Search Results (126)

A comprehensive understanding of plant diversity is essential for ecological research, conservation planning, and sustainable resource management. Advances in genetic technologies have transformed the assessment of plant biodiversity, enabling more precise and efficient characterization of genetic variation. Early molecular markers, widely used in the late 2000s, have largely been replaced by polymerase chain reaction (PCR)-based tools that require less DNA, are easier to use, and are supported by accessible commercial kits. The 2020s have seen the emergence of new, more accessible tools driven by cost reduction and efficiency improvements. High-throughput sequencing (HTS) technologies have further revolutionized the field by providing genome-wide insights into allelic diversity, structural polymorphisms, and epigenetic modifications. These innovations enhance the detection of adaptive variation, improve understanding of spatial genetic structure, and support the evaluation of environmental impacts on plant populations. Marker-assisted selection, now common in modern breeding, leverages genomic data to develop cultivars with enhanced resistance and desirable agronomic traits. Emerging tools such as environmental DNA (eDNA) analysis, high-throughput phenotyping, and advanced bioinformatics workflows expand the capacity to monitor species, assess population viability, and identify key traits linked to adaptation. The present review aims to highlight these technological advancements and the more recent and useful tools available from Next-Generation Sequencing to genotyping-by-sequencing, discussing their role for conserving plant genetic resources, improving breeding programs, and deepening knowledge of plant biodiversity within changing ecosystems. Full article

Accurate prediction of hard disk drive (HDD) health states is critical for enabling proactive data maintenance and ensuring data reliability in large-scale data centers. However, conventional models often suffer from semantic entanglement among heterogeneous SMART attributes and from the masking of incipient failure signatures by stochastic noise. To address these challenges, we propose SDGR-Net, a spatiotemporally decoupled learning framework designed to model the complex degradation dynamics of HDDs. SDGR-Net introduces three synergistic innovations: (1) a spatiotemporally decoupled dual-branch encoder that disentangles longitudinal temporal evolution from cross-variable correlations via parameter-isolated branches, thereby reducing representational interference; (2) a parsimonious dual-view temporal extraction mechanism that captures early-stage anomalies through forward–reverse sequence concatenation, enabling high-fidelity preservation of non-stationary pre-failure patterns; and (3) a cross-branch dynamic gated residual fusion module that functions as an adaptive information bottleneck to emphasize failure-critical features while suppressing redundant noise. Extensive experiments conducted on three heterogeneous HDD datasets, ST4000DM000, HUH721212ALN604, and MG07ACA14TA, demonstrate that SDGR-Net consistently outperforms six state-of-the-art baselines. In particular, SDGR-Net achieves a peak fault detection rate (FDR) of 0.9898 and a 69.6% relative reduction in false alarm rate (FAR) under high-reliability operating conditions. These results, corroborated by comprehensive ablation studies, indicate that SDGR-Net effectively balances detection sensitivity and operational robustness, offering a practical solution for intelligent HDD health monitoring. Full article

Human papillomavirus (HPV)-driven oropharyngeal squamous cell carcinoma (OPSCC) has emerged as a biologically distinct entity, typically affecting younger, non-smoking patients and showing improved survival compared to HPV-negative tumors. Accurate HPV status determination is essential for correct staging, prognostic assessment, and treatment de-escalation. Despite advances, substantial variability persists among diagnostic methods and clinical workflows. A narrative review of PubMed, Scopus, and Web of Science databases was conducted up to July 2025. Studies addressing HPV detection techniques in OPSCC—including p16^INK4a^ immunohistochemistry (IHC), HPV DNA and RNA assays, liquid biopsy approaches, and computational surrogates—were critically analyzed regarding diagnostic accuracy, clinical applicability, and emerging innovations. Tissue-based assays remain the diagnostic reference standard. p16 IHC provides high sensitivity but limited specificity and should be confirmed with nucleic acid-based methods such as DNA PCR, in situ hybridization (ISH), or E6/E7 mRNA detection. Combined or “orthogonal” testing minimizes discordance and refines risk stratification. Liquid biopsy detection of circulating HPV DNA using droplet digital PCR or next-generation sequencing has shown high sensitivity and specificity in cohorts of patients with HPV-associated OPSCC, supporting its potential role as a complementary biomarker for treatment monitoring and surveillance. However, circulating HPV DNA alone does not unequivocally identify the anatomic source of HPV DNA and should be interpreted together with clinical, radiologic, and tissue-based findings. Oral rinse and saliva assays show moderate diagnostic performance, while artificial intelligence-based radiomic and histopathologic models are emerging as complementary tools. Reliable HPV attribution in OPSCC requires a multimodal diagnostic strategy integrating p16 IHC, molecular confirmation, and ctHPV-DNA monitoring. Methodological standardization and prospective validation are essential to implement precision-guided, cost-effective workflows in routine clinical practice. Full article

