Biology subjects

Lau, K. J. X.

Publications and source records attributed to Lau, K. J. X..

5 recordsLinked to original sources

bioRxiv2026

Multi-agentic system for primer design in qPCR and LAMP diagnostics tests

Primer design is a fundamental component of molecular diagnostics in both quantitative polymerase chain reaction qPCR and loop-mediated isothermal amplification LAMP assays. However, assay design is often performed manually as nucleotide databases, sequence alignment tools and resources are found at different places on the Internet. In this study, an AI-orchestrated bioinformatics workflow was developed to automate the end-to-end qPCR and LAMP primers and probes. The workflow was implemented using LangGraph, LangChain and Biopython, where a series of specialized agents were coordinated to execute sequential bioinformatics tasks with minimal human intervention. Target sequences were then retrieved based on the user request from the National Center for Biotechnology Information nucleotide database and the requested sequence records were then subjected to multiple sequence alignment for the identification of conserved genomic regions. The multi-agentic primer design system can be used for assay development for applications in infectious disease diagnostics, outbreak surveillance and environmental monitoring. This study also demonstrates how multi-agentic systems can be combined with established bioinformatics methods to automate qPCR and LAMP assay design.

molecular biology↗

bioRxiv2026

Syntrophic microbiomes associated with methane-suppressive irrigation in rice

Rice, a staple crop of nearly half of the world population, is grown predominantly in flooded paddies which are one of the largest contributors to methane emissions. An effective approach is to minimise the anaerobic flooded conditions that favour the growth of methanogenic archaea. Empirical measurements showed that controlled irrigation regime reduces methane emissions by 70% to 90%. The soil microbiomes of both flood and drip irrigated soil were characterised using whole-genome shotgun metagenomics. Controlled irrigation was shown to suppress methanogens and lower methane emissions. While emissions are correlated with mcrA gene abundance, empty flooded fields exhibited relatively high mcrA levels above baseline despite undetectable methane emissions. Rice cultivar genotype had no significant effect on the soil microbiomes. Co-occurrence network analysis indicates that soil microbial communities stratify according to their oxygen preferences along a gradient. Methanogens were increased in flooded paddies, and methane production attributed to the microorganisms involved in the anaerobic decay of organic matter. Controlled irrigation altered the microbiome by raising the soil redox potential by enhancing aeration and promoting ammonia oxidation and nitrification pathways. IMPORTANCEThe temporal dynamics of microbial communities in drip-irrigated rice fields remain poorly characterized to-date. Empirical measurements demonstrate that controlled drip irrigation effectively suppresses methanogens and lowers methane emissions by up to 90%. Statistical analysis further revealed a moderate correlation between methane emissions and the mcrA gene with R = 0.6 and p-value = 2.9e-05. The correlation plot showed that the outliers corresponded to samples from empty flooded fields, where high mcrA gene abundance was observed despite low methane emissions. Methane produced in the soil is likely released into the atmosphere via transport through the aerenchyma of rice plants. Controlled irrigation is shown to be climate friendly as it reduces methane emissions by improving soil aeration and increasing the soil redox potential.

microbiology↗

bioRxiv2026

StomaQuant: Deep Learning-Based Quantification for Stomatal Trait Assessment

Stomata are microscopic pores that play a vital role in transpiration and gaseous exchange from leaf surfaces in plants. The stomatal density and size directly influence photosynthesis and hydrodynamics capacity. Conventional approaches for counting and determining stomatal density is labour-intensive and lack scalability. Although there are several AI-based stomata finder tools that were published in the last decade, existing models were trained on model plants like wheat, barley and Arabidopsis. Stomata in such model plants are generally elliptical, but applying a universal model to all plant species is not feasible due to their diverse morphological characteristics. Previous studies have suggested using the stomatal index to quantify the ratio between epidermal cells and total stomatal count. However, this approach can be difficult to apply consistently, as epidermal cell shape and size vary across plant species. Instead, we propose measuring stomatal density based on the number of stomata per total imaged pixel area in the captured images. In this study, a comparison between YOLOv12 and RF-DETR models were made for real-time stomata detection in normal and difficult-to-image and out-of-focus occluded images. The in-house training dataset consisted of images of 300 rice,100 barley and 50 sugarcane leaves that were captured against a dark background. YOLOv12 outperformed RF-DETR with higher mAP50:95 score. The models were trained with image augmentation for 300 epochs and YOLOv12 achieved a peak mean average precision of 98.5% and exceled at detecting stomata across abaxial and adaxial surfaces of leaves of both monocot and dicot plants. StomaQuant has also been shown to be effective for both epidermal peel and ethanol decolorised samples. Thus, StomaQuant can be used to effectively and efficiently estimate the stomatal density and size in a wide range of host plant species.

