For many animals, sperm can be a limiting resource and impact lifetime reproductive success. Sperm limitation can arise from reduced male availability in a population, but may also be a consequence ...of external influences, such as sperm wastage. If sperm is finite and not always cost‐free to produce, do males vary sperm use strategies in response to sperm limitation? One way to answer this is to examine male sperm allocation in response to sexual deception that elicits sperm wastage.
Cryptostylis orchids trick their parasitoid wasp pollinator, male Lissopimpla excelsa, into mating with the flower and ejaculating. For many parasitoids sperm is limited; so this exploitative interaction could impose a high cost to males. Here, we ask whether this duped wasp can become sperm limited, and whether this impacts his ejaculate size following deception. Sperm limitation has implications for his future and lifetime reproductive success, and consequently, the ultimate fitness of the orchid, and the evolutionary maintenance of orchid deception systems in general.
We compared sperm use and availability for male L. excelsa wasps from wild populations that either did or did not co‐occur with sexually deceptive Cryptostylis orchids.
On average, males had ~50,000 sperm cells in their seminal vesicles and ejaculated ~10% of their existing sperm stock in a single encounter. Pollinators that were permitted to mate with an orchid had significantly less sperm than males that were not, suggesting they may become temporarily sperm limited. Pollinators from sites with orchids ejaculate significantly less sperm on orchids than naïve males.
The difference in ejaculate size for pollinators that do and do not co‐occur with orchids may be a consequence of males learning to avoid orchids, or an adaptation to avoid sperm depletion. Alternatively, males may reduce sperm allocation when they perceive more available ‘females’ (either orchids or real females) in the environment. These costs and responses to exploitation show how plants can influence the population dynamics of their pollinators, and, more broadly, provides an explanation for the maintenance antagonistic co‐evolutionary relationships. We suggest that by interfering with the population dynamics of a duped species, exploiters might improve their own persistence.
A free plain language summary can be found within the Supporting Information of this article.
A free plain language summary can be found within the Supporting Information of this article.
SINDy-PI Kaheman, Kadierdan; Kutz, J. Nathan; Brunton, Steven L.
Proceedings of the Royal Society. A, Mathematical, physical, and engineering sciences,
10/2020, Letnik:
476, Številka:
2242
Journal Article
Recenzirano
Odprti dostop
Accurately modelling the nonlinear dynamics of a system from measurement data is a challenging yet vital topic. The sparse identification of nonlinear dynamics (SINDy) algorithm is one approach to ...discover dynamical systems models from data. Although extensions have been developed to identify implicit dynamics, or dynamics described by rational functions, these extensions are extremely sensitive to noise. In this work, we develop SINDy-PI (parallel, implicit), a robust variant of the SINDy algorithm to identify implicit dynamics and rational nonlinearities. The SINDy-PI framework includes multiple optimization algorithms and a principled approach to model selection. We demonstrate the ability of this algorithm to learn implicit ordinary and partial differential equations and conservation laws from limited and noisy data. In particular, we show that the proposed approach is several orders of magnitude more noise robust than previous approaches, and may be used to identify a class of ODE and PDE dynamics that were previously unattainable with SINDy, including for the double pendulum dynamics and simplified model for the Belousov–Zhabotinsky (BZ) reaction.
Machine learning (ML) and artificial intelligence (AI) algorithms are now being used to automate the discovery of physics principles and governing equations from measurement data alone. However, ...positing a universal physical law from data is challenging without simultaneously proposing an accompanying discrepancy model to account for the inevitable mismatch between theory and measurements. By revisiting the classic problem of modeling falling objects of different size and mass, we highlight a number of nuanced issues that must be addressed by modern data-driven methods for automated physics discovery. Specifically, we show that measurement noise and complex secondary physical mechanisms, like unsteady fluid drag forces, can obscure the underlying law of gravitation, leading to an erroneous model. We use the sparse identification of non-linear dynamics (SINDy) method to identify governing equations for real-world measurement data and simulated trajectories. Incorporating into SINDy the assumption that each falling object is governed by a similar physical law is shown to improve the robustness of the learned models, but discrepancies between the predictions and observations persist due to subtleties in drag dynamics. This work highlights the fact that the naive application of ML/AI will generally be insufficient to infer universal physical laws without further modification.
