An Integrative Pharmacology Model for Decoding the Underlying Therapeutic Mechanisms of Ermiao Powder for Rheumatoid Arthritis

As a systemic inflammatory arthritis disease, rheumatoid arthritis (RA) is complex and hereditary. Traditional Chinese medicine (TCM) has evident advantages in treating complex diseases, and a variety of TCM formulas have been reported that have effective treatment on RA. Clinical and pharmacological studies showed that Ermiao Powder, which consists of Phellodendron amurense Rupr. (PAR) and Atractylodes lancea (Thunb.) DC. (ALD), can be used in the treatment of RA. Currently, most studies focus on the anti-inflammatory mechanism of PAR and ALD and are less focused on their coordinated molecular mechanism. In this research, we established an integrative pharmacological strategy to explore the coordinated molecular mechanism of the two herbs of Ermiao Powder in treating RA. To explore the potential coordinated mechanism of PAR and ALD, we firstly developed a novel mathematical model to calculate the contribution score of 126 active components and 85 active components, which contributed 90% of the total contribution scores that were retained to construct the coordinated functional space. Then, the knapsack algorithm was applied to identify the core coordinated functional components from the 85 active components. Finally, we obtained the potential coordinated functional components group (CFCG) with 37 components, including wogonin, paeonol, ethyl caffeate, and magnoflorine. Also, functional enrichment analysis was performed on the targets of CFCG to explore the potential coordinated molecular mechanisms of PAR and ALD. The results indicated that the CFCG could treat RA by coordinated targeting to the genes involved in immunity and inflammation-related signal pathways, such as phosphatidylinositol 3‑kinase/protein kinase B signaling pathway, mitogen-activated protein kinase signaling pathway, tumor necrosis factor signaling pathway, and nuclear factor-kappa B signaling pathway. The docking and in vitro experiments were used to predict the affinity and validate the effect of CFCG and further confirm the reliability of our method. Our integrative pharmacological strategy, including CFCG identification and verification, can provide the methodological references for exploring the coordinated mechanism of TCM in treating complex diseases and contribute to improving our understanding of the coordinated mechanism

Detecting Key Functional Components Group and Speculating the Potential Mechanism of Xiao-Xu-Ming Decoction in Treating Stroke

Stroke is a cerebrovascular event with cerebral blood flow interruption which is caused by occlusion or bursting of cerebral vessels. At present, the main methods in treating stroke are surgical treatment, statins, and recombinant tissue-type plasminogen activator (rt-PA). Relatively, traditional Chinese medicine (TCM) has widely been used at clinical level in China and some countries in Asia. Xiao-Xu-Ming decoction (XXMD) is a classical and widely used prescription in treating stroke in China. However, the material basis of effect and the action principle of XXMD are still not clear. To solve this issue, we designed a new system pharmacology strategy that combined targets of XXMD and the pathogenetic genes of stroke to construct a functional response space (FRS). The effective proteins from this space were determined by using a novel node importance calculation method, and then the key functional components group (KFCG) that could mediate the effective proteins was selected based on the dynamic programming strategy. The results showed that enriched pathways of effective proteins selected from FRS could cover 99.10% of enriched pathways of reference targets, which were defined by overlapping of component targets and pathogenetic genes. Targets of optimized KFCG with 56 components can be enriched into 166 pathways that covered 80.43% of 138 pathways of 1,012 pathogenetic genes. A component potential effect score (PES) calculation model was constructed to calculate the comprehensive effective score of components in the components-targets-pathways (C-T-P) network of KFCGs, and showed that ferulic acid, zingerone, and vanillic acid had the highest PESs. Prediction and docking simulations show that these components can affect stroke synergistically through genes such as MEK, NFκB, and PI3K in PI3K-Akt, cAMP, and MAPK cascade signals. Finally, ferulic acid, zingerone, and vanillic acid were tested to be protective for PC12 cells and HT22 cells in increasing cell viabilities after oxygen and glucose deprivation (OGD). Our proposed strategy could improve the accuracy on decoding KFCGs of XXMD and provide a methodologic reference for the optimization, mechanism analysis, and secondary development of the formula in TCM

