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Objective A retrospective analysis was conducted to evaluate the application of the intraoperative fluoroscopic positioning and anatomical landmark positioning methods in medial patellofemoral ligament (MPFL) reconstruction for recurrent patellar dislocation. The aim was to summarize the positioning accuracy and clinical efficacy of each method, to serve as a reference for femoral positioning. Method We conducted a retrospective analysis of a cohort comprising 75 patients who underwent treatment for recurrent patellar dislocation at our institution between January 2014 and September 2020.Based on the different positioning methodologies utilized for identifying the MPFL femoral footprint, the included patients were systematically allocated to either the fluoroscopy group or the palpation group.Preoperative evaluations and assessments at the latest follow-up encompassed the International Knee Documentation Committee (IKDC) score, Lysholm score, and Kujala score for both groups.We utilized immediate postoperative CT scans for our evaluations. A total of 48 knee 3D-CT scans were acquired using Mimics Medical 21.0 for both groups. From these scans, we constructed a standard lateral Schottle point on a 3D-CT image. To assess the relative positions between the actual and standard location points in both groups, we established a coordinate system based on a simplified, constructed standard point baseline (as illustrated in Chart e). Subsequently, the relative positions of the actual points were evaluated. Result All 75 patients were followed up for a period ranging from 36 to 96 months( mean: 62.27 ± 21.36 months). Significant improvements were observed in the IKDC score, Lysholm score, and Kujala score from preoperative to the latest follow-up ( p  < 0.05) (Table 2), indicating statistical significance.Furthermore, the latest follow-up revealed no significant differences in knee function scores between the two groups ( P  > 0.05) (Table 3). Similarly, the latest evaluation showed no significant differences in knee function scores between patients undergoing MPFLR and MPFLR + TTO In their respective groups ( P  > 0.05) (Table 4).CT-3D reconstruction was conducted on 48 postoperative patients (24 in the fluoroscopy group and 24 in the palpation group). Evaluation of the positioning revealed that most cases in the palpation group were located in quadrants 1 and 3, whereas those in the fluoroscopy group were primarily distributed across quadrants 1, 3, and 4 ( p  < 0.05), indicating statistical significance.In the palpation group, the isometric distance was 3.90 ± 2.17 mm, with an isometric rate of 75%. In the fluoroscopy group, the isometric distance was 7.55 ± 3.94 mm, with an isometric rate of 29.2%.The femoral tunnel isometric rate was significantly higher in the palpation group, at 75%, compared to 29.2% in the fluoroscopy group. among the two positioning methods, there was no statistical difference in the positioning of the femoral footprint at the anterior and posterior ends of the standard point, but there was a statistical difference at the proximal and distal ends ( P  < 0.05). Conclusion Clinical outcomes significantly improved and were similar in both groups. Nevertheless, the palpation of femoral anatomical landmarks exhibited superior convenience and efficiency for experienced sports medicine practitioners, and additionally, it frequently achieved a more isometric femoral footprint than fluoroscopic positioning in certain scenarios.

Rapid and accurate intraoperative examination of tumor margins is crucial for precise surgical treatment, yet current methods are limited by incomplete tissue sampling and time‐consuming sample sectioning. The Rapid Arbitrary‐Shape Microscope (RAM) is developed, a bedside imaging system that enables high‐speed, 3D microscopy of irregular tissue surfaces without sectioning, providing cellular‐resolution images within minutes while preserving tissue morphology post‐excision. RAM precisely integrates a 3D scanning module with a robotic platform, seamlessly combining morphological measurements with robotic‐driven pathological microscopy. In studies involving metastatic tumors in 39 mouse liver and spleen samples, RAM demonstrated high accuracy in detecting positive margins containing cancer cells, achieving a sensitivity of 99.1% and a specificity of 82.9%. The system's capability is further validated on 12 skin cancer samples from 10 human subjects using nondestructive imaging, highlighting its potential to reduce surgical time and minimize the risk of overlooking residual cancer at surgical margins during intraoperative evaluation. This study presents a novel microscopic imaging system capable of rapid, section‐free scanning of irregular tissue surfaces, delivering high sensitivity for detecting cancer cell clusters during intraoperative tumor margin assessment.

In the field of neighborhood rough set, attribute reduction is considered as a key topic. Neighborhood relation and rough approximation play crucial roles in the process of obtaining the reduct. Presently, many strategies have been proposed to accelerate such process from the viewpoint of samples. However, these methods speed up the process of obtaining the reduct only from binary relation or rough approximation, and then the obtained results in time consumption may not be fully improved. To fill such a gap, a combined acceleration strategy based on compressing the scanning space of both neighborhood and lower approximation is proposed, which aims to further reduce the time consumption of obtaining the reduct. In addition, 15 UCI data sets have been selected, and the experimental results show us the following: (1) our proposed approach significantly reduces the elapsed time of obtaining the reduct; (2) compared with previous approaches, our combined acceleration strategy will not change the result of the reduct. This research suggests a new trend of attribute reduction using the multiple views.