Inferring the structure and dynamics of network models is critical to understanding the functionality and control of complex systems, such as metabolic and regulatory biological networks. The ...increasing quality and quantity of experimental data enable statistical approaches based on information theory for model selection and goodness-of-fit metrics. We propose an alternative data-driven method to infer networked nonlinear dynamical systems by using sparsity-promoting optimization to select a subset of nonlinear interactions representing dynamics on a network. In contrast to standard model selection methods-based upon information content for a finite number of heuristic models (order 10 or less), our model selection procedure discovers a parsimonious model from a combinatorially large set of models, without an exhaustive search. Our particular innovation is appropriate for many biological networks, where the governing dynamical systems have rational function nonlinearities with cross terms, thus requiring an implicit formulation and the equations to be identified in the null-space of a library of mixed nonlinearities, including the state and derivative terms. This method, implicit-SINDy, succeeds in inferring three canonical biological models: 1) Michaelis-Menten enzyme kinetics; 2) the regulatory network for competence in bacteria; and 3) the metabolic network for yeast glycolysis.
Challenges in dynamic mode decomposition Wu, Ziyou; Brunton, Steven L; Revzen, Shai
Journal of the Royal Society interface,
12/2021, Letnik:
18, Številka:
185
Journal Article
Recenzirano
Odprti dostop
Dynamic mode decomposition (DMD) is a powerful tool for extracting spatial and temporal patterns from multi-dimensional time series, and it has been used successfully in a wide range of fields, ...including fluid mechanics, robotics and neuroscience. Two of the main challenges remaining in DMD research are noise sensitivity and issues related to Krylov space closure when modelling nonlinear systems. Here, we investigate the combination of noise and nonlinearity in a controlled setting, by studying a class of systems with linear latent dynamics which are observed via multinomial observables. Our numerical models include system and measurement noise. We explore the influences of dataset metrics, the spectrum of the latent dynamics, the normality of the system matrix and the geometry of the dynamics. Our results show that even for these very mildly nonlinear conditions, DMD methods often fail to recover the spectrum and can have poor predictive ability. Our work is motivated by our experience modelling multilegged robot data, where we have encountered great difficulty in reconstructing time series for oscillatory systems with intermediate transients, which decay only slightly faster than a period.
The cause for the high prevalence of cefotaximase-producing Escherichia coli reported in dairy calves is unknown but may be partly due to the selective pressure of antimicrobial residues in waste ...milk (milk unfit for human consumption) fed to the calves. Antimicrobial use and waste milk feeding practices were investigated in 557 dairy farms in 2010/2011 that responded to a randomised stratified postal survey. The mean number of cases of mastitis per herd in the previous year was 47, and 93 per cent of respondents used antibiotic intra-mammary tubes to treat mastitis. The most frequently used lactating cow antibiotic tubes contained dihydrostreptomycin, neomycin, novobiocin, and procaine penicillin (37 per cent), and cefquinome (29 per cent). Ninety-six per cent of respondents used antibiotic tubes at the cessation of lactation (‘drying off’). The most frequently used dry cow antibiotic tube (43 per cent) contained cefalonium. Frequently used injectable antibiotics included tylosin (27 per cent), dihydrostreptomycin and procaine penicillin (20 per cent) and ceftiofur (13 per cent). Eighty-three per cent of respondents (413) fed waste milk to calves. Of these 413, 87 per cent fed waste milk from cows with mastitis, and only one-third discarded the first milk after antibiotic treatment. This survey has shown that on more than 90 per cent of the farms that feed waste milk to calves, waste milk can contain milk from cows undergoing antibiotic treatment. On some farms, this includes treatment with third- and fourth-generation cephalosporins. Further work is underway to investigate the presence of these antimicrobials in waste milk.
