The socially responsible choice in a duopolistic market: a dynamic problem of “ethical product” differentiation

The increasing attention of profit maximizing corporations to corporate social responsibility (CSR) is a new stylized fact of the contemporary economic environment. In our theoretical analysis we model CSR adoption as the optimal response of a profit maximizing firm to the competition of a not for profit corporate pioneer in the presence of a continuum of consumers with heterogeneous preferences towards the social and environmental features of the final good. CSR adoption implies a trade-off since, on the one side, it raises production costs but, on the other side, it leads to accumulation of “ethical capital”.We investigate conditions under which the profit maximizing firm switches from price to price and CSR competition by comparing monopoly and duopoly equilibria and their consequences on aggregate social responsibility and consumer welfare. Our findings provide a theoretical background for competition between profit maximizing incumbents and not for profit entrants in markets such as fair trade, organic food, ethical banking and ethical finance

Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: A comparative study

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Neural mechanisms underlying probalistic category learning in normal aging.

Probabilistic category learning engages neural circuitry that includes the prefrontal cortex and caudate nucleus, two regions that show prominent changes with normal aging. However, the specific contributions of these brain regions are uncertain, and the effects of normal aging have not been examined previously in probabilistic category learning. In the present study, using a blood oxygenation level-dependent functional magnetic resonance imaging block design, 18 healthy young adults (mean age, 25.5 ± 2.6 years) and 15 older adults (mean age, 67.1 ± 5.3 years) were assessed on the probabilistic category learning "weather prediction" test. Whole-brain functional images acquired using a 1.5T scanner (General Electric, Milwaukee, WI) with gradient echo, echo planar imaging (3/1 mm; repetition time, 3000 ms; echo time, 50 ms) were analyzed using second-level random-effects procedures [SPM99 (Statistical Parametric Mapping)]. Young and older adults displayed equivalent probabilistic category learning curves, used similar strategies, and activated analogous neural networks, including the prefrontal and parietal cortices and the caudate nucleus. However, the extent of caudate and prefrontal activation was less and parietal activation was greater in older participants. The percentage correct and reaction time were mainly positively correlated with caudate and prefrontal activation in young individuals but positively correlated with prefrontal and parietal cortices in older individuals. Differential activation within a circumscribed neural network in the context of equivalent learning suggests that some brain regions, such as the parietal cortices, may provide a compensatory mechanism for healthy older adults in the context of deficient prefrontal cortex and caudate nuclei responses. Copyright © 2005 Society for Neuroscience

MicroRNA expression profiles in pediatric dysembryoplastic neuroepithelial tumors.

© Springer Science+Business Media New York 2015Among noncoding RNAs, microRNAs (miRNAs) have been most extensively studied, and their biology has repeatedly been proven critical for central nervous system pathological conditions. The diagnostic value of several miRNAs was appraised in pediatric dysembryoplastic neuroepithelial tumors (DNETs) using miRNA microarrays and receiving operating characteristic curves analyses. Overall, five pediatric DNETs were studied. As controls, 17 samples were used: the FirstChoice Human Brain Reference RNA and 16 samples from deceased children who underwent autopsy and were not present with any brain malignancy. The miRNA extraction was carried out using the mirVANA miRNA Isolation Kit, while the experimental approach included miRNA microarrays covering 1211 miRNAs. Quantitative real-time polymerase chain reaction was performed to validate the expression profiles of miR-1909* and miR-3138 in all samples initially screened with miRNA microarrays. Our findings indicated that miR-3138 might act as a tumor suppressor gene when down-regulated and miR-1909* as a putative oncogenic molecule when up-regulated in pediatric DNETs compared to the control cohort. Subsequently, both miRNA signatures might serve as putative diagnostic biomarkers for pediatric DNETs.Peer reviewedFinal Accepted Versio

Spontaneous migraine attack causes alterations in default mode network connectivity

BACKGROUND: Although migraine is one of the most investigated neurologic disorders, we do not have a perfect neuroimaging biomarker for its pathophysiology. One option to improve our knowledge is to study resting-state functional connectivity in and out of headache pain. However, our understanding of the functional connectivity changes during spontaneous migraine attack is partial and incomplete. CASE PRESENTATION: Using resting-state functional magnetic resonance imaging we assessed a 24-year old woman affected by migraine without aura at two different times: during a spontaneous migraine attack and in interictal phase. Seed-to-voxel whole brain analysis was carried out using the posterior cingulate cortex as a seed, representing the default mode network (DMN). Our results showed decreased intrinsic connectivity within core regions of the DMN with an exception of a subsystem including the dorsal medial and superior frontal gyri, and the mid-temporal gyrus which is responsible for pain interpretation and control. In addition, increased connectivity between the DMN and pain and specific migraine-related areas, such as the pons and hypothalamus, developed during the spontaneous migraine attack. CONCLUSION: Our preliminary results provide further support for the hypothesis that alterations of the DMN functional connectivity during migraine headache may lead to maladaptive top-down modulation of migraine pain-related areas which might be a specific biomarker for migraine

Inter-hemispheric EEG coherence analysis in Parkinson's disease : Assessing brain activity during emotion processing

Parkinson’s disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3–AF4, F7–F8, F3–F4, FC5–FC6, T7–T8, P7–P8, and O1–O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities

Effects of pitch size and skill level on tactical behaviours of Association Football players during small-sided and conditioned games

In Association Football, the study of variability in players' movement trajectories during performance can provide insights on tactical behaviours. This study aimed to analyse the movement variability present in: i) the players' actions zones and ii), distances travelled over time, considered as a player's positional spatial reference. Additionally, we investigated whether the movement variability characteristics of players from different skill levels varied. Two groups of U-17 yrs players of different performance levels (national and regional) performed in three small-sided games with varying pitch dimensions (small, intermediate and large). Linear and non-linear analyses were used to capture the magnitude and structure of their movement variability. Results showed that increases in pitch size resulted in more restricted action zones and higher distance values from personal spatial positional references for both groups. National-level players were more sensitive to pitch modifications and displayed more variability than regional-level players in the small and intermediate pitches. These findings advance understanding about individual tactical behaviours in Association Football and have implications for training design, using pitch size manipulation