Hasil untuk "Plant culture"

B. Reinhold-Hurek, T. Hurek

H. N. Murthy, Eun-Jung Lee, K. Paek

M. Bairu, A. Aremu, J. Staden

E. Epstein

Rajendran Jeyasri, Pandiyan Muthuramalingam, K. Karthick et al.

Plant secondary metabolites are bioactive scaffolds that are crucial for plant survival in the environment and to maintain a defense mechanism from predators. These compounds are generally present in plants at a minimal level and interestingly, they are found to have a wide variety of therapeutic values for humans. Several medicinal plants are used for pharmaceutical purposes due to their affordability, fewer adverse effects, and vital role in traditional remedies. Owing to this reason, these plants are exploited at a high range worldwide and therefore many medicinal plants are on the threatened list. There is a need of the hour to tackle this major problem, one effective approach called elicitation can be used to enhance the level of existing and novel plant bioactive compounds using different types of elicitors namely biotic and abiotic. This process can be generally achieved by in vitro and in vivo experiments. The current comprehensive review provides an overview of biotic and abiotic elicitation strategies used in medicinal plants, as well as their effects on secondary metabolites enhancement. Further, this review mainly deals with the enhancement of biomass and biosynthesis of different bioactive compounds by methyl jasmonate (MeJA) and salicylic acid (SA) as elicitors of wide medicinal plants in in vitro by using different cultures. The present review was suggested as a significant groundwork for peers working with medicinal plants by applying elicitation strategies along with advanced biotechnological approaches.

R. Dressler, H. Rasmussen

Harshul Raj Surana, Arijit Maji, Aryan Vats et al.

Large Language Models (LLMs) have made significant progress in reasoning tasks across various domains such as mathematics and coding. However, their performance deteriorates in tasks requiring rich socio-cultural knowledge and diverse local contexts, particularly those involving Indian Culture. Existing Cultural benchmarks are (i) Manually crafted, (ii) contain single-hop questions testing factual recall, and (iii) prohibitively costly to scale, leaving this deficiency largely unmeasured. To address this, we introduce VIRAASAT, a novel, semi-automated multi-hop approach for generating cultural specific multi-hop Question-Answering dataset for Indian culture. VIRAASAT leverages a Knowledge Graph comprising more than 700 expert-curated cultural artifacts, covering 13 key attributes of Indian culture (history, festivals, etc). VIRAASAT spans all 28 states and 8 Union Territories, yielding more than 3,200 multi-hop questions that necessitate chained cultural reasoning. We evaluate current State-of-the-Art (SOTA) LLMs on VIRAASAT and identify key limitations in reasoning wherein fine-tuning on Chain-of-Thought(CoT) traces fails to ground and synthesize low-probability facts. To bridge this gap, we propose a novel framework named Symbolic Chain-of-Manipulation (SCoM). Adapting the Chain-of-Manipulation paradigm, we train the model to simulate atomic Knowledge Graph manipulations internally. SCoM teaches the model to reliably traverse the topological structure of the graph. Experiments on Supervised Fine-Tuning (SFT) demonstrate that SCoM outperforms standard CoT baselines by up to 20%. We release the VIRAASAT dataset along with our findings, laying a strong foundation towards building Culturally Aware Reasoning Models.

F. J. Gutiérrez-Mañero, B. Ramos-Solano, A. Probanza et al.

A. Varma, A. Varma, Savita Verma et al.

Xiaonan Hu, Xuebing Li, Jinyu Xu et al.

Accurate plant counting provides valuable information for agriculture such as crop yield prediction, plant density assessment, and phenotype quantification. Vision-based approaches are currently the mainstream solution. Prior art typically uses a detection or a regression model to count a specific plant. However, plants have biodiversity, and new cultivars are increasingly bred each year. It is almost impossible to exhaust and build all species-dependent counting models. Inspired by class-agnostic counting (CAC) in computer vision, we argue that it is time to rethink the problem formulation of plant counting, from what plants to count to how to count plants. In contrast to most daily objects with spatial and temporal invariance, plants are dynamic, changing with time and space. Their non-rigid structure often leads to worse performance than counting rigid instances like heads and cars such that current CAC and open-world detection models are suboptimal to count plants. In this work, we inherit the vein of the TasselNet plant counting model and introduce a new extension, TasselNetV4, shifting from species-specific counting to cross-species counting. TasselNetV4 marries the local counting idea of TasselNet with the extract-and-match paradigm in CAC. It builds upon a plain vision transformer and incorporates novel multi-branch box-aware local counters used to enhance cross-scale robustness. Two challenging datasets, PAC-105 and PAC-Somalia, are harvested. Extensive experiments against state-of-the-art CAC models show that TasselNetV4 achieves not only superior counting performance but also high efficiency.Our results indicate that TasselNetV4 emerges to be a vision foundation model for cross-scene, cross-scale, and cross-species plant counting.

Mohammad Ibrahim Qani

Language serves as a foundation of cultural identity, deeply entangled with the social and historical contexts of a community. This paper examines the ineffectiveness of interference by alien words within a culture. Drawing on sociolinguistic theories and case studies from diverse linguistic environments, it is argued that the forced introduction or adoption of foreign lexicon often fails to achieve its intended socio-cultural objectives. Instead, indigenous languages demonstrate resilience, adapting to or resisting external influences through unique strategies. The effectiveness of this research highlights the futility of attempting to impose linguistic uniformity and underscores the importance of understanding local cultural dynamics in preserving linguistic heritage. This pure language understanding directly relates to translation knowledge where linguists and translators need to work and research to eradicate misunderstanding. Misunderstandings mostly appear in non-equivalent words because there are different local and internal words like food, garment, cultural and traditional words, and others in every notion. Truly, most of these words do not have an equivalent in the target language and these words need to be worked and find their equivalent in the target language to fully understand both languages. The purpose of this research is to introduce the challenges and ineffectiveness of cultural influences in different notions where people do not see the facts of cultural enrichment. However, some of these ineffectiveness have been clearly mentioned in this research but some effective ways have also been dictated.

Kepeng Lin, Qizhe Zhang, Rui Wang et al.

Understanding the underlying linguistic rules of plant genomes remains a fundamental challenge in computational biology. Recent advances including AgroNT and PDLLMs have made notable progress although, they suffer from excessive parameter size and limited ability to model the bidirectional nature of DNA strands respectively. To address these limitations, we propose PlantBiMoE, a lightweight and expressive plant genome language model that integrates bidirectional Mamba and a Sparse Mixture-of-Experts (SparseMoE) framework. The bidirectional Mamba enables the model to effectively capture structural dependencies across both the forward and reverse DNA strands, while SparseMoE significantly reduces the number of active parameters, improving computational efficiency without sacrificing modeling capacity. We evaluated and tested our model on the Modified Plants Genome Benchmark (MPGB), an enhanced genomic benchmark, which consolidates 31 datasets across 11 representative tasks, with input sequence lengths ranging from 50 to 6,000 bp. Experimental results demonstrate that PlantBiMoE achieves the best performance on 20 out of 31 datasets and the average best when comparing with existing models. In summary, all above results demonstrate that our model can effectively represent plant genomic sequences, serving as a robust computational tool for diverse genomic tasks, while making substantive contributions to plant genomics, gene editing, and synthetic biology. The code is available at: https://github.com/HUST-Keep-Lin/PlantBiMoE

Marika Lamendola, Giacomo Fiore, Piotr Gulczynski et al.

The use of biostimulants and corroborants is increasing worldwide. Laboratory and field assays show their effectiveness in improving the vegetative performance of plants and their tolerance to abiotic stresses. This study aims to evaluate the <i>in vitro</i> activity of a biostimulant, based on pine bark extract, against some fungal phytopathogens. This research was carried out at the Laboratory of Plant Pathology (SAAF Department, University of Palermo, Italy), employing the poison food technique. Artificial agar media (Potato Dextrose Agar, PDA), simple or added with different concentrations of the biostimulant, were used to evaluate the differences in diametral growth of the fungi <i>Aspergillus niger</i>, <i>Aspergillus tubingensis</i>, <i>Botrytis cinerea</i>, <i>Coriolopsis gallica</i>, <i>Fomitiporia mediterranea</i>, <i>Fusarium oxysporum</i>, <i>Pleurostoma richardsiae</i> and <i>Pleurotus ostreatus</i>. The biostimulant was shown to contain the growth of most of the tested fungi, with the greatest effectiveness on <i>A. tubingensis</i>, <i>C. gallica</i>, <i>F. mediterranea</i> and <i>P. richardsiae</i> at the highest concentration, moderate effects on <i>A. niger</i>, <i>F. oxysporum</i> and <i>P. ostreatus</i> and no effect on <i>B. cinerea</i>. The observed fungistatic effects suggest that this biostimulant could contribute to integrated disease management while supporting more sustainable crop protection practices. In vivo tests aimed at evaluating the efficacy of these products on the evolution of different diseases in the field are ongoing, and preliminary results are promising but they are part of future work.

Loredana-Elena Mantea, Amada El-Sabeh, Marius Mihasan et al.

Climate change significantly impacts plant growth by reducing the availability of essential nutrients, including phosphorus (P). As an alternative to chemical fertilizers, climate-smart agriculture should prioritize the use of beneficial microorganisms such as P-solubilizing bacteria (PSB). Here, we report the ability of the P1.5S strain of <i>Bacillus safensis</i> to solubilize P under the stress caused by different pH, temperature, and salinity. Genomic data and the TBLASTN algorithm were used to identify genes involved in stress tolerance and P solubilization. Stress tolerance was confirmed by cultivation under varying conditions, while the mechanism of P solubilization was investigated using HPLC. Bioinformatic analysis revealed at least 99 genes related to stress tolerance, 32 genes responsible for organic acids synthesis, as well as 10 genes involved in phosphatase production. <i>B. safensis</i> P1.5S can grow at 37 °C, high NaCl concentrations (15 g/L), and is tolerant of alkaline and acidic conditions. The P1.5S strain primarily solubilizes P by releasing organic acids, including lactic, acetic, and succinic acid. Our data revealed that the efficacy of P solubilization was not affected by abiotic stressors (19.54 µg P/mL). By evaluating the P solubilization ability of <i>B. safensis</i> P1.5S induced by stressors represented by varying pH, temperature, and salinity conditions, this work introduces a new avenue for increasing P availability, which enables and endorses the future development of practical applications of <i>B. safensis</i> P1.5S in challenging agricultural environments.

P. Marschner, D. Crowley, Ching-Hong Yang

N. Yeh, Jen-Ping Chung

Hilbert Yuen In Lam, Xing Er Ong, Marek Mutwil

Large Language Models (LLMs), such as ChatGPT, have taken the world by storm and have passed certain forms of the Turing test. However, LLMs are not limited to human language and analyze sequential data, such as DNA, protein, and gene expression. The resulting foundation models can be repurposed to identify the complex patterns within the data, resulting in powerful, multi-purpose prediction tools able to explain cellular systems. This review outlines the different types of LLMs and showcases their recent uses in biology. Since LLMs have not yet been embraced by the plant community, we also cover how these models can be deployed for the plant kingdom.

Simon Alexander Wiese, Johannes Lehmann, Michael Beckmann

Using novel establishment-level observational data from Switzerland, we empirically examine whether the usage of key technologies of Industry 4.0 distinguishes across firms with different types of organizational culture. Based on the Technology-Organization-Environment and the Competing Values framework, we hypothesize that the developmental culture has the greatest potential to promote the usage of Industry 4.0 technologies. We also hypothesize that companies with a hierarchical or rational culture are especially likely to make use of automation technologies, such as AI and robotics. By means of descriptive statistics and multiple regression analysis, we find empirical support for our first hypothesis, while we cannot con-firm our second hypothesis. Our empirical results provide important implications for managerial decision-makers. Specifically, the link between organizational culture and the implementation of Industry 4.0 technologies is relevant for managers, as this knowledge helps them to cope with digital transformation in turbulent times and keep their businesses competitive.

Zhengle Wang, Ruifeng Wang, Minjuan Wang et al.

Pest and disease classification is a challenging issue in agriculture. The performance of deep learning models is intricately linked to training data diversity and quantity, posing issues for plant pest and disease datasets that remain underdeveloped. This study addresses these challenges by constructing a comprehensive dataset and proposing an advanced network architecture that combines Contrastive Learning and Masked Image Modeling (MIM). The dataset comprises diverse plant species and pest categories, making it one of the largest and most varied in the field. The proposed network architecture demonstrates effectiveness in addressing plant pest and disease recognition tasks, achieving notable detection accuracy. This approach offers a viable solution for rapid, efficient, and cost-effective plant pest and disease detection, thereby reducing agricultural production costs. Our code and dataset will be publicly available to advance research in plant pest and disease recognition the GitHub repository at https://github.com/WASSER2545/GPID-22

Mohit Tomar, Abhisek Tiwari, Tulika Saha et al.

In recent times, there has been an increasing awareness about imminent environmental challenges, resulting in people showing a stronger dedication to taking care of the environment and nurturing green life. The current $19.6 billion indoor gardening industry, reflective of this growing sentiment, not only signifies a monetary value but also speaks of a profound human desire to reconnect with the natural world. However, several recent surveys cast a revealing light on the fate of plants within our care, with more than half succumbing primarily due to the silent menace of improper care. Thus, the need for accessible expertise capable of assisting and guiding individuals through the intricacies of plant care has become paramount more than ever. In this work, we make the very first attempt at building a plant care assistant, which aims to assist people with plant(-ing) concerns through conversations. We propose a plant care conversational dataset named Plantational, which contains around 1K dialogues between users and plant care experts. Our end-to-end proposed approach is two-fold : (i) We first benchmark the dataset with the help of various large language models (LLMs) and visual language model (VLM) by studying the impact of instruction tuning (zero-shot and few-shot prompting) and fine-tuning techniques on this task; (ii) finally, we build EcoSage, a multi-modal plant care assisting dialogue generation framework, incorporating an adapter-based modality infusion using a gated mechanism. We performed an extensive examination (both automated and manual evaluation) of the performance exhibited by various LLMs and VLM in the generation of the domain-specific dialogue responses to underscore the respective strengths and weaknesses of these diverse models.