Smart biomimetic construction materials for next generation infrastructure

The resilience of building and civil engineering structures is typically associated with the design of individual elements such that they have sufficient capacity or potential to react in an appropriate manner to adverse events. Traditionally this has been achieved by using ‘robust’ design procedures that focus on defining safety factors for individual adverse events and providing redundancy. As such, construction materials are designed to meet a prescribed specification; material degradation is viewed as inevitable and mitigation necessitates expensive maintenance regimes; ~£40 billion/year is spent in the UK on repair and maintenance of existing, mainly concrete, structures. More recently, based on a better understanding and knowledge of microbiological systems, materials that have the ability to adapt and respond to their environment have been developed. This fundamental change has the potential to facilitate the creation of a wide range of ‘smart’ materials and intelligent structures, including both autogenous and autonomic self‐healing materials and adaptable, self‐sensing and self‐repairing structures, which can transform our infrastructure by embedding resilience in the materials and components of these structures so that rather than being defined by individual events, they can evolve over their lifespan. We therefore advocate that next generation infrastructure will include next generation infrastructure materials based on smart biomimetic construction materials. This paper presents details of the national consortium that is leading international efforts in the development of those next generation infrastructure materials. It presents details of the work done to date, over the past three years, as part of the EPSRC funded project Materials for Life and the plans for work to be done over the next five years as part of a follow-on Programme grant: Resilient Materials for Life

Novel Observation of Isospin Structure of Short-Range Correlations in Calcium Isotopes

Short-range correlations (SRCs) have been identified as being responsible for the high-momentum tail of the nucleon momentum distribution, n(k). Hard, short-range interactions of nucleon pairs generate the high-momentum tail and imprint a universal character on n(k) for all nuclei at large momentum. Triple coincidence experiments have shown a strong dominance of np pairs, but these measurements involve large final-state interactions. This paper presents the results from Jefferson Lab experiment E08014 which measured inclusive electron scattering cross section from Ca isotopes. By comparing the inclusive cross section from 48Ca to 40Ca in a kinematic region dominated by SRCs we provide a new way to study the isospin structure of SRCs

The relationship between white matter microstructure and self-perceived cognitive decline

Subjective cognitive decline (SCD) is a perceived cognitive change prior to objective cognitive deficits, and although it is associated with Alzheimer's disease (AD) pathology, it likely results from multiple underlying pathologies. We investigated the association of white matter microstructure to SCD as a sensitive and early marker of cognitive decline and quantified the contribution of white matter microstructure separate from amyloidosis. Vanderbilt Memory & Aging Project participants with diffusion MRI data and a 45-item measure of SCD were included [n = 236, 137 cognitively unimpaired (CU), 99 with mild cognitive impairment (MCI), 73 ± 7 years, 37% female]. A subset of participants (64 CU, 40 MCI) underwent a fasting lumbar puncture for quantification of cerebrospinal fluid (CSF) amyloid-β(CSF Aβ42), total tau (CSF t-tau), and phosphorylated tau (CSF p-tau). Diffusion MRI data was post-processed using the free-water (FW) elimination technique, which allowed quantification of extracellular (FW) and intracellular compartment (fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) microstructure. Microstructural values were quantified within 11 cognitive-related white matter tracts, including medial temporal lobe, frontal transcallosal, and fronto-parietal tracts using a region of interest approach. General linear modeling related each tract to SCD scores adjusting for age, sex, race/ethnicity, education, Framingham Stroke Risk Profile scores, APOE ε4 carrier status, diagnosis, Geriatric Depression Scale scores, hippocampal volume, and total white matter volume. Competitive models were analyzed to determine if white matter microstructural values have a unique role in SCD scores separate from CSF Aβ42. FW-corrected radial diffusivity (RDT) was related to SCD scores in 8 tracts: cingulum bundle, inferior longitudinal fasciculus, as well as inferior frontal gyrus (IFG) pars opercularis, IFG orbitalis, IFG pars triangularis, tapetum, medial frontal gyrus, and middle frontal gyrus transcallosal tracts. While CSF Aβ42 was related to SCD scores in our cohort (Radj2 = 39.03%; β = −0.231; p = 0.020), competitive models revealed that fornix and IFG pars triangularis transcallosal tract RDT contributed unique variance to SCD scores beyond CSF Aβ42 (Radj2 = 44.35% and Radj2 = 43.09%, respectively), with several other tract measures demonstrating nominal significance. All tracts which demonstrated nominal significance (in addition to covariates) were input into a backwards stepwise regression analysis. ILF RDT, fornix RDT, and UF FW were best associated with SCD scores (Radj2 = 46.69%; p = 6.37 × 10-12). Ultimately, we found that medial temporal lobe and frontal transcallosal tract microstructure is an important driver of SCD scores independent of early amyloid deposition. Our results highlight the potential importance of abnormal white matter diffusivity as an early contributor to cognitive decline. These results also highlight the value of incorporating multiple biomarkers to help disentangle the mechanistic heterogeneity of SCD as an early stage of cognitive decline

