Swift Monitoring Observations of Mrk 231: Detection of Ultraviolet Variability

We analyze 168 Swift monitoring observations of the nearest broad absorption line quasar Mrk 231 in the UV and X-ray bands, where we detect significant variability in the UV ($\sim$2246\AA) light curve with a null probability of $4.3\times10^{-10}$ for a constant model. Separately, from an archival sample of Swift observed active galactic nuclei (AGN), we measure the relation between UV excess variance and luminosity, finding that the normalized UV excess variance decreases with luminosity. Comparing to this mean relation, the normalized UV excess variance of Mrk 231 is smaller, however within the scatter characterising the full population. The upper limit of the X-ray excess variance is consistent with other AGN. The power spectrum density of the UV light curve can be well fit by a power law model with a slope of $1.82\pm0.14$ between $10^{-7.5}$ and $10^{-6}$ Hz, consistent with those for typical AGN, with no obvious quasi-periodical oscillation peaks. The UV variability and its power spectrum suggest that a significant amount of the UV emission of Mrk 231 is from the accretion disk. The consistencies in the normalized UV variability and the shape of the power spectrum density between Mrk 231 and other normal AGN suggest that the origin of UV variability of broad absorption line quasars is similar to other AGN, and dust scattering at large scales such as the torus is not a dominating process for the UV emission of Mrk 231. Significant scattering, if present, is constrained to smaller than $\sim$10 light days. We perform lagged correlation analysis between the UV and X-ray light curves and find the correlation insignificant within the present data.Comment: 8 pages, 4 figures, accepted by MNRA

Identifying Strongly Lensed Gravitational Waves with the Third-generation Detectors

The joint detection of GW signals by a network of instruments will increase the detecting ability of faint and far GW signals with higher signal-to-noise ratios (SNRs), which could improve the ability of detecting the lensed GWs as well, especially for the 3rd generation detectors, e.g. Einstein Telescope (ET) and Cosmic Explorer (CE). However, identifying Strongly Lensed Gravitational Waves (SLGWs) is still challenging. We focus on the identification ability of 3G detectors in this article. We predict and analyze the SNR distribution of SLGW signals and prove only 50.6\% of SLGW pairs detected by ET alone can be identified by Lens Bayes factor (LBF), which is a popular method at present to identify SLGWs. For SLGW pairs detected by CE\&ET network, owing to the superior spatial resolution, this number rises to 87.3\%. Moreover, we get an approximate analytical relation between SNR and LBF. We give clear SNR limits to identify SLGWs and estimate the expected yearly detection rates of galaxy-scale lensed GWs that can get identified with 3G detector network.Comment: 9 pages, 7 figure

DEP and AFO Regulate Reproductive Habit in Rice

Sexual reproduction is essential for the life cycle of most angiosperms. However, pseudovivipary is an important reproductive strategy in some grasses. In this mode of reproduction, asexual propagules are produced in place of sexual reproductive structures. However, the molecular mechanism of pseudovivipary still remains a mystery. In this work, we found three naturally occurring mutants in rice, namely, phoenix (pho), degenerative palea (dep), and abnormal floral organs (afo). Genetic analysis of them indicated that the stable pseudovivipary mutant pho was a double mutant containing both a Mendelian mutation in DEP and a non-Mendelian mutation in AFO. Further map-based cloning and microarray analysis revealed that dep mutant was caused by a genetic alteration in OsMADS15 while afo was caused by an epigenetic mutation in OsMADS1. Thus, OsMADS1 and OsMADS15 are both required to ensure sexual reproduction in rice and mutations of them lead to the switch of reproductive habit from sexual to asexual in rice. For the first time, our results reveal two regulators for sexual and asexual reproduction modes in flowering plants. In addition, our findings also make it possible to manipulate the reproductive strategy of plants, at least in rice

Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences

The geometries and topologies of leaves, flowers, roots, shoots, and their arrangements have fascinated plant biologists and mathematicians alike. As such, plant morphology is inherently mathematical in that it describes plant form and architecture with geometrical and topological techniques. Gaining an understanding of how to modify plant morphology, through molecular biology and breeding, aided by a mathematical perspective, is critical to improving agriculture, and the monitoring of ecosystems is vital to modeling a future with fewer natural resources. In this white paper, we begin with an overview in quantifying the form of plants and mathematical models of patterning in plants. We then explore the fundamental challenges that remain unanswered concerning plant morphology, from the barriers preventing the prediction of phenotype from genotype to modeling the movement of leaves in air streams. We end with a discussion concerning the education of plant morphology synthesizing biological and mathematical approaches and ways to facilitate research advances through outreach, cross-disciplinary training, and open science. Unleashing the potential of geometric and topological approaches in the plant sciences promises to transform our understanding of both plants and mathematics

Growth couples temporal and spatial fluctuations of tissue properties during morphogenesis

Living tissues display fluctuations -- random spatial and temporal variations of tissue properties around their reference values -- at multiple scales. It is believed that such fluctuations may enable tissues to sense their state or their size. Recent theoretical studies developed specific models of fluctuations in growing tissues and predicted that fluctuations of growth show long-range correlations. Here we elaborated upon these predictions and we tested them using experimental data. We first introduced a minimal model for the fluctuations of any quantity that has some level of temporal persistence or memory, such as concentration of a molecule, local growth rate, or mechanical properties. We found that long-range correlations are generic, applying to to any such quantity, and that growth couples temporal and spatial fluctuations. We then analysed growth data from sepals of the model plant Arabidopsis and we quantified spatial and temporal fluctuations of cell growth using the previously developed Cellular Fourier Transform. Growth appears to have long-range correlations. We compared different genotypes and growth conditions: mutants with altered response to mechanical stress have lower temporal correlations and longer-range spatial correlations than wild-type plants. Finally, we used a theoretical prediction to collapse experimental data from all conditions and developmental stages, validating the notion that temporal and spatial fluctuations are coupled by growth. Altogether, our work reveals kinematic constraints on spatiotemporal fluctuations that have an impact on the robustness of morphogenesis