The promotion and assessment of generic skills from interdisciplinary teaching teams

The paper explains a teaching project financed by the University of Barcelona (UB). It focuses on ageneric skill of the University of Barcelona, which is defined as "the learning capability andresponsibility”, and in which analytical and synthesis skills are included. It follows a multidisciplinaryapproach including teachers of Mathematics, World Economics and Economic History. All of us sharethe same students during the first and the second course of the grade in Economics at the Faculty ofEconomics and Business. The project has been developed in three stages. The first one has beendone during the first semester of the course 2012/13, being applied to first year students on thesubjects of Mathematics and Economic History. The second phase is being to be done during thesecond semester only on the Economic History subject. A third stage is going to be done next course2013/14 to second year students on the subject of World Economics. Each different teaching teamhas developed specific materials and assessment tools for each one of the subjects included in theproject. The project emphasizes two teaching dimensions: the elaboration of teaching materials topromote the acquisition of generic skills from an interdisciplinary point of view, and the design ofspecific tools to assess such skills. The first results of the first phase of the project shows cleardeficiencies in the analytical skill regarding to first year students.

Spatial mapping of exciton lifetimes in single ZnO nanowires

We investigate the spatial dependence of the exciton lifetimes in single ZnO nanowires. We have found that the free exciton and bound exciton lifetimes exhibit a maximum at the center of nanowires, while they decrease by 30% towards the tips. This dependence is explained by considering the cavity-like properties of the nanowires in combination with the Purcell effect. We show that the lifetime of the bound-excitons scales with the localization energy to the power of 3/2, which validates the model of Rashba and Gurgenishvili at the nanoscale.

Automated Lesion Detection-Based Symptome Mapping In Patients With Sensory Deficits

The human brain is the most complex structure known. With its high number of cells, number of connections and number of pathways it is the source of every thought in the world. It consumes 25% of our oxygen and suffers very fast from a disruption of its supply. An acute event, like a stroke, results in rapid dysfunction referable to the affected area. A few minutes without oxygen and neuronal cells die and subsequently degenerate. Changes in the brains incoming blood flow alternate the anatomy and physiology of the brain. All stroke events leave behind a brain tissue lesion. To rapidly react and improve the prediction of outcome in stroke patients, accurate lesion detection and reliable lesion-based function correlation would be very helpful. With a number of neuroimaging and clinical data of cerebral injured patients this study aims to investigate correlations of structural lesion locations with sensory functions.

Feasibility of direct mapping of cerebral fluorodeoxy-D-glucose metabolism in situ at subcellular resolution using soft X-ray fluorescence.

Glucose metabolism is difficult to image with cellular resolution in mammalian brain tissue, particularly with (18) fluorodeoxy-D-glucose (FDG) positron emission tomography (PET). To this end, we explored the potential of synchrotron-based low-energy X-ray fluorescence (LEXRF) to image the stable isotope of fluorine (F) in phosphorylated FDG (DG-6P) at 1 μm(2) spatial resolution in 3-μm-thick brain slices. The excitation-dependent fluorescence F signal at 676 eV varied linearly with FDG concentration between 0.5 and 10 mM, whereas the endogenous background F signal was undetectable in brain. To validate LEXRF mapping of fluorine, FDG was administered in vitro and in vivo, and the fluorine LEXRF signal from intracellular trapped FDG-6P over selected brain areas rich in radial glia was spectrally quantitated at 1 μm(2) resolution. The subsequent generation of spatial LEXRF maps of F reproduced the expected localization and gradients of glucose metabolism in retinal Müller glia. In addition, FDG uptake was localized to periventricular hypothalamic tanycytes, whose morphological features were imaged simultaneously by X-ray absorption. We conclude that the high specificity of photon emission from F and its spatial mapping at ≤1 μm resolution demonstrates the ability to identify glucose uptake at subcellular resolution and holds remarkable potential for imaging glucose metabolism in biological tissue. © 2012 Wiley Periodicals, Inc.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Communication skills training in oncology: it works!

While the previous chapter by L. Fallowfield and V. Jenkins focuses on different communication skills training (CST) concepts currently being utilized, this chapter reviews and comments the scientific evidence of the impact of CST on improving communication skills. The aim of this chapter is not to provide a complete review of the evidence-this has already been done in systematic reviews-but to discuss the scientific evidence and reflect on the available results and relevant topics for further investigations.

MRS glucose mapping and PET joining forces: re-evaluation of the lumped constant in the rat brain under isoflurane anaesthesia.

