Individual-based modeling of genome evolution in haplodiploid organisms

Ants, bees, wasps, bark beetles, and other species have haploid males and diploid females. Although such haplodiploid species play key ecological roles and are threatened by environmental changes, no general framework exists for simulating their genetic evolution. Here, we use the SLiM simulation environment to build a novel model for individual-based forward simulation of genetic evolution in haplodiploid populations. We compare the fates of adaptive and deleterious mutations and find that selection is more effective in haplodiploid species than in diploid species. Our open-source model will help understand the evolution of sociality and how ecologically important species may adapt to changing environments.

Deep learning for improving non-destructive grain mapping in 3D

Laboratory X-ray diffraction contrast tomography (LabDCT) is a novel imaging technique for non-destructive 3D characterization of grain structures. An accurate grain reconstruction critically relies on precise segmentation of diffraction spots in the LabDCT images. The conventional method utilizing various filters generally satisfies segmentation of sharp spots in the images, thereby serving as a standard routine, but it also very often leads to over or under segmentation of spots, especially those with low signal-to-noise ratios and/or small sizes. The standard routine also requires a fine tuning of the filtering parameters. To overcome these challenges, a deep learning neural network is presented to efficiently and accurately clean the background noise, thereby easing the spot segmentation. The deep learning network is first trained with input images, synthesized using a forward simulation model for LabDCT in combination with a generic approach to extract features of experimental backgrounds. Then, the network is applied to remove the background noise from experimental images measured under different geometrical conditions for different samples. Comparisons of both processed images and grain reconstructions show that the deep learning method outperforms the standard routine, demonstrating significantly better grain mapping.

Vision-Based Motion Planning of Cable-Driven Parallel Robots in the Presence of Moving Obstacle Using Sampling-Based Method

In the past decades, cable-driven parallel robots (CDPRs) have been proven the extraordinary performance for various applications. However, the multiple cables lead the robot easy to interfere with environments. Especially the large workspace of CDPR may introduce unknown moving obstacles. In this study, a sampling-based path planning method is presented for a CDPR to find the collision-free path in the presence of the moving obstacle. The suggested method is based on rapidly exploring random tree (RRT) algorithm which gives CDPRs advantages to handle complex constraints such as cable collision and dynamic feasible workspace (DFW). Moreover, we conduct the forward simulation to check the feasibility in a closed-loop system. The moving parts of both CDPRs and the moving obstacle are assumed as convex bodies, so that Gilbert-Johnson-Keerthi (GJK) algorithm is adopted to detect collision in real-time. Finally, the related simulation is carried out to illustrate the algorithm. The experiment is also presented using the drone as a moving obstacle and YOLO vision algorithm to detect the drone. The experiment results demonstrate the reliability of the suggested method.

Improved grain mapping by laboratory X-ray diffraction contrast tomography

Laboratory diffraction contrast tomography (LabDCT) is a novel technique for non-destructive imaging of the grain structure within polycrystalline samples. To further broaden the use of this technique to a wider range of materials, both the spatial resolution and detection limit achieved in the commonly used Laue focusing geometry have to be improved. In this work, the possibility of improving both grain indexing and shape reconstruction was investigated by increasing the sample-to-detector distance to facilitate geometrical magnification of diffraction spots in the LabDCT projections. LabDCT grain reconstructions of a fully recrystallized iron sample, obtained in the conventional Laue focusing geometry and in a magnified geometry, are compared to one characterized by synchrotron X-ray diffraction contrast tomography, with the latter serving as the ground truth. It is shown that grain indexing can be significantly improved in the magnified geometry. It is also found that the magnified geometry improves the spatial resolution and the accuracy of the reconstructed grain shapes. The improvement is shown to be more evident for grains smaller than 40 µm than for larger grains. The underlying reasons are clarified by comparing spot features for different LabDCT datasets using a forward simulation tool.

Adjoint tomography with full envelope for scattering and intrinsic attenuation - resolution and trade-off

<p>A rigorous framework exists for deterministic imaging the subsurface seismic velocity structure. Full-waveform inversion (FWI) that combines the forward simulation of waveforms with an adjoint (backward) simulation of the data misfit provides the gradient of the model misfit with respect to the changes in the model parameters. This gradient is used for iterative improvements of the model to minimize the data misfit. To investigate the small scale heterogeneity of the medium below the resolution limits the waveform tomography the envelopes of high-frequency seismic waves have been used to derive a statistical description of the small scale structure. Such studies employed a variety of misfit measures or empirical parameters and various assumptions about the spatial sensitivity of the measurements to derive some information about the spatial distribution of the high-frequency attenuation and scattering properties. A rigorous framework for the inversion of seismogram envelopes for the spatial imaging of heterogeneity and attenuation has been missing so far. Here we present a mathematical framework for the full envelope inversion that is based on a forward simulation of seismogram envelopes and an adjoint (backward) simulation of the envelope misfit, in full analogy to FWI. </p><p>Different from FWI that works with the wave equation, our approach is based on the Radiative Transfer Equation. In this study, the forward problem is solved by modelling the 2-D multiple nonisotropic scattering in a random elastic medium with spatially variable heterogeneity and attenuation using the Monte-Carlo method. The fluctuation strength <em>ε</em> and intrinsic quality factors <em>Q<sub>P</sub><sup>-1</sup></em> and <em>Q<sub>S</sub><sup>-1</sup></em> in the random medium are used to describe the spatial variability of attenuation and scattering. The misfit function is defined as the differences between the full observed and modelled envelopes.</p><p>We derived the sensitivity kernels corresponding to this misfit function that is minimized during the iterative adjoint inversion with the L-BFGS method. We have applied this algorithm in some numerical tests in the acoustic approximation. We show that it is possible in a rigorous way to image the spatial distribution of small scale heterogeneity and attenuation separately using seismogram envelopes. The resolution and the trade-off between scattering and intrinsic attenuation are discussed. Our analysis shows that relative importance of scattering and attenuation anomalies need to be considered when the model resolution is assessed. The inversions confirm, that the early coda is important for imaging the distribution of heterogeneity while later coda waves are more sensitive to intrinsic attenuation.</p>