O câncer é a primeira causa de mortes por doença, após 1 ano de idade, até o final da adolescência, excetuando aquelas relacionadas aos acidentes e à violência. A Leucemia Linfoide Aguda (LLA) afeta células linfoides e agrava-se rapidamente. São os tumores mais frequentes na infância e representam um terço de todas as neoplasias malignas nesta faixa etária. Em média, a taxa de cura excede 70%, todavia, apesar dos avanços das últimas décadas, os índices de crianças que apresentam recidiva da doença continua significativo. Danos endógenos ao DNA ocorrem numa frequência altíssima, além dos danos causados por terapias antitumorais. Alteração no reparo ao dano do DNA pode induzir mecanismos de resistência ao tratamento quimioterápico, resultando em aumento do reparo de lesões do DNA. Reparo por Excisão de Nucleotídeos (NER) é a via de reparo de DNA mais versátil e flexível nas células. Seus componentes estão sendo estudados como biomarcadores de prognóstico e terapias-alvo. No entanto, alguns relatórios têm abordado danos de DNA em Leucemia Linfoide Aguda (LLA) pediátrica. Neste estudo, realizamos um estudo de acompanhamento observacional em pacientes pediátricos para avaliar os danos do DNA pelo Ensaio Cometa Alcalino e expressão gênica da via de NER durante a indução da quimioterapia. Amostras de medula óssea (MO) ao diagnóstico, dia 15 (D15) e 30 (D30) do tratamento foram coletadas de 28 pacientes com LLA. Não houve aumento no índice de dano. No entanto, houve uma redução de células com baixo danos na comparação do D35 com o diagnóstico. Este resultado se confirmou em pacientes que apresentaram doença residual mínima positiva. A via de NER permaneceu constante, no entanto, em um único paciente, foi observada uma diminuição significativa da expressão dos genes, talvez devido ao silenciamento ou a regulação negativa das vias de reparo. Níveis de danos e reparação do DNA podem influenciar o resultado clínico, estar envolvidos na resistência aos fármacos e potencializar o risco de recidiva. Este é o primeiro estudo que avalia o dano ao DNA em amostras de MO de pacientes pediátricos com LLA. Apesar do pequeno número de pacientes alocados para o estudo, a partir dos achados é possível concluir que complexos de reparo merecem ser investigados a curto e a longo prazo. Acompanhamento dos resultados do paciente vai ajudar a elucidar a implicação dos nossos achados em taxas de cura e de recidiva. / Cancer is the leading cause of death by disease after 1 year old until the end of adolescence, except those related to accidents and violence. Acute Lymphoid Leukemia (ALL) affects lymphoid cells and worsens quickly. They are the most frequent tumors in childhood and account for a third of all malignancies in this age group. On average, the cure rate exceeds 70%, however, despite the progress of recent decades, rates of children with disease recurrence remains significant. Endogenous DNA damage occurs at a very high frequency, in addition to the damage caused by anti-tumor therapies. Change in the repair of DNA damage can induce resistance mechanisms to chemotherapy, resulting in increased repair of DNA lesions. Nucleotide Excision Repair (NER) pathway is the more versatile and flexible DNA repair in cells. Its components are being studied as prognostic biomarkers and targeted therapies. However, there are some reports of DNA damage in pediatric Acute Lymphoid Leukemia (ALL). In this study, we conducted an observational follow-up study in pediatric patients to assess DNA damage by alkaline comet assay and gene expression of NER pathway during induction chemotherapy. Bone marrow (BM) samples at diagnosis, 15th (D15) and 30th (D30) of treatment were collected from 28 patients with ALL. There was no increase in damage index. However, there was a reduction of cells with low damage in comparison to the D35 diagnosis. This result was confirmed in patients with positive minimal residual disease. The NER pathway remained constant, however, in one patient, a significant decrease of gene expression was observed perhaps due to the silencing or down-regulation of repair pathways. Damage levels and DNA repair can influence the clinical result and may be involved in drug resistance and enhance the risk of recurrence. This is the first study to assess DNA damage in BM samples of pediatric patients with ALL. Despite the small number of patients allocated to the study, from the findings we conclude that repair complex deserves to be investigated in the short and long term. Monitoring patient’s outcomes will help to access the implication of our findings in cure and relapse rates.

