A genetic graph-based approach for partitional clustering

Clustering is one of the most versatile tools for data analysis. In the recent years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the spectral clustering (SC) algorithm, which is based on graph cut: It initially generates a similarity graph using a distance measure and then studies its graph spectrum to find the best cut. This approach is sensitive to the parameters of the metric, and a correct parameter choice is critical to the quality of the cluster. This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution. The new algorithm, named genetic graph-based clustering (GGC), takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph. The experimental validation shows that GGC increases robustness of SC and has competitive performance in comparison with classical clustering methods, at least, in the synthetic and real dataset used in the experiments

A multi-objective genetic graph-based clustering algorithm with memory optimization

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. H. D. Menéndez, D. F. Barrero, and D. Camacho, "A multi-objective genetic graph-based clustering algorithm with memory optimization", in 2013 IEEE Congress on Evolutionary Computation (CEC), 2013, pp. 3174 - 3181Clustering is one of the most versatile tools for data analysis. Over the last few years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the Spectral Clustering algorithm, which is based on graph cut: it initially generates a Similarity Graph using a distance measure and then uses its Graph Spectrum to find the best cut. Memory consuption is a serious limitation in that algorithm: The Similarity Graph representation usually requires a very large matrix with a high memory cost. This work proposes a new algorithm, based on a previous implementation named Genetic Graph-based Clustering (GGC), that improves the memory usage while maintaining the quality of the solution. The new algorithm, called Multi-Objective Genetic Graph-based Clustering (MOGGC), uses an evolutionary approach introducing a Multi-Objective Genetic Algorithm to manage a reduced version of the Similarity Graph. The experimental validation shows that MOGGC increases the memory efficiency, maintaining and improving the GGC results in the synthetic and real datasets used in the experiments. An experimental comparison with several classical clustering methods (EM, SC and K-means) has been included to show the efficiency of the proposed algorithm.This work has been partly supported by: Spanish Ministry of Science and Education under project TIN2010-19872

A new web-based genomics resource for bioinformatics analysis of Rhipicephalus (Boophilus) microplus: CattleTickBase

No abstract availabl

3D bioprinting for orthopaedic applications: Current advances, challenges and regulatory considerations

In the era of personalised medicine, novel therapeutic approaches raise increasing hopes to address currently unmet medical needs by developing patient-customised treatments. Three-dimensional (3D) bioprinting is rapidly evolving and has the potential to obtain personalised tissue constructs and overcome some limitations of standard tissue engineering approaches. Bioprinting could support a wide range of biomedical applications, such as drug testing, tissue repair or organ transplantation. There is a growing interest for 3D bioprinting in the orthopaedic field, with remarkable scientific and technical advances. However, the full exploitation of 3D bioprinting in medical applications still requires efforts to anticipate the upcoming challenges in translating bioprinted products from bench to bedside. In this review we summarised current trends, advances and challenges in the application of 3D bioprinting for bone and cartilage tissue engineering. Moreover, we provided a detailed analysis of the applicable regulations through the 3D bioprinting process and an overview of available standards covering bioprinting and additive manufacturing

Exploiting Historical Data and Diverse Germplasm to Increase Maize Grain Yield in Texas

The U.S. is the largest maize producer in the world with a production of 300 million tons in 2012. Approximately 86% of the maize production is focused on the Midwestern states. The rest of the production is focused in the Southern states, where Texas is the largest maize producer. Grain yield in Texas ranges from 18 tons/ha in the irrigated production zones to 3 tons/ha in the dryland production zones. As a result, grain yield has increased slowly because of the poor production in the non-irrigated acres. Methods to improve the grain yield in Texas is to breed for maize varieties adapted to Texas growing conditions, including mapping genes that can be incorporated into germplasm through marker assisted selection. This dissertation includes two separate projects that exploit historical data and maize diversity to increase grain yield in Texas. For the first project, a large dataset collected by Texas AgriLife program was analyzed to elucidate past trends and future hints on how to improve maize yield within Texas. This study confirmed previous reports that the rate of increase for grain yield in Texas is less than the rate observed in the Midwestern US. For the second project, a candidate gene and whole genome association mapping analysis was performed for drought and aflatoxin resistance in maize. In order to do so, maize inbred lines from a diversity panel were testcrossed to isogenic versions of Tx714. The hybrids were evaluated under irrigated and non-irrigated conditions. The irrigated trials were inoculated with Aspergillus flavus and the aflatoxin level was quantified. This study found that the gene ZmLOX4 was associated with days to silk, and the gene ZmLOX5 gene was associated with plant and ear height. In addition, this study identified 13 QTL variants for grain yield, plant height, days to anthesis and days to silk. Furthermore, this study shows that diverse maize inbred lines can make hybrids that out yield commercial hybrids under heat and drought stress. Therefore, there are useful genes present in these diverse lines that can be exploited in maize breeding program

