Search Results (848)

The vast majority of the ocean’s microbial natural product biosynthetic potential remains undescribed. To access this chemical diversity, we employed Small Molecule In Situ Resin Capture (SMIRC) across three ecologically distinct sites in San Diego, California. Using high-resolution LC-MS/MS, we detected spatial and temporal variability in the metabolomes captured. Low annotation rates and evidence of extensive halogenation supported the chemical novelty of the compounds captured. We detected rare chlorinated polyketides in the pinnaic acid class, previously known only from filter-feeding invertebrates. We also report the first detection of chlorosulfolipids in the Pacific Ocean including one that contained 11 chlorine atoms. We linked compound abundances to weekly phytoplankton counts to identify candidate producers and found evidence that different taxa produce chlorosulfolipids of different carbon chain lengths. This study provides evidence of the chemical novelty that can be captured directly from the environment and a framework for integrating environmental metabolomics with phytoplankton counts as a method to identify candidate compound producers. Full article

Alzheimer’s disease (AD) is characterized by cognitive decline and amyloid-β (Aβ)-related pathology. Non-rodent models that capture selected aspects of human AD remain limited. We established and characterized a human APP-driven, Aβ-related AD-like tree shrew model following AAV-mediated delivery of triple-mutant human amyloid precursor protein (hAPP-SLA) carrying the Swedish, Austrian, and London mutations by bilateral stereotaxic injection directed at CA1 coordinates. Adult tree shrews received bilateral AAV-hAPP-SLA injections directed at CA1 coordinates and were evaluated by bioluminescence imaging, behavioral testing, PCR, RT-qPCR, Western blotting, ELISA, and histopathology. Vector-associated reporter signals remained detectable for 6 months. The experimental group showed exogenous hAPP expression and reduced endogenous tsAPP expression, increased relative hippocampal Aβ₄₂ protein level, enhanced 4G8-reactive APP/Aβ-related signals, elevated total Aβ immunoreactivity, increased serum Aβ₄₂/Aβ₄₀ ratio, cytoarchitectural alterations, reduced Nissl staining, and Thioflavin S-reactive aggregate-associated signals. AT8 (Ser202/Thr205), GFAP, and Iba-1 immunoreactivity increased, whereas Synaptophysin and PSD-95 immunoreactivity was reduced. These changes were accompanied by reduced short-delay recognition-related performance and reduced social approach and social novelty preference. Aged tree shrews showed partly overlapping alterations. This model provides a non-rodent platform for studying human APP-driven Aβ-related pathology. Full article

Single-entity electrochemistry (SEE) expands the scope of analytical electrochemical measurement by shifting attention from ensemble-averaged currents to individually resolved stochastic events. This review evaluates progress toward two analytical endpoints, trustworthy detection of single molecules and context-preserving interrogation of single cells, with emphasis on quantitative rigor rather than platform novelty alone. Across nanoparticle collisions, nanopores, confined nanoelectrodes, vesicle electrochemical cytometry, intracellular nanopipettes, and array-enabled single-cell devices, the decisive analytical issue is no longer simply whether one entity can be detected, but whether event assignment, calibration, throughput, and reproducibility are sufficient to support credible inference. Representative primary studies are compared through shared metrics including event frequency, temporal resolution, bandwidth, molecular counts, detection limit, affinity, and effective yield of analyzable events. Particular attention is given to three recurring bottlenecks: interfacial variability, model-dependent event interpretation, and incomplete reporting of denominators such as rejected events, insertion success, and pore-to-pore or cell-to-cell reproducibility. The current evidence base is strongest in secretion and vesicle studies, whereas confinement-enabled and multimodal routes define the leading edge of single-molecule analysis. Overall, SEE is developing not as a single universal platform, but as a family of interface-controlled, data-rich analytical strategies whose future analytical value will depend on standardized reporting, multimodal validation, and benchmarking practices that preserve both sensitivity and confidence of assignment. Full article

