Limb phase flexibility in walking:a test case in the squirrel monkey (Saimiri sciureus)

Abstract Background Previous analyses of factors influencing footfall timings and gait selection in quadrupeds have focused on the implications for energetic cost or gait mechanics separately. Here we present a model for symmetrical walking gaits in quadrupedal mammals that combines both factors, and aims to predict the substrate contexts in which animals will select certain ranges of footfall timings that (1) minimize energetic cost, (2) minimize rolling and pitching moments, or (3) balance the two. We hypothesize that energy recovery will be a priority on all surfaces, and will be the dominant factor determining footfall timings on flat, ground-like surfaces. The ability to resist pitch and roll, however, will play a larger role in determining footfall choice on narrower and more complex branch-like substrates. As a preliminary test of the expectations of the model, we collected sample data on footfall timings in a primate with relatively high flexibility in footfall timings – the squirrel monkey (Saimiri sciureus) – walking on a flat surface, straight pole, and a pole with laterally-projecting branches to simulate simplified ground and branch substrates. We compare limb phase values on these supports to the expectations of the model. Results As predicted, walking steps on the flat surface tended towards limb phase values that promote energy exchange. Both pole substrates induced limb phase values predicted to favor reduced pitching and rolling moments. Conclusions These data provide novel insight into the ways in which animals may choose to adjust their behavior in response to movement on flat versus complex substrates and the competing selective factors that influence footfall timing in mammals. These data further suggest a pathway for future investigations using this perspective

Literary studies and the academy

In 1885 the University of Oxford invited applications for the newly created Merton Professorship of English Language and Literature. The holder of the chair was, according to the statutes, to ‘lecture and give instruction on the broad history and criticism of English Language and Literature, and on the works of approved English authors’. This was not in itself a particularly innovatory move, as the study of English vernacular literature had played some part in higher education in Britain for over a century. Oxford University had put English as a subject into its pass degree in 1873, had been participating since 1878 in extension teaching, of which literary study formed a significant part, and had since 1881 been setting special examinations in the subject for its non-graduating women students. What was new was the fact that this ancient university appeared to be on the verge of granting the solid academic legitimacy of an established chair to an institutionally marginal and often contentious intellectual pursuit, acknowledging the study of literary texts in English to be a fit subject not just for women and the educationally disadvantaged but also for university men

Non-structural carbohydrates in woody plants compared among laboratories

Non-structural carbohydrates (NSC) in plant tissue are frequently quantified to make inferences about plant responses to environmental conditions. Laboratories publishing estimates of NSC of woody plants use many different methods to evaluate NSC. We asked whether NSC estimates in the recent literature could be quantitatively compared among studies. We also asked whether any differences among laboratories were related to the extraction and quantification methods used to determine starch and sugar concentrations. These questions were addressed by sending sub-samples collected from five woody plant tissues, which varied in NSC content and chemical composition, to 29 laboratories. Each laboratory analyzed the samples with their laboratory-specific protocols, based on recent publications, to determine concentrations of soluble sugars, starch and their sum, total NSC. Laboratory estimates differed substantially for all samples. For example, estimates for Eucalyptus globulus leaves (EGL) varied from 23 to 116 (mean = 56) mg g⁻¹ for soluble sugars, 6–533 (mean = 94) mg g⁻¹ for starch and 53–649 (mean = 153) mg g⁻¹ for total NSC. Mixed model analysis of variance showed that much of the variability among laboratories was unrelated to the categories we used for extraction and quantification methods (method category R² = 0.05–0.12 for soluble sugars, 0.10–0.33 for starch and 0.01–0.09 for total NSC). For EGL, the difference between the highest and lowest least squares means for categories in the mixed model analysis was 33 mg g⁻¹ for total NSC, compared with the range of laboratory estimates of 596 mg g⁻¹. Laboratories were reasonably consistent in their ranks of estimates among tissues for starch (r = 0.41–0.91), but less so for total NSC (r = 0.45–0.84) and soluble sugars (r = 0.11–0.83). Our results show that NSC estimates for woody plant tissues cannot be compared among laboratories. The relative changes in NSC between treatments measured within a laboratory may be comparable within and between laboratories, especially for starch. To obtain comparable NSC estimates, we suggest that users can either adopt the reference method given in this publication, or report estimates for a portion of samples using the reference method, and report estimates for a standard reference material. Researchers interested in NSC estimates should work to identify and adopt standard methods.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Oxford University Press. The published article can be found at: http://treephys.oxfordjournals.org/Keywords: soluble sugars, starch, particle size, reference method, standardization, non-structural carbohydrate chemical analysis, extraction and quantification consistenc

Data-Driven Information-Optimal Computational Microscopy

Optical microscopes have been an indispensable tool in biology and medicine for over three centuries. Unlike their simple predecessors, contemporary microscopes often employ complex robotic automation and customized algorithms. In the past decade, advances in high-performance computer processors, the ease of collecting massive datasets, and machine learning have created many new possibilities for data-driven approaches to microscope control and image analysis.This dissertation covers the challenges and opportunities in modern microscopy. First, it shows how neural networks can be used to create microscopes that adapt to the samples they are imaging in real time. For example, this paradigm can be used to quickly focus microscopes using inexpensive hardware or visualize developing immune responses at large scales. Next, new open-source software that facilitates development of these and other microscopy techniques is presented. Next, it turns to how microscopes can make measurements of the intrinsic optical properties of cells, from which their biological function can be inferred. Development of techniques that do so requires comparing approaches on standardized datasets, and the creation of such a dataset containing hundreds of thousands of images of single cells is described. Finally, a new theoretical framework for modeling the information transmission of both microscopes and image-processing algorithms is introduced. This perspective provides a new set of engineering principles for microscopes and opens a range of new research questions