.Jammu, Mar 31: Commissioner Secretary to Govt, Tourism Department, Farooq Ahmed Shah Friday flagged off a contingent of 32 youth, comprising of boys and girls, in presence of M S Punoo President and Aamir Ali, Vice President, J&K Dragon Boat Association.
The youth are going to participate in the 8th Dragon Boat National Championship, scheduled to be held in Guwahati Assam, from 4th to 6th April 2017.
According to the statement issued to KNS, the championship is being organised by Dragon Boat Federation of India and Assam Dragon Boat Association.
23 teams across India, including J&K are participating in the championship.
The J&K team underwent 10 days special coaching camp in Dal Lake Srinagar, said Shabir Ahmad team Manager.Genetic variation in Aquaporin-4 moderates the relationship between sleep and brain Aβ-amyloid burden

The glymphatic system is postulated to be a mechanism of brain Aβ-amyloid clearance and to be most effective during sleep. Ablation of the astrocytic end-feet expressed water-channel protein, Aquaporin-4, in mice, results in impairment of this clearance mechanism and increased brain Aβ-amyloid deposition, suggesting that Aquaporin-4 plays a pivotal role in glymphatic function. Currently there is a paucity of literature regarding the impact of AQP4 genetic variation on sleep, brain Aβ-amyloid burden and their relationship to each other in humans. To address this a cross-sectional observational study was undertaken in cognitively normal older adults from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. Genetic variants in AQP4 were investigated with respect to self-reported Pittsburgh Sleep Quality Index sleep parameters, positron emission tomography derived brain Aβ-amyloid burden and whether these genetic variants moderated the sleep-Aβ-amyloid burden relationship. One AQP4 variant, rs72878776, was associated with poorer overall sleep quality, while several SNPs moderated the effect of sleep latency (rs491148, rs9951307, rs7135406, rs3875089, rs151246) and duration (rs72878776, rs491148 and rs2339214) on brain Aβ-amyloid burden. This study suggests that AQP4 genetic variation moderates the relationship between sleep and brain Aβ-amyloid burden, which adds weight to the proposed glymphatic system being a potential Aβ-amyloid clearance mechanism and suggests that AQP4 genetic variation may impair this function. Further, AQP4 genetic variation should be considered when interpreting sleep-Aβ relationships.


Introduction
Estimates suggest that dysfunctional sleep may be present in up to 45% of Alzheimer's disease (AD) patients 1 ; manifesting commonly as frequent awakenings, increased sleep latency (time to fall asleep) and poor sleep maintenance 2. However, accumulating evidence also suggests that there is a bi-directional relationship between sleep and AD phenotypes 3-5 ; i.e., in addition to the AD phenotype leading to sleep dysfunction, dysfunctional sleep contributes to the AD phenotype.
Aβ-amyloid (Aβ) accumulates gradually in the brain of individuals as they progress towards a diagnosis of AD 6. This accumulation of Aβ is thought to begin about 20 years before the onset of AD symptomology 7, and in the sporadic form of the disease, is hypothesized to be driven by poor clearance mechanisms 6. The mechanisms of Aβ clearance from the human brain are multiple, with some specific factors involved yet to be fully understood 8. Significantly, however, it has been observed in mice that good quality sleep enhances brain Aβ clearance 9, while dysfunctional sleep exacerbates Aβ accumulation in humans 10 and animal models 11. Further, we have recently shown that in cognitively normal older adults, increased sleep latency is associated with higher brain Aβ burden 12. It is hypothesized that the brain has a lymphatic-like clearance system that operates parallel to the human lymphatic system through the employment of a network of paravascular clearing mechanisms 13. This lymphaticlike clearance system has been termed the glymphatic system 14,15, and is postulated to function almost entirely during sleep 9.
Evidence from animal models supports a cerebral perivascular and paravascular clearing mechanism that involves the bulk flow of interstitial fluid and the involvement of the water-channel protein, Aquaporin-4 (AQP4) 16, located primarily in the subpial and perivascular end-feet of astrocytic processes. Further, evidence from Aqp4 gene knockout mice supports the notion that the functionality of AQP4 is related to the efficacy of Aβ clearance 17, likely via the glymphatic system 18. Additionally, a study of autopsied human brains observed that AQP4 immunoreactivity is distributed in a manner similar to neuritic Aβ plaques 19 ; suggesting that AQP4 is likely to be linked to Aβ plaque deposition in the brain 20. Further, a decrease in AQP4 expression or loss of perivascular localization could contribute to reduced Aβ clearance 21.
Rare in silico-predicted functional variants have been identified in human AQP4, which have been shown to impair water permeability in vitro 22. However, no studies to date have investigated the role of AQP4 genetic variation in AD, sleep and Aβ clearance. To deepen our understanding of the role of AQP4 in AD, we investigated genetic variation across the AQP4 gene with respect to the relationship with, and between, sleep quality/quantity and brain Aβ burden. We hypothesized that poorer quality sleep would be associated with higher Aβ brain burden and that genetic variants in AQP4 would moderate this relationship. This hypothesis was derived from the premise that both poor sleep quality decreases Aβ clearance (i.e., results in higher brain Aβ burden), and also that suboptimal glymphatic clearance during dysfunctional sleep will result in a higher cerebral Aβ burden; ergo, AQP4 genetic variation would have a functional impact on brain Aβ burden.

Study participants
This cross-sectional investigation utilized data collected from Cognitively Normal (CN) older adults of the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study; a prospective longitudinal study of ageing launched in 2006.
All volunteers were aged 60 years and above at baseline. Further details regarding the design, enrolment process, neuropsychological assessments, and diagnostic criteria of the AIBL Study have been previously described elsewhere 23. The AIBL Study is approved by the institutional ethics committees of Austin Health, St Vincent's Health, Hollywood Private Hospital, and Edith Cowan University (ECU), and informed written consent was given by all volunteers.

Sleep measures
Subjective sleep quality and disturbances were assessed via the Pittsburgh Sleep Quality Index (PSQI) 24 in 462 CN older adults at the 72-month time point of the study. Several parameters are subsequently derived from this 19item self-report measure, including sleep quality, latency (in min), duration (reported in hours), efficiency, sleep disturbance, medication use and daytime dysfunction. A further global score of sleep quality, PSQI Total, is also derived, with a score > 5 indicating poor sleep. For the present study, analyses focused on parameters related to night-time function and thus, the factors studied herein were limited initially to overall sleep quality (PSQI Total), then subsequently to the sub-scales of sleep latency, sleep duration, sleep efficiency, and sleep disturbances.

Brain imaging
Of these 462 participants, 222 also underwent Αβ imaging. Αβ imaging was performed via positron emission tomography (PET) using one of the following radiolabeled Αβ tracers; 11 C-Pittsburgh Compound B (PiB), 18 Fflorbetapir or 18 F-flutemetamol, as previously described [25][26][27]. Images were analyzed using CapAIBL, a webbased freely available magnetic resonance (MR)-less methodology, to generate PET standardized uptake value (SUV) ratios (SUVR) for all tracers 28. Briefly, SUVs were summed and normalized to either the cerebellar cortex SUV (PiB), whole cerebellum SUV (florbetapir), or pons SUV (flutemetamol), to yield the target-region to reference-region SUVR. To allow for the analysis of these different tracers as a single continuous variable, a linear regression transformation was applied to generate PiBlike SUVR units termed the 'Before the Centiloid Kernel Transformation' (BeCKeT) scale 29. PiB SUVR and florbetapir/flutemetamol BeCKeT were utilized in this crosssectional study.

Genetic data
Genetic data were derived from a genome-wide singlenucleotide polymorphism (SNP) array conducted on the Illumina OmniExpressHumanExome + BeadChip with subsequent imputation using impute2 ver2.3, with the 1000 genome reference panel (2015 release). SNP data from the AQP4 genomic region (GRCh37 Chr18:24,432,000-24,446,000) were extracted and subjected to quality control in GoldenHelix SNP and Variation Suite (SVS version 8.7.1), which included removal of SNPs with call rate < 95%, Minor allele frequency < 5% and departure from Hardy-Weinberg Equilibrium (p < 0.05), leaving 32 SNPs (Supplementary Table 1). After Linkage Disequilibrium (LD) pruning (r 2 cutoff of 0.8, window size 10, increment 5), 13 AQP4 SNPs were selected to provide full coverage of the gene (Supplementary Table 1, Supplementary Figure 1). Apolipoprotein E (APOE) genotype, specifically the presence of the ε4 allele, the major genetic risk factor for AD, was determined using TaqMan genotyping assays (Life