Individual mRNA expression profiles reveal the effects of specific microRNAs

BACKGROUND: MicroRNAs (miRNAs) are oligoribonucleotides with an important role in regulation of gene expression at the level of translation. Despite imperfect target complementarity, they can also significantly reduce mRNA levels. The validity of miRNA target gene predictions is difficult to assess at the protein level. We sought, therefore, to determine whether a general lowering of predicted target gene mRNA expression by endogenous miRNAs was detectable within microarray gene expression profiles. RESULTS: The target gene sets predicted for each miRNA were mapped onto known gene expression data from a range of tissues. Whether considering mean absolute target gene expression, rank sum tests or 'ranked ratios', many miRNAs with significantly reduced target gene expression corresponded to those known to be expressed in the cognate tissue. Expression levels of miRNAs with reduced target mRNA levels were higher than those of miRNAs with no detectable effect on mRNA expression. Analysis of microarray data gathered after artificial perturbation of expression of a specific miRNA confirmed the predicted increase or decrease in influence of the altered miRNA upon mRNA levels. Strongest associations were observed with targets predicted by TargetScan. CONCLUSION: We have demonstrated that the effect of a miRNA on its target mRNAs' levels can be measured within a single gene expression profile. This emphasizes the extent of this mode of regulation in vivo and confirms that many of the predicted miRNA-mRNA interactions are correct. The success of this approach has revealed the vast potential for extracting information about miRNA function from gene expression profiles.

Effects of territorial intrusions on eavesdropping neighbors: communication networks in nightingales

Animal communication often occurs in communication networks in which multiple signalers and receivers are within signaling range of each other. In such networks, individuals can obtain information on the quality and motivation of territorial neighbors by eavesdropping on their signaling interactions. In songbirds, extracting information from interactions involving neighbors is thought to be an important factor in the evolution of strategies of territory defense. In a playback experiment with radio-tagged nightingales Luscinia megarhynchos we here demonstrate that territorial males use their familiar neighbors' performance in a vocal interaction with an unfamiliar intruder as a standard for their own response. Males were attracted by a vocal interaction between their neighbor and a simulated stranger and intruded into the neighbor's territory. The more intensely the neighbor had interacted with playback, the earlier the intrusions were made, indicating that males eavesdropped on the vocal contest involving a neighbor. However, males never intruded when we had simulated by a second playback that the intruder had retreated and sang outside the neighbor's territory. These results suggest that territorial males use their neighbors' singing behavior as an early warning system when territorial integrity is threatened. Simultaneous responses by neighboring males towards unfamiliar rivals are likely to be beneficial to the individuals in maintaining territorial integrity.

Regional Knowledge Maps - Potential and Challenges

Regional knowledge map is a tool recently demanded by some actors in an institutional level to help regional policy and innovation in a territory. Besides, knowledge maps facilitate the interaction between the actors of a territory and the collective learning. This paper reports the work in progress of a research project which objective is to define a methodology to efficiently design territorial knowledge maps, by extracting information of big volumes of data contained in diverse sources of information related to a region. Knowledge maps facilitate management of the intellectual capital in organisations. This paper investigates the value to apply this tool to a territorial region to manage the structures, infrastructures and the resources to enable regional innovation and regional development. Their design involves the identification of information sources that are required to find which knowledge is located in a territory, which actors are involved in innovation, and which is the context to develop this innovation (structures, infrastructures, resources and social capital). This paper summarizes the theoretical background and framework for the design of a methodology for the construction of knowledge maps, and gives an overview of the main challenges for the design of regional knowledge maps.

Recommender System Based on Consumer Product Reviews

Consumer reviews, opinions and shared experiences in the use of a product is a powerful source of information about consumer preferences that can be used in recommender systems. Despite the importance and value of such information, there is no comprehensive mechanism that formalizes the opinions selection and retrieval process and the utilization of retrieved opinions due to the difficulty of extracting information from text data. In this paper, a new recommender system that is built on consumer product reviews is proposed. A prioritizing mechanism is developed for the system. The proposed approach is illustrated using the case study of a recommender system for digital cameras

Uses of airborne scanning laser altimetry for river flood modelling and coastal geomorphology

Two ongoing projects at ESSC that involve the development of new techniques for extracting information from airborne LiDAR data and combining this information with environmental models will be discussed. The first project in conjunction with Bristol University is aiming to improve 2-D river flood flow models by using remote sensing to provide distributed data for model calibration and validation. Airborne LiDAR can provide such models with a dense and accurate floodplain topography together with vegetation heights for parameterisation of model friction. The vegetation height data can be used to specify a friction factor at each node of a model’s finite element mesh. A LiDAR range image segmenter has been developed which converts a LiDAR image into separate raster maps of surface topography and vegetation height for use in the model. Satellite and airborne SAR data have been used to measure flood extent remotely in order to validate the modelled flood extent. Methods have also been developed for improving the models by decomposing the model’s finite element mesh to reflect floodplain features such as hedges and trees having different frictional properties to their surroundings. Originally developed for rural floodplains, the segmenter is currently being extended to provide DEMs and friction parameter maps for urban floods, by fusing the LiDAR data with digital map data. The second project is concerned with the extraction of tidal channel networks from LiDAR. These networks are important features of the inter-tidal zone, and play a key role in tidal propagation and in the evolution of salt-marshes and tidal flats. The study of their morphology is currently an active area of research, and a number of theories related to networks have been developed which require validation using dense and extensive observations of network forms and cross-sections. The conventional method of measuring networks is cumbersome and subjective, involving manual digitisation of aerial photographs in conjunction with field measurement of channel depths and widths for selected parts of the network. A semi-automatic technique has been developed to extract networks from LiDAR data of the inter-tidal zone. A multi-level knowledge-based approach has been implemented, whereby low level algorithms first extract channel fragments based mainly on image properties then a high level processing stage improves the network using domain knowledge. The approach adopted at low level uses multi-scale edge detection to detect channel edges, then associates adjacent anti-parallel edges together to form channels. The higher level processing includes a channel repair mechanism.

An overview of the use of neural networks for data mining tasks

In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks. © 2012 Wiley Periodicals, Inc.

Forensisk analys av volatilt minne från operativsystemet OS X

Behovet av att analysera volatilt minne från Macintosh-datorer med OS X har blivit allt mer betydelsefull på grund av att deras datorer blivit allt populärare och att volatil minnesanalysering blivit en allt viktigare del i en IT-forensikers arbete. Anledningen till att volatil minnesanalysering blivit allt viktigare är för att det går att finna viktig information som inte finns lagrad permanent på datorns interna hårddisk. Problemet som låg till grunden för det här examensarbetet var att det uppenbart fanns brist på undersökningsmetoder av det volatila minnet för Mac-datorer med OS X.Syftet med detta arbete var därför att undersöka möjligheten att utvinna information från ett volatilt minne från en Mac-dator med OS X genom att kartlägga och bedöma olika undersökningsmetoder. För att göra denna undersökning har litteraturstudier, informella intervjuer, egna kunskaper och praktiska försök genomförts.Slutsatsen blev att möjligheten att utvinna information från det volatila minnet från en Mac-dator med OS X är relativt begränsad. Det största problemet är själva dumpningen av minnet. Många av dumpningsmetoderna som finns att tillgå kräver administrativa rättigheter. Vid analysering av en minnesdump bör man aldrig förlita sig på en analysmetod då olika analysmetoder ger olika resultat som kan vara till nytta för en vidare undersökning av en Mac-dator.

Green logistics: key elements for a well-functioning sustainable logistics system

A growing awareness of the modern society about the direct relationship between a growing global community with increasing total industrial activities on one hand and various environmental problems and a natural limitation of natural resources on the other hand set the base for sustainable or “green” approaches within the supply chain. This paper therefore will look at the issue of “Green Logistics” which seeks to reduce the environmental impact of logistics activities by taking into account functions such as recycling, waste and carbon emission reduction and the use of alternative sources of energy. In order to analyze how these approaches and ideas are being perceived by the system as a whole two models from the area of prospective and scenario planning are being used and described to identify the main drivers and tendencies within the system in order to create feasible hypothesis. Using the URCA/CHIVAS model allows us to identify the driver variables out of a high number of variables that best describe the system “Green Logistics”. Followed by the analysis of the actor’s strategies in the system with the Mactor model it is possible to reduce the complexity of a completely holistic system to a few key drivers that can be analyzed further on. Here the implications of URCA/CHIVAS and Mactor are being used to formulate hypotheses about the perception of Green Logistics and its successful implementation among logistics decision makers by an online survey. This research seeks to demonstrate the usefulness of scenario planning to a highly complex system observing it from all angles and extracting information about the relevant factors of it. The results of this demonstration indicate that there are drivers much beyond the factory walls that need to be considered when implementing successfully a system such as Green Logistics.

Mathematical tests about the existence and applications of PPS-wavelets in function approximation problems

Neural networks and wavelet transform have been recently seen as attractive tools for developing eficient solutions for many real world problems in function approximation. Function approximation is a very important task in environments where computation has to be based on extracting information from data samples in real world processes. So, mathematical model is a very important tool to guarantee the development of the neural network area. In this article we will introduce one series of mathematical demonstrations that guarantee the wavelets properties for the PPS functions. As application, we will show the use of PPS-wavelets in pattern recognition problems of handwritten digit through function approximation techniques.

Tvoice :um sistema de manipulação de linguagens para auxiliar portadores de necessidades especiais através da web

This work presents the tVoice, software that manipulates tags languages, extracting information and, being integral part of the VoiceProxy system, it aids bearers of special needs in the access to the Web. This system is responsible for the search and treatment of the documents in the Web, extracting the textual information contained in those documents and preceding the capability of generating eventually through translation techniques, an audio script, used by the of interface subsystem of VoiceProxy, the iVoice, in the process of voice synthesis. In this stage the tVoice, besides the treatment of the tag language HTML, processes other two formats of documents, PDF and XHTML. Additionally to allow that, besides the iVoice, other interface subsystems can make use of the tVoice through remote access, we propose distribution systems techniques based in the model Client-Server providers operations of the fashion of a proxy server treatment of documents

Nonlocal description of the nuclear interaction

Extensive systematizations of theoretical and experimental nuclear densities and of optical potential strengths extracted from heavy-ion elastic scattering data analyses at low and intermediate energies are presented. The energy-dependence of the nuclear potential is accounted for within a model based on the nonlocal nature of the interaction. The systematics indicates that the heavy-ion nuclear potential can be described in a simple global way through a double-folding shape, which basically depends only on the density of nucleons of the partners in the collision. The possibility of extracting information about the nucleon-nucleon interaction from the heavy-ion potential is investigated.

Complementarity of eastern and western hemisphere long-baseline neutrino oscillation experiments

We present a general formalism for extracting information on the fundamental parameters associated with neutrino masses and mixings from two or more long baseline neutrino oscillation experiments. This formalism is then applied to the current most likely experiments using neutrino beams from the Japan Hadron Facility (JHF) and Fermilab's NuMI beamline. Different combinations of muon neutrino or muon anti-neutrino running are considered. The type of neutrino mass hierarchy is extracted using the effects of matter on neutrino propogation. Contrary to naive expectation, we find that both beams using neutrinos is more suitable for determining the hierarchy provided that the neutrino energy divided by baseline (E/L) for NuMI is smaller than or equal to that of JHF, whereas to determine the small mixing angle, theta(13), and the CP or T violating phase delta, one neutrino and the other anti-neutrino are most suitable. We make extensive use of bi-probability diagrams for both understanding and extracting the physics involved in such comparisons.

Feedforward neural networks based on PPS-wavelet activation functions

Function approximation is a very important task in environments where computation has to be based on extracting information from data samples in real world processes. Neural networks and wavenets have been recently seen as attractive tools for developing efficient solutions for many real world problems in function approximation. In this paper, it is shown how feedforward neural networks can be built using a different type of activation function referred to as the PPS-wavelet. An algorithm is presented to generate a family of PPS-wavelets that can be used to efficiently construct feedforward networks for function approximation.

Toward a global description of the nucleus-nucleus interaction

Extensive systematizations of theoretical and experimental nuclear densities and of optical potential strengths extracted from heavy-ion elastic scattering data analyses at low and intermediate energies are presented. The energy dependence of the nuclear potential is accounted for within a model based on the nonlocal nature of the interaction. The systematics indicates that the heavy-ion nuclear potential can be described in a simple global way through a double-folding shape, which basically depends only on the density of nucleons of the partners in the collision. The possibility of extracting information about the nucleon-nucleon interaction from the heavy-ion potential is investigated.

Desenvolvimento de um programa computacional para classificação do uso da terra em imagens CBERS 2

Pós-graduação em Agronomia (Energia na Agricultura) - FCA