Motor output and control input in flapping flight: a compact model of the deforming wing kinematics of manoeuvring hoverflies

Insects are conventionally modelled as controlling flight by varying a few summary kinematic parameters that are defined on a per-wingbeat basis, such as the stroke amplitude, mean stroke angle and mean wing pitch angle. Nevertheless, as insects have tens of flight muscles and vary their kinematics continuously, the true dimension of their control input space is likely to be much higher. Here, we present a compact description of the deforming wing kinematics of 36 manoeuvring Eristalis hoverflies, applying functional principal components analysis to Fourier series fits of the wingtip position and wing twist measured over 26 541 wingbeats. This analysis offers a high degree of data reduction, in addition to insight into the natural kinematic couplings. We used statistical resampling techniques to verify that the principal components (PCs) were repeatable features of the data, and analysed their coefficient vectors to provide insight into the form of these natural couplings. Conceptually, the dominant PCs provide a natural set of control input variables that span the control input subspace utilized by this species, but they can also be thought of as output states of the flight motor. This functional description of the wing kinematics is appropriate to modelling insect flight as a form of limit cycle control.

Motor output and control input in flapping flight: a compact model of the deforming wing kinematics of manoeuvring hoverflies

Insects are conventionally modelled as controlling flight by varying a few summary kinematic parameters that are defined on a per-wingbeat basis, such as the stroke amplitude, mean stroke angle, and mean wing pitch angle. Nevertheless, as insects have tens of flight muscles and vary their kinematics continuously, the true dimension of their control input subspace is likely to be much higher. Here we present a compact description of the deforming wing kinematics of 36 manoeuvring Eristalis hoverflies, applying functional principal components analysis to Fourier series fits of the wingtip position and wing twist measured over 26,541 wingbeats. This analysis offers a high degree of data reduction, in addition to insight into the natural kinematic couplings. We used statistical resampling techniques to verify that the principal components were repeatable features of the data, and analysed their coefficient vectors to provide insight into the form of these natural couplings. Conceptually, the dominant principal components provide a natural set of control input variables that span the control input subspace of this species, but they can also be thought of as output states of the flight motor. This functional description of the wing kinematics is appropriate to modelling insect flight as a form of limit cycle control.

Metatarsophalangeal proportions of Homo naledi

Post-cranial differences between extant apes and humans include differences in the length, shape and size of bone elements relative to each other; i.e. differences in proportions. Foot proportions are influenced by the different functional requirements of climbing and bipedal locomotion. Phalangeal length is generally correlated with locomotor behaviour in primates and there is variation in hominins in relative phalangeal lengths – the functional and evolutionary significance of which is unclear and currently debated. Homo naledi has a largely modern rearfoot (i.e. tarsal skeleton) and midfoot (i.e. metatarsal skeleton). The proximal pedal phalanges of H. naledi are curved, but the relative lengths are unknown, because the phalanges cannot reliably be associated with metatarsals, or in many cases even with ray number. Here, we assess the lengths of the proximal pedal phalanges relative to the metatarsals in H. naledi with resampling from modern human and chimpanzee (Pan troglodytes) samples. We use a novel resampling method that employs two boundary conditions, assuming at one extreme that elements in the sample are associated, and at the other extreme that no elements are associated. The associated metatarsophalangeal proportions from digits 1 and 2 are within the 95% confidence interval of the modern human distribution. However, the associated and unassociated proportions from digits 3–5 fall above the 95% confidence interval of the human distribution, but below and outside of the chimpanzee distribution. While these results may indicate fossil preservation bias or other sample-derived statistical limitations, they potentially raise the intriguing possibility of unique medial versus lateral pedal column functional evolution in H. naledi. Additionally, the relevant associated proportions of H. naledi are compared to and are different from those of H. floresiensis. Both species suggest deep phylogenetic placement so the ancestral condition of the pedal phalanges in the genus Homo remains unclear. Significance: Modern humans demonstrate straight and relatively short pedal phalanges, whereas H. naledi demonstrates curved phalanges of an unknown relative length. This research analyses the relative length of the proximal phalanges to the metatarsals to determine if naledi has relatively short phalanges similar to modern humans or is distinct from modern humans in both its phalangeal length and curvature. This analysis further develops a statistical resampling method that was previously applied to large fossil assemblages with little association between bones.

Eph-ephrin signaling modulated by polymerization and condensation of receptors

Eph receptor signaling plays key roles in vertebrate tissue boundary formation, axonal pathfinding, and stem cell regeneration by steering cells to positions defined by its ligand ephrin. Some of the key events in Eph-ephrin signaling are understood: ephrin binding triggers the clustering of the Eph receptor, fostering transphosphorylation and signal transduction into the cell. However, a quantitative and mechanistic understanding of how the signal is processed by the recipient cell into precise and proportional responses is largely lacking. Studying Eph activation kinetics requires spatiotemporal data on the number and distribution of receptor oligomers, which is beyond the quantitative power offered by prevalent imaging methods. Here we describe an enhanced fluorescence fluctuation imaging analysis, which employs statistical resampling to measure the Eph receptor aggregation distribution within each pixel of an image. By performing this analysis over time courses extending tens of minutes, the information-rich 4D space (x, y, oligomerization, time) results were coupled to straightforward biophysical models of protein aggregation. This analysis reveals that Eph clustering can be explained by the combined contribution of polymerization of receptors into clusters, followed by their condensation into far larger aggregates. The modeling reveals that these two competing oligomerization mechanisms play distinct roles: polymerization mediates the activation of the receptor by assembling monomers into 6- to 8-mer oligomers; condensation of the preassembled oligomers into large clusters containing hundreds of monomers dampens the signaling. We propose that the polymerization–condensation dynamics creates mechanistic explanation for how cells properly respond to variable ligand concentrations and gradients.

