The Effects of Two Types of Sleep Deprivation on Visual Working Memory Capacity and Filtering Efficiency

Sleep deprivation has adverse consequences for a variety of cognitive functions. The exact effects of sleep deprivation, though, are dependent upon the cognitive process examined. Within working memory, for example, some component processes are more vulnerable to sleep deprivation than others. Additionally, the differential impacts on cognition of different types of sleep deprivation have not been well studied. The aim of this study was to examine the effects of one night of total sleep deprivation and 4 nights of partial sleep deprivation (4 hours in bed/night) on two components of visual working memory: capacity and filtering efficiency. Forty-four healthy young adults were randomly assigned to one of the two sleep deprivation conditions. All participants were studied: 1) in a well-rested condition (following 6 nights of 9 hours in bed/night); and 2) following sleep deprivation, in a counter-balanced order. Visual working memory testing consisted of two related tasks. The first measured visual working memory capacity and the second measured the ability to ignore distractor stimuli in a visual scene (filtering efficiency). Results showed neither type of sleep deprivation reduced visual working memory capacity. Partial sleep deprivation also generally did not change filtering efficiency. Total sleep deprivation, on the other hand, did impair performance in the filtering task. These results suggest components of visual working memory are differentially vulnerable to the effects of sleep deprivation, and different types of sleep deprivation impact visual working memory to different degrees. Such findings have implications for operational settings where individuals may need to perform with inadequate sleep and whose jobs involve receiving an array of visual information and discriminating the relevant from the irrelevant prior to making decisions or taking actions (e.g., baggage screeners, air traffic controllers, military personnel, health care providers)

Complete genome sequences of elephant endotheliotropic herpesviruses 1A and 1B determined directly from fatal cases

A highly lethal hemorrhagic disease associated with infection by elephant endotheliotropic herpesvirus (EEHV) poses a severe threat to Asian elephant husbandry. We have used high-throughput methods to sequence the genomes of the two genotypes that are involved in most fatalities, namely EEHV1A and EEHV1B (species Elephantid herpesvirus 1, genus Proboscivirus, subfamily Betaherpesvirinae, family Herpesviridae). The sequences were determined from postmortem tissue samples, despite the data containing tiny proportions of viral reads among reads from a host for which the genome sequence was not available. The EEHV1A genome is 180,421 bp in size and consists of a unique sequence (174,601 bp) flanked by a terminal direct repeat (2,910 bp). The genome contains 116 predicted protein-coding genes, of which six are fragmented, and seven paralogous gene families are present. The EEHV1B genome is very similar to that of EEHV1A in structure, size, and gene layout. Half of the EEHV1A genes lack orthologs in other members of subfamily Betaherpesvirinae, such as human cytomegalovirus (genus Cytomegalovirus) and human herpesvirus 6A (genus Roseolovirus). Notable among these are 23 genes encoding type 3 membrane proteins containing seven transmembrane domains (the 7TM family) and seven genes encoding related type 2 membrane proteins (the EE50 family). The EE50 family appears to be under intense evolutionary selection, as it is highly diverged between the two genotypes, exhibits evidence of sequence duplications or deletions, and contains several fragmented genes. The availability of the genome sequences will facilitate future research on the epidemiology, pathogenesis, diagnosis, and treatment of EEHV-associated disease

Resisting Sleep Pressure:Impact on Resting State Functional Network Connectivity

In today's 24/7 society, sleep restriction is a common phenomenon which leads to increased levels of sleep pressure in daily life. However, the magnitude and extent of impairment of brain functioning due to increased sleep pressure is still not completely understood. Resting state network (RSN) analyses have become increasingly popular because they allow us to investigate brain activity patterns in the absence of a specific task and to identify changes under different levels of vigilance (e.g. due to increased sleep pressure). RSNs are commonly derived from BOLD fMRI signals but studies progressively also employ cerebral blood flow (CBF) signals. To investigate the impact of sleep pressure on RSNs, we examined RSNs of participants under high (19 h awake) and normal (10 h awake) sleep pressure with three imaging modalities (arterial spin labeling, BOLD, pseudo BOLD) while providing confirmation of vigilance states in most conditions. We demonstrated that CBF and pseudo BOLD signals (measured with arterial spin labeling) are suited to derive independent component analysis based RSNs. The spatial map differences of these RSNs were rather small, suggesting a strong biological substrate underlying these networks. Interestingly, increased sleep pressure, namely longer time awake, specifically changed the functional network connectivity (FNC) between RSNs. In summary, all FNCs of the default mode network with any other network or component showed increasing effects as a function of increased 'time awake'. All other FNCs became more anti-correlated with increased 'time awake'. The sensorimotor networks were the only ones who showed a within network change of FNC, namely decreased connectivity as function of 'time awake'. These specific changes of FNC could reflect both compensatory mechanisms aiming to fight sleep as well as a first reduction of consciousness while becoming drowsy. We think that the specific changes observed in functional network connectivity could imply an impairment of information transfer between the affected RSNs

Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity

Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity.Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI). Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability.The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual's working memory capacity suggests that the organization of this network into high connectivity within modules and sparse connections between modules may reflect effective signaling across brain regions, perhaps through the modulation of signal or the suppression of the propagation of noise

