Sustained and elevated activity during the working memory delay period has long been considered the primary neural correlate for maintaining information over short time intervals. This idea has recently been reinterpreted in light of findings generated from multiple neural recording modalities and levels of analysis. To further investigate the sustained or transient nature of activity, the temporal-spectral evolution (TSE) of delay period activity was examined in humans with high density EEG during performance of a Sternberg working memory paradigm with a relatively long six second delay and with novel scenes as stimuli. Multiple analyses were conducted using different trial window durations and different baseline periods for TSE computation. Sensor level analyses revealed transient rather than sustained activity during delay periods. Specifically, the consistent finding among the analyses was that high amplitude activity encompassing the theta range was found early in the first three seconds of the delay period. These increases in activity early in the delay period correlated positively with subsequent ability to distinguish new from old probe scenes. Source level signal estimation implicated a right parietal region of transient early delay activity that correlated positively with working memory ability. This pattern of results adds to recent evidence that transient rather than sustained delay period activity supports visual working memory performance. The findings are discussed in relation to synchronous and desynchronous intra- and inter-regional neural transmission, and choosing an optimal baseline for expressing temporal-spectral delay activity change.

Nucleolin (NCL) is an abundant stress-responsive, RNA-binding phosphoprotein that controls gene expression by regulating either mRNA stability and/or translation. NCL binds to the AU-rich element (ARE) in the 3'UTR of target mRNAs, mediates miRNA functions in the nearby target sequences, and regulates mRNA deadenylation. However, the mechanism by which NCL phosphorylation affects these functions and the identity of the deadenylase involved, remain largely unexplored. Earlier we demonstrated that NCL phosphorylation is vital for cell cycle progression and proliferation, whereas phosphorylation-deficient NCL at six consensus CK2 sites confers dominant-negative effect on proliferation by increasing p53 expression, possibly mimicking cellular DNA damage conditions. In this study, we show that NCL phosphorylation at those CK2 consensus sites in the N-terminus is necessary to induce deadenylation upon oncogenic stimuli and UV stress. NCL-WT, but not hypophosphorylated NCL-6/S*A, activates poly (A)-specific ribonuclease (PARN) deadenylase activity. We further demonstrate that NCL interacts directly with PARN, and under non-stress conditions also forms (a) complex (es) with factors that regulate deadenylation, such as p53 and the ARE-binding protein HuR. Upon UV stress, the interaction of hypophosphorylated NCL-6/S*A with these proteins is favored. As an RNA-binding protein, NCL interacts with PARN deadenylase substrates such as TP53 and BCL2 mRNAs, playing a role in their downregulation under non-stress conditions. For the first time, we show that NCL phosphorylation offers specificity to its protein-protein, protein-RNA interactions, resulting in the PARN deadenylase regulation, and hence gene expression, during cellular stress responses.

Researchers classify critical neural events during sleep called spindles that are related to memory consolidation using the method of scalp electroencephalography (EEG). Manual classification is time consuming and is susceptible to low inter-rater agreement. This could be improved using an automated approach. This study presents an optimized filter based and thresholding (FBT) model to set up a baseline for comparison to evaluate machine learning models using naïve features, such as raw signals, peak frequency, and dominant power. The FBT model allows us to formally define sleep spindles using signal processing but may miss examples most human scorers would agree are spindles. Machine learning methods in theory should be able to approach performance of human raters but they require a large quantity of scored data, proper feature representation, intensive feature engineering, and model selection. We evaluate both the FBT model and machine learning models with naïve features. We show that the machine learning models derived from the FBT model improve classification performance. An automated approach designed for the current data was applied to the DREAMS dataset [1]. With one of the expert’s annotation as a gold standard, our pipeline yields an excellent sensitivity that is close to a second expert’s scores and with the advantage that it can classify spindles based on multiple channels if more channels are available. More importantly, our pipeline could be modified as a guide to aid manual annotation of sleep spindles based on multiple channels quickly (6–10 seconds for processing a 40-minute EEG recording), making spindle detection faster and more objective.

Sustained and elevated activity during the working memory delay period has long been considered the primary neural correlate for maintaining information over short time intervals. This idea has recently been reinterpreted in light of findings generated from multiple neural recording modalities and levels of analysis. To further investigate the sustained or transient nature of activity, the temporal-spectral evolution (TSE) of delay period activity was examined in humans with high density EEG during performance of a Sternberg working memory paradigm with a relatively long six second delay and with novel scenes as stimuli. Multiple analyses were conducted using different trial window durations and different baseline periods for TSE computation. Sensor level analyses revealed transient rather than sustained activity during delay periods. Specifically, the consistent finding among the analyses was that high amplitude activity encompassing the theta range was found early in the first three seconds of the delay period. These increases in activity early in the delay period correlated positively with subsequent ability to distinguish new from old probe scenes. Source level signal estimation implicated a right parietal region of transient early delay activity that correlated positively with working memory ability. This pattern of results adds to recent evidence that transient rather than sustained delay period activity supports visual working memory performance. The findings are discussed in relation to synchronous and desynchronous intra-and inter-regional neural transmission, and choosing an optimal baseline for expressing temporal-spectral delay activity change.