Exposure to ambient air pollution and cognitive function: an analysis of the English Longitudinal Study of Ageing cohort.

BACKGROUND: An increasing number of studies suggest adverse effects of exposure to ambient air pollution on cognitive function, but the evidence is still limited. We investigated the associations between long-term exposure to air pollutants and cognitive function in the English Longitudinal Study of Ageing (ELSA) cohort of older adults. METHODS: Our sample included 8,883 individuals from ELSA, based on a nationally representative study of people aged ≥ 50 years, followed-up from 2002 until 2017. Exposure to air pollutants was modelled by the CMAQ-urban dispersion model and assigned to the participants' residential postcodes. Cognitive test scores of memory and executive function were collected biennially. The associations between these cognitive measures and exposure to ambient concentrations of NO2, PM10, PM2.5 and ozone were investigated using mixed-effects models adjusted for time-varying age, physical activity and smoking status, as well as baseline gender and level of education. RESULTS: Increasing long-term exposure per interquartile range (IQR) of NO2 (IQR: 13.05 µg/m3), PM10 (IQR: 3.35 µg/m3) and PM2.5 (IQR: 2.7 µg/m3) were associated with decreases in test scores of composite memory by -0.10 (95% confidence interval [CI]: -0.14, -0.07), -0.02 [-0.04, -0.01] and -0.08 [-0.11, -0.05], respectively. The same increases in NO2, PM10 and PM2.5 were associated with decreases in executive function score of -0.31 [-0.38, -0.23], -0.05 [-0.08, -0.02] and -0.16 [-0.22, -0.10], respectively. The association with ozone was inverse across both tests. Similar results were reported for the London-dwelling sub-sample of participants. CONCLUSIONS: The present study was based on a long follow-up with several repeated measurements per cohort participant and long-term air pollution exposure assessment at a fine spatial scale. Increasing long-term exposure to NO2, PM10 and PM2.5 was associated with a decrease in cognitive function in older adults in England. This evidence can inform policies related to modifiable environmental exposures linked to cognitive decline.

The expert eye? An inter-rater comparison of elite tennis serve kinematics and performance.

This study examined the reliability of expert tennis coaches/biomechanists to qualitatively assess selected features of the serve with the aid of two-dimensional (2D) video replays. Two expert high-performance coaches rated the serves of 150 male and 150 female players across three different age groups from two different camera viewing angles. Serve performance was rated across 13 variables that represented commonly investigated and coached (serve) mechanics using a 1-7 Likert rating scale. A total of 7800 ratings were performed. The reliability of the experts' ratings was assessed using a Krippendorffs alpha. Strong agreement was shown across all age groups and genders when the experts rated the overall serve score (0.727-0.924), power or speed of the serve (0.720-0.907), rhythm (0.744-0.944), quality of the trunk action (0.775-1.000), leg drive (0.731-0.959) and the likelihood of back injury (0.703-0.934). They encountered greater difficulty in consistently rating shoulder internal rotation speed (0.688-0.717). In high-performance settings, the desire for highly precise measurement and large data sets powered by new technologies, is commonplace but this study revealed that tennis experts, through the use of 2D video, can reliably rate important mechanical features of the game's most important shot, the serve.

Cross-Sectional 4D-Printing: Upscaling Self-Shaping Structures with Differentiated Material Properties Inspired by the Large-Flowered Butterwort (Pinguicula grandiflora).

Extrusion-based 4D-printing, which is an emerging field within additive manufacturing, has enabled the technical transfer of bioinspired self-shaping mechanisms by emulating the functional morphology of motile plant structures (e.g., leaves, petals, capsules). However, restricted by the layer-by-layer extrusion process, much of the resulting works are simplified abstractions of the pinecone scale's bilayer structure. This paper presents a new method of 4D-printing by rotating the printed axis of the bilayers, which enables the design and fabrication of self-shaping monomaterial systems in cross sections. This research introduces a computational workflow for programming, simulating, and 4D-printing differentiated cross sections with multilayered mechanical properties. Taking inspiration from the large-flowered butterwort (Pinguicula grandiflora), which shows the formation of depressions on its trap leaves upon contact with prey, we investigate the depression formation of bioinspired 4D-printed test structures by varying each depth layer. Cross-sectional 4D-printing expands the design space of bioinspired bilayer mechanisms beyond the XY plane, allows more control in tuning their self-shaping properties, and paves the way toward large-scale 4D-printed structures with high-resolution programmability.

