An accurate and transferable machine learning potential for carbon

We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology. The potential, named GAP-20, describes the properties of the bulk crystalline and amorphous phases, crystal surfaces, and defect structures with an accuracy approaching that of direct ab initio simulation, but at a significantly reduced cost. We combine structural databases for amorphous carbon and graphene, which we extend substantially by adding suitable configurations, for example, for defects in graphene and other nanostructures. The final potential is fitted to reference data computed using the optB88-vdW density functional theory (DFT) functional. Dispersion interactions, which are crucial to describe multilayer carbonaceous materials, are therefore implicitly included. We additionally account for long-range dispersion interactions using a semianalytical two-body term and show that an improved model can be obtained through an optimization of the many-body smooth overlap of atomic positions descriptor. We rigorously test the potential on lattice parameters, bond lengths, formation energies, and phonon dispersions of numerous carbon allotropes. We compare the formation energies of an extensive set of defect structures, surfaces, and surface reconstructions to DFT reference calculations. The present work demonstrates the ability to combine, in the same ML model, the previously attained flexibility required for amorphous carbon [V. L. Deringer and G. Csányi, Phys. Rev. B 95, 094203 (2017)] with the high numerical accuracy necessary for crystalline graphene [Rowe et al., Phys. Rev. B 97, 054303 (2018)], thereby providing an interatomic potential that will be applicable to a wide range of applications concerning diverse forms of bulk and nanostructured carbon

Managing women in pregnancy after bariatric surgery: The midwife as the co-ordinator of care

Bariatric surgery is a recommended, cost-effective, evidenced-based intervention to reduce weight and associated comorbidities in severely obese people. People with a BMI of 40 kg/m2 or more, or a BMI between 35–40 kg/m2 with other medical conditions such as diabetes, hypertension, high cholesterol and obstructive sleep apnoea meet the criteria to be considered for bariatric surgery. Over the past 10 years, bariatric surgery in the UK has been more widely accessible and consequently midwives may be required to care for pregnant women who have undergone bariatric surgery such as a gastric band, sleeve gastrectomy and gastric bypass. Midwives are required to work co-operatively, recognising and working within the limits of their competence and providing leadership. The aim of this article is to consider the midwife's role as co-ordinator of care for pregnant women who have undergone bariatric surgery. It outlines the most common bariatric procedures and specific considerations, including nutritional supplementation required when providing care to women in the antenatal and postnatal period

Beyond Zeno: Approaching Infinite Temperature upon Repeated Measurements

The influence of repeated projective measurements on the dynamics of the state of a quantum system is studied in dependence of the time lag $\tau$ between successive measurements. In the limit of infinitely many measurements of the occupancy of a single state the total system approaches a uniform state. The asymptotic approach to this state is exponential in the case of finite Hilbert space dimension. The rate characterizing this approach undergoes a sharp transition from a monotonically increasing to an erratically varying function of the time between subsequent measurements

Plasma apolipoprotein J as a potential biomarker for Alzheimer\u27s disease: Australian Imaging, Biomarkers and Lifestyle study of aging

Introduction: For early detection of Alzheimer\u27s disease (AD), the field needs biomarkers that can be used to detect disease status with high sensitivity and specificity. Apolipoprotein J (ApoJ, also known as clusterin) has long been associated with AD pathogenesis through various pathways. The aim of this study was to investigate the potential of plasma apoJ as a blood biomarker for AD. Methods: Using the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, the present study assayed plasma apoJ levels over baseline and 18 months in 833 individuals. Plasma ApoJ levels were analyzed with respect to clinical classification, age, gender, apolipoprotein E (APOE) ε4 allele status, mini-mental state examination score, plasma amyloid beta (Aβ), neocortical Aβ burden (as measured by Pittsburgh compound B-positron emission tomography), and total adjusted hippocampus volume. Results: ApoJ was significantly higher in both mild cognitive impairment (MCI) and AD groups as compared with healthy controls (HC; P \u3c .0001). ApoJ significantly correlated with both standardized uptake value ratio (SUVR) and hippocampus volume and weakly correlated with the plasma Aβ1-42/Aβ1-40 ratio. Plasma apoJ predicted both MCI and AD from HC with greater than 80% accuracy for AD and greater than 75% accuracy for MCI at both baseline and 18-month time points. Discussion: Mean apoJ levels were significantly higher in both MCI and AD groups. ApoJ was able to differentiate between HC with high SUVR and HC with low SUVR via APOE ε4 allele status, indicating that it may be included in a biomarker panel to identify AD before the onset of clinical symptoms. © 2016 The Authors

