Changes in older people's experiences of providing care and of volunteering during the COVID-19 pandemic

Engagement in socially productive activities, such as care provision and voluntary work, make important contributions to society, and may have been especially important during the coronavirus pandemic. They have also been associated with better health, well-being, and longer survival for older people. The ELSA COVID-19 Substudy provided data to allow for an exploration of how changes in caring and volunteering may have occurred during the pandemic, and to examine this in relation to factors such as sex, age, employment status, wealth, COVID-19 vulnerability and symptoms, and pre-pandemic experiences of health. Overall, there have been important changes in both the level of care provided by older people and the extent of their involvement in volunteering, with, on average, care provision more likely to have increased or stayed the same (65% of older carers reported this), and volunteering more likely to have decreased or stopped (61% of older volunteers reported this). However, a large number of older people took on new caring roles for someone outside the household (12%) and 4% of older people registered to volunteer as part of the NHS scheme. Both economic characteristics (such as paid employment and wealth) and health-related characteristics (such as being vulnerable, self-isolating, having experienced COVID-19 symptoms, and reporting functional limitations) were related to changes the frequency of caring and voluntary work. It is yet unclear how these changes in caring and volunteering have influenced older people’s health and well-being during the coronavirus outbreak. Investigating the impact of the pandemic on broader health and well-being outcomes for older people, the role of changes in care provision and volunteering in this, and how we might respond to this, is a crucial next step

An optimal sensor placement method for SHM based on Bayesian experimental design and polynomial chaos expansion

We present an optimal sensor placement methodology for structural health monitoring (SHM) purposes, relying on a Bayesian experimental design approach. The unknown structural properties, e.g. the residual strength and stiffness, are inferred from data collected through a network of sensors, whose architecture, i.e., type and position may largely affect the accuracy of the monitoring system. In tackling this issue, an optimal network configuration is herein sought by maximizing the expected information gain between prior and posterior probability distributions of the parameters to be estimated. Since the objective function linked to the network topology cannot be analytically computed, a numerical approximation is provided by means of a Monte Carlo analysis, wherein each realization is obtained via finite element modeling. Since the computational burden linked to this procedure often grows infeasible, a Polynomial Chaos Expansion (PCE) approach is adopted for accelerating the computation of the forward problem. The analysis expands over joint samples covering both structural state and design variables, i.e., sensor locations. Via increase of the number of deployed sensors in the network, the optimization procedure soon turns computationally costly due to the curse of dimensionality. To this end, a stochastic optimization method is adopted for accelerating the convergence of the optimization process and thereby the damage detection capability of the SHM system. The proposed method is applied to thin flexible structures, and the resulting optimal sensor configuration is shown. The effects of the number of training samples, the polynomial degree of the approximation expansion and the optimization settings are also discussed

Reactivity to AQP4 epitopes in relapsing–remitting multiple sclerosis

Autoantibodies against the water channel AQP4, expressed predominately in central nervous system astrocytes, are markers and pathogenic factors in Devic's disease. In this study we examined whether Multiple Sclerosis (MS) patients recognize antigenic epitopes on AQP4 that may define distinct disease subsets. We screened sera from 45 patients with relapsing–remitting MS (RRMS) and 13 patients with primary progressive MS (PMS). 23 Neuromyelitis Optica (NMO) patients previously characterized were used as assay positive/negative controls. Sera from 23 patients with Systemic Lupus Erythematosus, 23 with primary Sjogren syndrome without neurological involvement and from 28 healthy individuals were also used as controls. NMO-positive sera exhibited reactivity against the intracellular epitope AQPaa252-275, confirming previous observations. All RRMS sera tested negative for anti-AQP4 antibodies using a cell-based assay, but surprisingly, 13% of them reacted with the epitope AQPaa252-275. PMS, healthy and disease controls showed no specific reactivity. Whether these antibodies define distinct MS subsets and have a pathogenic potential pointing to convergent pathogenetic mechanism with NMO, or are simply markers of astrocytic damage, remains to be determined

An application of generative adversarial networks in structural health monitoring

In the current work, the use of generative adversarial networks (GANs) in a simulated structural health monitoring (SHM) application is studied. A specific type of GAN is considered, aiming at a disentangled representation of underlying features and clusters of data through some latent variables. This idea could prove useful in SHM, since explanation of how damage mechanisms or environmental conditions affect a structure may be exploited in order to monitor structures more effectively. In a simulated mass-spring example, different damage cases are introduced by reducing the stiffness of specific springs and different damage levels by applying different extents of stiffness reduction. The GAN implementation proves able to capture different damage cases through its categorical latent variables, as well as the damage extent within its continuous latent variables. The results demonstrate that the latent variables are indeed capturing the effect of damage in the structure and can be exploited for the purpose of condition assessment

Leptin, acylcarnitine metabolites and development of adiposity in the Rhea mother-child cohort in Crete, Greece.

