Notes

Rendering associated with an Educational Encouraging Stop for the Countrywide School Fitness Connection Split III Athletic Plan.
The objective was to provide psychometric evidence of the 14-item and 10-item version of the Perceived Stress Scale in nursing professionals from Peru.

Data on 2848 professionals (92.275% women) between 23 and 69 years old (M = 21.876; SD = 10.763) was extracted from ENSUSALUD 2015. An exploratory structural equation modelling (ESEM) was applied with Mplus 7.0 software to analyse several measurement models in the PSS unidimensional, bidimensional, models with method factor, and bifactor. Finally, the reliability was analysed.

The two-factor structure obtained adequate fit indices, and acceptable factorial loadings (>.50), while the unidimensional model has poor statistical support. The construct and score reliability was also adequate.

The two-factor model of the PSS-10 and PSS-14 presents adequate psychometric properties to expand its use to empirical research.
The two-factor model of the PSS-10 and PSS-14 presents adequate psychometric properties to expand its use to empirical research.With emerging SARS-CoV-2 variants, vaccines approved so far are under scrutiny for long term effectiveness against the circulating strains. There is a prevalent obsession with humoral immunity as in vitro studies have indicated diminished effects of vaccine-induced neutralizing antibodies. However, this need not clinically translate to vaccine resistance as immune response against all forms of present vaccine preparations is T dependent unlike that against native viral particles which can induce T independent immune responses. Thus, we focused on this major correlate of protection against infections, T cell response. Using bioinformatics tools, we analyzed SARS-CoV-2 Spike protein T cell epitopes and their diversity across Delta plus/B.1.617.2.1, Gamma/P.1 (variant of concern), B.1.1.429, Zeta/P.2 and Mink cluster 5/B.1.1.298 variants as well as Omicron/B.1.1.529 (variant of concern). We also compared HLA restriction profiles of the mutant epitopes with that of the native epitopes (from Wuhan_hu_1 strain, used in vaccine formulations). Our observations show ~90% conservation of CD4+ and CD8+ epitopes across Delta plus/B.1.617.2.1, Gamma/P.1 (variant of concern), B.1.1.429, Zeta/P.2 and Mink cluster 5/B.1.1.298. For the Omicron/B.1.1.529 variant, ~75% of CD4+ and ~ 87% CD8+ epitopes were conserved. Majority of the mutated CD4+ and CD8+ epitopes of this variant were predicted to retain the HLA restriction pattern as their native epitopes. The results of our bioinformatics analysis suggest largely conserved T cell responses across the studied variants, ability of T cells to tackle new SARS-CoV-2 variants and aid in protection from COVID-19 post vaccination. In conclusion, the results suggest that current vaccines may not be rendered completely ineffective against new variants.In decision neuroscience, the motor system has primarily been considered to be involved in executing choice actions. However, a competing perspective suggests its engagement in the evaluation of options, traditionally considered to be performed by the brain's valuation system. Here, we investigate the role of the motor system in value-based decision making by determining the neural circuitries associated with the sensorimotor beta oscillations previously identified to encode decision options. In a simultaneous EEG-fMRI study, participants evaluated reward and risk associated with a forthcoming action. A significant sensorimotor beta desynchronization was identified prior to and independent of response. The level of beta desynchronization showed evidence of encoding the reward levels. This beta desynchronization covaried, on a trial-by-trial level, with BOLD activity in the cortico-basal ganglia-thalamic circuitry. In contrast, there was only a weak covariation within the valuation network, despite significant modulation of its BOLD activity by reward levels. These results suggest that the way in which decision variables are processed differs in the valuation network and in the cortico-basal ganglia-thalamic circuitry. We propose that sensorimotor beta oscillations indicate incentive motivational drive towards a choice action computed from the decision variables even prior to making a response, and it arises from the cortico-basal ganglia-thalamic circuitry.Optical coherence tomography (OCT) images of ex vivo human brain tissue are corrupted by multiplicative speckle noise that degrades the contrast to noise ratio (CNR) of microstructural compartments. This work proposes a novel algorithm to reduce noise corruption in OCT images that minimizes the penalized negative log likelihood of gamma distributed speckle noise. The proposed method is formulated as a majorize-minimize problem that reduces to solving an iterative regularized least squares optimization. We demonstrate the usefulness of the proposed method by removing speckle in simulated data, phantom data and real OCT images of human brain tissue. We compare the proposed method with state of the art filtering and non-local means based denoising methods. We demonstrate that our approach removes speckle accurately, improves CNR between different tissue types and better preserves small features and edges in human brain tissue.Intracranial EEG (iEEG) performed during the pre-surgical evaluation of refractory epilepsy provides a great opportunity to investigate the neurophysiology of human cognitive functions with exceptional spatial and temporal precisions. A difficulty of the iEEG approach for