Search Results (510)

Insects rely on a variety of sensory cues for orientation, with antennae playing a central role in receiving and transmitting information about the environment. Philaenus spumarius (Hemiptera: Aphrophoridae), a spittlebug and vector of the bacterium Xylella fastidiosa, has a reduced number of antennal sensilla, yet demonstrates effective multimodal communication through olfactory and vibrational signals. This study aimed to investigate how the simplified sensory system of P. spumarius relates to the primary neuropils of the brain. We examined the ultrastructural organization of Johnston’s organ using scanning and transmission electron microscopy, complemented by previous data on antennal sensilla. Brain organization was investigated by Micro-CT and confocal laser scanning microscopy, which enabled us to identify the primary neuropiles. In addition, we conducted antennal and single sensillum backfills to trace sensory neurons to the brain. Our findings provide insight into the adaptation of a simplified sensory system for effective communication and orientation in P. spumarius. Full article

Background: HER2-positive breast cancer patients receiving chemotherapy and targeted therapy (including anthracyclines and trastuzumab) face an elevated risk of cardiotoxicity, which can lead to long-term cardiovascular complications. Identifying predictive biomarkers is essential for early intervention. Circulating microRNAs (miRNAs), known regulators of gene expression and cardiovascular function, have emerged as potential indicators of cardiotoxicity. This study aims to evaluate the differential expression of circulating miRNAs in HER2-positive breast cancer patients undergoing chemotherapy and to assess their prognostic ability for therapy-induced cardiotoxicity using machine learning models. Methods: Forty-seven patients were assessed for cardiac toxicity at baseline and every 3 months, up to 15 months. Blood samples were collected at baseline. MiRNA expression profiling for 84 microRNAs was performed using the miRCURY LNA miRNA PCR Panel. Differential expression was calculated via the 2^−∆∆Ct method. The five most upregulated and five most downregulated miRNAs were further assessed using univariate logistic regression and receiver operating characteristic (ROC) analysis. Five machine learning models (Decision Tree, Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), k-Nearest Neighbors (KNN)) were developed to classify cardiotoxicity based on miRNA expression. Results: Forty-five miRNAs showed significant differential expression between cardiac toxic and non-toxic groups. ROC analysis identified hsa-miR-155-5p (AUC 0.76, p = 0.006) and hsa-miR-124-3p (AUC 0.75, p = 0.007) as the strongest predictors. kNN, SVM, and RF models demonstrated high prognostic accuracy. The decision tree model identified hsa-miR-17-5p and hsa-miR-185-5p as key classifiers. SVM and RF highlighted additional miRNAs associated with cardiotoxicity (SVM: hsa-miR-143-3p, hsa-miR-133b, hsa-miR-145-5p, hsa-miR-185-5p, hsa-miR-199a-5p, RF: hsa-miR-185-5p, hsa-miR-145-5p, hsa-miR-17-5p, hsa-miR-144-3p, and hsa-miR-133a-3p). Performance metrics revealed that SVM, kNN, and RF models outperformed the decision tree in overall prognostic accuracy. Pathway enrichment analysis of top-ranked miRNAs demonstrated significant involvement in apoptosis, p53, MAPK, and focal adhesion pathways, all known to be implicated in chemotherapy-induced cardiac stress and remodeling. Conclusions: Circulating miRNAs show promise as biomarkers for predicting cardiotoxicity in breast cancer patients. Machine learning approaches may enhance miRNA-based risk stratification, enabling personalized monitoring and early cardioprotective interventions. Full article

Duchenne muscular dystrophy (DMD) is a severe X-linked hereditary disorder caused by pathogenic variants in the DMD gene encoding the dystrophin protein. The absence of functional dystrophin leads to destabilization of the dystrophin-associated glycoprotein complex (DAPC), sarcolemmal damage, and progressive degeneration of muscle fibers. Current therapeutic strategies focus on restoring dystrophin expression using genome editing approaches. Adeno-associated virus (AAV) vectors represent the primary delivery platform due to their strong tropism for muscle tissue, low immunogenicity, and ability to achieve long-term transgene expression. However, the limited packaging capacity of AAV (~4.7 kb) necessitates the use of truncated mini- and micro-dystrophin transgenes as well as compact genome editing systems (SaCas9, NmeCas9, Cas12f, TIGR-Tas, and others). Major challenges include immune responses against the viral capsid and transgene products, as well as the inability to perform repeated administrations. Moreover, the duration of expression is limited by the episomal nature of AAV genomes and their loss during muscle fiber regeneration. Despite substantial progress, unresolved issues concerning safety, immunogenicity, and stability of genetic correction remain, defining the key directions for future research in DMD therapy. Full article

