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42 pages, 939 KiB  
Review
B7-H3 in Cancer Immunotherapy—Prospects and Challenges: A Review of the Literature
by Sylwia Mielcarska, Anna Kot, Miriam Dawidowicz, Agnieszka Kula, Piotr Sobków, Daria Kłaczka, Dariusz Waniczek and Elżbieta Świętochowska
Cells 2025, 14(15), 1209; https://doi.org/10.3390/cells14151209 - 6 Aug 2025
Abstract
In today’s oncology, immunotherapy arises as a potent complement for conventional cancer treatment, allowing for obtaining better patient outcomes. B7-H3 (CD276) is a member of the B7 protein family, which emerged as an attractive target for the treatment of various tumors. The molecule [...] Read more.
In today’s oncology, immunotherapy arises as a potent complement for conventional cancer treatment, allowing for obtaining better patient outcomes. B7-H3 (CD276) is a member of the B7 protein family, which emerged as an attractive target for the treatment of various tumors. The molecule modulates anti-cancer immune responses, acting through diverse signaling pathways and cell populations. It has been implicated in the pathogenesis of numerous malignancies, including melanoma, gliomas, lung cancer, gynecological cancers, renal cancer, gastrointestinal tumors, and others, fostering the immunosuppressive environment and marking worse prognosis for the patients. B7-H3 targeting therapies, such as monoclonal antibodies, antibody–drug conjugates, and CAR T-cells, present promising results in preclinical studies and are the subject of ongoing clinical trials. CAR-T therapies against B7-H3 have demonstrated utility in malignancies such as melanoma, glioblastoma, prostate cancer, and RCC. Moreover, ADCs targeting B7-H3 exerted cytotoxic effects on glioblastoma, neuroblastoma cells, prostate cancer, and craniopharyngioma models. B7-H3-targeting also delivers promising results in combined therapies, enhancing the response to other immune checkpoint inhibitors and giving hope for the development of approaches with minimized adverse effects. However, the strategies of B7-H3 blocking deliver substantial challenges, such as poorly understood molecular mechanisms behind B7-H3 protumor properties or therapy toxicity. In this review, we discuss B7-H3’s role in modulating immune responses, its significance for various malignancies, and clinical trials evaluating anti-B7-H3 immunotherapeutic strategies, focusing on the clinical potential of the molecule. Full article
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22 pages, 1029 KiB  
Review
Inter-Organellar Ca2+ Homeostasis in Plant and Animal Systems
by Philip Steiner and Susanna Zierler
Cells 2025, 14(15), 1204; https://doi.org/10.3390/cells14151204 - 6 Aug 2025
Abstract
The regulation of calcium (Ca2+) homeostasis is a critical process in both plant and animal systems, involving complex interplay between various organelles and a diverse network of channels, pumps, and transporters. This review provides a concise overview of inter-organellar Ca2+ [...] Read more.
The regulation of calcium (Ca2+) homeostasis is a critical process in both plant and animal systems, involving complex interplay between various organelles and a diverse network of channels, pumps, and transporters. This review provides a concise overview of inter-organellar Ca2+ homeostasis, highlighting key regulators and mechanisms in plant and animal cells. We discuss the roles of key Ca2+ channels and transporters, including IP3Rs, RyRs, TPCs, MCUs, TRPMLs, and P2XRs in animals, as well as their plant counterparts. Here, we explore recent innovations in structural biology and advanced microscopic techniques that have enhanced our understanding of these proteins’ structure, functions, and regulations. We examine the importance of membrane contact sites in facilitating Ca2+ transfer between organelles and the specific expression patterns of Ca2+ channels and transporters. Furthermore, we address the physiological implications of inter-organellar Ca2+ homeostasis and its relevance in various pathological conditions. For extended comparability, a brief excursus into bacterial intracellular Ca2+ homeostasis is also made. This meta-analysis aims to bridge the gap between plant and animal Ca2+ signaling research, identifying common themes and unique adaptations in these diverse biological systems. Full article
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24 pages, 3858 KiB  
Review
Emerging Strategies for Aflatoxin Resistance in Peanuts via Precision Breeding
by Archana Khadgi, Saikrisha Lekkala, Pankaj K. Verma, Naveen Puppala and Madhusudhana R. Janga
Toxins 2025, 17(8), 394; https://doi.org/10.3390/toxins17080394 - 6 Aug 2025
Abstract
Aflatoxin contamination, primarily caused by Aspergillus flavus, poses a significant threat to peanut (Arachis hypogaea L.) production, food safety, and global trade. Despite extensive efforts, breeding for durable resistance remains difficult due to the polygenic and environmentally sensitive nature of resistance. [...] Read more.