Abstract
Data-driven transformations that reformulate nonlinear systems in a linear framework have the potential to enable the prediction, estimation, and control of strongly nonlinear dynamics using ...linear systems theory. The Koopman operator has emerged as a principled linear embedding of nonlinear dynamics, and its eigenfunctions establish intrinsic coordinates along which the dynamics behave linearly. Previous studies have used finite-dimensional approximations of the Koopman operator for model-predictive control approaches. In this work, we illustrate a fundamental closure issue of this approach and argue that it is beneficial to first validate eigenfunctions and then construct reduced-order models in these validated eigenfunctions. These coordinates form a Koopman-invariant subspace
by design
and, thus, have improved predictive power. We show then how the control can be formulated directly in these intrinsic coordinates and discuss potential benefits and caveats of this perspective. The resulting control architecture is termed
Koopman Reduced Order Nonlinear Identification and Control
(KRONIC). It is further demonstrated that these eigenfunctions can be approximated with data-driven regression and power series expansions, based on the partial differential equation governing the infinitesimal generator of the Koopman operator. Validating discovered eigenfunctions is crucial and we show that lightly damped eigenfunctions may be faithfully extracted from EDMD or an implicit formulation. These lightly damped eigenfunctions are particularly relevant for control, as they correspond to nearly conserved quantities that are associated with persistent dynamics, such as the Hamiltonian. KRONIC is then demonstrated on a number of relevant examples, including (a) a nonlinear system with a known linear embedding, (b) a variety of Hamiltonian systems, and (c) a high-dimensional double-gyre model for ocean mixing.
Over 50 % of children and youth with cerebral palsy (CP) experience mental health challenges, with anxiety and depression most common. Youth with CP also experience several physiological symptoms ...such as fatigue, pain, sedentary lifestyle, and sleep disturbances that impact their daily living; however, little is known about the impact of these symptoms on mental health outcomes in these youth. This study addressed this gap and examined the individual and cumulative impacts of physiological symptoms on anxiety and depression symptoms in youth with CP. Forty youth with CP aged 8 to 18 years, and their caregiver, participated in this cross-sectional observational study. Pain, fatigue, anxiety, and depressive symptoms were measured using caregiver- and self-reported questionnaires and participants wore accelerometers for seven consecutive days, providing non-invasive physical activity and sleep pattern data. Youth with CP experienced substantial physiological symptoms and elevated anxiety and depression symptoms. Linear regression models determined that all physiological factors were predictive of caregiver-reported youth anxiety (R2 = 0.23) and youth depressive symptoms (R2 = 0.48). Fatigue, pain severity, sleep efficiency, and physical activity outcomes individually and cumulatively contributed to caregiver-reported youth anxiety and depression symptoms. These findings highlight the important role of physiological symptoms as potential risk factors and potential targets for intervention for mental health issues for in youth with CP.
•Children with CP experienced pain, fatigue, disordered sleep, and low levels of physical activity.•Children with CP experienced elevated symptoms of anxiety and depression.•Linear regressions determined all physiological factors contributed to caregiver-related anxiety and depression symptoms.
Periodic orbits are among the simplest non-equilibrium solutions to dynamical systems, and they play a significant role in our modern understanding of the rich structures observed in many systems. ...For example, it is known that embedded within any chaotic attractor are infinitely many unstable periodic orbits (UPOs) and so a chaotic trajectory can be thought of as 'jumping' from one UPO to another in a seemingly unpredictable manner. A number of studies have sought to exploit the existence of these UPOs to control a chaotic system. These methods rely on introducing small, precise parameter manipulations each time the trajectory crosses a transverse section to the flow. Typically these methods suffer from the fact that they require a precise description of the Poincaré mapping for the flow, which is a difficult task since there is no systematic way of producing such a mapping associated to a given system. Here we employ recent model discovery methods for producing accurate and parsimonious parameter-dependent Poincaré mappings to stabilize UPOs in nonlinear dynamical systems. Specifically, we use the sparse identification of nonlinear dynamics (SINDy) method to frame model discovery as a sparse regression problem, which can be implemented in a computationally efficient manner. This approach provides an explicit Poincaré mapping that faithfully describes the dynamics of the flow in the Poincaré section and can be used to identify UPOs. For each UPO, we then determine the parameter manipulations that stabilize this orbit. The utility of these methods are demonstrated on a variety of differential equations, including the Rössler system in a chaotic parameter regime.
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