Search for: All records

Objective: To establish the sensitivity of magnetic resonance elastography (MRE) to active muscle contraction in multiple muscles of the forearm. Methods: We combined MRE of forearm muscles with an MRI-compatible device, the MREbot, to simultaneously measure the mechanical properties of tissues in the forearm and the torque applied by the wrist joint during isometric tasks. We measured shear wave speed of thirteen forearm muscles via MRE in a series of contractile states and wrist postures and fit these outputs to a force estimation algorithm based on a musculoskeletal model. Results: Shear wave speed changed significantly upon several factors, including whether the muscle was recruited as an agonist or antagonist (p = 0.0019), torque amplitude (p ≤ 0.0001), and wrist posture (p = 0.0002). Shear wave speed increased significantly during both agonist (p ≤ 0.0001) and antagonist (p = 0.0448) contraction. Additionally, there was a greater increase in shear wave speed at greater levels of loading. The variations due to these factors indicate the sensitivity to functional loading of muscle. Under the assumption of a quadratic relationship between shear wave speed and muscle force, MRE measurements accounted for an average of 70% of the variance in the measured joint torque. Conclusion: This study shows the ability of MM-MRE to capture variations in individual muscle shear wave speed due to muscle activation and presents a method to estimate individual muscle force through MM-MRE derived measurements of shear wave speed. Significance: MM-MRE could be used to establish normal and abnormal muscle co-contraction patterns in muscles of the forearm controlling hand and wrist function. 

What conditions enable novel intellectual contributions to diffuse and become integrated into later scientific work? Prior work tends to focus on whole cultural products, such as patents and articles, and emphasizes external social factors as important. This article focuses on concepts as reflections of ideas, and we identify the combined influence that social factors and internal intellectual structures have on ideational diffusion. To develop this perspective, we use computational techniques to identify nearly 60,000 new ideas introduced over two decades (1993 to 2016) in the Web of Science and follow their diffusion across 38 million later publications. We find new ideas diffuse more widely when they socially and intellectually resonate. New ideas become core concepts of science when they reach expansive networks of unrelated authors, achieve consistent intellectual usage, are associated with other prominent ideas, and fit with extant research traditions. These ecological conditions play an increasingly decisive role later in an idea’s career, after their relations with the environment are established. This work advances the systematic study of scientific ideas by moving beyond products to focus on the content of ideas themselves and applies a relational perspective that takes seriously the contingency of their success.

 

We report the implementation of a symmetry-adapted perturbation theory algorithm based on a density functional theory [SAPT(DFT)] description of monomers. The implementation adopts a density-fitting treatment of hybrid exchange–correlation kernels to enable the description of monomers with hybrid functionals, as in the algorithm by Bukowski, Podeszwa, and Szalewicz [Chem. Phys. Lett. 414, 111 (2005)]. We have improved the algorithm by increasing numerical stability with QR factorization and optimized the computation of the exchange–correlation kernel with its 2-index density-fitted representation. The algorithm scales as O( N 5 ) formally and is usable for systems with up to ∼3000 basis functions, as demonstrated for the C 60 –buckycatcher complex with the aug-cc-pVDZ basis set. The hybrid-kernel-based SAPT(DFT) algorithm is shown to be as accurate as SAPT(DFT) implementations based on local effective exact exchange potentials obtained from the local Hartree–Fock (LHF) method while avoiding the lower-scaling [ O( N 4 )] but iterative and sometimes hard-to-converge LHF process. The hybrid-kernel algorithm outperforms Hartree–Fock-based SAPT (SAPT0) for the S66 test set, and its accuracy is comparable to the many-body perturbation theory based SAPT2+ approach, which scales as O( N 7 ), although SAPT2+ exhibits a more narrow distribution of errors. 

To enhance the solubility of orally administered pharmaceuticals, liquid capsules or amorphous tablets are often preferred over crystalline drug products. However, little is known regarding the variation in bonding mechanisms between pharmaceutical molecules in their different disordered forms. In this study, liquid and melt-quenched glassy carbamazepine have been studied using high energy X-ray diffraction and modeled using Empirical Potential Structure Refinement. The results show significant structural differences between the liquid and glassy states. The liquid shows a wide range of structures; from isolated molecules, to aromatic ring correlations and NH-O hydrogen bonding. Upon quenching from the liquid to the glass the number of hydrogen bonds per molecule increases by ~50% at the expense of a ~30% decrease in the close contact (non-bonded) carbon-carbon interactions between aromatic rings. During the cooling process, there is an increase in both singly and doubly hydrogen-bonded adjacent molecules. Although hydrogen-bonded dimers found in the crystalline states persist in the glassy state, the absence of a crystalline lattice also allows small, hydrogen-bonded NH-O trimers and tetramers to form. This proposed model for the structure of glassy carbamazepine is consistent with the results from vibrational spectroscopy and nuclear magnetic resonance.

