Overall average N100-AEP waveforms of the mid-frontal region.

<p>A: Waveforms of the low WM load task; B: Waveforms of the high WM load task. Solid lines represent the average N100-AEP waveform in the WM state; dotted lines represent the average N100-AEP waveform in the rest state.</p

Time Latencies of the N100-AEP trough in each brain region in WM states and rest states in the low WM load tasks.

<p>There were no statistically significant differences (<i>P</i>>0.05) in the N100-AEP latencies between WM states and rest states in the low load tasks. L-PF: left-prefrontal; M-PF: mid-prefrontal; R-PF: right-prefrontal; L-F: left-frontal; M-F: mid-frontal; R-F: right-frontal; L-T: left-temporal; R-T: right-temporal. Data is shown as mean (SD).</p

The percentage of correct responses in the visual WM tasks.

<p>The percentage of correct responses is greater than 85% in both the low and high WM load tasks. Data is shown as mean (SD).</p

Mean N100-AEP amplitudes in rest states (white bars) and WM states (gray bars) following an irrelevant auditory stimulus.

<p>A: N100-AEP amplitudes in low WM load task; B: N100-AEP amplitudes in High WM load task. L-PF: left-prefrontal; M-PF: mid-prefrontal; R-PF: right-prefrontal; L-F: left-frontal; M-F: mid-frontal; R-F: right-frontal; L-T: left-temporal; R-T: right-temporal. Results are expressed as mean±SEM; *<i>P</i><0.05.</p

Time Latencies of the N100-AEP trough in each brain region in WM states and rest states in the high WM load tasks.

<p>There were no statistically significant differences (<i>P</i>>0.05) in the N100-AEP latencies between WM states and rest states in the high load tasks. L-PF: left-prefrontal; M-PF: mid-prefrontal; R-PF: right-prefrontal; L-F: left-frontal; M-F: mid-frontal; R-F: right-frontal; L-T: left-temporal; R-T: right-temporal. Data is shown as mean (SD).</p

Hyperthermia-Induced Disruption of Functional Connectivity in the Human Brain Network

<div><p>Background</p><p>Passive hyperthermia is a potential risk factor to human cognitive performance and work behavior in many extreme work environments. Previous studies have demonstrated significant effects of passive hyperthermia on human cognitive performance and work behavior. However, there is a lack of a clear understanding of the exact affected brain regions and inter-regional connectivities.</p> <p>Methodology and Principal Findings</p><p>We simulated 1 hour environmental heat exposure to thirty-six participants under two environmental temperature conditions (25°C and 50°C), and collected resting-state functional brain activity. The functional connectivities with a preselected region of interest (ROI) in the posterior cingulate cortex and precuneus (PCC/PCu), furthermore, inter-regional connectivities throughout the entire brain using a prior Anatomical Automatic Labeling (AAL) atlas were calculated. We identified decreased correlations of a set of regions with the PCC/PCu, including the medial orbitofrontal cortex (mOFC) and bilateral medial temporal cortex, as well as increased correlations with the partial orbitofrontal cortex particularly in the bilateral orbital superior frontal gyrus. Compared with the normal control (NC) group, the hyperthermia (HT) group showed 65 disturbed functional connectivities with 50 of them being decreased and 15 of them being increased. While the decreased correlations mainly involved with the mOFC, temporal lobe and occipital lobe, increased correlations were mainly located within the limbic system. In consideration of physiological system changes, we explored the correlations of the number of significantly altered inter-regional connectivities with differential rectal temperatures and weight loss, but failed to obtain significant correlations. More importantly, during the attention network test (ANT) we found that the number of significantly altered functional connectivities was positively correlated with an increase in executive control reaction time.</p> <p>Conclusions/Significance</p><p>We first identified the hyperthermia-induced altered functional connectivity patterns. The changes in the functional connectivity network might be a possible explanation for the cognitive performance and work behavior alteration.</p> </div

Autophagy contributes to sulfonylurea herbicide tolerance via GCN2-independent regulation of amino acid homeostasis

<p>Sulfonylurea (SU) herbicides inhibit branched-chain amino acid (BCAA) biosynthesis by targeting acetolactate synthase. Plants have evolved target-site resistance and metabolic tolerance to SU herbicides; the GCN2 (general control non-repressible 2) pathway is also involved in SU tolerance. Here, we report a novel SU tolerance mechanism, autophagy, which we call ‘homeostatic tolerance,’ is involved in amino acid signaling in <i>Arabidopsis</i>. The activation and reversion of autophagy and GCN2 by the SU herbicide tribenuron-methyl (TM) and exogenous BCAA, respectively, confirmed that TM-induced BCAA starvation is responsible for the activation of autophagy and GCN2. Genetic and biochemical analyses revealed a lower proportion of free BCAA and more sensitive phenotypes in <i>atg5</i>, <i>atg7</i>, and <i>gcn2</i> single mutants than in wild-type seedlings after TM treatment; the lowest proportion of free BCAA and the most sensitive phenotypes were found in <i>atg5 gcn2</i> and <i>atg7 gcn2</i> double mutants. Immunoblotting and microscopy revealed that TM-induced activation of autophagy and GCN2 signaling do not depend on the presence of each other, and these 2 pathways may serve as mutually compensatory mechanisms against TM. TM inhibited the TOR (target of rapamycin), and activated autophagy in an estradiol-induced <i>TOR</i> RNAi line, suggesting that TM-induced BCAA starvation activates autophagy, probably via TOR inactivation. Autophagy and GCN2 were also activated, and independently contributed to TM tolerance in plants conferring metabolic tolerance. Together, these data suggest that autophagy is a proteolytic process for amino acid recycling and contributes to GCN2-independent SU tolerance, probably by its ability to replenish fresh BCAA.</p

Maps of brain regions showing significant positive or negative correlations with the PCC/PCu in a voxel-wise manner of within-group analysis.

<p>(a) NC group, (b) HT group and between-group analysis (c).</p

Results of functional connectivity analysis throughout the entire brain.

<p>(a–b) Mean zFC matrices of both groups throughout the entire brain divided into 90 regions by the AAL atlas. Visually different correlations between both groups mainly focused on the prefrontal regions (red rectangle) and temporal regions (yellow rectangle). (c–d) Significant correlations throughout the whole brain between both groups are depicted in the matrix figure (c) and brain network figure (d). The top-left triangle in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061157#pone-0061157-g003" target="_blank">Fig. 3 (c)</a> exhibits the p value of the significant correlations and the bottom-right one exhibits the T value of the corresponding correlations. The rectangles in green, pink, blue and black represent significant altered correlations. Details can be seen in the Result part.</p

La Démocratie pacifique : journal des intérêts des gouvernements et des peuples

22 juin 18461846/06/22 (A15,T6,N162)-1846/06/23 (A15,T6,N163)