The distance between the surface of the scalp and the surface of the grey matter of the brain is a key factor in determining the effective dose of non‐invasive brain stimulation for an individual ...person. The highly folded nature of the cortical surface means that the depth of a particular brain area is likely to vary between individuals. The question addressed here is: what is the variability of this measure of cortical depth? Ninety‐four anatomical MRI images were taken from the OASIS database. For each image, the minimum distance from each point in the grey matter to the scalp surface was determined. Transforming these estimates into standard space meant that the coefficient of variation could be determined across the sample. The results indicated that depth variability is high across the cortical surface, even when taking sulcal depth into account. This was true even for the primary visual and motor areas, which are often used in setting TMS dosage. The correlation of the depth of these areas and the depth of other brain areas was low. The results suggest that dose setting of TMS based on visual or evoked potentials may offer poor reliability, and that individual brain images should be used when targeting non‐primary brain areas.
Safe and effective modulation of brain activity with magnetic or electric stimulation needs a good understanding of brain anatomy. A crucial parameter is the distance from the scalp to the brain surface, as this is related to the neural effect of stimulation. The variability of this measure is high across the cortical surface in a sample of healthy brain images, meaning that person‐specific imaging may be needed for some applications.
The quantum anomalous Hall (QAH) effect appears in ferromagnetic topological insulators (FMTIs) when a Dirac mass gap opens in the spectrum of the topological surface states (SSs). Unaccountably, ...although the mean mass gap can exceed 28 meV (or ∼320 K), the QAH effect is frequently only detectable at temperatures below 1 K. Using atomic-resolution Landau level spectroscopic imaging, we compare the electronic structure of the archetypal FMTI Cr0.08(Bi0.1Sb0.9)1.92Te3 to that of its nonmagnetic parent (Bi0.1Sb0.9)2Te3, to explore the cause. In (Bi0.1Sb0.9)2Te3, we find spatially random variations of the Dirac energy. Statistically equivalent Dirac energy variations are detected in Cr0.08(Bi0.1Sb0.9)1.92Te3 with concurrent but uncorrelated Dirac mass gap disorder. These two classes of SS electronic disorder conspire to drastically suppress the minimum mass gap to below 100 μeV for nanoscale regions separated by <1 μm. This fundamentally limits the fully quantized anomalous Hall effect in Sb2Te3-based FMTI materials to very low temperatures.
A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment ...conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.
Net anthropogenic emissions of carbon dioxide (CO
) must approach zero by mid-century (2050) in order to stabilize the global mean temperature at the level targeted by international efforts
. Yet ...continued expansion of fossil-fuel-burning energy infrastructure implies already 'committed' future CO
emissions
. Here we use detailed datasets of existing fossil-fuel energy infrastructure in 2018 to estimate regional and sectoral patterns of committed CO
emissions, the sensitivity of such emissions to assumed operating lifetimes and schedules, and the economic value of the associated infrastructure. We estimate that, if operated as historically, existing infrastructure will cumulatively emit about 658 gigatonnes of CO
(with a range of 226 to 1,479 gigatonnes CO
, depending on the lifetimes and utilization rates assumed). More than half of these emissions are predicted to come from the electricity sector; infrastructure in China, the USA and the 28 member states of the European Union represents approximately 41 per cent, 9 per cent and 7 per cent of the total, respectively. If built, proposed power plants (planned, permitted or under construction) would emit roughly an extra 188 (range 37-427) gigatonnes CO
. Committed emissions from existing and proposed energy infrastructure (about 846 gigatonnes CO
) thus represent more than the entire carbon budget that remains if mean warming is to be limited to 1.5 degrees Celsius (°C) with a probability of 66 to 50 per cent (420-580 gigatonnes CO
)
, and perhaps two-thirds of the remaining carbon budget if mean warming is to be limited to less than 2 °C (1,170-1,500 gigatonnes CO
)
. The remaining carbon budget estimates are varied and nuanced
, and depend on the climate target and the availability of large-scale negative emissions
. Nevertheless, our estimates suggest that little or no new CO
-emitting infrastructure can be commissioned, and that existing infrastructure may need to be retired early (or be retrofitted with carbon capture and storage technology) in order to meet the Paris Agreement climate goals
. Given the asset value per tonne of committed emissions, we suggest that the most cost-effective premature infrastructure retirements will be in the electricity and industry sectors, if non-emitting alternatives are available and affordable
.
It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data ...analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
We review the physics of pair-density wave (PDW) superconductors. We begin with a macroscopic description that emphasizes order induced by PDW states, such as charge-density wave, and discuss related ...vestigial states that emerge as a consequence of partial melting of the PDW order. We review and critically discuss the mounting experimental evidence for such PDW order in the cuprate superconductors, the status of the theoretical microscopic description of such order, and the current debate on whether the PDW is a mother order or another competing order in the cuprates. In addition, we give an overview of the weak coupling version of PDW order, Fulde-Ferrell-Larkin-Ovchinnikov states, in the context of cold atom systems, unconventional superconductors, and noncentrosymmetric and Weyl materials.