A Survey on Multihop Ad Hoc Networks for Disaster Response Scenarios

Disastrous events are one of the most challenging applications of multihop ad hoc networks due to possible damages of existing telecommunication infrastructure.The deployed cellular communication infrastructure might be partially or completely destroyed after a natural disaster. Multihop ad hoc communication is an interesting alternative to deal with the lack of communications in disaster scenarios. They have evolved since their origin, leading to differentad hoc paradigms such as MANETs, VANETs, DTNs, or WSNs.This paper presents a survey on multihop ad hoc network paradigms for disaster scenarios.It highlights their applicability to important tasks in disaster relief operations. More specifically, the paper reviews the main work found in the literature, which employed ad hoc networks in disaster scenarios.In addition, it discusses the open challenges and the future research directions for each different ad hoc paradigm

Variable length-based genetic representation to automatically evolve wrappers

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-12433-4_44Proceedings 8th International Conference on Practical Applications of Agents and Multiagent SystemsThe Web has been the star service on the Internet, however the outsized information available and its decentralized nature has originated an intrinsic difficulty to locate, extract and compose information. An automatic approach is required to handle with this huge amount of data. In this paper we present a machine learning algorithm based on Genetic Algorithms which generates a set of complex wrappers, able to extract information from theWeb. The paper presents the experimental evaluation of these wrappers over a set of basic data sets.This work has been partially supported by the Spanish Ministry of Science and Innovation under the projects Castilla-La Mancha project PEII09-0266-6640, COMPUBIODIVE (TIN2007-65989), and by V-LeaF (TIN2008-02729-E/TIN)

Nacimiento de una masa mixta: regeneración del pino salgareño tras aplicar resalveos de distinto peso en tallares envejecidos de encina en el centro peninsular

En este trabajo se presentan los resultados obtenidos tras ejecutar resalveos de conversion de diferente peso en un tallar envejecido de encina (Quercus ilex subsp. ballota) situado en Guadalajara. El dispositivo experimental se instaló e inventarió en 1994. En 1995 se aplicaron resalveos de pesos variables entre 0 % (control) y 100 % de area basimétrica extraída. En 2010 se han realizado nuevos inventarios, encontrando una abundante egeneración de Pinus nigra a partir de pinos adultos dispersos en la zona de estudio. Dicha regeneración está significativamente relacionada con los pesos de clara aplicados y con la proximidad de pinos adultos de grandes diámetros

Multi-objective performance optimization of a probabilistic similarity/dissimilarity-based broadcasting scheme for mobile ad hoc networks in disaster response scenarios

Communications among crewmembers in rescue teams and among victims are crucial to relief the consequences and damages of a disaster situation. A common communication system for establishing real time communications between the elements (victims, crewmem-bers, people living in the vicinity of the disaster scenario, among others) involved in a disaster scenario is required. Ad hoc networks have been envisioned for years as a possible solution. They allow users to establish decentralized communications quickly and using common devices like mobile phones. Broadcasting is the main mechanism used to dissemi-nate information in all-to-all fashion in ad hoc networks. The objective of this paper is to optimize a broadcasting scheme based on similari-ty/dissimilarity coefficient designed for disaster response scenarios through a multi-objective optimization problem in which several per-formance metrics such as reachability, number of retransmissions and delay are optimized simultaneously