The importance of data assimilation components for initial conditions and subsequent error growth

Despite a specific data assimilation method,data assimilation(DA)in general can be decomposed into components of the prior information,observation forward operator that is given by the observation type,observation error covariances,and background error covariances.In a classic Lorenz model,the influences of the DA components on the initial conditions(ICs)and subsequent forecasts are systematically investigated,which could provide a theoretical basis for the design of DA for different scales of interests.The forecast errors undergo three typical stages:a slow growth stage from 0 h to 5 d,a fast growth stage from 5 d to around 15 d with significantly different error growth rates for ensemble and deterministic forecasts,and a saturation stage after 15 d.Assimilation strategies that provide more accurate ICs can improve the predictability.Cycling assimilation is superior to offline assimilation,and a flow-dependent background error covariance matrix(Pf)provides better analyses than a static background error covariance matrix(B)for instantaneous observations and frequent time-averaged observations;but the opposite is true for infrequent time-averaged observations,since cycling simulation cannot construct informative priors when the model lacks predictive skills and the flow-dependent Pf cannot effectively extract information from low-informative observations as the static B.Instantaneous observations contain more information than time-averaged observations,thus the former is preferred,especially for infrequent observing systems.Moreover,ensemble forecasts have advantages over deterministic forecasts,and the advantages are enlarged with less informative observations and lower predictive-skill model priors.

Impact of different organic matters on the occurrence of antibiotic resistance genes in activated sludge

The occurrence of antibiotic resistance genes(ARGs)in various environments has drawn worldwide attention due to their potential risks.Previous studies have reported that a variety of substances can enhance the occurrence and dissemination of ARGs.However,few studies have compared the response of ARGs under the stress of different organicmatters in biologicalwastewater treatment systems.In this study,seven organic pollutantswere added into wastewater treatment bioreactors to investigate their impacts on the ARG occurrence in activated sludge.Based on high-throughput sequencing,it was found that the microbial communities and ARG patterns were significantly changed in the activated sludge exposed to these organic pollutants.Compared with the non-antibiotic refractory organic matters,antibiotics not only increased the abundance of ARGs but also significantly changed the ARG compositions.The increase of Gram-negative bacteria(e.g.,Archangium,Prosthecobacter and Dokdonella)carrying ARGs could be the main cause of ARG proliferation.In addition,significant co-occurrence relationships between ARGs and mobile genetic elements were also observed in the sludge samples,which may also affect the ARG diversity and abundance during the organic matter treatment in the bioreactors.Overall,these findings provide new information for better understanding the ARG occurrence and dissemination caused by organic pollutants in wastewater treatment systems.

Bioimaging of Dissolvable Microneedle Arrays: Challenges and Opportunities

The emergence of microneedle arrays(MNAs)as a novel,simple,and minimally invasive administration approach largely addresses the challenges of traditional drug delivery.In particular,the dissolvable MNAs act as a promising,multifarious,and well-controlled platform for micro-nanotransport in medical research and cosmetic formulation applications.The effective delivery mostly depends on the behavior of the MNAs penetrated into the body,and accurate assessment is urgently needed.Advanced imaging technologies offer high sensitivity and resolution visualization of cross-scale,multidimensional,and multiparameter information,which can be used as an important aid for the evaluation and development of new MNAs.The combination of MNA technology and imaging can generate considerable new knowledge in a cost-effective manner with regards to the pharmacokinetics and bioavailability of active substances for the treatment of various diseases.In addition,noninvasive imaging techniques allow rapid,receptive assessment of transdermal penetration and drug deposition in various tissues,which could greatly facilitate the translation of experimental MNAs into clinical application.Relying on the recent promising development of bioimaging,this review is aimed at summarizing the current status,challenges,and future perspective on in vivo assessment of MNA drug delivery by various imaging technologies.