The quality of meteorological data from a certain type of commercial aircraft flying between Hong Kong and Shanghai is investigated in this study with a special focus on wind-related parameters, including the horizontal wind speed, horizontal wind direction, and eddy dissipation rate (EDR). The novelty of the study is the analysis of flight data on a new route between Hong Kong and Shanghai. The method for calculating the EDR from Quick Access Recorder (QAR) data of the studied aircraft type is first described. Then, we analyze seven flights operating between Hong Kong and Shanghai in 2025, when Hong Kong was affected by two typhoons, Wipha and Ragasa. Both low-level and enroute wind data are considered. The quality of QAR-based wind data is established through comparison with (a) QAR data from other airline flights separated by 10 min and by one runway from the studied aircraft; (b) headwind and EDR observations from Doppler Light Detection and Ranging (LIDAR) systems at Hong Kong International Airport (HKIA); and (c) reanalysis data of a global numerical weather prediction (NWP) model for the enroute phase of the studied aircraft type. The QAR-based wind data is found to have sufficient quality for the study of low-level windshear and turbulence as well as meteorological applications such as upper-air wind monitoring and data assimilation into NWP models. The wind data collected in the enroute phase is studied further by considering an extended period of July and September 2025 with 151 sets of valid QAR data. The horizontal wind speed and wind direction from the QAR are in general agreement with the model reanalysis data, noting the different nature of the matched data (e.g., averaging period, model grid resolution). Full article

The Knyszyn Forest is one of the major lowland forest complexes of north-eastern Poland, yet its myxobiota remains insufficiently documented. We surveyed Eumycetozoa in three forest nature reserves—Krzemianka, Jałówka, and Las Cieliczański—to assess local species richness, substrate occurrence, and new distributional records. Field surveys were combined with moist-chamber cultures, and species were identified from mature sporocarps using macro- and microscopic morphology. The inventory yielded 761 occurrence records representing 80 identified taxa. Moist-chamber cultures produced more records and species than field collection, while field surveys detected taxa fruiting naturally during the study period; together, both methods produced a more complete inventory than either method alone. Three species, Paradiacheopsis solitaria, Macbrideola rutilipedata, and Licea operculata, were recorded for the first time in Poland; these national novelties were included among the 14 taxa recorded for the first time in north-eastern Poland. Most records came from dead wood and bark, although other dead organic substrates also contributed to the recorded substrate spectrum. The resulting dataset provides a regional baseline for future biodiversity surveys in the Knyszyn Forest. Full article

Parkinson’s disease is a progressive neurodegenerative disorder characterized by motor impairments that significantly affect handwriting and fine motor control. Recent advances in artificial intelligence have enabled the non-invasive analysis of handwritten patterns as reliable digital biomarkers for early Parkinson’s disease detection. However, existing deep-learning approaches often struggle with diagnostic uncertainty and lack interpretability, limiting their clinical reliability and practical adoption. Moreover, models trained on single datasets frequently exhibit poor generalization across heterogeneous handwriting sources. This study uses two image-based handwriting datasets and one CSV-based HandPD feature dataset, including the Parkinson’s Augmented Handwriting Dataset, Parkinson’s Drawings Dataset, and HandPD Spiral/Meander feature records. A Transformer-based architecture is employed to learn global motor patterns from handwriting images, followed by a fuzzy-logic-based decision layer to handle uncertainty and improve robustness. The novelty of this work lies in integrating Transformer-driven deep feature learning with fuzzy clinical reasoning, supported by an AIC-based handcrafted feature analysis for interpretability. The model performance is evaluated using accuracy, precision, recall, F1-score, MCC, and AUC metrics. The experimental results demonstrate that the proposed Transformer–Fuzzy framework consistently outperforms CNN and Transformer-only baselines, achieving superior classification performance and robust generalization across all datasets, thereby establishing its effectiveness for reliable and interpretable Parkinson’s disease screening. Full article

As calibration errors have a direct impact on epipolar consistency, rectification accuracy, and metric 3D reconstruction performance, stereo camera calibration is a fundamental requirement for high-accuracy 3D modeling and reliable digital twin data acquisition. Because current calibration workflows (based on pairwise calibration methods) lack systematic data-quality checks mechanisms, there is a clear need for more robust data selection strategies. The novelty of the approach consists in the development of a new outlier-aware stereo calibration algorithm (OutAw) that introduces a unified multi-stage approach that integrates hard geometric selection, candidate subset generation, multi-criterion ranking, bootstrap stability analysis, and triangulation assessment into a comprehensive and systematic calibration framework. Unlike conventional approaches, OutAw (through its mechanism of detecting and rejecting inconsistent pairs) redefines the calibration strategy from arbitrary to criterion-based data selection. Also, the proposed algorithm is compared with BSC (a baseline OpenCV all-pairs calibration algorithm) and InterFil (an intermediate filtered variant) using 49 stereo pairs (at 1280 × 720 resolution) captured using a planar checkerboard. OutAw algorithm achieved (using only nine image pairs) superior results (epipolar error 0.5119 px, stereo RMS 0.7666 px) to the BSC ones (epipolar error 1.3687 px, stereo RMS 1.9385 px), representing statistically significant improvements (60.5%, respectively 62.3%). OutAw geometric consistency was validated by triangulation-based metrics (square-length standard deviation 0.1140 mm and square absolute error 0.1097 mm). Contamination analysis revealed that as the outlier rate increases, the calibration process degrades progressively. Also, the results obtained highlight that geometric quality-driven image selection is critical for achieving a reliable stereo calibration for DT applications. Full article

Background/Objectives: Anthracycline-induced cardiotoxicity remains a major challenge in cancer treatment, and researchers are showing interest in artificial intelligence (AI) to improve the prediction and detection of cancer therapy-related cardiac dysfunction (CTRCD). Current surveillance strategies rely mainly on left ventricular ejection fraction and, more recently, global longitudinal strain. Methods: The present study was designed to evaluate cardiac performance in a rat model of doxorubicin-induced cardiotoxicity and empagliflozin-mediated cardioprotection using a machine learning-based analytical framework. Eighteen adult male Sprague–Dawley rats were assigned to five experimental groups. We aimed to quantify ventricular wall dynamics and contractility using an advanced image-processing and object-detection model that has not been previously used to distinguish normal from impaired cardiac kinetics. During real-time recording, simultaneous electrocardiogram monitoring was performed, enabling direct correlation between deep learning-based ventricular wall motion metrics and cardiac electrical activity. The cardioprotective effects of empagliflozin were further validated by immunofluorescence staining (cTnI, vimentin, α-SMA, and Cx43) of rat cardiomyocytes and paraffin-embedded cardiac tissue, demonstrating attenuation of cellular injury and structural remodeling. Results: The integrated analysis of cardiac kinetic patterns derived via machine learning distinguishes not only extreme cardiotoxicity, but also tracks a graded pattern consistent with ECG-derived severity and treatment-related functional preservation. These findings indicate that the algorithm captures the gradient of empagliflozin’s cardioprotective effect within this internally validated preclinical setting. Additionally, immunofluorescence results validated the benefits of SGLT2 inhibition on myocardial integrity. Conclusions: The novelty of the present work lies at the intersection of advanced cardiac kinetic analysis using AI, preclinical modeling, and SGLT2-mediated cardioprotection in cardio-oncology. Full article

Medicinal plants are rich sources of bioactive secondary metabolites, yet their long-term effects on the rhizosphere (RS) microbial communities remain poorly understood, particularly with respect to microbial selection and functional potential. This study evaluated the number of selected groups of microorganisms culturable in vitro in the RS and bulk soil (BS) within 10-year monocultures of 11 medicinal plant species, and as a targeted case study, we performed shotgun metagenomic profiling for Allium ursinum. The abundance of microorganisms differed markedly among plant species, indicating species-specific RS selection. Azotobacter spp. showed the strongest variation: they were not detected in the RS of Allium ursinum, Thymus vulgaris, and Carum carvi, whereas higher counts were observed under Artemisia dracunculus (135.1 × 10² CFU g⁻¹ DM), Melissa officinalis (67.1 × 10² CFU g⁻¹ DM) and Calendula officinalis (38.8× 10² CFU g⁻¹ DM). Azotobacter spp. may serve as a sensitive candidate indicator of RS imbalance. Metagenomic analysis of the A. ursinum-associated soil revealed fine-scale taxonomic restructuring, while major functional categories remained broadly similar between the RS and BS. The novelty of this study lies in the development of the Integrated Microbiological Health Soil Index (IMHSI) and the proposal of a Nitrogen Enrichment Index (NEI) as exploratory composite metrics that integrate selected functional microbial groups. Full article

