Odometry-Based Viterbi Localization with Artificial Neural Networks and Laser Range Finders for Mobile Robots

This paper proposes an approach that solves the Robot Localization problem by using a conditional state-transition Hidden Markov Model (HMM). Through the use of Self Organized Maps (SOMs) a Tolerant Observation Model (TOM) is built, while odometer-dependent transition probabilities are used for building an Odometer-Dependent Motion Model (ODMM). By using the Viterbi Algorithm and establishing a trigger value when evaluating the state-transition updates, the presented approach can easily take care of Position Tracking (PT), Global Localization (GL) and Robot Kidnapping (RK) with an ease of implementation difficult to achieve in most of the state-of-the-art localization algorithms. Also, an optimization is presented to allow the algorithm to run in standard microprocessors in real time, without the need of huge probability gridmaps.     

Modeling of the relative humidity via functional networks and control of the temperature via classic controls for a bird incubator

The control of the relative humidity and the temperature is important for the birds to be born. It is not easy to control the relative humidity, but it is possible to obtain the measure of the relative humidity as a consequence of the control of the temperature in a bird incubator. In this article, (1) the mathematical model for the control of temperature in the bird incubator is presented, (2) a functional network to approximate the relative humidity behavior in the bird incubator is proposed, (3) a control for the temperature in the bird incubator is proposed, the error of the proportional control applied to the mathematical model of the temperature of the bird incubator is assured to be uniformly stable, (4) the comparison results of four classic control laws for the control of the temperature considering the proposed mathematical model of the temperature and the functional network to approximate the relative humidity behavior in the bird incubator are presented.     

A Color Vision-Based Lane Tracking System for Autonomous Driving on Unmarked Roads

This work describes a color Vision-based System intended to perform stable autonomous driving on unmarked roads. Accordingly, this implies the development of an accurate road surface detection system that ensures vehicle stability. Although this topic has already been documented in the technical literature by different research groups, the vast majority of the already existing Intelligent Transportation Systems are devoted to assisted driving of vehicles on marked extra urban roads and highways. The complete system was tested on the BABIECA prototype vehicle, which was autonomously driven for hundred of kilometers accomplishing different navigation missions on a private circuit that emulates an urban quarter. During the tests, the navigation system demonstrated its robustness with regard to shadows, road texture, and weather and changing illumination conditions.     

Influence of Powder Morphology and Chemical Composition on Metallic Foams produced by SlipReactionFoamSintering (SRFS)‐ Process

The SlipReactionFoamSintering (SRFS)‐ process is a metallurgical method to produce open‐cell metallic foams of iron based materials, steels and nickel based materials. In this process several chemical reactions take place. Through the most influential parameter, the morphology of the metallic powder, different properties of the foam can be adjusted, such as the viscosity of the slip, the cell structure and the mechanical properties. Several examples are presented in this paper. Moreover, the thermal conductivity of three foams is measured with the transient‐heat‐source‐technique.     

In Vitro Assays to Identify Metabolism-Disrupting Chemicals with Diabetogenic Activity in a Human Pancreatic β-Cell Model

There is a need to develop identification tests for Metabolism Disrupting Chemicals (MDCs) with diabetogenic activity. Here we used the human EndoC-βH1 β-cell line, the rat β-cell line INS-1E and dispersed mouse islet cells to assess the effects of endocrine disruptors on cell viability and glucose-stimulated insulin secretion (GSIS). We tested six chemicals at concentrations within human exposure (from 0.1 pM to 1 µM). Bisphenol-A (BPA) and tributyltin (TBT) were used as controls while four other chemicals, namely perfluorooctanoic acid (PFOA), triphenylphosphate (TPP), triclosan (TCS) and dichlorodiphenyldichloroethylene (DDE), were used as “unknowns”. Regarding cell viability, BPA and TBT increased cell death as previously observed. Their mode of action involved the activation of estrogen receptors and PPARγ, respectively. ROS production was a consistent key event in BPA-and TBT-treated cells. None of the other MDCs tested modified viability or ROS production. Concerning GSIS, TBT increased insulin secretion while BPA produced no effects. PFOA decreased GSIS, suggesting that this chemical could be a “new” diabetogenic agent. Our results indicate that the EndoC-βH1 cell line is a suitable human β-cell model for testing diabetogenic MDCs. Optimization of the test methods proposed here could be incorporated into a set of protocols for the identification of MDCs.     

