Data-Intensive architecture for scientific knowledge discovery

This paper presents a data-intensive architecture that demonstrates the ability to support applications from a wide range of application domains, and support the different types of users involved in defining, designing and executing data-intensive processing tasks. The prototype architecture is introduced, and the pivotal role of DISPEL as a canonical language is explained. The architecture promotes the exploration and exploitation of distributed and heterogeneous data and spans the complete knowledge discovery process, from data preparation, to analysis, to evaluation and reiteration. The architecture evaluation included large-scale applications from astronomy, cosmology, hydrology, functional genetics, imaging processing and seismology.

Secure neighbor discovery in wireless sensor networks using range-free localization techniques

Si una red inalámbrica de sensores se implementa en un entorno hostil, las limitaciones intrínsecas a los nodos conllevan muchos problemas de seguridad. En este artículo se aborda un ataque particular a los protocolos de localización y descubrimiento de vecinos, llevada a cabo por dos nodos que actúan en connivencia y establecen un "agujero de gusano" para tratar de engañar a un nodo aislado, haciéndole creer que se encuentra en la vecindad de un conjunto de nodos locales. Para contrarrestar este tipo de amenazas, se presenta un marco de actuación genéricamente denominado "detection of wormhole attacks using range-free methods" (DWARF) dentro del cual derivamos dos estrategias para de detección de agujeros de gusano: el primer enfoque (DWARFLoc) realiza conjuntamente la localización y la detección de ataques, mientras que el otro (DWARFTest) valida la posición estimada por el nodo una vez finalizado el protocolo de localización. Las simulaciones muestran que ambas estrategias son eficaces en la detección de ataques tipo "agujero de gusano", y sus prestaciones se comparan con las de un test convencional basado en la razón de verosimilitudes.

Knowledge Discovery in Spectral Data by Means of Complex Networks

In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease. The structural analysis of the resulting network can then be used to feed standard data-mining algorithms, for instance for the classification of new (unlabeled) subjects. Furthermore, we show how the structure of the network is resilient to the presence of external additive noise, and how it can be used to extract relevant knowledge about the development of the disease.

Discovery of ferromanganese hydrocarbon-related nodules associated with the Meknes mud volcano (Western Moroccan margin)

A suite of ferromanganese nodules were sampled during the MVSEIS-2008 cruise aboard of the R/V Hespérides in the flanks of Meknes mud volcano (Moroccan margin, NE Central Atlantic). The nodules were collected at water depths between 750-850 m within a seabed area characterized by high acoustic backscatter values. Debris of cold water corals and hydrocarbon-derived authigenic carbonate crusts were sampled at same time. The nodules show tabular morphology, up to 20 cm in maximum diameter and 2 kg of weight, brown-reddish external color and they are internally composed by a concentric to complex arrangement of laminae. The results of X-ray diffraction analysis show that these ferromanganese nodules are essentially composed of goethite and lepidocrocite, being Mn-oxides, silicates (quartz and clay minerals) and carbonates (calcite, dolomite and siderite) accessory to occasional minerals. All the samples display micritic to micro-sparitic mosaic under the petrographic microscope which forms massive, laminated or dendritic-mottled textures. The nodules show a high abundance of Fe, minor Mn and low contents of trace metals and REEs. Mature hydrocarbons, as n-alkanes derived from marine bacterial activity, and phenanthrene have been detected in all the ferromanganese nodules analyzed. These nodules display analogous characteristics (textural, mineralogical and geochemical) to the nodules studied by González et al (2009) in the carbonate mud-mounds in the Gulf of Cadiz, offshore Iberian margin. In this way, the same preliminary genetic model proposed for these nodules might be applicable to those find in the Meknes mud volcano. Therefore, the anaerobic oxidation of hydrocarbon-rich fluids within the mud-breccia sediments in the flanks of Meknes mud volcano would induce the formation of early diagenetic Fe-(Mn) carbonate nodules. Thus, the nodules were later exhumed by the erosive action of sea bottom currents generating the replacement of ferromanganese carbonates by Fe-Mn oxy-hydroxides. Thus, the hydrocarbon-rich fluid venting from deep seated reservoirs and erosive action of bottom currents must have been essential actors, as mineralization controls, for ferromanganese nodules generation and evolution. These findings imply that this type of nodules must be considered as new product as derived from the anaerobic/aerobic oxidation of hydrocarbons in areas of active seepages.

Discovery of a low-mass brown dwarf companion of the young nearby star G196-3.

A substellar-mass object in orbit at about 300 astronomical units from the young low-mass star G 196-3 was detected by direct imaging. Optical and infrared photometry and low- and intermediate-resolution spectroscopy of the faint companion, hereafter referred to as G 196-3B, confirm its cool atmosphere ?15 Jupiter masses. The separation and allow its mass to be estimated at 25?10 between the objects and their mass ratio suggest the fragmentation of a collapsing cloud as the most likely origin for G 196-3B, but alternatively it could have originated from a protoplanetary disc that has been dissipated. Whatever the formation process was, the young age of the primary star (about 100 million years) demonstrates that substellar companions can form on short time scales.

