Panax ginseng C. A. Meyer (ginseng) is one of the most widely used traditional Chinese medicinal herbs, with its roots as the primary medicinal part garnering significant attention due to their therapeutic potential. The GRAS [GRI (Gibberellic Acid Insensitive), RGA (Repressor of GAI-3 mutant), and SCR (Scarecrow)] genes are a class of widely distributed plant-specific transcription factors that play crucial roles in various physiological processes including root formation, fruit development, hormone signaling, and stem cell maintenance. This study systematically identified 139 GRAS genes (PgGRAS) in the ginseng genome for the first time, analyzing their complexity and diversity through protein domain structure, phylogenetic relationships, gene structure, and cis-acting element prediction. Evolutionary analysis revealed that all PgGRAS members were divided into 14 evolutionary branches, including a novel species-specific subfamily PG28, with segmental duplication being the primary driver of family expansion. RNA-seq analysis uncovered tissue-specific expression patterns of the PgGRAS gene family. qRT-PCR validation demonstrated that PgGRAS48, a member of the SCL3 subfamily, was significantly highly expressed in the main root and upregulated upon GA treatment, suggesting its potential regulatory role in main root development. Therefore, this gene was selected for further investigation. Overexpression of PgGRAS48 significantly increased the main root length in Arabidopsis thaliana (A. thaliana), accompanied by elevated endogenous GA levels. Subcellular localization, molecular docking, Bimolecular Fluorescence Complementation (BIFC) and yeast two-hybrid (Y2H) experiments confirmed the interaction between PgGRAS48 (SCL3) and PgGRAS2 (DELLA) in the nucleus, revealing the molecular mechanism by which SCL3-DELLA regulates main root elongation through gibberellin (GA) biosynthesis or signaling pathways. This study elucidates the molecular network of the GRAS family in root development in ginseng, providing key targets for the targeted improvement of root architecture in medicinal plants.
Photosynthesis is vital for the survival of entire ecosystems on Earth. While light is fundamental to this process, excessive exposure can be detrimental to plant cells. Chloroplasts, the photosynthetic organelles, actively move in response to light and self-organize within the cell to tune light absorption. These disk-shaped motile organelles must balance dense packing for enhanced light absorption under dim conditions with spatial rearrangements to avoid damage from excessive light exposure. Here, we reveal that the packing characteristics of chloroplasts within plant cells show signatures of optimality. Combining measurements of chloroplast densities and three-dimensional cell shape in the water plant Elodea densa, we construct an argument for optimal cell shape versus chloroplast size to achieve two targets: dense packing into a two-dimensional monolayer for optimal absorption under dim light conditions and packing at the sidewalls for optimal light avoidance. We formalize these constraints using a model for random close packing matched with packing simulations of polydisperse hard disks confined within rectangular boxes. The optimal cell shape resulting from these models corresponds closely to that measured in the box-like plant cells, highlighting the importance of particle packing in the light adaptation of plants. Understanding the interplay between structure and function sheds light on how plants achieve efficient photo adaptation. It also highlights a broader principle: how cell shape relates to the optimization of packing finite and relatively small numbers of organelles under confinement. This universal challenge in biological systems shares fundamental features with the mechanics of confined granular media and the jamming transitions in dense active and passive systems across various scales and contexts.
Plant disease is a critical factor affecting agricultural production. Traditional manual recognition methods face significant drawbacks, including low accuracy, high costs, and inefficiency. Deep learning techniques have demonstrated significant benefits in identifying plant diseases, but they still face challenges such as inference delays and high energy consumption. Deep learning algorithms are difficult to run on resource-limited embedded devices. Offloading these models to cloud servers is confronted with the restriction of communication bandwidth, and all of these factors will influence the inference's efficiency. We propose a collaborative inference framework for recognizing plant diseases between edge devices and cloud servers to enhance inference speed. The DNN model for plant disease recognition is pruned through deep reinforcement learning to improve the inference speed and reduce energy consumption. Then the optimal split point is determined by a greedy strategy to achieve the best collaborated inference acceleration. Finally, the system for collaborative inference acceleration in plant disease recognition has been implemented using Gradio to facilitate friendly human-machine interaction. Experiments indicate that the proposed collaborative inference framework significantly increases inference speed while maintaining acceptable recognition accuracy, offering a novel solution for rapidly diagnosing and preventing plant diseases.
Bosubabu Sambana, Hillary Sunday Nnadi, Mohd Anas Wajid
et al.
Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to mitigating their effects and preventing widespread damage, as outbreaks can severely impact the productivity and quality of crops. With advancements in technology, there are increasing opportunities for automating the monitoring and detection of disease outbreaks in plants. This study proposed a system designed to identify and monitor plant diseases using a transfer learning approach. Specifically, the study utilizes YOLOv7 and YOLOv8, two state-ofthe-art models in the field of object detection. By fine-tuning these models on a dataset of plant leaf images, the system is able to accurately detect the presence of Bacteria, Fungi and Viral diseases such as Powdery Mildew, Angular Leaf Spot, Early blight and Tomato mosaic virus. The model's performance was evaluated using several metrics, including mean Average Precision (mAP), F1-score, Precision, and Recall, yielding values of 91.05, 89.40, 91.22, and 87.66, respectively. The result demonstrates the superior effectiveness and efficiency of YOLOv8 compared to other object detection methods, highlighting its potential for use in modern agricultural practices. The approach provides a scalable, automated solution for early any plant disease detection, contributing to enhanced crop yield, reduced reliance on manual monitoring, and supporting sustainable agricultural practices.
Simeon Adebola, Shuangyu Xie, Chung Min Kim
et al.
Accurate temporal reconstructions of plant growth are essential for plant phenotyping and breeding, yet remain challenging due to complex geometries, occlusions, and non-rigid deformations of plants. We present a novel framework for building temporal digital twins of plants by combining 3D Gaussian Splatting with a robust sample alignment pipeline. Our method begins by reconstructing Gaussian Splats from multi-view camera data, then leverages a two-stage registration approach: coarse alignment through feature-based matching and Fast Global Registration, followed by fine alignment with Iterative Closest Point. This pipeline yields a consistent 4D model of plant development in discrete time steps. We evaluate the approach on data from the Netherlands Plant Eco-phenotyping Center, demonstrating detailed temporal reconstructions of Sequoia and Quinoa species. Videos and Images can be seen at https://berkeleyautomation.github.io/GrowSplat/
Large language models (LLMs) are increasingly deployed in culturally diverse environments, yet existing evaluations of cultural competence remain limited. Existing methods focus on de-contextualized correctness or forced-choice judgments, overlooking the need for cultural understanding and reasoning required for appropriate responses. To address this gap, we introduce a set of benchmarks that, instead of directly probing abstract norms or isolated statements, present models with realistic situational contexts that require culturally grounded reasoning. In addition to the standard Exact Match metric, we introduce four complementary metrics (Coverage, Specificity, Connotation, and Coherence) to capture different dimensions of model's response quality. Empirical analysis across frontier models reveals that thin evaluation systematically overestimates cultural competence and produces unstable assessments with high variance. In contrast, thick evaluation exposes differences in reasoning depth, reduces variance, and provides more stable, interpretable signals of cultural understanding.
Cross-commodity valuation approaches to value gas fire power plants are well studied in the literature. Hence, the value of the gas fire power plant is identical to the value of a spark spread option wherein the underlying are electricity and gas with a strike price assimilated to operating and maintenance costs. Power and fuels spot prices account for uncertain futures cash-flows for power-plant generator owners. For instance, for gas-fired turbine plant, spot prices of electricity and gas determine the random cash-flows of the power-plant. Other than the spot prices, the valuation of such plant involves among other deterministic cost the plant heat rate and operating costs. Recently, the cost of emissions is considered into the valuation to tackle environmental issues. Given some simplifications in the plant cash-flow modelling, the value of such plant can either be expressed as the price of i) a cross-commodity option or ii) the price of a real option. Here, we focus on cross-commodity option valuation approach where the value of the power plant is approached as the value of a spark spread option. When spot prices of the underlying commodities are log-normal, closed formulae or approximations can be obtained using Kirk's approximation. Naturally, the spot price of electricity and gas present spikes due to seasonality among other factors. However, in that case it is not possible to get a closed formula for the spark spread option. In this paper we explore possibilities to approximate spark spread options when spot prices fall into a class of jump diffusion processes.
In a globalized world, cultural elements from diverse origins frequently appear together within a single visual scene. We refer to these as culture mixing scenarios, yet how Large Vision-Language Models (LVLMs) perceive them remains underexplored. We investigate culture mixing as a critical challenge for LVLMs and examine how current models behave when cultural items from multiple regions appear together. To systematically analyze these behaviors, we construct CultureMix, a food Visual Question Answering (VQA) benchmark with 23k diffusion-generated, human-verified culture mixing images across four subtasks: (1) food-only, (2) food+food, (3) food+background, and (4) food+food+background. Evaluating 10 LVLMs, we find consistent failures to preserve individual cultural identities in mixed settings. Models show strong background reliance, with accuracy dropping 14% when cultural backgrounds are added to food-only baselines, and they produce inconsistent predictions for identical foods across different contexts. To address these limitations, we explore three robustness strategies. We find supervised fine-tuning using a diverse culture mixing dataset substantially improve model consistency and reduce background sensitivity. We call for increased attention to culture mixing scenarios as a critical step toward developing LVLMs capable of operating reliably in culturally diverse real-world environments.
