Influence of sex on the age‐related adaptations of neuromuscular function and motor unit properties in elite masters athletes

Motor unit (MU) remodelling acts to minimise loss of muscle fibres following denervation in older age, which may be more successful in masters athletes. Evidence suggests performance and neuromuscular function decline with age in this population, although the majority of studies have focused on males, with little available data on female athletes. Functional assessments of strength, balance and motor control were performed in 30 masters athletes (16 male) aged 44–83 years. Intramuscular needle electrodes were used to sample individual motor unit potentials (MUPs) and near‐fibre MUPs in the tibialis anterior (TA) during isometric contractions at 25% maximum voluntary contraction, and used to determine discharge characteristics (firing rate, variability) and biomarkers of peripheral MU remodelling (MUP size, complexity, stability). Multilevel mixed‐effects linear regression models examined effects of age and sex. All aspects of neuromuscular function deteriorated with age (P < 0.05) with no age × sex interactions, although males were stronger (P < 0.001). Indicators of MU remodelling also progressively increased with age to a similar extent in both sexes (P < 0.05), whilst MU firing rate progressively decreased with age in females (p = 0.029), with a non‐significant increase in males (p = 0.092). Masters athletes exhibit age‐related declines in neuromuscular function that are largely equal across males and females. Notably, they also display features of MU remodelling with advancing age, probably acting to reduce muscle fibre loss. The age trajectory of MU firing rate assessed at a single contraction level differed between sexes, which may reflect a greater tendency for females to develop a slower muscle phenotype

Simulating neutron radiation damage of graphite by in-situ electron irradiation

Radiation damage in nuclear grade graphite has been investigated using transmission electron microscopy (TEM) and electron energy loss spectroscopy (EELS). Changes in the structure on the atomic scale and chemical bonding, and the relationship between each were of particular interest. TEM was used to study damage in nuclear grade graphite on the atomic scale following 1.92×108 electrons nm-2 of electron beam exposure. During these experiments EELS spectra were also collected periodically to record changes in chemical bonding and structural disorder, by analysing the changes of the carbon K-edge. Image analysis software from the 'PyroMaN' research group provides further information, based on (002) fringe analysis. The software was applied to the micrographs of electron irradiated virgin 'Pile Grade A' (PGA) graphite to quantify the extent of damage from electron beam exposure

Bayesian optimization for materials design

We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as possible. We focus on the case when materials designs are parameterized by a low-dimensional vector. Bayesian optimization is built on a statistical technique called Gaussian process regression, which allows predicting the performance of a new design based on previously tested designs. After providing a detailed introduction to Gaussian process regression, we introduce two Bayesian optimization methods: expected improvement, for design problems with noise-free evaluations; and the knowledge-gradient method, which generalizes expected improvement and may be used in design problems with noisy evaluations. Both methods are derived using a value-of-information analysis, and enjoy one-step Bayes-optimality

Tunable magnetic exchange interactions in manganese-doped inverted core/shell ZnSe/CdSe nanocrystals

Magnetic doping of semiconductor nanostructures is actively pursued for applications in magnetic memory and spin-based electronics. Central to these efforts is a drive to control the interaction strength between carriers (electrons and holes) and the embedded magnetic atoms. In this respect, colloidal nanocrystal heterostructures provide great flexibility via growth-controlled `engineering' of electron and hole wavefunctions within individual nanocrystals. Here we demonstrate a widely tunable magnetic sp-d exchange interaction between electron-hole excitations (excitons) and paramagnetic manganese ions using `inverted' core-shell nanocrystals composed of Mn-doped ZnSe cores overcoated with undoped shells of narrower-gap CdSe. Magnetic circular dichroism studies reveal giant Zeeman spin splittings of the band-edge exciton that, surprisingly, are tunable in both magnitude and sign. Effective exciton g-factors are controllably tuned from -200 to +30 solely by increasing the CdSe shell thickness, demonstrating that strong quantum confinement and wavefunction engineering in heterostructured nanocrystal materials can be utilized to manipulate carrier-Mn wavefunction overlap and the sp-d exchange parameters themselves.Comment: To appear in Nature Materials; 18 pages, 4 figures + Supp. Inf

