MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction

Predicting protein properties such as solvent accessibility and secondary structure from its primary amino acid sequence is an important task in bioinformatics. Recently, a few deep learning models have surpassed the traditional window based multilayer perceptron. Taking inspiration from the image classification domain we propose a deep convolutional neural network architecture, MUST-CNN, to predict protein properties. This architecture uses a novel multilayer shift-and-stitch (MUST) technique to generate fully dense per-position predictions on protein sequences. Our model is significantly simpler than the state-of-the-art, yet achieves better results. By combining MUST and the efficient convolution operation, we can consider far more parameters while retaining very fast prediction speeds. We beat the state-of-the-art performance on two large protein property prediction datasets.Comment: 8 pages ; 3 figures ; deep learning based sequence-sequence prediction. in AAAI 201

Adaptive output feedback stabilization for nonlinear systems with unknown polynomial-of-output growth rate and sensor uncertainty

summary:In this paper, the problem of adaptive output feedback stabilization is investigated for a class of nonlinear systems with sensor uncertainty in measured output and a growth rate of polynomial-of-output multiplying an unknown constant in the nonlinear terms. By developing a dual-domination approach, an adaptive observer and an output feedback controller are designed to stabilize the nonlinear system by directly utilizing the measured output with uncertainty. Besides, two types of extension are made such that the proposed methods of adaptive output feedback stabilization can be applied for nonlinear systems with a large range of sensor uncertainty. Finally, numerical simulations are provided to illustrate the correctness of the theoretical results

Global stabilization for triangular formations under mixed distance and bearing constraints

This paper addresses the triangular formation control problem for a system of three agents under mixed distance and bearing constraints. The main challenge is to find a fully distributed control law for each agent to guarantee the global convergence towards a desired triangular formation. To solve this problem, we invoke the property that a triangle can be uniquely determined by the lengths of its two sides together with the magnitude of the corresponding included angle. Based on this feature, we design a class of control strategies, under which each agent is only responsible for a single control variable, i.e., a distance or an angle, such that the control laws can be implemented in local coordinate frames. The global convergence is shown by analyzing the dynamics of the closed-loop system in its cascade form. Then we discuss some extensions on more general formation shapes and give the quadrilateral formation as an example. Simulation results are provided to validate the effectiveness of the proposed control strategies

Research progress on the mechanism of probiotics regulating cow milk allergy in early childhood and its application in hypoallergenic infant formula

Some infants and young children suffer from cow's milk allergy (CMA), and have always mainly used hypoallergenic infant formula as a substitute for breast milk, but some of these formulas can still cause allergic reactions. In recent years, it has been found that probiotic nutritional interventions can regulate CMA in children. Scientific and reasonable application of probiotics to hypoallergenic infant formula is the key research direction in the future. This paper discusses the mechanism and clinical symptoms of CMA in children. This review critically ex- amines the issue of how probiotics use intestinal flora as the main vector to combine with the immune system to exert physiological functions to intervene CMA in children, with a particular focus on four mechanisms: promoting the early establishment of intestinal microecological balance, regulating the body's immunity and alleviating allergic response, enhancing the intestinal mucosal barrier function, and destroying allergen epitopes. Additionally, it overviews the development process of hypoallergenic infant formula and the research progress of probiotics in hypoallergenic infant formula. The article also offers suggestions and outlines potential future research directions and ideas in this field

AGSPNet: A framework for parcel-scale crop fine-grained semantic change detection from UAV high-resolution imagery with agricultural geographic scene constraints

Real-time and accurate information on fine-grained changes in crop cultivation is of great significance for crop growth monitoring, yield prediction and agricultural structure adjustment. Aiming at the problems of serious spectral confusion in visible high-resolution unmanned aerial vehicle (UAV) images of different phases, interference of large complex background and salt-and-pepper noise by existing semantic change detection (SCD) algorithms, in order to effectively extract deep image features of crops and meet the demand of agricultural practical engineering applications, this paper designs and proposes an agricultural geographic scene and parcel-scale constrained SCD framework for crops (AGSPNet). AGSPNet framework contains three parts: agricultural geographic scene (AGS) division module, parcel edge extraction module and crop SCD module. Meanwhile, we produce and introduce an UAV image SCD dataset (CSCD) dedicated to agricultural monitoring, encompassing multiple semantic variation types of crops in complex geographical scene. We conduct comparative experiments and accuracy evaluations in two test areas of this dataset, and the results show that the crop SCD results of AGSPNet consistently outperform other deep learning SCD models in terms of quantity and quality, with the evaluation metrics F1-score, kappa, OA, and mIoU obtaining improvements of 0.038, 0.021, 0.011 and 0.062, respectively, on average over the sub-optimal method. The method proposed in this paper can clearly detect the fine-grained change information of crop types in complex scenes, which can provide scientific and technical support for smart agriculture monitoring and management, food policy formulation and food security assurance

Leaders’ response to employee overqualification:An explanation of the curvilinear moderated relationship

This research aimed to advance overqualification literature by examining how leaders’ perceived employee overqualification (LPEO) influences their empowering behaviour and employee work behaviours. Drawing upon the individualized leadership theory, we proposed that LPEO has an inverted U-shape relationship with their empowering behaviour such that leaders are more motivated to empower employees from low to moderate levels of overqualification, but this tendency decreases after a certain inflection point. We also predicted that the inflection point occurs at a lower level of employee overqualification when leaders perceive higher (vs. lower) status threats. Leader empowering behaviour was hypothesized to positively predict employees’ voice and negatively predict their withdrawal behaviour. Two multi-source and time-lagged studies were conducted to examine this moderated mediation curvilinear model. In Study 1, survey data from 372 leader–employee dyads supported the inverted U-shape mediation model from leaders’ perceived overqualification to empowering behaviour, then to employee outcomes (i.e., voice and withdrawal behaviour). In Study 2, we collected data from a sample of 73 team leaders and 286 employees, and the results supported the full model. Taken together, these findings offer a perspective to enrich the understanding of employee overqualification and have important practical implications.Practitioner pointsWhen leaders regard employees as overqualified, they can assist them via the means of appropriate empowerment to best utilize their skills to benefit the company.Leader empowerment can inspire employees’ voice behaviour but reduce their withdrawal behaviours.To minimize potentially negative aspects from highly overqualified employees, organizations should reduce leaders’ concern about the status threat, and encourage leaders’ proactive responses to these employees so as to achieve positive returns from overqualified employees