The article presents an original approach to automatic musical key detection, combining Constant-Q Transform (CQT) analysis with the Triple Composite Signature of Fifths (TCSF). The method’s novelty lies primarily in the construction of the Signature of Fifths (SF), which is grounded in fundamental principles of music theory and builds on earlier SF-based studies. The proposed approach aims to preserve the algorithmic simplicity typical of SF approaches while strengthening their key advantages. In addition, the method reflects the analytical approach of experienced musicians by assigning greater importance to the initial and final sections of a piece. The use of CQT enables efficient audio analysis and offers a practical compromise between frequency resolution and alignment with the pitch-class representation. Experiments conducted on Franz Schubert’s songs from the Winterreise song cycle and Frédéric Chopin’s Preludes, Op. 28, confirm the effectiveness of the proposed algorithm, achieving 87.5% and 79.2% key-detection accuracy, respectively. The obtained results demonstrate that the proposed method is competitive with tonal profile-based key-detection approaches. Full article

Unmanned Aerial Vehicle (UAV) image object detection holds significant application value in the low-altitude economy, traffic monitoring, intelligent agriculture, and disaster rescue. However, due to the top-down perspective, UAV images typically suffer from challenges such as small target scales, dense object distribution, severe occlusions, and complex backgrounds. These issues often limit the recall and localization accuracy of general-purpose detectors when they are directly applied to UAV small-object detection scenarios. To address these aforementioned challenges, this paper proposes an Adaptive Dynamic Aggregation YOLO network, termed ADA-YOLO. The novelty of ADA-YOLO lies in its highly efficient combinatorial design specifically tailored for UAV small object detection, while retaining the efficient backbone of YOLOv8, we systematically reconstruct the neck and detection head to improve accuracy. Specifically, a high-resolution P2 detection branch is incorporated to construct a P2–P5 multi-scale prediction structure. Furthermore, the lightweight DySample dynamic upsampling module is adopted to replace traditional upsampling methods, and an Adaptive Spatial Feature Fusion (ASFF) mechanism is introduced to alleviate semantic conflicts and noise interference during multi-scale feature fusion. This synergistic combination explicitly addresses multi-scale representation challenges and enhances small-object detection performance in complex scenes. Comparative experiments with the baseline YOLOv8n on the VisDrone2019 dataset demonstrate that ADA-YOLO achieves an improvement of 11.3% in mAP@0.5 and 8.2% in mAP@0.5:0.95. The improved model achieves these performance gains with a modest parameter increase and acceptable computational complexity. Finally, ablation experiments further validate the effectiveness of each individual module and their synergistic gains. Full article

The edible seeds of pumpkin plants (genus Cucurbita) are becoming increasingly appreciated as functional foods for their nutritional benefits, medicinal properties, and bioactive compounds, including lipids, proteins, and antioxidants. Particularly, the naked seeds of Cucurbita pepo var. styriaca have proved to yield both an edible oil showing anti-inflammatory properties in treating skin disorders and hydro-alcoholic extracts effective in inhibiting the growth of cancer cells. In this study, a detailed and extensive analysis of the eco-friendly alcoholic extract of the seeds of this variety was accomplished by using LC-HRMSMS techniques, with the main aim to broaden the knowledge on bioactive lipids other than the already reported fatty acids. The obtained results highlighted the occurrence of numerous compounds belonging to different classes of polar and neutral lipids, such as phospholipids, sphingolipids, glycolipids, acylglycerols, and oxylipins. Noteworthily, a significant presence of Cer-(EO)LCBs, i.e., Cer-EOS-type ceramides with different long chain base (LCB) and fatty acid composition, was detected, representing a real novelty for pumpkin. Additionally, a good number of multiflorane-type triterpenoids were detected, only some of which were previously reported in this plant. These findings highlight the nutraceutical value of these edible seeds. Full article

