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More often than not, a multimedia data described by multiple features, such as color and shape features, can be naturally decomposed of multi-views. Since multi-views provide complementary information to each other, great endeavors have been dedicated by leveraging multiple views instead of a single view to achieve the better clustering performance. To effectively exploit data correlation consensus among multi-views, in this paper, we study subspace clustering for multi-view data while keeping individual views well encapsulated. For characterizing data correlations, we generate a similarity matrix in a way that high affinity values are assigned to data objects within the same subspace across views, while the correlations among data objects from distinct subspaces are minimized. Before generating this matrix, however, we should consider that multi-view data in practice might be corrupted by noise. The corrupted data will significantly downgrade clustering results. We first present a novel objective function coupled with an angular based regularizer. By minimizing this function, multiple sparse vectors are obtained for each data object as its multiple representations. In fact, these sparse vectors result from reaching data correlation consensus on all views. For tackling noise corruption, we present a sparsity-based approach that refines the angular-based data correlation. Using this approach, a more ideal data similarity matrix is generated for multi-view data. Spectral clustering is then applied to the similarity matrix to obtain the final subspace clustering. Extensive experiments have been conducted to validate the effectiveness of our proposed approach.

Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain signals produced by brain neurons. Neurons are connected to each other in a complex way to communicate with human organs and generate signals. The monitoring of these brain signals is commonly done using Electroencephalogram (EEG) and Electrocorticography (ECoG) media. These signals are complex, noisy, non-linear, non-stationary and produce a high volume of data. Hence, the detection of seizures and discovery of the brain-related knowledge is a challenging task. Machine learning classifiers are able to classify EEG data and detect seizures along with revealing relevant sensible patterns without compromising performance. As such, various researchers have developed number of approaches to seizure detection using machine learning classifiers and statistical features. The main challenges are selecting appropriate classifiers and features. The aim of this paper is to present an overview of the wide varieties of these techniques over the last few years based on the taxonomy of statistical features and machine learning classifiers—‘black-box’ and ‘non-black-box’. The presented state-of-the-art methods and ideas will give a detailed understanding about seizure detection and classification, and research directions in the future.

Signet ring cell carcinoma is a type of rare adenocarcinoma with poor prognosis. Early detection leads to huge improvement of patients' survival rate. However, pathologists can only visually detect signet ring cells under the microscope. This procedure is not only laborious but also prone to omission. An automatic and accurate signet ring cell detection solution is thus important but has not been investigated before. In this paper, we take the first step to present a semi-supervised learning framework for the signet ring cell detection problem. Self-training is proposed to deal with the challenge of incomplete annotations, and cooperative-training is adapted to explore the unlabeled regions. Combining the two techniques, our semi-supervised learning framework can make better use of both labeled and unlabeled data. Experiments on large real clinical data demonstrate the effectiveness of our design. Our framework achieves accurate signet ring cell detection and can be readily applied in the clinical trails. The dataset will be released soon to facilitate the development of the area.

International audienceWandering is among the most frequent, problematic, and dangerous behaviors for elders with dementia. Frequent wanderers likely suffer falls and fractures, which affect the safety and quality of their lives. In order to monitor outdoor wandering of elderly people with dementia, this paper proposes a real-time method for wandering detection based on individuals' GPS traces. By representing wandering traces as loops, the problem of wandering detection is transformed into detecting loops in elders' mobility trajectories. Specifically, the raw GPS data is first preprocessed to remove noisy and crowded points by performing an online mean shift clustering. A novel method called θ_WD is then presented that is able to detect loop-like traces on the fly. The experimental results on the GPS datasets of several elders have show that the θ_WD method is effective and efficient in detecting wandering behaviors, in terms of detection performance (AUC > 0.99, and 90% detection rate with less than 5 % of the false alarm rate), as well as time complexit

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Cx43 undergoes both distribution and concentration changes following acute cardiac ischemia. The loss of Cx43 protein is heterogeneous. Cx43 dephosphorylation occurred within 1 hour following ischemia.

Gallic acid is an active phenolic acid widely distributed in plants, and there is compelling evidence to prove its anti-inflammatory effects. NLRP3 inflammasome dysregulation is closely linked to many inflammatory diseases. However, how gallic acid affects the NLRP3 inflammasome remains unclear. Therefore, in the present study, we investigated the mechanisms underlying the effects of gallic acid on the NLRP3 inflammasome and pyroptosis, as well as its effect on gouty arthritis in mice. The results showed that gallic acid inhibited lactate dehydrogenase (LDH) release and pyroptosis in lipopolysaccharide (LPS)-primed and ATP-, nigericin-, or monosodium urate (MSU) crystal-stimulated macrophages. Additionally, gallic acid blocked NLRP3 inflammasome activation and inhibited the subsequent activation of caspase-1 and secretion of IL-1β. Gallic acid exerted its inhibitory effect by blocking NLRP3-NEK7 interaction and ASC oligomerization, thereby limiting inflammasome assembly. Moreover, gallic acid promoted the expression of nuclear factor E2-related factor 2 (Nrf2) and reduced the production of mitochondrial ROS (mtROS). Importantly, the inhibitory effect of gallic acid could be reversed by treatment with the Nrf2 inhibitor ML385. NRF2 siRNA also abolished the inhibitory effect of gallic acid on IL-1β secretion. The results further showed that gallic acid could mitigate MSU-induced joint swelling and inhibit IL-1β and caspase 1 (p20) production in mice. Moreover, gallic acid could moderate MSU-induced macrophages and neutrophils migration into joint synovitis. In summary, we found that gallic acid suppresses ROS generation, thereby limiting NLRP3 inflammasome activation and pyroptosis dependent on Nrf2 signaling, suggesting that gallic acid possesses therapeutic potential for the treatment of gouty arthritis.