The paper investigates the applicability of time series models for processing data obtained from a customized crack-propagation sensor. Because the sensor records a variable and noise-affected waveform, the study focuses on models capable of forecasting signals composed of both trend and stochastic components. Adaptive, analytical, and autoregressive approaches were examined, with particular attention to their suitability for short, non-stationary sequences typical of fatigue-related measurements. Based on the statistical characteristics of the sensor output during crack growth, the ARIMA model was selected for further analysis and algorithm development. The forecasting performance of ARIMA was evaluated for different parameter configurations by comparing the range and variability of the base and predicted data. Initial tests using first-order parameters produced unsatisfactory results, with high variance observed in both raw and modeled signals. Therefore, model parameters were optimized using the aicbic function, and the analyses were repeated. For the selected datasets, variance reduction by 3–4 orders of magnitude was achieved, demonstrating a substantial improvement in prediction stability. The presented results confirm that the proposed methodology is effective for processing complex sensor signals and highlight the broader significance of applying statistically grounded time series models in structural health monitoring. The study introduces an innovative framework for evaluating fatigue-related sensor data and establishes a reliable baseline for future predictive methods. Full article

Background: Liquid biopsy, particularly through the analysis of circulating tumor DNA (ctDNA), represents a significant advancement in oncology. Unlike traditional tissue biopsies, ctDNA offers a minimally invasive, real-time approach to cancer management. It has demonstrated considerable potential in early cancer detection, monitoring of therapeutic responses, and assessing minimal residual disease (MRD) to predict recurrence. By enabling comprehensive molecular profiling through a simple blood test, ctDNA supports the core principles of precision oncology, facilitating more personalized and adaptive treatment strategies. Methods: In the following article we describe the recent developments focused on refining ctDNA detection assays to improve sensitivity and specificity. Advanced technologies, including next-generation sequencing (NGS) and digital PCR, are commonly employed. The integration of artificial intelligence (AI) and multi-omics approaches—such as combining genomic, epigenomic, and transcriptomic data—has further enhanced the analytical power of ctDNA assays. Results: Emerging evidence shows that ctDNA-based liquid biopsy enables dynamic, real-time tracking of tumor evolution and therapeutic resistance. Clinical studies have demonstrated its efficacy in detecting early-stage cancers, guiding treatment selection, and predicting relapse with higher accuracy than some conventional methods. Moreover, AI-enhanced algorithms have improved signal detection, allowing for more precise and earlier identification of actionable mutations and MRD. Conclusions: ctDNA analysis via liquid biopsy is poised to revolutionize cancer care by offering a non-invasive, precise, and adaptive tool for tumor characterization and monitoring. Although obstacles remain—particularly regarding assay sensitivity, standardization, and economic feasibility—ongoing technological innovations and multi-omics integration are rapidly advancing its clinical viability. With continued progress, ctDNA-based liquid biopsy is likely to become a cornerstone of routine oncology practice. Full article

Environmental DNA (eDNA) metabarcoding is an innovative tool that is transforming ecological research. It offers a simple and effective method for simultaneously detecting numerous species across a wide range of environments. The method relies on assigning DNA sequences sampled from the environment to taxa, which is straightforward for species that have already been sequenced and are represented in reference databases. However, existing bioinformatics tools often fail to deliver accurate, fine-grained assignments when target species are absent from these databases. This limitation arises from handcrafted classification thresholds that do not account for nucleotide positional information. Here, we propose a deep neural architecture specifically designed to exploit both nucleotide identity and positional patterns in short TELEO sequences. Using an in-silico validation framework based on NCBI genbank sequences, we compare our approach with several state-of-the-art bioinformatics tools (Obitools, Kraken2, Lolo), as well as alternative sequence embedding methods, under controlled conditions. Our approach yields significantly higher classification accuracy at the genus and family levels, achieving average accuracies of 94.7% at the genus level and 86.5% at the family level, substantially outperforming the tested reference-based pipelines. The method remains robust with limited training data and shows improved performance when nucleotide positional information is preserved through sequence alignment. These results demonstrate the potential of AI-powered eDNA metabarcoding to complement existing taxonomic assignment tools, particularly in contexts where reference databases are incomplete or species-level resolution is not achievable, thereby supporting biodiversity monitoring and ecosystem management. Full article