Codesign of Biobased Cellulose-Filled Filaments and Mesostructures for 4D Printing Humidity Responsive Smart Structures.

Hygromorphic smart structures are advantageous as passively actuated systems for generating movement, with applications ranging from weather-responsive architectural building skins to adaptive wearables and microrobotics. Four-dimensional (4D) printing is a valuable method for multiscale fabrication and physical programming of such structures. However, material limitations in terms of printability, responsiveness, and mechanical properties are major bottlenecks in achieving reliable and repeatable humidity-responsive actuation. We propose a codesign method for 4D printing hygromorphic structures through fused filament fabrication, incorporating parallel development of (1) biobased cellulose-filled filaments with varying stiffness and hygroresponsiveness, and (2) designed mesoscale structuring in printed elements. We first describe the design of a pallet of filaments produced by compounding cellulose powder in mass ratios of 0-30% within two matrix polymers with high and low stiffness. We then present the design, fabrication, and testing of a series of 4D-printed prototypes tuned to change shape, that is, open and close, in response to relative humidity (RH). The structures can fully transform in conditions of 35-90% RH, which corresponds to naturally occurring shifts in RH in daily and seasonal weather cycles. Furthermore, their motion is fast (within the range of minutes), fully reversible, and repeatable in numerous cycles. These results open new opportunities for the utilization of 4D printing and natural resources for the development of functional humidity-responsive smart structures.

Exposure to Ambient Air Pollution and the Incidence of Dementia in the Elderly of England: The ELSA Cohort.

Increasing evidence suggests an adverse association between ambient air pollution and the incidence of dementia in adult populations, although results at present are mixed and further work is required. The present study investigated the relationships between NO2, PM10, PM2.5 and ozone on dementia incidence in a cohort of English residents, aged 50 years and older, followed up between 2004 and 2017 (English Longitudinal Study of Ageing; n = 8525). Cox proportional hazards models were applied to investigate the association between time to incident dementia and exposure to pollutants at baseline. Hazard ratios (HRs) were calculated per 10 µg/m3. Models were adjusted for age, gender, physical activity, smoking status and level of education (the latter as a sensitivity analysis). A total of 389 dementia cases were identified during follow-up. An increased risk of developing dementia was suggested with increasing exposure to PM2.5 (HR: 1.10; 95% confidence interval (CI): 0.88, 1.37), whilst NO2, PM10 and ozone exhibited no discernible relationships. Hazard ratios were 0.97 (CI: 0.89, 1.05) for NO2; 0.98 (CI: 0.89, 1.08) for PM10; 1.01 (CI: 0.94, 1.09) for ozone. In the London sub-sample (39 dementia cases), a 10 µg/m3 increase in PM10 was found to be associated with increased risk of dementia by 16%, although not statistically significant (HR: 1.16; CI: 0.90, 1.48), and the magnitude of effect for PM2.5 increased, whilst NO2 and ozone exhibited similar associations as observed in the England-wide study. Further work is required to fully elucidate the potentially adverse associations between air pollution exposure and dementia incidence.

Development of a Material Design Space for 4D-Printed Bio-Inspired Hygroscopically Actuated Bilayer Structures with Unequal Effective Layer Widths.

(1) Significance of geometry for bio-inspired hygroscopically actuated bilayer structures is well studied and can be used to fine-tune curvatures in many existent material systems. We developed a material design space to find new material combinations that takes into account unequal effective widths of the layers, as commonly used in fused filament fabrication, and deflections under self-weight. (2) For this purpose, we adapted Timoshenko's model for the curvature of bilayer strips and used an established hygromorphic 4D-printed bilayer system to validate its ability to predict curvatures in various experiments. (3) The combination of curvature evaluation with simple, linear beam deflection calculations leads to an analytical solution space to study influences of Young's moduli, swelling strains and densities on deflection under self-weight and curvature under hygroscopic swelling. It shows that the choice of the ratio of Young's moduli can be crucial for achieving a solution that is stable against production errors. (4) Under the assumption of linear material behavior, the presented development of a material design space allows selection or design of a suited material combination for application-specific, bio-inspired bilayer systems with unequal layer widths.

