Search Results (2,277)

Introduction: Accurate nuclear segmentation is essential for the reliable diagnostic interpretation of fluorescence in situ hybridization (FISH) results. However, automated 2D digital algorithms often fail in samples with dense or overlapping nuclei, such as lymphomas, due to the loss of spatial depth information. Here, we tested if AI-based 3D nuclear segmentation can improve the accuracy, reproducibility, and diagnostic reliability of FISH reading in critical situations. Materials and Methods: Formalin-fixed follicular lymphoma sections were FISH-labeled for BCL2 gene rearrangements and digitally scanned in multilayer Z-stacks. The analytic performance in nuclear segmentation of the adaptive thresholding-based FISHQuant, and the freely accessible AI-based NucleAIzer, StarDist, and Cellpose algorithms, were compared to the eye control-based traditional FISH testing, primarily focusing on nuclear segmentation. Results: We revealed that the Cellpose algorithm showed limited sensitivity to low-intensity signals and the adaptive thresholding 2D segmentation, and FISHQuant struggled to resolve densely packed nuclei, occasionally underestimating their counts. In contrast, 3D segmentation across focal planes significantly improved the nuclear separation and signal localization. AI-driven 3D models, especially NucleAIzer and StarDist, showed improved precision, lower variance (VP/VS ≈ 0.96), and improved gene spot correlation (r > 0.82) across multiple focal planes. The similar average number of gene spots per cell nuclei in the AI-based analyses as the eye control counting, despite the elevated number of cell nuclei found with AI, validated the AI nuclear segmentation results. Conclusions: Inaccurate segmentation limits automated diagnostic FISH signal evaluation. Deep learning 3D approaches, particularly NucleAIzer and StarDist, may overcome thresholding and 2D constraints and improve the consistency of nuclear detection, resulting in better classification of pathogenetic gene aberrations with automated workflows in digital pathology. Full article

Background: Hypoxic–ischemic (HI) brain injury triggers a dynamic, multi-phase response involving early microglial efferocytosis followed by extracellular matrix (ECM) deposition and scar formation. Arginase-1 (ARG1), a key enzyme in tissue repair, is implicated in both processes, yet its role in neonatal microglia remains poorly defined. We characterize ARG1-linked pathways in neonatal microglia, identifying distinct efferocytic and fibrotic phases post-HI. Methods: HI was induced in P9 mice using the Vannucci model, and brains were collected at 24 h (D1) and 5 days (D5). Spatially resolved single-cell transcriptomics (seqFISH) was performed using a targeted panel enriched for microglial, ARG1-pathway, efferocytosis, and profibrotic genes. Cell segmentation, clustering, and spatial mapping were conducted using Navigator and Seurat. Differential expression, GSEA, and enrichment analyses were used to identify time- and injury-dependent pathways. Results: Spatial transcriptomics identified 12 transcriptionally distinct cell populations with preserved neuroanatomical organization. HI caused the expansion of microglia and astrocytes and the loss of glutamatergic neurons by D5. Microglia rapidly activated regenerative and profibrotic programs—including TGF-β, PI3K–Akt, cytoskeletal remodeling, and migration—driven by early DEGs such as Cd44, Reln, TGF-β1, and Col1a2. By D5, microglia adopted a collagen-rich fibrotic state with an upregulation of Bgn, Col11a1, Anxa5, and Npy. Conclusion: Neonatal microglia transition from early efferocytic responses to later fibrotic remodeling after HI, driven by the persistent activation of PI3K–Akt, TGF-β, and Wnt/FZD4 pathways. These findings identify microglia as central regulators of neonatal scar formation and highlight therapeutic targets within ARG1-linked signaling. Full article

The aquaporin 0 (AQP0)/major intrinsic protein of eye lens (MIP) cDNA was cloned and sequenced. Initial studies of the tissue distribution of mRNA expression proved to be incorrect. Subsequent experiments showed that AQP0 mRNA is expressed strongly in the eye with moderately strong expression in the kidneys and some expression was seen in the brain and muscle tissue, and very low expression in the esophagus/fundic stomach. Another set of PCR reactions with five times the amount of cDNA additionally showed mRNA/cDNA expression in the liver, rectal gland, and a very low level in the intestine. Sporadic expression of different pieces of AQP0 cDNA was seen in various experiments in gill and pyloric stomach. A custom polyclonal antibody was produced against a region near the C-terminal end of the AQP0 protein sequence. The antibody gave a band of around the correct size (for the AQP0 protein) on the Western blot, which also showed a few other higher-molecular-weight bands. The antibody was also used in immunohistochemistry, and in the kidney, it showed staining in the proximal II (PII), intermediate segment I (IS I), and late distal tubule (LDT) parts of the sinus zone region of nephrons as well as some staining in the bundle zone tubule segments, suggesting a role for AQP0 as a water channel. In the rectal gland, the antibody showed weak apical membrane staining in a few secretory tubules near the duct, but also somewhat stronger staining in cells appearing to connect various secretory tubules, suggesting a role in cell–cell adhesion. In the spiral valve intestine side wall and valve flap, after signal amplification, weak antibody staining was seen in the apical and lateral membranes of epithelial cells adjacent to the luminal surface. There was also some staining in the intestinal muscle. In the rectum/colon, staining was seen in a layer of cells underlying the epithelium and in some muscle layers. In the gill, there was very weak staining in secondary lamellae epithelial cells and in connective tissue surrounding blood vessels and blood sinuses. The low level of transcript expression in the rectal gland, gill, and intestinal tissues suggests caution in the interpretation of the immunohistochemical staining in these tissues. Full article

Camera, lidar, and wearable-based gesture recognition technologies face practical limitations such as lighting sensitivity, occlusion, hardware cost, and user inconvenience. Wi-Fi channel state information (CSI) can be used as a contactless alternative to capture subtle signal variations caused by human motion. However, existing CSI-based methods are highly sensitive to domain shifts and often suffer notable performance degradation when applied to environments different from the training conditions. To address this issue, we propose a domain-robust touchless finger location sensing framework that operates reliably even in a single-link environment composed of commercial Wi-Fi devices. The proposed system applies preprocessing procedures to reduce noise and variability introduced by environmental factors and introduces a multi-domain segment combination strategy to increase the domain diversity during training. In addition, the dual-axis transformer learns temporal and spatial features independently, and the GNN-based integration module incorporates relationships among segments originating from different domains to produce more generalized representations. The proposed model is evaluated using CSI data collected from various users and days; experimental results show that the proposed method achieves an in-domain accuracy of 99.31% and outperforms the best baseline by approximately 4% and 3% in cross-user and cross-day evaluation settings, respectively, even in a single-link setting. Our work demonstrates a viable path for robust, calibration-free finger-level interaction using ubiquitous single-link Wi-Fi in real-world and constrained environments, providing a foundation for more reliable contactless interaction systems. Full article

Synthesizing anatomically plausible facial expressions for embodied avatars requires bridging the gap between high-level semantic intent and low-level physical constraints. This study presents a unified architecture that establishes a “Semantic-Kinematic Loop,” explicitly coupling FACS-based control with biomechanical regularization. Unlike black-box neural renderers or purely geometric BlendShape systems, our framework employs a multi-stage pipeline: semantic intent is first mapped to Action Units (AUs), which then drive a coarse linear deformation, followed by a fine grained refinement stage using a topology-aware Inverse Kinematics (IK) solver. This solver enforces segment length constraints and inter-region coupling, effectively translating abstract affective signals into physically grounded surface deformations. Furthermore, the framework exploits this kinematic structure to enable controlled perturbation strategies, facilitating the generation of diverse, anatomically valid synthetic training data. The experimental results indicate that this hybrid approach effectively eliminates surface tearing artifacts and achieves superior anatomical fidelity in reproducing complex emotional states. Full article

Parkinson’s disease (PD) is characterized by alterations in movement dynamics that are difficult to quantify with conventional clinical assessment. This study proposes an integrated approach combining graph-based kinematic analysis with explainable machine learning to identify digital biomarkers of Parkinsonian motor impairment. Kinematic signals were acquired using Xsens inertial sensors from 51 patients with PD and 53 healthy controls. For each participant, subject-specific kinematic networks were constructed by modeling inter-segment similarities through Jensen–Shannon divergence, from which global and local graph-theoretical metrics were extracted. A machine learning pipeline incorporating voting feature selection, and XGBoost classification was evaluated using a nested cross-validation design. The model achieved robust performance (AUC = 0.87), and explainability analyses using SHAP identified a subset of 13 features capturing alterations in velocity, inter-segment connectivity, and network centrality. PD was characterized by increased positional variability, reduced distal limb velocity, and a redistribution of network centrality towards proximal body segments. These features were associated with clinical severity, confirming their physiological relevance. By integrating graph-theoretical modeling, explainable artificial intelligence, and machine learning methodology, this work provides a method of discovering quantitative biomarkers capturing alterations in motor coordination. These findings highlight the potential of ML and kinematic networks to support objective motor assessment in PD. Full article

