Determining jumping performance from a single body-worn accelerometer using machine learning

External peak power in the countermovement jump is frequently used to monitor athlete training. The gold standard method uses force platforms, but they are unsuitable for field-based testing. However, alternatives based on jump flight time or Newtonian methods applied to inertial sensor data have not been sufficiently accurate for athlete monitoring. Instead, we developed a machine learning model based on characteristic features (functional principal components) extracted from a single body-worn accelerometer. Data were collected from 69 male and female athletes at recreational, club or national levels, who performed 696 jumps in total. We considered vertical countermovement jumps (with and without arm swing),sensor anatomical locations, machine learning models and whether to use resultant or triaxial signals. Using a novel surrogate model optimisation procedure, we obtained the lowest errors with a support vector machine when using the resultant signal from a lower back sensor in jumps without arm swing. This model had a peak power RMSE of 2.3 W·kg-1 (5.1% of the mean), estimated using nested cross validation and supported by an independent holdout test (2.0 W·kg-1). This error is lower than in previous studies, although it is not yet sufficiently accurate for a field-based method. Our results demonstrate that functional data representations work well in machine learning by reducing model complexity in applications where signals are aligned in time. Our optimisation procedure also was shown to be robust can be used in wider applications with low-cost, noisy objective functions

DEVELOPING METHODS TO ASSESS THE RELATIONSHIP BETWEEN ERGOMETER AND ON-WATER ROWING PERFORMANCE FROM INDEPENDENT DATASETS

Rowing ergometers are often used by internationally competitive athletes alongside on-water rowing. This study proposes methods to develop a generalisable relationship between maximal effort 2000 m ergometer and on-water rowing performance using independent datasets. Ergometer times for 2000 m tests (n = 153) and 2000 m on-water times from international races (n = 139) were collated. Percentiles from the raw data and fitted probability density functions were mapped to develop a generalisable performance relationship. Bootstrapping was utilised to estimate the uncertainty in the percentile mappings. When built on a larger sample of athletes, this approach could be useful to identify athletes who under or overperform on water compared to ergometers, and this could provide valuable context for future biomechanical investigations of rowing technique

Distribution, composition and functions of gelatinous tissues in deep-sea fishes

Many deep-sea fishes have a gelatinous layer, or subdermal extracellular matrix, below the skin or around the spine. We document the distribution of gelatinous tissues across fish families (approx. 200 species in ten orders), then review and investigate their composition and function. Gelatinous tissues from nine species were analysed for water content (96.53 ± 1.78% s.d.), ionic composition, osmolality, protein (0.39 ± 0.23%), lipid (0.69 ± 0.56%) and carbohydrate (0.61 ± 0.28%). Results suggest that gelatinous tissues are mostly extracellular fluid, which may allow animals to grow inexpensively. Further, almost all gelatinous tissues floated in cold seawater, thus their lower density than seawater may contribute to buoyancy in some species. We also propose a new hypothesis: gelatinous tissues, which are inexpensive to grow, may sometimes be a method to increase swimming efficiency by fairing the transition from trunk to tail. Such a layer is particularly prominent in hadal snailfishes (Liparidae); therefore, a robotic snailfish model was designed and constructed to analyse the influence of gelatinous tissues on locomotory performance. The model swam faster with a watery layer, representing gelatinous tissue, around the tail than without. Results suggest that the tissues may, in addition to providing buoyancy and low-cost growth, aid deep-sea fish locomotion. © 2017 The Authors

Image-Based Cell Profiling Enables Quantitative Tissue Microscopy in Gastroenterology.

Immunofluorescence microscopy is an essential tool for tissue-based research, yet data reporting is almost always qualitative. Quantification of images, at the per-cell level, enables "flow cytometry-type" analyses with intact locational data but achieving this is complex. Gastrointestinal tissue, for example, is highly diverse: from mixed-cell epithelial layers through to discrete lymphoid patches. Moreover, different species (e.g., rat, mouse, and humans) and tissue preparations (paraffin/frozen) are all commonly studied. Here, using field-relevant examples, we develop open, user-friendly methodology that can encompass these variables to provide quantitative tissue microscopy for the field. Antibody-independent cell labeling approaches, compatible across preparation types and species, were optimized. Per-cell data were extracted from routine confocal micrographs, with semantic machine learning employed to tackle densely packed lymphoid tissues. Data analysis was achieved by flow cytometry-type analyses alongside visualization and statistical definition of cell locations, interactions and established microenvironments. First, quantification of Escherichia coli passage into human small bowel tissue, following Ussing chamber incubations exemplified objective quantification of rare events in the context of lumen-tissue crosstalk. Second, in rat jejenum, precise histological context revealed distinct populations of intraepithelial lymphocytes between and directly below enterocytes enabling quantification in context of total epithelial cell numbers. Finally, mouse mononuclear phagocyte-T cell interactions, cell expression and significant spatial cell congregations were mapped to shed light on cell-cell communication in lymphoid Peyer's patch. Accessible, quantitative tissue microscopy provides a new window-of-insight to diverse questions in gastroenterology. It can also help combat some of the data reproducibility crisis associated with antibody technologies and over-reliance on qualitative microscopy. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC. on behalf of International Society for Advancement of Cytometry.UK Medical Research Council (grant number MR/R005699/1) UK Engineering and Physical Sciences Research Council (grant EP/H008683/1) UK Biotechnology and Biological Sciences Research Council (grant number BB/P026818/1

High stakes and low bars: How international recognition shapes the conduct of civil wars

When rebel groups engage incumbent governments in war for control of the state, questions of international recognition arise. International recognition determines which combatants can draw on state assets, receive overt military aid, and borrow as sovereigns—all of which can have profound consequences for the military balance during civil war. How do third-party states and international organizations determine whom to treat as a state's official government during civil war? Data from the sixty-one center-seeking wars initiated from 1945 to 2014 indicate that military victory is not a prerequisite for recognition. Instead, states generally rely on a simple test: control of the capital city. Seizing the capital does not foreshadow military victory. Civil wars often continue for many years after rebels take control and receive recognition. While geopolitical and economic motives outweigh the capital control test in a small number of important cases, combatants appear to anticipate that holding the capital will be sufficient for recognition. This expectation generates perverse incentives. In effect, the international community rewards combatants for capturing or holding, by any means necessary, an area with high concentrations of critical infrastructure and civilians. In the majority of cases where rebels contest the capital, more than half of its infrastructure is damaged or the majority of civilians are displaced (or both), likely fueling long-term state weakness

Characterizing Nanoparticles in Biological Matrices: Tipping Points in Agglomeration State and Cellular Delivery In Vitro.

Understanding the delivered cellular dose of nanoparticles is imperative in nanomedicine and nanosafety, yet is known to be extremely complex because of multiple interactions between nanoparticles, their environment, and the cells. Here, we use 3-D reconstruction of agglomerates preserved by cryogenic snapshot sampling and imaged by electron microscopy to quantify the "bioavailable dose" that is presented at the cell surface and formed by the process of individual nanoparticle sequestration into agglomerates in the exposure media. Critically, using 20 and 40 nm carboxylated polystyrene-latex and 16 and 85 nm silicon dioxide nanoparticles, we show that abrupt, dose-dependent "tipping points" in agglomeration state can arise, subsequently affecting cellular delivery and increasing toxicity. These changes are triggered by shifts in the ratio of the total nanoparticle surface area to biomolecule abundance, with the switch to a highly agglomerated state effectively changing the test article midassay, challenging the dose-response paradigm for nanosafety experiments. By characterizing nanoparticle numbers per agglomerate, we show these tipping points can lead to the formation of extreme agglomeration states whereby 90% of an administered dose is contained and delivered to the cells by just the top 2% of the largest agglomerates. We thus demonstrate precise definition, description, and comparison of the nanoparticle dose formed in different experimental environments and show that this description is critical to understanding cellular delivery and toxicity. We further empirically "stress-test" the commonly used dynamic light scattering approach, establishing its limitations to present an analysis strategy that significantly improves the usefulness of this popular nanoparticle characterization technique

ACS Nano

open access articleUnderstanding the effect of variability in the interaction of individual cells with nanoparticles on the overall response of the cell population to a nanoagent is a fundamental challenge in bionanotechnology. Here, we show that the technique of time-resolved, high-throughput microscopy can be used in this endeavor. Mass measurement with single-cell resolution provides statistically robust assessments of cell heterogeneity, while the addition of a temporal element allows assessment of separate processes leading to deconvolution of the effects of particle supply and biological response. We provide a specific demonstration of the approach, in vitro, through time-resolved measurement of fibroblast cell (HFF-1) death caused by exposure to cationic nanoparticles. The results show that heterogeneity in cell area is the major source of variability with area-dependent nanoparticle capture rates determining the time of cell death and hence the form of the exposure–response characteristic. Moreover, due to the particulate nature of the nanoparticle suspension, there is a reduction in the particle concentration over the course of the experiment, eventually causing saturation in the level of measured biological outcome. A generalized mathematical description of the system is proposed, based on a simple model of particle depletion from a finite supply reservoir. This captures the essential aspects of the nanoparticle–cell interaction dynamics and accurately predicts the population exposure–response curves from individual cell heterogeneity distributions

A low density of 0.8 g/cc for the Trojan binary asteroid 617 Patroclus

The Trojan population consists of two swarms of asteroids following the same orbit as Jupiter and located at the L4 and L5 Lagrange points of the Jupiter-Sun system (leading and following Jupiter by 60 degrees). The asteroid 617 Patroclus is the only known binary Trojan (Merline et al. 2001). The orbit of this double system was hitherto unknown. Here we report that the components, separated by 680 km, move around the system centre of mass, describing roughly a circular orbit. Using the orbital parameters, combined with thermal measurements to estimate the size of the components, we derive a very low density of 0.8 g/cc. The components of Patroclus are therefore very porous or composed mostly of water ice, suggesting that they could have been formed in the outer part of the solar system.Comment: 10 pages, 3 figures, 1 tabl