Hasil untuk "Plant culture"

Fatemeh Jamshidi-kia, Z. Lorigooini, Hossein Amini-Khoei

Human societies have been in close contact with their environments since the beginning of their formation and used the ingredients of the environment to obtain food and medicine. Awareness and application of plants to prepare food and medicine have been realized through trial and error, and gradually human became able to meet his needs from his surroundings. Information about medicinal plants has long been transmitted gradually and from generation to generation, a human knowledge has gradually become complete with the formation of civilizations and the provision of more facilities. Medicinal plants are used as a medical resource in almost all cultures. Ensuring the safety, quality and effectiveness of medicinal plants and herbal drugs very recently became a key issue in industrialized and developing countries. By standardizing and evaluating the health of active plant-derived compounds, herbal drugs can help the emergence of a new era of the healthcare system to treat human diseases in the future. Awareness of traditional knowledge and medicinal plants can play a key role in the exploitation and discovery of natural plant resources. In order to maintain this knowledge, comprehensive approach and collaboration are needed to maintain historical records on medicinal plants and use these resources in favour of human beings, before they are destroyed forever. Therefore, this review was conducted to investigate and describe the process of using medicinal plants throughout history. This review focuses on the recent various important challenges in quality evaluation of medicinal plants in the authenticity, efficacy, toxicity and consistency.

Vineet Kumar, S. Yadav

J. Clifford

Introduction: The Pure Products Go Crazy Part One: Discourses 1. On Ethnographic Authority 2. Power and Dialogue in Ethnography: Marcel Griaule's Initiation 3. On Ethnographic Self-Fashioning: Conrad and Malinowski Part Two: Displacements 4. On Ethnographic Surrealism 5. A Poetics of Displacement: Victor Segalen 6. Tell about Your Trip: Michel Leiris 7. A Politics of Neologism: Aime Cesaire 8. The Jardin des Plantes: Postcards Part Three: Collections 9. Histories of the Tribal and the Modern 10. On Collecting Art and Culture Part Four: Histories 11. On Orientalism 12. Identity in Mashpee References Sources Index

S. Grayston, Shen S. J. Wang, C. Campbell et al.

L. Bogorad, F. Constabel, S. Baron et al.

M. F. Balandrin, J. Klocke, E. Wurtele et al.

Chang‐Zheng Chen, Ling Li, H. Lodish et al.

B. Niu, J. Paulson, Xiaoqi Zheng et al.

D. Hope, C. Gries, Weixing Zhu et al.

H. Hirochika, Kazuhiko Sugimoto, Yoshiaki Otsuki et al.

Wanjia Tang, HongFen Li, Yidi Huang et al.

Anthocyanins play a crucial role in shaping the visual appeal and nutritional quality of fruits. Previous research on anthocyanin biosynthesis in sweet cherry (Prunus avium L.) has primarily relied on single-omics approaches or focused on a limited range of metabolites, leaving the regulatory mechanisms and dynamic metabolism of anthocyanins during ripening inadequately characterized. This study integrated anthocyanin-targeted metabolomics and transcriptomics to identify key anthocyanins in sweet cherry and construct a transcriptional regulatory network for anthocyanin biosynthesis. A novel bHLH transcription factor, Prunus avium bHLH transcription factor 102 (PavbHLH102), was identified, and its role in regulating cyanidin levels was validated through overexpression and silencing experiments. Both in vitro and in vivo assays demonstrated that PavbHLH102 activates key anthocyanin biosynthetic genes, including PavF3H, PavDFR, and PavUFGT, thereby enhancing fruit coloration. Notably, PavF3′H upregulation significantly increased cyanidin accumulation. This study provides new insights into anthocyanin regulation in sweet cherry and offers valuable resources for improving fruit quality.

Raphael Perez, Valentin Torrelli, Sandrine Roques et al.

