isolates. Conclusions: Pyometra-associated E. coli from northern Italian pets belong to globally disseminated high-risk B2 lineages that combine extensive virulence with a largely intrinsic resistome, and currently retain susceptibility to several key drug classes, underscoring an important but vulnerable therapeutic window. Full article

(This article belongs to the Special Issue Gram-Negative Infections in Humans and Companion Animals: A Global Threat)

24 pages, 7488 KB

Open AccessArticle

Preparation and Characterisation of a Halloysite Nanoclay–Anthocyanin Hybrid Under Variable Conditions

by Teresa Rutschi-De-Cea, Daniel López-Rodríguez, Bárbara Micó-Vicent and Jorge Jordán-Núñez

Textiles 2026, 6(1), 24; https://doi.org/10.3390/textiles6010024 (registering DOI) - 15 Feb 2026

Abstract

The development of sustainable pigments from natural sources is gaining interest due to environmental concerns and the need for bio-based alternatives to synthetic dyes. This study investigates the synthesis of hybrid pigments by adsorbing anthocyanins—extracted from pomegranate agro-waste—onto halloysite (HA) nanotubes. A full [...] Read more.

The development of sustainable pigments from natural sources is gaining interest due to environmental concerns and the need for bio-based alternatives to synthetic dyes. This study investigates the synthesis of hybrid pigments by adsorbing anthocyanins—extracted from pomegranate agro-waste—onto halloysite (HA) nanotubes. A full factorial design was applied to evaluate the influence of pH and surfactant type (cetylpyridinium bromide and sodium dodecyl sulfate) on pigment colour and the thermal and structural stability of the hybrids. Adsorption was carried out in 400 mL dispersion baths containing 10 g of HA and 5% w/w anthocyanins. Surfactants (2% w/w) were added before the pigment, followed by 200 µL of silane. Dispersions were stirred at high speed for 1 h and then at 500 rpm for 23 h to ensure adsorption without premature desorption. Characterisation (TGA, XRD, FTIR, UV-Vis/NIR, SEM, EDX, BET) confirmed the preservation of HA structure and minimal changes in thermal behaviour. Pigment colour varied with synthesis conditions, especially pH: a higher pH increased brightness and yielded yellowish tones, while a lower pH resulted in reddish-blue hues with greater variability. The results confirm halloysite’s potential as a stable carrier for natural dyes and demonstrate that pH effectively tunes hybrid pigment colour. Full article

29 pages, 1015 KB

Open AccessReview

The Epigenetic Battleground: Host Chromatin at the Core of Infection

by Fabrício Castro Machado and Nilmar Silvio Moretti

Epigenomes 2026, 10(1), 13; https://doi.org/10.3390/epigenomes10010013 (registering DOI) - 15 Feb 2026

Abstract

Chromatin dynamics are usually modulated by histone epigenetic post-translational modifications, which rapidly and reversibly govern accessibility and transcriptional responsiveness. During microbial infection, this regulatory layer becomes a highly contested interface where host defense mechanisms and pathogen-driven subversion strategies converge and compete. Many infectious [...] Read more.

Chromatin dynamics are usually modulated by histone epigenetic post-translational modifications, which rapidly and reversibly govern accessibility and transcriptional responsiveness. During microbial infection, this regulatory layer becomes a highly contested interface where host defense mechanisms and pathogen-driven subversion strategies converge and compete. Many infectious agents exploit chromatin to reprogram gene expression, creating cellular environments that are conducive to infection, proliferation, and persistence. Diverse strategies have been described for viruses, bacteria, fungi, protozoa and nematodes, including the direct secretion of acetyltransferases and methyltransferases, interference with host chromatin-binding proteins, subcellular localization of transcriptional factors or epigenetic regulators, and metabolic availability manipulation. Concurrently, host cells activate immune and stress-response genes to mount rapid, adaptable antimicrobial responses. Recent advances in genome-wide, single-cell, and spatial omics profiling have begun to reveal the temporal and cell-type-specific dynamics of the host genome at the core of infection. This review synthesizes current insights into how chromatin is rewired by the major categories of pathogens during infection, highlighting representative case studies across infective agents and the functional consequences for immunity and cell fate. In addition, we discuss emerging techniques for epigenomic and transcriptomic data collection, and the potential of targeted host-directed therapeutic strategies. Chromatin regulation is thus a promising field of study and a possible target for next-generation interventions. Full article