Aflatoxin contamination, primarily caused by Aspergillus flavus, poses a significant threat to peanut (Arachis hypogaea L.) production, food safety, and global trade. Despite extensive efforts, breeding for durable resistance remains difficult due to the polygenic and environmentally sensitive nature of resistance. Although germplasm such as J11 have shown partial resistance, none of the identified lines demonstrated stable or comprehensive protection across diverse environments. Resistance involves physical barriers, biochemical defenses, and suppression of toxin biosynthesis. However, these traits typically exhibit modest effects and are strongly influenced by genotype–environment interactions. A paradigm shift is underway with increasing focus on host susceptibility (S) genes, native peanut genes exploited by A. flavus to facilitate colonization or toxin production. Recent studies have identified promising S gene candidates such as AhS5H1/2, which suppress salicylic acid-mediated defense, and ABR1, a negative regulator of ABA signaling. Disrupting such genes through gene editing holds potential for broad-spectrum resistance. To advance resistance breeding, an integrated pipeline is essential. This includes phenotyping diverse germplasm under stress conditions, mapping resistance loci using QTL and GWAS, and applying multi-omics platforms to identify candidate genes. Functional validation using CRISPR/Cas9, Cas12a, base editors, and prime editing allows precise gene targeting. Validated genes can be introgressed into elite lines through breeding by marker-assisted and genomic selection, accelerating the breeding of aflatoxin-resistant peanut varieties. This review highlights recent advances in peanut aflatoxin resistance research, emphasizing susceptibility gene targeting and genome editing. Integrating conventional breeding with multi-omics and precision biotechnology offers a promising path toward developing aflatoxin-free peanut cultivars. Full article
(This article belongs to the Special Issue Strategies for Mitigating Mycotoxin Contamination in Food and Feed)
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26 pages, 1203 KiB  
Review
Deciphering the Role of Functional Ion Channels in Cancer Stem Cells (CSCs) and Their Therapeutic Implications
by Krishna Samanta, Gali Sri Venkata Sai Rishma Reddy, Neeraj Kumar Sharma and Pulak Kar
Int. J. Mol. Sci. 2025, 26(15), 7595; https://doi.org/10.3390/ijms26157595 - 6 Aug 2025
Abstract
Despite advances in medicine, cancer remains one of the foremost global health concerns. Conventional treatments like surgery, radiotherapy, and chemotherapy have advanced with the emergence of targeted and immunotherapy approaches. However, therapeutic resistance and relapse remain major barriers to long-term success in cancer [...] Read more.
Despite advances in medicine, cancer remains one of the foremost global health concerns. Conventional treatments like surgery, radiotherapy, and chemotherapy have advanced with the emergence of targeted and immunotherapy approaches. However, therapeutic resistance and relapse remain major barriers to long-term success in cancer treatment, often driven by cancer stem cells (CSCs). These rare, resilient cells can survive therapy and drive tumour regrowth, urging deeper investigation into the mechanisms underlying their persistence. CSCs express ion channels typical of excitable tissues, which, beyond electrophysiology, critically regulate CSC fate. However, the underlying regulatory mechanisms of these channels in CSCs remain largely unexplored and poorly understood. Nevertheless, the therapeutic potential of targeting CSC ion channels is immense, as it offers a powerful strategy to disrupt vital signalling pathways involved in numerous pathological conditions. In this review, we explore the diverse repertoire of ion channels expressed in CSCs and highlight recent mechanistic insights into how these channels modulate CSC behaviours, dynamics, and functions. We present a concise overview of ion channel-mediated CSC regulation, emphasizing their potential as novel diagnostic markers and therapeutic targets, and identifying key areas for future research. Full article
(This article belongs to the Special Issue Ion Channels as a Potential Target in Pharmaceutical Designs 2.0)
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30 pages, 2414 KiB  
Review
Melittin-Based Nanoparticles for Cancer Therapy: Mechanisms, Applications, and Future Perspectives
by Joe Rizkallah, Nicole Charbel, Abdallah Yassine, Amal El Masri, Chris Raffoul, Omar El Sardouk, Malak Ghezzawi, Therese Abou Nasr and Firas Kreidieh
Pharmaceutics 2025, 17(8), 1019; https://doi.org/10.3390/pharmaceutics17081019 - 6 Aug 2025
Abstract
Melittin, a cytolytic peptide derived from honeybee venom, has demonstrated potent anticancer activity through mechanisms such as membrane disruption, apoptosis induction, and modulation of key signaling pathways. Melittin exerts its anticancer activity by interacting with key molecular targets, including downregulation of the PI3K/Akt [...] Read more.