bioinformatics↗

bioRxiv2025

An Integrated Decarbonising Approach to Mitigate Methane Emissions and Enhance Productivity in Rice Cultivation

Flooded rice paddies contribute approximately 12% of global anthropogenic methane emissions, accounting for 1.5% of the total warming effect from all greenhouse gases. With the rising global demand for rice due to population growth, the need for effective methane mitigation strategies in rice cultivation is increasingly critical. This study investigates the combined impact of irrigation methods, fertiliser combinations, and varietal differences on productivity, water use and methane emissions in rice. Field trials were conducted across five land parcels covering 8 Ha in the Sathyamangalam region of Tamil Nadu, India, from October 2024 to January 2025. Results revealed that drip irrigation significantly reduced seasonal methane emissions by up to 68% (128 kg/ha/season) compared to continuous flood irrigation (402.32 kg/ha/season) offering a sustainable solution to address climate change. Furthermore, our modified package of practices coupled with tailored fertiliser combination, led to a 28% reduction in methane emissions (222 kg/ha/season) relative to continuous flooding methods used by the farmers in the region (309 kg/ha/season). Methane emission differences of up to 23.8% were also evident across rice varieties ADT-45 (229.7 kg/ha/season and BPT-5204 (301.7 kg/ha/season) for. Although flood irrigation yielded a 5-6% higher grain productivity than drip irrigation, the TLL fertiliser package under flood irrigation still provided distinct benefits with a yield increase of 5.4%. Notably, water usage under drip irrigation was 42.5% lower on average across the five locations, with minimal impact on yield, resulting in a marked improvement in water use efficiency (0.62 kg.m-3 under drip vs. 0.39 kg.m-3 under flood irrigation). Our findings highlight the value of integrating modern irrigation techniques, optimizing fertiliser management, and appropriate varietal selection with higher environmental sustainability and improved farm productivity to mitigate climate impact in rice cultivation.

plant biology↗

bioRxiv2024

Drip irrigation suppresses methane emissions by reshaping the rhizosphere microbiomes in rice

The rhizosphere microbiomes of rice plants under conventional flood irrigation consist of highly complex consortia of microorganisms and in particular methanogens purportedly associated with methane emissions therein. Controlled irrigation has been proposed as a cultivation method of choice over continuous flooding to reduce water and fertilizer usage in an aerobic environment. However, a systemic understanding of the assembly and function of microbiota in the rhizosphere under drip and flood irrigation remains unclear. Using empirical analyses, we report a significant reduction in methane emissions in controlled irrigation compared to the flooded environment. Genotypic or varietal differences did not influence such methane emissions under conventional flooded cultivation of rice. Using metagenomic sequencing and computational analyses, we provide a deeper understanding of how drip irrigation or continuous flooding affect the root-associated microbiomes in rice. Rhizosphere soil from two different rice varieties, Huanghuazhan and Temasek rice, grown under drip or flood conditions in a greenhouse was collected over 2 months post-transplantation for metagenomic analysis. Our results reveal that drip irrigation favours microbes involved in the nitrifying-denitrifying processes while continuous flooding enriches for methanotrophs and methanogenic archaea. Syntrophic microbiomes associated with methanogenesis were significantly reduced in drip irrigation. Several keystone taxa were evident in the co-occurrence network model related to methanogenic, methanotrophic, nitrifying, sulphur-oxidising and sulphur-reducing activities. Lastly, oxygen availability and redox potential were identified as key drivers that reshape rhizosphere microbiota and the associated metabolic functional differences observed between the two irrigation regimes leading up to the microbial mitigation of climate impact. IMPORTANCEUnlike previous studies in alternate wet-dry irrigation systems, this study characterised the rice microbiomes in a controlled drip irrigation settings where water levels were maintained at low levels and soil remained unflooded throughout the entire season in a greenhouse. A reduction of more than 90% in methane emissions was observed with drip irrigation compared to flood irrigation. Significant correlation was found between levels of methane emitted and mcrA gene copies detected with a Pearson correlation coefficient R of 0.77 and p-value of 2.3e-10. Methanogens are highly abundant in continuous flooded rice soil and are significantly reduced in drip-irrigated soil. Metagenomic profiling indicates that the shifts in microbial diversity under drip irrigation favour nitrifying microorganisms and is likely influenced by increased oxygen availability due to higher soil redox potential.

microbiology↗