The differential adhesion forces of anterior cruciate and medial collateral ligament fibroblasts: effects of tropomodulin, talin, vinculin, and alpha-actinin.

We have determined the effects of tropomodulin (Tmod), talin, vinculin, and alpha-actinin on ligament fibroblast adhesion. The anterior cruciate ligament (ACL), which lacks a functional healing response, and the medial collateral ligament (MCL), a functionally healing ligament, were selected for this study. The micropipette aspiration technique was used to determine the forces needed to separate ACL and MCL cells from a fibronectin-coated surface. Delivery of exogenous tropomodulin, an actin-filament capping protein, into MCL fibroblasts significantly increased adhesion, whereas its monoclonal antibody (mAb) significantly decreased cell adhesiveness. However, for ACL fibroblasts, Tmod significantly reduced adhesion, whereas its mAb had no effect. mAbs to talin, vinculin, and alpha-actinin significantly decreased the adhesion of both ACL and MCL cells with increasing concentrations of antibody, and also reduced stress fiber formation and cell spreading rate as revealed by immunofluorescence microscopy. Disruption of actin filament and microtubule assembly with cytochalasin D and colchicine, respectively, also significantly reduced adhesion in ACL and MCL cells. In conclusion, both ACL and MCL fibroblast adhesion depends on cytoskeletal assembly; however, this dependence differs between ACL and MCL fibroblasts in many ways, especially in the role of Tmod. These results add yet another possible factor in explaining the clinical differences in healing between the ACL and the MCL.

Opinion and generic question answering systems: a performance analysis

The importance of the new textual genres such as blogs or forum entries is growing in parallel with the evolution of the Social Web. This paper presents two corpora of blog posts in English and in Spanish, annotated according to the EmotiBlog annotation scheme. Furthermore, we created 20 factual and opinionated questions for each language and also the Gold Standard for their answers in the corpus. The purpose of our work is to study the challenges involved in a mixed fact and opinion question answering setting by comparing the performance of two Question Answering (QA) systems as far as mixed opinion and factual setting is concerned. The first one is open domain, while the second one is opinion-oriented. We evaluate separately the two systems in both languages and propose possible solutions to improve QA systems that have to process mixed questions.

Evaluating knowledge-based approaches to the multilingual extension of a temporal expression normalizer

The extension to new languages is a well known bottleneck for rule-based systems. Considerable human effort, which typically consists in re-writing from scratch huge amounts of rules, is in fact required to transfer the knowledge available to the system from one language to a new one. Provided sufficient annotated data, machine learning algorithms allow to minimize the costs of such knowledge transfer but, up to date, proved to be ineffective for some specific tasks. Among these, the recognition and normalization of temporal expressions still remains out of their reach. Focusing on this task, and still adhering to the rule-based framework, this paper presents a bunch of experiments on the automatic porting to Italian of a system originally developed for Spanish. Different automatic rule translation strategies are evaluated and discussed, providing a comprehensive overview of the challenge.

EmotiBlog: a finer-grained and more precise learning of subjectivity expression models

The exponential growth of the subjective information in the framework of the Web 2.0 has led to the need to create Natural Language Processing tools able to analyse and process such data for multiple practical applications. They require training on specifically annotated corpora, whose level of detail must be fine enough to capture the phenomena involved. This paper presents EmotiBlog – a fine-grained annotation scheme for subjectivity. We show the manner in which it is built and demonstrate the benefits it brings to the systems using it for training, through the experiments we carried out on opinion mining and emotion detection. We employ corpora of different textual genres –a set of annotated reported speech extracted from news articles, the set of news titles annotated with polarity and emotion from the SemEval 2007 (Task 14) and ISEAR, a corpus of real-life self-expressed emotion. We also show how the model built from the EmotiBlog annotations can be enhanced with external resources. The results demonstrate that EmotiBlog, through its structure and annotation paradigm, offers high quality training data for systems dealing both with opinion mining, as well as emotion detection.

EMOCause: an easy-adaptable approach to emotion cause contexts

In this paper we present a method to automatically identify linguistic contexts which contain possible causes of emotions or emotional states from Italian newspaper articles (La Repubblica Corpus). Our methodology is based on the interplay between relevant linguistic patterns and an incremental repository of common sense knowledge on emotional states and emotion eliciting situations. Our approach has been evaluated with respect to manually annotated data. The results obtained so far are satisfying and support the validity of the methodology proposed.

Multilingual extension of a temporal expression normalizer using annotated corpora

This paper presents the automatic extension to other languages of TERSEO, a knowledge-based system for the recognition and normalization of temporal expressions originally developed for Spanish. TERSEO was first extended to English through the automatic translation of the temporal expressions. Then, an improved porting process was applied to Italian, where the automatic translation of the temporal expressions from English and from Spanish was combined with the extraction of new expressions from an Italian annotated corpus. Experimental results demonstrate how, while still adhering to the rule-based paradigm, the development of automatic rule translation procedures allowed us to minimize the effort required for porting to new languages. Relying on such procedures, and without any manual effort or previous knowledge of the target language, TERSEO recognizes and normalizes temporal expressions in Italian with good results (72% precision and 83% recall for recognition).