Yedra Vieites-Álvarez, Yedra Vieites-Álvarez, Manuel J. Reigosa
et al.
During the last decade, research has shown the environment and human health benefits of growing buckwheat (Fagopyrum spp.). This comprehensive review aims to summarize the major advancements made in the study of buckwheat from 2013 to 2023, focusing on its agronomic characteristics, nutritional value, and potential applications in sustainable agriculture. The review examines the diverse applications of buckwheat in organic and agroecological farming systems, and discusses the ability of buckwheat to control weeds through allelopathy, competition, and other sustainable farming methods, such as crop rotation, intercropping and green manure, while improving soil health and biodiversity. The review also explores the nutritional value of buckwheat. It delves into the composition of buckwheat grains, emphasizing their high protein content, and the presence of essential amino acids and valuable micronutrients, which is linked to health benefits such as lowering cholesterol levels, controlling diabetes and acting against different types of cancer, among others. Finally, the review concludes by highlighting the gaps in current knowledge, and proposing future research directions to further optimize buckwheat production in organic or agroecological farming systems. It emphasizes the need for interdisciplinary collaboration, and the integration of traditional knowledge with modern scientific approaches to unlock the full potential of buckwheat as a sustainable crop.
Online reviews have become essential for users to make informed decisions in everyday tasks ranging from planning summer vacations to purchasing groceries and making financial investments. A key problem in using online reviews is the overabundance of online that overwhelms the users. As a result, recommendation systems for providing helpfulness of reviews are being developed. This paper argues that cultural background is an important feature that impacts the nature of a review written by the user, and must be considered as a feature in assessing the helpfulness of online reviews. The paper provides an in-depth study of differences in online reviews written by users from different cultural backgrounds and how incorporating culture as a feature can lead to better review helpfulness recommendations. In particular, we analyze online reviews originating from two distinct cultural spheres, namely Arabic and Western cultures, for two different products, hotels and books. Our analysis demonstrates that the nature of reviews written by users differs based on their cultural backgrounds and that this difference varies based on the specific product being reviewed. Finally, we have developed six different review helpfulness recommendation models that demonstrate that taking culture into account leads to better recommendations.
There are two main life cycles in plants, annual and perennial. These life cycles are associated with different traits that determine ecosystem function. Although life cycles are textbook examples of plant adaptation to different environments, we lack comprehensive knowledge regarding their global distributional patterns. Here, we assembled an extensive database of plant life cycle assignments of 235,000 plant species coupled with millions of georeferenced data points to map the worldwide biogeography thereof. We found that annuals are half as common as initially thought, accounting for only 6% of plant species. Our analyses indicate annuals are favored in hot and dry regions. However, a more accurate model shows annual species' prevalence is driven by temperature and precipitation in the driest quarter (rather than yearly means), explaining, for example, why some Mediterranean systems have more annuals than deserts. Furthermore, this pattern remains consistent among different families, indicating convergent evolution. Finally, we demonstrate that increasing climate variability and anthropogenic disturbance increase annual favorability. Considering future climate change, we predict an increase in annual prevalence for 69% of the world's ecoregions by 2060. Overall, our analyses raise concerns for ecosystem services provided by perennials, as ongoing changes are leading to a more annuals-dominated world.
Veronica Vinciotti, Luca De Benedictis, Ernst C. Wit
Cultural values vary significantly around the world. Despite a large heterogeneity, similarities across national cultures are present. This paper studies cross-country culture heterogeneity via the joint inference of country-specific copula graphical models from world-wide survey data. To this end, a random graph generative model of the cultural networks is introduced, with a latent space and proximity measures that embed cultural relatedness across countries. Within-country heterogeneity is also accounted for, via parametric modelling of the marginal distributions of each cultural trait. All together, the different components of the model are able to identify several dimensions of culture.
Marta Magnani, Rubén Díaz-Sierra, Luke Sweeney
et al.
