Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.

Optimization of snowmaking in high mountains ski resort

Ski resorts are deploying more and more systems of artificial snow. These tools are necessary to ensure an important economic activity for the high alpine valleys. However, artificial snow raises important environmental issues that can be reduced by an optimization of its production. This paper presents a software prototype based on artificial intelligence to help ski resorts better manage their snowpack. It combines on one hand a General Neural Network for the analysis of the snow cover and the spatial prediction, with on the other hand a multiagent simulation of skiers for the analysis of the spatial impact of ski practice. The prototype has been tested on the ski resort of Verbier (Switzerland).

Identification of Research Portfolio for the Development of Filtration Equipment

This piece of work which is Identification of Research Portfolio for Development of Filtration Equipment aims at presenting a novel approach to identify promising research topics in the field of design and development of filtration equipment and processes. The projected approach consists of identifying technological problems often encountered in filtration processes. The sources of information for the problem retrieval were patent documents and scientific papers that discussed filtration equipments and processes. The problem identification method adopted in this work focussed on the semantic nature of a sentence in order to generate series of subject-action-object structures. This was achieved with software called Knowledgist. List of problems often encountered in filtration processes that have been mentioned in patent documents and scientific papers were generated. These problems were carefully studied and categorized. Suggestions were made on the various classes of these problems that need further investigation in order to propose a research portfolio. The uses and importance of other methods of information retrieval were also highlighted in this work.

Advertising in Mobile Games - A Business Model Evaluation

Tämän tutkimuksen tavoitteena oli selvittää kuinka houkuttelevan liiketoimintamahdollisuuden mobiilipelit tarjoavat mainostusalustana. Tutkimus suoritettiin tapaustutkimuksena. Tutkimus aloitettiin määrittelemällä liiketoimintamalli, jonka jälkeen suoritettiin yleinen katsaus Suomen mobiilipelimarkkinoille. Tämän jälkeen arvoketju-, arvoverkko- sekä markkina-analyysin avulla selvitettiin liiketoimintamallin mahdollisuudet sekä rajoitukset. Tutkimukseen käytettiinteorettista viitekehystä joka pohjautui Hamelin liiketoimintamalliin, Porterin arvoketjuun sekä Alleenin arvoverkoon. Tutkimuksen tuloksena todettiin, että mainostaminen mobiilipeleissä tarjoaa liiketoimintamahdollisuuden ilman esteitä sentoteuttamiselle. Suomalaiset mobiilipelimarkkinat ovat kuitenkin pirstoutuneet,minkä johdosta tutkittu 'mainosten hallinta-alusta'-liiketoimintamalli aiheuttaa liian suuret integraatiokustannukset. Myös suuri määrä pelitoimittajia heikentää mallin tehokkuutta.

Are Evaluation of ICTs Investment Using Decision Support Systems:Case Applications from Distributor's and End User's Perspective Group Decision

The evaluation of investments in advanced technology is one of the most important decision making tasks. The importance is even more pronounced considering the huge budget concerning the strategic, economic and analytic justification in order to shorten design and development time. Choosing the most appropriate technology requires an accurate and reliable system that can lead the decision makers to obtain such a complicated task. Currently, several Information and Communication Technologies (ICTs) manufacturers that design global products are seeking local firms to act as their sales and services representatives (called distributors) to the end user. At the same time, the end user or customer is also searching for the best possible deal for their investment in ICT's projects. Therefore, the objective of this research is to present a holistic decision support system to assist the decision maker in Small and Medium Enterprises (SMEs) - working either as individual decision makers or in a group - in the evaluation of the investment to become an ICT's distributor or an ICT's end user. The model is composed of the Delphi/MAH (Maximising Agreement Heuristic) Analysis, a well-known quantitative method in Group Support System (GSS), which is applied to gather the average ranking data from amongst Decision Makers (DMs). After that the Analytic Network Process (ANP) analysis is brought in to analyse holistically: it performs quantitative and qualitative analysis simultaneously. The illustrative data are obtained from industrial entrepreneurs by using the Group Support System (GSS) laboratory facilities at Lappeenranta University of Technology, Finland and in Thailand. The result of the research, which is currently implemented in Thailand, can provide benefits to the industry in the evaluation of becoming an ICT's distributor or an ICT's end user, particularly in the assessment of the Enterprise Resource Planning (ERP) programme. After the model is put to test with an in-depth collaboration with industrial entrepreneurs in Finland and Thailand, the sensitivity analysis is also performed to validate the robustness of the model. The contribution of this research is in developing a new approach and the Delphi/MAH software to obtain an analysis of the value of becoming an ERP distributor or end user that is flexible and applicable to entrepreneurs, who are looking for the most appropriate investment to become an ERP distributor or end user. The main advantage of this research over others is that the model can deliver the value of becoming an ERP distributor or end user in a single number which makes it easier for DMs to choose the most appropriate ERP vendor. The associated advantage is that the model can include qualitative data as well as quantitative data, as the results from using quantitative data alone can be misleading and inadequate. There is a need to utilise quantitative and qualitative analysis together, as can be seen from the case studies.

Visiosta toimivaksi yritysverkostoksi –verkostoitumisprosessin tutkiminen ja tulevaisuuden toimintamallin rakentaminen Case: Imatran Seudun Koivukeskus

Tutkimuksen tarkoitus oli tutkia, miten case-verkosto Imatran Seudun Koivukeskus on syntynyt ja luoda sille sopiva tulevaisuuden toimintamalli. Tutkimuksessa hyödynnettiin käsiteanalyyttista ja konstruktiivista tutkimusotetta. Tutkimuksen tavoitteita olivat keskeisten käsitteiden määrittely; onnistuneiden ja epäonnistuneiden verkostoitumisesimerkkien kuvaaminen ja analysointi; case-verkoston esittely; case-verkoston historiallinen analyysi, sen toimintamallin tutkiminen ja vertaaminen esimerkkitapauksiin sekä case-verkoston tulevaisuuden kehitysnäkymien analysointi ja sille sopivan toimintamallin muodostaminen. Tutkielma koostui teoreettisesta osasta ja empiirisestä osasta. Teoriaosio käsitteli verkostoitumisen teoriaa. Tämä osio ja suoritetut teemahaastattelut loivat pohjan empiiriselle osuudelle ja toimintamallin muodostamiselle case-yritykselle. Tutkimuksen perusteella kriittisimmiksi case-verkoston tulevaisuuden toimintamallin osa-alueiksi osoittautuivat raaka-aineen saatavuus sekä avoimuus ja tiedonkulku verkostossa. Näiden lisäksi toimintamallissa tulee painottaa alihankinnan ja jatkojalostuksen lisäämistä, tutkimus- ja kehitystoimintaa sekä kuivaustoiminnan osaamistason kehittämistä.

Tratamento estatístico dos parâmetros da qualidade das águas da bacia do alto curso do Rio das Velhas

Water quality was monitored at the upper course of the Rio das Velhas, a major tributary of the São Francisco basin located in the state of Minas Gerais, over an extension of 108 km from its source up to the limits with the Sabara district. Monitoring was done at 37 different sites over a period of 2 years (2003-2004) for 39 parameters. Multivariate statistical techniques were applied to interpret the large water-quality data set and to establish an optimal long-term monitoring network. Cluster analysis separated the sampling sites into groups of similarity, and also indicated the stations investigated for correlation and recommended to be removed from the monitoring network. Principal component analysis identified four components, which are responsible for the data structure explaining 80% of the total variance of the data. The principal parameters are characterized as due to mining activities and domestic sewage. Significant data reduction was achieved.

Effect of scanner acoustic background noise on strict resting-state fMRI

Functional MRI (fMRI) resting-state experiments are aimed at identifying brain networks that support basal brain function. Although most investigators consider a ‘resting-state’ fMRI experiment with no specific external stimulation, subjects are unavoidably under heavy acoustic noise produced by the equipment. In the present study, we evaluated the influence of auditory input on the resting-state networks (RSNs). Twenty-two healthy subjects were scanned using two similar echo-planar imaging sequences in the same 3T MRI scanner: a default pulse sequence and a reduced “silent” pulse sequence. Experimental sessions consisted of two consecutive 7-min runs with noise conditions (default or silent) counterbalanced across subjects. A self-organizing group independent component analysis was applied to fMRI data in order to recognize the RSNs. The insula, left middle frontal gyrus and right precentral and left inferior parietal lobules showed significant differences in the voxel-wise comparison between RSNs depending on noise condition. In the presence of low-level noise, these areas Granger-cause oscillations in RSNs with cognitive implications (dorsal attention and entorhinal), while during high noise acquisition, these connectivities are reduced or inverted. Applying low noise MR acquisitions in research may allow the detection of subtle differences of the RSNs, with implications in experimental planning for resting-state studies, data analysis, and ergonomic factors.

Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.

La distribution des connaissances dans la gestion du risque : analyse des interactions dans le cadre du Comité de la protection civile de la Mairie de Tecoluca - Salvador

Le sujet de la gestion du risque m’a toujours interpelée, surtout après que j’ai vécu deux ouragans et un tremblement de terre dévastateurs au Salvador. Bien qu’on ait assez écrit sur le sujet en le reliant souvent aux changements climatiques, on ne sait pas comment les organisations gouvernementales et civiles vivent cette gestion du risque au quotidien. À partir d’une étude ethnographique de la Commission de la protection civile de la Mairie de Tecoluca au Salvador, j’observais les processus qui se mettent en place dans la recherche et l’analyse des facteurs structuraux causant les situations de vulnérabilité. Pour ce faire, j’adoptais une approche basée sur l’étude des interactions, mobilisant les théories de la cognition distribuée et de l’acteur réseau. Comme je le montre, la gestion du risque, vue comme un processus participatif, se caractérise, d’une part, par la coopération et la coordination entre les personnes et, d’autre part, par la contribution d’outils, de technologies, de documents et de méthodes contribuant à la détection de risques. Ceci exige la mobilisation de connaissances qui doivent être produites, partagées et distribuées entre les membres d’un groupe à travers les divers artéfacts, outils, méthodes et technologies qu’ils mobilisent et qui les mobilisent. À ce sujet, la théorie de la cognition distribuée permet d’explorer des interactions qui se produisent au sein d’un groupe de travail en se focalisant sur ce qui contribue à l’acte de connaitre, conçu comme une activité non pas seulement individuelle, mais surtout collective et distribuée. Par ailleurs, la théorie de l’acteur-réseau me permet, quant à elle, de montrer comment dans l’exécution de cette tâche (la gestion du risque), la contribution active d’acteurs non humains, tant en soi qu’en relations avec les acteurs humains, participe de l’activité de détection et de prévention du risque.

Identification of spectral lines of elements using artificial neural networks

Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. This paper describes how an ANN can be used to identify the spectral lines of elements. The spectral lines of Cadmium (Cd), Calcium (Ca), Iron (Fe), Lithium (Li), Mercury (Hg), Potassium (K) and Strontium (Sr) in the visible range are chosen for the investigation. One of the unique features of this technique is that it uses the whole spectrum in the visible range instead of individual spectral lines. The spectrum of a sample taken with a spectrometer contains both original peaks and spurious peaks. It is a tedious task to identify these peaks to determine the elements present in the sample. ANNs capability of retrieving original data from noisy spectrum is also explored in this paper. The importance of the need of sufficient data for training ANNs to get accurate results is also emphasized. Two networks are examined: one trained in all spectral lines and other with the persistent lines only. The network trained in all spectral lines is found to be superior in analyzing the spectrum even in a noisy environment.

Adaptive Real-time Anomaly-based Intrusion Detection using Data Mining and Machine Learning Techniques

Die zunehmende Vernetzung der Informations- und Kommunikationssysteme führt zu einer weiteren Erhöhung der Komplexität und damit auch zu einer weiteren Zunahme von Sicherheitslücken. Klassische Schutzmechanismen wie Firewall-Systeme und Anti-Malware-Lösungen bieten schon lange keinen Schutz mehr vor Eindringversuchen in IT-Infrastrukturen. Als ein sehr wirkungsvolles Instrument zum Schutz gegenüber Cyber-Attacken haben sich hierbei die Intrusion Detection Systeme (IDS) etabliert. Solche Systeme sammeln und analysieren Informationen von Netzwerkkomponenten und Rechnern, um ungewöhnliches Verhalten und Sicherheitsverletzungen automatisiert festzustellen. Während signatur-basierte Ansätze nur bereits bekannte Angriffsmuster detektieren können, sind anomalie-basierte IDS auch in der Lage, neue bisher unbekannte Angriffe (Zero-Day-Attacks) frühzeitig zu erkennen. Das Kernproblem von Intrusion Detection Systeme besteht jedoch in der optimalen Verarbeitung der gewaltigen Netzdaten und der Entwicklung eines in Echtzeit arbeitenden adaptiven Erkennungsmodells. Um diese Herausforderungen lösen zu können, stellt diese Dissertation ein Framework bereit, das aus zwei Hauptteilen besteht. Der erste Teil, OptiFilter genannt, verwendet ein dynamisches "Queuing Concept", um die zahlreich anfallenden Netzdaten weiter zu verarbeiten, baut fortlaufend Netzverbindungen auf, und exportiert strukturierte Input-Daten für das IDS. Den zweiten Teil stellt ein adaptiver Klassifikator dar, der ein Klassifikator-Modell basierend auf "Enhanced Growing Hierarchical Self Organizing Map" (EGHSOM), ein Modell für Netzwerk Normalzustand (NNB) und ein "Update Model" umfasst. In dem OptiFilter werden Tcpdump und SNMP traps benutzt, um die Netzwerkpakete und Hostereignisse fortlaufend zu aggregieren. Diese aggregierten Netzwerkpackete und Hostereignisse werden weiter analysiert und in Verbindungsvektoren umgewandelt. Zur Verbesserung der Erkennungsrate des adaptiven Klassifikators wird das künstliche neuronale Netz GHSOM intensiv untersucht und wesentlich weiterentwickelt. In dieser Dissertation werden unterschiedliche Ansätze vorgeschlagen und diskutiert. So wird eine classification-confidence margin threshold definiert, um die unbekannten bösartigen Verbindungen aufzudecken, die Stabilität der Wachstumstopologie durch neuartige Ansätze für die Initialisierung der Gewichtvektoren und durch die Stärkung der Winner Neuronen erhöht, und ein selbst-adaptives Verfahren eingeführt, um das Modell ständig aktualisieren zu können. Darüber hinaus besteht die Hauptaufgabe des NNB-Modells in der weiteren Untersuchung der erkannten unbekannten Verbindungen von der EGHSOM und der Überprüfung, ob sie normal sind. Jedoch, ändern sich die Netzverkehrsdaten wegen des Concept drif Phänomens ständig, was in Echtzeit zur Erzeugung nicht stationärer Netzdaten führt. Dieses Phänomen wird von dem Update-Modell besser kontrolliert. Das EGHSOM-Modell kann die neuen Anomalien effektiv erkennen und das NNB-Model passt die Änderungen in Netzdaten optimal an. Bei den experimentellen Untersuchungen hat das Framework erfolgversprechende Ergebnisse gezeigt. Im ersten Experiment wurde das Framework in Offline-Betriebsmodus evaluiert. Der OptiFilter wurde mit offline-, synthetischen- und realistischen Daten ausgewertet. Der adaptive Klassifikator wurde mit dem 10-Fold Cross Validation Verfahren evaluiert, um dessen Genauigkeit abzuschätzen. Im zweiten Experiment wurde das Framework auf einer 1 bis 10 GB Netzwerkstrecke installiert und im Online-Betriebsmodus in Echtzeit ausgewertet. Der OptiFilter hat erfolgreich die gewaltige Menge von Netzdaten in die strukturierten Verbindungsvektoren umgewandelt und der adaptive Klassifikator hat sie präzise klassifiziert. Die Vergleichsstudie zwischen dem entwickelten Framework und anderen bekannten IDS-Ansätzen zeigt, dass der vorgeschlagene IDSFramework alle anderen Ansätze übertrifft. Dies lässt sich auf folgende Kernpunkte zurückführen: Bearbeitung der gesammelten Netzdaten, Erreichung der besten Performanz (wie die Gesamtgenauigkeit), Detektieren unbekannter Verbindungen und Entwicklung des in Echtzeit arbeitenden Erkennungsmodells von Eindringversuchen.

The web of connections between tourism companies: Structure and dynamics

Tourism destination networks are amongst the most complex dynamical systems, involving a myriad of human-made and natural resources. In this work we report a complex network-based systematic analysis of the Elba (Italy) tourism destination network, including the characterization of its structure in terms of several traditional measurements, the investigation of its modularity, as well as its comprehensive study in terms of the recently reported superedges approach. In particular, structural (the number of paths of distinct lengths between pairs of nodes, as well as the number of reachable companies) and dynamical features (transition probabilities and the inward/outward activations and accessibilities) are measured and analyzed, leading to a series of important findings related to the interactions between tourism companies. Among the several reported results, it is shown that the type and size of the Companies influence strongly their respective activations and accessibilities, while their geographical position does not seem to matter. It is also shown that the Elba tourism network is largely fragmented and heterogeneous, so that it could benefit from increased integration. (C) 2009 Elsevier B.V. All rights reserved.

A formação de rede para o atendimento de desastres de massa: o caso do acidente aéreo do voo 447 da Air France

Este estudo teve por objetivo verificar como as características do modelo de rede se manifestam em resposta a desastres de massa, por meio de um estudo de caso. O objeto dessa pesquisa foi o acidente aéreo do voo 447 da Air France, que aconteceu em meados de 2009, no Oceano Atlântico em área próxima ao arquipélago de São Pedro e São Paulo, circunscrição do estado de Pernambuco. A rede formada para a resposta a esse acidente foi composta pela Aeronáutica, Marinha, Polícia Federal, Secretaria de Defesa Social de Pernambuco, INTERPOL e Secretaria de Estado da Segurança e da Defesa Social da Paraíba, tendo ainda a participação de outras instituições, que forneceram suporte logístico de pessoas e recursos em geral para a missão. As atividades desenvolvidas durante o evento compreenderam busca e resgate dos corpos, sob a coordenação da Aeronáutica e da Marinha, pré-identificação dos corpos, sob a coordenação da Polícia Federal e da Secretaria de Defesa Social de Pernambuco, necrópsia dos corpos e coleta de material postmortem, sob a coordenação da Secretaria de Defesa Social de Pernambuco e da Secretaria de Estado da Segurança e da Defesa Social da Paraíba, coleta de material antemortem, sob a coordenação da INTERPOL e da Polícia Federal, e identificação dos corpos, que teve como coordenadores a Polícia Federal e a Secretaria de Defesa Social de Pernambuco. A pesquisa realizada compreendeu três momentos distintos: a) montagem da estrutura da rede, b) análise das etapas de gerenciamento e, c) identificação das características da rede. A montagem da estrutura da rede permitiu conhecer em detalhes a rede formada, seus integrantes, objetivos e funcionamento, e subsidiar as etapas seguintes. Com isso foi possível fazer uma análise das etapas de gerenciamento da rede, a ativação, o enquadramento, a mobilização e a síntese, e como cada uma delas aconteceu na rede de atendimento do voo 447 da Air France. Por fim, foi possível identificar as principais características do modelo de rede, a pluralidade, a horizontalidade, a capilaridade, a interdependência, a flexibilidade e a dinâmica do estado, e verificar como elas se manifestaram na missão de resposta ao acidente aéreo do voo 447 da Air France.