The Function of microRNAs in Pulmonary Embolism: Review and Research Outlook

Pulmonary embolism (PE) is a common pathologic condition that frequently occurs in patients with deep venous thrombosis. Severe PE may critically suppress cardiopulmonary function, thereby threatening the life of patients. Chronic pulmonary hypertension caused by PE may lead to deterioration of respiratory dysfunction, resulting in complete disability. MicroRNAs (miRNAs) are a group of abundantly expressed non-coding RNAs that exert multiple functions in regulating the transcriptome via post-transcriptional targeting of mRNAs. Specifically, miRNAs bind to target mRNAs in a matching mechanism between the miRNA seed sequence and mRNA 3ʹ UTR, thus modulating the transcript stability or subsequent translation activity by RNA-induced silencing complex. Current studies have reported the function of miRNAs as biomarkers of PE, revealing their mechanism, function, and targetome in venous thrombophilia. This review summarizes the literature on miRNA functions and downstream mechanisms in PE. We conclude that various related miRNAs play important roles in PE and have great potential as treatment targets. For clinical application, we propose that miRNA biomarkers combined with traditional biomarkers or miRNA signatures generated from microchips may serve as a great predictive tool for PE occurrence and prognosis. Further, therapies targeting miRNAs or their upstream/downstream molecules need to be developed more quickly to keep up with the progress of routine treatments, such as anticoagulation, thrombolysis, or surgery.

A GAN-Like Approach for Physics-Based Imitation Learning and Interactive Control

We present a simple and intuitive approach for interactive control of physically simulated characters. Our work builds upon generative adversarial networks (GAN) and reinforcement learning, and introduces an imitation learning framework where an ensemble of classifiers and an imitation policy are trained in tandem given pre-processed reference clips. The classifiers are trained to discriminate the reference motion from the motion generated by the imitation policy, while the policy is rewarded for fooling the discriminators. Using our GAN-like approach, multiple motor control policies can be trained separately to imitate different behaviors. In runtime, our system can respond to external control signal provided by the user and interactively switch between different policies. Compared to existing method, our proposed approach has the following attractive properties: 1) achieves state-of-the-art imitation performance without manually designing and fine tuning a reward function; 2) directly controls the character without having to track any target reference pose explicitly or implicitly through a phase state; and 3) supports interactive policy switching without requiring any motion generation or motion matching mechanism. We highlight the applicability of our approach in a range of imitation and interactive control tasks, while also demonstrating its ability to withstand external perturbations as well as to recover balance. Overall, our approach has low runtime cost and can be easily integrated into interactive applications and games.

A Design of a Developable Automatic Avoidance System of UAV Based on ADS-B

There are two primary defects in the existing UAV avoidance systems: the system is memoryless; airborne radars are used to detect long-distance barriers, which are unreliable and expensive. The paper adopts the deep learning algorithm and ADS-B communication system based on a satellite base station to solve the above problems. It divides the avoidance problem into two parts: short-distance obstacle avoidance and long-distance route planning. On the one hand, the system establishes the knowledge base storing the previous avoidance experience and the matching mechanism, realizing the correspondence between input and experience through a deep learning algorithm. They can dramatically improve the reaction speed and safety of UAVs. On the other hand, the system realizes the interconnection between UAV and the satellite base station through the ADS-B communication system to replace the radars, putting the task of route planning on the satellite platform. Therefore, the satellite can achieve large-scale and all-weather detection to improve the overall safety of UAVs depending on its high and long-range characteristics. The paper also illustrates the design elements of the RF baseband integrated ADS-B transceiver and the simulation performance of the short-distance avoidance system in the end, whose results show that the system can be applied to dense obstacle environments and significantly improve the security of UAVs in a complex domain.

Spatial Distribution of Ink at Keypoints (SDIK): A Novel Feature for Word Spotting in Arabic Documents

This paper addresses the challenging task of word spotting in Arabic handwritten documents. We proposed a novel feature that we called Spatial Distribution of Ink at Keypoints (SDIK). The proposed feature captures the characteristics of Arabic handwriting concentrated at endpoints and branch points. SDIK feature quantizes the spatial repartition of ink pixels in the neighborhoods of keypoints. The resulting SDIK features are very fast to match, we take this advantage to match a query word with lines images rather than words images. By this matching mechanism, we overcome the hard task of segmenting an Arabic document into words. The method proposed in this study is tested on historical Arabic document with IBN SINA dataset and on modern handwriting with IFN/ENIT database. The obtained results are great of interest for retrieving query words in an Arabic document.