Functional-structural plant models (FSPM) replicate plants' responses to their environment and are useful for predicting behavior in a changing climate. However, they rely on detailed measurements of traits, which are difficult to collect consistently across scales, often limiting model parameterization and thorough evaluation, and thereby reducing confidence in model predictions. Here, we provided a comprehensive dataset of structural and biophysical measurements from four oil palm plants (Elaeis guinnensis) grown under multiple controlled environmental scenarios, including varying CO2 concentrations, light, temperature and humidity conditions. The dataset included detailed reconstructions of the three-dimensional plant structures derived from terrestrial LiDAR point clouds, and enabled the parametrization of biophysical processes at the leaf scale such as photosynthesis and stomatal conductance, as well as the collection of plant-scale measurements (gas exchange measurements of CO2 and H20), which can be compared with FSPM simulations. The tree-dimensional reconstructions effectively represented the architecture of the plants and showed strong correlation with the measured total leaf area. Hence, future comparisons between simulated and observed physiological traits could be used to evaluate the quality of the physiological formalisms independently. By bridging the scales from individual leaves to the entire plant, this database allows modellers to both calibrate their biophysical models at a fine spatial resolution and evaluate their predictive accuracy at the plant scale. The provided data will facilitate benchmarking of biophysical models, help identify sources of model uncertainty, and ultimately enhance model predictions, which can be applied in various fields, from cognitive studies to decision support applications.

A. Namdeo

M. Fridlender, Y. Kapulnik, H. Koltai

Plants have had an essential role in the folklore of ancient cultures. In addition to the use as food and spices, plants have also been utilized as medicines for over 5000 years. It is estimated that 70–95% of the population in developing countries continues to use traditional medicines even today. A new trend, that involved the isolation of plant active compounds begun during the early nineteenth century. This trend led to the discovery of different active compounds that are derived from plants. In the last decades, more and more new materials derived from plants have been authorized and subscribed as medicines, including those with anti-cancer activity. Cancer is among the leading causes of morbidity and mortality worldwide. The number of new cases is expected to rise by about 70% over the next two decades. Thus, there is a real need for new efficient anti-cancer drugs with reduced side effects, and plants are a promising source for such entities. Here we focus on some plant-derived substances exhibiting anti-cancer and chemoprevention activity, their mode of action and bioavailability. These include paclitaxel, curcumin, and cannabinoids. In addition, development and use of their synthetic analogs, and those of strigolactones, are discussed. Also discussed are commercial considerations and future prospects for development of plant derived substances with anti-cancer activity.

SHENG-HE XU, Sai Wang

The rapid development of intelligent technologies has transformed various industries, and agriculture benefits greatly from precision farming innovations. One of the remarkable achievements in agriculture is enhancing pest and disease identification for better crop health control and higher yields. This paper presents novel models of a multimodal data fusion technique to meet the growing need for accurate and timely wheat pest and disease identification. It combines image processing, sensor - derived environmental data, and machine learning for reliable wheat pest and disease diagnosis. First, deep - learning algorithms in image analysis detect early - stage pests and diseases on wheat leaves. Second, environmental data such as temperature and humidity improve diagnosis. Third, the data fusion process integrates image data for further analysis. Finally, several criteria compare the proposed model with previous methods. Experimental results show the proposed techniques achieve a detection accuracy of 96.5%, precision of 94.8%, recall of 97.2%, F1 score of 95.9%, MCC of 0.91, and AUC - ROC of 98.4%. The training time is 15.3 hours, and the inference time is 180 ms. Compared with CNN - based and SVM - based techniques, the proposed model’s improvement is analyzed. It can be adapted for real - time use and applied to more crops and diseases.

Sabrina Gennis, Matthew D. Biviano, Kristoffer P. Lyngbirk et al.

Plants respond to biotic and abiotic stresses through complex and dynamic mechanisms that integrate physical, chemical, and biological cues. Here, we present a multi-physics platform designed to systematically investigate these responses across scales. The platform combines a six-axis micromanipulator with interchangeable probes to deliver precise mechanical, electrostatic, optical, and chemical stimuli. Using this system, we explore calcium signaling in Arabidopsis thaliana, thigmonastic motion in Mimosa pudica, and chemical exchange via microinjection in Rosmarinus officinalis L. and Ocimum basilicum. Our findings highlight stimulus-specific and spatially dependent responses: mechanical and electrostatic stimuli elicit distinct calcium signaling patterns, while repeated electrostatic stimulation exhibited evidence of response fatigue. Thigmonastic responses in Mimosa pudica depend on the location of perturbation, highlighting the intricate bi-directional calcium signaling. Microinjection experiments successfully demonstrate targeted chemical perturbations in glandular trichomes, opening avenues for biochemical studies. This open-source platform provides a versatile tool for dissecting plant stress responses, bridging the gap between fundamental research and applied technologies in agriculture and bioengineering. By enabling precise, scalable, and reproducible studies of plant-environment interactions, this work offers new insights into the mechanisms underlying plant resilience and adaptability.

