Emergence and spread patterns of antibiotic resistance genes during two different aerobic granular sludge cultivation processes

The prevalence and accumulation of antibiotic resistance genes (ARGs) were frequently detected in biological wastewater treatment processes, which might cause potential health crisis to human. In present study, the fates of ARGs during two different aerobic granular sludge (AGS) cultivation processes were investigated. The results showed that traditional AGS (T-AGS) cultivation process and enhanced AGS (E-AGS) cultivation process had significant differences (P < 0.005) in ARGs shift patterns. E-AGS process had higher average relative abundance (0.280 ± 0.079) of ARGs than T-AGS process (0.130 ± 0.041), while the intensity of ARGs enrichment during E-AGS (1.52-5.29 fold) was lower than T-AGS (3.79-75.31 fold) process. TnpA and intI1 as two different types of mobile genetic elements (MGEs) carrying ARGs, were observed to contribute significantly to the horizontal gene transfer (HGT) during T-AGS (r = 0.902, P < 0.050) and E-AGS (r = 0.823, P < 0.001) processes, respectively. Higher HGT level took place and more possible potential hosts (25 hosts) harboring ARGs were detected during E-AGS process comparing with T-AGS process (17 hosts). Meanwhile, over large AGS might increase the propagation of several antibiotic deactivation ARGs, so it was not advised. Overall, whether during T-AGS or during E-AGS process which was applied in a pilot-scale sequencing batch reactor treating municipal wastewater, the accumulation and spread of ARGs were inevitable. It should be valued that some suitable pre-treatments of seed sludge should be executed, meanwhile, advanced treatment for removing of ARGs in AGS should be conducted to maintain the relative abundances of ARGs at relatively low level.

Characterizing pandemic waves: A latent class analysis of COVID-19 spread across US counties

The spread of the COVID-19 pandemic is observed to follow the shape of "waves" (i.e., the rise and fall of population-adjusted daily new infection cases with time). Different geographic regions of the world have experienced different position and span of these waves over time. The presence and strength of these waves broadly characterize the dynamics of the pandemic spread in a given area, so their characterization is important to draw meaningful intervention and mitigation plans tailored for that area. In this paper, we propose a novel technique to represent the trend of COVID-19 spread as a sequence of a fixed-length text string defined on three symbols: R (rise), S (Steady), and F (fall). These strings, termed as trend strings, enabled us searching for specific patterns in them (such as for waves). After analyzing county-level infection data, we observe that, US counties-despite their wide variation in trend strings-can be grouped into a number of heterogeneous classes each of which might have a representative COVID spread pattern over time (in terms of presence and propensity of waves). To this end, we conduct a latent class analysis to cluster 3142 US counties into four distinct classes based on their wave characteristics for one year pandemic data (January 2020 to January 2021). We observe that counties in each class have distinct socio-demographics, location, and human mobility characteristics. In short summary, counties have differing number of waves (class 1 counties have only one wave and class 3 counties have three) and their positions also vary (class 1 had the wave later in the year whereas class 3 had waves throughout the year). We believe that this way of characterizing pandemic waves would provide better insights in understanding the complex dynamics of COVID-19 spread and its future evolution, and would, therefore, help in taking class-specific policy interventions.