Structural effects of sulfonamides on the proliferation dynamics of sulfonamide resistance genes in the sequencing batch reactors and the mechanism

Antibiotic resistance genes(ARGs)can be easily promoted by antibiotics,however,the structural effects of antibiotics on the proliferation of ARGs dynamic and the associated mechanisms remain obscure in,especially,activated sludge sequencing batch reactors.In the present study,the effects of 9 sulfonamides(SAs)with different structures on the proliferation dynamic of sulfonamide resistance genes(Suls)in the activated sludge sequencing batch reactors and the corresponding mechanisms were determined(30 days),and the results showed that the largest proliferation value(ΔA^(R))of Suls dynamic for SAs(sulfachloropyridazine)was approximately 2.9 times than that of the smallest one(sulfadiazine).The proliferation of Suls was significantly related to the structural features(minHBint6,SssNH,SHBd and SpMax2_Bhm)that represent the biological activity of SAs.To interpret the phenomenon,a mechanistic model was developed and the results indicated that the biodegradation of SAs(T_(1/2))rather than conjugative transfer frequency or mutation frequency tends to be the key process for affecting Suls proliferation.T_(1/2)was proved to be dependent on the interactions between SAs and receptors(E_(binding)),the cleavage mode(bond dissociation energy),and the site of nucleophilic assault.Besides,the metagenomic analysis showed that SAs posed significant effect on antibiotic resistome and Tnp31 played a vital role in the proliferation of Suls.Overall,our findings provide important insight into a theoretical basis for understanding the structural effects of SAs on the proliferation of ARGs in SBR systems.

Method to characterize color of biochar and its prediction with biochar yield as model property

Biochar has trigged increasing attention in the last decade due to its multiple functionalities,in which,color is not only the visual appearance,but also a useful indicator of relevant properties as well as an important tool to predict biochar’s properties.However,biochar color characterization remains challenged currently.In this work,three different feedstock-derived biochars were produced and basic color information(from color spaces RGB,HSB and CIE L^(*)a^(*)b^(*))of their scanned images was extracted.Then principal component analysis(PCA)and nonmetric multidimensional scaling analysis(NMDS)were employed on the combinations of different color indexes to cluster biochars.With the assumption that clusters from both PCA and NMDS were as consistent as possible with that of visual intuition of biochar color,we identified feedstock-independent color indexes[(R+G-B)/(R+G+B),(R+B-G)/(R+G+B),(G+B-R)/(R+G+B),L,a,b]to characterize color of bio-char,which can in microscopic perspective elucidate color differences with respect to pyrolysis temperature and feedstock type.Finally,prediction ability of color indexes with biochar yield as model property was explored and good performance was observed for both partial least squares regression(R2=0.8653)and neural network regression(R^(2)=0.9720)with the latter being more powerful.The proposed color indexes will find more applications in prediction of biochar properties and functions in future.