Bio-Inspired Motion Mechanisms: Computational Design and Material Programming of Self-Adjusting 4D-Printed Wearable Systems.

This paper presents a material programming approach for designing 4D-printed self-shaping material systems based on biological role models. Plants have inspired numerous adaptive systems that move without using any operating energy; however, these systems are typically designed and fabricated in the form of simplified bilayers. This work introduces computational design methods for 4D-printing bio-inspired behaviors with compounded mechanisms. To emulate the anisotropic arrangement of motile plant structures, material systems are tailored at the mesoscale using extrusion-based 3D-printing. The methodology is demonstrated by transferring the principle of force generation by a twining plant (Dioscorea bulbifera) to the application of a self-tightening splint. Through the tensioning of its stem helix, D. bulbifera exhibits a squeezing force on its support to provide stability against gravity. The functional strategies of D. bulbifera are abstracted and translated to customized 4D-printed material systems. The squeezing forces of these bio-inspired motion mechanisms are then evaluated. Finally, the function of self-tightening is prototyped in a wrist-forearm splint-a common orthotic device for alignment. The presented approach enables the transfer of novel and expanded biomimetic design strategies to 4D-printed motion mechanisms, further opening the design space to new types of adaptive creations for wearable assistive technologies and beyond.

Programming material compliance and actuation: hybrid additive fabrication of biocomposite structures for large-scale self-shaping.

We present a hybrid approach to manufacturing a new class of large-scale self-shaping structures through a method of additive fabrication combining fused granular fabrication (FGF) and integrated hygroscopic wood actuators (HWAs). Wood materials naturally change shape with high forces in response to moisture stimuli. The strength and simplicity of this actuation make the material suitable for self-shaping architectural-scale components. However, the anisotropic composition of wood, which enables this inherent behavior, cannot be fully customized within existing stock. On the other hand, FGF allows for the design of large physical parts with multi-functional interior substructures as inspired by many biological materials. We propose to encode passively actuated movement into physical structures by integrating HWAs within 3D-printed meta-structures with functionally graded stiffnesses. By leveraging robotic manufacturing platforms, self-shaping biocomposite material systems can be upscaled with variable resolutions and at high volumes, resulting in large-scale structures capable of transforming from flat to curved simply through changes in relative humidity.

Programming sequential motion steps in 4D-printed hygromorphs by architected mesostructure and differential hygro-responsiveness.

Through their anisotropic cellular mesostructure and differential swelling and shrinking properties, hygroscopic plant structures move in response to changes in the environment without consuming metabolic energy. When the movement is choreographed in sequential time steps, either in individual structures or with a coordinated interplay of various structural elements, complex functionalities such as dispersal and protection of seeds are achieved. Inspired by the multi-phase motion in plant structures, this paper presents a method to physically program the timescale and the sequences of shape-change in 4D-printed hygromorphic structures. Using the FDM 3D-printing method, we have developed multi-layered, multi-material functional bilayers that combine highly hygroscopic active layers (printed with hygroscopic bio-composite materials) with hydrophobic restrictive and blocking layers (printed with PLA and TPC materials). The timescale of motion is programmed through the design of the mesostructured layers and 3D-printing process parameters, including thickness (number of printed active layers), porosity (filling ratio of the active layer), and water permeability (filling ratio of the blocking layer). Through a series of experiments, it is shown that the timescale of motion can be extended by increasing the thickness of the active layer, decreasing the porosity of the active layer, or increasing the filling ratio of the hydrophobic restrictive and blocking layers. Similarly, a lower thickness of the active layer and lower filling ratio of all layers result in a faster motion. As a proof of concept, we demonstrate several prototypes that exhibit sequential motion, including an aperture with overlapping elements where each completes its movement sequentially to avoid collision, and a self-locking mechanism where defined areas of the structure are choreographed to achieve a multi-step self-shaping and locking function. The presented method extends the programmability and the functional capabilities of hygromorphic 4D-printing, allowing for novel applications across fields such as robotics, smart actuators, and adaptive architecture.

