Disruption of gradient expression of Zic3 resulted in abnormal intra-retinal axon projection

The targeting of retinal ganglion axons toward the optic disc is the first step in axon pathfinding in the visual system. The molecular mechanisms involved in guiding the retinal axons to project towards the optic disc are not well understood. We report that a gene encoding a zinc-finger transcription factor, Zic3, is expressed in a periphery-high and center-low gradient in the retina at the stages of active axon extension inside the retina. The gradient expression of Zic3 recedes towards the periphery over the course of development, correlating with the progression of retinal cell differentiation and axonogenesis. Disruption of gradient expression of Zic3 by retroviral overexpression resulted in mis-targeting of retinal axons and some axons misrouted to the sub-retinal space at the photoreceptor side of the retina. Misexpression of Zic3 did not affect neurogenesis or differentiation inside the retina, or grossly alter retinal lamination. By stripe assay, we show that misexpression of Zic3 may induce the expression of an inhibitory factor to the retinal axons. Zic3 appears to play a role in intra-retinal axon targeting, possibly through regulation of the expression of specific downstream genes involved in axon guidance

An adaptation reference-point-based multiobjective evolutionary algorithm

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.It is well known that maintaining a good balance between convergence and diversity is crucial to the performance of multiobjective optimization algorithms (MOEAs). However, the Pareto front (PF) of multiobjective optimization problems (MOPs) affects the performance of MOEAs, especially reference point-based ones. This paper proposes a reference-point-based adaptive method to study the PF of MOPs according to the candidate solutions of the population. In addition, the proportion and angle function presented selects elites during environmental selection. Compared with five state-of-the-art MOEAs, the proposed algorithm shows highly competitive effectiveness on MOPs with six complex characteristics

An improved memory prediction strategy for dynamic multiobjective optimization

The file attached to this record is the author's final peer reviewed version.In evolutionary dynamic multiobjective optimization (EDMO), the memory strategy and prediction method are considered as effective and efficient methods. To handling dynamic multiobjective problems (DMOPs), this paper studies the behavior of environment change and tries to make use of the historical information appropriately. And then, this paper proposes an improved memory prediction model that uses the memory strategy to provide valuable information to the prediction model to predict the POS of the new environment more accurately. This memory prediction model is incorporated into a multiobjective evolutionary algorithm based on decomposition (MOEA/D). In particular, the resultant algorithm (MOEA/D-MP) adopts a sensor-based method to detect the environment change and find a similar one in history to reuse the information of it in the prediction process. The proposed algorithm is compared with several state-of-the-art dynamic multiobjective evolutionary algorithms (DMOEA) on six typical benchmark problems with different dynamic characteristics. Experimental results demonstrate that the proposed algorithm can effectively tackle DMOPs

Handling Dynamic Multiobjective Optimization Environments via Layered Prediction and Subspace-Based Diversity Maintenance

Peer reviewedPostprin

Fairness-enhancing deep learning for ride-hailing demand prediction

Short-term demand forecasting for on-demand ride-hailing services is one of the fundamental issues in intelligent transportation systems. However, previous travel demand forecasting research predominantly focused on improving prediction accuracy, ignoring fairness issues such as systematic underestimations of travel demand in disadvantaged neighborhoods. This study investigates how to measure, evaluate, and enhance prediction fairness between disadvantaged and privileged communities in spatial-temporal demand forecasting of ride-hailing services. A two-pronged approach is taken to reduce the demand prediction bias. First, we develop a novel deep learning model architecture, named socially aware neural network (SA-Net), to integrate the socio-demographics and ridership information for fair demand prediction through an innovative socially-aware convolution operation. Second, we propose a bias-mitigation regularization method to mitigate the mean percentage prediction error gap between different groups. The experimental results, validated on the real-world Chicago Transportation Network Company (TNC) data, show that the de-biasing SA-Net can achieve better predictive performance in both prediction accuracy and fairness. Specifically, the SA-Net improves prediction accuracy for both the disadvantaged and privileged groups compared with the state-of-the-art models. When coupled with the bias mitigation regularization method, the de-biasing SA-Net effectively bridges the mean percentage prediction error gap between the disadvantaged and privileged groups, and also protects the disadvantaged regions against systematic underestimation of TNC demand. Our proposed de-biasing method can be adopted in many existing short-term travel demand estimation models, and can be utilized for various other spatial-temporal prediction tasks such as crime incidents predictions

Irx4-mediated regulation of Slit1 expression contributes to the definition of early axonal paths inside the retina

Although multiple axon guidance cues have been discovered in recent years, little is known about the mechanism by which the spatiotemporal expression patterns of the axon guidance cues are regulated in vertebrates. We report that a homeobox gene Irx4 is expressed in a pattern similar to that of Slit1 in the chicken retina. Overexpression of Irx4 led to specific downregulation of Slit1 expression, whereas inhibition of Irx4 activity by a dominant negative mutant led to induction of Slit1 expression, indicating that Irx4 is a crucial regulator of Slit1 expression in the retina. In addition, by examining axonal behavior in the retinas with overexpression of Irx4 and using several in vivo assays to test the effect of Slit1, we found that Slit1 acts positively to guide the retinal axons inside the optic fiber layer (OFL). We further show that the regulation of Slit1 expression by Irx4 is important for providing intermediate targets for retinal axons during their growth within the retina

A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Traditional dynamic multiobjective evolutionary algorithms usually imitate the evolution of nature, maintaining diversity of population through different strategies and making the population track the Pareto optimal solution set efficiently after the environmental change. However, these algorithms neglect the role of the dynamic environment in evolution, leading to the lacking of active guieded search. In this paper, a dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model is proposed (DEE-DMOEA). When the environment has not changed, this algorithm makes use of the evolutionary environment to record the knowledge and information generated in evolution, and in turn, the knowledge and information guide the search. When a change is detected, the algorithm helps the population adapt to the new environment through building a dynamic evolutionary environment model, which enhances the diversity of the population by the guided method, and makes the environment and population evolve simultaneously. In addition, an implementation of the algorithm about the dynamic evolutionary environment model is introduced in this paper. The environment area and the unit area are employed to express the evolutionary environment. Furthermore, the strategies of constraint, facilitation and guidance for the evolution are proposed. Compared with three other state-of-the-art strategies on a series of test problems with linear or nonlinear correlation between design variables, the algorithm has shown its effectiveness for dealing with the dynamic multiobjective problems

A proportion-based selection scheme for multi-objective optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Classical multi-objective evolutionary algorithms (MOEAs) have been proven to be inefficient for solving multiobjective optimizations problems when the number of objectives increases due to the lack of sufficient selection pressure towards the Pareto front (PF). This poses a great challenge to the design of MOEAs. To cope with this problem, researchers have developed reference-point based methods, where some well-distributed points are produced to assist in maintaining good diversity in the optimization process. However, the convergence speed of the population may be severely affected during the searching procedure. This paper proposes a proportion-based selection scheme (denoted as PSS) to strengthen the convergence to the PF as well as maintain a good diversity of the population. Computational experiments have demonstrated that PSS is significantly better than three peer MOEAs on most test problems in terms of diversity and convergence