Günther J.L. Gerhardt

Topography-specific spindle frequency changes in Obstructive Sleep Apnea

BackgroundSleep spindles, as detected on scalp electroencephalography (EEG), are considered to be markers of thalamo-cortical network integrity. Since obstructive sleep apnea (OSA) is a known cause of brain dysfunction, the aim of this study was to investigate sleep spindle frequency distribution in OSA. Seven non-OSA subjects and 21 patients with OSA (11 mild and 10 moderate) were studied. A matching pursuit procedure was used for automatic detection of fast (≥13Hz) and slow (<13Hz) spindles obtained from 30min samples of NREM sleep stage 2 taken from initial, middle and final night thirds (sections I, II and III) of frontal, central and parietal scalp regions.ResultsCompared to non-OSA subjects, Moderate OSA patients had higher central and parietal slow spindle percentage (SSP) in all night sections studied, and higher frontal SSP in sections II and III. As the night progressed, there was a reduction in central and parietal SSP, while frontal SSP remained high. Frontal slow spindle percentage in night section III predicted OSA with good accuracy, with OSA likelihood increased by 12.1%for every SSP unit increase (OR 1.121, 95% CI 1.013 - 1.239, p=0.027).ConclusionsThese results are consistent with diffuse, predominantly frontal thalamo-cortical dysfunction during sleep in OSA, as more posterior brain regions appear to maintain some physiological spindle frequency modulation across the night. Displaying changes in an opposite direction to what is expected from the aging process itself, spindle frequency appears to be informative in OSA even with small sample sizes, and to represent a sensitive electrophysiological marker of brain dysfunction in OSA.

Loss of sleep spindle frequency deceleration in Obstructive Sleep Apnea

OBJECTIVE Sleep spindles have been suggested as surrogates of thalamo-cortical activity. Internal frequency modulation within a spindles time frame has been demonstrated in healthy subjects, showing that spindles tend to decelerate their frequency before termination. We investigated internal frequency modulation of slow and fast spindles according to Obstructive Sleep Apnea (OSA) severity and brain topography. METHODS Seven non-OSA subjects and 21 patients with OSA contributed with 30min of Non-REM sleep stage 2, subjected to a Matching pursuit procedure with Gabor chirplet functions for automatic detection of sleep spindles and quantification of sleep spindle internal frequency modulation (chirp rate). RESULTS Moderate OSA patients showed an inferior percentage of slow spindles with deceleration when compared to Mild and Non-OSA groups in frontal and parietal regions. In parietal regions, the percentage of slow spindles with deceleration was negatively correlated with global apnea-hypopnea index (rs=-0.519, p=0.005). DISCUSSION Loss of physiological sleep spindle deceleration may either represent a disruption of thalamo-cortical loops generating spindle oscillations or some compensatory mechanism, an interesting venue for future research in the context of cognitive dysfunction in OSA. SIGNIFICANCE Quantification of internal frequency modulation (chirp rate) is proposed as a promising approach to advance description of sleep spindle dynamics in brain pathology.

Effect of Plastic Deformation on the Excess Loss of Electrical Steel

The interpretation of the effect of plastic deformation on the calculated excess loss component (anomalous-loss) supports the concept of loss separation. Magnetic losses and Barkhausen noise of nonoriented electrical steel sheets were measured on Epstein strips taken from a single coil of 0.8% Si nonoriented electrical steel. Sheets were extracted in the annealed condition, without any skin pass and with a grain size of 18 μm. This material was cold rolled in order to obtain sets of samples with true strain from 2% up to 29%. X-ray diffraction was used to estimate the dislocation density. The analysis of magnetic properties was performed by Barkhausen noise measurements and also by analyzing the hysteresis loops obtained from Epstein frame measurements for different inductions and different frequencies (including the quasi-static regime for hysteresis loss measurements). These data allowed us to observe that most of the well known total loss increase with plastic deformation is due to an increase in the hysteresis loss component, while excess loss decreases to become negligible. This behavior can be explained if it is assumed that the plastic deformation lead to an increase in the number of domain walls per unit volume, thereby decreasing the excess loss. Barkhausen peak area increases with plastic deformation, reproducing results taken from samples of different silicon content.