Splitting complex temporal questions for question answering systems

This paper presents a multi-layered Question Answering (Q.A.) architecture suitable for enhancing current Q.A. capabilities with the possibility of processing complex questions. That is, questions whose answer needs to be gathered from pieces of factual information scattered in different documents. Specifically, we have designed a layer oriented to process the different types of temporal questions. Complex temporal questions are first decomposed into simpler ones, according to the temporal relationships expressed in the original question. In the same way, the answers of each simple question are re-composed, fulfilling the temporal restrictions of the original complex question. Using this architecture, a Temporal Q.A. system has been developed. In this paper, we focus on explaining the first part of the process: the decomposition of the complex questions. Furthermore, it has been evaluated with the TERQAS question corpus of 112 temporal questions. For the task of question splitting our system has performed, in terms of precision and recall, 85% and 71%, respectively.

Going beyond traditional QA systems: challenges and keys in opinion question answering

The treatment of factual data has been widely studied in different areas of Natural Language Processing (NLP). However, processing subjective information still poses important challenges. This paper presents research aimed at assessing techniques that have been suggested as appropriate in the context of subjective - Opinion Question Answering (OQA). We evaluate the performance of an OQA with these new components and propose methods to optimally tackle the issues encountered. We assess the impact of including additional resources and processes with the purpose of improving the system performance on two distinct blog datasets. The improvements obtained for the different combination of tools are statistically significant. We thus conclude that the proposed approach is adequate for the OQA task, offering a good strategy to deal with opinionated questions.

Concit-Corpus: Context Citation Analysis to learn Function, Polarity and Influence

Citation corpus composed by 85 articles taken randomly from ACL Anthology with a total of 2195 bibliography cites.

Relationship between body mass index and mean arterial pressure in normotensive and chronic hypertensive pregnant women: a prospective, longitudinal study

Being overweight is associated with both higher systolic blood pressure (SBP) and diastolic blood pressure (DBP) during pregnancy and increased risk of gestational hypertensive disorders. The objective of this study was to determine and quantify the effect of body mass index (BMI) on mean arterial pressure (MAP) at several time points throughout pregnancy in normotensive (NT) and chronic hypertensive pregnant (HT) women.

Impact of lung function testing on hemodynamic values : concordance between non-invasive and invasive measures

Trabalho Final do Curso de Mestrado Integrado em Medicina, Faculdade de Medicina, Universidade de Lisboa, 2014

Blood markers and recurrence of stroke in patients with transient ischemic attack

Trabalho Final do Curso de Mestrado Integrado em Medicina, Faculdade de Medicina, Universidade de Lisboa, 2014

TOC, CaCO3, n-alkane and alkenone concentrations of ODP Sites 202-1237 and 202-1239

We generated preliminary downcore records of total organic carbon content, calcium carbonate, long-chain n-alkane concentration, total alkenone concentration, and alkenone-based sea-surface temperature for samples from the easternmost flank of Nazca Ridge (Site 1237) and the eastern crest of Carnegie Ridge (Site 1239). Total organic carbon and long-chain n-alkane concentrations will be used to evaluate terrestrial sediment sources. Downcore records of alkenone sea-surface temperature will benefit studies of paleoceanography of the southeastern Pacific. Since these sites are located under the influence of major tectonic events, such as the uplift of the Andes Mountains and the closure of the Isthmus of Panama, the records will help us to examine the effects of the tectonic events on the oceanic environment.

Real-time detection, tracking, and monitoring of automatically discovered events in social media

We introduce ReDites, a system for realtime event detection, tracking, monitoring and visualisation. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. Events are automatically detected from the Twitter stream. Then those that are categorised as being security-relevant are tracked, geolocated, summarised and visualised for the end-user. Furthermore, the system tracks changes in emotions over events, signalling possible flashpoints or abatement. We demonstrate the capabilities of ReDites using an extended use case from the September 2013 Westgate shooting incident. Through an evaluation of system latencies, we also show that enriched events are made available for users to explore within seconds of that event occurring.

A simple Bayesian modelling approach to event extraction from Twitter

With the proliferation of social media sites, social streams have proven to contain the most up-to-date information on current events. Therefore, it is crucial to extract events from the social streams such as tweets. However, it is not straightforward to adapt the existing event extraction systems since texts in social media are fragmented and noisy. In this paper we propose a simple and yet effective Bayesian model, called Latent Event Model (LEM), to extract structured representation of events from social media. LEM is fully unsupervised and does not require annotated data for training. We evaluate LEM on a Twitter corpus. Experimental results show that the proposed model achieves 83% in F-measure, and outperforms the state-of-the-art baseline by over 7%.© 2014 Association for Computational Linguistics.