Interfaces play an important role in modifying the dynamics of polymers confined to the nanoscale. We demonstrate that the distance over which an interface suppresses molecular mobility in ...poly(styrene) thin films can be systematically increased by tens of nanometers by controlling the chain of conformation, i.e., the height of the loops in irreversibly adsorbed nanolayers. These effects arise from topological interaction between adsorbed and neighboring unadsorbed chains, respectively, which increase their motional coupling to facilitate the propagation of suppressed dynamics originating at the interface, thus highlighting the ability to manipulate interfacial effects by local conformation of chains in adsorbed nanolayers.
Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a ...straightforward process. Biases, artifacts and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalization.
We have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalization and visualization of scRNA-seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single-cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development.
The open-source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http://bioconductor.org/packages/scater .
davis@ebi.ac.uk.
Supplementary data are available at Bioinformatics online.
The effect caffeine elicits on endurance performance is well founded. However, comparatively less research has been conducted on the ergogenic potential of anaerobic performance. Some studies showing ...no effect of caffeine on performance used untrained subjects and designs often not conducive to observing an ergogenic effect. Recent studies incorporating trained subjects and paradigms specific to intermittent sports activity support the notion that caffeine is ergogenic to an extent with anaerobic exercise. Caffeine seems highly ergogenic for speed endurance exercise ranging in duration from 60 to 180 seconds. However, other traditional models examining power output (i.e. 30-second Wingate test) have shown minimal effect of caffeine on performance. Conversely, studies employing sport-specific methodologies (i.e. hockey, rugby, soccer) with shorter duration (i.e. 4-6 seconds) show caffeine to be ergogenic during high-intensity intermittent exercise. Recent studies show caffeine affects isometric maximal force and offers introductory evidence for enhanced muscle endurance for lower body musculature. However, isokinetic peak torque, one-repetition maximum and muscular endurance for upper body musculature are less clear. Since relatively few studies exist with resistance training, a definite conclusion cannot be reached on the extent caffeine affects performance. It was previously thought that caffeine mechanisms were associated with adrenaline (epinephrine)-induced enhanced free-fatty acid oxidation and consequent glycogen sparing, which is the leading hypothesis for the ergogenic effect. It would seem unlikely that the proposed theory would result in improved anaerobic performance, since exercise is dominated by oxygen-independent metabolic pathways. Other mechanisms for caffeine have been suggested, such as enhanced calcium mobilization and phosphodiesterase inhibition. However, a normal physiological dose of caffeine in vivo does not indicate this mechanism plays a large role. Additionally, enhanced Na+/K+ pump activity has been proposed to potentially enhance excitation contraction coupling with caffeine. A more favourable hypothesis seems to be that caffeine stimulates the CNS. Caffeine acts antagonistically on adenosine receptors, thereby inhibiting the negative effects adenosine induces on neurotransmission, arousal and pain perception. The hypoalgesic effects of caffeine have resulted in dampened pain perception and blunted perceived exertion during exercise. This could potentially have favourable effects on negating decreased firing rates of motor units and possibly produce a more sustainable and forceful muscle contraction. The exact mechanisms behind caffeine's action remain to be elucidated.
Impaired insulin-mediated suppression of hepatic glucose production (HGP) plays a major role in the pathogenesis of type 2 diabetes (T2D), yet the molecular mechanism by which this occurs remains ...unknown. Using a novel in vivo metabolomics approach, we show that the major mechanism by which insulin suppresses HGP is through reductions in hepatic acetyl CoA by suppression of lipolysis in white adipose tissue (WAT) leading to reductions in pyruvate carboxylase flux. This mechanism was confirmed in mice and rats with genetic ablation of insulin signaling and mice lacking adipose triglyceride lipase. Insulin’s ability to suppress hepatic acetyl CoA, PC activity, and lipolysis was lost in high-fat-fed rats, a phenomenon reversible by IL-6 neutralization and inducible by IL-6 infusion. Taken together, these data identify WAT-derived hepatic acetyl CoA as the main regulator of HGP by insulin and link it to inflammation-induced hepatic insulin resistance associated with obesity and T2D.
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•Insulin inhibits gluconeogenesis by suppressing lipolysis and hepatic acetyl CoA•Hyperglycemia associated with HFD is due to increased WAT-derived hepatic acetyl CoA•ATGL KOs are protected from HFD-induced insulin resistance due to decreased lipolysis•mφJNK KOs are protected from HFD-induced insulin resistance due to decreased lipolysis
Metabolic abnormalities associated with a high-fat diet are found to be driven by increased hepatic acetyl CoA levels, which are shown to be a consequence of white adipose tissue inflammation and inappropriately increased lipolysis.
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