Comparative transcriptomics uncovers alternative splicing changes and signatures of selection from maize improvement

Background: Alternative splicing (AS) is an important regulatory mechanism that greatly contributes to eukaryotic transcriptome diversity. A substantial amount of evidence has demonstrated that AS complexity is relevant to eukaryotic evolution, development, adaptation, and complexity. In this study, six teosinte and ten maize transcriptomes were sequenced to analyze AS changes and signatures of selection in maize domestication and improvement. Results In maize and teosinte, 13,593 highly conserved genes, including 12,030 multiexonic genes, were detected. By identifying AS isoforms from mutliexonic genes, we found that AS types were not significantly different between maize and teosinte. In addition, the two main AS types (intron retention and alternative acceptor) contributed to more than 60% of the AS events in the two species, but the average unique AS events per each alternatively spliced gene in maize (4.12) was higher than that in teosinte (2.26). Moreover, 94 genes generating 98 retained introns with transposable element (TE) sequences were detected in maize, which is far more than 9 retained introns with TEs detected in teosinte. This indicates that TE insertion might be an important mechanism for intron retention in maize. Additionally, the AS levels of 3864 genes were significantly different between maize and teosinte. Of these, 151 AS level-altered genes that are involved in transcriptional regulation and in stress responses are located in regions that have been targets of selection during maize improvement. These genes were inferred to be putatively improved genes. Conclusions We suggest that both maize and teosinte share similar AS mechanisms, but more genes have increased AS complexity during domestication from teosinte to maize. Importantly, a subset of AS level-increased genes that encode transcription factors and stress-responsive proteins may have been selected during maize improvement

Identification of transcriptome induced in roots of maize seedlings at the late stage of waterlogging

<p>Abstract</p> <p>Background</p> <p>Plants respond to low oxygen stress, particularly that caused by waterlogging, by altering transcription and translation. Previous studies have mostly focused on revealing the mechanism of the response at the early stage, and there is limited information about the transcriptional profile of genes in maize roots at the late stage of waterlogging. The genetic basis of waterlogging tolerance is largely unknown. In this study, the transcriptome at the late stage of waterlogging was assayed in root cells of the tolerant inbred line HZ32, using suppression subtractive hybridization (SSH). A forward SSH library using RNA populations from four time points (12 h, 16 h, 20 h and 24 h) after waterlogging treatment was constructed to reveal up-regulated genes, and transcriptional and linkage data was integrated to identify candidate genes for waterlogging tolerance.</p> <p>Results</p> <p>Reverse Northern analysis of a set of 768 cDNA clones from the SSH library revealed a large number of genes were up-regulated by waterlogging. A total of 465 ESTs were assembled into 296 unigenes. Bioinformatic analysis revealed that the genes were involved in complex pathways, such as signal transduction, protein degradation, ion transport, carbon and amino acid metabolism, and transcriptional and translational regulation, and might play important roles at the late stage of the response to waterlogging. A significant number of unigenes were of unknown function. Approximately 67% of the unigenes could be aligned on the maize genome and 63 of them were co-located within reported QTLs.</p> <p>Conclusion</p> <p>The late response to waterlogging in maize roots involves a broad spectrum of genes, which are mainly associated with two response processes: defense at the early stage and adaption at the late stage. Signal transduction plays a key role in activating genes related to the tolerance mechanism for survival during prolonged waterlogging. The crosstalk between carbon and amino acid metabolism reveals that amino acid metabolism performs two main roles at the late stage: the regulation of cytoplasmic pH and energy supply through breakdown of the carbon skeleton.</p

TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models

The full potential of large pretrained models remains largely untapped in control domains like robotics. This is mainly because of the scarcity of data and the computational challenges associated with training or fine-tuning these large models for such applications. Prior work mainly emphasizes effective pretraining of large models for decision-making, with little exploration into how to perform data-efficient continual adaptation of these models for new tasks. Recognizing these constraints, we introduce TAIL (Task-specific Adapters for Imitation Learning), a framework for efficient adaptation to new control tasks. Inspired by recent advancements in parameter-efficient fine-tuning in language domains, we explore efficient fine-tuning techniques -- e.g., Bottleneck Adapters, P-Tuning, and Low-Rank Adaptation (LoRA) -- in TAIL to adapt large pretrained models for new tasks with limited demonstration data. Our extensive experiments in large-scale language-conditioned manipulation tasks comparing prevalent parameter-efficient fine-tuning techniques and adaptation baselines suggest that TAIL with LoRA can achieve the best post-adaptation performance with only 1\% of the trainable parameters of full fine-tuning, while avoiding catastrophic forgetting and preserving adaptation plasticity in continual learning settings.Comment: 21 pages, 8 figures, 8 table

Constrained Decision Transformer for Offline Safe Reinforcement Learning

Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the environment. We aim to tackle a more challenging problem: learning a safe policy from an offline dataset. We study the offline safe RL problem from a novel multi-objective optimization perspective and propose the $\epsilon$-reducible concept to characterize problem difficulties. The inherent trade-offs between safety and task performance inspire us to propose the constrained decision transformer (CDT) approach, which can dynamically adjust the trade-offs during deployment. Extensive experiments show the advantages of the proposed method in learning an adaptive, safe, robust, and high-reward policy. CDT outperforms its variants and strong offline safe RL baselines by a large margin with the same hyperparameters across all tasks, while keeping the zero-shot adaptation capability to different constraint thresholds, making our approach more suitable for real-world RL under constraints.Comment: 15 pages, 7 figure

On the Robustness of Safe Reinforcement Learning under Observational Perturbations

Safe reinforcement learning (RL) trains a policy to maximize the task reward while satisfying safety constraints. While prior works focus on the performance optimality, we find that the optimal solutions of many safe RL problems are not robust and safe against carefully designed observational perturbations. We formally analyze the unique properties of designing effective state adversarial attackers in the safe RL setting. We show that baseline adversarial attack techniques for standard RL tasks are not always effective for safe RL and proposed two new approaches - one maximizes the cost and the other maximizes the reward. One interesting and counter-intuitive finding is that the maximum reward attack is strong, as it can both induce unsafe behaviors and make the attack stealthy by maintaining the reward. We further propose a more effective adversarial training framework for safe RL and evaluate it via comprehensive experiments. This paper provides a pioneer work to investigate the safety and robustness of RL under observational attacks for future safe RL studies.Comment: 30 pages, 4 figures, 8 table

Bradyrhizobium diazoefficiens USDA 110–glycine max interactome provides candidate proteins associated with symbiosis

Although the legume−rhizobium symbiosis is a most-important biological process, there is a limited knowledge about the protein interaction network between host and symbiont. Using interolog- and domain-based approaches, we constructed an interspecies protein interactome containing 5115 protein−protein interactions between 2291 Glycine max and 290 Bradyrhizobium diazoefficiens USDA 110 proteins. The interactome was further validated by the expression pattern analysis in nodules, gene ontology term semantic similarity, co-expression analysis, and luciferase complementation image assay. In the G. max−B. diazoefficiens interactome, bacterial proteins are mainly ion channel and transporters of carbohydrates and cations, while G. max proteins are mainly involved in the processes of metabolism, signal transduction, and transport. We also identified the top 10 highly interacting proteins (hubs) for each species. Kyoto Encyclopedia of Genes and Genomes pathway analysis for each hub showed that a pair of 14-3-3 proteins (SGF14g and SGF14k) and 5 heat shock proteins in G. max are possibly involved in symbiosis, and 10 hubs in B. diazoefficiens may be important symbiotic effectors. Subnetwork analysis showed that 18 symbiosis-related soluble N-ethylmaleimide sensitive factor attachment protein receptor proteins may play roles in regulating bacterial ion channels, and SGF14g and SGF14k possibly regulate the rhizobium dicarboxylate transport protein DctA. The predicted interactome provide a valuable basis for understanding the molecular mechanism of nodulation in soybean

Genome-Wide Association Studies Identified Three Independent Polymorphisms Associated with α-Tocopherol Content in Maize Kernels

