Recognizing the central role of microorganisms in greenhouse gas (GHG) cycling in aquaculture systems, we provide a genome- and gene-centric perspective on the metabolic potential for CO₂ and CH₄ cycling in prawn aquaculture ponds across seasons and contrasting culture practices. Using TaxVAMB, we recovered 78 high- and medium-quality metagenome-assembled genomes (MAGs), including previously underappreciated taxa such as Bathyarchaeia and Terriglobia. Metabolic profiling revealed that CO₂ and CH₄ cycling constitute a minor fraction of the pond’s metabolic potential, dominated instead by heterotrophic processes such as fermentation, oxygen metabolism, and iron reduction. The relative metabolic weight of these carbon-cycling pathways was lower than that reported for permafrost, wetland, peatland, deep-sea, and human gut microbiomes. An integrated metabolic network suggested that genetic potential for CO₂ production is primarily driven by pyruvate and acetyl-CoA oxidation, while methanogenesis and methane oxidation genes together encode the potential for internal carbon-recycling loops via canonical archaeal and bacterial pathways. Seasonal dynamics, rather than management treatment, strongly influenced functional gene abundances, with CO₂ fixation and CH4 oxidation genes increasing toward the late season. Bathyarchaeia emerged as the most versatile taxon for CO₂ cycling and methanogenesis, with stable relative abundance across seasons and treatments. This study underscores the role of seasonally evolving microbial networks in regulating carbon turnover and the potential for CO2 and CH4 emissions in prawn aquaculture ponds.
A. Bashar, A. M. Djurhuus, P. Browne et al.· bioRxiv· 0 citations
Peripheral Artery Disease (PAD) is a serious and potentially limb and life-threatening condition that demands a timely and accurate diagnosis to prevent severe complications. Traditional diagnostic methods are often slow and error prone. Although PAD localization remains relatively less explored using machine learning, the development of efficient and deployable models for real-time arterial blockage identification demands more investigation. In this paper, we propose PAD-Net, a novel end-to-end object detection framework specifically optimized for the identification and localization of arterial blockage. By integrating Light-Weight Feature Adapter (LFA) and Transformer block into You Only Look Once (YOLO) architecture, our model enhances the possibility of capturing multi-scale vascular structures often obscured in complex medical backgrounds/conditions. The proposed model was evaluated on a novel dataset of lower-extremity CT angiograms collected from the National Institute of Cardiovascular Disease, Bangladesh. Experimental results demonstrate that the model achieves a Mean Average Precision of 0.87 (mAP@0.5) and 0.423 (mAP@0.5:0.95), achieving competitive performance compared with state-of-theart YOLO and RT-DETR (Real Time Detection Transformer) models. At the same time, it maintains real-time speed to make predictions. The proposed PAD-Net gives a strong and scalable solution for AI-based PAD diagnosis. Furthermore, to validate real-world applicability, the model is deployed on a web-based Streamlit Community Cloud. The deployment of the proposed model highlights the effectiveness of reliable PAD detection and its integration into clinical workflows.
Md Raisul Islam, M. Zaman, S. Ahmmed et al.· International Conference on...· 0 citations