The bulk of Zimbabwe’s groundnut (Arachis hypogaea L.) crop is grown on sandy soils in the smallholder sector where sustainable production is hindered by acid soil infertility. The study goal was thus to examine the effects of soil acidity amelioration by four Ca-containing materials on nutrient composition, vegetative and reproductive growth, and quality of groundnut to formulate ameliorative strategies to improve productivity on acid soils. The effectiveness of calcitic lime (CL), dolomitic lime (DL), gypsum (G) and single superphosphate (SSP) in ameliorating soil acidity was determined in field experiments conducted for three seasons at two Research Stations in Zimbabwe, and in greenhouse experiments conducted for two seasons at Harare Research Station. In both experiments the lime application rates were from 0 to 4000 kg ha-1, while G application rates were from 0 to 3450 kg ha-1, and those of SSP were from 0 to 250 kg ha-1. Calcitic or dolomitic lime applied at 2000 or 4000 kg ha-1 increased soil pH and Ca and Mg contents in the pod and root zones, and in the plant material. Gypsum and SSP applications at 200 and 250 kg ha-1 respectively, had no significant effects on pH, Ca and Mg levels, but when applied in equivalent amounts of Ca as lime, gypsum improved soil Ca status. Effects of the four ameliorants on the N, P and K levels in the soils and in plant material were generally neither significant nor consistent. The direct and residual benefits of application of CL or DL were manifested in improved plant stands, better growth, nodulation, productivity and quality of groundnut. Gypsum applied at equal Ca rates as CL or DL was the superior Ca-source in improving pod and kernel quality. By the end of the third season, the increases in cumulative kernel yields due to application of 4000 kg ha-1 lime over non-application were up to 319%. The major growth-limiting factors on the studied acid soils were identified as deficiencies of Ca and Mg, and low pH per se. In a field experiment conducted to evaluate the tolerance of 15 groundnut genotypes to soil acidity, significant differences in yield and nutrient utilization efficiency of the genotypes were observed, implying that productivity on acid soils can be increased by growing genotypes efficient in uptake and utilization of nutrients. Results from greenhouse and growth chamber studies conducted to examine the effects of pH (3.0 - 7.0) and its interactions with Ca (0 - 2000 µM Ca) on early seedling growth and reproductive growth of groundnut indicated that low pH per se has a major detrimental impact on seedling survival, growth, pod formation, yield and quality of groundnut, but not on germination. The adverse effects of low pH were more pronounced in the absence of Ca, and became progressively less as the solution Ca concentrations increased. Further experiments showed that it is feasible to mitigate the adverse effects of soil acidity on groundnut germination and seedling survival by pelleting seeds with small amounts of CaCO3, or priming with CaSO4. / Dissertation (PhD (Plant Production: Agronomy))--University of Pretoria, 2003. / Plant Production and Soil Science / unrestricted

