UV and chemical aging alter the adsorption behavior of microplastics for tetracycline

As two pollutants with similar transport pathways, microplastics (MPs) and antibiotics (ATs) inevitably co-exist in water environments, and their interaction has become a topic of intense research interest for scholars over the past few years. This paper comprehensively and systematically reviews the current interaction between MPs and ATs, in particular, the role played by biofilm developed MPs (microplastic biofilm). A summary of the formation process of microplastic biofilm and its unique microbial community structure is presented in the paper. The formation of microplastic biofilm can enhance the adsorption mechanisms of ATs on primary MPs. Moreover, microplastic biofilm system is a diverse and vast reservoir of genetic material, and this paper reviews the mechanisms by which microplastics with biofilm drive the production of antibiotic resistance genes (ARGs) and the processes that selectively enrich for more ARGs. Meanwhile, the enrichment of ARGs may lead to the development of microbial resistance and the gradual loss of the antimicrobial effect of ATs. The transfer pathways of ARGs affected by microplastic biofilm are outlined, and ARGs dependent transfer of antibiotic resistance bacteria (ARB) is mainly through horizontal gene transfer (HGT). Furthermore, the ecological implications of the interaction between microplastic biofilm and ATs and perspectives for future research are reviewed. This review contributes to a new insight into the aquatic ecological environmental risks and the fate of contaminants (MPs, ATs), and is of great significance for controlling the combined pollution of these two pollutants. 

Interaction between microplastic biofilm formation and antibiotics: Effect of microplastic biofilm and its driving mechanisms on antibiotic resistance gene.

The studies on microplastics are significant in the world. According to the literature, microplastics have greatly specific surface areas, indicating high adsorption capacities for highly toxic pollutants in aquatic and soil environments, and these could be used as adsorbents. The influencing factors of microplastic adsorption, classification of microplastics, and adsorption mechanisms using microplastics for adsorbing organic, inorganic, and mixed pollutants are summarized in the paper. Furthermore, the influence of pH, temperature, functional groups, aging, and other factors related to the adsorption performances of plastics are discussed in detail. We found that microplastics have greater advantages in efficient adsorption performance and cost-effectiveness. In this paper, the adsorptions of pollutants by microplastics and their performance is proposed, which provides significant guidance for future research in this field.

https://www.mdpi.com/2079-4991/12/13/2256/pdf?version=1656585002

Adsorption of Different Pollutants by Using Microplastic with Different Influencing Factors and Mechanisms in Wastewater: A Review

Press perturbations of microplastics and antibiotics on freshwater micro-ecosystem: Case study for the ecological restoration of submerged plants.

Adsorption of antibiotics on different microplastics (MPs): Behavior and mechanism

Distinct chemical adsorption behaviors of sulfanilamide as a model antibiotic onto weathered microplastics in complex systems

This paper looks at regional water security in eastern China in the context of global climate change. The response of runoff to climate change in the Qinhuai River Basin, a typical river in eastern China, was quantitatively investigated by using the Soil and Water Assessment Tool (SWAT) model and the ensemble projection of multiple general circulation models (GCMs) under three different shared socioeconomic pathways (SSPs) emission scenarios. The results show that the calibrated SWAT model is applicable to the Qinhuai River Basin and can accurately characterize the runoff process at daily and monthly scales with the Nash–Sutcliffe efficiency coefficients (NSE), correlation coefficients (R), and the Kling–Gupta efficiency (KGE) in calibration and validation periods being above 0.75 and relative errors (RE) are ±3.5%. In comparison to the baseline of 1980–2015, the mean annual precipitation in the future period (2025–2060) under the three emission scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5 will probably increase by 5.64%, 2.60%, and 6.68% respectively. Correspondingly, the multiple-year average of daily maximum and minimum air temperatures are projected to rise by 1.6–2.1 °C and 1.4–2.0 °C, respectively, in 2025–2060. As a result of climate change, the average annual runoff will increase by 16.24%, 8.84%, and 17.96%, respectively, in the period of 2025–2060 under the three SSPs scenarios. The increase in runoff in the future will provide sufficient water supply to support socioeconomic development. However, increases in both rainfall and runoff also imply an increased risk of flooding due to climate change. Therefore, the impact of climate change on flooding in the Qinhuai River Basin should be fully considered in the planning of flood control and the basin’s development. 

/pdf/investigating-impacts-of-climate-change-on-runoff-from-the-1segkd9l.pdf

Investigating Impacts of Climate Change on Runoff from the Qinhuai River by Using the SWAT Model and CMIP6 Scenarios

Purpose: To derive a multinomial probability function and quantitative measures of the data and epistemic uncertainty as direct output of a 3D U-Net segmentation network. Approach: A set of T1 brain MRI images were downloaded from the Connectome Project and segmented using FMRIB’s FAST algorithm to be used as ground truth. A 3D U-Net neural network was trained with sample sizes of 200, 500, and 898 T1 brain images using a loss function defined as the negative logarithm of the likelihood based on a derivation of the definition of the multinomial probability function. From this definition, the epistemic and aleatoric uncertainty equations were derived and used to quantify maps of the uncertainty along with tissue segmentations. Results: Maps of the tissue segmentation along with the epistemic and aleatoric uncertainty, per voxel, are presented. The uncertainty decreased based on the increasing number of training data used to train the neural network. The neural network trained with 898 volumes resulted in uncertainty maps that were high primarily in the tissue boundary regions. The epistemic and aleatoric uncertainty were averaged over all test data (connectome and tumor separately), and the epistemic uncertainty showed a decreasing trend, as expected, with increasing numbers of data used to train the model. The aleatoric uncertainty showed a similar trend which was also expected as the aleatoric uncertainty is not expected to be as dependent on the number of training data. Conclusion: The derived uncertainty equations from a multinomial probability distribution were able to quantify the aleatoric and epistemic uncertainty per voxel and are applicable for all two-dimensional and three-dimensional neural networks. 

Direct quantification of epistemic and aleatoric uncertainty in 3D U-net segmentation

Let $R$ be a finite commutative unitary ring. An idempotent in $R$ is an element $e\in R$ with $e^2=e$. Let $\Psi$ be a subgroup of the group ${\rm Aut}(R)$ of all automorphisms of $R$. The $\Psi-$weighted Erd\H{o}s-Burgess constant ${\rm I}_{\Psi}(R)$ is defined as the smallest positive integer $\ell$ such that every sequence over $R$ of length at least $\ell$ must contain a nonempty subsequence $a_1,\ldots, a_{r}$ such that $\prod\limits_{i=1}^r \psi_i(a_i)$ is one idempotent of $R$ where $\psi_1,\ldots,\psi_r\in \Psi$. In this paper, for the finite quotient ring of a Dedekind domain $R$, a connection is established between the $\Psi-$weighted-Erd\H{o}s-Burgess constant of $R$ and the $\Psi-$weighted Davenport constant of its group of units by all the prime ideals of $R$. 

/pdf/weighted-erd-h-o-s-burgess-and-davenport-constant-in-28l37mqr.pdf

Weighted Erd\H{o}s-Burgess and Davenport constant in commutative rings

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Distinct chemical adsorption behaviors of sulfanilamide as a model antibiotic onto weathered microplastics in complex systems

Investigating Impacts of Climate Change on Runoff from the Qinhuai River by Using the SWAT Model and CMIP6 Scenarios

Direct quantification of epistemic and aleatoric uncertainty in 3D U-net segmentation

Weighted Erd\H{o}s-Burgess and Davenport constant in commutative rings