Melittin, a cytolytic peptide derived from honeybee venom, has demonstrated potent anticancer activity through mechanisms such as membrane disruption, apoptosis induction, and modulation of key signaling pathways. Melittin exerts its anticancer activity by interacting with key molecular targets, including downregulation of the PI3K/Akt and NF-κB signaling pathways, and by inducing mitochondrial apoptosis through reactive oxygen species generation and cytochrome c release. However, its clinical application is hindered by its systemic and hemolytic toxicity, rapid degradation in plasma, poor pharmacokinetics, and immunogenicity, necessitating the development of targeted delivery strategies to enable safe and effective treatment. Nanoparticle-based delivery systems have emerged as a promising strategy for overcoming these challenges, offering improved tumor targeting, reduced off-target effects, and enhanced stability. This review provides a comprehensive overview of the mechanisms through which melittin exerts its anticancer effects and evaluates the development of various melittin-loaded nanocarriers, including liposomes, polymeric nanoparticles, dendrimers, micelles, and inorganic systems. It also summarizes the preclinical evidence for melittin nanotherapy across a wide range of cancer types, highlighting both its cytotoxic and immunomodulatory effects. The potential of melittin nanoparticles to overcome multidrug resistance and synergize with chemotherapy, immunotherapy, photothermal therapy, and radiotherapy is discussed. Despite promising in vitro and in vivo findings, its clinical translation remains limited. Key barriers include toxicity, manufacturing scalability, regulatory approval, and the need for more extensive in vivo validation. A key future direction is the application of computational tools, such as physiologically based pharmacokinetic modeling and artificial-intelligence-based modeling, to streamline development and guide its clinical translation. Addressing these challenges through focused research and interdisciplinary collaboration will be essential to realizing the full therapeutic potential of melittin-based nanomedicines in oncology. Overall, this review synthesizes the findings from over 100 peer-reviewed studies published between 2008 and 2025, providing an up-to-date assessment of melittin-based nanomedicine strategies across diverse cancer types. Full article
(This article belongs to the Special Issue Development of Novel Tumor-Targeting Nanoparticles, 2nd Edition)
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25 pages, 6821 KiB  
Article
Hierarchical Text-Guided Refinement Network for Multimodal Sentiment Analysis
by Yue Su and Xuying Zhao
Entropy 2025, 27(8), 834; https://doi.org/10.3390/e27080834 - 6 Aug 2025
Abstract
Multimodal sentiment analysis (MSA) benefits from integrating diverse modalities (e.g., text, video, and audio). However, challenges remain in effectively aligning non-text features and mitigating redundant information, which may limit potential performance improvements. To address these challenges, we propose a Hierarchical Text-Guided Refinement Network [...] Read more.
