Interactions between microplastics and heavy metals in leachate: Implications for landfill stabilization process.

The emission of microplastics and heavy metals in landfills has attracted widespread attention for its stabilization process. Microplastics have become carriers of heavy metals due to their adsorption properties, affecting their environmental behavior. However, the effects of landfill stabilization on the interaction between microplastics and heavy metals in leachate are ambiguous. This work explored the abundance characteristics of microplastics and heavy metals in leachate from 10 landfills in Beijing. Overall, the average abundance of microplastics was 196.3 items/L, dominated by small particle size (20-50 µm) and film microplastics. The levels of Cr and As were much higher than other heavy metals. The average abundance of microplastics and polymer types tended to decrease as the landfill stabilization proceeded, and the surface composition of microplastics became more complex. Statistical analysis revealed that the correlations between microplastics and heavy metals in the leachate of landfill stabilization presented significant parabolic characteristics, and Cr and As were more susceptible to landfill stabilization with significant positive correlation with a wide range of microplastics such as 20-30 µm. These results were intended to provide a scientific basis for the treatment and disposal of waste leachate and the synergistic prevention and control of new and traditional pollutants.

Microbiome regulation for sustainable wastewater treatment.

Sustainable wastewater treatment is essential for attaining clean water and sanitation, aligning with UN Sustainable Development Goals. Wastewater treatment plants (WWTPs) have utilized environmental microbiomes in biological treatment processes in this effort for over a century. However, the inherent complexity and redundancy of microbial communities, and emerging chemical and biological contaminants, challenge the biotechnology applications. Over the past decades, understanding and utilization of microbial energy metabolism and interaction relationships have revolutionized the biological system. In this review, we discuss how microbiome regulation strategies are being used to generate actionable performance for low-carbon pollutant removal and resource recovery in WWTPs. The engineering application cases also highlight the real feasibility and promising prospects of the microbiome regulation approaches. In conclusion, we recommend identifying environmental risks associated with chemical and biological contaminants transformation as a prerequisite. We propose the integration of gene editing and enzyme design to precisely regulate microbiomes for the synergistic control of both chemical and biological risks. Additionally, the development of integrated technologies and engineering equipment is crucial in addressing the ongoing water crisis. This review advocates for the innovation of conventional wastewater treatment biotechnology to ensure sustainable wastewater treatment.

Molecular Insights to the Structure-Interaction Relationships of Human Proton-Coupled Oligopeptide Transporters (PepTs).

Human proton-coupled oligopeptide transporters (PepTs) are important membrane influx transporters that facilitate the cellular uptake of many drugs including ACE inhibitors and antibiotics. PepTs mediate the absorption of di- and tri-peptides from dietary proteins or gastrointestinal secretions, facilitate the reabsorption of peptide-bound amino acids in the kidney, and regulate neuropeptide homeostasis in extracellular fluids. PepT1 and PepT2 have been the most intensively investigated of all PepT isoforms. Modulating the interactions of PepTs and their drug substrates could influence treatment outcomes and adverse effects with certain therapies. In recent studies, topology models and protein structures of PepTs have been developed. The aim of this review was to summarise the current knowledge regarding structure-interaction relationships (SIRs) of PepTs and their substrates as well as the potential applications of this information in therapeutic optimisation and drug development. Such information may provide insights into the efficacy of PepT drug substrates in patients, mechanisms of drug-drug/food interactions and the potential role of PepTs targeting in drug design and development strategies.

The spatiotemporal response of China's vegetation greenness to human socio-economic activities.

Climate change and human socioeconomic activities both strongly impact long-term vegetation greenness. It is more a challenge to evaluate the impacts of socioeconomic activities on vegetative greenness than climate change, partially due to the lack of appropriate quantitative indicators of the former. Here we examined the relationship between the remote sensing nighttime light (NTL) data and the Normalized Difference Vegetation Index (NDVI), which in this study are used as the proxies of socioeconomic activities and vegetation greenness, respectively. We first eliminated the vegetation greenness changes in response to climate change and calculated the human-activities-induced NDVI (HNDVI). After explored the spatiotemporal patterns of the HNDVI and NTL data across China from 1998 to 2018, we studied the relationship between the HNDVI and NTL at the grid and county levels, respectively. Our results show that the mean adjusted DN values of the NTL data (NTLI) continuously increase (+0.2938) across our study area from 1998 to 2018, whereas the HNDVI values fluctuate with a general upward trend (+0.0018). Most grids (91.2%) with increased HNDVI were found in rural areas, particularly in the Northeast forest shelterbelt and the Loess Plateau. By contrast, the HNDVI values in rapidly urbanized areas in Chinese major urban agglomerations mainly show a downward trend, especially in the Yangtze River Delta (YRD) urban agglomeration. The relationships between the NTLI and HNDVI are inconsistent over time and across space, which could be attributed to land use conditions, afforestation projects in rural areas, and greening activities in urban areas over different periods and regions.

An atlas and analysis of bovine skeletal muscle long noncoding RNAs.

Long noncoding RNAs (lncRNAs) have various biological functions and have been extensively studied in recent years. However, the identification and characterization of bovine lncRNAs in skeletal muscle has been very limited compared with that of lncRNAs in other model organisms. In this study, 7188 bovine skeletal muscle lncRNAs were identified by RNA-Seq and a stringent screening procedure in four different muscle tissues. These lncRNAs shared many characteristics with other mammalian lncRNAs, such as a shorter open reading frame and lower expression level than for mRNAs. Furthermore, the chromosomal locations and global expression patterns for these lncRNAs are also described in detail. More importantly, we detected the important interaction relationships of lncRNAs-miRNAs-mRNAs related to muscle development among 36 lncRNAs, 62 miRNAs and 12 mRNAs. Our results provide a global expression pattern of lncRNAs specific to bovine skeletal muscle and provide important targets for revealing the function of bovine muscle development by thoroughly studying the interaction relationships of lncRNAs-miRNAs-mRNAs.

The interplay between molecular modeling and chemoinformatics to characterize protein-ligand and protein-protein interactions landscapes for drug discovery.

Protein-ligand and protein-protein interactions play a fundamental role in drug discovery. A number of computational approaches have been developed to characterize and use the knowledge of such interactions that can lead to drug candidates and eventually compounds in the clinic. With the increasing structural information of protein-ligand and protein-protein complexes, the combination of molecular modeling and chemoinformatics approaches are often required for the efficient analysis of a large number of such complexes. In this chapter, we review the progress on the developments of in silico approaches that are at the interface between molecular modeling and chemoinformatics. Although the list of methods and applications is not exhaustive, we aim to cover representative cases with a special emphasis on interaction fingerprints and their applications to identify "hot spots." We also elaborate on proteochemometric modeling and the emerging concept of activity landscape, structure-based interpretation of activity cliffs and structure-protein-ligand interaction relationships. Target-ligand relationships are discussed in the context of chemogenomics data sets.

Progress in the analysis of multiple activity profile of screening data using computational approaches.

The increasing awareness that drugs may have the clinical effect through the interaction with multiple targets is encouraging the screening of investigational compounds across multiple biological endpoints. As the number and complexity of chemogenomics data sets increase, more computational approaches are being developed for the efficient analysis of structure-multiple activity relationships. In silico methods cover a wide range of applications including visual, qualitative, and quantitative approaches to describe in detail multiple ligand-protein relationships, find associations between targets and, whenever possible, to predict the bioactivity profile of small molecules. Here, we present a commentary of representative computational methods and their applications to characterize structure-multiple activity relationships and conduct the rational design of polypharmacology for the advancement of drug discovery.