Tocopherols are a class of four natural compounds that can provide nutrition and function as antioxidant in both plants and animals. Maize kernels have low α-tocopherol content, the compound with the highest vitamin E activity, thus, raising the risk of vitamin E deficiency in human populations relying on maize as their primary vitamin E source. In this study, two insertion/deletions (InDels) within a gene encoding γ-tocopherol methyltransferase, Zea mays VTE4 (ZmVTE4), and a single nucleotide polymorphism (SNP) located ∼85 kb upstream of ZmVTE4 were identified to be significantly associated with α-tocopherol levels in maize kernels by conducting an association study with a panel of ∼500 diverse inbred lines. Linkage analysis in three populations that segregated at either one of these three polymorphisms but not at the other two suggested that the three polymorphisms could affect α-tocopherol content independently. Furthermore, we found that haplotypes of the two InDels could explain ∼33% of α-tocopherol variation in the association panel, suggesting ZmVTE4 is a major gene involved in natural phenotypic variation of α-tocopherol. One of the two InDels is located within the promoter region and associates with ZmVTE4 transcript level. This information can not only help in understanding the underlying mechanism of natural tocopherol variations in maize kernels, but also provide valuable markers for marker-assisted breeding of α-tocopherol content in maize kernels, which will then facilitate the improvement of maize as a better source of daily vitamin E nutrition

Magnetically-dressed CrSBr exciton-polaritons in ultrastrong coupling regime

The strong coupling between photons and matter excitations such as excitons, phonons, and magnons is of central importance in the study of light-matter interactions. Bridging the flying and stationary quantum states, the strong light-matter coupling enables the coherent transmission, storage, and processing of quantum information, which is essential for building photonic quantum networks. Over the past few decades, exciton-polaritons have attracted substantial research interest due to their half-light-half-matter bosonic nature. Coupling exciton-polaritons with magnetic orders grants access to rich many-body phenomena, but has been limited by the availability of material systems that exhibit simultaneous exciton resonances and magnetic ordering. Here we report magnetically-dressed microcavity exciton-polaritons in the van der Waals antiferromagnetic (AFM) semiconductor CrSBr coupled to a Tamm plasmon microcavity. Angle-resolved spectroscopy reveals an exceptionally high exciton-polariton coupling strength attaining 169 meV, demonstrating ultrastrong coupling that persists up to room temperature. Temperature-dependent exciton-polariton spectroscopy senses the magnetic order change from AFM to paramagnetism in CrSBr, confirming its magnetic nature. By applying an out-of-plane magnetic field, an effective tuning of the polariton energy is further achieved while maintaining the ultrastrong exciton-photon coupling strength, which is attributed to the spin canting process that modulates the interlayer exciton interaction. Our work proposes a hybrid quantum platform enabled by robust opto-electronic-magnetic coupling, promising for quantum interconnects and transducers.Comment: 8 pages, 4 figure

Wafer-Size and Single-Crystal MoSe_2 Atomically Thin Films Grown on GaN Substrate for Light Emission and Harvesting

Two-dimensional (2D) atomic-layered semiconductors are important for next-generation electronics and optoelectronics. Here, we designed the growth of an MoSe_2 atomic layer on a lattice-matched GaN semiconductor substrate. The results demonstrated that the MoSe_2 films were less than three atomic layers thick and were single crystalline of MoSe_2 over the entire GaN substrate. The ultrathin MoSe_2/GaN heterojunction diode demonstrated ∼850 nm light emission and could also be used in photovoltaic applications

Identification of significant loci for drought resistance and root traits at seedling stage with a set of maize introgression lines

Genetic dissection of the genetic basis of drought resistance is important for crop improvement. In this study, significant loci for drought resistance related traits at seedling stage were identified with a set of maize introgression lines under two environments. Two drought resistance indicators, leaf-drying degree under medium drought stress (LDM) and severe drought stress (LDS), and three root traits, root dry weight under drought stress (RWS), root length under drought stress (RLS), and root number under drought stress (RNS) were investigated. A total of 31 and 25 significant loci for LDM and LDS were identified, respectively, and 20 of them were commonly detected under both environments. For the three root traits, 41 significant loci were detected with 20 loci identified under both environments. Of the 97 significant loci, only 19 loci related to both leaf-drying degree and root traits under stress simultaneously, suggesting that these root traits had limited contribution to drought resistance at seedling stage. The significant loci detected in this study would be useful in molecular breeding for drought resistance in maize