Activity

less then  0.0001), but only in HP where it peaked at day 56. In contrast, no difference in BW gain (BWG) (P = 0.78) between HP and LP was observed. In conclusion, this study shows that behavioural measurements monitored with sensors were affected even at low infection levels not affecting BWG. These combined results demonstrate the potential of automated behavioural recordings as a tool for detection of subclinical parasitism.The ongoing coronavirus disease 19s pandemic has yet again demonstrated the importance of the human-animal interface in the emergence of zoonotic diseases, and in particular the role of wildlife and livestock species as potential hosts and virus reservoirs. As most diseases emerge out of the human-animal interface, a better understanding of the specific drivers and mechanisms involved is crucial to prepare for future disease outbreaks. Interactions between wildlife and livestock systems contribute to the emergence of zoonotic diseases, especially in the face of globalization, habitat fragmentation and destruction and climate change. As several groups of viruses and bacteria are more likely to emerge, we focus on pathogenic viruses of the Bunyavirales, Coronaviridae, Flaviviridae, Orthomyxoviridae, and Paramyxoviridae, as well as bacterial species including Mycobacterium sp., Brucella sp., Bacillus anthracis and Coxiella burnetii. Noteworthy, it was difficult to predict the drivers of disease emergence in the past, even for well-known pathogens. Thus, an improved surveillance in hotspot areas and the availability of fast, effective, and adaptable control measures would definitely contribute to preparedness. We here propose strategies to mitigate the risk of emergence and/or re-emergence of prioritized pathogens to prevent future epidemics.Extending laying cycle is a tendency in hen breeding, but egg quality declines as laying hens age. The present study was conducted to investigate the interactive effects of vitamins A and K3 on laying performance, egg and tibia quality, and antioxidative status of aged Roman Pink laying hens. In a 3 × 3 factorial arrangement, 1 080 87-week-old laying hens were allocated to nine groups with eight replicates in each group. Deficient, adequate and excess vitamins A (0, 7 000 and 14 000 IU/kg) and K3 (0, 2.0 and 4.0 mg/kg) were supplemented into a basal diet with 1 320 IU/kg of vitamin A and 0.5 mg/kg of vitamin K3. check details After 2 weeks of adaption to basal diet, hens were fed corresponding diets for 8 weeks. Vitamins A and K3 did not significantly affect the laying performance. However, they showed interactive effects on yolk ratio at week 93 as well as tibia weight and diameter (P less then 0.05), and hens fed deficient vitamins A and K3 had the highest yolk ratio and tibia weight, but the lowest tibia diameter. Comand K3 had the highest CAT mRNA levels. In conclusion, dietary addition of vitamins A and K3 improved the eggshell quality and yolk color as well as antioxidative status in eggshell gland of aged laying hens. Adequate vitamins A and K3 showed beneficial effects and excess levels did not exhibit superior effects.The optimal operation and functional stability of a wastewater treatment plant (WWTP) strongly depend on the properties of its microbial community. However, a knowledge gap remains regarding the seasonal dynamics of microbial community properties, especially phylogenetic group based assembly and co-occurrence patterns. Accordingly, in this study, influent and activated sludge (AS) samples were weekly collected from 2 full-scale WWTPs for one year (89 influent and 103 AS samples in total) and examined by high-throughput Illumina-MiSeq sequencing. The results suggested that the microbial community diversity and composition in the influent fluctuated substantially with season, while those in the AS had a relatively more stable pattern throughout the year. The phylogenetic group based assembly mechanisms of AS community were identified by using “Infer Community Assembly Mechanisms by Phylogenetic-bin-based null model (iCAMP)”. The results showed that drift accounted for the largest proportion (52.8%), while homogeneous selection (18.2%) was the most important deterministic process. Deterministic processes dominated in 47 microbial groups (bins), which were also found (~40%) in the AS core taxa dataset. Moreover, the results suggested that Nitrospira were more susceptible to stochastic processes in winter, which may provide a possible explanation for nitrification failure in winter. Network analysis results suggested that the network structure of the AS community could be more stable in summer and autumn. In addition, there were no identical keystone taxa found in different networks (constructed from different plants, sources, and seasons), which supported the context dependency theory. The results of this study deepened our understanding of the microbial ecology in AS systems and provided a foundation for further studies on the community regulation strategy of WWTPs.The increasing amount of data on biofilter treatment performance over the past decade has made it possible to use data-driven approaches to explore the relationships between biofilter performance and a range of input variables. The knowledge gap lies in lack of models to predict the biofilter performance considering both design and operational variables, especially for heavy metals. In this study, we tested three machine learning (ML) approaches, namely multilinear regression (MLR), artificial neural network (NN), and random forest (RF), to predict biofilter outflow concentrations of heavy metals (Cd, Cr, Cu, Fe, Ni, Pb and Zn) using a range of design and operational factors as input variables. The results show that RF performed relatively better than other two models, with median Nash-Sutcliffe Efficiency (NSE) values of 0.995, 0.317, 0.762, 0.636, 0.726, 0.896 and 0.656 for Cd, Cr, Cu, Fe, Ni, Pb and Zn, respectively during model training. However, all the models were less accurate during model validation, ontamination exists. Explorative analysis also demonstrated how the key operational and design variables can be optimised to further reduce the health risks that can be fit for drinking purposes (i.e., RQ value less then 1).