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Frontiers in Human Neuroscience
Motor Neuroscience
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Cognitive Fatigue: Underlying Mechanisms and Impact on Performance View all 17 Articles
Stephane Perrey
Université de Montpellier, France
Makii Muthalib
Muthuraman Muthuraman
Division of Movement Disorders and Neurostimulation, Department of Neurology, University Medical Centre, Johannes Gutenberg University Mainz, Germany
Front. Hum. Neurosci., 20 September 2018 | https://doi.org/10.3389/fnhum.2018.00378
Cognitive Fatigue, Sleep and Cortical Activity in Multiple Sclerosis Disease. A Behavioral, Polysomnographic and Functional Near-Infrared Spectroscopy Investigation
Guillermo Borragán1,2*, Médhi Gilson1,2, Anne Atas2,3, Hichem Slama1,2,4,5, Andreas Lysandropoulos6, Melanie De Schepper1,2 and Philippe Peigneux1,2
1Neuropsychology and Functional Neuroimaging Research Unit, Université Libre de Bruxelles, Brussels, Belgium
2Centre de Recherches en Cognition et Neurosciences and ULB Neurosciences Institute, Université Libre de Bruxelles, Brussels, Belgium
3Consciousness, Cognition and Computational Group, Université Libre de Bruxelles, Brussels, Belgium
4Cognitive Neurosciences Research Unit, Université Libre de Bruxelles, Brussels, Belgium
5Department of Clinical and Cognitive Neuropsychology, Erasme Hospital, Université Libre de Bruxelles, Brussels, Belgium
6Neuroimmunology Unit – Multiple Sclerosis, Erasme Hospital, Université Libre de Bruxelles, Brussels, Belgium
Patients with multiple sclerosis (MS) disease frequently experience fatigue as their most debilitating symptom. Fatigue in MS partially refers to a cognitive component, cognitive fatigue (CF), characterized by a faster and stronger than usual development of the subjective feeling of exhaustion that follows sustained cognitive demands. The feeling of CF might result from supplementary task-related brain activity following MS-related demyelination and neurodegeneration. Besides, CF in MS disease might also stem from disrupted sleep. The present study investigated the association between the triggering of CF, task-related brain activity and sleep features. In a counterbalance mixed design, 10 patients with MS and 11 healthy controls were exposed twice for 16 min to a CF-inducing dual working memory updating task (TloadDback) under low or high cognitive demands conditions, counterbalanced. Considering known inter-individual differences and potential cognitive deficits in MS, the maximal cognitive load of the task was individually adapted to each participant's own upper limits. During the experimental sessions, cortical brain activity was measured using near-infrared spectroscopy (NIRS) during the CF-induction task, and in a resting state immediately before and after. Ambulatory polysomnography recordings were obtained on the nights preceding experimental sessions. When cognitive load was individually adapted to their processing capabilities, patients with MS exhibited similar than healthy controls levels of subjectively perceived CF, evolution of performance during the task, and brain activity patterns. Linear mixed models indicate a negative association between oxygenation level changes in the dorsolateral prefrontal cortex (DLPFC) and the triggering of subjective CF in patients with MS only. Longer total sleep time was also associated with higher CF in MS patients. These results suggest that controlling for cognitive load between individuals with and without MS results in a similar task-related development of subjective CF. Besides comparable performance and cortical brain activity between groups, mixed model analyses suggest a possible association between CF, DLPFC activity and sleep duration in MS disease.
Multiple sclerosis (MS) is a neurodegenerative disease characterized by inflammation, demyelination and axonal degeneration, often accompanied by motor and cognitive deficits (Trapp et al., 1998). MS is the first cause of disability in young populations in modern societies (Rosati, 2001), with high social care costs (Adelman et al., 2013). Fatigue is the most common symptom, reported by up to 90% of patients with MS (Walker et al., 2012). Moreover, fatigue is for more than half of the patients reported as the main cause of interference with daily living activities and quality of life (Comi et al., 2001). Multifactorial in nature, the phenomenon can be classified into primary fatigue, if the causal mechanisms are directly related to the disease, and secondary fatigue, if the mechanisms are not specific to the disease (Kos et al., 2008). Primary fatigue has been associated with brain atrophy (Calabrese et al., 2010; Genova et al., 2013), functional brain connectivity changes (Cader et al., 2006; Cruz Gómez et al., 2013), disease severity (Pittion-Vouyovitch et al., 2006) as usually assessed with the Expanded Disability Status Scale (EDSS; Kurtzke, 1983), dopamine imbalance (Dobryakova et al., 2015), neuroendocrine level variations (e.g., pro-inflammatory cytokines; Heesen et al., 2006) and compensatory brain overactivation patterns (Kos et al., 2008; Leocani et al., 2008). For its part, secondary fatigue has been associated with factors such as mood and personality (Bol et al., 2009), depression (Strober and Arnett, 2005) or sleep disturbances (Veauthier and Paul, 2014). In spite of the large number of variables associated with fatigue, the symptom is still barely understood, and pharmacological treatments are barely efficient (Bol et al., 2009). One potential explanation for inconclusive results might stem from a lack of specification of the term "fatigue." Indeed, the physical, cognitive and psychosocial aspects of fatigue are often intermixed during the assessment of the symptoms. For instance, these different aspects are not explicitly segregated in the most popular fatigue severity scale inventory (FSS; Krupp et al., 1989). Physical and cognitive fatigue (CF) are better distinguished in the fatigue scale for motor and cognitive functions (FSMC; Penner et al., 2009), but fatigue is only assessed as a trait in this scale. However, patients with MS experience variations in their fatigue state in daily life, and might be especially vulnerable to CF triggered online by task-related cognitive demands. Although several studies might support the assumption that CF develops faster in patients with MS than in controls (Lehmann et al., 2012; Genova et al., 2013; Sandry et al., 2014), it is worth noticing that in these studies, all participants were exposed to the same task conditions, which might bias the evaluation of CF. Indeed, since attentional (Urbanek et al., 2010) and working memory (Brissart et al., 2012) deficits are often reported in MS, cognitive demands for the same task conditions are likely to be much higher for patients than controls, thus eventually leading to higher pressure and CF levels. Therefore, to delineate the development of task-related CF in MS disease from other factors, task demands should be carefully adapted to each patient's individual cognitive processing capacity.
As mentioned above, compensatory cortical overactivity needed to maintain performance at normal levels in visual (Werring et al., 2000; Colorado et al., 2012), motor (Pantano, 2002; Tomassini et al., 2012) and cognitive (Forn et al., 2006; Audoin et al., 2008; Colorado et al., 2012) tasks is viewed as a potential cause for CF in MS disease. Besides, it was proposed that compensatory brain overactivity required to cope with the neurodegenerative process in MS is a cause of acute fatigue (Leocani et al., 2008). However, evidence remains scarce (DeLuca et al., 2008; Genova et al., 2013) and no predictors of this capacity have been described beyond cognitive reserve (Bonnet et al., 2010; Das et al., 2014). Additionally, sleep disturbances have been reported in MS, and especially fragmented sleep (Kaminska et al., 2011). Sleep is involved in brain plasticity maintenance (Dang-Vu et al., 2006) and is hypothesized to support the cellular homeostasis restorative processes allowing the brain to cope with daily cognitive challenges (Tononi and Cirelli, 2014). Thus, sleep fragmentation in MS might reduce the efficacy of brain compensatory mechanisms to maintain performance, eventually resulting in a faster triggering of CF. If this relation is proven true, then sleep medicine interventions in the clinical management might contribute to improve quality of life in MS patients.
In this framework, we aimed at investigating the putative triangular relationship between the triggering of CF, cortical overactivity patterns and sleep disruptions. To assess CF unbiased by interindividual differences face to the same task conditions, we used a paradigm in which task-related cognitive load can be individually adapted to each participant's maximal performance level (TloadDback task; Borragán et al., 2017). Participants were then administered in a counterbalanced design the same task on two separate occasions, once in a high cognitive load (HCL) and once in a low cognitive load (LCL) condition. Cortical activity was recorded before, during and after the CF-inducing task (HCL and LCL on two separate days) using functional near infrared spectroscopy (fNIRS). Polysomnography recordings were obtained for the nights preceding each of the experimental days.
Ten patients with relapsing-remitting MS (RRMS) according to the criteria of McDonald et al. (2001) and 11 healthy controls were included in the present study. Patients were recruited over a 12-month period by an experienced neurologist (AL), and were in remission from the time of the clinical examination until the NIRS imaging session. All participants gave their written informed consent to participate in the present study conducted in agreement with the Declaration of Helsinki, and approved by the institutional ethics committee of the ULB-Erasme Hospital. MS and control groups had similar level of education (in years) but were not matched for age (Table 1). Therefore, age was included as a confounding covariate in the statistical analyses. Demographic and neuropsychological data are reported in Table 1. Due to a data transfer problem, fNIRS data were not accessible for two patients. Likewise, PSG was not obtained in one MS patient and one control participant, due to bad quality in the EEG signal.
TABLE 1. Demographic and neuropsychological data in healthy controls and patients with MS disease.
Experimental Design and Procedure
The experiment was carried out over 7 consecutive days (Figure 1). On Day 1, a Pretest session (35–40 min duration) aimed at determining for each participant separately her/his maximal cognitive load capacity on the CF-inducing TloadDback task (see below). I.e., maximal load was defined as the fastest stimulus presentation rate at which the participant was still able to maintain an accuracy performance > 85%. This fastest presentation rate defined the HCL condition. For the LCL condition, stimulus presentation rate was slowed down by 50%, making it easier to process the ongoing stimuli. This procedure was shown efficient to induce high vs. low levels of CF in healthy participants (Borragán et al., 2016, 2017). Following the Pretest session at Day 1 and after a brief rest period (∼ 10 min), a neuropsychological battery test (average duration 45 min) was administered. On Day 5 and Day 7, participants were first equipped with the fNIRS optodes (20–25 min), and then administered during 16 min the TloadDback task under either the high (HCL) or the low (LCL) cognitive load condition, in a counterbalanced order. Cortical activity was recorded using fNIRS during the 16 min of TloadDback task practice, and during two 4-min resting state (Rst1) sessions, immediately before and after the TloadDback task. In the resting state sessions, participants were asked to look at a fixation cross on a computer screen. Before and after each resting state and the TloadDback task (i.e., at 4 time points per testing day, see Figure 1), participants were asked to report their subjectively perceived levels of CF and sleepiness using visual analog scales Thus, a whole testing session lasted approximately for 50 min. The night before each testing session, sleep was recorded using ambulatory polysomnography at the participant's home. Polysomnography was set-up and configured by the principal investigator (GB) 1–2 h before participants' usual bedtime. To prevent circadian confounds, all testing sessions took place at the same time of day within each participant, either in the morning (9–12 h) or in the afternoon (14–19 h).
FIGURE 1. Experimental design (overview). On Day 1, participants' maximal cognitive load is calibrated on the TloadDback task (pretest), followed by a neuropsychological assessment. On the nights of Day 4 and Day 6, nocturnal sleep is recorded at home using ambulatory PSG. On Day 5 and Day 7 after the PSG night, participants are administered the CF-inducing TloadDback task under either the high (HCL) or low (LCL) cognitive load condition, counterbalanced. Evolution of subjective feelings of CF and sleepiness were assessed throughout the testing session using visual analog scales (VAS). Cortical brain activity was recorded using fNIRS both during the TloadDback task and during the resting state sessions immediately before and after the task.
Tasks and Material
Cognitive Fatigue Induction Paradigm (TloadDback)
Cognitive fatigue was induced using the TloadDback task (for details see Borragán et al., 2017), a dual task that combines a working memory updating task (N-back; Kirchner, 1958) and a parity number decision task. In this task, digits and letters are displayed on screen in alternation. Participants are instructed to press the space bar with their left hand every time the displayed letter is the same than the penultimate letter, or to indicate whether the displayed digit is odd or even by pressing "1" or "2" on the numeric keypad. Combining two tasks featuring different processing requirements ensures a large recruitment of working memory resources, eventually leading to a decrease in performance and the intensification of the subjective feeling of mental exhaustion (Borragán et al., 2017). The task includes a separate pretest session (on Day 1) during which the individual's maximal cognitive load capacity is determined as the shortest duration the stimuli can be presented (stimulus time duration; STD) for a performance still > 85% accuracy. During this pre-test session, STD was initially set at 1500 ms for the first block (30 letters and 30 digits, alternating), then speeded up by 100 ms for the next block if accuracy performance was >85%. The procedure was repeated until accuracy performance dropped < 85%, indicating that the participant's cognitive load limit had been reached. STD was then slowed down by 100 ms, and again speeded up by 100 ms in case of success, to improve the reliability in the pre-test. After three failures to reach accuracy > 85% at a given STD, the immediately preceding STD at which performance was still successful was determined as the individual's maximal capacity, which was used in the testing session to define the HCL condition. For the LCL condition, STD was made longer by 50%, eventually ensuring comparable levels of low vs. high cognitive demands between individuals. CF was assessed subjectively using a Visual Analog Scale for fatigue (VASf: Lee et al., 1991), and objectively by computing the evolution of performance (accuracy) across four 4-min quartiles (i.e., t1, t2, t3, and t4) during the 16 min duration of the TloadDback task. Additionally, self-reported sleepiness was assessed using a VAS scale (VASs).
Neuropsychological Battery
Executive function and information processing capacities were evaluated using several neuropsychological tests (French versions). The digit and visual spans of the Wechsler Adult Intelligence Scale (WAIS; Wechsler, 1981) consist on a verbal repetition of numbers (Digit) or spatial positions (Visual) in direct and reverse order; they are usually employed to assess short-term and working memory functioning. The Trail-Making Test (Reitan, 1958) mostly tests mental flexibility. It features 25 circles distributed over a sheet. In the Numbers Sequencing condition, the circles are numbered 1 – 25 and the individuals must draw lines to connect the numbers in ascending order. In the Switching Part condition, the circles include both numbers (1 – 13) and letters (A – L) and the individuals must connect the circles alternating numbers and letters in ascending order again (i.e., 1-A-2-B-3-C, etc.). Successful TMT performance requires adequate letter and number recognition, mental flexibility, visual scanning and motor function (Gaudino et al., 1995). In the Stroop task (Stroop, 1935), participants are asked to provide the ink color of a written color name. Interference due to automatic reading makes providing the ink color more difficult and slower when the written color name is different from the printed color. The test thus assess the inhibitory component of selective attention and processing speed ability (Lamers et al., 2010). The written section of the Symbol Digit Modalities Test (SDMT; Smith, 1968) requires individuals to identify nine different symbols corresponding to the numbers 1 through 9 and to manually fill the blank space under each symbol with the corresponding number, as fast as possible. The final score is computed as the number of correct symbols identified and written down in a 90 s interval. The neuropsychological assessment ended up with the Paced Auditory Serial Addition Test (PASAT; Gronwall, 1977). In this test, single digits are presented either every 3 (trial 1) or 2 (trial 2) seconds, and individuals must add each new digit to the one immediately prior to it. Final score is the total number of correct sums given (out of 60 possible) in each trial. The test is widely employed in MS disease to evaluate processing speed, flexibility and calculation ability (Rudick et al., 1997). To moderate the cognitive workload caused by neuropsychological assessment, tests were administered in the order in which they are described in the text.
Polysomnography
To investigate sleep characteristics in the nights preceding the behavioral and fNIRS imaging sessions, we used ambulatory polysomnography (BrainRT EEG/PSG system, OSG BVBA, Belgium). PSG recording included standard electroencephalogram (EEG) at locations Fp1, Fp2, F3, F4, C3, C4, P3 and P4 according to the standard 10–20 system, electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG). Respiratory events were detected using abdominal and thoracic belts, oral/nasal airflow with a nasal pressure transducer, an oronasal thermocouple and oxygen saturation levels measured by finger pulse oximetry. PSG scoring was performed in agreement with the American Academy of Sleep Medicine (AASM) criteria (Iber et al., 2007). Each 30-s epoch was visually classified by a qualified expert somnologist (MG) as Wake, N1, N2, N3 or REM sleep stage. Sleep onset was defined as the first 30-s epoch scored N2, SWS or REM and final awakening as the last 30-s epoch scored N2, SWS or REM. Sleep period time was defined as the time interval separating sleep onset from final awakening. Total sleep time was calculated as the sleep period time minus the duration of intra-sleep awakenings, and sleep efficiency was expressed as the percentage of total sleep time during the sleep period time. The apnoea-hypopnea index (AHI) was computed in all participants. The cut-off score for the presence of apnoea was AHI > 5 events per hour. Micro arousals were computed as awakening periods > 3 s and <15 s. EEG data were visualized using the PRANA software package (PhiTools, Strasbourg, France).
fNIRS Acquisition
Variations in haemodynamic cortical activity were recorded using a multichannel fNIRS system (BrainSight, V2.3b16, NIRS, Rogue Research Inc., Canada) with two continuous wavelengths (685 and 830 nm). The set-up featured a total of 24 channels (detectors) created around 6 source optodes encompassing 3 areas in each cerebral hemisphere: ventrolateral prefrontal cortex (VPFC), dorsolateral prefrontal cortex (DLPFC), and Inferior Parietal Cortex (IPC). Channels in each area were averaged to provide a general measure of brain activity. Optodes were positioned and located using a 3-D coordinates system combined with a Polaris localization device (see Figure 2). Channels displacements during the experiment were minimized using elastic tape. Detector optodes were located at an approximate distance of 3 cm from the source optodes. Functional NIRS signals were recorded at a sample rate of 20 Hz. To limit the natural trend appearing within signal recording, separate recordings were made for the Rst1, TloadDback and Rst2 periods. This made also easier to remind the TloadDback task instructions. Thus, there is a short temporal gap between the 3 successive recordings conducted within one individual.
FIGURE 2. Rendering of the estimated optodes positions on a MNI template brain. Red and blue numbers represent sources and detectors, respectively; yellow lines illustrate their interconnection. The figure was created using Homer2-AtlasViewer (Aasted et al., 2015).
fNIRS Analysis
Oxyhemoglobin ΔHbO and deoxyhemoglobin ΔHbR were computed at each time point for each channel using the modified Beer–Lambert law. For each participant, raw recorded absorption units were normalized then low-pass filtered (0.009 – 0.08 Hz) to attenuate high-frequency noises arising from Mayor waves: respiration, cardiac pulsations and optodes movements. Furthermore, to improve signal quality, noisy channels were automatically detected and removed from the analysis using an automated algorithm based on correlation analysis (Guerrero-Mosquera et al., 2016). In a nutshell, data preprocessing was performed as follows. First, data were located using digital triggers, then a first normalization of AUs (Absorption Unities) was performed followed by the band pass filtering implemented in Homer2 (Huppert et al., 2009). Then, a new normalization was performed to allow computing new analysis coming from a corrected baseline output. This normalization was done with regard to the individual data points. Finally, the concentration of the different chromophores was computed using Homer2's code hmrOD2Conc.
The fNIRS signal was analyzed computing two different indicators of brain activity. First, cortical activity changes during practice of the TloadDback task and during resting states were estimated computing Cerebral Oxygen Exchange (COE; see e.g., Sano et al., 2013; Yoshino et al., 2013), an indirect measure of brain metabolism computed as the difference between HbR and HbO. Higher COE indicates reduced oxygenation levels, and vice-versa. Task-related changes in brain activity during the ensuing resting state period (Rst) were estimated by computing the amplitude of low frequency fluctuations (ALFF; Yu-Feng et al., 2007) of the total hemoglobin concentration. ALFF is viewed as a good measure of spontaneous neural activity, appropriate to detect state-dependent resting brain changes associated with CF (Gui et al., 2015).
Statistical analyses were computed following Fritz et al. (2012) recommendations. Mean (m) ± Standard Deviation (std) are reported as measures of central tendency, and size effects are reported as partial eta squares (η2). Mean squared errors (MSE) are included in the ANOVAs. Significance level was set at p < 0.05 (two-tailed) and Tukey HSD test was employed for post hoc corrections. Bayesian analyses were conducted to estimate the likelihood of the null hypothesis.
Demographic parameters and results on neuropsychological tests are provided Table 1. MS patients were significantly older than controls (p < 0.05, Bayes factor [BF] > 3). For this reason, age was considered as a covariate in the subsequent analyses. When considering the covariate age, sleep quality as measured through the PSQI was lower (p < 0.05, BF > 3) and levels of cognitive and physical fatigue higher (p < 0.05, BFs > 3) in MS patients compared to healthy controls. Besides, patients' performance was lower in both direct and indirect visual span tests (p < 0.05, BFs = inconclusive), the Stroop task (p < 0.01, BF > 17), the SDMT (p < 0.05, BF > 8) and the PASAT-3s (p < 0.01, BF = inconclusive) and PASAT-2s (p < 0.01, BFs > 3).
Pretest
First, we investigated between-group differences in the optimal stimulus time duration (STD) needed to maintain successful performance (accuracy > 85%) in the TloadDback task at Pretest on Day 1. An ANCOVA performed on the STD with a between-subject factor Group and the confound covariate Age disclosed significant differences [F(1,18) =5.78; p < 0.05; MSE = 0.23; ηp2 = 0.24]. Participants in the control group had a shorter (faster) STD (average ± standard deviation 811 ± 0.094 ms) than participants with MS (960 ± 126 ms).
Self-Reported Evolution of Cognitive Fatigue and Sleepiness (VAS)
Subjective evolution of CF was assessed using the VASf before (p1) and after (p2) the resting state session Rst1, after the TloadDback task (p3) and after resting session Rst2 (p4), see Figure 3. A mixed repeated-measure ANCOVA was computed on VASf scores with within-subject factors Condition (HCL vs. LCL) and Period (p1 vs. p2 vs. p3 vs. p4), between-subjects factor Group (Healthy controls vs. MS patients) and confounding covariate Age. Results disclosed a triple interaction Condition × Period × Group [F(3.51) = 3.56; p < 0.05; MSE = 0.97; ηp2 = 0.17; BF was < 0.33]. Tukey post hoc tests showed that CF similarly increased in both groups in the LCL condition (all p < 0.88) with significant differences found between p1- p3 and p1- p4 for both groups (all p < 0.05), i.e., from before the pre-task resting state session (p1) to after the TloadDback (p3) and after the post-task resting state session (p4). CF was not significantly modified from immediately before (p2) to immediately after (p3) the TloadDback task in the LCL condition (all p > 0.16). In the HCL condition, CF increased in both groups (all p < 0.54) with significant differences found again between p1 – p3 and p1 – p4, but also from immediately before the task to immediately after (p2 – p3) and to after the second resting state session (p2 – p4), in both groups (all p < 0.05). Finally, CF significantly increased in MS patients from the pre- (p1) to the post- (p2) resting state session Rst1 (p < 0.05), i.e., before exposure to the CF-inducing TloadDback task. This effect was not present in healthy controls (p > 0.9).
FIGURE 3. Evolution of self-reported cognitive fatigue (CF) using visual analog scales for fatigue (VASf). HCL: high cognitive condition; LCL: low cognitive condition; Rst: pre- (Rst1) and post- (Rst2) task resting state sessions (5 min); TloadDback: CF-inducing dual task (16 min). The interaction between Condition (HCL vs. LCL), Period (pre-Rst1 p1, pre-TloadDback p2, post-TloadDback p3, post-Rst2 p4) and Group (Healthy controls vs. MS patients) was significant (p < 0.05). Asterisks indicate significant p-values after Tukey post hoc correction: ∗p < 0.05; ∗∗p < 0.01, and ∗∗∗p < 0.001. n.s, non-significant.
Similar analyses computed on sleepiness scores failed to disclose any significant effects (all p > 0.16; all BFs < 0.033).
TloadDback
Accuracy performance during the 16 min practice on the TloadDback task was computed across four successive 4-min quartiles (t1, t2, t3 and t4; see Table 2), and entered in a mixed repeated-measure ANCOVA with within-subject factors Condition (HCL vs. LCL) and Time (t1 vs. t2 vs. t3 vs. t4), between-subjects factor Group (Healthy controls vs. MS patients) and confound covariate Age. The triple interaction Condition × Time × Group did not reach significance (p > 0.1, BF < 0.33). There was, however, a main Condition effect [F(1,18) = 20.76; p < 0.001; MSE = 0.003; ηp2 = 0.54; BF > 100] with higher performance in the LCL (95.2 ± 3.5%) than in the HCL (86.7 ± 6.3%) condition. The Condition × Group interaction was also significant [F(1,18)= 8.1; p < 0.05; MSE = 0.003; ηp2 = 0.31; BF = 0.64], but Tukey post hoc tests failed to disclose between-groups differences in performance either in the LCL (Controls 95.5 ± 3.5% vs. MS 95 ± 3.6%) or the HCL (Controls 88 ± 6.3% vs. MS 85 ± 6.3%) condition (all p > 0.6). There was also a significant covariate effect of Age on Condition [F(1,18) = 10.1; p < 0.01; MSE = 0.003; ηp2 = 0.36].
TABLE 2. Evolution of accuracy performance across the four quartiles (t1 to t4) during the TloadDback by Condition (HCL vs. LCL) and Group (Controls vs. MS patients).
fNIRS Results
CF-Induction Related COE During Task Practice
As a reminder, COE values were computed across four successive 4-min quartiles (t1, t2, t3, and t4) for each of the 6 target areas (3 per hemisphere), and entered in a repeated-measure ANCOVA with within-subject factors Area (VLPFC vs. DLPFC vs. IPC), Hemisphere (Right vs. Left), Time on Task (ToT; t1 vs. t2 vs. t3 vs. t4) and Condition (HCL vs. LCL), between-subjects factor Group (Healthy controls vs. MS patients), and confound covariate Age. No significant effect was found (all p > 0.15).
CF-Induction Related COE During Pre- and Post-task Resting States
COE values were similarly recorded for each of the 6 target areas (3 per hemisphere) during the resting states immediately before and after the TloadDback task. A repeated-measure ANCOVA was conducted on COE values during Rst periods with within-subject factors Area (VLPFC vs. DLPFC and IPC), Hemisphere (Right vs. Left), Condition (HCL vs. LCL) and Resting State (pre Rst1 vs. post Rst2), between-subjects factor Group (Controls vs. MS patients) and confounding covariate Age. All effects were non-significant (all p > 0.07).
CF-Induction Related ALFF During Pre- and Post-task Resting States
The Amplitude of Low Frequency Fluctuations (ALFF) is an index of spontaneous neural activity sensitive to activity changes during resting state sessions (Yu-Feng et al., 2007). A repeated-measure ANCOVA was conducted on ALFF values with within-subject factors Area (VLPFC vs. DLPFC and IPC), Hemisphere (Right vs. Left), Condition (HCL vs. LCL) and Resting State (pre Rst1 vs. post Rst2), between-subjects factor Group (Healthy controls vs. MS patients) and confounding covariate Age highlighted an Area × Group interaction effect [F(2,26) = 4.9; p < 0.05; MSE = 0.003; ηp2 = 0.14; BF = 0.27]. Tukey post hoc disclosed lower ALFF in IPC than VLPFC and DLPFC both in Controls (p < 0.005) and MS patients (p < 0.001). Separate ANCOVA in each region of interest failed to disclose significant effects in the VLPFC (all p > 0.20), the DLPFC (all p > 0.32) and the IPC (all p > 0.27).
Sleep Architecture
To investigate possible structural differences in sleep patterns between patients with MS and Controls (as previously reported, e.g., Côté et al., 2012; Veauthier and Paul, 2014), regular and Bayesian ANCOVAs were computed separately on sleep parameters with Age as a confounding covariate. Given the small size of the sample for these results, relevant values were averaged over the two nights to improve data reliability. No significant difference was found between group but for a higher sleep arousal index in MS patients. Results are reported Table 3.
TABLE 3. Polysomnography results.
Relationship Between Cognitive Fatigue, Brain Activity and Sleep
Finally, we investigated the potential dual relatioship between the triggering of CF, brain activity patterns and sleep architecture, using three different multiple regression models with between-subjects factor Group (Controls vs. MS patients) and confounding covariate Age. Again, given the restricted sample size, relevant values (self-reported feelings of CF, brain activity and sleep parameters) were averaged over the two sessions to reduce the loss of statistical power due to the inclusion of a new factor in the modeling. Predictors were centered and outliers excluded before computing the analyses.
Model A. Relationship Between Brain Activity and Triggering of CF (N Controls = 10; N MS patients = 8)
The first model aimed at investigating the relationship between the COE in the three regions of interest (ROIs: VLPFC, DLPFC, IPC) included in the fNIRS set-up (calculated as the average values of COE during TloadDback in both HCL and LCL conditions) and the feeling of CF triggered as a consequence of performing in the TloadDback (i.e., average of self-reported CF during HCL and LCL conditions). COE values at the three ROI were initially included as predictors of CF. Nevertheless, the variance inflation factor (VIF) of the model revealed the existence of collinearity among the predictors (all VIFs > 0.7). Therefore, we computed three explorative regression models for each predictor. Models with COE activity in IPC and VLPFC as predictors did not show any significant result (all p> 0.1). At variance, COE activity in the DLPFC region exhibited a significant interaction by group (Figure 4A), suggesting that activity in this area was associated to the triggering of CF only in MS patients (p < 0.005; Adjusted R squared > 42% variance). The beta of the slope for the Healthy controls group was -1,7e05 whereas for the group of MS patients it was 6.2e05.
FIGURE 4. (A) Scatterplot illustrating the association between self-reported feelings of cognitive fatigue (CF; VASf) and DLPFC activity (COE). (B) Scatterplot between self-reported feelings of CF and total sleep time on the preceding night.
Model B. Relationship Between Sleep Parameters and the Triggering of CF (N Controls = 10; N MS patients = 9)
Given the large number of potential sleep predictors (Côté et al., 2012; # Microarousal, Stages 1-3 and REM, Sleep Efficiency, # ISAs, Total Sleep Time, Arousal index and # of Hypopneas) and the existent collinearity among them, we performed one regression model with each of them. Overall, the predictor Total Sleep Time (Figure 4B) disclosed a significant between-group effect (p < 0.05; Adjusted R squared > 29% variance) suggesting that MS patients who were sleeping more experienced a higher triggering of CF during the TloadDback. Betas were 9.1e-01 for the control group and 9.3e-01 for the MS control.
Model C. Relationship Between Sleep Parameters and CF-Related Brain Activity (N controls = 9; N MS patients = 7)
A new model was created to investigate a possible association between the sleep parameters previously defined and the levels of COE activity in the 3 ROIs. Results failed to disclose significant differences (all p > 0.1).
The purpose of the present study was to investigate cortical brain activity and sleep architecture as possible explanatory factors for the triggering of CF in MS disease. To induce comparable levels of CF between healthy controls and MS patients, we used a task that allows adapting cognitive demands to each participant's optimal performance levels (and cognitive load) during a pretest session (Borragán et al., 2017). Our results show that the manipulation was appropriate for two main reasons. First, there was a significant difference in Pretest STD values between group. Indeed, the final STD was longer at Pretest in patients with MS than in Controls. This suggests that using the same STD for all participants would have resulted from the start in higher cognitive demands in patients with MS than in Controls. This is important because it might have automatically led to higher levels of CF in the patients group. What is more, it may explain why a faster triggering of CF in MS has been reported in prior studies exposing all participants to the same task conditions (Krupp and Elkins, 2000; Lehmann et al., 2012; Claros-Salinas et al., 2013). Second, results of the neuropychological testing showed that patients with MS presented lower scores than Controls on inhibitory and sustained attentional functions as assessed through the Stroop and PASAT tests. Processing speed evaluated via the SDMT and the PASAT was also significantly lower in MS patients. Besides, direct and inverse visual spans scores also suggest that working memory was altered in MS patients, though these latter results were not supported by Bayesian evidence. These results strengthen the need to adapt task demands to the capacities of each individual, especially when the goal of the study is to compare a common phenomenon triggered in two diverse populations. Therefore, the manipulation inherent to the task paradigm used in the present study (TloadDback) helped accounting for cognitive difficulties in determining levels of cognitive load exposure. This fact likely reduced between-group differences in the evolution of self-reported CF, sleepiness and performance in the TloadDback, explaining why we found a similar evolution between the two populations. It can be speculated that equalizing the number of stimuli between groups might have led to a decrease of performance in the MS group, but in this case it would entail that either MS participants would have a faster STD (i.e., being put above their processing capacity) or would need to be exposed longer to the task (being time in itself also a potential contributor in the ToT effect). In this respect, the TloadDback task was designed to allow comparing the evolution of performance across group that exhibit different processing level capabilities. Therefore, task demands (i.e., time needed to process a stimulus) are adapted to each individual eventually leading to similar task durations.
Although the authors acknowledge that a limited sample size in this study asks for caution in the interpretations, Bayesian analyses confirmed an absence of differences in task performance and sleepiness evolution. Moreover, the differential evolution of CF was observed over all the experiment, and was apparently not driven by cognitive load exposure, as patients with MS increased CF within the 5 min of the first resting state session, that is even before the administration of the CF-inducing task. Overall higher fatigability in MS patients was reported in the literature, and might result from the influence of external variables such as anxiety, depression or sleep disorders (lriarte et al., 2000). Notwithstanding those reservations, regression models evidence potential associations between the subjective triggering of CF, brain activity and sleep.
The first regression model disclosed a direct relationship, only present in patients with MS, between brain activity in the DLPFC region and the triggering of subjective CF during the TloadDback task. Diminished oxygenation levels (i.e., higher COE) in the DLPFC were associated with higher feelings of CF. This result may corroborate recent evidence (Sepulcre et al., 2009; Genova et al., 2013) in support of the Chaudhuri and Behan (2000, 2004) model, which proposes that a disruption of the striato-thalamo-cortical pathway is responsible for an exacerbated feeling of CF in neurological disorders such as MS. At variance, a decrease, rather than an increase in activity (oxygenation) associated with higher feelings of CF contrast with the proposal that cerebral overactivation is a causal factor for the triggering of CF in MS (DeLuca et al., 2008; Leocani et al., 2008). Instead, they support earliest evidence suggesting a deactivation in cortical and subcortical regions (Roelcke et al., 1997; Filippi et al., 2002). Another explanation for these different findings relies on task demands (Wylie et al., 2017). Indeed, Wylie et al. (2017) described in healthy participants higher activation levels in the anterior cingulate cortex (ACC) area associated to fatigue when subjects were performing a 2-back task, and deactivation in the same area associated to fatigue during the less complicated zero-back task (Wylie et al., 2017). However, the task condition in our experiment did not appear to modulate changes in cortical oxygenation.
The second regression model evidenced an association between self-reported CF and total sleep time, again only in patients with MS. The analysis suggests that participants who sleep more presented a higher sensitivity to the triggering of CF. That MS patients sleeping longer experience a faster triggering of CF might be explained from two different perspectives. First, it might suggest that patients need longer sleep periods to recover from daytime activity because their sleep is less efficient. Although this possibility was previously discussed (Induruwa et al., 2012; Lehmann et al., 2012), our polysomnography analysis did not support this proposal, considering that patients with MS and Controls exhibited a similar sleep architecture, with only a higher arousal index in patients with MS. Comparable arousal alterations were also reported in MS patients experiencing higher levels of fatigue (Veauthier et al., 2011; Côté et al., 2012). Higher arousal during sleep and increased fatigue might also be associated with a reduced restorative quality of sleep in MS patients. Indeed, sleep in patients with chronic fatigue syndrome was characterized by a disruption in the relative Non-REM sleep power spectra distribution, suggesting a pattern of power exchange in higher frequency bands at the expense of central ultra slow power, that might compromise the homeostatic function of sleep (Neu et al., 2014). Second, it is possible that the higher sensibility of MS patients to the triggering of CF relates to other causes, such as for instance the above-mentioned disruption in the striato-thalamo-cortical pathway. As a consequence, they might require more sleep to recover from the CF accumulated during the day.
Finally, we are willing to acknowledge limitations in our study linked to reduced sample size, and hence reduced statistical power. Nevertheless, the robust nature of our intra-subject comparison models substantiated by Bayesian evidence emphasize the importance of considering individual differences in cognitive capacity prior to the assessment of CF, especially when clinical group are investigated. Besides, mixed models provide further evidence linking cortical brain activity, sleep and CF in MS disease.
GB: substantial contributions to acquisition, analysis, interpretation, and drafting and revising of last version to be published. MG: substantial contributions to the analysis of polysomnography recordings. AA: substantial contributions to the statistical analysis. HS: substantial contributions to data acquisition and interpretation of the data. AL: substantial contributions to patient's recruitment. MDS: substantial contributions to data acquisition. PP: substantial contributions to acquisition, analysis, interpretation, and drafting and revising of last version to be published.
GB was supported by the Program P7/33 IPA/PAI COOL. MG is FRS-FNRS Research Fellow. PP is Francqui Research Professor 2013–2016. The NIRS equipment was supported by FRS-FNRS "Grands Equipements" Fund 2.5020.12.
The reviewer MMuthalib and handling Editor declared their shared affiliation.
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Keywords: cognitive fatigue, sleep, cortical activity, multiple sclerosis, fNRS
Citation: Borragán G, Gilson M, Atas A, Slama H, Lysandropoulos A, De Schepper M and Peigneux P (2018) Cognitive Fatigue, Sleep and Cortical Activity in Multiple Sclerosis Disease. A Behavioral, Polysomnographic and Functional Near-Infrared Spectroscopy Investigation. Front. Hum. Neurosci. 12:378. doi: 10.3389/fnhum.2018.00378
Received: 28 February 2018; Accepted: 03 September 2018;
Published: 20 September 2018.
Stephane Perrey, Université de Montpellier, France
Muthuraman Muthuraman, Universität Mainz, Germany
Makii Muthalib, Université de Montpellier, France
Copyright © 2018 Borragán, Gilson, Atas, Slama, Lysandropoulos, De Schepper and Peigneux. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Guillermo Borragán, gborraganpedraz@gmail.com


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Amphenol RADSOK® RadFin™ was designed to address the need for amperes greater than 100A will maintain small foot prints for PCB applications.
Another unique feature of the RadFin is its ability to act as a great heat dissipating connector. This dramatically helps reduce T-rise with minimal air-flow required.
Amphenol RADSOK® RadStack™ was designed to address the need for high current and small foot prints for PCB mezzanine applications.
Amphenol's RADSOK® enabled SMT RADSERT™ provides a versatile interconnect for high current/high power board to board mezzanine applications. The SMT RADSERT™ is ideal for bringing power to the board from mezzanine cards or busbars suspended above the board. Custom board stack heights are easily accommodated.
RADSERT™ interconnects are low profile, single point, high current contacts that can be applied to a PCB or busbar. Also available with "Super Twist" technology for enhanced lead in and radial mis-alignment tolerance to accommodate multiple mating positions or blind mate applications. RADSERT™ termination options include SMT, knurl "Press-fit" or wave solder with a current carrying capability up to 200A.
RCx is a simple, high density, low cost, passive connector and cable system specifically designed for intra-rack connectivity of 25 Gb, 50 Gb and 100 Gb Ethernet allowing for a seamless intra-rack switch to adapter deployments. The streamlined design eliminates the need to include provision for active electrical components such as EEPROMs, optics, retimers, or management ICs. RCx is density optimized and can accommodate more than 128 25 G lanes in a RU faceplate. That leads two RCx2 receptables to fit on a half-height PCIe card faceplate or two Rcx4 to fit on a full-height PCIe card faceplate. Versatile, the 4-lane RCx4 receptacle can be used as a 4-lane port, or can be bifurcated to be used as multiple 2-lane and 1-lane ports.
Rib-Cage® connectors provide optimal precision and reliability at a competitive commercial cost.
Rib-Cage® connectors meet high performance standards for use in applications with shock and vibration.
The versatile Rib-Cage® system includes dual-row, pinand-socket connectors for right-angle and mezzanine connections between printed circuit boards (PCB) or flexible printed circuits (FPC).
RJMG Connector offer a wide variety of fully RoHS compliant Magnetic Modular Jacks for RJ45. The right angle, through-hole type is offered in both single port and multi-port (1x2, 1x4 and 2x2 configurations) with options of tab up and tab down.
RJ11 is the most commonly recognized registered jack, a standardized telecommunication network interface for connecting voice and data equipment to a telecommunications carrier and is found in homes and offices in most countries for single-line telephones.
This modular jack connector is wired for one analog telephone line, using the centre 2 contacts of 6 available positions (6P2C). It is available in right angle surface mount configuration.
RJ11 is specified with shields to protect against electromagnetic interference (EMI) and radio frequency signals.
Amphenol ICC's RJ11 is designed to meet Category 3 (Cat 3) cable performance. Cat 3 cable features a 10Mb/s transfer rate and supports a transmission speed of 16MHz. It supports 10Mb/s transmission in accordance with the IEEE802.3u standard.
Amphenol ICC Modular Jacks include single and multi-port solutions that are widely used in Telecom, Data, Networking and Home Entertainment markets for various applications. The product features a simple, flexible and low profile design for LAN application, in particular, the Network Interface Card. Modular Jacks Cat 3 supports the transmission of 16Mbps signals targeted to phone, modem, POS terminal machines and Cat 5 supports the transmission of 100Mbps signals targeted to switch, router, hub, etc.
Ruggedized D-sub connectors with die cast housings and IP67 sealing for Harsh Environment applications, provide reliable performance in extreme conditions for the most demanding applications, and mate with standard connectors.
Amphenol's MUSBR series USB Connectors is a line of Generation 2 ruggedized USB connectors with die cast housings and IP67 sealing, designed for Harsh Environment applications. Rugged series provide reliable performance in extreme conditions for the most demanding applications, and mate with standard USB plugs.
Rugged USB Connector Series serve many markets and applications across the globe including Automotive, Communications, Consumer, Data, Industrial and Medical.
1. Transmission rate 6 Gbps.
2. Meet SAS 3.0 specification.
Condition: EIA 364-13. Insert and extract at a speed of 25mm/minute.
Condition: EIA 364-09. Mated and Unmated connector for 25cycles at a rate of 200 cycles per hour.
No physical damage and shall meet requirements of subsequent test.
Condition: EIA 364-23. Subject mated contacts assembled in housing to 20mV maximum. Open circuit at 100mA maximum.
30 mohm max, initial per contact mated pair.
Condition: EIA 364-20. Method B. Test between adjacent of mated and unmated connector assemblies. The dielectric shall withstand 500 VAC for 1 minute at sea level.
There shall be no physical damage and shall meet requirements of subsequent test.
2. Durability: Condition: EIA 364-09. Mated and Unmated connector for 25 cycles at a rate of 200 cycles per hour.
2. Meet SAS 2.1 specification.
Condition: EIA 364-13. Insert and extract at a speed of 12.5mm/minute.
UnMating Force: 20 N Min.
2. Durability:Condition: EIA 364-09. Mated and Unmated connector for 50cycles at a rate of 200 cycles per hour. No physical damage and shall meet requirements of subsequent test.
HIGH SPEED DESIGN WITH FLEXIBILITIESSerial-Attached SCSI/Peripheral Component Interconnect Express (SAS/PCIe 3.0) connectors for next-generation servers with 12Gb/s speeds. The 68-position, SAS/PCIe receptacle and header enables implementation of high speed Serial Attached SCSI (SAS) hard disk drive (HDD) interface as well as Peripheral Component Express (PCIe)-based devices.
CONNECTORS FOR HIGH CAPACITY STORAGE SERVERSAmphenol offers a wide range of high performance SATA connectors. They are designed to support up to 12Gb/s, enabling the implementation of low cost, high speed, high capacity hard disk drive (HDD). The connectors are SATA compliant and meet a wide range of vertical and right angle configurations for usage across server and storage equipment, HDDs and HDD carriers.
2. Meet SATA 3.0 specification.
Mating Force: 45 N max.
Condition: EIA 364-09. Mated and Unmated connector for 50cycles at a rate of 200 cycles per hour.
Condition: EIA 364-23. Subject mated contacts assembled in housing to 20mV max Open circuit at 100mA maximum.
30 mohm max, initial per contact mated pair. 15 mohm maxi, after stress per contact mated pair.
No flashover, no spark over, no excess leakage and no breakdown.
Mated and Unmated connector for 50cycles at a rate of 200 cycles per hour.
SAS 4.0 connectors for next-generation servers with 24Gb/s speeds. The 29-position receptacle and plug connectors feature hot-plugging and blind-mating, connector misalignment compensation, and a PCB retention mechanism for robust SMT attachment. The connectors are SATA compliant and meet a wide range of vertical and right angle configurations for usage across server and storage equipment, HDDs, and HDD carriers.
The SFP interconnect system consists of a 20-position hot swappable I/O connector enclosed in a metal cage mounted to a host PCB. It is designed to support Gigabit Ethernet and Fiber Channel applications. The connector accepts multiple transceivers per INF-8074i and combines,transmits, and receives functions in a low cost, compact and flexible format. We offer a wide variety of cage configurations, which have a two-piece construction with enhanced transceiver mating tabs available in press-fit or solder tail versions. Stacked versions ( 2XN ) consist of a 2-row cage with integrated connectors. The entire assembly in press-fit pin compliant. Main applications of the SFP series include servers, storage, switches, and networking equipment.
SheerPwr™ Circular is a high-current, low-resistance interface designed for connecting busbars to circuit boards. It uses a robust and compliant power contact assembled in a circular orientation.
The result is a power socket, designed to mate with traditional machined pins, which provides repeated low resistance, high mis-alignment and high current carrying capabilities.
Slim Cool Edge Hybrid Power and Signal connectors bring high speed and high power into a one-piece card-edge package with the added benefits of cost competitive and high density. These versatile solutions based on a 0.65mm signal pitch design offer multiple BTB configurations like right angle, mezzanine and coplanar. Moreover, the connectors are designed as Open Pin Field and are hot plug capable.
Unmating Force: 15 N minimum for cycles 1through 5.Extraction force 10N min after 50 mating/unmating(at rate of 200cycles per hour) cycles.


Are you looking to moisturise, hydrate and renew your body and achieve radiant, glowing, whole body beauty?
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Client: Joe Petrocelli, J.Petrocelli Construction, Inc.
This new five-story hotel consists of one hundred guest rooms, an informal lounge, and riverfront pool and deck area. The site is next-door to the Atlantis Marine World Aquarium, located on the Peconic River, has access to a marina.
The structural framing system for the Hyatt Place Hotel consists of 8" and 10" thick pre-stressed, pre-cast hollow core concrete planks with 2" concrete topping slab supported by 8" wide reinforced load-bearing masonry walls at building exteriors and one of the interior corridor walls. Wind and seismic forces on building are resisted by reinforced masonry shear walls. The foundation system consists of 20 tons capacity pressure treated timer piles placed in a staggered layout and caped with continuous concrete grade beams for load-bearing wall support.
Construction of the facility was completed on time and within budget in July 2011.


The City of Beverly Hills has developed an integrated approach to waste collection services, which includes trash, green waste and recycling collection. These programs help the City reduce waste sent to landfills.
ANNOUNCEMENT: PHASE 1 OF THE CITY'S PLASTIC BAG ORDINANCE GOES INTO EFFECT JULY 1, 2014!
Recycling is easy! You no longer need to sort out recyclables...the City does it for you!
The City has developed an integrated approach to refuse collection services including trash, green waste and recycling. BLACK containers are for all household refuse including recyclables and GREEN containers for yard clippings only. The mixed waste is sent to a material recovery facility where the recyclables are sorted and the yard clippings are sent to a composting facility.
This new system has enabled to City to meet and exceed a state mandated 50% diversion of waste from landfills. In 2011, the City's overall diversion rate was 78%.
The City recycles more than 70% of the residential and commercial garbage.
Green waste, i.e. yard clippings, is turned into compost.
The City collects e-waste (electronic devices, batteries, etc) at the Beverly Hills Farmers' Market on the first Sunday of each month.
Batteries may also be brought to the Beverly Hills Public Library for recycling.
For more information call the Public Works Customer Service Department at (310) 285-2467.


aldo shoes sale men shoes ,aldo viralian oxford shoes in brown black men aldo accessories bags,no sale tax product details shoes by aldo suede upper lace-up fastening shaped cuff classic shape almond toe shape wipe with a damp cloth 100% real leather upper brand aldo bensadoun established his first namesake store in montreal, canada in 1972 with global expansion quickly following. a footwear brand with a conscience, aldo have been supporting and working with charities and communities for 25 years. always adapting to suit the fast changing desires of the customer, the brand research worldwide trends to ensure they always offer a perfectly edited collection of cutting-edge, wearable footwear with fine attention to detail. look after me wipe clean with a damp cloth about me lining: 50% real leather, 50% textile, sole: 100% rubber, upper: 100% real leather.


But for some reason, I was getting kind of tired of this wall.
It just didn't sit well with me. I was a little disappointed that it didn't turn out as good as Leanne's wall.
Isn't hers pretty, organized and symmetrical?
So I took it all down.
…put the kids' artwork on the side wall leading to the laundry area and garage.
I like how it looks here plus it works better on this smaller wall since the art doesn't get lost on this wall.
And the bonus is that I see the artwork more often and that makes me smile.
It makes this corner feel so much cooler and cozy all at the same time. I tell you, kids artwork is the best deal out there as it costs next to nothing (frame not included) AND it makes the kids feel special.
So this is how the wall looks now, a little bare, I know but I like it so much better since I felt it was a little cluttered before. And this mirror was tucked in that infamous landing of the stairway.
…and was this tan-ish/blah/pukey color that I didn't really like. I just painted it with some Heirloom White spray paint and it pops so much more. I got this thing for free hosting a Southern Living At Home party years ago and now I get to actually enjoy it and show it off.


Where does custom user deployment information get stored and how does it work in App-V 5.0?
Further to our Boston connection, Ryan Cobb's blog around how to publish individual applications inside a package to different users found here, a few people have been asking me how that data configuration actually works.
So how is this actually working? Let's go behind the scenes….
Here we can see the applications published and for the Microsoft Office 2013 package we can clearly see there is an additional reference to the custom configuration with an ID that equals 17 as stored in the database! I hope that explains how custom user configurations actually work!
One Response to Where does custom user deployment information get stored and how does it work in App-V 5.0?


We run weekly drama classes for children and young people aged 4 to 18 years old in Pyrford and Maybury, Woking. We offer a variety of classes to suit all students whether you are looking for a fun class to build confidence skills or a class for your budding stage performer. Our classes help build confidence through drama and acting in an inclusive and supportive environment.
Every student will have the opportunity to perform in our end of term shows in July and December each year where we raise money for local charities. We also take part in the Woking Drama Festival in October as well as Godalming Speech and Drama Festival in March. For students aged 5 years and over they can work towards the LAMDA Examination syllabus and gain recognised qualifications.
A fun 1 hour class for our youngest students. We aim to develop their confidence, improve speech, make new friends, stimulate their imagination and encourage creativity. Children will learn a range of dramatic skills through improvisation, poetry, plays and stories. Each term is centred on a theme using a mixture of suitable material to introduce the wonderful world of the performing arts. The class begins with a warm up to get us focussed. The children use songs, games and exercises and then listen to a story or poem. This is then acted out with all the children taking part.
A fun and educational 1 hour class each week. Drama is a fun way to help children develop confidence, self-esteem and social skills. Children will explore a wide range of dramatic skills through poetry, stories, plays and role-play. Each term is centred on a particular theme and we use a combination of materials to explore characters and gain new dramatic skills.
A fun and educational 1 hour class each week. Students will develop confidence, self-esteem and social skills whilst exploring a wide range of dramatic skills through poetry, stories, plays and role-play. Each term is centred on a particular theme and we use a combination of materials to explore characters and gain new dramatic skills. Each class begins with warm up activities to get us focussed on drama. The children use games and drama exercises to gain new skills.
Students will attend a one hour workshop each week and they will gain confidence, self-esteem and develop skills to help them in their future lives. We can also help with GCSE, AS and A Level work.
We set and expect high standards from our students in these classes. We will explore a variety of dramatic skills and use a range of dramatic material. Workshops will include Improvisation, Characterisation, Mask work, Role-play; we use poetry, stories, and plays as well as exploring theatre practitioners and key theatre practices.


Donald Glover and Brian Tyree Henry aren't the only rapper-actors on FX's Atlanta. Lakeith Stanfield is also proving to be a powerhouse in his own right. Not only does he star in the trippy film Sorry to Bother You, he also contributed a rap verse for the movie's soundtrack.
The song, "OYAHYTT" comes from the Coup, the same hip-hop and funk group fronted by the film's director, Boots Riley. The track, which seems to stand for the song's first words and hook ("Oh Yeah, Alright, Hell Yeah, That's Tight"), was released last night.
This isn't the first time Stanfield showed off his rap skills. He also portrayed Snoop Dogg in the Oscar-nominated N.W.A. biopic, Straight Outta Compton. The Coup has a decades-long history in the music industry with their last album bearing the same name as Riley's directorial debut, Sorry to Bother You, with features from Killer Mike and Living Colour's Vernon Reid.
The rest of the Atlanta co-stars are pretty busy as well. Glover followed up "This Is America" with the two-song Summer Pack, while Henry is starring in a series of films including the biopic White Boy Rick, the Miles Morales–led animated film Spider-Man: Into the Spider-Verse, and the James Baldwin adaptation If Beale Street Could Talk. Atlanta, which earned 16 Emmy nominations this week, returns in 2019.
Sorry to Bother You also stars Tessa Thompson, Omari Hardwick, Terry Crews, Armie Hammer, Steven Yeun, and more. It was released in select theaters on July 6, but is widely available today (July 13). Check out a clip on the "art of the white voice" below.


text:
Q: Valgrind errors: heap overflow for pollfds struct Running valgrind throws me this massive error for each element of pollfd(fd,events,revents):
==14524== 286699 errors in context 6 of 6:
==14524== Syscall param poll(ufds.fd) points to unaddressable byte(s)
==14524==    at 0x4984D47: poll (poll.c:29)
==14524==    by 0x10AD7C: connmgr_listen (connmgr.c:95)
==14524==    by 0x109549: main (main.c:12)
==14524==  Address 0x4a97a10 is 0 bytes after a block of size 32 alloc'd
==14524==    at 0x484DA83: calloc (in /usr/libexec/valgrind/vgpreload_memcheck-amd64-linux.so)
==14524==    by 0x10B285: element_copy (connmgr.c:196)
==14524==    by 0x10A07F: dpl_insert_at_index (dplist.c:117)
==14524==    by 0x10AE6E: connmgr_listen (connmgr.c:113)
==14524==    by 0x109549: main (main.c:12)

It has to do with the fact that I have a struct called connection which hold my TCP sockets and it contains a struct called socket_descriptor which holds the pollfds information for polling (fs,events,revents). I call the polling function which throws the error(line 95):
int rc = poll(&(connection->socket_descriptor), nr_connections, 0);

It's caused by this block here in which I initialise a new connection and add it to my double pointed list of connections.
          if (connection->socket_descriptor.revents & POLLIN))
          { // add latest connection from listening node
                connection_t *new_node = calloc(1,sizeof(connection));
                if (tcp_wait_for_connection(connection->socket, &client) != TCP_NO_ERROR)
                      exit(EXIT_FAILURE);
                tcp_get_sd(client, &(new_node->socket_descriptor.fd)); //gets fd of the socket descriptor
                new_node->socket_descriptor.events = POLLIN | POLLRDHUP;
                new_node->socket_descriptor.revents = 0;
                new_node->socket = client;
                dpl_insert_at_index(connections, (void *)new_node, nr_connections, true);
                free(new_node);
          }

I tried calloc so that the pollfd elements would have an address but it didn't work. I understand that it probably comes from a struct within a struct but I don't know how to fix it.
The struct of my connection:
struct connection
{
      time_t ts;
      tcpsock_t *socket;
      fds_t socket_descriptor;
};

redpajama_set_name: RedPajamaStackExchange


The Waitemata waterfront is the meeting place of the City and the Harbour; the City and the Region; the City's and the Region's public transport systems.
The Passenger Transport Interchange is the central tenet of the brief. The Waitemata waterfront is the place where Auckland's three major modes of public transport (ferry, bus and train) meet. It is also where visitors and guests from the rest of the region, the country, and the world encounter Auckland. It is the place where Greater Auckland has its functional and symbolic hub, and where the future regional transport network demonstrates its efficiency.
Waitemata waterfront is particularly the meeting point of Auckland's growing water-based passenger transport (ferries, water taxis, etc) with the growing land-based public transport (buses and coaches, light rail, heavy rail, taxis, etc).
Three interweaving themes are derived from our analysis to inform and support this proposal: transport, navigation and destination. These themes relate the site, the city and the region, and bring together the more expansive themes of country and city, city and harbour, water and land, local and global. They also provide a link between the historic influences of Maori and Pakeha, New Zealand and the South Pacific.
This entry was posted in Masterplanning, Projects, Schemes by admin. Bookmark the permalink.


text:
U.S. near deal for Nvidia supercomputer as it waits for delayed Intel machine -sources
Stephen Nellis
Aug 24 (Reuters) - The U.S. Department of Energy is nearing a deal to purchase a supercomputer made with chips from Nvidia Corp and Advanced Micro Devices Inc as a key lab waits for a larger supercomputer from Intel Corp that has been delayed for months, two people familiar with the matter told Reuters.
The Nvidia and AMD machine, to be called Polaris, will not be a replacement for the Intel-based Aurora machine slated for the Argonne National Lab near Chicago, which was poised to be the nation's fastest computer when announced in 2019. Instead, Polaris, which will come online this year, will be a test machine for Argonne to start readying its software for the Intel machine, the people familiar with the matter said.
Argonne spokesperson Ben Schiltz did not immediately respond to a request for comment, nor did Nvidia spokesperson Hector Marinez or AMD spokesperson Aaron Grabein.
Intel, AMD and Nvidia are battling for market share for chips used in data centers. The United States' array of supercomputers do scientific work for healthcare, climate and other researchers, as well as performing virtual testing of the country's nuclear weapons.
Key technologies pioneered in the systems often filter down to commercial data centers in subsequent years, giving an advantage to chip companies which win the contract work.
When Aurora was first announced, Intel and Argonne said the machine would be delivered in 2021, but Intel still has not delivered the key Ponte Vecchio and Sapphire Rapids chips. Intel said in June that the Sapphire Rapids chips would not be in production until 2022, and on Tuesday Intel spokesperson Will Moss said the company remained committed to delivering the computer in 2022.
The $500 million contract called for Intel and partners to deliver a computer with so-called exaflop performance, or the ability to perform 1 quintillion - or 1,000,000,000,000,000,000 - calculations per second. Now, the first exascale computer in the United States will likely be a different machine at a different lab - Oak Ridge National Lab in Tennessee - built by Hewlett Packard Enterprise Co with chips from AMD expected to be delivered later this year.
The Polaris machine at Argonne will not be as powerful as the Intel machine, the sources said. Based on Nvidia's A100 chips and AMD's Rome and Milan chips, the Polaris computer will be able to make some exaflop calculations but mostly work at slower speeds. (Reporting by Stephen Nellis in San Francisco; editing by Peter Henderson and Jonathan Oatis)

redpajama_set_name: RedPajamaCommonCrawl


On January 01, 2016, CMS launched the QIC Telephone Discussion Demonstration project to determine if engagement between suppliers and the QIC could improve the understanding of the cause of appeal denials, and lead to increased proper claim submissions. The project was originally only limited to certain DME claims, however in October 2016, the projected was expanded to include all DME claim types, including orthotics and prosthetics, but only in Jurisdictions C and D.
CMS has recently announced that the Formal Telephone Discussion Demonstration has been expanded to include Jurisdictions A & B starting on November 1, 2018.
Participation in the telephone discussion demonstration is voluntary, but unfortunately there is no way to "sign up" for this program. Your claim(s) must be selected for review by representatives of the QIC, currently that contractor is C2C solutions. C2C Solutions reviews reconsideration requests to identify appeals that may benefit from participation in the telephone discussion process. Eligible suppliers are then offered the opportunity to engage in a telephone discussion with QIC prior to the issuance of the reconsideration decision.
This extra step allows suppliers to discuss the facts of the appeal directly with C2C medical review staff and to submit any missing documentation that may have been identified by C2C. Early results of the initial phases of the demonstration project proved to be very successful with a denial overturn rate approaching 86%.
Questions? Contact Joe McTernan at jmcternan@AOPAnet.org or Devon Bernard at dbernard@AOPAnet.org.


text:
College basketball odds and lines
Injury status of Ohio State's Kyle Young, D.J. Carton for Thursday's game against Nebraska
Ohio State will be without at least one of its key players when the Buckeyes head to Lincoln.
By Erik Buchinger Feb 27, 2020, 5:21am PST
Share All sharing options for: Injury status of Ohio State's Kyle Young, D.J. Carton for Thursday's game against Nebraska
Rich Barnes-USA TODAY Sports
Ohio State Buckeyes guard D.J. Carton and forward Kyle Young top the injury report for the Buckeyes' matchup at the Nebraska Cornhuskers on Thursday night.
Carton will not be playing in this game as he continues to address a personal situation. The freshman guard took a leave of absence in late January to deal with his mental health. He returned to campus last week Friday and will go through extensive medical protocols before rejoining the team. In 20 games this season, Carton is averaging 10.4 points, three assists and 2.8 rebounds. He's also shooting 40% from the three-point line.
Young is a big question mark for Thursday night's matchup, and his status will be determined during the afternoon shoot-around. The junior forward suffered a high ankle sprain during Sunday's win against Maryland. He is averaging 7.5 points and 5.8 rebounds over 25 games. He missed two games due to an appendectomy earlier this season.
Ohio State is coming into Thursday as 9.5-point favorites and is 6-2 against the spread in its last eight games. Nebraska is looking to snap a 12-game losing streak for its third conference victory this season.
Gambling problem? Call 1-800-GAMBLER (NJ/WV/PA) or 1-800-9-WITH-IT (IN) or 1-800-522-4700 (NH). NH/NJ/WV/IN/PA only. 21+ (18+ NH). Eligibility Restrictions Apply. See website for details.
List of player prop bets for Quez Watkins available on DraftKings for Super Bowl 57 We take a look at the player prop bets available for Quez Watkins available on DraftKings Sportsbook ahead of the Super Bowl 57 matchup between the Chiefs and Eagles
How to live stream 2023 Senior Bowl practices We take a look at how to watch coverage of National and American team practices ahead of the 2023 Senior Bowl in Mobile, Alabama.


The Continuity of Care Record (CCR) is a health record standard specification developed jointly by ASTM International, the Massachusetts Medical Society(MMS), the HIMSS, the American Academy of Family Physicians (AAFP), the American Academy of Pediatrics (AAP), and other health informatics vendors.
sections such as patient demographics, insurance information, diagnosis and problem list, medications, allergies and care plan. These represent a "snapshot" of a patient's health data that can be useful or possibly lifesaving, if available at the time of clinical encounter. The ASTM CCR standard is designed to permit easy creation by a physician using an electronic health record (EHR) system at the end of an encounter.
Because it is expressed in the standard data interchange language known as XML, a CCR can potentially be created, read and interpreted by any EHR or EMR software application. A CCR can also be exported in other formats, such as PDF.


I love this quote. It is so true!
Oh my. I love this quote...thanks for sharing it, Margaret!
It's one of those things we "know" but Lewis put it in words and it strikes us with - "Yeah, I feel that way, too!"
I love all the quotes you select, Margaret--and this one about friends is so true, even with you and I and FB! Stay cozy and warm!
I love that Lewis quote!


text:
Q: How can the state in react-js turns back to their original value I'm exploring state in ReactJS and encountered this problem. What I want to happen is when the state === 0 it will go back to its original value. Here's my code:
export default class Test extends React.Component {
  constructor (props) {
    super(props);
    this.state ={
      stateValue: 5
    }
  }

And inside componentDidUpdate() is I decrement the value of state by 1 and have a condition in setState but I don't know what to put inside setState in order for the state to come back to its original value again.
if (stateValue === 0) {
  this.setState({ })
}

and inside return is

{stateValue}

The result is it counts from 5 down to 0 but when it gets 0. It doesn't change back to its original value.
What I want to happen is when it turns to 0 it will go back to its value again which is 5 then continue to count down again. How can I do this? Thanks in advance

A: You can store another property in your set state.
export default class Test extends React.Component {
  const originalValue = 5;

  constructor (props) {
     super(props);
     this.state ={
       stateValue: originalValue
     }
  }
}

And then while updating you can assign that property
if (stateValue === 0) {
    this.setState({ stateValue: originalValue })
}

A: You can impliment this kind of logic with initial state with class property:
class Example extends Component {
  defaultState = {
    stateValue: 5,
  };
  constructor(props) {
    super(props);
    this.state = this.defaultState;
  }

  componentDidUpdate(_prevProps, _prevState) {
    if (this.state.stateValue === 0) {
      this.setState(this.defaultState);
    }
  }

  render() {
    const { stateValue } = this.state;

    return (

{stateValue}

    );
  }
}


Builder confidence is up in the single-family 55+ housing market, according to the latest 55+ Housing Market Index (HMI) from the National Association of Homebuilder (NAHB) The Index score rose to 66 in Q4 2018, up six points from the previous quarter.
All three components of the index, including present sales, expected sales, and prospective buyer traffic increased in Q4. Present sales rose six points to 72, expected sales for the next six months increased five points to 70 and traffic of prospective buyers jumped 10 points to 53.
NAHB also notes increases in the multifamily 55+ condo market, with a three-point gain to 47. The index component for present sales for condos increased three points to 51, expected sales for the next six months fell four points to 49 and traffic of prospective buyers rose seven points to 38. In the 55+ multifamily rental market, two out of four components saw increases. According to the NAHB, present production increased six points to 60 and present demand for existing units rose four points to 67, while future expected production and future expected demand both fell two points to 54 and 62, respectively.
The full 55+ HMI tables can be found here.