Earl Ranario, Ismael Mayanja, Heesup Yun et al.

Accurate plant segmentation in thermal imagery remains a significant challenge for high throughput field phenotyping, particularly in outdoor environments where low contrast between plants and weeds and frequent occlusions hinder performance. To address this, we present a framework that leverages synthetic RGB imagery, a limited set of real annotations, and GAN-based cross-modality alignment to enhance semantic segmentation in thermal images. We trained models on 1,128 synthetic images containing complex mixtures of crop and weed plants in order to generate image segmentation masks for crop and weed plants. We additionally evaluated the benefit of integrating as few as five real, manually segmented field images within the training process using various sampling strategies. When combining all the synthetic images with a few labeled real images, we observed a maximum relative improvement of 22% for the weed class and 17% for the plant class compared to the full real-data baseline. Cross-modal alignment was enabled by translating RGB to thermal using CycleGAN-turbo, allowing robust template matching without calibration. Results demonstrated that combining synthetic data with limited manual annotations and cross-domain translation via generative models can significantly boost segmentation performance in complex field environments for multi-model imagery.

Manavendra Desai, Himanshu Sharma, Sayak Mukherjee et al.

Given the advancements in data-driven modeling for complex engineering and scientific applications, this work utilizes a data-driven predictive control method, namely subspace predictive control, to coordinate hybrid power plant components and meet a desired power demand despite the presence of weather uncertainties. An uncertainty-aware data-driven predictive controller is proposed, and its potential is analyzed using real-world electricity demand profiles. For the analysis, a hybrid power plant with wind, solar, and co-located energy storage capacity of 4 MW each is considered. The analysis shows that the predictive controller can track a real-world-inspired electricity demand profile despite the presence of weather-induced uncertainties and be an intelligent forecaster for HPP performance.

Worasit Sangjan, Piyush Pandey, Norman B. Best et al.

Accurate identification of individual plants from unmanned aerial vehicle (UAV) images is essential for advancing high-throughput phenotyping and supporting data-driven decision-making in plant breeding. This study presents MatchPlant, a modular, graphical user interface-supported, open-source Python pipeline for UAV-based single-plant detection and geospatial trait extraction. MatchPlant enables end-to-end workflows by integrating UAV image processing, user-guided annotation, Convolutional Neural Network model training for object detection, forward projection of bounding boxes onto an orthomosaic, and shapefile generation for spatial phenotypic analysis. In an early-season maize case study, MatchPlant achieved reliable detection performance (validation AP: 89.6%, test AP: 85.9%) and effectively projected bounding boxes, covering 89.8% of manually annotated boxes with 87.5% of projections achieving an Intersection over Union (IoU) greater than 0.5. Trait values extracted from predicted bounding instances showed high agreement with manual annotations (r = 0.87-0.97, IoU >= 0.4). Detection outputs were reused across time points to extract plant height and Normalized Difference Vegetation Index with minimal additional annotation, facilitating efficient temporal phenotyping. By combining modular design, reproducibility, and geospatial precision, MatchPlant offers a scalable framework for UAV-based plant-level analysis with broad applicability in agricultural and environmental monitoring.

Yuhang Wang, Yanxu Zhu, Jitao Sang

The advanced reasoning capabilities of Large Reasoning Models enable them to thoroughly understand and apply safety policies through deliberate thought processes, thereby improving the models' safety. Beyond safety, these models must also be able to reflect the diverse range of human values across various cultures. This paper presents the Cultural Norm-based Cultural Alignment (CNCA) framework, which enables models to leverage their powerful reasoning ability to align with cultural norms. Specifically, we propose three methods to automatically mine cultural norms from limited survey data and explore ways to effectively utilize these norms for improving cultural alignment. Two alignment paradigms are examined: an in-context alignment method, where cultural norms are explicitly integrated into the user context, and a fine-tuning-based method, which internalizes norms through enhanced Chain-of-Thought training data. Comprehensive experiments demonstrate the effectiveness of these methods, highlighting that models with stronger reasoning capabilities benefit more from cultural norm mining and utilization. Our findings emphasize the potential for reasoning models to better reflect diverse human values through culturally informed alignment strategies.