OBJECTIVE: This study aims to investigate relations of serum leptin at age 4 with development of adiposity and linear growth during 3 years of follow-up among 75 Greek children and to identify serum metabolites associated with leptin at age 4 and to characterize their associations with adiposity gain and linear growth. METHODS: Linear regression models that accounted for maternal age, education and gestational weight gain and child's age and sex were used to examine associations of leptin and leptin-associated metabolites measured at age 4 with indicators of adiposity and linear growth at age 7. RESULTS: Each 1-unit increment in natural log-(ln)-transformed leptin corresponded with 0.33 (95% CI: 0.10, 0.55) units greater body mass index-for-age z-score gain during follow-up. Likewise, higher levels of the leptin-associated metabolites methylmalonyl-carnitine and glutaconyl-carnitine corresponded with 0.14 (95% CI: 0.01, 0.27) and 0.07 (95% CI: -0.01, 0.16) units higher body mass index-for-age z-score gain, respectively. These relationships did not differ by sex or baseline weight status and were independent of linear growth. CONCLUSIONS: These findings suggest that leptin, methylmalonyl-carnitine and possibly glutaconyl-carnitine are associated with weight gain during early childhood. Future studies are warranted to confirm these findings in other populations

A nexus of intrinsic dynamics underlies translocase priming

The cytoplasmic ATPase SecA and the membrane-embedded SecYEG channel assemble to form the Sec translocase. How this interaction primes and catalytically activates the translocase remains unclear. We show that priming exploits a nexus of intrinsic dynamics in SecA. Using atomistic simulations, smFRET, and HDX-MS, we reveal multiple dynamic islands that cross-talk with domain and quaternary motions. These dynamic elements are functionally important and conserved. Central to the nexus is a slender stem through which rotation of the preprotein clamp of SecA is biased by ATPase domain motions between open and closed clamping states. An H-bonded framework covering most of SecA enables multi-tier dynamics and conformational alterations with minimal energy input. As a result, cognate ligands select preexisting conformations and alter local dynamics to regulate catalytic activity and clamp motions. These events prime the translocase for high-affinity reception of non-folded preprotein clients. Dynamics nexuses are likely universal and essential in multi-liganded proteins.</p

Monitoring-Supported Value Generation for Managing Structures and Infrastructure Systems

To maximize its value, the design, development and implementation of Structural Health Monitoring (SHM) should focus on its role in facilitating decision support. In this position paper, we offer perspectives on the synergy between SHM and decision-making. We propose a classification of SHM use cases aligning with various dimensions that are closely linked to the respective decision contexts. The types of decisions that have to be supported by the SHM system within these settings are discussed along with the corresponding challenges. We provide an overview of different classes of models that are required for integrating SHM in the decision-making process to support management and operation and maintenance of structures and infrastructure systems. Fundamental decision-theoretic principles and state-of-the-art methods for optimizing maintenance and operational decision-making under uncertainty are briefly discussed. Finally, we offer a viewpoint on the appropriate course of action for quantifying, validating and maximizing the added value generated by SHM. This work aspires to synthesize the different perspectives of the SHM, Prognostic Health Management (PHM), and reliability communities, and deliver a roadmap towards monitoring-based decision support

Architected frames for elastic wave attenuation: Experimental validation and local tuning via affine transformation

We experimentally demonstrate the capability of architected plates, with a frame-like cellular structure, to inhibit the propagation of elastic flexural waves. By leveraging the octet topology as a unit cell to design the tested prototypes, a broad and easy-to-tune bandgap is experimentally generated. The experimental outcomes are supported by extensive numerical analyses based on 3D solid elements. Drawing from the underlying dynamic properties of the octet cell, we numerically propose a tailorable design with enhanced filtering capabilities. We transform the geometry of the original unit cell by applying a uniaxial scaling factor that, by breaking the in-plane symmetry of the structure, yields independently tuned struts and consequently multiple tunable bandgaps within the same cell. Our findings expand the spectrum of available numerical analyses on the octet lattice, taking it a significant step closer to its physical implementation. The ability of the octet lattice to control the propagation of flexural vibrations is significant within various applications in the mechanical and civil engineering domains, and we note such frame-like designs could lead to advancements in energy harvesting and vibration protection devices (e.g., lightweight and resonance-tunable absorbers)

Preproteins couple the intrinsic dynamics of SecA to its ATPase cycle to translocate via a catch and release mechanism

Protein machines undergo conformational motions to interact with and manipulate polymeric substrates. The Sec translocase promiscuously recognizes, becomes activated, and secretes >500 non-folded preprotein clients across bacterial cytoplasmic membranes. Here, we reveal that the intrinsic dynamics of the translocase ATPase, SecA, and of preproteins combine to achieve translocation. SecA possesses an intrinsically dynamic preprotein clamp attached to an equally dynamic ATPase motor. Alternating motor conformations are finely controlled by the γ-phosphate of ATP, while ADP causes motor stalling, independently of clamp motions. Functional preproteins physically bridge these independent dynamics. Their signal peptides promote clamp closing; their mature domain overcomes the rate-limiting ADP release. While repeated ATP cycles shift the motor between unique states, multiple conformationally frustrated prongs in the clamp repeatedly “catch and release” trapped preprotein segments until translocation completion. This universal mechanism allows any preprotein to promiscuously recognize the translocase, usurp its intrinsic dynamics, and become secreted