BacPP: Bacterial promoter prediction—A tool for accurate sigma-factor specific assignment in enterobacteria

Promoter sequences are well known to play a central role in gene expression. Their recognition and assignment in silico has not consolidated into a general bioinformatics method yet. Most previously available algorithms employ and are limited to σ70-dependent promoter sequences. This paper presents a new tool named BacPP, designed to recognize and predict Escherichia coli promoter sequences from background with specific accuracy for each σ factor (respectively, σ24, 86.9%; σ28, 92.8%; σ32, 91.5%; σ38, 89.3%, σ54, 97.0%; and σ70, 83.6%). BacPP is hence outstanding in recognition and assignment of sequences according to σ factor and provide circumstantial information about upstream gene sequences. This bioinformatic tool was developed by weighing rules extracted from neural networks trained with promoter sequences known to respond to a specific σ factor. Furthermore, when challenged with promoter sequences belonging to other enterobacteria BacPP maintained 76% accuracy overall.

Quantifying chirp in sleep spindles.

Sleep spindles are considered as a marker of integrity for thalamo-cortical circuits. Recently, attention has been given to internal frequency variation in sleep spindles. In this study, a procedure based on matching pursuit with a Gabor-chirplet dictionary was applied in order to measure chirp rate in atoms representing sleep spindles, also categorized into negative, positive or zero chirp types. The sample comprised 707 EEG segments containing visual sleep spindles, labeled TP, obtained from nine healthy male volunteers (aged 20-34, average 24.6 y). Control datasets were 333 non-REM (NREM) sleep background segments and 287 REM sleep intervals, each with 16s duration. Analyses were carried out on the C3-A2 EEG channel. In TP and NREM groups, the proportion of non-null chirp types was non-random and total chirp distribution was asymmetrical towards negative values, in contrast to REM. Median negative chirp rate in the TP and NREM groups was significantly lower than in REM (-0.4 Hz/s vs -0.3 Hz/s, P < 0.05). Negative chirp atoms outnumbered positives by 50% in TP, while in NREM and REM, they were, respectively, only 22% and 12% more prevalent. TP negative chirp atoms were significantly higher in amplitude compared to positive or zero types. Considering individual subjects, 88.9% had a TP negative/positive chirp ratio above 1 (mean ± sd=1.64 ± 0.65). We propose there is increasing evidence, corroborated by the present study, favoring systematic measurement of sleep spindle chirp rate or internal frequency variation. Preferential occurrence of negatively chirping spindles is consistent with the hypothesis of electrophysiological modulation of neocortical memory consolidation.

Rules extraction from neural networks applied to the prediction and recognition of prokaryotic promoters

Promoters are DNA sequences located upstream of the gene region and play a central role in gene expression. Computational techniques show good accuracy in gene prediction but are less successful in predicting promoters, primarily because of the high number of false positives that reflect characteristics of the promoter sequences. Many machine learning methods have been used to address this issue. Neural Networks (NN) have been successfully used in this field because of their ability to recognize imprecise and incomplete patterns characteristic of promoter sequences. In this paper, NN was used to predict and recognize promoter sequences in two data sets: (i) one based on nucleotide sequence information and (ii) another based on stability sequence information. The accuracy was approximately 80% for simulation (i) and 68% for simulation (ii). In the rules extracted, biological consensus motifs were important parts of the NN learning process in both simulations.

Barkhausen Noise and Magnetic Properties of Plastically Deformed Silicon Steels

We present Barkhausen noise and magnetic measurements on two fully processed, nonoriented electrical steels which had been cold-rolled to thickness reductions of up to 60%. Both coercive field Hc and hysteresis loss Wh show an almost linear increase with thickness reduction up to the highest deformations. These changes are almost fully reversed after vacuum annealing for 2 h at 760°C. The hysteresis loss can be conveniently subdivided into high and low induction components as suggested by recent modelling. Electron back-scatter diffraction (EBSD) shows no texture change during initial phases of cold-rolling. Barkhausen noise measurements were obtained on both cold-rolled and annealed samples. The undeformed material shows a Barkhausen signature consisting of two small peaks which coalesce into one peak upon plastic deformation and thereafter grow steadily. Annealing the material brings back the two-peaked signature. These results are discussed and hypotheses are presented for the behavior of the Barkhausen noise.

Synchronization and Propagation of Global Sleep Spindles

Sleep spindles occur thousands of times during normal sleep and can be easily detected by visual inspection of EEG signals. These characteristics make spindles one of the most studied EEG structures in mammalian sleep. In this work we considered global spindles, which are spindles that are observed simultaneously in all EEG channels. We propose a methodology that investigates both the signal envelope and phase/frequency of each global spindle. By analysing the global spindle phase we showed that 90% of spindles synchronize with an average latency time of 0.1 s. We also measured the frequency modulation (chirp) of global spindles and found that global spindle chirp and synchronization are not correlated. By investigating the signal envelopes and implementing a homogeneous and isotropic propagation model, we could estimate both the signal origin and velocity in global spindles. Our results indicate that this simple and non-invasive approach could determine with reasonable precision the spindle origin, and allowed us to estimate a signal speed of 0.12 m/s. Finally, we consider whether synchronization might be useful as a non-invasive diagnostic tool.

NREM sleep alpha and sigma activity in Parkinson’s disease: Evidence for conflicting electrophysiological activity?

OBJECTIVES Sleep EEG spectral patterns were investigated in eight newly diagnosed, non-depressed, non-demented, drug-naïve Parkinsons disease patients compared to nine controls. METHODS Mean relative spectral power density calculated for 0.25 Hz frequency bins and for classical EEG frequency bands. RESULTS Differences between patients and controls were most prominent in non-REM sleep, specially around 8.6 Hz (slow alpha), 12.5 Hz (fast alpha/slow sigma) and 15 Hz (fast sigma). Slow alpha showed lower p-values over frontal and occipital electrodes, whereas fast sigma activity was more important on central and parietal sites. Significantly increased NREM sleep alpha activity was found in left and right frontal (Mann-Whitney U=12,000, p=.021; U=14,000, p=.036), left and right central (U=14,000, p=.036), left parietal and left occipital (U=13,000, p=.027; U=15,000, p=.046) areas. Increased sigma activity was found in right frontal (U=14,000, p=.036), left central (U=12,000, p=.021), left and right parietal (U=12,000, p=.021; U=13,000, p=.027) and left occipital (U=15,000, p=.046) areas. CONCLUSIONS Concomitantly increased scalp EEG alpha and sigma activity was found during NREM sleep in initial Parkinsons disease. SIGNIFICANCE These non-REM sleep microstructure changes may represent evidence for altered electrophysiological mechanisms leading to sleep-wake instability in early disease stages.

Case depth in SAE 1020 steel using barkhausen noise

The most widely used thermochemical process for surface hardening of steels is case hardening. Using several different heat treatments, martensitic surface layers were formed on SAE 1020 steel into which carbon had been diffused. Case depths were measured by traditional destructive techniques. Barkhausen noise measurements were made and both the RMS Barkhausen pulse envelope and the fast Fourier transform (FFT) were obtained from numerical calculation. The FFT amplitudes, functions of frequency, were associated with distance from the sample surface using the skin depth equation δ = 1/ (πfσµ)½ , where f is the frequency of the electromagnetic wave, s is the electrical conductivity, and µ is the magnetic permeability. We define a normalized power index (NPI) which can be used to estimate case depths. The NPI is discussed in relation to the sample microstructure and it is shown that the case depth is most easily determined when the magnetic properties of the surface layer and core are substantially different.