In-Place Gestures Classification via Long-term Memory Augmented Network

In-place gesture-based virtual locomotion techniques enable users to control their viewpoint and intuitively move in the 3D virtual environment. A key research problem is to accurately and quickly recognize in-place gestures, since they can trigger specific movements of virtual viewpoints and enhance user experience. However, to achieve real-time experience, only short-term sensor sequence data (up to about 300ms, 6 to 10 frames) can be taken as input, which actually affects the classification performance due to limited spatio-temporal information. In this paper, we propose a novel long-term memory augmented network for in-place gestures classification. It takes as input both short-term gesture sequence samples and their corresponding long-term sequence samples that provide extra relevant spatio-temporal information in the training phase. We store long-term sequence features with an external memory queue. In addition, we design a memory augmented loss to help cluster features of the same class and push apart features from different classes, thus enabling our memory queue to memorize more relevant long-term sequence features. In the inference phase, we input only short-term sequence samples to recall the stored features accordingly, and fuse them together to predict the gesture class. We create a large-scale in-place gestures dataset from 25 participants with 11 gestures. Our method achieves a promising accuracy of 95.1% with a latency of 192ms, and an accuracy of 97.3% with a latency of 312ms, and is demonstrated to be superior to recent in-place gesture classification techniques. User study also validates our approach. Our source code and dataset will be made available to the community.Comment: This paper is accepted to IEEE ISMAR202

Masked Autoencoders in 3D Point Cloud Representation Learning

Transformer-based Self-supervised Representation Learning methods learn generic features from unlabeled datasets for providing useful network initialization parameters for downstream tasks. Recently, self-supervised learning based upon masking local surface patches for 3D point cloud data has been under-explored. In this paper, we propose masked Autoencoders in 3D point cloud representation learning (abbreviated as MAE3D), a novel autoencoding paradigm for self-supervised learning. We first split the input point cloud into patches and mask a portion of them, then use our Patch Embedding Module to extract the features of unmasked patches. Secondly, we employ patch-wise MAE3D Transformers to learn both local features of point cloud patches and high-level contextual relationships between patches and complete the latent representations of masked patches. We use our Point Cloud Reconstruction Module with multi-task loss to complete the incomplete point cloud as a result. We conduct self-supervised pre-training on ShapeNet55 with the point cloud completion pre-text task and fine-tune the pre-trained model on ModelNet40 and ScanObjectNN (PB\_T50\_RS, the hardest variant). Comprehensive experiments demonstrate that the local features extracted by our MAE3D from point cloud patches are beneficial for downstream classification tasks, soundly outperforming state-of-the-art methods ($93.4\%$ and $86.2\%$ classification accuracy, respectively).Comment: Accepted to IEEE Transactions on Multimedi

DHGCN: Dynamic Hop Graph Convolution Network for Self-Supervised Point Cloud Learning

Recent works attempt to extend Graph Convolution Networks (GCNs) to point clouds for classification and segmentation tasks. These works tend to sample and group points to create smaller point sets locally and mainly focus on extracting local features through GCNs, while ignoring the relationship between point sets. In this paper, we propose the Dynamic Hop Graph Convolution Network (DHGCN) for explicitly learning the contextual relationships between the voxelized point parts, which are treated as graph nodes. Motivated by the intuition that the contextual information between point parts lies in the pairwise adjacent relationship, which can be depicted by the hop distance of the graph quantitatively, we devise a novel self-supervised part-level hop distance reconstruction task and design a novel loss function accordingly to facilitate training. In addition, we propose the Hop Graph Attention (HGA), which takes the learned hop distance as input for producing attention weights to allow edge features to contribute distinctively in aggregation. Eventually, the proposed DHGCN is a plug-and-play module that is compatible with point-based backbone networks. Comprehensive experiments on different backbones and tasks demonstrate that our self-supervised method achieves state-of-the-art performance. Our source code is available at: https://github.com/Jinec98/DHGCN.Comment: Accepted to AAAI 202

Effect of intracranial electrical stimulation on dynamic functional connectivity in medically refractory epilepsy

ObjectiveThe objective of this study was to explore the distributed network effects of intracranial electrical stimulation in patients with medically refractory epilepsy using dynamic functional connectivity (dFC) and graph indicators.MethodsThe time-varying connectivity patterns of dFC (state-based metrics) as well as topological properties of static functional connectivity (sFC) and dFC (graph indicators) were assessed before and after the intracranial electrical stimulation. The sliding window method and k-means clustering were used for the analysis of dFC states, which were characterized by connectivity strength, occupancy rate, dwell time, and transition. Graph indicators for sFC and dFC were obtained using group statistical tests.ResultsDFCs were clustered into two connectivity configurations: a strongly connected state (state 1) and a sparsely connected state (state 2). After electrical stimulation, the dwell time and occupancy rate of state 1 decreased, while that of state 2 increased. Connectivity strengths of both state 1 and state 2 decreased. For graph indicators, the clustering coefficient, k-core, global efficiency, and local efficiency of patients showed a significant decrease, but the brain networks of patients exhibited higher modularity after electrical stimulation. Especially, for state 1, there was a significant decrease in functional connectivity strength after stimulation within and between the frontal lobe and temporary lobe, both of which are associated with the seizure onset.ConclusionOur findings demonstrated that intracranial electrical stimulation significantly changed the time-varying connectivity patterns and graph indicators of the brain in patients with medically refractory epilepsy. Specifically, the electrical stimulation decreased functional connectivity strength in both local-level and global-level networks. This might provide a mechanism of understanding for the distributed network effects of intracranial electrical stimulation and extend the knowledge of the pathophysiological network of medically refractory epilepsy

