Latest news with #TropicalPlants
The Guardian
4 days ago
- Health
- The Guardian
Houseplant clinic: how can I restore my cheese plant's shine?
What's the problem? My Swiss cheese plant (Monstera deliciosa) has survived for years, but recently the leaves have become dull and lost their gloss. Diagnosis Swiss cheese plants are renowned for their lush, naturally glossy leaves. However, they can lose their sheen because of dust accumulation, unsuitable potting conditions, or stress, often linked to watering habits or inadequate nutrients. Prescription Repot your plant into well-draining compost enriched with organic matter and perlite or bark chips. Regularly wipe the leaves gently with a damp cloth. Ensure consistent watering, only when the top inch of soil feels dry to the touch. And ensure the plant is exposed to bright, indirect light to support healthy growth. Prevention Gently dust the leaves every few weeks, and keep your plant well fed with occasional doses of diluted liquid fertiliser during the growing season. With suitable soil, proper watering, good airflow and careful cleaning, your Swiss cheese plant should regain its natural, healthy glossiness. Got a plant dilemma? Email saturday@ with 'Houseplant clinic' in the subject line
Hans India
01-06-2025
- Science
- Hans India
Large language models can accurately predict plant gene functions: Study
Large language models (LLMs), when trained on extensive plant genomic data, can accurately predict gene functions and regulatory elements, researchers said on Sunday. By leveraging the structural parallels between genomic sequences and natural language, these AI-driven models can decode complex genetic information, offering unprecedented insights into plant biology. This advancement holds promise for accelerating crop improvement, enhancing biodiversity conservation, and bolstering food security in the face of global challenges, said the study published in Tropical Plants journal. Traditionally, plant genomics has grappled with the intricacies of vast and complex datasets, often limited by the specificity of traditional machine learning models and the scarcity of annotated data. While LLMs have revolutionised fields like natural language processing, their application in plant genomics remained nascent. The primary hurdle has been adapting these models to interpret the unique "language" of plant genomes, which differ significantly from human linguistic patterns. In this study, researchers explored the potential of LLMs in plant genomics. By drawing parallels between the structures of natural language and genomic sequences, the study highlights how LLMs can be trained to understand and predict gene functions, regulatory elements, and expression patterns in plants. The research discusses various LLM architectures, including encoder-only models like DNABERT, decoder-only models such as DNAGPT, and encoder-decoder models like ENBED. The team employed a methodology that involved pre-training LLMs on vast datasets of plant genomic sequences, followed by fine-tuning with specific annotated data to enhance accuracy. By treating DNA sequences akin to linguistic sentences, the models could identify patterns and relationships within the genetic code. These models have shown promise in tasks like promoter prediction, enhancer identification, and gene expression analysis. Notably, plant-specific models like AgroNT and FloraBERT have been developed, demonstrating improved performance in annotating plant genomes and predicting tissue-specific gene expression. However, the study also notes that most existing LLMs are trained on animal or microbial data, which often lack comprehensive genomic annotations, showcasing the versatility and robustness of LLMs in diverse plant species. In summary, this study underscores the immense potential of integrating artificial intelligence, particularly large language models, into plant genomics research. The study was conducted by Meiling Zou, Haiwei Chai and Zhiqiang Xia's team from Hainan University.
