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    Gravitational lensing describes the bending of the trajectories of light and gravitational waves due to the gravitational potential of a massive object. Strong lensing by galaxies can create multiple images with different overall amplifications, arrival times, and image types. If, furthermore, the gravitational wave encounters a star along its trajectory, microlensing will take place. Previously, it has been shown that the effects of microlenses on strongly-lensed type-I images could be negligible in practice, at least in the low magnification regime. In this work, we study the same effect on type-II strongly-lensed images by computing the microlensing amplification factor. As opposed to being magnified, type-II images are typically demagnified. Moreover, microlensing on top of type-II images induces larger mismatches with un-microlensed waveforms than type-I images. These results are broadly consistent with recent literature and serve to confirm the findings. In addition, we investigate the possibility of detecting and analysing microlensed signals through Bayesian parameter estimation with an isolated point mass lens template, which has been adopted in recent parameter estimation literature. In particular, we simulate gravitational waves microlensed by a microlens embedded in a galaxy potential near moderately magnified type-I and II macroimages, with variable lens masses, source parameters and macromagnifcations. Generally, an isolated point mass model could be used as an effective template to detect a type-II microlensed image but not for type-I images, demonstrating the necessity for more realistic microlensing search templates.

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  2. Abstract

    Gene expression states persist for varying lengths of time at the single-cell level, a phenomenon known as gene expression memory. When cells switch states, losing memory of their prior state, this transition can occur in the absence of genetic changes. However, we lack robust methods to find regulators of memory or track state switching. Here, we develop a lineage tracing-based technique to quantify memory and identify cells that switch states. Applied to melanoma cells without therapy, we quantify long-lived fluctuations in gene expression that are predictive of later resistance to targeted therapy. We also identify the PI3K and TGF-β pathways as state switching modulators. We propose a pretreatment model, first applying a PI3K inhibitor to modulate gene expression states, then applying targeted therapy, which leads to less resistance than targeted therapy alone. Together, we present a method for finding modulators of gene expression memory and their associated cell fates.