Multimodal sentiment analysis (MSA) benefits from integrating diverse modalities (e.g., text, video, and audio). However, challenges remain in effectively aligning non-text features and mitigating redundant information, which may limit potential performance improvements. To address these challenges, we propose a Hierarchical Text-Guided Refinement Network (HTRN), a novel framework that refines and aligns non-text modalities using hierarchical textual representations. We introduce Shuffle-Insert Fusion (SIF) and the Text-Guided Alignment Layer (TAL) to enhance crossmodal interactions and suppress irrelevant signals. In SIF, empty tokens are inserted at fixed intervals in unimodal feature sequences, disrupting local correlations and promoting more generalized representations with improved feature diversity. The TAL guides the refinement of audio and visual representations by leveraging textual semantics and dynamically adjusting their contributions through learnable gating factors, ensuring that non-text modalities remain semantically coherent while retaining essential crossmodal interactions. Experiments demonstrate that the HTRN achieves state-of-the-art performance with accuracies of 86.3% (Acc-2) on CMU-MOSI, 86.7% (Acc-2) on CMU-MOSEI, and 80.3% (Acc-2) on CH-SIMS, outperforming existing methods by 0.8–3.45%. Ablation studies validate the contributions of SIF and the TAL, showing 1.9–2.1% performance gains over baselines. By integrating these components, the HTRN establishes a robust multimodal representation learning framework. Full article
(This article belongs to the Section Information Theory, Probability and Statistics)
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26 pages, 514 KiB  
Article
Improving Voice Spoofing Detection Through Extensive Analysis of Multicepstral Feature Reduction
by Leonardo Mendes de Souza, Rodrigo Capobianco Guido, Rodrigo Colnago Contreras, Monique Simplicio Viana and Marcelo Adriano dos Santos Bongarti
Sensors 2025, 25(15), 4821; https://doi.org/10.3390/s25154821 - 5 Aug 2025
Abstract
Voice biometric systems play a critical role in numerous security applications, including electronic device authentication, banking transaction verification, and confidential communications. Despite their widespread utility, these systems are increasingly targeted by sophisticated spoofing attacks that leverage advanced artificial intelligence techniques to generate realistic [...] Read more.
Voice biometric systems play a critical role in numerous security applications, including electronic device authentication, banking transaction verification, and confidential communications. Despite their widespread utility, these systems are increasingly targeted by sophisticated spoofing attacks that leverage advanced artificial intelligence techniques to generate realistic synthetic speech. Addressing the vulnerabilities inherent to voice-based authentication systems has thus become both urgent and essential. This study proposes a novel experimental analysis that extensively explores various dimensionality reduction strategies in conjunction with supervised machine learning models to effectively identify spoofed voice signals. Our framework involves extracting multicepstral features followed by the application of diverse dimensionality reduction methods, such as Principal Component Analysis (PCA), Truncated Singular Value Decomposition (SVD), statistical feature selection (ANOVA F-value, Mutual Information), Recursive Feature Elimination (RFE), regularization-based LASSO selection, Random Forest feature importance, and Permutation Importance techniques. Empirical evaluation using the ASVSpoof 2017 v2.0 dataset measures the classification performance with the Equal Error Rate (EER) metric, achieving values of approximately 10%. Our comparative analysis demonstrates significant performance gains when dimensionality reduction methods are applied, underscoring their value in enhancing the security and effectiveness of voice biometric verification systems against emerging spoofing threats. Full article
(This article belongs to the Special Issue Sensors and Machine-Learning Based Signal Processing)
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25 pages, 13175 KiB  
Article
Fault Diagnosis for CNC Machine Tool Feed Systems Based on Enhanced Multi-Scale Feature Network
by Peng Zhang, Min Huang and Weiwei Sun
Lubricants 2025, 13(8), 350; https://doi.org/10.3390/lubricants13080350 - 5 Aug 2025
Abstract
Despite advances in Convolutional Neural Networks (CNNs) for intelligent fault diagnosis in CNC machine tools, bearing fault diagnosis in CNC feed systems remains challenging, particularly in multi-scale feature extraction and generalization across operating conditions. This study introduces an enhanced multi-scale feature network (MSFN) [...] Read more.
Despite advances in Convolutional Neural Networks (CNNs) for intelligent fault diagnosis in CNC machine tools, bearing fault diagnosis in CNC feed systems remains challenging, particularly in multi-scale feature extraction and generalization across operating conditions. This study introduces an enhanced multi-scale feature network (MSFN) that addresses these limitations through three integrated modules designed to extract critical fault features from vibration signals. First, a Soft-Scale Denoising (S2D) module forms the backbone of the MSFN, capturing multi-scale fault features from input signals. Second, a Multi-Scale Adaptive Feature Enhancement (MS-AFE) module based on long-range weighting mechanisms is developed to enhance the extraction of periodic fault features. Third, a Dynamic Sequence–Channel Attention (DSCA) module is incorporated to improve feature representation across channel and sequence dimensions. Experimental results on two datasets demonstrate that the proposed MSFN achieves high diagnostic accuracy and exhibits robust generalization across diverse operating conditions. Moreover, ablation studies validate the effectiveness and contributions of each module. Full article
(This article belongs to the Special Issue Advances in Tool Wear Monitoring 2025)
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15 pages, 1353 KiB  
Review
Fyn Kinase: A Potential Target in Glucolipid Metabolism and Diabetes Mellitus
by Ruifeng Xiao, Cong Shen, Wen Shen, Xunan Wu, Xia Deng, Jue Jia and Guoyue Yuan
Curr. Issues Mol. Biol. 2025, 47(8), 623; https://doi.org/10.3390/cimb47080623 - 5 Aug 2025
Abstract
Fyn is widely involved in diverse cellular physiological processes, including cell growth and survival, and has been implicated in the regulation of energy metabolism and the pathogenesis of diabetes mellitus through multiple pathways. Fyn plays a role in increasing fat accumulation and promoting [...] Read more.
