Papers by Claudia Becerra
Cancer Cell, 2014
Sonic hedgehog (Shh), a soluble ligand overexpressed by neoplastic cells in pancreatic ductal ade... more Sonic hedgehog (Shh), a soluble ligand overexpressed by neoplastic cells in pancreatic ductal adenocarcinoma (PDAC), drives formation of a fibroblast-rich desmoplastic stroma. To better understand its role in malignant progression, we deleted Shh in a well-defined mouse model of PDAC. As predicted, Shh-deficient tumors had reduced stromal content. Surprisingly, such tumors were more aggressive and exhibited undifferentiated histology, increased vascularity, and heightened proliferation--features that were fully recapitulated in control mice treated with a Smoothened inhibitor. Furthermore, administration of VEGFR blocking antibody selectively improved survival of Shh-deficient tumors, indicating that Hedgehog-driven stroma suppresses tumor growth in part by restraining tumor angiogenesis. Together, these data demonstrate that some components of the tumor stroma can act to restrain tumor growth.
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Boletin Micologico, 1997
Base de dados : LILACS. Pesquisa : 255726 [Identificador único]. Referências encontradas : 1 [ref... more Base de dados : LILACS. Pesquisa : 255726 [Identificador único]. Referências encontradas : 1 [refinar]. Mostrando: 1 .. 1 no formato [Detalhado]. página 1 de 1, 1 / 1, LILACS, seleciona. para imprimir. Fotocópia. experimental, Documentos relacionados. Id: 255726. ...
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In this paper we describe the system used to participate in the sub task 5b in the Phrasal Se- ma... more In this paper we describe the system used to participate in the sub task 5b in the Phrasal Se- mantics challenge (task 5) in SemEval 2013. This sub task consists in discriminating lit- eral and figurative usage of phrases with compositional and non-compositional mean- ings in context. The proposed approach is based on part-of-speech tags, stylistic features and distributional statistics
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In this paper we describe our system submit- ted for evaluation in the CLTE-SemEval-2013 task, wh... more In this paper we describe our system submit- ted for evaluation in the CLTE-SemEval-2013 task, which achieved the best results in two of the four data sets, and finished third in av- erage. This system consists of a SVM clas- sifier with features extracted from texts (and their translations SMT) based on a cardinality function. Such function was the soft
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Soft cardinality has been shown to be a very strong text-overlapping baseline for the task of mea... more Soft cardinality has been shown to be a very strong text-overlapping baseline for the task of measuring semantic textual similarity (STS), obtaining 3 rd place in SemEval-2012. At *SEM-2013 shared task, beside the plain text- overlapping approach, we tested within soft cardinality two distributional word-similarity functions derived from the ukWack corpus. Unfortunately, we combined these measures with other features using
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In this paper we describe the system used to participate in the sub task 5b in the Phrasal Se- ma... more In this paper we describe the system used to participate in the sub task 5b in the Phrasal Se- mantics challenge (task 5) in SemEval 2013. This sub task consists in discriminating lit- eral and figurative usage of phrases with compositional and non-compositional mean- ings in context. The proposed approach is based on part-of-speech tags, stylistic features and distributional statistics
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Revista Tecnura, 2015
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We present an approach for the construction of text similarity functions using a parameterized re... more We present an approach for the construction of text similarity functions using a parameterized resem-blance coefficient in combination with a softened cardinality function called soft cardinality. Our ap-proach provides a consistent and recursive model, varying levels of granularity from sentences to char-acters. Therefore, our model was used to compare sentences divided into words, and in turn, words di-vided into q-grams of characters. Experimentally, we observed that a performance correlation func-tion in a space defined by all parameters was rel-atively smooth and had a single maximum achiev-able by "hill climbing." Our approach used only sur-face text information, a stop-word remover, and a stemmer to tackle the semantic text similarity task 6 at SEMEVAL 2012. The proposed method ranked 3rd (average), 5th (normalized correlation), and 15th (aggregated correlation) among 89 systems submit-ted by 31 teams.
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This paper presents a novel approach for building adaptive similarity functions based on cardinal... more This paper presents a novel approach for building adaptive similarity functions based on cardinality us-ing machine learning. Unlike current approaches that build feature sets using similarity scores, we have developed these feature sets with the cardinal-ities of the commonalities and differences between pairs of objects being compared. This approach al-lows the machine-learning algorithm to obtain an asymmetric similarity function suitable for direc-tional judgments. Besides using the classic set cardi-nality, we used soft cardinality to allow flexibility in the comparison between words. Our approach used only the information from the surface of the text, a stop-word remover and a stemmer to address the cross-lingual textual entailment task 8 at SEMEVAL 2012. We have the third best result among the 29 systems submitted by 10 teams. Additionally, this paper presents better results compared with the best official score.
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Lecture Notes in Computer Science, 2009
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In this paper we describe our system submit- ted for evaluation in the CLTE-SemEval-2013 task, wh... more In this paper we describe our system submit- ted for evaluation in the CLTE-SemEval-2013 task, which achieved the best results in two of the four data sets, and finished third in av- erage. This system consists of a SVM clas- sifier with features extracted from texts (and their translations SMT) based on a cardinality function. Such function was the soft cardinal- ity. Furthermore, this system was simplified by providing a single model for the 4 pairs of languages obtaining better (unofficial) re- sults than separate models for each language pair. We also evaluated the use of additional circular-pivoting translations achieving results 6.14% above the best official results.
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Proceedings of the 6th International Workshop on Semantic Evaluation (SemEval 2012), 2012
This paper presents a novel approach for building adaptive similarity functions based on cardinal... more This paper presents a novel approach for building adaptive similarity functions based on cardinality using machine learning. Unlike current approaches that build feature sets using similarity scores, we have developed these feature sets with the cardinalities of the commonalities and differences between pairs of objects being compared. This approach allows the machine-learning algorithm to obtain an asymmetric similarity function suitable for directional judgments. Besides using the classic set cardinality, we used soft cardinality to ...
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Papers by Claudia Becerra