The main objective of our current study was evaluating the effects of NFC supplementation and forage type on rumen microbiota and metabolism, by comparing microbial structures and composition among samples collected from cows fed AH (alfalfa-based diet), H-NFC (CS-based diet with high NFC) and L-NFC (CS-based diet with low NFC) diets. Our results show that microbial communities were structurally different but functionally similar among groups. When compared with L-HFC, NFC increased the population of Treponema, Ruminobacter, Selenomonas and Succinimonas that were negatively correlated with ruminal NH3-N, and urea nitrogen in blood, milk and urine, as well as significantly increasing the number of genes involved in amino acid biosynthesis. However, when compared to the AH group, H-NFC showed a higher abundance of bacteria relating to starch degradation and lactate production, but a lower abundance of bacteria utilizing pectin and other soluble fibers. This may lead to a slower proliferation of lignocellulose bacteria, such as Ruminococcus, Marvinbryantia and Syntrophococcus. Lower fibrolytic capacity in the rumen may reduce rumen rotation rate and may limit dry matter intake and milk yield in cows fed H-NFC. The enzyme activity assays further confirmed that cellulase and xylanase activity in AH were significantly higher than H-NFC. In addition, the lower cobalt content in Gramineae plants compared to legumes, might have led to the significantly down-regulated microbial genes involved in vitamin B12 biosynthesis in H-NFC compared to AH. A lower dietary supply with vitamin B12 may restrict the synthesis of milk lactose, one of the key factors influencing milk yield. In conclusion, supplementation of a CS-based diet with additional NFC was beneficial for nitrogen conversion by increasing the activity of amino acid biosynthesis in rumen microbiota in dairy cattle. However, lower levels of fibrolytic capacity may limit dry matter intake of cows fed H-NFC and may prevent increased milk yield.

Cryptocaryon irritans is a ciliate parasite that causes “white spot disease” in marine teleosts. The disease outbreak is influenced by hosts and a range of abiotic factors, such as temperature, salinity, and pH.

Background: Disease outbreaks, particularly cryptocaryoniasis caused by the ciliate Cryptocaryon irritans , pose significant barriers to sustainable marine fish aquaculture, undermining productivity, profitability, and biosecurity. Despite its impact, early warning tools for parasitic diseases leveraging advanced technologies remain underdeveloped. Methods: We developed a machine learning (ML)-driven early warning system for cryptocaryoniasis, integrating seven years of outbreak surveillance data ( n = 429 events from 2016 to 2023) with 17 high-resolution oceanographic predictors influencing parasite life cycles along China’s coast. Five supervised ML models: logistic regression (LR), support vector machine (SVM), random forest (RF), XGBoost (XGB), and artificial neural network (ANN), were trained using cross-validation and benchmarked in commercial open-sea cages and recirculating aquaculture systems (RAS). Results: The RF model achieved the highest sensitivity (98.6%), with RF and XGB excelling in F1 scores (0.93 and 0.938, respectively), identifying stocking density, water temperature, salinity, pH, and novel predictors such as silicate and nitrate as key risk factors. The predictive engine was deployed as an open-source web-based platform, delivering weekly, spatially resolved outbreak forecasts. Field validation across 12 open-sea cage events and weekly RAS monitoring confirmed high predictive accuracy (91.67% in sea cages; 87.5% in RAS), revealing seasonal and latitudinal disease trends. Conclusions: This study establishes a robust, scalable framework for real-time disease forecasting in marine aquaculture, adaptable to other aquatic pathogen-host species to support parasite surveillance and precision health management across diverse global aquaculture systems. While further validation with larger datasets and integration of pathogen and host data will enhance future models, this system provides a flexible foundation for advancing disease control in aquatic environments. Graphical