An “Observational Large Ensemble” to Compare Observed and Modeled Temperature Trend Uncertainty due to Internal Variability

Estimates of the climate response to anthropogenic forcing contain irreducible uncertainty due to the presence of internal variability. Accurate quantification of this uncertainty is critical for both contextualizing historical trends and determining the spread of climate projections. The contribution of internal variability to uncertainty in trends can be estimated in models as the spread across an initial condition ensemble. However, internal variability simulated by a model may be inconsistent with observations due to model biases. Here, statistical resampling methods are applied to observations in order to quantify uncertainty in historical 50-yr (1966–2015) winter near-surface air temperature trends over North America related to incomplete sampling of internal variability. This estimate is compared with the simulated trend uncertainty in the NCAR CESM1 Large Ensemble (LENS). The comparison suggests that uncertainty in trends due to internal variability is largely overestimated in LENS, which has an average amplification of variability of 32% across North America. The amplification of variability is greatest in the western United States and Alaska. The observationally derived estimate of trend uncertainty is combined with the forced signal from LENS to produce an “Observational Large Ensemble” (OLENS). The members of OLENS indicate the range of observationally constrained, spatially consistent temperature trends that could have been observed over the past 50 years if a different sequence of internal variability had unfolded. The smaller trend uncertainty in OLENS suggests that is easier to detect the historical climate change signal in observations than in any given member of LENS.

High-resolution bootstrapped differential semblance

We formulated two coherency measures, based on the bootstrapped differential semblance (BDS) estimator, that offered higher resolution in parameter tracking than did standard normalized differential semblance. Bootstrapping is a statistical resampling procedure used to infer estimates of standard errors and confidence intervals from data samples for which the statistical properties are unattainable via simple means, or when the probability density function is unkown or difficult to estimate. The first proposed estimator was based on a deterministic sorting of original offset traces by alternating near and far offsets to achieve maximized time shifts between adjacent traces. The near offsets were indexed with odd integers, while the even integers were used to index far offsets that were located at a constant index increment from the previous trace. The second was the product of several BDS terms, with the first term being the deterministic BDS defined above. The other terms were generated by random sorting of traces that alternated near and far offsets in an unpredictible manner. The proposed estimators could be applied in building velocity (and anellipticity) spectra for time-domain velocity analysis, depth-domain residual velocity update, or to any parameter-fitting algorithm involving discrete multichannel data. The gain in resolution provided by the suggested estimators over the differential semblance coefficient was illustrated on a number of synthetic and field data examples.

Bankruptcy Prediction by Genetic Ant Colony Algorithm

Corporate bankruptcy is a hot topic in economical research. Traditional methods cannot reach satisfying classification accuracy due to the high dimensional features. In this study, we proposed a novel method based on wrapper-based feature selection. Moreover, a novel genetic ant colony algorithm (GACA) was proposed as the search method, and the rule-based model was employed as the classifier. Stratified K-fold cross validation method was taken as the statistical resampling to reduce overfitting. Simulations take 1,000 runs of each algorithm on the dataset of 800 corporations during the period 2006-2008. The results of the training subset show that the GACA obtains 84.3% success rate, while GA obtains only 48.8% and ACA obtains 22.1% success rate. The results on test subset demonstrate that the mean misclassification error of GACA is only 7.79%, less than those of GA (19.31%) and ACA (23.89%). The average computation time of GACA is only 0.564s compared to the GA (1.203s) and ACA (1.109s).

Resampling Methods in Paleontology

This chapter reviews major types of statistical resampling approaches used in paleontology. They are an increasingly popular alternative to the classic parametric approach because they can approximate behaviors of parameters that are not understood theoretically. The primary goal of most resampling methods is an empirical approximation of a sampling distribution of a statistic of interest, whether simple (mean or standard error) or more complicated (median, kurtosis, or eigenvalue). This chapter focuses on the conceptual and practical aspects of resampling methods that a user is likely to face when designing them, rather than the relevant mathematical derivations and intricate details of the statistical theory. The chapter reviews the concept of sampling distributions, outlines a generalized methodology for designing resampling methods, summarizes major types of resampling strategies, highlights some commonly used resampling protocols, and addresses various practical decisions involved in designing algorithm details. A particular emphasis has been placed here on bootstrapping, a resampling strategy used extensively in quantitative paleontological analyses, but other resampling techniques are also reviewed in detail. In addition,ad hocand literature-based case examples are provided to illustrate virtues, limitations, and potential pitfalls of resampling methods.