Constrained Optimization as the Allocation Method in Local Flexibility Markets

Local flexibility markets or smart markets are new tools used to harness regional flexibility for congestion management. In order to benefit from the available flexibility potential for grid-oriented or even grid-supportive applications, complex but efficient and transparent allocation is necessary. This paper proposes a constrained optimization method for matching the flexibility demand of grid operators to the flexibility supply using decentralized flexibility options located in the distribution grid. Starting with a definition of the operational and stakeholder environment of smart market design, various existing approaches are analyzed based on a literature review and a resulting meta-analysis. In the next step, a categorization of the allocation method is conducted followed by the definition of the optimization goal. The optimization problem, including all relevant input parameters, is identified and formulated by introducing the relevant boundary conditions and constraints of flexibility demand and offers. A proof of concept of the approach is presented using a case study and the Altdorfer Flexmarkt (ALF) field test within the project C/sells. In this paper, we analyze the background of the local flexibility market, provide the methodology (including publishing the code of the matching mechanism), and provide the results of the field test.

A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks

This paper proposes and implements a lightweight, “real-time” localization system (SORLA) with artificial landmarks (reflectors), which only uses LiDAR data for the laser odometer compensation in the case of high-speed or sharp-turning. Theoretically, due to the feature-matching mechanism of the LiDAR, locations of multiple reflectors and the reflector layout are not limited by geometrical relation. A series of algorithms is implemented to find and track the features of the environment, such as the reflector localization method, the motion compensation technique, and the reflector matching optimization algorithm. The reflector extraction algorithm is used to identify the reflector candidates and estimates the precise center locations of the reflectors from 2D LiDAR data. The motion compensation algorithm predicts the potential velocity, location, and angle of the robot without odometer errors. Finally, the matching optimization algorithm searches the reflector combinations for the best matching score, which ensures that the correct reflector combination could be found during the high-speed movement and fast turning. All those mechanisms guarantee the algorithm’s precision and robustness in the high speed and noisy background. Our experimental results show that the SORLA algorithm has an average localization error of 6.45 mm at a speed of 0.4 m/s, and 9.87 mm at 4.2 m/s, and still works well with the angular velocity of 1.4 rad/s at a sharp turn. The recovery mechanism in the algorithm could handle the failure cases of reflector occlusion, and the long-term stability test of 72 h firmly proves the algorithm’s robustness. This work shows that the strategy used in the SORLA algorithm is feasible for industry-level navigation with high precision and a promising alternative solution for SLAM.

Convolutional Neural Network with an Elastic Matching Mechanism for Time Series Classification

Recently, some researchers adopted the convolutional neural network (CNN) for time series classification (TSC) and have achieved better performance than most hand-crafted methods in the University of California, Riverside (UCR) archive. The secret to the success of the CNN is weight sharing, which is robust to the global translation of the time series. However, global translation invariance is not the only case considered for TSC. Temporal distortion is another common phenomenon besides global translation in time series. The scale and phase changes due to temporal distortion bring significant challenges to TSC, which is out of the scope of conventional CNNs. In this paper, a CNN architecture with an elastic matching mechanism, which is named Elastic Matching CNN (short for EM-CNN), is proposed to address this challenge. Compared with the conventional CNN, EM-CNN allows local time shifting between the time series and convolutional kernels, and a matching matrix is exploited to learn the nonlinear alignment between time series and convolutional kernels of the CNN. Several EM-CNN models are proposed in this paper based on diverse CNN models. The results for 85 UCR datasets demonstrate that the elastic matching mechanism effectively improves CNN performance.

Question answering system for deterministic fault diagnosis of intelligent railway signal equipment

Purpose The railway signal equipment failure diagnosis is a vital element to keep the railway system operating safely. One of the most difficulties in signal equipment failure diagnosis is the uncertainty of causality between the consequence and cause for the accident. The traditional method to solve this problem is based on Bayesian Network, which needs a rigid and independent assumption basis and prior probability knowledge but ignoring the semantic relationship in causality analysis. This paper aims to perform the uncertainty of causality in signal equipment failure diagnosis through a new way that emphasis on mining semantic relationships. Design/methodology/approach This study proposes a deterministic failure diagnosis (DFD) model based on the question answering system to implement railway signal equipment failure diagnosis. It includes the failure diagnosis module and deterministic diagnosis module. In the failure diagnosis module, this paper exploits the question answering system to recognise the cause of failure consequences. The question answering is composed of multi-layer neural networks, which extracts the position and part of speech features of text data from lower layers and acquires contextual features and interactive features of text data by Bi-LSTM and Match-LSTM, respectively, from high layers, subsequently generates the candidate failure cause set by proposed the enhanced boundary unit. In the second module, this study ranks the candidate failure cause set in the semantic matching mechanism (SMM), choosing the top 1st semantic matching degree as the deterministic failure causative factor. Findings Experiments on real data set railway maintenance signal equipment show that the proposed DFD model can implement the deterministic diagnosis of railway signal equipment failure. Comparing massive existing methods, the model achieves the state of art in the natural understanding semantic of railway signal equipment diagnosis domain. Originality/value It is the first time to use a question answering system executing signal equipment failure diagnoses, which makes failure diagnosis more intelligent than before. The EMU enables the DFD model to understand the natural semantic in long sequence contexture. Then, the SMM makes the DFD model acquire the certainty failure cause in the failure diagnosis of railway signal equipment.