Abstract
Glycosomes of trypanosomatids are peroxisome-like organelles comprising unique metabolic features, among which the lack of the hallmark peroxisomal enzyme catalase. The absence of this highly efficient peroxidase from glycosomes is presumably compensated by other antioxidants, peroxidases of the peroxiredoxin (PRX) family being the most promising candidates for this function. Here, we follow on this premise and investigate the product of a Leishmania infantum gene coding for a putative glycosomal PRX (LigPRX). First, we demonstrate that LigPRX localizes to glycosomes, resorting to indirect immunofluorescence analysis. Second, we prove that purified recombinant LigPRX is an active peroxidase in vitro. Third, we generate viable LigPRX-depleted L. infantum promastigotes by classical homologous recombination. Surprisingly, phenotypic analysis of these knockout parasites revealed that promastigote survival, replication, and protection from oxidative and nitrosative insults can proceed normally in the absence of LigPRX. Noticeably, we also witness that LigPRX-depleted parasites can infect and thrive in mice to the same extent as wild type parasites. Overall, by disclosing the dispensable character of the glycosomal peroxiredoxin in L. infantum, this work excludes this enzyme from being a key component of the glycosomal hydroperoxide metabolism and contemplates alternative players for this function.

Abstract

Abstract
Monocular Visual Odometry techniques represent a challenging and appealing research area in robotics navigation field. The use of a single camera to track robot motion is a hardware-cheap solution. In this context, there are few Visual Odometry methods on the literature that estimate robot pose accurately using a single camera without any other source of information. The use of omnidirectional cameras in this field is still not consensual. Many works show that for outdoor environments the use of them does represent an improvement compared with the use of conventional perspective cameras. Besides that, in this work we propose an open-source monocular omnidirectional version of the state-of-the-art method Libviso2 that outperforms the original one even in outdoor scenes. This approach is suitable for central dioptric omnidirectional cameras and takes advantage of their wider field of view to calculate the robot motion with a really positive performance on the context of monocular Visual Odometry. We also propose a novel approach to calculate the scale factor that uses matches between laser measures and 3-D triangulated feature points to do so. The novelty of this work consists in the association of the laser ranges with the features on the omnidirectional image. Results were generate using three open-source datasets built in-house showing that our unified system largely outperforms the original monocular version of Libviso2.

Abstract
A new probabilistic approach for microgrids (MGs) optimal energy management considering ac network constraints is proposed in this paper. The economic model of an energy storage system (ESS) is considered in the problem. The reduced unscented transformation (RUT) is applied in order to deal with the uncertainties related to the forecasted values of load demand, market price, and available outputs of renewable energy sources (RESs). Moreover, the correlation between market price and load demand is taken into account. Besides, the impact of the correlated wind turbines (WT) on MGs' energy management is studied. An enhanced JAYA (EJAYA) algorithm is suggested to achieve the best solution of the considered problem. The effective performance of the presented approach is verified by applying the suggested strategy on a modified IEEE 33-bus system. It can be observed that for dealing with probabilistic problems, the suggested RUT-EJAYA shows accurate results such as those of Monte Carlo (MC) while the computational burden (time and complexity) is lower.

Abstract
Tackling multi-agent environments where each agent has a local limited observation of the global state is a non-trivial task that often requires hand-tuned solutions. A team of agents coordinating in such scenarios must handle the complex underlying environment, while each agent only has partial knowledge about the environment. Deep reinforcement learning has been shown to achieve super-human performance in single-agent environments, and has since been adapted to the multi-agent paradigm. This paper proposes A3C3, a multi-agent deep learning algorithm, where agents are evaluated by a centralized referee during the learning phase, but remain independent from each other in actual execution. This referee's neural network is augmented with a permutation invariance architecture to increase its scalability to large teams. A3C3 also allows agents to learn communication protocols with which agents share relevant information to their team members, allowing them to overcome their limited knowledge, and achieve coordination. A3C3 and its permutation invariant augmentation is evaluated in multiple multi-agent test-beds, which include partially-observable scenarios, swarm environments, and complex 3D soccer simulations.

Abstract
This work proposes a new control framework for power converters with a dual half bridge (DHB) configuration. The new framework exploits the multi-port structure of the DHB to simultaneously: i) regulate the current in the primary side of the DIM and ii) equalize the voltage in the two secondary ports of the DHB. To implement these functions, we combine input-output linearization methods with pragmatic voltage balance algorithms. We then apply this framework to a hybrid energy storage system composed of a battery pack and two supercapacitor modules. Numerical simulation results demonstrate the effectiveness of the proposed approach in regulating the power between the energy storage units and balancing the supercapacitors' voltages.