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Periodontitis exacerbates Alzheimer's disease (AD) through multiple pathways. Both periodontitis and AD are intricately correlated to intestinal homeostasis, yet there is still a lack of direct evidence regarding whether periodontitis can regulate the progression of AD by modulating intestinal homeostasis. The current study induced experimental periodontitis in AD mice by bilaterally ligating the maxillary second molars with silk and administering Pg-LPS injections in APPswe/PS1ΔE9 (APP/PS1) mice. Behavioral tests and histological analyses of brain tissue were conducted after 8 weeks. Gut microbiota was analyzed and colon tissue were also evaluated. Then, fecal microbiota from mice with periodontitis was transplanted into antibiotic-treated mice to confirm the effects of periodontitis on AD and the potential mechanism was explored. The results indicated periodontitis exacerbated cognitive impairment and anxious behaviour in APP/PS1 mice, with increased Aβ deposition, microglial overactivation and neuroinflammation in brain. Moreover, the intestinal homeostasis of AD mice was altered by periodontitis, including affecting gut microbiota composition, causing colon inflammation and destroyed intestinal epithelial barrier. Furthermore, AD mice that underwent fecal transplantation from mice with periodontitis exhibited worsened AD progression and disrupted intestinal homeostasis. It also impaired intestinal barrier function, elevated peripheral inflammation, damaged blood-brain barrier (BBB) and caused neuroinflammation and synapses impairment. Taken together, the current study demonstrated that periodontitis could disrupt intestinal homeostasis to exacerbate AD progression potential via causing gut microbial dysbiosis, intestinal inflammation and intestinal barrier impairment to induce peripheral inflammation and damage BBB, ultimately leading to neuroinflammation and synapse impairment. It underscores the importance of maintaining both periodontal health and intestinal homeostasis to reduce the risk of AD. [ABSTRACT FROM AUTHOR]

Objective: To compare the differences between Ultrasound Volume Navigation (UVN), O-arm Navigation, and conventional X-ray fluoroscopy-guided screw placement in Minimally Invasive Transforaminal Lumbar Interbody Fusion (MIS-TLIF) surgeries. Methods: A total of 90 patients who underwent MIS-TLIF due to lumbar disc herniation from January 2022 to January 2023 were randomly assigned to the UVN group, O-arm group, and X-ray group. UVN, O-arm navigation, and X-ray guidance were used for screw placement in the respective groups, while the remaining surgical procedures followed routine MIS-TLIF protocols. Intraoperative data including average single screw placement time, total radiation dose, and average effective radiation dose per screw were recorded and calculated. On the 10th day after surgery, postoperative X-ray and CT examinations were conducted to assess screw placement accuracy and facet joint violation. Results: There were no significant differences in general characteristics among the three groups, ensuring comparability. Firstly, the average single screw placement time in the O-arm group was significantly shorter than that in the UVN group and X-ray group (P<0.05). Secondly, in terms of total radiation dose during surgery, for single-level MIS-TLIF, the O-arm group had a significantly higher radiation dose compared to the UVN group and X-ray group (P<0.05). However, for multi-level MIS-TLIF, the X-ray group had a significantly higher radiation dose than the O-arm group and UVN group (P<0.05). In terms of average single screw radiation dose, the O-arm group and X-ray group were similar (P>0.05), while the UVN group was significantly lower than the other two groups (P<0.05). Furthermore, no significant differences were found in screw placement assessment grades among the three groups (P>0.05). However, in terms of facet joint violation rate, the UVN group (10.3%) and O-arm group (10.7%) showed no significant difference (P>0.05), while the X-ray group (26.7%) was significantly higher than both groups (P<0.05). Moreover, in the UVN group, there were significant correlations between average single screw placement time and placement grade with BMI index (r = 0.637, P<0.05; r = 0.504, P<0.05), while no similar significant correlations were found in the O-arm and X-ray groups. Conclusion: UVN-guided screw placement in MIS-TLIF surgeries demonstrates comparable efficiency, visualization, and accuracy to O-arm navigation, while significantly reducing radiation exposure compared to both O-arm navigation and X-ray guidance. However, UVN may be influenced by factors like obesity, limiting its application. [ABSTRACT FROM AUTHOR]