Fyn is widely involved in diverse cellular physiological processes, including cell growth and survival, and has been implicated in the regulation of energy metabolism and the pathogenesis of diabetes mellitus through multiple pathways. Fyn plays a role in increasing fat accumulation and promoting insulin resistance, and it also contributes to the development of diabetic complications such as diabetic kidney disease and diabetic retinopathy. The primary mechanism by which Fyn modulates lipid metabolism is that it inhibits AMP-activated protein kinase (AMPK). Additionally, it affects energy homeostasis through regulating specific signal pathways affecting lipid metabolism including pathways related to CD36, through enhancement of adipocyte differentiation, and through modulating insulin signal transduction. Inflammatory stress is one of the fundamental mechanisms in diabetes mellitus and its complications. Fyn also plays a role in inflammatory stress-related signaling cascades such as the Akt/GSK-3β/Fyn/Nrf2 pathway, exacerbating inflammation in diabetes mellitus. Therefore, Fyn emerges as a promising therapeutic target for regulating glucolipid metabolism and alleviating type 2 diabetes mellitus. This review synthesizes research on the role of Fyn in the regulation of energy metabolism and the development of diabetes mellitus, while exploring its specific regulatory mechanisms. Full article
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22 pages, 2053 KiB  
Article
Enhanced Real-Time Method Traffic Light Signal Color Recognition Using Advanced Convolutional Neural Network Techniques
by Fakhri Yagob and Jurek Z. Sasiadek
World Electr. Veh. J. 2025, 16(8), 441; https://doi.org/10.3390/wevj16080441 - 5 Aug 2025
Abstract
Real-time traffic light detection is essential for the safe navigation of autonomous vehicles, where timely and accurate recognition of signal states is critical. YOLOv8, a state-of-the-art object detection model, offers enhanced speed and precision, making it well-suited for real-time applications in complex driving [...] Read more.
Real-time traffic light detection is essential for the safe navigation of autonomous vehicles, where timely and accurate recognition of signal states is critical. YOLOv8, a state-of-the-art object detection model, offers enhanced speed and precision, making it well-suited for real-time applications in complex driving environments. This study presents a modified YOLOv8 architecture optimized for traffic light detection by integrating Depth-Wise Separable Convolutions (DWSCs) throughout the backbone and head. The model was first pretrained on a public traffic light dataset to establish a strong baseline and then fine-tuned on a custom real-time dataset consisting of 480 images collected from video recordings under diverse road conditions. Experimental results demonstrate high detection performance, with precision scores of 0.992 for red, 0.995 for yellow, and 0.853 for green lights. The model achieved an average mAP@0.5 of 0.947, with stable F1 scores and low validation losses over 80 epochs, confirming effective learning and generalization. Compared to existing YOLO variants, the modified architecture showed superior performance, especially for red and yellow lights. Full article
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23 pages, 85184 KiB  
Article
MB-MSTFNet: A Multi-Band Spatio-Temporal Attention Network for EEG Sensor-Based Emotion Recognition
by Cheng Fang, Sitong Liu and Bing Gao
Sensors 2025, 25(15), 4819; https://doi.org/10.3390/s25154819 - 5 Aug 2025
Abstract
Emotion analysis based on electroencephalogram (EEG) sensors is pivotal for human–machine interaction yet faces key challenges in spatio-temporal feature fusion and cross-band and brain-region integration from multi-channel sensor-derived signals. This paper proposes MB-MSTFNet, a novel framework for EEG emotion recognition. The model constructs [...] Read more.
