The energy crossroads: Exploring the moderating role of the energy crisis on the acceptance of the development of a uranium mine.

The energy crisis caused by the lack of supply from some countries involved in armed conflicts, coupled with society's continuous demand for energy production, is leading to the proposal of new energy sources, such as the development of uranium mines to increase nuclear energy production. Mine projects (MPs) trigger numerous conflicts in the local societies involved. While for some people, they represent an opportunity for development and benefits, for others these proposals are perceived as a threat and a health risk. However, energy scarcity and price increases create a social dilemma that moderates the established relationships between the perceived risks and benefits and the valuation of energy proposals. This research aims to analyze both the weight of the factors influencing the acceptance of a uranium MP and the moderating role of the energy crisis on these factors. A total of 629 people participated in this study (371 before and 258 after the energy crisis). The participants answered a questionnaire assessing their socio-demographic characteristics, beliefs about the environment, the perceived risks and benefits, and their emotional balance, as well as their level of knowledge and acceptance of the uranium mine proposal. The results show that age, gender, and level of education and knowledge both directly and indirectly influence the acceptance of a mine. In addition, the perception of risks and benefits is associated with the emotional balance generated, explaining the level of final acceptance of the proposal. The moderation-mediated energy crisis hypothesis influences the relationship between the perceived risks and acceptance of the uranium mine. The results are discussed in order to determine factors for intervention to alleviate the social conflicts created by new energy proposals.

Network analysis to examine sex differences linked to emotional well-being in cardiovascular disease.

Psychosocial dimensions are essential to guarantee an optimal approach to improve emotional well-being in patients with cardiovascular disease (CVD). There is evidence of sex differences regarding these dimensions. Thus, the connections between them are crucial to implement personalized therapies. Network model analyses were conducted on data from 593 CVD patients. The models included scores from the Hospital Anxiety and Depression Scale (HADS), positive (PA) and negative affect (NA), positivity (PS), satisfaction of life (SLS), social support (SS), self-efficacy on emotion regulation (RESE), cardiac self-efficacy (CSE) and the Short Form-12 Health Survey. The main sex differences were found in: PA-PS (p = 0.03), SS-RESE (p = 0.04), for which the positive associations are stronger for men than for women and PA-RESE (p < 0.01) for which the positive association is stronger for women than for men. These nuances should be considered to implement tailored and integrative therapies for each CVD patient.

Explanatory model of the psychosocial variables related to the social acceptance of a uranium mine project in northwest Spain.

Introduction: The demographic growth and the development of the welfare system have been accompanied by an important social dilemma between preserving nature or promoting energy development by assuming the benefits and risks of both proposals. This research attempts to address this social dilemma by analyzing the psychosocial factors that influence the acceptance or rejection of a new uranium mining development and exploitation project. The main objective was to test an explanatory theoretical model of uranium mining project acceptance, based on the interrelation of sociodemographic variables (e.g., age, gender, economic and educational situation, and level of knowledge about uranium energy) and cognitive variables (e.g., environmental beliefs, risk, and benefit perceptions), along with the activation of an emotional balance in response to the proposal of constructing a uranium mine. Method: Three hundred seventy-one individuals responded to the questionnaire about the variables included in the model. Results: The results showed that older participants showed lower levels of agreement with the mining proposal people, while women and those with greater knowledge of nuclear energy perceived greater risks and had a more negative emotional balance. The proposed explanatory model based on sociodemographic, cognitive, and affective variables showed good fit indices for explaining the assessment of the uranium mine. Thus, age, level of knowledge, risks and benefits, and emotional balance had a direct effect on the acceptance of the mine. Likewise, emotional balance showed a partial mediation effect between the relationships existing between the perception of benefits and risks and the acceptance of the mining proposal. Discussion: The results are discussed based on the consideration of analyzing sociodemographic, cognitive, and affective variables to understand potential conflicts in communities affected by energy projects.

Driving anxiety and anxiolytics while driving: Their impacts on behaviour and cognition behind the wheel.

Introduction: The interaction between road safety and drivers' mental health is an important issue to take into consideration on transportation and safety research. The present review deals specifically with the link between anxiety and driving activity from two complementary points of view. Method: A systematic review into primary studies, following the PRISMA statement, was carried out in four databases: Scopus, Web of Science, Transport Research International Documentation and Pubmed. A total of 29 papers were retained. On the one hand, we present a systematic review of research articles exploring the cognitive and behavioural effects of driving anxiety, regardless its onset, when concerned people have to drive. The second goal of the review is to compile the available literature on the influence of legal drugs, which are used to fight against anxiety, on actual driving tasks. Results: Eighteen papers have been retained for the first question, whose main findings show that exaggerated cautious driving, negative feelings and avoidance are associated with driving anxiety. Most of the conclusions were drawn from self-reported questionnaires and little is known about the effects in situ. Concerning the second question, benzodiazepines are the most studied legal drugs. They affect different attentional processes and could slow reaction times down depending on the population and treatment features. Conclusions: The two standpoints included in the present work allow us to propose some possible lines of research to study certain aspects that have not been explored in depth about people who either feel apprehensive about driving or who drive under the effects of anxiolytics. Practical applications: The study on driving anxiety may be crucial to estimate the consequences for traffic safety. Furthermore, it is relevant to design effective campaigns to raise awareness about the issues discussed. To propose standard evaluations of driving anxiety and exhaustive research works to find out the extent of anxiolytics use are also important to be considered for traffic policies.

Stress estimation by the prefrontal cortex asymmetry: Study on fNIRS signals.

BACKGROUND: Functional near-infrared spectroscopy (fNIRS) is a non-invasive technique frequently used to measure the brain hemodynamic activity in applications to evaluate affective disorders and stress. Using two wavelengths of light, it is possible to monitor relative changes in the concentrations of oxyhemoglobin and deoxyhemoglobin. Besides, the spatial asymmetry in the prefrontal cortex activity has been correlated with the brain response to stressful situations. METHODS: We measured prefrontal cortex activity with a NIRS multi-distance device during a baseline period, under stressful conditions (e.g., social stress), and after a recovery phase. We calculated a laterality index for the contaminated brain signal and for the brain signal where we removed the influence of extracerebral hemodynamic activity by using a short channel. RESULTS: There was a significant right lateralization during stress when using the contaminated signals, consistent with previous investigations, but this significant difference disappeared using the corrected signals. Indeed, exploration of the susceptibility to contamination of the different channels showed non-homogeneous spatial patterns, which would hint at detection of stress from extracerebral activity from the forehead. LIMITATIONS: There was no recovery phase between the social and the arithmetic stressor, a cumulative effect was not considered. CONCLUSIONS: Extracerebral hemodynamic activity provided insights into the pertinence of short channel corrections in fNIRS studies dealing with emotions. It is important to consider this issue in clinical applications including modern monitoring systems based on fNIRS technique to assess emotional states in affective disorders.

Spontaneous Breathing Rate Variations Linked to Social Exclusion and Emotion Self-assessment.

The emotional reactions to social exclusion can be associated with physiological responses that could allow researchers to estimate the valence and intensity of the ongoing affective state. In this work, respiratory activity was analysed to verify whether breathing rate variations can be considered as predictive factors of subsequent positive and negative affect after inclusion and exclusion in young women. A standard Cyberball task was implemented and manipulated information was provided to the participants to create both conditions. The participants were socially excluded by limiting their participation to 6% of the total number of passes among three teammates and providing negative feedback about them. The results suggest that breathing rate can be a good option to infer subjective feelings during social interactions and a promising feature to incorporate into modern emotion monitoring systems as an alternative to other physiological measures. Furthermore, the interaction between metaemotion and physiology was studied by recording breathing rate while completing the Positive and Negative Affect Schedule, evidencing a breathing rate increase during the emotion self-assessment only after exclusion.

Cognitive and body manifestations of driving anxiety according to different onsets.