Background: Atherosclerosis forms the background of several cardiovascular pathologies. LDL receptor knockout (LDLR-KO) mice kept on a high-fat diet (HFD) develop high cholesterol levels. Previously we found that vasodilation responses in HFD LDLR-KO mice were improved in the absence of type 1 cannabinoid receptors (CB₁Rs). We aimed to reveal the effects of HFD and CB₁Rs on vascular contractile and structural properties. Methods: Experiments were performed on LDLR-CB₁R double knockout and wild type (WT) mice, kept on an HFD or control diet (CD) for 5 months. Thoracic aortas were isolated for Oil Red plaque staining and abdominal aorta segments for myography to obtain phenylephrine (Phe)-induced (100 nM–10 µM) contractile responses. Aorta samples were subjected to histology stainings with hematoxylin–eosin and resorcin–fuchsin (elastin density) and for smooth muscle actin (SMA) immunohistochemistry. Results: Phe-induced contractions significantly increased in HFD groups (p < 0.05) similarly in all genotypes. However, contractions were stronger with CD in CB₁R-KO compared to WT. Plaque areas were increased in LDLR-KO mice compared to WT, significant in HFD groups (p < 0.05). SMA increased to HFD, while elastin density remained similar, with the highest value in double KO-HFD. Intima/media ratio significantly decreased in double KO-HFD vs. CD. Conclusions: Our results indicate that HFD-treated LDLR-KO mice develop atherosclerosis with functional contractile and structural alterations modulated by CB₁Rs: absence of CB₁Rs elicited higher contraction properties with some modification in vascular remodeling indicating contribution of the CB₁R to cellular signalization controlling wall thickness and elasticity in pathological conditions. Full article

Continuous missing data is a prevalent challenge in long-term air quality monitoring, undermining the reliability of public health protection and sustainable urban development. In this paper, we propose ConFill, a novel contrastive learning-based framework for reconstructing continuous missing data in air quality time series. By leveraging temporal continuity as a supervisory signal, our method constructs positive sample pairs from adjacent subsequences and negative pairs from distant and shuffled segments. Through contrastive learning, the model learns robust representations that preserve intrinsic temporal dynamics, and enable accurate imputation of continuous missing segments. A novel data augmentation strategy is proposed, to integrate noise injection, subsequence masking, and time warping to enhance the diversity and representativeness of training samples. Extensive experiments are conducted on a large scale real-world dataset comprising multi-pollutant observations from 209 monitoring stations across China over a three-year period. Results show that ConFill outperforms baseline imputation methods under various missing scenarios, especially in reconstructing long consecutive gaps. Ablation studies confirm the effectiveness of both the contrastive learning module and the proposed augmentation technique. Full article

This study systematically characterized functional compartmentalization along the intestinal tract of New Zealand rabbits by analyzing mucosal tissue and luminal contents from distinct segments, including the duodenum, jejunum, ileum, cecum, and colon, using RNA-seq and 16S rRNA sequencing. Transcriptomic analysis revealed that differentially expressed genes identified between the small and large intestines were mainly enriched in digestion, absorption, and immune functions. Genes associated with the transport of amino acids, sugars, vitamins, and bile salts showed significantly higher expression in the small intestine, whereas genes related to water absorption, short-chain fatty acids (SCFAs), nucleotides, and metal ion transport were preferentially expressed in the large intestine. From an immunological perspective, genes involved in fungal responses were enriched in the small intestine, while bacterial response pathways and pattern recognition receptor (PRR) signaling genes were upregulated in the large intestine. Microbiota analysis demonstrated significantly greater diversity and abundance in the large intestine compared with the small intestine. Specifically, Proteobacteria and Actinobacteria were enriched in the small intestine, whereas Firmicutes, Verrucomicrobia, and Bacteroidetes dominated the large intestine. Correlation analysis further identified significant associations between gut microbiota composition and host genes involved in nutrient digestion and absorption. Together, these findings provide transcriptome-based evidence for regional specialization of nutrient transport, immune responses, and microbial ecology along the rabbit intestine. Full article

In the diagnostics of plasmas heated by neutral beam injection (NBI), which serves as a fundamental heating technique, critical core parameters such as ion temperatures and rotational velocities can be measured through NBI’s associated diagnostic methods. However, conventional spectral analysis methods applied in NBI-based Beam Emission Spectroscopy diagnostics face a significant limitation: a relatively high false detection rate during characteristic peak detection and boundary determination. This issue stems from three primary factors: persistent noise interference, overlapping spectral peaks, and dynamic broadening effects. To address this critical issue, we propose a spectral feature extraction method based on morphological segmentation and optimized clustering, with three key innovations that work synergistically: (1) an adaptive chunking algorithm driven by gradient, Laplacian, and curvature features to dynamically partition spectral regions, laying a foundation for localized analysis; (2) a hierarchical residual iteration mechanism combining dynamic thresholding and Gaussian template subtraction to enhance weak peak signals; (3) optimized DBSCAN clustering integrated with morphological closure to refine peak boundaries accurately. Among them, the adaptive chunking technique is distinct from general adaptive methods: its chunking granularity can be dynamically adjusted according to peak structures and can accurately adapt to low signal-to-noise ratio (SNR) scenarios. Experimental results based on measured data from the EAST device demonstrate that the adaptive chunking strategy maintains a missed detection rate of 0–20% across the full signal-to-noise ratio (SNR) range, with false positive rates limited to 16.67–50.00%. Notably, it achieves effective peak detection even under extremely low SNR conditions. Full article

This paper introduces a novel architecture for a navigation system that is designed to estimate the position and velocity of a moving vehicle specifically for remote piloting scenarios where GPS availability is intermittent and can be lost for extended periods of time. The purpose of the navigation system is to keep velocity estimation as reliable as possible to allow the vehicle guidance and control systems to maintain close-to-nominal performance. The cornerstone of this system is a landmark-extraction algorithm, which identifies pertinent features within the environment. These features serve as landmarks, enabling continuous and precise adjustments to the vehicle’s estimated velocity. State estimations are performed by a Sequential Kalman filter, which processes camera data regarding the vehicle’s relative position to the identified landmarks. Tracking the landmarks supports a state-of-the-art LiDAR odometry segment and keeps the velocity error low. During an extensive testing phase, the system’s performance was evaluated across various real word trajectories. These tests were designed to assess the system’s capability in maintaining stable velocity estimation under different conditions. The results from these evaluations indicate that the system effectively estimates velocity, demonstrating the feasibility of its application in scenarios where GPS signals are compromised or entirely absent. Full article

Background/Objectives: Improper landing mechanics in Taekwondo can lead to non-contact injuries such as ankle sprains and knee ligament tears, highlighting the necessity for objective methods to evaluate landing stability and injury risk. Electromyography (EMG) enables the examination of muscle activation patterns; however, conventional analyses based on simple averages have limited predictive value. Methods: This study analyzed EMG signals recorded during single-leg landings (45 cm height) in 30 elite male Taekwondo athletes. Participants were divided into regular exercise groups (REG, n = 15) and non-exercise groups (NEG, n = 15). Signals were segmented into two phases. Eight features were extracted per muscle per phase. Classification models (Random Forest, XGBoost, Logistic Regression, Voting Classifier) were used to classify between groups, while regression models (Ridge, Random Forest, XGBoost) predicted continuous muscle activation changes as injury risk indicators. Results: The Random Forest Classifier achieved an accuracy of 0.8365 and an F1-score of 0.8547. For regression, Ridge Regression indicated high performance (R² = 0.9974, MAE = 0.2620, RMSE = 0.4284, 5-fold CV MAE: 0.2459 ± 0.0270), demonstrating strong linear correlations between EMG features and outcomes. Conclusions: The AI-enabled EMG analysis can be used as an objective measure of the study of the individual landing stability and risk of injury in Taekwondo athletes, but its clinical application has to be validated in the future by biomechanical injury indicators and prospective cohort studies. Full article

