Reservoirs play a vital role in the control and management of surface water resources. However, the long water residence time in the reservoir potentially increases the storage and accumulation of antibiotic resistant genes (ARGs). The full profiles and potential health risks of antibiotic resistomes in reservoirs are largely unknown. In this study, we investigated the antibiotic resistomes of water and sediment during different seasons in the Danjiangkou Reservoir, which is one of the largest reservoirs in China, using a metagenomic sequencing approach. A total of 436 ARG subtypes belonging to 20 ARG types were detected from 24 water and 18 sediment samples, with an average abundance of 0.138 copies/cell. The overall ARG abundance in the sediment was higher than that in the water, and bacitracin and vancomycin resistance genes were the predominant ARG types in the water and sediment, respectively. The overall ARG abundance in the dry season was higher than that in the wet season, and a significant difference in ARG subtype compositions was observed in water, but not in the sediment, between the different seasons. The potential horizontal gene transfer frequency in the water was higher than that in the sediment, and the ARGs in water mainly came from the sediment upstream of the reservoir. The metagenomic assembly identified 14 contigs as ARG-carrying pathogens including Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa, and 3 of 14 carried virulence factors. Overall, the potential public health risks posed by resistomes in the water of the Danjiangkou Reservoir were higher in the dry season than in the wet season. Based on these results, strategies including sediment control and pathogen monitoring are suggested for water safety management in drinking water reservoirs.
Reservoirs play a vital role in the control and management of surface water resources. However, the long water residence time in the reservoir potentially increases the storage and accumulation of antibiotic resistant genes (ARGs). The full profiles and potential health risks of antibiotic resistomes in reservoirs are largely unknown. In this study, we investigated the antibiotic resistomes of water and sediment during different seasons in the Danjiangkou Reservoir, which is one of the largest reservoirs in China, using a metagenomic sequencing approach. A total of 436 ARG subtypes belonging to 20 ARG types were detected from 24 water and 18 sediment samples, with an average abundance of 0.138 copies/cell. The overall ARG abundance in the sediment was higher than that in the water, and bacitracin and vancomycin resistance genes were the predominant ARG types in the water and sediment, respectively. The overall ARG abundance in the dry season was higher than that in the wet season, and a significant difference in ARG subtype compositions was observed in water, but not in the sediment, between the different seasons. The potential horizontal gene transfer frequency in the water was higher than that in the sediment, and the ARGs in water mainly came from the sediment upstream of the reservoir. The metagenomic assembly identified 14 contigs as ARG-carrying pathogens including Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa, and 3 of 14 carried virulence factors. Overall, the potential public health risks posed by resistomes in the water of the Danjiangkou Reservoir were higher in the dry season than in the wet season. Based on these results, strategies including sediment control and pathogen monitoring are suggested for water safety management in drinking water reservoirs.
Polarization-based passive millimeter-wave imaging has been applied in several applications, including material clustering, pattern recognition, and target detection. We present here a general formulation of a metal target detection method called dual linear polarization discriminator (DLPD), utilizing passive millimeter-wave polarimetric imagery. Several potential discriminators are defined, and linear polarization difference ratio (LPDR) is selected and proposed to be a new feature discriminator that is sensitive to material composition and able to reduce ambient radiation effects when detecting target with different material and shape. Furthermore, the detection criterion is verified utilizing the threshold values determined by a statistical analysis of LPDR. Outdoor experiments demonstrate that the proposed detection method is highly effective for detecting a metal target in a complex background.
Average bioequivalence tests are used in clinical trials to determine whether a generic drug has the same effect as an original drug in the population. For highly variable drugs whose intra-subject variances of direct drug effects are high, extra criteria are needed in bioequivalencestudies. Currently used average bioequivalence tests for highly variable drugs recommended by the European Medicines Agency and the US Food and Drug Administration use sample estimators in the null hypotheses of interest. They cannot control the empirical type I error rate, so the consumer's risk is higher than the predetermined level. In this paper, we propose two new statistically sound methods that can control the empirical type I error rate without involving any sample estimators in the null hypotheses. In the proposed methods, we consider the average level of direct drug effects and the intra-subject variance of the direct drug effects. The first proposed method tests the latter parameter first to determine whether a product should be regarded as a highly variable drug, and then tests the former using corresponding bioequivalence limits. The second proposed method tests these two parameters simultaneously to capture the bioequivalence region. Extensive simulations are done to compare these methods. The simulation results show that the proposed methods have good performance on controlling the empirical type I error rate. The proposed methods are useful for pharmaceutical manufacturers and regulators.
Reverse osmosis (RO) technology is promising in the sustainable production of fresh water. However, expansion of RO use has been hindered by membrane fouling, mainly inorganic fouling known as scaling. Although membrane mineral scaling by chemical means have been investigated extensively, mineral scaling triggered by microbial activity has been largely neglected. In this study, the simultaneous biomineralization of CaCO3 and CaSO4 in the presence of three different microbial communities from fresh water, wastewater, and seawater was investigated. In the presence of either 13 or 79 mM of Ca2+ and SO42- in the media, the fresh water microbial community produced calcite/vaterite and vaterite/gypsum, respectively; the wastewater community produced vaterite and vaterite/gypsum, respectively; and the seawater community produced aragonite in both conditions. The results showed that the concentration of salts and the microbial composition influence the types of precipitates produced. The mechanisms of crystal formation of CaCO3 and gypsum by these communities were also investigated by determining the need for metabolic active cells, the effect of a calcium channel blocker, and the presence of extracellular polymeric substances (EPS). The results showed that metabolically active cells can lead to production of EPS and formation of Ca2+ gradient along the cells through calcium channels, which will trigger formation of biominerals. The prevention of biomineralization by these consortia was also investigated with two common polymeric RO antiscalants, i.e. polyacrylic acid (PAA) and polymaleic acid (PMA). Results showed that these antiscalants do not prevent the formation of the bio-precipitates suggesting that novel approaches to prevent biomineralization in RO systems still needs to be investigated.