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  3. Free, publicly-accessible full text available May 24, 2024
  4. Castric, Vincent (Ed.)
    Abstract Macroalgal (seaweed) genomic resources are generally lacking as compared with other eukaryotic taxa, and this is particularly true in the red algae (Rhodophyta). Understanding red algal genomes is critical to understanding eukaryotic evolution given that red algal genes are spread across eukaryotic lineages from secondary endosymbiosis and red algae diverged early in the Archaeplastids. The Gracilariales is a highly diverse and widely distributed order including species that can serve as ecosystem engineers in intertidal habitats and several notorious introduced species. The genus Gracilaria is cultivated worldwide, in part for its production of agar and other bioactive compounds with downstream pharmaceutical and industrial applications. This genus is also emerging as a model for algal evolutionary ecology. Here, we report new whole-genome assemblies for two species (Gracilaria chilensis and Gracilaria gracilis), a draft genome assembly of Gracilaria caudata, and genome annotation of the previously published Gracilaria vermiculophylla genome. To facilitate accessibility and comparative analysis, we integrated these data in a newly created web-based portal dedicated to red algal genomics ( These genomes will provide a resource for understanding algal biology and, more broadly, eukaryotic evolution. 
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    Free, publicly-accessible full text available July 1, 2024
  5. Speed-dependent interlimb coordination allows animals to maintain stable locomotion under different circumstances. The V3 neurons are known to be involved in interlimb coordination. We previously modeled the locomotor spinal circuitry controlling interlimb coordination (Danner et al., 2017). This model included the local V3 neurons that mediate mutual excitation between left and right rhythm generators (RGs). Here, our focus was on V3 neurons involved in ascending long propriospinal interactions (aLPNs). Using retrograde tracing, we revealed a subpopulation of lumbar V3 aLPNs with contralateral cervical projections. V3 OFF mice, in which all V3 neurons were silenced, had a significantly reduced maximal locomotor speed, were unable to move using stable trot, gallop, or bound, and predominantly used a lateral-sequence walk. To reproduce this data and understand the functional roles of V3 aLPNs, we extended our previous model by incorporating diagonal V3 aLPNs mediating inputs from each lumbar RG to the contralateral cervical RG. The extended model reproduces our experimental results and suggests that locally projecting V3 neurons, mediating left–right interactions within lumbar and cervical cords, promote left–right synchronization necessary for gallop and bound, whereas the V3 aLPNs promote synchronization between diagonal fore and hind RGs necessary for trot. The model proposes the organization of spinal circuits available for future experimental testing. 
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  6. Mammalian locomotion is generated by central pattern generators (CPGs) in the spinal cord, which produce alternating flexor and extensor activities controlling the locomotor movements of each limb. Afferent feedback signals from the limbs are integrated by the CPGs to provide adaptive control of locomotion. Responses of CPG-generated neural activity to afferent feedback stimulation have been previously studied during fictive locomotion in immobilized cats. Yet, locomotion in awake, behaving animals involves dynamic interactions between central neuronal circuits, afferent feedback, musculoskeletal system, and environment. To study these complex interactions, we developed a model simulating interactions between a half-center CPG and the musculoskeletal system of a cat hindlimb. Then, we analyzed the role of afferent feedback in the locomotor adaptation from a dynamic viewpoint using the methods of dynamical systems theory and nullcline analysis. Our model reproduced limb movements during regular cat walking as well as adaptive changes of these movements when the foot steps into a hole. The model generates important insights into the mechanism for adaptive locomotion resulting from dynamic interactions between the CPG-based neural circuits, the musculoskeletal system, and the environment. 
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  7. Neuronal circuits in the spinal cord are essential for the control of locomotion. They integrate supraspinal commands and afferent feedback signals to produce coordinated rhythmic muscle activations necessary for stable locomotion. For several decades, computational modeling has complemented experimental studies by providing a mechanistic rationale for experimental observations and by deriving experimentally testable predictions. This symbiotic relationship between experimental and computational approaches has resulted in numerous fundamental insights. With recent advances in molecular and genetic methods, it has become possible to manipulate specific constituent elements of the spinal circuitry and relate them to locomotor behavior. This has led to computational modeling studies investigating mechanisms at the level of genetically defined neuronal populations and their interactions. We review literature on the spinal locomotor circuitry from a computational perspective. By reviewing examples leading up to and in the age of molecular genetics, we demonstrate the importance of computational modeling and its interactions with experiments. Moving forward, neuromechanical models with neuronal circuitry modeled at the level of genetically defined neuronal populations will be required to further unravel the mechanisms by which neuronal interactions lead to locomotor behavior. 
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  8. ABSTRACT This paper is the second in a pair of papers on the topic of the generation of a two-colour artificial star [which we term a laser photometric ratio star (LPRS)] of de-excitation light from neutral sodium atoms in the mesosphere, for use in precision telescopic measurements in astronomy and atmospheric physics, and more specifically for the calibration of measurements of dark energy using type Ia supernovae. The two techniques, respectively, described in both this and the previous paper would each generate an LPRS with a precisely 1:1 ratio of yellow (589/590 nm) photons to near-infrared (819/820 nm) photons produced in the mesosphere. Both techniques would provide novel mechanisms for establishing a spectrophotometric calibration ratio of unprecedented precision, from above most of Earth’s atmosphere, for upcoming telescopic observations across astronomy and atmospheric physics; thus greatly improving the performance of upcoming measurements of dark energy parameters using type Ia supernovae. The technique described in this paper has the advantage of producing a much brighter (specifically, brighter by approximately a factor of 103) LPRS, using lower power (≤30 W average power) lasers, than the technique using a single 500 W average power laser described in the first paper of this pair. However, the technique described here would require polarization filters to be installed into the telescope camera in order to sufficiently remove laser atmospheric Rayleigh backscatter from telescope images, whereas the technique described in the first paper would only require more typical wavelength filters in order to sufficiently remove laser Rayleigh backscatter. 
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  9. ABSTRACT The largest uncertainty on measurements of dark energy using type Ia supernovae (SNeIa) is presently due to systematics from photometry; specifically to the relative uncertainty on photometry as a function of wavelength in the optical spectrum. We show that a precise constraint on relative photometry between the visible and near-infrared can be achieved at upcoming survey telescopes, such as at the Vera C. Rubin Observatory, via a laser source tuned to the 342.78 nm vacuum excitation wavelength of neutral sodium atoms. Using a high-power laser, this excitation will produce an artificial star, which we term a ‘laser photometric ratio star’ (LPRS) of de-excitation light in the mesosphere at wavelengths in vacuum of 589.16, 589.76, 818.55, and 819.70 nm, with the sum of the numbers of 589.16 and 589.76 nm photons produced by this process equal to the sum of the numbers of 818.55 and 819.70 nm photons, establishing a precise calibration ratio between, for example, the r and $z$ filters of the LSST camera at the Rubin Observatory. This technique can thus provide a novel mechanism for establishing a spectrophotometric calibration ratio of unprecedented precision for upcoming telescopic observations across astronomy and atmospheric physics; thus greatly improving the performance of upcoming measurements of dark energy parameters using type SNeIa. The second paper of this pair describes an alternative technique to achieve a similar, but brighter, LPRS than the technique described in this paper, by using two lasers near resonances at 589.16 and 819.71 nm, rather than the single 342.78 nm on-resonance laser technique described in this paper. 
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