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

Manipulating adenovirus hexon hypervariable loops dictates immune neutralisation and coagulation factor X-dependent cell interaction in vitro and in vivo

Adenoviruses are common pathogens, mostly targeting ocular, gastrointestinal and respiratory cells, but in some cases infection disseminates, presenting in severe clinical outcomes. Upon dissemination and contact with blood, coagulation factor X (FX) interacts directly with the adenovirus type 5 (Ad5) hexon. FX can act as a bridge to bind heparan sulphate proteoglycans, leading to substantial Ad5 hepatocyte uptake. FX “coating” also protects the virus from host IgM and complement-mediated neutralisation. However, the contribution of FX in determining Ad liver transduction whilst simultaneously shielding the virus from immune attack remains unclear. In this study, we demonstrate that the FX protection mechanism is not conserved amongst Ad types, and identify the hexon hypervariable regions (HVR) of Ad5 as the capsid proteins targeted by this host defense pathway. Using genetic and pharmacological approaches, we manipulate Ad5 HVR interactions to interrogate the interplay between viral cell transduction and immune neutralisation. We show that FX and inhibitory serum components can co-compete and virus neutralisation is influenced by both the location and extent of modifications to the Ad5 HVRs. We engineered Ad5-derived HVRs into the rare, native non FX-binding Ad26 to create Ad26.HVR5C. This enabled the virus to interact with FX at high affinity, as quantified by surface plasmon resonance, FX-mediated cell binding and transduction assays. Concomitantly, Ad26.HVR5C was also sensitised to immune attack in the absence of FX, a direct consequence of the engineered HVRs from Ad5. In both immune competent and deficient animals, Ad26.HVR5C hepatic gene transfer was mediated by FX following intravenous delivery. This study gives mechanistic insight into the pivotal role of the Ad5 HVRs in conferring sensitivity to virus neutralisation by IgM and classical complement-mediated attack. Furthermore, through this gain-of-function approach we demonstrate the dual functionality of FX in protecting Ad26.HVR5C against innate immune factors whilst determining liver targeting

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

ConCarE:Personalized Clinical Feature Embedding via Capturing the Healthcare Context

Predicting the patient’s clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics. Most deep learning-based solutions for EMR analysis concentrate on learning the clinical visit embedding and exploring the relations between vis- its. Although those works have shown superior performances in healthcare prediction, they fail to thoroughly explore the personal characteristics during the clinical visits. Moreover, existing work usually assumes that a more recent record has a larger weight in the prediction, but this assumption is not true for certain clinical features. In this paper, we propose ConCare to handle the irregular EMR data and extract feature interrelationship to perform individualized healthcare prediction. Our solution can embed the feature sequences separately by modeling the time-aware distribution. ConCare further improves the multi-head self-attention via the cross-head decorrelation, so that the inter-dependencies among dynamic features and static baseline information can be diversely captured to form the personal health context. Experimental results on two real-world EMR datasets demonstrate the effectiveness of ConCare. More importantly, ConCare is able to extract medical findings which can be confirmed by human experts and medical literature

The importance of aboveground and belowground interspecific interactions in determining crop growth and advantages of peanut/maize intercropping

Intercropping of maize (Zea mays L.) and peanut (Arachis hypogaea L.) often results in greater yields than the respective sole crops. However, there is limited knowledge of aboveground and belowground interspecific interactions between maize and peanut in field. A two-year field experiment was conducted to investigate the effects of interspecific interactions on plant growth and grain yield for a peanut/maize intercropping system under different nitrogen (N) and phosphorus (P) levels. The method of root separation was employed to differentiate belowground from aboveground interspecific interactions. We observed that the global interspecific interaction effect on the shoot biomass of the intercropping system decreased with the coexistence period, and belowground interaction contributed more than aboveground interaction to advantages of the intercropping in terms of shoot biomass and grain yield. There was a positive effect from aboveground and belowground interspecific interactions on crop plant growth in the intercropping system, except that aboveground interaction had a negative effect on peanut during the late coexistence period. The advantage of intercropping on grain came mainly from increased maize yield (means 95%) due to aboveground interspecific competition for light and belowground interaction (61%72% vs. 28%-39% in fertilizer treatments). There was a negative effect on grain yield from aboveground interaction for peanut, but belowground interspecific interaction positively affected peanut grain yield. The supply of N, P, or N + P increased grain yield of intercropped maize and the contribution from aboveground interspecific interaction. Our study suggests that the advantages of peanut/maize intercropping for yield mainly comes from aboveground interspecific competition for maize and belowground interspecific facilitation for peanut, and their respective yield can be enhanced by N and P. These findings are important for managing the intercropping system and optimizing the benefits from using this system. (C) 2021 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd