Borregaardhughes8974
The calcification of Anaerobic Granular Sludge is a serious problem in the application of anaerobic methanization biotechnology. Regular replacement of calcified sludge with exogenous sludge is an effective method, but it is costly and troublesome. A new DOuble Circulation Anaerobic Sludge bed reactor was developed for the enhanced production of endogenous sludge to self-balance the discharge of calcified sludge. The sludge washout rate was demonstrated to fall by 45% and the sludge proliferation rate was shown to rise by 230%, offsetting the regular discharge of calcified sludge. The zoogloea in 100 μm dimension was revealed to be the intermediate component of sludge. The sludge proliferation mode was proposed as follows (i) Growth of sludge; (ii) Self-cracking of sludge to release fragmental sludge; (iii) Migration of fragmental sludge by self-floatation; (iv) Accumulation of suspended sludge in the sedimentation chamber; (v) Re-granulation of suspended sludge with the aid of Venturi effect.
Investigate whether resting-state EEG parameters recorded early poststroke can predict upper extremity motor impairment reflected by the Fugl-Meyer motor score (FM-UE) after six months, and whether they have prognostic value in addition to FM-UE at baseline.
Quantitative EEG parameters delta/alpha ratio (DAR), brain symmetry index (BSI) and directional BSI (BSIdir) were derived from 62-channel resting-state EEG recordings in 39 adults within three weeks after a first-ever ischemic hemispheric stroke. FM-UE scores were acquired within three weeks (FM-UE
) and at 26weeks poststroke (FM-UE
). Linear regression analyses were performed using a forward selection procedure to predict FM-UE
.
BSI calculated over the theta band (BSI
) (β=-0.40; p=0.013) was the strongest EEG-based predictor regarding FM-UE
. BSI
(β=-0.27; p=0.006) remained a significant predictor when added to a regression model including FM-UE
, increasing explained variance from 61.5% to 68.1%.
Higher BSI
values, reflecting more power asymmetry over the hemispheres, predict more upper limb motor impairment six months after stroke. Moreover, BSI
shows additive prognostic value regarding FM-UE
next to FM-UE
scores, and thereby contains unique information regarding upper extremity motor recovery.
To our knowledge, we are the first to show that resting-state EEG parameters can serve as prognostic biomarkers of stroke recovery, in addition to FM-UE
scores.
To our knowledge, we are the first to show that resting-state EEG parameters can serve as prognostic biomarkers of stroke recovery, in addition to FM-UEbaseline scores.
The current study investigated the behavioral, cognitive, and electrophysiological impact of mild (only a few hours) and acute (one night) sleep loss via simultaneously recorded behavioural and physiological measures of vigilance.
Participants (N=23) came into the lab for two testing days where their brain activity and vigilance were recorded and assessed. The night before the testing session, participants either slept from 12am to 9am (Normally Rested), or from 1am to 6am (Sleep Restriction).
Vigilance was reduced and sleepiness was increased in the Sleep Restricted vs. Normally Rested condition, and this was exacerbated over the course of performing the vigilance task. As well, sleep restriction resulted in more intense alpha bursts. Semagacestat chemical structure Lastly, EEG spectral power differed in Sleep Restricted vs. Normally Rested conditions as sleep onset progressed, particularly for frequencies reflecting arousal (e.g., delta, alpha, beta).
The findings of this study suggest that only one night of mild sleep loss significantly increases sleepiness and, importantly, reduces vigilance. In addition, this sleep loss has a clear impact on the physiology of the brain in ways that reflect reduced arousal.
Understanding the neural correlates and cognitive processes associated with loss of sleep may lead to important advancements in identifying and preventing deleterious or potentially dangerous, sleep-related lapses in vigilance.
Understanding the neural correlates and cognitive processes associated with loss of sleep may lead to important advancements in identifying and preventing deleterious or potentially dangerous, sleep-related lapses in vigilance.
To determine the quantitative EEG responses in a population of drug-naïve patients with Temporal Lobe Epilepsy (TLE) after Levetiracetam (LEV) initiation as first antiepileptic drug (AED). We hypothesized that the outcome of AED treatment can be predicted from EEG data in patients with TLE.
Twenty-three patients with TLE and twenty-five healthy controls were examined. Clinical outcome was dichotomized into seizure-free (SF) and non-seizure-free (NSF) after two years of LEV. EEG parameters were compared between healthy controls and patients with TLE at baseline (EEG
) and after three months of AED therapy (EEG
) and between SF and NSF patients. Receiver Operating Characteristic curves models were built to test whether EEG parameters predicted outcome.
AED therapy induces an increase in EEG power for Alpha (p=0.06) and a decrease in Theta (p<0.05). Connectivity values were lower in SF compared to NSF patients (p<0.001). Quantitative EEG predicted outcome after LEV treatment with an estimated accuracy varying from 65.2% to 91.3% (area under the curve [AUC]=0.56-0.93) for EEG
and from 69.9% to 86.9% (AUC=0.69-0.94) for EEG
.
AED therapy induces EEG modifications in TLE patients, and such modifications are predictive of clinical outcome.
Quantitative EEG may help understanding the effect of AEDs in the central nervous system and offer new prognostic biomarkers for patients with epilepsy.
Quantitative EEG may help understanding the effect of AEDs in the central nervous system and offer new prognostic biomarkers for patients with epilepsy.
Brain Age Index (BAI), calculated from sleep electroencephalography (EEG), has been proposed as a biomarker of brain health. This study quantifies night-to-night variability of BAI and establishes probability thresholds for inferring underlying brain pathology based on a patient's BAI.
86 patients with multiple nights of consecutive EEG recordings were selected from Epilepsy Monitoring Unit patients whose EEGs reported as within normal limits. While EEGs with epileptiform activity were excluded, the majority of patients included in the study had a diagnosis of chronic epilepsy. BAI was calculated for each 12-hour segment of patient data using a previously established algorithm, and the night-to-night variability in BAI was measured.
The within-patient night-to-night standard deviation in BAI was 7.5years. Estimates of BAI derived by averaging over 2, 3, and 4 nights had standard deviations of 4.7, 3.7, and 3.0years, respectively.
Averaging BAI over n nights reduces night-to-night variability of BAI by a factor of n, rendering BAI a more suitable biomarker of brain health at the individual level.
