Skip to content
#protein folding Open access

Integrative omics and field studies reveal the efficacy of PlantGuard, a next-generation biostimulant

Sep 2026 · Frontiers in Plant Science · 0 citations · 25 references
Plant Growth Enhancement Techniques

Abstract

This study presents the development and evaluation of a novel, natural plant oil-based biostimulant called PlantGuard, formulated from a blend of essential oil components and emulsifiers designed to enhance crop performance under field and controlled-environment hydroponics conditions. The biostimulant was applied to horticultural vegetables and crops such as tomato ( Solanum lycopersicum ) and shallot (Allium cepa var. aggregatum) , as well as rice ( Oryza sativa ) across multiple trial sites in Indonesia over two growing seasons. Treated plots demonstrated statistically significant improvements in flowering synchrony, fruit set, biomass accumulation, and overall yield, with increases ranging from 15% to 32% compared to untreated controls. We also studied the effect of PlantGuard on tomatoes and kale ( Brassica oleracea var. sabellica) , grown under hydroponic conditions and recorded an almost 3-fold increase in total yield. To characterize biological responses associated with PlantGuard, a comprehensive molecular analysis was conducted comprising RNA sequencing, quantitative proteomics, and untargeted metabolomics. RNA-sequencing profiling revealed differential expression of genes associated with photosynthesis, nutrient transport, hormonal signaling, including jasmonic acid- and auxin-related responses, and defense processes. Proteomic analyses confirmed the enhanced expression of stress-related proteins, antioxidant enzymes, and metabolic regulators. Metabolomic fingerprinting identified elevated levels of phenylpropanoids, flavonoids, and lipid-derived signaling molecules associated with abiotic stress mitigation and plant growth promotion. Collectively, these multi-omics findings validate the functional activity of PlantGuard at the molecular level and support its application as a sustainable input for enhancing crop and vegetable productivity by increasing plant growth and resilience in diverse agricultural systems.

Read PDF

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.