Multi-satellite sequential detection is crucial for maritime target identification and tracking. However, inherent satellite revisit patterns and maritime target motion often result in fragmented track segments, necessitating effective multi-satellite track association to ensure continuity. Existing methods predominantly rely on track information and statistical signal parameters, rendering them susceptible to localization errors and ineffective in scenarios characterized by dense targets and overlapping radar parameters. To overcome these limitations, this paper proposes an attention-guided track-pulse-sequence target association network (AG-TPS-TAN). First, the asymmetric dual-branch network operates by incorporating both track data and electromagnetic signal data, processing the latter in the form of raw pulse sequences instead of the conventional statistical parameters. Second, within the track branch, we enhance the feature representation by incorporating a novel track-point-aware attention mechanism which can autonomously identify and weight critical points indicative of motion continuity, such as interruption boundaries and maneuvering points. Third, we introduce a dual-feature fusion module optimized with a combined loss function, which pulls feature representations of the same target closer together while pushing apart those from different targets, thereby enhancing both feature consistency and discriminability. Experiments were conducted on a public AIS trajectory dataset, constructing a dataset containing both motion trajectories and electromagnetic signals. Evaluations under varying target numbers showed that the proposed AG-TPS-TAN achieved average association accuracies of 93.91% for 5 targets and 63.83% for 50 targets. Against this, the track-only method TSADCNN scored 76.08% and 25.64%, and the signal-statistics-based method scored 77.12% and 29.56%, for 5 and 50 targets, respectively, thus exhibiting a clear advantage for the proposed approach. Full article

Background/Objectives: Parkinsonian gait is characterized by impaired motor adaptability, altered temporal organization, and reduced movement variability. While data augmentation is commonly used to mitigate class imbalance in gait-based machine learning models, conventional strategies often ignore physiological differences between healthy and pathological movements, potentially distorting meaningful motor dynamics. This study explores whether preserving healthy motor variability while selectively augmenting pathological gait signals can improve the robustness and physiological coherence of gait pattern classification models. Methods: Eight patients with Parkinsonian gait patterns and forty-eight healthy participants performed walking tasks on the Motigravity platform under hypogravity conditions. Full-body kinematic data were acquired using wearable inertial sensors. A selective augmentation strategy based on smooth time-warping was applied exclusively to pathological gait segments (×5, σ = 0.2), while healthy gait signals were left unaltered to preserve natural motor variability. Model performance was evaluated using a hybrid convolutional neural network–long short-term memory (CNN–LSTM) architecture across multiple augmentation configurations. Results: Selective augmentation of pathological gait signals achieved the highest classification performance (94.1% accuracy, AUC = 0.97), with balanced sensitivity (93.8%) and specificity (94.3%). Performance decreased when augmentation exceeded an optimal range of variability, suggesting that beneficial augmentation is constrained by physiologically plausible temporal dynamics. Conclusions: These findings demonstrate that physiology-informed, selective data augmentation can improve gait pattern classification under constrained data conditions. Rather than supporting disease-specific diagnosis, this proof-of-concept study highlights the importance of respecting intrinsic differences in motor variability when designing augmentation strategies for clinical gait analysis. Future studies incorporating disease-control cohorts and subject-independent validation are required to assess specificity and clinical generalizability. Full article

NFT prices are shaped by heterogeneous signals including visual appearance, textual narratives, transaction trajectories, and on-chain interactions, yet existing studies often model these factors in isolation and rarely unify multimodal alignment, temporal non-stationarity, and heterogeneous relational dependencies in a leakage-safe forecasting setting. We propose MM-Temporal-Graph, a cross-modal temporal graph transformer framework for explainable NFT valuation and information-centric risk forecasting. The model encodes image, text, transaction time series, and blockchain behavioral features, constructs a heterogeneous NFT interaction graph (co-transaction, shared creator, wallet relation, and price co-movement), and jointly performs relation-aware graph attention and global temporal–structural transformer reasoning with an adaptive fusion gate. A contrastive multimodal alignment objective improves robustness under market drift, while a risk-aware regularizer and a multi-source risk index enable early warning and interpretable attribution across modalities, time segments, and relational neighborhoods. On MultiNFT-T, MM-Temporal-Graph improves MAE from 0.162 to 0.153 and R² from 0.823 to 0.841 over the strongest multimodal graph baseline, and achieves 87.4% early risk detection accuracy. These results support accurate, robust, and explainable NFT valuation and proactive risk monitoring in Web3 markets. Full article