A visual framework to accelerate knowledge discovery based on dimensionality reduction minimizing degradation of quality

Tradicionalmente, el uso de técnicas de análisis de datos ha sido una de las principales vías para el descubrimiento de conocimiento oculto en grandes cantidades de datos, recopilados por expertos en diferentes dominios. Por otra parte, las técnicas de visualización también se han usado para mejorar y facilitar este proceso. Sin embargo, existen limitaciones serias en la obtención de conocimiento, ya que suele ser un proceso lento, tedioso y en muchas ocasiones infructífero, debido a la dificultad de las personas para comprender conjuntos de datos de grandes dimensiones. Otro gran inconveniente, pocas veces tenido en cuenta por los expertos que analizan grandes conjuntos de datos, es la degradación involuntaria a la que someten a los datos durante las tareas de análisis, previas a la obtención final de conclusiones. Por degradación quiere decirse que los datos pueden perder sus propiedades originales, y suele producirse por una reducción inapropiada de los datos, alterando así su naturaleza original y llevando en muchos casos a interpretaciones y conclusiones erróneas que podrían tener serias implicaciones. Además, este hecho adquiere una importancia trascendental cuando los datos pertenecen al dominio médico o biológico, y la vida de diferentes personas depende de esta toma final de decisiones, en algunas ocasiones llevada a cabo de forma inapropiada. Ésta es la motivación de la presente tesis, la cual propone un nuevo framework visual, llamado MedVir, que combina la potencia de técnicas avanzadas de visualización y minería de datos para tratar de dar solución a estos grandes inconvenientes existentes en el proceso de descubrimiento de información válida. El objetivo principal es hacer más fácil, comprensible, intuitivo y rápido el proceso de adquisición de conocimiento al que se enfrentan los expertos cuando trabajan con grandes conjuntos de datos en diferentes dominios. Para ello, en primer lugar, se lleva a cabo una fuerte disminución en el tamaño de los datos con el objetivo de facilitar al experto su manejo, y a la vez preservando intactas, en la medida de lo posible, sus propiedades originales. Después, se hace uso de efectivas técnicas de visualización para representar los datos obtenidos, permitiendo al experto interactuar de forma sencilla e intuitiva con los datos, llevar a cabo diferentes tareas de análisis de datos y así estimular visualmente su capacidad de comprensión. De este modo, el objetivo subyacente se basa en abstraer al experto, en la medida de lo posible, de la complejidad de sus datos originales para presentarle una versión más comprensible, que facilite y acelere la tarea final de descubrimiento de conocimiento. MedVir se ha aplicado satisfactoriamente, entre otros, al campo de la magnetoencefalografía (MEG), que consiste en la predicción en la rehabilitación de lesiones cerebrales traumáticas (Traumatic Brain Injury (TBI) rehabilitation prediction). Los resultados obtenidos demuestran la efectividad del framework a la hora de acelerar y facilitar el proceso de descubrimiento de conocimiento sobre conjuntos de datos reales. ABSTRACT Traditionally, the use of data analysis techniques has been one of the main ways of discovering knowledge hidden in large amounts of data, collected by experts in different domains. Moreover, visualization techniques have also been used to enhance and facilitate this process. However, there are serious limitations in the process of knowledge acquisition, as it is often a slow, tedious and many times fruitless process, due to the difficulty for human beings to understand large datasets. Another major drawback, rarely considered by experts that analyze large datasets, is the involuntary degradation to which they subject the data during analysis tasks, prior to obtaining the final conclusions. Degradation means that data can lose part of their original properties, and it is usually caused by improper data reduction, thereby altering their original nature and often leading to erroneous interpretations and conclusions that could have serious implications. Furthermore, this fact gains a trascendental importance when the data belong to medical or biological domain, and the lives of people depends on the final decision-making, which is sometimes conducted improperly. This is the motivation of this thesis, which proposes a new visual framework, called MedVir, which combines the power of advanced visualization techniques and data mining to try to solve these major problems existing in the process of discovery of valid information. Thus, the main objective is to facilitate and to make more understandable, intuitive and fast the process of knowledge acquisition that experts face when working with large datasets in different domains. To achieve this, first, a strong reduction in the size of the data is carried out in order to make the management of the data easier to the expert, while preserving intact, as far as possible, the original properties of the data. Then, effective visualization techniques are used to represent the obtained data, allowing the expert to interact easily and intuitively with the data, to carry out different data analysis tasks, and so visually stimulating their comprehension capacity. Therefore, the underlying objective is based on abstracting the expert, as far as possible, from the complexity of the original data to present him a more understandable version, thus facilitating and accelerating the task of knowledge discovery. MedVir has been succesfully applied to, among others, the field of magnetoencephalography (MEG), which consists in predicting the rehabilitation of Traumatic Brain Injury (TBI). The results obtained successfully demonstrate the effectiveness of the framework to accelerate and facilitate the process of knowledge discovery on real world datasets.

