Current Visceral Leishmaniasis Research: A Research Review to Inspire Future Study.

Visceral leishmaniasis (VL), one of the deadliest parasitic diseases in the world, causes more than 50,000 human deaths each year and afflicts millions of people throughout South America, East Africa, South Asia, and Mediterranean Region. In 2015 the World Health Organization classified VL as a neglected tropical disease (NTD), prompting concentrated study of the VL epidemic using mathematical and simulation models. This paper reviews literature related to prevalence and prevention control strategies. More than thirty current research works were reviewed and classified based on VL epidemic study methods, including modeling approaches, control strategies, and simulation techniques since 2013. A summarization of these technical methods, major findings, and contributions from existing works revealed that VL epidemic research efforts must improve in the areas of validating and verifying VL mathematical models with real-world epidemic data. In addition, more dynamic disease control strategies must be explored and advanced simulation techniques must be used to predict VL pandemics.

Iterative reconstruction of scene depth with fidelity based on light field data.

Depth reconstruction from the light field, as a depth extracting approach, is a vibrant research field in computational imaging. In this paper, depth reconstruction from the light field was approached as an optimization problem by analyzing the forward and inverse models. The forward and inverse models present the connection between the light field and the depth of the scene. We proposed an iterative method for scene depth reconstruction with fidelity from 4D light field data. The objective function of the optimization problem entails three terms, of which the matching term is used as the fidelity term, while the gradient term and classification term constitute the penalty terms. The iterative method minimized the objective function by correcting mismatching pixels iteratively. In this way, the reconstruction accuracy of the scene depth was significantly improved with high robustness, especially in the weak texture and occlusion regions. The experimental results show that the depth map can be reconstructed with high accuracy by the iterative method, in alignment with high depth resolution in the weak texture and occlusion regions. An accurate 3D surface in the field of view can be achieved using the depth information reconstructed from the 4D light field.

Zoonotic visceral leishmaniasis transmission: modeling, backward bifurcation, and optimal control.

Visceral leishmaniasis (VL), a vector-borne disease caused by protozoan flagellates of the genus Leishmania, is transmitted by sand flies. After malaria, VL is the second-largest parasitic killer, responsible for an estimated 500,000 infections and 51,000 deaths annually worldwide. Mathematical models proposed for VL have included the impact of dogs versus wild canids in disease dissemination and models developed to assist in control approaches. However, quantitative conditions that are required to control or eradicate VL transmission are not provided and there are no mathematical methods proposed to quantitatively calculate optimal control strategies for VL transmission. The research objective of this work was to model VL disease transmission system (specifically Zoonotic VL), perform bifurcation analysis to discuss control conditions, and calculate optimal control strategies. Three time-dependent control strategies involving dog populations, sand fly population, and humans are mainly discussed. Another strategy sometimes used in attempts to control zoonotic VL transmission, dog culling, is also evaluated in this paper.

A neural computational model for bottom-up attention with invariant and overcomplete representation.

BACKGROUND: An important problem in selective attention is determining the ways the primary visual cortex contributes to the encoding of bottom-up saliency and the types of neural computation that are effective to model this process. To address this problem, we constructed a two-layered network that satisfies the neurobiological constraints of the primary visual cortex to detect salient objects. We carried out experiments on both synthetic images and natural images to explore the influences of different factors, such as network structure, the size of each layer, the type of suppression and the combination strategy, on saliency detection performance. RESULTS: The experimental results statistically demonstrated that the type and scale of filters contribute greatly to the encoding of bottom-up saliency. These two factors correspond to the mechanisms of invariant encoding and overcomplete representation in the primary visual cortex. CONCLUSIONS: (1) Instead of constructing Gabor functions or Gaussian pyramids filters for feature extraction as traditional attention models do, we learn overcomplete basis sets from natural images to extract features for saliency detection. Experiments show that given the proper layer size and a robust combination strategy, the learned overcomplete basis set outperforms a complete set and Gabor pyramids in visual saliency detection. This finding can potentially be applied in task-dependent and supervised object detection. (2) A hierarchical coding model that can represent invariant features, is designed for the pre-attentive stage of bottom-up attention. This coding model improves robustness to noises and distractions and improves the ability of detecting salient structures, such as collinear and co-circular structures, and several composite stimuli. This result indicates that invariant representation contributes to saliency detection (popping out) in bottom-up attention. The aforementioned perspectives will significantly contribute to the in-depth understanding of the information processing mechanism in the primary visual system.