Although numerous positron emission tomography (PET) studies with (18) F-fluoro-deoxyglucose (FDG) have reported quantitative results on cerebral glucose kinetics and consumption, there is a large variation between the absolute values found in the literature. One of the underlying causes is the inconsistent use of the lumped constants (LCs), the derivation of which is often based on multiple assumptions that render absolute numbers imprecise and errors hard to quantify. We combined a kinetic FDG-PET study with magnetic resonance spectroscopic imaging (MRSI) of glucose dynamics in Sprague-Dawley rats to obtain a more comprehensive view of brain glucose kinetics and determine a reliable value for the LC under isoflurane anaesthesia. Maps of Tmax /CMRglc derived from MRSI data and Tmax determined from PET kinetic modelling allowed to obtain an LC-independent CMRglc . The LC was estimated to range from 0.33 ± 0.07 in retrosplenial cortex to 0.44 ± 0.05 in hippocampus, yielding CMRglc between 62 ± 14 and 54 ± 11 μmol/min/100 g, respectively. These newly determined LCs for four distinct areas in the rat brain under isoflurane anaesthesia provide means of comparing the growing amount of FDG-PET data available from translational studies.

Interest rates mapping

The present study deals with the analysis and mapping of Swiss franc interest rates. Interest rates depend on time and maturity, defining term structure of the interest rate curves (IRC). In the present study IRC are considered in a two-dimensional feature space - time and maturity. Exploratory data analysis includes a variety of tools widely used in econophysics and geostatistics. Geostatistical models and machine learning algorithms (multilayer perceptron and Support Vector Machines) were applied to produce interest rate maps. IR maps can be used for the visualisation and pattern perception purposes, to develop and to explore economical hypotheses, to produce dynamic asset-liability simulations and for financial risk assessments. The feasibility of an application of interest rates mapping approach for the IRC forecasting is considered as well. (C) 2008 Elsevier B.V. All rights reserved.

Detection and mapping of a lethal locus in a eucalyptus hybrid population

The objective of this work was to verify the existence of a lethal locus in a eucalyptus hybrid population, and to quantify the segregation distortion in the linkage group 3 of the Eucalyptus genome. A E. grandis x E. urophylla hybrid population, which segregates for rust resistance, was genotyped with 19 microsatellite markers belonging to linkage group 3 of the Eucalyptus genome. To quantify the segregation distortion, maximum likelihood (ML) models, specific to outbreeding populations, were used. These models consider the observed marker genotypes and the lethal locus viability as parameters. The ML solutions were obtained using the expectation‑maximization algorithm. A lethal locus in the linkage group 3 was verified and mapped, with high confidence, between the microssatellites EMBRA 189 e EMBRA 122. This lethal locus causes an intense gametic selection from the male side. Its map position is 25 cM from the locus which controls the rust resistance in this population.

The role of interval nodes in sentinel lymph node mapping and dissection for melanoma patients.

In sentinel node (SN) biopsy, an interval SN is defined as a lymph node or group of lymph nodes located between the primary melanoma and an anatomically well-defined lymph node group directly draining the skin. As shown in previous reports, these interval SNs seem to be at the same metastatic risk as are SNs in the usual, classic areas. This study aimed to review the incidence, lymphatic anatomy, and metastatic risk of interval SNs. METHODS: SN biopsy was performed at a tertiary center by a single surgical team on a cohort of 402 consecutive patients with primary melanoma. The triple technique of localization was used-that is, lymphoscintigraphy, blue dye, and gamma-probe. Otolaryngologic melanoma and mucosal melanoma were excluded from this analysis. SNs were examined by serial sectioning and immunohistochemistry. All patients with metastatic SNs were recommended to undergo a radical selective lymph node dissection. RESULTS: The primary locations of the melanomas included the trunk (188), an upper limb (67), or a lower limb (147). Overall, 97 (24.1%) of the 402 SNs were metastatic. Interval SNs were observed in 18 patients, in all but 2 of whom classic SNs were also found. The location of the primary was truncal in 11 (61%) of the 18, upper limb in 5, and lower limb in 2. One patient with a dorsal melanoma had drainage exclusively in a cervicoscapular area that was shown on removal to contain not lymph node tissue but only a blue lymph channel without tumor cells. Apart from the interval SN, 13 patients had 1 classic SN area and 3 patients 2 classic SN areas. Of the 18 patients, 2 had at least 1 metastatic interval SN and 2 had a classic SN that was metastatic; overall, 4 (22.2%) of 18 patients were node-positive. CONCLUSION: We found that 2 of 18 interval SNs were metastatic: This study showed that preoperative lymphoscintigraphy must review all known lymphatic areas in order to exclude an interval SN.

Weed or wheel! FMRI, behavioural, and toxicological investigations of how cannabis smoking affects skills necessary for driving.

