An Event-Based Neurobiological Recognition System with Orientation Detector for Objects in Multiple Orientations

A new multiple orientation event-based neurobiological recognition system is proposed by integrating recognition and tracking function in this paper, which is used for asynchronous address-event representation (AER) image sensors. The characteristic of this system has been enriched to recognize the objects in multiple orientations with only training samples moving in a single orientation. The system extracts multi-scale and multi-orientation line features inspired by models of the primate visual cortex. An orientation detector based on modified Gaussian blob tracking algorithm is introduced for object tracking and orientation detection. The orientation detector and feature extraction block work in simultaneous mode, without any increase in categorization time. An addresses lookup table (addresses LUT) is also presented to adjust the feature maps by addresses mapping and reordering, and they are categorized in the trained spiking neural network. This recognition system is evaluated with the MNIST dataset which have played important roles in the development of computer vision, and the accuracy is increase owing to the use of both ON and OFF events. AER data acquired by a DVS are also tested on the system, such as moving digits, pokers, and vehicles. The experimental results show that the proposed system can realize event-based multi-orientation recognition.The work presented in this paper makes a number of contributions to the event-based vision processing system for multi-orientation object recognition. It develops a new tracking-recognition architecture to feedforward categorization system and an address reorder approach to classify multi-orientation objects using event-based data. It provides a new way to recognize multiple orientation objects with only samples in single orientation

Extracellular calcium reduction strongly increases the lytic capacity of pneumolysin from streptococcus pneumoniae in brain tissue

Background. Streptococcus pneumoniae causes serious diseases such as pneumonia and meningitis. Its major pathogenic factor is the cholesterol-dependent cytolysin pneumolysin, which produces lytic pores at high concentrations. At low concentrations, it has other effects, including induction of apoptosis. Many cellular effects of pneumolysin appear to be calcium dependent. Methods. Live imaging of primary mouse astroglia exposed to sublytic amounts of pneumolysin at various concentrations of extracellular calcium was used to measure changes in cellular permeability (as judged by lactate dehydrogenase release and propidium iodide chromatin staining). Individual pore properties were analyzed by conductance across artificial lipid bilayer. Tissue toxicity was studied in continuously oxygenated acute brain slices. Results. The reduction of extracellular calcium increased the lytic capacity of the toxin due to increased membrane binding. Reduction of calcium did not influence the conductance properties of individual toxin pores. In acute cortical brain slices, the reduction of extracellular calcium from 2 to 1 mM conferred lytic activity to pathophysiologically relevant nonlytic concentrations of pneumolysin. Conclusions. Reduction of extracellular calcium strongly enhanced the lytic capacity of pneumolysin due to increased membrane binding. Thus, extracellular calcium concentration should be considered as a factor of primary importance for the course of pneumococcal meningitis

Unravelling cancer subtype-specific driver genes in single-cell transcriptomics data with CSDGI

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Novel Hybrid Excitation High Temperature Superconducting Homopolar Inductor Alternator for Aviation Turbo-Electric System

The high temperature superconducting homopolar inductor alternator (HTS-HIA) connected with a high-speed gas turbine can achieve higher efficiency and higher power density. However, limited by the insulation and quench of the HTS winding, the HTS-HIA cannot change excitation current quickly to meet the requirements of output voltage regulation. To satisfy the load demand of multiple voltage levels, a novel hybrid excitation HTS-HIA (HEHTS-HIA) is proposed in this paper. Its excitation windings include a HTS winding and an adjusting winding made of copper wire. In steady-state operation, only the HTS winding carries the excitation current, while the adjusting winding only operates when the output voltage needs to be regulated. Firstly, the structure and operation principle of the proposed HEHTS-HIA is illustrated. To describe the principle of voltage regulation, the equivalent circuits of HTS winding and adjusting winding are established. Then, the electromagnetic performance of HEHTS-HIA is analyzed, including adjusting characteristics, response speed, transient characteristics and output performance. The results show that the proposed HEHTS-HIA can achieve a full range of voltage regulation under the premise of ensuring the safety of HTS winding, indicating that the proposed HEHTS-HIA is a promising candidate for the application of aviation turbo-electric system

Optimal Design of a High Temperature Superconducting Homopolar Inductor Machine

A high temperature superconducting homopolar inductor machine (HTS-HIM) is optimally designed considering the effect of magnetic field on HTS coil in this paper. Firstly, the structure and operation principle of HTS-HIM are presented. The three-dimensional HTS-HIM finite element analysis model and two-dimensional axisymmetric direct coupling model of HTS-HIM based on T-A formulation are established. Secondly, the excitation window parameters, the excitation current and number of turns of HTS coil are optimized, taking into account the HTS-HIM performance and the safety of HTS coil. Thirdly, the magnetic field weakening capabilities of the U-shaped flux diverter and copper layer are analyzed and their parameters are optimized. Finally, the optimal design scheme and the critical current of HTS coil in HTS-HIM are obtained

Enhancing Online Epidemic Supervising System by Compartmental and GRU Fusion Model

The global pandemic, COVID-19, is an acute respiratory infectious disease caused by the 2019 novel coronavirus. Building the online epidemic supervising system to provide COVID-19 dynamic prediction and analysis has attracted the attention of the industry and applications community. In previous studies, the compartmental models and deep neural networks (DNNs) played important roles in predicting and analyzing the dynamics of the pandemic. Nevertheless, the compartmental model has limited ability to fit historical data and thus leads to unsatisfactory prediction accuracy due to the difficulty in parameter estimation. For DNNs, the lack of interpretability makes it difficult to explain the prediction results; thus, it cannot provide an in-depth understanding of the transmission mechanism of the pandemic. We propose a fusion model to leverage the merits of both models and resolve their shortcomings. The fusion model extracts epidemic-related knowledge from the state-of-the-art SEIDR compartmental model to guide the training of the GRU model, which can preserve the interpretability and achieve a good performance in predicting epidemic dynamics. This model can help to enhance the online epidemic supervising system by providing more accurate prediction results and deeper analysis. Our extensive experiments across multiple epidemic datasets from six European countries demonstrate that our model outperforms existing state-of-the-art baselines in predicting the active confirmed cases. More importantly, by analyzing the effective reproductive number, our method can reveal the risk of the second wave of the epidemic in Europe and justify the importance of social distancing to control the outbreak of the epidemic.</jats:p

AdaCare:Explainable Clinical Health Status Representation Learning via Scale Adaptive Feature Extraction and Recalibration

Deep learning-based health status representation learning and clinical prediction have raised much research interest in recent years. Existing models have shown superior performance, but there are still several major issues that have not been fully taken into consideration. First, the historical variation pattern of the biomarker in diverse time scales plays an important role in indicating the health status, but it has not been explicitly extracted by existing works. Second, key factors that strongly indicate the health risk are different among patients. It is still challenging to adaptively make use of the features for patients in diverse conditions. Third, using the prediction model as a black box will limit the reliability in clinical practice. However, none of the existing works can provide satisfying interpretability and meanwhile achieve high prediction performance. In this work, we develop a general health status representation learning model, named AdaCare. It can capture the long and short-term variations of biomarkers as clinical features to depict the health status in multiple time scales. It also models the correlation between clinical features to enhance the ones which strongly indicate the health status and thus can maintain a state-of-the-art performance in terms of prediction accuracy while providing qualitative in- interpretability. We conduct health risk prediction experiment on two real-world datasets. Experiment results indicate that AdaCare outperforms state-of-the-art approaches and provides effective interpretability which is verifiable by clinical experts