BACKGROUND: Driving anxiety can have deleterious effects not only on driving behavior, but also on life quality. The interaction between motor vehicle collision (MVC) experiences and driving anxiety has been studied from different standpoints. However, the comparison with other events triggering it has been scarcely considered. Objectives: To analyze the body manifestations and the driving cognitions related to the accident, social and panic concerns in people suffering from different levels of driving anxiety. Method: A total of 260 participants suffering from driving anxiety were included in a survey, including Driving Cognition Questionnaire and Body Sensation Questionnaire. Results: Panic attacks and criticisms are the most relevant onsets of driving anxiety, more than MVC. Only 11.4% of MVC victims considered it as the onset. People with MVC history showed lower scores in social concerns than people without MVC experience and neither the responsibility of the MVC nor the role (driver/passenger) seemed to have an impact on the anxiety level. Conclusions: Although the most relevant body sensations, heart palpitations and sweating, were the same in people with panic attack experiences and MVC victims, a discrimination of the emotions behind the concept of "driving anxiety" is desirable to clarify the psychological effects of different onsets.

The Arousal Effect of Exclusionary and Inclusionary Situations on Social Affiliation Motivation and Its Subsequent Influence on Prosocial Behavior.

Given the negative costs of exclusion and the relevance of belongingness for humans, the experience of exclusion influences social affiliation motivation, which in turn is a relevant predictor of prosocial behavior. Skin conductance is a typical measure of the arousal elicited by emotions. Hence, we argued that both inclusion and exclusion will increase skin conductance level due to the increase of either positive affect or anger affects, respectively. Moreover, we argued that emotional arousal is also related to social affiliation motivation and prosocial behavior. A total of 48 students were randomly allocated to either an inclusionary or exclusionary condition and their skin conductance levels were recorded during an experiment in which they completed an online questionnaire and played the game "Cyberball." Results indicated that (a) individuals who perceived high exclusion felt angrier than individuals perceiving high inclusion, who feel positive affect; (b) no differences were evidenced in terms of skin conductance between exclusion and inclusion situations; (c) over-aroused individuals were less motivated to affiliate; and (d) individuals with lower affiliation motivation behaved in a less prosocial way. The results were congruent to the argument that behaving prosocially may be a way to gain the desired affiliation.

Cortical Activity Linked to Clocking in Deaf Adults: fNIRS Insights with Static and Animated Stimuli Presentation.

The question of the possible impact of deafness on temporal processing remains unanswered. Different findings, based on behavioral measures, show contradictory results. The goal of the present study is to analyze the brain activity underlying time estimation by using functional near infrared spectroscopy (fNIRS) techniques, which allow examination of the frontal, central and occipital cortical areas. A total of 37 participants (19 deaf) were recruited. The experimental task involved processing a road scene to determine whether the driver had time to safely execute a driving task, such as overtaking. The road scenes were presented in animated format, or in sequences of 3 static images showing the beginning, mid-point, and end of a situation. The latter presentation required a clocking mechanism to estimate the time between the samples to evaluate vehicle speed. The results show greater frontal region activity in deaf people, which suggests that more cognitive effort is needed to process these scenes. The central region, which is involved in clocking according to several studies, is particularly activated by the static presentation in deaf people during the estimation of time lapses. Exploration of the occipital region yielded no conclusive results. Our results on the frontal and central regions encourage further study of the neural basis of time processing and its links with auditory capacity.

Towards Mindless Stress Regulation in Advanced Driver Assistance Systems: A Systematic Review.

Background: Stress can frequently occur in the driving context. Its cognitive effects can be deleterious and lead to uncomfortable or risky situations. While stress detection in this context is well developed, regulation using dedicated advanced driver-assistance systems (ADAS) is still emergent. Objectives: This systematic review focuses on stress regulation strategies that can be qualified as "subtle" or "mindless": the technology employed to perform regulation does not interfere with an ongoing task. The review goal is 2-fold: establishing the state of the art on such technological implementation in the driving context and identifying complementary technologies relying on subtle regulation that could be applied in driving. Methods: A systematic review was conducted using search operators previously identified through a concept analysis. The patents and scientific studies selected provide an overview of actual and potential mindless technology implementations. These are then analyzed from a scientific perspective. A classification of results was performed according to the different stages of emotion regulation proposed by the Gross model. Results: A total of 47 publications were retrieved, including 21 patents and 26 studies. Six of the studies investigated mindless stress regulation in the driving context. Patents implemented strategies mostly linked to attentional deployment, while studies tended to investigate response modulation strategies. Conclusions: This review allowed us to identify several ADAS relying on mindless computing technologies to reduce stress and better understand the underlying mechanisms allowing stress reduction. Further studies are necessary to better grasp the effect of mindless technologies on driving safety. However, we have established the feasibility of their implementation as ADAS and proposed directions for future research in this field.

