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"Wood, Dylan"
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Weather-responsive adaptive shading through biobased and bioinspired hygromorphic 4D-printing
In response to the global challenge of reducing carbon emissions and energy consumption from regulating indoor climates, we investigate the applicability of biobased cellulosic materials and bioinspired 4D-printing for weather-responsive adaptive shading in building facades. Cellulose is an abundantly available natural material resource that exhibits hygromorphic actuation potential when used in 4D-printing to emulate motile plant structures in bioinspired bilayers. Three key aspects are addressed: (i) examining the motion response of 4D-printed hygromorphic bilayers to both temperature and relative humidity, (ii) verifying the responsiveness of self-shaping shading elements in lab-generated conditions as well as under daily and seasonal weather conditions for over a year, and (iii) deploying the adaptive shading system for testing in a real building facade by upscaling the 4D-printing manufacturing process. This study demonstrates that hygromorphic bilayers can be utilized for weather-responsive facades and that the presented system is architecturally scalable in quantity. Bioinspired 4D-printing and biobased cellulosic materials offer a resource-efficient and energy-autonomous solution for adaptive shading, with potential contributions towards indoor climate regulation and climate change mitigation.
Inspired by motile plant structures that respond passively to external stimuli, in this work the authors validate biobased cellulosic materials and bioinspired 4D-printed hygromorphic bilayers for a weather-responsive, energy-autonomous shading system with a demonstration at an architectural scale.
Journal Article
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 NO
2
, PM
10
, PM
2.5
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 NO
2
(IQR: 13.05 μg/m
3
), PM
10
(IQR: 3.35 μg/m
3
) and PM
2.5
(IQR: 2.7 μg/m
3
) 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 NO
2
, PM
10
and PM
2.5
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 NO
2
, PM
10
and PM
2.5
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.
Journal Article
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 ed 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.
This paper presents a design and material programming approach for adaptive material systems through a case study of biomimetic design. The twining air potato (Dioscorea bulbifera) provides stability against gravity by tensioning its stem helix and generating a squeezing force. Based on D. bulbifera's force generation, 4D‐printing is employed to produce adaptive wearable systems with passive, targeted shape changes.
Journal Article
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.
Journal Article
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.
•Experimental data to assess inter-individual differences (n=125) in DMN activity.•Data includes a rsfMRI scan, 2 DMN regulating tasks, and a DMN neurofeedback scan.•Data includes several self-report measures related to behavioral phenotypes.•Neurofeedback data indicates that the participants are able to modulate their DMN.•Self-reported sleep and task metrics indicate up to 73 participants may be excluded.
Journal Article
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.
Journal Article
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.
Journal Article
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.
Journal Article
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.
Journal Article