Label distribution learning (LDL) is an emerging learning paradigm designed to capture the relative importance of labels for each instance. Label-specific features (LSFs), constructed by LIFT, have proven effective for learning tasks with label ambiguity by leveraging clustering-based prototypes for each label to re-characterize instances. However, directly introducing LIFT into LDL tasks can be suboptimal, as the prototypes it collects primarily reflect intra-cluster relationships while neglecting cross-cluster interactions. Additionally, constructing LSFs using multi-perspective information, rather than relying solely on Euclidean distance, provides a more robust and comprehensive representation of instances, mitigating noise and bias that may arise from a single distance perspective. To address these limitations, we introduce Structural Anchor Points (SAPs) to capture inter-cluster interactions. This leads to a novel LSFs construction strategy, LIFT-SAP, which enhances LIFT by integrating both distance and directional information of each instance relative to SAPs. Furthermore, we propose a novel LDL algorithm, Label Distribution Learning via Label-specifIc FeaTure with SAPs (LDL-LIFT-SAP), which unifies multiple label description degrees predicted from different LSF spaces into a cohesive label distribution. Extensive experiments on 15 real-world datasets demonstrate the effectiveness of LIFT-SAP over LIFT, as well as the superiority of LDL-LIFT-SAP compared to seven other well-established algorithms.

Feature selection is crucial for fuzzy decision systems (FDSs), as it identifies informative features and eliminates rule redundancy, thereby enhancing predictive performance and interpretability. Most existing methods either fail to directly align evaluation criteria with learning performance or rely solely on non-directional Euclidean distances to capture relationships among decision classes, which limits their ability to clarify decision boundaries. However, the spatial distribution of instances has a potential impact on the clarity of such boundaries. Motivated by this, we propose Spatially-aware Separability-driven Feature Selection (S\\(^2\\)FS), a novel framework for FDSs guided by a spatially-aware separability criterion. This criterion jointly considers within-class compactness and between-class separation by integrating scalar-distances with spatial directional information, providing a more comprehensive characterization of class structures. S\\(^2\\)FS employs a forward greedy strategy to iteratively select the most discriminative features. Extensive experiments on ten real-world datasets demonstrate that S\\(^2\\)FS consistently outperforms eight state-of-the-art feature selection algorithms in both classification accuracy and clustering performance, while feature visualizations further confirm the interpretability of the selected features.

Fuzzy rough feature selection (FRFS) is an effective means of addressing the curse of dimensionality in high-dimensional data. By removing redundant and irrelevant features, FRFS helps mitigate classifier overfitting, enhance generalization performance, and lessen computational overhead. However, most existing FRFS algorithms primarily focus on reducing uncertainty in pattern classification, neglecting that lower uncertainty does not necessarily result in improved classification performance, despite it commonly being regarded as a key indicator of feature selection effectiveness in the FRFS literature. To bridge uncertainty characterization and pattern classification, we propose a Margin-aware Fuzzy Rough Feature Selection (MAFRFS) framework that considers both the compactness and separation of label classes. MAFRFS effectively reduces uncertainty in pattern classification tasks, while guiding the feature selection towards more separable and discriminative label class structures. Extensive experiments on 15 public datasets demonstrate that MAFRFS is highly scalable and more effective than FRFS. The algorithms developed using MAFRFS outperform six state-of-the-art feature selection algorithms.

Symbiodiniaceae are the source of essential coral symbionts of reef building corals. The growth and density of endosymbiotic Symbodiniaceae within the coral host is highly dependent on nutrient availability, yet little is known about how Symbiodiniaceae respond to the dynamics of the nutrients, including switch between different chemical forms and changes in abundance. In this study, we investigated physiological, cytometric, and transcriptomic responses in Fugacium kawagutii to nitrogen (N)-nutrient deficiency and different chemical N forms (nitrate and ammonium) in batch culture conditions. We mainly found that ammonium was consumed faster than nitrate when provided separately, and was preferentially utilized over nitrate when both nitrogen compounds were supplied at 1:2, 1:1 and 2:1 molarity ratios. Besides, N-deficiency caused decreases in growth, energy production, antioxidative capacity and investment in photosynthate transport but increased energy consumption. Growing on ammonium produced a similar cell yield as nitrate, but with a decreased investment in nutrient transport and assimilation. These all have important implications of N nutrient to support symbiosis in coral ecosystem, especially regarding ammonium. In addition, by integrating our current results with previous data, we identified ten highly and stably expressed genes as candidate reference genes, which will be potentially useful for gene expression studies in the future.