Practical rationality for poverty mitigation policies – A contrast between Onora O'Neill and Alasdair MacIntyre

In this article, we will present the contrast between the epistemic aspects of an approach that we will conveniently call - and not from a rigorous historical pretension – “Aristotelian”, such as that of Alasdair MacIntyre, and epistemic aspects of an approach that we will also conveniently call “Kantian”, such as that of Onora O'Neill. Our hypothesis is that the presentation of these different perspectives, in terms of practical rationality for the formulation of poverty mitigation policies, would allow us to verify that the Aristotelian approach is contextually efficient, while the Kantian approach is universally demanding. However, if we take into account that a certain international political and economic conjuncture makes poverty mitigation difficult, the addressing of this problem would need to occur in a globally efficient and universally demanding manner. In this sense a Kantian theory of obligation seems to provide epistemic requirements necessary for the formulation of efficient policies

A tool for the automatic and manual annotation of biomedical documents

The techniques developed within the field of Biomedical Text Mining (BioTM) have been mainly tested and evaluated over a set of known corpora built by a few researchers with a specific goal or to support scientific competitions. The generalized use of BioTM software therefore requires that an enlarged set of corpora is made available covering a wider range of biomedical research topics. This work proposes a software tool that facilitates the task of building a BioTM corpus by providing a userfriendly and interoperable tool that allows both automatic and manual annotation of biomedical documents (supporting both abstracts and full text). This tool is also integrated in a more comprehensive BioTM framework.Fundação para a Ciência e a Tecnologia (FCT

Reconstructing transcriptional regulatory networks using data integration and text mining

Transcriptional Regulatory Networks (TRNs) are powerful tool for representing several interactions that occur within a cell. Recent studies have provided information to help researchers in the tasks of building and understanding these networks. One of the major sources of information to build TRNs is biomedical literature. However, due to the rapidly increasing number of scientific papers, it is quite difficult to analyse the large amount of papers that have been published about this subject. This fact has heightened the importance of Biomedical Text Mining approaches in this task. Also, owing to the lack of adequate standards, as the number of databases increases, several inconsistencies concerning gene and protein names and identifiers are common. In this work, we developed an integrated approach for the reconstruction of TRNs that retrieve the relevant information from important biological databases and insert it into a unique repository, named KREN. Also, we applied text mining techniques over this integrated repository to build TRNs. However, was necessary to create a dictionary of names and synonyms associated with these entities and also develop an approach that retrieves all the abstracts from the related scientific papers stored on PubMed, in order to create a corpora of data about genes. Furthermore, these tasks were integrated into @Note, a software system that allows to use some methods from the Biomedical Text Mining field, including an algorithms for Named Entity Recognition (NER), extraction of all relevant terms from publication abstracts, extraction relationships between biological entities (genes, proteins and transcription factors). And finally, extended this tool to allow the reconstruction Transcriptional Regulatory Networks through using scientific literature

Combining syntactic and ontological knowledge to extract biologically relevant relations from scientific papers

Bringing biologists and text miners closer together is a major aim towards the general usage of literature mining tools. Our contribution to this aim is an end-user tool for the extraction of problem-specific biologically relevant relations. Development efforts are being focused on easy-to-use text mining workflows including commonly available entity recognisers and syntactic processors, and the construction of a userfriendly environment that enables problemspecific tailoring by biologists.Fundação para a Ciência e a Tecnologia (FCT)Systems Biology as a Driver for Industrial Biotechnology (SYSINBIO

Semantic annotation of biological concepts interplaying microbial cellular responses

<p>Abstract</p> <p>Background</p> <p>Automated extraction systems have become a time saving necessity in Systems Biology. Considerable human effort is needed to model, analyse and simulate biological networks. Thus, one of the challenges posed to Biomedical Text Mining tools is that of learning to recognise a wide variety of biological concepts with different functional roles to assist in these processes.</p> <p>Results</p> <p>Here, we present a novel corpus concerning the integrated cellular responses to nutrient starvation in the model-organism <it>Escherichia coli</it>. Our corpus is a unique resource in that it annotates biomedical concepts that play a functional role in expression, regulation and metabolism. Namely, it includes annotations for genetic information carriers (genes and DNA, RNA molecules), proteins (transcription factors, enzymes and transporters), small metabolites, physiological states and laboratory techniques. The corpus consists of 130 full-text papers with a total of 59043 annotations for 3649 different biomedical concepts; the two dominant classes are <it>genes </it>(highest number of unique concepts) and <it>compounds </it>(most frequently annotated concepts), whereas other important cellular concepts such as <it>proteins </it>account for no more than 10% of the annotated concepts.</p> <p>Conclusions</p> <p>To the best of our knowledge, a corpus that details such a wide range of biological concepts has never been presented to the text mining community. The inter-annotator agreement statistics provide evidence of the importance of a consolidated background when dealing with such complex descriptions, the ambiguities naturally arising from the terminology and their impact for modelling purposes.</p> <p>Availability is granted for the full-text corpora of 130 freely accessible documents, the annotation scheme and the annotation guidelines. Also, we include a corpus of 340 abstracts.</p

13C-based metabolic flux analysis

The determination of fluxes is an important parameter to define the extent to which enzymes participate in metabolic networks and to simulate organism behaviour to various types of genetic and environmental perturbations. Metabolic flux analysis is the ultimate measurement of metabolic pathway activity during steady-state conditions, operating as a valuable tool in the detection of physiological alterations and to describe cell phenotypes. Since intracellular metabolic fluxes cannot be directly measured, available methodologies often estimate fluxes by applying mass balances around intracellular metabolites and from experimentally determined nutrient uptake and product secretion rates. 13C-isotopic tracing is a technique that can also be used to measure fluxes. When cells are grown on a 13C-labeled carbon substrate, the 13C-labelling pattern in their proteinogenic amino acids can be determined through nuclear magnetic resonance or mass spectrometry (e.g.; GC-MS). In this work we adapted a sensitive and high-throughput GC-MS method for measurement of metabolic flux distribution based on methylchlorofomate (MCF) derivatization to convert the amino acids into volatile compounds. Using the 13C-labelling distribution of these compounds we determine the metabolic flux ratios in the central carbon metabolism. Great part of this work was the development of a flexible software to calculate flux ratios and estimate flux distribution in different metabolic models. Although our case studies are based on GC-MS coupled to MCF derivatization, this software is generic for different mass spectrometric methods

A framework for the development of biomedical text mining software tools

Over the last few years, a growing number of techniques has been successfully proposed to tackle diverse challenges in the Biomedical Text Mining (BioTM) arena. However, the set of available software tools to researchers has not grown in a similar way. This work makes a contribution to close this gap, proposing a framework to ease the development of user-friendly and interoperable applications in this field, based on a set of available modular components. These modules can be connected in diverse ways to create applications that fit distinct user roles. Also, developers of new algorithms have a framework that allows them to easily integrate their implementations with state-of-the-art BioTM software for related tasks.This work was supported in part by the research projects recSysBio (ref. POCI/BIO/60139/2004) and MOBioPro (ref. POSC/EW59899/2004) of the University of Minho, financed by the Portuguese Fundaao para a Ciencia e Tecnologia. The work of SC is supported by a PhD grant from the same institution (ref. SFRH/BD/22863/2005)