Emotion analysis based on electroencephalogram (EEG) sensors is pivotal for human–machine interaction yet faces key challenges in spatio-temporal feature fusion and cross-band and brain-region integration from multi-channel sensor-derived signals. This paper proposes MB-MSTFNet, a novel framework for EEG emotion recognition. The model constructs a 3D tensor to encode band–space–time correlations of sensor data, explicitly modeling frequency-domain dynamics and spatial distributions of EEG sensors across brain regions. A multi-scale CNN-Inception module extracts hierarchical spatial features via diverse convolutional kernels and pooling operations, capturing localized sensor activations and global brain network interactions. Bi-directional GRUs (BiGRUs) model temporal dependencies in sensor time-series, adept at capturing long-range dynamic patterns. Multi-head self-attention highlights critical time windows and brain regions by assigning adaptive weights to relevant sensor channels, suppressing noise from non-contributory electrodes. Experiments on the DEAP dataset, containing multi-channel EEG sensor recordings, show that MB-MSTFNet achieves 96.80 ± 0.92% valence accuracy, 98.02 ± 0.76% arousal accuracy for binary classification tasks, and 92.85 ± 1.45% accuracy for four-class classification. Ablation studies validate that feature fusion, bidirectional temporal modeling, and multi-scale mechanisms significantly enhance performance by improving feature complementarity. This sensor-driven framework advances affective computing by integrating spatio-temporal dynamics and multi-band interactions of EEG sensor signals, enabling efficient real-time emotion recognition. Full article
(This article belongs to the Section Intelligent Sensors)
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16 pages, 745 KiB  
Review
Bidirectional Interplay Between Microglia and Mast Cells
by Szandra Lakatos and Judit Rosta
Int. J. Mol. Sci. 2025, 26(15), 7556; https://doi.org/10.3390/ijms26157556 - 5 Aug 2025
Abstract
Microglia, the brain’s resident innate immune cells, play a fundamental role in maintaining neural homeostasis and mediating responses to injury or infection. Upon activation, microglia undergo morphological and functional changes, including phenotypic switching between pro- and anti-inflammatory types and the release of different [...] Read more.
Microglia, the brain’s resident innate immune cells, play a fundamental role in maintaining neural homeostasis and mediating responses to injury or infection. Upon activation, microglia undergo morphological and functional changes, including phenotypic switching between pro- and anti-inflammatory types and the release of different inflammatory mediators. These processes contribute to neuroprotection and the pathogenesis of various central nervous system (CNS) disorders. Mast cells, although sparsely located in the brain, exert a significant influence on neuroinflammation through their interactions with microglia. Through degranulation and secretion of different mediators, mast cells disrupt the blood–brain barrier and modulate microglial responses, including alteration of microglial phenotypes. Notably, mast cell-derived factors, such as histamine, interleukins, and tryptase, activate microglia through various pathways including protease-activated receptor 2 and purinergic receptors. These interactions amplify inflammatory cascades via various signaling pathways. Previous studies have revealed an exceedingly complex crosstalk between mast cells and microglia suggesting a bidirectional regulation of CNS immunity, implicating their cooperation in both neurodegenerative progression and repair mechanisms. Here, we review some of the diverse communication pathways involved in this complex interplay. Understanding this crosstalk may offer novel insights into the cellular dynamics of neuroinflammation and highlight potential therapeutic targets for a variety of CNS disorders. Full article
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17 pages, 4371 KiB  
Article
Adaptive Filtered-x Least Mean Square Algorithm to Improve the Performance of Multi-Channel Noise Control Systems
by Maha Yousif Hasan, Ahmed Sabah Alaraji, Amjad J. Humaidi and Huthaifa Al-Khazraji
Math. Comput. Appl. 2025, 30(4), 84; https://doi.org/10.3390/mca30040084 (registering DOI) - 5 Aug 2025
Abstract
This paper proposes an optimized control filter (OCF) based on the Filtered-x Least Mean Square (FxLMS) algorithm for multi-channel active noise control (ANC) systems. The proposed OCF-McFxLMS algorithm delivers three key contributions. Firstly, even in difficult noise situations such as White Gaussian, Brownian, [...] Read more.
This paper proposes an optimized control filter (OCF) based on the Filtered-x Least Mean Square (FxLMS) algorithm for multi-channel active noise control (ANC) systems. The proposed OCF-McFxLMS algorithm delivers three key contributions. Firstly, even in difficult noise situations such as White Gaussian, Brownian, and pink noise, it greatly reduces error, reaching nearly zero mean squared error (MSE) values across all Microphone (Mic) channels. Secondly, it improves computational efficiency by drastically reducing execution time from 58.17 s in the standard McFxLMS algorithm to just 0.0436 s under White Gaussian noise, enabling real-time noise control without compromising accuracy. Finally, the OCF-McFxLMS demonstrates robust noise attenuation, achieving signal-to-noise ratio (SNR) values of 137.41 dB under White Gaussian noise and over 100 dB for Brownian and pink noise, consistently outperforming traditional approaches. These contributions collectively establish the OCF-McFxLMS algorithm as an efficient and effective solution for real-time ANC systems, delivering superior noise reduction and computational speed performance across diverse noise environments. Full article
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22 pages, 2517 KiB  
Article
Characterization and Engineering of Two Novel Strand-Displacing B Family DNA Polymerases from Bacillus Phage SRT01hs and BeachBum
by Yaping Sun, Kang Fu, Wu Lin, Jie Gao, Xianhui Zhao, Yun He and Hui Tian
Biomolecules 2025, 15(8), 1126; https://doi.org/10.3390/biom15081126 - 5 Aug 2025
Abstract
Polymerase-coupled nanopore sequencing requires DNA polymerases with strong strand displacement activity and high processivity to sustain continuous signal generation. In this study, we characterized two novel B family DNA polymerases, SRHS and BBum, isolated from Bacillus phages SRT01hs and BeachBum, respectively. Both enzymes [...] Read more.
Polymerase-coupled nanopore sequencing requires DNA polymerases with strong strand displacement activity and high processivity to sustain continuous signal generation. In this study, we characterized two novel B family DNA polymerases, SRHS and BBum, isolated from Bacillus phages SRT01hs and BeachBum, respectively. Both enzymes exhibited robust strand displacement, 3′→5′ exonuclease activity, and maintained processivity under diverse reaction conditions, including across a broad temperature range (10–45 °C) and in the presence of multiple divalent metal cofactors (Mg2+, Mn2+, Fe2+), comparable to the well-characterized Phi29 polymerase. Through biochemical analysis of mutants designed using AlphaFold3-predicted structural models, we identified key residues (G96, M97, D486 in SRHS; S97, M98, A493 in BBum) that modulated exonuclease activity, substrate specificity and metal ion utilization. Engineered variants SRHS_F and BBum_Pro_L efficiently incorporated unnatural nucleotides in the presence of Mg2+—a function not observed in Phi29 and other wild-type strand-displacing B family polymerases. These combined biochemical features highlight SRHS and BBum as promising enzymatic scaffolds for nanopore-based long-read sequencing platforms. Full article
(This article belongs to the Section Enzymology)
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23 pages, 3055 KiB  
Article
RDPNet: A Multi-Scale Residual Dilated Pyramid Network with Entropy-Based Feature Fusion for Epileptic EEG Classification
by Tongle Xie, Wei Zhao, Yanyouyou Liu and Shixiao Xiao
Entropy 2025, 27(8), 830; https://doi.org/10.3390/e27080830 - 5 Aug 2025
Abstract
Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide. Electroencephalogram (EEG) signals play a vital role in the diagnosis and analysis of epileptic seizures. However, traditional machine learning techniques often rely on handcrafted features, limiting their robustness and generalizability across [...] Read more.
Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide. Electroencephalogram (EEG) signals play a vital role in the diagnosis and analysis of epileptic seizures. However, traditional machine learning techniques often rely on handcrafted features, limiting their robustness and generalizability across diverse EEG acquisition settings, seizure types, and patients. To address these limitations, we propose RDPNet, a multi-scale residual dilated pyramid network with entropy-guided feature fusion for automated epileptic EEG classification. RDPNet combines residual convolution modules to extract local features and a dilated convolutional pyramid to capture long-range temporal dependencies. A dual-pathway fusion strategy integrates pooled and entropy-based features from both shallow and deep branches, enabling robust representation of spatial saliency and statistical complexity. We evaluate RDPNet on two benchmark datasets: the University of Bonn and TUSZ. On the Bonn dataset, RDPNet achieves 99.56–100% accuracy in binary classification, 99.29–99.79% in ternary tasks, and 95.10% in five-class classification. On the clinically realistic TUSZ dataset, it reaches a weighted F1-score of 95.72% across seven seizure types. Compared with several baselines, RDPNet consistently outperforms existing approaches, demonstrating superior robustness, generalizability, and clinical potential for epileptic EEG analysis. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information II)
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