Investigation into background levels of small organic samples at the NERC Radiocarbon Laboratory

Recent progress in preparation/combustion of submilligram organic samples at our laboratories is presented. Routine methods had to be modified/refined to achieve acceptable and consistent procedural blanks for organic samples smaller than 1000 g C. A description of the process leading to a modified combustion method for smaller organic samples is given in detail. In addition to analyzing different background materials, the influence of different chemical reagents on the overall radiocarbon background level was investigated, such as carbon contamination arising from copper oxide of different purities and from different suppliers. Using the modified combustion method, small amounts of background materials and known-age standard IAEA-C5 were individually combusted to CO2. Below 1000 g C, organic background levels follow an inverse mass dependency when combusted with the modified method, increasing from 0.13 0.05 pMC up to 1.20 0.04 pMC for 80 g C. Results for a given carbon mass were lower for combustion of etched Iceland spar calcite mineral, indicating that part of the observed background of bituminous coal was probably introduced by handling the material in atmosphere prior to combustion. Using the modified combustion method, the background-corrected activity of IAEA-C5 agreed to within 2 s of the consensus value of 23.05 pMC down to a sample mass of 55 g C

Progress in AMS target production in sub-milligram samples at the NERC Radiocarbon Laboratory

. Recent progress in graphite target production for sub-milligram environmental samples in our facility is presented. We describe an optimized hydrolysis procedure now routinely used for the preparation of CO2 from inorganic samples, a new high-vacuum line dedicated to small sample processing (combining sample distillation and graphitization units), as well as a modified graphitization procedure. Although measurements of graphite targets as small as 35 µg C have been achieved, system background and measurement uncertainties increase significantly below 150 µg C. As target lifetime can become critically short for targets <150 µg C, the facility currently only processes inorganic samples down to 150 µg C. All radiocarbon measurements are made at the Scottish Universities Environmental Research Centre (SUERC) accelerator mass spectrometry (AMS) facility. Sample processing and analysis are labor-intensive, taking approximately 3 times longer than samples ≥500 µg C. The technical details of the new system, graphitization yield, fractionation introduced during the process, and the system blank are discussed in detail

Does treating insomnia with digital cognitive behavioural therapy (Sleepio) mediate improvements in anxiety for those with insomnia and comorbid anxiety?:An analysis using individual participant data from two large randomised controlled trials

Background: Considerable comorbidity exists between insomnia and anxiety, and evidence shows that the benefits of CBT for insomnia extend to anxiety. Using data from two large trials of digital CBT (dCBT) for insomnia, we evaluated whether improving sleep is an effective treatment target to reduce both insomnia and anxiety symptoms in individuals with insomnia and clinically significant anxiety. Methods: This was a controlled sub-analysis combining individual participant data from two previous randomised controlled trials of dCBT for insomnia (Sleepio). Participants (N = 2172) with insomnia disorder and clinically significant anxiety symptoms were included in this sub-analysis and received either dCBT or control (usual care or sleep hygiene education). Assessments were evaluated at baseline, post-intervention (week 8 or 10), and follow-up (week 22 or 24). Mediation was evaluated using structural equation models. Results: dCBT for insomnia was superior to control at reducing both insomnia (Hedges' g range = 0.77–0.81; both p &lt; 0.001) and anxiety symptoms (Hedges' g range = 0.39–0.44; both p &lt; 0.001) at all time points. Baseline insomnia symptoms moderated the effects of dCBT on insomnia, however no variables moderated treatment effects on anxiety. Reductions in anxiety symptoms at follow-up were mediated by improvements in sleep at post-intervention (% mediated = 84 %), suggesting a causal pathway. Limitations: Participants did not have a formal anxiety disorder diagnosis and so the effects of dCBT for insomnia on anxiety may differ by anxiety disorder. Conclusions: Addressing sleep using dCBT for insomnia may serve as a treatment target from which to improve anxiety in individuals with insomnia and clinically significant comorbid anxiety. Clinical trial registrations: Digital Insomnia therapy to Assist your Life as well as your Sleep (DIALS) - ISRCTN60530898 http://www.isrctn.com/ISRCTN60530898. Oxford Access for Students Improving Sleep (OASIS) - ISRCTN61272251 http://www.isrctn.com/ISRCTN61272251.</p

Contribution of Berry Polyphenols to the Human Metabolome

Diets rich in berries provide health benefits, however, the contribution of berry phytochemicals to the human metabolome is largely unknown. The present study aimed to establish the impact of berry phytochemicals on the human metabolome. A "systematic review strategy" was utilized to characterize the phytochemical composition of the berries most commonly consumed in the USA; (poly)phenols, primarily anthocyanins, comprised the majority of reported plant secondary metabolites. A reference standard library and tandem mass spectrometry (MS/MS) quantitative metabolomics methodology were developed and applied to serum/plasma samples from a blueberry and a strawberry intervention, revealing a diversity of benzoic, cinnamic, phenylacetic, 3-(phenyl)propanoic and hippuric acids, and benzyldehydes. 3-Phenylpropanoic, 2-hydroxybenzoic, and hippuric acid were highly abundant (mean > 1 µM). Few metabolites at concentrations above 100 nM changed significantly in either intervention. Significant intervention effects (P < 0.05) were observed for plasma/serum 2-hydroxybenzoic acid and hippuric acid in the blueberry intervention, and for 3-methoxyphenylacetic acid and 4-hydroxyphenylacetic acid in the strawberry intervention. However, significant within-group effects for change from baseline were prevalent, suggesting that high inter-individual variability precluded significant treatment effects. Berry consumption in general appears to cause a fluctuation in the pools of small molecule metabolites already present at baseline, rather than the appearance of unique berry-derived metabolites, which likely reflects the ubiquitous nature of (poly)phenols in the background diet