Evaluating methods for the analysis of rare variants in sequence data

A number of rare variant statistical methods have been proposed for analysis of the impending wave of next-generation sequencing data. To date, there are few direct comparisons of these methods on real sequence data. Furthermore, there is a strong need for practical advice on the proper analytic strategies for rare variant analysis. We compare four recently proposed rare variant methods (combined multivariate and collapsing, weighted sum, proportion regression, and cumulative minor allele test) on simulated phenotype and next-generation sequencing data as part of Genetic Analysis Workshop 17. Overall, we find that all analyzed methods have serious practical limitations on identifying causal genes. Specifically, no method has more than a 5% true discovery rate (percentage of truly causal genes among all those identified as significantly associated with the phenotype). Further exploration shows that all methods suffer from inflated false-positive error rates (chance that a noncausal gene will be identified as associated with the phenotype) because of population stratification and gametic phase disequilibrium between noncausal SNPs and causal SNPs. Furthermore, observed true-positive rates (chance that a truly causal gene will be identified as significantly associated with the phenotype) for each of the four methods was very low (<19%). The combination of larger than anticipated false-positive rates, low true-positive rates, and only about 1% of all genes being causal yields poor discriminatory ability for all four methods. Gametic phase disequilibrium and population stratification are important areas for further research in the analysis of rare variant data

Using patient management as a surrogate for patient health outcomes in diagnostic test evaluation

<p>Abstract</p> <p>Background</p> <p>Before a new test is introduced in clinical practice, evidence is needed to demonstrate that its use will lead to improvements in patient health outcomes. Studies reporting test accuracy may not be sufficient, and clinical trials of tests that measure patient health outcomes are rarely feasible. Therefore, the consequences of testing on patient management are often investigated as an intermediate step in the pathway. There is a lack of guidance on the interpretation of this evidence, and patient management studies often neglect a discussion of the limitations of measuring patient management as a surrogate for health outcomes.</p> <p>Methods</p> <p>We discuss the rationale for measuring patient management, describe the common study designs and provide guidance about how this evidence should be reported.</p> <p>Results</p> <p>Interpretation of patient management studies relies on the condition that patient management is a valid surrogate for downstream patient benefits. This condition presupposes two critical assumptions: the test improves diagnostic accuracy; and the measured changes in patient management improve patient health outcomes. The validity of this evidence depends on the certainty around these critical assumptions and the ability of the study design to minimise bias. Three common designs are test RCTs that measure patient management as a primary endpoint, diagnostic before-after studies that compare planned patient management before and after testing, and accuracy studies that are extended to report on the actual treatment or further tests received following a positive and negative test result.</p> <p>Conclusions</p> <p>Patient management can be measured as a surrogate outcome for test evaluation if its limitations are recognised. The potential consequences of a positive and negative test result on patient management should be pre-specified and the potential patient benefits of these management changes clearly stated. Randomised comparisons will provide higher quality evidence about differences in patient management using the new test than observational studies. Regardless of the study design used, the critical assumption that patient management is a valid surrogate for downstream patient benefits or harms must be discussed in these studies.</p

Ethnic differences in ovulatory function in nulliparous women

African-American women have a long-standing approximately 20% higher breast cancer incidence rate than USA White women under age 40 while rates among Latinas are lower than those of Whites. The reasons for this are not clear, however they may be due to ethnic differences in circulating oestradiol and progesterone levels. In a cross-sectional study, we investigated whether anovulation frequency and circulating serum oestradiol and/or progesterone levels vary among normally cycling nulliparous African-American (n=60), Latina (n=112) and non-Latina White (n=69) women. Blood and urine specimens were collected over two menstrual cycles among healthy 17- to 34-year-old women. Frequency of anovulation was greater among White women (nine out of 63, 14.3%) than African-American women (four out of 56, 7.1%) or Latina women (seven out of 102, 6.9%), although these differences were not statistically significant. African-American women had 9.9% (P=0.26) higher follicular phase oestradiol concentrations than Latina women and 17.4% (P=0.13) higher levels than White women. African-American women also had considerably higher levels of luteal phase oestradiol (vs Latinas, +9.4%, P=0.14; vs Whites, +25.3%, P=0.003) and progesterone (vs Latinas, +15.4%, P=0.07; vs Whites, +36.4%, P=0.002). Latina women were also observed to have higher follicular oestradiol, and luteal oestradiol and progesterone levels than White women (follicular oestradiol: +6.8%, P=0.48; luteal oestradiol: +14.6%, P=0.04; luteal progesterone: +18.2%, P=0.06). These results suggest that exposure to endogenous steroid hormones may be greater for young African-American and Latina women than for Whites

Understanding children’s experiences of self-wetting in humanitarian contexts: An evaluation of the Story Book methodology

Little is known about how children in humanitarian contexts experience self-wetting. Children can wet themselves due to having the medical condition of urinary incontinence (the involuntary leakage of urine), or due to them not wanting to or not being able to use the toilet facilities available (social or functional incontinence). Self-wetting is a global public health challenge: the physical health of children can suffer; they can miss out on educational and social opportunities; they may face increased protection risks; and the emotional effect on daily life can be significantly negative. The Story Book methodology was developed to facilitate conversations with children aged five to eleven in humanitarian contexts (specifically refugee settlements in Adjumani District, Uganda; and refugee camps in Cox’s Bazar, Bangladesh) about self-wetting to understand how humanitarian professionals can best meet the needs of children that wet themselves. This paper has evaluated how far the Story Book methodology meets the specific requirements of conducting research a) in a humanitarian context; b) with young children; and c) on a personal and highly sensitive topic. Data has been used from Story Book sessions held with children in Adjumani District and Cox’s Bazar, and from semi-structured interviews held with adults known to have participated in the planning and/or facilitation of the sessions. The evaluation found that although the Story Book methodology provided deep insights into how children in humanitarian contexts experience self-wetting, it was not always implemented as designed; it is not practical to implement in humanitarian settings; and it was not acceptable to all participants and facilitators as a research tool. Changes have been recommended to improve the methodology as a research tool to better understand how children experience personal health issues, but even with such changes the methodology will remain better suited to non-humanitarian contexts

Comparing distribution of harbour porpoise using generalized additive models and hierarchical Bayesian models with integrated nested laplace approximation

Species Distribution Models (SDMs) are used regularly to develop management strategies, but many modelling methods ignore the spatial nature of data. To address this, we compared fine-scale spatial distribution predictions of harbour porpoise (Phocoena phocoena) using empirical aerial-video-survey data collected along the east coast of Scotland in August and September 2010 and 2014. Incorporating environmental covariates that cover habitat preferences and prey proxies, we used a traditional (and commonly implemented) Generalized Additive Model (GAM), and two Hierarchical Bayesian Modelling (HBM) approaches using Integrated Nested Laplace Approxi�mation (INLA) model-fitting methodology. One HBM-INLA modelled gridded space (similar to the GAM), and the other dealt more explicitly in continuous space using a Log-Gaussian Cox Process (LGCP). Overall, predicted distributions in the three models were similar; however, HBMs had twice the level of certainty, showed much finer-scale patterns in porpoise distribution, and identified some areas of high relative density that were not apparent in the GAM. Spatial differences were due to how the two methods accounted for autocorrelation, spatial clustering of animals, and differences between modelling in discrete vs. continuous space; consequently, methods for spatial analyses likely depend on scale at which results, and certainty, are needed. For large-scale analysis (>5–10 km resolution, e.g. initial impact assessment), there was little difference be�tween results; however, insights into fine-scale (<1 km) distribution of porpoise from the HBM model using LGCP, while more computationally costly, offered potential benefits for refining conservation management or mitigation measures within offshore developments or protected areas