Global diversity and distribution of antibiotic resistance genes in human wastewater treatment systems
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Engineering topics
Publications and source records attributed to Zhang, Wen.
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Rising atmospheric CO₂ and warming spring temperatures increase vegetation growth and the terrestrial carbon sink. However, drought, heat stress, phenology, and resource limitations may stabilize or limit theses projected increases. We investigate the balance between these amplifying and stabilizing ecological factors by asking whether enhanced early-season growth leads to continued late-season growth. Using the Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) dataset, we identify three seasonal growth patterns based on early- and peak-season positive LAI anomalies: (1) amplification, where late-season LAI anomalies exceed earlier ones; (2) weak stabilization, where late-season anomalies remain similar or slightly lower; and (3) strong stabilization, where late-season anomalies become negative. Weak and strong stabilization events dominate across 67% and 26% of Northern Hemisphere ecosystems above 30°N, respectively. The absence of any trend in amplifying or stabilizing events suggests stabilizing factors seasonally offset CO₂ and temperature-induced spring greening. Terrestrial biosphere models underestimate strong stabilization and overestimate amplification events. This inconsistency arises from the models’ underestimation late-season LAI sensitivity to precipitation in water-limited regions; overlook negative legacy effects of early enhanced LAI on late-season soil moisture via evapotranspiration losses in energy-limited regions. Our findings suggest water/heat stress and resource limitations limit greening and the land carbon sink.
Volatile fatty acids (VFAs) derived from arrested anaerobic digestion (AD) can be recovered as a valuable commodity for value-added synthesis. However, separating VFAs from digestate with complex constituents and a high-water content is an energy-prohibitive process. This study developed an innovative technology to overcome this barrier by integrating deep eutectic solvents (DESs) with an omniphobic membrane into a membrane contactor for efficient extraction of anhydrous VFAs with low energy consumption. Here, a kinetic model was developed to elucidate the mechanistic differences between this novel omniphobic membrane-enabled DES extraction and the previous hydrophobic membrane-enabled NaOH extraction. Experimental results and mechanistic modeling suggested that VFA extraction by the DES is a reversible adsorption process facilitating subsequent VFA separation via anhydrous distillation. High vapor pressure of shorter-chain VFAs and low Nernst distribution coefficients of longer-chain VFAs contributed to DES-driven extraction, which could enable continuous and in-situ recovery and conversion of VFAs from AD streams.
The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper – yet more efficient – alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.
RNA self-copying using an activated adenosine substrate is challenging. Our structure studies reveal the binding motifs of adenosine substrates. The obtained structures explain the disfavorable conformations for nucleophilic reactions.
A master metadata repository (MMR) software system manages the storage and searching of metadata pertaining to data from national and international satellite sources of the Global Ocean Data Assimilation Experiment (GODAE) High Resolution Sea Surface Temperature Pilot Project [GHRSSTPP]. These sources produce a total of hundreds of data files daily, each file classified as one of more than ten data products representing global sea-surface temperatures. The MMR is a relational database wherein the metadata are divided into granulelevel records [denoted file records (FRs)] for individual satellite files and collection-level records [denoted data set descriptions (DSDs)] that describe metadata common to all the files from a specific data product. FRs and DSDs adhere to the NASA Directory Interchange Format (DIF). The FRs and DSDs are contained in separate subdatabases linked by a common field. The MMR is configured in MySQL database software with custom Practical Extraction and Reporting Language (PERL) programs to validate and ingest the metadata records. The database contents are converted into the Federal Geographic Data Committee (FGDC) standard format by use of the Extensible Markup Language (XML). A Web interface enables users to search for availability of data from all sources.