Using datamining approaches to selectacupoints in acupuncture and Moxibustion for knee osteoarthritis

Background: Acupuncture and moxibustion are traditional Chinese medicine therapies commonly used to treat knee osteoarthritis (KOA). Although acupoint selection affects the effectiveness of acupuncture and moxibustion, the basic rules of acupoint selection are little understood and there is a lack of guidelines regarding prescription. In this study, we used data mining approaches to investigate the principles of acupoint selection and provide a framework for formulation prescription in acupuncture and moxibustion for clinical treatment of KOA.Materials and Methods: PubMed, Cochrane Library, Science Citation Index, Wanfang database, VIP database, and China National Knowledge Infrastructure were searched for randomized controlled clinical trials published in English or Chinese from January 1, 2009 to October 1, 2015 evaluating the effect of acupuncture and moxibustion on KOA. Databases were established. Frequency statistics and association rule were used to extract and analyze the data.Results: A total of 876 acupuncture prescriptions and 122 acupoints were included in the analysis. Acupoints were concentrated in acupoints of fourteen meridians. The most frequently used acupoints were Dubi (ST35), Neixiyan (EX-LE4), Yanglingquan (GB34), Xuehai (SP10), Liangqiu (ST34), Zusanli (ST36), Yinlingquan (SP9), and Ashi point. The most frequently used meridian was Stomach Meridian of Foot-Yangming. Acupoints were concentrated mainly in the lower limbs. 42 acupoint pairs occurred frequently, and the top acupoint pairing was Dubi (ST35) and Neixiyan (EX-LE4).Conclusion: Acupoint selection and formulation prescription should focus on locally affected areas, and follow the theory of meridians, which helps establish guidelines for acupuncture and moxibustion in KOA patients.Key words: acupuncture and moxibustion, knee osteoarthritis, acupoint, data mining technolog

DHGCN: Dynamic hop graph convolution network for self-supervised point cloud learning

Recent works attempt to extend Graph Convolution Networks (GCNs) to point clouds for classification and segmentation tasks. These works tend to sample and group points to create smaller point sets locally and mainly focus on extracting local features through GCNs, while ignoring the relationship between point sets. In this paper, we propose the Dynamic Hop Graph Convolution Network (DHGCN) for explicitly learning the contextual relationships between the voxelized point parts, which are treated as graph nodes. Motivated by the intuition that the contextual information between point parts lies in the pairwise adjacent relationship, which can be depicted by the hop distance of the graph quantitatively, we devise a novel self-supervised part-level hop distance reconstruction task and design a novel loss function accordingly to facilitate training. In addition, we propose the Hop Graph Attention (HGA), which takes the learned hop distance as input for producing attention weights to allow edge features to contribute distinctively in aggregation. Eventually, the proposed DHGCN is a plug-and-play module that is compatible with point-based backbone networks. Comprehensive experiments on different backbones and tasks demonstrate that our self-supervised method achieves state-of-the-art performance. Our source code is available at: https://github.com/Jinec98/DHGCN

Efficacy and Safety of Chinese Medicinal Herbs for the Treatment of Hyperuricemia: A Systematic Review and Meta-Analysis

Background. Chinese medicinal herbs may be useful for the treatment of hyperuricemia, but there has been no systematic assessment of their efficacy and safety. Objectives. To systematically assess the efficacy and safety of Chinese medicinal herbs for the treatment of hyperuricemia. Methods. Six electronic databases were searched from their inception to December 2015. Randomized controlled clinical trials (RCTs) were included. Cochrane criteria were applied to assess the risk of bias. Data analysis was performed using RevMan software version 5.2. Results. Eleven RCTs with 838 patients were included. There was no significant difference in serum uric acid between Chinese medicinal herbs and traditional Western medicine (SME: 0.19, 95% CI: −0.04 to 0.43; p=0.10). In terms of overall efficacy, the Chinese medicinal herbs were significantly superior to Western medicine (RR: 1.11; 95% CI: 1.04 to 1.17; p=0.0007). The Chinese medicinal herbs were better than Western medicine in reducing the adverse reactions (RR: 0.30; 95% CI: 0.15 to 0.62; p=0.001). And all these funnel plots showed unlikelihood of publishing bias. Conclusions. The results indicate that Chinese medicinal herbs may have greater overall efficacy with fewer adverse drug reactions, although the evidence is weak owing to the low methodological quality and the small number of the included trials

