Numerical resolution of Emden's equation using Adomian polynomials

Purpose: In this paper the authors aim to show the advantages of using the decomposition method introduced by Adomian to solve Emden's equation, a classical non‐linear equation that appears in the study of the thermal behaviour of a spherical cloud and of the gravitational potential of a polytropic fluid at hydrostatic equilibrium. Design/methodology/approach: In their work, the authors first review Emden's equation and its possible solutions using the Frobenius and power series methods; then, Adomian polynomials are introduced. Afterwards, Emden's equation is solved using Adomian's decomposition method and, finally, they conclude with a comparison of the solution given by Adomian's method with the solution obtained by the other methods, for certain cases where the exact solution is known. Findings: Solving Emden's equation for n in the interval [0, 5] is very interesting for several scientific applications, such as astronomy. However, the exact solution is known only for n=0, n=1 and n=5. The experiments show that Adomian's method achieves an approximate solution which overlaps with the exact solution when n=0, and that coincides with the Taylor expansion of the exact solutions for n=1 and n=5. As a result, the authors obtained quite satisfactory results from their proposal. Originality/value: The main classical methods for obtaining approximate solutions of Emden's equation have serious computational drawbacks. The authors make a new, efficient numerical implementation for solving this equation, constructing iteratively the Adomian polynomials, which leads to a solution of Emden's equation that extends the range of variation of parameter n compared to the solutions given by both the Frobenius and the power series methods.This work has been supported by the Ministerio de Ciencia e Innovación, project TIN2009-10581

Learning Probabilistic Features for Robotic Navigation Using Laser Sensors

SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N2), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.This work has been supported by the Spanish Ministerio de Ciencia e Innovación (www.micinn.es), project TIN2009-10581

Modelling Oil-Spill Detection with Swarm Drones

Nowadays, swarm robotics research is having a great increase due to the benefits derived from its use, such as robustness, parallelism, and flexibility. Unlike distributed robotic systems, swarm robotics emphasizes a large number of robots, and promotes scalability. Among the multiple applications of such systems we could find are exploring unstructured environments, resource monitoring, or distributed sensing. Two of these applications, monitoring, and perimeter/area detection of a given resource, have several ecological uses. One of them is the detection and monitoring of pollutants to delimit their perimeter and area accurately. Maritime activity has been increasing gradually in recent years. Many ships carry products such as oil that can adversely affect the environment. Such products can produce high levels of pollution in case of being spilled into sea. In this paper we will present a distributed system which monitors, covers, and surrounds a resource by using a swarm of homogeneous low cost drones. These drones only use their local sensory information and do not require any direct communication between them. Taking into account the properties of this kind of oil spills we will present a microscopic model for a swarm of drones, capable of monitoring these spills properly. Furthermore, we will analyse the proper macroscopic operation of the swarm. The analytical and experimental results presented here show the proper evolution of our system.This work has been supported by the Spanish Ministerio de Ciencia e Innovación, Project TIN2009-10581

A macroscopic model for high intensity radiofrequency signal detection in swarm robotics systems

In recent years, there has been a growing interest in resource location in unknown environments for robotic systems, which are composed of multiple simple robots rather than one highly capable robot [M. Sempere, F. Aznar, M. Pujol, and R. Rizo, On cooperative swarm foraging for simple, non explicitly connected, agents, 2010]. This tradeoff reduces the design and hardware complexity of the robots and removes single point failures, but adds complexity in algorithm design. The challenge is to programme a swarm of simple robots, with minimal intercommunication and individual capability, to perform a useful task as a group. This paper is focused on finding the highest intensity area of a radiofrequency (RF) signal in urban environments. These signals are usually more intense near the city centre and its proximity, since in these zones the risk of signal saturation is high. RF radiation (RFR) is boosted or blocked mainly depending on orography or building structures. RF providers need to supply enough coverage, setting up different antennas to be able to provide a minimum quality of service. We will define a micro/macroscopic mathematical model to efficiently study a swarm robotic system, predict their long-term behaviour and gain insight into the system design. The macroscopic model will be obtained from Rate Equations, describing the dynamics of the swarm collective behaviour. In our experimental section, the Campus of the University of Alicante will be used to simulate our model. Three RFR antennas will be taken into account, one inside our Campus and the other two in its perimeter. Several tests, that show the convergence of the swarm towards the RFR, will be presented. In addition, the obtained RFR maps and the macroscopic behaviour of the swarm will be discussed.This work has been supported by the Spanish Ministerio de Ciencia e Innovación, project TIN2009-10581

Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy

Face detection is the first step of any automated face recognition system. One of the most popular approaches to detect faces in color images is using a skin color segmentation scheme, which in many cases needs a proper representation of color spaces to interpret image information. In this paper, we propose a fuzzy system for detecting skin in color images, so that each color tone is assumed to be a fuzzy set. The Red, Green, and Blue (RGB), the Hue, Saturation and Value (HSV), and the YCbCr (where Y is the luminance and Cb,Cr are the chroma components) color systems are used for the development of our fuzzy design. Thus, a fuzzy three-partition entropy approach is used to calculate all of the parameters needed for the fuzzy systems, and then, a face detection method is also developed to validate the segmentation results. The results of the experiments show a correct skin detection rate between 94% and 96% for our fuzzy segmentation methods, with a false positive rate of about 0.5% in all cases. Furthermore, the average correct face detection rate is above 93%, and even when working with heterogeneous backgrounds and different light conditions, it achieves almost 88% correct detections. Thus, our method leads to accurate face detection results with low false positive and false negative rates.This work has been supported by the Ministerio de Economía y Competitividad (Spain), Project TIN2013-40982-R. Project co-financed with FEDER funds

The HERC proteins and the nervous system

The HERC protein family is one of three subfamilies of Homologous to E6AP C-terminus (HECT) E3 ubiquitin ligases. Six HERC genes have been described in humans, two of which encode Large HERC proteins -HERC1 and HERC2- with molecular weights above 520 kDa that are constitutively expressed in the brain. There is a large body of evidence that mutations in these Large HERC genes produce clinical syndromes in which key neurodevelopmental events are altered, resulting in intellectual disability and other neurological disorders like epileptic seizures, dementia and/or signs of autism. In line with these consequences in humans, two mice carrying mutations in the Large HERC genes have been studied quite intensely: the tambaleante mutant for Herc1 and the Herc2+/530 mutant for Herc2. In both these mutant mice there are clear signs that autophagy is dysregulated, eliciting cerebellar Purkinje cell death and impairing motor control. The tambaleante mouse was the first of these mice to appear and is the best studied, in which the Herc1 mutation elicits: (i) delayed neural transmission in the peripheral nervous system; (ii) impaired learning, memory and motor control; and (iii) altered presynaptic membrane dynamics. In this review, we discuss the information currently available on HERC proteins in the nervous system and their biological activity, the dysregulation of which could explain certain neurodevelopmental syndromes and/or neurodegenerative diseases

BMP-2 induces osterix expression through upregulation of DLX5 and its phosphorylation by p38

Osterix, a zinc-finger transcription factor, is specifically expressed in osteoblasts and osteocytes of all developing bones. Because no bone formation occurs in Osterix null mice, Osterix is thought to be an essential regulator of osteoblast differentiation. We report that bone morphogenetic protein-2 (BMP-2) induces an increase in Osterix expression, which is mediated through a homeodomain sequence located in the proximal region of the Osterix promoter. Our results demonstrate that induction of Dlx5 by BMP-2 mediates Osterix transcriptional activation. First, BMP-2 induction of Dlx5 precedes the induction of Osterix. Second, Dlx5 binds to the BMP-responsive homeodomain sequences both in vitro and in vivo. Third, Dlx5 overexpression and knock-down assays demonstrate its role in activating Osterix expression in response to BMP-2. Furthermore, we show that Dlx5 is a novel substrate for p38 MAPK in vitro and in vivo and that Ser-34 and Ser-217 are the sites phosphorylated by p38. Phosphorylation at Ser-34/217 increases the transactivation potential of Dlx5. Thus, we propose that BMP activates expression of Osterix through the induction of Dlx5 and its further transcriptional activation by p38-mediated phosphorylation

p38 regulates expression of osteoblast-specific genes by phosphorylation of osterix