Les techniques d'apprentissage profond ont fait un bond au cours des dernières années, et ont considérablement changé la manière dont les tâches de traitement automatique du langage naturel (TALN) sont traitées. En quelques années, les réseaux de neurones et les plongements de mots sont rapidement devenus des composants centraux à adopter dans le domaine. La supervision distante (SD) est une technique connue en TALN qui consiste à générer automatiquement des données étiquetées à partir d'exemples partiellement annotés. Traditionnellement, ces données sont utilisées pour l'entraînement en l'absence d'annotations manuelles, ou comme données supplémentaires pour améliorer les performances de généralisation. Dans cette thèse, nous étudions comment la supervision distante peut être utilisée dans un cadre d'un TALN moderne basé sur l'apprentissage profond. Puisque les algorithmes d'apprentissage profond s'améliorent lorsqu'une quantité massive de données est fournie (en particulier pour l'apprentissage des représentations), nous revisitons la génération automatique des données avec la supervision distante à partir de Wikipédia. On applique des post-traitements sur Wikipédia pour augmenter la quantité d'exemples annotés, tout en introduisant une quantité raisonnable de bruit. Ensuite, nous explorons différentes méthodes d'utilisation de données obtenues par supervision distante pour l'apprentissage des représentations, principalement pour apprendre des représentations de mots classiques (statistiques) et contextuelles. À cause de sa position centrale pour de nombreuses applications du TALN, nous choisissons la reconnaissance d'entité nommée (NER) comme tâche principale. Nous expérimentons avec des bancs d’essai NER standards et nous observons des performances état de l’art. Ce faisant, nous étudions un cadre plus intéressant, à savoir l'amélioration des performances inter-domaines (généralisation). / Recent years have seen a leap in deep learning techniques that greatly changed the way Natural Language Processing (NLP) tasks are tackled. In a couple of years, neural networks and word embeddings quickly became central components to be adopted in the domain. Distant supervision (DS) is a well-used technique in NLP to produce labeled data from partially annotated examples. Traditionally, it was mainly used as training data in the absence of manual annotations, or as additional training data to improve generalization performances. In this thesis, we study how distant supervision can be employed within a modern deep learning based NLP framework. As deep learning algorithms gets better when massive amount of data is provided (especially for representation learning), we revisit the task of generating distant supervision data from Wikipedia. We apply post-processing treatments on the original dump to further increase the quantity of labeled examples, while introducing a reasonable amount of noise. Then, we explore different methods for using distant supervision data for representation learning, mainly to learn classic and contextualized word representations. Due to its importance as a basic component in many NLP applications, we choose Named-Entity Recognition (NER) as our main task. We experiment on standard NER benchmarks showing state-of-the-art performances. By doing so, we investigate a more interesting setting, that is, improving the cross-domain (generalization) performances.

The goal of information extraction is to retrieve relational data from texts written in natural human language. Applications of such obtained information is wide - from text summarization, through ontology creation up to answering questions by QA systems. This work describes design and implementation of a system working in computer cluster which transforms a dump of Wikipedia articles to a set of extracted information that is stored in distributed RDF database with a possibility to query it using created user interface.

Der Großteil des geschäftsrelevanten Wissens liegt heute als unstrukturierte Information in Form von Textdaten auf Internetseiten, in Office-Dokumenten oder Foreneinträgen vor. Zur Extraktion und Verwertung dieser unstrukturierten Informationen wurde eine Vielzahl von Text-Mining-Lösungen entwickelt. Viele dieser Systeme wurden in der jüngeren Vergangenheit als Webdienste zugänglich gemacht, um die Verwertung und Integration zu vereinfachen. Die Kombination verschiedener solcher Text-Mining-Dienste zur Lösung konkreter Extraktionsaufgaben erscheint vielversprechend, da so bestehende Stärken ausgenutzt, Schwächen der Systeme minimiert werden können und die Nutzung von Text-Mining-Lösungen vereinfacht werden kann. Die vorliegende Arbeit adressiert die flexible Kombination von Text-Mining-Diensten in einem serviceorientierten System und erweitert den Stand der Technik um gezielte Methoden zur Auswahl der Text-Mining-Dienste, zur Aggregation der Ergebnisse und zur Abbildung der eingesetzten Klassifikationsschemata. Zunächst wird die derzeit existierende Dienstlandschaft analysiert und aufbauend darauf eine Ontologie zur funktionalen Beschreibung der Dienste bereitgestellt, so dass die funktionsgesteuerte Auswahl und Kombination der Text-Mining-Dienste ermöglicht wird. Des Weiteren werden am Beispiel entitätsextrahierender Dienste Algorithmen zur qualitätssteigernden Kombination von Extraktionsergebnissen erarbeitet und umfangreich evaluiert. Die Arbeit wird durch zusätzliche Abbildungs- und Integrationsprozesse ergänzt, die eine Anwendbarkeit auch in heterogenen Dienstlandschaften, bei denen unterschiedliche Klassifikationsschemata zum Einsatz kommen, gewährleisten. Zudem werden Möglichkeiten der Übertragbarkeit auf andere Text-Mining-Methoden erörtert.

This thesis deals with the named entity recognition (NER) in text. It is realized by machine learning techniques. Recently, techniques for creating word embeddings models have been introduced. These word vectors can encode many useful relationships between words in text data, such as their syntactic or semantic similarity. Modern NER systems use these vector features for improving their quality. However, only few of them investigate in greater detail how much these vectors have impact on recognition and whether they can be optimized for even greater recognition quality. This thesis examines various factors that may affect the quality of word embeddings, and thus the resulting quality of the NER system. A series of experiments have been performed, which examine these factors, such as corpus quality and size, vector dimensions, text preprocessing techniques, and various algorithms (Word2Vec, GloVe and FastText) and their parameters. Their results bring useful findings that can be used within creation of word vectors and thus indirectly increase the resulting quality of NER systems.

This thesis investigates automated annotation approaches to assist public authorities in Sweden in optimizing resource allocation and gaining valuable insights to enhance the preservation of high-quality welfare. The study uses pre-trained Swedish language models for the named entity recognition (NER) task of finding job titles in job advertisements from The Swedish Public Employment Service, Arbetsförmedlingen. Specifically, it evaluates the performance of the Swedish Bidirectional Encoder Representations from Transformers (BERT), developed by the National Library of Sweden (KB), referred to as KB-BERT. The thesis explores the impact of training data size on the models’ performance and examines whether active learning can enhance efficiency and accuracy compared to random sampling. The findings reveal that even with a small training dataset of 220 job advertisements, KB-BERT achieves a commendable F1-score of 0.770 in predicting job titles. The model’s performance improves further by augmenting the training data with an additional 500 annotated job advertisements, yielding an F1-score of 0.834. Notably, the highest F1-score of 0.856 is achieved by applying the active learning strategy of uncertainty sampling and the measure of mean entropy. The test data provided by Arbetsförmedlingen was re-annotated to evaluate the complexity of the task. The human annotator achieved an F1-score of 0.883. Based on these findings, it can be inferred that KB-BERT performs satisfactorily in classifying job titles from job ads.

This research presents an innovative approach to extracting information from Positive Energy Districts (PEDs), urban areas generating surplus energy. PEDs are integral to the European Commission's SET Plan, tackling housing challenges arising from population growth. The study refines BERT to categorize PED-related entities, producing a cutting-edge NER model and an integrated pipeline of diverse NER tools and data sources. The model achieves an accuracy of 0.81 and an F1 Score of 0.55 with notably high confidence scores through pipeline evaluations, confirming its practical applicability. While the F1 score falls short of expectations, this pioneering exploration in PED information extraction sets the stage for future refinements and studies, promising enhanced methodologies and impactful outcomes in this dynamic field. This research advances NER processes for Positive Energy Districts, supporting their development and implementation.

No description available.

This master's thesis describes Semantic Enrichment Component (SEC), that searches entities (e.g., persons or places) in the input text document and returns information about them. The goals of this component are to create a single interface for named entity recognition tools, to enable parallel document processing, to save memory while using the knowledge base, and to speed up access to its content. To achieve these goals, the output of the named entity recognition tools in the text was specified, the tool for storing the preprocessed knowledge base into the shared memory was implemented, and the client-server scheme was used to create the component.