Blocking the PCNA/NKp44 Checkpoint to Stimulate NK Cell Responses to Multiple Myeloma

Simple SummaryMembrane-associated PCNA is expressed on the surface of human MM cell lines and primary MM cells. Mab 14-25-9 interacts with membrane-associated PCNA and blocks its binding to NK-expressed NKp44, thus activating NK function. We showed that mAb 14-25-9 can serve as an immune checkpoint blocker, enhancing the function of NK cells on target human MM cell lines and primary cells.AbstractMultiple Myeloma (MM) is a devastating malignancy that evades immune destruction using multiple mechanisms. The NKp44 receptor interacts with PCNA (Proliferating Cell Nuclear Antigen) and may inhibit NK cells’ functions. Here we studied in vitro the expression and function of PCNA on MM cells. First, we show that PCNA is present on the cell membrane of five out of six MM cell lines, using novel anti-PCNA mAb developed to recognize membrane-associated PCNA. Next, we stained primary bone marrow (BM) mononuclear cells from MM patients and showed significant staining of membrane-associated PCNA in the fraction of CD38⁺CD138⁺ BM cells that contain the MM cells. Importantly, blocking of the membrane PCNA on MM cells enhanced the activity of NK cells, including IFN-γ-secretion and degranulation. Our results highlight the possible blocking of the NKp44-PCNA immune checkpoint by the mAb 14-25-9 antibody to enhance NK cell responses against MM, providing a novel treatment option.     

The Interaction of Anti-DNA Antibodies with DNA: Evidence for Unconventional Binding Mechanisms

Antibodies to DNA (anti-DNA) are the serological hallmark of systemic lupus erythematosus, a prototypic autoimmune disease. These antibodies bind to conserved sites on single-stranded and double-stranded DNA and display variable region somatic mutations consistent with antigen selection. Nevertheless, the interaction of anti-DNA with DNA has unconventional features. Anti-DNA antibodies bind by a mechanism called monogamous bivalency, in which stable interaction requires contact of both Fab sites with determinants on the same extended DNA molecule; the size of this DNA can be hundreds to thousands of bases, especially in solid phase assays. This binding also requires the presence of the Fc portion of IgG, a binding mechanism known as Fc-dependent monogamous bivalency. As shown by the effects of ionic strength in association and dissociation assays, anti-DNA binding is primarily electrostatic. Like anti-DNA autoantibodies, anti-DNA antibodies that bind specifically to non-conserved sites on bacterial DNA, a type of anti-DNA found in otherwise healthy individuals, also interact by monogamous bivalency. The unconventional features of anti-DNA antibodies may reflect the highly charged and polymeric nature of DNA and the need for molecular rearrangements to facilitate monogamous bivalency; the Fc portion contributes to binding in an as yet unknown way.     

Modelization of hybrid systems with hydrogen and renewable energy oriented to electric propulsion in sailboats

This paper presents a conceptual model of a hybrid electric sailboat in which energy from electric grid is stored in batteries and energy from renewable energies (eolic, solar and hydro) is stored as hydrogen. The main objective of this model is to study the viability of electrifying traditional sailboats with internal combustion engines into hybrid systems with batteries and fuel cell. The most important advantage of this design is the possibility to reduce up to zero emissions of traditional sailboat. Conversion of renewable energy to hydrogen is performed through an electrolyzer and post conversion to energy is carried out by a fuel cell. The fuel cell with the batteries forms the hybrid system (batteries-fuel cell) for propulsion electrical energy supply. In order to model the boat dynamic and energy systems, modular mathematical models were developed under Matlab^®-Simulink^®, using a fixed-step solver for the simulation of global model. A simulated logic controller manages the global model. In this paper, many models have been used: some of them are based in literature models and others were developed from experimental data. A control strategy has also been developed to manage energy flows and then it has been embedded to Matlab^® language. The global model permits test the performance of the sailboat.     

Integrating toxin gene expression,growth and fumonisin B1 and B2 production by a strain of Fusarium verticillioides under different environmental factors

The objective of this study was to integrate data on the effect of water activity (a_w; 0.995–0.93) and temperature (20–35°C) on activation of the biosynthetic FUM genes, growth and the mycotoxins fumonisin (FB₁, FB₂) by Fusarium verticillioides in vitro. The relative expression of nine biosynthetic cluster genes (FUM1, FUM7, FUM10, FUM11, FUM12, FUM13, FUM14, FUM16 and FUM19) in relation to the environmental factors was determined using a microarray analysis. The expression was related to growth and phenotypic FB₁ and FB₂ production. These data were used to develop a mixed-growth-associated product formation model and link this to a linear combination of the expression data for the nine genes. The model was then validated by examining datasets outside the model fitting conditions used (35°C). The relationship between the key gene (FUM1) and other genes in the cluster (FUM11, FUM13, FUM9, FUM14) were examined in relation to a_w, temperature, FB₁ and FB₂ production by developing ternary diagrams of relative expression. This model is important in developing an integrated systems approach to develop prevention strategies to control fumonisin biosynthesis in staple food commodities and could also be used to predict the potential impact that climate change factors may have on toxin production.