Japanese apricot (Prunus mume Sieb. et Zucc.) is a dicotyledonous plant from the Rosaceae family that originated in China. Functional genomic studies in Japanese apricot are essential to elucidate the molecular mechanisms underlying key agronomic traits and to accelerate crop improvement. However, the lack of an efficient genetic transformation system has hindered gene function analysis and impeded molecular breeding efforts. Agrobacterium rhizogenes-mediated transformation has emerged as a robust tool for functional gene validation and studying root-specific processes across diverse plant species, due to its simple protocol and rapid turnaround time. Notably, Agrobacterium-mediated transformation remains notoriously recalcitrant in Rosaceae species, particularly in Japanese apricot. Through screening of ten Japanese apricot varieties, we identified ‘Muguamei’ (MGM) as the optimal cultivar for tissue culture. Using its genotype, we established an Agrobacterium rhizogenes-mediated transformation system for Japanese apricot via an in vitro approach. The binary vector incorporated the RUBY reporter for visual selection and eYGFPuv for fluorescent validation of transformation events. Furthermore, CRISPR/Cas9-mediated knockout of PmPDS in ‘Muguamei’ calli generated albino phenotypes, confirming successful genome editing. Through optimization of antibiotics, the study achieved an 80% explant survival rate using Woody Plant Medium (WPM) supplemented with 6-BA (0.5 mg/L) and TDZ (0.05 mg/L). For in vitro micropropagation, we found that ‘Muguamei’ exhibited optimal shoot growth in the presence of 6-BA (0.06 mg/L) and TDZ (0.1 mg/L), and up to 8 bud proliferation lines could be reached under 4.0 mg/L 6-BA. During the rooting of micro shoots, ½MS medium performed better and reached the optimum root length (35.70 ± 4.56 mm) and number (6.00 ± 1.00) under IAA (0.5 mg/L) and IBA (0.4 mg/L). Leaf explants were cultured on WPM supplemented with TDZ (4.0 mg/L) and NAA (0.2 mg/L). 50 mg/L kanamycin concentrations were the suitable screening concentration. Full article

The incorporation of carbon nanotubes (CNTs) enhances the mechanical properties of cement-based materials by inhibiting micro-crack propagation. Machine learning provides an efficient approach for predicting the compressive strength of CNT-reinforced concrete, yet existing studies often lack important features and rely on less adaptive models. To address these issues, a multi-dimensional database (429 experimental data points) covering 11 factors (including cement mix ratio, CNT morphology, and dispersion process) was constructed. A hierarchical model verification and optimization was conducted: traditional regression models (Multiple Linear Regression, Multiple Polynomial Regression (MPR), Multivariate Adaptive Regression Splines), mainstream model (Support Vector Regression (SVR)), and ensemble learning models (Random Forest, eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine optimized by Particle Swarm Optimization (PSO)/Bayesian Optimization (BO)) are trained, compared, and evaluated. MPR performs best (test set R² = 0.856) among traditional regression models, while SVR (test set R² = 0.824) is less accurate. The highest accuracy in ensemble models is achieved by the PSO-optimized XGB model, with R² = 0.910 (test set). PSO outperforms BO in optimization precision, while BO is much more efficient. Water–cement ratio, age, and sand–cement ratio are the primary influencing factors for strength. Among CNT parameters, the inner diameter has greater impact than the length and outer diameter. Optimal CNT parameters are CNT–cement mass ratio 0.1–0.3%, inner diameter ≥ 7.132 nm, and length 1–15 μm. Surfactant polycarboxylate can increase strength, while OH⁻ functional groups can decrease it. These findings, integrated into the high-precision PSO-XGB model, provide a powerful tool for optimizing the mix design of CNT-reinforced concrete, accelerating its development and application in the industry. Full article