(This article belongs to the Special Issue Epigenetics Meets Immunology: Mechanisms, Crosstalk, and Therapeutic Implications)

26 pages, 2078 KB

Open AccessArticle

Adversarial Distributed Multi-Task Meta-Inverse Reinforcement Learning with Theory of Mind and Mean-Field Method

by Li Song, Kun Yang and Chao Chen

Mathematics 2026, 14(4), 691; https://doi.org/10.3390/math14040691 (registering DOI) - 15 Feb 2026

Abstract

Maximum entropy adversarial inverse reinforcement learning (ME-AIRL) has garnered widespread attention for its ability to learn rewards and optimize policies from expert demonstrations. In complex multi-task environments, applying meta-learning ME-AIRL to acquire rewards requires a substantial volume of homogeneous expert demonstrations across all [...] Read more.

Maximum entropy adversarial inverse reinforcement learning (ME-AIRL) has garnered widespread attention for its ability to learn rewards and optimize policies from expert demonstrations. In complex multi-task environments, applying meta-learning ME-AIRL to acquire rewards requires a substantial volume of homogeneous expert demonstrations across all tasks, which is often impractical in real-world scenarios. Moreover, interference between tasks further escalates computational complexity. To solve these challenges, this paper proposes a distributed multi-task meta ME-AIRL framework based on theory of mind and mean field, referred to as TMMF-MTAIRL. In TMMF-MTAIRL, the theory of mind is used to capture the relationships and representational information among multiple tasks. Furthermore, TMMF-MTAIRL integrates mean-field theory to transform interactions between complex tasks into interactions between the main task and the average of the remaining tasks. Furthermore, additional latent variables are introduced to enhance adaptation to novel tasks. We evaluate the proposed TMMF-MTAIRL on point-maze benchmarks and a real-world rolling bearing fault diagnosis dataset using metrics such as classification accuracy, mean rewards or cumulative rewards. TMMF-MTAIRL achieves the best performance across all tasks, with an average improvement of 0.16 in accuracy of fault classification over the strongest baseline. Full article

29 pages, 4058 KB

Open AccessArticle

Reliability-Based Recycling of Reclaimed Asphalt Pavement Using a t-Distribution Guarantee Rate Method and a Ternary Composite Rejuvenation System

by Yuanyuan Li, Bowen Hu, Kefeng Bi, Chonghui Wang, Hongbin Zhu and Gangping Jiang

Materials 2026, 19(4), 762; https://doi.org/10.3390/ma19040762 (registering DOI) - 15 Feb 2026

Abstract

Large-scale use of reclaimed asphalt pavement (RAP) is limited by strong gradation variability, uneven recovery of aged asphalt (AA), and an incomplete understanding of the rejuvenation mechanism. This study combines source evaluation, composite rejuvenation, and multi-scale analysis to improve AA recovery. A gradation [...] Read more.

Large-scale use of reclaimed asphalt pavement (RAP) is limited by strong gradation variability, uneven recovery of aged asphalt (AA), and an incomplete understanding of the rejuvenation mechanism. This study combines source evaluation, composite rejuvenation, and multi-scale analysis to improve AA recovery. A gradation variability model was developed using the t-distribution, and a reliability-based method was proposed for reclaimed material selection and mix design. Rejuvenator 1 (R1) was identified as the best option, and a ternary composite rejuvenation system was formed using R1, SBS-modified asphalt, and base asphalt (BA). AA performance was assessed using physical and rheological tests, supported by Fourier-transform infrared spectroscopy, fluorescence microscopy, and gel permeation chromatography. The t-distribution guarantee rate method quantified RAP gradation fluctuations effectively. At a 90% guarantee rate, the deviation in key sieve pass rates was below 3%, indicating stable sources. In the composite system, 10% R1 restored AA high temperature performance, while adding 30% SBS modified asphalt and BA improved low-temperature crack resistance. The micro analyses showed no new functional groups after rejuvenation. Recovery was mainly driven by physical blending, dilution, and optimisation of the molecular-weight distribution. Full article

(This article belongs to the Section Construction and Building Materials)

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