Across plant communities worldwide, fire regimes reflect a combination of climatic factors and plant characteristics. To shed new light on the complex relationships between plant characteristics and fire regimes, we developed a new conceptual, mechanistic model that includes plant competition, stochastic fires, and fire-vegetation feedback. Considering a single standing plant functional type, we observed that highly flammable and slowly colonizing plants can persist only when they have a strong fire response, while fast colonizing and less flammable plants can display a larger range of fire responses. At the community level, the fire response of the strongest competitor determines the existence of alternative ecological states, i.e. different plant communities, under the same environmental conditions. Specifically, when the strongest competitor had a very strong fire response, such as in Mediterranean forests, only one ecological state could be achieved. Conversely, when the strongest competitor was poorly fire-adapted, alternative ecological states emerged, for example between tropical humid savannas and forests, or between different types of boreal forests. These findings underline the importance of including the plant fire response when modeling fire ecosystems, e.g. to predict the vegetation response to invasive species or to climate change.
Plant disease recognition has witnessed a significant improvement with deep learning in recent years. Although plant disease datasets are essential and many relevant datasets are public available, two fundamental questions exist. First, how to differentiate datasets and further choose suitable public datasets for specific applications? Second, what kinds of characteristics of datasets are desired to achieve promising performance in real-world applications? To address the questions, this study explicitly propose an informative taxonomy to describe potential plant disease datasets. We further provide several directions for future, such as creating challenge-oriented datasets and the ultimate objective deploying deep learning in real-world applications with satisfactory performance. In addition, existing related public RGB image datasets are summarized. We believe that this study will contributing making better datasets and that this study will contribute beyond plant disease recognition such as plant species recognition. To facilitate the community, our project is public https://github.com/xml94/PPDRD with the information of relevant public datasets.
Nariane Q. Vilhena, Rebeca Gil, Mario Vendrell
et al.
This study investigated the effect of preharvest 1-MCP treatment on maintaining ‘Rojo Brillante’ persimmon firmness. Early in the season, preharvest 1-MCP was applied 1, 7 and 10 days after ethephon treatment. The fruit firmness was evaluated during three different harvests and after the commercialization period of 3 d at 3 °C, plus 6 d at 20 °C. Late in the season, 1-MCP was applied 3 days before harvest in the fruit treated with gibberellic acid (GA) and then cold-stored for up to 60 days, plus a 6-day shelf life at 20 °C. The results showed that preharvest 1-MCP delayed the fruit softening induced by ethephon during the harvest period, and was the most effective treatment when performed 1 day after ethephon application. Therefore, preharvest 1-MCP extended the harvest period of ethephon-treated fruit. At the end of the season, preharvest 1-MCP had the same effect on maintaining the fruit firmness as the commercial postharvest application.
Highly efficient genetic transformation technology is greatly beneficial for crop gene function analysis and precision breeding. However, the most commonly used genetic transformation technology for woody plants, mediated by Agrobacterium tumefaciens, is time-consuming and inefficient, which limits its utility for gene function analysis. In this study, a simple, universal, and highly efficient genetic transformation technology mediated by A. rhizogenes K599 is described. This technology can be applied to multiple citrus genotypes, and only 2–8 weeks were required for the entire workflow. Genome-editing experiments were simultaneously conducted using 11 plasmids targeting different genomic positions and all corresponding transformants with the target knocked out were obtained, indicating that A. rhizogenes-mediated genome editing was highly efficient. In addition, the technology is advantageous for investigation of specific genes (such as ACD2) for obtaining “hard-to-get” transgenic root tissue. Furthermore, A. rhizogenes can be used for direct viral vector inoculation on citrus bypassing the requirement for virion enrichment in tobacco, which facilitates virus-induced gene silencing and virus-mediated gene expression. In summary, we established a highly efficient genetic transformation technology bypassing tissue culture in citrus that can be used for genome editing, gene overexpression, and virus-mediated gene function analysis. We anticipate that by reducing the cost, required workload, experimental period, and other technical obstacles, this genetic transformation technology will be a valuable tool for routine investigation of endogenous and exogenous genes in citrus.
Large scale adoption of large language models has introduced a new era of convenient knowledge transfer for a slew of natural language processing tasks. However, these models also run the risk of undermining user trust by exposing unwanted information about the data subjects, which may be extracted by a malicious party, e.g. through adversarial attacks. We present an empirical investigation into the extent of the personal information encoded into pre-trained representations by a range of popular models, and we show a positive correlation between the complexity of a model, the amount of data used in pre-training, and data leakage. In this paper, we present the first wide coverage evaluation and comparison of some of the most popular privacy-preserving algorithms, on a large, multi-lingual dataset on sentiment analysis annotated with demographic information (location, age and gender). The results show since larger and more complex models are more prone to leaking private information, use of privacy-preserving methods is highly desirable. We also find that highly privacy-preserving technologies like differential privacy (DP) can have serious model utility effects, which can be ameliorated using hybrid or metric-DP techniques.