Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models

Background: Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Results: Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity. Conclusions: Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.

Electric Motor and Frequency Converter Markets in Latin America and Especially in the Mining Industry of Chile

Latinalaisen Amerikan osuus maailmantaloudesta on pieni verrattuna sen maantieteelliseen kokoon, väkilukuun ja luonnonvaroihin. Aluetta pidetään kuitenkin yhtenä tulevaisuuden merkittävistä kasvumarkkinoista. Useissa Latinalaisen Amerikan maissa on teollisuutta, joka hyödyntää luonnonvaroja ja tuottaa raaka-aineita sekä kotimaan että ulkomaiden markkinoille. Tällaisia tyypillisiä teollisuudenaloja Latinalaisessa Amerikassa ovat kaivos- ja metsäteollisuus sekä öljyn ja maakaasun tuotanto. Näiden teollisuudenalojen tuotantolaitteiden ja koneiden valmistusta ei Latinalaisessa Amerikassa juurikaan ole. Ne tuodaan yleensä Pohjois-Amerikasta ja Euroopasta. Tässä diplomityössä tutkitaan sähkömoottorien ja taajuusmuuttajien markkinapotentiaalia Latinalaisessa Amerikassa. Tutkimuksessa perehdytään Latinalaisen Amerikan maiden kansantalouksien tilaan sekä arvioidaan sähkömoottorien ja taajuusmuuttajien markkinoiden kokoa tullitilastojen avulla. Chilen kaivosteollisuudessa arvioidaan olevan erityistä potentiaalia. Diplomityössä selvitetään ostoprosessin kulkua Chilen kaivosteollisuudessa ja eri asiakastyyppien roolia siinä sekä tärkeimpiä päätöskriteerejä toimittaja- ja teknologiavalinnoissa.

Automatic target validation based on neuroscientific literature mining for tractography.

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/.

Impact of the Spanish smoking control law on exposure to second-hand smoke and respiratory health in hospitality workers: a cohort study

A smoke-free law came into effect in Spain on 1st January 2006, affecting all enclosed workplaces except hospitality venues, whose proprietors can choose among totally a smoke-free policy, a partial restriction with designated smoking areas, or no restriction on smoking on the premises. We aimed to evaluate the impact of the law among hospitality workers by assessing second-hand smoke (SHS) exposure and the frequency of respiratory symptoms before and one year after the ban.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

The scope of Baker's law.

656 I. 657 II. 658 III. 660 IV. 661 V. 663 VI. 663 VII. 664 VIII. 664 665 References 665 SUMMARY: Baker's law refers to the tendency for species that establish on islands by long-distance dispersal to show an increased capacity for self-fertilization because of the advantage of self-compatibility when colonizing new habitat. Despite its intuitive appeal and broad empirical support, it has received substantial criticism over the years since it was proclaimed in the 1950s, not least because it seemed to be contradicted by the high frequency of dioecy on islands. Recent theoretical work has again questioned the generality and scope of Baker's law. Here, we attempt to discern where the idea is useful to apply and where it is not. We conclude that several of the perceived problems with Baker's law fall away when a narrower perspective is adopted on how it should be circumscribed. We emphasize that Baker's law should be read in terms of an enrichment of a capacity for uniparental reproduction in colonizing situations, rather than of high selfing rates. We suggest that Baker's law might be tested in four different contexts, which set the breadth of its scope: the colonization of oceanic islands, metapopulation dynamics with recurrent colonization, range expansions with recurrent colonization, and colonization through species invasions.

A Dependency Parsing Approach to Biomedical Text Mining

Biomedical research is currently facing a new type of challenge: an excess of information, both in terms of raw data from experiments and in the number of scientific publications describing their results. Mirroring the focus on data mining techniques to address the issues of structured data, there has recently been great interest in the development and application of text mining techniques to make more effective use of the knowledge contained in biomedical scientific publications, accessible only in the form of natural human language. This thesis describes research done in the broader scope of projects aiming to develop methods, tools and techniques for text mining tasks in general and for the biomedical domain in particular. The work described here involves more specifically the goal of extracting information from statements concerning relations of biomedical entities, such as protein-protein interactions. The approach taken is one using full parsing—syntactic analysis of the entire structure of sentences—and machine learning, aiming to develop reliable methods that can further be generalized to apply also to other domains. The five papers at the core of this thesis describe research on a number of distinct but related topics in text mining. In the first of these studies, we assessed the applicability of two popular general English parsers to biomedical text mining and, finding their performance limited, identified several specific challenges to accurate parsing of domain text. In a follow-up study focusing on parsing issues related to specialized domain terminology, we evaluated three lexical adaptation methods. We found that the accurate resolution of unknown words can considerably improve parsing performance and introduced a domain-adapted parser that reduced the error rate of theoriginal by 10% while also roughly halving parsing time. To establish the relative merits of parsers that differ in the applied formalisms and the representation given to their syntactic analyses, we have also developed evaluation methodology, considering different approaches to establishing comparable dependency-based evaluation results. We introduced a methodology for creating highly accurate conversions between different parse representations, demonstrating the feasibility of unification of idiverse syntactic schemes under a shared, application-oriented representation. In addition to allowing formalism-neutral evaluation, we argue that such unification can also increase the value of parsers for domain text mining. As a further step in this direction, we analysed the characteristics of publicly available biomedical corpora annotated for protein-protein interactions and created tools for converting them into a shared form, thus contributing also to the unification of text mining resources. The introduced unified corpora allowed us to perform a task-oriented comparative evaluation of biomedical text mining corpora. This evaluation established clear limits on the comparability of results for text mining methods evaluated on different resources, prompting further efforts toward standardization. To support this and other research, we have also designed and annotated BioInfer, the first domain corpus of its size combining annotation of syntax and biomedical entities with a detailed annotation of their relationships. The corpus represents a major design and development effort of the research group, with manual annotation that identifies over 6000 entities, 2500 relationships and 28,000 syntactic dependencies in 1100 sentences. In addition to combining these key annotations for a single set of sentences, BioInfer was also the first domain resource to introduce a representation of entity relations that is supported by ontologies and able to capture complex, structured relationships. Part I of this thesis presents a summary of this research in the broader context of a text mining system, and Part II contains reprints of the five included publications.

Treaty shopping in international investment law

Is "treaty shopping" in international investment law "legitimate nationality planning" or "treaty abuse"? This is the question investment arbitral tribunals have been increasingly faced with over past years. This PhD thesis will examine in a systematic and comprehensive manner investment arbitral decisions that have attempted to draw this line. It will show that while some legal approaches taken by arbitral tribunals have started to consolidate, others remain unsettled, contributing to the picture of an overall inconsistent jurisprudence. The thesis will also make proposals de lege ferenda on how States could reform their international investment agreements in order to make them less susceptible to the practice of treaty shopping.

Construction of an index of information from clinical practice in Radiology and Imaging Diagnosis based on text mining and thesaurus

Objective To construct a Portuguese language index of information on the practice of diagnostic radiology in order to improve the standardization of the medical language and terminology. Materials and Methods A total of 61,461 definitive reports were collected from the database of the Radiology Information System at Hospital das Clínicas – Faculdade de Medicina de Ribeirão Preto (RIS/HCFMRP) as follows: 30,000 chest x-ray reports; 27,000 mammography reports; and 4,461 thyroid ultrasonography reports. The text mining technique was applied for the selection of terms, and the ANSI/NISO Z39.19-2005 standard was utilized to construct the index based on a thesaurus structure. The system was created in *html. Results The text mining resulted in a set of 358,236 (n = 100%) words. Out of this total, 76,347 (n = 21%) terms were selected to form the index. Such terms refer to anatomical pathology description, imaging techniques, equipment, type of study and some other composite terms. The index system was developed with 78,538 *html web pages. Conclusion The utilization of text mining on a radiological reports database has allowed the construction of a lexical system in Portuguese language consistent with the clinical practice in Radiology.