Multiple object tracking with attention to appearance, structure, motion and size

Objective of multiple object tracking (MOT) is to assign a unique track identity for all the objects of interest in a video, across the whole sequence. Tracking-by-detection is the most common approach used in addressing MOT problem. In this work, we propose a method to address MOT by defining a dissimilarity measure based on object motion, appearance, structure, and size. We calculate the appearance and structure-based dissimilarity measure by matching histograms following a grid architecture. Motion and size for each track are predicted using the information from track's history. These dissimilarity values are then used in the Hungarian algorithm, in the data association step for track identity assignment. In addition, we introduce a method to address any false detection in stable tracks. The proposed method runs in real time following an online approach. We evaluate our method in both MOT17 benchmark data-set for pedestrian tracking and KITTI benchmark data-set for vehicle tracking using the same system parameters to verify the robustness of the proposed method. The method can achieve state-of-the-art results in both benchmarks.National Research Foundation (NRF)Published versio

Real time multiple object tracking using deep features and localization information

In this paper we propose a tracking by detection method using a dissimilarity measure calculated based on the location and the appearance information of the object. These dissimilarity values are used in Hungarian Algorithm [1] in the data association step for track identity assignment. We make use of YOLO [2] deep learning based object detector in the detection step from camera image feed. Location measure is calculated using the predicted object location and bounding box, while the appearance measure is from the last feature layer from the detection network. Main focus in this work is to propose a tracking framework that can be used in real time automated vehicle guiding applications, by striking a balance between computational complexity and tracking accuracy. Therefore, we make use of the deep features available from detection framework rather than calculating a new appearance measure during the tracking step. The method proposed is very efficient and enables to achieve speeds up to 500+ frames per second (fps) in KITTI [3] tracking benchmark while achieving state-of-the-art results.NRF (Natl Research Foundation, S’pore)Accepted versio

A Consistent and Long-term Mapping Approach for Navigation

The construction and maintenance of a robocentric map is key to high-level mobile robotic tasks like path planning and smart navigation. But the challenge of dynamic environment and huge amount of dense sensor data makes it hard to be implemented in a real-world application for long-term use. In this paper we present a novel mapping approach by incorporating semantic cuboid object detection and multi-view geometry information. The proposed system can precisely describe the incremental 3D environment in real-time and maintain a long-term map by extracting out moving objects. The representation of the map is a collection of sub-volumes which can be utilized to perform pose graph optimization to address the challenge of building a consistent and scalable map. These sub-volumes are first aligned by localization module and refined by fusing the active volumes using co-visible graph. With the proposed framework we can obtain the object-level constraints and propose a consistent obstacle mapping system combining multi-view geometry with obstacle detection to obtain robust static map in a complex environment. Public dataset and self-collected data demonstrate the efficiency and consistency of our proposed approach

Energy minimization approach for negative obstacle region detection

Obstacle detection is an important area of research for automatic robot navigation and obstacle avoidance. However, the main focus has been on detecting positive obstacles such as vehicles, pedestrians and anything above the ground plane. Equally important task is detecting negative obstacles such as holes, ditches etc.That lies below the ground surface, which has not been studied extensively. We propose to detect such holes and potholes as an energy minimization problem. We use stereo information combined with saliency to initialize the energy function and use colour information to optimize the result. The optimized result is then subjected to hysteresis thresholding to arrive at the final negative obstacle region. Experimental results prove that the introduced method can successfully detect negative obstacles and run in real time (in the range 14-20 Hz) when using GPU for stereo matching. The proposed method was tested under three camera configurations with corridor, concrete-road and tar-road scenes. Proposed method perform well compared to the recent works