Hemodynamic responses to visual cues during attentive listening in autonomous versus manual simulated driving: A pilot study.

Emerging automation technologies could have a strong impact on the allocation of drivers' attentional resources. The first objective of this pilot study is to investigate the hemodynamic responses evoked to relevant visual stimuli in manual and autonomous driving. The second aim is to examine how the inclusion of a secondary task (attentive listening to a broadcast) modulates these hemodynamic responses in both driving situations. Frontal, temporo-parietal and occipital activations were recorded using a functional Near-InfraRed Spectroscopy (fNIRS) system. Event-related analysis was used to determine whether visual cue processing (specifically, the lighting of a lead vehicle's brake-lights) could induce different brain responses depending on the driving mode and on the presence or absence of a competing task. Mind-wandering as reported by the participants was more pronounced during autonomous compared to manual driving. Our results showed an increase in the OxyHb concentration in the right temporo-parietal and occipital areas during manual compared to autonomous driving, suggesting greater allocation of attentional resources for processing visual cues in the first condition. Finally, an event-related decrease in right frontal activity during autonomous driving when listening was observed, suggesting that attentional resources were more focused on the secondary task than on monitoring the driving scene.

Cardiovascular correlates of emotional state, cognitive workload and time-on-task effect during a realistic flight simulation.

In aviation, emotion and cognitive workload can considerably increase the probability of human error. An accurate online physiological monitoring of pilot's mental state could prevent accidents. The heart rate (HR) and heart rate variability (HRV) of 21 private pilots were analysed during two realistic flight simulator scenarios. Emotion was manipulated by a social stressor and cognitive workload with the difficulty of a secondary task. Our results confirmed the sensitivity of the HR to cognitive demand and training effects, with increased HR when the task was more difficult and decreased HR with training (time-on-task). Training was also associated with an increased HRV, with increased values along the flight scenario time course. Finally, the social stressor seemed to provoke an emotional reaction that enhanced motivation and performance on the secondary task. However, this was not reflected by the cardiovascular activity.

Respiration and Heart Rate Modulation Due to Competing Cognitive Tasks While Driving.

Research works on operator monitoring underline the benefit of taking into consideration several signal modalities to improve accuracy for an objective mental state diagnosis. Heart rate (HR) is one of the most utilized systemic measures to assess cognitive workload (CW), whereas, respiration parameters are hardly utilized. This study aims at verifying the contribution of analyzing respiratory signals to extract features to evaluate driver's activity and CW variations in driving. Eighteen subjects participated in the study. The participants carried out two different cognitive tasks requiring different CW demands, a single task as well as a competing cognitive task realized while driving in a simulator. Our results confirm that both HR and breathing rate (BR) increase in driving and are sensitive to CW. However, HR and BR are differently modulated by the CW variations in driving. Specifically, HR is affected by both driving activity and CW, whereas, BR is suitable to evidence a variation of CW only when driving is not required. On the other hand, spectral features characterizing respiratory signal could be also used similarly to HR variability indices to detect high CW episodes. These results hint the use of respiration as an alternative to HR to monitor the driver mental state in autonomic vehicles in order to predict the available cognitive resources if the user has to take over the vehicle.

Spectral and spatiotemporal variability ECG parameters linked to catheter ablation outcome in persistent atrial fibrillation.

