Biology subjects

Nguyen, N. P.

Publications and source records attributed to Nguyen, N. P..

3 recordsLinked to original sources

bioRxiv2026

Macroalgal fucoidan can activate the biological carbon pump

Macroalgae secrete complex carbohydrate polymers, their extracellular matrix, as protection against microbial degradation. By resisting breakdown, these carbohydrates can contribute to marine carbon sequestration, though mechanisms, extent, and timescales remain unknown. Using ship-based sampling and experiments, we found that brown macroalgae release 1.7-4.2% of carbon fixation as fucoidan, equivalent to 0.32-0.88 mg fucoidan per gram of dry seaweed tissue per day. A Bayesian model trained on our empirical data, coupled with Monte Carlo simulations suggests an annual global release of 13-37 megatons fucoidan carbon. Moreover, degradation resistance combined with surface-activity enabled fucoidan to act as glue that cross-linked allochthonous organic carbon including microbes and proteins into marine snow. Notably, substantial fucoidan exudation was universally conserved across all tested species and regions. Thus, any brown macroalgal species can be used e.g. via aquafarming to enhance the formation of marine snow.

ecology↗

bioRxiv2024

A novel GLYCEROPHOSPHODIESTER PHOSPHODIESTERASE 13 is involved in the Phosphate starvation-induced lipid remodeling in rice

Phosphorus (P) is one of the most vital macronutrient determinants in plant development and productivity. However, the bioavailability of inorganic phosphate (Pi), the only form that plants can assimilate, is limited in the soil, thus significantly affecting plant development. Plants have adopted various specialized strategies to modify their morphological, physiological, and biochemical properties for better adaptation to Pi deficiency conditions. GLYCEROPHOSPHODIESTER PHOSPHODIESTERASES (GDPDs), generally known as phospholipid remodeling proteins, have been suggested to play essential roles in maintaining phosphate homeostasis. The previous genome-wide association studies (GWAS) in a Vietnamese rice collection led to the discovery of a robust QTL named qRST9.14 associating with the phosphate adaption in rice, in which OsGDPD13 is located within this locus. Interestingly, we discovered an absence of OsGDPD13 from the Indica reference genome. A subcellular localization study showed that the GDPD13 protein localizes to the plasma membrane, cytoplasmic speckles, and plasmodesmata. The osgdpd13 knockout mutant was generated in the Japonica cv Kitaake background to characterize its function. Phenotypic analysis indicated that mutation of OsGDPD13 significantly lowered crown root number and reduced the plant height under Pi deficient condition. Moreover, osgdpd13 lines reduce the capability to degrade total phospholipids compared to the wild type under Pi starvation conditions. This finding suggests the involvement of the OsGDPD13 gene in rice growth and the Phosphate starvation-induced lipid remodeling under P deficiency. This work will provide valuable information for developing crop plants with higher Phosphate use efficiency.

plant biology↗

bioRxiv2019

DRPnet - Automated Particle Picking In Cryo-Electron Micrographs Using Deep Regression

AO_SCPLOWBSTRACTC_SCPLOWSelection of individual protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based method to automatically detect particle centers from cryoEM micrographs. This is a challenging task because of the low signal-to-noise ratio of cryoEM micrographs and the size, shape, and grayscale-level variations in particles. We propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined (or classified) to reduce false particle detections by the second CNN. This approach, entitled Deep Regression Picker Network or \"DRPnet\", is simple but very effective in recognizing different grayscale patterns corresponding to 2D views of 3D particles. Our experiments showed that DRPnets first CNN pretrained with one dataset can be used to detect particles from a different datasets without retraining. The performance of this network can be further improved by re-training the network using specific particle datasets. The second network, a classification convolutional neural network, is used to refine detection results by identifying false detections. The proposed fully automated \"deep regression\" system, DRPnet, pretrained with TRPV1 (EMPIAR-10005) [1], and tested on {beta}-galactosidase (EMPIAR-10017) [2] and {beta}-galactosidase (EMPIAR-10061) [3], was then compared to RELIONs interactive particle picking. Preliminary experiments resulted in comparable or better particle picking performance with drastically reduced user interactions and improved processing time.

molecular biology↗