Definitional verbal patterns for semantic relation extraction

In this paper we present a description of the role of definitional verbal patterns for the extraction of semantic relations. Several studies show that semantic relations can be extracted from analytic definitions contained in machine-readable dictionaries (MRDs). In addition, definitions found in specialised texts are a good starting point to search for different types of definitions where other semantic relations occur. The extraction of definitional knowledge from specialised corpora represents another interesting approach for the extraction of semantic relations. Here, we present a descriptive analysis of definitional verbal patterns in Spanish and the first steps towards the development of a system for the automatic extraction of definitional knowledge.

Biomedical relation extraction:from binary to complex

Biomedical relation extraction aims to uncover high-quality relations from life science literature with high accuracy and efficiency. Early biomedical relation extraction tasks focused on capturing binary relations, such as protein-protein interactions, which are crucial for virtually every process in a living cell. Information about these interactions provides the foundations for new therapeutic approaches. In recent years, more interests have been shifted to the extraction of complex relations such as biomolecular events. While complex relations go beyond binary relations and involve more than two arguments, they might also take another relation as an argument. In the paper, we conduct a thorough survey on the research in biomedical relation extraction. We first present a general framework for biomedical relation extraction and then discuss the approaches proposed for binary and complex relation extraction with focus on the latter since it is a much more difficult task compared to binary relation extraction. Finally, we discuss challenges that we are facing with complex relation extraction and outline possible solutions and future directions.

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

Frame-driven Extraction of Linked Data and Ontologies from Text

Ontology design and population -core aspects of semantic technologies- re- cently have become fields of great interest due to the increasing need of domain-specific knowledge bases that can boost the use of Semantic Web. For building such knowledge resources, the state of the art tools for ontology design require a lot of human work. Producing meaningful schemas and populating them with domain-specific data is in fact a very difficult and time-consuming task. Even more if the task consists in modelling knowledge at a web scale. The primary aim of this work is to investigate a novel and flexible method- ology for automatically learning ontology from textual data, lightening the human workload required for conceptualizing domain-specific knowledge and populating an extracted schema with real data, speeding up the whole ontology production process. Here computational linguistics plays a fundamental role, from automati- cally identifying facts from natural language and extracting frame of relations among recognized entities, to producing linked data with which extending existing knowledge bases or creating new ones. In the state of the art, automatic ontology learning systems are mainly based on plain-pipelined linguistics classifiers performing tasks such as Named Entity recognition, Entity resolution, Taxonomy and Relation extraction [11]. These approaches present some weaknesses, specially in capturing struc- tures through which the meaning of complex concepts is expressed [24]. Humans, in fact, tend to organize knowledge in well-defined patterns, which include participant entities and meaningful relations linking entities with each other. In literature, these structures have been called Semantic Frames by Fill- 6 Introduction more [20], or more recently as Knowledge Patterns [23]. Some NLP studies has recently shown the possibility of performing more accurate deep parsing with the ability of logically understanding the structure of discourse [7]. In this work, some of these technologies have been investigated and em- ployed to produce accurate ontology schemas. The long-term goal is to collect large amounts of semantically structured information from the web of crowds, through an automated process, in order to identify and investigate the cognitive patterns used by human to organize their knowledge.

Ontology-based protein-protein interactions extraction from literature using the hidden vector state model

This paper proposes a novel framework of incorporating protein-protein interactions (PPI) ontology knowledge into PPI extraction from biomedical literature in order to address the emerging challenges of deep natural language understanding. It is built upon the existing work on relation extraction using the Hidden Vector State (HVS) model. The HVS model belongs to the category of statistical learning methods. It can be trained directly from un-annotated data in a constrained way whilst at the same time being able to capture the underlying named entity relationships. However, it is difficult to incorporate background knowledge or non-local information into the HVS model. This paper proposes to represent the HVS model as a conditionally trained undirected graphical model in which non-local features derived from PPI ontology through inference would be easily incorporated. The seamless fusion of ontology inference with statistical learning produces a new paradigm to information extraction.

Frame detection over the semantic web

In the past, research in ontology learning from text has mainly focused on entity recognition, taxonomy induction and relation extraction. In this work we approach a challenging research issue: detecting semantic frames from texts and using them to encode web ontologies. We exploit a new generation Natural Language Processing technology for frame detection, and we enrich the frames acquired so far with argument restrictions provided by a super-sense tagger and domain specializations. The results are encoded according to a Linguistic MetaModel, which allows a complete translation of lexical resources and data acquired from text, enabling custom transformations of the enriched frames into modular ontology components.