Traditional Network Intrusion Detection Systems (NIDSs) face persistent challenges in detecting zero-day attacks due to concept drift, high false-positive rates, and limited adaptability. This research introduces a Cognitive Network Intrusion Detection System (CNIDS) whose central novelty is that effective zero-day handling does not arise from any single mechanism but from the interaction between continual representation learning, persistent vector memory, and human-aligned feedback. By reframing zero-day resilience as a continuous learning process rather than a static detection task, CNIDS emphasizes adaptive operational behavior over raw automated accuracy. The proposed framework integrates Continual Pre-Training (CPT) to align representations with evolving traffic, Supervised Fine-Tuning (SFT) to preserve precision on known attacks, and a Human-in-the-Loop Reinforcement Signal (HRS) that converts low-confidence alerts into structured learning updates. These components are unified through a vector database that functions as long-term episodic memory, enabling similarity-based reasoning and cross-dataset generalization. Ablation results show that disabling any component degrades zero-day adaptation: removing CPT increases drift sensitivity, removing vector memory prevents knowledge retention, and removing human feedback collapses learning to static inference. Using a class-exclusion zero-day protocol on NSL-KDD, UNSW-NB15, and CICIDS2017, CNIDS raises zero-day detection from 0% to 18.2% while maintaining precision above 80% and stabilizing false positives. Full article

Determining the 3D position of a vehicle from a 2D image plays a key role in video surveillance, autonomous driving, and spatial localization. However, localization accuracy can significantly degrade in conditions of incomplete or synthetic measurement noise and keypoint jitter. In this paper, we propose a new iterative 3D position estimation algorithm (KGA). This algorithm includes geometric correction and calibration steps for converting from 2D to 3D coordinates; trajectory prediction and correction using a Kalman filter; and adaptive tuning of the filter parameters using the Goldschmidt algorithm. Experiments confirm that KGA outperforms the standard (FK) and modified (MFK) Kalman filters in accuracy and convergence speed, demonstrating robustness to various camera angles and noise levels. The novelty of this approach lies in the integration of the Goldschmidt algorithm into the Kalman filter to create an adaptation mechanism that dynamically adjusts the measurement noise covariance based on instantaneous innovation magnitude. Unlike end-to-end deep learning trackers or nonlinear filters (EKF/UKF), KGA is designed as a lightweight post-processing stage that can be seamlessly integrated into existing detection pipelines while maintaining the low computational footprint required for UAV-based edge deployment. The algorithm is of practical value for computer vision systems requiring accurate and robust tracking under varying observational conditions, with current implementation suitable for offline or buffered processing, and clear pathways to real-time deployment through code optimization. The algorithm is of practical value for computer vision systems requiring accurate and robust tracking under varying observational conditions. Full article

The Tonglushan ore field is an important component of the polymetallic mineralization belt in the middle and lower reaches of the Yangtze River in China. The skarn-type Cu, Fe, Au, and Mo molybdenum deposits are mainly developed in the contact zone between the rock mass and the strata, as well as in the contact zone between residual and capturing bodies in the rock body. The distribution of ore bodies is controlled by faults and strata, but there is a lack of large-scale geophysical information on the contact relationship between the ore-forming geological body and the host rock and on the deep spatial morphology of the ore-forming structure and intrusion rock. The study uses the JS-WEM2 wide-field electromagnetic instrument and the RS230 spectrometer to conduct the ground frequency domain electromagnetic and radiometric spectrometry measurements on four profiles. The measurement results indicate that the fault distribution in the Tonglushan ore field is predominantly in the NW-trending and NE-trending directions. The NW-trending Tonglushan–Lijiashan fault (F2) is a steeply dipping fault; the NE-trending faults are minor, with steep dips, generally extending no deeper than −1000 m. The Tonglushan stock exhibits the northeastward uplift, characterized by southward overlap and southeastward dip. The deep resistivity is greater than 3000 Ω·m, while the resistivity below −1000 m is less than 2000 Ω·m due to the fault influence. The ore bodies are mainly distributed along the contact zones where variations in the occurrence of the rock intersect with the strata. On resistivity profiles, these zones show the gradient variation in resistivity and the distorted shape of the resistivity contour line. The radioactive element contents of wall rock above the ore bodies are characterized by high U, high Th, and low K. The Wide-Field Electromagnetic Method (WFEM) can effectively detect the distribution and morphology of rocks and faults, and combined with the radioactive characteristics of geological bodies, it can effectively identify concealed faults and the favorable mineralization target areas. Novelty: The study combines the WFEM with radiometric measurements to reduce uncertainty in exploration compared to using only one method. It improves the detection accuracy and target identification ability of deep hidden ore bodies, providing the new technical method for deep mineral exploration in complex structural areas. Full article