cognitive neuroscience, however, is the potential variability across patients in the anatomical location of implantations and in the functional responses therein recorded. In this context, we designed, implemented, and tested a user-friendly and efficient open-source toolbox for Multi-Patient Intracranial data Analysis (MIA), which can be used as standalone program or as a Brainstorm plugin. MIA helps analyzing event related iEEG signals while following good scientific practice recommendations, such as building reproducible analysis pipelines and applying robust statistics. The signals can be analyzed in the temporal and time-frequency domains, and the similarity of time courses across patients or contacts can be assessed within anatomical regions. MIA allows visualizing all these results in a variety of formats at every step of the analysis. Here, we present the toolbox architecture and illustrate the different steps and features of the analysis pipeline using a group dataset collected during a language task.The vast majority of fMRI studies of task-related brain activity utilize common levels of task demands and analyses that rely on the central tendencies of the data. This approach does not take into account perceived difficulty nor regional variations in brain activity between people. The results are findings of brain-behavior relationships that weaken as sample sizes increase. Participants of the current study included twenty-six healthy young adults evenly split between the sexes. EGFR inhibitor The current work utilizes five parametrically modulated levels of memory load centered around each individual's predetermined working memory cognitive capacity. Principal components analyses (PCA) identified the group-level central tendency of the data. After removing the group effect from the data, PCA identified individual-level patterns of brain activity across the five levels of task demands. Expression of the group effect significantly differed between the sexes across all load levels. Expression of the individual level patterns demonstrated a significant load by sex interaction. Furthermore, expressions of the individual maps make better predictors of response time behavior than group-derived maps. We demonstrated that utilization of an individual's unique pattern of brain activity in response to increasing a task's perceived difficulty is a better predictor of brain-behavior relationships than study designs and analyses focused on identification of group effects. Furthermore, these methods facilitate exploration into how individual differences in patterns of brain activity relate to individual differences in behavior and cognition.The self is characterized by an intrinsic temporal component consisting in continuity across time. On the neural level, this temporal continuity manifests in the brain's intrinsic neural timescales (INT) that can be measured by the autocorrelation window (ACW). Recent EEG studies reveal a relationship between resting state ACW and self-consciousness. However, it remains unclear whether ACW exhibits different degrees of task-related changes during self-specific compared to non-self-specific activities. To this end, participants in our study initially recorded an eight-minute autobiographical narrative. Following a resting-state session, participants were presented with their own narrative and the narrative of a stranger while undergoing concurrent EEG recording. Behaviorally, subjects evaluated both of the narratives and indicated their perceptions of positivity or negativity on a moment-to-moment basis by positioning a cursor relative to the center of the computer screen. Our results indicate (a) greater spatial extension and velocity in the behavioral cursor movement during the self narrative assessment compared to the non-self narrative assessment; and (b) longer neural ACWs in response to the self- compared to the non-self narrative and rest. These findings demonstrate the importance of longer temporal windows in neural activity measured by ACW for self-specificity. More broadly, the results highlight the relevance of temporal continuity for the self on the neural level. Such temporal continuity may, correspondingly, also manifest on the psychological level as a "common currency" between brain and self.The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects observed in imaging data and their derived structural and functional features have prevented the derivation of consistent findings across multiple studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we propose a deep learning-based framework to harmonize imaging data obtained from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling subject dataset from the Strategic Research Program for Brain Sciences (SRPBS) and harmonized the gray matter volume maps derived from eight source sites to a target site. The proposed framework significantly eliminated intersite differences in gray matter volumes. The embedded encoders successfully captured both the abstract textures of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in terms of site effect removal, data distribution homogenization, and intrasubject similarity improvement. Finally, the proposed harmonization network provided fixable expandability, through which new sites could be linked to the target site via indirect schema without retraining the whole model. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that can enhance reliability and reproducibility in multisite studies regarding brain development and brain disorders.
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