Removing noisy mentions for distant supervision

Los métodos para Extracción de Información basados en la Supervisión a Distancia se basan en usar tuplas correctas para adquirir menciones de esas tuplas, y así entrenar un sistema tradicional de extracción de información supervisado. En este artículo analizamos las fuentes de ruido en las menciones, y exploramos métodos sencillos para filtrar menciones ruidosas. Los resultados demuestran que combinando el filtrado de tuplas por frecuencia, la información mutua y la eliminación de menciones lejos de los centroides de sus respectivas etiquetas mejora los resultados de dos modelos de extracción de información significativamente.

Photosynthesis, Chlorophylls, and SPAD Readings in Coffee Leaves in Relation to Nitrogen Supply

The export of nitrogen (N) from senescent plant parts is important for the efficient use of this macronutrient. The objective of this study was to establish correlations among the photosynthetic pigment content, total N, and the photosynthetic variables with the SPAD-502 readings in Coffea arabica leaves. Correlations were established among the chlorophyll content, N content, and chlorophyll a and b with SPAD-502 readings taken on coffee leaves at different months. The results show that all variables decreased with time. However, correlation increased linearly with N doses. Total chlorophyll presented a direct linear correlation with readings of the portable chlorophyll meter. The SPAD readings have shown to be a good tool to diagnose the integrity of the photosynthetic system in coffee leaves. Thus, the portable chlorophyll SPAD-502 instrument can be used to evaluate the N status and can also help to evaluate the photosynthetic process in coffee plants.

Use of solvent extraction to remediate soils contaminated with hydrocarbons

The main objective of this research is to exploit the possibility of using an ex situ solvent extraction technique for the remediation of soils contaminated with semi-volatile petroleum hydrocarbons. The composition of the organic phase was chosen in order to form a single phase mixture with an aqueous phase and simultaneously not being disturbed (forming stable emulsions) by the soil particles hauling the contaminants. It should also permit a regeneration of the organic solvent phase. As first, we studied the miscibility domain of the chosen ternary systems constituted by ethyl acetate–acetone–water. This system proved to satisfy the previous requirements allowing for the formation of a single liquid phase mixture within a large spectrum of compositions, and also allowing for an intimate contact with the soil. Contaminants in the diesel range within different functional groups were selected: xylene, naphthalene and hexadecane. The analytical control was done by gas chromatography with FID detector. The kinetics of the extractions proved to be fast, leading to equilibrium after 10 min. The effect of the solid–liquid ratio on the extraction efficiency was studied. Lower S/L ratios (1:8, w/v) proved to be more efficient, reaching recoveries in the order of 95%. The option of extraction in multiple contacts did not improve the recovery in relation to a single contact. The solvent can be regenerated by distillation with a loss around 10%. The contaminants are not evaporated and they remain in the non-volatile phase. The global results show that the ex situ solvent extraction is technically a feasible option for the remediation of semi-volatile aromatic, polyaromatic and linear hydrocarbons.

Influence of abiotic stress factors on VOCs emission from Portuguese rice paddy fields: relation with increased climate change

Dissertação para obtenção do Grau de Mestre em Engenharia do Ambiente Perfil de Gestão de Sistemas Ambientais

Boron extraction and vertical mobility in Paraná State oxisol, Brazil

The deficiency or excess of micronutrients has been determined by analyses of soil and plant tissue. In Brazil, the lack of studies that would define and standardize extraction and determination methods, as well as lack of correlation and calibration studies, makes it difficult to establish limits of concentration classes for analysis interpretation and fertilizer recommendations for crops. A specific extractor for soil analysis is sometimes chosen due to the ease of use in the laboratory and not in view of its efficiency in determining a bioavailable nutrient. The objectives of this study were to: (a) evaluate B concentrations in the soil as related to the fertilizer rate, soil depth and extractor; (b) verify the nutrient movement in the soil profile; (c) evaluate efficiency of Hot Water, Mehlich-1 and Mehlich-3 as available B extractors, using sunflower as test plant. The experimental design consisted of complete randomized blocks with four replications and treatments of five B rates (0, 2, 4, 6, and 8 kg ha-1) applied to the soil surface and evaluated at six depths (0-0.05, 0.05-0.10, 0.10-0.15, 0.15-0.20, 0.20-0.30, and 0.30-0.40 m). Boron concentrations in the soil extracted by Hot Water, Mehlich-1 and Mehlich-3 extractors increased linearly in relation to B rates at all depths evaluated, indicating B mobility in the profile. The extractors had different B extraction capacities, but were all efficient to evaluate bioavailability of the nutrient to sunflower. Mehlich-1 and Mehlich-3 can therefore be used to analyze B as well as Hot Water.