The Recent Star Formation History of NGC 5102

We present Hubble Space Telescope photometry of young stars in NGC 5102, a nearby gas-rich post-starburst S0 galaxy with a bright young stellar nucleus. We use the IAC-pop/MinnIAC algorithm to derive the recent star formation history in three fields in the bulge and disk of NGC 5102. In the disk fields, the recent star formation rate has declined monotonically and is now barely detectable, but a starburst is still in progress in the bulge and has added about 2 percent to the mass of the bulge over the last 200 Myr. Other studies of star formation in NGC 5102 indicate that about 20 percent of its stellar mass was added over the past Gyr. If this is correct, then much of the stellar mass of the bulge may have formed over this period. It seems likely that this star formation was fueled by the accretion of a gas-rich system with HI mass of about 2 x 10^9 Msol which has now been almost completely converted into stars. The large mass of recently formed stars and the blue colours of the bulge suggest that the current starburst, which is now fading, may have made a significant contribution to build the bulge of NGC 5102.Comment: 36 pages, 16 figures, accepted in A

Entropy of Molecular Binding at Solvated Mineral Surfaces

We present thermodynamic integration simulations for the binding of mannose and methanoic acid onto the {10.4} calcite surface producing free energy of binding values of −2.89 and −1.64 kJ mol–1, respectively. We extract the entropy of binding from vacuum-based simulations and use these values to determine the entropy of binding for surface water molecules which is ∼6 J mol–1 K–1

How does an amorphous surface influence molecular binding? - ovocleidin-17 and amorphous calcium carbonate

Atomistic molecular dynamics simulations of dehydrated amorphous calcium carbonate interacting with the protein ovocleidin-17 are presented. These simulations demonstrate that the amorphisation of the calcium carbonate surface removes water structure from the surface. This reduction of structure allows the protein to bind with many residues, unlike on crystalline surfaces where binding is strongest when only a few residues are attached to the surface. Basic residues are observed to dominate the binding interactions. The implications for protein control over crystallisation are discussed

Selenium hyperaccumulation offers protection from cell disruptor herbivores

<p>Abstract</p> <p>Background</p> <p>Hyperaccumulation, the rare capacity of certain plant species to accumulate toxic trace elements to levels several orders of magnitude higher than other species growing on the same site, is thought to be an elemental defense mechanism against herbivores and pathogens. Previous research has shown that selenium (Se) hyperaccumulation protects plants from a variety of herbivores and pathogens. Selenium hyperaccumulating plants sequester Se in discrete locations in the leaf periphery, making them potentially more susceptible to some herbivore feeding modes than others. In this study we investigate the protective function of Se in the Se hyperaccumulators <it>Stanleya pinnata </it>and <it>Astragalus bisulcatus </it>against two cell disrupting herbivores, the western flower thrips (<it>Frankliniella occidentalis</it>) and the two-spotted spider mite (<it>Tetranychus urticae</it>).</p> <p>Results</p> <p><it>Astragalus bisulcatus </it>and <it>S. pinnata </it>with high Se concentrations (greater than 650 mg Se kg^-1) were less subject to thrips herbivory than plants with low Se levels (less than 150 mg Se kg^-1). Furthermore, in plants containing elevated Se levels, leaves with higher concentrations of Se suffered less herbivory than leaves with less Se. Spider mites also preferred to feed on low-Se <it>A. bisulcatus </it>and <it>S. pinnata </it>plants rather than high-Se plants. Spider mite populations on <it>A. bisulcatus </it>decreased after plants were given a higher concentration of Se. Interestingly, spider mites could colonize <it>A. bisulcatus </it>plants containing up to 200 mg Se kg^-1dry weight, concentrations which are toxic to many other herbivores. Selenium distribution and speciation studies using micro-focused X-ray fluorescence (μXRF) mapping and Se K-edge X-ray absorption spectroscopy revealed that the spider mites accumulated primarily methylselenocysteine, the relatively non-toxic form of Se that is also the predominant form of Se in hyperaccumulators.</p> <p>Conclusions</p> <p>This is the first reported study investigating the protective effect of hyperaccumulated Se against cell-disrupting herbivores. The finding that Se protected the two hyperaccumulator species from both cell disruptors lends further support to the elemental defense hypothesis and increases the number of herbivores and feeding modes against which Se has shown a protective effect. Because western flower thrips and two-spotted spider mites are widespread and economically important herbivores, the results from this study also have potential applications in agriculture or horticulture, and implications for the management of Se-rich crops.</p

Exploiting machine learning in multiscale modelling of materials

Recent developments in efficient machine learning algorithms have spurred significant interest in the materials community. The inherently complex and multiscale problems in Materials Science and Engineering pose a formidable challenge. The present scenario of machine learning research in Materials Science has a clear lacunae, where efficient algorithms are being developed as a separate endeavour, while such methods are being applied as ‘black-box’ models by others. The present article aims to discuss pertinent issues related to the development and application of machine learning algorithms for various aspects of multiscale materials modelling. The authors present an overview of machine learning of equivariant properties, machine learning-aided statistical mechanics, the incorporation of ab initio approaches in multiscale models of materials processing and application of machine learning in uncertainty quantification. In addition to the above, the applicability of Bayesian approach for multiscale modelling will be discussed. Critical issues related to the multiscale materials modelling are also discussed