Cognitive and emotional effects of carotid stenosis.

PRINCIPLES: Patients with carotid artery stenosis (CAS) are at risk of ipsilateral stroke and chronic compromise of cerebral blood flow. It is under debate whether the hypo-perfusion or embolism in CAS is directly related to cognitive impairment. Alternatively, CAS may be a marker for underlying risk factors, which themselves influence cognition. We aimed to determine cognitive performance level and the emotional state of patients with CAS. We hypo-thesised that patients with high grade stenosis, bilateral stenosis, symptomatic patients and/or those with relevant risk factors would suffer impairment of their cognitive performance and emotional state. METHODS: A total of 68 patients with CAS of ≥70% were included in a prospective exploratory study design. All patients underwent structured assessment of executive functions, language, verbal and visual memory, motor speed, anxiety and depression. RESULTS: Significantly more patients with CAS showed cognitive impairments (executive functions, word production, verbal and visual memory, motor speed) and anxiety than expected in a normative sample. Bilateral and symptomatic stenosis was associated with slower processing speed. Cognitive performance and anxiety level were not influenced by the side and the degree of stenosis or the presence of collaterals. Factors associated with less co-gnitive impairment included higher education level, female gender, ambidexterity and treated hypercholesterolemia. CONCLUSIONS: Cognitive impairment and increased level of anxiety are frequent in patients with carotid stenosis. The lack of a correlation between cognitive functioning and degree of stenosis or the presence of collaterals, challenges the view that CAS per se leads to cognitive impairment.

Pequín, London, Roma: un viatge lingüístic a través de la història de tres llengües: gramàtica comparada entre el xinès, l'anglès i el llatí

Treball de recerca realitzat per un alumne d'ensenyament secundari i guardonat amb un Premi CIRIT per fomentar l'esperit científic del Jovent l'any 2009. En aquest treball es vol trobar un vincle entre les cultures més influents al llarg de la història d’Àsia i Europa, prenent com a element bàsic les seves llengües: el xinès, l’anglès i el llatí. Ara bé, la comparació lingüística entre elles només té veritable interès si es cerca allò comú sobretot semàntica i sintàcticament, posat que d'entrada, visual i fonèticament, qualsevol profà pot observar-ne prou diferències. Aquestes característiques comunes, doncs, tenen valor independentment del temps i l'espai, ja que el que hi ha en joc aquí és, en el fons, el llenguatge verbal com a nervi del fet de ser humà. Els continguts s'estructuren mitjançant l'ús d'un mètode inductiu com a regla d’anàlisi comparativa; no es pot, tanmateix, prescindir d'una introducció teòrica de lingüística, absolutament necessària per a un estudi comparatiu de gramàtica. Una vegada conclòs el treball, es veu que els conceptes que totes tres expressen són comuns i el canvi, quan es dóna, afecta la forma i està relacionat amb les regles de la lògica: aquests casos, doncs, no fan sinó confirmar la tesi, ja que esperar una equivalència total seria no només ingenu, sinó gens natural ni realista. L'anhel del traductor és arribar a expressar, precisament, aquest univers.

Oscillations in a molecular structured cell population model

We consider a nonlinear cyclin content structured model of a cell population divided into proliferative and quiescent cells. We show, for particular values of the parameters, existence of solutions that do not depend on the cyclin content. We make numerical simulations for the general case obtaining, for some values of the parameters convergence to the steady state but also oscillations of the population for others.

Language knowledge and earnings in Catalonia

In this paper we are aimed to investigate the relationship between Catalan knowledge and individual earnings in Catalonia. Using data from 2006, we find a positive earning return to Catalan proficiency; however, when accounting for self-selection into Catalan knowledge, we find a higher language return (20% of extra earnings), suggesting that individuals who are more prone to know Catalan are also less remunerated than others (negative selection effect). Moreover, we also find important complementarities between language knowledge and completed education, which means that only more educated individuals benefit from Catalan knowledge.

Language knowledge and earnings in Catalonia

This paper investigates the economic value of Catalan knowledge for national and foreign first- and second-generation immigrants in Catalonia. Specifically, drawing on data from the “Survey on Living Conditions and Habits of the Catalan Population (2006)”, we want to quantify the expected earnings differential between individuals who are proficient in Catalan and those who are not, taking into account the potential endogeneity between knowledge of Catalan and earnings. The results indicate the existence of a positive return to knowledge of Catalan, with a 7.5% increase in earnings estimated by OLS; however, when we account for the presence of endogeneity, monthly earnings are around 18% higher for individuals who are able to speak and write Catalan. However, we also find that language and education are complementary inputs for generating earnings in Catalonia, given that knowledge of Catalan increases monthly earnings only for more educated individuals.

Analyzing complexity in foreign language monologic oral production in a CLIL context

The study tested three analytic tools applied in SLA research (T-unit, AS-unit and Idea-unit) against FL learner monologic oral data. The objective was to analyse their effectiveness for the assessment of complexity of learners' academic production in English. The data were learners' individual productions gathered during the implementation of a CLIL teaching sequence on Natural Sciences in a Catalan state secondary school. The analysis showed that only AS-unit was easily applicable and highly effective in segmenting the data and taking complexity measures

Diagnostic interview for genetic studies (DIGS): inter-rater and test-retest reliability of the French version.

The National Institute of Mental Health developed the semi-structured Diagnostic Interview for Genetic Studies (DIGS) for the assessment of major mood and psychotic disorders and their spectrum conditions. The DIGS was translated into French in a collaborative effort of investigators from sites in France and Switzerland. Inter-rater and test-retest reliability of the French version have been established in a clinical sample in Lausanne. Excellent inter-rater reliability was found for schizophrenia, bipolar disorder, major depression, and unipolar schizoaffective disorder while fair inter-rater reliability was demonstrated for bipolar schizoaffective disorder. Using a six-week test-retest interval, reliability for all diagnoses was found to be fair to good with the exception of bipolar schizoaffective disorder. The lower test-retest reliability was the result of a relatively long test-retest interval that favored incomplete symptom recall. In order to increase reliability for lifetime diagnoses in persons not currently affected, best-estimate procedures using additional sources of diagnostic information such as medical records and reports from relatives should supplement DIGS information in family-genetic studies. Within such a procedure, the DIGS appears to be a useful part of data collection for genetic studies on major mood disorders and schizophrenia in French-speaking populations.

Structured output SVM for remote sensing image classification

In the recent years, kernel methods have revealed very powerful tools in many application domains in general and in remote sensing image classification in particular. The special characteristics of remote sensing images (high dimension, few labeled samples and different noise sources) are efficiently dealt with kernel machines. In this paper, we propose the use of structured output learning to improve remote sensing image classification based on kernels. Structured output learning is concerned with the design of machine learning algorithms that not only implement input-output mapping, but also take into account the relations between output labels, thus generalizing unstructured kernel methods. We analyze the framework and introduce it to the remote sensing community. Output similarity is here encoded into SVM classifiers by modifying the model loss function and the kernel function either independently or jointly. Experiments on a very high resolution (VHR) image classification problem shows promising results and opens a wide field of research with structured output kernel methods.

Speech, Language and Communication Therapy Action Plan (2011/12 - 2012/13) - Easy Read (PDF 454 KB)

Easy Read version of Speech, Language and Communication Therapy Action Plan (2011/12 - 2012/13)