Tabagisme et système digestif: une relation complexe. Partie 2: Microbiote intestinal et tabagisme [Smoking and digestive tract: a complex relationship. Part 2: Intestinal microblota and cigarette smoking].

Le système digestif est colonisé dès la naissance par une population bactérienne, le microbiote, qui influence le développement du système immunitaire. Des modifications dans sa composition sont associées à des pathologies comme l'obésité et les maladies inflammatoires chroniques de l'intestin. Outre les antibiotiques, des facteurs environnementaux comme le tabagisme semblent aussi avoir une influence sur la composition de la flore intestinale, pouvant en partie expliquer la prise de poids à l'arrêt du tabac avec une modification de la composition du microbiote proche de celle observée chez des personnes obèses (profil microbiotique montrant des capacités accrues d'extraction calorique des aliments ingérés). Ces découvertes permettent d'imaginer de nouvelles approches diagnostiques et thérapeutiques via la régulation de ce microbiome. The digestive tract is colonized from birth by a bacterial population called the microbiota which influences the development of the immune system. Modifications in its composition are associated with problems such as obesity or inflammatory bowel diseases. Antibiotics are known to influence the intestinal microbiota but other environmental factors such as cigarette smoking also seem to have an impact on its composition. This influence might partly explain weight gain which is observed after smoking cessation. Indeed there is a modification of the gut microbiota which becomes similar to that of obese people with a microbiotical profile which is more efficient to extract calories from ingested food. These new findings open new fields of diagnostic and therapeutic approaches through the regulation of the microbiota.

Relation between the level of self-mutilation and theconcentration of fecal metabolites of glucocorticoids incaptive chimpanzees (Pan troglodytes)

The influence of stress in an environment, according with the behavioral and endocrine variables of primates, are increasingly being studied by a diversity of authors, and have shown that abnormal behaviors associated with increased glucocorticoids may be directly related with the impairment of their well-being. In this work were used 22 adult chimpanzees (Pan troglodytes), 11 males and 11 females, kept in captivity in three different institutions. All animals had their behavior registered by focal session using a 30 seconds sample interval, during six months, totaling 4,800 registries per each animal. During this period, fecal samples were collected 3 times a week for the extraction and measurement of the concentration of fecal metabolites of glucocorticoid by radioimmunoassay. Of the total observed, stereotypical behaviors represented 13,45±2.76%, and among them, self-mutilation represented 38.28±3.98 %. The animals were classified into three different scores, according with the percentage of body surface with alopecia due to self-mutilation. It was found a positive correlation of high intensity between the scores of alopecia due to the observed mutilation and the average concentrations of fecal metabolites of glucocorticoids. This result strongly suggests that this measurement of self-mutilation in a chimpanzee can be used as an important auxiliary tool to evaluate de conditions of adaptation of an animal in captivity, functioning as a direct indicator of the presence of chronic stress.

Corn germ with pericarp in relation to whole corn: nutrient contents, food and protein efficiency, and protein digestibility-corrected amino acid score

The germ fraction with pericarp (bran) is generated in the industrial processing of corn kernel, and it is used for oil extraction and animal feed. This study evaluated the nutritional and protein quality of this fraction in relation to whole corn. The proximate composition, mineral contents, and amino acid profile of the germ fraction with pericarp and of whole corn were determined. A 4-week experiment was conducted using 36 weanling male Wistar rats, and three 10%-protein diets (reference, germ with 15% lipids and casein with 15% lipids), two 6%-protein diets (whole corn and casein), and a protein-free diet were prepared. The germ showed higher contents of proteins, lipids, dietary fiber (27.8 g.100 g-1), ash, minerals (Fe and Zn- approximately 5 mg.100 g-1), and lysine (57.2 mg.g-1 protein) than those of corn. The germ presented good quality protein (Relative Protein Efficiency Ratio-RPER = 80%; Protein Digestibility-Corrected Amino Acid Score-PDCAAS = 86%), higher than that of corn (RPER = 49%; PDCAAS = 60%). The corn germ fraction with pericarp is rich in dietary fiber, and it is a source of good quality protein as well as of iron and zinc, and its use as nutritive raw material is indicated in food products for human consumption.

The relationship between abortion and suicide : a systematic review = La relation entre l’avortement et le suicide : une recension systématique

Essai doctoral présenté à la Faculté des arts et des sciences en vue de l’obtention du grade de Doctorat en psychologie clinique (D.Psy.)