Marijuana is the most widely used illicit drug, however its effects on cognitive functions underling safe driving remain mostly unexplored. Our goal was to evaluate the impact of cannabis on the driving ability of occasional smokers, by investigating changes in the brain network involved in a tracking task. The subject characteristics, the percentage of Δ(9)-Tetrahydrocannabinol in the joint, and the inhaled dose were in accordance with real-life conditions. Thirty-one male volunteers were enrolled in this study that includes clinical and toxicological aspects together with functional magnetic resonance imaging of the brain and measurements of psychomotor skills. The fMRI paradigm was based on a visuo-motor tracking task, alternating active tracking blocks with passive tracking viewing and rest condition. We show that cannabis smoking, even at low Δ(9)-Tetrahydrocannabinol blood concentrations, decreases psychomotor skills and alters the activity of the brain networks involved in cognition. The relative decrease of Blood Oxygen Level Dependent response (BOLD) after cannabis smoking in the anterior insula, dorsomedial thalamus, and striatum compared to placebo smoking suggests an alteration of the network involved in saliency detection. In addition, the decrease of BOLD response in the right superior parietal cortex and in the dorsolateral prefrontal cortex indicates the involvement of the Control Executive network known to operate once the saliencies are identified. Furthermore, cannabis increases activity in the rostral anterior cingulate cortex and ventromedial prefrontal cortices, suggesting an increase in self-oriented mental activity. Subjects are more attracted by intrapersonal stimuli ("self") and fail to attend to task performance, leading to an insufficient allocation of task-oriented resources and to sub-optimal performance. These effects correlate with the subjective feeling of confusion rather than with the blood level of Δ(9)-Tetrahydrocannabinol. These findings bolster the zero-tolerance policy adopted in several countries that prohibits the presence of any amount of drugs in blood while driving.

Quantitative multi-parameter mapping of R1, PD(*), MT, and R2(*) at 3T: a multi-center validation.

Multi-center studies using magnetic resonance imaging facilitate studying small effect sizes, global population variance and rare diseases. The reliability and sensitivity of these multi-center studies crucially depend on the comparability of the data generated at different sites and time points. The level of inter-site comparability is still controversial for conventional anatomical T1-weighted MRI data. Quantitative multi-parameter mapping (MPM) was designed to provide MR parameter measures that are comparable across sites and time points, i.e., 1 mm high-resolution maps of the longitudinal relaxation rate (R1 = 1/T1), effective proton density (PD(*)), magnetization transfer saturation (MT) and effective transverse relaxation rate (R2(*) = 1/T2(*)). MPM was validated at 3T for use in multi-center studies by scanning five volunteers at three different sites. We determined the inter-site bias, inter-site and intra-site coefficient of variation (CoV) for typical morphometric measures [i.e., gray matter (GM) probability maps used in voxel-based morphometry] and the four quantitative parameters. The inter-site bias and CoV were smaller than 3.1 and 8%, respectively, except for the inter-site CoV of R2(*) (<20%). The GM probability maps based on the MT parameter maps had a 14% higher inter-site reproducibility than maps based on conventional T1-weighted images. The low inter-site bias and variance in the parameters and derived GM probability maps confirm the high comparability of the quantitative maps across sites and time points. The reliability, short acquisition time, high resolution and the detailed insights into the brain microstructure provided by MPM makes it an efficient tool for multi-center imaging studies.

ADC mapping of chronic cerebral hypoperfusion induced by carotid artery stenosis.

BACKGROUND: Carotid artery stenosis is associated with the occurrence of acute and chronic ischemic lesions that increase with age in the elderly population. Diffusion Imaging and ADC mapping may be an appropriate method to investigate patients with chronic hypoperfusion consecutive to carotid stenosis. This non-invasive technique allows to investigate brain integrity and structure, in particular hypoperfusion induced by carotid stenosis diseases. The aim of this study was to evaluate the impact of a carotid stenosis on the parenchyma using ADC mapping. METHODS: Fifty-nine patients with symptomatic (33) and asymptomatic (26) carotid stenosis were recruited from our multidisciplinary consultation. Both groups demonstrated a similar degree of stenosis. All patients underwent MRI of the brain including diffusion-weighted MR imaging with ADC mapping. Regions of interest were defined in the anterior and posterior paraventricular regions both ipsilateral and contralateral to the stenosis (anterior circulation). The same analysis was performed for the thalamic and occipital regions (posterior circulation). RESULTS: ADC values of the affected vascular territory were significantly higher on the side of the stenosis in the periventricular anterior (P<0.001) and posterior (P<0.01) area. There was no difference between ipsilateral and contralateral ADC values in the thalamic and occipital regions. CONCLUSIONS: We have shown that carotid stenosis is associated with significantly higher ADC values in the anterior circulation, probably reflecting an impact of chronic hypoperfusion on the brain parenchyma in symptomatic and asymptomatic patients. This is consistent with previous data in the literature.