This work gives an overview of integrated microwave to millimeter wave sensors and their applications covering frequencies from 28 GHz to 240 GHz. The designs are capable to address versatile application fields from liquid compound measurements to plaque detection and classification in arteries, glucose detection in continuous glucose monitoring (CGS) systems and virus detection in the context of respiratory diseases. The demonstrated approaches represent powerful and miniaturized solutions for highly sensitive contactless sensing of sample properties. Exploiting millimeter wave frequencies enables highest levels of integration to implement miniaturized sensing solutions including on-chip readout systems.
Thermography detects surface temperature and subsurface thermal activity of an object based on the Stefan-Boltzmann law. Impacts of the technology would be more far-reaching with finer thermal sensitivity, called noise-equivalent differential temperature (NEDT). Existing efforts to advance NEDT are all focused on improving registration of radiation signals with better cameras, driving the number close to the end of the roadmap at 20 to 40 mK. In this work, we take a distinct approach of sensitizing surface radiation against minute temperature variation of the object. The emissivity of the thermal imaging sensitizer (TIS) rises abruptly at a preprogrammed temperature, driven by a metal-insulator transition in cooperation with photonic resonance in the structure. The NEDT is refined by over 15 times with the TIS to achieve single-digit millikelvin resolution near room temperature, empowering ambient thermography for a broad range of applications such as in operando electronics analysis and early cancer screening.
High concentrations of PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 mu m) in China have caused severe visibility degradation. Accurate simulations of PM2.5 and its chemical components are essential for evaluating the effectiveness of pollution control strategies and the health and climate impacts of air pollution. In this study, we compared the GEOS-Chem model simulations with comprehensive datasets for organic aerosol (OA), sulfate, nitrate, and ammonium in China. Model results are evaluated spatially and temporally against observations. The new OA scheme with a simplified secondary organic aerosol (SOA) parameterization significantly improves the OA simulations in polluted urban areas, highlighting the important contributions of anthropogenic SOA from semivolatile and intermediate-volatility organic compounds. The model underestimates sulfate and overestimates nitrate for most of the sites throughout the year. More significant underestimation of sulfate occurs in winter, while the overestimation of nitrate is extremely large in summer. The model is unable to capture some of the main features in the diurnal pattern of the PM2.5 chemical components, suggesting inaccuracies in the presented processes. Potential model adjustments that may lead to a better representation of the boundary layer height, the precursor emissions, hydroxyl radical concentrations, the heterogeneous formation of sulfate and nitrate, and the wet deposition of nitric acid and nitrate have been tested in the sensitivity analysis. The results show that uncertainties in chemistry perhaps dominate the model biases. The proper implementation of heterogeneous sulfate formation and the good estimates of the concentrations of sulfur dioxide, hydroxyl radical, and aerosol liquid water are essential for the improvement of the sulfate simulation. The update of the heterogeneous uptake coefficient of nitrogen dioxide significantly reduces the modeled concentrations of nitrate. However, the large overestimation of nitrate concentrations remains in summer for all tested cases. The possible bias in the chemical production and the wet deposition of nitrate cannot fully explain the model overestimation of nitrate, suggesting issues related to the atmospheric removal of nitric acid and nitrate. A better understanding of the atmospheric nitrogen budget, in particular, the role of the photolysis of particulate nitrate, is needed for future model developments. Moreover, the results suggest that the remaining underestimation of OA in the model is associated with the underrepresented production of SOA.
Aim Statistical species distribution models (SDMs) are the most common tool to predict the impact of climate change on biodiversity. They can be tuned to fit relationships at various levels of complexity (defined here as parameterization complexity, number of predictors, and multicollinearity) that may co-determine whether projections to novel climatic conditions are useful or misleading. Here, we assessed how model complexity affects the performance of model extrapolations and influences projections of species ranges under future climate change. Location Europe. Taxon 34 European tree species. Methods We sampled three replicates of predictor sets for all combinations of 10 levels (n = 3-12) of environmental variables (climate, terrain, soil) and 10 levels of multicollinearity. We used these sets for each species to fit four SDM algorithms at three levels of parameterization complexity. The >100,000 resulting SDM fits were then evaluated under environmental block cross-validation and projected to environmental conditions for 2061-2080 considering four climate models and two emission scenarios. Finally, we investigated the relationships of model design with model performance and projected distributional changes. Results Model complexity affected both model performance and projections of species distributional change. Fits of intermediate parameterization complexity performed best, and more complex parameterizations were associated with higher projected loss of current ranges. Model performance peaked at 10-11 variables but increasing number of variables had no consistent effect on distributional change projections. Multicollinearity had a low impact on model performance but distinctly increased projected loss of current ranges. Main conclusions SDM-based climate change impact assessments should be based on ensembles of projections, varying SDM algorithms as well as parameterization complexity, besides emission scenarios and climate models. The number of predictor variables should be kept reasonably small and the classical threshold of maximum absolute Pearson correlation of 0.7 restricts collinearity-driven effects in projections of species ranges.