Aneuploid Embryonic Stem Cells Drive Teratoma Metastasis

Aneuploidy, a deviation of the chromosome number from euploidy, is one of the hallmarks of cancer. High levels of aneuploidy are generally correlated with metastasis and poor prognosis in cancer patients. However, the causality of aneuploidy in cancer metastasis remains to be explored. Here we demonstrate that teratomas derived from aneuploid murine embryonic stem cells (ESCs), but not from isogenic diploid ESCs, disseminated to multiple organs, for which no additional copy number variations were required. Notably, no cancer driver gene mutations were identified in any metastases. Aneuploid circulating teratoma cells were successfully isolated from peripheral blood and showed high capacities for migration and organ colonization. Single-cell RNA sequencing of aneuploid primary teratomas and metastases identified a unique cell population with high stemness that was absent in diploid ESCs-derived teratomas. Further investigation revealed that aneuploid cells displayed decreased proteasome activity and overactivated endoplasmic reticulum (ER) stress during differentiation, thereby restricting the degradation of proteins produced from extra chromosomes in the ESC state and causing differentiation deficiencies. Noticeably, both proteasome activator Oleuropein and ER stress inhibitor 4-PBA can effectively inhibit aneuploid teratoma metastasis

FSCN1 Promotes Epithelial-Mesenchymal Transition Through Increasing Snail1 in Ovarian Cancer Cells

Background/Aims: Epithelial-mesenchymal transition (EMT) is one of the key mechanisms mediating cancer progression. Snail1 has a pivotal role in the regulation of EMT, involving the loss of E-cadherin and concomitant upregulation of vimentin, among other biomarkers. We have found FSCN1 promoted EMT in ovarian cancer cells, but the precise mechanism of FSCN1 in EMT process has not been clearly elucidated. Methods: The levels of FSCN1 and snail1 were determined in epithelial ovarian cancer(EOC) specimen and in ovarian cancer cells by RT-qPCR. The changes of EMT makers and effects on snail1 by FSCN1 were examined by overexpression or depletion of FSCN1 in EOC cells by RT-qPCR and western blotting. The invasiveness of the FSCN1-modified EOC cells was examined in transwell assay. Co-immunoprecipitation (IP) was performed to detect the interaction between snail1 and FSCN1 in EOC cells. Results: We found FSCN1 and snail1 significantly increased in EOC, and especially in EOC with metastasis. FSCN1 was positively correlated with snail1 expression at the cellular/histological levels. Moreover, we further showed that FSCN1 physiologically interacted with and increased the levels of snail1 to promote ovarian cancer cell EMT. Conclusion: FSCN1 promote EMT through snail1 in ovarian cancer cells. FSCN1 is an attractive novel target for inhibiting invasion and metastasis of EOC cells

USING DATA MINING APPROACHES TO SELECT ACUPOINTS IN ACUPUNCTURE AND MOXIBUSTION FOR KNEE OSTEOARTHRITIS

Background: Acupuncture and moxibustion are traditional Chinese medicine therapies commonly used to treat knee osteoarthritis (KOA). Although acupoint selection affects the effectiveness of acupuncture and moxibustion, the basic rules of acupoint selection are little understood and there is a lack of guidelines regarding prescription. In this study, we used data mining approaches to investigate the principles of acupoint selection and provide a framework for formulation prescription in acupuncture and moxibustion for clinical treatment of KOA. Materials and Methods: PubMed, Cochrane Library, Science Citation Index, Wanfang database, VIP database, and China National Knowledge Infrastructure were searched for randomized controlled clinical trials published in English or Chinese from January 1, 2009 to October 1, 2015 evaluating the effect of acupuncture and moxibustion on KOA. Databases were established. Frequency statistics and association rule were used to extract and analyze the data. Results: A total of 876 acupuncture prescriptions and 122 acupoints were included in the analysis. Acupoints were concentrated in acupoints of fourteen meridians. The most frequently used acupoints were Dubi (ST35), Neixiyan (EX-LE4), Yanglingquan (GB34), Xuehai (SP10), Liangqiu (ST34), Zusanli (ST36), Yinlingquan (SP9), and Ashi point. The most frequently used meridian was Stomach Meridian of Foot-Yangming. Acupoints were concentrated mainly in the lower limbs. 42 acupoint pairs occurred frequently, and the top acupoint pairing was Dubi (ST35) and Neixiyan (EX-LE4). Conclusion: Acupoint selection and formulation prescription should focus on locally affected areas, and follow the theory of meridians, which helps establish guidelines for acupuncture and moxibustion in KOA patients