Constructing living buildings: a review of relevant technologies for a novel application of biohybrid robotics

Biohybrid robotics takes an engineering approach to the expansion and exploitation of biological behaviours for application to automated tasks. Here, we identify the construction of living buildings and infrastructure as a high-potential application domain for biohybrid robotics, and review technological advances relevant to its future development. Construction, civil infrastructure maintenance and building occupancy in the last decades have comprised a major portion of economic production, energy consumption and carbon emissions. Integrating biological organisms into automated construction tasks and permanent building components therefore has high potential for impact. Live materials can provide several advantages over standard synthetic construction materials, including self-repair of damage, increase rather than degradation of structural performance over time, resilience to corrosive environments, support of biodiversity, and mitigation of urban heat islands. Here, we review relevant technologies, which are currently disparate. They span robotics, self-organizing systems, artificial life, construction automation, structural engineering, architecture, bioengineering, biomaterials, and molecular and cellular biology. In these disciplines, developments relevant to biohybrid construction and living buildings are in the early stages, and typically are not exchanged between disciplines. We, therefore, consider this review useful to the future development of biohybrid engineering for this highly interdisciplinary application.publishe

Defining anti-synthetase syndrome: a systematic literature review

OBJECTIVES: Anti-synthetase syndrome (ASSD) is a heterogeneous autoimmune disease characterised by multi-system involvement with a wide variety of manifestations. Validated classification criteria are necessary to improve recognition and prevent misclassification, especially given the lack of reliable and standardised autoantibody testing. We systematically reviewed the literature to analyse proposed ASSD criteria, characteristics, and diagnostic performance. METHODS: We searched PubMed and Embase databases (01/01/1984 to 06/11/2018) and the ACR and EULAR meeting abstracts (2017-2018). Sensitivities, specificities, positive, negative likelihood ratios and risk of bias were calculated for ASSD criteria and key variables reported in the literature. We performed meta-analysis when appropriate. RESULTS: We retrieved 4,358 studies. We found 85 proposed ASSD criteria from a total of 82 studies. All but one study included anti-synthetase autoantibody (ARS) positivity in the ASSD criteria. Most studies required only one ASSD feature plus anti-ARS to define ASSD (n=64, 78%), whereas 16 studies required more than one ASSD variable plus anti-ARS. The only criteria not including anti-ARS positivity required 5 ASSD clinical features. We found limited data and wide variability in the diagnostic performance of each variable and definition proposed in the literature. Given these limitations we only meta-analysed the performance of individual muscle biopsy and clinical variables in diagnosing ASSD, which performed poorly. CONCLUSIONS: The current ASSD criteria include a variety of serological, clinical, and histological features with wide variability amongst proposed definitions and the performance of these definitions has not been tested. This systematic literature review suggests the need for additional data and consensus-driven classification criteria for ASSD

Heteroskedasticity testing through a comparison of Wald statistics

This paper shows that a test for heteroskedasticity within the context of classical linear regression can be based on the difference between Wald statistics in heteroskedasticity-robust and nonrobust forms. The test is asymptotically distributed under the null hypothesis of homoskedasticity as chi-squared with one degree of freedom. The power of the test is sensitive to the choice of parametric restriction used by the Wald statistics, so the supremum of a range of individual test statistics is proposed. Two versions of a supremum-based test are considered: the first version does not have a known asymptotic null distribution, so the bootstrap is employed to approximate its empirical distribution. The second version has a known asymptotic distribution and, in some cases, is asymptotically pivotal under the null. A simulation study illustrates the use and finite-sample performance of both versions of the test. In this study, the bootstrap is found to provide better size control than asymptotic critical values, namely with heavy-tailed, asymmetric distributions of the covariates. In addition, the use of well-known modifications of the heteroskedasticity consistent covariance matrix estimator of OLS coefficients is also found to benefit the tests’ overall behaviour.info:eu-repo/semantics/publishedVersio

Lifetime self-reported arthritis is associated with elevated levels of mental health burden: A multi-national cross sectional study across 46 low- and middle-income countries

Population-based studies investigating the relationship of arthritis with mental health outcomes are lacking, particularly among low- and middle-income countries (LMICs). We investigated the relationship between arthritis and mental health (depression spectrum, psychosis spectrum, anxiety, sleep disturbances and stress) across community-dwelling adults aged ≥18 years across 46 countries from the World Health Survey. Symptoms of psychosis and depression were established using questions from the Mental Health Composite International Diagnostic Interview. Severity of anxiety, sleep problems, and stress sensitivity over the preceding 30 days were self-reported. Self-report lifetime history of arthritis was collected, including presence or absence of symptoms suggestive of arthritis: pain, stiffness or swelling of joints over the preceding 12-months. Multivariable logistic regression analyses were undertaken. Overall, 245,706 individuals were included. Having arthritis increased the odds of subclinical psychosis (OR = 1.85; 95%CI = 1.72–1.99) and psychosis (OR = 2.48; 95%CI = 2.05–3.01). People with arthritis were at increased odds of subsyndromal depression (OR = 1.92; 95%CI = 1.64–2.26), a brief depressive episode (OR = 2.14; 95%CI = 1.88–2.43) or depressive episode (OR = 2.43; 95%CI = 2.21–2.67). Arthritis was also associated with increased odds for anxiety (OR = 1.75; 95%CI = 1.63–1.88), sleep problems (OR = 2.23; 95%CI = 2.05–2.43) and perceived stress (OR = 1.43; 95%CI = 1.33–1.53). Results were similar for middle-income and low-income countries. Integrated interventions addressing arthritis and mental health comorbidities are warranted to tackle this considerable burden

Wavelet penalized likelihood estimation in generalized functional models

The paper deals with generalized functional regression. The aim is to estimate the influence of covariates on observations, drawn from an exponential distribution. The link considered has a semiparametric expression: if we are interested in a functional influence of some covariates, we authorize others to be modeled linearly. We thus consider a generalized partially linear regression model with unknown regression coefficients and an unknown nonparametric function. We present a maximum penalized likelihood procedure to estimate the components of the model introducing penalty based wavelet estimators. Asymptotic rates of the estimates of both the parametric and the nonparametric part of the model are given and quasi-minimax optimality is obtained under usual conditions in literature. We establish in particular that the LASSO penalty leads to an adaptive estimation with respect to the regularity of the estimated function. An algorithm based on backfitting and Fisher-scoring is also proposed for implementation. Simulations are used to illustrate the finite sample behaviour, including a comparison with kernel and splines based methods

Do Consumption-Based Asset Pricing Models Explain Serial Dependence in Stock Returns?

We show that the Bansal-Yaron, Campbell-Cochrane and Cecchetti-Lam-Mark models of asset prices cannot explain the serial correlation structure of stock returns. We show this by estimating these models and deriving expected returns from them and then testing whether the difference between observed and expected returns is a martingale difference sequence. We use variance ratio and rescaled range tests which we modify to account for the expected returns being functions of estimated parameters. We also use a weighted quantilogram test based on a bootstrap procedure robust to this estimation. The evidence against the BansalYaron and Campbell-Cochrane models is significant. While the evidence against the Cecchetti-Lam-Mark model is not in general significant, our point estimates strongly suggest its residuals are not a martingale difference sequence. Furthermore, a semi-parametric maximal predictability test suggests there is some evidence that the three models’ state variables struggle to explain the degree of predictability observed in the market return. A timing strategy designed to exploit predictability in the market can significantly outperform the market in certainty equivalent terms under the Bansal-Yaron model. The timing strategy may underperform the market by less than it ought to under the Campbell-Cochrane model