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Historically, xenia effects were hypothesized to be unique genetic contributions of pollen to seed phenotype, but most examples represent standard complementation of Mendelian traits. We identified the imprinted dosage-effect defective1 (ded1) locus in maize (Zea mays) as a paternal regulator of seed size and development. Hypomorphic alleles show a 5–10% seed weight reduction when ded1 is transmitted through the male, while homozygous mutants are defective with a 70–90% seed weight reduction. Ded1 encodes an R2R3-MYB transcription factor expressed specifically during early endosperm development with paternal allele bias. DED1 directly activates early endosperm genes and endosperm adjacent to scutellum cell layer genes, while directly repressing late grain-fill genes. These results demonstrate xenia as originally defined: Imprinting of Ded1 causes the paternal allele to set the pace of endosperm development thereby influencing grain set and size.

Deep neural networks (DNN) have achieved great success in machine learning due to their powerful ability to learn and present knowledge. However, models of such DNN often have massive trainable parameters, which lead to big resource burden in practice. As a result, reducing the amount of parameters and preserving its competitive performance are always critical tasks in the field of DNN. In this paper, we focused on one type of convolution neural network that has many repeated or same-structure convolutional layers. Residual net and its variants are widely used, making the deeper model easy to train. One type block of such a model contains two convolutional layers, and each block commonly has two trainable parameter layers. However, we used only one layer of trainable parameters in the block, which means that the two convolutional layers in one block shared one layer of trainable parameters. We performed extensive experiments for different architectures of the Residual Net with trainable parameter sharing on the CIFAR-10, CIFAR-100, and ImageNet datasets. We found that the model with trainable parameter sharing can obtain fewer errors on the training datasets and had a very close recognition accuracy (within 0.5%), compared to the original models. The parameters of the new model were reduced by more than 1/3 of the total of the original.

Maize (Zea mays) is a leading cereal crop in the world. The maize kernel is the storage organ and the harvest portion of this crop and is closely related to its yield and quality. The development of maize kernel is initiated by the double fertilization event, leading to the formation of a diploid embryo and a triploid endosperm. The embryo and endosperm are then undergone independent developmental programs, resulting in a mature maize kernel which is comprised of a persistent endosperm, a large embryo, and a maternal pericarp. Due to the well-characterized morphogenesis and powerful genetics, maize kernel has long been an excellent model for the study of cereal kernel development. In recent years, with the release of the maize reference genome and the development of new genomic technologies, there has been an explosive expansion of new knowledge for maize kernel development. In this review, we overviewed recent progress in the study of maize kernel development, with an emphasis on genetic mapping of kernel traits, transcriptome analysis during kernel development, functional gene cloning of kernel mutants, and genetic engineering of kernel traits.

A human's, or lower insects', behavior is dominated by its nervous system. Each stable behavior has its own inner steps and control rules, and is regulated by a neural circuit. Understanding how the brain influences perception, thought, and behavior is a central mandate of neuroscience. The phototactic flight of insects is a widely observed deterministic behavior. Since its movement is not stochastic, the behavior should be dominated by a neural circuit. Based on the basic firing characteristics of biological neurons and the neural circuit's constitution, we designed a plausible neural circuit for this phototactic behavior from logic perspective. The circuit's output layer, which generates a stable spike firing rate to encode flight commands, controls the insect's angular velocity when flying. The firing pattern and connection type of excitatory and inhibitory neurons are considered in this computational model. We simulated the circuit's information processing using a distributed PC array, and used the real-time average firing rate of output neuron clusters to drive a flying behavior simulation. In this paper, we also explored how a correct neural decision circuit is generated from network flow view through a bee's behavior experiment based on the reward and punishment feedback mechanism. The significance of this study: firstly, we designed a neural circuit to achieve the behavioral logic rules by strictly following the electrophysiological characteristics of biological neurons and anatomical facts. Secondly, our circuit's generality permits the design and implementation of behavioral logic rules based on the most general information processing and activity mode of biological neurons. Thirdly, through computer simulation, we achieved new understanding about the cooperative condition upon which multi-neurons achieve some behavioral control. Fourthly, this study aims in understanding the information encoding mechanism and how neural circuits achieve behavior control. Finally, this study also helps establish a transitional bridge between the microscopic activity of the nervous system and macroscopic animal behavior.

The objective of the study was to investigate the role of neuregulin-ErbB signaling in neuropathic pain in different types of injury. Neuregulin-1(NRG-1) was injected into animals with either formalin-induced pain model or spared nerve injury (SNI) model. Formalin tests or paw withdrawal tests were performed to study the role of NRG-1 in neuropathic pain. siRNA specific to different erbB receptors were then introduced to test which specific signaling pathway was required for NRG-1 signaling in the different pain models. NRG-1 inhibits neuropathic pain after SNI in a dose-dependent manner, while NRG-1 aggravates formalin-induced neuropathic pain. ErbB2 and erbB4 receptors were activated after neuregulin administration. Knockdown of ErbB2 relieves the aggravation of NRG-1 on formalin-induced neuropathic pain, and knockdown of ErbB4 could relieve the inhibition of NRG-1 on neuropathic pain in the SNI model. NRG-1 has two distinct functions depending on the different receptor activation in different models of neuropathic pain. These novel findings may provide new therapeutic approaches for the treatment of neuropathic pain in different injury types.