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Abstract Background Cotton fibers provide a powerful model for studying cell differentiation and elongation. Each cotton fiber is a singular and elongated cell derived from epidermal-layer cells of a cotton seed. Efforts to understand this dramatic developmental shift have been impeded by the difficulty of separation between fiber and epidermal cells. Results Here we employed laser-capture microdissection (LCM) to separate these cell types. RNA-seq analysis revealed transitional differences between fiber and epidermal-layer cells at 0 or 2 days post anthesis. Specifically, down-regulation of putative cell cycle genes was coupled with upregulation of ribosome biosynthesis and translation-related genes, which may suggest theirmore »respective roles in fiber cell initiation. Indeed, the amount of fibers in cultured ovules was increased by cell cycle progression inhibitor, Roscovitine, and decreased by ribosome biosynthesis inhibitor, Rbin-1. Moreover, subfunctionalization of homoeologs was pervasive in fiber and epidermal cells, with expression bias towards 10% more D than A homoeologs of cell cycle related genes and 40–50% more D than A homoeologs of ribosomal protein subunit genes. Key cell cycle regulators were predicted to be epialleles in allotetraploid cotton. MYB-transcription factor genes displayed expression divergence between fibers and ovules. Notably, many phytohormone-related genes were upregulated in ovules and down-regulated in fibers, suggesting spatial-temporal effects on fiber cell development. Conclusions Fiber cell initiation is accompanied by cell cycle arrest coupled with active ribosome biosynthesis, spatial-temporal regulation of phytohormones and MYB transcription factors, and homoeolog expression bias of cell cycle and ribosome biosynthesis genes. These valuable genomic resources and molecular insights will help develop breeding and biotechnological tools to improve cotton fiber production.« less

Dielectric elastomers (DEs) deform and change shape when an electric field is applied across them. They are flexible, resilient, lightweight, and durable and as such are suitable for use as soft actuators. In this paper a physics-based and control-oriented model is developed for a DE tubular actuator using a physics-lumped parameter modeling approach. The model derives from the nonlinear partial differential equations (PDE) which govern the nonlinear elasticity of the DE actuator and the ordinary differential equation (ODE) that governs the electrical dynamics of the DE actuator. With the boundary conditions for the tubular actuator, the nonlinear PDEs are numericallymore »solved and a quasi-static nonlinear model is obtained and validated by experiments. The full nonlinear model is then linearized around an operating point with an analytically derived Hessian matrix. The analytically linearized model is validated by experiments. Proportional–Integral–Derivative (PID) and H∞ control are developed and implemented to perform position reference tracking of the DEA and the controllers’ performances are evaluated according to control energy and tracking error.« less

Focusing on graph-structured prediction tasks, we demon- strate the ability of neural networks to provide both strong predictive performance and easy interpretability, two proper- ties often at odds in modern deep architectures. We formulate the latter by the ability to extract the relevant substructures for a given task, inspired by biology and chemistry appli- cations. To do so, we utilize the Local Relational Pooling (LRP) model, which is recently introduced with motivations from substructure counting. In this work, we demonstrate that LRP models can be used on challenging graph classification tasks to provide both state-of-the-art performance and inter- pretability, throughmore »the detection of the relevant substructures used by the network to make its decisions. Besides their broad applications (biology, chemistry, fraud detection, etc.), these models also raise new theoretical questions related to com- pressed sensing and to computational thresholds on random graphs.« less

This study examined age differences in barriers to preparing for disasters and how caregiving responsibilities are associated with these barriers among different age groups. Using a sample of 1142 individuals from the 2017 Federal Emergency Management Agency National Household Survey, binary and multinomial logistic regressions were conducted to investigate the likelihood of encountering any or one of the two types of barriers, namely, barriers related to coping appraisal (i.e., capacity) and those related to threat appraisal (i.e., risk perception). Age was the key predictor and was categorized into five groups: 18–34, 35–49, 50–64, 65–74, and 75+. The results showed thatmore »the 18–34, 35–49, and 75+ age groups were more likely to have coping appraisal barriers than those aged between 65 and 74. In addition, being a caregiver increased the likelihood of having coping appraisal barriers. Interestingly, relative to the 65–74 age group, being a caregiver in the 18–34, 35–49, and 50–64 age groups would be more likely to have coping appraisal barriers. Our findings highlighted age patterns and heterogeneity among older adults. This study also directed attention to how disaster preparation behaviors were shaped by life course experiences.« less

From the perspective of expressive power, this work compares multi-layer Graph Neural Networks (GNNs) with a simplified alternative that we call Graph-Augmented Multi-Layer Perceptrons (GA-MLPs), which first augments node features with certain multi-hop operators on the graph and then applies an MLP in a node-wise fashion. From the perspective of graph isomorphism testing, we show both theoretically and numerically that GA-MLPs with suitable operators can distinguish almost all non-isomorphic graphs, just like the Weifeiler-Lehman (WL) test. However, by viewing them as node-level functions and examining the equivalence classes they induce on rooted graphs, we prove a separation in expressive powermore »between GA-MLPs and GNNs that grows exponentially in depth. In particular, unlike GNNs, GA-MLPs are unable to count the number of attributed walks. We also demonstrate via community detection experiments that GA-MLPs can be limited by their choice of operator family, as compared to GNNs with higher flexibility in learning.« less

Polyploidy is a prominent feature for genome evolution in many animals and all flowering plants. Plant polyploids often show enhanced fitness in diverse and extreme environments, but the molecular basis for this remains elusive. Soil salinity presents challenges for many plants including agricultural crops. Here we report that salt tolerance is enhanced in tetraploid rice through lower sodium uptake and correlates with epigenetic regulation of jasmonic acid (JA)–related genes. Polyploidy induces DNA hypomethylation and potentiates genomic loci coexistent with many stress-responsive genes, which are generally associated with proximal transposable elements (TEs). Under salt stress, the stress-responsive genes including those inmore »the JA pathway are more rapidly induced and expressed at higher levels in tetraploid than in diploid rice, which is concurrent with increased jasmonoyl isoleucine (JA-Ile) content and JA signaling to confer stress tolerance. After stress, elevated expression of stress-responsive genes in tetraploid rice can induce hypermethylation and suppression of the TEs adjacent to stress-responsive genes. These induced responses are reproducible in a recurring round of salt stress and shared between twojaponicatetraploid rice lines. The data collectively suggest a feedback relationship between polyploidy-induced hypomethylation in rapid and strong stress response and stress-induced hypermethylation to repress proximal TEs and/or TE-associated stress-responsive genes. This feedback regulation may provide a molecular basis for selection to enhance adaptation of polyploid plants and crops during evolution and domestication.

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