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Abstract Our legs act as our primary contact with the surrounding environment, generating external forces that enable agile motion. To be agile, the nervous system has to control both the magnitude of the force that the feet apply to the ground and the point of application of this force. The purpose of this study was to characterize the performance of the healthy human neuromechanical system in controlling the force-magnitude and position of an externally applied force. To accomplish this, we built an apparatus that immobilized participants but allowed them to exert variable but controlled external forces with a single leg onto a ground embedded force plate. We provided real-time visual feedback of either the leg force-magnitude or force-position that participants were exerting against the force platform and instructed participants to best match their real-time signal to prescribed target step functions. We tested target step functions of a range of sizes and quantified the responsiveness and accuracy of the control. For the control of force-magnitude and for intermediate step sizes of 0.45 bodyweights, we found a bandwidth of 1.8 ± 0.5 Hz, a steady-state error of 2.6 ± 0.9%, and a steady-state variability of 2.7 ± 0.9%. We found similar control performance in terms of responsiveness and accuracy across step sizes and between force-magnitude and position control. Increases in responsiveness correlated with reductions in other measures of control performance, such as a greater magnitude of overshooting. We modelled the observed control performance and found that a second-order model was a good predictor of external leg force control. We discuss how benchmarking force control performance in young healthy humans aids in understanding differences in agility between humans, between humans and other animals, and between humans and engineered systems.

Traditional approaches used in the pharmaceutical industry are not precise or versatile enough for customized medicine formulation and manufacture. Here the authors produce a method to form coatings, with accurate dosages, as well as a means of closely controlling dissolution kinetics.

Mangroves are found globally throughout tropical and sub-tropical inter-tidal coastlines. These highly biodiverse and carbon-dense ecosystems have multi-faceted value, providing critical goods and services to millions living in coastal communities and making significant contributions to global climate change mitigation through carbon sequestration and storage. Despite their many values, mangrove loss continues to be widespread in many regions due primarily to anthropogenic activities. Accessible, intuitive tools that enable coastal managers to map and monitor mangrove cover are needed to stem this loss. Remotely sensed data have a proven record for successfully mapping and monitoring mangroves, but conventional methods are limited by imagery availability, computing resources and accessibility. In addition, the variable tidal levels in mangroves presents a unique mapping challenge, particularly over geographically large extents. Here we present a new tool—the Google Earth Engine Mangrove Mapping Methodology (GEEMMM)—an intuitive, accessible and replicable approach which caters to a wide audience of non-specialist coastal managers and decision makers. The GEEMMM was developed based on a thorough review and incorporation of relevant mangrove remote sensing literature and harnesses the power of cloud computing including a simplified image-based tidal calibration approach. We demonstrate the tool for all of coastal Myanmar (Burma)—a global mangrove loss hotspot—including an assessment of multi-date mapping and dynamics outputs and a comparison of GEEMMM results to existing studies. Results—including both quantitative and qualitative accuracy assessments and comparisons to existing studies—indicate that the GEEMMM provides an accessible approach to map and monitor mangrove ecosystems anywhere within their global distribution.

To help educators deliver their physiology laboratory courses remotely, we developed an inexpensive, customizable hardware kit along with freely available teaching resources. We based the course design on four principles that should allow students to conduct insightful experiments on different physiological systems. First, the experimental setup should not be constrained to laboratory environments. Second, students should be able to take this course without prior coding and electronics experience. Third, the hardware kit should be relatively inexpensive, and all other resources should be freely available. Fourth, all resources should be customizable for educators. The hardware kit consists of commercially available electronic components, with a microcontroller as its hub (Arduino friendly). All measurement systems can be assembled without soldering. The hardware kit is cost-effective (approximately the cost of a textbook) and can be customized depending upon instructional needs. All software is freely available, and we share all necessary codes in open-access online repositories for simple use and customizability. All lab manuals and additional video tutorials are also freely available online and customizable. In our particular course, we have weekly asynchronous physiology lectures and one synchronous laboratory session, where students can get help with their equipment. In this article, we only focus on the novel and open-source laboratory part of the course. The laboratory includes four units [data acquisition, ECG, electromyography (EMG), activity classification] and one final project. It is our intent that these resources will allow other educators to rapidly implement their own remote physiology laboratories or to extend our work into other pedagogical applications of wearable technology.

Let n\in \mathbb {N} and let \Theta \subset \{1,\dots,n\} be a nonempty subset. We prove that if \Theta contains an odd integer, then any P_\Theta-Anosov subgroup of \operatorname {Sp}(2n,\mathbb {R}) is virtually isomorphic to a free group or a surface group. In particular, any Borel Anosov subgroup of \operatorname {Sp}(2n,\mathbb {R}) is virtually isomorphic to a free or surface group. On the other hand, if \Theta does not contain any odd integers, then there exists a P_\Theta-Anosov subgroup of \operatorname {Sp}(2n,\mathbb {R}) which is not virtually isomorphic to a free or surface group. We also exhibit new examples of maximally antipodal subsets of certain flag manifolds; these arise as limit sets of rank 1 subgroups. [ABSTRACT FROM AUTHOR]

Low‐grade serous ovarian carcinoma (LGSC) is a rare and lethal subtype of ovarian cancer. LGSC is pathologically, biologically, and clinically distinct from the more common high‐grade serous ovarian carcinoma (HGSC). LGSC arises from serous borderline ovarian tumours (SBTs). The mechanism of transformation for SBTs to LGSC remains poorly understood. To better understand the biology of LGSC, we performed whole proteome profiling of formalin‐fixed, paraffin‐embedded tissue blocks of LGSC (n = 11), HGSC (n = 19), and SBTs (n = 26). We identified that the whole proteome is able to distinguish between histotypes of the ovarian epithelial tumours. Proteins associated with the tumour microenvironment were differentially expressed between LGSC and SBTs. Fibroblast activation protein (FAP), a protein expressed in cancer‐associated fibroblasts, is the most differentially abundant protein in LGSC compared with SBT. Multiplex immunohistochemistry (IHC) for immune markers (CD20, CD79a, CD3, CD8, and CD68) was performed to determine the presence of B cells, T cells, and macrophages. The LGSC FAP+ stroma was associated with greater abundance of Tregs and M2 macrophages, features not present in SBTs. Our proteomics cohort reveals that there are changes in the tumour microenvironment in LGSC compared with its putative precursor lesion, SBT. These changes suggest that the tumour microenvironment provides a supportive environment for LGSC tumourigenesis and progression. Thus, targeting the tumour microenvironment of LGSC may be a viable therapeutic strategy. © 2024 The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

Animals are much better at running than robots. The difference in performance arises in the important dimensions of agility, range, and robustness. To understand the underlying causes for this performance gap, we compare natural and artificial technologies in the five subsystems critical for running: power, frame, actuation, sensing, and control. With few exceptions, engineering technologies meet or exceed the performance of their biological counterparts. We conclude that biology's advantage over engineering arises from better integration of subsystems, and we identify four fundamental obstacles that roboticists must overcome. Toward this goal, we highlight promising research directions that have outsized potential to help future running robots achieve animal-level performance. Editor's summary: Strategies for robot locomotion have often taken inspiration from animals. But robots still fall short when compared to the inherent performance of animals. Burden et al. review the literature and discuss why animals outrun robots in categories such as agility, range, and robustness. The authors highlight that, with few exceptions, engineering outperforms biology in the components critical for running, so they conclude that there must be as-yet-undiscovered principles of integration and control that give animals their advantage over robots. —Amos Matsiko [ABSTRACT FROM AUTHOR]