Myopathies represent a highly heterogeneous group of primary muscle disorders, traditionally classified based on clinical presentation and histopathological findings. Recent breakthroughs in molecular genetics, immunology, and pathophysiology have revolutionized the understanding, diagnosis, and management of these conditions. Both inherited and acquired forms of myopathy, including structural, metabolic, inflammatory, endocrine, and mitochondrial subtypes, are now recognized to arise from diverse pathogenic mechanisms such as impaired calcium handling, mitochondrial dysfunction, chronic inflammation, altered metabolism, and defective muscle regeneration. The advent of next-generation sequencing technologies has enabled more precise diagnosis of genetic forms, while the discovery of novel molecular biomarkers and immunological signatures offers promising avenues for disease monitoring and stratification across the broader spectrum. Importantly, molecular and mechanistic insights have redefined clinical classifications, allowing for better prognostic predictions and patient-tailored therapeutic approaches. Innovative treatments, including gene therapy, antisense oligonucleotide therapies, immune-modulating agents, metabolic support strategies, and targeted pharmacological interventions, are progressively translating molecular knowledge into clinical applications. However, technical limitations, biological variability, and ethical considerations continue to pose significant challenges to the implementation of precision medicine in myopathies. In this narrative review, we comprehensively discuss the latest molecular findings, their integration into clinical practice, and the emerging therapeutic strategies based on these discoveries. We also highlight current limitations and propose future research directions aimed at bridging the gap between molecular insights and effective, equitable patient care. Full article

Effective monitoring of maize phenology under stress conditions is crucial for optimizing agricultural management and mitigating yield losses. Crop prediction models constructed from Convolutional Neural Network (CNN) have been widely applied. However, CNNs often struggle to capture long-range temporal dependencies in phenological data, which are crucial for modeling seasonal and cyclic patterns. The Transformer model complements this by leveraging self-attention mechanisms to effectively handle global contexts and extended sequences in phenology-related tasks. The Transformer model has the global understanding ability that CNN does not have due to its multi-head attention. This study, proposes a synergistic framework, in combining CNN with Transformer model to realize global-local feature synergy using two models, proposes an innovative phenological monitoring model utilizing near-ground remote sensing technology. High-resolution imagery of maize fields was collected using unmanned aerial vehicles (UAVs) equipped with multispectral and thermal infrared cameras. By integrating this data with CNN and Transformer architectures, the proposed model enables accurate inversion and quantitative analysis of maize phenological traits. In the experiment, a network was constructed adopting multispectral and thermal infrared images from maize fields, and the model was validated using the collected experimental data. The results showed that the integration of multispectral imagery and accumulated temperature achieved an accuracy of 92.9%, while the inclusion of thermal infrared imagery further improved the accuracy to 97.5%. This study highlights the potential of UAV-based remote sensing, combined with CNN and Transformer as a transformative approach for precision agriculture. Full article

Small object detection in remote sensing images provides significant value for urban monitoring, aerospace reconnaissance, and other fields. However, detection accuracy still faces multiple challenges including limited target information, weak feature representation, and complex backgrounds. This research aims to improve the performance of the YOLO11 model for small object detection in remote sensing imagery by addressing key issues in long-distance spatial dependency modeling, multi-scale feature adaptive fusion, and computational efficiency. We constructed a specialized Remote Sensing Airport-Plane Detection (RS-APD) dataset and used the public VisDrone2019 dataset for generalization verification. Based on the YOLO11 architecture, we proposed the DAE-YOLO model with three innovative modules: Dynamic Spatial Sequence Module (DSSM) for enhanced long-distance spatial dependency capture; Adaptive Multi-scale Feature Enhancement (AMFE) for multi-scale feature adaptive receptive field adjustment; and Efficient Dual-level Attention Mechanism (EDAM) to reduce computational complexity while maintaining feature expression capability. Experimental results demonstrate that compared to the baseline YOLO11, our proposed model improved mAP₅₀ and mAP_50:95 on the RS-APD dataset by 2.1% and 2.5%, respectively, with AP_s increasing by 2.8%. This research provides an efficient and reliable small object detection solution for remote sensing applications. Full article