Machine-learning of atomic-scale properties based on physical principles

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear functionals of the potential energy, such as the total energy and atomic forces. We then give a detailed account of the Smooth Overlap of Atomic Positions (SOAP) representation and kernel, showing how it arises from an abstract representation of smooth atomic densities, and how it is related to several popular density-based representations of atomic structure. We also discuss recent generalisations that allow fine control of correlations between different atomic species, prediction and fitting of tensorial properties, and also how to construct structural kernels---applicable to comparing entire molecules or periodic systems---that go beyond an additive combination of local environments

Aβ Imaging: feasible, pertinent, and vital to progress in Alzheimer’s disease

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Logopenic and nonfluent variants of primary progressive aphasia are differentiated by acoustic measures of speech production

Differentiation of logopenic (lvPPA) and nonfluent/agrammatic (nfvPPA) variants of Primary Progressive Aphasia is important yet remains challenging since it hinges on expert based evaluation of speech and language production. In this study acoustic measures of speech in conjunction with voxel-based morphometry were used to determine the success of the measures as an adjunct to diagnosis and to explore the neural basis of apraxia of speech in nfvPPA. Forty-one patients (21 lvPPA, 20 nfvPPA) were recruited from a consecutive sample with suspected frontotemporal dementia. Patients were diagnosed using the current gold-standard of expert perceptual judgment, based on presence/absence of particular speech features during speaking tasks. Seventeen healthy age-matched adults served as controls. MRI scans were available for 11 control and 37 PPA cases; 23 of the PPA cases underwent amyloid ligand PET imaging. Measures, corresponding to perceptual features of apraxia of speech, were periods of silence during reading and relative vowel duration and intensity in polysyllable word repetition. Discriminant function analyses revealed that a measure of relative vowel duration differentiated nfvPPA cases from both control and lvPPA cases (r2 = 0.47) with 88% agreement with expert judgment of presence of apraxia of speech in nfvPPA cases. VBM analysis showed that relative vowel duration covaried with grey matter intensity in areas critical for speech motor planning and programming: precentral gyrus, supplementary motor area and inferior frontal gyrus bilaterally, only affected in the nfvPPA group. This bilateral involvement of frontal speech networks in nfvPPA potentially affects access to compensatory mechanisms involving right hemisphere homologues. Measures of silences during reading also discriminated the PPA and control groups, but did not increase predictive accuracy. Findings suggest that a measure of relative vowel duration from of a polysyllable word repetition task may be sufficient for detecting most cases of apraxia of speech and distinguishing between nfvPPA and lvPPA

Food loss and waste metrics: a proposed nutritional cost footprint linking linear programming and life cycle assessment

Purpose: The main purpose of this article is to assess the nutritional and economic efficiency of food loss and waste (FLW) along the supply of 13 food categories included in the Spanish food basket by means of the definition of a new method which combines two indexes. Methods: The nutrient-rich foods index and the economic food loss and waste (EFLW) index were combined by means of linear programming to obtain the nutritional cost footprint (NCF) indicator under a life cycle perspective. The functional unit used was the daily supply of food for a Spanish citizen in year 2015. Results and discussion: Results showed that vegetables and cereals were the food categories most affected by the inefficiencies in the food supply chain under a nutritional perspective, being agricultural production and household consumption the main stages in which the nutritional content of food is lost or wasted. Moreover, according to the NCF index, vegetables represented 27% of total nutritional-economic wastage throughout the entire Spanish agri-food chain. They are followed by fruits, which add up to 19%. Hence, specific food waste management strategies should be established for these specific products and supply stages. Finally, the sensitivity analysis performed highlighted that results were mostly independent from the importance attributed to either nutritional or economic variables. Conclusions: The methodology described in this study proposes an indicator quantifying the nutritional-economic cost of different food categories in the Spanish food basket. This NCF indicator makes it possible to define reduction strategies to promote the use of food waste fractions for waste-to-energy valorization approaches or the extraction of different types of pharmacological, chemical, or cosmetic compounds.The authors are grateful for the funding of the Spanish Ministry of Economy and Competitiveness through the Ceres-Procom: Food production and consumption strategies for climate change mitigation (CTM2016-76176-C2-1-R) (AEI/FEDER, UE)