Background: Adenomyosis remains difficult to diagnose non-invasively due to clinical overlap with endometriosis and the limited specificity of imaging techniques. This pilot study evaluated whether serum- and urine-derived microRNA (miRNA) profiles, combined with machine-learning approaches, could support non-invasive diagnosis. Methods: Serum and urine samples were collected from 59 patients undergoing surgery for chronic pelvic pain at the Endometriosis Centre of RWTH Aachen University Hospital. Seven patients had isolated adenomyosis, 34 had histologically confirmed endometriosis, and 18 served as negative controls. miRNAs were profiled using next-generation sequencing. A structured feature-selection pipeline (variance filtering, univariate testing, mutual information, recursive feature elimination) was applied before training Logistic Regression, Random Forest, Support Vector Machine, and Decision Tree models using cross-validation. Model performance was evaluated using accuracy, precision, recall, F1 score, and ROC-AUC. Results: Distinct miRNA signatures were detected in both serum and urine, with urine-based models showing superior discriminatory performance. Logistic Regression and Support Vector Machine achieved excellent separation in urine datasets, although perfect AUC values must be interpreted cautiously due to the small number of adenomyosis cases. In serum, Random Forest achieved the highest AUC values (up to 0.98). Several miRNAs, including miR-183-3p, miR-320d-2, and miR-17, emerged as promising candidate biomarkers for differentiating adenomyosis from endometriosis and from negative controls. Conclusions: This pilot study demonstrates the feasibility of liquid-biopsy miRNA profiling combined with machine learning for non-invasive adenomyosis detection. Although results are preliminary and require validation in larger cohorts, urine miRNA profiles may represent a promising complementary tool to improve diagnostic accuracy and reduce diagnostic delay. Full article

This study introduces an energy-aware hybrid security framework that safeguards embedded systems against code theft, closing a critical gap. The approach integrates bitstream encryption, dynamic key generation, and Dynamic Function eXchange (DFX)-based memory obfuscation, yielding a layered hardware–software countermeasure to Read-Only Memory (ROM) scraping, side-channel attacks, and Man-in-the-Middle (MITM) intrusions by eavesdropping on communications on pins, cables, or Printed Circuit Board (PCB) routes. Prototyped on a Xilinx Zynq-7020 System-on-Chip (SoC) and applicable to MicroBlaze-based designs, it derives a fresh Authenticated Encryption with Associated Data (AEAD) key for each record via an Ascon-eXtendable-Output Function (XOF)–based Key Derivation Function (KDF) bound to a device identifier and a rotating slice from a secret pool, while relocating both the pool and selected Block RAM (BRAM)-resident code pages via Dynamic Function eXchange (DFX). This moving-target strategy frustrates ROM scraping, probing, and communication-line eavesdropping, while cryptographic confidentiality and integrity are provided by a lightweight AEAD (Ascon). Hardware evaluation reports cycles/byte, end-to-end latency, and per-packet energy under identical conditions across lightweight AEAD baselines; the framework’s key-derivation and DFX layers are orthogonal to the chosen AEAD. The threat model, field layouts (Nonce/AAD), receiver-side acceptance checks, and quantitative bounds are specified to enable reproducibility. By avoiding online key exchange and keeping long-lived secrets off Programmable Logic (PL)-based external memories while continuously relocating their physical locus, the framework provides a deployable, energy-aware defense in depth against code-theft vectors in FPGA-based systems. Overall, the work provides an original and deployable solution for strengthening the security of commercial products against code theft in embedded environments. Full article

In general, epilepsy is considered to be one of most prevalent neurological disorders and frequently appears as sudden seizures resulting in injuries, accidents, sudden unexpected death, etc. Also, it is reported that around 60 million people across the globe are experiencing various seizures due to epilepsy. So, there is demand for ambulatory seizure detection devices to prevent such accidents and to improve the quality of life for epilepsy patients. In this work, an intelligent Internet of Medical Things (IoMT)-based wearable device is designed and developed to monitor seizures in epilepsy patients. Due to the lack of an accelerometer dataset for epileptic seizures, the proposed device was developed, and a dataset mimicking seizure-like activities was generated. Further, the proposed device utilizes an MPU6500-based inertial measurement unit (IMU) which is integrated into an ESP32 microcontroller board. The ESP32 has a built-in wireless fidelity (WiFi) + Bluetooth (BLE) un that supports MicroPython v1.22.1 programming. Also, the machine learning algorithms such as Decision Trees (DT), Support Vector Machines (SVM), and Random Forest (RF) were programmed using MicroPython v1.22.1 programming and deployed on a tiny edge computing device to monitor the activity of the epileptic patients. All the adopted machine learning algorithms were compared in terms of performance metrics such as accuracy, precision, recall, false positive rate (FPR), etc., and the efficacy of the device was analysed. Results demonstrate that the proposed device is capable of identifying the activities of individuals, which is highly useful for epilepsy patients to monitor epileptic seizures. Furthermore, the proposed device was deployed with an RF algorithm since it exhibits an accuracy of 95% which is better compared to other machine learning algorithms. Also, the proposed device is simple and cost-effective and, in the event of a seizure event, can alert caretakers of epilepsy patients with an FPR of less than 4%. Full article

To address information isolation and incomplete parameter extraction among multi-modal data (e.g., drawings, text, and tables) in the operation and maintenance stage of buildings, this paper proposes a multi-modal data parsing, automatic parameter extraction, and standardized integration method oriented toward 3D modeling. First, by employing vector element parsing and layer semantic analysis, the method enables structured extraction of key component geometry from architectural drawings and improves modeling accuracy via spatial topological relationship analysis. Second, by combining regular expressions, a domain-specific terminology dictionary, and a BiLSTM-CRF deep learning model, the extraction accuracy of unstructured parameters from architectural texts is significantly improved. Third, a multi-scale sliding window and geometric feature analysis are used to achieve automatic detection and parameter extraction from complex nested tables. Regarding the experimental setup: the drawings consist of a large-scale collection of DXF files stratified and randomly split into train/val/test with an approximate 8:1:1 ratio; the text set includes 1550 PDF-derived specification fragments (8:1:1 split); and the tables cover typical door/window, structural, and electrical schedules (also split ~8:1:1). F1 scores use micro-F1 (instance-level aggregation), and 95% confidence intervals and their computation are described in the main text. Experimental results show that the F1 scores for wall line, wall, and column recognition reach 98.1%, 84.9%, and 92.2%, respectively, while the F1 scores for door and window recognition are 74.3% and 76.2%. For text parameter extraction, the proposed PENet model achieves a precision of 83.56% and a recall of 86.91%. For the table task, the parameter extraction recalls for doors/windows and structure are 95.0% and 96.7%, respectively. The proposed method enables efficient parameter extraction and standardization from multi-modal architectural data, demonstrates significant advantages in handling heterogeneous data and improving modeling efficiency, and provides practical technical support for the digital reconstruction and intelligent management of existing buildings. Full article

Structure-Modulated Long-Period Fiber Gratings (SM-LPFGs) represent an advancement in fiber optic sensor technology, moving beyond traditional photosensitivity-based fabrication to achieve enhanced performance through the direct physical modification of the geometry of the fiber. This review provides a comprehensive analysis of the primary fabrication techniques enabling this approach, including CO₂ laser inscription, femtosecond laser micromachining, electric-arc discharge, chemical etching, and fusion tapering. The central focus of this work is the elucidation of the definitive structure–performance relationship, systematically detailing how engineered geometries such as helical profiles, micro-tapers, and asymmetric grooves unlock novel sensing capabilities. We demonstrate how these specific structures are strategically designed to induce circular birefringence for torsion measurement, enhance evanescent field interaction for ultra-sensitive refractive index detection, and create localized stress concentrations for high-resolution strain and vector bending sensing. Furthermore, the review surveys the practical implementation of these sensors in critical application domains, including structural health monitoring, biomedical diagnostics, and environmental sensing. Finally, we conclude by summarizing key achievements and identifying promising future research directions, such as the development of hybrid fabrication processes, the integration of machine learning for advanced signal demodulation, and the path towards industrial-scale production. Full article

This paper introduces TRIDENT-DE, a novel ensemble-based variant of Differential Evolution (DE) designed to tackle complex continuous global optimization problems. The algorithm leverages three complementary trial vector generation strategies best/1/bin, current-to-best/1/bin, and pbest/1/bin executed within a self-adaptive framework that employs jDE parameter control. To prevent stagnation and premature convergence, TRIDENT-DE incorporates adaptive micro-restart mechanisms, which periodically reinitialize a fraction of the population around the elite solution using Gaussian perturbations, thereby sustaining exploration even in rugged landscapes. Additionally, the algorithm integrates a greedy line-refinement operator that accelerates convergence by projecting candidate solutions along promising base-to-trial directions. These mechanisms are coordinated within a mini-batch update scheme, enabling aggressive iteration cycles while preserving diversity in the population. Experimental results across a diverse set of benchmark problems, including molecular potential energy surfaces and engineering design tasks, show that TRIDENT-DE consistently outperforms or matches state-of-the-art optimizers in terms of both best-found and mean performance. The findings highlight the potential of multi-operator, restart-aware DE frameworks as a powerful approach to advancing the state of the art in global optimization. Full article