Osterix, a zinc finger transcription factor, is specifically expressed in osteoblasts and osteocytes of all developing bones. Because no bone formation occurs in Osx-null mice, Osterix is thought to be an essential regulator of osteoblast differentiation. We report that, in several mesenchymal and osteoblastic cell types, BMP-2 induces an increase in expression of the two isoforms of Osterix arising from two alternative promoters. We identified a consensus Sp1 sequence (GGGCGG) as Osterix binding regions in the fibromodulin and the bone sialoprotein promoters in vitro and in vivo. Furthermore, we show that Osterix is a novel substrate for p38 MAPK in vitro and in vivo and that Ser-73 and Ser-77 are the regulatory sites phosphorylated by p38. Our data also demonstrate that Osterix is able to increase recruitment of p300 and Brg1 to the promoters of its target genes fibromodulin and bone sialoprotein in vivo and that it directly associates with these cofactors through protein-protein interactions. Phosphorylation of Osterix at Ser-73/77 increased its ability to recruit p300 and SWI/SNF to either fibromodulin or bone sialoprotein promoters. We therefore propose that Osterix binds to Sp1 sequences on target gene promoters and that its phosphorylation by p38 enhances recruitment of coactivators to form transcriptionally active complexes

The p38α MAPK function in osteoprecursors is required for bone formation and bone homeostasis in adult mice

p38 MAPK activity plays an important role in several steps of the osteoblast lineage progression through activation of osteoblast-specific transcription factors and it is also essential for the acquisition of the osteoblast phenotype in early development. Although reports indicate p38 signalling plays a role in early skeletal development, its specific contributions to adult bone remodelling are still to be clarified. Methodology/Principal Findings: We evaluated osteoblast-specific deletion of p38 alpha to determine its significance in early skeletogenesis, as well as for bone homeostasis in adult skeleton. Early p38 alpha deletion resulted in defective intramembranous and endochondral ossification in both calvaria and long bones. Mutant mice showed reduction of trabecular bone volume in distal femurs, associated with low trabecular thickness. In addition, knockout mice also displayed decreased femoral cortical bone volume and thickness. Deletion of p38 alpha did not affect osteoclast function. Yet it impaired osteoblastogenesis and osteoblast maturation and activity through decreased expression of osteoblast-specific transcription factors and their targets. Furthermore, the inducible Cre system allowed us to control the onset of p38 alpha disruption after birth by removal of doxycycline. Deletion of p38 alpha at three or eight weeks postnatally led to significantly lower trabecular and cortical bone volume after 6 or 12 months. Conclusions: Our data demonstrates that, in addition to early skeletogenesis, p38 alpha is essential for osteoblasts to maintain their function in mineralized adult bone, as bone anabolism should be sustained throughout life. Moreover, our data also emphasizes that clinical development of p38 inhibitors should take into account their potential bone effects

Epidemiological and clinical characteristics of SARS-CoV-2 reinfections in a Spanish region

Objective: We aimed to assess the prevalence and clinical characteristics of SARS-CoV-2 reinfections in a Spanish region. Methods: This is a retrospective observational study in all patients with SARS-CoV-2 infections in the Lleida health region from 1 March to 30 November 2020. Reinfections were classified as patients with positive SARS-CoV-2 PCR tests separated by at least 90days plus a negative test result between both infection episodes. Primary and secondary outcomes: The primary outcome was the percentage of SARS-CoV-2 reinfections among all SARS-CoV-2 infections detected during our study period. Secondary outcomes were the clinical and sociodemographic characteristics of patients with SARS-CoV-2 reinfections. Results: Of the 27,758 patients diagnosed with SARS-CoV-2 infection in the study period, 14 were identified as coronavirus reinfection (0.050%). Of the reinfected sample, 12 patients (85.7%) were women. The median age was 41.5 years. Two patients died in the second coronavirus episode. Conclusion: The reinfection rate of SARS-CoV-2 in the Spanish region Lleida was relatively low during the observational period in 2020 (less than 1%). These data are in line with the notion that previous SARS-CoV-2 infections may offer a significant protection by so called natural immunity