Computational analysis of hygromorphic self-shaping wood gridshell structures.

Bi-layered composites capable of self-shaping are of increasing relevance to science and engineering. They can be made out of anisotropic materials that are responsive to changes in a state variable, e.g. wood, which swells and shrinks by changes in moisture. When extensive bending is desired, such bilayers are usually designed as cross-ply structures. However, the nature of cross-ply laminates tends to prevent changes of the Gaussian curvature so that a plate-like geometry of the composite will be partly restricted from shaping. Therefore, an effective approach for maximizing bending is to keep the composite in a narrow strip configuration so that Gaussian curvature can remain constant during shaping. This represents a fundamental limitation for many applications where self-shaped double-curved structures could be beneficial, e.g. in timber architecture. In this study, we propose to achieve double-curvature by gridshell configurations of narrow self-shaping wood bilayer strips. Using numerical mechanical simulations, we investigate a parametric phase-space of shaping. Our results show that double curvature can be achieved and that the change in Gaussian curvature is dependent on the system's geometry. Furthermore, we discuss a novel architectural application potential in the form of self-erecting timber gridshells.

Analysis of hygroscopic self-shaping wood at large scale for curved mass timber structures.

The growing timber manufacturing industry faces challenges due to increasing geometric complexity of architectural designs. Complex and structurally efficient curved geometries are nowadays easily designed but still involve intensive manufacturing and excessive machining. We propose an efficient form-giving mechanism for large-scale curved mass timber by using bilayered wood structures capable of self-shaping by moisture content changes. The challenge lies in the requirement of profound material knowledge for analysis and prediction of the deformation in function of setup and boundary conditions. Using time- and moisture-dependent mechanical simulations, we demonstrate the contributions of different wood-specific deformation mechanisms on the self-shaping of large-scale elements. Our results outline how to address problems such as shape prediction, sharp moisture gradients, and natural variability in material parameters in light of an efficient industrial manufacturing.

The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository.

This data descriptor describes a repository of openly shared data from an experiment to assess inter-individual differences in default mode network (DMN) activity. This repository includes cross-sectional functional magnetic resonance imaging (fMRI) data from the Multi Source Interference Task, to assess DMN deactivation, the Moral Dilemma Task, to assess DMN activation, a resting state fMRI scan, and a DMN neurofeedback paradigm, to assess DMN modulation, along with accompanying behavioral and cognitive measures. We report technical validation from n=125 participants of the final targeted sample of 180 participants. Each session includes acquisition of one whole-brain anatomical scan and whole-brain echo-planar imaging (EPI) scans, acquired during the aforementioned tasks and resting state. The data includes several self-report measures related to perseverative thinking, emotion regulation, and imaginative processes, along with a behavioral measure of rapid visual information processing. Technical validation of the data confirms that the tasks deactivate and activate the DMN as expected. Group level analysis of the neurofeedback data indicates that the participants are able to modulate their DMN with considerable inter-subject variability. Preliminary analysis of behavioral responses and specifically self-reported sleep indicate that as many as 73 participants may need to be excluded from an analysis depending on the hypothesis being tested. The present data are linked to the enhanced Nathan Kline Institute, Rockland Sample and builds on the comprehensive neuroimaging and deep phenotyping available therein. As limited information is presently available about individual differences in the capacity to directly modulate the default mode network, these data provide a unique opportunity to examine DMN modulation ability in relation to numerous phenotypic characteristics.

COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data.

The field of neuroimaging has embraced the need for sharing and collaboration. Data sharing mandates from public funding agencies and major journal publishers have spurred the development of data repositories and neuroinformatics consortia. However, efficient and effective data sharing still faces several hurdles. For example, open data sharing is on the rise but is not suitable for sensitive data that are not easily shared, such as genetics. Current approaches can be cumbersome (such as negotiating multiple data sharing agreements). There are also significant data transfer, organization and computational challenges. Centralized repositories only partially address the issues. We propose a dynamic, decentralized platform for large scale analyses called the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). The COINSTAC solution can include data missing from central repositories, allows pooling of both open and "closed" repositories by developing privacy-preserving versions of widely-used algorithms, and incorporates the tools within an easy-to-use platform enabling distributed computation. We present an initial prototype system which we demonstrate on two multi-site data sets, without aggregating the data. In addition, by iterating across sites, the COINSTAC model enables meta-analytic solutions to converge to "pooled-data" solutions (i.e., as if the entire data were in hand). More advanced approaches such as feature generation, matrix factorization models, and preprocessing can be incorporated into such a model. In sum, COINSTAC enables access to the many currently unavailable data sets, a user friendly privacy enabled interface for decentralized analysis, and a powerful solution that complements existing data sharing solutions.

COINS Data Exchange: An open platform for compiling, curating, and disseminating neuroimaging data.

Neuroimaging data collection is inherently expensive. Maximizing the return on investment in neuroimaging studies requires that neuroimaging data be re-used whenever possible. In an effort to further scientific knowledge, the COINS Data Exchange (DX) (http://coins.mrn.org/dx) aims to make data sharing seamless and commonplace. DX takes a three-pronged approach towards improving the overall state of data sharing within the neuroscience community. The first prong is compiling data into one location that has been collected from all over the world in many different formats. The second prong is curating the data so that it can be stored in one consistent format and so that data QA/QC measures can be assured. The third prong is disseminating the data so that it is easy to consume and straightforward to interpret. This paper explains the concepts behind each prong and describes some challenges and successes that the Data Exchange has experienced.

Corrigendum: Harnessing modern web application technology to create intuitive and efficient data visualization and sharing tools.

[This corrects the article on p. 71 in vol. 8, PMID: 25206330.].

Harnessing modern web application technology to create intuitive and efficient data visualization and sharing tools.

Neuroscientists increasingly need to work with big data in order to derive meaningful results in their field. Collecting, organizing and analyzing this data can be a major hurdle on the road to scientific discovery. This hurdle can be lowered using the same technologies that are currently revolutionizing the way that cultural and social media sites represent and share information with their users. Web application technologies and standards such as RESTful webservices, HTML5 and high-performance in-browser JavaScript engines are being utilized to vastly improve the way that the world accesses and shares information. The neuroscience community can also benefit tremendously from these technologies. We present here a web application that allows users to explore and request the complex datasets that need to be shared among the neuroimaging community. The COINS (Collaborative Informatics and Neuroimaging Suite) Data Exchange uses web application technologies to facilitate data sharing in three phases: Exploration, Request/Communication, and Download. This paper will focus on the first phase, and how intuitive exploration of large and complex datasets is achieved using a framework that centers around asynchronous client-server communication (AJAX) and also exposes a powerful API that can be utilized by other applications to explore available data. First opened to the neuroscience community in August 2012, the Data Exchange has already provided researchers with over 2500 GB of data.

Automated collection of imaging and phenotypic data to centralized and distributed data repositories.