The rise in diabetes and obesity worldwide has created an urgent demand for low-sugar, nutrient-dense foods with appealing flavors. This study established an endogenous and “rapid validation–stable production” platform to enhance the flavor of edible tomato fruits by integrating two key technologies in the MicroTom cherry tomato: (1) TRV viral vector-mediated transient expression and (2) Agrobacterium-mediated stable genetic transformation. We employed the human sweet taste receptor TAS1R2 for in vitro functional validation and objectively demonstrated that tomato-derived recombinant thaumatin II exhibits receptor-binding activity equivalent to that of the native protein, overcoming the limitations of traditional sensory evaluation. Non-targeted metabolomic analysis (covering 1236 metabolites) confirmed that thaumatin II expression did not significantly alter the profiles of sugars, organic acids, or key flavor compounds in tomato fruits. This provides safety data supporting the development of “ready-to-eat sugar-substitute fruits.” Our strategy offers a solution and theoretical technical support for the development of low-sugar, high-nutrient foods. Full article

The accelerating global urbanization process has posed new challenges to urban planning. With the rapid advancement of artificial intelligence (AI) technology, the application of AI in urban planning has gradually emerged as a prominent research focus. This study systematically reviews the current state, development trends, and challenges of AI applications in urban planning through a combination of bibliometric analysis using Citespace, AI-assisted reading based on generative models, and predictive analysis via support vector machine (SVM) algorithms. The findings reveal the following: (1) The application of AI in urban planning has undergone three stages—namely, the budding stage (January 1984 to January 2017), the rapid development stage (January 2017 to January 2023), and the explosive growth stage (January 2023 to January 2025). (2) Research hotspots have shifted from early-stage basic data integration and fundamental technology exploration to a continuous fusion and iteration of foundational and emerging technologies. (3) Globally, China, the United States, and India are the leading contributors to research in this field, with inter-country collaborations demonstrating regional clustering. (4) High-frequency keywords such as “deep learning,” “machine learning,” and “smart city” are prevalent in the literature, reflecting the application of AI technologies across both macro and micro urban planning scenarios. (5) Based on current research and predictive analysis, the application scenarios of technologies like deep learning and machine learning are expected to continue expanding. At the same time, emerging technologies, including generative AI and explainable AI, are also projected to become focal points of future research. This study offers a technical application guide for urban planning, promotes the scientific integration of AI technologies within the field, and provides both theoretical support and practical guidance for achieving efficient and sustainable urban development. Full article

Silver nanoparticles (AgNPs) have drawn great attention, owing to their unique physico-chemical and biological properties and various applications, particularly in the biomedical field. In addition to conventional chemical and physical methods, materials scientists have been exploring the capabilities endowed by several bioresources, such as plants, bacteria, fungi and algae, in the cost-effective and eco-friendly production of AgNPs. This review article provides a comprehensive overview of the current state of research on the bioapplications of biogenic AgNPs (bio-AgNPs). The various bioresources used and methodologies followed to synthesize bio-AgNPs are briefly examined, along with some aspects of the underlying mechanisms. Then, the review surveys the toxicity of AgNPs, in general, and presents the unique biological properties of bio-AgNPs. Furthermore, the review details numerous applications of bio-AgNPs with paramount importance to human health, such as the control of infectious disease vectors, cancer therapy, antibiofilm activity and environmental remediation. Importantly, the review highlights the paradoxical effect of these nano-objects since they specifically seem to exert their action solely on targeted cells and (micro)organisms. By featuring the unique advantages of biogenic methods and their challenges, this article aims at serving as a valuable resource to attract research on bio-AgNPs and elicit further developments towards the scalable and sustainable production of AgNPs for large scale industrial and clinical use. Full article

Gene therapy and RNA (ribonucleic acid)-based therapeutic strategies have emerged as promising alternatives to conventional diabetes treatments, significantly expanding the therapeutic landscape using viral and non-viral vectors, and RNA modalities such as mRNA (messenger ribonucleic acid), siRNA (small interfering ribonucleic acid) and miRNA (micro ribonucleic acid). Recent advancements in these fields have led to notable preclinical successes and ongoing clinical trials, yet they are accompanied by debates over safety, efficacy and ethical considerations that underscore the complexity of clinical translation. This review offers a comprehensive analysis of the underlying mechanisms by which these treatments target diabetes, critically evaluating the fundamental concepts and mechanistic insights that form their basis, while highlighting current research gaps, such as the challenges in long-term stability and efficient delivery of RNA-based therapies, and potential adverse effects associated with gene therapy techniques. By synthesizing diverse perspectives and controversies, the review outlines future directions and interdisciplinary approaches aimed at overcoming existing hurdles, ultimately setting the stage for innovative, personalized diabetes management and addressing the broader clinical and regulatory implications of these emerging therapeutic strategies. Full article