Variability in Leaf Color Induced by Chlorophyll Deficiency: Transcriptional Changes in Bamboo Leaves.

The diversity of leaf characteristics, particularly leaf color, underscores a pivotal area of inquiry within plant science. The synthesis and functionality of chlorophyll, crucial for photosynthesis, largely dictate leaf coloration, with varying concentrations imparting different shades of green. Complex gene interactions regulate the synthesis and degradation of chlorophyll, and disruptions in these pathways can result in abnormal chlorophyll production, thereby affecting leaf pigmentation. This study focuses on Bambusa multiplex f. silverstripe, a natural variant distinguished by a spectrum of leaf colors, such as green, white, and green-white, attributed to genetic variations influencing gene expression. By examining the physiological and molecular mechanisms underlying chlorophyll anomalies and genetic factors in Silverstripe, this research sheds light on the intricate gene interactions and regulatory networks that contribute to leaf color diversity. The investigation includes the measurement of photosynthetic pigments and nutrient concentrations across different leaf color types, alongside transcriptomic analyses for identifying differentially expressed genes. The role of key genes in pathways such as ALA biosynthesis, chlorophyll synthesis, photosynthesis, and sugar metabolism is explored, offering critical insights for advancing research and plant breeding practices.

Identification and characterization of the cupin_1 domain-containing proteins in ma bamboo (Dendrocalamus latiflorus) and their potential role in rhizome sprouting.

Cupin_1 domain-containing protein (CDP) family, which is a member of the cupin superfamily with the most diverse functions in plants, has been found to be involved in hormone pathways that are closely related to rhizome sprouting (RS), a vital form of asexual reproduction in plants. Ma bamboo is a typical clumping bamboo, which mainly reproduces by RS. In this study, we identified and characterized 53 Dendrocalamus latiflorus CDP genes and divided them into seven subfamilies. Comparing the genetic structures among subfamilies showed a relatively conserved gene structure within each subfamily, and the number of cupin_1 domains affected the conservation among D. latiflorus CDP genes. Gene collinearity results showed that segmental duplication and tandem duplication both contributed to the expansion of D. latiflorus CDP genes, and lineage-specific gene duplication was an important factor influencing the evolution of CDP genes. Expression patterns showed that CDP genes generally had higher expression levels in germinating underground buds, indicating that they might play important roles in promoting shoot sprouting. Transcription factor binding site prediction and co-expression network analysis indicated that D. latiflorus CDPs were regulated by a large number of transcription factors, and collectively participated in rhizome buds and shoot development. This study significantly provided new insights into the evolutionary patterns and molecular functions of CDP genes, and laid a foundation for further studying the regulatory mechanisms of plant rhizome sprouting.

Interpretable Compositional Representations for Robust Few-Shot Generalization.

We propose Recognition as Part Composition (RPC), an image encoding approach inspired by human cognition. It is based on the cognitive theory that humans recognize complex objects by components, and that they build a small compact vocabulary of concepts to represent each instance with. RPC encodes images by first decomposing them into salient parts, and then encoding each part as a mixture of a small number of prototypes, each representing a certain concept. We find that this type of learning inspired by human cognition can overcome hurdles faced by deep convolutional networks in low-shot generalization tasks, like zero-shot learning, few-shot learning and unsupervised domain adaptation. Furthermore, we find a classifier using an RPC image encoder is fairly robust to adversarial attacks, that deep neural networks are known to be prone to. Given that our image encoding principle is based on human cognition, one would expect the encodings to be interpretable by humans, which we find to be the case via crowd-sourcing experiments. Finally, we propose an application of these interpretable encodings in the form of generating synthetic attribute annotations for evaluating zero-shot learning methods on new datasets.