Accurate data collection at the ground level is vital to the integrity of neuroimaging research. Similarly important is the ability to connect and curate data in order to make it meaningful and sharable with other investigators. Collecting data, especially with several different modalities, can be time consuming and expensive. These issues have driven the development of automated collection of neuroimaging and clinical assessment data within COINS (Collaborative Informatics and Neuroimaging Suite). COINS is an end-to-end data management system. It provides a comprehensive platform for data collection, management, secure storage, and flexible data retrieval (Bockholt et al., 2010; Scott et al., 2011). It was initially developed for the investigators at the Mind Research Network (MRN), but is now available to neuroimaging institutions worldwide. Self Assessment (SA) is an application embedded in the Assessment Manager (ASMT) tool in COINS. It is an innovative tool that allows participants to fill out assessments via the web-based Participant Portal. It eliminates the need for paper collection and data entry by allowing participants to submit their assessments directly to COINS. Instruments (surveys) are created through ASMT and include many unique question types and associated SA features that can be implemented to help the flow of assessment administration. SA provides an instrument queuing system with an easy-to-use drag and drop interface for research staff to set up participants' queues. After a queue has been created for the participant, they can access the Participant Portal via the internet to fill out their assessments. This allows them the flexibility to participate from home, a library, on site, etc. The collected data is stored in a PostgresSQL database at MRN. This data is only accessible by users that have explicit permission to access the data through their COINS user accounts and access to MRN network. This allows for high volume data collection and with minimal user access to PHI (protected health information). An added benefit to using COINS is the ability to collect, store and share imaging data and assessment data with no interaction with outside tools or programs. All study data collected (imaging and assessment) is stored and exported with a participant's unique subject identifier so there is no need to keep extra spreadsheets or databases to link and keep track of the data. Data is easily exported from COINS via the Query Builder and study portal tools, which allow fine grained selection of data to be exported into comma separated value file format for easy import into statistical programs. There is a great need for data collection tools that limit human intervention and error while at the same time providing users with intuitive design. COINS aims to be a leader in database solutions for research studies collecting data from several different modalities.

The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry.

The National Institute of Mental Health strategic plan for advancing psychiatric neuroscience calls for an acceleration of discovery and the delineation of developmental trajectories for risk and resilience across the lifespan. To attain these objectives, sufficiently powered datasets with broad and deep phenotypic characterization, state-of-the-art neuroimaging, and genetic samples must be generated and made openly available to the scientific community. The enhanced Nathan Kline Institute-Rockland Sample (NKI-RS) is a response to this need. NKI-RS is an ongoing, institutionally centered endeavor aimed at creating a large-scale (N > 1000), deeply phenotyped, community-ascertained, lifespan sample (ages 6-85 years old) with advanced neuroimaging and genetics. These data will be publically shared, openly, and prospectively (i.e., on a weekly basis). Herein, we describe the conceptual basis of the NKI-RS, including study design, sampling considerations, and steps to synchronize phenotypic and neuroimaging assessment. Additionally, we describe our process for sharing the data with the scientific community while protecting participant confidentiality, maintaining an adequate database, and certifying data integrity. The pilot phase of the NKI-RS, including challenges in recruiting, characterizing, imaging, and sharing data, is discussed while also explaining how this experience informed the final design of the enhanced NKI-RS. It is our hope that familiarity with the conceptual underpinnings of the enhanced NKI-RS will facilitate harmonization with future data collection efforts aimed at advancing psychiatric neuroscience and nosology.

COINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets.

The availability of well-characterized neuroimaging data with large numbers of subjects, especially for clinical populations, is critical to advancing our understanding of the healthy and diseased brain. Such data enables questions to be answered in a much more generalizable manner and also has the potential to yield solutions derived from novel methods that were conceived after the original studies' implementation. Though there is currently growing interest in data sharing, the neuroimaging community has been struggling for years with how to best encourage sharing data across brain imaging studies. With the advent of studies that are much more consistent across sites (e.g., resting functional magnetic resonance imaging, diffusion tensor imaging, and structural imaging) the potential of pooling data across studies continues to gain momentum. At the mind research network, we have developed the collaborative informatics and neuroimaging suite (COINS; http://coins.mrn.org) to provide researchers with an information system based on an open-source model that includes web-based tools to manage studies, subjects, imaging, clinical data, and other assessments. The system currently hosts data from nine institutions, over 300 studies, over 14,000 subjects, and over 19,000 MRI, MEG, and EEG scan sessions in addition to more than 180,000 clinical assessments. In this paper we provide a description of COINS with comparison to a valuable and popular system known as XNAT. Although there are many similarities between COINS and other electronic data management systems, the differences that may concern researchers in the context of multi-site, multi-organizational data sharing environments with intuitive ease of use and PHI security are emphasized as important attributes.