University of Coimbra

About: University of Coimbra is a education organization based out in Coimbra, Portugal. It is known for research contribution in the topics: Population & Mitochondrion. The organization has 14318 authors who have published 43067 publications receiving 994733 citations. The organization is also known as: UC & Universidade dos Estudos Gerais.

The development of compassionate engagement and action scales for self and others

Abstract: Studies of the value of compassion on physical and mental health and social relationships have proliferated in the last 25 years. Although, there are several conceptualisations and measures of compassion, this study develops three new measures of compassion competencies derived from an evolutionary, motivational approach. The scales assess 1. the compassion we experience for others, 2. the compassion we experience from others, and 3. self-compassion based on a standard definition of compassion as a ‘sensitivity to suffering in self and others with a commitment to try to alleviate and prevent it’. We explored these in relationship to other compassion scales, self-criticism, depression, anxiety, stress and well-being. Participants from three different countries (UK, Portugal and USA) completed a range of scales including compassion for others, self-compassion, self-criticism, shame, depression, anxiety and stress with the newly developed ‘The Compassionate Engagement and Actions’ scale. All three scales have good validity. Interestingly, we found that the three orientations of compassion are only moderately correlated to one another (r < .5). We also found that some elements of self-compassion (e.g., being sensitive to, and moved by one’s suffering) have a complex relationship with other attributes of compassion (e.g., empathy), and with depression, anxiety and stress. A path-analysis showed that self-compassion is a significant mediator of the association between self-reassurance and well-being, while self-criticism has a direct effect on depressive symptoms, not mediated by self-compassion. Compassion evolved from caring motivation and in humans is associated with a range of different socially intelligent competencies. Understanding how these competencies can be inhibited and facilitated is an important research endeavour. These new scales were designed to assess these competencies. This is the first study to measure the three orientations of compassion derived from an evolutionary model of caring motivation with specified competencies. Our three new measures of compassion further indicate important complex relationships between different potentiation’s of compassion, well-being, and vulnerability to psychopathologies.

Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization

Abstract: Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new multilevel thresholding method is introduced for the segmentation of hyperspectral and multispectral images. The new method is based on fractional-order Darwinian particle swarm optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. In this paper, the so-called Otsu problem is solved for each channel of the multispectral and hyperspectral data. Therefore, the problem of n-level thresholding is reduced to an optimization problem in order to search for the thresholds that maximize the between-class variance. Experimental results are favorable for the FODPSO when compared to other bioinspired methods for multilevel segmentation of multispectral and hyperspectral images. The FODPSO presents a statistically significant improvement in terms of both CPU time and fitness value, i.e., the approach is able to find the optimal set of thresholds with a larger between-class variance in less computational time than the other approaches. In addition, a new classification approach based on support vector machine (SVM) and FODPSO is introduced in this paper. Results confirm that the new segmentation method is able to improve upon results obtained with the standard SVM in terms of classification accuracies.

Observation of Long-Range Elliptic Azimuthal Anisotropies in s =13 and 2.76 TeV pp Collisions with the ATLAS Detector

Abstract: ATLAS has measured two-particle correlations as a function of relative azimuthal-angle, $\Delta \phi$, and pseudorapidity, $\Delta \eta$, in $\sqrt{s}$=13 and 2.76 TeV $pp$ collisions at the LHC using charged particles measured in the pseudorapidity interval $|\eta|$<2.5. The correlation functions evaluated in different intervals of measured charged-particle multiplicity show a multiplicity-dependent enhancement at $\Delta \phi \sim 0$ that extends over a wide range of $\Delta\eta$, which has been referred to as the "ridge". Per-trigger-particle yields, $Y(\Delta \phi)$, are measured over 2<$|\Delta\eta|$<5. For both collision energies, the $Y(\Delta \phi)$ distribution in all multiplicity intervals is found to be consistent with a linear combination of the per-trigger-particle yields measured in collisions with less than 20 reconstructed tracks, and a constant combinatoric contribution modulated by $\cos{(2\Delta \phi)}$. The fitted Fourier coefficient, $v_{2,2}$, exhibits factorization, suggesting that the ridge results from per-event $\cos{(2\phi)}$ modulation of the single-particle distribution with Fourier coefficients $v_2$. The $v_2$ values are presented as a function of multiplicity and transverse momentum. They are found to be approximately constant as a function of multiplicity and to have a $p_{\mathrm{T}}$ dependence similar to that measured in $p$+Pb and Pb+Pb collisions. The $v_2$ values in the 13 and 2.76 TeV data are consistent within uncertainties. These results suggest that the ridge in $pp$ collisions arises from the same or similar underlying physics as observed in $p$+Pb collisions, and that the dynamics responsible for the ridge has no strong $\sqrt{s}$ dependence.

Resident Neural Stem Cells Restrict Tissue Damage and Neuronal Loss After Spinal Cord Injury in Mice

Abstract: Central nervous system injuries are accompanied by scar formation. It has been difficult to delineate the precise role of the scar, as it is made by several different cell types, which may limit the damage but also inhibit axonal regrowth. We show that scarring by neural stem cell–derived astrocytes is required to restrict secondary enlargement of the lesion and further axonal loss after spinal cord injury. Moreover, neural stem cell progeny exerts a neurotrophic effect required for survival of neurons adjacent to the lesion. One distinct component of the glial scar, deriving from resident neural stem cells, is required for maintaining the integrity of the injured spinal cord.

Luminosity Determination in pp Collisions at sqrt(s)=7 TeV Using the ATLAS Detector at the LHC

Abstract: Measurements of luminosity obtained using the ATLAS detector during early running of the Large Hadron Collider (LHC) at root s = 7 TeV are presented. The luminosity is independently determined using several detectors and multiple algorithms, each having different acceptances, systematic uncertainties and sensitivity to background. The ratios of the luminosities obtained from these methods are monitored as a function of time and of mu, the average number of inelastic interactions per bunch crossing. Residual time- and mu-dependence between the methods is less than 2% for 0 < mu < 2.5. Absolute luminosity calibrations, performed using beam separation scans, have a common systematic uncertainty of +/- 11%, dominated by the measurement of the LHC beam currents. After calibration, the luminosities obtained from the different methods differ by at most +/- 2%. The visible cross sections measured using the beam scans are compared to predictions obtained with the PYTHIA and PHOJET event generators and the ATLAS detector simulation.