Probing for High-Momentum Protons in ⁴He via the ⁴He (e, e\u27p) X Reactions

Experimental cross sections for the 4He(e,e′p) X reactions in the missing energy range from 0.017 to 0.022 GeV and up to a missing momentum of 0.632 GeV/c at xB = 1.24 and Q2 = 2 (GeV/c)2 are reported. The data are compared to relativistic distorted-wave impulse approximation calculations for the 4He(e,e′p)3H channel. Significantly more events are observed for pm ≥ 0.45 GeV/c than are predicted by the theoretical model, and striking fluctuations in the ratio of data to the theoretical model around pm = 0.3GeV/c are possible signals of initial-state multinucleon correlations

Determination of the Argon Spectral Function From (e, e\u27p) Data

The E12-14-012 experiment, performed in Jefferson Lab Hall A, has measured the (e,e′p) cross section in parallel kinematics using a natural argon target. Here, we report the full results of the analysis of the data set corresponding to beam energy 2.222 GeV, and spanning the missing momentum and missing energy range 15 ≲ pm ≲ 300  MeV /c and 12 ≲ Em ≲ 80  MeV. The reduced cross section, determined as a function of pm and Em with ≈ 4% accuracy, has been fitted using the results of Monte Carlo simulations involving a model spectral function and including the effects of final state interactions. The overall agreement between data and simulations turns out to be quite satisfactory (χ2/d. o. f. =1.9). The resulting spectral function will provide valuable new information, needed for the interpretation of neutrino interactions in liquid argon detectors

Physical interventions to interrupt or reduce the spread of respiratory viruses: systematic review

Objective To review systematically the evidence of effectiveness of physical interventions to interrupt or reduce the spread of respiratory viruses

Measurement of the Ar(e, e\u27 p) and Ti(e, e\u27 p) Cross Sections in Jefferson Lab Hall A

The E12-14-012 experiment, performed in Jefferson Lab Hall A, has collected exclusive electron-scattering data (e, e\u27p) in parallel kinematics using natural argon and natural titanium targets. Here we report the first results of the analysis of the data set corresponding to beam energy 2222 GeV, electron scattering angle 21.5 degrees, and proton emission angle -50°. The differential cross sections, measured with ≈ 4% uncertainty, have been studied as a function of missing energy and missing momentum, and compared to the results of Monte Carlo simulations, obtained from a model based on the distorted-wave impulse approximation

Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank - rationale, challenges and approaches

PMCID: PMC3668194SEP was directly funded by the National Institute for Health Research Cardiovascular Biomedical Research Unit at Barts. SN acknowledges support from the Oxford NIHR Biomedical Research Centre and from the Oxford British Heart Foundation Centre of Research Excellence. SP and PL are funded by a BHF Senior Clinical Research fellowship. RC is supported by a BHF Research Chair and acknowledges the support of the Oxford BHF Centre for Research Excellence and the MRC and Wellcome Trust. PMM gratefully acknowledges training fellowships supporting his laboratory from the Wellcome Trust, GlaxoSmithKline and the Medical Research Council

Cerebrospinal fluid and plasma neurofilament light relate to abnormal cognition

Introduction Neuroaxonal damage may contribute to cognitive changes preceding clinical dementia. Accessible biomarkers are critical for detecting such damage. Methods Plasma and cerebrospinal fluid (CSF) neurofilament light (NFL) were related to neuropsychological performance among Vanderbilt Memory & Aging Project participants (plasma n = 333, 73 ± 7 years; CSF n = 149, 72 ± 6 years) ranging from normal cognition (NC) to mild cognitive impairment (MCI). Models adjusted for age, sex, race/ethnicity, education, apolipoprotein E ε4 carriership, and Framingham Stroke Risk Profile. Results Plasma NFL was related to all domains (P values ≤ .008) except processing speed (P values ≥ .09). CSF NFL was related to memory and language (P values ≤ .04). Interactions with cognitive diagnosis revealed widespread plasma associations, particularly in MCI participants, which were further supported in head-to-head comparison models. Discussion Plasma and CSF NFL (reflecting neuroaxonal injury) relate to cognition among non-demented older adults albeit with small to medium effects. Plasma NFL shows particular promise as an accessible biomarker with relevance to cognition in MCI

Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets

The use of mechanistic models in clinical studies is limited by the lack of multi-modal patients data representing different anatomical and physiological processes. For example, neuroimaging datasets do not provide a sufficient representation of heart features for the modeling of cardiovascular factors in brain disorders. To tackle this problem we introduce a probabilistic framework for joint cardiac data imputation and personalisation of cardiovascular mechanistic models, with application to brain studies with incomplete heart data. Our approach is based on a variational framework for the joint inference of an imputation model of cardiac information from the available features, along with a Gaussian Process emulator that can faithfully reproduce personalised cardiovascular dynamics. Experimental results on UK Biobank show that our model allows accurate imputation of missing cardiac features in datasets containing minimal heart information, e.g. systolic and diastolic blood pressures only, while jointly estimating the emulated parameters of the lumped model. This allows a novel exploration of the heart-brain joint relationship through simulation of realistic cardiac dynamics corresponding to different conditions of brain anatomy