Retroperitoneal liposarcomas: The experience of a tertiary Asian center

10.1186/1477-7819-9-12World Journal of Surgical Oncology9

A systematic review of tests for lymph node status in primary endometrial cancer

<p>Abstract</p> <p>Background</p> <p>The lymph node status of a patient is a key determinate in staging, prognosis and adjuvant treatment of endometrial cancer. Despite this, the potential additional morbidity associated with lymphadenectomy makes its role controversial. This study systematically reviews the accuracy literature on sentinel node biopsy; ultra sound scanning, magnetic resonance imaging (MRI) and computer tomography (CT) for determining lymph node status in endometrial cancer.</p> <p>Methods</p> <p>Relevant articles were identified form MEDLINE (1966–2006), EMBASE (1980–2006), MEDION, the Cochrane library, hand searching of reference lists from primary articles and reviews, conference abstracts and contact with experts in the field. The review included 18 relevant primary studies (693 women). Data was extracted for study characteristics and quality. Bivariate random-effect model meta-analysis was used to estimate diagnostic accuracy of the various index tests.</p> <p>Results</p> <p>MRI (pooled positive LR 26.7, 95% CI 10.6 – 67.6 and negative LR 0.29 95% CI 0.17 – 0.49) and successful sentinel node biopsy (pooled positive LR 18.9 95% CI 6.7 – 53.2 and negative LR 0.22, 95% CI 0.1 – 0.48) were the most accurate tests. CT was not as accurate a test (pooled positive LR 3.8, 95% CI 2.0 – 7.3 and negative LR of 0.62, 95% CI 0.45 – 0.86. There was only one study that reported the use of ultrasound scanning.</p> <p>Conclusion</p> <p>MRI and sentinel node biopsy have shown similar diagnostic accuracy in confirming lymph node status among women with primary endometrial cancer than CT scanning, although the comparisons made are indirect and hence subject to bias. MRI should be used in preference, in light of the ASTEC trial, because of its non invasive nature.</p

Cross-sectional associations between multiple lifestyle behaviors and health-related quality of life in the 10,000 steps cohort

Background: The independent and combined influence of smoking, alcohol consumption, physical activity, diet, sitting time, and sleep duration and quality on health status is not routinely examined. This study investigates the relationships between these lifestyle behaviors, independently and in combination, and health-related quality of life (HRQOL). Methods: Adult members of the 10,000 Steps project (n = 159,699) were invited to participate in an online survey in November-December 2011. Participant socio-demographics, lifestyle behaviors, and HRQOL (poor self-rated health; frequent unhealthy days) were assessed by self-report. The combined influence of poor lifestyle behaviors were examined, independently and also as part of two lifestyle behavior indices, one excluding sleep quality (Index 1) and one including sleep quality (Index 2). Adjusted Cox proportional hazard models were used to examine relationships between lifestyle behaviors and HRQOL. Results: A total of 10,478 participants provided complete data for the current study. For Index 1, the Prevalence Ratio (p value) of poor self-rated health was 1.54 (p = 0.001), 2.07 (p≤0.001), 3.00 (p≤0.001), 3.61 (p≤0.001) and 3.89 (p≤0.001) for people reporting two, three, four, five and six poor lifestyle behaviors, compared to people with 0-1 poor lifestyle behaviors. For Index 2, the Prevalence Ratio (p value) of poor self-rated health was 2.26 (p = 0.007), 3.29 (p≤0.001), 4.68 (p≤0.001), 6.48 (p≤0.001), 7.91 (p≤0.001) and 8.55 (p≤0.001) for people reporting two, three, four, five, six and seven poor lifestyle behaviors, compared to people with 0-1 poor lifestyle behaviors. Associations between the combined lifestyle behavior index and frequent unhealthy days were statistically significant and similar to those observed for poor self-rated health. Conclusions: Engaging in a greater number of poor lifestyle behaviors was associated with a higher prevalence of poor HRQOL. This association was exacerbated when sleep quality was included in the index. © 2014 Duncan et al

Evaluation of methods and marker systems in genomic selection of oil palm (Elaeis guineensis Jacq.)

Background Genomic selection (GS) uses genome-wide markers as an attempt to accelerate genetic gain in breeding programs of both animals and plants. This approach is particularly useful for perennial crops such as oil palm, which have long breeding cycles, and for which the optimal method for GS is still under debate. In this study, we evaluated the effect of different marker systems and modeling methods for implementing GS in an introgressed dura family derived from a Deli dura x Nigerian dura (Deli x Nigerian) with 112 individuals. This family is an important breeding source for developing new mother palms for superior oil yield and bunch characters. The traits of interest selected for this study were fruit-to-bunch (F/B), shell-to-fruit (S/F), kernel-to-fruit (K/F), mesocarp-to-fruit (M/F), oil per palm (O/P) and oil-to-dry mesocarp (O/DM). The marker systems evaluated were simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs). RR-BLUP, Bayesian A, B, Cπ, LASSO, Ridge Regression and two machine learning methods (SVM and Random Forest) were used to evaluate GS accuracy of the traits. Results The kinship coefficient between individuals in this family ranged from 0.35 to 0.62. S/F and O/DM had the highest genomic heritability, whereas F/B and O/P had the lowest. The accuracies using 135 SSRs were low, with accuracies of the traits around 0.20. The average accuracy of machine learning methods was 0.24, as compared to 0.20 achieved by other methods. The trait with the highest mean accuracy was F/B (0.28), while the lowest were both M/F and O/P (0.18). By using whole genomic SNPs, the accuracies for all traits, especially for O/DM (0.43), S/F (0.39) and M/F (0.30) were improved. The average accuracy of machine learning methods was 0.32, compared to 0.31 achieved by other methods. Conclusion Due to high genomic resolution, the use of whole-genome SNPs improved the efficiency of GS dramatically for oil palm and is recommended for dura breeding programs. Machine learning slightly outperformed other methods, but required parameters optimization for GS implementation