A framework for goal-oriented discovery of resources in the RESTful architecture

One of the challenges facing the current web is the efficient use of all the available information. The Web 2.0 phenomenon has favored the creation of contents by average users, and thus the amount of information that can be found for diverse topics has grown exponentially in the last years. Initiatives such as linked data are helping to build the Semantic Web, in which a set of standards are proposed for the exchange of data among heterogeneous systems. However, these standards are sometimes not used, and there are still plenty of websites that require naive techniques to discover their contents and services. This paper proposes an integrated framework for content and service discovery and extraction. The framework is divided into several layers where the discovery of contents and services is made in a representational stateless transfer system such as the web. It employs several web mining techniques as well as feature-oriented modeling for the discovery of cross-cutting features in web resources. The framework is used in a scenario of electronic newspapers. An intelligent agent crawls the web for related news, and uses services and visits links automatically according to its goal. This scenario illustrates how the discovery is made at different levels and how the use of semantics helps implement an agent that performs high-level tasks.

Linked Data for sharing, discovery and re-use of Language Resources at a Web scale

Conferencia por invitación, impartida el 31 d mayo de 2014 en el Workshop on Language Technology Service Platforms: Synergies, Standards, Sharing at LREC2014

The Annual of scientific discovery: or, Year-book of facts in science and art.

1866-1867

The Annual of scientific discovery: or, Year-book of facts in science and art.

1870

The Annual of scientific discovery: or, Year-book of facts in science and art.

1859

The Annual of scientific discovery: or, Year-book of facts in science and art.

1863

Bacteria lacking a multidrug pump: A sensitive tool for drug discovery

Microorganisms express multidrug resistance pumps (MDRs) that can confound antibiotic discovery. We propose the use of mutants deficient in MDRs to overcome this problem. Sensitivity to quinolones and to amphipathic cations (norfloxacin, benzalkonium chloride, cetrimide, pentamidine, etc.) was increased 5- to 30-fold in a Staphylococcus aureus mutant with a disrupted chromosomal copy of the NorA MDR. NorA was required both for increased sensitivity to drugs in the presence of an MDR inhibitor and for increased rate of cation efflux. This requirement suggests that NorA is the major MDR protecting S. aureus from the antimicrobials studied. A 15- to 60-fold increase in sensitivity to antimicrobials also was observed in wild-type cells at an alkaline pH that favors accumulation of cations and weak bases. This effect was synergistic with a norA mutation, resulting in an increase up to 1,000-fold in sensitivity to antimicrobials. The usefulness of applying MDR mutants for natural product screening was demonstrated further by increased sensitivity of the norA− strain to plant alkaloid antimicrobials, which might be natural MDR substrates.

Phylogenomic analysis of natural products biosynthetic gene clusters allows discovery of arseno-organic metabolites in model streptomycetes

We are indebted with Marnix Medema, Paul Straight and Sean Rovito, for useful discussions and critical reading of the manuscript, as well as with Alicia Chagolla and Yolanda Rodriguez of the MS Service of Unidad Irapuato, Cinvestav, and Araceli Fernandez for technical support in high-performance computing. This work was funded by Conacyt Mexico (grants No. 179290 and 177568) and FINNOVA Mexico (grant No. 214716) to FBG. PCM was funded by Conacyt scholarship (No. 28830) and a Cinvestav posdoctoral fellowship. JF and JFK acknowledge funding from the College of Physical Sciences, University of Aberdeen, UK.

Gene discovery in the wood-forming tissues of poplar: Analysis of 5,692 expressed sequence tags

A rapidly growing area of genome research is the generation of expressed sequence tags (ESTs) in which large numbers of randomly selected cDNA clones are partially sequenced. The collection of ESTs reflects the level and complexity of gene expression in the sampled tissue. To date, the majority of plant ESTs are from nonwoody plants such as Arabidopsis, Brassica, maize, and rice. Here, we present a large-scale production of ESTs from the wood-forming tissues of two poplars, Populus tremula L. × tremuloides Michx. and Populus trichocarpa ‘Trichobel.’ The 5,692 ESTs analyzed represented a total of 3,719 unique transcripts for the two cDNA libraries. Putative functions could be assigned to 2,245 of these transcripts that corresponded to 820 protein functions. Of specific interest to forest biotechnology are the 4% of ESTs involved in various processes of cell wall formation, such as lignin and cellulose synthesis, 5% similar to developmental regulators and members of known signal transduction pathways, and 2% involved in hormone biosynthesis. An additional 12% of the ESTs showed no significant similarity to any other DNA or protein sequences in existing databases. The absence of these sequences from public databases may indicate a specific role for these proteins in wood formation. The cDNA libraries and the accompanying database are valuable resources for forest research directed toward understanding the genetic control of wood formation and future endeavors to modify wood and fiber properties for industrial use.