With the increasing prevalence of atrial fibrillation (AF), there is a strong clinical interest in determining whether a patient suffering from persistent AF will benefit from catheter ablation (CA) therapy at long term. This work presents several regression models based on noninvasive measures automatically computed from the standard 12-lead electrocardiogram (ECG) such as AF dominant frequency (DF), spectral concentration and spatiotemporal variability (STV). Sixty-two AF patients referred to CA were enrolled in this study. Forty-seven of them had no recurrence after CA during an average follow-up of 14 ± 8 months. The ECG features were extracted from an ECG recorded before the CA intervention and they were combined by means of logistic regression. The combination of DF and STV values from different precordial leads reached AUC = 0.939, outperforming the best results by using only one kind of features, such as DF (AUC = 0.801), and yielding a global accuracy of 93.5% for discriminating the best long-term responders to CA. These results point out the need to take into consideration the spatial variation of spectral ECG parameters to build predictive models dealing with AF.

Non-invasive prediction of catheter ablation outcome in persistent atrial fibrillation by fibrillatory wave amplitude computation in multiple electrocardiogram leads.

BACKGROUND: Catheter ablation (CA) of persistent atrial fibrillation (AF) is challenging, and reported results are capable of improvement. A better patient selection for the procedure could enhance its success rate while avoiding the risks associated with ablation, especially for patients with low odds of favorable outcome. CA outcome can be predicted non-invasively by atrial fibrillatory wave (f-wave) amplitude, but previous works focused mostly on manual measures in single electrocardiogram (ECG) leads only. AIM: To assess the long-term prediction ability of f-wave amplitude when computed in multiple ECG leads. METHODS: Sixty-two patients with persistent AF (52 men; mean age 61.5±10.4years) referred for CA were enrolled. A standard 1-minute 12-lead ECG was acquired before the ablation procedure for each patient. F-wave amplitudes in different ECG leads were computed by a non-invasive signal processing algorithm, and combined into a mutivariate prediction model based on logistic regression. RESULTS: During an average follow-up of 13.9±8.3months, 47 patients had no AF recurrence after ablation. A lead selection approach relying on the Wald index pointed to I, V1, V2 and V5 as the most relevant ECG leads to predict jointly CA outcome using f-wave amplitudes, reaching an area under the curve of 0.854, and improving on single-lead amplitude-based predictors. CONCLUSION: Analysing the f-wave amplitude in several ECG leads simultaneously can significantly improve CA long-term outcome prediction in persistent AF compared with predictors based on single-lead measures.

Atrial signal extraction in atrial fibrillation electrocardiograms using a tensor decomposition approach.

Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice and remains a major challenge in cardiology. The noninvasive analysis of AF usually requires the estimation of the atrial activity (AA) signal in surface electrocardiogram (ECG) recordings. The present contribution puts forward a tensor decomposition approach for noninvasive AA extraction in AF ECG recordings. As opposed to the matrix approach, tensor decompositions are generally unique under mild conditions and have the potential to perform source separation in scenarios with a limited number of electrodes. An experimental study on a synthethic signal model and a real AF ECG recording evaluates the performance of the so-called block term tensor decomposition approach as compared to matrix techniques such as principal component analysis and independent component analysis.

Empirical mode decomposition of multiple ECG leads for catheter ablation long-term outcome prediction in persistent atrial fibrillation.

Predictive models arouse increasing interest in clinical practice, not only to improve successful intervention rates but also to extract information of diverse physiological disorders. This is the case of persistent atrial fibrillation (AF), the most common cardiac arrhythmia in adults. Currently, catheter ablation (CA) is one of the preferred therapies to face this disease. However, selecting the best responders to CA by standard noninvasive techniques such as the electrocardiogram (ECG) remains a challenge. This work presents different predictive models for determining long-term CA outcome based on the dominant frequency (DF) of atrial activity measured in the ECG. The ensemble empirical mode decomposition (EEMD) is employed to obtain the intrinsic mode functions (IMFs) composing the ECG signal in each lead. The IMF DFs computed in multiple leads are then combined into a logistic regression (LR) model. The IMF DF features are discriminant enough to reach 79% accuracy for long-term CA outcome prediction, outperforming other methods based on DF computation. Our study shows EEMD as a valuable alternative to extract clinically relevant spectral information from AF ECGs and confirms the advantage of LR to build multivariate predictive models as compared with univariate analysis.