 

Janzen–Connell effects (JCEs), specialised predation of seeds and seedlings near conspecific trees, are hypothesised to maintain species richness. While previous studies show JCEs can maintain high richness relative to neutral communities, recent theoretical work indicates JCEs may weakly inhibit competitive exclusion when species exhibit interspecific fitness variation. However, recent models make somewhat restrictive assumptions about the functional form of specialised predation—that JCEs occur at a fixed rate when offspring are within a fixed distance of a conspecific tree. Using a theoretical model, I show that the functional form of JCEs largely impacts their ability to maintain coexistence. If predation pressure increases additively with adult tree density and decays exponentially with distance, JCEs maintain considerably higher species richness than predicted by recent models. Loosely parameterising the model with data from a Panamanian tree community, I elucidate the conditions under which JCEs are capable of maintaining high species richness.

 

Abstract Evaluating the social impacts of engineered products is critical to ensuring that products are having their intended positive impacts and learning how to improve product designs for a more positive social impact. Quantitative evaluation of product social impacts is made possible through the use of social impact indicators, which combine the user data in a meaningful way to give insight into the current social condition of an individual or population. Most existing methods for collecting these user data for social impact indicators require direct human interaction with users of a product (e.g., interviews, surveys, and observational studies). These interactions produce high-fidelity data that help indicate the product impact but only at a single snapshot in time and are typically infrequently collected due to the large human resources and cost associated with obtaining them. In this article, a framework is proposed that outlines how low-fidelity data often obtainable using remote sensors, satellites, or digital technology can be collected and correlated with high-fidelity, infrequently collected data to enable continuous, remote monitoring of engineered products via the user data. These user data are critical to determining current social impact indicators that can be used in a posteriori social impact evaluation. We illustrate an application of this framework by demonstrating how it can be used to collect data for calculating several social impact indicators related to water hand pumps in Uganda. Key to this example is the use of a deep learning model to correlate user type (man, woman, or child statured) with the raw hand pump data obtained via an integrated motion unit sensor for 1200 hand pump users. 

On September 1 2019, Hurricane Dorian made landfall in Elbow Cay in the Bahamas with sustained winds of 295 km/h and a central pressure of 910 mb, with subsequent landfalls in Marsh Harbour and Grand Bahama Island, where it stalled for two days. This paper presents field observations of Dorian’s coastal hazards and impacts on the built environment in these locales, collected by the Structural Extreme Events Reconnaissance (StEER) Network. Data were collected using a mixed methodological approach: (1) surveying high-water marks and inundation extent, including an approximately 8 m high water mark in Marsh Harbour, (2) conducting surface-level forensic assessments of damage to 358 structures, and (3) rapidly imaging 475 km of routes using street-level panoramas. Field observations are complemented by a debris field analysis using high-resolution satellite imagery. Observed performance reiterates the potential for well-confined, elevated construction to perform well under major hurricanes, but with the need to codify such practices through the addition of storm surge design provisions and an increase in the design wind speeds in the Bahamas Building Code. This study further demonstrates the value of robust reconnaissance infrastructure for capturing perishable data following hurricanes and making such data rapidly available using publicly accessible platforms. 

Abstract Those working in Engineering for Global Development seek to improve the conditions in developing countries. A common metric for understanding the development state of a given country is the Human Development Index (HDI), which focuses on three dimensions: health, education, and income. An engineer’s expertise does not always align with any of those dimensions directly, while they still hope to perform impactful work for human development. To discover other areas of expertise that are highly associated with the HDI, correlations and variable selection were performed between all World Development Indicators and the HDI. The resultant associations are presented according to industry sector for a straightforward connection to engineering expertise. The associated areas of expertise can be used during opportunity development as surrogates for focusing on the HDI dimensions themselves. The data analysis shows that work related to “Trade, Transportation, and Utilities,” such as electricity distribution, and exports or imports, “Natural Resources and Mining,” such as energy resources, agriculture, or access to clean water, and “Manufacturing,” in general, are most commonly associated with improvements in the HDI in developing countries. Also, because the associations were discovered at country-level, they direct where geographically particular areas of expertise have been historically associated with improving HDI.