45 research outputs found
Eighth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
No full textA probabilistic author-centered model for Twitter discussions
No full textIn a recent work some of the authors have developed an argumentative approach for discovering relevant opinions in Twitter discussions with probabilistic valued relationships. Given a Twitter discussion, the system builds an argument graph where each node denotes a tweet and each edge denotes a criticism relationship between a pair of tweets of the discussion. Relationships between tweets are associated with a probability value, indicating the uncertainty that the relationships hold. In this work we introduce and investigate a natural extension of the representation model, referred as probabilistic author-centered model, in which tweets within a discussion are grouped by authors, in such a way that tweets of a same author describe his/her opinion in the discussion and are rep- resented with a single node in the graph, and criticism relationships denote controversies between opinions of Twitter users in the discussion. In this new model, the interactions between authors can give rise to circular criticism relationships, and the probability of one opinion criticizing another has to be evaluated from the probabilities of criticism among the tweets that compose both opinions.This work was partially funded by the Spanish MICINN Projects TIN2015-71799-C2-1-P and TIN2015-71799-C2-2-PPeer Reviewe
Nuevo método para flatos, hypocondria, vapores y ataques hystéricos de las mugeres de todos estados y en todo estado ...
No full textHernández Morejón, t. 7º, p. 26
Anomaly Detection for Diagnosing Failures in a Centrifugal Compressor Train
No full textPredicting machine failures is of the utmost importance in industrial systems as it can turn expensive crashes and repair costs into affordable maintenance costs. To this end, this paper presents preliminary work for detecting failures in a centrifugal compressor train based on sensorial data. We show the detection capabilities of a two-step process consisting of: (1) a preprocessing step to reduce the dimensionality of the input data using Principal Component Analysis, and (2) an anomaly detection step using the Mahalanobis distance to detect anomalous observations on the sensors' data. The experiments using real-world data demonstrate the feasibility of our approach and the ability of the method to detect the failures eight days in advance
The DeuteroNoise Dataset: An Initial Exploration of an Underwater Vessel Sound Dataset Within the Framework of a JPI-Oceans Project
No full textAn efficient solver for weighted Max-SAT
No full textWe present a new branch and bound algorithm for weighted Max-SAT, called Lazy which incorporates original data structures and inference rules, as well as a lower bound of better quality. We provide experimental evidence that our solver is very competitive and outperforms some of the best performing Max-SAT and weighted Max-SAT solvers on a wide range of instances. © 2007 Springer Science+Business Media LLC.Acknowledgements Research partially supported by projects TIN2006-15662-C02-02 and TIN2004-07933-C03-03 funded by the Ministerio de Ciencia y Tecnología. The second author was supported by a grant Ramón y Cajal.Peer Reviewe
Different models of fuzzy logic programming with fuzzy unification (towards a revision of fuzzy databases)
No full textMicroRNA-based classification of hepatocellular carcinoma and oncogenic role of miR-517a
No full textBackground & Aims
Hepatocellular carcinoma (HCC) is a heterogeneous tumor that develops via activation of multiple pathways and molecular alterations. It has been a challenge to identify molecular classes of HCC and design treatment strategies for each specific subtype. MicroRNAs (miRNAs) are involved in HCC pathogenesis, and their expression profiles have been used to classify cancers. We analyzed miRNA expression in human HCC samples to identify molecular subclasses and oncogenic miRNAs.
Methods
We performed miRNA profiling of 89 HCC samples using a ligation-mediated amplification method. Subclasses were identified by unsupervised clustering analysis. We identified molecular features specific for each subclass using expression pattern (Affymetrix U133 2.0; Affymetrix, Santa Clara, CA), DNA change (Affymetrix STY Mapping Array), mutation (CTNNB1), and immunohistochemical (phosphor[p]–protein kinase B, p–insulin growth factor–IR, p-S6, p–epidermal growth factor receptor, β-catenin) analyses. The roles of selected miRNAs were investigated in cell lines and in an orthotopic model of HCC.
Results
We identified 3 main clusters of HCCs: the wingless-type MMTV integration site (32 of 89; 36%), interferon-related (29 of 89; 33%), and proliferation (28 of 89; 31%) subclasses. A subset of patients with tumors in the proliferation subclass (8 of 89; 9%) overexpressed a family of poorly characterized miRNAs from chr19q13.42. Expression of miR-517a and miR-520c (from ch19q13.42) increased proliferation, migration, and invasion of HCC cells in vitro. MiR-517a promoted tumorigenesis and metastatic dissemination in vivo.
Conclusions
We propose miRNA-based classification of 3 subclasses of HCC. Among the proliferation class, miR-517a is an oncogenic miRNA that promotes tumor progression. There is rationale for developing therapies that target miR-517a for patients with HCC