In facial landmark localization, facial region initialization usually plays an important role in guiding the model to learn critical face features. Most facial landmark detectors assume a well-cropped face as input and may underperform in real applications if the input is unexpected. To alleviate this problem, we present a region-aware deep feature-fused network (RDFN). The RDFN consists of a region detection subnetwork and a region-wise landmark localization subnetwork to explicitly solve the input initialization problem and derive the landmark score maps, respectively. To exploit the association between tasks, we develop a cross-task feature fusion scheme to extract multi-semantic region features while trading off their importance in different dimensions via global channel attention and global spatial attention. Furthermore, we design a within-task feature fusion scheme to capture the multi-scale context and improve the gradient flow for the landmark localization subnetwork. At the inference stage, a location reweighting strategy is employed to transform the score maps into 2D landmark coordinates. Extensive experimental results demonstrate that our method has competitive performance compared to recent state-of-the-art methods, achieving NMEs of 3.28%, 1.48%, and 3.43% on the 300W, AFLW, and COFW datasets, respectively. [ABSTRACT FROM AUTHOR]

Indoor localization and navigation have become an increasingly important problem in both industry and academia with the widespread use of mobile smart devices and the development of network techniques. The Wi-Fi-based technology shows great potential for applications due to the ubiquitous Wi-Fi infrastructure in public indoor environments. Most existing approaches use trilateration or machine learning methods to predict locations from a set of annotated Wi-Fi observations. However, annotated data are not always readily available. In this paper, we propose a robot-aided data collection strategy to obtain the limited but high-quality labeled data and a large amount of unlabeled data. Furthermore, we design two deep learning models based on a variational autoencoder for the localization and navigation tasks, respectively. To make full use of the collected data, a hybrid learning approach is developed to train the models by combining supervised, unsupervised and semi-supervised learning strategies. Extensive experiments suggest that our approach enables the models to learn effective knowledge from unlabeled data with incremental improvements, and it can achieve promising localization and navigation performance in a complex indoor environment with obstacles. [ABSTRACT FROM AUTHOR]

Face alignment is widely used in high-level face analysis applications, such as human activity recognition and human–computer interaction. However, most existing models involve a large number of parameters and are computationally inefficient in practical applications. In this paper, we aim to build a lightweight facial landmark detector by proposing a network-level architecture-slimming method. Concretely, we introduce a selective feature fusion mechanism to quantify and prune redundant transformation and aggregation operations in a high-resolution supernetwork. Moreover, we develop a triple knowledge distillation scheme to further refine a slimmed network, where two peer student networks could learn the implicit landmark distributions from each other while absorbing the knowledge from a teacher network. Extensive experiments on challenging benchmarks, including 300W, COFW, and WFLW, demonstrate that our approach achieves competitive performance with a better trade-off between the number of parameters (0.98 M–1.32 M) and the number of floating-point operations (0.59 G–0.6 G) when compared to recent state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel refined fused model combining masked Res-C3D network and skeleton LSTM for abnormal gesture recognition in RGB-D videos. The key to our design is to learn discriminative representations of gesture sequences in particular abnormal gesture samples by fusing multiple features from different models. First, deep spatiotemporal features are well extracted by 3D convolutional neural networks with residual architecture (Res-C3D). As gestures are mainly derived from the arm or hand movements, a masked Res-C3D network is built to decrease the effect of background and other variations via exploiting the skeleton of the body to reserve arm regions with discarding other regions. And then, relative positions and angles of different key points are extracted and used to build a time-series model by long short-term memory network (LSTM). Based the above representations, a fusion scheme for blending classification results and remedy model disadvantage by abnormal gesture via a weight fusion layer is developed, in which the weights of each voting sub-classifier being advantage to a certain class in our ensemble model are adaptively obtained by training in place of fixed weights. Our experimental results show that the proposed method can distinguish the abnormal gesture samples effectively and achieve the state-of-the-art performance in the IsoGD dataset. [ABSTRACT FROM AUTHOR]

Impact cratering process is the major geologic activity on the surface of the Moon, and the spatial distribution and size-frequency distribution of lunar craters are indicative to the bombardment history of the Solar System. The substantial efforts on the development of automated crater detection algorithms (CDAs) have been carried out on the images from the remote sensing observations. Recently, CDAs via convolutional neural network (CNN) on digital elevation model (DEM) has been developed as it can combine the discrimination ability of CNN with the robust characteristic of the DEM data. However, most of the existing algorithms adopt a traditional two-stage detection pipeline including an edge segmentation and a template matching step. In this paper, we attempt to reduce the gap between the existing DEM-based CDAs and the advanced CNN methods for object detection, and propose a complete workflow including an end-to-end deep learning pipeline for lunar crater detection, in particular for craters smaller than 50 km in diameter. Based on the workflow, we benchmark nine representative CNN models involving three popular types of detection architectures. Moreover, we elaborate on the practical application of the proposed workflow, and provide an example method to demonstrate the performance advantage in terms of the precision ( 82.97 % ) and recall ( 79.39 % ). Furthermore, we develop a crater verification tool to manually validate the detection results, and the visualization results show that our detected craters are reasonable and can be used as a supplement to the existing hand-labeled datasets. [ABSTRACT FROM AUTHOR]

• For multi-view input, we propose the global-aware fusion to adapt the global state when fusing features from different views. • Our algorithm includes a powerful but relatively lightweight network, novel loss function, and specified training strategy. • The reconstructor exceeds existing SOTA methods while the amount of parameters is far less than the same type of algorithm. • An extra view-reduction method based on maximizing diversity for our model can be used to tradeoff cost and performance. Deep learning technology has made great progress in multi-view 3D reconstruction tasks. At present, the mainstream solutions adopt different ways to fusion the features from several views. Among them, attention-based aggregation function performs relatively well and stably, however, it still has an obvious shortcoming the strong independence of each view during predicting the weights for merging leads to a lack of adaption of the global state. In this paper, we propose a global-aware attention-based fusion approach that builds the correlation between each branch and the global feature to provide a comprehensive foundation for weights inference. On the basis of this, we design a complete reconstruction algorithm. Experiments on ShapeNet verify that our method outperforms existing SOTA methods. Furthermore, we propose a view-reduction method based on maximizing diversity and discuss the cost-performance tradeoff of our model to achieve a better performance when facing a heavy input amount and limited computational cost. [ABSTRACT FROM AUTHOR]

Double-quantitative decision-theoretic rough sets (Dq-DTRS) provide more comprehensive description methods for rough approximations of concepts, which lay foundations for the development of attribute reduction and rule extraction of rough sets. Existing researches on concept approximations of Dq-DTRS pay more attention to the equivalence class of each object in approximating a concept, and calculate concept approximations from the whole data set in a batch. This makes the calculation of approximations time consuming in dynamic data sets. In this paper, we first analyze the variations of equivalence classes, decision classes, conditional probability, internal grade and external grade in dynamic data sets while objects vary sequentially or simultaneously over time. Then we propose the updating mechanisms for the concept approximations of two types of Dq-DTRS models from incremental perspective in dynamic decision information systems with the sequential and batch variations of objects. Meanwhile, we design incremental sequential insertion, sequential deletion, batch insertion, batch deletion algorithms for two Dq-DTRS models. Finally, we present experimental comparisons showing the feasibility and efficiency of the proposed incremental approaches in calculating approximations and the stability of the incremental updating algorithms from the perspective of the runtime under different inserting and deleting ratios and parameter values. • We study dynamic maintenance approaches for the approximations of Dq-DTRS. • We propose incremental updating mechanisms of Dq-DTRS with the variation of objects. • We design incremental sequential and batch updating algorithms for Dq-DTRS models. • The validity, stability and efficiency of our methods are verified by comparisons. [ABSTRACT FROM AUTHOR]