To enhance the monitoring and conservation efforts for China’s Class II endangered species, specifically the wild giant salamander and its ecosystems, this study addresses the urgent need to counteract the rapid decline of its wild population caused by habitat loss and insufficient surveillance. We present an innovative localization system based on passive Ultra-High-Frequency Radio Frequency Identification (UHF RFID) technology, employing a Double-Transform (D-Tr) methodology that integrates an enhanced 3D LANDMARC algorithm with GAIN generative adversarial networks. This system effectively reconstructs missing Received Signal Strength Indicator (RSSI) data due to environmental barriers by applying a log-distance path loss model. The D-Tr framework simultaneously generates RSSI sequences alongside their first-order differential characteristics, allowing for a comprehensive analysis of spatiotemporal signal relationships. Field tests conducted in the Hubei Xianfeng Zhongjian River Giant Salamander National Nature Reserve reveal that the positioning error consistently remains within 10 cm, with average accuracy improvements of 20.075%, 15.331%, and 12.925% along the X, Y, and Z axes, respectively, compared to traditional time-series models such as long short-term memory (LSTM) and gated recurrent unit (GRU). This system, designed to investigate the behavioral patterns and movement paths of farmed giant salamanders, achieves centimeter-level tracking of their cave-dwelling activities. It provides essential technical support for quantitatively assessing their daily activity patterns, habitat choices, and population trends, thereby promoting a shift from passive oversight to proactive monitoring in the conservation of endangered species. Full article

Permanent magnet synchronous generators (PMSGs) are suitable for offshore applications due to their high efficiency and power density. Inter-turn short circuits (ITSCs) stand as one of the most critical faults in these machines due to their rapid evolution in phase or ground short circuits. It is therefore necessary to detect ITSCs at an early stage. In the literature, ITSC detection is often based on current signal processing methods. One of the challenges that these methods face is the presence of imperfections in the stator coils, which also affects the three-phase symmetry. Moreover, when the stator coils are connected in parallel, this type of fault becomes important, as circulating currents will flow between the parallel windings. This, in turn, increases the thermal stress on the insulation and the permanent magnets, while also exacerbating the vibrations of the generator. In this study, a finite-element analysis (FEA) model has been developed to simulate a dual-rotor PMSG under conditions of coil asymmetry. To further investigate the impact of this asymmetry, mathematical modeling has been conducted. For fault detection, negative-sequence current (NSC) analysis and torque monitoring have been used to distinguish coil asymmetry from ITSCs. While both methods demonstrate potential for fault identification, NSC induced small amplitudes and the torque analysis was unable to detect ITSCs under low-severity conditions, thereby underscoring the importance of developing advanced strategies for early-stage ITSC detection. The innovative aspect of this work is that, despite these limitations, the combined use of NSC phase-angle tracking and torque harmonic analysis provides, for the first time in a core-less PMSG with parallel-connected coils, a practical way to distinguish ITSC from coil asymmetry, even though both faults produce almost identical signatures in conventional current-based indices. Full article

Liquid biopsy and multi-omic biomarker integration are transforming precision oncology in breast cancer, providing real-time, minimally invasive insights into tumor biology. By analyzing circulating tumor DNA, circulating tumor cells, exosomal non-coding RNAs, and proteomic or metabolomic profiles, clinicians can monitor clonal evolution, therapeutic response, and recurrence risk in real time. Recent advances in sequencing technologies, methylation profiling, and artificial intelligence–driven data integration have markedly improved diagnostic sensitivity and predictive accuracy. Multi-omic frameworks combining genomic, transcriptomic, and proteomic data enable early detection of resistance, molecular stratification, and identification of actionable targets, while machine learning models enhance outcome prediction and therapy optimization. Despite these advances, key challenges persist. Pre-analytical variability, lack of standardized protocols, and disparities in access continue to limit reproducibility and clinical adoption. High costs, incomplete regulatory validation, and the absence of definitive evidence for mortality reduction underscore the need for larger, prospective trials. Integrating multi-omic assays into clinical workflows will require robust bioinformatics pipelines, clinician-friendly reporting systems, and interdisciplinary collaboration among molecular scientists, data engineers, and oncologists. In the near future, liquid biopsy is expected to complement, not replace, traditional tissue analysis, serving as a cornerstone of adaptive cancer management. As sequencing becomes faster and more affordable, multi-omic and AI-driven analyses will allow earlier detection, more precise treatment adjustments, and continuous monitoring across the disease course. Ultimately, these innovations herald a shift toward real-time, data-driven oncology that personalizes breast cancer care and improves patient outcomes. Full article

Autonomous exploration is essential for various mapping tasks, including data collection, environmental monitoring, and search and rescue operations. Unmanned aerial vehicles (UAVs), owing to their low cost and high maneuverability, have become key enablers of such applications, particularly in complex or hazardous environments. However, existing approaches often suffer from issues such as redundant exploration and unstable flight behavior. In this study, we propose a hierarchical exploration approach specifically designed for limited-field-of-view UAVs in geospatial mapping applications. The approach addresses these challenges through hybrid viewpoint generation, an innovative boundary exploration sequence, and a two-stage global path planning strategy. To balance exploration efficiency and computational cost, we adopt a hybrid approach that combines collision-free spherical sampling with adaptive viewpoint generation based on stochastic differential equations. This approach generates high-quality candidate viewpoints while minimizing computational overhead. Furthermore, we introduce a novel heuristic evaluation function to prioritize frontiers within small regions, thereby facilitating optimal path planning. Based on this formulation, the global coverage path is modeled as a traveling salesman problem (TSP). The two-stage global planning framework consists of an initial stage that applies a history-aware trajectory enhancement strategy with smoothing corrections, followed by a second stage employing a sliding-window TSP algorithm to construct the global path. This design mitigates motion inconsistencies caused by frequent heuristic updates and enhances flight stability and trajectory smoothness. To evaluate the performance of the proposed framework, we compare it with state-of-the-art approaches in both simulated and real-world environments. Experimental results demonstrate that our approach shortens flight paths and reduces exploration time, thereby improving overall exploration efficiency. Full article

The main objective of this study was to develop a comprehensive testing method for powered roof supports operating under real mining conditions and to establish guidelines for a monitoring system designed to record their geometric and operational parameters. The proposed methodology included analyses of load-bearing capacity limits, laboratory model tests, bench tests, and in situ investigations under actual working conditions. Based on these studies, a detailed testing procedure was developed, defining the sequence of experimental stages, the selection and calibration of sensors, their installation and servicing methods, as well as the integration of measuring equipment with the support structure. The key results demonstrate that the proposed method allows for reliable acquisition and interpretation of data concerning the operational behavior of powered roof supports. The findings enabled the identification of critical geometric and operational parameters influencing the stability, durability, and efficiency of the support system. The developed monitoring procedure, supported by both laboratory and field tests, provides a consistent and replicable framework for assessing the performance of roof supports in real-time mining operations. The conclusions confirm that the presented approach represents an innovative and systematic method for evaluating and monitoring powered roof supports under real conditions. The main contribution of this work lies in the formulation of universal guidelines for the design and implementation of monitoring systems, significantly improving the safety, reliability, and efficiency of mining processes. Full article

The proliferation of power converters in modern energy production systems has led to increased harmonic content due to the commutation of active switching devices. This increase in harmonics contributes to lower system efficiency, reduced power factor, and consequently, a higher reactive power requirement. To address these issues, this paper presents both simulation and experimental results of various control strategies implemented on Parallel Voltage Source Inverters (PVSI) for harmonic mitigation. The proposed control strategies are categorized into direct and indirect control methods. The direct control techniques implemented include the instantaneous power method (PQ), the synchronous algorithm (DQ), the maximum principle method (MAX), the algorithm based on synchronization of current with the voltage positive-sequence component (SEC-POZ), and two methods employing the separating polluting components approach using a band-stop filter and a low-pass filter. The main innovation in these active power filter (APF) control strategies, compared to traditional or existing technologies, is the real-time digital implementation on high-speed platforms, specifically FPGAs. Unlike slower microcontroller-based systems with limited processing capabilities, FPGA-based implementations allow parallel processing and high-speed computation, enabling the execution of complex control algorithms with minimal latency. Additionally, the enhanced reference current generation achieved through the seven applied methods provides precise harmonic compensation under highly distorted and nonlinear load conditions. Another key advancement is the integration with Smart Grid functionalities, allowing IoT connectivity and remote diagnostics, which enhances system monitoring and operational flexibility. Following validation on an experimental test bench, these algorithms were implemented and tested on industrial APF prototypes powered by a standardized three-phase network supply. All control strategies demonstrated an effective reduction in total harmonic distortion (THD) and improvement in power factor. Experimental findings were used to provide recommendations for choosing the most effective control solution, focusing on minimizing THD and enhancing system performance. Full article