Disentangling the attention network test: behavioral, event related potentials, and neural source analyses.

BACKGROUND: The study of the attentional system remains a challenge for current neuroscience. The "Attention Network Test" (ANT) was designed to study simultaneously three different attentional networks (alerting, orienting, and executive) based in subtraction of different experimental conditions. However, some studies recommend caution with these calculations due to the interactions between the attentional networks. In particular, it is highly relevant that several interpretations about attentional impairment have arisen from these calculations in diverse pathologies. Event related potentials (ERPs) and neural source analysis can be applied to disentangle the relationships between these attentional networks not specifically shown by behavioral measures. RESULTS: This study shows that there is a basic level of alerting (tonic alerting) in the no cue (NC) condition, represented by a slow negative trend in the ERP trace prior to the onset of the target stimuli. A progressive increase in the CNV amplitude related to the amount of information provided by the cue conditions is also shown. Neural source analysis reveals specific modulations of the CNV related to a task-related expectancy presented in the NC condition; a late modulation triggered by the central cue (CC) condition and probably representing a generic motor preparation; and an early and late modulation for spatial cue (SC) condition suggesting specific motor and sensory preactivation. Finally, the first component in the information processing of the target stimuli modulated by the interaction between orienting network and the executive system can be represented by N1. CONCLUSIONS: The ANT is useful as a paradigm to study specific attentional mechanisms and their interactions. However, calculation of network effects is based in subtractions with non-comparable experimental conditions, as evidenced by the present data, which can induce misinterpretations in the study of the attentional capacity in human subjects.

Neural correlates of alerting and orienting impairment in multiple sclerosis patients.

BACKGROUND: A considerable percentage of multiple sclerosis patients have attentional impairment, but understanding its neurophysiological basis remains a challenge. The Attention Network Test allows 3 attentional networks to be studied. Previous behavioural studies using this test have shown that the alerting network is impaired in multiple sclerosis. The aim of this study was to identify neurophysiological indexes of the attention impairment in relapsing-remitting multiple sclerosis patients using this test. RESULTS: After general slowing had been removed in patients group to isolate the effects of each condition, some behavioral differences between them were obtained. About Contingent Negative Variation, a statistically significant decrement were found in the amplitude for Central and Spatial Cue Conditions for patient group (p<0.05). ANOVAs showed for the patient group a significant latency delay for P1 and N1 components (p<0.05) and a decrease of P3 amplitude for congruent and incongruent stimuli (p<0.01). With regard to correlation analysis, PASAT-3s and SDMT showed significant correlations with behavioral measures of the Attention Network Test (p<0.01) and an ERP parameter (CNV amplitude). CONCLUSIONS: Behavioral data are highly correlated with the neuropsychological scores and show that the alerting and orienting mechanisms in the patient group were impaired. Reduced amplitude for the Contingent Negative Variation in the patient group suggests that this component could be a physiological marker related to the alerting and orienting impairment in relapsing-remitting multiple sclerosis. P1 and N1 delayed latencies are evidence of the demyelination process that causes impairment in the first steps of the visual sensory processing. Lastly, P3 amplitude shows a general decrease for the pathological group probably indexing a more central impairment. These results suggest that the Attention Network Test give evidence of multiple levels of attention impairment, which could help in the assessment and treatment of relapsing-remitting multiple sclerosis patients.

Regions of interest computed by SVM wrapped method for Alzheimer's disease examination from segmented MRI.

Accurate identification of the most relevant brain regions linked to Alzheimer's disease (AD) is crucial in order to improve diagnosis techniques and to better understand this neurodegenerative process. For this purpose, statistical classification is suitable. In this work, a novel method based on support vector machine recursive feature elimination (SVM-RFE) is proposed to be applied on segmented brain MRI for detecting the most discriminant AD regions of interest (ROIs). The analyses are performed both on gray and white matter tissues, achieving up to 100% accuracy after classification and outperforming the results obtained by the standard t-test feature selection. The present method, applied on different subject sets, permits automatically determining high-resolution areas surrounding the hippocampal area without needing to divide the brain images according to any common template.