Dataset Information

Tuning the instrument: sonic properties in the spider's web.

ABSTRACT: Spider orb webs are multifunctional, acting to absorb prey impact energy and transmit vibratory information to the spider. This paper explores the links between silk material properties, propagation of vibrations within webs and the ability of the spider to control and balance web function. Combining experimental and modelling approaches, we contrast transverse and longitudinal wave propagation in the web. It emerged that both transverse and longitudinal wave amplitude in the web can be adjusted through changes in web tension and dragline silk stiffness, i.e. properties that can be controlled by the spider. In particular, we propose that dragline silk supercontraction may have evolved as a control mechanism for these multifunctional fibres. The various degrees of active influence on web engineering reveals the extraordinary ability of spiders to shape the physical properties of their self-made materials and architectures to affect biological functionality, balancing trade-offs between structural and sensory functions.

SUBMITTER: Mortimer B

PROVIDER: S-EPMC5046944 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Tuning the instrument: sonic properties in the spider's web.

Mortimer B B Soler A A Siviour C R CR Zaera R R Vollrath F F

Journal of the Royal Society, Interface 20160901 122

Spider orb webs are multifunctional, acting to absorb prey impact energy and transmit vibratory information to the spider. This paper explores the links between silk material properties, propagation of vibrations within webs and the ability of the spider to control and balance web function. Combining experimental and modelling approaches, we contrast transverse and longitudinal wave propagation in the web. It emerged that both transverse and longitudinal wave amplitude in the web can be adjusted ...[more]

PMID: 27605164

Similar Datasets

Project description:BackgroundAs the Internet becomes the number one destination for obtaining health-related information, there is an increasing need to identify health Web pages that convey an accurate and current view of medical knowledge. In response, the research community has created multicriteria instruments for reliably assessing online medical information quality. One such instrument is DISCERN, which measures health Web page quality by assessing an array of features. In order to scale up use of the instrument, there is interest in automating the quality evaluation process by building machine learning (ML)-based DISCERN Web page classifiers.ObjectiveThe paper addresses 2 key issues that are essential before constructing automated DISCERN classifiers: (1) generation of a robust DISCERN training corpus useful for training classification algorithms, and (2) assessment of the usefulness of the current DISCERN scoring schema as a metric for evaluating the performance of these algorithms.MethodsUsing DISCERN, 272 Web pages discussing treatment options in breast cancer, arthritis, and depression were evaluated and rated by trained coders. First, different consensus models were compared to obtain a robust aggregated rating among the coders, suitable for a DISCERN ML training corpus. Second, a new DISCERN scoring criterion was proposed (features-based score) as an ML performance metric that is more reflective of the score distribution across different DISCERN quality criteria.ResultsFirst, we found that a probabilistic consensus model applied to the DISCERN instrument was robust against noise (random ratings) and superior to other approaches for building a training corpus. Second, we found that the established DISCERN scoring schema (overall score) is ill-suited to measure ML performance for automated classifiers.ConclusionUse of a probabilistic consensus model is advantageous for building a training corpus for the DISCERN instrument, and use of a features-based score is an appropriate ML metric for automated DISCERN classifiers.AvailabilityThe code for the probabilistic consensus model is available at https://bitbucket.org/A_2/em_dawid/ .

Dataset Information

Tuning the instrument: sonic properties in the spider's web.

Publications

Tuning the instrument: sonic properties in the spider's web.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets