Lung function, COPD and cognitive function: a multivariable and two sample Mendelian randomization study

BACKGROUND: Observational studies show an association between reduced lung function and impaired cognition. Cognitive dysfunction influences important health outcomes and is a precursor to dementia, but treatments options are currently very limited. Attention has therefore focused on identifying modifiable risk factors to prevent cognitive decline and preserve cognition. Our objective was to determine if lung function or risk of COPD causes reduced cognitive function using Mendelian randomization (MR). METHODS: Single nucleotide polymorphisms from genome wide association studies of lung function and COPD were used as exposures. We examined their effect on general cognitive function in a sample of 132,452 individuals. We then performed multivariable MR (MVMR), examining the effect of lung function before and after conditioning for covariates. RESULTS: We found only weak evidence that reduced lung function (Beta − 0.002 (SE 0.02), p-value 0.86) or increased liability to COPD (− 0.008 (0.008), p-value 0.35) causes lower cognitive function. MVMR found both reduced FEV(1) and FVC do cause lower cognitive function, but that after conditioning for height (− 0.03 (0.03), p-value 0.29 and − 0.01 (0.03) p-value 0.62, for FEV1 and FVC respectively) and educational attainment (− 0.03 (0.03) p-value 0.33 and − 0.01 (0.02), p-value 0.35) the evidence became weak. CONCLUSION: We did not find evidence that reduced lung function or COPD causes reduced cognitive function. Previous observational studies are probably affected by residual confounding. Research efforts should focus on shared risk factors for reduced lung function and cognition, rather than lung function alone as a modifiable risk factor. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-021-01611-6

Refresh Triggered Computation: Improving the Energy Efficiency of Convolutional Neural Network Accelerators

To employ a Convolutional Neural Network (CNN) in an energy-constrained embedded system, it is critical for the CNN implementation to be highly energy efficient. Many recent studies propose CNN accelerator architectures with custom computation units that try to improve energy-efficiency and performance of CNNs by minimizing data transfers from DRAM-based main memory. However, in these architectures, DRAM is still responsible for half of the overall energy consumption of the system, on average. A key factor of the high energy consumption of DRAM is the refresh overhead, which is estimated to consume 40% of the total DRAM energy. In this paper, we propose a new mechanism, Refresh Triggered Computation (RTC), that exploits the memory access patterns of CNN applications to reduce the number of refresh operations. We propose three RTC designs (min-RTC, mid-RTC, and full-RTC), each of which requires a different level of aggressiveness in terms of customization to the DRAM subsystem. All of our designs have small overhead. Even the most aggressive RTC design (i.e., full-RTC) imposes an area overhead of only 0.18% in a 16 Gb DRAM chip and can have less overhead for denser chips. Our experimental evaluation on six well-known CNNs show that RTC reduces average DRAM energy consumption by 24.4% and 61.3%, for the least aggressive and the most aggressive RTC implementations, respectively. Besides CNNs, we also evaluate our RTC mechanism on three workloads from other domains. We show that RTC saves 31.9% and 16.9% DRAM energy for Face Recognition and Bayesian Confidence Propagation Neural Network (BCPNN), respectively. We believe RTC can be applied to other applications whose memory access patterns remain predictable for a sufficiently long time

High COVID-19 transmission potential associated with re-opening universities can be mitigated with layered interventions

Reopening of universities to students following COVID-19 restrictions risks increased transmission due to high numbers of social contacts and the potential for asymptomatic transmission. Here, the authors use a mathematical model with social contact data to estimate the impacts of reopening a typical non-campus based university in the UK

Contacts and behaviours of university students during the COVID-19 pandemic at the start of the 2020/2021 academic year

University students have unique living, learning and social arrangements which may have implications for infectious disease transmission. To address this data gap, we created CONQUEST (COroNavirus QUESTionnaire), a longitudinal online survey of contacts, behaviour, and COVID-19 symptoms for University of Bristol (UoB) staff/students. Here, we analyse results from 740 students providing 1261 unique records from the start of the 2020/2021 academic year (14/09/2020–01/11/2020), where COVID-19 outbreaks led to the self-isolation of all students in some halls of residences. Although most students reported lower daily contacts than in pre-COVID-19 studies, there was heterogeneity, with some reporting many (median = 2, mean = 6.1, standard deviation = 15.0; 8% had ≥ 20 contacts). Around 40% of students’ contacts were with individuals external to the university, indicating potential for transmission to non-students/staff. Only 61% of those reporting cardinal symptoms in the past week self-isolated, although 99% with a positive COVID-19 test during the 2 weeks before survey completion had self-isolated within the last week. Some students who self-isolated had many contacts (mean = 4.3, standard deviation = 10.6). Our results provide context to the COVID-19 outbreaks seen in universities and are available for modelling future outbreaks and informing policy

Characterisation of Genome-Wide Association Epistasis Signals for Serum Uric Acid in Human Population Isolates

Genome-wide association (GWA) studies have identified a number of loci underlying variation in human serum uric acid (SUA) levels with the SLC2A9 gene having the largest effect identified so far. Gene-gene interactions (epistasis) are largely unexplored in these GWA studies. We performed a full pair-wise genome scan in the Italian MICROS population (n = 1201) to characterise epistasis signals in SUA levels. In the resultant epistasis profile, no SNP pairs reached the Bonferroni adjusted threshold for the pair-wise genome-wide significance. However, SLC2A9 was found interacting with multiple loci across the genome, with NFIA - SLC2A9 and SLC2A9 - ESRRAP2 being significant based on a threshold derived for interactions between GWA significant SNPs and the genome and jointly explaining 8.0% of the phenotypic variance in SUA levels (3.4% by interaction components). Epistasis signal replication in a CROATIAN population (n = 1772) was limited at the SNP level but improved dramatically at the gene ontology level. In addition, gene ontology terms enriched by the epistasis signals in each population support links between SUA levels and neurological disorders. We conclude that GWA epistasis analysis is useful despite relatively low power in small isolated populations

Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure